saving work

scripts/ml/combined_model/cm_logo_skf.py

@@ -9,6 +9,72 @@ import sys, os
 import pandas as pd
 import numpy as np
 import re
+from copy import deepcopy
+from sklearn import linear_model
+from sklearn import datasets
+from collections import Counter
+
+from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
+from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
+
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
+from sklearn.naive_bayes import GaussianNB
+from sklearn.gaussian_process import GaussianProcessClassifier, kernels
+from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
+
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
+from sklearn.neural_network import MLPClassifier
+
+from sklearn.svm import SVC
+from xgboost import XGBClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+
+from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_transformer
+
+from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
+from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
+
+# added
+from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
+
+from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
+from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
+
+from sklearn.pipeline import Pipeline, make_pipeline
+
+from sklearn.feature_selection import RFE, RFECV
+
+import itertools
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from statistics import mean, stdev, median, mode
+
+from imblearn.over_sampling import RandomOverSampler
+from imblearn.under_sampling import RandomUnderSampler
+from imblearn.over_sampling import SMOTE
+from sklearn.datasets import make_classification
+from imblearn.combine import SMOTEENN
+from imblearn.combine import SMOTETomek
+
+from imblearn.over_sampling import SMOTENC
+from imblearn.under_sampling import EditedNearestNeighbours
+from imblearn.under_sampling import RepeatedEditedNearestNeighbours
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.base import BaseEstimator
+from sklearn.impute import KNNImputer as KNN
+import json
+import argparse
+import re
+import itertools
+from sklearn.model_selection import LeaveOneGroupOut
 ###############################################################################
 homedir = os.path.expanduser("~")
 sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
@@ -22,7 +88,7 @@ from MultClfs_logo_skf import *
 #from GetMLData import *
 #from SplitTTS import *
 
-skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True,**rs)
+skf_cv = StratifiedKFold(n_splits = 10 , shuffle = True, random_state = 42)
 
 #logo = LeaveOneGroupOut()
 
@@ -38,13 +104,17 @@ def CMLogoSkf(combined_df
 
     for bts_gene in bts_genes:
         print('\n BTS gene:', bts_gene)
+        if not std_gene_omit:
+            training_genesL = ['alr']
+        else:
+            training_genesL = []
             
         tr_gene_omit = std_gene_omit + [bts_gene]
         n_tr_genes = (len(bts_genes) - (len(std_gene_omit)))
         #n_total_genes = (len(bts_genes) - len(std_gene_omit))
         n_total_genes = len(all_genes)
         
-        training_genesL = std_gene_omit + list(set(bts_genes) - set(tr_gene_omit))
+        training_genesL = training_genesL + list(set(bts_genes) - set(tr_gene_omit))
         #training_genesL = [element for element in bts_genes if element not in tr_gene_omit]
     
         print('\nTotal genes: ', n_total_genes
@@ -53,7 +123,7 @@ def CMLogoSkf(combined_df
               , '\nOmitted genes:', tr_gene_omit
               , '\nBlind test gene:', bts_gene) 
             
-        tts_split_type = "logoBT_" + bts_gene
+        tts_split_type = "logo_skf_BT_" + bts_gene
         
         outFile = "/home/tanu/git/Data/ml_combined/" + str(n_tr_genes+1) + "genes_" + tts_split_type + ".csv"
         print(outFile)
@@ -67,7 +137,6 @@ def CMLogoSkf(combined_df
         #cm_y = cm_training_df.loc[:,'dst_mode']
         cm_y = cm_training_df.loc[:, target_var]
     
-    
         gene_group = cm_training_df.loc[:,'gene_name']
         
         print('\nTraining data dim:', cm_X.shape
@@ -87,14 +156,14 @@ def CMLogoSkf(combined_df
         #cm_bts_y = cm_test_df.loc[:, 'dst_mode']
         cm_bts_y = cm_test_df.loc[:, target_var]
     
-        print('\nTraining data dim:', cm_bts_X.shape
-              , '\nTraining Target dim:', cm_bts_y.shape)
+        print('\nTEST data dim:', cm_bts_X.shape
+              , '\nTEST Target dim:', cm_bts_y.shape)
         
         
         #%%:Running Multiple models on LOGO with SKF
         cD3_v2 = MultModelsCl_logo_skf(input_df = cm_X
                         , target = cm_y
-                        , group = 'none'
+                        #, group = 'none'
                         , sel_cv = skf_cv
         
                         , blind_test_df = cm_bts_X
@@ -116,5 +185,5 @@ def CMLogoSkf(combined_df
         cD3_v2.to_csv(outFile)
 
 #%%
-CMLogoSkf(combined_df)
+#CMLogoSkf(combined_df)
 CMLogoSkf(combined_df, std_gene_omit=['alr'])