added UQ scripts to do hyperparam ML models

UQ_LR.py

@@ -0,0 +1,207 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon May 16 05:59:12 2022
+
+@author: tanu
+"""
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 15 11:09:50 2022
+
+@author: tanu
+"""
+#%% Import libs
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import GridSearchCV
+from sklearn import datasets
+from sklearn.ensemble import ExtraTreesClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.svm import SVC
+
+from sklearn.base import BaseEstimator
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+from xgboost import XGBClassifier
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+#%% Get train-test split and scoring functions
+# X_train, X_test, y_train, y_test = train_test_split(num_df_wtgt[numerical_FN]
+#                                                     , num_df_wtgt['mutation_class']
+#                                                     , test_size    = 0.33
+#                                                     , random_state = 2
+#                                                     , shuffle      = True
+#                                                     , stratify     = num_df_wtgt['mutation_class'])
+
+y.to_frame().value_counts().plot(kind = 'bar')
+blind_test_df['dst_mode'].to_frame().value_counts().plot(kind = 'bar')
+
+scoring_fn =  ({'accuracy'      : make_scorer(accuracy_score)
+                 , 'fscore'     : make_scorer(f1_score)
+                 , 'mcc'        : make_scorer(matthews_corrcoef)
+                 ,  'precision' : make_scorer(precision_score)
+                 ,  'recall'    : make_scorer(recall_score)
+                 ,  'roc_auc'   : make_scorer(roc_auc_score)
+                 ,  'jaccard'   : make_scorer(jaccard_score)
+            })    
+
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
+
+#%% Logistic Regression + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        A Custom BaseEstimator that can switch between classifiers.
+        :param estimator: sklearn object - The classifier
+        """ 
+        self.estimator = estimator
+    
+    def fit(self, X, y=None, **kwargs):
+        self.estimator.fit(X, y)
+        return self
+
+    def predict(self, X, y=None):
+        return self.estimator.predict(X)
+    
+    def predict_proba(self, X):
+        return self.estimator.predict_proba(X)
+    
+    def score(self, X, y):
+        return self.estimator.score(X, y)
+    
+parameters = [
+    {
+        'clf__estimator': [LogisticRegression(**rs)],
+        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'clf__estimator__C': np.logspace(0, 4, 10),
+        'clf__estimator__penalty': ['none', 'l1', 'l2', 'elasticnet'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['saga']
+    },
+    {
+        'clf__estimator': [LogisticRegression(**rs)],
+        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'clf__estimator__C': np.logspace(0, 4, 10),
+        'clf__estimator__penalty': ['l2', 'none'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['newton-cg', 'lbfgs', 'sag']
+    }, 
+    {
+        'clf__estimator': [LogisticRegression(**rs)],
+        #'clf__estimator__C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
+        'clf__estimator__C': np.logspace(0, 4, 10),
+        'clf__estimator__penalty': ['l1', 'l2'],
+        'clf__estimator__max_iter': list(range(100,800,100)),
+        'clf__estimator__solver': ['liblinear']
+    }
+
+]    
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_lr = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_lr_fit = gscv_lr.fit(X, y)
+gscv_lr_fit_be_mod = gscv_lr_fit.best_params_
+gscv_lr_fit_be_res = gscv_lr_fit.cv_results_
+
+print('Best model:\n', gscv_lr_fit_be_mod)
+print('Best models score:\n', gscv_lr_fit.best_score_, ':' , round(gscv_lr_fit.best_score_, 2))
+
+#print('\nMean test score from fit results:', round(mean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_lr_fit_be_res['mean_test_mcc']),2))
+
+
+######################################
+# Blind test
+######################################
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )) 
+
+test_predict = gscv_lr_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_bts, test_predict))
+print(matthews_corrcoef(y_bts, test_predict))
+
+# create a dict with all scores
+lr_bts_dict = {#'best_model': list(gscv_lr_fit_be_mod.items())
+               'bts_fscore':None
+               , 'bts_mcc':None
+               , 'bts_precision':None
+               , 'bts_recall':None
+               , 'bts_accuracy':None
+               , 'bts_roc_auc':None
+               , 'bts_jaccard':None }
+lr_bts_dict
+lr_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+lr_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+lr_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+lr_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+lr_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+lr_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+lr_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+lr_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(lr_bts_dict, orient = 'index', columns = 'best_model')
+
+lr_bts_df = pd.DataFrame.from_dict(lr_bts_dict,orient = 'index')
+lr_bts_df.columns = ['Logistic_Regression']
+print(lr_bts_df)
+
+# d2 = {'best_model_params': lis(gscv_lr_fit_be_mod.items() )}
+# d2
+# def Merge(dict1, dict2):
+#     res = {**dict1, **dict2}
+#     return res
+# d3 = Merge(d2, lr_bts_dict)
+# d3
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_lr_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+lr_bts_df.columns
+lr_output = pd.concat([model_params_df, lr_bts_df], axis = 0)
+lr_output
+
+# Format the combined df
+# Drop the best_model_params row from lr_output
+lr_df = lr_output.drop([0], axis = 0)
+lr_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################

