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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StackGenVis/cache_dir/joblib/run/GridSearchForParameters/func_code.py

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1.2 KiB

# first line: 484
def GridSearchForParameters(clf, params):
grid = GridSearchCV(estimator=clf,
param_grid=params,
scoring='accuracy',
cv=crossValidation,
n_jobs = -1)
grid.fit(XData, yData)
cv_results = []
cv_results.append(grid.cv_results_)
df_cv_results = pd.DataFrame.from_dict(cv_results)
number_of_classifiers = len(df_cv_results.iloc[0][0])
number_of_columns = len(df_cv_results.iloc[0])
df_cv_results_per_item = []
df_cv_results_per_row = []
for i in range(number_of_classifiers):
df_cv_results_per_item = []
for column in df_cv_results.iloc[0]:
df_cv_results_per_item.append(column[i])
df_cv_results_per_row.append(df_cv_results_per_item)
df_cv_results_classifiers = pd.DataFrame(data = df_cv_results_per_row, columns= df_cv_results.columns)
global allParametersPerformancePerModel
parametersPerformancePerModel = df_cv_results_classifiers[['mean_test_score','params']]
parametersPerformancePerModel = parametersPerformancePerModel.to_json()
allParametersPerformancePerModel.append(parametersPerformancePerModel)
return 'Everything is okay'