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@ -12,6 +12,7 @@ import warnings |
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import copy |
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import copy |
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from joblib import Memory |
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from joblib import Memory |
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from itertools import chain |
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from itertools import chain |
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import ast |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.neighbors import KNeighborsClassifier |
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from sklearn.neighbors import KNeighborsClassifier |
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@ -72,15 +73,6 @@ def Reset(): |
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# Initializing models |
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# Initializing models |
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global classifiersId |
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classifiersId = [] |
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global classifiersIDwithFI |
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classifiersIDwithFI = [] |
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global classifiersIDPlusParams |
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classifiersIDPlusParams = [] |
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global classifierID |
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classifierID = 0 |
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global resultsList |
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global resultsList |
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resultsList = [] |
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resultsList = [] |
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@ -146,15 +138,6 @@ def RetrieveFileName(): |
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# Initializing models |
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# Initializing models |
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global classifiersId |
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classifiersId = [] |
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global classifiersIDwithFI |
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classifiersIDwithFI = [] |
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global classifiersIDPlusParams |
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classifiersIDPlusParams = [] |
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global classifierID |
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classifierID = 0 |
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global RetrieveModelsList |
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global RetrieveModelsList |
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RetrieveModelsList = [] |
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RetrieveModelsList = [] |
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@ -170,9 +153,7 @@ def RetrieveFileName(): |
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global crossValidation |
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global crossValidation |
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crossValidation = 3 |
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crossValidation = 3 |
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global scoring |
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#scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'} |
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#scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted', 'neg_log_loss': 'neg_log_loss', 'r2': 'r2', 'neg_mean_absolute_error': 'neg_mean_absolute_error', 'neg_mean_absolute_error': 'neg_mean_absolute_error'} |
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scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'} |
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global yPredictProb |
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global yPredictProb |
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yPredictProb = [] |
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yPredictProb = [] |
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@ -296,7 +277,7 @@ def GridSearch(clf, params): |
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number_of_columns = len(df_cv_results.iloc[0]) |
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number_of_columns = len(df_cv_results.iloc[0]) |
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df_cv_results_per_item = [] |
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df_cv_results_per_item = [] |
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df_cv_results_per_row = [] |
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df_cv_results_per_row = [] |
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for i in range(number_of_classifiers): |
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for i in range(number_of_classifiers): |
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df_cv_results_per_item = [] |
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df_cv_results_per_item = [] |
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for column in df_cv_results.iloc[0]: |
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for column in df_cv_results.iloc[0]: |
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@ -398,21 +379,15 @@ def Preprocessing(): |
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global resultsList |
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global resultsList |
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df_cv_results_classifiersList = [] |
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df_cv_results_classifiersList = [] |
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parametersList = [] |
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parametersList = [] |
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#FeatureImportanceList = [] |
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PerClassMetricsList = [] |
