From bb711180bade4eba945ab4d149b88c8d8270be5e Mon Sep 17 00:00:00 2001 From: Rodolfo Wottrich Date: Sun, 13 Dec 2015 19:21:25 -0700 Subject: [PATCH] Final code for linear regression and SVR, and condo/residential datasets Final implementations of linear regression and support vector regression, and the detached/non-detached datasets, which we called "condo" and "residential". This commit contains Rodolfo's and Diogo's code, because Diogo had trouble with his copy of the repository. --- code/regression/regression.ipynb | 774 ++++- .../SVM_RBF-checkpoint.ipynb | 345 +++ .../SVM_RBF_Assessed-checkpoint.ipynb | 552 ++++ .../SVM_RBF_Condo-checkpoint.ipynb | 322 ++ .../SVM_RBF_Condo_Assessed-checkpoint.ipynb | 534 ++++ .../SVM_RBF_Condo_LatLong-checkpoint.ipynb | 534 ++++ .../SVM_RBF_FS-checkpoint.ipynb | 257 ++ .../SVM_RBF_FS_Condo-checkpoint.ipynb | 427 +++ .../SVM_RBF_FS_Residential-checkpoint.ipynb | 428 +++ .../SVM_RBF_LatLong-checkpoint.ipynb | 567 ++++ .../SVM_RBF_Residential-checkpoint.ipynb | 362 +++ ..._RBF_Residential_Assessed-checkpoint.ipynb | 378 +++ ...M_RBF_Residential_LatLong-checkpoint.ipynb | 552 ++++ code/svm_regression/SVM_RBF.ipynb | 565 ++++ code/svm_regression/SVM_RBF_Assessed.ipynb | 561 ++++ code/svm_regression/SVM_RBF_Condo.ipynb | 534 ++++ .../SVM_RBF_Condo_Assessed.ipynb | 552 ++++ .../SVM_RBF_Condo_LatLong.ipynb | 543 ++++ code/svm_regression/SVM_RBF_FS.ipynb | 428 +++ code/svm_regression/SVM_RBF_FS_Condo.ipynb | 418 +++ .../SVM_RBF_FS_Residential.ipynb | 425 +++ code/svm_regression/SVM_RBF_LatLong.ipynb | 567 ++++ code/svm_regression/SVM_RBF_Residential.ipynb | 572 ++++ .../SVM_RBF_Residential_Assessed.ipynb | 543 ++++ .../SVM_RBF_Residential_LatLong.ipynb | 552 ++++ data/realestate/realestate_condo.csv | 2247 ++++++++++++++ data/realestate/realestate_residential.csv | 2599 +++++++++++++++++ 27 files changed, 17135 insertions(+), 3 deletions(-) create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Assessed-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_Assessed-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_LatLong-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Condo-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Residential-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_LatLong-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_Assessed-checkpoint.ipynb create mode 100644 code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_LatLong-checkpoint.ipynb create mode 100644 code/svm_regression/SVM_RBF.ipynb create mode 100644 code/svm_regression/SVM_RBF_Assessed.ipynb create mode 100644 code/svm_regression/SVM_RBF_Condo.ipynb create mode 100644 code/svm_regression/SVM_RBF_Condo_Assessed.ipynb create mode 100644 code/svm_regression/SVM_RBF_Condo_LatLong.ipynb create mode 100644 code/svm_regression/SVM_RBF_FS.ipynb create mode 100644 code/svm_regression/SVM_RBF_FS_Condo.ipynb create mode 100644 code/svm_regression/SVM_RBF_FS_Residential.ipynb create mode 100644 code/svm_regression/SVM_RBF_LatLong.ipynb create mode 100644 code/svm_regression/SVM_RBF_Residential.ipynb create mode 100644 code/svm_regression/SVM_RBF_Residential_Assessed.ipynb create mode 100644 code/svm_regression/SVM_RBF_Residential_LatLong.ipynb create mode 100644 data/realestate/realestate_condo.csv create mode 100644 data/realestate/realestate_residential.csv diff --git a/code/regression/regression.ipynb b/code/regression/regression.ipynb index cc1fc62..a0432fa 100644 --- a/code/regression/regression.ipynb +++ b/code/regression/regression.ipynb @@ -2,12 +2,30 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Automatically created module for IPython interactive environment\n", + "Average error, non-normalized data: 0.178070746577\n", + "Average error, normalized data: inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n" + ] + } + ], "source": [ "print(__doc__)\n", "\n", @@ -36,7 +54,7 @@ "# Create linear regression object\n", "regr = linear_model.LinearRegression()\n", "\n", - "kf = KFold(len(X), n_folds=5)\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", "error = list()\n", "for train, test in kf:\n", " X_train = X.ix[train]\n", @@ -76,10 +94,760 @@ "\n", "print 'Average error, normalized data: ', np.mean(error)\n", "\n", + "y_ = regr.fit(X, y)\n", + "\n", "## Partial results:\n", "## 18.7% mean relative error for non-normalized data;\n", "## For normalized data, weird predictions overflow error calculation" ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ True False False False True True False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False True False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True True False False False False False False False\n", + " False]\n", + "Average error, feature selection: 0.168150913843\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate.csv')\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "X = realestate.ix[:,0:109]\n", + "y = realestate.ix[:,110:111]\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "X = kbest.fit_transform(X, y)\n", + "retained2 = kbest.get_support()\n", + "print retained2\n", + "X = pd.DataFrame(X)\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, feature selection: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, feature selection: inf\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n", + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:420: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n", + " warnings.warn(msg, DataConversionWarning)\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate.csv')\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "X = realestate.ix[:,0:109]\n", + "y = realestate.ix[:,110:111]\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "X = kbest.fit_transform(X, y)\n", + "retained2 = kbest.get_support()\n", + "X = pd.DataFrame(X)\n", + "\n", + "X_original = X.values #returns a numpy array\n", + "min_max_scaler_x = preprocessing.MinMaxScaler()\n", + "X_scaled = min_max_scaler_x.fit_transform(X_original)\n", + "X_normalized = pd.DataFrame(X_scaled)\n", + "\n", + "y_original = y.values #returns a numpy array\n", + "min_max_scaler_y = preprocessing.MinMaxScaler()\n", + "y_scaled = min_max_scaler_y.fit_transform(y_original)\n", + "y_normalized = pd.DataFrame(y_scaled)\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X_normalized.ix[train]\n", + " X_test = X_normalized.ix[test]\n", + " y_train = y_normalized.ix[train]\n", + " y_test = y_normalized.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, feature selection: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, lat/long only: 0.476007524248\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate.csv')\n", + "\n", + "X = realestate.ix[:,1:3]\n", + "y = realestate.ix[:,110:111]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, lat/long only: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, assessed values only: 0.247622994529\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate.csv')\n", + "\n", + "X = realestate.ix[:,0:1]\n", + "y = realestate.ix[:,110:111]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, assessed values only: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, non-normalized data, condo dataset: 0.187490341788\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_condo.csv')\n", + "\n", + "X = realestate.ix[:,0:105]\n", + "y = realestate.ix[:,105:106]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + "\n", + " error.append(np.mean( np.abs(y_ - y_test)/ y_test))\n", + "\n", + "print 'Average error, non-normalized data, condo dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, non-normalized data, residential dataset: 0.159796206428\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_residential.csv')\n", + "\n", + "X = realestate.ix[:,0:82]\n", + "y = realestate.ix[:,82:83]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + "\n", + " error.append(np.mean( np.abs(y_ - y_test)/ y_test))\n", + "\n", + "print 'Average error, non-normalized data, residential dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, lat/long only, condo dataset: 0.389881454078\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_condo.csv')\n", + "\n", + "X = realestate.ix[:,1:3]\n", + "y = realestate.ix[:,105:106]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, lat/long only, condo dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, lat/long only: 0.321407925808\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_residential.csv')\n", + "\n", + "X = realestate.ix[:,1:3]\n", + "y = realestate.ix[:,82:83]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, lat/long only: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, assessed values only, condo dataset: 0.264715158048\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_condo.csv')\n", + "\n", + "X = realestate.ix[:,0:1]\n", + "y = realestate.ix[:,105:106]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, assessed values only, condo dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, assessed values only, residential dataset: 0.225016019177\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_residential.csv')\n", + "\n", + "X = realestate.ix[:,0:1]\n", + "y = realestate.ix[:,82:83]\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, assessed values only, residential dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, feature selection, condo dataset: 0.195096943044\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_condo.csv')\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "X = realestate.ix[:,0:105]\n", + "y = realestate.ix[:,105:106]\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "X = kbest.fit_transform(X, y)\n", + "retained2 = kbest.get_support()\n", + "X = pd.DataFrame(X)\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, feature selection, condo dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average error, feature selection, residential dataset: 0.164841899523\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/lib64/python2.7/site-packages/sklearn/utils/validation.py:515: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", + " y = column_or_1d(y, warn=True)\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import datasets, linear_model, cross_validation, metrics\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn import preprocessing\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "\n", + "pd.set_option('display.max_rows', 1000)\n", + "\n", + "# Load realestate dataset\n", + "\n", + "realestate = pd.read_csv('../../data/realestate/realestate_residential.csv')\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "X = realestate.ix[:,0:82]\n", + "y = realestate.ix[:,82:83]\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "X = kbest.fit_transform(X, y)\n", + "retained2 = kbest.get_support()\n", + "X = pd.DataFrame(X)\n", + "\n", + "# Create linear regression object\n", + "regr = linear_model.LinearRegression()\n", + "\n", + "kf = KFold(len(X), n_folds=5, shuffle=True)\n", + "\n", + "error = list()\n", + "for train, test in kf:\n", + " X_train = X.ix[train]\n", + " X_test = X.ix[test]\n", + " y_train = y.ix[train]\n", + " y_test = y.ix[test]\n", + "\n", + " # Train the model using the training sets\n", + " y_ = regr.fit(X_train, y_train).predict(X_test)\n", + " \n", + " individual_errors = np.abs(y_ - y_test)/ y_test\n", + " error.append(np.mean(individual_errors))\n", + "\n", + "print 'Average error, feature selection, residential dataset: ', np.mean(error)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF-checkpoint.ipynb new file mode 100644 index 0000000..bd2a6ce --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF-checkpoint.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 110\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.65500000e+05 5.35870927e+01 -1.13441766e+02 ..., 2.02500000e+03\n", + " 1.60500000e+03 2.84100000e+03]\n", + " [ 4.75500000e+05 5.36128236e+01 -1.13430047e+02 ..., 1.93000000e+03\n", + " 1.48000000e+03 2.09700000e+03]\n", + " [ 2.68000000e+05 5.35955564e+01 -1.13378465e+02 ..., 2.48500000e+03\n", + " 2.56900000e+03 1.60800000e+03]\n", + " ..., \n", + " [ 1.54000000e+05 5.34268642e+01 -1.13456094e+02 ..., 4.70400000e+03\n", + " 5.43900000e+03 1.71500000e+03]\n", + " [ 1.50500000e+05 5.34268642e+01 -1.13456094e+02 ..., 4.70400000e+03\n", + " 5.43900000e+03 1.71500000e+03]\n", + " [ 1.56000000e+05 5.34292446e+01 -1.13468327e+02 ..., 5.32900000e+03\n", + " 4.90800000e+03 1.12600000e+03]]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=0\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=17782.7941" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Assessed-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Assessed-checkpoint.ipynb new file mode 100644 index 0000000..2625c86 --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Assessed-checkpoint.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar=1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 165500. 475500. 268000. ..., 154000. 150500. 156000.]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=1.5\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 0.5623\n", + " Cost = 5623413.2519\n", + " Relative Accuracy = 0.1581\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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is/G+de9saN8CNm4HbQxeXwznT/fANDeO5sSl3jN82w4wuxKumwnjl8K9ffLb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\nIiIiUggFmiIiIiJSiP8H1gG2Aqw8aLUAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1581\n", + "Train set Accuracy: 0.1567\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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FxKuA5cCBmXl/WfOnwCeAfTNzQ0ScSRHUZmXmprLmfODMzDygfP4B4KTMnF3xOa4AnpuZ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\n5ZZVd3Eu8FUCa5xzX6e2tpp0EbkcFX4LiMXupqxsHNdee1uDC/RPf/o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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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MdKBQJyIiIrJPlQY6UKgTERERCVRxoAOFOhEREUm4VCpFa+tCWlsXkkqlRv+FYlR5oAPN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\nelFLnVrqpOJGWhR3eLdo55DWucxZrdHu0fHj9y0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4n6js0zM88MAn9T8H30FlpobFiX0P\neJdvSzT6FFNTrwduBF4mzXsYI84wb+MEfw28BDxKNPogXV3rOXfuMKVi95YtWxK4Hn+c27PP3lxw\nTHf3yoJtM3E9VxNTViyrNmwear23APDgg7vLupgdFg6cu9yh6VBvVtksDWepa3qEuSZTqe0FBedV\nnwFtxRsQ8ArZK0vaFt389V1V9Yf2aYtdLNYh2Wx2OgvVy3JdJ3ZtVtXfrv9q9uW0G9acIymtrT3T\n1+PVft01be3LZPoLrJGZTP+0Bcyfkeo/th5Wr2rcxc79urDg7pfDfIAGs9TVfQLN0hypc/CX1JJp\n12S47EiPFNZRHRGP9PUKLJUlS1Zp0jek96/yET1VWzXoct0WOJchcUnxE8sWPa5HvGz3az6fLyCW\n+Xxex9YNaHexJ4GSz+etl+yWislUGGoVOxkmd1KqJFmp885XPGejx402Cpy73GE+4EhdkzZH6hYv\nZhuTFRbU7xEsr68XR7dWk6128ZOwgZBjktpCt1Z2c5/4Y+XMceExevH4qlDSVu56iu3zE6jCuRaL\nvQtb75lq1gXvUTGiXS3myyrkrE+Vw5E6h/mAI3VN2hypW5yoNiA/rK8/CcKQtb6CF5La1q1JX6cE\nkyyU9c7v0kyT0C7X66ZJXDK5VpLJlLS1bZBYrMUau5DcRKPL9Dm7JRpd5ru22ZG68P2VEJRqX9Yj\nIyOaoHpCyLY7uNi+ajBfBMIRlcrhCLDDfMCRuiZtjtQtTlTzkjWSH8lkSjKZAV15YUAikaR4MXMd\n+vOIeFmrZvsWvV3Ey2z1CFw83uGzrKVp0YQuIV5m7Apfn3h8laRS2yUSaRPb/aq2b5VSLtJSL82w\nfX73a04KLY25il681RKbcta4WrgzHalrTDhXtcNcw5G6Jm2O1C1OVPqSDZKceLzTZyHyx8wZWZIR\n/Tkpfr050Z87rePbJZXaPt3H06FrEZVAkbJa4XxHRkYkldoqsdhqSSTWTX8OWv6C8WbVxpj5EyWG\n9HyMTEvx9Su1luWIYFvbhoJrbmvbUOktrgjO/erg0JxwpK5JmyN1ixOVvmQLyV+Ye3VAwmPetmqr\nlm01M3VYveOXLFGJCWneJOMs07VctwXIX2fBeZPJlGSz2ZAM3F4Jy9atBbz1mJnlqRoLTGtrMOmk\n25fFWyu4RAkHh+ZDo5G6iJqTw1wjEomIW+vFiUq0sC655PWcPv0OPA2416LKfT2hv29CleU6D/wS\nuFhv/x5wAlXC6ziwnEjkF6xd28VTT33UGu84sI80v2SMmNahu0+PlwR+HfgJ8CTwc+BT+rgDeoy7\ngHXAVmAPSgvvo8DHrHMcoK3tr+jru2zWml+eTt01eu63AqqEV2Wl0irHJZe8jtOnz6CuDeBRMpnt\nfOtbp2p2DgcHh/lHI+gQRiIRRCQy7ycugmi9J+DgsFgxOjrKzp1D7Nw5xHPPPYMiUMd1+2cUkbpK\nt7uANxCJLCGfP0Qq9RKemPDTwF2kUj1kMhvZvLmXp556OjDe9aTp0oTuc5zgc8CngV8DXkSRxu8D\nfwi8G9iPEhe+F0WoPg106rm8DfgySph4+mqA4zz//McYG7uKK698G5dc8jpGR0dntDZGHPiKK54g\nk3k1mczdXHHFyaoInb2+peZx5MhHiMe9P3XxeJQjRz4yo3k7ODg0Bsw/hmNjVzE2dhVXX52d8d+j\nRYV6mwqbpeHcr4sSxRICgoLCSgC4V2C9wAbxkiDEcr/2SSq1Y9q9lkrtkOXLkxKLrZZUaoeVaGBc\nt0aKpE/S7JBxluosV/+YnqBwOffvLvHH6xkx4/DsWOirW0xXtbFlzmXp4LC40ChJQzSY+9WVCXNw\nmAUKS2Wd4aMfPcrU1KuBT05vV7z+VpTV7VbCSnDBD3jssZe48sq3MjX1Gb1tL/AOHnsMbrzxKHAB\nngVtEBgkzWHGuIlhlmqXa5/efwCYAK7Dc/Ma9Ouxsfrea33fAqzRY91Y5OrXMTFxHUeP3jlrV2y1\nLpSwEmWl5lFNiTEHBweHhQpH6hwcaoqvMzX1SUx9Vz8mUIQuC6wFrrH27QdyQC9TU3fgxbEB3IBy\nof4q8ALwA2AfcEa7XD+iCd0U+fyHuPvu2/nhD8dZvnwpfX07eOCBP0Xk3SgSZ8jko8B5Eokb2LLl\nIk6fnkARzuPAQT23J4Dt+ueewHwP6r5Pc/bsz2a0UjCzGrAODg4OudweTp3KMjGhvicSB8nljtd3\nUo2AepsKm6Xh3K+LEkE3oNJ769OuS1uypFNnsdrugn6d6eqX9PAkTcz37oLsTZXl2qZdrgfFZM16\nQsZbBDaKlyUbrBW7SkzlipGREclms6KkU3pFSY106p/d1txy0ta2PqCr1y6RSJdkMgM1Fe0t5y51\n0h4ODg6NEFZBg7lf6z6BZmmO1C1eGFHhtrb1mhgNaWLWqolUSrzSX4ZYGfHdnBRKlbTo43OiYu/W\nFRAfJVvSIbt5jUX+usQTL+4OxMH1ixdbZ+vhbZNUamtAzkQRukikQ1+DmoshTpmMGctPXGdCrMJI\nXSbTXxFhK/YHvZQ+noupc3BwqCUcqWvS5kjd3KBWL+vZjBO0GnlkzVSC6LSI1HpRenS9FpkxIsNr\nRFV8sMlVVjzdOvUbq2q5LtNJEX3WOZfpz4Z0GTI5In7tuzXilRobKhjfrx+nxo9Gu6brvs5WYy64\ndvF4p5iEjni8M7QCRKXjlq9k4ax6Dg4OtUOjkToXU+ewYFGreKzZjhMM2lc4idFeU3Fsd6LkQiaA\nbuCM1XcQFc82DPxxYJwbgXcC1wOQ5kkdQ/dmnRSxTPeZBNr0uI+ikjRAxdH9P9hJGwrDwC+JRP4e\nkVeXuLp1QJapKXjwwZMcOmTHsmwqszKFCCZFKCxFJXOgr/N81eMahCVQ3HbbzVUlVTg4ODgsVDhS\n57BgUW0G5FyPUxrj+IV+fwF8EEXIWlHJCJ0oArgWRfRAJUZsBz5ImhsZY5xhYpzgm0AEuEX32wss\nBz6rx/kQijxei0poAKU1d6eeSxuwmmj0Z7zyyguoxAuDfSh9PH9GrEmIMBpzN9xwMw8/vJ+pKbW/\nXKByGHnesmULk5O3YNZ+chLgbhKJgzUOgD4DDOnP1ZPRIBpB9HQ+0WzX61Ad3PPRQKi3qbBZGs79\nWnPUSqdotuOUdr92a5fqFrETDvxJC+0SVtxe/ewRWCFpOnUM3XXaXdoW4jJtCRl3hcBm7QYOxu61\nWt9NfVkT97dNH+PNKR7vrKi+azXrrGrAzixRolg8XdDVqpJA/OtiXMkzQbMlaTTb9TpUh2Z/Pmgw\n92vdJ9AszZG62qNWf0xqMY5NMlKpraLi2QxR6he/WHCv+Ou2hgn7mni4Lp3l2iG7uc/avzbkmN4i\n2zolLNkimGW7ZMkqSaW2SibTL/H4Kj13f3LFbMQ9Z5MUUc39ChK+WouUNoro6Xyh2a7XoTo0+/PR\naKTOuV8dFiyMG9Az+89M36wW49jitpdc8jqUu68d5Qp9M8qlGUPF2V2Fcm1egedmDWIVcBdpJhnj\nZYb5fU7wNmv/L4H3Wt8PogSDwxAFNpS9hldeSTE+/gSf/extALz1rXt4/vkPY9eXnY0mXZiu1JEj\nyq1azdqXc5cHhYbN2A4ODg6LHvVmlc3ScJa6RYugZain51e0lc7IknRry1uYpcy4Qm1Nu3aBwpKz\niwAAIABJREFUbkmzScaJyG5+PeA+NDpxxg1rdO5GpNDFulz3zQustOaU1BY8OyPWb41TFke/2zKV\n2lrRGlS6VjNBtZaBWruHms3d1GzX61Admv35oMEsdXWfQLM0R+oWFqohKd4ftJxEIl2abOUssmRI\n1JAmdynxBH5tF+2AGH07JVvSIbtp04RvhXh1XI1EihnXFgNeoft3a0K3UTy9O5vsDekxUhIUPzYE\nqbW1R1RsXUrPLyfJZKrMGsz9H/WZnK/WOnXNpnvXbNfrUB2a+flwpK5JmyN1CwfVkAbPajQSIE6e\n5Qv6ZMmSZQVWL1gqfl06lVSR5nKtQ/caTch6xV/Z4ZioBIqkdawRCh4SvzWwVcJ16IyVzp+0EY+v\nkkymX2vFtQSuJyeZzECJNfDGn+uYmmZ+iTg4NBMa/Xe90Uidi6lzcAhgZhInR4CgVp3Rpvsur7yy\nFNiK0q/bA3waJWfS6zsmzXsY4wGGuYAT/DuUHEkPqi7snXgxeEuA2wLnuwN4ECVdYvptA74bMt84\n0egH6ezs5i1vuZrx8ZOcPfszHnnkl5w+fa3u8wi2vEokMsyRI/eVWIP5QzBuDpysgoPDYoOrDV09\nHKlzcKgCQeKQy+3hwQd3MzkZDen9/wFfRWnNteAJ7GaBawp6p/kqY7ykkyL6UEK8WeAh3WMcRfL2\nAxtDzrdOn+MmlJjxQd3/LpSOnYHSy5uauptz53Zwzz1/x8UXbwNgcvJ2Comp+gO6Y8f20D+mjVBY\n2/3xd3BYfJgfDdHFBUfqHBwCKEZSihGHdPpiTp/uBt6PspYB/DPwHpRw8F5UputJve8a4M9Q1SXO\nAcd1pYg/14Tuc9Zs7kCRwvcCcZR1743AA6iqEAZ7gV/Xn3+gjzPE6kGUCPEdet8wcEjPLcfU1FFO\nn4ZoNBeyGopI2pmqQdQqC3k2cH/8HRwcHBypc3AoQDGSsnPnUChxUGWtxvCXuxrGL1kyjHKXngHu\n1tuWAMbl+hLDtHGCb+N3n/4AVQIsBtyutx1EVXy4Q2/bjKkcEY1+k6mpNwKnUNa6O1AyKsb6dhyP\nXAK8anrf1NQZolGvQoSa82pgP+vW9ZRdM0OgRkdH2blzSK9dcTfo3LpLz/DNbz7Mzp1DzhXr4LBA\n0QhegAWHegf1NUvDJUrUHbMNuFUJAf4M1mQyJanUdp0lGiYgvEsnI5gMU694vcpy7ZRxorKbRCBh\nIqeTH/qLJDr06XPusrYlJZMJEzouJqXSKX4RZCUGrKo8+DNioa8mwsDV9qsUwSxkO/mj2SQWgmj0\nQHMHh1Jo9OeXBkuUqPsEmqU5Uldf1IJEhJWbgn6JRrukeIUHQzDaJViqSxG6Jbr0VzC7tVsTSFPq\nKzj2Sossqrn09GwouM54vFNXhyjMbk2ltkok0unbV6wCgyKI5bNaK82EnYuMWTP3YqXHmhHNriHm\n4DDXaDRS59yvDk2B2cRcGTfhV796CpW1mrX23szU1CeB38efjGDi6O5FuUa/DjwJ5IEsab7DGMIw\nyznBfwD68Ge3XohyoV6Lcsfus8beB7wJ+BywDJUZ28u2bVtCXMcnpq9ffb/H5ya96qrdTE6qOMCp\nqRe54YYjAMTj+5icNOfbj8rcPVN2reoJ4wLeuXOIsbF6z6Yx4GINHRyaC47UOTgUwejoKDfccISH\nH/4OU1O/B/xDSK8JVMxaAhXnZuLVrgWeQCVF3AMsB7oANKG7gmHexglO4UmfmOxWk7VqYuJagbQe\n+2FUDN8o6tf3k/p8e1m37jUFsWyl4taOHr3TynYd5fz5azh9+h0AxOPX09PzMZ566py+LpXwMTDw\nodB1MucYGLiEU6cOTsfARKP7GRgoTMCYy1iZesXhOEkVBweHuqPepsJmaSwi92ujxziEoVo3VLC/\nqtLQGuJ+XWa5S/u0m9KIDveLv2zXKkmzXFeKuE6PaVyo7aJKfm0JuGGTokSA7W1rdeUKv4vR26bc\nrqnUDolGjZu28Jr9LtBCd2gpN6Z5BjKZfp97N5FYI9lsVp9XlSQrFVc3V8/RfD+jjermbNR5OTgs\nFtBg7te6T8A3GSWwNRVo4yF9fgy8iBIB2xrYvwz4DPAMSgvib4ELAn26UOaTn+v250BHoM8G4Mt6\njGeATwFLA322o/QiXkT51j5S4toqe0IaHAv5JVHNi14lHARJWp8maknxYuS2CfyKqGSGbeLVem2X\nYPKEKv0Vkd20iFeDtUOTNlPmy5TwMp/bBV8Shaq/mkj0FBAuNa+8nmPpuq5mPbx72VcwXjFSN9Pj\nFjPqUVWjUizEf8IcHBYKHKkrT+oeRekomLbS2n8QeA64GuWP+ktN8FqtPp/T294EZDTxOw1ErT7/\nHRUg9H+hgpm+A5y09i/R+/8e2AFcrsf8tNWnHeUfO4EKOBrScxsucm3VPCcNi0Z+edUKIyMjOvnB\nXyJLkaUOUdY0mzS1W6TMWN/Wi8p+PaYJ3RltoduoCVafqESK1hBy1hUgeisFtsiSJaskn8+LiKnL\nalsBu/Wc2kOJVrFEh1IWt3w+H0rgZ2PhW6xoht+L2cKRS4fFCEfqypO6M0X2RYCngBusbcs1kdqj\nv3cALwNvs/r0Aq8AO/X3X9UWwNdaffr1tlfp72/Wx1xg9fnPqACqVv39PdrKt8zqcwh4ssj8K3g8\nGh/N8PIKz/5MCmwWz80aRprM522iarNuECVbktWErk38btRdRcYykifetkgk6XsRplI7NOnbJZ5s\nygYfgQuOaYhasRdr2Es3bJt/fUZ85LIUGVzMWMgW7PmAWx+HxQpH6sqTul9oq9jjwBeATXrfZk28\nLg0c83fAMf35jbrPykCf7wB/oD+/E3gusD8CPA9k9fePBcklsEqPPaC//znw5UCf1+g+G0Ourcyj\nsTDQDH+cw0ldpyZ2xUiTTeq6xLhQlWwJ2uXaOr1unkvUuGCDVkE/qWtrWz9NrEZGRiQW88ujKGue\nrVHn3aNIpFMSiW5JJFb5JExmeu/CZFMymQHf/IzeXSYzMOPnY6FZdhbafOcTzfDPoENzotFIXaNl\nv34DlYr3PWANqibSP0QikTSqsjjATwLH/BRV9BLd5xUR+Vmgz0+s49eiYuSmISISiUR+GugTPM9Z\nlPXO7vNvIecx+34YfokLG41QEmomqDQzcXR0lLNnfxKorLAX9Yg9q7/vwS9rshdT0UHVVX0ncBdp\n1mnZkgs5wc9RoZcfRHn334Hy3t8L/AdU9YaLUNmyd6P+Nzg+Pf7zz1/L2Nh2Tp3Ksm5dD+fPfwoV\nxnkzyji9BBVJsBclu3INsI+entU884wwMXErKpP2OmYrbxEmm2LLpNil1CYmDlY1tsFCrOVqV9Vw\ncHBwqAcaitSJyIj19TuRSOQfUboQWeCfSh1aZujIDKZT7phy5yzATTfdNP359a9/Pa9//eurHaIh\nsNBeXpUQBCVfcjPf/vZ3EPkUKqRyH96vyIf1Nps0DaPI3iSKiF2EImmDpOlijD9gmGt16a9nUI/U\np1Cc/30o7brjKG26A3qM7+rzvRtFwr6P0ru7FVBE7Ikncnou9wIf1/336rHsGrPv4uWXv6QJYBZ/\nebDZodgzUCtdNKevtrjgyj05LBZ87Wtf42tf+1q9p1EUDUXqghCRFyORyCMoJdYv6c1rUCYKrO9P\n689PA0sikcjKgLVuDSpL1fRZZZ8nEolEUEkZ9ji/EZhON8ocYvdZG+izxtpXAJvUOcwfyhGE0dFR\nfuu3/hPnzydQIZdrdd/tKMvaOpRFrFePeACIAm8B3oAiaNHp8ykdulsYpoMT3IfSlduAZ8gdBD6L\nIoZPo8jYn6L+T5hCCRl/Hfg+8fgrTE6+xXc9U1MvAZ9HadTZFsMDwK9gCKAa90vW/j36nApz/2Id\nBe7gm998htHR0UVPyJxOXXEsVAu/g0MQQYPMH/7hH9ZvMmGot/+3VEMlQjwF3Ki/j1OYKPF/gGv1\n91KJElfo72GJEr+BP1HiNylMlHg7/kSJ6/S57USJ/wL8qMi1lPTLO8wdysXzqNqtdnyaHe+2IrDP\nZLoO6di5ToHl0/tVDF2LruW6VffdpmPkUuLVcT0mKsN1jd5m9O9yekwjj9Iifm08E3Nn4vDsuL5t\nYmfixuMqW9bOaoV2iUS6ZhXrVgpevJ2RdplZ/N5Ci91caPN1cHCoDWiwmLq6T8A3GWVi+HfAJpTc\nyN+hMkzX6/0f0t+vBrah5ESeBFqsMf4Y+BF+SZNvARGrz/8L/DMqCOm1KF/W31r7o3r/A3iSJk8C\nn7L6tGvC+QWUvMouTfL2F7m2qh4Uh9qh3As3FlsdQpD6NLkKy07dKoUixHlLtqRNlF6c6W8EhNv1\nsSbrdYv+uU1UEoWROckFzmdkSmzNvDYplDRRiRKx2GofaVOJCwM6caF/zsnGyMhITWRNFlLigUsE\ncHBoTjQaqWs09+sFKJLUjQpC+kegT0R+BCAin4hEIgmU76oLlVixU0R+YY2xD+Xv+ktU7ab/AVyj\nF9/g7SiB4lH9/W+B95udIjIViUT+PYogfh1lobsXuN7q81wkErlCz+Uh4Bxwq4iYuk0OGvV2SwVd\nPwMDH+Do0Tu54YabgRhTU+dDjnoMlYj9KCpf527gBr3vLMEasGluZIw/0qW/vogyEB9HxbpNAH+g\nt92KKgl2ABWL97+BLcBS1P8roNy+NjbreVyHctfuRUUCZPHi5LKo8FN4wxteRy63h6NH7+To0TvJ\n5fbwrW99rdLlmjUGBwe59NKLZ11/daHFbjo4ODjUHfVmlc3SaFJLXaO5pcLdg73iFxPuFKUztyyw\nvUNgqcBan1VGVYqI6tJf7QKXa+ucXTXCWNhWiicunBBPJsW4VvOWZa5PW/BGRLl7Vwv0Siq1VfL5\nvPilULoFchKNdjWETlyj3fe5RrNdr4ODgwINZqmr+wSapTUrqZtPt1Ql7jqv/FdKuyt7xCvNtVKT\nqJRFqIKu1w6BJWLcr17pr2X6uCGBdSHH9eljNwgMCPRJNBoWFzcQIHpd4sXZ+euo5vN5XWN1m8AW\niUZXTosLN4IrcCG5T2uBZrteBwcHaThS12juVweHorDduAMDl/Dgg98ClEsXqEi25OGHH0VljYJy\nY3ahvPH3Ar+HcrN+RO//ICrc0kYE5dW/ljT3MMYphnkzJziHco8eAF4KOe4JVGjmGEqjDqam9ob0\newQljbIWlSULcCOtrQle+9onfFmDhw4d4rLLLitwbT/44FDB2n31q6c4fPgwhw4dKtg3V2g292mz\nXa+Dg0PjwZE6hzlFrfSpglpzY2NG8FcJ8m7ZcmGobAl4P8+e/RlTU0EZkH2o8MtNqLyY20L2mxi3\nAyiy9VOtQ/cIw9zNCSZRsW3muFuBu/Tn7SjymAC+jSoTbBM2e3zvmtRYap2i0Qn++q//IpQwhBGJ\ngYFLeOABWzz5AOfPZ7nxxk8AFCV2tYp9rHcMpYODg0PTot6mwmZpNKn7VaQ2bqnw0l1eaa6wbMtM\nZsAX56RclWE1Xe1YuuD+Lu2mNfVV+yRNQrtcr7Pi2YI1XY9pd6w5zpYy6RIVO2dkSFK6BbNe+6Zj\n5CqFcskal62pU+vNra1tQ+i9qFVMmIstc3BwaCbQYO7Xuk+gWVozk7paoBypCxK4RGKNjp+zj8mJ\nX4qkQ8fAmf3dUigTYpoiSR6h+3V9/gEp1JEzSRF2PVh/LVdFJjv1uLskrJ5sMpkqIESlCPLIyEiA\nuBYSXZvE2oSrVnF4jRLP5+Dg4DAfaDRS59yvDg2FYq67oBvXrreaSBzkyBHlqrQV681nD9tJJhOc\nO7cPJSPyblQM3SjKHdoCtKGqR5iqEg+hYuD2kWZK13Lt4gT/CrxXj/u/9DE/R8XlGdkRUw/WfLZx\nEfACyk27Dlip+ykkEge5777CmMBScYNHj97J1NSrrHO8wzemF0Oo3L8TE2d4+9vfx6WXXszZs8Fy\nyY2L+XLvOjeyg4PDgkO9WWWzNJylrizKue5sK5XJ8izm0s3n89LWtkFbpoZEyX2slEikWCWGMCve\nsmk3aZp2baGLi3LHtoqSQunV/XLaQmeEhbfqn0k9li2NYkuqbLHOrdy7Jos1uDbKxex3p9pWMGUl\ny+nxzbmWSyLRI8rlO6SPXymQ9Vkl4/FOX+WJ+XC/zsQtP1/uXedGrg4u89ehWUGDWerqPoFmaY7U\nlUetXHdKwy1Y8WGpRbLC4ubC4uk6BbZImpW69NeF4kmT2LF43eLJn3iacapfXn9eJn7tuqQmdAMS\n5nq1rztIMGwXb3g/PzkMJ3sdEozhy2T6a/JiruQFP1PSNF/uXedGrhyOADs0MxqN1Dn3q8OigXGX\nffWrpwhWfIBhlOTI+1FuyDv09kdRBUiiettJVOF7gFbS/FS7XH+HE3xbb49SmCX7YX38d4EVwJ+h\nJEx6gduBZaiyxE+i3L3nUaWMVwKFrk/bHXr06J2+zF6FfUQiP+Yb3+jikktez5EjNwQqZ6wjl7vJ\nkjj5OhAc4w5sdHev4Stf+ZuCuVSLSqQ9gtdkspWdi3Phwd1LB4fGgSN1Dg2D2cif+OPN/iGw9wyK\niN2NKtnVgiJ4oAheRH/uR8mJXANMkmYlYzzHMO/mBH+NKuuFHiuIl1Elv17W37cBgid1chxV/e6X\nwE/1HH4D+DGK1PmJ5ve+F2fnzqFpDb7C6/k3RFbw/PMf4/RpuOqq3+HkyXtCCVUut4cHHvjPlsSJ\nwb/gyabsJ5f7Qsi5Ggu1kshplPM4ODg41BT1NhU2S8O5X0VEuUaTyZQkk6lQqY6ZxuZ4lSL6BVZY\n7tdgrFxXgVtNHWdcqP0COUnTJuMgu1ktqixYq8AqPd5WKSwf1q3dqCbz1XPPqli1VlExbX63cCq1\nVVeWsN25SfHKfq2UVGqHxOPe+VSGa19V7kFP6sR2Se8QlSG7RTKZ/hndz5liNi67+YrfcnFilaHZ\n3a/uOWlu0GDu17pPoFmaI3XhsW7VaLAVg5LyMITFxKz9iqiEgGAM3bYQUrd+Og4NtkmaTp0UcaVF\n+Fo0KVuhxzX9O0XF620Rf6JEq2QyA9N/6LPZrITF7V1xxS5pazPnN9ImJonCe1HG46umY94ymYGq\nSZ1Zpyuu2CWp1PY5uQ/Vwr0MFw+a9V42O6F1EEfqmrU5Uic6G7VQi2028LJCzbjGUmcSAXoD5+wt\nIDRqW7fARp3l2iK7SVjHGytbdygxU306xc4mjUbbJJMZ0E2RsZ6eiwqOzWQGJB5vF38SRbcoq2Cw\nr0fqYrEW8Wevrqr4ZeKSABwcagP3u+TQaKTOxdQ5zAtGR0d5/vkXajKOXf/18OHPMDGxCRVnNgT8\nAFiKSlL4PPBOVCkugxdRmnEn9fdrgS8BedK8nzFeYJh1nOAlVIzbDuBPUdpzP0YlVgTrtZ4HlgP/\nDRU/N8rUVITTp0183aNAH/H43xOPX8+kDs2Lx6/nzJkXOX8+hkq8ADiox/iLwDnO8PDDjzI1da0+\n9lFSqVU8++zNbNzYy5Ej97jAdAcHB4cmhyN1DvMCRcTeQFAMd3j4Q6H9w4Rfg+K7qr7pO4G/RxG4\nV/TRU8BXAVPntQuV/XoRKkFhO6o+K6hEgYdI8yRjvMwwF3KCcyjh3rv0mDcB3wNOoMhdsF7ry0Qi\nUZRBFpQQcRwvGeMA8A0mJ28nk7mL7m5FKM+evYjTp1/R/bLW1d9BW9tSfvELr35rNHrMV7d2chI2\nbz5Zdbbq6OgoZ8/+hGjUG9slATjUA4tB3Nkl1Dg0HOptKmyWRpO7Xz03RV5UcH6vpFJbQ/uOjIz4\nhHCNa9Ebw45daxdPuNe4I7eIJ+ybEqUF1y/Qpl2z/qSENFkZJ2rF0Bk3qZ1Y0CkmiUK5do3wcItA\nThKJHiu2JujyNXVgw8SCC2PjoFPy+bwvTknF0c3OzeOP/1FJGJnMgIyMjBSNiWrWWKlq4Naoeiym\nWDR3/5sbNJj7te4TaJY2l6SuXEZpGMwfIhPzlcn0S2vrajE1SVtbuySZTEkmMyDZbFaSyZS0tW2Q\nnp4Nsnx5UpSY7nLdv0sgrn+26u0t+meX/rlCVEJBqz6mXSBa5Phlmjgt12SqXW8zIsFL9M9Ovb1N\nt22aNLZax9qxc8s1iRoQ2C7QoZMiorpShCGIG8Wr+WpntPZZ41xukbB2PbcuPTcT77bSGiepr9fM\nu1X/RP808XQdAv3S03ORxGJmrTolHu/Q4/dMj7d06XIxQsjRaIf09GzQZNiso2lxfY4ufW39uqVk\nyZJV0tOzWc9ZrVUk0imZTL/OmF0hhsBGoytKEsDg89jWtkFSqa2+CiDqWRvwHVvq+a3VC7OaaiSm\nfybTP/07EEZ0M5l+SaV26Gzk3IzISTXZ4KXWsNw1h/UrtX8mf1OqwXzFolX7/DiC5lAOwWfEkbom\nbXNF6maSUVpYoaBdv/y7A9tyUigJ0q6Jy7KQ7SaZYIXVjIzHUv097Bxhxw+F7C/2uUUfk9Ofw7Je\njbXNkyFJk5BxluhKEe3iVYLo0vM15zBVGsyLaJvuf7koeZPgOkRCtvUH5t0thRInndYaBNfXzK3b\nGic4nlm3Y1a/Vr39WKBfV2Bbu9jlx9Q1Li+4jmRyTUkLS3g1D/vau33Hqqzg8Oe3Vtac8Oe9OAlT\nlmJ/4ouxFvurdtjPcniVj1Io97sbPu+hgjUsRtjKldwrtn+ustRthFmeM5mBmp6j2udnMVkPHeYG\nYc+II3VN2uaK1PkzP9Ufx3IZpYX/JfdJuBvQSGwEtyellItRjdWrf5pt3SXOEXZ8KmR/sc9GWmS7\neGQu7FxJMcQlTV7GWSq7abNe0EZGJOfrq45dKZ7Fbr01XpjuXRihXF3ieoPrcazIuGZfn/U5uA6p\nwHfT7HHC7p2xYNpzC7uOwm02iQl7Hv3X7t8Xi60u2Gae31pZc8LGsdctOGYxt7j577zYtZhtlc6x\n3O9u+LwLjwk7X7m1K7V/Jn9TqoXSlbRJcXfNtRKrfX5cJqtDOYT/TiLSABzDtDBpfAeHBYZ1qFJc\nT+GV77oJldBwXLeDqOSHO0nzHca4hWF2cILPAE+gEiciuu92VFLFndY5elBJEgdQ2a4GEeYGS+Zo\n3FI4g7q+A8Ba5u7aFivGiUb3c/bsTxgdHa33ZBoa3d1rUL+nJzFVV9Q2BweHWaHerLJZGs79KnPj\nfvXcXoUaclvEs2qpPml+TcbpkN3T7kbb6rLNGtOICxvrVL94wsDG/bpVaut+tV2s2RL75sL92ime\nLp8aNxotvMeXX36579mxRZFHRkaa1v0aiXRKJNJWctxqf3cXs/t1Jvd2JvFxzv3qUEs496trc07q\nRBZOokQ0ulzv75LyiRLb9AusWKKESRwwpMaU6moXz+VoRIjVL2CaFl36a4X4yZAhSC3iJSy0i1du\nzCZFNlHq0PMxZb6SoshrcG1iEokkJRJZKv5EiaQokeGk3pbQn3OiSGlejLBxPN4hsdhqice7JJFY\nJW1tG2Tp0sT0OkSj7RUlSiQSSUkkVkk02iGRSJcev18MwU0k1vlIWjablVhstcRiqyWbzRY8O3b5\nMvMSbKZECXMtqkpH9a67Zk2UqGR+wb4zIVwuUcKh1nCJEq6phW4iSZPZ/GEMj1lIWtmftqWvRfyV\nGDpCiFlEYKWuFBGR3fSKZzHssEimqSBhl+paK55FLaXbkHgWPDNGMP5orfgzWtt9hMhvfVmjyZu5\nLr9lNBrtKvqyn61VoZIxSt3LhRKDNNcv6pGREZ0B2/hrsVCxUJ41h+aDI3VN2pqJ1M0GYVlxXiLE\n2sC+sGQIs824yDolzeXa5ZoQ5S61LXvHrO+5wFidEpYBqkiY6bPWtz8eXyWJRHCex6Stbf30NXql\nzfrE09yz++ckmUyVJCG1esmVIjzlSN9CeNHOlUvNXjcV9J8Tu1ZvNNrlLD01xEJ41hyaE47UNWlz\npK40jLsrEjH6bbYlKycqPs5sN0SoN4SIDfgInoqhi8huDuptpv5sYW1Vf5xZUpQVr1zG7oCewyqB\nXolE2kKtNrHY6oLr9chGeKZlKdT6JRdG7sqdo5K4rXq7srxr8ASrZ5tlGbzuaNSEAdTuHA5+uHg3\nh0aFI3VN2hypKw7vD7ZxV27QBGutJm5domQxWiQ8oaNVvLi7DvGSItplnBatQ2de7sayFia9sUHP\nwejU2dUn7H6GhK3S58oFiGhwjt2SSm33XW8wntGuoFEJOarlS67YWJUQx2LErVFewuoaamtFC1sX\nj8g7wjFXaIR/EhwcgnCkrkmbI3WFMH+klSsyJypgf0WAIJlEBpOduUoKY9/6xMvq7BfYpitFRKyk\nCFMNYqkmij0WYTPn2SKeZdBY7Qqzf+PxpEQiJqHhmBQSv5yei0rYiMU6fGRMJReoffF4py+5IBiY\nrvoXlkyz12+2L7li5G02xKxR3GVzEe8Wdm0mccERDgeH5oIjdU3ampHUVROvpUjV2iKWMeNq7RSv\nrqt9nMk87BOVFJGUcbpkN9eJyqLt0CRuSJO1FousmUzZFrGzQNX5zByGxMtuXSaZTL/P2lYopXJM\nli9PhpK0MNHVVGp7UfI0H8r7pQjYTLMgqyF1c22BqUXdXBuNYoV0cHCoPxypa9LWbKRuJkH2XiZr\ncHu/eJazgZD9JqZpm3a5LpXd3GeRs2D5qw2aKJo4vZQUypYM6WNGJOjuXb486btWJWfh7xOJFEp9\niIi0tZmYPm/+YVUVaq3uP5OEiNmQl0qPnQ+CVM05KiWYzhU4O7j1c1gscKSuSVuzkbqZlClSljp/\n/JOnU2dKk5lyY56gsPqe1LVckd1caZGz5eLXrTMCu2bsFlEu3SHx4vds0d1CkrlkySrftSoX3zLx\nSo21FxxjXl5KG64wM7bYWhWz7FWDmUqXFLuHlVrvKnlxz5ebtpK5OAvc/MCts8NigiN1TdocqSud\nOakyCIcsgtWqSVbSIknB+DavEkKaXh1Dt1RgnT42mEnbIZ5osCGII7r1iUrGMC7aNvFOr5C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bV6mnx5blKTFLFc13I9aPUPypuYz8ZStd363C+QC42bKtyWk7a29Zb1rhSRUiQmldrhq0VajAwW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\nX4/hlQ+Db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E8IYUMmz99J6rftX9sSeSznYRR/ngyV1LUV39kgAMMlrZK0WNJtkr6UWu8y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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo-checkpoint.ipynb new file mode 100644 index 0000000..d773f8d --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo-checkpoint.