added different scaling options
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ebef0c7967
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3 changed files with 99 additions and 31 deletions
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@ -142,7 +142,9 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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# Run Multiple Classifiers
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############################
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target, skf_cv
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def MultModelsCl(input_df, target
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#, skf_cv
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, sel_cv
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, blind_test_df
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, blind_test_target
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, tts_split_type
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@ -151,6 +153,7 @@ def MultModelsCl(input_df, target, skf_cv
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, add_cm = True # adds confusion matrix based on cross_val_predict
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, add_yn = True # adds target var class numbers
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, var_type = ['numerical', 'categorical','mixed']
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, scale_numeric = ['min_max', 'std', 'min_max_neg', 'none']
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, run_blind_test = True
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, return_formatted_output = True):
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@ -182,24 +185,52 @@ def MultModelsCl(input_df, target, skf_cv
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#======================================================
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# Determine preprocessing steps ~ var_type
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#======================================================
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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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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# if var_type == 'mixed':
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# t = [('cat', OneHotEncoder(), categorical_ix) ]
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if type(var_type) == list:
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var_type = str(var_type[0])
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else:
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var_type = var_type
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if var_type in ['numerical','mixed']:
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if scale_numeric == ['none']:
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if scale_numeric != ['none']:
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if scale_numeric == ['min_max']:
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scaler = MinMaxScaler()
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if scale_numeric == ['min_max_neg']:
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scaler = MinMaxScaler(feature_range=(-1, 1))
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if scale_numeric == ['std']:
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scaler = StandardScaler()
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t = [('num', scaler, numerical_ix)
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, ('cat', OneHotEncoder(), categorical_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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#======================================================
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# Specify multiple Classification Models
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#======================================================
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models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
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, ('Decision Tree' , DecisionTreeClassifier(**rs) )
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, ('Extra Tree' , ExtraTreeClassifier(**rs) )
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, ('Extra Trees' , ExtraTreesClassifier(**rs) )
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@ -211,18 +242,18 @@ def MultModelsCl(input_df, target, skf_cv
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, ('Logistic Regression' , LogisticRegression(**rs) )
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, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
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, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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, ('Multinomial' , MultinomialNB() )
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#, ('Multinomial' , MultinomialNB() )
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, ('Naive Bayes' , BernoulliNB() )
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, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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, ('QDA' , QuadraticDiscriminantAnalysis() )
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, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) )
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, ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
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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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, **rs
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, max_features = 'auto') )
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# , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
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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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# , **rs
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# , max_features = 'auto') )
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, ('Ridge Classifier' , RidgeClassifier(**rs) )
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, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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, ('SVC' , SVC(**rs) )
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@ -254,7 +285,7 @@ def MultModelsCl(input_df, target, skf_cv
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skf_cv_modD = cross_validate(model_pipeline
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, input_df
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, target
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, cv = skf_cv
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, cv = sel_cv
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, scoring = scoring_fn
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, return_train_score = True)
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#==============================
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@ -283,7 +314,7 @@ def MultModelsCl(input_df, target, skf_cv
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cmD = {}
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# Calculate cm
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y_pred = cross_val_predict(model_pipeline, input_df, target, cv = skf_cv, **njobs)
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y_pred = cross_val_predict(model_pipeline, input_df, target, cv = sel_cv, **njobs)
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#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
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tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
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@ -334,7 +365,8 @@ def MultModelsCl(input_df, target, skf_cv
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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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#print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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print('\nMCC on Training:' , mm_skf_scoresD[model_name]['test_mcc'] )
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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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@ -26,7 +26,7 @@ from GetMLData import *
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combined_model_paramD = {'data_combined_model' : True
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, 'use_or' : False
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, 'omit_all_genomic_features': False
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, 'write_maskfile' : False
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, 'write_maskfile' : False # true once for writing and checking
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, 'write_outfile' : False }
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pnca_df = getmldata('pncA', 'pyrazinamide' , **combined_model_paramD)
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@ -14,7 +14,8 @@ sys.path
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# import
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from GetMLData import *
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from SplitTTS import *
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from MultClfs_fi import *
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#from MultClfs_fi import *
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from MultClfs import *
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#%%
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# X,y = load_boston(return_X_y=True)
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@ -33,7 +34,7 @@ from MultClfs_fi import *
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#%%
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sel_cv = StratifiedKFold(n_splits = 10
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skf_cv = StratifiedKFold(n_splits = 10
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, shuffle = True,**rs)
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#sel_cv = logo
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# sel_cv = RepeatedStratifiedKFold(n_splits = 5
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@ -48,10 +49,21 @@ gene_model_paramD = {'data_combined_model' : False
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#df = getmldata(gene, drug, **gene_model_paramD)
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df = getmldata('pncA', 'pyrazinamide', **gene_model_paramD)
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df = getmldata('embB', 'ethambutol' , **gene_model_paramD)
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df = getmldata('katG', 'isoniazid' , **gene_model_paramD)
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df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD)
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df = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
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#df = getmldata('alr' , 'cycloserine' , **combined_model_paramD)
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all(df.columns.isin(['gene_name'])) # should be False
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spl_type = '70_30'
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spl_type = '80_20'
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spl_type = 'sl'
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df2 = split_tts(df
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, data_type = 'actual'
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, split_type = '70_30'
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, split_type = spl_type
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, oversampling = False
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, dst_colname = 'dst'
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, target_colname = 'dst_mode'
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@ -61,19 +73,43 @@ df2 = split_tts(df
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all(df2['X'].columns.isin(['gene_name'])) # should be False
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fooD = MultClfs_fi (input_df = df2['X']
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fooD = MultModelsCl(input_df = df2['X']
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, target = df2['y']
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, sel_cv = sel_cv
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, sel_cv = skf_cv
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, run_blind_test = True
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, blind_test_df = df2['X_bts']
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, blind_test_target = df2['y_bts']
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, tts_split_type = '70_30'
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, var_type = 'mixed'
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, tts_split_type = spl_type
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, resampling_type = 'none' # default
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)
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, var_type = ['mixed']
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, scale_numeric = ['min_max_neg']
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, return_formatted_output = False
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)
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for k, v in fooD.items():
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print('\nModel:', k
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, '\nTRAIN MCC:', fooD[k]['test_mcc']
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, '\nBTS MCC:' , fooD[k]['bts_mcc']
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, '\nDIFF:',fooD[k]['bts_mcc'] - fooD[k]['test_mcc'] )
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#%% CHECK SCALING
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embb_df = getmldata('embB', 'ethambutol' , **combined_model_paramD)
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all(embb_df.columns.isin(['gene_name'])) # should be False
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scaler = MinMaxScaler(feature_range=(-1, 1))
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bar = embb_df[['vdwclashes_rr', 'electro_rr']]
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bar_df1 = scaler.fit_transform(bar)
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bar_df1 = pd.DataFrame(bar_df1)
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bar_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
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bar2 = pd.concat([bar, bar_df1], axis = 1)
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scaler2 = StandardScaler()
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baz = embb_df[['vdwclashes_rr', 'electro_rr']]
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baz_df1 = scaler2.fit_transform(baz)
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baz_df1 = pd.DataFrame(baz_df1)
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baz_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
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baz2 = pd.concat([baz, baz_df1], axis = 1)
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a = pd.concat([bar2, baz2], axis = 1)
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