tidying script to run from cmd and via ssh
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4 changed files with 271 additions and 76 deletions
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@ -27,17 +27,42 @@ from sklearn.model_selection import train_test_split, cross_validate, cross_val_
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# Metric
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from sklearn.metrics import mean_squared_error, make_scorer, roc_auc_score, f1_score, matthews_corrcoef, accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report
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# other vars
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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, n_repeats = 3
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, **rs)
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% YC
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#def run_all_ML(input_pd, target_label, bts_input, bts_target, var_type):
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def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'):
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def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, preprocess = True, var_type = 'numerical'):
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#y = input_pd[target_label]
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#X = input_pd.drop(target_label,axis=1)
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y = target_label
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X = input_pd
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# determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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# Determine categorical and numerical features
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numerical_ix = input_pd.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix = input_pd.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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@ -53,17 +78,21 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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result_pd = pd.DataFrame()
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result_pd = pd.DataFrame()
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result_bts_pd = pd.DataFrame()
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#results_btsD = {}
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results_all = {}
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for name, algorithm in all_estimators(type_filter="classifier"):
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try:
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estmator = algorithm()
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temp_pd = pd.DataFrame()
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temp_cm = pd.DataFrame()
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# orig
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pipe = Pipeline([
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("model" , algorithm())
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])
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# # orig
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# pipe = Pipeline([
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# ("model" , algorithm())
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# ])
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# turn on and off preprocessing
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if preprocess == True:
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@ -76,11 +105,17 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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("model" , algorithm())
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])
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y_pred = cross_val_predict(pipe, X, y, cv = 10, n_jobs=10)
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_mcc = round(matthews_corrcoef(y_pred, y), 3)
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_bacc = round(balanced_accuracy_score(y_pred, y), 3)
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_f1 = round(f1_score(y_pred, y), 3)
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# cross val scores
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y_pred = cross_val_predict(pipe, X, y, cv = 10, **njobs)
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# CHANGE to cross_validate: ONLY THEN CAN YOU TRUST
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# y_pred = cross_validate(pipe, X, y
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# , cv = 10
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# , scoring = scoring_fn
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# , **njobs)
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_mcc = round(matthews_corrcoef(y_pred, y), 3)
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_bacc = round(balanced_accuracy_score(y_pred, y), 3)
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_f1 = round(f1_score(y_pred, y), 3)
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_roc_auc = round(roc_auc_score(y_pred, y), 3)
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_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel()
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@ -88,7 +123,88 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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'roc_auc', 'matthew', 'bacc', 'f1']),\
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ignore_index=True)
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#=========================
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# Blind test: BTS results
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#=========================
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#Build the final results with all scores for a feature selected model
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pipe.fit(input_pd, target_label)
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bts_predict = pipe.predict(blind_test_input_df)
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
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print('\nMCC on Blind test:' , bts_mcc_score)
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#print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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_mccBTS = round(matthews_corrcoef(bts_predict, blind_test_target), 3)
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_baccBTS = round(balanced_accuracy_score(bts_predict, blind_test_target), 3)
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_f1BTS = round(f1_score(bts_predict, blind_test_target), 3)
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_roc_aucBTS = round(roc_auc_score(bts_predict, blind_test_target), 3)
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_tnBTS, _fpBTS, _fnBTS, _tpBTS = confusion_matrix(bts_predict, blind_test_target).ravel()
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result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
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, _tpBTS, _tnBTS
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, _fpBTS, _fnBTS
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, _roc_aucBTS
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, _mccBTS
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, _baccBTS, _f1BTS]),\
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columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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'roc_auc', 'matthew', 'bacc', 'f1']),\
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ignore_index=True)
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results_all['CrossValResultsDF'] = result_pd
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results_all['BlindTestResultsDF'] = result_bts_pd
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except Exception as e:
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print("Got an error while running {}".format(name))
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print("XXXGot an error while running {}".format(name))
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print(e)
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return(result_pd)
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#return(result_pd)
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return(results_all)
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#%% CALL function
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#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF = YC_resD2['CrossValResultsDF']
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CVResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF = YC_resD2['BlindTestResultsDF']
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BTSResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
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# from sklearn.utils import all_estimators
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# for name, algorithm in all_estimators(type_filter="classifier"):
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# clf = algorithm()
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# print('Name:', name, '\nAlgo:', clf)
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# Random Oversampling
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YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
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CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
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BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
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# Random Undersampling
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YC_resD_rus = run_all_ML(input_pd=X_rus, target_label=y_rus, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
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CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
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BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
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# Random Oversampling+Undersampling
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YC_resD_rouC = run_all_ML(input_pd=X_rouC, target_label=y_rouC, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
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CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
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BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
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# SMOTE NC
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YC_resD_smnc = run_all_ML(input_pd=X_smnc, target_label=y_smnc, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
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CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
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CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
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BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
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BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
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