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PerClassMetricsList = [] |
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FeatureAccuracyList = [] |
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FeatureAccuracyList = [] |
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#RFEListPD = [] |
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#perm_imp_rfpimp = [] |
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perm_imp_eli5PD = [] |
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perm_imp_eli5PD = [] |
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featureScores = [] |
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featureScores = [] |
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for j, result in enumerate(resultsList): |
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for j, result in enumerate(resultsList): |
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df_cv_results_classifiersList.append(resultsList[j][0]) |
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df_cv_results_classifiersList.append(resultsList[j][0]) |
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parametersList.append(resultsList[j][1]) |
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parametersList.append(resultsList[j][1]) |
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#FeatureImportanceList.append(resultsList[j][2]) |
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PerClassMetricsList.append(resultsList[j][2]) |
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PerClassMetricsList.append(resultsList[j][2]) |
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FeatureAccuracyList.append(resultsList[j][3]) |
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FeatureAccuracyList.append(resultsList[j][3]) |
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#RFEListPD.append(resultsList[j][5]) |
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#perm_imp_rfpimp.append(resultsList[j][6]) |
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perm_imp_eli5PD.append(resultsList[j][4]) |
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perm_imp_eli5PD.append(resultsList[j][4]) |
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featureScores.append(resultsList[j][5]) |
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featureScores.append(resultsList[j][5]) |
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@ -427,9 +402,8 @@ def Preprocessing(): |
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featureScoresCon = pd.concat(featureScores, ignore_index=True, sort=False) |
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featureScoresCon = pd.concat(featureScores, ignore_index=True, sort=False) |
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global factors |
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global factors |
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factors = [1,1,1,1,1,1] |
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factors = [1,1,1,1,1,1] |
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global scoring |
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NumberofscoringMetrics = len(scoring) |
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global df_cv_results_classifiers_metrics |
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global df_cv_results_classifiers_metrics |
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global NumberofscoringMetrics |
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del df_cv_results_classifiers['params'] |
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del df_cv_results_classifiers['params'] |
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df_cv_results_classifiers_metrics = df_cv_results_classifiers.copy() |
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df_cv_results_classifiers_metrics = df_cv_results_classifiers.copy() |
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del df_cv_results_classifiers_metrics['mean_fit_time'] |
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del df_cv_results_classifiers_metrics['mean_fit_time'] |
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@ -441,10 +415,18 @@ def sumPerMetric(factors): |
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sumPerClassifier = [] |
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sumPerClassifier = [] |
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preProcessResults = [] |
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preProcessResults = [] |
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preProcessResults = Preprocessing() |
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preProcessResults = Preprocessing() |
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loopThroughMetrics = preProcessResults[4] |
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loopThroughMetrics = preProcessResults[3] |
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global scoring |
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global metricsPerModel |
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metricsPerModel = [] |
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metricsPerModel.append(loopThroughMetrics['mean_test_accuracy'].sum()/loopThroughMetrics['mean_test_accuracy'].count()) |
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metricsPerModel.append(loopThroughMetrics['mean_test_f1_macro'].sum()/loopThroughMetrics['mean_test_f1_macro'].count()) |
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metricsPerModel.append(loopThroughMetrics['mean_test_precision'].sum()/loopThroughMetrics['mean_test_precision'].count()) |
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metricsPerModel.append(loopThroughMetrics['mean_test_recall'].sum()/loopThroughMetrics['mean_test_recall'].count()) |
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metricsPerModel.append(loopThroughMetrics['mean_test_jaccard'].sum()/loopThroughMetrics['mean_test_jaccard'].count()) |
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for index, row in loopThroughMetrics.iterrows(): |
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for index, row in loopThroughMetrics.iterrows(): |
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rowSum = 0 |
