git add UQ_imbalance.py
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4 changed files with 134 additions and 56 deletions
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@ -21,6 +21,7 @@ mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
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mm_skf_scores_df_all
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mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
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mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
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baseline_BT = mm_skf_scores_df_all.filter(like='bts_', axis=0)
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#%%
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mm_skf_scoresD2 = MultClassPipeSKFCV(input_df = X_sm
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@ -29,28 +30,70 @@ mm_skf_scoresD2 = MultClassPipeSKFCV(input_df = X_sm
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, skf_cv = skf_cv)
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sm_all = pd.DataFrame(mm_skf_scoresD2)
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sm_df_CT = sm_all.filter(like='test_', axis=0)
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sm_df_BT = sm_all.filter(like='bts_', axis=0)
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#%%
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mm_skf_scoresD3 = MultClassPipeSKFCV(input_df = X_ros
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, target = y_ros
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, var_type = 'mixed'
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, skf_cv = skf_cv)
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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ros_all = pd.DataFrame(mm_skf_scoresD3)
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ros_CT = ros_all.filter(like='test_', axis=0)
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ros_BT = ros_all.filter(like='bts_', axis=0)
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#--------- combined
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mm_skf_scoresD3v2 = MultClassPipeSKFCV(input_df = X_rouC
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, target = y_rouC
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, var_type = 'mixed'
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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rouC_all = pd.DataFrame(mm_skf_scoresD3v2)
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rouC_CT = ros_all.filter(like='test_', axis=0)
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rouC_BT = ros_all.filter(like='bts_', axis=0)
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#%%
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mm_skf_scoresD4 = MultClassPipeSKFCV(input_df = X_rus
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, target = y_rus
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, var_type = 'mixed'
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, skf_cv = skf_cv)
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, var_type = 'numerical'
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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rus_all = pd.DataFrame(mm_skf_scoresD4)
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rus_CT = rus_all.filter(like='test_', axis=0)
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rus_BT = rus_all.filter(like='bts_' , axis=0)
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#%%
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mm_skf_scoresD5 = MultClassPipeSKFCV(input_df = X_enn
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, target = y_enn
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, var_type = 'mixed'
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, skf_cv = skf_cv)
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, var_type = 'numerical'
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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enn_all = pd.DataFrame(mm_skf_scoresD5)
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enn_CT = enn_all.filter(like='test_', axis=0)
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enn_BT = enn_all.filter(like='bts_', axis=0)
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#%%
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mm_skf_scoresD6 = MultClassPipeSKFCV(input_df = X_renn
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, target = y_renn
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, var_type = 'numerical'
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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renn_all = pd.DataFrame(mm_skf_scoresD6)
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renn_CT = renn_all.filter(like='test_', axis=0)
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renn_BT = renn_all.filter(like='bts_', axis=0)
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#%%: with categorical values + oversampling
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mm_skf_scoresD7 = MultClassPipeSKFCV(input_df = X_smnc
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, target = y_smnc
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, var_type = 'mixed'
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, skf_cv = rskf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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smnc_all = pd.DataFrame(mm_skf_scoresD7)
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smnc_CT = smnc_all.filter(like='test_', axis=0)
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smnc_BT = smnc_all.filter(like='bts_', axis=0)
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@ -76,8 +76,8 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({ 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'fscore' : make_scorer(f1_score)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_score)
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, 'accuracy' : make_scorer(accuracy_score)
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@ -87,7 +87,10 @@ scoring_fn = ({ 'fscore' : make_scorer(f1_score)
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# Multiple Classification - Model Pipeline
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def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categorical','mixed']):
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def MultClassPipeSKFCV(input_df, target, skf_cv
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, blind_test_input_df
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, blind_test_target
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, var_type = ['numerical', 'categorical','mixed']):
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'''
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@ param input_df: input features
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@ -120,8 +123,8 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix) ]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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@ -137,7 +140,7 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
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rf = RandomForestClassifier(**rs, n_estimators = 1000 )
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rf2 = RandomForestClassifier(
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min_samples_leaf = 5
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, n_estimators = 100 #10
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, n_estimators = 1000
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, bootstrap = True
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, oob_score = True
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, **njobs
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@ -158,16 +161,16 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
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, ('K-Nearest Neighbors', knn)
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, ('SVM' , svm)
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, ('MLP' , mlp)
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# , ('Decision Tree' , dt)
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# , ('Extra Trees' , et)
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# , ('Random Forest' , rf)
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# , ('Naive Bayes' , nb)
