StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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76 lines
3.0 KiB
76 lines
3.0 KiB
# first line: 705
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def GridSearchForParameters(clf, params, eachAlgor, factors):
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global scoring
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global NumberofscoringMetrics
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# instantiate spark session
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spark = (
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SparkSession
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.builder
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.getOrCreate()
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)
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sc = spark.sparkContext
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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 crossValidation
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NumberofscoringMetrics = len(scoring)
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grid = DistGridSearchCV(
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estimator=clf, param_grid=params,
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sc=sc, cv=crossValidation, refit='accuracy', scoring=scoring,
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verbose=0, n_jobs=-1)
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grid.fit(XData, yData)
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yPredict = grid.predict(XData)
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cv_results = []
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cv_results.append(grid.cv_results_)
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df_cv_results = pd.DataFrame.from_dict(cv_results)
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number_of_classifiers = len(df_cv_results.iloc[0][0])
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df_cv_results_per_item = []
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df_cv_results_per_row = []
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for i in range(number_of_classifiers):
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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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df_cv_results_per_item.append(column[i])
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df_cv_results_per_row.append(df_cv_results_per_item)
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df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
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global allParametersPerformancePerModel
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global parametersPerformancePerModel
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metrics = df_cv_results_classifiers.copy()
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metrics = metrics.filter(['mean_test_accuracy','mean_test_f1_macro','mean_test_precision','mean_test_recall','mean_test_jaccard'])
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sumperModel = []
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global rowSum
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for index, row in metrics.iterrows():
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rowSum = 0
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lengthFactors = NumberofscoringMetrics
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for loop,elements in enumerate(row):
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lengthFactors = lengthFactors - 1 + factors[loop]
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rowSum = elements*factors[loop] + rowSum
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if lengthFactors is 0:
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sumperModel = 0
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else:
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sumperModel.append(rowSum/lengthFactors)
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global target_names
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global PerClassMetric
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global PerClassMetricPandas
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PerClassMetric = []
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yPredictProb.append(grid.predict_proba(XData))
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PerClassMetric.append(classification_report(yData, yPredict, target_names=target_names, digits=2, output_dict=True))
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PerClassMetricPandas = pd.DataFrame(PerClassMetric)
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del PerClassMetricPandas['accuracy']
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del PerClassMetricPandas['macro avg']
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del PerClassMetricPandas['weighted avg']
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summarizedMetrics = pd.DataFrame(sumperModel)
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summarizedMetrics.rename(columns={0:'sum'})
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parameters = pd.DataFrame(df_cv_results_classifiers['params'])
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parametersPerformancePerModel = pd.concat([summarizedMetrics, parameters], axis=1)
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PerClassMetricPandas = PerClassMetricPandas.to_json()
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parametersPerformancePerModel = parametersPerformancePerModel.to_json()
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allParametersPerformancePerModel.append(parametersPerformancePerModel)
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allParametersPerformancePerModel.append(PerClassMetricPandas)
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return 'Everything is okay'
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