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 105\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=1\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=9\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=1000000000.0000" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_Assessed-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_Assessed-checkpoint.ipynb new file mode 100644 index 0000000..bfc51d8 --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_Assessed-checkpoint.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar = 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-3\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 5.6234\n", + " Cost = 316227.7660\n", + " Relative Accuracy = 0.1487\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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ldOi/Z7y7c9pvYfAZXbkFOk2BZi/j7jea2WLACUTl0Rhgh8w7NEeQeauIu08y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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1487\n", + "Train set Accuracy: 0.1470\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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2Kw/tB6yqBbrSLcATlevsB9xdC3SlQWDD8h61mptqga5Ss11EvLxSM8i6BoF9yt5ASZJ6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\n8JyI5BB/hsaebwhwxDm3K9Rme6jNjuBJp/+ahO8T7mcn+l9RY222B86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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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qzycDXwCeDVwYER9oYv0kSRUGOkljGDfUAc8DVlZ9fhtwama+APifwMnNqJgk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_LatLong-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_LatLong-checkpoint.ipynb new file mode 100644 index 0000000..f91ceee --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Condo_LatLong-checkpoint.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar = X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=7\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 17.7828, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 17.7828, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 17.7828, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 17.7828, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 17.7828, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 17.7828, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 17.7828, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 17.7828, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 17.7828, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 17.7828, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 17.7828, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 17.7828, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 17.7828, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 17.7828, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 17.7828, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 17.7828, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 17.7828, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 17.7828, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 17.7828, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 17.7828, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 17.7828, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 17.7828, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 17.7828, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 17.7828, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 17.7828, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 100.0000\n", + " Cost = 1000000.0000\n", + " Relative Accuracy = 0.2796\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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68IU8bgqULgWHNPRhZv4SuPFhOLcjZAS3DJrwJvS4BYYO8LdQWbXWl5fM8BdF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.2796\n", + "Train set Accuracy: 0.2506\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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6I4rerM2BZ4D/FxHnRcSrR3ntzcvr/7bc3gWYAiyuXPsp4LtALSDtB2xUV7Mc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\nvpGOjrDmWrSO60X+5y24NVt/5l9v9O2acTFuj+Ni3EqoZzo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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ZwEAHhjpJkjRMBjTQgaFOkiQNiwEOdGCokyRJw2DAAx0Y6iRJ0qAbgkAHhjpJkjTIhiTQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS-checkpoint.ipynb new file mode 100644 index 0000000..b0a0c4a --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS-checkpoint.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 110\n", + "[ True False False False True True False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False True False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True True False False False False False False False\n", + " False False]\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Condo-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Condo-checkpoint.ipynb new file mode 100644 index 0000000..0b5855c --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Condo-checkpoint.ipynb @@ -0,0 +1,427 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 105\n", + "[ True False False True True False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False True\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True False False False False False False False True\n", + " False False False False False False False False False]\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + "Best parameters: \n", + " Sigma = 100.0000\n", + " Cost = 562341.3252\n", + " Relative Accuracy = 0.1165" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n", + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cross-Validation Accuracy: 0.1165\n", + "Train set Accuracy: 0.0956\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4Km7Y5tdToVOyYZu70agdXPcl/PGzwsegy6BZF/93tCur8yBY/W3R8UKrv4Gs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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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K/BjYp9y0D7BiMNCVbgeeqhxnH+C+wUBXWgxsUJ5jsOaWwUBXqdk6Il5TqVnMmhYDe5W9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\nrkJggg==\n", 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9SLU06jp1EfEXwFXAfiWnHgLellJaWaO6tRXXqZNUDQMDAyxZsgyAC+b0cOKi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\nKIqiKEoOoKJOURRFURQlB/j/tLzFgQYN5O0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Residential-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Residential-checkpoint.ipynb new file mode 100644 index 0000000..41dec2d --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_FS_Residential-checkpoint.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 110\n", + "[ True False False False True True False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False True False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True True False False False False False False False\n", + " False False]\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + "Best parameters: \n", + " Sigma = 1.0000\n", + " Cost = 17782794.1004\n", + " Relative Accuracy = 0.1116" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n", + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cross-Validation Accuracy: 0.1116\n", + "Train set Accuracy: 0.1052\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tXgiKl55hV69t/m7v67Yz2eNh5fgSFS1XIch7n+ec+xoYALwZM+t44PUki00B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\n/T/Uqn03nUFTxAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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ZM5h+/bV0z17J7Nk3sHz5VcyZM6fRrZNUJRXvYxcRARxFtiJ2HrAW+Hfg2pTS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\njaTbet0OPOLPvxwVjsei1rZxFBTcz/DhA3nppe04dyTqvr2AloKzGLX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9d8RdNYlBo89+fSOwMXzsSxDiNoZ/vgQcD3yTIEytJJzVmg90oY8SjK/7KsH6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\nXJ1xUTipoUvaThH+2rY4UucidGtM0P4kziokbr9cU1+/1tTXrzF1das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5iKeeejr/e9im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\neC2TdQDwHrC4+1/Vz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_LatLong-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_LatLong-checkpoint.ipynb new file mode 100644 index 0000000..e27e6ec --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_LatLong-checkpoint.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar=X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 53.58709268 -113.4417665 ]\n", + " [ 53.61282363 -113.4300466 ]\n", + " [ 53.59555643 -113.3784654 ]\n", + " ..., \n", + " [ 53.42686423 -113.4560936 ]\n", + " [ 53.42686423 -113.4560936 ]\n", + " [ 53.42924459 -113.4683265 ]]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=4\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 1000.0000\n", + " Cost = 10000000.0000\n", + " Relative Accuracy = 0.2796\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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SWyq2uj6HPTEPdl77MPq3DjVoG+xH9CY2YLQEOwll3pFUSu4Bqrm0iMp4ifKv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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.2796\n", + "Train set Accuracy: 0.1864\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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HZub3IuJVwHXAvpl5b1nzBuAS4GmZ+VBEnEIR1IYz89Gy5izglMzcp3z+YeA1mXlg3fe4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\n9UFE4nXuvOeRLg6ea/Q9xI1b45tzrtsegWTaRpHyJPN99zHXzXcvfhF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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IiIhIMqWkyTVKoU5EREQkKoWBDhTqRERERPZLaaADhToRERGRQIoDHSjUiYiIiKQ+0IFC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\nERGRCqCgTkRkGMzsq2b2oxzH7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential-checkpoint.ipynb new file mode 100644 index 0000000..1b4bc87 --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential-checkpoint.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 82\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.55150000e+06 3.10941360e-01 3.98108300e-01 4.64200000e+01\n", + " 8.00000000e+00 9.00000000e+00 6.00000000e+00 9.42000000e+02\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 7.00000000e+00 3.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.20000000e+01 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 8.43400000e+03\n", + " 1.00000000e+00 2.50000000e+01 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.09000000e+02 1.76100000e+04 1.38220000e+04\n", + " 1.10770000e+04 1.00210000e+04 1.08960000e+04 1.40240000e+04\n", + " 1.58800000e+04 1.41740000e+04]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "print( diff_X )\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=0\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_Assessed-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_Assessed-checkpoint.ipynb new file mode 100644 index 0000000..8ab65e2 --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_Assessed-checkpoint.ipynb @@ -0,0 +1,378 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar = 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=4\n", + "maxcost=9\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 5)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m kf = KFold(ndata, n_folds=nfold. shuffle=True)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cvmape' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# Get indexes of parameter combination with minimum error\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0midxsigma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midxcost\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munravel_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcvmape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcvmape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'cvmape' is not defined" + ] + } + ], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'idxsigma' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m# Print out Results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0moptsigma\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcvsigma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midxsigma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0moptcost\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcvcost\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midxcost\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcvsigma\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midxsigma\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcvcost\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midxcost\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcvmape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midxsigma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0midxcost\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'idxsigma' is not defined" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cvmape' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'font.size'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;36m14\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcvmape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'nearest'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnsigma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncost\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'cvmape' is not defined" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_LatLong-checkpoint.ipynb b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_LatLong-checkpoint.ipynb new file mode 100644 index 0000000..ed72a71 --- /dev/null +++ b/code/svm_regression/.ipynb_checkpoints/SVM_RBF_Residential_LatLong-checkpoint.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar = X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.31094136 0.3981083 ]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "print( diff_X )\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0032, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0032, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0032, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0032, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0032, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 3.1623, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0032, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0032, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0032, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0032, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0032, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 3.1623, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0032, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0032, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0032, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0032, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0032, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 3.1623, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0032, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0032, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0032, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0032, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0032, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 3.1623, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0032, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0032, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0032, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0032, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0032, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 3.1623, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 100.0000\n", + " Cost = 17782794.1004\n", + " Relative Accuracy = 0.1839\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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tJ6ymYl/EVBl/94A5KeVzKD62N2ouPoieTclDIubjj0vqhWjpOPyY0cT+3tW2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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1839\n", + "Train set Accuracy: 0.1638\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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FEfGpyuejgS8Bfw6cFRGfaGH7JEk1hjpJIxgx2AH7AEsqn98NvC8z/wJ4PXB0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\nyytzsn5Jy1+6tbOqak5Gq2BP0JtWtPasnYZhGEZ2YBY7I9c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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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k84EOFOpERESk3bVAoAOFOhEREWlnLRLoQKFORERE2lULBTpQqBMREZF21GKBDhTqRERE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\nAjaKyJdE5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF.ipynb b/code/svm_regression/SVM_RBF.ipynb new file mode 100644 index 0000000..0c554b1 --- /dev/null +++ b/code/svm_regression/SVM_RBF.ipynb @@ -0,0 +1,565 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 110\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 1.65500000e+05 5.35870927e+01 -1.13441766e+02 ..., 2.02500000e+03\n", + " 1.60500000e+03 2.84100000e+03]\n", + " [ 4.75500000e+05 5.36128236e+01 -1.13430047e+02 ..., 1.93000000e+03\n", + " 1.48000000e+03 2.09700000e+03]\n", + " [ 2.68000000e+05 5.35955564e+01 -1.13378465e+02 ..., 2.48500000e+03\n", + " 2.56900000e+03 1.60800000e+03]\n", + " ..., \n", + " [ 1.54000000e+05 5.34268642e+01 -1.13456094e+02 ..., 4.70400000e+03\n", + " 5.43900000e+03 1.71500000e+03]\n", + " [ 1.50500000e+05 5.34268642e+01 -1.13456094e+02 ..., 4.70400000e+03\n", + " 5.43900000e+03 1.71500000e+03]\n", + " [ 1.56000000e+05 5.34292446e+01 -1.13468327e+02 ..., 5.32900000e+03\n", + " 4.90800000e+03 1.12600000e+03]]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=0\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 0.0100\n", + " Cost = 5623413.2519\n", + " Relative Accuracy = 0.1227\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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DFtvD9OHQ4fCi6dNf9XFB5VWwhBKdIJb8HQqhZV8Y+EnRvBDg00tgxVzY9lZo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1227\n", + "Train set Accuracy: 0.1080\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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Ekoj4UOX5scBngT8Fzo2I97ewfZKkGkOdpHGMG+yA5wHLKs/fArwjM18A/BVw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\nnWEYhmEYRg/h/wOZa0ALgZ9DUQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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7y0mgA4U6ERERaVc5CnSgUCciIiLtKGeBDhTqREREpN3kMNCBQp2IiIi0k5wGOlCoExER\nkXaR40AHCnUiIiLSDnIe6EChTkRERPKuDQIdKNSJiIhInrVJoAOFOhEREcmrNgp0oFAnIiIiedRm\ngQ4U6kRERCRv2jDQgUKdiIiI5EmbBjpQqBMREZG8aONABwp1IiIikgdtHuhAoU5ERESyToEOUKgT\nERGRLFOg202hTkRERLJJga6IQp2IiIhkjwLdJAp1IiIiki0KdIkU6kRERCQ7FOhKUqgTERGRbFCg\nm5JCnYiIiKSfAt20FOpEREQk3RToyqJQJyIiIumlQFc2hToRERFJJwW6iijUiYiISPoo0FVMoU5E\nRETSRYGuKgp1IiIikh4KdFVTqBMREZF0UKCriUKdiIiItJ4CXc0U6kRERKS1FOjqQqFOREREWkeB\nrm4U6kRERKQ1FOjqSqFOREREmk+Bru4U6kRERKS5FOgaQqFOREREmkeBrmEU6kRERKQ5FOgaSqFO\ncmd0dJQlS1awZMkKRkdHW10cEREBBbomMHdvdRnagpm5nnXjjY6OsmzZIOPjFwLQ3b2GjRs3MDAw\n0OKSiYi0sZwGOjPD3a3V5ShQqGsShbrmWLJkBWNjS4HBcM8G+vs3sXnzNa0slohI+8ppoIP0hTo1\nv4qIiEhj5DjQpVFnqwsgUk9DQ6vYunWQ8fHgdXf3GoaGNrS2UCIi7UiBrunU/Nokan5tntHRUdat\nWw8EIU/96UREmqxNAl3aml8V6ppEoU5ERNpCmwQ6SF+oU586ERERqY82CnRppFAnIiIitVOgazmF\nOhEREamNAl0qKNSJiIhI9RToUkOhTkRERKqjQJcqCnUiIiJSOQW61FGoExERkcoo0KWSQp2IiIiU\nT4EutRTqREREpDwVBrrR0VGWLFnBkiUrGB0dbUIB25tWlGgSrSghIiKZVkWgW7ZskPHxC4FgLe6N\nGzfkaunGtK0ooVDXJAp1IiKSWVU0uS5ZsoKxsaXAYLhnA/39m9i8+ZqGFbPZ0hbq1PwqIiIipakP\nXWZ0troAIiIiklI1BLqhoVVs3TrI+Hjwurt7DUNDGxpQSClQ82uTqPlVREQypQ41dKOjo6xbtx4I\nQl6e+tNB+ppfFeqaRKFOREQyQ02uZUlbqFOfOhEREZmgQJdZCnUiIiISUKDLNIU6ERERUaDLAYU6\nERGRdqdAN0kWV8PQQIkm0UAJERFJJQW6ScpdDSNtAyUU6ppEoU5ERFJHgS5RuathpC3UqflVRESk\nHSnQ5Y5WlBAREWk3CnRTyupqGGp+bRI1v4qISCoo0JWlnNUw0tb8qlDXJAp1IiLScgp0dZW2UKc+\ndSIiIu1AgS73FOpEpCGyOMeTSG4p0LUFNb82iZpfpZ2UO8eTiDSBAl3DpK35VaGuSRTqpJ2UO8eT\niDSYAl1DpS3UqflVREQkjxTo2o7mqRORusvqHE8iuaFA15bU/Nokan6VdlPOHE8i0gAKdE2TtuZX\nhbomUagTEZGGU6BrqrSFOvWpExERyQMFuranUCciJWmuOZGMUKAT1PzaNGp+lazRXHMiGaFA1zJp\na35VqGsShTrJGs01J5IBCnQtlbZQp+ZXERGRLFKgkxjNUyciiTTXnEiKKdBJAjW/NomaXyWLNNec\nSAop0KVG2ppfFeqaRKFORERqpkCXKmkLdepTJyIiQgam8FGgk2mopq5JVFMnIpJeqZ/CR4EuldJW\nU6dQ1yQKdSIi6ZXqKXwU6FIrbaFOza8iIiJppUAnFdCUJiIi0vZSOYWPAp1USM2vTaLmVxGRdEvV\nFD4KdJmQtuZXhbomUagTEZGyKNBlRtpCnfrUiYiIpIUCXU1SPy1Ng6mmrklUUyciIlNSoKtJK6al\nSVtNnUJdkyjUiYhISQp0NWvFtDRpC3VqfhUREWklBTqpE4U6ERGRCtS135YCXd0MDa2iu3sNsAHY\nEE5Ls6rVxWoqNb82iZpfRUSyr679thTo6q7Z09KkrflVoa5JFOpERLKvbv22FOhyIW2hTs2vIiIi\nzaRAJw2iZcJERETKVPNyYgp00kBqfm0SNb+KiORD1f22FOhyJ23Nrwp1TaJQJyLSxhTociltoU59\n6kRERBpJgU6aRKFORESkURTopIkU6kRERBpBgU6aTKFORESk3hTopAWaGurM7AVmtsnM7jSzXWY2\nGDt+Zbg/ut0QO2cPM7vUzB4ws8fM7Jtm9tTYOQeY2ZfM7OFw+6KZ7Rc75zAz+1Z4jQfM7JNm9pTY\nOceY2XVm9kRY5nMT7mmxmf3MzMbN7FYze0ftT0pERDIrY4GursueSUs1u6Zub+BXwLuBcSA+HNSB\nMeCgyPby2DmfAJYDK4HnAzOBa80sei9XAccCA8BLgeOALxUOmtkM4NtheU4CTgFeC6yLnDMzLMs9\nwAlhmc8ys9WRcw4HvgNsDT/vAuBSM1te/iMREZHcyGCgW7ZskLGxpYyNLWXZskEFuwxr2ZQmZvYo\n8C53/2Jk35XALHd/VYn37AfcD7zJ3b8S7psH3AG8zN03m9mzgF8Di9z9h+E5i4AfAEe4++/N7GXA\ntcBh7n5XeM4bgH8B5rj7Y2Z2GkFIm+vufw3POQc4zd3nha8vBF7j7kdEyvg5YIG7Py9Wdk1pIiKS\nZxkLdFDHZc/alKY0mZoDJ5nZfWb2WzNbb2ZzIsePB54CbN79Bvc7gd8AJ4a7TgQeKwS60A3A48Dz\nIuf8dyHQhTYDe4SfUTjnB4VAFznnEDObHzlnM8U2AyeEtYEiItIOMhjoJH/StkzYCHANcBtwODAM\nfM/Mjnf37QTNsTvd/cHY++4LjxH++0D0oLu7md0fO+e+2DW2ATtj5/wx4XMKx+4A5iZc5z6C5zo7\n4ZiIiORNhgNdzcueSaqkKtS5+1cjL39tZj8jCE+vADZO8dZqqj6ne0/d20rXrl27++eTTz6Zk08+\nud4fISIizZThQAcwMDDAxo0bIsuebSh/2bM2tGXLFrZs2dLqYpSUqlAX5+73mNmdwDPCXfcCM8xs\nVqy2bi5wXeScaJMtZmbAgeGxwjlFfd4IatZmxM45KHbO3Mixqc7ZQVDzVyQa6kREJOMyHugKBgYG\nFOTKFK+Q+dCHPtS6wiRIW5+6ImF/uqcSjEAF+BnwJLAkcs484EiCfnMAPwT2MbMTI5c6kWCka+Gc\nG4BnxaZC6Qf+Gn5G4TrPN7M9Yufc5e53RM7pjxW7H/iJu++s4FZFRCRLchLoJF+aOvrVzPYGnhm+\nvB74KPAt4EHgIeBDwNcJasCeRjD69KnAs9z98fAanwZeBbwpfM/FwH7A8YXhpWb2HWAesIqgmXU9\n8Ad3f3V4vAP4BUHfuyGCWrorgWvc/d3hOTOB3wJbCPr2HQFcAax190vCc54G3Ax8LvyMRcBlwEp3\nL2ou1uhXEZGcUKCTUNpGvzY71J0MfC986Uz0a7sSeCfwb0AfsD9B7dz3gHOjo1TNrAu4CHg90A18\nF3hn7Jz9gUuBpeGubwKnu/sjkXMOBT4NvIhgzrwvA2e5+5ORc44mCGnPJQiQl7v7+bF7egFwCbAA\nuAu40N3XJ9y7Qp2ISNYp0ElEW4e6dqZQJyKScQp0EpO2UJfqPnUiIiKpoEAnGaBQJyIiMhUFupbT\n+rTlUfNrk6j5VUQkgxToWq6wPu34+IVAMEHyxo3pmE8vbc2vCnVNolAnIpIxCnSpkOb1adMW6tT8\nKiIiEqdAJxmkUCciIhJVQaBTX6/GGxpaRXf3GmADsCFcn3ZVq4uVSmp+bRI1v4qIZECFgS6tfb3y\nZnR0NLI+7arUPOO0Nb8q1DWJQp2ISMpV2OSa5r5e0hxpC3VqfhUREVEfOsmBzlYXQEREpKWqDHRD\nQ6vYunWQ8fHgddDXa0ODCikyPTW/NomaX0VEUqjGGrq09vWS5khb86tCXZMo1ImIpIyaXKVGaQt1\n6lMnIiLtJ0OBTtOmSLlUU9ckqqkTEUmJjAU6TZuSXmmrqVOoaxKFOhGRFMhQoANNm5J2aQt1an4V\nEZH2kLFAJ1IpTWkiIiL5l9FAp2lTpBJqfm0SNb+KiLRIRgNdgaZNSa+0Nb8q1DWJQp2ISAtkPNBJ\nuqUt1KlPnYiI5JMCnbQZhToREckfBbpM0px8tdFACRERyRcFukyKz8m3detgKufki/ZxTBv1qWsS\n9akTEWkCBbrMqmVOvmYNJokHT3iT+tSJiIjUXU4DnZokp1YIWmNjSxkbW8qyZYMNe07r1q0PA90g\nE+EzPdT8KiIi2ZfjQJeFJsl6qHZOvuKgBePjwb48PqPpKNSJiEi25TTQQXsFloGBATZu3BBpRk1f\neI0Hz7RRqBMRkezKcaBrRwMDAxUHuWauuhEPnmNjDfmYqmmgRJNooISISJ21QaCLN792d6/JbfNr\nLVq16kbaJh9WqGsShToRkTpqg0BXoGXC0kuhrk0p1ImI1EkbBTpJt7SFOk1pIiIi2aFAJ1KSQp2I\niGSDAp3IlBTqREQk/RToRKalUCciIummQCdSFoU6ERFJrxwHOi3/VX/t/kw1+rVJNPpVRKRCOQ90\njZx/rh2nQWnFnH5pG/2qUNckCnUiIhXIcaADWLJkBWNjS5lYFH4D/f2b2Lz5mpqv3a4TFjfymZaS\ntlCnZcJERCRdch7oGq2d1ouVYgp1IiKSHm0S6Jq5Xmm70DNV82vTqPlVRGQabRLoChrV761dm1+h\n+X0J09b8qlDXJAp1IiJTaLNA12jtOFCiFRTq2pRCnYhICQp0klFpC3Wap05ERFpHgU6kbhTqRESk\nNRToROpKoU5ERJpPgU6k7hTqRESkuRToptXuy11JdTRQokk0UEJEBAW6MrTzlCRZk7aBEgp1TaJQ\nJyJtT4GuLEnLXfX0nM/xxz9b05OkTNpCnZpfRURyLDXNeAp0NXnooTmMjS1l2bJBNcdKSQp1IiI5\nVWjGGxtb2tpAoEBXkaGhVXR3rwE2hNv7gLVA0CRbmFS4WqkJ+lJ3CnUiIjlVvLB7fQJBxRToKjYw\nMMDGjRvo799ET8/5BL+/+i4h1vKgLw3R2eoCiIhITinQVW1gYICBgYHIoIljgNoXqS8O+jA+HuxT\nP718UE2diEhOxZvxgkCwqjkfrkBXF9Fau/7+TQ0bBasm2XzQ6Ncm0ehXEWmFlizsrkCXWknTpZxz\nzhl85COXagqVKqRt9KtCXZMo1IlIW1CgS7140F+3bv2kKVT6+zexefM1LStjVqQt1KlPnYiI1IcC\nXSYU+usVNH3wjDSMQp2IiNROgS6zhoZWsXXrIOPjwetaB2NI66j5tUnU/CoiuaVAl3kt6XtZgbSW\nL23Nr2WHOjPbAzgE6AYecPcHGlmwvFGoE5FcUqCTBkvzWriZCnVmNhN4I3AK8FzgKZHDdwEjwOfc\n/ceNLGQeKNSJSO4o0EkTJK2Fm5aBHGkLdSXnqTOz1cBtwJuBzcCrgWOBI4ATCdYseQqw2cxGzOyZ\nDS+tiIikgwKdSOpMNVBiIbDY3W8ucfy/gM+b2Z7AW4GTgd/Xt3giIpI6CnTSRBrIUT4NlGgSNb+K\nSC4o0EkLaKBEeSoKdWY2G3B3f7BxRconhToRyTwFOpEiaQt10679amZzzexKM3sYuB94wMz+ZGaf\nN7MDG19EERFpOQU6kdSbbvTr3sCNQA/wr8BvAAOOAl4PbAOOc/fHG1/UbFNNnYhklgKdSKK01dRN\nt6LEGQQjXI9293ujB8zsn4Efhud8tDHFExGRllKgE8mM6ZpfXwVcEA90AO5+D/DP4TkiIpI3CnRS\nhtHRUZYsWcGSJSsYHR2t+hyp3XTNrw8CJ7n7b0ocXwD8wN17GlS+3FDzq4jUQ9NGASrQSRnKWe0h\nzStC1Cptza/ThbongXnufl+J4wcD/+vu0zXjtj2FOhGpVdO+HBXopEzlrPaQ5hUhapW2UDddGJsB\nTJVEdlHGCFoREandunXrw0AXfDmOjwf76hrqFOhEMqucGrYtZrazhveLiEgWKNBJhcpZ7UErQjTP\ndM2va8u4hrv7h+pWopxS86uI1Kqhza8KdFKlcvp5pnVFiFqlrflVy4Q1iUKdiNRDQ74cFehEqpKb\nUGdm3cBK4K3uflJdS5VDCnUikkoKdCJVS1uoq3iQg5k918zWA/cCFwO31r1UIiJSF1POD6ZAJ5Ir\nZdXUmVmKo8SQAAAgAElEQVQP8I/AW4FeoBtYBXzR3bc3tIQ5oZo6EWm2KfvgKdCJ1CxtNXVTjl41\ns5cAbwOWAv8FXAJcAzwI3KBAJyKSXiWnQHnqUxXoRHJouilJRgiaWI909z8WdpqlJpSKiEgFnvbY\nnxXoRHJqulD3HeCdwOFm9mXg2+6+o/HFEhGRWg0NreK66/6R7WGbyrGd7+XSW4DLLlOgE8mhKQdK\nuPtS4JnAz4GLgHvN7NOAqupERDLhSeByFnAx39nxMLesWpWKQKcF3qUR2v3vquwpTSxoc11M0Mdu\nBXA/8DXg6+7+o4aVMCc0UEJEmq2w5uYCjmeMflbzGh7sv7/la27meYF3aZ1W/F2lbaBE2VOaeGCL\nu78ROBj4GPAi4PpGFU5ERGqzgDvDQHcxV7Ow1cUB4gM4gi/hwoTKItXS31UV89QBuPvD7n6Zux8H\nPKfOZRIRkTr44IolfJfzWM1ruJrt4Zqbq1pdLBFpkClDnZkdbWbXmtnMhGP7mdm1wM6GlU5ERKpz\n880sWruW+9acxYP999Pfvyk1TZxDQ6vo7l4DbAA2KGxKXejvapo+dWZ2BXCPu/+fEsfPB57u7m9o\nUPlyQ33qRKRpMjCxcF4XeJfWavbfVdr61E0X6n4PrHT3n5U4fhzw/7n7MxpUvtxQqBORpshAoBPJ\ni7SFuun61B0KbJvi+EPAvPoVR0REqpahQNfuU0+INMJ0oe5PwFS1cM8AHq5fcUREpCopDHSlglth\n6omxsaWMjS1l2bJBBTuROpiu+fWrwF7u/qoSx68FnnD3v29Q+XJDza8i0jApDXSl5gwrzJ9XWJMW\nNtDfv6nl8+eJVCprza8XAEvM7N/MbGE44nU/MzvRzL4J9AMfbXwxRUQkUSTQjfb0pKZJU3OGiTTf\nlGu/uvsvzGwFcAVwQ+zwNuB17v7zRhVORESmEAt00ZqxrVsHUzOFSdzQ0Cq2bh1kfDx4HUw9saG1\nhRLJgbKWCTOzvYABgnVgDfgdMOruTzS2ePmh5lcRqatYk2vamjSnW7JJU5pIHqSt+XXKmrqCMLxt\nbHBZRESkHCnsQxc3MDDAxo0bIsEtCHQKcwq00jhl1dRNepPZ3wOLgBvd/cp6FyqPVFMnInVRItC1\nYjHzSmWhjI2Wp2egcJq+mrppQ52ZbQDuKqwqYWZvBj4LXA+cAFzs7h9sdEGzTqFORGo2TQ1d2r9k\n09ZE3Ap5eQZ5Cqe1SFuoK6f59XnA2yOvTwfe6+6XmdlLgfWAQp2ISCOV0eQ6MDCQkS/VUYKvjrvZ\ntm1GqwsjVSge3Qzj48G+bPz95VfJKU3M7Ipw7ddDgTMjr58NvCT8+Q3AIZFjIiJSb2Gg++XgIEuu\n+HoqpiypxtDQKrq63gO8EVgKnMqvf/27TN5LtbTovDRSyeZXM5tPMNL1h8BpwI3AC4CPAM8PT9sH\n+C9gQXit2xtc3sxS86uIVCUS6E781JWZb+467riTufHGN5P15sdapL2ZvBxqfg1kpvnV3e8AMLMf\nAWuATwNnAv8WOfYc4LbCaxERqaNIk+tZV3w9F81ds2fPanURWi47zeSllRrdLK1VTp+61cAXCULd\n9cCHIsdOBb7VgHKJiLS3eB+6K77e6hLVhSYezo88hNO8qWpKE6mcml9FpGwJgyLy1NyVh+ZHEUhf\n86tCXZMo1IlIWaYY5aowJJIumQl1ZnYucIm7PzbtRcxOAnrcfVOdy5cbCnUi+VaXwJWBlSJEZELa\nQl3JKU2ApwN/NLP1ZvYqMzu4cMDM9jSz48zs3Wb2Y+BLwJ8aXVgRkTQqNI2OjS1lbGwpy5YNljVN\nx+joKEuWrGDJkhVc/9nPtjzQRctT7jQj1bxHRBrE3UtuwDEEM0T+CdgF7AT+Ev68C/gpsArYY6rr\naPPwUYtIHvX3L3e40sHD7Urv718+5XtGRka8u3uuw5W+gGG/hw7/xZo1Jc/t71/u/f3LfWRkpBG3\nUFQeuNI7Og7wvr5FU35e/D3d3XMbVj6RNAq/21ueMQrblKNf3f0mYJWZnQb8LTAf6Aa2Ab9w9wca\nkDNFRHKvMCP/Ao5njA/wXlbx4M9/z+bYefEBElu3DjZkgER8hYBdu+DGGy9n2bLSn6dVBUTSZarm\n193cfae73+ju/+buX3H3MQU6EZFAtasELOBOxuhnNRdzNQsTzykOTkG4K/Tda7xD6vZ5aqYVabxy\n5qkTEZEpVDMR6wdXLKF37J28l1Vczfaa52urdaBGfP64YM75DcC9Zb+n1D00q7ZRpO21uv23XTbU\np05ECm66yf2gg/wXa9ZM21eunH5r9erbNjIy4n19i72jY5bDUFnXKqe/XzV9DkWygJT1qdM8dU2i\nKU1EBKhq2pLpauGWLFnB2NhS6rWear3nw6t3+UTSIm1Tmqj5VUSkWaqch65RyzGVCm+Vft50IVBL\ng4k0SaurCttlQ82vIu0tbHL1q66q+6WraX6t9j3xptZyr1PrtCzNmNZFpFJkpfnVzK4ACgct8nNS\nMHxLnbNm7qj5VaSNNWGliEqbTCttEi219uy6desb3rRaybq3WkpNmiltza9TTWkyJ7LNBlYAy4Bn\nAM8Mf14RHi+Lmb3AzDaZ2Z1mtsvMBhPOWWtmd5nZE2b2fTM7KnZ8DzO71MweMLPHzOybZvbU2DkH\nmNmXzOzhcPuime0XO+cwM/tWeI0HzOyTZvaU2DnHmNl1YVnuDJdOi5d3sZn9zMzGzexWM3tHuc9D\nRNpATpb+auXUKuV+drUre4jkRclQ5+6vdPdXufurgBuAUWCeu7/A3Z8PzANGgB9V8Hl7A78C3g2M\nE6v9M7M1wGrgdOA5wP3AmJntEzntE8ByYCXwfGAmcK2ZRe/lKuBYYAB4KXAcwVJmhc+ZAXw7LM9J\nwCnAa4F1kXNmAmPAPcAJYZnPMrPVkXMOB74DbA0/7wLgUjNbXsEzEZG8alKgqybMVDu3XqOuUw+t\nndNPmk1zHyYop42WYKKiBQn7FwD3VtPuCzwK/FPktREEqLMj+/YEHgFWha/3A/4KnBI5Zx7B8mVL\nwtfPIljC7MTIOYvCfc8MX78sfM9TI+e8gSBo7hO+Pg14mMgSaMA5wJ2R1xcCv43d1+eAGxLud9q2\neRHJkQb2oYurdsqQSvqpTdV3rtH93crtt6epU9pHWpaoI2V96ioJYP0J+18CPFrVB08OdU8Pg9fx\nsfOuBa4Mf35ReM6s2Dk3Ax8Mf34L8EjsuIWfNxi+/jBwU+ycOeG1F4evvwh8K3bOc8Jz5oev/xO4\nNHbO64DtwIzY/nL+PkQkD5oY6NybF2ZaOVihnM9Oyxe9NF5aAnzaQl25U5pcA1xhZmcBPwz3nRjW\nVH2jzGtM56Dw3/ti++8HDomcs9PdH4ydc1/k/QcBRUuYubub2f2xc+Kfs42g9i56zh8TPqdw7A5g\nbsJ17iOYKmZ2wjERyaFo5/wPrljCorVrm9qHrllThjRqapV6fXY1K3uI5Em5oe6dwEXAFUBXuO9J\n4PPA+xpQrrjpho1WM/JkuvdoqKqITOsjH/kI5523jl27LmEBd9I79k5+ueYsnt3EQREKMxNaGTyl\neTT3YbKyQp27PwG808zeD/SGu29198fqWJbCAoNzgTsj++dGjt0LzDCzWbHaurnAdZFzikbkmpkB\nB8au87zY588GZsTOOSh2ztxYWUuds4Og5q/I2rVrd/988sknc/LJJ8dPEUmtRk0VkeUpKEZHRznv\nvEvCQHc8Y3yA97KKB3/+ezY3uSx5DzNZ/juR+mvV/5HZsmULW7ZsafjnVK2StlqC4PN3wJ61tvuS\nPFDibiYPlPgz8Pbw9VQDJfrD10kDJZ5H8UCJlzJ5oMTrKR4ocWr42dGBEv8H+N/I648yeaDEeuD6\nhPstp3leJJUa1Vcp632ggn49C30Bw343B/lKrlLn/IhSkxVX2i8v638nkl+krE9duQFsX+BrYTDa\nCTw93H85sLbsDwumEDk23B4Hzg1/PjQ8/n6CEafLgKOBqwlq7faOXOPTwP8CLwb6gO8DP4dgIuXw\nnO8QTJ2ykKDv303ANyPHO8Lj/xF+/kvCz/lk5JyZBKNxv0Iwynd5GPLeGznnacBjwCVhmHxbGDqX\nJdx7DX82Iq3VqE7JaensXK3+/uW+gEG/mw5fyakOV3pHxwEKHJ4cxIaHh6sKZ1n/O5H8Sluom2ry\n4agLgacSzPc2Htl/bRh2yvWcMID9nKAW7kPhzx8KU8/HwoB0GfATgqbMJe7+eOQa7wE2Al8lmB/u\nEeBV4cMteD3wS4K59UaAG4F/LBx0913AK4AngOsJwuPXifQPdPdHgH6CQRo/BS4FLnL3SyLn3A68\nHHhB+BlnA2e4+8YKnomIZNQHVyzhu3yJ1byUq9kCrObww+e3ulipkDRn3MUXX6F55EQaqZzkR1CL\n9dzw50eZqKl7BvBYq5NpFjZUUycZlsbm15avBRpOW/KLNWu8r2+Rd3QckMrmwVY9p6TatZ6e3qrn\n01Pzq6QRKaupKzeQPA70+uRQ1wf8udU3kYVNoU6yrlHhIE19rMouS2weumY2D9ZrwuBGm7r5dchh\noXd0zPLh4eGyr9fSEJ9jerbVy2qou46wL1ks1H0G+PdW30QWNoU6kfppRIgqOwAlTCzczMl/Kwlp\nzVhpotLrDA8Pp7ZWsx2pFrQ2WQ11zwvD3L8AfyHoX/b9sAbv+EYWMC+bQp1I/TQiRJV1zRIrRTTr\ni7HS+67mOVVzL5WEQA16SBf9PmqTtlBX1kAJd78hDHZdwK0EI0/vAha6+8/K78EnIlKsmkW5W7KI\n/M03Q39/4koRhTmz+vs30d+/iY0b0zH5b6XPaXR0lNe//l2Mjx9OMAXn9IMZRkdHWbZskLGxpYyN\nLWXZskEtri7SKq1Ole2yoZo6kUnSNFBiyrI0eS3XqspYwvDwsPf09HpPT++U/dfi14a5DiN1rw1U\nc1+66PdRG1JWU1duINkJHJiwfzbBWqwtv5G0bwp1IpOlreknMSimJNAVNGqgRNLvAhZ6V9ecuvfb\na1bHfA0AKE+1z0nPN7uhbleJUHcIMN7qm8jCplAnMlnaQt0kKQt0lark+SaHunne17doys9