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rowSum = 0 |
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global scoring |
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lengthFactors = len(scoring) |
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lengthFactors = len(scoring) |
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for loop,elements in enumerate(row): |
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for loop,elements in enumerate(row): |
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lengthFactors = lengthFactors - 1 + factors[loop] |
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lengthFactors = lengthFactors - 1 + factors[loop] |
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@ -470,7 +452,7 @@ def RetrieveFactors(): |
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ModelSpaceTSNENew = [] |
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ModelSpaceTSNENew = [] |
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preProcessResults = [] |
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preProcessResults = [] |
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preProcessResults = Preprocessing() |
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preProcessResults = Preprocessing() |
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XClassifiers = preProcessResults[4] |
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XClassifiers = preProcessResults[3] |
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flagLocal = 0 |
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flagLocal = 0 |
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countRemovals = 0 |
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countRemovals = 0 |
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for l,el in enumerate(FactorsInt['Factors']): |
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for l,el in enumerate(FactorsInt['Factors']): |
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@ -489,10 +471,12 @@ def UpdateOverview(): |
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global sumPerClassifierSel |
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global sumPerClassifierSel |
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global ModelSpaceMDSNew |
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global ModelSpaceMDSNew |
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global ModelSpaceTSNENew |
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global ModelSpaceTSNENew |
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global metricsPerModel |
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ResultsUpdateOverview = [] |
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ResultsUpdateOverview = [] |
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ResultsUpdateOverview.append(sumPerClassifierSel) |
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ResultsUpdateOverview.append(sumPerClassifierSel) |
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ResultsUpdateOverview.append(ModelSpaceMDSNew) |
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ResultsUpdateOverview.append(ModelSpaceMDSNew) |
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ResultsUpdateOverview.append(ModelSpaceTSNENew) |
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ResultsUpdateOverview.append(ModelSpaceTSNENew) |
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ResultsUpdateOverview.append(metricsPerModel) |
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response = { |
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response = { |
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'Results': ResultsUpdateOverview |
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'Results': ResultsUpdateOverview |
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} |
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} |
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@ -506,7 +490,7 @@ def InitializeEnsemble(): |
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mergedPredListListForm = [] |
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mergedPredListListForm = [] |
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for el in mergedPredList: |
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for el in mergedPredList: |
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mergedPredListListForm.append(list(chain(*el))) |
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mergedPredListListForm.append(list(chain(*el))) |
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XClassifiers = preProcessResults[4] |
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XClassifiers = preProcessResults[3] |
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PredictionSpace = FunTsne(mergedPredListListForm) |
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PredictionSpace = FunTsne(mergedPredListListForm) |
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DataSpace = FunTsne(XData) |
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DataSpace = FunTsne(XData) |
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ModelSpaceMDS = FunMDS(XClassifiers) |
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ModelSpaceMDS = FunMDS(XClassifiers) |
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@ -522,38 +506,30 @@ def InitializeEnsemble(): |
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def ReturnResults(sumPerClassifier,ModelSpaceMDS,ModelSpaceTSNE,preProcessResults,DataSpaceList,PredictionSpaceList): |
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def ReturnResults(sumPerClassifier,ModelSpaceMDS,ModelSpaceTSNE,preProcessResults,DataSpaceList,PredictionSpaceList): |
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global Results |
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global Results |
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Results = [] |
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Results = [] |
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#FeatureImportanceListPD = preProcessResults[1] |
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parametersGen = preProcessResults[0] |
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PerClassMetrics = preProcessResults[1] |
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PerClassMetrics = preProcessResults[1] |