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# , ('Random Forest2' , rf2)
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# , ('XGBoost' , xgb)
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# , ('LDA' , lda)
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# , ('MultinomialNB' , mnb)
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# , ('PassiveAggresive' , pa)
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# , ('StochasticGDescent' , sgd)
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, ('Decision Tree' , dt)
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, ('Extra Trees' , et)
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, ('Random Forest' , rf)
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, ('Naive Bayes' , nb)
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, ('Random Forest2' , rf2)
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, ('XGBoost' , xgb)
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, ('LDA' , lda)
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, ('MultinomialNB' , mnb)
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, ('PassiveAggresive' , pa)
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, ('StochasticGDescent' , sgd)
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]
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mm_skf_scoresD = {}
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@ -196,48 +199,41 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
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#pp.pprint(mm_skf_scoresD)
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#return(mm_skf_scoresD)
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#%%
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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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#bts_predict = gscv_fs.predict(X_bts)
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#bts_predict = gscv_fs.predict(blind_test_input_df)
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model_pipeline.fit(input_df, target)
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bts_predict = model_pipeline.predict(X_bts)
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bts_predict = model_pipeline.predict(blind_test_input_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
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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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# Diff b/w train and bts test scores
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# train_test_diff = train_bscore - bts_mcc_score
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# print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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# create a dict with all scores
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lr_btsD = { 'model_name': model_name
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, 'bts_mcc':None
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, 'bts_fscore':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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# # create a dict with all scores
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# lr_btsD = { 'model_name': model_name
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# , 'bts_mcc':None
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# , 'bts_fscore':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_btsD
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lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
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lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
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lr_btsD
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return(lr_btsD)
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mm_skf_scoresD[model_name]['bts_mcc'] = bts_mcc_score
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mm_skf_scoresD[model_name]['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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mm_skf_scoresD[model_name]['bts_jaccard'] = round(jaccard_score(blind_test_target, bts_predict),2)
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return(mm_skf_scoresD)
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@ -81,7 +81,11 @@ from sklearn.model_selection import cross_validate, cross_val_score
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from sklearn.model_selection import RepeatedStratifiedKFold
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from sklearn.ensemble import AdaBoostClassifier
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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from imblearn.over_sampling import SMOTENC
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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from sklearn.model_selection import GridSearchCV
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@ -117,7 +121,8 @@ os.chdir(homedir + "/git/ML_AI_training/")
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#from MultClassPipe import MultClassPipeline
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from MultClassPipe2 import MultClassPipeline2
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from loopity_loop import MultClassPipeSKFLoop
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from MultClassPipe3 import MultClassPipeSKFCV
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#from MultClassPipe3 import MultClassPipeSKFCV
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from UQ_MultClassPipe4 import MultClassPipeSKFCV
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gene = 'pncA'
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drug = 'pyrazinamide'
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@ -285,9 +290,9 @@ all_df_wtgt.shape
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#------
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# X
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#------
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#X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
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X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
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X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
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X = all_df_wtgt[numerical_FN] # training numerical only
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#X = all_df_wtgt[numerical_FN] # training numerical only
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#X_bts = blind_test_df[numerical_FN] # blind test data numerical
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#------
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@ -27,22 +27,56 @@ 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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def run_all_ML(input_pd, target_label):
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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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#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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numerical_ix
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categorical_ix = input_df.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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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('num', MinMaxScaler(), numerical_ix)
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, ('cat', OneHotEncoder(), categorical_ix)]
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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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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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("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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pipe = Pipeline([
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('prep' , col_transform),
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("model" , algorithm())
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])
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else:
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pipe = Pipeline([
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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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