added Mult_clfs_logo and Mult_clsf.py with consistency
This commit is contained in:
parent
06f2ce97b6
commit
de5c1270be
5 changed files with 201 additions and 115 deletions
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@ -105,6 +105,7 @@ def CMLogoSkf(cm_input_df
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, target_var = 'dst_mode'
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, gene_group = 'gene_name'
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, std_gene_omit = []
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, output_dir = outdir
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, file_suffix = ""
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):
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@ -138,7 +139,7 @@ def CMLogoSkf(cm_input_df
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# else:
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# file_suffix = file_suffix
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outFile = outdir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + ".csv"
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outFile = output_dir + str(n_tr_genes+1) + "genes_" + tts_split_type + '_' + file_suffix + ".csv"
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print(outFile)
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@ -170,7 +171,7 @@ def CMLogoSkf(cm_input_df
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#cm_bts_y = cm_test_df.loc[:, 'dst_mode']
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cm_bts_y = cm_test_df.loc[:, target_var]
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print('\nTEST data dim:', cm_bts_X.shape
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print('\nTEST data dim:' , cm_bts_X.shape
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, '\nTEST Target dim:', cm_bts_y.shape)
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print("Running Multiple models on LOGO with SKF")
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@ -209,4 +210,4 @@ def CMLogoSkf(cm_input_df
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# Actual Data
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#===============
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CMLogoSkf(cm_input_df = combined_df_actual, file_suffix = "actual")
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CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")
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# CMLogoSkf(cm_input_df = combined_df_actual, std_gene_omit=['alr'], file_suffix = "actual")
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@ -74,10 +74,13 @@ from sklearn.impute import KNNImputer as KNN
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import json
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import argparse
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import re
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import itertools
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from sklearn.model_selection import LeaveOneGroupOut
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from sklearn.decomposition import PCA
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
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#rs = {'random_state': 42}
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#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
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scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'fscore' : make_scorer(f1_score)
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@ -88,13 +91,13 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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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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#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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#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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@ -137,6 +140,7 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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, 'bts_jcc' : 'JCC'
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}
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#gene_group = 'gene_name'
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#%%############################################################################
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############################
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# MultModelsCl()
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@ -145,17 +149,23 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target
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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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, resampling_type
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#, group = None
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, resampling_type = 'none' # default
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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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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = int)
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
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):
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'''
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@ param input_df: input features
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@ -174,6 +184,24 @@ def MultModelsCl(input_df, target
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Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
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'''
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#%% Func globals
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rs = {'random_state': random_state}
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njobs = {'n_jobs': n_jobs}
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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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logo = LeaveOneGroupOut()
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# select CV type:
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# if group == None:
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# sel_cv = skf_cv
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# else:
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# sel_cv = logo
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#======================================================
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# Determine categorical and numerical features
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#======================================================
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@ -196,8 +224,9 @@ def MultModelsCl(input_df, target
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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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# col_transform = ColumnTransformer(transformers = t
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# , remainder='passthrough')
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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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@ -229,37 +258,37 @@ def MultModelsCl(input_df, target
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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, 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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# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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# , ('Gaussian NB' , GaussianNB() )
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# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
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, ('LDA' , LinearDiscriminantAnalysis() )
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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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# , ('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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# , ('Ridge Classifier' , RidgeClassifier(**rs) )
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# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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# , ('SVC' , SVC(**rs) )
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# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