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FeatureAccuracy = preProcessResults[2] |
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FeatureAccuracy = preProcessResults[2] |
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#RFEListPDCon = preProcessResults[5] |
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#perm_imp_rfpimpCon = preProcessResults[6] |
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perm_imp_eli5PDCon = preProcessResults[4] |
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perm_imp_eli5PDCon = preProcessResults[4] |
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featureScoresCon = preProcessResults[5] |
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featureScoresCon = preProcessResults[5] |
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#FeatureImportanceListPD = FeatureImportanceListPD.to_json(orient='records') |
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parametersGenPD = parametersGen.to_json(orient='records') |
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PerClassMetrics = PerClassMetrics.to_json(orient='records') |
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PerClassMetrics = PerClassMetrics.to_json(orient='records') |
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FeatureAccuracy = FeatureAccuracy.to_json(orient='records') |
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FeatureAccuracy = FeatureAccuracy.to_json(orient='records') |
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#RFEListPDCon = RFEListPDCon.to_json(orient='records') |
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#perm_imp_rfpimpCon = perm_imp_rfpimpCon.to_json(orient='records') |
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perm_imp_eli5PDCon = perm_imp_eli5PDCon.to_json(orient='records') |
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perm_imp_eli5PDCon = perm_imp_eli5PDCon.to_json(orient='records') |
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featureScoresCon = featureScoresCon.to_json(orient='records') |
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featureScoresCon = featureScoresCon.to_json(orient='records') |
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XDataJSON = XData.columns.tolist() |
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XDataJSON = XData.columns.tolist() |
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global metricsPerModel |
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Results.append(json.dumps(sumPerClassifier)) # Position: 0 |
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Results.append(json.dumps(sumPerClassifier)) # Position: 0 |
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Results.append(json.dumps(ModelSpaceMDS)) # Position: 1 |
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Results.append(json.dumps(ModelSpaceMDS)) # Position: 1 |
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Results.append(json.dumps(classifiersIDPlusParams)) # Position: 2 |
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Results.append(json.dumps(parametersGenPD)) # Position: 2 |
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#Results.append(FeatureImportanceListPD) # Position: 3 |
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Results.append(PerClassMetrics) # Position: 3 |
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Results.append(PerClassMetrics) # Position: 3 |
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Results.append(json.dumps(target_names)) # Position: 4 |
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Results.append(json.dumps(target_names)) # Position: 4 |
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Results.append(FeatureAccuracy) # Position: 5 |
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Results.append(FeatureAccuracy) # Position: 5 |
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Results.append(json.dumps(XDataJSON)) # Position: 6 |
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Results.append(json.dumps(XDataJSON)) # Position: 6 |
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Results.append(json.dumps(classifiersId)) # Position: 7 |
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Results.append(json.dumps(DataSpaceList)) # Position: 7 |
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Results.append(json.dumps(classifiersIDwithFI)) # Position: 8 |
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Results.append(json.dumps(PredictionSpaceList)) # Position: 8 |
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Results.append(json.dumps(DataSpaceList)) # Position: 9 |
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Results.append(json.dumps(metricsPerModel)) # Position: 9 |
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Results.append(json.dumps(PredictionSpaceList)) # Position: 10 |
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Results.append(perm_imp_eli5PDCon) # Position: 10 |
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Results.append(json.dumps(ModelSpaceTSNE)) # Position: 11 |
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Results.append(featureScoresCon) # Position: 11 |
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#Results.append(RFEListPDCon) # Position: 13 |
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#Results.append(perm_imp_rfpimpCon) # Position: 14 |
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Results.append(perm_imp_eli5PDCon) # Position: 12 |
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Results.append(featureScoresCon) # Position: 13 |
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return Results |
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return Results |
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# Retrieve data from client |
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# Retrieve data from client |
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@ -581,7 +557,9 @@ def FeatureSelPerModel(): |
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resultsList = [] |
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resultsList = [] |
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global loopFeatures |
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global loopFeatures |