UQ_RF.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed May 18 06:03:24 2022
+
+@author: tanu
+"""
+#%% RandomForest + hyperparam: BaseEstimator: ClfSwitcher()
+class ClfSwitcher(BaseEstimator):
+    def __init__(
+        self, 
+        estimator = SGDClassifier(),
+    ):
+        """
+        A Custom BaseEstimator that can switch between classifiers.
+        :param estimator: sklearn object - The classifier
+        """ 
+        self.estimator = estimator
+    
+    def fit(self, X, y=None, **kwargs):
+        self.estimator.fit(X, y)
+        return self
+
+    def predict(self, X, y=None):
+        return self.estimator.predict(X)
+    
+    def predict_proba(self, X):
+        return self.estimator.predict_proba(X)
+    
+    def score(self, X, y):
+        return self.estimator.score(X, y)
+    
+parameters = [
+    {
+        'clf__estimator': [RandomForestClassifier(**rs
+                                                  , **njobs
+                                                  , bootstrap = True
+                                                  , oob_score = True)],
+        'clf__estimator__max_depth': [4, 6, 8, 10, 12, 16, 20, None]
+        , 'clf__estimator__class_weight':['balanced','balanced_subsample']
+        , 'clf__estimator__n_estimators': [10, 25, 50, 100]
+        , 'clf__estimator__criterion': ['gini', 'entropy']#, 'log_loss']
+        #, 'clf__estimator__max_features': ['auto', 'sqrt']
+        , 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
+        , 'clf__estimator__min_samples_split': [2, 5, 15, 20]
+        }
+]
+
+# Create pipeline
+pipeline = Pipeline([
+    ('pre', MinMaxScaler()),
+    ('clf', ClfSwitcher()),
+])
+
+# Grid search i.e hyperparameter tuning and refitting on mcc
+gscv_rf = GridSearchCV(pipeline
+                    , parameters
+                    #, scoring = 'f1', refit = 'f1'
+                    , scoring = mcc_score_fn, refit = 'mcc'
+                    , cv = skf_cv
+                    , **njobs
+                    , return_train_score = False
+                    , verbose = 3)
+
+# Fit 
+gscv_rf_fit  = gscv_rf.fit(X, y)
+
+gscv_rf_fit_be_mod = gscv_rf_fit.best_params_
+gscv_rf_fit_be_res = gscv_rf_fit.cv_results_
+
+print('Best model:\n', gscv_rf_fit_be_mod)
+print('Best models score:\n', gscv_rf_fit.best_score_, ':' , round(gscv_rf_fit.best_score_, 2))
+
+print('\nMean test score from fit results:', round(mean(gscv_rf_fit_be_re['mean_test_mcc']),2))
+print('\nMean test score from fit results:', round(np.nanmean(gscv_rf_fit_be_res['mean_test_mcc']),2))
+
+######################################
+# Blind test
+######################################
+
+# See how it does on the BLIND test
+#print('\nBlind test score, mcc:', )
+
+test_predict = gscv_rf_fit.predict(X_bts)
+print(test_predict)
+print(np.array(y_bts))
+y_btsf = np.array(y_bts)
+
+print(accuracy_score(y_btsf, test_predict))
+print(matthews_corrcoef(y_btsf, test_predict))
+
+# create a dict with all scores
+rf_bts_dict = {#'best_model': list(gscv_rf_fit_be_mod.items())
+               'bts_fscore'     : None
+               , 'bts_mcc'      : None
+               , 'bts_precision': None
+               , 'bts_recall'   : None
+               , 'bts_accuracy' : None
+               , 'bts_roc_auc'  : None
+               , 'bts_jaccard'  : None }
+rf_bts_dict
+rf_bts_dict['bts_fscore']    = round(f1_score(y_bts, test_predict),2)
+rf_bts_dict['bts_mcc']       = round(matthews_corrcoef(y_bts, test_predict),2)
+rf_bts_dict['bts_precision'] = round(precision_score(y_bts, test_predict),2)
+rf_bts_dict['bts_recall']    = round(recall_score(y_bts, test_predict),2)
+rf_bts_dict['bts_accuracy']  = round(accuracy_score(y_bts, test_predict),2)
+rf_bts_dict['bts_roc_auc']   = round(roc_auc_score(y_bts, test_predict),2)
+rf_bts_dict['bts_jaccard']   = round(jaccard_score(y_bts, test_predict),2)
+rf_bts_dict
+
+# Create a df from dict with all scores
+pd.DataFrame.from_dict(rf_bts_dict, orient = 'index', columns = 'best_model')
+
+rf_bts_df = pd.DataFrame.from_dict(rf_bts_dict,orient = 'index')
+rf_bts_df.columns = ['Logistic_Regression']
+print(rf_bts_df)
+
+# Create df with best model params
+model_params = pd.Series(['best_model_params',  list(gscv_rf_fit_be_mod.items() )])
+model_params_df = model_params.to_frame()
+model_params_df
+model_params_df.columns = ['Logistic_Regression']
+model_params_df.columns
+
+# Combine the df of scores and the best model params
+rf_bts_df.columns
+rf_output = pd.concat([model_params_df, rf_bts_df], axis = 0)
+rf_output
+
+# Format the combined df
+# Drop the best_model_params row from rf_output
+rf_df = rf_output.drop([0], axis = 0)
+rf_df
+
+#FIXME: tidy the index of the formatted df
+
+###############################################################################
+
+
+