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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, 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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, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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, ('Gaussian NB' , GaussianNB() )
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, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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, ('K-Nearest Neighbors' , KNeighborsClassifier() )
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, ('LDA' , LinearDiscriminantAnalysis() )
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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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, ('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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, ('Ridge Classifier' , RidgeClassifier(**rs) )
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, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
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, ('SVC' , SVC(**rs) )
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, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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, ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder = False, **njobs) )
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]
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mm_skf_scoresD = {}
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@ -289,10 +318,11 @@ def MultModelsCl(input_df, target
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print('\nRunning model pipeline:', model_pipeline)
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skf_cv_modD = cross_validate(model_pipeline
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cv_modD = cross_validate(model_pipeline
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, input_df
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, target
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, cv = sel_cv
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#, groups = group
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, scoring = scoring_fn
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, return_train_score = True)
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#==============================
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@ -300,7 +330,7 @@ def MultModelsCl(input_df, target
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#==============================
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mm_skf_scoresD[model_name] = {}
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for key, value in skf_cv_modD.items():
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for key, value in cv_modD.items():
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print('\nkey:', key, '\nvalue:', value)
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print('\nmean value:', np.mean(value))
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mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
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@ -308,7 +338,7 @@ def MultModelsCl(input_df, target
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# ADD more info: meta data related to input df
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mm_skf_scoresD[model_name]['resampling'] = resampling_type
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mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
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#mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
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mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
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mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
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mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
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@ -321,7 +351,12 @@ def MultModelsCl(input_df, target
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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 = sel_cv, **njobs)
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y_pred = cross_val_predict(model_pipeline
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, input_df
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, target
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, cv = sel_cv
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#, groups = group
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, **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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@ -357,7 +392,7 @@ def MultModelsCl(input_df, target
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# Build bts numbers dict
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btD = {'n_blindY_neg' : Counter(blind_test_target)[0]
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, 'n_blindY_pos' : Counter(blind_test_target)[1]
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#, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
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, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
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, 'n_test_size' : len(blind_test_df) }
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# Update cmD+tnD dicts with btD
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@ -371,9 +406,9 @@ def MultModelsCl(input_df, target
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bts_predict = model_pipeline.predict(blind_test_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('\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('\nMCC on Training:' , mm_skf_scoresD[model_name]['test_mcc'] )
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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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@ -526,7 +561,8 @@ def ProcessMultModelsCl(inputD = {}, blind_test_data = True):
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sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs')
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else:
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print('\nConcatenting dfs not possible [WF],check numbers ')
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# print('\nConcatenting dfs not possible [WF],check numbers ')
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print('\nOnly combining CV and metadata')
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#-------------------------------------
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# Combine WF+Metadata: Final output
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@ -76,7 +76,12 @@ import argparse
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import re
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import itertools
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from sklearn.model_selection import LeaveOneGroupOut
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from sklearn.decomposition import PCA
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#%% GLOBALS
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#rs = {'random_state': 42}
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#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
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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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@ -86,7 +91,13 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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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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@ -139,21 +150,23 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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def MultModelsCl_logo_skf(input_df
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, target
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, sel_cv
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, tts_split_type
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, resampling_type
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#, group = None
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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = int)
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, tts_split_type = "none"
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#, group = 'none'
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, resampling_type = 'none' # default
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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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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = int)
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