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loopFeatures = 2 |
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loopFeatures = 2 |
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for index, eachalgor in enumerate(algorithmList): |
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algorithmsWithoutDuplicates = list(dict.fromkeys(algorithmList)) |
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for index, eachalgor in enumerate(algorithmsWithoutDuplicates): |
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if (eachalgor == 'KNN'): |
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if (eachalgor == 'KNN'): |
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clf = KNeighborsClassifier() |
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clf = KNeighborsClassifier() |
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params = detailsParams[index] |
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params = detailsParams[index] |
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@ -595,8 +573,7 @@ def FeatureSelPerModel(): |
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if (featureSelection['featureSelection'] == ''): |
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if (featureSelection['featureSelection'] == ''): |
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key = 0 |
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key = 0 |
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else: |
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else: |
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key = 2 |
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key = 2 |
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EnsembleModel(ClassifierIDsList, key) |
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return 'Everything Okay' |
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return 'Everything Okay' |
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def FunMDS (data): |
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def FunMDS (data): |
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@ -621,11 +598,13 @@ def EnsembleModel (ClassifierIDsList, keyRetrieved): |
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global all_classifiers |
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global all_classifiers |
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global algorithmList |
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global algorithmList |
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algorithmsWithoutDuplicates = list(dict.fromkeys(algorithmList)) |
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if (keyRetrieved == 0): |
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|
|
if (keyRetrieved == 0): |
|
|
|
columnsInit = [] |
|
|
|
columnsInit = [] |
|
|
|
all_classifiers = [] |
|
|
|
all_classifiers = [] |
|
|
|
columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData] |
|
|
|
columnsInit = [XData.columns.get_loc(c) for c in XData.columns if c in XData] |
|
|
|
for index, eachelem in enumerate(algorithmList): |
|
|
|
|
|
|
|
|
|
|
|
for index, eachelem in enumerate(algorithmsWithoutDuplicates): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
for each in resultsList[index][1]: |
|
|
|
for each in resultsList[index][1]: |
|
|
|
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**each))) |
|
|
|
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsInit), KNeighborsClassifier().set_params(**each))) |
|
|
|
@ -640,7 +619,7 @@ def EnsembleModel (ClassifierIDsList, keyRetrieved): |
|
|
|
random_state=RANDOM_SEED, |
|
|
|
random_state=RANDOM_SEED, |
|
|
|
n_jobs = -1) |
|
|
|
n_jobs = -1) |
|
|
|
elif (keyRetrieved == 1): |
|
|
|
elif (keyRetrieved == 1): |
|
|
|
ClassifierIDsList = json.loads(ClassifierIDsList) |
|
|
|
ClassifierIDsList = json.loads(ClassifierIDsList) |
|
|
|
for loop in ClassifierIDsList['ClassifiersList']: |
|
|
|
for loop in ClassifierIDsList['ClassifiersList']: |
|
|
|
temp = [int(s) for s in re.findall(r'\b\d+\b', loop)] |
|
|
|
temp = [int(s) for s in re.findall(r'\b\d+\b', loop)] |
|
|
|
all_classifiersSelection.append(all_classifiers[temp[0]]) |
|
|
|
all_classifiersSelection.append(all_classifiers[temp[0]]) |
|
|
|
@ -656,7 +635,7 @@ def EnsembleModel (ClassifierIDsList, keyRetrieved): |
|
|
|
lr = LogisticRegression() |
|
|
|
lr = LogisticRegression() |
|
|
|
if (len(all_classifiersSelection) == 0): |
|
|
|
if (len(all_classifiersSelection) == 0): |
|
|
|
all_classifiers = [] |
|
|
|
all_classifiers = [] |
|
|
|
for index, eachelem in enumerate(algorithmList): |
|
|
|
for index, eachelem in enumerate(algorithmsWithoutDuplicates): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
for j, each in enumerate(resultsList[index][1]): |
|
|
|
for j, each in enumerate(resultsList[index][1]): |
|
|
|
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), KNeighborsClassifier().set_params(**each))) |
|
|
|
all_classifiers.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), KNeighborsClassifier().set_params(**each))) |
|
|
|
@ -671,7 +650,7 @@ def EnsembleModel (ClassifierIDsList, keyRetrieved): |
|
|
|
random_state=RANDOM_SEED, |
|
|
|
random_state=RANDOM_SEED, |
|
|
|
n_jobs = -1) |
|
|
|
n_jobs = -1) |
|
|
|
else: |
|
|
|
else: |
|
|
|
for index, eachelem in enumerate(algorithmList): |
|
|
|
for index, eachelem in enumerate(algorithmsWithoutDuplicates): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
if (eachelem == 'KNN'): |
|
|
|
for j, each in enumerate(resultsList[index][1]): |
|
|
|
for j, each in enumerate(resultsList[index][1]): |
|
|
|
all_classifiersSelection.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), KNeighborsClassifier().set_params(**each))) |
|
|
|
all_classifiersSelection.append(make_pipeline(ColumnSelector(cols=columnsReduce[j]), KNeighborsClassifier().set_params(**each))) |
|
|
|
@ -726,6 +705,8 @@ def RetrieveModel(): |
|
|
|
global parametersPerformancePerModel |
|
|
|
global parametersPerformancePerModel |
|
|
|
parametersPerformancePerModel = [] |
|
|
|
parametersPerformancePerModel = [] |
|
|
|
global algorithms |
|
|
|
global algorithms |
|
|
|
|
|
|
|
global factors |
|
|
|
|
|
|
|
factors = [1,1,1,1,1,1] |
|
|
|
algorithms = RetrievedModel['Algorithms'] |
|
|
|
algorithms = RetrievedModel['Algorithms'] |