Ia01P\nWsuVF3q+gbSFuinXfjWzocjL08zs0cjrGQST7v62xhZgEWlTqV6Ue4o+dHk0NLSK//iPU9i1q7Bn\nDfBGZs++bcr3tWoNzukUT34M4+PBvjSULQ/0fNNpylAHnAF4+PNbCZphC7YDtwPvqH+xRKQdpDUQ\ncPPNPHbiibzX9+Ibp5/L6j/8gXPOOafVpapYJaF5YGCAD394iPPOG2LXrmcCb6S7+8tlheyBgYF0\n/N5E2pwFtYfTnGS2hWA90z81vEQ5ZWZezrMWkRYLA93bH9vJ1Xwm3Hkmw8Pvz2SwGx0djYTmVdOG\nr0rPT6vCqNygNikItGkZlZwHer4BM8PdrdXlKCgr1EntFOpEMiBscn37o7v4l8c/RqFpCTbQ03M+\nDz74P60snVQoLwE1rfR8MxzqzOwI4LXAoQTz1QEYQSfBtzSmePmhUCeScpE+dLNOP5eHHjoXhToR\nmUraQl1Zkw+b2SuAXwGvJOhbdwTwCmAZMKdhpRMRaYbYoIjVq98MnElh0l44M9wn5apmUmkRqU1Z\noQ74MPAhdz+RYJmwfwLmA98lWC5MRCSbEka5nnPOOQwPv5+envPp6Tl/2v50o6OjHHfcycya9QyO\nO+6ktg8xWmVCpDXKHSjxGPC37v4HM3sIeIG732xmxwDfdvfDGl3QrFPzq0gK1WHaktHRUZYu/Ue2\nb/94uOd9dHXtYNOmq9uyjxHAkiUrGBtbSrT5ur9/E5s3X9PKYonUXSabX4FHge7w53uAZ4Y/dwI9\n9S6UiEjDRQLdaE9P1U2F69atDwPdYLhdxPbtR065XmoaqblUJPumm6eu4MfAIuDXwLeBdWb2t8By\n4IcNKpuISGPEAl10aoatWwfbbmqG+PQUtT6DVE8qLZJj5Ta/9gJ7u/uvzGxv4CKCkPc7YLW7/7Gx\nxcw+Nb+KtE506oUPrljCorVrdze5VtpUGJ/GAch882sjmkvbZbqLdrlPSZa25teyaurc/dbIz48D\npzWsRCIidRSthVrAnfSOvZNfrjmLZ1fRh65UjdamTV/i7LMv4I477mT+/CO44IJzi77c2/GLvx1W\nmah3DadIzSpdLBbYE9grurV6AdssbMGjFsmvkZER7+9f7v39y1O1sHdhofoF3OR3c5Cv5NSiRe0r\nWZg8adH76LWSZGHh8yyUMY2q+XuQfAm/21ueMQpbufPUPc3MNpnZo8ATwGOR7dH6R00RyZJKp7Bo\ndqf8BdzJGP2s5mKuZmHRscL6s/39m+jv31T3mpbihc+DWp1qB1E06rk1+hmISJOUk/yAHwA/Ifiv\n0suAl0a3VifTLGyopk5yrNwai5GREe/rW+QdHQc0rVZo6+WX+z10+EpOrfnzpqrRKlVTWa/aHNWm\npc/w8HBT/5YlfUhZTV25geQx4KhWFzbLm0Kd5Fk5wWUilCycdG5PT29jmm1vusn9oIP8G697nff0\n9HpPT68PDw/XdMmk8DZd2KtHGFNT34Q0NPVP/F6HHBZ6R8esmv+2WiUNzzOrshrqbgAWt7qwWd4U\n6iTPygkuE6FkcjgpBL2k91X9hRMGul+sWdPwGq7pAlc9vjT7+haFz2m5w0gmQ109nkNaaizzErLT\n8jyzKquh7mjge8BrgF7gsOjW6pvIwqZQJ3k33Rf2xJfgiMPElwgc4HCwQ6/DiikHMXR1zfG+vkVT\nhoKRkRF/+4kv9m1de/gv1qwJP3coDEPBz/X+8m30F/zIyIh3dc2JPLP9vbNz70x9+eatxjIt5ahV\nXu6jVdIW6sqdfNiAA4FvJHXLA2aUeR0RyanpprCYmJD2QuCNdHQM0dnZwfbtTwBHhGf9O3/4w9N2\nv6d4kAFs3w433ng5sDRx+ojR0VHOefUb+NZfd3A6b+abn7qSQw6ZBfwnwfSaAO9j27YjqKdqJ9st\nd6qT4lUrAjt3rq653M0U/12Ojwf7sjogQxMsSxqVu0zYBuB+4FXA3wHPjWx/15iiiUieFI+wvI3v\nfOdfmTEDYF/g1HDbi7vvfmCaKx1CqVGk13zo43zrrztYzWe4ms8wPn4h99//GEGgG6SwjFd8is5y\nR5WWOq+a0aO1Lnrv/jcNWYos7cuFDQ2tort7DcHX0oYwTK1qejnyMmI4Lc9T6qSc6jyCaUyOaHW1\nYpY31PwqMkln54GTmn46Ow/cfTzeZAezw+bbhGaim27ybV17hKNcJ67X09M7bX+3cpoFpzuv0v5i\nlTR7jYyMFI2yDJqvy2tGrqRcjexfVc9rq2N/fel5Vo+UNb+WG0iuQ1OX1PagFeqkzZTzRdHbe+yk\nYNPbe2zidfr6FntX1/7JoWCKQRHDw8NTholyw9VU51UTWCrtyxRMnzErHCwxVNZnVFquZvQNVHiQ\nPElbqCu3T92ngUvM7FDgV8CTsdq+n9ej1lBE8qHU8klAUR+yyy77KK94xSns3Bm8b8aM93LZZV8p\nula0r16hD9q2bfcBR7Ju3Xr2uf323Wu5PvuUU9j4whdGPmOiSezii88HYPXqM+raTDY6OsrrX/+u\nivuLTdcnK97f7pxzzuGEE04I991WdG+lpK0fWzssHSbSUuUkP2DXFNvOVifTLGyopk5yoJyalpGR\nkbDJc2FRU2lf36LEWrSg9m2hw0Lv6tp/2hqc6ISvCxj2e+jwX6xZM2WZOzv3dpjnMG/SqNH4yNKu\nrjllN79O1AJOnnuvlqbRVo0U1fQWIpUhZTV15QaSp021tfomsrAp1EnWlfOFP7kP3FwvzKmW1Ldt\nuv5uSWUImiCT13JNCkm9vUc5zIyUaab39h5VdM1yg2X8+qWmaak1DLVyFQo1kYqUL22hrqzmV3e/\nvdYaQRHJnmgT4LZtD07blBdv7guspbv7NubPP5KHHqqtPOvWrWfXrmeGa7l+IFzLdTv9bCrZ5HvH\nHduATxWV6Y473l90ze3bP8HEtCkbSjZRxpsPJ0afDhCMHlxLT88DXHVVOkZCFkZoJjVHT/WeNJRd\nRCpXMtSZ2XLgWnffHv5ckrsnzV8nIhkWD0kdHUNVXacQcoDwesH+7u41rF59Bh/+8HvYvv1yALq6\nbmFo6Oopr7eAIxjjPFaziqvZTkfHexka+krJ/mPd3Xvy6KPF1+ju3rOqe4mb3C/utroEunrOgaaQ\nJtJGSlXhEfSXOzDyc8mt1dWNWdhQ86tkzOQmwKFpFy+vdNqPcvqzRd/z2TPO8Hvo8JW83OPrbQbL\naB3twcoUi7ww5cfw8PCk5tfoGp219iNrVHOlmkFF0o+UNb9aUCZpNDNzPWvJkiVLVjA2djhwW7jn\ncPr6fsTs2XOB0isglLtKQmHU6EMPzQHWUmjC7O/fxObN1+w+p1BbuIA7+S7n8cPXreAzD+8suv7o\n6CivfOUb2LFjXXj19wFPMDz8fzjhhBN45Sv/gR07ngVAZ+dvuPbar05aiaKcMk+lHtcQkWwxM9zd\nWl2O3cpJfsALgKck7O8EXtDqZJqFDdXUSYol1QpNV8NV6+eVGlDR17d493mF2sKkQRFRSQMLYOHu\ne2r02paNGjVaj9o61fiJNA4pq6krd566LcBBBEuFRe0fHit3uTERSZlSAwyuu+7nxAcYXHfdJk44\nYeoaqXJqrEoNqID/5pFH5hWdmzQoolGqrW1rxHxwpX4vlVyzHtcQkQwpJ/kR6V8X2/83wCOtTqZZ\n2FBNnaRUqZqspP1Jc81VM79acs1ar8OQ9/T07j5v6+WXh33oTi263nR982D27qlJ6rUMWDXPsBZJ\n1+zp6a2otq3e5cparV/WyivZQ8pq6qYLIt8Kt13AaOT1JuA7wB+B0VbfRBY2hTpJo1ITBRe+BOMh\np69v8ZQhodwQUVbza2Tpr3iASwpfIyMj3te32Ht6er2vb1HF67LWEoAa0fxaqkm5VWGzlntsRbjS\nRMrSDFkLdVeG2y7g6sjrK4H1wNnA7FbfRBY2hTpJm8nBarYX1hQdHh4O11td5H19ixMm260t1BU+\n/+CD/8Zhf4eh3f32BgcHdwc6v+qqSe9rVB+5Wq9b7+AyVfBtRdis9vm0Klw1oy+lSKZC3e6Tgs4u\ne7e6sFneFOokbUo17w0ODpZcOH66KUgq/QIPaglXhE2vwc/PmzmvZKArVe56fFm3InxMFwSnqkmt\n12eUq9rn3qpwpVAnzZDVUDcDmBF5fTDwNmBRq28gK5tCnSRpZZ+f5D5zi4vmogtqh4aKluFKWlIr\neh+FWr6ke4rfb3f3bId9dl9vAd1+D1Yy0BWuUc+lr8ot+3Qq/V02o69fPVVbjlLhqtF/+2l5bpJv\nWQ11I8C7w5/3Ae4E/gTsAAZbfRNZ2BTqJK7VXzrJfeYWhU2hy8NthcO83R30SwXBasNJR8deHjT7\nXukLGPa7MX9DR3dZZS83EEz1nOv1O6jmOpU2Vaehw3815Uh6NsPDw03520/Lc5P8ymqoewD42/Dn\nfwJ+AzwFeBPwq1bfRBY2hTqJS0PzUPxLLwh1syM1dTO90N+t1ECJzs4Dy7qP5I7/PWGgm5iHDmZV\nXO6pTPWc6/U7qOY6zfr9pyHYxMtQzb1XGyhbfe+Sb2kLdeXOU7dPWDMHsATY6O5Pmtn3gU+XeQ0R\nSb1O4CKK54/bBFwUrkP6Obq71+xekxTOZMeOQ2r6xPg8dF1d1xQdj88dB+Ri7rVq1netdB69tMxT\nF19/tnAP5armPtJy7yJNVU7yA34HnEIQ7h4AXhju7wO2tTqZZmFDNXUSk97m13ht2vLEvlBBB/4h\nDzrwT1wnaf3WpM+D2b6AQ/xubPc8dDDbe3uPmaaMiyuq5RkeHi65Zu10TbP1aOKd7n2N/Iw01AYn\nqfRe0lwTKu2NlNXUlRtI3gE8CTwM/JJw0ATwbuB7rb6JLGwKdZIkHQMlRsLgttB7e4+JBa/i5tdo\nGYu/NEc8GOwwb/fgiSQveclLHGY59PgCnu53s4ev5OU+0YdvqERwnPhiDvaV3xctuJ8hh4Xe0TFr\n0lJnSb+Deg/GqIe8BZt6NaHX8z0ilcpkqAvKzQnAcmCfyL5XoBGw5T4/F0mT4EtvqKiWraPjgKIR\noNONZK1kHrXBwUEvrCVbGBSxklkeX192cHAwYf684UjwPKroeKHMpe8xO9Nw1LtMtdYGp6VPWrUh\ne6rpd+pVrjQ8H2mdzIY6bQp1km3xL6CRkZFwPrrJc9VVMi3H5HnUhhKvURhQUTwo4oAw1C0Mt5m+\n776HTipTYUBF4ct5qrn0ovIU6oaHhycF4FJhth7TtLS6e0BSeSqdMiZp+p16lidNz0daI1OhDrgB\n2D/y+gKCoWmF13OAP7b6JrKwKdRJK5X6AkrqnxZ8CU79JRX9gp2ogRtyONqjK0REa0dmzJgV1tAd\n5Cu5KhbWJj4/aTRtUKaJ1+U2wdbS1y1tX9gTNavFTdVx9Sp7I4NtM2q4Gh3M0xj8pfnSFuqmG/26\nEOiKvD4d+BzwYPh6BjBv+uEYItIqo6OjvP717wpHAQ4CMD4ejEC84IKzwxGChbPfB3wZGNh9Tny0\n4OjoKEuX/iPbt38cALP3EPxn4AvAEcBbw2tsYPv2j3P22RcAcNSucUY5l9W8g6vZDpxJT89ePPTQ\nTcCK8OqHM3/+XO6+e2KErdl7cH9rURm2b38CuJxgZO6qKe//yCOfwR13nM/8+fO44ILyRj8ODAxw\nzjlncPHF5wOwevUZKRk1eQzB6GSADcBtk85Yt2594u86HeXXqFSRhpoq8RGs+Xpg5PWjwNMjrw8C\ndrU6mWZhQzV10gITtTYLS9YqFOanC2rIjvboclSFdV+jNSrJtXv7R5oFCwMTlu+ueXvtkcf63ezn\nK1njhb5xPT0HeW/vsR5f+3V4eHh3TU5f32Lv7Nzbo3PndXbu552dsyKfN9s7O/cuWqO2+N4rr7Fq\ndBPHZ0UAACAASURBVNNdNcq9n3rVIDWqtrKZ8/M1srY1jbW50nykrKZOoa5ZD1qhru21olN18QjX\n8lZVCALUkHd1zQmDTfF7kpo+YV5Cc+lCD6YtOTwcFPHyyDlDCcuRTR5kkTRCN6nPnVnPpHLWEh4m\nT8I82/v6FjXkd1SJcv6G6hk2GvE328xmy0b/b04DJSRvoW6uQp1CnUyvVf+vPmnakcKSX8nnBF+y\n++57WOKcdUHt2eTAA0fGQt0BDkf6Arr9bp7iK3l5ZFDGlYkDNGCxF2oHk6c0Ccof1CgO+eQQWVzO\nWsJDUnDt7DwwM1/eaQ4bquGSPElbqCtnRYkvmdlfAQP2BNab2Tjg4WsRmUar+jkNDa3iuutWsn37\n5QB0dd3CVVddXfS527Y9OOl9jz32KJT4z8MFF5zL0qUT1+zsfJKOjnvZvr2wGsKZwHYWcAdjdLCa\nt3I1X6f38EOYOfMK7rjjTp58ci8efTR+5ZuAd3LvvbMjfa6Whte7HbgOuIgdOwqfAXAMHR3vZdeu\ntyTee6UrNhTMnz+Phx4q3rdjx4GMjS3NRB+w+AoOaTIwMMDGjRsiK2Ok+1mKZMl0oe6LBOHNwtf/\nmnBOef+VFJGmiC4ltXjxcQTLNJ8aHj0r4R07CAZIFLwP97k88sgDTIQngDNZvPj9DAwMsGnT1ZEv\n5bUAvOtdH+DWW28H+lnA7xnjJlbzWq7mMwRjrj7BLbfcEoa1m2LXXgO8GRjlgQfuZ8eOjxFdqqyz\n8/3s2FG8fFlPz/kcf/xtLF48xEc+cinj48cA0NV1Ftu2/Q3r1q3nnHPO4LrrNoXlLD88XHDB2eFg\nkMKe1cBVTDWApNEqXSIsbdePSnPoFMm0VlcVtsuGml/bStKccM1ocop/jtkB0zZBJk2VUWimnW4K\njYkBDYt8xow5DkO+gFnhoIjCPHRBX7nJTZpHhs2my3efE/SZO2xSmYNrl76P6MCKpH6A1T7LUqta\nlJpKpFFNnpP7Pe7n++xzcMl56mq9vppERcpDyppfW16AdtkU6tpHqS/I5s/NNeLBPHDTB7Ng5v2h\nMGQd4B0de4QjU4uDVF/f4oT7HAr71S30BfxtGOiuKurvlryu7Iow9C3cfY3Ozv18eHg4YamyvTza\nj6+ra07ipLqN6IRfTuCpZT68cv4mku6rMFlzPYKd5lwTqY5CXZtuCnXto5VfkMWfvTwMTpOX4YoH\niegSXoXzDj74MI+u5AA9RSNAg5B2tMOBHgyKOCMyyjVa6zcrobZyhQdTmRQC3Uzfc8+e3QGluJbs\nSI+PgI2vUTvdiNdaA/V072/0El7JoS7Y19PTW/H91KP80lhpHuwiExTq2nRTqGsf9fiCrGZJpMnN\nj4VpRYrLEh2FOtU0JUGTZ/ESXoVQNzIy4mYTzZwL2D8MdDYprPX0zCkq5z77HOywXyQsFmr7ghq9\naA1cEBwn30OpFSWSglK85q8RTYvV/M4rec/k5teJKWDqEeqKrz/kHR2zdo9CluZTc3h2KNS16aZQ\n1z5q/Q/yVO9PCnvx87u65nhv71FheJrcpy5p+o/SoS65+TU6AXGwlut+vpJDvLi2L2jS3Xffw4ru\nLanP3ETfuuL567q65kyafLira07J6VaSntF04amWGpFa+vJVGgRHRkZKTtZcD4VJqKPPX2GiNVRz\nmh0KdW26KdS1l1qCwlRNiOU2OU7UxhU3vwZf2JM7/SctFt/be1RiOYJzezzoQ3d5uJbrqT4xufHE\n+XC077vvobHgM7nmDWZ5YR66pCDZ17fIe3p6d9ceDQ8Ph/cY1AhOFXynWjO11lUn4mG6r29RRbWr\n1Xz28PCw9/T0ek9Pb90CXYHCRDpM/j0MeU9Pb1s2xaa9GVqhrk03hTopV6kv1vL2j3jQz222w6Lw\n9cQXQqmmyJGRkbAP3SyfMWOODw4OJi6VFfS9OyAMdIORPnSzPblW8ACHvSNhcbbDSyYFyKCsyZMS\nJw3siN5DR8cBRX3x4veX1F+wcH41tWXFTcPNbWZvNIW6dIg3h0f/ftup9jQLzdAKdW26KdRJuSqp\nkSuuwRvy4oENs73QVDdVc+NEeJs8ujQYFRvs6+zcb3c/ugUM+910hIHuwDCUzffJK03sPanMSSNy\nOzsP9J6eXh8cHJz2P+JTBY9Sq2OU04ycNMJ3qt9LqVrPLMvCl2irNDuAFw8Was+gnYX/k6FQ16ab\nQp1Uopy+c/Emx4n/+E+MEoUjvaNjVuKXUPGXRjmDEYJ55RbwYr+bA8Im14UeLO+1PDy+l08MrNjf\nJ0auTlw3WCc2aV9QG5g0VUlUpaEuWFZs8r25V7bG69TN3NUFoLTV1LlXV6Y03kc9tTLsTtV9IO8U\n6hTqUrsp1Ek9TPXlOfEf/+goyf29t/eYxOtMfEkdnRjqghquwpfJYoe9wxq6/Xwle4XvO8Bhj8jn\n7eFwkEOvw3D4/omO90FN34pYGWf6xETEM0uGquSyT4xwLTVgIegbmBzcyvnCnGoS4r6+xTUNskhr\nrVglIS3N91EvrQwXSf1d692XMq2y8LelUNemm0KdNFowzUjPpC+fgw9+2qSO9cVfUosc9okFn5kO\nXUVfJsG0JQf4StYknDvsQQ1hvPl1ZhjienzGjFmRPn0TEx0HtXsT79lnn4PLutdC6Ij3E4wPWEjq\nG1hO7WfS8eB+huryBVOPGphG1JBV+kWahdqUWrXyHtvh+U4l7bXACnVtuinUSaWq+Y/Z5D5ikycf\nLtRqTTTVHupBU+m8cCv8fOjua01MW/IcL0x6W9x82lti/1xPmuOucF/d3ZOnTYlOgVLO8yjnS2+q\nEaPT135ObrqNn1vd76r8pt9Sz6MRtRiVhoh2CB2tb37N9/PNMoW6Nt0U6qQS5dQgJYWIoFZqTiQo\nTK656+4+JAx/+8cC31wPatwWOhzmsK8HNXQ3RaYtSZ7QuNAnrlSoi9aORQXlKK6tShqoUOp5TPQl\nXBgGVJ/0pVfLF3I5X6jVXr+SQRrVlq0a1YwITnsTWT20qsaoXZ5vVinUtemmUCflmi6oTPxHfkUY\npnp8cHBw97F99z003L8oDGfxoFW83urEZxT3f4OZvoC9wxq6U8OAuI93dc30+KoRcFT4ecWjb832\n8b6+RSW/hMrtL5QUNPr6FseaRmd7fM66Uu8tN/yU84VabTNqraFsuvcXQkhhnr9yw0g1ISLtTWRZ\np+ebXgp1bbop1Ek5JvfhmlgOqniEZ/KarpPfOzjpvOC9heOFMOIer2kLBkWYr+QZ4bG9vLNzn6IV\nB2A/7+iYGChhto8HAyiWTyp3knKDTalm0Pi+zs4DJy1vVWt4mu4Ltdpm1FprYKYbDV1O4K32nkUk\noFDXpptCnZQjKYAU1kQtbm6cPC1I0tQdQRh7igcrNvSEgS5+/Ojw58J0JMvDlSL285V0h+871IPB\nFMkrPhQCQKUT8pZbyxVvVi61VFihfIXnNTw8HNZcFuaUq3/NUy3NqLWGp0r6GRb6PJZ6vgpxIpVL\nW6jrRERSY9u2B4GbgBXhnsPZd9+7+NrXNgCwbNkg4+NvBK6Y9N4dO3YkXPEWYB9gHXAR8Kpw/yhw\nOfBbYJyeng/w0EOdwKks4E7GOI3V7MnVzAKeAP4CvAO4bdIn3HTTb5g5c19Wr34zJ5xwQljG4Fh3\n9xqGhjaUvN/Fi49jbOxjwKfCPWeyePH7gxKOjrJu3frd58GTYZkBnmTFipdxyy1rdn8WvA/4MjDA\n+Di8611nceutdxRdu7d3PpddtoGBgYGSZRodHQ3v4UIAtm4dZOPG4vdEy/bIIw+F5doE/z977x4e\nV3nea98zlsfIlmRpLB/kCDt4HFA8dkBAu526X5W02EqaXRrQbkPYZGuTFIeGYEBjEHyGlB3LddJg\nQnNoiEliHChR2nK5VXqQUNLG3dDTx8k1JiTgEDdEQGLcJiYRCFvv98fzvrPetWaNTtZh5Hnu61qX\nNDNrvWutGZn58Rx+D5sBqK9fVHR9n9bW1hGvZaqPh7Hdr6Ios4SZVpXlsqGROmUMiKdaOF1aWVmf\nj6QEUa2ayH71Jn7OqzP/dV2uLloXThcmkwtNYVPESiO1c37TRZeBxaHriw6XH0/Up1hqdKzTG0by\nkIuLXFZULDklc2NjRh7hBPVFm0JOlVPxjhsp/ardlYoycSixSN2MX0C5bCrqlLF8KcfViUn36GJT\nWVlvghFbOQML7eMmEzQ7tBk3v1UaGhpNoSHxwgIBBHW2hm6ZuYwHPBEXZ11SY4Iu2BrjN3Ok05lQ\ngX4ms86k05mizRLFBEWxNPTYhJakWCXtGl2jyYw2R3M0kRN+Pb6BY7I5leaF0RolVNQpysRRUVem\nm4q68qarq8saA0vHaCq1eASLj/haqKCebbEJR9BqTbz5b62RjtTamDXDAilLo22KuNoEEbi446JC\nL2cCj7qcqapqiI0QufWqq1eEBF4xsRIv6uoK9vOJiubCztqlRiZjnJpFyWiibioE0VQKL7XMUJSJ\no6KuTDcVdeWLTHqojYiLnMlk1hVE7gp95oLu16BLtVCUBRG7OCEUF7EKrkdsS84w1y0506TTGVNd\nfaZJJqtNYHkSpF9dmlbWiYrIGtPQsCrmXC0manUSneoQ9z4UisM2k0wuKuhuHYmuri6bhnX2MGMT\nRyNFVUdKv06VIJrqaJo2SijKxFBRV6abirrTg4l8+cVHnZqMpEHj7Siam1vssPicJ2p6TXFRt94E\nUbGgk1Ser4pErGqNzGhtMVl+wwywwLyfBZHuWl+4rTeSxp0fsU0p7IRNpZYWPCeRvMJIoUtTFntP\nCwWZmZCYmQoRJp/RBpNOZ0wmc964fOAmgkbTFKU0UVFXppuKutnPeL5YfaESb71RWK8WlwbcuPFS\nWxvmi7K0Cc9LdX5zK028J916KyLXG79RIst5+aaI6uozTVdXlxWSrg7Pv97ADsNdV7yFiqvz8681\nLoK43iSTMgs2bi5r8F4XCseJeMz5dWWjNUpM9t/CZKHRNEUpPVTUlemmom72M9YUWPQLP5VabCoq\nFnpCp9bE1av5EyP8L+84L7e5c6uMpFYbrUhLe4IuWFP86XyDYRFo0hSRtDV09SaTWRMxFfa7WoMU\nsN8EUGi6u9BeywITzJGdZxKJ+SaRWBhZu87AWnPGGWkTZ9wbvNe9xm/yGI94ikboxpu6He/fgrtu\nFV2KUj6UmqhTnzpFmWR27dptPb/aARgagubmPcAejhx5gZUrs7S1vZsdOwKPtWTyBo4eXcOOHTvY\nseOzIc+w3/3ddwH34PutvfHGXODT+cfwOjA/5moagL2If5uQ5af08ynrQ7ePVOoENTWLGR6+KX/N\nQg4wwJXAS4gP3DmAeJtBBYnE6xizHfhPe65DQCXQZdfYyqpVDXz+85/id37nKo4fHwSS+Wt/7bUt\nwHJgGSC+aEeObKe+fqk9vtVe/+2k0z/hgQfG7p8WfA7LgE6Gh3fxxBNw8cUfoKfnvvw6vudcLrd5\nTOsfPfoyYW+6gxw48DTDw1cB6vWmKMoMMdOqslw2NFI36xlrym08Eb1o7VycH1tVVYORurRFNqq1\nLjadWeiZVmujZkGqNkuNGWCBnRTRZiorl43QbersU8LTHgqbGJxFSq+JSyun05n8/SYSdTHnWV8Q\nDRxtBNZYImLBPRW3HZmoVUhFxaJQdDHuvtQWRFFOfyixSN2MX0C5bCrqTg/GIijihEKxOq7R/dic\nH53fBVpVIPyC9KpLxS4yMM9UVFQZN/4ry9vMAJjL8mItbSorF8des6RH242fGh3ZbsRd83kjCqh4\n77vAssW3ehlLV+xIIiywNCmsy6uuXlH0/R9NjMXZzlRWLldRpyhliIq6Mt1U1JUXviDp6uqKFSJB\np2m4u1Mid/dGfjcRAeVbpLjO2KjFSH3+cXhSRMYTjOm8aIq/njZTUbHEpNMZ09XVZYyJF0LBdYaj\nhb5IC2oDffEYROjctIeRGI8IC85XaaINJHPnVo17PUecQbQYQ4fPMdq9KIoy+1FRV6abirrypVhR\nfWEKs9Ekk4tMe3u71znbUkTUrTUSTas3QXq1MCIFi2ImRSwxYlZcGIUbi/1HXLSsvb3dpNMZk05n\nzEUXXZT/3Rc24eaHFhOkd12quGnUEVtjGeFV+N4tNtE08pw58RHKsaRfM5l1BeJZUuSFqWolYDZ0\n786Ga1RKCxV1ZbqpqCttJvM/5vHdq06IiO9bvB2IiJxo/VjYjLjeCqBeKyIa7eOFJpi4EKyZpdqm\nXP1JEW32mMJZqv71x43ZSqczBbYg4UhkcR+4wntxUyt8cSfdryO9tyPV2oW7jmvt+ZYV3IdLv473\ns5d7qDX+qLSKioWx4ltFXcBs8NmbDdeolB4q6sp0U1FXukzmf8yL1dPJc/6Ehrio2nob5QmnPF1D\nhYifeiOjv7qM1Na5GazzDSRDgkomRWAuI2UC+5Mu73xNochSJnNe6D5krFncNYbfo9HGZrl5sM6w\nt7p6hamuPjP/e9z+I9HV1TVKJDBYq7l5g8lk1pjJSo2Go42XGjFR3qCCYBRmw3zZ2XCNSulRaqJO\nLU2UsidqQTI4KM+N1Y7Ct8Q4evSVgrX27+9h3769XH75NRw7dod9bRnwPm+VG4H/BXwL+D4nTnyG\nY8fg1lu30N5+CT/72THgBIGtyUeAM4Cr7eOt9udVQI+1LYEOVtPNq8AbiM1Iu93vIDAA3Gwfb+Hw\n4dfZsWMHF154IZdffg3GXAl0ete4FbFGaR3lPToItNnfz+LYscVcfPEHgDcYGroLgMrKTnbuvI1b\nbtnJE0+Ej165srGozUhfX1/I8mXHjk4uvPDCop9Vff1SHn/8YXbs2MGdd24HoKPjJrZt2xa7/9hp\nxdmt1Nf30NrayrZt13rnuFbtTBRFmX5mWlWWy4ZG6kqW8dRpjTwHNL6xwaUsw5MleiORthq71Rcc\nH0xlcJE8N7orup/YoYSbItabwBS4xjtfVczxa82cOYvtPbgpEC4iVThlwk/XBu+BO4+fYu3yonzh\n46Mp2VRqcdHGktE+q+mIlhU7R9x9aKQuYDZEMmfDNSqlByUWqZvxCyiXTUVd6TKeOq3of+gLRUau\n6GQG+dJf4AmcaAdp2sRbfiwz4W7XpUYaJaL71ZosZ1gfuqtN0ERRa6DBBDV3fm2dPyfWP8diE9Td\nFc6PTSbr8mlH9z4VHx0WWJaMVMMXX4MY3vdUBPhk/r1EzxFXU+dP31BmRxPCbLhGpbRQUVemm4q6\n0qbYf8xHExFxrzc0vNkEY7syJoiuuS7PtJHIV1T8OBHmxJMTWs6c2N83LLKgxmRJmwHmmsuoNoGf\n3UidsbUmLPLmG99aBc4O7VNRsdBkMueFzJKjIjeuuQLWmlRqsW0wGD0KMtFo3Ex+IYvNSbj7dbTa\nQEVRZj8q6sp0U1E3OxlLZCjacRlMWPBtL+qMNDg0GrHzWBMRVS7FmjYwx+4fNR32Bdc6K8JkxmqW\nlBkgYS5jtSfg/G7YOMPgtZHHLr3rHruu1GCfOI82//0otPuoNcnkwnzDx1hE10gpTmdV4uasTsSU\neCqIa8bIZNZM2/lLDY14KeWCiroy3VTUzU7GIhb8L7BMxk1UiBNR0fRmm5EoXMZAtQlGfdWaOHuS\nQKwttaLOGQt3WUE3zwoLJ/78aKA/zcGJRH9ft76rnXOCLiwm47pVCyOXOStcg4jeeGvMoqJg/Gnw\n6U3Lavo1YKYFtqJMJ6Um6rT7VVFGoLW1lX379nqdmIVD2ltbW/PPLVq0eoTVmgi6TwGuR7pSXwE+\na5/rBD4EfDXm+O8CHUAd8CJwB1kuoJ+b6eDDdPN14Nftvm3APKRL1vE6MoR+OdLF+hJwu/25FRhC\numfvAQaBjcA6b58tVFXVcuJEJ4ODsmJlZSctLdeyaZN0uy5fXm2PXwPsyt/v0BDccsvO0HvnOlyP\nHn0ZqKC+flG+09V/TwE2bWqbcIdyX18fl1zSnu+YffjhdvbtK/wcT4X6+kVjem4mKNZJPFWcaje5\noiinwEyrynLZ0EjdaY2LBMlkgXpTmH71h95faqNi1SZIsboIjzMUXmSCejhX8xatoWuPTIo400b5\ngohgIrHQSG2d/7w7l4sYutSvH7VLG0kX32uvR2rGnOdcc3OLSaczJpNZE+r6lCaRNrt/YRew/34V\nevcVj+qMNw0+1o7Zyfz8SzE6NRPXpX5vSjlBiUXqZvwCymVTUVc6THYqrnC01nwr2pqMpFHXWmE0\n34Q7UGtN0A1rTOHs1oUmmN+6MvRFGaRcr/bWcmnT8BdqRYVLtUaFZtoKsLhUb6PdnPCSL+rm5pbI\n/cY1YDiTY38MWr3JZNbl37Pgi39sAmC8afDxpGYni1KsI5sJgVWqAldRpgIVdWW6qaibeVyEaaTu\nzYkQ1JFtsBGqM03gQScWJ9JAUB0jgOpMEIGLE0jOusStd6nJcrcZYKG5jEYr0pzP3XwT+Mv5a0Rr\n69bb4+YZOCNyDb7diRN0Ul9XUbEoxnIkrnbQrd9mj11roCY//qu3t9c2XKw3QTQwON63Nok2Q0xE\nNLW3txfc30QnSsw2ZipqVooCV1GmglITdVpTp5QF0boquAFYw+DgFadc7yM1Yb3IhIezgQ3APSST\n32HBggE6O3NceOGFvOtdl8UcPQ+pZbsH+EnM6y8CLwAp4GqyvEA/v08Hc+nmIuDbSI3cBlKp+xga\neg/hKRDXA2d6j1uR+ri7ge8BrwGfRqZAXG+vZyPQD9QAjyC1eH9DMnky5vo2A1d4jzuBvfYcNwNv\nBZYCrdTXPx/zOXQAW/JHuxq9YjVw4/2c+vr6uO++HqRW8G7gWWAj+/c/zikPlZgF5HKbefjh9lAN\nZC63d8rPO5HPSlGUSWCmVWW5bGikbkaJi1i4iNJIA+SjxEUgJAoXNQfO5aNVqdRi611XZcLpz3oj\npsDuWuKmMQT1aeFJERn7+pL8WpWV9aaiwkUh15vARqXWxNuruP3898RF1pqM1PW5aRCXhqJoURsX\n6Ypdb/y6vGC6hnS/NjdviLVEkTmwLWMyHx7v5yTni5osrx/TeqdLtGky7uN0eS8UZbJBI3WKUios\nR2an7hnT3nFdlNu2XcsPfvAScBcyz3U3cBbwN/ZxP0ND63jxxe8Ajci81bvtikPAOfb355FIncxu\nFa4C+oDXbYTuZjq4k26GgO8AtwHbcV2GJ0/eyIkTJ4GvA3OQztpDSDftRrvez4EPIl2tW4G5Me/J\nTwAXSbsOid4BfI+jRxcC0NTUxJEj21m5chk7d3YD2PfmJWAvlZWdbNt2A/v393D06CscOvQGTzzh\nomVh1q//JR566MH8e/zYYweAi4t9DKMS/pwuRiKBV+XvOZF4nVzu9nGsMTUds9PFqUbNTqf3QlFO\ne2ZaVZbLhkbqpoSJGtpKNK13XFGg4iPB1toIkL9+jRGDYRcZc/stMOF5ry6iV2PE262w5i7LvEhT\nRL0JatFaTLg+zz9/zvu9xcAKUziZws2FdZ2rcTV5wbpz5tQVnQxRzBw4/L6Fm0HiR7KNrSN27J+T\nizTK75nMeRNaY7S/k9M1mqXdrIpSHDRSpyiTw3giCM5v7pZbdnLgwFMMD/9v4KVTrDF6hOHhTyM1\nb7uQ2rR27/Xt3u+ftj87gP9Cas6WIxG6+5Eatvvt6weRqFIHWQbpZ4gO3k03P0aieO1IrZuLQO1F\nImq/Fzl/DxKhSwFXeuf/F6TO7Syk5u3bVFf/G0uWNHD48Isx93kOQTQQTp68mzgPMve+X3zxBxga\n+hQA+/d/gGz2bG+tVqCddHo7F1xwbsj3L+xvthG4nXT6JzzwwORGhVatWjVpazk0mqUoSkkw06qy\nXDY0UjfpTDSCMNGISm9vb8iTLZi6sNQU71x1dXH+87VG6utc1CxqZVJjoNZkqbQRunoTHb0lEbY1\nRmrqMiZqeRJEp4rVEt5rpFs2412P60atiZwrGt0L1+H573ncZIVM5rwxWVwEXcRB/dtEIkJdXV22\nns9N6AiilmON+o3XluN0jmapRYmiFAeN1CnKzDLWGqOoE7/wBnArUh/XQRChewG/i1N+PwGkgd+K\nrJy02xfs40eAO/CjbFly9HPSTor4DvAE4Vq8ecBh4Iv2uY8g0Tr//FfZtaMsR+r95iN1eSD1dRuA\nr9h7uwGJ0P02cK9dR7p6U6kkQ0MS3YxGOo8ceaHgbEeODHD77VvYv19qBeOmcgC0tJxPf/8fAZ/J\n30NLy00x11+cvr4+duz4LMPDuwBIJm/gAx+4hIGB54Hni547ylgmiZQLp+N7Md1TNhRl2phpVVku\nGxqpm3SmKoIgfnYbbI1ZsHZzs+sYdR5r7SaYCFEYZZJI3jpvH78z1tXVLTZR89/AWLjTi4zNt/u6\nyJmL6HV5EbQqb915BjImkag2c+b4tXZulmuxujOJQFZWuqkY4TrBhoYVI0Y65T2KdvjmRvxswp2q\n4ajgeKNdM+nL5kdxxzvrVpk+NPKoTCZopE5RJofxRBDG+n/mQW3UWfg1coOD8O//fiPSderq074M\n1CP+bk1Ip+Ud9jUXvXoReJVg5qrzcOtDInknkWiezGiVLtfb6GA53bwVibjVeVd4F4V1e85wbS3w\nz97592DMYiorj/CWt+wB4MknX8OYl5Au3GKczfLlr/H97+/BmDtD51u2bM+Ikc6dO2/j4osvY2jI\nRTPvB1oZHFwX6wdY2Km6Famnm9zIyfREZt4giKa+MQXrK5OBzqZVTmtmWlWWy4ZG6maM8YyYCqJF\nLTGRLFeLVmujZi4atdDIZIaox5wfUZsbiV7Ni+yfNlnOMwPMM5fxmzZ658Ztmfw+8rPXXl+jdx43\nW9a/3sAnzt1zUG9WHzm/H5nL2fdiZcF70NBw9pje7zg/uriIWXH/wIlFUOIiZl1dXVMemTmda+pO\nN/SzUiYTSixSN+MXUC6birrRmSpLiPEOgxfxtCAmjeiMdeOaIlzK09mX+Ps7WxAZ8xWM6oqm0xiW\newAAIABJREFUXJNeyrXWRM18odJIGtaJu6hArPOEZGGDQ3PzBtPc3GINihcYsS9xzRzBWDMnepJJ\nN3s2OFcyuXDMn+VIQmqklGs6nRmTRU3c34qIOvferTepVK1NCU/tl/h0CIXT1TJlutH0qzKZqKgr\n001F3chM5X9oR/vCjeu6DATVpVbYrC0qyOT3FlPYwbrAOC+8QNQVCkOZFLHQXMYCe1yNCfzi3AzV\nWhuBOjPm3L7IW2hglRV/TkR22fX82r5a4893rapqMM3NLSadzpjm5g2mt7fXVFQsMcFEC5nMUFGx\nJP95jSYwurq6TDqdMel0Jj9rNa5eMa47daT1R/pbifus5T5Ovat2JKZaKKgQmVxUICuThYq6Mt1U\n1I3MVEY6CsdaLbZRK4lcVVU1FESkRAQZK8J8AeKieL6QWmriBtPLOkvt/m58l0tzzjew0EboFprL\n8sJmvRWQK+yaa+2+vhiLixL6j+uMRN580RRnKhyMFKuuPrNANGQybhRZsE5zc/OY09nRfYI0aKEo\n9qNzo60/0t9KfDq38D6cyJxMplIoaMpQUUqTUhN12iihnPb4DRVHj77CwYO/4IknXkEaFu5Cmhu2\nEm5AuB54O/ADZKyW/9pdQMbuk0QaJ+KsQ1YjY8jc2vfbn3uBFFmS9PMHdHA+3Wy31/MIYo/yIbtf\nOzLGbBnSPHCQeOsSnzfZtdwxEDZCdrzFnu8Zliw5k8OHc/jF40eO3ITYiwT3fuTI9thC81tu2R5q\nRIjb5847t9vneohywQXn5keFbdrUNuFC9ugAe+hEPofbQvexf38P27YVHn8q6BB7RVFmGhV1SkkQ\n/TI+tUkPxXnuue9z4sQcoBa4GfmiLxQZItautr9HOzJfAr4PrLDPPY/MVPV96rbiOj+FHmSe6h3A\nSbJspZ9P0UGKbn7NrrkV8aAzSMfrauA+4J3ATrvWOmARgY9cDSL+1nnnPcf+vAq4xz5fbY9xdAJX\nAF+hvf23GRg4zuHDI71zI3GQAweeZnhYxOXDD7fT1LR6hP034wus8X7WI/2tOAF/+eXXcOzYYuS9\n2T2+2ylBpuvfh6Ios5yZDhWWy4amX0dlqtJXhY0Q/uxUYwonOsRNUWi0acMqA9X29WUm7BvnN0pE\nj2/Jp0Yl5brMXMYDJkjpNtpr6vWe89OnLn1Yb6DBu/ZLTbTuzfebC9fotZmgASNnEonaUK1btGu0\nvb09Nm0ZfT+lmzacGmxubhkh/XqvPX+dqa5eka/hK/Z5jdStXOxvJbxGLnQfs7UeTevAFKX0oMTS\nrzN+AeWyqaibOeLrrM4zUlPmj5JqMeGGB39/VwdWY8Wb33Sw0ISNgatNYY3eSiOjvxpsDd0D3trp\nIuczkX1cE8cSb/+uAuHlN0AENXqNJmiaWG8qKpaE6sqiXaOJRJVpb283yWR1/rlksjovJnyBkcmc\nFyvq4kSIe665eUNIRCaTdSFxNxkCxl+jq6tLBZGiKJOOirqRhc/twHBkG4jZ50fAL4B/ANZEXp8H\nfBb4CeL6+lfAmyL71CF5rf+y21eBhZF9VgDfsGv8BPhjYG5kn3XAfnstLwC3jXBvY/sLUSadeFEX\n7Rj157PmIt2ZUXuSYiLMNR8sMIXdtLUmyyozwFzb5eqfuyYiAhcaiay5btt7TeCLV2/E2mRh5Lz+\ntWS849bb66014fvJjdo1Gp10UUysxU2RaG7eMIHPZH1R65NTFWMa5VIUZSpQUTe6qHsaWOJti7zX\nO4GfAZcAWeDrVuBVeft8wT73G0CzFX5PAElvn79DKs7/G7AeeAro8V6fY1//e+A84CK75me8fWqQ\nQqhuYA3QZq+to8i9jefvRDkFol/g0XReIlGYXg1SiDmTTC4yDQ2rTCJRZwVRXCo2Tkg5Aed86gKR\nk6XWjv76TSviltr9XEq0yUiU8Dzje8YFJsG19hxu/Q32fCtGEJj1VmB2mcCKpdH4AnXkrtHCiGV1\n9YqC1GgwPi1sGTKSkIo/36Wha5osGw+1A1EUZapQUTe6qDtY5LUEMnPpFu+5M6yQ2mwfLwReB97v\n7dOIzGLaZB+/1UYA3+7ts8E+9xb7+N32mDd5+/xPYNAJSOD3bZRvnrfPNuCFItc/hj8P5VQp9gUe\nTheuKxAUmcx5prr6TFNo4DvfFE5e6DLhiRL+9Ih6K8pWWhG2wWRpNwNUmMu42gT1bs4nrsbARfaY\ntZHzuxTqWiPi0glAmekaiD4/Cueico12zTPy60n0MX62aldXlwm89NxkiUZ7/43538X+ZaT6ORHF\nmcx5Np0bL6QK6xyXGhddHEloTsTGQ+1AFEWZKkpN1CUpPVYlEokfJRKJ7ycSia8lEomz7PNnAUuB\nh9yOxpjXgH8EfsU+dQEwN7LPC8jAzrfbp94OvGqMcUMyAf4JaV/8FW+fp40xP/L2eQhJ7V7g7fN/\njTGvR/ZZnkgkVo7/tpXJIGylITNFnR3GQw89SC63mR/+8IdId+heu13H4cPf5fjxNyH2JfcjdiAf\nQrpgTyIzPTvsuhcSzPl0sz432tfuAN6M/Dm9lyyH6Oertst1gXel1UiX61XAAXuerci8WXf+TxJY\nnLwGfNNe02L7Pwr32PN9yF7bHsRe5deBHwKfQwLNX6K5+R4+/vEclZX35+9bOig309fXx44dn7Xn\nvhqZafurJBI/QRrku+xWQSo1XPCe19cvYt++vTQ330My+RWGh3dx+PD1DA1V2PsIPgeH61Jtbt5D\nMplDOnFfyl+ToiiKMn5KzdLkX5BvxmcQAXcr8E+JRCKLfDsAvBw55sfIpHTsPieNMa9E9nnZO34Z\nUiOXxxhjEonEjyP7RM9zFPl29/f5j5jzuNeOxN+iMl7cMPajR18GKqivXzTuoexujcceO8DQ0F2I\nUMohXm2/hwikV4GvIJn1zchHDpBG/mTm2P0eQbzq2u3rzjbDXc/ZwFay3Ec/hg6a6KYTEV5XErY7\n2Yv8WYX94GS9i5F/Cq8hQekP2eM+iYjJq71j1iECb6/dx7EcuJpnn72FBx/sp6mpCbiH+vql5HJ7\naW1tLfCFA0int/PTn9Zw8uSnQs//9Kc3UlnZWWCt0drayq5du62tSfQ+4j8n5+vmPht4Pr8WTJ6N\nh9qBKIpSLpSUqDPG9HoPn0okEv+MmIC1A/860qGjLJ2YwOWMdsxo5yzg9ttvz//+jne8g3e84x3j\nXaLs6Ovr45JL2hkcvAIJyt4BiBfavn17C4Rd9As8lbqR739/Mb/5m+9nePjTwIDd83FgF/Kn1YeI\noa32NWcW/AWCSBWID93riCCMMmDX2ALcRJYX6OdhOriSbv7F7jMM/CXSV/OSt38cA4i5cRVwAhF1\nX0ciWu3Ee+sNkEoNMzTk1u60P/t59dVBnnjiSkBEzb59t40oii+44Fz+/u//b+xrzsgZCImw4vch\n99nSclPsHsVMe33T6Oi5AiHIqAJ/pHUURVHGw7e//W2+/e1vz/RlFGem87+jbUizwueR9OswcEHk\n9b8B9tjff93usyiyzyHgD+zvHwR+Fnk9ARwH2u3jjwNPRfZZbNdusY/3An8d2eeX7D4rY+4jmopX\nxkBQDzX2uijXkVldvcI2O/gjspwnnd81Wmy0VIMJ5qe6btK19ne/ps5vXMiZLG/zmiJcjd18+zNn\npCYvY+vU5hlpZoj65FUbqbeLq+nrNYXeevWmqqrB3nuLbfxwtXmFPnL+e1esDrGh4c0mapfS0PDm\nop9VYZ1cOvS+RD+viXakauODoiilAlpTN3YSicQZSGPDi8aY55HwxqbI67+K1MQBPIYUO/n7NAJN\n3j7/DFQlEglXYwdSH7fA2+efgLcmEok3eftsRMI0j3nr/D+JRGJeZJ8fGWM09TqN9PX1sWlTG5s2\ntdHX1wfAM888x/HjH8eYTyNZ8YN271Yk0nUIGbe1lyB651OJ1MVdjaRB2+0aP7av+zV1J5ExVA+S\npY5+nqKDt9DNGfa4tUhK93NIevd+5P8RjiEp3XUE0bceJM061z6/iiA162r2bkf+KfzCPr4b+AU3\n33wNIDVu557bRHX1n9nX1o74/rW2trJt27Wk09tJp7ezbdu1tLa2snbt+cif9Ha7bbTPFV9n3769\nbNzYQzq9HUk1Pww8SDDxQnAR2P7+i+nvv5hLLmnPf3ajUaxuUlEUpeyZaVXpb8g31K8hUbn/Bvw1\n0mF6pn39Jvv4EuSbqhvJhS3w1vgTpErctzR5HEh4+/wt8O+IncnbkW/rv/JeT9rXv0VgafIC8Mfe\nPjVIN+7XEHuVS4GfAjcUubdxaH/FEURlXKdnYVdrvMVGNPK2yPgRJ1nPdZO6SRCu83OxCU+ccGuk\nbVQtztJkvZ0UkTCXkfLO5a69Jb+f84kLjIJ77X7+9S2x+xZ6xUGjqahYYpqbm82cOYsMLDLp9JtM\nJrMm5K+XSi22Hajx752jq6srdFxFxQJTXb3CVFevMBUVC4oeN7bPLb4LOZ3OjBg9HAntZlUUpVSg\nxCJ1M34BoYsRgfQjgsKlPweaIvv8ARJaGSTefDiFhDaOIqGWOPPhWsR8+Kd2+ypQE9nnTMR8+Od2\nrbsoNB9ei5gPD9rrVvPhKcCfQtDc3BJK18V9wVdXx3m3OVsQJ3KckGryhJMz/j3DCru2yBrOEqTQ\nfDhLjRV0c6wg9EXiQhPYhLg0rj/Ky6VTnYiba4JpEHVWSIZFYnQiQ+BJ5+7tUgPrTUPDKpNOZ0xV\nVYPJZM4rSHX29vZGxnwVTqjIZNZMyLR3ZL/AQtPksQqzwjFldQWjxhRFUaYDFXVluqmomxqam1ti\nBFxtgTCRrc6If9waE8xbLaw3C48Ea/PEXrsnxAIhmGWBGWCB9aFz4mqDXT9tRVrY9DcQdW6M171W\nSOas0PQjdwvtmiISk8k666kXd91rTWHUT2rrUqnFprl5Q4wo9gVWYQQtnc5MymcVFuDhmsDx1sXF\n1Q7G3Z+i0zQUZTKJ/ntSUVemm4q6ySOI3LXY9KAzuc1ZkbbUBIPt3ZSHtVa8zIsIvtoCESPHuN9d\no4VLYc43gRFw2kboFkRmuS40hWO5qiKPXeQuZ/f1I22F6d3KymWmuvpMk0i4CRdrC/YJRoKNdD/r\nQyJKhJa7lntjz11RsWRSBEFwLve5tJl0OmOam1smJMaKjRrT5okAbSpRlMkj7t+Tiroy3VTUTQ6F\nHZb1RiJv0TRquPMyiMj5oqXXFE5xiM55bTThtOlCK9DqbA3dXBuhMxEhGBUbLh2bNoVjwNxWY4Xd\nwpjjnSi9115LrQnPr3XHF07LCIu64HcnooKaxfVG5spGo5xtkyII2tvbC9a+6KKLJiw6io8aC+6v\n3NH6Q0WZPOL/m4MxJaAx3FZSPnWKMhrS+XgFgU9bC/D/IcbBHyRsfNuJ9MLcg/S1LEM6VT+BdKv+\nHOnQXId4wlUgzja32+3fgd8B3gnstPvWAL8gyw308zk66KCbL9vzgPjOnYi58hNIJ+0eoB54EvGs\na0FKNq9EOlV/APwnYf+6rfb4rH28GynxXGav6zBiUPxhgskWji3I1ArnwxcYEz/22AF27drNtm3X\nsn//48BycrnbefTRR7nzzu387GfHOXFiI/AXAAwOkp/OMRG+8Y2HiZosf/vbN3HixB/lnxvPOaKe\nhNH7UxRFKTtmWlWWy4ZG6iYF6WytN+FIUrEom2uMaDOwzEbL5kWOdx5zLppXF/OaW0t847JUmAEW\neinXnJFatPVGIoZNdt/1dqsxgVfdyphIWJcJUoct9ncXzfM98pbb55fZ1/x7rfNmrbpmjha79npT\nXb0iNIvVf9+KRcfi6hWbm1sm/NnFdbxWVCw5pUiSn4ofadZsuaLpV0WZPDT9qlvwRpexqJusQu3e\n3t4iDQKFNWNBvVqTCdKZfgerX2/nbEbi0p6NxrclCduWONFWa8JdrRtMODXqrFCc0IurA3Pn99O8\nfprY7eOLznBtnqtNC0yXXQNBrX2txWQya6yQcibKpqiQKhTQ9aa5ecOEP++ursLO2oaGFSE7lVMR\nHdoQEI++L4oyeWijhG7yRpeRqPP/6Lu6uiYlUhD8H1KcKPJFXaMJR7eihf9NBUIlHI2Lrp02LpKW\npdE2RTiB5os2Vx/Xa4Jom7+OE2lxNRl1RiJwTSaI7LkpE77HXNy9r8jfq9ic1ObFZiJRZTKZNRHr\nEz+yGdQKxom6wsaGXP4/ZMVEQm9vb+h8qdTiAmGXTmdMdfUKk0zONy6ymEikTXNzi4oORVFmFSrq\nynQrF1EX5yEmwiAQIhMp1A4KVOOMenPe7/NN0B3qmhL888d1jbbYn64L1Rd8IgID25JKIyncOGHm\nREqc+HLRqOj1u45aP5qYNpKm7TJiQuzWihOEIlorK5eaTGZdwfVXVTXEHOOL4LVFhXb0s0ylak0m\nc17IRiR67FhTtpOd2lUURZkJSk3UaaOEMqmERzjB8DBIA8D4iA5sD7MIyAEG8Zp+FHgeuIqKii9y\n4sSXgD+2+24FvmB/X0f8SLDXkYaCYWQQiWvCaAeeJ8v59PNxOriXbobsuaPMtevcDXyfcKPDFqSB\noRP4JHAFcAPwNqSw/yV7nXvsvV2JNDZ8FhkT5thsj3VspapqLm9/ew+53F4uv/waZChLe36P1167\nKeZaDwBuJNeP2LYtF9uY4MZ+7dq1m6NHX+bQobkcPny9fbUT2Jsf0eWOP3LkhYJ1nnvu+YLn4vaL\ne05RFEUZOyrqlCknmXyW4eG9AFRWdpLL7R1xfzcXVMQh7N9/GWee+WYSiWtt1DMF3Gn33oqIlG7g\nJU6cABkX3IOIIDcb9euI8OuwxzhuANYgHaKPAP3293VAJ1n+D/100sG5dPN+RGydtMc5rkcE5p8g\nHantdo27ge8iYu+37HO7EWG5FPi2dw9vIGLOXdMbQCPS1ZpDJtmtI5j3WkUqdYKbb76e/fsfZ9eu\n3dTVzefYsfB7uXLlcgYGOiMdou2IODwBfIj9+x9n27bYj4LW1lZaW1vZtKmNoaGrCHfW7kbm4vrn\nW8axY/77u5VXX32dvr6+kHCM22/lynPiL0JRFEUZGzMdKiyXjTJNv1ZWLjVdXV3jKtQuPnnANSbE\nebg12lSrX+vm+6/5Ex1cd2i0g9SdV0ZzZWm3Xa6VJpzidZ2s55mg4zUdWcO/vjNN2IzYpYn9dGvc\nPfmpVJcabjPJ5CLT3NxSUK+YSi02FRULQ4/deC7pPI3eb4sZazq8mNFvNP3a29trEgnXueuaSHIF\n55Dau6D+L5Wq1Xo6RVFmHZRY+nXGL6BctnIRdcacerddWEBEf3emvX73atoEoi+u0WGBEVsT13hQ\nY2S+a1QA9ubXkRq6CnMZS4w0UmTs1maCUVq+mbATXXHXsMhIfdxKKxidzYo7/ryYYwrtP0SEmbwQ\nixNabjZudEpDXNNDnCgb6TMd67xV6ZoN27HECcfRGi60Y1NRlFJHRV2ZbuUk6k6V4oPfe41EuKI+\nb25yxIoioq7NBFG7M03QSDHXE1lBR2iWu70IXZcp9MVzvnKLjETp5tvnek3gUedH2Lrs5jdD+CJy\ngylsnnAduf69OJG0Pj9eK7pPeEpEEC2Nm+aQyawZ97zVsQitU/VGG8/xKv4URZlJVNSV6XY6ibrp\n+CINTGU3RCw54ma1OkuRNvu6P4IrZ2CxCdKwrnOzxXs9Y8VdvclyrRlgmR395daab8XgehN009Ya\nqLbnrLOizUUS3aitpSaY51psJqsTrm6fjD1fW4EIE6EXCMxUqjb03oTnuYbPFWf8Oxnjoor9LZzK\n38hYR1upsa6iKDNNqYk6bZRQxkW0ieHhh9vZt2/vhEdHRbtcW1tb6evr45ZbdnLkyAusXLmMj33s\nOvbv7+Ho0Zd58skkopF9zga+ijQX3GWfuwZ4EzKKaxEy6upV4H8DX0S6VY39fS3wXrJ82Y7+ejfd\n/AXSSPAc0hixyq6zAfgK8CGkcWGL/fkpgo7bLcAg0hgB0tV6dszdDxA0ajwKLEE6YQH+1R7vOnGv\noqLiPk6cCLpbh4agufke6ut77Psnn4N7P6eakf4W3DaVRDutRxoxFvd3piiKctox06qyXDZOk0jd\nZA4IL9ZUEUSfcsalR12zRTBBwUWwFhpoN0FzgbEpyuiw+/l2P1dT57+WM1kWWR+61TZa5vZrN/He\ndcH9Fxoc32sjdMtsNK4p5rqdwXAuclydjdIVppIl2lZoBjzW93Wyo1pTNSx+rBE4jegpijLTUGKR\nuhm/gHLZVNSNba0gTRg26U0kak0ms8YKJDej1YmlGiuEFpqgqaGwU1O2wjFhWS60NXSrTdApO3+E\ntVw9nHscTa269KyfMnamyHINDQ0rrECMis+c3XdBaI1UanFsXVxXV1fsexuX/pzMtHnQURtf03eq\n5xnLGpMt/hRFUcZLqYk6Tb8q4yKX28zDD7fnfc/G4js3MXYjRr3tgHwVHz58A5BA/NzER05Mfdch\nacy5SKp0cZE1n0U84/YiXm+Q5Xr6+alNuT4C/BhJe94LrEf87aKchfi8tQNfQrzjrvNevxdJA7d7\nz12L+OBBKmVYtuxMXnwR4EnEO241YkSMd30HEY+6BrLZsxkYOA58JrTu/v09sR5zcelP93jXrt35\nVKS/z1hTlEHa9Qp8z7/Kyk5aWq6dlPT8WNK3vjmyXPPEywAURVFOC2ZaVZbLxmkSqTNm8iI+XV1d\nduTUeuP81zKZ82ykqtjgexf5ajGSbm2wz1eb8NzXuPTrBhsBE7uNoMu1xgTWJ2tM0JVaa8T6ZJG3\nlt+12uitPc8E3ndxo8jSoccy0stF3XyblvgoYyZz3ilHnEaayzqeFGWhj6B04xZr0pjpqJimXxVF\nmSoosUjdjF9AuWynk6ibDMJftNE6txormOq852qtgPDNiP2UZY0VVr7B8FL7epWR9GyVcbYiWbrM\nAElzGb9pz7XWBClaZ3/ixFmNkfq4qHlvrQksU6pNIEajtib19rwmf2w4ddkVEXhxgnC+ueiiiwre\np0xmzZjF9UjzVscjxkbatxRFnTGTn3pWGxVFUYwxKurKdVNRF6a4wbDxhExQgybCqdi+7rli0yTO\ntGKs0Qq6gxHbkvlWdOW8tZy/nPHW9n3m3DWtN4EInW/F31oTRASdJcoZxgnYZHKRqa5eYYImiWIT\nNNx5xBevomKJidYDOhE4luhTXA1cOp2J+TzMiGJspMjX6R4VO93vT1GU8VFqoi45Y3lfpSTp6+tj\n06Y2Nm1qo6+vb8L7nCqJhMHVoIk9SD9SazYwwlFJ4GrgVuBX7f7/jsyKvRw4TpZ/oJ+NdHAn3ay3\nx60gsDc53z7XCDzurb0G+D1kzusfAl9D5rkuR+rcksjs1wVIndmD9jWAxVRXLyaT2UUy+RWGh3dx\n/PjHgXuA/wH8GzIntg9oRWrmcva5rwF+0dw6u/aD9vflgNSwjWZlsnLlMntte+221T4nNXSVlZ35\n16RWcnPsOq6WbePGHjZu7AnVzI30WpTp+DuabMI2KmN73xVFUaaNmVaV5bIxCyJ1Y4lCTFakIjz7\nc63xZ6H682IlZVhlghFcG4yYCUfTry4y5+xF3HzVILqWpdYMgE25umP9dKqbt+omTKwvsp87h19f\n56Jg0ekQtaaiYkGk3qzXRtrC9+3updgEiPb29tB7L9e5wcSN4irW/TrSvNXpTCvO1ohXqaaXFUWZ\nGSixSN2MX0C5bLNB1I3lC2uyvtSiRfsVFYtMc/OGgpmlxhgzZ85iE0x3uNSmI92w+JVWUG0wwfSI\njCfQ5Fol5epsS+qsUGyKiDV3b2utoKozQRrYePtFR4s5S5UaEzfZYs6cusj81ehIsOAaXMOBMdJI\nkk5nTDqdyVuX9Pb2mubmFpNIpL3z14cE2mjp0VKoB5ut4mi2ilFFUaYGFXVluqmoG/1c1dUrbCTJ\ndWfWmubmFpNK1ZmgCcHVsNUYaC6IZsGcyONcpIbONUS4CJ8f6fO7XudZcTav4BzJ5HyTSFQaidgt\nM3CGaW5uMe3t7babNxCTQbfreaa3t9e+HlcTOPb3Mu69cw0PxsQ3RJSaYJqtos6Y0hHGiqLMPKUm\n6tSnTskzFg+6qfSpO358OeL5JnVeQ0MVPPHElcAngKNIvRxIXdgC+1x4lBZ82R4vdVxZrqWfL9HB\n++3or9eBP0fGcfUgfnA3AB+0z90ADAEfRUaMLbDXtBWoAE4wPDzfHtduz7sX2MPAwHGGh3cBNxXc\n2+HDPwTg3HPX8sQT0VcHCOrYJvZe1tcvAqRO7ckn/73g9aNHX5nQulPF9PkdTj7TMQJNKURHvSnK\nGJhpVVkuG7MgUmfM2J38J2NigJ9+DerD2gwssZuL5MR5t6VtBC1qb1KTj3yJbUnCXMbZJugWbYlE\nyO414kPnUrt+jZzfZer/Xng91dUrvOhTQ8x11ceO60qlFhekm8fy3hVLAco1NBWcv7l5w4Q+p6lE\nI17KWNG0t1KqUGKRuhm/gHLZZouomy6iRfuFdWm+L9x5MaLONTREn19sYL0n6JaboOHBre1bptRE\nBKRbx1mWuGvyU7rtBaKpsrLe++JpM+F08XzjbEmmeoyWiDrfzLj4jFhFmUnG8+9gNqfrldMbFXVl\nuqmoCxP3H2mJgLlmB19EnWHCs1RrjEx+qDVS1xZ0gEKtyXKeGWCe7XJ1PnfLjNTH+UKryW51Jmx0\nnDbSSDHfiKlwNFLnulZdfV6NqapqMMYEX1SZzBrb4NFofL+7qf4iCsRyIDr9yRGKUgqMN/Kmok4p\nVVTUlelWrqJu5IhSVNStMIUTFVwq0VmMpE2QZvVFmFiYZEnZLterjZvEIPu7KQ9+t+siu246RvA1\n2t/jGhvcFIlA6GUy62ItRGYiZSQdshtMOp0xzc0tKuiUkmO8Ik3Tr0qpUmqiLiHXpEw1iUTClNt7\nvWPHDj72sV0MD38akGL4bduuZf/+xzl69GUOHnyGEyd22b23AmcgTQl3EjQhnAm8D3jGNDB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8eCCQpiJvxRYK3d84+QZoUc0rBwFdKBegci6O4kHGW7DRFgLyHmwRuBO6xtyRxrLOzq4K5DrEeu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\nGwwN3UWWF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Assessed.ipynb b/code/svm_regression/SVM_RBF_Assessed.ipynb new file mode 100644 index 0000000..6b058a9 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Assessed.ipynb @@ -0,0 +1,561 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar=1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 165500. 475500. 268000. ..., 154000. 150500. 156000.]