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, ):
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):
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'''
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@ param input_df: input features
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@ -165,7 +178,7 @@ def MultModelsCl_logo_skf(input_df
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@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
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@type: int or StratifiedKfold()
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@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
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@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-hot encoder)
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@type: list
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returns
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@ -185,8 +198,8 @@ def MultModelsCl_logo_skf(input_df
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, **rs)
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logo = LeaveOneGroupOut()
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# # select CV type:
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# if group == 'none':
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# select CV type:
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# if group == None:
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# sel_cv = skf_cv
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# else:
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# sel_cv = logo
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@ -201,52 +214,81 @@ def MultModelsCl_logo_skf(input_df
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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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# col_transform = ColumnTransformer(transformers = t
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# , remainder='passthrough')
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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
|
||||
|
||||
if var_type in ['numerical','mixed']:
|
||||
if scale_numeric == ['none']:
|
||||
t = [('cat', OneHotEncoder(), categorical_ix)]
|
||||
if scale_numeric != ['none']:
|
||||
if scale_numeric == ['min_max']:
|
||||
scaler = MinMaxScaler()
|
||||
if scale_numeric == ['min_max_neg']:
|
||||
scaler = MinMaxScaler(feature_range=(-1, 1))
|
||||
if scale_numeric == ['std']:
|
||||
scaler = StandardScaler()
|
||||
|
||||
t = [('num', scaler, numerical_ix)
|
||||
, ('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
|
||||
if var_type == 'categorical':
|
||||
t = [('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
if var_type == 'mixed':
|
||||
t = [('num', MinMaxScaler(), numerical_ix)
|
||||
, ('cat', OneHotEncoder(), categorical_ix) ]
|
||||
|
||||
col_transform = ColumnTransformer(transformers = t
|
||||
, remainder='passthrough')
|
||||
|
||||
|
||||
#======================================================
|
||||
# Specify multiple Classification Models
|
||||
#======================================================
|
||||
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
||||
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
|
||||
, ('Decision Tree' , DecisionTreeClassifier(**rs) )
|
||||
, ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
, ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
|
||||
, ('Gaussian NB' , GaussianNB() )
|
||||
, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
|
||||
, ('K-Nearest Neighbors' , KNeighborsClassifier() )
|
||||
, ('LDA' , LinearDiscriminantAnalysis() )
|
||||
, ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
|
||||
, ('Multinomial' , MultinomialNB() )
|
||||
, ('Naive Bayes' , BernoulliNB() )
|
||||
, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
|
||||
, ('QDA' , QuadraticDiscriminantAnalysis() )
|
||||
, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
|
||||
, ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
|
||||
, n_estimators = 1000
|
||||
, bootstrap = True
|
||||
, oob_score = True
|
||||
, **njobs
|
||||
, **rs
|
||||
, max_features = 'auto') )
|
||||
, ('Ridge Classifier' , RidgeClassifier(**rs) )
|
||||
, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
, ('SVC' , SVC(**rs) )
|
||||
, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
, ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) )
|
||||
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
||||
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
|
||||
# , ('Decision Tree' , DecisionTreeClassifier(**rs) )
|
||||
# , ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
# , ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
|
||||
# , ('Gaussian NB' , GaussianNB() )
|
||||
# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
|
||||
# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
|
||||
# , ('LDA' , LinearDiscriminantAnalysis() )
|
||||
# , ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
# , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
|
||||
# , ('Multinomial' , MultinomialNB() )
|
||||
# , ('Naive Bayes' , BernoulliNB() )
|
||||
# , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
|
||||
# , ('QDA' , QuadraticDiscriminantAnalysis() )
|
||||
# , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) )
|
||||
# , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
|
||||
# , n_estimators = 1000
|
||||
# , bootstrap = True
|
||||
# , oob_score = True
|
||||
# , **njobs
|
||||
# , **rs
|
||||
# , max_features = 'auto') )
|
||||
# , ('Ridge Classifier' , RidgeClassifier(**rs) )
|
||||
# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
# , ('SVC' , SVC(**rs) )
|
||||
# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder = False, **njobs) )
|
||||
]
|
||||
|
||||
mm_skf_scoresD = {}
|
||||
|
|
@ -269,6 +311,12 @@ def MultModelsCl_logo_skf(input_df
|
|||
('prep' , col_transform)
|
||||
, ('model' , model_fn)])
|
||||
|
||||
# model_pipeline = Pipeline([
|
||||
# ('prep' , col_transform)
|
||||
# , ('pca' , PCA(n_components = 2))
|
||||
# , ('model' , model_fn)])
|
||||
|
||||
|
||||
print('\nRunning model pipeline:', model_pipeline)
|
||||
cv_modD = cross_validate(model_pipeline
|
||||
, input_df
|
||||
|
|
@ -358,8 +406,9 @@ def MultModelsCl_logo_skf(input_df
|
|||
bts_predict = model_pipeline.predict(blind_test_df)
|
||||
|
||||
bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
|
||||
print('\nMCC on Blind test:' , bts_mcc_score)
|
||||
print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
|
||||
print('\nMCC on Blind test:' , bts_mcc_score)
|
||||
#print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
|
||||
print('\nMCC on Training:' , mm_skf_scoresD[model_name]['test_mcc'] )
|
||||
|
||||
mm_skf_scoresD[model_name]['bts_mcc'] = bts_mcc_score
|
||||
mm_skf_scoresD[model_name]['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
|
||||
|
|
@ -387,8 +436,7 @@ def MultModelsCl_logo_skf(input_df
|
|||
############################
|
||||
#Processes the dict from above if use_formatted_output = True
|
||||
|
||||
def ProcessMultModelsCl(inputD = {}
|
||||
, blind_test_data = True):
|
||||
def ProcessMultModelsCl(inputD = {}, blind_test_data = True):
|
||||
|
||||
scoresDF = pd.DataFrame(inputD)
|
||||
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ skf_cv = StratifiedKFold(n_splits = 10
|
|||
# , n_repeats = 3
|
||||
# , **rs)
|
||||
# param dict for getmldata()
|
||||
gene_model_paramD = {'data_combined_model' : False
|
||||
gene_model_paramD = {'data_combined_model' : False
|
||||
, 'use_or' : False
|
||||
, 'omit_all_genomic_features': False
|
||||
, 'write_maskfile' : False
|
||||
|
|
@ -77,7 +77,7 @@ fooD = MultModelsCl(input_df = df2['X_ros']
|
|||
, blind_test_df = df2['X_bts']
|
||||
, blind_test_target = df2['y_bts']
|
||||
, tts_split_type = spl_type
|
||||
, resampling_type = 'none' # default
|
||||
, resampling_type = 'XXXX' # default
|
||||
, var_type = ['mixed']
|
||||
, scale_numeric = ['min_max']
|
||||
, return_formatted_output = False
|
||||
|
|
|
|||
|
|
@ -93,6 +93,7 @@ for gene, drug in ml_gene_drugD.items():
|
|||
, sel_cv = skf_cv
|
||||
, blind_test_df = tempD['X_bts']
|
||||
, blind_test_target = tempD['y_bts']
|
||||
, scale_numeric = ['min_max']
|
||||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
|
|
@ -103,5 +104,5 @@ for gene, drug in ml_gene_drugD.items():
|
|||
out_wf= pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_f = out_wf.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
out_wf_f.to_csv(('/home/tanu/git/Data/ml_combined/genes/'+ out_filename), index = False)
|
||||
out_wf_f.to_csv(out_filename, index = False)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue