136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon May 16 05:59:12 2022
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@author: tanu
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"""
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Tue Mar 15 11:09:50 2022
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@author: tanu
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"""
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parameters = [
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{
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'clf': [LogisticRegression(**rs)],
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#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['saga']
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},
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{
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'clf': [LogisticRegression(**rs)],
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#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l2', 'none'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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},
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{
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'clf': [LogisticRegression(**rs)],
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#'clf__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l1', 'l2'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['liblinear']
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}
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]
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# Create pipeline
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pipeline = Pipeline([
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('pre', MinMaxScaler()),
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('clf', ClfSwitcher()),
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])
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# Grid search i.e hyperparameter tuning and refitting on mcc
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gscv_lr = GridSearchCV(pipeline
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, parameters
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#, scoring = 'f1', refit = 'f1'
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, scoring = mcc_score_fn, refit = 'mcc'
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, cv = skf_cv
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, **njobs
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, return_train_score = False
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, verbose = 3)
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# Fit
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gscv_lr_fit = gscv_lr.fit(X, y)
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gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
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gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
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print('Best model:\n', gscv_lr_fit_be_mod)
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print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
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#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
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print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
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######################################
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# Blind test
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######################################
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# See how it does on the BLIND test
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#print('\nBlind test score, mcc:', ))
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test_predict = gscv_lr_fit.predict(X_bts)
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print(test_predict)
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print(np.array(y_bts))
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y_btsf = np.array(y_bts)
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print(accuracy_score(y_bts, test_predict))
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print(matthews_corrcoef(y_bts, test_predict))
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# create a dict with all scores
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lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
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'bts_fscore':None
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, 'bts_mcc':None
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, 'bts_precision':None
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, 'bts_recall':None
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, 'bts_accuracy':None
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, 'bts_roc_auc':None
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, 'bts_jaccard':None }
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lr_bts_dict
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lr_bts_dict['bts_fscore'] = round(f1_score(y_bts, test_predict),2)
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lr_bts_dict['bts_mcc'] = round(matthews_corrcoef(y_bts, test_predict),2)
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lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
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lr_bts_dict['bts_recall'] = round(recall_score(y_bts, test_predict),2)
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lr_bts_dict['bts_accuracy'] = round(accuracy_score(y_bts, test_predict),2)
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lr_bts_dict['bts_roc_auc'] = round(roc_auc_score(y_bts, test_predict),2)
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lr_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
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lr_bts_dict
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# Create a df from dict with all scores
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lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
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lr_bts_df.columns = ['Logistic_Regression']
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print(lr_bts_df)
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# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
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# d2
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# def Merge(dict1, dict2):
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# res = {**dict1, **dict2}
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# return res
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# d3 = Merge(d2, lr_bts_dict)
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# d3
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# Create df with best model params
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model_params = pd.Series(['best_model_params', list(gscv_lr_fit_be_mod.items() )])
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model_params_df = model_params.to_frame()
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model_params_df
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model_params_df.columns = ['Logistic_Regression']
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model_params_df.columns
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# Combine the df of scores and the best model params
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lr_bts_df.columns
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lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
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lr_output
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# Format the combined df
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# Drop the best_model_params row from lr_output
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lr_df = lr_output.drop([0], axis = 0)
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lr_df
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#FIXME: tidy the index of the formatted df
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###############################################################################
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