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=1.5\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0750, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 4.2170, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 0.5623\n", + " Cost = 5623413.2519\n", + " Relative Accuracy = 0.1572\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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2C96MeGwZyyYiIo1ATeciUhZxZPXl+C/0DMb7CX6F90m8MITwVhmLJyIijUCBpoiIiIjk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1572\n", + "Train set Accuracy: 0.1567\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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FxKuA5cCBmXl/WfOnwCeAfTNzQ0ScSRHUZmXmprLmfODMzDygfP4B4KTMnF3xOa4AnpuZ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\n5ZZVd3Eu8FUCa5xzX6e2tpp0EbkcFX4LiMXupqxsHNdee1uDC/RPf/o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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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MdKBQJyIiIrJPlQY6UKgTERERCVRxoAOFOhEREUm4VCpFa+tCWlsXkkqlRv+FYlR5oAPN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\nelFLnVrqpOJGWhR3eLdo55DWucxZrdHu0fHj9y0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4n6js0zM88MAn9T8H30FlpobFiX0P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bV6mnx5blKTFLFc13I9aPUPypuYz8ZStd363C+QC42bKtyWk7a29Zb1rhSRUiQmldrhq0VajAwW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\nX4/hlQ+Db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E8IYUMmz99J6rftX9sSeSznYRR/ngyV1LUV39kgAMMlrZK0WNJtkr6UWu8y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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Condo.ipynb b/code/svm_regression/SVM_RBF_Condo.ipynb new file mode 100644 index 0000000..7d35570 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Condo.ipynb @@ -0,0 +1,534 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 105\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=1\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=9\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.0018, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0316, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0316, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.0316, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0316, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.0316, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 0.5623, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=31622.7766\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=31622776.6017\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 1.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 1.0000, Sigma= 0.0018, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0316, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0316, Cost=31622.7766\n", + " Working on: Fold= 1.0000, Sigma= 0.0316, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0316, Cost=31622776.6017\n", + " Working on: Fold= 1.0000, Sigma= 0.0316, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=31622.7766\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=31622776.6017\n", + " Working on: Fold= 1.0000, Sigma= 0.5623, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=31622.7766\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=31622776.6017\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 2.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 2.0000, Sigma= 0.0018, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0316, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0316, Cost=31622.7766\n", + " Working on: Fold= 2.0000, Sigma= 0.0316, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0316, Cost=31622776.6017\n", + " Working on: Fold= 2.0000, Sigma= 0.0316, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=31622.7766\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=31622776.6017\n", + " Working on: Fold= 2.0000, Sigma= 0.5623, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=31622.7766\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=31622776.6017\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 3.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 3.0000, Sigma= 0.0018, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0316, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0316, Cost=31622.7766\n", + " Working on: Fold= 3.0000, Sigma= 0.0316, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0316, Cost=31622776.6017\n", + " Working on: Fold= 3.0000, Sigma= 0.0316, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=31622.7766\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=31622776.6017\n", + " Working on: Fold= 3.0000, Sigma= 0.5623, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=31622.7766\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=31622776.6017\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=31622.7766\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=31622776.6017\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0018, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0018, Cost=31622.7766\n", + " Working on: Fold= 4.0000, Sigma= 0.0018, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0018, Cost=31622776.6017\n", + " Working on: Fold= 4.0000, Sigma= 0.0018, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0316, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0316, Cost=31622.7766\n", + " Working on: Fold= 4.0000, Sigma= 0.0316, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0316, Cost=31622776.6017\n", + " Working on: Fold= 4.0000, Sigma= 0.0316, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=31622.7766\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=31622776.6017\n", + " Working on: Fold= 4.0000, Sigma= 0.5623, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=31622.7766\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=31622776.6017\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 0.0018\n", + " Cost = 1000000000.0000\n", + " Relative Accuracy = 0.1353\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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xVtQpeMvp32LrC8A3+CfJFaTHgWlMX+FBuW7qM9qMhBCWAf/FX31xu+INctm8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\nmZlZaf4Lk0v1IrFl3voAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1353\n", + "Train set Accuracy: 0.0861\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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CHJaZ15c1hwHXAvtm5u0RcRRwFbBXZt5V1ryubMOumbkhIk6mCGlTMvPhsuZ9wMmZuUf5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\nhRS9dMuAr2TmKWXNDsBtFFu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rPMe0MucIljNr1hV98xzTwcFBjjzy74Dzy0d/vZ8FTOUy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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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9bcKWusxcCDwFuBZ4H/DTiPgw0DWDAiW1xpo1F5aBbglQhLvq7EP1IAOd1Pcm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\nAEuWePzxL1FffxPWMeLisGcqUADkcn2+F8YuB6aS4DRquSdY6KI6dHO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Condo_Assessed.ipynb b/code/svm_regression/SVM_RBF_Condo_Assessed.ipynb new file mode 100644 index 0000000..8556eb1 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Condo_Assessed.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar = 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-3\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.0178, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 0.3162, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 5.6234, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782.7941\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=316227.7660\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=5623413.2519\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 5.6234\n", + " Cost = 316227.7660\n", + " Relative Accuracy = 0.1473\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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aEQ/vxbKJiEgPUNO5iPSKNLL6TOI/9CxH9BN8h+iTeLq7P9GLxRMRkR6gQFNERERESqE+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\nrvnTL4C13rbg6537ND2aOLc65Zd53eePWTKDzHnPwb1nw9r70cPYZXJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1473\n", + "Train set Accuracy: 0.1470\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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2Kw/tB6yqBbrSLcATlevsB9xdC3SlQWDD8h61mptqga5Ss11EvLxSM8i6BoF9yt5ASZJ6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\nYQzR6BJURA6pve7IkcI6Iw3XO50w4TuUlNxTm2R43br6PnZ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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qzycDXwCeDVwYER9oYv0kSRUGOkljGDfUAc8DVlZ9fhtwama+APifwMnNqJgk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Condo_LatLong.ipynb b/code/svm_regression/SVM_RBF_Condo_LatLong.ipynb new file mode 100644 index 0000000..8ffd6f8 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Condo_LatLong.ipynb @@ -0,0 +1,543 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar = X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=3\n", + "maxcost=7\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost= 1000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost= 1000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost= 1000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost= 1000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost= 1000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 1000.0000\n", + " Cost = 1000000.0000\n", + " Relative Accuracy = 0.2349\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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IrtEYRBEpEPPyXLUbdKVdBHyrcCgism/TGEQRKagywC9m9hG+izgbP67rJHx3W+8irJuI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.2349\n", + "Train set Accuracy: 0.1512\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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mGfALYGq5ayqwqhLoSrcCj1WdZyrw80qgKy0GNi+vUam5uRLoqmp2jYg9qmoWs6HFwP5l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\n/0XLmVyIxtMt69dZGYZhGIYxpDBR102cc78UkTHAImACsB74jHPulf6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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qo0AHhjpJktSJ2izQgaFOkiR1mjYMdGCokyRJnaRNAx0Y6iRJUqdo40AHhjpJktQJ2jzQ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\n21s2YBWON9Iv/KVLlw5qVZs4cZcBx8j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/g9WtS3ZZLvJZrtPkTP7AC7jZXtTt8K/T3bi6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\nuM5dd+2kuXnXwDWGk6Ha3r6W8vINBO+1vHzDqN+rMTxG+6wMwzAmhWy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_FS.ipynb b/code/svm_regression/SVM_RBF_FS.ipynb new file mode 100644 index 0000000..12f0fe4 --- /dev/null +++ b/code/svm_regression/SVM_RBF_FS.ipynb @@ -0,0 +1,428 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 110\n", + "[ True False False False True True False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False True False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True True False False False False False False False\n", + " False False]\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + "Best parameters: \n", + " Sigma = 1.0000\n", + " Cost = 17782794.1004\n", + " Relative Accuracy = 0.1116" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n", + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cross-Validation Accuracy: 0.1116\n", + "Train set Accuracy: 0.1052\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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tXgiKl55hV69t/m7v67Yz2eNh5fgSFS1XIch7n+ec+xoYALwZM+t44PUki00B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\n/T/Uqn03nUFTxAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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ZM5h+/bV0z17J7Nk3sHz5VcyZM6fRrZNUJRXvYxcRARxFtiJ2HrAW+Hfg2pTS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\njaTbet0OPOLPvxwVjsei1rZxFBTcz/DhA3nppe04dyTqvr2AloKzGLX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9d8RdNYlBo89+fSOwMXzsSxDiNoZ/vgQcD3yTIEytJJzVmg90oY8SjK/7KsH6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\nXJ1xUTipoUvaThH+2rY4UucidGtM0P4kziokbr9cU1+/1tTXrzF1das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5iKeeejr/e9im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\nW4B/I8t/W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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_FS_Condo.ipynb b/code/svm_regression/SVM_RBF_FS_Condo.ipynb new file mode 100644 index 0000000..e97b0ec --- /dev/null +++ b/code/svm_regression/SVM_RBF_FS_Condo.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2246 \n", + "Number of variables: 105\n", + "[ True False False True True False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False True False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False True\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False True False False False False False False False True\n", + " False False False False False False False False False]\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + "Best parameters: \n", + " Sigma = 316.2278\n", + " Cost = 562341.3252\n", + " Relative Accuracy = 0.1137\n", + "Cross-Validation Accuracy: 0.1137\n", + "Train set Accuracy: 0.0855\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2396FiIgP+rWlxVDoNKl8qoSuIO4izKwZEE0fKcBeZtYfyHXOLTezu4BrzOxr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\nAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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qlgM/Bg4udx0MrK4FutJtwGOV8xwM/KgW6EqLga3Ka9Rqbq0FukrN8yLiBZWaxWxoMXBg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\nVJnoXrnmtPUrhBBid1FFiU6ioypKZFc/uPLKq1mxYieWqHc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lAFwyvYuTFyww0El10mh16soqPhwRzwOmkt0HNoDfAr0ppaer273WYaiTNFa9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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_condo.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_FS_Residential.ipynb b/code/svm_regression/SVM_RBF_FS_Residential.ipynb new file mode 100644 index 0000000..1833de6 --- /dev/null +++ b/code/svm_regression/SVM_RBF_FS_Residential.ipynb @@ -0,0 +1,425 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 82\n", + "[ True False False False True False False False False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False True False False False True True False False\n", + " False False False False False False True False False False False False\n", + " False False False False False False False True False False False False\n", + " False False False False False False False False False False False False\n", + " False False False False False False False False False False]\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + "Best parameters: \n", + " Sigma = 1.0000\n", + " Cost = 17782794.1004\n", + " Relative Accuracy = 0.1038" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n", + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Cross-Validation Accuracy: 0.1038\n", + "Train set Accuracy: 0.0865\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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N10dERMpPY4JE4jhT7GmooNn/dGCZAiARkbpPY4JEimsIrHTOvYV1Z+Vj4yyOwboGLq3B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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ZwNVV95kEfBnYDTgaOAl4CzBcVbMnsAr4GXAEcAZwdkScWVVzEHAzcHt5vwuAyyLihB36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\n98J62HphPXTpZbugkBiLqWALcDillEcSJV4KXyeF43ZgvWF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Zpxvwu16dc6+Z2XeA68xso/+aDmAN8JBf5hkzWwHcYWYteN2sdwAPOOfSgx26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\n4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Op9+nIOHQr8vbODkLQScCKwsaTPi6wBpEewt3W2bGEoqUI3s5jXeI9wbg+WN7M24kqd\nmdXdXGA1SWtGRKPDxC6VmGeAHSPi1aVY7yvAB6SK04udxCyMiJsljSQ9Rr0EeBaYAZwlaXxELC4X\nkLROfv/uG6QK2G7AZ0XI5sC9kjaLiNc727nc2/Y04NGIeLfI2hH4HJje80M1s3bgx69mVndPklrv\nLpK0taQjSO/LlS4G9pB0paRdc9yhkqo9R/8jV8b+DOxbzpc0RtIISWunSe0FHESqOJI7R5xEeqz7\nuKRR+Zt1O0k6G/hTXtVo4L6IeDYiZhTpfuAl0qPZYrMaLGkjSdtKOhaYAqzZ5Fj3BR6LiOpjYzNr\nc67UmVk7q/Yebdab9D3gGGAk6dMio0mPR6OImQ6MIHUeeJTUmnYh8HY3278aOLJ4Xw5gFul9utfz\nOu8k9aq9sNjeNFIL3IukHqozSJ9GGQacKWkwMAr4QyfbnQCcmD+PEqTPubwFdABTgdOBu0itjy9V\nlj2K9KjZzGpGTXrUm5lZD0maDFwRETc1yZsYEQe0YLeakjSK1Cq5c/Wxr5m1P7fUmZktn1Npn3vp\nasBJrtCZ1ZNb6szMzMxqoF1+XZqZmZlZF1ypMzMzM6sBV+rMzMzMasCVOjMzM7MacKXOzMzMrAZc\nqTMzMzOrAVfqzMzMzGrg3xpDGRMnyA1WAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline\n", + "\n", + "from sklearn.feature_selection import VarianceThreshold\n", + "from sklearn.feature_selection import SelectKBest\n", + "from sklearn.feature_selection import f_regression\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )\n", + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X\n", + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "\n", + "#sel = VarianceThreshold(threshold=(.7 * (1 - .7)))\n", + "#nrm_X = sel.fit_transform(nrm_X)\n", + "#retained = sel.get_support()\n", + "#print retained\n", + "kbest = SelectKBest(f_regression, k=7)\n", + "nrm_X = kbest.fit_transform(nrm_X, y)\n", + "retained2 = kbest.get_support()\n", + "print(retained2)\n", + "#X = pd.DataFrame(X)\n", + "\n", + "poly = PolynomialFeatures(2)\n", + "nrm_X = poly.fit_transform(nrm_X)\n", + "\n", + "\n", + "nfold=5\n", + "\n", + "minsigma=-2\n", + "maxsigma=2\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)\n", + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + "\n", + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)\n", + "\n", + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)\n", + "\n", + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))\n", + "\n", + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()\n", + "\n", + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n", + "\n", + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))\n", + "\n", + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n", + "\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_LatLong.ipynb b/code/svm_regression/SVM_RBF_LatLong.ipynb new file mode 100644 index 0000000..e27e6ec --- /dev/null +++ b/code/svm_regression/SVM_RBF_LatLong.ipynb @@ -0,0 +1,567 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 4844 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar=X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 53.58709268 -113.4417665 ]\n", + " [ 53.61282363 -113.4300466 ]\n", + " [ 53.59555643 -113.3784654 ]\n", + " ..., \n", + " [ 53.42686423 -113.4560936 ]\n", + " [ 53.42686423 -113.4560936 ]\n", + " [ 53.42924459 -113.4683265 ]]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "print(X)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=4\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=1000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 1000.0000\n", + " Cost = 10000000.0000\n", + " Relative Accuracy = 0.2796\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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SWyq2uj6HPTEPdl77MPq3DjVoG+xH9CY2YLQEOwll3pFUSu4Bqrm0iMp4ifKv\nA4jfCe+KXThew05KjbGTT/wuoRS7UCzDDqhM3ZJP4xWa3tg+GY+tewn2qHlmMO9S1r5Pkq10m0Rl\nvIAFsE2xZupki+FCrPsuFDwR1efv0b/F2A/9WCo+OVdIBmPb/HFsu2+OXaQzNwaLqPgUR1Jyf5RE\nZdxD9nUAJ1H+xYQvYCf1vybm3YPs+33ATj5zqfjKgEI1FLto3ot1w2yBjUHJdN0swG7eMupiD4jM\nwE64bbHxQfGbrTTWpfltlL9/VH78SZwy7C54FjYubDAWGMcfetgaCwhGY/u2LdaiewiFbV+sy3cM\n2UDyLrJByHzKPzFdF3sv2PToe3vgN1iwlZHC9ts3Uf6dsICzfSzP77Bj60bsGGyJBU7xY+FjsoGs\nw1p6b8a6v0et85pWPef9z6Hhq3ZxzvlLa7oS8pNCfZy6Nuu99iyykW3owxxStXK9ukNqRg/Ae1/h\nXZYF954kERERkaqgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgS\nERERCVCQJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgS\nERERCVCQJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgS\nERERCVCQJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgS\nERERCVCQJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgS\nERERCVCQJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAXVrugK1Ve+aroD8pHNNV0Aq\n6FynpmsgSZtuVtM1kHK61HQFpJxXw8lqSRIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERER\nkQAFSSIiIiIBCpJEREREAhQkiYiIiAQoSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERER\nkQAFSSIiIiIBCpJEREREAhQkiYiIiAQoSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERER\nkQAFSSIiIiIBCpJEREREAhQkiYiIiAQoSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERER\nkQAFSSLcEhA4AAAgAElEQVQiIiIBCpJEREREAhQkiYiIiAQoSBIREREJUJAkIiIiEqAgSURERCRA\nQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQoSBIREREJUJAkIiIiEqAgSURERCRA\nQZKIiIhIQEEGSc65XZ1z/3TOzXTOpZ1zx+YxT2/n3OvOueXRfH/cGHXNx4vA74AjgfOAz/Kcbw7w\nm+iT9GlU1pHAacC4xPSJ0fRjo/nPAcZXYd1quyeAA4BBwNHA5EryTgNOBvaK8h8I3AasSeR7HDgk\nyvNr4LnE9DXAXcCvojxHAv8JlHEEsFv0OQGYkP9q1Vr3pmGHFHRMwS9S8LbPnfcLDwemYOsof78U\nXJmG1Yl57knDzinolIKdUvB4uvz0R9PQOlX+0yYFZbFyUh5GpaFvtKy+KfueqqR+hWL0EugyAxpO\nh36zYMLK3HmnlMHuc6Dtd5a/2wy4aGHFfXLbEthqJjSaDj1nwoNLy0+/awnsMhtafgubfAt7zIG3\nEssdtRj6z4Lm06HNt3DAPPi0bMPX9+du9EzoMhEajod+78GExbnzTimF3SdB2wmWv9tEuOhrWJ04\nBh6ZC33ehcbjod0EOPpTmBfblkMmQdGrFT+93ilfzpxVcOwUaPOmLW+bd+CNRVWz3huqbk1XoJo0\nBj4CxgIPAJWekpxzzYCXsTigH7AVcJ9zrtR7f331VrVybwH3AyOAnlhQciVwI9CqkvlWAzcAW1Mx\ncJkHXAX8Ajgrmn430AzYKcrTDDgU6ADUAd4HxkTpO2xg3Wq7ccB1wPlAHyxgOhMLUNoG8tcH9se2\nURNgKradUtF8AH8HbgUuBnoBn0R5mgG7RHlGA88DfwS6YIHsOcC9wJZRnpKozE7Yj/5fUZ6HgC02\ncL1/rp5Kw0Ue/upggIN7PQxPw8Qi6OAq5q8PHOmgt4PmwMfAyLQFoZdG+e9Nw+UebnTQ18EHHs72\n0NzDXrEyGwEfFJU/wdSPTb/ZW31uK7Jj8RPgjLTV4Q+BuhWKx5bB7xfAmFYwuBhuWwr7zIUpHaFT\n4KpT7OD4JrB9MbQogsllMGK+7ZOrW1qeMUvg/IVwdysYUAzvrLI8m9SBXzayPK+vhCOawKBiaFgE\nN/wIe82FyR1gi3rZPKc3g/7FkAYuWQR7zoUpHaysQvTYPPj9lzBmSxjcHG6bBfv8F6YMgE4NKuYv\ndnB8O9i+KbSoC5OXwYjPYY2Hq6MTyVuL4ZjP4Lot4FetYG4ZnDYVjvoUXtne8jzVu3yguzINvd+B\n4W2yaYtXw6APYNcW8Px20LoeTFsJbepX3/ZYF877wr6lcc4tBU7z3j9QSZ5TgVFAifd+VZR2EXCq\n975jIL//e3VVOOF87IJ4ciztDCyYOaqS+e4DVmAn5ruxi2TGg8B7wM2xtDHADCx4yuU8LCg4cgPr\nVtU6b8RlgbWu9QAuiqUdjAWdp+VZxvXYBfPe6PsJQG/g7FieG6M8d0ff9waOAw6P5TkPKAb+XMmy\nfgGcDhyUZ92qQueNeLEZloJeDq6PtYvvmIIDHFycZ1v5xWkLhF6I6r1PCvo5+HNs/kvSMMnDs1Ge\nR9NwvodvK1nXI1LQysEtsXJOS8NiDw9v5AvyppttvGUNmA196sMdsbulHjPgkMZwVcv8yhi5AN5e\nBRPb2/eBs2HnYrhu02yecxZYsPRm+9zltPsOLm4BpzULTy9NQ/Nv4ZkS2K9RfnWrEl023qIGvA99\nmsAdPbNpPf4Dh7SBq7rlV8bIL+HtH2FiP/t+7Xdw60yYPjCb577ZcOaXsHS3cBkPz4XjPrN5OhRb\n2oVfw5uL4c2+675eVcm9Ct77CrcuBdndth52Bt7MBEiRcUB759zmNVQnVgPfANsl0rcDvqhkvg+A\nScBvCTehTc1R5jSsdSPJY81ys7Gga0PqVtutBj4n2+KWMQD4b55lzADeBuLnhNVY60JcfaxbNFVJ\nnuJKlpsCXsKC5W3zrFttU+btt7l74tS2u4N387z/m+bhNQ+DYmWUYds2rgF2XMW7ylYC26dg2xQc\nmYKPE8vc2cGbHr6M0r/wMMHDngXcilTmYdIqGNawfPqwhjBxVXiepK9Ww0srYEislaPMWwtHXAMH\n767K3X25ysNKD5tUcqVbkrYWpcry1GZlaZi0FIYlgtNhLWHij/mV8dVyeGkhDNkkmza4uXWTPTsf\nvIf5ZfC372G/TXOXc9ds2GfTbIAE8PQPsGMzGP4JlLwJ278Lt83Mf/2qW6F2t62rtsB3ibR5sWnf\nbtzqmKXYwds8kd4cyNWdvBC4g2wLQ8iPgTJbYBfVpdHfAKVYK9EaLJo+EWtJWt+6FYLF2HonzwMt\ngXfXMu8JWABZhrXq/C42bSfgn8DuWF/vZ8Az2D5ZHC1vZ+BRLLjqFC3v1cByvgKOj5bTCPgrkOfN\nYq2zANtGrRPprYDv1zLvPinralsFHOPgotgFeA8HD3vYz9tvfjLwkLdjYQHQBuju4BZgG2fHw51p\n2C8N44uga1TWmUWwNA2D0tZtvQYY6eD4Ar0gA8xP2T4pSbSUtakDcysZlwTWWvRhmQU3JzWFK2MX\n5b0awj3L4ODG0Lc+fFAGdy+zbTo/XXF5ABcvgqYODqikheishbB9fWulKkTzV1sQWZK4w2pTH+au\nZdzPwPfhw2WwKg0ntYcru2an7dQcHt3GutdWpK0rbmhLuH+rcFlTl8Mbi+GZxB3btJUwehaM7AQX\nbm7LO2OqTTutQj/Oxqcgyaxzn+Njsb+3wcaR/BzcDAyjasafNMLG3qzALib3Yxej3lVQ9v+iUdi2\n/ALbT2Ox7jOwAHQBFkh57CL/S2xAXeZ6eg5wBXBY9L0TNnj8n4nldMaCqWXAv4FLscC5UAOl9XVP\nkd0IfOzhMm/75KwouPmDsyBr37TtjzbA4Q5u8dn90c/ZJ2PHItg9DXd7uCpK/0caHvdwp4OeDj7y\nNn5qszQcVcCB0vp6vA0sS9uYpHMXwtV14fzoru2PLWBuygIpD7StA8c1gWt+DHeJ3PQj3LkE/t0O\nmuTY1iMXwMSVMKEduAJu3Vtfj/eCZSkbk3TuV3D1t3B+Z5s2pdSCmUu6wF4tYfYqOPdrOPkLGLt1\nxbLumg3tiyu2NKW9tSRdGZ2gtmsKXy631qTqDJLGL7LP2ihIMnOpOOa2JDatguHVWh3TFDv4ky2i\ni4FNKmYHrHvmM2wwMdjJxGP1HQHsibUUJVt7FmN3uk1jaY7sRugMzAT+gQVJ61O3QtACW+8FifSF\nrH2wenxbprGA55iovGLgEmycU6asJ7FANbM9WwDXYt1uP0Z5bgaS55G6sbSe2G/iEWzAd6HZFPvd\n/pBI/4Hs9s6lfXRR7O4glbaB2Wd4KHLWjXOTg+u9BUttgfu8DbxvleNiWuRgW2fddxmXeTjDwa+i\ni3RPBzPTcKPfuOP2NqZWdWyfzEv03c9LQbu1jMPqGF2Reta31qgT58N5zaN9UgT3tIY7W2XLun2p\ntRS1TpR74482IPvFttAvRwvR2Qvg8VJ4rR10rrc+a1o7tKoHdVz5p87Avrdby+DojlF3Z8/G1hp1\n4udw3ua2P0ZNt9akP0Rj3Xo1gcZ1YJdJMKqbBUQZZWkYOwdO7mDzxrUvhq0bl0/r2Qi+y7Nrdn0N\n2aR89+Gfpofz6V7G/AfYxTkXP5yGArO89zXS1QZQD+hKxTEnH5F9minpeuxCmvkMx8axXIt114AN\nOv4oUGY37OSWS5rsY+vrU7dCUA/rDns7kf4O6zbuJ4Vty8QTtdTBWuscNihu1xx1aBXN/yr2qH9l\n0lhgVYjqOxsH91qiLXi8hx3XoWUg89tOjsmr46Cds1aGpxJPtiV5D594KInlWUnFk2yhn3TrO+hb\nDONWlE9/eQUMXIcurZS3LpzQPmlf1/bJ30ph/0RX2vVRgPR8WxgYeHIL4KwF8FgpvNoWehRwgARQ\nvwj6NoVxC8unv7wQBibHS1Tip/0RHWsr0oHfdvTbTyeOx6d/gAWr4bftKpY7qDl8Xlo+beoK6Jxj\n321sBdmS5JxrDHSPvhYBmzvn+gALvPcznHOjgP7e+z2jPI9gvRL3O+euwK7z/wdctnFrXtH+WGvB\nFlilxmGtNcOi6Q9jY1Aujb53Ssz/FXbBjacPwx7Xvw+LBD/H3n0Qf7LqSWwDlmAX2EnAm9hg8Hzr\nVqiOwlp9tsEu0E9iLUu/jqbfCkzBHtkHe99RMbad6mItfaOxVr3MAfgd1qXZG1iC7ddpwOWx5X6C\ntWr0wFpK7ozSj4nluQV7ZUAbYDm2nycBN23QGv+8nergdx52SFtgdH/U+nNcdML+cxo+9PCP6A7g\n8bS1FG2FBZyTPVzh7Wm4etE8X3t72q2vs1a70d4eeBgTC4CuSUN/Zw8pLQXu8taVen0sz14ObvKw\nmbdj5GPgdg/DC7xrZ2QzOPoH2LHYAqPbl1pX2SnRE2YXLIT3VsEr0UXzwaX2yH6vehZkvb8KLlwE\nhzbO7pMvV8PbK2GnBrAobcHQlDJ4sEN2uX9dbOOQHmoDW9SFudFdXaMiaBZd0U+bDw8tg6dLoHlR\nNk/TImhcoBHsyE5w9BTr1hrYHG6fZY/snxJtuwu+hveWZB/df3AONKwDvRpbkPX+ErhwGhzaBupF\n22j/VvZagNtn2SDwOavsNQN9m2ZboDLunA17toTOicH8AGd3goEfwFXT4bA2NibplpkwqmvFvDWh\nIIMkoD/ZMa0e+FP0uR8b9tEWawixDN4vcc4Nxd7x9z7W43Gt9/6GjVjnoIHYCfhJYBGwGXAh2a6d\nxWRHmOeSPB+3icq4HwtsWmLBz4BYnpXYiwsXYC1RHbHH+wetQ90K1VDswnkvMB8Lfm4i21+7AJgV\ny18X29YziMZSYOOKjozlSWOR+rdR/v5R+fE+4DLg9qjshsBg7NH/JrE8C7FutQVRencskE0+jVdI\nflUEC9PWNTbPW/Dzt9g7kr6n/JMXdYEb0xaEeuwG4kQHp8QOlDQwxluwVBcLPJ8vgo6xPEuw9yt9\nj73PalvgX0WwfSzPKGdj0c5L22+lBBskfk6BB0mHNYEFabhiMcxJQe961rKTeUfS3BRMi71NtZ6z\nlzx+udr2yeZ17V1GZ8daOlIeblgCXyyw/Hs0sNcDbBa7io1eai2CwxOj9o9rAvdGo/vHLLVz4i8S\nAykuawGXFOhYgcNKrCXniukwpwx6N7Z3EmXekTR3FUyLtfzVK4JR39rYIA9s3gBO7wBnx14jcWw7\nWJqy1wD84Ut7n9Iem2Tfo5QxbQW8tggeyzFwt18zeLq3BWF/nm7LuqIrnPozGLQN/wPvSaoOG/M9\nSbJ2nWu6AlLBxnxPkuRnY74nSfKwEd+TJGun9ySJiIiIrAMFSSIiIiIBCpJEREREAhQkiYiIiAQo\nSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQo\nSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQo\nSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQo\nSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQo\nSBIREREJUJAkIiIiEqAgSURERCRAQZKIiIhIgIIkERERkQAFSSIiIiIBCpJEREREAhQkiYiIiAQ4\n731N16HWcc5536umayE/2bemKyAV7FbTFZAKetd0BSTuh05NaroKEtPGLcN775LpakkSERERCVCQ\nJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgSERERCVCQ\nJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgSERERCVCQ\nJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAQqSRERERAIUJImIiIgEKEgSERERCVCQ\nJCIiIhKgIElEREQkQEGSiIiISICCJBEREZEABUkiIiIiAXXzzeic2wM4AugEFAM+M817v0fVV01E\nRESk5uTVkuScOw54AWgC7A58D7QEdgA+q67KiYiIiNSUfLvbzgFO994fAZQBFwDbAw8DS6upbiIi\nIiI1Jt8gqSvwcvT3KqCJ994DtwDHV0fFRERERGpSvkHSAqBZ9PdsoHf096ZAw6qulIiIiEhNy3fg\n9gRgKPAR8Bhws3NuT2BPsi1MIiIiIgUj3yDpNKBB9PdfgDXAYCxguqIa6iUiIiJSo/IKkrz3C2N/\np4Cro4+IiIhIQcr7PUkAzrmWQBsSY5m891OqslIiIiIiNS2vIMk5tz1wP9kB23EeqFOFdRIRERGp\ncfm2JN0LzATOxF4k6SvPLiIiIlK75RskdQcO895/WZ2VEREREfm5yPc9SW8BPauzIiIiIiI/J/m2\nJP0WuNs51w34GFgdn+i9f6OqKyYiIiJSk/JtSdoC6ANcj708cnzs81o11Csn59yuzrl/OudmOufS\nzrljA3kuc87Ncs4td8695pzbOo9yd3POfeCcW+Gc+9o5d3L1rMG6G70AunwBDT+Ffl/BhNLceaes\nhN2/gbafW/5uX8BF82B1YhTZbQtgqy+h0afQcyo8uKhiWU/+CFt/CQ0+hW2+hKeXlJ8+6gfo/zU0\nnwJtPoMDvoVPV274+tYGoz+ELndAw+uh31iYMDN33inzYfdHoe1tlr/bnXDRG7A6VT7fI1Ogz/3Q\n+AZodxsc/SzMS+zrJ7+Are+BBtfBNvfA04kO8MsmQNE15T/tb6uSVf5ZG/0sdDkBGh4E/c6CCZ/m\nzjvlO9j9fGh7lOXv9lu4aCysXpPNc9z1UPTLip8mv87muetF2OU8aDkcNjkM9rgA3go857sudSsk\nox+ALoOgYXfotx9MeDd33ilTYffh0Lav5e82GC66BlavLp+vrAwuuQ66DoIG3WHzneGW+8rnWbIU\nzrwEOvS3PN13hSeeXf+6FYp7R6+mX5dSNmu4jKH9lvP2hFTOvF9MSXPQ7ivYpq3l79+tlKsuWsXq\nxIWkrMzzl0tW0a9rKZ0aLGOHzUu5+5ayn6Z//mmKEw5ZQf9upZQULeOvfypLLoprLltFSdGycp/e\n7Su5yG1k+bYk3QH8G7iKmh+43Rh78/dY4IFkXZxz/weMBI4FpgKXAC8757b03i8LFeic6wI8D9wN\nHAnsAox2zv3gvf9Hda1IPh77EX4/F8a0g8GNLbjZ51uYsgV0ql8xf7GD41vA9g2hRRFMXgkjZsMa\nD1e3tTxjFsD58+DuDjCgIbyzAkbMgk3qwC+j/3zmP8vh8BlweQkc3MwCpkO/g7e6wo6NLM/rpXB6\nS+jfENLAJd/DntNhSncrq1A99hn8/t8wZhgM7gC3fQj7PAFTfgudmlXMX1wHju8N25dAi2KY/D2M\neAnWpOHqIZbnrZlwzHNw3e7wq+4wtxROexmOehZeGW55/jMLDv8XXD4YDu4OT06FQ5+Bt46CHdtl\nl9dzUxh/ePZ7nXxvhWqpx96A398FY34Hg7eB256FfS6FKWOgU+uK+YvrwfFDYftu0KIxTJ4GI26G\nNSm4+gTLc/MpcM0J2Xm8h0Hnwm69smmvfwxH7AqDtoaGxXDD07DXH2HyLbBF+/WrW6F47J/w+z/B\nmCthcH+4bSzscyxM+Td0al8xf3F9OP5Q2L4XtGgGkz+FEefDmjVw9YXZfIefDrPnwV1XQ/cuMG8+\nLF+Rnb56NQw9Clq1hCfGQMd2MHMO1K+3/nUrBE8/tpo//n4V14wpZsDgOtx722qO2GcFE6Y0okOn\niieI+sVw+PF16b19Ec1bOD6ZnGbkiFWsWQOXXF38U76TDl/JvNme6+8qpmv3In6Y51mxPFvOyhWw\nedcifvnruoy6uAznwvXr3tPx1Pjs/3BWp06OjDXA2f9Tu5ZMzpUC23nvv6r+KuXPObcUOM17/0D0\n3WH/t9zN3vtRUVoDLLA7x3t/Z45yrgZ+5b3fMpZ2F7CN935gIL/3vZKp1WPA19CnAdzRIZvWYyoc\n0gyuaptfGSPnwNvLYWI3+z7wa9i5EVwXu7CeM8eCpTe72vfh38HiNLzUOZtn6DfQui480im8nNK0\ntSo9szns1zTvVdxw+27EZQEDHoQ+beCOvbJpPe6CQ7aEq3bNr4yRr8Lbs2Hib+z7te/CrZNg+inZ\nPPd9DGe+AkvPtu/Dn4HFq+Clw7J5hj4GrRvBI/vb98smWPD0cewCXyN223iLGnA29OkKd5yRTesx\nAg4ZDFdVaGcOG3kXvP05TLwuPP2tKdZqNPFa2KmS0ZntfgMXDYfT96+6ulWZ0AtcqsmAA6DP1nDH\nX7JpPXaDQ/aFq/4vvzJGXg5vfwgTn7Lv496Aw34H0yZAyxbhee58GK65HT5/DermaAKoirpVhR86\nNdloy9p7wHK26VPEdXc0+Cltpx6l7H9IXS66qriSObP+OHIVH7yd4vmJdpf82rg1jDhsJe9Na8wm\nLdce1OzWezn7H1qXcy4pf3d/zWWreO7JFK9/3Ggd1qjqtXHL8N5XWJF87zFfAfpWbZWqRRegBBiX\nSfDerwTeACoEOzE7x+eJjAP6OedqrE2kLA2TVsCwxLE0rAlMXB6eJ+mrVfDSMhjSOFautxanuAZF\n8O4KSEUx89vrsdwlKWtRKuRWpLIUTJoHwzqXTx/WGSbOyq+MrxbBS9/AkM2yaYM7wpxSePYra7WY\nvxz+9hns1y2b5+05+S132o/QYTR0vQOO+Cd8szi/etVGZath0tcwbIfy6cN2gImf5VfGV7PhpUkw\nZNvcee56EXptXnmAtGo1rCyDlk2rrm61UVkZTPoEhiVuGIbtChM/yK+Mr6bDS6/DkJ2yaU+/BP23\nhWvvgE4DLLA561IojZ2Tnh4HA/vCaRdDu76wzS/gTzdYi1RV1a22KSvzfDQpzZBh5aPGIcPq8t7E\n3F1ucdO+SjP+pRSDhmRP7i88vYY+/esw+toy+nQqZacepVx01ipKS9e9o+nbaWm27VBKv66lnHzE\nSr79Jr3OZVSXfLvbXgCuc85ti3V1JQdu12iXVEymbWVeIv17oLKG1JLAPPOw7dMqMG2jmJ+CFFCS\n2Ett6sLcNcFZfjLwa/hwJazycNImcGVJdtpeTeGeRXBwc+jbAD5YCXcvsi65+Slb3tw1FZdbspbl\nnjUHtm8AOzfMnae2m78cUmkoaVw+vU0j6yKrzMCH4MPvYdUaOGk7uHKX7LSd2sOj+1v32oo11hU3\ntDPcH2slm1tacbkljcsvd6f2MHZf6NkS5i2HKybCwIfh0xOgZQHul/lLov2RaFlo0xzmBsbZxQ38\nA3w4zYKbk/aGK48J5/uxFJ6YAH85rvLyLn4AmjaCAwZseN1qs/kLIZWCklbl09tsCnN/qHzegQfB\nh5/AqjI46Ui48rzstGnfwYT3oEEx/ONOWPQjnHGJdb89cXs2z2sT4aiD4Pmx8M0MC5iWLYe/XrRh\ndautFs73pFLQuqT8nXGrNo7v51Ye0Ow7cDmffJhm1So4+qS6XHhlthXo22medyekaNAA7vtHAxYv\n8lx4xirmzk5zzxP5n2z67VSHW8bWYYuejh/meW64YjX7DVzBm582yquFqrrlGySNjv69IMf02jDq\n4X/qBZiPbwbL0jB5BZw7F66eD+dHYyD+2BrmroaB06zVom1dOK4FXDN//XfkyDnWyjShKzn7nf/X\nPX4gLCuzMUnnjoer34HzozvlKfPhjFfgkoGwVxeYvczynPwSjN0v/2Xs3TX7dy9g5/Y2wHzsJ3B2\n/ypcmQLw+AWwbIWNSTr3Xrj6CTj/sIr5HnoN0h6O3iN3WTc9A3e+CP++CpoUYDC6sTw+GpaVwuQp\ncO6VcPVoOP80m5ZOQ1ERPHILNI1auW+9HPY6Gn5YAK03tTwlrW3MknM2xmnBIjj7cguSZN3c/XgD\nSpfBJ5NT/OncMm65ejVnnm+BUmZ/3P5IA5o0tZP+qFth+F4rmf+Dp1Xr/C4Ee+ydDUO26gX9dq5D\nvy7LeWzsak45OzDwdiPL9z+4rQ1BEMDc6N8S7A3hxL7PrZi93HzJET4lwBpgfmiGy2JtS0Maw5Bq\n6F5uVcf+v5d5idabeWugXb3gLD/pGE3vWWytUSfOgvNaQZGzrrV7OsKdHaKy6sLtC6FpkY05Aguc\nkq1G89ZYetLZc+DxH+G1LtC55n/T1apVIxsInXzqbN5yaNc4PE9Gx6gbpuem1sJw4ktw3gDbJ6Pe\ntlagP+xoeXq1hsb1YJdHYNSu0L4ptG1csbVqXqml59KoHmzTCr4q0C63Vs2i/ZFYv3mLod0mlc/b\nMWpN6Nkp2h83w3mH2Ik/7q4X4ZBB0CLHMX7j03DJQ/Di5dCve9XUrTZr1RLq1LFB1XHz5kO7NpXP\n2zEaJ9lzC2vxOfE8OO9U2yft2kD7kmyAlMkH8N0sC5Lal9gg7fiNWs9uNrh7waINq1tt1bKVo04d\n+GFe+XaCH+Z5StpVfmlv39Gmd+9ZRCoFI09cxenn1aOoyFHSzlHS3v0UIGXyAcz6Lk2r1us37qJR\nI0fPbYr45qvqbdd4a/wa3hq/9u7G2hL85OsbLOAZlkmIBm4PBiZWMt9/gKGJtKHAe9774Fa8rCT7\nqY4ACaB+EfRtCOMSz+S9vAwGrsMYt5S3rrTkitRx0D46ofztR9g/Nth650a2nORyByWWe9YcewLv\n1S7QI7/xf7Va/TrQtwTGTS+f/vJ0GNghNEdYyluXWirqel+xxoKluMz3TO/8zu1tOeWW+y0MqmS5\nK9fAZwvWHsDVVvXrQd8tYNyk8ukvfwgDt8q/nFTanm5LJYZCvPsFfDQdRuwdnu/6pyxAev5PMDDx\not/WSxcAACAASURBVJGqqlttU78+9O1tA63jXn7TxgvlK5WK9kl04hrc37rW4mOQpk6zfzfvaP8O\n6gdfTrcW8p/yfAONGsKmm1Rd3WqT+vUd2/UtYvy48ne9r7+8hv4D8w8BUikb25XZHwMG12HebF9u\nDNLXU+0A6rj5+ocWK1d6pn6WpqRd9XZJDBpSl/MuK/7pk0u+/8HtpYS7qzywEvgKeNF7vyKQp0o5\n5xpj/00KWJC3uXOuD7DAez/DOXcjcKFz7nPgS+BiYCnwSKyMBwDvvc88X3I7cLpz7gbgTmAQ9gqB\n2IPUNWNkKzh6JuzY0AKj2xdZC88p0Z3oBXPhvRXwShf7/uAiaFgEvRpAfQfvr4AL58GhzaFe9Jv7\ncpU97bZTI1iUgusXwJRV8GDH7HLP2hR2nQZX/wAHNoOnlsD45fBWl2ye02bDQ4vh6c2geZF14QE0\nrQONCy38jhnZH45+zh67H9gBbp9sLTyn9LHpF7wO783NPrr/4KfQsC70amVB1vtz4cI34NAtoV50\ns7X/FjDiRbj9QxjWBeYsg9+/Cn3bZlugzuoLuz5q3XQHbgFPfQnjv7NXAGSc8xocsAV0agrfL4c/\nT7QA7NiN9DRmTRh5EBx9LezYw4KP21+wMT+nROO5Lrgf3pvK/7d33+FRVIsbx78nPSH03qUIKERB\nmmDDhmIvCDbsol71KliwoOIVRfRnV7A3FBUR9epFBVQUpAgigqIIIiBIAqGlt93z++Nskt3NJAQh\nCQnv53n2gZ05M3N2Z3fnnTNnTpj1kHs+6SuIj3EdsWOiYPFquOsNOO9IiA77RXzxc+jUEo72eP8e\n/cD1Q3rrVujYHJK3uekJcVAnoXx1q6lGXgXDRkCf7i58PP+W6/NzbeBuzjsfhkU/wax33PNJH0B8\nHHTr7MLl4mVw1yNw3qkQHWgVv/AseOBpuPwWGDPC9Um6aYwr06iBK3PdMHj2Ddeh+/pLYe0GGPME\nXH9J+etWE107Mprrh+VyWJ98eveP5I3n89mcbLn0Wvfmjr0zlx8X+flglrtWPGVSPvHxhi7dIoiO\ngZ8W+3jorjzOOC+K6MCB5JwLo3j8gTxuujyX28bEsGO7ZfRNrkzDRq5Mfr7lt19ccMrOtqRs8rN8\nqY9aiYb2Hd1B4r5bczn5jChatDakbrY8/kAeOdmWoZeWtzdQxSpvLc4D2gAJuFvswXWEzsZ1am4N\nbDHGHG2tXbPXaxmqN/BV4P8WuD/weB24wlr7iDEmHngOqA8sAAZaa4MvVLQmKPRZa9caY04BngCu\nAzYCN1prP6zg17JLQ+rC1gIYuwU2FUBSHExvWzxGUnIBrAkanyvauEEeV+W5F9g2Gm5oCCMaFpfx\nAU9shZV/u/LHJcK89tAm6FJZvwR4tzWM3uzGP+oYA1NaQ++glqSJ28AAx68NrfOYJnBvDW26BhjS\nBbZmw9j5LswkNYbpg4vHSErOhDVBl1iiI9zltFXb3Rlu27pww2EwoldxmUu7QXoePPsj3DLbjad0\nXBsYH3Qrfb+W8O7pMHoO3DsXOtZz/Zx6Bw3lsDEdLvgEUrOhcbxbZsHF3uM31RRDjoKtaTD2Pdi0\nDZIOcC07heMQJW+HNUEX26MjYdwUWPV34DvSBG44DUacHbre9Cx4bw7cd4H3dif8z7UGDh0fOv2y\nE+DVm8tXt5pqyOmwdQeMfQY2bYakzjD99eJxiJK3uE7WhaKjYdxzxa1AbVvCDZfCiKuKy9RKgFmT\nXWft3qdD/bpw9snw8B3FZVo1hxlvueEDegyCZo3hyqEw+t/lr1tNdOaQaLZthSfG5pGyyXJQUgST\np8cXjZG0Odmybk1xM2p0NDw1Lo81q/xYC63bRnDlDdFcM6K4n0etWoaps+K588ZcTuqdRd36hlPO\njmL0w8UHkk0bLScc5tpOjIE3XyjgzRcKOGJAJNO+coEseaPlmgty2JZqadjY0KtfBJ8t8B6/qSqU\nd5ykS4BLgMustRsC01oBrwFvAf8D3gMyrLVnVlx19w2VOU6SlEMNPyuvlipxnCQpp0ocJ0l2rTLH\nSZJd29Nxku4HbikMSACB/98G3G+tTQXuxo03JCIiIlLtlTckNQXiPKbHBuaBG4uoaofMFBEREdlL\ndmfE7eeNMX2MMRGBRx9gIu4P3oJrzK3o/kgiIiIilaK8IelqXAftBUBe4LEgMO3qQJk04Na9XUER\nERGRqlDewSRTgJONMZ2Bwr9e9Ju1dmVQma8roH4iIiIiVWK3BiIIhKKVuywoIiIiUs2VGpKMMU8D\nd1prM40xz+A9mKTBDcr4b495IiIiItVWWS1JhwCFI0clURySwscR2K/+cKyIiIjsH0oNSdbaAV7/\nBzDGRANx1tr0CquZiIiISBUq8+42Y8wJxpghYdPuBDKA7caYL4wx9SqygiIiIiJVYVdDANyB+ztn\nAATGRnoQeBO4HTgU9wdkRURERGqUXYWkbsA3Qc/PA+Zba6+21j4O3AicUVGVExEREakquwpJ9XAD\nRhY6Avg86PlioOXerpSIiIhIVdtVSNoEdAQwxsQCPYD5QfNrA7kVUzURERGRqrOrkPQZMN4Ycxzw\nCJAFzAmanwSsrqC6iYiIiFSZXY24fR/wAe4P3GYAl1lrg1uOrqT4D9yKiIiI1BhlhiRr7Rbg6MBt\n/hnW2oKwIucBGitJREREapzy/oHbHaVM37p3qyMiIiKyb9hVnyQRERGR/ZJCkoiIiIgHhSQRERER\nDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERER\nDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERER\nDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERER\nD8ZaW9V1qHaMMfYP26yqqyEBUxlc1VWQMB9xdlVXQcKs9HWu6ipIkB2p9aq6ChLE3ywRa60Jn66W\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh4UEgSERER8aCQJCIiIuJBIUlERETEg0KSiIiIiAeF\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh4UEgSERER8aCQJCIiIuJBIUlERETEg0KSiIiIiAeF\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh4UEgSERER8aCQJCIiIuJBIUlERETEg0KSiIiIiAeF\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh4UEgSERER8aCQJCIiIuJBIUlERETEg0KSiIiIiAeF\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh4UEgSERER8aCQJCIiIuJBIUlERETEg0KSiIiIiAeF\nJBEREREPCkkiIiIiHhSSRERERDwoJImIiIh42KdCkjHmaGPMf40xG4wxfmPMpR5lxhhjNhpjsowx\nXxtjDg6bH2uMecYYs8UYk2GM+dgY07Ic2z7XGLPCGJNjjPnFGHPW3nxte+KtCVkc024LB8cnc2av\nVBbPzSu17KoVBVx47Db6NtvMwfHJHNthC/93dzr5+baozILZuXSMSC7x+PP3gqIy+fmWZ/6TwbEd\n3XZP657Kt1/khmxr0nOZnHpoKofWTeHQuikM7r+Vr6eHlqmp5k9YzsPt3uDu+Ik83es9/pz7d6ll\nU1Zs44VjP+SBZq9yd/xExnd4k8/vno8v3+dZ/s+5f3Nn1HM8kTS5xLyctDw+/ve3PNjyNe6Om8gj\nB05i2furPdfz9bjF3BHxLB/f+M0/e5HVSPKEj1nS7iIWxA9iWa/rSJu7vNSyWSvW8suxI1ncbDAL\n4gexpMPFrL/7Ffz5xZ//nd/8xPL+N7Ko0dksTDiFpQddzt+PTQlZz9b3v2FZr+v4vv6ZLEw8lZ96\nXMOWN2eElPlrzBvMjzgh5LG4xZC9++L3UTkT32BHx35sS+zAzr6nkD/3+1LL+lb8Ttrx57G9ZQ+2\nJXZgR6cjyBo9HpufX1Qm78PppJ18IdubH8q2+l3Y2f908j6ZGbKetOMGsy26dYnHzkOPLypj0zPI\nHHkfOzoczrbaHUk76iwKFv+099+AfYx97UX8vbviP6AR/oFHYRfOK73syl/xnzMIf1J7V75vEv5x\nY0L2h/3fx/iHnoG/6wH4OzbHf8qx2BnTQ9fz7lv4m9cOfbSog80LPYbtTt0qW1RVVyBMLWAZ8Abw\nJmCDZxpjRgEjgUuB34F7gZnGmM7W2oxAsSeBM4DzgW3A48Cnxpie1lq/10aNMf2AdwPrmwacC7xv\njDnCWlv6N7sSfPpeNmNvTuM/E+vQ68gYJj2XxRWDtvP5ika0aB1ZonxMLAy+PJ6uPaKoXS+CX5fm\nc9fVafgKYNT42iFlv1jRiLoNTNHzBo2KM/PjozP4aFI2416pS8eDIvn28zyuO3s7789ryMHdowFo\n3jqSUY/U5oADI/H74YPXs7nurO18/ENDOidFV9A7UvV+em8Vn9w8h7MmDqDdkc2Z99xyXh30Cbes\nuJB6rWuXKB8VG0mvyw+iRY9GxNeL5e+lqXxw9df4CyynjO8fUjZrew5TLplFxxNak/Z3Zsg8X76P\nl0/8mFqN4rjo/ZOp2yqRnRsyiIopea6zbkEy37+0gmaHNAJjSsyvSVLf+5q1N0+g3cSbqHNkEsnP\nfcyvg+6k+4pXiW3dpET5iNgYmlx+Mgk9OhJVL5HMpatZc/Xj2AIfbccPByCydjzNbz6XhKR2RCbE\nkTZ3OWuueYKIhDiaXXcGAFGN6tDq3mHEd2mNiY5i+yfz+ePK/yOqcV3qD+pbtL34Lq3pOvvx4gpE\n7lPnphUid8p/yRo5hlrPPUTUEX3Imfg66acNo+7yr4ls3aLkArExxF42lMjuXTH16uJb+guZ194O\nBQUkPHw3APlzFhJ9/JEkjB2FaVCPvLenkTH4Kmp/+T7RR/YBIPGDlyEo7NqcXNK6n0DMeacXTcsc\nfhu+X1ZS67UniWjVnLy3PiD9pAuou/wrIlo0q9g3porYj6Zi7x2FGf8k9OmHfe1F7IXnwLeLMS1b\nlVwgNhZz/jBIOgTq1INflmFvuQFb4MPc84Bb54LvMEcdC3eOgfr1Yeq72MsvgGmfYfoG/a7FJ2C+\n/xls8eHcxMT887pVMmOt3XWpKmCMSQeut9a+GXhugL+Bp6214wLT4oDNwK3W2heNMXUDzy+z1r4T\nKNMKWAcMstbO8NgUxpj3gHrW2pOCps0EtlhrL/Qob/+wlfNlOqfvVg7qHsWDL9QtmnZ8py0MGhzH\nrQ+VPCB7eXBkGj8uyGfqvIaAa0m6+LjtLNrShPoNvX+w+7XYzLV31uLSG2sVTbt+8Hbi4g2PTapX\n6rZ6Nkzhtodrc/7VCeWq294wlcGVti2AZ/u+T/PujTj3hWOLpj3aaRJJgzty8kP9yrWOT0bOYf2C\nFK6fF1r3N8+ZTosejbF+y89TVzNiefHHb+GLP/PNIz9yy28XERlV+oE2e2cuz/ScwuBXjmPmmO9p\nltSQM58+ejdf5Z75iLMrbVvL+15PQvcOdHhhZNG0HztdQsPBR9PmoavKtY61IyeQvuBXkuY9U2qZ\nlefcR0R8LAe+fVepZZb1vJZ6J/emzYNXAq4laesHc+i+/OVyvpqKs9LXudK2tbPfaUR170qtieOL\npu046ChizjmVhAfvKNc6Mm+5H9/CJdSZ+3GZ24k+si8Jj97jOT938jQyrxhJvT/mE9GyOTY7m+31\nDyLx/ZeIOf3E4vX0PYXok44l4T+3lfMV7rkdqaX/ju5t/kEDoFsSEY8Wf779/bvDaWcRcdeY8q3j\nvjvgh0VEfPpl2dvp25+IMQ8BriXJ3n0rEX8kV2jd9gZ/s0SstSXOKKvTKU07oClQFHSstTnAt0Bh\nbO0JRIeV2QD8GlTGy+HBywTM2MUyFS4vz/LLknyOGhgbMv2ogbEsmVf6Jbdga1cXMOeLPA4fEFNi\n3lm9ttKvxWaGnbCNBbNDL5Pl51liYkM/L7FxhsVz8/Hi81k+eTebrEzLYf1rbitSQZ6PjUu20Glg\n65DpBw5sw7p5m8q1jtTVO/j9i7/oMCD0KvD8CcvJ3JLN8aN7hZx1Ffrloz9p278ZH13/DWObv8pj\nXScz8/7v8RWENpBOG/41Sed1oP0xLT3XU5P48/LJXLKKegN7hUyvO7AX6fNWlGsd2as3suOLxdQd\ncGipZTJ/XEX6/BXUOeYQz/nWWnZ+uYTslX9R5+jQMrlrNrG45RCWtL+Y3y8YS86f5fucVFc2Lw/f\njz8TfWJoMI8+8WgK5i8u1zp8q/+kYOY3RB1T9kmHTc/ANCg9bOS+PJnok48lomVzN6HABz4fJjb0\n99DExlLwXZVeNKgwNi8Pli/FHHN86IxjjoNFC8q3jj//gK9nQf+jyi6YkYapXz90Wk42/l4H4z+s\nM/5h52F/XrZX61bR9rXLbWUpbLpJCZu+GWgRVMZnrd0aViYFF7DKWnf4elOCtlkltqf68fmgUdPQ\nLNuwSQRbkj2vHBYZ3H8rK37MJy8Xzh8ezy0PJhbNa9oikgeer8MhvaPJy7V8NCmHYcdv551vGtDr\nSPfjcdRJsbz+ZCaHD4ihbcdI5n2ZxxfTckocc1cuz2dwv23k5VoSEg0TP6xPp641NyRlpWZjfX4S\nm4a2lCU2iWd1claZyz7Xfyp//7gFX66PPsO7ctKDhxfN27Q8lVn/WcQNCwdjSrk8tm3NTv74egM9\nLurM5dNPZ/ufaXx0/TfkZeRz6qNHALDwpV/YtiaNCyYPdAvV8EttBak7sT4/0U1Df5ijm9RjZ/K2\nMpdd3v9GMn9cjc3Np+nwU2kdaP0J9kOroeSn7sQW+Gg95lKaDj8tdPs7M/ih5VBsXgFERtB+wk3U\nO6l30fzEww+i4xu3E9+lDfkp29kw9i1+7v9vDv3lFaIb1NmDV77vsqnbXBBp0jhkekTjRuSnzC1z\n2bQjz6Rg6c+Qm0fs1RcRP3ZUqWVzJryO/TuFmIvP9Zzv+30NBXMWkjjt1aJppnYiUYf3JPuhp4ns\n1hnTtDF5735EwcIlRBzYbjdeZTWybSv4fNA49NKzadQYuyX8sBfKf9rx8PNPkJsLwy7H3HlfqWXt\nqy9AcjIMvqB44oGdME8+D12TID0N+9IE7OknwFfzMe067FHdKkt1CkllqfTT5afGpBf9v++AGA4f\nEFtG6cr3zJR6ZGVYVizN5+Hb0nlhfCbX3uGCUrtOUbTrVLzrexwew4a1Pl56NLMoJN3zVB3uunon\nJx2cijHQtmMk512RwPuvhgaB9l2i+N+yhqTvtHz2fg63XbKDt2c3qNFB6Z+6aMrJ5GXk8/fSLUy/\nbR6zxy/h2Dt6UpDrY/LQLzj1/46gftvSD5zWb6ndNIFzXzoWYwwtezQmc2sOn46Yy6mPHsGWldv5\n4u4FXDf3HCIK+71YW+Nbk/6pTlPuxZ+RTebS1ay77UVix79LyzsuCCnT7bun8WVkkz7/F9aPeonY\nA5rS+OLiyzSRdWpx6LKX8GVks3PWEtaOmEBs26bUPa4HAPVP7hO0snYk9juYH9tdxJY3ZtBiROVe\nJq4OEt+diM3IouCnX8geNZacR54jftQNJcrlTfsfWXc8SOI7z3v3cQJyX34b06Ip0aeGtlLUeuMp\nMq+6hR1te0NkJJGHJRFz/pkULCm9s//+yrz4JmRmuD5J/xkNzz4ON95Sopz99CPsA/dgXnwzpB+R\n6dkHegZ9B3ofjj2hP/aV5zFjH62Ml1Aq+9232HlzdlmuOoWkwouaTYENQdObBs1LBiKNMQ3DWpOa\n4S7LlbXu8Faj4PWWcNOY8vUH2hP1G0UQGQmpKaGtRqkpfho3L9lpO1jzVm5+hy5R+H1w51U7GX57\nLSIivFsWDu0Tzf/eyy563qBRBM9/WJ+8PMuOrX6aNI9k/Kh02nQI/chERxvatHfTuvaIZtmifF57\nIotxL9elJkpoFI+JjCAjJTQsZqRkU7t5rVKWcuq1ciG1SZf6WJ9l6lVfcczth5G2KZMtv23n/cu/\n5P3L3fV+63fh5s7oCVzx2ekceEJr6rSoRWRMZEhLU5Mu9cnPyidzazbr5ieTlZrN413fKZpvfX7+\nnLOJhS/8wgOZ1xAZXfbnprqJalQXExlBfsr2kOn5KduJad6gzGVjW7mWjvgubbA+P39c9Rgtbh+K\niShuuY1t6xqgE7oeQH7KDv4a82ZISDLGENfeHaRrHdKB7F/Xs/GhyUUhKVxkQhzxXQ8gZ/XG3X+x\n1YRp1AAiI7Gbt4RM929OJaJZyY70wSJaufcysktH8PnIHH4bcbf9K2Sf5H3wKRmXjyDxjaeICQtA\nhWxeHrmTphJ79cUhywJEtm9Lna+mYrOzsWkZRDRtTMYF1xHZvu0/ebn7vgYNITIStmwOmWy3bIYm\nZV8sMS0CXQIO7Aw+P/aW6+H6ESHvqf3kQ+y/r8E8+xLmxJPLXl9EBDapO6z5Y4/rtqfMEUdjjii+\nJOx/bJxnuerUJ+lPXGgZWDgh0HH7SKDwfsEfgPywMq2ALkFlvMwHTgybdiLw3R7Xeg/ExBi69Yxm\nzozQ/kJzZ+buVr8fnw98Be7f0qxYmk+TFh53y8UYmjSPJD/f8vkHOZxwZtktZj6f60tVU0XFRNKy\nZ2N+n/FXyPRVM9fTtn/5v9R+n8VfYLE+P3VbJTLi5wu5+afzix6HX9uNhh3rcvNP59O2n1tv2yOa\nk7pqB8E3W2z5fQfRtaKp1TCerme3D1nPTUuH0rJXE7pfcCA3LT2/xgUkgIiYaGr17MSOGaF9XXbO\n/IHa/buWf0U+PxT4sL7SL2Nbn89dViuD9fnx53n32wPw5+SR/et6Ypo3LH/dqhkTE0PkYUnkzww9\nL82f9S1R/XqVspSHwD4J/uHKff8TMi67mcTXniDm7FNKXTTv4y+wW7cTe8X5pdczPp6Ipo3xb99B\n/sxviT5jYKllqzMTEwOH9MB+E9bh+tuvoXdf74W8+HxQEHogsR9/gP33cMzTL2BOPXOXq7DWworl\n0Kz53q1bBdqnWpKMMbWAAwNPI4C2xpjuwFZr7V/GmCeBu4wxvwGrgNFAOjAZwFq70xjzCvCIMWYz\nxUMA/ATMCtrOl8BCa23hbSpPAd8Ghhj4GDgbGAAcUZGvtzyuGJnArcN2cmifaA7rH8Pk57NITfZz\n4bWuT8yjd6azbFE+k2a5s+YPJ2UTF2/o1C2K6BhYvjifx+5KZ9B5cURHuxaI157MpFW7SDoeHEV+\nnuXjt3KY9XEuE6YVd4D86fs8kjf4Oah7FCkb/Tw1xo2wMPz24taSR+5I57jTYmnWKoLMdMt/J+fw\n/Td5vDI9rONeDXPUyO68N2wmrfs0pW3/Zix8/mfSk7M4/NpuAHx25zw2LNrM1bPcUFtLJv1GVHwU\nzbo1JDImgg2LN/P5XfM55LwORcGl6cGhrR61GscRFRsZMv3w65KY9+xy/nvTHPpfn8S2tWnMGvM9\n/f6VBEB83Vji64aG2JiEKOLrx5ZYf03SfORgVg97mMQ+Xajdvyspz39CfvJ2ml7rbvted+fLZC5a\nycGzXPP+lkkziYiPIaFbO0xMFBmLf2f9Xa/Q4LxjiIh2P4mbnvmQuPbNievkLh2kfbuMTY9Npdn1\nZxRtd8ODb1P78IOIbdcMf24+O6YvJPWtWbR79saiMmtvfZ4GZ/QnpnVj8jfvYMMDk/Bn59L40pp5\nQC4UN2I4mZfeRFTv7kT160XOi5PwJ28h9pphAGTdNY6CxT9RZ8a7AOS+NRUTH0dk1y4QE03BD8vI\nGv0wMYNPxUS7E8Lc9z4m89KbSPi/e4k6og/+5EDrQ0w0EQ1Cf3NyX3qbqOOPIvKA0BssAPJnfIP1\n+Yjs0hH/6rVk3TGWyC4dib1saAW+I1XLXHMD9sarsT16Qa++2Ddfgc0pmEvc3Z/+B++DpT8Q8f6n\nANj334G4OOhyMMTEwNIl2HFj4PSzi/aH/eh97A1XY8aMg779sZsDfYiiozH13e+N/b+HoFdfaNce\n0tOxL0+Elb9igu5k21Xdqto+FZKA3sBXgf9b4P7A43XgCmvtI8aYeOA5oD6wABhorQ0eUOZmoAB4\nD4jHhaOLbehYB+1xwwK4DVk73xhzPjAW+A+wGhhirV2011/hbjp1SDw7tlqeG5vJ5k1pdE6K4pXp\n9YvGSNqS7OevNcXJPioaJo7LYN0qH9ZCi7aRDLshgctHFIeb/Hx4+LZ0kjf4igLVK9Prc8zJxQfY\n3Bx44p4M1q8poFaiYcCpsTzxdl1q1ylufExN8TPy4h1sSfZTu24EBx0axWuf1+fIE/et/ll726FD\nDiRraw5fjV1E+qYsmiU15PLppxeNkZSenMW2NWlF5SOiI5g97odAKxDUb1ub/jccwlEjupe+EWNK\ndLqu1yqRq2acwacj5/JUj/eo3SyB3lcexHGje5eyEu/11DSNhgygYGsaG8e+Td6mrSQktafL9IeK\nxkjKT95GzpriO8pMdCQbx71DzqqNWGuJbduUZjecRfMRQR2A/X7WjXqJ3LXJmKhI4jq2pM34q2l6\nTXHHbX9mNmuue5K8DalExMcQf1BbOk66g0ZDi4eGyNuYyu8XjKUgNY3oxnVJ7HcwSQue9Ry/qSaJ\nPe907NbtZD/0NP5Nm4lM6kLtT94s6j/kT9mC/8/1xQtER5M9/jl8q/4Ea4ls24q4f11G3M1XFxXJ\nffEt8PvJGnEfWSOKOxBHHdOPOrOKB/r0rVlHwex5JL4zwbNuNi2drLvH4d+QjGlQj5hzTyHhgVGY\nyJrX0lrInHkubN+GfeIR2JwMXbpi3v6guP/Q5hRYt7Z4gego7DOPucti1kKr1pgrroHhxf3D7Juv\ngt+Pved2uOf24mX7H4X5wA0qadPT4NYbYUsK1K4DSd0xH32B6X5Y+etWxfbZcZL2ZZU5TpLsx+uN\nTQAAHexJREFUWmWPkyS7VpnjJEn5VOY4SbJrlTlOkuxaTRgnSURERKTSKCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExINCkoiIiIgHhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGg\nkCQiIiLiQSFJRERExIOx1lZ1HaodY4wdYl+v6mpIwBRzWlVXQUp4u6orICVsq+oKSLBGY6q6BhIs\n1WCtNeGT1ZIkIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQ\nSBIRERHxoJAkIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQ\nSBIRERHxoJAkIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQ\nSBIRERHxoJAkIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQ\nSBIRERHxoJAkIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQ\nSBIRERHxoJAkIiIi4kEhSURERMSDQpKIiIiIh6jK2pAx5mjgVuAwoAVwubX2jbAyY4CrgfrAQuB6\na+2KoPmxwP8B5wPxwJfAv6y1G4PK1AeeBk4PTPovcKO1ducu6vcv4DagGfALcLO1du4/fb170+oJ\nX7Ly0c/ISd5Jna4t6f7khTQ+spNn2Z0rNrLk+kmk//o3+TuziWtRjzbn96XrmLOIiHa7e/M3v7H8\nzqmk/56MLyuPhLYNaX/V0XS+ZVDIun5/agZ/TPyKrPXbiGmYSMsze3DI+CFE1Yr9R3WrWV4FngU2\nA52BB4HDSyk7F3ge+BFIB9oB1wAXhpV7BXgZ2AC0BEYCQ4LmvwlMAX4DLJAE3An03YO61RRzgK9w\n728z4GygQyllVwGzgfVADtAIOIaS79HiwDq3AHFAJ+BMoE5g/ibgM9z+2gacBAwKW0cOMB1YHqhb\nK+AcoM1uv8LqZREwD8gAGgMnU/prXgssADYCuUAD3Ge6R1CZj4CfPJaNBu4K/N+H+679hHuvGwIn\nAB3DlknHHTpWAXm4w82pQNtyvrZqKnsCZD8K/mSI6gq1noToI73L5s2GnCegYBH4d0JkR4i/GeIu\nD1vnc5DzLPjWQWQbiL8b4oaFlXkKciaCbz1ENISYM6HWeDC1Sm43axxk3Q1x10PiM3vlZe+pSgtJ\nQC1gGfAG7tfeBs80xozCHRUuBX4H7gVmGmM6W2szAsWeBM7AhaRtwOPAp8aYntZaf6DMZNwv0UmA\nwR11JgWW82SMGRpY93W4b9n1wGfGmIOttX/t4eveI+vfW8jSmydz2MRLaHRkJ1Y/9yVzBj3OySse\nJKF1wxLlI2OjaXf5UdTr0YaYegnsWLqexVe/hr/Az6Hj3QE3unYcnW4eSN2kVkQmxJA693d+uOYN\nIhNi6XjdcQCsmzyfZaOm0PuVK2h0VGcy/9jMoitfxZeTT++Xr/hHdas5PgTuBh7F/Zi/CgzFHRRa\nepRfDHQFbgKa4n6gRwKxwLmBMq8C/8F9DHsCPwAjgLq4jzKB9Z8D9MEdtJ/Hhaivgfb/sG41wRLc\n6z4P9z7MBV7ABcj6HuXX4t6LE3CB51fgPdwBt2egzBrgbVwoOgRIA6bifkquD5TJxx2IDwX+h/u5\nCfcuLkxdBNTDhYcJgbrV/Uevdt/3M/A5Lni0wb3mt4F/4f2a/8J9L44AagOrgU9xh6ekQJlBwIlB\ny1jcZzs42HyFO8ScgQtmq3H79UpccAYXWguXuwhIALbjDk81WO57kHkzJE6EqCMh5zlIGwT1VkBk\n65LlC+ZD5KEQfwdENIe8zyFjOJg4iL3AlcmeCFl3QOLLENUXChZCxtUQUR9iTnNlciZD5ihIfAWi\njwLfH5BxJdgcqP1y6DbzF0DOSxB5CN7fpaphrLW7LrW3N2pMOq6V6M3AcwP8DTxtrR0XmBaHOxW+\n1Vr7ojGmbuD5ZdbadwJlWgHrgEHW2hnGmINwrUBHWGvnB8ocgTvN7GKt/b2U+iwEllprrwma9jsw\n1Vp7l0d5O8S+vjfeil2a1fc/1Ovehl4vXFY0bXqnUbQa3JtDHhpcrnUsHfkOWxf8wfHzRpda5rtz\nniEyLprDJ18LwJIbJrHz5w0cO/vOojI/3/chG6f9wEnLx+61uu0NU8xplbYtZyDQDZfRC/XB/TiX\n/h6HuhLwA68Fng8CegEPBJW5FxcAPi1jPQfjAtdVe7Fue8Pblbitx3GhZ2jQtLG48HK65xIlvY7b\nH1cEnn+F+9m4L6jMAmAa8IjH8g8D3XEtJoXygFG4fd0taPr/AQfhQkRl2lZJ23kZF3qC3/tncJ/V\n48u5jqm4/TGklPnrcd+dK3HnxACPAUcS2rI6BRd+zw48/zKwbFiLSFVoNKbytrWjL0R1h8QXiqdt\n6wSxg6HWQ+VbR9pQwAd1pgbW2R+i+kHiY8VlMm+F/IVQb457nnEDFPwM9WYHlbkP8qZB/eXF0/w7\nYUdPqP0KZI2ByCRIfPofvNA9kGqw1pZIZ/tKn6R2uG/VjMIJ1toc4Fugf2BST9ynPbjMBtxpYL/A\npH5ARmFACpgHZAaVCWGMicFdApwRNmtG0LarhC+vgO1L1tFsYNeQ6c0GdmPrvFXlWkf66hSSv1hO\n4wFdSi2z/cd1bJ2/OqRMo6M6sWPperYu/AOAzPVb+fu/P9L81EP2Wt2qpzzc2eqxYdOPBb7fjfWk\n41oWgtcbG1YmDheSfKWsIzfwKFzP3qpbdVKAa4noHDa9C67FqLyyca0KhdoDO3GtIhZ32ehH3IG+\nvPyBZcMb7KNwLVU1kQ/XchZ+qbMDbj+VVw6uR0VplgBNKA5Ihdv2eq/XBz3/DdfbYyourL5Azf1u\nBNg8KFgC0QNDp8cMhPx5u7GenWAaBE3IA+Pxm1XwPdjAb1b0UeBb6oITuEtuef+FmLAThIzhEHse\nRB8DVdBwU5bKvNxWlsK20JSw6Ztxn+jCMj5r7dawMilByzfDdSAoYq21xpjNQWXCNQIiS9l2actU\nirzUdKzPT2zT0Cbq2CZ1yEkus4sVX/Yfy44f1+HLLaD98GNIevDcEmU+aTWC3NR0bIGfrmPOosPw\nAUXz2gztS25qOl8fPQ6sxV/g54BL+nPIw0P2uG7V21bcj3HjsOmNcB+Z8vgC10oxPWjacbjWl1Nx\nLRJLgbdwIWAr7oAQ7iEgkeLWi71Rt+omExdEaodNT8RdIiuPn3H9U24OmnYA7sr/JNxlNT8uiF20\nG3WLC6xnBtA8UMcfcI3f4fuopsjCvVeJYdNr4YJmefwO/IlrJfKSA6ygZKtUR1xr3wG4fk1rcOfQ\nwbbjLn8fjmt1Ssb1KwPX4loD+VMBH0Q0DZ0e0QRscvnWkfcp5H8FdYNCVfRJkPMKxJwDUT2h4AfI\neRkoAJsKpinEDnXb33k07ntaALGXQK2Hi9eT8xL410Dtye652XcutcG+E5LKsqtYuW+9o/uIflP+\nRUFGDjuWruen26bw2/j/cdAdoZeljvvubgoyctk6fzXLRk2h1gGNaHuxazzb/M1v/Dr2Ew6beAkN\n+3YgY1UyP940mZ/v+5Bu95/ttUkpl4XAtcA4Qjum3oILMqfgPvJNcF3vnsG7wfcFXNe+aZQ8IEn5\nrcEFoXMJ7VicDHyA6w/WBRe4Psb1cbl4N9Z/MfAO7rKdAVrjGq6rtKvjPmw97jM9iOLz43DLcN+R\nQ8Omnwx8AjyHe68b4L5jPwaVsYH1FgasZriTi0XU2JC0p/K/g/SLoNYzEN2reHrCPa4T+M7+gIWI\nZhB3GWQ/QtFvVv43kDU20BeqL/hWQeZN7pJbrfuhYCVk3g315oKJdMtYy64P+5VnXwlJhXG2Ke5W\nEYKeJweViTTGNAxrTWoKfBNUJuQULdDfqUnQesIFYjZhMZumuHZjTz+P+bDo/00GdKHJgINKK/qP\nxTSqjYmMIDcltGUmJ2Uncc3rlbKUk9DKNYvW6dIC6/Oz+KrX6HL7KZiI4gNurbaNAKjbtSU5KTv5\nZcxHRSHp59HTaHPh4bS/4uiiMgWZuSy+6jW63nfmHtWtemuIa3jcEjZ9CyU/QuEWABfgOu1eFjYv\nDngK17+msBHzNVwAahRW9nlcH5gphAatPalbdVULd0BMD5ueTvFdaKX5A3gRF0yPCJs3E9e597jA\n8xZADMU3zpa303Uj4EbcpdCcQJ1ep+Q+rSkScAfI8FajDEq29oVbj7vv5lhc/7zSLMFd9ozz2Hag\n3wxZge3NxIWlQrXxbmmtwa3fEYGLJf6wiyX+FNcpuyz5cyHtVEh4AOKvCZ1n4lwfosQXi9eV8zyY\n2hAReI8zR0PshRAX6OsX1RVsJmRcBQn3uQ7iNhW2B3fb8EHBHMh5ARpmgonek1dfurzZkD97l8X2\nlT5Jf+JCTNFF00DH7SNxfYrAtVPnh5VphTvNKywzH0g0xgT3P+qH+yX1vPhqrc0LrDvsgi0nlrYM\nQLcxZxc9KiIgAUTGRFG/Z1uSZ/wSMj1l5i806h9+W2vprM9dLrM+f9ll8gqKnvuy8zARoY10JiKC\nwo7+e6tu1U8M7gz267Dpsyn7THQermVoFDC8jHKRuEszBnfH1klh8yfgAtK7Htv7p3WrzqJwrTMr\nw6avxHV1LM1qXGvcINzt/+HyKfnzWPh9+CdnuTG4gJSF6xeTVHbxaqvw8/tH2PQ1hPYfCrcOd7l5\nACWHtAi2Edcz4rBd1KE2Liz9Smh/tTa48+JgWwntH1jDmBh3OSw/rNtt3kyIKqPbbf63kHYKJNwP\n8f8uY/2RENnCXSbLfRdigjvsZ4MJ+x6ZCIq+QzFnQ72fod5PgcdSiOrl7qCrt7TiAhJAzACoNab4\nUYrKHCepFnBg4GkE0NYY0x3Yaq39yxjzJHCXMeY3XAeB0bjTwckA1tqdxphXgEcCfYwKhwD4CZgV\nKPOrMeZz4AVjzHDcr9oLwCfW2lVBdfkNeMZa+1xg0uPAJGPM97ij2bW4U/nnK+jtKLfOI09m4bAX\nadCnHY36H8gfz39NTvJOOlzrOucuu/N9ti36kwGzbgdg7aTviIyPoW63lkTERLF98VqW3zWV1uf1\nLhonadUzM6nVvjG1O7kuV1u+Xcnvj31Oh+uLr/G3OL07vz/+BfV7taNBn/ZkrE7h53um0eL07kWt\nUbuqW811He525sNw4eN1AjdeBuY/gGvinxZ4Phc3JtKVuFv4C8/oIiluUfgDl9V74s5qJ+D6ZkwM\n2u4zuMt0E3EBoHA9CRSfpe+qbjXRsbhLZm1w78t3uMtjha1Dn+BaKQpv3V+Fa0E6Cvc+FfZdiqD4\n0mU3XBCdS/Hltmm4A33hAbWwkzK4UJWGawiPpbi14jdcH52muBa9/wb+X1YQqO764QJ+S1yAXYxr\nSSpsHZqFu5n5ksDztbif+d64972wFcpQ8tb8H3Atpl5jGm3E7YNmgX8LLzAEtxIejhuPbA5uWI5N\nuI7b5b3rrpqKHwnpwyCqjwtGOc+7/khx7m5mMu90YyLVneWe5812LUjxN7jA4i+8EBNZ3ErkW+Vu\n248+HPzbIftx8K2A2pOKtxtzupse1ctt27caMu9x000EmLoQEd4qmwCmPkTtzk0SFacyL7f1xt1X\nCy5G3h94vA5cYa19xBgTj7ugXB93bWKgtTYzaB0343qyvoe79WEWcLENHcfgQtzR5IvA84+BG8Lq\n0gn3TXOVsXaKMaYhLpg1x438dkpVj5EE0HpIH3K3ZvDr2E/I3rSDukmtOWr6yKJxiHKSd5K5pvjy\nSkR0JL+N+5T0VSlgIaFtQzrecDydRhS3SFi/Zdmo98lam4qJiiCxY1OSxp9Hh2uKw81Bo88AY/h5\n9DSyN24ntnFtWpzenW5BHcB3Vbea6yyKM3oK7nbudykeh2gz7sy40Hu4Sy3PBh6F2uB+9MEdSCfi\nwlIU7gA+ndCz79dwH/+rCHUB7jJQeepWE/XAdeCegTs4NscN1lk4RlIarrWg0Pe4UPMVxT9J4C7L\n3Bv4fx/cPpuD+wmJx53jBQ+3tgN3h1SheYFHR4p/crJxQzjswIXZ7rjO+ftKI35F6IprMZuDO89t\niuvwXngwzMR1oC70E+5zXfj+FaqHG1usUC5uhBevlj8C6/g6sO4Y3P46h9C7RlvgWnS/xN08XRd3\nSbX3bry+aih2CPi3uv5B/k0QlQR1phePkeRPBl/QHZe5bwA5bvDJ7EeLp0ccAA0C5awPsp+AjJWu\nxSf6OKg3zw0qWSh+NGDcZTf/RhewYk6HhAdLr6sx7EtdjatknKTqrjLHSZJdq/xxkmTXKnOcJCmf\nyhonScqlMsdJkl3bx8dJEhEREdmnKCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgH\nhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgH\nhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgH\nhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgH\nhSQRERERDwpJIiIiIh4UkkREREQ8KCSJiIiIeFBIEhEREfGgkCQiIiLiQSFJRERExINCkoiIiIgH\nhSQRERERDwpJIiIiIh4UkkREREQ8KCTtxzbP/rWqqyAh5lZ1BaSEVVVdAQmxtqorIMHyZld1DSqc\nQtJ+bPPs36q6ChLiu6qugJSwuqorICHWVnUFJFj+7KquQYVTSBIRERHxoJAkIiIi4sFYa6u6DtWO\nMUZvmoiISA1irTXh0xSSRERERDzocpuIiIiIB4UkEREREQ8KSSIiIiIeFJKqgDHmaGPMf40xG4wx\nfmPMpeVYJskY840xJiuw3D0eZY4xxvxgjMk2xvxhjLmmYl5ByDbbGGM+McZkGGO2GGOeMsZEl1L2\nQGNMujEmvaLrtbv2p31ijDkg8BrDHwMrum7ltT/tj6ByNxtjfjPG5Bhj/jbGjKvouu2O/WmfGGPG\nlPId8RtjGlV0/cpjf9ofgTInGWPmG2PSAmU+MsYcWNF1U0iqGrWAZcBNQDZQZu95Y0wdYCawCegV\nWO42Y8zIoDLtgOm4YZu7A+OAZ4wx5+xJRY0xa40xx5QyLxL4X+D1HAlcAAwGHvMoGwO8C3zDLl5v\nFdnv9glwEtAs6PH1ntRrL9uv9ocx5nHgOuA2oAswCPdd2ZfsT/vkUUK/G81x++Nra23qntRtL9pv\n9kegXh/j9kF34AQgLlDXimWt1aMKH0A6cMkuylwH7ABig6bdDWwIej4eWBm23EvAvLBplwMrcF+q\nlcDNBO5yLGXbfwJHlzJvEOADWgZNuyiw7sSwsk8ArwCXAulV/b7vz/sEOADwAz2r+r3W/rAAnYE8\noHNVv9faJ6G/W0HzWwMFwPlV/d7vj/sDF5oKgrcBHBv4HWtQke+tWpKqh37AHGttbtC0GUALY0zb\noDIzwpabAfQKJHWMMVcDDwKjcWertwCjgH/tQb1WWGs3hm0zFuhZOMEYcypwKnAjUGIcimqqWu+T\ngGnGmBRjzFxjzLn/cHv7iuq8P84E1gCnGGPWGGP+NMa8boxp/A+3ua+ozvsk3JXANuCDf7jNfUF1\n3h+LgHzgamNMpDGmNnAZ8L21dts/3G65KCRVD82AlLBpKUHzAJqWUiYKKLyGfg9wm7V2mrV2nbX2\nU9yZw64+3KUFG696peLOCpoBGGNaAC8CF1lrs3axneqk2u4T3FnnLcB5uLO4L4H3jDEX7WKb+7Lq\nvD/aA22BIcAlwDDcwecTY0x1PqmozvukeCUuHFwBTLLW5u9im/uyars/rLXrgIHAf4AcXItYV+D0\nXWxzj0VV9AZkr9jjPjyBs9JWwIvGmOeDZkWFlfsMd124UALwmTHGV1gXa22d4EV2selJwERr7aJ/\nVvN9VrXdJ9barbjLn4WWGGMaArcDb+/eq9hnVNv9gTtZjQWGWWtXB7YxDHcZoxfuLLo6qs77JNjJ\ngTq8tBvL7Iuq7f4wxjTDddd4A5gM1MEFpinGmONs4PpbRVBIqh6SKXmG0zRoXlllCnCpvPAs4Bpg\nXhnbuhLXIQ7cB3c27uC5sJR69Q+b1giIDKrXscDRxpj7gtYZYYzJB66z1r5cRl32ZdV5n3hZhDtb\nrq6q8/7YBBQUBqSA1bgz6TZU35BUnfdJsOHAd9ba38rYfnVQnffH9bi+rKMKCxhjLgb+wl2uK6su\ne0QhqXqYD4w3xsQGXU8+EdgYaIYsLHN22HInAoustT4gxRjzN9DRWvtWaRuy1v4d/NwYUxDYzhqP\n4vOAu40xLYOuJ58I5AI/BJ53C1vmLFxnwd7A31Rf1XmfeOmO9kdV7Y+5QJQxpn3QOtrjDhLrqL6q\n8z4pXE8L4BTcQb+6q877Ix7XSTtY4fOK7TZUkb3C9Si1p38t3EGpO5CJu8bbHWgdmD8OmBVUvg7u\nbPMd3HXYc4CdwIigMgcAGbjLKAcBV+E+ZGcHlbkSyMLdidAZF2AuAe4oo65l3ZUQgbsF9UuKb8vc\nADxVxvouYx+8u21/2ie4OwwvCNSpM3BroF43VfV+2E/3hwEW4862uwM9cLc6z9vd9037ZO/sk6Cy\no4HtQFxVv//78/7AXZHwBV7jgcBhwOfAWiC+Qt/nqt7R++MDGIBLwf7Aji/8/6uB+a8Ba8KW6Rb4\n4cwGNgL3eKz3aFzyzgH+AIZ7lDk/UCYbd7fGt8CQMupa6oc7ML818EngS5oKPAlEl1H+MiCtqvfB\n/rxPAj9ovwR+DHcC3wMXVvU+2F/3R6BMM2AKkIbrxDoJaFzV+2E/3ycGd9fhs1X93mt/WIChgW2m\nB74jHwFdKvp9NoGNi4iIiEgQDQEgIiIi4kEhSURERMSDQpKIiIiIB4UkEREREQ8KSSIiIiIeFJJE\nREREPCgkiYiIiHhQSBIRERHxoJAkIvsFY0xTY8xTxpjVxpgcY8wGY8x0Y8ygvbDuA4wxfmPMYXuj\nriKyb9AfuBWRGs8YcwDwHe7PsNwB/IQ7STwBmIj7m1V7ZVN7aT0isg9QS5KI7A8m4P6uVS9r7VRr\n7Spr7Upr7XPAIQDGmDbGmA+NMWmBxwfGmJaFKzDGtDbGfGyM2WqMyTTG/GqMGRqYXfjXzRcFWpS+\nqtRXJyIVQi1JIlKjGWMaACcBd1trs8LnW2vTjDERwMe4P7A5ANci9Czuj2j2DhSdAMQE5qcBXYJW\n0wf3h4JPwrVS5VXASxGRSqaQJCI1XUdc6Pm1jDLHA0lAe2vtegBjzIXAamPMcdbar4A2wAfW2uWB\nZdYFLZ8a+HertXbzXq29iFQZXW4TkZquPP2EDgL+LgxIANbaP4G/gYMDk54CRhtj5hljHlAnbZGa\nTyFJRGq6VYClOOzsLgtgrX0VaAe8BnQC5hlj7tsrNRSRfZJCkojUaNbabcAXwA3GmFrh840x9YAV\nQAtjTNug6e2BFoF5hevaaK19yVo7FLgXGB6YVdgHKbJiXoWIVAVjra3qOoiIVChjTDuKhwC4B1iO\nuwx3LHCHtbatMWYJkAXcFJj3DBBpre0TWMdTwHRcy1Qd4Akg31o70BgTFVj3w8CLQI61dmclvkQR\nqQBqSRKRGi/Qv+gwYCYwHncH2pfAmcDNgWJnAluAr4GvcP2RzgpaTWFw+gWYAWwCLg2svwD4N3AV\nsBH4sEJfkIhUCrUkiYiIiHhQS5KIiIiIB4UkEREREQ8KSSIiIiIeFJJEREREPCgkiYiIiHhQSBIR\nERHxoJAkIiIi4kEhSURERMSDQpKIiIiIh/8HhkRBhIk4GfQAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.2796\n", + "Train set Accuracy: 0.1864\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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HZub3IuJVwHXAvpl5b1nzBuAS4GmZ+VBEnEIR1IYz89Gy5izglMzcp3z+YeA1mXlg3fe4\nGDgoM18+xnfMTl5vSZKexFDXMhFBZka321GvGz12zy6HWn8QEVeWvV4A+wHDwGitMDN/A3wJqIWk\nlwBDDTX3AHcCh5UvHQY8VAt1pVuAh+s+5zDgjlqoK40C25fnqNXcVAt1dTVPj4hn1dWMsrVR4JCy\nV1CSpOow1PW9Tge7rwCLKYZI/4JiKPaWiNit/B1gXcN71tcd2wvYnJkPNtSsa6h5oP5g2U3W+DmN\n5/kpsHmCmnV1x6AIomPVzAL2QJKkqjDUDYRZnTxZZq6ue/qtiLgV+CFF2PvqeG+d4KOn0g060Xva\nMma6bNmyx3+fO3cuc+fObcdpJEl6gqGuJdauXcvatWu73YxxdTTYNcrMRyLi28ABwL+ULw8D99SV\nDQP3l7/fD8yMiN0beu2GgRvrap5Wf55y/t6eDZ/TOAduD2BmQ81eDTXDdcfGq9lE0QP4JPXBTpKk\ntjPUtUxjh8wHPvCB7jVmG7q6j125OOJ5wE8y84cUQWl+w/EjKObIAXwd2NhQsw/w3LqaW4GdIqI2\n5w6KuXA71tXcAjyvYZuUecCj5Tlqn3NkRGzfUHNvZt5VVzOv4WvNA76WmZsnvACSJLWToW7gdHpV\n7HnANcCPKXrQ3kcR3F6YmT+OiHcB7wFOAr4HvLc8fmBmPlx+xieAY4A3Aj8Dzgd2AV5SW3IaEdcD\n+wBLKIZcVwA/yMzjyuMzgH+nmIu3lKK37jLgqsw8razZGfgusBY4GzgQuBRYlpkXlDW/DXwLuLg8\nx+HARcDrMnPVGN/fVbGSpM4w1LVdFVfFdnoo9hnAlRRB6gGKHq9DM/PHAJn5kYiYTRGOdqVYbDG/\nFupKb6cY6vwnYDbwBeBPGhLT64ELgZHy+dUUe+NRnmdLRLwa+ATwZWAD8BngnXU1v4qIeWVb/o0i\nRJ5XC3VlzY8i4mjgAuAU4F7grWOFOkmSOsZQN7A62mM36OyxkyS1naGuY6rYY+e9YiVJ6heGuoFn\nsJMkqR8Y6oTBTpKk3meoU8lgJ0lSLzPUqY7BTpKkXmWoUwODnSRJvchQpzEY7CRJ6jWGOm2DwU6S\npF5iqNM4DHaSJPUKQ50mYLCTJKkXGOrUBIOdJElVZ6hTkwx2kiRVmaFOk2CwkySpqgx1miSDnSRJ\nVWSo0xQY7CRJqhpDnabIYCdJUpUY6jQNBjtJkqrCUKdpMthJklQFhjq1gMFOkqRuM9SpRQx2kiR1\nk6FOLWSwkySpWwx1ajGDnSRJ3WCoUxsY7CRJ6jRDndrEYCdJUicZ6tRGBjtJkjrFUKc2M9hJktQJ\nhjp1gMFOkqR2M9SpQwx2kiS1k6FOHWSwkySpXQx16jCDnSRJ7WCoUxcY7CRJajVDnbrEYCdJUisZ\n6tRFBjtJklrFUKcuM9hJktQKhjpVgMFOkqTpMtSpIgx2kiRNh6FOFWKwkyRpqgx1qhiDnSRJU2Go\nUwUZ7CRJmixDnSrKYCdJ0mQY6lRhBjtJkpplqFPFGewkSWqGoU49wGAnSdJEDHXqEQY7SZLGY6hT\nDzHYSZK0LYY69RiDnSRJYzHUqQcZ7CRJamSoU48y2EmSVM9Qpx5msJMkqcZQpx5nsJMkCQx16gsG\nO0mSDHXqEwY7SdJgM9SpjxjsJEmDy1CnPmOwkyQNJkOd+pDBTpI0eAx16lMGO0nSYDHUqY8Z7CRJ\ng8NQpz5nsJMkDQZDnQaAwU6S1P8MdRoQBjtJUn8z1GmAGOwkSf3LUKcBY7CTJPUnQ50GkMFOktR/\nDHUaUAY7SVJ/MdRpgBnsJEn9w1CnAWewkyT1B0OdZLCTJPUBQ50EGOwkSb3OUCc9zmAnSepdhjpp\nKwY7SVJvMtRJT2KwkyT1HkOdNCaDnSSptxjqpG3qWrCLiHdHxJaIuLDh9WURcW9EPBIRN0TE8xuO\nbx8RF0bEAxHxUERcHRHPaKjZNSKuiIhflI9PR8QuDTX7RsS15Wc8EBEfi4ihhpoXRsSNZVvuiYj3\njfE9joqIr0fEhoj4fkS8afpXR5I0JkOdNK6uBLuIOBT4C+A/gKx7/UzgdOAtwEuB9cCaiNip7u0f\nBY4HXgccCewMXBcR9d/ls8CLgQXAK4GDgSvqzjMT+DywI3AEcCLwWmB5Xc3OwBrgJ8AhwGnAOyPi\n9Lqa/YDrgZvL850LXBgRx0/tykiStslQJ00oMnPiqlaesOg5+zrw58Ay4D8z820REcB9wMcz89yy\ndgeKcHdGZq4o37seeGNmXlnW7APcBbwqM0cj4nnAt4HDM/PWsuZw4CbgwMz8XkS8CrgO2Dcz7y1r\n3gBcAjwtMx+KiFMogtpwZj5a1pwFnJKZ+5TPPwy8JjMPrPt+FwMHZebLx/ju2enrLUl9wVCnCooI\nMjO63Y563eixWwH8c2beCNRfjP2AYWC09kJm/gb4ElALSS8Bhhpq7gHuBA4rXzoMeKgW6kq3AA/X\nfc5hwB21UFcaBbYvz1GruakW6upqnh4Rz6qrGWVro8AhZa+gJGm6DHVS0zoa7CLiL4BnA+8tX6rv\nvtqr/Lmu4W3r647tBWzOzAcbatY11DxQf7DsJmv8nMbz/BTYPEHNurpjUATRsWpmAXsgSZoeQ500\nKbM6daKIOBA4BzgiMzfXXmbrXrttmWj8cirdoBO9xzFTSeomQ500aR0LdhTDlnsA3y6m0wEwEziy\nXEn6gvK1YeCeuvcNA/eXv98PzIyI3Rt67YaBG+tqnlZ/4nL+3p4Nn9M4B26Psj31NXs11AzXHRuv\nZhNFD+CTLFu27PHf586dy9y5c8cqk6TBZqhTBa1du5a1a9d2uxnj6tjiiXLhQ/22JAFcCvwX8CGK\neXL3Ahc2LJ5YR7F44uIJFk+8MjPXbGPxxMspVq7WFk+8kmJVbP3iidcDn+KJxRN/CXwY2LNu8cR7\nKBZPPLN8/jfAwobFEysoFk8cPsY1cPGEJE3EUKceUcXFEx1fFbvVySPWUqyKfWv5/F3Ae4CTgO9R\nzMU7giKQPVzWfAI4Bngj8DPgfGAX4CW11BQR1wP7AEsoAuQK4AeZeVx5fAbw7xRz8ZZS9NZdBlyV\nmaeVNTsD3wXWAmcDB1IE0WWZeUFZ89vAt4CLy3McDlwEvC4zV43xfQ12kjQeQ516SBWDXSeHYseS\n1M1ly8yPRMRsinC0K/AVYH4t1JXeTjHU+U/AbOALwJ80JKbXAxcCI+Xzqyn2xqudZ0tEvBr4BPBl\nYAPwGeCddTW/ioh5ZVv+jSJEnlcLdWXNjyLiaOAC4BSKHse3jhXqJEkTMNRJ09bVHrtBY4+dJG2D\noU49qIo9dt4rVpLUXYY6qWUMdpKk7jHUSS1lsJMkdYehTmo5g50kqfMMdVJbGOwkSZ1lqJPaxmAn\nSeocQ53UVgY7SVJnGOqktjPYSZLaz1AndYTBTpLUXoY6qWMMdpKk9jHUSR1lsJMktYehTuo4g50k\nqfUMdVJXGOwkSa1lqJO6xmAnSWodQ53UVQY7SVJrGOqkrjPYSZKmz1AnVYLBTpI0PYY6qTJmNVsY\nEdsDTwdmAw9k5gNta5UkqTcY6qRKGbfHLiJ2jog3R8RNwK+A7wPfAtZFxI8j4uKIeFknGipJqhhD\nnVQ52wx2EXE68EPgJGAUOA54MXAgcBiwDBgCRiNidUQ8p+2tlSRVg6FOqqTIzLEPRKwEPpiZ3xr3\nAyJ2AP4ceCwzL259E/tHROS2rrck9QxDnQRARJCZ0e121NtmsFPrGewk9TxDnfS4Kga7Sa2KjYg9\nImL3djVGklRhhjqp8iYMdhExHBGXRcQvgPXAAxHx84j4VETs2f4mSpK6zlAn9YRxh2IjYkfgG8Bu\nwD8AdwIBPB94PfBT4ODMfLj9Te19DsVK6kmGOmlMVRyKnWgfu7dSrHx9QWbeX38gIj4E3FrW/E17\nmidJ6ipDndRTJhqKPQY4tzHUAWTmT4APlTWSpH5jqJN6zkTB7rnATeMc/zLwvNY1R5JUCYY6qSdN\nFOx2Bn42zvGflTWSpH5hqJN61kTBbiYw3mz/LU18hiSpVxjqpJ420eIJgLURsXka75ck9QJDndTz\nJgpmH2ziM9y/Q5J6naFO6gveUqyD3MdOUiUZ6qQpqeI+dlOeHxcRsyPipIi4uZUNkiR1kKFO6iuT\nniMXES8DTgb+J8XiiWta3ShJUgcY6qS+01Swi4jdgP8F/DmwPzAbWAJ8OjMfa1/zJEltYaiT+tK4\nQ7ER8UcR8Y/APcBrgAuAvYHNwC2GOknqQYY6qW9N1GO3GjgfeG5m3l17MaJS8wQlSc0y1El9baLF\nE9cDbwaWR8RxEeG+dZLUqwx1Ut8bN9hl5rHAc4DbgfOA+yPiE4BddpLUSwx10kBoeh+7KMZfj6JY\nEXsCsB74Z+BzmfmVtrWwj7iPnaSuMNRJbVHFfeymtEFxRPwW8AaKVbIvysyZrW5YPzLYSeo4Q53U\nNn0T7Lb6gIiDM/P2FrWnrxnsJHWUoU5qqyoGu4m2O3lBRFwXETuPcWyXiLiOYusTSVKVGOqkgTTR\nqtilwH9k5q8aD2TmL4FvAO9qR8MkSVNkqJMG1kTB7gjgqnGOrwJ+r3XNkSRNi6FOGmgTBbtnAj8d\n5/jPgH1a1xxJ0pQZ6qSBN1Gw+zlwwDjHDwB+0brmSJKmxFAniYmD3ZeAt49z/O1ljSSpWwx1kkoT\nBbtzgfkR8S8RcWi5EnaXiDgsIq4G5gF/0/5mSpLGZKiTVGfCfewi4o+BS4HdGw79FDg5M69pU9v6\njvvYSWopQ53UVVXcx66pDYoj4inAAor7xgbwX8BIZj7S3ub1F4OdpJYx1Eld17PBTq1hsJPUEoY6\nqRKqGOxmTeVNEbEIOBz4RmZe1tIWSZK2zVAnaRwTLZ4gIi6PiA/VPT8J+Azwu8CFEfGBNrZPklRj\nqJM0gQmDHfByYLTu+VuAd2Tm7wP/AzipHQ2TJMHIyAjz55/A0X+0kHVz5xYvGuokbcM2h2Ij4tLy\n12cCb4uIxeXzFwF/FBGHlO9/eq02Mw15ktQiIyMjLFy4mE0bzmElF3HbjDvY/urPMd9QJ2kbtrl4\nIiKeRbEC9lbgFOAbwCuAc4Ajy7KdgK8CB5Wf9aM2t7enuXhC0mTMn38Ca9cczUquBWARxzB33vWM\njo53C29JnVLFxRPbHIrNzLvKoPYV4EyKUPc24F/KY3cBTwF+WFcrSWqRWVu2sJKLAFjESjZObb2b\npAHSzBy704FNwCeAB4H6xRJ/CeX/SkqSWuexx7j0kXXMnHEHiziGjVzJ7NlnsnTpkm63TFKFuY9d\nBzkUK6kpdatfR08+mfM+Xkx5Xrp0CQsWLOhmyyTVqeJQrMGugwx2kibkliZSz6hisNvmUGxEvC8i\ndmrmQyLiiIg4tnXNkqQBZKiTNE3jzbF7NnB3RKyIiGMiYu/agYjYISIOjojTIuI24Arg5+1urCT1\nLUOdpBYYdyg2Il4IvJViI+JdgAQ2ArW/cW4HVgCXZ+aj7W1q73MoVtKYDHVST6riUGxTc+wiYibF\nLcSeBcwGfgr8e2Y+0N7m9ReDnaQnMdRJPatng51aw2AnaSuGOqmnVTHYNbOPnSSp1Qx1ktrAYCdJ\nnWaok9QmBjtJ6iRDnaQ2MthJUqcY6iS1mcFOkjrBUCepA2Zt60BEXEqxbx1A1P3+JJn5Zy1ulyT1\nD0OdpA4Zr8fuaXWPPYATgIXAAcBzyt9PKI83JSJOjYhvRsQvy8ctEXF0Q82yiLg3Ih6JiBsi4vkN\nx7ePiAsj4oGIeCgiro6IZzTU7BoRV0TEL8rHpyNil4aafSPi2vIzHoiIj0XEUEPNCyPixrIt90TE\n+8b4TkdFxNcjYkNEfD8i3tTs9ZA0AAx1kjpom8EuM/84M4/JzGOAW4ARYJ/MfEVmHgnsA6wGvjKJ\n8/0YeBcwB3gJ8K/Av5R3uCAizgROB94CvBRYD6xpuGftR4HjgdcBRwI7A9dFRP13+SzwYmAB8Erg\nYIrbnlGeZybweWBH4AjgROC1wPK6mp2BNcBPgEOA04B3RsTpdTX7AdcDN5fnOxe4MCKOn8Q1kdSv\nDHWSOqzZO0/cD/xhZn674fWDgC9m5l5TbkDEg8BfAZcA9wEfz8xzy2M7UIS7MzJzRdnrth54Y2Ze\nWdbsA9y5rqD3AAAgAElEQVQFvCozRyPiecC3gcMz89ay5nDgJuDAzPxeRLwKuA7YNzPvLWveULbh\naZn5UEScQhHUhmu3S4uIs4BTMnOf8vmHgddk5oF13+di4KDMfPkY39UNiqVBYaiT+l4vb1C8I/D0\nMV7fuzw2aRExMyJeV77/FmA/YBgYrdVk5m+ALwG1kPQSYKih5h7gTuCw8qXDgIdqoa50C/Bw3ecc\nBtxRC3WlUWD78hy1mpsa7oE7Cjw9Ip5VVzPK1kaBQ8peQUmDyFAnqUuaDXZXAZdGxIkR8dvl40Tg\n/wL/bzInLOetPQT8Bvg7YGHZE1jr9VvX8Jb1dcf2AjZn5oMNNesaara6h23ZTdb4OY3n+SmweYKa\ndXXHoAiiY9XMopiXKGnQGOokddE2V8U2eDNwHnApUPtbaiPwKeCMSZ7zO8DvArsA/wP4dETMneA9\nE41fTqUbdKL3OGYqaXIMdZK6rKlgl5mPAG+OiHcB+5cvfz8zH5rsCTNzI/CD8uk3IuKlwDuAc8rX\nhoF76t4yDNxf/n4/MDMidm/otRsGbqyr2WqlbkQEsGfD5zTOgdsDmNlQ0zh3cLju2Hg1myh6AJ9k\n2bJlj/8+d+5c5s6dO1aZpF5jqJP63tq1a1m7dm23mzGuphZPPF4csQdFsPtmOf9t+g2I+Ffgnsz8\n04i4D7iwYfHEOorFExdPsHjilZm5ZhuLJ15OsXK1tnjilRSrYusXT7yeogeytnjiL4EPA3vWLZ54\nD8XiiWeWz/+GYii5fvHECorFE4eP8V1dPCH1I0OdNJB6dvFERDw1Iv6ZIlTdQrmQIiI+GRHLmj1Z\nRPxNRBxRztF7YUScCxwF/ENZ8lHgzIhYGBEvAC4Dfk2xfQmZ+UuK8PWRiPjDiJhDsY3JN4EvlDV3\nUmzD8vcRcWhEHAb8PXBtZn6vPM8oRfj7dES8OCL+CPgIsKKuF/KzwCPAZRFxULmFyZnA+XVf6ZPA\nMyLigoh4XkScDCymGLaWNAgMdZIqpNnFEx8GnkGxH9yGutevo9hTrlnDwGco5tl9gWIF6iszcwQg\nMz8CXABcBHytrJ+fmQ/XfcbbgVXAP1H0wv0KOKahK+z1FGFvhCLkfQP4X7WDmbkFeDVFcPsy8I/A\n56ibL5iZvwLmUYTYfwMuBM7LzAvqan4EHA28ojzHu4G3ZuaqSVwTSb3KUCepYprdx+4e4PjMvC0i\nfg28KDN/EBEHAP+emTtN8BHCoViprxjqpIHXs0OxwK5A4xYjAE+l2CJEkvrayMgI8+efwPz5JzB6\n3XWGOkmV1Ox2J/8GHEsxTFpvCcWcO0nqWyMjIyxcuJgNGz7MEJt46xdfy7rfO5jhtWsNdZIqpdlg\n925gpLyF2BDwjnJxw8so5pdJUt9avnxFGepOZCWL2Lzl+Zz0lGGuN9RJqpimhmIz8xaKfd+2A74P\n/CFwL3BoZn69fc2TpGoYYhMrKYZfF3Eqm2Y0O5NFkjpnUvvYaXpcPCH1ptHrruPR417L5i3PZxGn\nMmv2WaxadTkLFizodtMkdVEVF080uyp2M7B3Zq5veH0PYF1mesP7JhjspB5Urn5dt349Jz1lmE0z\nZrB06RJDnaRKBrtm59htq9HbAY+1qC2SVC11W5oMr13rnDpJlTdusIuIpXVPTyn3sKuZSbFw4rvt\naJgkdZX71EnqQeMOxUbEj4AEngXcw9Z71j0G/Aj435n51fY1sX84FCv1CEOdpCZUcSi22Tl2aylu\ndv/ztreojxnspB5gqJPUpJ4NdmoNg51UcYY6SZNQxWDX7OIJIuJA4LXAMykWTUCxqCIz88/a0DZJ\n6hxDnaQ+0FSwi4hXA/8PuB04BLgNOADYHripba2TpE4w1EnqE81unf5B4AOZeRjwG+BPKRZUfAG4\noU1tk6T2M9RJ6iPNBrsDgX8sf98IzM7M3wAfAN7ejoZJUtsZ6iT1mWaD3a+B2eXvPwGeU/4+C9it\n1Y2SpLYz1EnqQ80unrgNOBz4NvB5YHlE/C5wPHBrm9omSe1hqJPUp5rdx25/YMfM/I+I2BE4jyLo\n/Rdwembe3d5m9ge3O5EqwFAnqUWquN2J+9h1kMFO6jJDnaQWqmKwa3ofu5qI2IGGuXmZ+UjLWiRJ\n7WCokzQAmlo8ERG/HRHXRMSvgUeAh+oev25j+yRp+gx1kgZEsz12VwA7AG8B1gOOJ0rqDYY6SQOk\n2cUTDwEvy8w72t+k/uUcO6nDDHWS2qiKc+ya3cfuP4CntbMhktRShjpJA6jZHrsXAB8vH/9JcfeJ\nx7ndSXPssZM6xFAnqQOq2GPX7By7APYE/t8YxxKY2bIWSdJ0GOokDbBmg93lFIsmzsTFE5KqylAn\nacA1OxT7CDAnM7/b/ib1L4dipTYy1EnqsCoOxTa7eOJrwH7tbIgkTZmhTpKA5odiPwFcEBHPpFgh\n27h44vZWN0ySmmKok6THNTsUu2Wcw5mZLp5ogkOxUosZ6iR1URWHYpvtsXt2W1shSZNlqJOkJ2mq\nx06tYY+d1CKGOkkV0FM9dhFxPHBdZj5W/r5NmTnW/naS1HqGOknapm322JXz6vbKzPUTzLEjM5td\nXTvQ7LGTpslQJ6lCeqrHrj6sGdwkdZ2hTpIm1FRgi4hXRMTQGK/PiohXtL5ZklTHUCdJTZnMdid7\nZeb6htf3ANbbo9cch2KlKTDUSaqoKg7FTjeQ7QY81IqGSNKTGOokaVLG3ccuIq6te3pFRDxW/p7l\ne18A3NqmtkkaZIY6SZq0iTYofrDu958Dv6l7/hhwE3BxqxslacAZ6iRpSsYNdpn5RoCI+BHwfzLz\n4Q60SdIgM9RJ0pQ1u3hiJkBmbi6f7w28GrgzM7/c1hb2ERdPSE82MjLC8uUrADjjbScx/5JLigOG\nOkkVV8XFE80Gu9XA/5eZH4uInYDvADsCTwX+PDMvb28z+4PBTtrayMgICxcuZsOGDzPEJq6acSov\n+72DGV671lAnqfKqGOyaXRX7EuCG8vfjgV8DewInA0vb0C5JA2D58hVlqDuRlVzL5i3P56SnDBvq\nJGmKmg12O1EsngCYD6zKzI0UYe+AdjRM0mAYYhMrKebULeJUNs1wW0xJmqpm/wb9MXBEOQy7AFhT\nvr4b8Eg7Giap/53xtpO4asapwN0s4hhmzT6LpUuXdLtZktSzmp1j9ybgb4GHgbuAgzNzc0ScBhyX\nmX/Q3mb2B+fYSXXK1a/r1q/npKcMs2nGDJYuXcKCBQu63TJJakoV59g1FewAIuIQYF9gNDMfKl97\nNfALV8Y2x2AnldzSRFIf6Olgp+kz2EkY6iT1jSoGu3Hn2EXELRHxW3XPz42I3euePy0i7m5nAyX1\nEUOdJLXVRIsnDgXq/+Z9C7BL3fOZwD6tbpSkPmSok6S2c18BSe1nqJOkjjDYSWovQ50kdcx0g50r\nASRtm6FOkjpqVhM1V0TEo0AAOwArImIDRajboZ2Nk9TDDHWS1HET9dh9GrgP+BnwIPAPwD3l7z8r\nj13ezgZK6kGGuoE3MjLC/PknMH/+CYyMjHS7OdLAcB+7DnIfOw2EFoa6kZERli9fAeBdKXrIyMgI\nCxcuZsOGDwMwe/aZrFp1uX9+6jtV3MfOYNdBBjv1vRaHOsNBb5o//wTWrDkWWFy+cjnz5l3D6OhV\n3WyW1HJVDHbNzLGTpIm1ePh1+fIVZagrwsGGDcVrBjtJ2jaDnaTpc06d6ixduoSbb17Mhg3F89mz\nz2TpUqdjS53gUGwHORSrvtSmUOdQbG9zfqQGQRWHYg12HWSwU99pc0+d4UBSlRnsBpzBTn3F4VdJ\nFdPp/xk02A04g536hqFOUsV0Y/qGwW7AGezUFwx1kiqoG9vsVDHYTfdesZIGiaFOkirN7U4kNcdQ\nJ6nC3Gan4FBsBzkUq55lqJPUA1w8YbDrKIOdepKhTpLGVMVg5xw7SdtmqJOknmKwkzS2KYa6kZER\n5s8/gfnzT2BkZKSNDZQkNXIotoMcilXPmEao8zZgkgbFwA/FRsS7I+JrEfHLiFgfEddExEFj1C2L\niHsj4pGIuCEint9wfPuIuDAiHoiIhyLi6oh4RkPNrhFxRUT8onx8OiJ2aajZNyKuLT/jgYj4WEQM\nNdS8MCJuLNtyT0S8b4z2HhURX4+IDRHx/Yh40/SulNRF0xh+Xb58RRnqFgNFwKtNZJYktV+nh2KP\nAv4WOAz4A2AT8IWI2LVWEBFnAqcDbwFeCqwH1kTETnWf81HgeOB1wJHAzsB1EVH/fT4LvBhYALwS\nOBi4ou48M4HPAzsCRwAnAq8FltfV7AysAX4CHAKcBrwzIk6vq9kPuB64uTzfucCFEXH8VC6Q1FXO\nqZOkntbVodiI2BH4JXBcZn4+IgK4D/h4Zp5b1uxAEe7OyMwVZa/beuCNmXllWbMPcBfwqswcjYjn\nAd8GDs/MW8uaw4GbgAMz83sR8SrgOmDfzLy3rHkDcAnwtMx8KCJOoQhqw5n5aFlzFnBKZu5TPv8w\n8JrMPLDue10MHJSZL2/4vg7FqrpaEOocipU0SAZ+KHYMO5dt+Hn5fD9gGBitFWTmb4AvAbWQ9BJg\nqKHmHuBOip5Ayp8P1UJd6Rbg4brPOQy4oxbqSqPA9uU5ajU31UJdXc3TI+JZdTWjbG0UOKTsFdQA\n6PkFAy3qqVuwYAGrVhW38Zk37xpDnSR1WLfvPPEx4BtALYDtVf5c11C3Hnh6Xc3mzHywoWZd3fv3\nAh6oP5iZGRHrG2oaz/NTYHNDzd1jnKd27C6KINr4Oesoru0eYxxTn2nspbr55sW9FWhaPPy6YMGC\n3vnuktRnuhbsIuJ8it6zI5ocn5yoZipdoRO9p+XjpsuWLXv897lz5zJ37txWn0IdtvWCAdiwoXit\nJ8KNc+okqWlr165l7dq13W7GuLoS7CLiAmAR8PuZ+aO6Q/eXP4eBe+peH647dj8wMyJ2b+i1GwZu\nrKt5WsM5A9iz4XO2mgNH0cM2s6Fmr4aa4Ya2bqtmE0UP4Fbqg53UVYY6SZqUxg6ZD3zgA91rzDZ0\nfI5dRHwM+J/AH2TmfzUc/iFFUJpfV78DxarVW8qXvg5sbKjZB3huXc2twE4RUZtzB8VcuB3ram4B\nntewTco84NHyHLXPOTIitm+ouTcz76qrmdfwPeYBX8vMzWNdA/WXpUuXMHv2mcDlwOXljaeXdLtZ\n4zPUSVJf6uiq2Ii4CPgT4DUUix1qfp2ZD5c17wLeA5wEfA94L0WwO7Cu5hPAMcAbgZ8B5wO7AC+p\nDetGxPXAPsASiiHXFcAPMvO48vgM4N8p5uItpeituwy4KjNPK2t2Br4LrAXOBg4ELgWWZeYFZc1v\nA98CLi7PcThwEfC6zFzV8P1dFdunOn3j6Wkx1ElSS1RxVWyng90WinlrjRdhWWZ+sK7u/cCbgF2B\nrwCnZuYddce3A84DXg/MBr4AvLl+hWtE/BZwIXBs+dLVwFsy81d1Nc8EPkGxp94G4DPAOzNzY13N\nCyiC2ssoQuQnM/OvG77XK4ALgIOAe4EPZ+aTdmU12KnrDHWS1DIDH+wGncFOXWWok6SWqmKw6/Z2\nJ5LaqDZEPGvLFi59ZB3De+5pqJOkPmawk/pUbX+9TRvOYSUXcduMO9j+6s8x31AnSX2r23eekNQm\ny5evKEPdtcC+nLDlIs77+KXdbpYkqY0MdlKfmrVlCyu5CIBFrGRjlzroe/52a5LUQxyKlfpI/Zy6\nC+77Lv814wecsOVUNnJlub/e5R1vT0/fbk2SeoyrYjvIVbFqp8Y5dTNn3MG3/ve7ueHL/wF0Z3+9\n+fNPYM2aY6ndbg0uZ968axgdvaqj7ZCkdnBVrKS2efKculOZ++XrDVGSNECcYyf1iarMqavX7O3W\nnIcnSa3hUGwHORSrtnnsMdbNncttX72dE7ZcxEZmMXv2mZWYzzbR7dYa5+FVpd2SNJEqDsUa7DrI\nYKe2qLujxOjJJz++pUnl71lbch6epF5VxWDX/bEaSVPXcJuw+dttx/w//uPutkmS1DUGO6lX9cm9\nX5cuXcLNNy9mw4bieTe2ZZGkfuFQbAc5FKuW6ZNQVzPRPDxJqqIqDsUa7DrIYKeW6LNQJ0m9qorB\nzu1OpF5iqJMkjcNgJ/UKQ50kaQIGO6kXNIS6kRtucENfSdKTGOykijrnnHPYffcD2Gu3/fnui19c\nvFiGuoULF7NmzbGsWXMsCxcuNtxJkgCDnVRJ55xzDu9970f49c/ezSd/vgt33vk9zp0zB7bbjuXL\nV5R3aVgMFHdsqK0olSQNNoOdVEHnn38pQ5zPSq4F9mURn+S8j1/R7WZJkirOYCdV0FAmK7kIgEWs\nZGPdXuJLly5h9uwzgcuBy8sNfZd0p6F63MjIiPMeJXWdd56Qquaxx7hxr+258+f/ySJOZSNXAm/j\n9NPfBcCCBQtYteryug19L3dD3y4bGRlh4cLF5RA53HzzYlat8s9FUue5QXEHuUGxJlS3+vXcOXMe\nH349/fSTOOuss7rZMo1j/vwTWLPmWIp5jwCXM2/eNYyOXtXNZklqsypuUGyPnVQVDVuavHu77Xj3\n+9/f3TZJknqKwU6qAjcf7mlLly7h5psXs2FD8byY93h5dxslaSC5eELqgpGREQ444HcZGhpm96c+\nc6t96qoU6lwQ0JzavMd5865h3rxrnF8nqWucY9dBzrETFGHp6KOPZ8uWHRjiAFbyIHAXdy57b6WG\nXhsXBMyefWZTgWVkZKRuYccSA46kvlXFOXYGuw4y2Ang4IOP4Bvf+DZDnFduaXIHiziFp+52LQ8+\n+N/dbt7jprIgYKphUJJ6URWDnUOxUofdddf9ZairbT58ERv5Sreb1RLeFUOSustgJ3XY/s98+hib\nD3+H008/qbsNa+BGyJLUexyK7SCHYsVjj7Fu7ly++pWv89r8RBnqTmPx4tdw2WWXdbt1TzLZ+XIO\nxUoaJFUcijXYdZDBbsDVbWkyevLJnPfxS4H+W2Dg4glJg8JgN+C6Gez8x7bL3KdOkvqOwW7AdSvY\nOTzWefVB+oy3ncT8Sy4pDhjqJKlvGOwGXLeCXS/dx7Ifehbrg/QQm7hqxqm87PcOZnjtWkOdJPWR\nKgY7V8WqMmqBaM2aY1mz5lgWLlzck3c7qG35McSJrORaNm95Pic9ZdhQJ0lqO4PdAOiVbSv6aQ+0\nITaxkmJO3SJOZdMM/1OTJLWf/9oMAO9j2VlnvO0krppxKnA3iziGWbPPqmSQ1uDyHsBS/3KOXQdV\nabuTKs5l64tFHuXq13Xr13PSU4bZNGNGZa6vBH3y35lUEVWcY2ew66CqBLsq/8VexcDZNLc0UQ/o\npcVUUtVVMdjN6nYD1Hlbz2WDDRuK16oQohYsWFCJdjSrFkRnbdnCpY+sY3jPPSsR6no6IEuSpsw5\ndtIU1Xo+1645miVfvIvbvno7oyef3NZQN9HcqJGREQ4+eC5HH/0G1qzZr6dXF6s9emUxlaQpykwf\nHXoUl7v7Vq9enbNnDydclnBZzp49nKtXr+52s3rK2WefnbNm7ZlDXJKrOC5XcVwOcUnOm3d82845\n0Z9b43EYTlidcNmT2rV69eqcN+/4nDfveP/sB5B//lJrlP+udz1f1D+63oBBelQl2GX6F/t0nH32\n2Qk75xAvy1XMKUPdo2MGqFaaN+/4MrBl+dj6fGMdh+OfVGew35r/LUiaqioGO+fYDahem8tWJeef\nfylDnM9KPgV8k0WcykauLIe0Lu928xrc96R2VXmOZac1LiS6+ebFlVlIJElT4Rw7VVbV9tqqtWfD\nL3/FSs4F9mQRn2MjlzBr1rvaHggmmhvVeHzGjHcwZ85Mg8o4+mlTbEkCV8WqoqrWk1Jrz6YN57CS\nHwL/ySKOYSMPAnewbNm72t622kbTT6x23fp6PPn4lWO2aenSJdx882I2bCieV7OnUZI0Fe5j10FV\n2ceunSbaZqPZbTiqttfW/PknsHbN0azkWgAWcQw56z3svPNTOf30kzjrrLO60q6pcjuUQpX3dJRU\nfe5jp742US9bK3rhuhVIZm3ZwkouAvZlESvZyJXM+/0jenZTV+dYFibqBZWkntPt1RuD9KBCq2Lb\nYSqrNre1inSslZtnn332pFZztmy146OP5v2HHZbXzNg+h7jElaSSpMzMSq6KdfGEKqnWkzJv3jXM\nm3cNq1Zdzo033t70RPda7+CaNceyZs2xHH30iRx88BFNLcKoX7Qxet11sGgRw3vuyfZXf465865/\nvD2t7tmp2mIRSVIP6nayHKQHfd5jN9kNdCfb6zWZHr+x93Q7dMJznn322Tljxu4Jh+YQb89rZmyf\n9x92WOajjzZ/IaZgWz2U7q8mSdVFBXvsut6AQXr0e7DLnHj4czrDo5MJhs1u1tv4+RG/lXBZeUeJ\n7XMV++er/vA1k2rnVIzV3iJguolwppsIS6qmKgY7F0+opSaalD+dSfvNTHSvLa74wQ++A3yh7kht\nf7f7t/n5p576V2R+lCFOZCWLgOeziCHmzujOjIUtW56DmwhXb+sbSaoyg516ynjBcOsAcB/wF8Cl\nwLeANwL3b3PPtpGREb7//bsZYlMZ6ijvKPFOli79YHu+TJ3GveVmzHgHW7b8WdvP2wu8U4YkNc/F\nE+obWweApwMvBNYC/wB8md12++sxe3pGRkZ4/etPZYhZrOQvgbvLzYfPYP/99+5IgGhcLPLBDy5l\n9uzPsK27TEiSNBZ77NSnlgB/8viz2bN/yGc/O3aoe+KOEhcBP2MRj7CRS4jYxEUXnd+2Fo61J199\n+w455BD3V8M7ZUjSZHjniQ4ahDtPdFPjXKzttns7Bx30IvbYY/dtbmY81h0lNrKMGTM28MEPvqNt\nd5TwjgeT450yJFVRFe88YbDroCoHu375h3Oy3+PoP1rIki/eRf0dJXbb7a/57GcvmtI16NVbpkmS\nJs9gN+CqGuwGrffonHPO4fzzL2Uok+t32si9967nhC0XsZFZzJ59Jmed9VZuvPF2YHIhdzLX0WAn\nSb3PYDfgqhrsWhUyeqHX75xzzuG97/0IQ5zPSs4F7uK0vfZj9733ZI89hjnqqIM555wLpxRyn7iO\newErgPuYM2cmt99+M7D19ZnOeSRJ1VDFYOfiCbVElfcaqw9Ut956axnqPgXcwyI+ycb7Z/HAL89k\n1ar3tWBrjf+k2DOvuA7f/OY7Hr892NbXp9YzeA3QvsURvRC2JUmtY7BTU6sOJwoIVd1rrDFwDvGF\nsqfuERZxERv5c+CJ9k7H0qVL+OIX38CWLcupXYctW5743Mbrc+ON7R16rXLYliS1h/vY6Ul7qDX+\n418LCGvWHMuaNceycOHitt2kfmRkhPnzT2D+/BNaco76wFncUWJ/4C4W8Uw2jvH/NUuXLmH27Npd\nKia3f9yCBQt40YteMO02t8rWYbsIeNMNr5Kkiuv2Pc0G6UGP3it2rPuYNt5vdTL3cR3L6tWrc86c\nw3PGjF3H/YzJ3jN0771/p7z366O5iuNyFXNyeNd9c6ed9k7YZcxzjXWOZs/beB1mzNg1zz777Glf\nn6mYM+eoJ/25zZlzVFvPKUmDhAreK7brDRikRy8Eu7ECTDPBblvvbfacReg5dNzzTDYcrV69OuEp\nOcTuuYo5uYo5OcSuudNOe0+qvZM979lnn50zZuxefp+lj9dP90b2471/rGNz5hyesMfj7YY9cs6c\nwyd9XknS2Ax2A/6oerDbVoBpd2/TE8Fx/ADZbMCsrx/iklzFYbmKvXOIlyU8N3fbbf8ptq/5806m\nvhnj/Rls61jRjqXldS1+n247pFaZ7v/oSFVQxWDn4gk9blsLIEZHr2LVqss7cHurJTyx5crUbh1V\nv8jjF+t/Ut4mbF8WsZaNXAm8ndNPP6N1Te6Q8RanbOvYE4tinthSxVtxqQpc2CO1UbeT5SA9qHiP\n3dY9TasTDs3ddtu/7f83vXWP09KcMWP3nDPnqDGHG8frOaw/PsQleXVsl1fHrBzikvI9u+TixYun\n2b7mhoBb3cM5Xi/geMfsFVEVtaNXW+oGKthj1/UGDNKj6sHuiUCydKu5WZ2Y6D9RAKkdnzPn8Jwz\n56gx62r/WNQvlHjpi47c6nOnMw9wMu9rdaCaylCsVFUGO/ULg92AP6oe7DKLkLDbbvu3dbHEVNrU\nTHB5Yk7dcbmK43KIS/7/9s49PKrq3P+fNckMBAKEEEQQRIwXRFACnh4o1lhrTLWVX5GetFI88Yai\nVAQCUor08BQseipejy1FK6BW27SWij2aQK3Q46W2ClrUohYRBbwhoqCRTJL1++NdO7Nnz+RKLpPk\n/TzPPMzee+21914zJN+81yYlX6S6daupyROKkqroHyNKZ0GFXRd/pbKw8wuDZGUymlPexD/n0qVL\nmy08Gvrr3rvOv5063j5iIi779Z6Ee5Is0XEukaAswWWpv2g6Nyp+Uwv9PJTOgAq7Lv5KVWEXFDWR\nSJaNRPrXK3KSia3MzIG1btKlS5fGxc1B72aLpoZiyDIyBjhLXZ59xKTXul+D1jh/jTwYEJclqq6h\nzo0Kd0VRWoMuL+yAM4B1wC6gBihOMmYxsBv4HHgSGBE43g24E/gQOAg8AhwVGNMXuB/Y7173AX0C\nY44GHnVzfAjcDoQDY0YBm9y97AIWJbnffOAFoALYDlxZz/M38qvStiQTNXl5E+r9azrxnBILWbW/\nOEVElbhjifM3JSkjWXJFbu4om5eXb7Ozc22YWXW6X+t7xlCoX5Nr9XU2uorVpKt+voqitC6pKOza\nuqVYT+AfwLVOCFn/QWPMfGAO8H3g34APgA3GmEzfsNuAC4DvAl8BegN/NMb4n+VBYDRQCHwdGIMI\nPe86acD/uvs5HbgQ+Daw3DemN7ABeBc4zd3zPGPMHN+YYcBjwFPuesuAO40xFzR5ZVKMnJwBrF//\nMOvXP5y0BEGw9RasRj6aYqCYmppbgafrnH/fvv6Nbk3mtTzLy7ubUOheamouZvv2d9my5RIO7FtA\nKV+iQQIAACAASURBVD8DPqCIUqKks3fv+41qS3bqqSNrn+1wWomlEk1pydaWreIURVGUNqK9FCVw\nAPhP37ZBRNQC377uwKfAFW67D3AIuNA3ZjBQDZzjtk9CrIHjfWMmuH3Hu+1z3TlH+cZ8DxGbmW77\nKsTa1803ZiGwy7d9E/Ba4LnuBp6p45kb9RdAc/HacmVn5yYtF1Lfec1xU8USLcZZyE+wiBjT1x0b\nHueKFTdofIybf866LEhidZls4QgLg22YxbXZr1J8WNzI6el93HXH2Ugkq9FFlmOZt/m1FsumthRr\nzHO0Fk39HLuSFUtdsYqitAakoMUulYTdsU58jQ2M+yOw2r0/y43pFxjzMvBf7v2lwKeB48Zdr9ht\n/xjYGhjT382d77bvAx4NjPk3N2ao2/4LcGdgzH8AlUBakmeu/xtyGJSVldlIJMv6y5REIv2bJO4O\nrx1YScK1RWDJdnp6H9ur19FOcJUlFRIN/fIdOPDoWoEoMXXpdi0n2TD32OzsXFtQcIEdOPAYC9nW\na+flb6PVmGdMdg/x8YLtU8euMaRCh4xUpqu4nRVFaTtU2NUv7L7sRNPgwLh7gTL3fgoQTTLXE8DP\n3fsfAtuTjNkOzHfvVwJ/Chw3QBT4jtteD9wTGHO0u8d/d9uvAdcHxpzhxgxIcg8NfUeajfySrr/X\namuRrMacZKAmxu3VV4tNrH/DnfUvvgVWWVmZ9WL4/HXqwhwV1/oMYmLSS5BoSguxZGKnseVf6puj\nLT6Hpl5XrVgtj4pHRelapKKw6ygtxWwDx00z5mzonIau2SwWL15c+/7MM8/kzDPPbI3LtCmFhYUJ\ncXjnnDM56djhw4ezc+cShg49kmXLpIVQrL3QVCTG7Qdu9Fz27j0RgAULlgCGMFWUUgRAETOIch2P\nrn2QwsJCd83LkfwcgKnA03TrZmvvx4ubi7VHi9/eu/f9w1yN9iPWQky2G2oh5sUutn6ruK6BtslS\nlM7Pxo0b2bhxY3vfRv20l6Kk8a7Y/wVWufd1uWJfoemu2JcDY4Ku2DXAHwNjgq7YTcD/BMZ0SFds\nY+ZvaoxZY0uoxCxNybJz86211pf92q22Th30tt27Z9deMzd3RIIbFrpbY7Li1kTWKfl9BY9nZAyw\nxcXFNj5GsLddunRpo5+9LS1hajFqP7qaa1tRlNS02KWSsDPAHhKTJz4Bprnt+pInCtx2suQJz83r\nJU98ncTkiSnEJ09Md9f2J0/8EHjHt30jickTK4Gn63jmRn1Rmktzkyf85ycTBYeTXNGYosf1Cbvc\n3NG2oOACm505xK4l165lvA3zLSfeetQKrLKyMmtMohtWEi3i55Rzve1E97XnTvbWQe6vxN1fvIu4\nqWupdF5U2ClK16PLCzukvMho9/oMWOTeD3HHr0MyUScBI4FfI/Xjevrm+BnwDvA1IA+pdbcZML4x\njyFlVcYB44GtwCO+4yF3/Al3/bPddW73jemNZOk+BJyMlFj5BJjtG3MMUgfvVicoL3fCc1Idz9/0\nb00bUVfSQEHBBS7GrMT3C6ukNlmhsaKlMUWGgwkYEi/Xo7b48FrSbZhZtceKi4vrnV9EW9869tct\n7IK/jFviF7YKvc6PxiwqStdDhR2c6SxnNc5i5r2/1zfmv5zlroLkBYojwB3AXicOkxUozkLq1n3i\nXvcBvQNjhiAFij9zc91GYoHikYi7tQIpmpysQPEZSIHiL5AEjSvqef7Gf1sOk6YKiWQFh+M7NeRY\nyWYtixNffgHY1GzToFUwljwx2kI/C8MTer/26nV0PaVQggKurx048OiAS7h+V2wo1Nfm5U1oEYtl\nS52vdBxUwCtK16LLC7uu/morYVef9a3xnSQSLVlebbigADQm0wbrxiW7p4bcxNL2q5uVOLkcG2aY\ns9T9PxvmkPUyVOt6Zr9Agyybnt6zNlvW/+zJtvPy8m0o1M9ZDaW7hf8+D+cXtrroFEVROicq7Lr4\nq62EXUPWt7qK8/rFoIicxLIfiaU/hgfcp7G6cXXNncxaJy7foRbCVooPH2XXEnLu13tq587MHFiv\nVdBrMxa0ujV+zcqsxOe1nHVNhZ2iKErnJBWFXUcpd6IcFk+7Fl/FAFRUSHkPfxmGYOmL/PzZ3HDD\n/LjSGQ8+KKUzpKSDd+YHwC21cwPs3Lkk7urLl690JSASrx9f6uR1oCdhvk0pm4D3KQKiXId44D/n\n4MGrmDQpeRkJb9t7Bo/y8vK48ibeOP/+WJmTlUhDkbrXqq756qKpZUgURVEUpbmosOuEBIWECKaG\nCdajO+2005LWOFu48BpuuUXEW7duObz7bvw8Q4cObvS9LliwhIqK/sBq4GLCnEQpM4AxFLGCKHMR\n4QgwDyigomIUU6bM4MEH70oQXME6YgsXXsMNN9yZUFsMiBsbicwjEplFZeXweu+3ObXKtF6coiiK\n0lYYsSQqbYExxrbVepeXlzNlygz27esPfBO4E7FEicVo4cJr2LRpM9A4q5N/3nhBNIuamjSqqpa7\n7XmsW3c/gM/6NyZOXGVkzK8VV+edd6GzJkKY6yglF/iCIoYS5VvACuBZd/U1wCrgEmAFGRk74kTV\nOedMZsOGicSsh2vIzl7Cvn2L4vYVFEgB4+DYvLxVQBUvvfRq7T1591rfNQoK1rF+/cONWj9FURSl\n82CMwVrbnCYJrYZa7Do9B4HHgeOAm8nOrmDOnORWrMaIu6BbtbIS8vLuJidHxFJJiYg6v/h74onZ\nXHTRRPbs8cbItcaMOZOamkuBdYSpoZRewFsUsYQo9wAzgWmBO9gKzAJ+TUXFewlu0sawd+9H7Ny5\nC0m+PhKQ83Ny+rF+/cMBV2vHta411WWsKI1Fv1uKksK0d5BfV3rRBskTiRmeXqmSEut1TDicYP74\nc8ssjLPZ2blxCQbJ5g+F+sVl5i5dutRCT5f96tWpi9gwJ1rp8tDfwggr/WG9ThJerbv82nnz8ibE\nzRlM0jj77LNtsGuEZN76y7iUNKnockcoXdJR7lPpeOh3S1FikILJE+1+A13p1drCLvgDV4RQmXs/\noVaE5eVNaLawi5UViS8m7G9flqzLBAwP1MXrbaF3Qp06mbOPhaWBbNu+bp9XdiV5m7JgWZdYceVY\n1wgYbIPZvk2tS5fqtco0E1dpLfS7pSgxUlHYqSu2ExF0kworgWHAq8Ct7NsHBw96iQIyor4sTb/L\nJT9/DA8/vIFotAq4F2m4EXPJLliwDICtW59HGnusACYADwD94zJzAcLMopS7gKMpopQoDwEWaeCx\nArg58CwriES2c/LJJ5CTs469e09ly5ZL8GewPvzwKpYtW8Dy5StZvnwl0WgUGOXmAonT+03cM44d\ne2qTXEnBJBNFURRFSRVU2HV69gAbCYqw3Nzb+PhjyWydM+eapEIlmCixYYMX83YJEv+2NW78zp27\nWLBgCVVVGcSE1Czga4RCG6mpiY0NU0UpVcCrFDHDibrZwKXA06SnV1JVFX8/vXrt5re/vT8ukSHI\niy9uZeLE71JZeRsA6enl7l49ZpKeXk1VlQjZzlp6REusKK2FfrcUJcVpb5NhV3rRxq7YUKivzc0d\nZXv1GpI05q2hGJnkbbpyfe7d7LhYNa+zRLzrc7KFfjYj4wgXUzfOhpll15Ju15Jrwzzqxo1z7uJY\nPF58J4mchK4W0qnC7971YvC8XrASA5iRcaTNzBxos7Nz7dKlSzuEK7Ul6CrPqbQ9+t1SFIEUdMW2\n+w10pVdrCztrk//ATSb4RADZpDEysU4QwS4TXnybJ6D6Wq+VWHp6T5ubO9qGQtk2mKwg4m6chWwb\nZpITdRk2TB8bS4zo7YRdH1tcXGzLysqcIB3s9pcljeWReMGRTnBO8Am7ujtIdNRfSh31vhVFUTor\nqSjstI5dG9KWdeyC+GPl3nzzDbZvL0FKfawE9pCZuZPx48eTnz+GxYtvdXXpHgU2AHe4WeYjMWrv\nIW7TY4B3gBqM+RxrV7hxc4AH3fu5wG7gUld8+CoghyIOEuVON2Y2cBbwZ+As0tP/RCgUobLypwnX\nzctbRU5OP0BcQs8//zzXX//fvnv0XK2jgOkEa9rNmXNJQk295tb0OxyaWi4i6BYP1tdTFEVR2p5U\nrGPX7sqyK704DItdS1lrJKs1y3pZqZ7FDXo4y5rnXl1qoZ+VXrBH+qxg1h0fksQyV+w7PirOYhbm\nCLuW8XYteTaMV4rFBiyB46yUMhnu3l/gc/uOs8ZkWcisPR6JZCXN8PX6xSazNsZc0GVu/uFu3rYr\n3dCcchGaiagoipJ6kIIWu1A760qlEXjWmg0bJrJhw0QmTSqmvLy8WXMtX77SJRYMRfqvTnevHkA5\nMAhJilgOXAxkAYeAlxFL3RrEgvYZMAJYh1j+7kAsfN597cHruRrmQko5Cik+PIMox7t5/M/wBpJB\n+6a7znTA6/CwFXgday8D/gfYC0ygsjKdbduSt0t78MG7yMjwLH3ePS+mpuZ4N1+xmz8La29z22IR\nC/aabWnis5fb5pqKoihK10CzYjsAwTImyRrTN53PiZUTKUc6U7wO7AfuQgTdKuCriMiaCXwf6Adc\nA/zU7cfNMRURh4uR0ippwArC9KOUewAooi9RFhJz5y52/0rmrOwH8ISWh5cte7Nv3zrgZg4dKkHc\nvR5zgRNr+7NKW7UM93wrgaOIL9WyLmFl9u79qDbjNlWq6msmoqIoitIYVNh1MfLzx/DEEyXU1Hgf\nfTkicG5y2zOBEHCjbzsTsc7tAj4E7gZuJ158zUKsfW8gX6tLXEzdZGAIRewnikFq2hUiIu4td94h\n4DngACIog6Qhtej8vAQMcy7ui4kJtGJycnYAUm9uzpxLEmLwsrP7sm+fN88ViCgV0tNLePHFSqw9\nGZjQpHZrfuqLoWuOSPOEamdodaYoiqK0Iu3tC+5KL5oZY5csJivYYcEbV1Bwgc3Lm2Dz8vIT4vHi\n5/EyUb2sVH9nhnG+WK4SK50g/C24Ml0sXqaNdYMYGhdzFx9Td6ybJ8s3T5aV8iclVrJaV1tjsmxu\n7gibnt4vcL0+gXNjLdIikT71xqsli00zppf1d7VIT+9j8/LybV5evjUm03cdyf5NFstWX8xjY2Lo\nNMNVURSl40MKxti1+w10pVdzhZ218UIg1hO1pDYhoLi42LcvJ0FUxNpr+UVOiRNn/tZdOS45wRsz\nLkkSQrYTY1kW0ix0s5JMIePCHHK9X3vYMLPcHH3duGwrJUwm+66VG5cQIAkRweSJ0W58fBJHr15H\nJ4gk/3by9mbeHFI/Ly9vgrW2rlZo4xKEXUPCTRMdFEVRugapKOzUFdtB8LexOuecyVRUTEXcmjdR\nUwNr1sxG4sYk9swfj7dgwTK2bdtGRcWwwKyjyMjIpKLiRuLdqtcSi3fbVscdeXFtMxFX6UEAwlRS\nShEARYwkyr1AJfANoAw4wZ33BHAZEsd3Uu2se/d+xM6d7wGL8JcqgbfdudMRV65w3HHD4tYmWBYk\nEplFJDKvtn1aKDSbmppL3RziEs7JWUd5eTkvvfRywlOGQm9QUrI4bl/rxDwm0tSSKKl6DUVRFKXt\nUGHXYXkaL+tUWFHnyJ07dzkh8iRSY+564N+Bx6mo6J7kDOPGVCHZr9f6jl0LDEQyYT0RMAewhLmK\nUm4HoIjdRLkPSY64GRF1Xhaud87/umuMQ8TbTLZsqQFOJT4h4lqkf2wBfgEaicxj2bL74+48KLoq\nKyEv725yciQGb9CgiaxZczexmL2Z5Odfx/LlK6mpuRjJno1d98c/ntdksdMSiQ5BgdrcWL/2voai\nKIrSxrS3ybArvWihzhPSSqufDbpVjemT1BUrrs3JNrHu3EA3foBvvzeHv3ODF4/X17lOvXM8N2mO\nDXOiXUuaXUuaDfMlG1/zbpyN1cezvv1ZVrpF5Lr3Jb57K7YwzmZn5wZq1ZXV7k8Wm5bMnZqXlx84\nHh9T6Llt4+vbxVy0yda/tWPo2sKdqy5jRVGUwwN1xSqHi+c6GzZsCG+++X1EL0Io9HNqakAseTnA\ntWRk9GD48OOYPPlctmy5BckM9btc5yKWqzXAMmA7UI1Y0v6AZIsWI5mzv0RcvSBWralIuZJ/EmYE\npbwAGIroQ5TXidW8m4u4YpMV5jbAP4BuJJY4WQIsYuzYdZSUXOEsS96zvsbQoSPqWKEqkpU/8dZO\n3K2nJZwVs7LdBEwkI2M+y5Ylt7I1JkPV7x5WFEVRlDajvZVlV3pxmBa7oKUoEunvMmAnOGtaMDFi\nsPU6NGRkDKrDYtbHxjpOeBmyOb5/PQtWsgSEwTbMLNf7daCz1HlJEkPc3H2tdJMIWgy9xIl+7j6D\n8w+wkGVzc0fXJkRI1qpnNZTnClrDxAqVaJGLHUu0XC5durR2fevLKm5LmtOdIhWvoSiK0pkhBS12\n7X4DXel1uMKuLteZ7PeyV8ucezNYWqRbgqCREiXB/X2d+PGuNbgO4ZVtwzxq1/L/XEmTo9w9eO3K\nymxitq031zgr2biTrbQr657k3nokCA4RsP42aL0T3KX1lYaRrODhSdewvvPbU9y1dkkULbuiKIrS\nfFJR2Bm5L6UtMMbYw1nvc86ZzIYNBnjR7RlNQYHMJ/v/jLg3ByGJDTvcuGGIO/VN4Fik3dcliBt2\nDnAL8Rmoc5BCwZPcdh/gXeBnbsxMwoQp5QTgCIo4nyjXIS7XK4F7gJHAtMC8NwO7EdfoBKTQcRbw\nvhvTAymOXE28a3YNBQXrePbZZzl4MEqsA8VcMjPDHDiwJ26dbrjhBm65ZRUA559/OqWlZbUJApLF\nO803h8y9fv3DvjX2WpklHk8lNKM1tdDPQ1G6HsYYrLXJYo3aDY2xS2H8vygGDerFpk3/B0SBk4Gd\nwJ95881soBfSxeEyRKxdQ0xIgcSZHQKyERE1CBF9BcTKj/g5AYl9u5tYx4ZZSEuxkYS5lFJ+BvyN\nIq4hyhwk4/V3iKAchWTVBnkHOBuw7vrTkPZeXhmWve6ek2f4GhNGYgGLfft+FDemvLycG264s1bI\nrVlzrbtmsW/UbLys2I7amkszWlML/TwURUkZ2ttk2JVeNMEVG+8SnOxcnOOcm9VzW5bYeBeml6k6\nMonrtK/bHxwfjDnz9iUWA4Zxrviw5371Yuq6J3XVxsfteZ0qst22t6+/lQ4UA3xzxLtxPXdobu7o\nhOvk5o6OW7dk7mq5jr+o8RCXbZufUNA4Vvy5aa7YtnZpakZry3M4n6F+HorSNSEFXbFqsUtRYvXY\njkRcrLe5I3OIZbdOJjHTdSXS23WrOw5iETsRqSE3l/gadLORWnWzgOHA6cAGYpa6YrxixWFqfMWH\nZxDlHjemh5vnRqQQ8d1Idqrnur0WsTR62/PctvdMs9w9bHXbhe66c8jO7suDD3qWj3mIK9Wz6L0K\nDK1rCX2cgGTwvgfM5MCBacAoKirm8/zzz8dZ+J56aj5FRV/n0UeXADBnzjUNWl3UWtPx0c9QUZRO\nQ3sry670ogkWu5gFwGuvle/e5/gsA8lab42zyRMllvosWBf43ntZsT2sl+maLAM2TC+7lpCz1N3j\n7qO3s8L5s2l71jlHw9u9rb+WXXp6zzjLSSTiWQG958q2kUh23LpJjb++vjHSZi07O9clT5TEXTex\nzdpqX43Axlns2sNak0pJHp2Bw/0M9fNQlK4JarFTGktJyRU8+eRkqqrSwXVziNVkm4lYt/6JJD6A\n1JWrAIYAGSTWhbsOeBzp8vAXpI3XO0jCw0YkqSINqSm3FT9h3qGUzwEoYhtRFiO18t4GrkLi1ea7\n6z0N7GrEE+5CEjo8i8ggxKI4GzBkZERYuHBWnMWkstIitfSKfftmx81aWFjIRRdNdC3WTgSmkpHx\nAA8+KHXnNmwYRUPU1BxPsF0Y0GaB8Y0Jwm9MLT2l7dDPQ1GUlKG9lWVXetHEGLuYNcyzyJVY6dIw\nxHfMHwOXZSXeLVyHJS/HWeYmOEtdWsCyl2WlHEiPWstZmBy7lvG+kiZZ7rhXTy7YYcKzvHkxgSVu\nbA/fdfx18oJdLDJr5wnWqUtL659gVUlL65+wbmI5KbEwzoZC/eLq1CUrheLfJ9a+eKteXt6Eeq0x\nLWmtUctP+6DrrihKcyAFLXbtfgNd6dUUYSc12/y16PrbxAK/QVHkJUd0s+Je9dywfS2MdkJssIVe\nPsEVdJmOdHPn2DBfcsWHxzv3q1eHzu8y9V8/2x3r6bvPPm7fYAtHJBGjfX1zeYIx9oz+OnXFxcU2\n6GIuLi6OW7eGXGrJAuQbSp5I1qYs6KZrqeQJDcJvP7Smn6IoTSUVhZ26YlOUnTvfI9GduiKwvQ64\nCUkM+BeSkDAN+A3wJ+BvSEUbr2bdXHf+L5HSJD2TXPld4GLCrKGUd4ChFPGJK2lSBeS5a6wiVk5l\nMeIWPgo4F3HHBu97m7uX6cTcr1IDKC3tfrp3T6OysieVlT+NO/df/4qVM1m9ejUAv/rVdQB873uT\navc1lmStvoL7TjvttDiXmve+qfMqHQv9DBVF6QyosEtRhg4dzL59jR39IfAAkvV5M/ApEi93FFII\n+F5gBCKYdiCCcTYwFslI9ZgNnEWY/6OU/Yio20+UQ0ANUqtukbvOy0iMHMBrwOWIeJwNXBq4Py9+\n7prA9WZi7TSqqkZRXT0/6ZNVVVXGba9evZr6tFys56tsN6dOnffLffnylSxfvpL8/DE89dT8w5qz\nsbTE/SuKoihdF+080YY0pfNEeXk5Eyde5CxYkFgixLO+rUFEXaF7fzWi171yJSLWpOPDv4AwMAWx\n2lUhyRJfAH2BTMLsdokSR1DEcUS5DEmMqEC6RPzYbU9FLHOvAv2RhI13kVIrHxIrbTLf3dd77p4f\nQIoMb3fXjd17KHQNNTXGd+8z6d49nYqKjxq1Zv61O5xEhxtuuIEf/ehWl0QxgYyMB1i48Bo2bdrc\n7DmbgnYwUBRF6RikYucJFXZtSFNbipWXl/P1r09BLF59EMH2OiKIqpFuEt2ICaG5iFiLb8clte9O\nItZK6203Lt3tuxuYRpiTKOVqoJoiIkTJQkTYKsSdOxux/HlWO080/gXpJuG5Zme7+cPE3MCz3PUe\ncPfmtfiKCb+0tHlUV/8n/lZoaWn3cdZZX2kzgVNeXs55511ITc2tvvubSkHBjpRsK6YoiqK0H6ko\n7NQVm/J8jgixmBVLxNgo934UEmsHIphWJ5ljELGP+gREAHpFfncAdxDmx5TyDHAyRbxNlGp37XnA\n/YiQSwM+wSv0Ky3KzgeeRAScdx+XIu7fOcAfEWHofIssRkSdPwZvMaHQaxxxRF/efXcU/j6u1dW9\n2bBhYm3BWGjdsiPLl690oi4YIzioRa+jKIqiKK2BCrsUZsGCJYiL81JiomkaIsb81rFp7v1MxIo3\n0zfLXKRW3QFiFjd/nbqPCFNFKR8CuyniKqI85K77GdAPEWP/QBIn/unmqUA6W8xEkjCCvWkrkbp5\n0xFrXS/ElZysxt3rXHTRRB5+eL0713/vYWAdFRVTWbBgGdu2bWux7gCNdXmGQm9QUrK4WddQFEVR\nlLZEXbFtSFNj7MQlmOb2+EXTicBTiJjyRFYPxD1bCRhgICLm0oAvI1myZyMWtpl4rtgwWZSyBwhT\nxJ1Emevm84SYVxx5FpJAUQ18HWk7lg4cjVjwvoff0iYuzDCSyFEAPOHu5WLEHeu5Ymdy9tlf4umn\nt1JR0R+Jt4u5YiWObwKwirQ0Q3X1xXHXKShY1ywXabCFVEbG/FqLoH9/KDSbH/+4hIULFzb5Goqi\nKErnRl2xSqMoLy9nypQZziW4CFhCvGvwZkQ8zQR6u31eB4prEdH2HmItG+UbZ33zLCLMZEr5H8BS\nRJgo6UB34DLEldorcN1rEeH4HjGx5mW5zgL+6u4XJIniY9+4ryFu2Z8jYvB64ACZmSE2b95BRcXp\nblzQ8vcNxL18AtXVIvDkOgMQ4Re/bo1108Z68crzeR0m1q9/ONBB4CFNXlAURVE6DCrsUoyYJckT\nLccmGfUBIvYKkGxXSF43bgXwMBKf9wMgll0aJkypy1wt4iiifIYIwOuAwYi17QvgdGJibQjiSv0I\ncckG6+xdDxQhlj3v9RxwMuIufgV4390LwGwOHhzgtmchgrIAWInU4MtHXND+RIZLECveRGAm+fnX\nBdatbjetX/jt3Vt3pq3WM1MURVE6LO1dIbkrvain84RX9T7WqL7Meg3s47sx+Ls+eC3C4ltgQb6v\nE8QE3/uhVtqE9bJrMXYt/V1HiWzXIaLExlqCha10r8ixsRZhXoeJXDcu2LXCa1s2ynev3n0PsNI9\nI3jOkMD53rESG+u8EbzGBQldGZJ1bMjOzo3rLuHvKBGJZNlIpH/ttraQUhRFUZoK2nlCSUa8tWki\nkk36VyRx4RdIzNwKxIJ2CCkM/Bu3nYeUK/Ga289E4uBOQzJgdxLLnn2VMMtcnTpDEZ8S5XEk4/MT\nN+Y3bp5BxEqYrEDKqni18nohcW/X+p7iWuBbiPt3idvnFUX2mJ3k6f2hCa+5+cGYX2Btsq/n60gy\nR8Ps29efSZOKa12rftdrZSXk5d1NTo4kpWjTdkVRFKUzoMkTbUhdyRNjxpzOli3ViJi6AhFqTyCx\ncmlIDTpPMC0iJpbGu38nkJhw8AaSqLAKyW6FMGMo5R/ACRSxiyhVSEmTXMS9WkOstMkPkKSLicRK\no0xHROcRwB6kYPGnwHB3D3cjrtTXkBItg90x775+gXSvOMGNXwPkAD8gFJrNWWeNZfNmeY6+fXux\nffvXiE+0uJb09Cqqqu4CYgkPhYWFCa5Yf328ggIRbxs2TMRf36+5iReKoiiKApo8oSShvLycl156\nlVgc2WREzN2BCKrpwJGIIKlGLGhLEAvaIWAfIpI8gbIGEXYDke4Sc4HBhLmGUp4DoIjurk6dRaxv\n7yGizk8FYk2bi4i/GnfdI4C9iIB7kvg4O6+l2Agkpu6XSMweSEeMCLEkj1mIxfELYAU1NZfy0hYr\npgAAGntJREFUpz/djVejb//+EjffGiTmbg95eSNZtmxRXB9Xz8pWWFjI2rVrmDJlBvv29XfneRZG\nbdWlKIqidBHa2xfclV4kibFLjA0b7Nv2HzvbF7NWEohf621huC8urZt7DbeQZcPMsmsJ27Wk2zCZ\nLmavu4WeLu6tn+9a49y+EhcvN9zN38PF3GW78/raWOyfd++rLRxdR8xcspi8kYExq33xcyU2FIrF\nFtYXA+fFJxYUXGCXLl0aF0vnP88/rr3j6VLpXhRFUZTmgcbYKY2nHMkgvRKJkQshVrxixKrnvfdY\nQSzWbiticdtLmCil3AlAEVlEsYil7lgkw3W/O/99d96HxNyxJwMbEavXCmLu4aHufv5FfDHkmYgl\nzyvFMs137GCSZ9yFZML6ed893x6GDRvIscfWHwOXmA073/V1TTwvVbJdG5PBqyiKoijNQWPs2pBk\nMXaJsWFXu397IOU+HkdcmCFEVA0G+iLu2Rfd2NGIWJMSICKoVhPGUsoBoIYiehClByL4RiHlSr7w\nzTuDWIzc+cT6zv4aEXlenN1uN4dXa+5Kdz89kdIor5GWtp20tENUVkZ8476P1MiL1agbOLA3+/d/\nFnj27njuWmNmMXr0ySxbtqhO0XPOOZM7XOxcR7xnRVEUJZFUjLELtfcNdHW82LC8vFX06rWISCQd\nCX28GXgGiaW7DRE7EeAt4HlE8C1yrw1IEgNIfN4vCFNJKZ8AORSR6YoPf4EkSkxDxNixSOuw49yc\ndyB159YhSQu3IRmoc5G6eBMQy9vNiCgpRhIiou4+voox2zjllBOIRHq64+vc65jAdjEjR45m7VoR\nNQUF68jNPcE9p8xt7W1s2VLNpEnFlJeXt8yCK4qiKEonRl2xKcIrr7xEZeVwxPKW5fZWI0kVQZer\n12/1SCRBAER8vQikEQZK+RSIUESIaK1+jyLWtrnErHXViFj0CvYOJj4R4zWky8M3gHvc/QUZiYi9\n3Vh7GVu2jCI9vQTpMvEzN+Zq0tLupbpakkQikXmUlNwf5x4dM+bMJHN3o6LiJpYvX5nUatcaSRFN\n6WDRHDSRQ1EURWktVNilAAsWLKGyMh3JgN2KCKhZSMZqXQxGBJ8nCGqAKsIMpJQ3gRBF9CfKQUTE\n9UFE4nXuvOeRLg6ea/Q9xI1b45tzrtsegWTaRpHyJPN99zHXzXcvfhF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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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IiIhIMqWkyTVKoU5EREQkKoWBDhTqRERERPZLaaADhToRERGRQIoDHSjUiYiIiKQ+0IFC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\nERGRCqCgTkRkGMzsq2b2oxzH7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Residential.ipynb b/code/svm_regression/SVM_RBF_Residential.ipynb new file mode 100644 index 0000000..707d8c4 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Residential.ipynb @@ -0,0 +1,572 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\fixes.py:64: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n", + " if 'order' in inspect.getargspec(np.copy)[0]:\n" + ] + } + ], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 82\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,:nvar-1]\n", + "y = dataset[:,nvar-1]\n", + "nvar = nvar - 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.55150000e+06 3.10941360e-01 3.98108300e-01 4.64200000e+01\n", + " 8.00000000e+00 9.00000000e+00 6.00000000e+00 9.42000000e+02\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 7.00000000e+00 3.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.20000000e+01 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 8.43400000e+03\n", + " 1.00000000e+00 2.50000000e+01 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n", + " 1.00000000e+00 1.09000000e+02 1.76100000e+04 1.38220000e+04\n", + " 1.10770000e+04 1.00210000e+04 1.08960000e+04 1.40240000e+04\n", + " 1.58800000e+04 1.41740000e+04]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "print( diff_X )\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-4\n", + "maxsigma=0\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0001, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0010, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.0100, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 0.0100\n", + " Cost = 17782794.1004\n", + " Relative Accuracy = 0.1190\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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BMcbYHnZ5xQVln0g6bkDFhWTfmv1kxWVkH6vO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\nSj4Htw0NQc3aRhMotOxMLaB52xjSk/NpHva4zdqGPm5yUg6fvLCZs27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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1190\n", + "Train set Accuracy: 0.0996\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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SCEZbPEFELIqID1S9Pg34NPBMYEFEvK+J7ZMkVRjqJI1i1GAHHAksr3r9FuCf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\nRVHX1raOsgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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woI3e3qsBqK3tYMOGzqJvTi+jp71fRUREiqXCAx3AqlVrw0DXBgThbtWqteWu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AywlNR+7rM9XL+xr2KN4Gv583jMrFb6uhGxmdmRtrc0f4j/D6SOKc0L+DGpPVxZuSau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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Residential_Assessed.ipynb b/code/svm_regression/SVM_RBF_Residential_Assessed.ipynb new file mode 100644 index 0000000..792ccc9 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Residential_Assessed.ipynb @@ -0,0 +1,543 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 1\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,0]\n", + "y = dataset[:,nvar-1]\n", + "nvar = 1\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i] = (X[i]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=-1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=4\n", + "maxcost=9\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=177827.9410\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=56234132.5190\n", + " Working on: Fold= 0.0000, Sigma= 0.1000, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=177827.9410\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=56234132.5190\n", + " Working on: Fold= 0.0000, Sigma= 1.0000, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=177827.9410\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=56234132.5190\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=177827.9410\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=56234132.5190\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=1000000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=177827.9410\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=56234132.5190\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=177827.9410\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=56234132.5190\n", + " Working on: Fold= 1.0000, Sigma= 0.1000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=177827.9410\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=56234132.5190\n", + " Working on: Fold= 1.0000, Sigma= 1.0000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=177827.9410\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=56234132.5190\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=177827.9410\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=56234132.5190\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=1000000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=177827.9410\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=56234132.5190\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=177827.9410\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=56234132.5190\n", + " Working on: Fold= 2.0000, Sigma= 0.1000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=177827.9410\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=56234132.5190\n", + " Working on: Fold= 2.0000, Sigma= 1.0000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=177827.9410\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=56234132.5190\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=177827.9410\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=56234132.5190\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=1000000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=177827.9410\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=56234132.5190\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=177827.9410\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=56234132.5190\n", + " Working on: Fold= 3.0000, Sigma= 0.1000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=177827.9410\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=56234132.5190\n", + " Working on: Fold= 3.0000, Sigma= 1.0000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=177827.9410\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=56234132.5190\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=177827.9410\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=56234132.5190\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=1000000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=177827.9410\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=56234132.5190\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=177827.9410\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=56234132.5190\n", + " Working on: Fold= 4.0000, Sigma= 0.1000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=177827.9410\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=56234132.5190\n", + " Working on: Fold= 4.0000, Sigma= 1.0000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=177827.9410\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=56234132.5190\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=177827.9410\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=56234132.5190\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=1000000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=10000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=177827.9410\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=56234132.5190\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=1000000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 1000.0000\n", + " Cost = 56234132.5190\n", + " Relative Accuracy = 0.1345\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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F2jrnhgPOez+7qQsfNwZ4GtgNC1TexVqSEq1DLwHLsK4wsDvZHgYOxG7hT7RN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\nA3bu8dmewEPA9NaylzXrfnhX2k8BJwEHAXP7fdxHWiaV72wMzAf2by1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1345\n", + "Train set Accuracy: 0.1293\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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twIcbUTFJ0igZ6qQxa7hgdzBwxRD7rwReU7/qSJK2iqFOGtOGC3bPBx4eYv+j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\nDoMJu+6AiTrDMAzDMJqBCbuujok6wzAMwzCaiQm7royJOsMwDMMwWoA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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xdQp1ZaJQJyISQ3WwbImUTtxCnbpfRUSkPinQSY1RqBMRkfqjQCc1SKFORETq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\nnUi8lfvDM9/rXXfeef4I5md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fNcvP2e/gESGiqWm2u48elCZPPrjgOpRK5vvX0HCQNzQc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\nAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/code/svm_regression/SVM_RBF_Residential_LatLong.ipynb b/code/svm_regression/SVM_RBF_Residential_LatLong.ipynb new file mode 100644 index 0000000..6cae013 --- /dev/null +++ b/code/svm_regression/SVM_RBF_Residential_LatLong.ipynb @@ -0,0 +1,552 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Import needed packages\n", + "#\n", + "import numpy as np\n", + "import time\n", + "import pandas as pd\n", + "import csv\n", + "from numpy import genfromtxt\n", + "from sklearn import cross_validation\n", + "from sklearn.cross_validation import KFold\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.learning_curve import learning_curve\n", + "from sklearn.svm import SVR\n", + "from sklearn import preprocessing\n", + "from sklearn.preprocessing import Imputer\n", + "from sklearn import metrics\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of data: 2598 \n", + "Number of variables: 2\n" + ] + } + ], + "source": [ + "#\n", + "# Read data\n", + "#\n", + "dataset = genfromtxt('../../data/realestate/realestate_residential.csv', delimiter=',')\n", + "dataset = dataset[1:,:]\n", + "\n", + "ndata=dataset.shape[0]\n", + "nvar=dataset.shape[1]\n", + "\n", + "X = dataset[:,1:3]\n", + "y = dataset[:,nvar-1]\n", + "nvar = X.shape[1]\n", + "\n", + "print('Number of data: %d \\nNumber of variables: %d' % (ndata,nvar) )" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.31094136 0.3981083 ]\n" + ] + } + ], + "source": [ + "#\n", + "# normalize the data attributes\n", + "#\n", + "min_X = np.amin(X,axis=0)\n", + "max_X = np.amax(X,axis=0)\n", + "diff_X = max_X-min_X;\n", + "print( diff_X )\n", + "nrm_X = np.zeros((ndata,nvar))\n", + "for i in range(ndata):\n", + " nrm_X[i,:] = (X[i,:]-min_X) / diff_X" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# define parameters for cross-validation\n", + "#\n", + "nfold=5\n", + "\n", + "minsigma=1\n", + "maxsigma=3\n", + "nsigma=5\n", + "\n", + "mincost=5\n", + "maxcost=8\n", + "ncost=5\n", + "\n", + "cvsigma=np.logspace(minsigma, maxsigma, nsigma)\n", + "cvcost=np.logspace(mincost,maxcost,ncost)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 0.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 1.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 2.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 3.0000, Sigma= 1000.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 10.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 31.6228, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 100.0000, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 316.2278, Cost=100000000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000.0000\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=562341.3252\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=3162277.6602\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=17782794.1004\n", + " Working on: Fold= 4.0000, Sigma= 1000.0000, Cost=100000000.0000\n" + ] + } + ], + "source": [ + "#\n", + "# 5-fold cross-validation to define parameters\n", + "#\n", + "i=0\n", + "kf = KFold(ndata, n_folds=nfold, shuffle=True)\n", + "cvape=np.zeros((nsigma,ncost,nfold))\n", + "for train, test in kf:\n", + " X_train=nrm_X[train]\n", + " y_train=y[train]\n", + " X_test=nrm_X[test]\n", + " y_test=y[test]\n", + " for j in range(nsigma):\n", + " for k in range(ncost):\n", + " print (\" Working on: Fold=%10.4f, Sigma=%10.4f, Cost=%10.4f\" % (i,cvsigma[j],cvcost[k]) )\n", + " svr_rbf=SVR(kernel='rbf', C=cvcost[k], gamma=cvsigma[j])\n", + " y_pred=svr_rbf.fit(X_train, y_train).predict(X_test)\n", + " cvape[i,j,k] = np.mean ( np.abs((y_test - y_pred)/y_test) )\n", + " i = i+1 \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Mean absolute percentage error\n", + "#\n", + "cvmape=np.mean(cvape,axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Get indexes of parameter combination with minimum error\n", + "#\n", + "idxsigma, idxcost = np.unravel_index(cvmape.argmin(), cvmape.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best parameters: \n", + " Sigma = 1000.0000\n", + " Cost = 100000.0000\n", + " Relative Accuracy = 0.1783\n" + ] + } + ], + "source": [ + "#\n", + "# Print out Results\n", + "#\n", + "optsigma=cvsigma[idxsigma];\n", + "optcost=cvcost[idxcost];\n", + "print(\"Best parameters: \\n Sigma = %10.4f\\n Cost = %10.4f\\n Relative Accuracy = %10.4f\" % (cvsigma[idxsigma],cvcost[idxcost],cvmape[idxsigma,idxcost]))" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:387: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", + " \"matplotlib is currently using a non-GUI backend, \"\n" + ] + }, + { + "data": { + "image/png": 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HH6Xw9tvJ+N+rSUNmwYjrKHrzTTLeeYuUDu03e11bhSa+T1hask9oUXqfuOlT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\nn7NDRtYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "# Plot results for all combinations tested\n", + "#\n", + "matplotlib.rcParams.update({'font.size': 14})\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.imshow(cvmape,interpolation='nearest')\n", + "for i in range(nsigma):\n", + " for j in range(ncost):\n", + " ax.text(i,j,(\"%.4f\" % cvmape[j,i]), va='center', ha='center')\n", + " \n", + "fig.suptitle('Average Absolute Percentual Error', fontsize=20)\n", + "plt.xlabel('Cost')\n", + "plt.ylabel('Sigma')\n", + "\n", + "plt.xticks(range(ncost),np.char.mod('%.2e', cvcost))\n", + "plt.yticks(range(nsigma),cvsigma)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#\n", + "# Run in all data set with optimal parameters\n", + "#\n", + "svr_rbf=SVR(kernel='rbf', C=optcost, gamma=optsigma)\n", + "y_pred=svr_rbf.fit(nrm_X, y).predict(nrm_X)\n", + "trainmape = np.mean ( np.abs((y - y_pred)/y) )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation Accuracy: 0.1783\n", + "Train set Accuracy: 0.1422\n" + ] + } + ], + "source": [ + "#\n", + "# Report Results\n", + "#\n", + "print(\"Cross-Validation Accuracy: %10.4f\\nTrain set Accuracy: %10.4f\" %(cvmape[idxsigma,idxcost],trainmape))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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BnwV2ysz1EXEyRVCbnZmPlTVnACdn5pzy/UeBV2Tm3pXruBDYLzMPHeYas533W5KkzRjq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\n/l4nRWaYDxwhK+sv3H//D6r3FhVdR3b2bNQd/DEqEINCw4W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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#\n", + "#\n", + "#\n", + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,np.max([axx[1],axy[1]]))\n", + "plt.ylim(0,np.max([axx[1],axy[1]]))\n", + "fig.suptitle('Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", + " if self._edgecolors == str('face'):\n" + ] + }, + { + "data": { + "image/png": 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CHSjUiYiISC/LSaADhToRERHpVTkKdKBQJyIiIr0oZ4EOFOpERESk1+Qw0IFCnYiIiPSS\nnAY6UKgTERGRXpHjQAcKdSIiItILch7oQKFORERE8q4HAh0o1ImIiEie9UigA4U6ERERyaseCnSg\nUCciIiJ51GOBDhTqREREJG96MNCBQp2IiIjkSY8GOlCoExERkbzo4UAHCnUiIiKSBz0e6EChTkRE\nRLJOgQ5QqBMREZEsU6CboVAnIiIi2aRAV0KhTkRERLJHga6MQp2IiIhkiwJdIoU6ERERyQ4FuooU\n6kRERCQbFOiqUqgTERGR9FOgq0mhTkRERNJNga4uCnUiIiKSXgp0dVOoExERkXRSoGuIQp2IiIik\njwJdwxTqREREJF0U6JqiUCciIiLpoUDXNIU6ERERSQcFullRqBMREZHuU6CbNYU6ERER6S4FupZQ\nqBMREZHuUaBrGYU6ERER6Q4FupZSqBMREZHOU6BrOYU6ERER6SwFurZQqBMREZHOUaBrG4U6ERER\n6QwFurZSqBMREZH2U6BrO4U6ERERaS8Fuo5QqBMREZH2UaDrGIU6ERERaQ8Fuo5SqBMREZHWU6Dr\nOIU6ERERaS0Fuq5QqBMREZHWUaDrGoU6ERERaQ0Fuq5SqBMREZHZU6DrOoU6ERERmR0FulRQqBMR\nEZHmKdClhkKdiIiINEeBLlUU6kRERKRxCnSpo1AnIiIijVGgSyWFOhEREamfAl1qKdSJiIhIfRTo\nUk2hTkRERGpToEs9hToRERGpToEuExTqREREpDIFusxQqBMREZFkCnSZolAnIiIi5RToMkehTkRE\nREop0GWSQp2IiIgUKdBllkKdiIiIBBToMk2hTkRERBTockChTkREpNcp0OWCQp2IiEgvU6DLDYU6\nERGRXqVAlysKdSIiIr1IgS53FOpERER6jQJdLinUiYiI9BIFutxSqBMREekVCnS5plAnIiLSCxTo\nck+hTkREJO8U6HqCQp2IiEieKdD1DIU6ERGRvFKg6ykKdSIiInmkQNdzFOpERETyRoGuJynUiYiI\n5IkCXc9SqBMREckLBbqeplAnIiKSBwp0PU+hTkREJOsU6ASFOhERkWxToJOQQp2IiEhWKdBJhEKd\niIhIFinQSYxCnYiISNYo0EkChToREZEsUaCTChTqREREskKBTqpQqBMREckCBTqpQaFOREQk7RTo\npA4KdSIiImmmQCd1UqgTERFJKwU6aYBCnYiISBop0EmDFOpERETSRoFOmqBQJyIikiYKdNIkhToR\nEZG0UKCTWVCoExERSQMFOpklhToREZFuU6CTFlCoExER6SYFOmkRhToREZFuUaCTFlKoExER6QYF\nOmkxhToREZFOU6CTNlCoExER6SQFOmkThToREZFOUaCTNlKoExER6QQFOmkzhToREZF2U6CTDlCo\nExERaScFOukQhToREZF2UaCTDlKoExERaQcFOukwhToREZFWU6CTLuhoqDOzk8zsGjO7w8z2mtl4\nbP0V4ePR5frYNvua2SVmdp+ZPWRmXzKzJ8W2WWRmnzWzB8PlM2a2ILbNIWb25fA17jOzj5nZPrFt\njjGza83skbDM7044plEzu8HM9pjZbWb2htmfKRERySwFOumSTtfUHQD8EHgrsAfw2HoHdgIrIssf\nxrb5KLAROA14HjAf+IqZRY/lSuA4YAx4AfBM4LOFlWY2B/hqWJ7nAqcDLwO2RLaZH5blLuCEsMzv\nMLOzI9scDnwN2B3u73zgEjPbWP8pERGR3FCgky4y93iu6tCOzX4L/IW7fyby2BXAYnd/cYXnLADu\nBV7j7p8PH1sJ3A680N13mNnTgVuBNe7+nXCbNcC3gSPc/adm9kLgK8Ah7n5nuM0rgL8Dlrr7Q2Z2\nJkFIW+7uvw+3ORc4091Xhr9fALzU3Y+IlPEyYJW7PydWdu/WuRYRkQ5QoOs5Zoa7W7fLUZC2PnUO\nPNfM7jGz/zSzrWa2NLL+eGAfYMfME9zvAH4EnBg+dCLwUCHQha4HHgaeE9nmPwqBLrQD2DfcR2Gb\nbxcCXWSbg8zs0Mg2Oyi1AzghrA0UEZFeoEAnKZC2UDcFvAp4PjABPBv4hpkNhOtXAE+4+wOx590T\nritsc190ZVhFdm9sm3tir3E/8ESNbe6JrANYXmGbfmBJ4hGKiEi+KNBJSvR3uwBR7v6PkV9vNbMb\nCJpW/wjYXuWpzVR91npOy9tKzzvvvJmfTz75ZE4++eRW70JERDpJga6n7Nq1i127dnW7GBWlKtTF\nuftdZnYH8JTwobuBOWa2OFZbtxy4NrJNtMkWMzNgWbiusE1JnzeCmrU5sW1WxLZZHllXbZvHCWr+\nSkRDnYiIZJwCXc+JV8i8973v7V5hEqSt+bVE2J/uSQQjUAFuAB4D1kW2WQkcSdBvDuA7wFwzOzHy\nUicSjHQtbHM98PTYVChrgd+H+yi8zvPMbN/YNne6++2RbdbGir0W+K67P9HAoYqISJYo0EkKdXT0\nq5kdADw1/PU64G+ALwMPAL8E3gt8gaAG7DCC0adPAp7u7g+Hr/EJ4MXAa8LnXAgsAI4vDC81s68B\nK4EzCJpZtwL/7e4vCdf3AT8g6Hs3QVBLdwVwtbu/NdxmPvCfwC5gEjgCuBw4z90vCrc5DLgFuCzc\nxxrg48Bp7l7SXKzRryIiOaFAJ6G0jX7tdKg7GfhG+KtT7Nd2BfAm4J+BEWAhQe3cN4B3R0ephoMm\nPgL8KTAI/Avwptg2C4FLgPXhQ18C3uzuv4lsczDwCYJBGXuAzwHvcPfHItscTRDSnk0QIC919/fH\njukk4CJgFXAncIG7b004doU6EZGsU6CTiJ4Odb1MoU5EJOMU6CQmbaEu1X3qREREUkGBTjJAoU5E\nRKQaBTrJCIU6ERGRShToJEMU6kRERJIo0EnGKNSJiIjEKdBJBinUiYiIRCnQSUYp1ImIiBQo0EmG\nKdSJiIiAAp1knkKdiIiIAp3kgEKdiIj0NgU6yQmFOhER6V0KdJIjCnUiItKbFOgkZxTqRESk9yjQ\nSQ4p1ImISG9RoJOcUqgTEZHeoUAnOaZQJyIivUGBTnJOoU5ERPJPgU56gEKdiIjkmwKd9AiFOhER\nyS8FOukhCnUiIpJPCnTSYxTqREQkfxTopAcp1ImISL4o0EmPUqgTEZH8UKCTHqZQJyIi+aBAJz1O\noU5ERLJPgU5EoU5ERDJOgU4EUKgTEZEsU6ATmaFQJyIi2aRAJ1JCoU5ERLJHgU6kjEKdiIhkiwKd\nSCKFOhERyQ4FOpGKFOpERCQbFOhEqlKoExGR9FOgE6lJoU5ERNJNgU6kLgp1IiKSXgp0InVTqBMR\nkXRSoBNpiEKdiIikjwKdSMMU6kREJF0U6ESaolAnIiLpoUCXK9PT06xbt4l16zYxPT3d7eLknrl7\nt8vQE8zMda5FRKpQoMuV6elpNmwYZ8+eCwAYHNzM9u3bGBsb63LJWsfMcHfrdjkKFOo6RKFORKQK\nBbrcWbduEzt3rgfGw0e2sXbtNezYcXU3i9VSaQt1an4VEZHuUqATaYn+bhdARER6mAJdbk1MnMHu\n3ePs2RP8Pji4mYmJbd0tVM6p+bVD1PwqIhKjQJd709PTbNmyFQhCXp7600H6ml8V6jpEoU5EJEKB\nTnIgbaFOfepERKSzUhroNP2GZJ1q6jpENXUiIqQ60OV9+g1pvbTV1CnUdYhCnYj0vJQGOuiN6Tek\n9dIW6tT8KiIi7ZfiQJd2ahaWemlKExERaa8MBLq0Tr8RbxbevXtczcJSkZpfO0TNryLSkzIQ6ArS\nOP2GmoXTLW3Nr6qpExGR9shQoAMYGxtLRZCDYsC84YabgMO7XRzJCIU6ERFpvYwFujQpbXJdD5wV\nrjkmNc3Ckk4KdSIi0loKdLOyZcvWMNCNzzw2NPR+jj/+Z0xMqD+dVKZQJyIiraNA1xbHH3+s+tFJ\nTQp1IiLSGgp0LZHWkbiSfhr92iEa/SoiuaZA11JpHIkr5dI2+lWhrkMU6kQktxTopEelLdTV3fxq\nZvsCBwGDwH3ufl/bSiUiItmgQCeSGlVvE2Zm883sTWb2beA3wG3ALcA9Zva/ZnaZmT27EwUVEZGU\nUaATSZWKoc7MzgZ+BrwW2AG8BDgOOAI4ETgP2AfYYWZTZvbUtpdWRETSQYFOJHUq9qkzs/8LvM/d\nb6n6Amb7AX8OPOrul7W+iPmgPnUikhsKdCJA+vrUaaBEhyjUiUguKNCJzEhbqKvapy7OzJaY2eJ2\nFUZERFJMgU4k1WqGOjNbbmZXmNmDwL3AfWb2KzP7ezNb1v4iiohI1ynQiaRe1eZXMzsA+D4wBPwD\n8CPAgKOAPwXuB57p7g+3v6jZpuZXEcksBTqRRGlrfq01T91bCEa4Hu3ud0dXmNkHge+E2/xNe4on\nIiJdpUAnkhm1ml9fDJwfD3QA7n4X8MFwGxERyRsFOpFMqRXqjgS+XWX9dcDTW1ccERFJBQU6kcyp\nFermA7+ssv6X4TYiIpIXCnQimVQr1M0BqvXu31vHa4iISFYo0IlkVq2BEgC7zOyJWTxfRESyQIFO\nJNNqhbL31fEamqdDRCTrFOhEMk+3CesQzVMnIqmlQCfSlLTNU9d0fzgzGzSz15rZ7lYWSEREOkiB\nTiQ3Gg51ZvZsM9sK3A1cCNzW8lKJiHTQ9PQ069ZtYt26TUxPT3e7OJ2jQCeSK3U1v5rZEPAq4M+B\nYWAQOAP4jLs/2tYS5oSaX0XSaXp6mg0bxtmz5wIABgc3s337NsbGxrpcsjZToBOZtUw1v5rZqWZ2\nFXAH8FLgIuBA4AngegU6Ecm6LVu2hoFuHAjC3ZYtW7tdrPZSoBPJpVqjX6cImliPdPf/KTxolppQ\nKiIijVCgE8mtWqHua8CbgMPN7HPAV9398fYXS0SkMyYmzmD37nH27Al+HxzczMTEtu4Wql0U6ERy\nrWrzq7uvB54K3Ah8BLjbzD4BqKpORHJhbGyM7du3sXbtNaxde01++9Mp0EmG9exgpgbVPU+dBW2u\no8DrgE3AvcA/AV9w939tWwlzQgMlRKRrFOgkw9I8mClTAyWiPLDL3V9JMFjiQ8DzgevaVTgREZmd\n6z71KR44/gQ+uORgpoeGul0ckYb15GCmJjU1+bC7P+juH3f3ZwLPanGZRESkBa771KcYfuObePOj\nr+XcW/6CDRvG1XQlkmO1pjQ52sy+YmbzE9YtMLOvEExvIiIiaXLLLRx51lt5G2dwFZ9ENRySVRMT\nZzA4uBnYBmwLBzOd0e1ipVKtmroJ4Ifu/pv4Cnf/NfB94J3tKJiIiDQp7EP3qac9g6tY3e3SiMxK\nzwxmaoGqAyXM7KfAae5+Q4X1zwT+r7s/pU3lyw0NlBCRjogMipgeGkptB3ORPEjbQIlaoe53wBHu\nfnuF9YcBP3b3/dpSuhxRqBORtksY5To9PT3T5DoxcYYCnUgLZS3U3QW80t2/XmH9qcDn3H1Fm8qX\nGwp1ItJWmrZEpOPSFupq9an7FvCXVdb/ZbiNiIh0iwKdiFA71J0PrDOzfzaz1eGI1wVmdqKZfQlY\nC/xN+4spIiKJFOhEJFTzjhJm9iLgcmBxbNX9wOvc/Zo2lS1X1PwqIi2nQCfSVWlrfq3rNmFmtj8w\nRnAfWAN+Aky7+yPtLV5+KNSJSEsp0Il0XSZDncyeQp2ItEwOAp1G5Uoe5CLUmdkfA2uA77v7Fa0u\nVB4p1IlIS+Qk0Gn+PMmDtIW6mvd+NbNtZvbByO+vBT4HPAO4xMze28byiYhIQQ4CHegG7SLtUjPU\nAc8BdkR+fzPwNnf/A+DlwGvbUTAREYnISaATkfapGOrM7HIzuxw4GDgr8vuxwKnhz68ADoqsExGR\nVqsj0E1PT7Nu3SbWrdvE9PR0hwvYGN2gXbIgS/+nCir2qTOzQwlGun4HOBP4PnAS8AHgeeFmc4F/\nA1aFr/XzNpc3s9SnTkSaUmegy1ofNQ2UkDSr9/9U2vrU1TNP3XZgBfAJ4CzgO+5+VrjuWcCn3f2Y\ndhc06xTqRKRhdTa5rlu3iZ071xP0UQPYxtq117Bjx9UdKWav6XQgVQDuvHr/T6Ut1PXXsc3ZwGcI\nQt11QHRgxBuBL7ehXCIivU196FIpXoOze/d4W2tFO70/ybaaoc7df0axuTW+7s9bXiIRkV7XYKCb\nmDiD3bt5b0vMAAAgAElEQVTH2bMn+D3oo7atzYXsTaUjd2HPnuCxdoWsTu9PAln9P1VPTZ2IiHRK\nEzV0Y2NjbN++LdJEp5ockdnI6v+pagMl3g1c5O4P1XwRs+cCQ7oPbGXqUyciNaW8yVV9uzo/KCWL\ng2B6Sdr61FULdZcDLwG+QNBv7nvufle4bj/gKIJm2VcAS4FXu/u3O1HoLFKoE5GqMhDoFC4CGigh\nBZkJdQBmdgzwFoJJhhcADjwGDISb3AhsBba5++/bW9RsU6gTkYpSHuhAI2xFkqQt1FW9o4S73+zu\nZwBLgOOBTQR3kBgDlrv7Ce6+VYFOJP+yOBFnJrQ50Ol9E+kdNeepk9ZQTZ1kmZre2qQDga5V75s+\nAyLl0lZTp1DXIQp1kmVqemuDDjS5tvp9U98ukVJpC3Wa0kREpNMy0IcuydjYmIKcSIop1IlITVmd\niDOVOhjo9L6J9BY1v3aIml8l69T01gJdqKHT+ybSPmlrflWo6xCFOpEel9EmVxGpLG2hrmLzazj5\ncCGFWOTnMu7+Zy0ul4hIfijQiUgHVJunbmlkWUIwR90G4CnAU8OfN4Xr62JmJ5nZNWZ2h5ntNbPx\nhG3OM7M7zewRM/ummR0VW7+vmV1iZveZ2UNm9iUze1Jsm0Vm9lkzezBcPmNmC2LbHGJmXw5f4z4z\n+5iZ7RPb5hgzuzYsyx3hrdPi5R01sxvMbI+Z3WZmb6j3fIhID1CgE5EOqRjq3P1F7v5id38xcD0w\nDax095Pc/XnASmAK+NcG9ncA8EPgrcAeYrV/ZrYZOBt4M/As4F5gp5nNjWz2UWAjcBrBbcrmA18x\ns+ixXAkcRzBJ8guAZwKfjexnDvDVsDzPBU4HXgZsiWwzH9gJ3AWcEJb5HWZ2dmSbw4GvAbvD/Z0P\nXGJmGxs4JyKSVwp0ItJBdfWpM7O7gVPc/dbY46uAr7v7ioZ3bPZb4C/c/TPh7wb8ArjY3c8PH9uP\nINi93d23hrVt9wKvcffPh9usBG4HXujuO8zs6cCtwBp3/064zRrg28AR7v5TM3sh8BXgEHe/M9zm\nFcDfAUvd/SEzO5MgpC0v3DHDzM4FznT3leHvFwAvdfcjIsd1GbDK3Z8TO171qRPpJQp0IrmXtj51\nVW8TFnEAcFDC4weG61rhcGA5sKPwgLv/DvgWUAhIxwP7xLa5A/gRcGL40InAQ4VAF7oeeDjyOicC\n/1EIdKEdwL7hPgrbfDt2C7QdwEFmdmhkmx2U2gGcENYGikgvUqATkS6oN9RdDVxuZqeb2WHhcjrw\naeCLLSpLobbvntjj90bWrQCecPcHYtvcE9vmvujKsIos/jrx/dwPPFFjm3si6yAIoUnb9BP0QxSR\nXqNAJyJdUu/kw28CPgJcDgyEjz0G/D3w9jaUK65Wu2UzVZ+1nqO2UhFpjAKdiHRRXaHO3R8B3mRm\n7wSGw4dvc/eHWliWu8N/lwN3RB5fHll3NzDHzBbHauuWA9dGtikZkRv211sWe52SPm8ENWtzYtvE\n+wouj5W10jaPE9T8lTjvvPNmfj755JM5+eST45uISFYp0Ink3q5du9i1a1e3i1FRQ5MPm9kSglB3\nU9jfrfkdJw+UuBO4JDZQ4h6CgRKX1Rgo8QJ331lhoMRzCEaoFgZKvIBg9Gt0oMSfEtQ8FgZKvBG4\nAFgWGSjxVwQDJQ4Of/8bYENsoMRWgoESa2LHq4ESIhG5utOBAl3PytXnWBqWtoESuHvNBZgH/BOw\nl6Df2ZPDxy8FzqvnNcLtDyCY+uM4goEL7w5/Pjhc/07gQYI58I4GriKotTsg8hqfAP4XOAUYAb4J\n3EgYUMNtvkYwdcpqgsEMNwNfiqzvC9d/Pdz/qeF+PhbZZj7BdCafB1YRTKPya+BtkW0OAx4CLgKe\nDrwO+D1B0Isfu4tIYGpqygcHlztc4XCFDw4u96mpqW4Xqzk33+y+YoX7lVd2uyTSYbn6HEtTwmt7\nXRmoE0u9YewTBCNIjwtDTCHUvQj4Yd07g5PDYFgIh4WfPx3Z5j0EU5vsCQPbUbHXGAAuJmjefBj4\nEvCk2DYLCeal+3W4fAaYH9vmYODL4WvcTzD/3T6xbY4maNbdQ1CL+O6EYzoJuAH4HXAbcEaFY2/i\n4yKST2vXbgwvhB4uV/jatRu7XazGKdD1tNx8jqVpaQt19Q6UWA9sdPcfmFm0DfHHwJPrfA3cfRc1\nRty6+3uB91ZZ/yhwVrhU2uZB4FU19vO/wItrbHMLMFpjm29RnAZFRHqFmlxFJGXqDXWLgPg0IhA0\nyz7RuuKISC+YmDiD3bvH2bMn+H1wcDMTE9u6W6hGKNAJOfgcS+7Ue0eJa4F/dveLwgEOx7r7f5vZ\nJ4HD3P2F7S5o1mmghPSKejuOZ7aDuQKdRGT2cywtkbaBEvWGuucQ3Pv1H4FXApcR9Dd7NnCSu9/Q\nzkLmgUKd9ILp6Wk2bBhnz54LgKDmYvv2bfm50HUx0Ck8iKRP2kJdXXeUcPfrCeZ1GyAYDHAKwcCB\n1Qp0IlKwZcvWMNCNA0G4KwSRzIsEuumhIdat28S6dZuYnp5u+64LYXnnzvXs3LmeDRvGO7JfEcmW\nevvU4e43A69uY1lERNIpFuiitZG7d4+3vTayNCzDnj3BY6qtk16m2utyddXUmdkTZrYs4fElZqaB\nEiICBF+sg4ObgW3AtrDj+Bkl20xPT3e0lqtZhXKe8ZxT+f1JJ800ueapNjIr74VInGqvK6hn3hOC\nueSWJTx+ELCn2/OyZGFB89RJj5iamvK1azf62rUbyyZizcpkrYVyrmLSf8ECf/U+82fK2Y25ydpx\n3rLyXiSp9hmT3pCWOQLJ0jx1ZjYR+fXMcORrwRyCiXf/s4UZU0QybmxsrGIzSFaaEbds2cqT97yF\nnfwtZ/NJrnrsUe4Ky9mNaSzGxsbYvn1bpKlp9s29WXkv4uKDcTrR/C2SFbX61L0FKAzZ/HNK56R7\nFPg58IbWF0tEpHsOe+jXbOPDQaDjdILm5EA7AlY9qoXlXpLVMCqtpTkCk1UNde5+GICZ7SK4n+mv\nOlAmEcmpTHwR33ILl/z4Rs7Yx7nqsUcp9g0sDXbxEJG1TtuZeC+kaVn7PDaqW39cpV632397ZUF9\n6kTcPeX9oSL3cm2knGnun1arj2M33ovZ7DfN5zotdI46h5T1qWsklBwBnAtcCnw6XC4HPt3tg8jC\nolAn9Up16MmzSKBrVFo6bcel8eLeijLp/0h1af08dkKnPxtpC3V1zVNnZn8EfBG4ETgB+HfgKcC+\nwLdbWHEo0tPUCbxLcnrrrzT2P2tFmdS/UJLo+7P+yYffB7zX3T8YjoB9NcEdJT4HXN+uwon0mjRe\nhHOvBYFO/dMkTXr186jvzzonHyZoer0q/PkxYNDdfwe8F/jLdhRMRHpXxybFbVENXaHT9tq117B2\n7TWpqR2oZzJolSl/0vp5lA6op40WuAtYFf58K8FIWIAR4KFutyFnYUF96qQOaewD1WkdOwdhH7of\nbN6c6/5Zaex/lsYySfZ14/uTlPWps6BM1ZnZl4CvuvtWM/sQ8DKCP7M2Ave6+9rWx818MTOv51yL\n5H0qglrWrdvEzp3rKTShQFDjsGPH1a3bSVhDd9P4OCdefMVMH5zBwc2pqdWIfg5GR5/JtdfeCGTj\nM9Hrn2Hpnk5/9swMd7e27qQR9SQ/YBh4RvjzAcAngR8CXwAO6XYyzcKCaupE6tKOkXuTk5M+NDTs\nQ0PD/qm3vGVmlGtaRwnGaxxgvsNEJmpv01TbrBpBaTdSVlPX9QL0yqJQJ71ithfSVoeCycnJMBRd\nEd7L1fyLL3+5u1cPkN0MBEnlgo2pCp6VpCUopylcSn6lLdTVO1BihpntZ2b7R5eWVBmKSOYVphTY\nuXM9O3euZ8OG8YYHOtTTybuRgRQXXng5cDGrOD68l+sbeN3Xi02ZSZ32W3EcUnT//Q90ZuBLROlI\nyGCai0KznEhu1ZP8gMOAa4DfAntjyxPdTqZZWFBNnfSATtTSNFIDMzU15XPmLA1r6Fb4aVzpcIUP\nDQ2XbBOvket2DV6eml8HBpb6wMDCjteYpaXGUPKNlNXU1TtP3WeB/YA3A/cC6vEvkjF56bxe71xU\n09PTrF//Ko584lh28m7O5g1cxaPAWZx99jtntmtkIttOTW4av6/l6Og7ufrqndx++z9z6KFPaem+\nWi1e9vvvfxrf//7r6fTcYb06V5v0uHqSH/AQcFS3E2iWF1RTJ13Uqf5FndhPvTUwa9dujNTQvdxh\npcMSHx4+qsHjmPC+vsU+MjLqIyNrulL7k+X+Yd2sMdNACWk3UlZTV28guR4Y7XZhs7wo1Ek3dfLC\n2u4Lab0B5/UnnuK/YEHY5Orh9qvrPu6pqSkfGVnjfX2LZvYV/DzR8YCS5abELAdSkVrSFurqbX49\nA7jYzC4Gbia4q0S0tu9/WlFrKCLtcf/9D9T1WCu0+76c8ea9iYmE5s9bbuGSH9/I6+bs5aonHiUY\nCPF2BgYeZ3T0Raxbtyl8buVm6LGxMbZs2crevcWmw717oa9vgr17jwHUpFePut6vDMhL9wXJt3pD\nnQHLgC8mrHNgTstKJCJt8Djw9sjvbye4+182xYNj9IL7nk3rWHPeeez78Y/zyqEhbj3nfG6//Q4O\nPfQINm16IR/4wCWz6hN37LFHs2TJNUBpQGnnRT/r/cPaHfTbTTeKl8yopzoPuBH4BvBHwLOAE6JL\nt6sbs7Cg5lfpoqD5biKc6yz4uRXNd2nosxRt3lvFpN9Fn/9g8+bEbRttxqy36bATTYzj4+Pe37/M\n+/uX+fj4eN3PS8N7lHVZbv6W9iKjza9HAiPu/p+tDpUi0n7Fmp5XAtfR13cto6Nvm9VrpqX2ojAa\nNpiH7l28jTN44MafsqMFr11v02G9I3Lj6q3d+8AHPsC2bduBiwHYtu0snvrUp3LuuefWfP00vEci\n0iH1JD/gWuAF3U6gWV5QTZ102eTkZEmn/9nWJqWl9qJ0lOuVVcvRrhq1Zs5FI2UZGhoue/3oXHut\nKFezNXq9UBOowR6dk7XPEymrqas3kPwJ8CPg9cD/BzwzunT7ILKwKNRJt7V6Qt20hLrdl17qd9Hn\np/HGihfc6PFNTk62/KLRzEW/kfPX7lBXLP+Ew2rv61vsk5OTsz7ubl+gW7n/bh5Lt89jp2QxPGc1\n1MXvIqE7SijUScZUusA3+0Xaqi/gWV2wbr7ZfcUK/8HmzT4yMupDQ8M+MrKmLFh0ao6+Ro6jkVAX\nvX9t4Q4T9YaugYGlM88bGFiaWLZin8vieerrWzSrYNrtC3S3998qeTmOeqTlD8VGZDXUHVZt6fZB\nZGFRqJPZmu1f65UuDrOpwWtXmWo9Z+3ajf76E0/x3y1a5H7llVXDSxouFEnnqdFjn5yc9KGhYR8a\nGq4Z6Ar7GxkZ9b6+fT2YeHml9/cfUCXUrS47TyMjo1X3U+3ctvq8tzM0d1ojx5Lm42i1LB5rJkOd\nFoU66a521oq1ugavEc2ORg360C3wV+8zP5wkeLRiIKm1j25OltyOfcfvhlFaw7fER0bWJD6nr29x\n2Xnq61tctVzVjq2VF+h2N293UqPHktbjaIcs1kpmJtQBG4GByM8Vl24fRBYWhTqZjXZ+sTdTgxd/\nfrPBpNHjqjQoolqfs1qhqlUXkUrnoVMX5cL+g3NRuOtF+b4r9cULmngXRAJg0MeungEfScfdynPb\nzICPkZFRHxhYmLqA0K5pdfIia/0HsxTq9gLLIj9XXLp9EFlYFOpkNtodDBqpwYs/bzYXnEafn3Tr\nr+ACvsZhScUaqUpNl606r52qsap3/8G5mEoMddWaVIPzuDp83lTdZa0W7FpxgW58wEexGX5kZE2q\nAkIzn4esBZ1ekplQp0WhTmavVV/G3fhrvZ59tiKwVDtH0XW7L73Uf7dokb96n/llZQr61C0MA8lq\nHxhYWFdtXKsCVycHDNQbwINzUdr8WmmgRPS1m+njWO8Ezc3+X6h3H1loqkxjzZtCY/MyGeqAk4B9\nEh7vB07q9kFkYVGo6z2duJg3s00r9lna1Nf+ZuHonSIarRXqRODqVL+9RprKh4aGfe3a+qdwiTZb\nNlK71Yka3Wj5qpUrC6HOPV0hKo0hM0uyGupmmmJjjy9R86tCnSTr9AWmU1/O1TriDw4urxgiGg0N\nhfO3ipvDPnRvbHktWrRcs7nAdurcVzqWTjeD11OmRrdpBQWUxmUlCKdV3kLd04DfdPsgsrAo1PWe\nZjp3zyZYdOrLuXw/E97fv8xHRkZ9cnIy8aKa3OdroupEwUNDw76K8bruFFFNpy70jUw50qxqfd46\nOWAlqlPN9PVKUy1YFijUzU6mQh3w5XDZC0xHfr8G+BrwP8B0tw8iC4tCXe9ppK9RJ5oAW6VS/63B\nweWJU4sULrDlz9mYWGsWbXL9Bean8YezDmPtvtB3IjgG/QaLc/HBkpK+g7Mx289OrfOrGrT00nsz\nO1kLdVeEy17gqsjvVwBbgXOAJd0+iCwsCnW9qbl+QBMz/aE61YTWSPnLa92Wz9QaVepjV2+oS2py\nbeZc1HMcrdSJQJ20j1qTA9erU6FUNWjppPemeZkKdTMbwXnAAd0ubJYXhbps6eSXXOnFesqjU3M0\n0zeq0u2yGlXtQj81NRUGuNVhmQshY82sml/Xrk2eh242qo2MbZVmQ10jn7N2B0dd2DtP5zz7shrq\n5gBzIr8fCLwOWNPtA8jKolCXHZ1ujijdX/mtmhptBmvthK8TYY1a8HOlptJ4gGtkoES0L9pfrV/v\nd9Hnp/HGhstfab+15rBrhWbOe6PP6eRAGAWN9lOzZz5kNdRNAW8Nf54L3AH8CngcGO/2QWRhUajL\njm50HI4ODpjNvltZ9nrCUKXm2XpDQfRG9YU+dJ943vMaDhXVLpDV7jbRSo2GoWbeq3r6rs0mkClo\ndI4GKORDVkPdfcAzwp9fDfwI2Ad4DfDDbh9EFhaFuuzo5pftbC+qrQ11o3X34YrWwtV7a6Yg0C32\npD50jSpvwl7tQ0PDM83R7eqLNhut/py1IpApaHSOznU+ZDXU7QEODn/+HPDB8OdDgUe6fRBZWBTq\nsqPbtRWzqW1pffNr47dmKt6iqvJzijV0q8NpS/b103iWFwaJ1DrG+PkplnXKg4EbpfPmRUeN1rqr\nQqe0+nPWipCgoNE53f6ekdbIaqj7CXB62PR6H/AH4eMjwP3dPogsLAp12eqrk6WyxrWq7PVedCqP\nbE0OBVNTU97fvywMdOvDaUveGD5vvo+PjzdcpuLjyX0S0/p+trJcs51rLqhpXVMSgBU02iutn0up\nX1ZD3RuAx4AHgZsKgyaAtwLf6PZBZGHp9VCnv0qzqbkpWYoDPuLv8+TkpPf1LfJoH7pgHrric4eH\nj6tYnmrBpTgit7X91LKi2f9j8ecNDCz0kZHRzJ8PkU7IZKgLys0JwEZgbuSxP9IIWIW6eqhZJ7/K\nQ8HSxNuATU1NeV9feR+6IAAWPxf9/csq7qvW5yitI0o7pZmAqv+bIs1LW6jrp07u/j3ge7HHvlrv\n80Ukn8bGxti+fRtbtmwFYGLis4yNjZVtt2XLVvbufSqruIOdvIuzuZCreJRgXvNt4VZvZ599rOy5\n09PTnHPO+fzXf/0Ms524fxm4k76+nzI6+rYqZdmWWJZomfbsuQAYB2DPnuCxas+pZHp6OrLfM5p6\njdkaGxtryX7vv/8e1q3bBHTvWKRz0vDZlRaplviA64GFkd/PBxZHfl8K/E+3k2kWFnq8pi5vNSJp\nlPZmxGBi4XH/RWQeumBQxf4OR4Y1dvv7gQceUnIsxYEOE+E2C8PndG4wSC1Z/nzHy97ff4Cb1TeC\nWbIvy5/dNCBlNXW1gsheYFnk998CT478vgLY2+2DyMLS66HOPf2hI8uqDSBo1R0mGilL0vu8+9JL\nwz508zx+U3pY5jDssMnnzTuk5FiCJtsJj45qjY+wbXaKklZd0LLehBkdKGE2lOljkcZk/bPbbWkL\ndXU3v4rMVquahqRcUjPiOee8n1tv/QmPPvphAH75y7fzohf9CcccczRLlixvSzPL9PQ0GzaMh2W5\nma9//RUce+zRXPKG0znhnHN43Zy5XPXEKuCNM2UNml6fC1wd/vzvJceyd++lwHXABZHnQHD76aD8\nN910C9PT0w0fT6PNtXlV+L+5bt0m3J/odnEkRs2jUrdqiQ/V1KmmrsdkqTYxWtbg7g/ld00oH5V6\n9ExTZ1/fYp+cnGxpmZLmi1vFpN9Fn7/r0KeVrStMYxLUxBUHWZSWe8JhUcKxrI48f1PV2oXorcha\nfczu+WnCKt4aLlpTuiiTx5IX7f5s5eWz2y2krKZutqFuuUKdQl1ezPbLrZOBsLSsE2425GaLZsLR\n4ODyxDspBOFo7sx2SRfseo6j0jbFULfR46NcS0PmVBjKhh3GHVZ6f/8yn5ycTHwfxsfHS/p5wZDD\nGi/ckxZWVwx10VuRFUJgu4JdVv4gqKR47ifaFvylMZ1oHs3DZ7dbshjqpoFrgC8TzFX3L+HP1wA7\nFOoU6vJitpO3djIQltaILQwD0mo3mzvTd25qaioWhIoX6+DnqbJjrOc4qm0zNTUV3iZspa/iGf4L\nFvlpXOmFfm+ld54IatiCgFZ6t4ek81F4LAiHEyXvU1/f4ornLa33fk2r+CCVPBxTlqW5z1saP/Od\nLlPWQt0VwOXhv5WWy7t9EFlYFOrSbzZfnp0OhMX9rfFg0EBxAMHcuQfOPD9oyowPSiitTYuWM+k4\n4oMQkl4zOgHwwMDScGLhBeGgiImZY4o2g46Pj/u8eQfX3F+t89XXt6hqbVInQl0em7DyeExZlNb3\nIY3l6kaZMhXqtCjU9ZLZzMhfz50MajdZVn5u5bKuLHsurPS+vkU+MrLGJycnY7VjxRo6WF0ySrae\nWrBgAuFFsdeb8JGRNT4yssb7+5eFgW7FTA3d0NDwzD7i5zcIdRNhQAyaUusJXI38Nd6J5tc016Y0\nK4/HlFVprBFL4+ejG2VKW6jT6FeRUDMjIYujPV8JvH3m8cHBzUxMbEvY7gIAdu8eZ/v25kdaFsr6\n8pe/nt/+Nr52mL17X8v3v38pN920hVe9aj0//OHl/OAHN+P+WuBugjv8PcEf/3EwwWyxbOuBs8LX\nOQbYzN69r5mZkDeYQPgioqNQzd7GzTfv5fHH92EVB7GTD3M2n+QqTge2cfzxx86MrIyP0N1vv7OB\ny4CLw1c7i1/96nEWL34KZ5/9Ws4999yKx1/vuTv33HP56U9/yj/8wzsBeMUrNlR83WZMT09zww03\nEZw7EZEu6naq7JUF1dTlUulfhkHn/0LNVOXtvKzJstkmg0JzZ7EWamlZM2uhpi1oNj3agz5swUAJ\ns4WJI2eDGsDy5tWk45g37xCH1WEN3SI/LVIrFj2WpHM1Z05hUuHovo9uaY1arfM7m1qQ0oEFSyru\nI4vS2LzWi9L6PqSxXGp+VfNr5060Ql0u1Vvdn9xXbc3MxMDDw8ck3i+1muiEscPDx4WTxk4kNrMW\nQksxMBXLMWfOYg/6yBUn8y3cozVpIET8S3NkZE04KGJB2OSaHG4rBaD4RMJBmAx+bkXft+I0HcUm\n3lYE6uJr1w71WZXGZr9ek8ZmzoI0fj40UCIFheiFRaEun+oNBfHtBgYWen//4pJgMzCwsMEauuKo\n14GBhT48fFQY2opTmxT6vBW+4IojTYsXiGK/vCVeGNQQHfUYHwE5NTXlw8PHeX//Mp8790D/q/Xr\nI3eKKB3FmlTu5PnzVobHMr8k4LUi1AU1kaUhcmRkjbvP/oKZ5guu5EOnPmOzrbFOW7jrFIW6Hl0U\n6vKr3i+06HbJc8hVnmstLimozJ17YPjzpEOh9m2iZDBEMEddvJZsTUmISq5dK4bWU0891QvTqAT3\ncjU/c+EyHx4+Kqx1PC6sPTzK5807pGzC36SLVOlEwhMzP7ei+TXpXBdG2DZ7wSzWko6G4To9TVBp\n18sBoBmdaFKcbReQtDXDdpJCXY8uCnWdlcYLRytDXVJtV+k9Uye8r2+xj4yMxqY3OTIMdqsTa8bi\nc9Yl16oFo1+DPnR9fhp/6HCk9/UtDmvwDvBgUuGFZQGt0LevdARtafNrf/+ylt75odX9GctrXZc2\n3HTeq3o9ADSr3d9n3ZrOKQ8U6np0UajrnHZeOJqplSufzmPCzea52YJIsFno/f0H1F3OSrVPlcpX\nOhXJhAfNsAs8OtVHtMm0WN7VZfsJauiKd4ooNvkWpwxJ6tuXFDqDQRalAyUauSDU8360eqBEK/vR\npfGPj3bKWwDIy/unUNc8hboeXRTqOqddXzKVwkH1AFccTBD8Hr3v6SYv3g1iomI/tCSTk5Mld4uo\n9dzkJs+jw/JsdFg908+sdPvS+7SaLQybXIvz0BX78EVfe2Ps56AGLl6GoB9g8hxytS6Y9b4f9b5W\nvWE9qL1c7UEz9+zuItLsvIhZDRJ5CgClA3+yfUs1Nb82T6GuRxeFus5p1YUjfvFMHsE6WjZgIakW\nrdiMGX2N5vtzVbqYVLrgJ4e6Ia/U9JpUGwUr/QUrn+x30RfW0BUHV5Q2oU540PxavC8rzA8DXNKU\nJmvC7YcdNs3UONa6UFR6P2bbnFptsEv0vYYDysJsuwdZpPUC2kgoTmP5m1EcVV16d5OsHk/aB0qk\n9Y8ZhboeXRTqOqcVF47k2rbRsotwcEeESgMWitsVw0a0ObO5UFcpDExOTpb0VYtPRVL5PrDJTZLB\na23yYGTqIl/Fk/3+gX39B5s3h8e40mHUo1OnBK9ZWvO2335DPjk5GZZvsQf9+oZKtik21y7xwcGl\ndQWepG2S+gCOjIxWvRjUG66SBqcEx9LY+9fofmf7nHZr9P9bWi/OjQrei/LuCd1+P/IozX8MKNT1\n6KJQ11nNNMFFJdcCrSn7YkkKcPPmHVyxWbB0kEBpAKq3r1dp02hwYRkePioyt9xUGLZWztwHNgh1\n8/6LPAAAACAASURBVDx+z9ahoeGK5+PAAw+cKV8wKML8XYceGuufFw2IKyOhJ3reRn1kZDQsXyEk\nxmsNi821fX1DdYWX6s3cHnm95Dn3qr3X0drZws/Jg0aGqr52rc9o8/f8rXxeOi2NZeqE4P/B4p48\n9k5L82dMoa5HF4W67mr0Alrvhb7SgIXojevj/WyirxGfA66e8k5NTYUjTIs1b0Et3JFhuIreYWJJ\n2CS8xqs1FRUC59DQ8Ez5i6Nco4MikgNXEG4WhmGycqgKQuKRVUPdnDmLG2oSrdafMQif1ZtIk/Y1\nOTlZcqeOgYGlPjx8TFm5h4ePm1WtU6MDb0ZG1pSUKw01Fs02I+ehtm58fNyDAUfpeT/ySKFOoS51\ni0JddwWhprSWqtqXQmP9rIoX2f7+xT48fEwYZCbq/qKvp/9eobxBrVvSJMJHezA/Xfzx1ZFapvKB\nEUkhMQhfQ2ENXXRQRKVQt9yDgQNrSl4nKVQFwW95bF8TM68/PHxM4jmpVzRQF/vxJQ8IqXT+k8L6\n8PBxZUGvExfw+Gex0G+zG4GoUg14o82vaW1Ka0SeBkqkXZo/Mwp1Pboo1LVHPRf+Ss2FrapNKNai\njIbhqNCRfmFdATLeF25gYGnYrFt6667Ca1TqyxMEyfLbgAVBpvIAgiD4DJXtbxWHh3eKKAyKmO+w\nX0LIWBoed2HQxNEz4Sz5vrKFYL3aBwcP8r6+/WfOWX//gqrvYz3vdeWy1R/GghBcemuxwrQlna5h\nSkstRa3a49l0bUhLrUsj8nIcWZHW2l2Fuh5dFOpar96/3pK+fAs3uW+lIMBEBwAMhTVXlb/sK/XL\nKYa2ICj19S2aqQWoNOpucnLSR0bWxAZEFG8/lvSlGDSzRgc2BPPKBTV0+4YTCxdu4bW/w74z5S6v\n2SotE8z3U089teQ9ig+KGBlZ01RYi9/KrPC8pBrZYC68xi6+SVOtDA8f1dTnYra6ER6S3pNWlKPY\nNzH5D5YsUagTd1eo69VFoa716v1STdpucHBFy/7iK1wAg+k5ypslqzUVFGvdSmuFSud5W+nx230F\nAacw6GDIx8fHS8ozMjIa9o9bU6MGszxQruIZ4Z0iNnuxZi1YNzCwvMoFP2mi4oU+Pj7ua9du9MHB\ng8IyF49zePi4us5xUnNofABEvMazUCObNMChWsheu3Zj4qTIhVuLddpsm54areGIdyko1GzONsTE\njyN6r+E01bzUK81NgtI5CnU9uijUtV69F5n4RSq4mMxvyQWl9Iu9PNTMmVN6l4bkMLSprFYo6J/m\nZaGqcHyVpi+pdAGvN4gVRrkGNXSFc1WsURkcXFpxv5VqHOfNO8SnpqZ8zpzFHtReFmvq5s49sK5z\nXLk2s/h7Ungr9HOqt39keehIR21StIm/kVuSNRM8qt2tZDYhJun/a7N34kiLtDYJSuco1PXoolDX\neo1cZJKa5QrzxM3mYl16oZry+Dxm0cEIlaY5qd38WgwWhQt6sc+Xl6xLqmEp3XfxnrDBOdnkpfdy\nNT+Np4X7X+BBk2s0bM4vK2vh/AUjAaPhNJjnbs6cpbGwtMiDZumgFq2+cxxv2l1QdvyV5qgrnP9a\nF9/kASDJ8/h1QzOhqpnataTzWHifGgkxjQz+ke5SOG2eQl2PLgp17VHvl1HyBXu06QtLYb/lF8DC\nfG2rZ/qyVdp/Yb+V7kBRvFtFMLrObFHJoIIgQBUDX1LfscIoyeDx0lt+9fcvDkPbRNjkan4acxzm\nerFJeK7DwV6420PSHHOlAzgKo1+LExHvs8/chHO/0mH/mUmJK03/Unp+Suflq2caklbUJDVSK9bO\nC2MzoaiZ5yRNspw0YriaSn0g1VyZPmpGnh2Fuh5dFOoa0+oL5NTUVBhiooMY5pcEr0ZeK1rzFa2d\n6u9f7CMja8qayWpNUVLpS7W8n1jpQIPCnG+Dg8vDu1skh5JoiC3WKga3/lrFpZF56AqBsfgapf37\njvRKc91V6lcX9KUrrwHr71+cULsX3Pu12Ny4xvv65nnS6NV438Eg1JXesq3Rvmdpvv9lMwGtmXIF\n3RWaP4/VyqoaofRRDersKNT16KJQV792XCCnpqbcbK4XQ0tQA9ZM5/fyL8HS2rmkGoniY8lzWlW6\n2CXXMBZDVn//ssgI1OQaluJFunD8o16YSy5ocu3z03j5TMiL3/qqGNTme1/f3LJjiPb3CvYR71e4\nONancbkXmsCTBpfMm3dI5PwVpmiJ1tIV57GrNdFwIVi3u7atExfGZv9fNHNMsw1fCgrZMZv3SiHd\nFep6dVGoq187LgiV+ks187q1glal0ZbV7s0aV7l5t1DjFvwcvaNBvKbKbO5MqBsaWuHFuegmHJbE\nauiS78Xa17fQYZ7DYj/11FPLvsTjQcMs3g9vgcMS7+8/IGweLp3Korjf4vH19y+LPFZ+/PPmHVLl\n3ERrGSfqPt+z1akQU6upOi3UpJcds/ljQe+xK9T16qJQV79Ohbpm56or3pi+GJCiQaVSqKvVBFto\nbhwePi68T+vR4RINScWRuzDf+/tLb1EUzFVXuM/qRBiSFkSes9phsa/iVP8F/ZE7RcSbXIdmbhdW\n7S/x5IC7wEv7/U04DPmBBx7m0VsqBTV2azxpPrjia5bXPgbnJlqDWPqeFmvuygegtGu0ZSdq0ZJq\nJusJdt0KgqrFyY5m3ivVxgYU6np0UairXzsukM1eEGuVDRZ4X9++ZcEqqU9ScRRncZ62QlmLTbNL\nIv9Ga7uO9NKRu8OedA/V5PBYCJ9BuYujXPf14nQqEx5v4qynb1XlEaPR3wvbFAd9BLczmwz3Hx38\nsb+Pj4/H+iwW1wdN6IUm1tLRxvEJiZMGoAR3sWh9jcLk5KTPm3ew9/cvmzl3jX6Wmrkfca0/TMon\nl56f6ho+yQ6FuoBCXY8uCnWNafQvx3oukK2oOSgdSVoYGDC3ZFBEEIbKR2FWusAGr7nJodDsWG9Q\nKq+pGh4+LuyndqQHzbQbw59Xh4Hu5kiTa+FesWvC4FQMSP39i31wcInH55WLj4Isb34tBLd4Wa9w\nWJFwTOXNr/HbcZUGtfhtxyYqjlAtD+DFvnytvPgkva+nnnpqA5+l4rFXK1czXQiqTU8iMhtqfg0o\n1PXoolDXXp36q7FY21Z666voLaQqlaXS48VbUhVCWvl2ZtE+b4XavP1LRvRGpygpremb77AwrKFb\nEWtyLVz0F5bts1ieYhiKB4LCAI3CFCzF+8jGy1oYqVt8/aGh4cSRsfPmHVLx/Dd6IQn6E7b3tlTJ\nffsW1qwRa/QzOzXV+D2MWxXq1JQqSfS5cIW6Xl0U6tqr2eke6vlCitcaFSbrjXfurza4oVqoK96S\natSDmrNNJaHMbOFMv6gghB0YLsO+335DPm/eIT40FPxcKRSu4vCwyfWNkbC1f7iv5V4cZVp8TulU\nJsFrRkcLJwWs0oB6tAejYY/2/v4FYVNuaRhr5h6rzdTizmb+uloqDdioFZ6aqemI9+es9ZxWNL+q\nRkakMoW6Hl0U6tqrmRqcZm4dNTi4PBIQihfxvr7FkcEJpXPXRe8ekRQu5s490Etr1hY6DHow6e/R\nM02exSlLqvW9K/TZK5YvqKHbN7yX6+owlM71YPqSQj+9CS/W1hVCX3SUajCvXK3pVpImQC7cJiwp\njBWbnguBdVNdtWiNDjCY7bxr1ZTPtVe852wtnZhuZLYDJdR3SrKgW7WGCnU9uijUtV8j/6nrvVAl\nbTc8fExZ7Uex31mhqbK8r1elcDE8fFxCTc/RM69duBAXO/4XypR067NCn7Yg7BUHRWwuCVkjI6Ml\nI2cL044EkyaPltSqRaclqRXqgoBaWqZaTYqN1qK1YoBBK0NJ8Prld9LIy4CEVp0/NdVJu3SzNlmh\nrkcXhbr0KPazKr9QxS88lS5ohdqPYE61+MCAUS80vyWHoOIgi8LdJ6o1fRYuoKWhbsKLzcDF2qED\nD3xaGC6O9FU8JTLKtTyADgws9eHhY2buyBAt69TUVHiHiuSBEkFfutKpUwYGFsbu2rGkaq1YMdAu\n8kKfu3pq0VoxwGC2oS76OSm+L5Me1DaurNmEnCWtuGCqCVfaqZu1yQp1Pboo1KVD+fQhxYtM9TtB\nlDelVp4AtzhQwGyhDw8fExm5WTrIoq9vUTh/W+lcbEHwmyoJh8VBCIW+auWDKYKasvgo15Xh85Z4\n0NRZfE61L75qTanR2jWzhRXDaaU7dsRfIyhbUMtY68u4mQEGrQwU8dcbGFhYcix5DCyzrWVTE640\nq57PnkJdcelHJEWmp6fZsmUrABMTZzA2NtbS19+yZSt79lwAjANrgfMYGrqPK6/cFlsHe/bAtdde\nw/bt2yJl2gbAhg3j4baHA2dF9vBW4HUzr+EOt912Kbfdtp6BgXdg9h+4XzSzfu9euO++d4a/XwM8\nADwK7AS2AR/hl7+E9etP47HHAD4a2c/NJcfmvpeHHprDKu5gJ+/ibC7kKm4AfgNcHG71dmAaCM7r\n/fc/UPIa0fP/+OOPhvvYFK49nEceeZiXv/wMHn30wyXHCJfXOvUltmzZWvIagUuB64CDqj53dPSZ\n7NwZPednMTr6zorbj42Nlb2Hs/lcxT8njz4KIyOXsWTJNS15/TQaGxvL3TFJ+k1PT0e+a2H37nG2\nby///zUxcQa7d4+zZ0/w++Dg5pnv6l6jUCepUe9/4NYZA+7m+OOvYWxs7P+19+7xdVV1/vd7naah\nadM0SdOWlEKFg1iaVpopM8apYxy17agzzI/mN48FcSKOIA4/uSTFyhRGniFYLxQUx5EHHoUKar0w\nYvU3pkRG6lN1/MlFLDioUxGFUqSDKEgghHyfP9Za2Wvvs8/JpUnO7ft+vfYrOXuvvfY6eyc5n3yv\nox/6OaMSH2gbNnTFPtQBmpuvZO3aUzh8eBX33bc6McNSoJuhof1Y8XM9cDReWM2enWF4+EZgpRs/\nzKxZn+Wll65219jD0FA90EdcBF0E+GtdAJxDG4cZ4DJ6OJFdXAc8APxz4rwrsKLxM9x3H5x44is5\n++y3ctttA/zoRz9GZJ4b9xzwKeBfRq/x0kvreeaZx3Lu0SOPPMry5UdjRaNnCw89ZNiwoWtUoHvR\neM8992MFcZxM5uf09l6Rsz9k7957sYL8SrdnPXv33su2bQVPm1ZaWpZwxx23FW8BJU6xPnSn+59E\nZXpJ+0d7x44bcp5j+I/b4cNPACtGn3vVPfNimwqrZUPdr2MyEyb0Qq64tPIP2ezKnGSHQnXPcmuJ\nha7UpIvVuoCt+zXeVcHGxvn4uyWSr9CwTXhYLDaG7no5SJNsZl7sPViXa9TFwiY+NCTW4uvb9aYc\nC7NgfQJE/L342Lxkxwy/7traRTmu7HiCSYsYU5+aXJB0v0RZwNH1k0WRx/vMp+JnqLa2UdrbOysi\nAWA6kxlmOlFC4/jKn2KHWowHSsz9WvQFVMumom5sZiouIt+HS1RY2IuSLrHxaDZTNVeUWGGW/MNh\nS1w0io198wIpV5TZDhK9KSVNGlxnhmaJxJ8Xd9Efq9mzZ4tNMmiWNurlIBnZzMmJ6yRFWoMwKvrC\ntfiM1bSuBcl6dX7eZe68eS5btnBZlLQYPdtlYpnAPOnu7k59VnEBtUjq6nJLyuSL3Yue6/QkSrS3\nr4vF02UyTTlJJ1PJdIuuShJBGsdX/pRapnsaKuqqdFNRNzZT+aEymQK1VnR4C1yfE1W+REWjZDK+\nDlz0ByOtQXw8YaDfzdGS88cGlkltbaNr6+Wv2y+5HRkagmOrpKZmsRhz1KhYi8qWzE65jl9/aD1L\nW0shUecFaYNELccWiC+p4pMz7HhfZsVb7aJ5rEUxTTDae+QLOIekt8ZaNqE/3NP5hz5f667pEETT\nLboqTQRV2vupVqajVNVUoqKuSjcVdeNjKiwRkylEHM/EbBZiLkyfLevFWWR98m43n/25fv0m1zM1\nWT+uXpLWuNbW4xLXXSK2HEqaqIrqn4VCK57l6t2oC4Lxyeu2CJwg8TZnhdyvXsitcmPCe2StlLki\nNq0wcotksytT7nOfJC2QY9XCs/do/M93Opvap69v07R8mMxMvb3KEUGVZnlUxkbdryrqZu5Gq6ib\nMaaijpkVRcnXm4LvrSswcjt6AZMWkzZXoFtguUCzzJq1ULq7u8dthbLXWybWVemPbUrp5bpQksLR\ndqZIE0Xegtgs2exqVy6lwx1bKbBM5s8/Vvr6+vKWbvFWyvgf0l4xpsn1dK1zYjByX4fFl+19yW1P\nFj6r5B/pfOVexv55iFsrp0qs5K4v6pNbbqKu1EXQTHTfUMqfmX7mKuqqdFNRN3NM9MMvvaOD/3D2\nrxdKZHVbKM3NWSeEIsuMF1u5c61yYileFy+96HC9xIsKh+7XSFjZpIiwl2uDWAtccr7cPrWRO7VZ\n6uoWjVoZ03qzjvee+gSGMEmkttbOXaiQc9r6kvFxUfxa4TVO1c/DREkrxFyO7ld/jVIUQaUuOJXq\nRUVdlW4q6maOiX4A5CYq2O4GkRvRZ5B68eTdlE1OsK0bh6hrEGttWyM+7sxmvfr5rNXMWq/6nAhc\nFsTx+Ri/RmmjWw6yQDZTKzYxoUmiWLbktVsk7mptcOOzTkCuGl1DUoT5e1lIUKV3VoiuH4qnNHFl\nTEPOvW9tPaHgs00THYXEyEwJgpkQRKUquqabSnMNK5WDiroq3SpJ1JXDB8tE1pheimOTRKKoKxBP\nuZmokRjrcGIwdL82CRyVEC4L3JwLJOqk4I81inV/ikCva9U1W3xfUSvokM00OmHmhWW925Ku33on\nCDe5cS1uPU0Sxd21jArTQq7PpOhLHreWqom4UhvcmuL3vqZm8YSf9Vii7Uib2ivFRUWdUqqoqKvS\nrVJEXSW6QXID6aO4KGsxE4nHuxVyI/Y6gbfCjV/gtuR476pLy0Rtcud6K549P8pyDZM4vDWxWaBe\nstnVkpukESZZJMuO+CSQVknGqI31QZp7vDfmfk372Yi3V0sTyC1SX986oec3Htdwpf3MVhv6DJVS\nRUVdlW6VIuoq9T/mvr4+ZxXz1jhvgYuC8uE4J4zS3JzLnJDqlGR/18h9G473Vr20mLdVEtWoWyiw\nysXQHS2bOTFlvH8mTXni9LxIzCdIVwXC09Za6+7uTk2OGMud6i15Y1lI4+f6+7tMYO64LWm5ArHQ\nOqcnUSJtPaVmwS6mW3gqr12q91epblTUVemmom56mMqMOB/wbpMgVqdktlqhV1MzV3LLfviYu4V5\nRF+Y/LDEjW9Imb9RYE5sv7XQZWQzW/PMvUm8+9OuOZrPFjHucusay8oY7vPnxZM7kvcrzGatqZk3\n7s4KyYxZ795+4xvfmPOMfAZuMtYvX3eK5Don2oFiMpSqJWmmkivSrlHo2irQlEpBRV2VbpUi6krp\nw2sya8l3Tl9fnxNAUfxYX1+fKwwcFzx2X1oMnkjcvRmdE8XkdaSMCS1V3pVqa9218QbX+us8icqA\nJLs2dAk0SybTLJFLs1OiRIsugdVirXFpIrI+Zb2rJCwMnFY6JLe+X35hle9ZWHdxXCB3d3ePKdjS\n/rlobs7G4v28aEjLbi7UgWIyTPafnekWNzPxT1i+a+TbX0p/QxTlSCk1UVczvZ1llUojbJwM0Nu7\ns2gNk8fT7DnZ0DvtnL/5m3N55plngI+N7h8agttuu4kFCxp46qn4dY0xwGrgardnJ3A70IVtUv8M\ncBFwPbAOuBEYBDqBe4FHgQXBjBuBQ8BubKP6PcBjtPEKBridHoRdzAMeAjLAG9z8APXAncDHGBkB\n2AI0ubEfdvsuAM5x4z9NTY0wPHw1cBKwCxhwYzxbgKHEGuHuu+/OuZdDQx8dvWeW3cDVDA7C+ee/\nn9/+9nwAenrOZtu2bbH5Nm7c6I5fF5vjc597H8PDH8k7r19DkrVrT+GOO25jz549nH56t3vOkMn0\n5oxtaVmYOsdMklznvn3dfPWrxft9minG26RdUZRJUGxVWS0bFWKpKyUmFiDfK5nMQtcKLB5/FcW2\n+VZdMmr5SetGkN+S1Cu2E0XSNbvSWeHCwrvzJN6hoUXC7gpxl6t3kfqkCO8ujdeuy03oCPeF/Vt9\n3GB0PJOZL7kJFlnJLWYcWczS4/fC6zTGzk2LlUuL28vfTix6xoWsPZNJ4DhSJmN9mgor2liWvlJ0\nv5ZaCIeiHAmUmKWu6Auolk1F3dQz1gdW9OGRzLD0tdF8Yd8wOSKqTZfNrpb+/n6pqZnnhNIygTnS\n3r5uNM4rEon+Gmkxb40CiyW3p2u92KzTTgkTMOKtv/x7yIrtKNEo8S4MaTFyhUURdATlR+x9S3NR\nprmew3na29fldZNG30fnNjdnc57hxETz+OKyjiSB40iYaNmUIxU34xVspZYooe5XpZJQUVelm4q6\n6aHQB1b0oZkmfHxx4TB5oT/4fp5ksyvzdEHoSLE6FLpWh6RZx6JixH2joi+39VeHRHFvy908893r\nZZJbF2+hwCKxAjIsmLwimKt3NKHB3zcrrprFx/zV1Cx0CQZjW8ysta3DvY/Vbh5fKiU6Nynq/P3L\nZtdIfX1rTAyNlShR6OchrbvDROaYDFMZ3zleytnipYkSSqWgoq5KNxV1M08UyJ9mPUtzW/oPyWaB\nBpk//9iUchhd7twOaW9fF3ww+wSITolb/ry4yVe6ZIFYV2yHtPHGlNZf85wQi0qORBbGuW49vuiw\n3xdauI52ItDvaxZj6lOyWMOEh0apqZnnerWmJUJYV3Z7e2fCzZasgRe33IXWq+mw1uQWQ24atapO\nt2WoGIkS5SzqFKVSUFFXpZuKupknKrmxQnIzRjtTRFZHIMIWyaxZi5xbMimUekdFgxc1ra2+hl04\nboVERX99F4dIXEVCrCUQdM1OpC10101veh/F5nlxerREZUvCcbn7MpnmmFUs3Rq5SurrW6WmZoGE\nJUtaW0+ICcwwfiotPg6WSU3NYunr64sJmLR4PD9uskw0C3MqsVbNFe55dMp01cILUTemohSH8G9Z\nqYk6zX5VSopktuqRZMTZ7Eyf0foObFbnCDY79VT32nMRIMAlwDJgiJdeehcHDqwGerAZrEuwWaQP\nA1czMmKv0dt7LocOPUsyi9Oe90VsRuud2AzYHmAucDJwOXCINv6eAe6kh3eziw63rm53zWHgMWD/\nGO/2eeAFN67L7Tvevd/4uSMjc3nqqUVcdtkHC8x3kGefzQAfH31Pw8M7efzxntg+n7l4xx23sXbt\nKQwMxGfJZAb5xjc+BzBmRurw8AlcdtlHAGKZslP5MzEd7Nmzh/37HwBmE2VEX0Bn5/um9bqllImu\nKNVCMmsd/rWo68mh2KqyWjbUUlcQGwu1Lpal6N1nhawP44upyw3Gj5IlGiXqAtHh9nm3piQsY97K\nt250f3t7p0uWSHPxdiReL3HbOrExdIuljTWByzUcuylx3dCVmXS/eutiWlHkdWKMz+z1de6ieerr\nW6W/vz9Wo6+wyzg3s9ZbpNLcn/mtgfGM1DCmMYy9m4g1ajJFcKcC+95yn7+6QhWl8sj9W4ZICWgM\nvxV9AdWyqajLTzwuLX9SQv7z0j+so+NpJT6yCfGVTJSICxA73sfXeXdog9juD14EhRmbvtBveE0v\nJpsEVkgb3YleruHYMPHC7zvaicF6iRIlwhIkaSKsQ4wJs2596zN7vKZmsYh492GypIl3EYci0YvI\nSLiNle0okj8j1WbYxkvJzJ9/bMHzkmVrwutNJAtzqlBRpyjVg4o63eyNVlGXl8JZqpvyfkCmCZHk\nOFuSJK3ERyjqfI04/7ozEC4+Ji4URXPcOVmxsXLzxWeV2vU0C8ySMJvUCq4oNq+NRifo3iw27m2e\nRNa0sH1YJHbiwnJeIOxWuXPSSpCkZd1GQrG19SQX47Yuj7Wu061psUCXs67Z95nJLJxQn9YomSI6\n13aUiIvObHZ14mcjvW9rqcSURbGbUTZ1be0ijW9TlAok+XdHRV2Vbirq8pO/nlxkPUsTa3H3nbV+\npfX0zK2FNld8BmtcQIVCyrfuSmv5FWaiNjhh1SzWktbqvg+FSmPwWlwdugWymRMlKneStIi1pOwL\na8DNldzCwC2BsLPrzGZXBqJonROAth3ZrFlNQX9b/15XSNxa55/NQoGunHpvE7GA9fX15RQBtuvz\nNfrs8wifYVodu0IJHvmsY9NdQsOHDzQ3Z0ezghVFqUxKOVGi6Auolk1FXS7+F6O9vdOJC2sZM6bJ\nuQzz9xHNVz8un4XEF4atr2+VWbNCMbhoVMDMmrUoWIcXSmkZpcuC18kyHkucsEueY62F8cLCXkjN\nTRl/rJurSXJduV4AJc/xFrtVAs1SUzNPuru7JS5cI7dpJPjyFRCOCjFbt6stDBw+v4lYytKemY1H\nTLplOwue44XbeEVdqVj0FIvWqFMqCRV1hYXPFdh0vXA7mDLmMeA54NvAysTxo4BPAE8CzwJfA45J\njGkCbgGedttngQWJMccBX3dzPIlN+ZudGLMa2OvW8ihweYH3Nr6fkCoh+UFbW7to1Ao0HgtQuqjL\n76odz3m+pMb69Zucy7bLCarQItYi1prlz08rN5ImBLPSxjxnoTtPooSMWrFWvKSLNLT0Jbsz5BN1\nvqTIYgmTDvK5tuvrW8UK1Nz2aLaUy3x3vC92rNC9nOi9TyuDEs5R6BrjFWtaz610SGvdp5ZNpZxR\nUTe2qPsJsDjYFgbHtwK/B04H2rD1Ih4D6oMxn3L73gC0O+F3H5AJxnwTW+fhVUAH8ACwOzg+yx3/\nd2AN8EY353XBmAZsF/ZdwEpsHYnfAz153ttEfk4qnqlukVTIVRuS1g7LChcrnHwnhbo635VhnRNf\nXkjNEe/qtSIqLWbNW8wiN6jNcq2XzSxzgi7Z+zXs+jA3Zc5k39h5EhebTW6tPiYwKZpCF/cmt8Z5\nUuj+pQmuIxF1aSJsrMLA40mGGcvqM92iTi1P4yceahH9TNfWNuq9U8oSFXVji7r9eY4Z4HHgU1cT\nuwAAIABJREFU0mDfHCekznWvF2CLdZ0RjFkGvARscK9PdhbAVwdj1rl9L3ev3+TOOSYY8zZsobF6\n9/o9zsp3VDBmG/BonvWP48ejepiKD9pCLaGy2TWSySyQWbMWSja7ZtT6Z4VMsgdrr1iL3DrxhYd9\n3FmUmLBJbAusXFdmbrxbn3ttY+usoEu2/krLyE2WL0mKutBl/Eb3XnyihC9v4l2l1sUa9VH1btSk\n1bE/dv1QNKXFImaza2LW1Mm0xkrrBVpIFB2paJpO96u6didG9Hu/LudnMS0eVpke9B+RqUNF3dii\n7g/OKvYL4AvA8e7YCU54rU2c8w3gZvf9692YhYkxDwAfcN+/E/h94rjBVqTtdq//KSkugUVu7k73\n+rPA1xNj/tiNWZ7y3sb40agupvLDMNkntKbGi7zoQ6OmZqGLIVsl1qrla7fFRY0VMb68Sr0Tcl58\nFcrSDTNTO0c/qNo4Xg6CbOZPAiHVKOlu27B8SVq7rXBsvjItXkwuE+iSurol0t3dLc3NWclkmgte\ns66uNeYCFxHp7u6WmprFMmvWQslkonZjYQ24iXw4FOvDZLqum8+lrB+U6RQqM5TsDaxMD/qPyNSi\noq6wqPsL4H8Cq5z79NvOOtcM/KkTTMsS53wG6Hffnwm8mDLvncCn3Pf/ABxIGXMA2Oq+vwH4VuK4\nAV4E3upe3wH8v4kxx7k1vipl/rF/OqqM6figLVweJUyQSKsj1ylx64Fv2dUk1vp1dIG5k/XkGqSN\nBa5syaxgnrB1WGgFS1rNvPBryiPg0uL2kgWLl0i8DElaJm9YVDkqTGxMo2SzKxO9X+Nr9M9tvM9w\npj5MJvNzNdmfxXwJO/pBmZ/+/n6ZP//YnPsWJsgo04fGmE4tpSbqSqpNmIj0By8fMMZ8H9uTqRv4\nQaFTx5jaTGI5Y50z1jVzuOKKK0a/f93rXsfrXve6iU5RUWzcuHHCbY2SLaPAtqk6fPgJoIZHHnkU\nOC3P2a8gt43Xavf9VuBEbJuncMz1wHnABdh2X76FV08w5gJs+7Cd7vjztNHFALfTQz27eAn7/8D7\nga8ANwFLsWGhPdhWYMPYEM2dwbXEzXe1W5/nQrLZYzhwIGxz5te1BbgVuBvbturbjIy8HvtrdDTx\n1mgXAq3AbnfOIff91Yjg5k/ejxsA+8wOH34iaJezn4GBM6irO4qlSxdxwgkvz2nptWPHDW6snc+3\nGJvK1lbJFj779nXz1a8Wbp8VP2c/d975Nk45ZRXbt186el6+VmW9veeyb183g4N+tq3ATgYHD035\ne6sUNm7cyJe/fCOnnfZ2hobsvtraS9i+/ZbiLkxRxsFdd93FXXfdVexl5KfYqnKsDZus8EmiRpZJ\n9+v/Bm5y3+dzvz7IxN2vDyTGJN2vO4FvJMao+3Uayc2YbXSWpNDV6q1g+eLekpa75RJln6ZllG5K\nfN/vrG1h8sRciZIcGlyniIxsZmtgDWsO5k12qAgLGi8W6E5Y3uK15Lq7u4OCtx1urnrXnaFXbExf\ncn3e9RwW8l0xxvtNSwLpGLWytbd3ShT0HiatNAusyClOPBMWgrR2ZM3N2XEmUsTfx3jbjPX397uk\nkrg7X60fhdG4ruKg7tephRKz1BV9AQUXZxMhHgcuc68Pkpso8TvgHPe6UKLEevc6LVHCu3Z9osRf\nkJsocSbxRInz3LXDRIl/AH6d572M48dDKUTuB7YXYbkf5NZluU6sS3WV2PIcUe27qNDuXLFdIVa5\n14Vcol7UpblDbRxbG+8N6tCF7tDQ9ZusBbcs5bp+TIMTZPOlpmbxaNKHSFq/3DAZIpkQsUByCxbP\nlXjXi+S6VkiYVBKWnfF1//K7o+2zCduIzcSHSfxnpD/2fscueZIuOscjRvWDUiknVFBPHSrqCou4\nq4HXOqvcq7BJEE8Dx7rj73OvT8fG3e3C1oebF8zxL8CviZc0uRcwwZh/A36MLWfyamz5kq8FxzPu\n+J1EJU0eBT4ejGlwgvMLWD/aJifyLs7z3ib0g6LESbeGeDGSW3ojElHJNl8NYuPbkuIlKQh9RmnY\nbWGlG++vG7Wugg5poy/FQudj1Y4LxndJVB9unkSJFUmRWC9RSZWoi0VoQcotO+Itf8n1eXHW7+7X\nCol3n2gQmC2Rdc9bBxfEMl79s4gyav39yG/xK9SrdTp+TiJxNb5+rNE56eMnUuRYPygVpbpQUVdY\n1H0Bm/n6ghNRXwZWJMZ8wFnsBkkvPlwLXAccxmbSphUfbsQWH/6d2z4LNCTGHIstPvwHN9fHyC0+\nvApbfHjQrVuLD08DuTXpbBmSmpp5ku5q9ZYq35EhzYoUvj46IYC8IPHFh5MWttzWVXELnc+irXfb\nLInXpGuWyAp2lMDSPGv0FjsvsKI1trd35hUi9j0n70e9RH1y1+U5b43YBIywX21XjoDJtYYtl0IW\nzpl2Q3pxNVZh4+Q5cavn+N2viqJULyrqqnRTUTd58pWNsDFdvoTIcU5YNDtx4stvpJUOSYo674r1\nVj3fQSFN+HhhFO23Frqjgjp0TWKtXn7O5uAaq5zAanRrDnvOelEUFQKOBGBcpNluEP69R3X67Lhk\ngWVfhy8UlfMk971597KP00sv5pz2PLLZldLcnJW6uqMlLGpcTAE0VXX0Cu1XFKW6UVFXpZuKusmT\nL/jdCpu0em7++06xVqlky62wk4JPBvD148KEibQSII0Sij3by3WBbObEQFR5IbdS4gkECyRKglgm\nvh9sZPHKbdll58kVpjZBJIyHa5S6upYgeSIt9jA6v7X1pJhVKvfe9brrNEp7e2ce92v+xIFSEUCl\ntBZFUSoPFXVVuqmomzzJfpGRqy/NktYpyQB5axlbIJFbNMwMXSCRVczvWyqRCzMpGlc6odjkLHQL\nZDPzBFpTBFlus3o7X4tYl++ixPHexPWaRq+VP2bQW/Z6R3toJt2ItuNGrhvSd+SwvV5XSza72o3t\nEugQY+bLrFnRPLW1i8bdBUJRFKUaUFFXpZuKuiPDi4h40dJCnR2S+5e5/Ukh5YWgF3VNYi15S5y4\nOUoi65wXlN3SxvVykNmymWaJulOkXTPNfeuLGccTIOz39cF83oLo1+DHeYtiOO+qvAkJ+fqrJi1u\nUZFiP2+uhVALxCqKokSUmqgrqeLDipJGWPg1XhP6XOCs4PUWoAWbR5NkGbYo8bdTjh1w5w5hiwh/\n2s17I1CHzZHpdmNX08YFDPAVeqhjF2djm5r8CFs02HMBtkpOuG9rsN67gPdQU/NphocvwiZcvwv4\nLrZaTnQ9uAibzL3b7TuaqGiy51E6OzePvkoWdj711FOD4rm2GO8f/dFrGBw83s17LiMj12KLLXue\nzrlTtrhzYfIV6p2q8aVCqa+71NenKMo0UGxVWS0baqmbFElrkjHzJbeosM8C9XXX6iUeRxdmY6Y1\nta8Xm4wQuk6XSJShutxZANcFhYV92ZAGZ3VrdF+zbvPlTxZJ5O7tFe8qjVy9fitkffTFi8P3HFr4\n7JwTyTLt7+9PxNTZOSJXbdq9Hrvp+kSTE8o1s7TU113q61OUSoESs9QVfQHVsqmomxy5SRLebZnW\nzWGuWJfnLCd6lkmUuCASdVpoSJzfLLnuzI5gv52jjQY5CLIZL3aybs6bxbpsk8IrTDyYkyLsmp2Q\nzF8wN3LVNoqNw/Nxe74sic9YnVjpkLQMVt8BInTbRokXHVJb2zimMJho14hy7UNZ6usu9fUpSqVQ\naqJO3a9KSXP48H8Hr/ZgywK+B/gumczPOeWUV3Dffb/Elh58D9ZlOo/IlfkQ8M9Yd+X12NrU38aW\nIJwLvMnN+Sl3jdX4/p22F+pNwNW0cQsDGHpYxi6eBZ7D1srGjT+eqP+qZ3cw5mK3Fohcu96F+gts\nD9gPAW/BmEFELsQ2P3kncCOtrY288MJsnnrqPHzvVTvPecAh6uq20tu7096lSbrd5s2by96998bO\nibttr1AXnqIoSilTbFVZLRtqqZswUX9Tb7nKTUbILTDr+5uGpUS8pcwnAuS6HSPLXDKDNRvUoTvP\nWf+8Ba9ZomzbsfrGNgVWuQ6JWpSFa2kRmCetrS9LtbLE+716K+Mq8f1gRUT6+vqcC9VaBcPWXsmy\nI/GCzpFl8Uhcdep+LQ1KfX2KUilQYpa6oi+gWjYVdRMn3mh9UyDUIrETz4b1rksfD7dJrHu0Q2yB\n3tmSXnvOiyRfvDjKPG3jeDmIkc28WaL4u/mJc70oSrYjC92vxwXX9K3N8mXHNufs96KspmZhcI1F\n4l2vzc3ZvHFyfo356slZYdybc73JMtFyJ+VaHqXU113q61OUSkBFXZVuKurGj6+fVlOzWOIJDGmd\nFRZL/lZhXQmh1ShRPF4Ui2ZF1Fyx1rxobBuzXQzdUon6tB4lNrZtnVgrWYv7vl8iC6FPmvDlS+qd\nqBxPnb0OsYkZkbUtk2kavSfp1kAr6tLiqKL3KnnFmsZfKYqiTI5SE3UaU6cUhXxxX3v27OG0097O\n0NBH3cgtwP8E3k0m8yQjI+8gKu3RzbPPfgYYxpYVuZZ4TNuV2DbA4b4ebBwa2Ji054CXYWPrbh8d\n28ajDHA5PcxjF83Ak8AaoAMb2/dT4jF1YGPk1mHj676ILaPykvu6xL2Xl4I1PUpuGZRBYD5wjdt3\nIbW1s4B85UQOAhfQ0/M+9u69N+X4z4ArYnuS976391z27etmcNAeD+PzFEVRlDKi2KqyWjbUUjdK\noXiffNamurqjXccDHxfn3ZS+40Ju14T0jg7Jvq+dwfc2GzVq/bUsz7y5jeLtvMmerZ0SdYnwMXjJ\n8iW97qv9vq5uaerctbWLZM4c30M2siZmMgukr68v9b4a0yg1NfE+rIUKEaurTlEUZWKgljql2tmx\n4wYGBz+Mt4oNDtp9hTIrBweHOHCgF5u1eiPW2gXWurU0+N7Tgy3+exE2q7QR2E9kpfO8gM103QIs\npo3/xQBP0sMIu6jHWvLCebcAr0hZ4U+xmaqHsJa7s7HZtzuBF7FWwDnAMUSWvYPYgsje4reTurrb\nRy1mEUsZGjoPm727zr23pdTWwu7dXxy9bxs3buSrX90ZWOF2AcSKDl966fbUe3/HHbdpZquiKEqZ\no6JOGZOZrEzf23su3/rWZqxxE6yIeh4riG4Efk2uS/V6rFi7ALjQ7XsRW77Ez/EWrPD6FFEpES8I\nrwdepI1XMcBn6WEpu9iMLWdytrvuOVhh+BzWBbsluP5WoBUr4h4mKodyO9AJ/DuwCCvIbgTWu2s+\ngDEXjr7Xurqt9PS8l6uu2hoIu7C8ylKsAFxNc/OVfP7zu3KeRbKThGfHjhu49NLt/OhHP845NpOk\n/Sxp5wNFUZQpotimwmrZKFP363SURhhrzr6+PjGmSWx26HKxyQkrJOrBOlbpkHxjvOu0WWCNRAWB\nO6SNeS7LtU5sQsQ8sZmz/vxGN77LzeUTJXx5kzABIl+ihs1G9UkNoduzvb1ztPRIX1+fy+r1cye7\nYow/kSG3dMncmAvXJ2FMJflcuWnPPZ87WFEUpRygxNyvRV9AtWzlKuqmKzMy+cGf73V9fasTNL7M\niI+huzlV7NhjuY3oI1Hny5ccPTomiqE7OZgzK7kxcy0p865yIqnBic8Ot29e7BrhPMn7Z+vPLRp9\nT762XBRv1xkThxMRPvHn50u+RDGJ2ezKI36WyfcykXjJ3DqD0c+XxvkpilLqlJqoU/erUhRCN+Ge\nPXs4/fRuF+sFe/dupq3tFFpaFrrRVwMfwbpDTwJej40rm4V1hx7CuigvxsbI/ZK4e9R3evCu3I1Y\nVyy08QADrKeHM9jFb9z4WcB/u/G+w8RZbt6LgnkvdGPfAOwDDPA01k07i7TYO2N+xuHDs9iwoWvU\n1Xjppdtdtm83AEND8Pvff4y6ultH70lt7f0ce+wOfvvb51i+fMV4bnEKN2DvZbd7vZOGhpsmOVc6\nk4mXTCP5M7FvXzdf/epOdc0qiqIUotiqslo2ytRSN52V6b0lZv784yTenzVyDxqzQKLep8miur4e\n3DJnoatz+xckrFwr3PEVErlJj3K9XBe4ThEtwbF5EnWe6EjsX5fYv1DiLtaoILAdm5aNai1lvtdq\nmrXKFxMO+7CO5zmkWTyj83Ld0lNdj66QZXci7tepthCr1U9RlOmAErPUFX0B1bKVq6gTmZ4PxNxY\nrxYn6NJKiDSl7PNFepNz9Lt5FgT7fTutTie0OlyniNmymRPd2Hp3/spAlDW6a/v4vt4Cawlfdwb7\neyWTWTgaM5dsYZbJNEk2u1KShZPb29fF7td4RE4+AR7F7a2LuXmnI35trH8C0n6W0vZNpajTllmK\nokwXKuqqdCtnUTcdpNejW5ZqTUoXUr5mW7zOmxVtJ7lzWtyc8QSGNrpdUsRJbt9iJ+QaJN5XtUts\na7G5wf4FKWtJirplUlOzQObPP1aam7OjdeTse859f+3tnbGerrW1jTmiYzwiZ7zCb7otVlNxjakU\nYtoxQ1GU6aLURJ3G1CklxCC27MfWYN+FwFHEa8VdDPQCt2Jj6XydNz/G17Dbgo1xezfxThFX0sO7\n2cU+oti7AffV17G7CPgmUXeH7mDOtHp4O0fPmzMnw8hILc88cyUAV121lVNPPdWVa/kbRK7HdsU4\nF4CWloXs3r0rKOtxRU7s2GS7Ptxzz/3s2bMnVstuuuPSpuIauTX3NJ5OURRlTIqtKqtlQy11MdLd\nr8skKgXSLNZl6q1sPoZumUS9VpNZrvl6qVpLWpTl+seBZXCuWJdvvszW3K4UtbXNsn79JhcL1+vW\n4q1w9ZLNrkm1DPX390tNjXcv2/ecZpUrdM8KWcDS72lv1bsb1f2qKMp0QYlZ6oq+gGrZVNTl4kVK\nNrtG6utbZf78Y8WYOifowjIk4oRTc0KwJF2h+URdo7TR5wTdfIkSH+oF3jjGuX0Sd982jJYBSZYi\nsevpcjX2cl2saW7A9vbOSd2zQsLOis0OmUxdu5lkJpMXNFFCUZTpoNREnbpflSNmsh0B/LiwdIUx\nF+UZfQNxNyjApVhXrOfHiddbgEHaeINzuS5iF78DbgZOx7YcOwvrOv0JcbfqhdiuFM9iS5VswXa1\nGKahoSkY9yK2OwRu3DmIPEhuSZW01mIEZVvGZjxlPjZu3MjatacwMHAatnRLaTLTJUtmwu2sKIpS\ndIqtKqtlo0ItdUfq2sqfMNHgLGreLZovgcIX0m0Sm9TQKzb7NCtRUsTRspnPS5Rg4S1YK9y5m9y+\nXoFmyWQWSiYzR+JFjqNSJdnsmpS197u5shKVTlnnXq+S9vZ103Kv0ixwpeJuLGQd0+QFRVEqAdRS\np1QSEy02m7TqWfYDXe7748lkBhkZaQC+CMwGPggcBP4XkVVsPzapwSdJ+OLDN+ITJdo43/VyfTe7\nGMJa4oaIihX/Bng/kfXP9lcdGdmCtdRdS9wyuB04m9/85qnku3LjPuxeX0Am8ylGRuaOru/BBy8B\nmILg//i9SiOZZNDZ+V527LiBHTtumLHeqlo8WFEUpQgUW1VWy0aFWuomYnGJLEi9zprVLM3Nx+TE\nrNXX+/pw4bzLJR5Tt8DNEbYIs/Fz1kLXKAfBlS1pdttcd61WZw2ck7h2aMVblseC2CL19a2J95Nr\nRbQFlafWEtXXlxvf58ul+PWk1YArhtXO/lzEy82E779UrImKoihHAiVmqSv6Aqplq1RRN5EPZ/tB\n3yW284PvyrAiRTz5zNewi0S++nC+E4QXi8uCOnRHSdRN4qjEXL4Q8HKJMm+7AhHiM3Fvligbt1ng\nqFhh4CgxIbcjRL5kickG60+0W8N0dGYYL7bQcv6Cyv39/dLevk6am7PS3t6pgk5RlLKk1ESdul+V\nI2Ii9cQOH34C+BG27ty1bu/FWJdikqexiQc92FpxJmXMUqwLdgvQAtxIG6czwC308CZ28SvgMTfX\nTcRdqddjXbC+F+w3sbXqfI27b2FdtZcBvw/2X8ArX3li7P1//vOfdK5Gu6+ubis9Pe/lqqu2ju6r\nrb2EBx98kaGhjwFT747M5wYvHjXE+8yCfQa5rtnBwa05ZyuKoigTR0WdMimSsXF33HHbOM6qAVZj\nhVj4Yd/j9vvvX8BmuoKNgzsE/CXx7NStWFF2CJgFNLrCwrvp4Vx28SMiYdiYshYrCFtbP8jTT+9j\ncPAErDiM1lVbewlDQ89hBV20/+tfvzI2Uz5he+qpp47uO3z4JO677xzSRNeOHTdw+PB/A8O0tCzJ\nG/c2mQLEky1afKSkZfX6fRONw1QURVHGSbFNhdWyUUHu16SrL5NpGs3uLES+NlnWHbrEuTznpRz3\nRYd7BdZIlPXq69U1pLT+ahQbM9crth5dGIu2RHwmq3f91dQsTnWh5nOtTpR8NeomWiw4X0ZpITd4\nMWq0FVqPZr4qilIpUGLu16IvoFq2ShJ16WVIOsYMdrfFehsTsVYNiXi3tD6vPgFipYtrW+hEni1F\nYgsLHyWbOc/tb3JxcNlgfl/qZGFMEPqODvliwMZKThgvaSLHXjP5XjdNWuSUWoHdyQhQRVGUckJF\nXZVulS/qxidGkgHyMD8xV69ESRHequZr0YWJEw0CK6WN6xOtv44dtcLBYifuGoNr+Hpyi8UnS3jR\nYQWnbSsWtu/q7u6WmprFUlOzWLq7uyd935Ii50juY7E5UgFZagJUURRlMqioq9KtkkRdbo/RyJ2Z\nLFuRVmLD77NWsGZJtrSyFjmfHRtmti5yQsyWyGhjoXO51knU+qs3Z012rnC94bgoK7Ovr0/q61ul\npmaxZLNrpL+/f1qtSuXaq1UtbYqiKBYVdVW6VZKoE7Ef7LZxfWMgkCLXZLIvam3tIunr6wvEgBdh\ncUFjv/ZL5GrtCPavGB1n69AZ2cybnWBb6ARfU86aamrmOYvdMjdnlyRj29Jcw7W1i5w1cfriv7zI\nbW/vlPb2dXktV319faMxfpNx/04lGhOnKIpiKTVRp9mvyqTYuHEjJ5xwAwcOvAF42G3r+fCHb+Ca\na25icPAPDA19FJ/hODQEH/hALy+9tMPt6yKZVWrLm7yAzWjNAGcHc3cDdwPvp41bGEDooYldPIXt\nOnEWcCOtrY2cf/65XHPNTcDt9PS8D4B//McdjIz4MioXAVcB2wCblbljxw0MDa0A1gG73Zr/lkce\nuX2K71yc8fQkveqqq7jsso/gy6pcdpnNAt62bduErjXZHr2KoihKmVBsVVktG2VqqUu6S/PHhPVL\nPNEgLeHBH+8Xm8SQlhAx2507T5KJC7AuJSmiUWwB4UYBZP7843KsWemxa80xV6cdsyLnmtnsyqK7\nGqciA3cqXabqflUURbGgljqlXIgXid3PwEBkLdq3r5tt297Lvn2+wO71xIvN7sdaxDw92HpyFwMv\nAedg68J5tgJ/AfwYeAXwc6ATbzWDbtoYYIDLXWHhrwCD2ALBBngGaOCZZ/4JGI816ySam2/n85+3\nNdtsYeRDwMcIrYcNDTdx9tlncs01tjZdT897y9LClVsbbj9nnnk+a9eeMmGr3UQKTiuKoigzSLFV\nZbVslKGlzpbc6BCbmNCZGkcVtclK65Va785fJbbvqreANbr4tz53nk+UaJV4f9cGZz3rlTbmuxi6\npW78XIHjxGa7+qzV+PXr6paKiLUsGRNmztrEC7/+yOq0KmeOZC25YlilpqKsSiGrqlraFEVRJgdq\nqVPKgT179nD//T8B3gl8F/gZae28Nm7cyNq1pzAwYIhb5rYALwK/xFrT3kNum65PAK/BtuQ6hG3Z\n9U4i69w5wK208UkGwFnovgc8BPwpNo5uC9a6tpskg4PPs2fPHgBmzXqR4eEe4CTgLGprP0tv7y0J\nC9bRbk5LXd1W4MRp7X4wnjg3b22MrIXvm3A8XbyzRNyqqh0dFEVRKgMVdUoqO3bcwMjIO4FbgQ+7\nvRcA/xt4C3V1t9LZ+V42bOjiF7/4BVb0vQe4EngK62KdA3zInXsR0IRPTrD9Xz+MdcfOxwqNQWx/\n0JOwCQs7aWOYAV6gh7kuKcILxNVuPUvdfOcSF40XAA2jgml4+JNY0XYD8F3a2k5i48aNif6oG4Fu\nmpuvdG7JndPaPzXZA7VQP9ht27ZNWMiFhC7Te+55kqeemvRUiqIoSomiok4pwHexwituYctkPsO6\ndWsTGaVbgPVuOxNYSXqP199ie7YOA/vJZpfz8MO/ZmRkHfATop6vW2njLQxwCz28m118EfgRUA98\nDngWK8J8P9UPY61sFwO1WFH5VmzmrGej23byyCNXsmFDF52dfxTEBUJd3a18/vNxYTVdvVNnugeq\nz7SNxKTdP1P9YBVFUZTpRUWdkkpv77nceefbGBlJHlnKyMh5fOtbPcC1xEWbt2pdQ5o71Frgbsda\n/w5hTA+f/OTn+cIXvsDOnZ8hLHHSxqMM8H/TwyvYRQewD/iVO74aKyIfxIs0uAJjfo7Ii8AS4Czq\n6m4dFSuhMIMLeOqpcxgYWM2+fVvZtu297N27273vuKArRlLAdJce0UQHRVGUCqXYQX3VslGGiRJ9\nfX2SyTRJPMHAd2lIS4zoCBIW+iUqaxK2+9o0Ot6YJunu7pZMZqHA0aPztbHftf6aJ1EnCX/dTRIv\nS5LbJSKTaZL29nU5/UbXr98k8+cf58ZF6y5W4dy00iDxAs2axKAoilLKoIkSSrmwbds2Tj31VC69\ndDv33/8AIyPvAA6RyVzMyMjrsWVILMZcyLx5cwF4/vlehod3AK/HxtKtwBcHtu7SncBWRF7Pzp23\nAx/HJmFc4Cx0H6WHF9lFDdYFfAuRRS4imz2OE07YzT333M9TT52DDf6HkRFoadmdY3EDePOb92It\nfcUnzWI20y5ZRVEUpXJQUafESHP9+Tgsu/9hOjt7ueqqTzA4+BrgMuA5jHmeZ5/9OAA1Neczf/4/\nMnv2bP7qr/6agwefAR7mF79YxoED38XGuVmXqRV03uV6p6tD18Qu3gh8Extnd8iNv4Bpyxh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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1,figsize=(9,9))\n", + "ax.scatter(y,y_pred)\n", + "ax.plot([0, np.max(y)], [0, np.max(y)], color=[1,0,0])\n", + "axy=ax.get_ylim()\n", + "axx=ax.get_xlim()\n", + "plt.xlim(0,2000000)\n", + "plt.ylim(0,2000000)\n", + "fig.suptitle('Zoom in Results', fontsize=20)\n", + "plt.xlabel('True ($CAD)')\n", + "plt.ylabel('Estimated ($CAD)')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/data/realestate/realestate_condo.csv b/data/realestate/realestate_condo.csv new file mode 100644 index 0000000..9b11e24 --- /dev/null +++ b/data/realestate/realestate_condo.csv @@ -0,0 +1,2247 @@ +PA_ASSESSED_VALUE,PA_LATITUDE,PA_LONGITUDE,Acres,Bathrooms,Bedrooms,Beds Above Grade,Condo Fee,Construction – Wood Frame,Construction – Concrete,Construction – Concrete Block,Construction – Insulated Concrete,Construction – Modular,Construction – Prefab,Construction – Steel Frame,Ensuite,Exposure – North,Exposure – Northwest,Exposure – West,Exposure – Southwest,Exposure – South,Exposure – Southeast,Exposure – East,Exposure – Northeast,Fireplace,Foundation – Concrete,Foundation – Block,Foundation – Brick/Stone/Block,Foundation – Concrete Slab,Foundation – Grade Beam,Foundation – Piling,Foundation – Preserved Wood,Foundation – Other,Full Baths,Half Baths,Has Basement,Has Pool,Heating – Forced Air-1,Heating – Forced Air-2,Heating – Baseboard,Heating – Fan Coil,Heating – Heat Pump,Heating – Hot Water,Heating – In Floor Heat System,Heating – Other,Is Waterfront,Parking Spaces,Roof – Asphalt Shingles,Roof – Cedar Shakes,Roof – Clay Tile,Roof – Concrete Tiles,Roof – EPDM Membrane,Roof – Metal,Roof – Pine Shakes,Roof – Roll Roofing,Roof – Tar & Gravel,Roof – Vinyl Shingles,Roof – Wood Shingles,Roof – Other,Square Footage,Style - 2 and Half Storey,Style - 2 Storey,Style - 2 Storey Split,Style - 3 Level Split,Style - 3 Storey,Style - 4 Level Split,Style - 5 Level Split,Style – Bi-Level,Style – Bungalow,Style - Bungalow Semi,Style - Hillside Bungalow,Style – Loft,Style - Modular Home,Style - Multi Level Apartment,Style- Other,Style - Not Applicable,Style – Penthouse,Style - Raised Bungalow,Style - Single Level Apartment,Style - Studio Suite,Sub-Type – 4Plex,Sub-Type - Apartment High Rise,Sub-Type – Carriage,Sub-Type - Detached Condominium,Sub-Type - Duplex Side By Side,Sub-Type - Duplex Up And Down,Sub-Type - Half Duplex,Sub-Type - Lowrise 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+275500,53.4164657,-113.4531132,0.04,3,3,3,150,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1203,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2012,6051,4625,1376,2109,1407,5041,6390,3287,285000 +276500,53.4164657,-113.4531132,0.04,3,3,3,142,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1203,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2011,6051,4625,1376,2109,1407,5041,6390,3287,283000 +376895.8472,53.4114948,-113.4515012,0,3,2,2,160,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1082,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2015,6478,5052,792,2537,823,5469,6817,3765,279900 +289500,53.42412106,-113.4607216,0.05,2,3,3,196,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1302,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2005,6178,4752,1981,2236,1278,5168,6069,1597,279900 +376895.8472,53.4042803,-113.5208995,0,3,3,3,90,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1203,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2015,8683,2068,3293,2635,2585,10504,6131,2282,279900 +253000,53.42603096,-113.4571726,0.05,2,3,3,163,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1167,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2005,5619,4192,2686,1677,365,4609,5476,1752,274900 +376895.8472,53.4031962,-113.5207027,0,3,2,2,90,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1121,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2015,8805,2190,3416,2757,2707,10626,6253,2405,269900 +201000,53.42554373,-113.4543844,0,2,2,2,295,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,969,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2007,5438,4012,2498,1497,450,4429,5561,1784,267500 +257500,53.42603096,-113.4571726,0.05,3,2,2,162,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1109,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2005,5619,4192,2686,1677,365,4609,5476,1752,264900 +216500,53.4208571,-113.434858,0,2,2,2,415,1,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,973,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2013,5479,4053,2142,611,1089,4470,5818,3608,259900 +259000,53.42945485,-113.4699748,0,2,3,3,182,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,0,0,0,1151,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2003,7856,4614,2984,2363,814,4938,4516,792,259900 +252000,53.43107452,-113.4654276,0.06,2,2,2,1780,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1137,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2003,8236,4994,3151,2743,654,5318,4896,1172,259000 +312000,53.61480367,-113.5489889,0.07,3,4,3,212,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1378,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2008,3382,2097,1420,512,532,2460,2155,2674,254900 +221500,53.4208571,-113.434858,0,2,2,2,409,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,973,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2013,5479,4053,2142,611,1089,4470,5818,3608,249900 +247000,53.43107452,-113.4654276,0.06,2,3,3,155,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,1204,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2003,8236,4994,3151,2743,654,5318,4896,1172,249000 +203500,53.4208571,-113.434858,0,2,2,2,404,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,938,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,2013,5479,4053,2142,611,1089,4470,5818,3608,248900 +225000,53.41192383,-113.4593744,0,1,2,2,166,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,872,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2013,7119,5693,220,3178,251,6110,7750,3545,237500 +252000,53.41973553,-113.4608445,0.05,1,2,2,259,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,953,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,2006,6466,5039,1854,2524,1267,5456,6356,1884,236999 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+202000,53.43740227,-113.622746,0.11,3,3,3,0,1,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2570,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,3450,5666,5015,2289,2162,6936,5215,4651,782000 +376895.8472,53.4007557,-113.5197981,0,4,4,4,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3405,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,9142,2527,3753,3094,3044,10963,6590,2742,779700 +724000,53.3993521,-113.5171717,0.16,4,4,4,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,3056,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,9550,3189,4161,3756,3706,11625,7252,3150,759900 +184000,53.40059654,-113.5187581,0.12,3,4,4,0,1,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2650,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,9845,3230,4456,3797,3747,11666,7293,3445,759900 +699500,53.41656156,-113.5146161,0.15,4,5,4,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,1,1,0,1,0,0,0,0,0,0,0,0,5,1,0,0,0,0,0,0,0,0,0,0,0,0,2774,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2013,7837,1335,2359,1000,527,9134,4496,2191,750000 +626000,53.41490987,-113.5695709,0.13,3,3,3,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2544,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2014,4869,4771,3839,2833,1966,10650,7877,4559,749900 +729000,53.41207095,-113.5236025,0.12,3,3,3,0,1,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2517,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2008,8326,922,1759,1489,1439,10232,4986,1733,749900 +769500,53.44373082,-113.6087985,0.13,3,3,3,0,1,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,2616,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2013,4692,6239,5588,2862,2735,7509,5788,5225,749900 +748000,53.40018157,-113.5200805,0.15,4,4,4,0,1,0,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,3,1,1,0,0,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,3174,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2014,8937,2590,3548,3157,3107,11026,6654,2537,749900 +198000,53.42327137,-113.6423985,0.13,3,3,3,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,2,1,0,0,0,0,0,0,0,0,0,0,0,0,2638,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2014,3690,7849,7198,4433,3235,9119,7398,6835,749888 +748000,53.40410428,-113.5246302,0.14,4,6,4,0,1,0,0,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,4,0,1,0,0,1,0,0,0,0,0,0,0,6,1,0,0,0,0,0,0,0,0,0,0,0,0,3168,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2011,8984,2148,3595,2855,2805,11598,6352,1484,745000 +180000,53.42227433,-113.5927169,0,3,3,3,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2777,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,2084,4673,4407,1462,2234,8863,7142,6113,745000 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+241000,53.42719774,-113.6282312,0,4,5,3,0,1,0,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,3,1,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2344,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2014,2746,6905,6254,3489,1939,8175,6454,5891,688600 +700000,53.41777246,-113.5063971,0.14,4,4,3,0,1,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,4,0,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2546,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2004,8235,1726,2750,1390,680,8911,4895,3040,688000 +210000,53.40059049,-113.5175098,0,4,3,3,0,1,0,0,1,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,2,2,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2390,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,9684,3069,4294,3636,3586,11505,7132,3283,686900 +622500,53.42173264,-113.5330933,0.13,4,4,3,0,0,1,0,1,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,3,1,1,0,0,1,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,2518,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2002,6557,1225,523,494,381,10122,4842,1861,685000 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+151500,53.4008785,-113.518761,0.1,4,3,3,0,1,0,0,1,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,2,2,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2379,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2015,9814,3199,4424,3766,3716,11635,7262,3413,668900 +640000,53.42837505,-113.6099542,0.11,3,4,3,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,2,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2273,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2010,1512,5609,4958,1999,376,6879,5158,4595,664800 +724500,53.41959219,-113.5080361,0.14,3,4,2,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,4,1,0,0,0,0,0,0,0,0,0,0,0,0,1525,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,2002,7875,1366,2390,1030,486,8551,4534,3165,660000 +402500,53.39815233,-113.5182565,0.12,3,5,5,0,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,3,0,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,2845,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2014,9403,3973,4014,4033,4096,13283,8036,3003,659900 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