|
|
|
for eachAlgor in algorithms: |
|
|
|
for eachAlgor in algorithms: |
|
|
|
if (eachAlgor) == 'KNN': |
|
|
|
if (eachAlgor) == 'KNN': |
|
|
|
@ -734,22 +715,31 @@ def RetrieveModel(): |
|
|
|
else: |
|
|
|
else: |
|
|
|
clf = RandomForestClassifier() |
|
|
|
clf = RandomForestClassifier() |
|
|
|
params = {'n_estimators': list(range(80, 120)), 'criterion': ['gini', 'entropy']} |
|
|
|
params = {'n_estimators': list(range(80, 120)), 'criterion': ['gini', 'entropy']} |
|
|
|
GridSearchForParameters(clf, params, eachAlgor) |
|
|
|
GridSearchForParameters(clf, params, eachAlgor, factors) |
|
|
|
SendEachClassifiersPerformanceToVisualize() |
|
|
|
SendEachClassifiersPerformanceToVisualize() |
|
|
|
return 'Everything Okay' |
|
|
|
return 'Everything Okay' |
|
|
|
|
|
|
|
|
|
|
|
def GridSearchForParameters(clf, params, eachAlgor): |
|
|
|
def GridSearchForParameters(clf, params, eachAlgor, factors): |
|
|
|
|
|
|
|
global scoring |
|
|
|
|
|
|
|
global NumberofscoringMetrics |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'} |
|
|
|
|
|
|
|
NumberofscoringMetrics = len(scoring) |
|
|
|
|
|
|
|
|
|
|
|
grid = GridSearchCV(estimator=clf, |
|
|
|
grid = GridSearchCV(estimator=clf, |
|
|
|
param_grid=params, |
|
|
|
param_grid=params, |
|
|
|
scoring='accuracy', |
|
|
|
scoring=scoring, |
|
|
|
cv=crossValidation, |
|
|
|
cv=crossValidation, |
|
|
|
|
|
|
|
refit='accuracy', |
|
|
|
n_jobs = -1) |
|
|
|
n_jobs = -1) |
|
|
|
grid.fit(XData, yData) |
|
|
|
grid.fit(XData, yData) |
|
|
|
|
|
|
|
yPredict = grid.predict(XData) |
|
|
|
cv_results = [] |
|
|
|
cv_results = [] |
|
|
|
cv_results.append(grid.cv_results_) |
|
|
|
cv_results.append(grid.cv_results_) |
|
|
|
df_cv_results = pd.DataFrame.from_dict(cv_results) |
|
|
|
df_cv_results = pd.DataFrame.from_dict(cv_results) |
|
|
|
number_of_classifiers = len(df_cv_results.iloc[0][0]) |
|
|
|
number_of_classifiers = len(df_cv_results.iloc[0][0]) |
|
|
|
number_of_columns = len(df_cv_results.iloc[0]) |
|
|
|
number_of_columns = len(df_cv_results.iloc[0]) |
|
|
|
|
|
|
|
|
|
|
|
df_cv_results_per_item = [] |
|
|
|
df_cv_results_per_item = [] |
|
|
|
df_cv_results_per_row = [] |
|
|
|
df_cv_results_per_row = [] |
|
|
|
|
|
|
|
|
|
|
|
@ -761,9 +751,42 @@ def GridSearchForParameters(clf, params, eachAlgor): |
|
|
|
df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns) |
|
|
|
df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns) |
|
|
|
|
|
|
|
|
|
|
|
global allParametersPerformancePerModel |
|
|
|
global allParametersPerformancePerModel |
|
|
|
parametersPerformancePerModel = df_cv_results_classifiers[['mean_test_score','params']] |
|
|
|
global parametersPerformancePerModel |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
metrics = df_cv_results_classifiers.copy() |
|
|
|
|
|
|
|
del metrics['mean_fit_time'] |
|
|
|
|
|
|
|
del metrics['mean_score_time'] |
|
|
|
|
|
|
|
metrics = metrics.ix[:, 0:NumberofscoringMetrics] |
|
|
|
|
|
|
|
sumperModel = [] |
|
|
|
|
|
|
|
global rowSum |
|
|
|
|
|
|
|
for index, row in metrics.iterrows(): |
|
|
|
|
|
|
|
rowSum = 0 |
|
|
|
|
|
|
|
lengthFactors = NumberofscoringMetrics |
|
|
|
|
|
|
|
for loop,elements in enumerate(row): |
|
|
|
|
|
|
|
lengthFactors = lengthFactors - 1 + factors[loop] |
|
|
|
|
|
|
|
rowSum = elements*factors[loop] + rowSum |
|
|
|
|
|
|
|
if lengthFactors is 0: |
|
|
|
|
|
|
|
sumperModel = 0 |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
sumperModel.append(rowSum/lengthFactors) |
|
|
|
|
|
|
|
global target_names |
|
|
|
|
|
|
|
global PerClassMetric |
|
|
|
|
|
|
|
global PerClassMetricPandas |
|
|
|
|
|
|
|
PerClassMetric = [] |
|
|
|
|
|
|
|
yPredictProb.append(grid.predict_proba(XData)) |
|
|
|
|
|
|
|
PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True)) |
|
|
|
|
|
|
|
PerClassMetricPandas = pd.DataFrame(PerClassMetric) |
|
|
|
|
|
|
|
del PerClassMetricPandas['accuracy'] |
|
|
|
|
|
|
|
del PerClassMetricPandas['macro avg'] |
|
|
|
|
|
|
|
del PerClassMetricPandas['weighted avg'] |
|
|
|
|
|
|
|
summarizedMetrics = pd.DataFrame(sumperModel) |
|
|
|
|
|
|
|
summarizedMetrics.rename(columns={0:'sum'}) |
|
|
|
|
|
|
|
parameters = pd.DataFrame(df_cv_results_classifiers['params']) |
|
|
|
|
|
|
|
parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1) |
|
|
|
|
|
|
|
PerClassMetricPandas = PerClassMetricPandas.to_json() |
|
|
|
parametersPerformancePerModel = parametersPerformancePerModel.to_json() |
|
|
|
parametersPerformancePerModel = parametersPerformancePerModel.to_json() |
|
|
|
allParametersPerformancePerModel.append(parametersPerformancePerModel) |
|
|
|
allParametersPerformancePerModel.append(parametersPerformancePerModel) |
|
|
|
|
|
|
|
allParametersPerformancePerModel.append(PerClassMetricPandas) |
|
|
|
return 'Everything is okay' |
|
|
|
return 'Everything is okay' |
|
|
|
|
|
|
|
|
|
|
|
#GridSearchForParameters = mem.cache(GridSearchForParameters) |
|
|
|
#GridSearchForParameters = mem.cache(GridSearchForParameters) |
|
|
|
@ -772,7 +795,7 @@ def GridSearchForParameters(clf, params, eachAlgor): |
|
|
|
@app.route('/data/PerformanceForEachModel', methods=["GET", "POST"]) |
|
|
|
@app.route('/data/PerformanceForEachModel', methods=["GET", "POST"]) |
|
|
|
def SendEachClassifiersPerformanceToVisualize (): |
|
|
|
def SendEachClassifiersPerformanceToVisualize (): |
|
|
|
response = { |
|
|
|
response = { |
|
|
|
'PerformancePerModel': allParametersPerformancePerModel |
|
|
|
'PerformancePerModel': allParametersPerformancePerModel, |
|
|
|
} |
|
|
|
} |
|
|
|
return jsonify(response) |
|
|
|
return jsonify(response) |
|
|
|
|
|
|
|
|
|
|
|
@ -780,7 +803,13 @@ def Remove(duplicate): |
|
|
|
final_list = [] |
|
|
|
final_list = [] |
|
|
|
for num in duplicate: |
|
|
|
for num in duplicate: |
|
|
|
if num not in final_list: |
|
|
|
if num not in final_list: |
|
|
|
final_list.append(num) |
|
|
|
if (isinstance(num, float)): |
|
|
|
|
|
|
|
if np.isnan(num): |
|
|
|
|
|
|
|
pass |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
final_list.append(int(num)) |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
final_list.append(num) |
|
|
|
return final_list |
|
|
|
return final_list |
|
|
|
|
|
|
|
|
|
|
|
# Retrieve data from client |
|
|
|
# Retrieve data from client |
|
|
|
@ -789,45 +818,174 @@ def Remove(duplicate): |
|
|
|
def RetrieveModelsParam(): |
|
|
|
def RetrieveModelsParam(): |
|
|
|
RetrieveModelsPar = request.get_data().decode('utf8').replace("'", '"') |
|
|
|
RetrieveModelsPar = request.get_data().decode('utf8').replace("'", '"') |
|
|
|
RetrieveModelsPar = json.loads(RetrieveModelsPar) |
|
|
|
RetrieveModelsPar = json.loads(RetrieveModelsPar) |
|
|
|
algorithm = RetrieveModelsPar['algorithm'] |
|
|
|
|
|
|
|
RetrieveModelsParPandas = pd.DataFrame(RetrieveModelsPar['brushed']) |
|
|
|
global algorithmList |
|
|
|
RetrieveModelsParPandas = RetrieveModelsParPandas.drop(columns=['performance']) |
|
|
|
algorithmList = RetrieveModelsPar['algorithms'] |
|
|
|
RetrieveModelsParPandas = RetrieveModelsParPandas.drop(columns=['model']) |
|
|
|
count = [] |
|
|
|
RetrieveModelsParPandas = RetrieveModelsParPandas.to_dict(orient='list') |
|
|
|
if ('KNN' in algorithmList): |
|
|
|
|
|
|
|
count.append('KNN') |
|
|
|
|
|
|
|
if ('RF' in algorithmList): |
|
|
|
|
|
|
|
count.append('RF') |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
global detailsParams |
|
|
|
|
|
|
|
results = [] |
|
|
|
|
|
|
|
counter1 = 0 |
|
|
|
|
|
|
|
counter2 = 0 |
|
|
|
|
|
|
|
for index, items in enumerate(algorithmList): |
|
|
|
|
|
|
|
if (items == 'KNN'): |
|
|
|
|
|
|
|
counter1 = counter1 + 1 |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
counter2 = counter2 + 1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
output = pd.DataFrame() |
|
|
|
|
|
|
|
for d in RetrieveModelsPar['parameters']: |
|
|
|
|
|
|
|
output = output.append(json.loads(d), ignore_index=True) |
|
|
|
|
|
|
|
RetrieveModelsPandSel = output.loc[0:counter1,:] |
|
|
|
|
|
|
|
RetrieveModelsPandSel2 = output.loc[counter1:counter1+counter2,:] |
|
|
|
|
|
|
|
RetrieveModelsPandSelDic = RetrieveModelsPandSel.to_dict(orient='list') |
|
|
|
|
|
|
|
RetrieveModelsPandSelDic2 = RetrieveModelsPandSel2.to_dict(orient='list') |
|
|
|
|
|
|
|
|
|
|
|
RetrieveModels = {} |
|
|
|
RetrieveModels = {} |
|
|
|
for key, value in RetrieveModelsParPandas.items(): |
|
|
|
for key, value in RetrieveModelsPandSelDic.items(): |
|
|
|
withoutDuplicates = Remove(value) |
|
|
|
withoutDuplicates = Remove(value) |
|
|
|
RetrieveModels[key] = withoutDuplicates |
|
|
|
RetrieveModels[key] = withoutDuplicates |
|
|
|
global RetrieveModelsList |
|
|
|
|
|
|
|
RetrieveModelsList.append(RetrieveModels) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
global classifierID |
|
|
|
RetrieveModels2 = {} |
|
|
|
global algorithmList |
|
|
|
for key, value in RetrieveModelsPandSelDic2.items(): |
|
|
|
global detailsParams |
|
|
|
withoutDuplicates = Remove(value) |
|
|
|
|
|
|
|
RetrieveModels2[key] = withoutDuplicates |
|
|
|
|
|
|
|
global resultsList |
|
|
|
|
|
|
|
resultsList = [] |
|
|
|
|
|
|
|
for alg in count: |
|
|
|
|
|
|
|
if (alg == 'KNN'): |
|
|
|
|
|
|
|
clf = KNeighborsClassifier() |
|
|
|
|
|
|
|
params = RetrieveModels |
|
|
|
|
|
|
|
detailsParams.append(params) |
|
|
|
|
|
|
|
results.append(GridSearch(clf, params)) |
|
|
|
|
|
|
|
resultsList.append(results[0]) |
|
|
|
|
|
|
|
elif (alg == 'RF'): |
|
|
|
|
|
|
|
clf = RandomForestClassifier() |
|
|
|
|
|
|
|
params = RetrieveModels2 |
|
|
|
|
|
|
|
detailsParams.append(params) |
|
|
|
|
|
|
|
results.append(GridSearch(clf, params)) |
|
|
|
|
|
|
|
resultsList.append(results[0]) |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
pass |
|
|
|
|
|
|
|
return 'Everything Okay' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Retrieve data from client |
|
|
|
|
|
|
|
@cross_origin(origin='localhost',headers=['Content-Type','Authorization']) |
|
|
|
|
|
|
|
@app.route('/data/FeaturesScoresUpdate', methods=["GET", "POST"]) |
|
|
|
|
|
|
|
def UpdateBarChartLine(): |
|
|
|
|
|
|
|
RetrieveModelsforUpdate = request.get_data().decode('utf8').replace("'", '"') |
|
|
|
|
|
|
|
RetrieveModelsforUpdate = json.loads(RetrieveModelsforUpdate) |
|
|
|
|
|
|
|
algorithms = RetrieveModelsforUpdate['algorithms'] |
|
|
|
|
|
|
|
count = [] |
|
|
|
|
|
|
|
if ('KNN' in algorithms): |
|
|
|
|
|
|
|
count.append('KNN') |
|
|
|
|
|
|
|
else: |
|
|
|
|
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count.append(0) |
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if ('RF' in algorithms): |
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count.append('RF') |
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else: |
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count.append(0) |
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results = [] |
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results = [] |
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algorithmList.append(algorithm) |
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counter1 = 0 |
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if (algorithm == 'KNN'): |
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counter2 = 0 |
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clf = KNeighborsClassifier() |
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for index, items in enumerate(algorithms): |
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params = RetrieveModels |
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if (items == 'KNN'): |
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detailsParams.append(params) |
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counter1 = counter1 + 1 |
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results.append(GridSearch(clf, params)) |
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else: |
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resultsList.append(results[0]) |
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counter2 = counter2 + 1 |
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for j, oneClassifier in enumerate(results[0][1]): |
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classifiersId.append(classifierID) |
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output = pd.DataFrame() |
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classifiersIDPlusParams.append(classifierID) |
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output2 = pd.DataFrame() |
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classifierID = classifierID + 1 |
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loop = 0 |
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elif (algorithm == 'RF'): |
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for d in RetrieveModelsforUpdate['parameters']: |
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clf = RandomForestClassifier() |
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if (loop < counter1): |
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params = RetrieveModels |
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output = output.append(json.loads(d), ignore_index=True) |
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detailsParams.append(params) |
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else: |
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results.append(GridSearch(clf, params)) |
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output2 = output2.append(json.loads(d), ignore_index=True) |
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resultsList.append(results[0]) |
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loop = loop + 1 |
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for oneClassifier, j in enumerate(results[0][1]): |
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output.dropna(axis='columns') |
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classifiersIDPlusParams.append(classifierID) |
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output2.dropna(axis='columns') |
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classifiersIDwithFI.append(classifierID) |
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classifierID = classifierID + 1 |
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if (output.empty): |
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pass |
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else: |
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else: |
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RetrieveModelsPandSel = output.loc[0:counter1,:] |
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RetrieveModelsPandSelDic = RetrieveModelsPandSel.to_dict(orient='list') |
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RetrieveModels = {} |
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for key, value in RetrieveModelsPandSelDic.items(): |
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withoutDuplicates = Remove(value) |
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RetrieveModels[key] = withoutDuplicates |
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if (output2.empty): |
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pass |
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pass |
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else: |
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RetrieveModelsPandSel2 = output2.loc[0:counter2,:] |
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RetrieveModelsPandSelDic2 = RetrieveModelsPandSel2.to_dict(orient='list') |
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RetrieveModels2 = {} |
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for key, value in RetrieveModelsPandSelDic2.items(): |
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withoutDuplicates = Remove(value) |
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|
RetrieveModels2[key] = withoutDuplicates |
|
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factors = [1,1,1,1,1,1] |
|
|
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|
global allParametersPerformancePerModelUpdate |
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|
|
allParametersPerformancePerModelUpdate = [] |
|
|
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|
|
|
for alg in count: |
|
|
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|
|
|
|
if (alg == 'KNN'): |
|
|
|
|
|
|
|
clf = KNeighborsClassifier() |
|
|
|
|
|
|
|
params = RetrieveModels |
|
|
|
|
|
|
|
GridSearchForUpdate(clf, params, factors) |
|
|
|
|
|
|
|
elif (alg == 'RF'): |
|
|
|
|
|
|
|
clf = RandomForestClassifier() |
|
|
|
|
|
|
|
params = RetrieveModels2 |
|
|
|
|
|
|
|
GridSearchForUpdate(clf, params, factors) |
|
|
|
|
|
|
|
else: |
|
|
|
|
|
|
|
allParametersPerformancePerModelUpdate.append(0) |
|
|
|
|
|
|
|
SendEachClassifiersPerformanceToVisualizeLinePlot() |
|
|
|
return 'Everything Okay' |
|
|
|
return 'Everything Okay' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def GridSearchForUpdate(clf, params, factors): |
|
|
|
|
|
|
|
global scoring |
|
|
|
|
|
|
|
global NumberofscoringMetrics |
|
|
|
|
|
|
|
scoring = {'accuracy': 'accuracy', 'f1_macro': 'f1_weighted', 'precision': 'precision_weighted', 'recall': 'recall_weighted', 'jaccard': 'jaccard_weighted'} |
|
|
|
|
|
|
|
NumberofscoringMetrics = len(scoring) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
grid = GridSearchCV(estimator=clf, |
|
|
|
|
|
|
|
param_grid=params, |
|
|
|
|
|
|
|
scoring=scoring, |
|
|
|
|
|
|
|
cv=crossValidation, |
|
|
|
|
|
|
|
refit='accuracy', |
|
|
|
|
|
|
|
n_jobs = -1) |
|
|
|
|
|
|
|
grid.fit(XData, yData) |
|
|
|
|
|
|
|
yPredict = grid.predict(XData) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
global allParametersPerformancePerModelUpdate |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
global target_names |
|
|
|
|
|
|
|
global PerClassUpd |
|
|
|
|
|
|
|
global PerClassMetricUpdate |
|
|
|
|
|
|
|
PerClassUpd = [] |
|
|
|
|
|
|
|
PerClassMetricUpdate = [] |
|
|
|
|
|
|
|
PerClassUpd.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True)) |
|
|
|
|
|
|
|
PerClassMetricUpdate = pd.DataFrame(PerClassUpd) |
|
|
|
|
|
|
|
del PerClassMetricUpdate['accuracy'] |
|
|
|
|
|
|
|
del PerClassMetricUpdate['macro avg'] |
|
|
|
|
|
|
|
del PerClassMetricUpdate['weighted avg'] |
|
|
|
|
|
|
|
PerClassMetricUpdate = PerClassMetricUpdate.to_json() |
|
|
|
|
|
|
|
allParametersPerformancePerModelUpdate.append(PerClassMetricUpdate) |
|
|
|
|
|
|
|
return 'Everything is okay' |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# Sending each model's results |
|
|
|
|
|
|
|
@app.route('/data/UpdatePerFeaturePerformance', methods=["GET", "POST"]) |
|
|
|
|
|
|
|
def SendEachClassifiersPerformanceToVisualizeLinePlot (): |
|
|
|
|
|
|
|
global allParametersPerformancePerModelUpdate |
|
|
|
|
|
|
|
response = { |
|
|
|
|
|
|
|
'PerformanceCheck': allParametersPerformancePerModelUpdate, |
|
|
|
|
|
|
|
} |
|
|
|
|
|
|
|
return jsonify(response) |