UQ_pnca_ML.py

@@ -0,0 +1,287 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar  6 13:41:54 2022
+
+@author: tanu
+"""
+import os, sys
+import pandas as pd
+import numpy as np
+import pprint as pp
+from copy import deepcopy
+from sklearn import linear_model
+from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.neural_network import MLPClassifier
+from xgboost import XGBClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+
+from sklearn.compose import ColumnTransformer
+from sklearn.compose import make_column_transformer
+
+from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score 
+from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score
+from sklearn.metrics import jaccard_score
+
+from sklearn.metrics import make_scorer
+from sklearn.metrics import classification_report
+
+from sklearn.metrics import average_precision_score
+
+from sklearn.model_selection import cross_validate
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import StratifiedKFold
+
+from sklearn.pipeline import Pipeline
+from sklearn.pipeline import make_pipeline
+
+from sklearn.feature_selection import RFE
+from sklearn.feature_selection import RFECV
+import itertools
+#import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+print(np.__version__)
+print(pd.__version__)
+from statistics import mean, stdev, median, mode
+
+#from imblearn.over_sampling import RandomOverSampler
+#from imblearn.over_sampling import SMOTE
+#from imblearn.pipeline import Pipeline
+#from sklearn.datasets import make_classification
+from sklearn.model_selection import cross_validate, cross_val_score
+from sklearn.model_selection import RepeatedStratifiedKFold
+from sklearn.ensemble import AdaBoostClassifier
+#from imblearn.combine import SMOTEENN
+#from imblearn.under_sampling import EditedNearestNeighbours
+
+from sklearn.model_selection import GridSearchCV
+from sklearn.base import BaseEstimator
+
+scoring_fn =  ({'accuracy'      : make_scorer(accuracy_score)
+                 , 'fscore'     : make_scorer(f1_score)
+                 , 'mcc'        : make_scorer(matthews_corrcoef)
+                 ,  'precision' : make_scorer(precision_score)
+                 ,  'recall'    : make_scorer(recall_score)
+                 ,  'roc_auc'   : make_scorer(roc_auc_score)
+                 , 'jcc'        : make_scorer(jaccard_score)
+            }) 
+  
+rs = {'random_state': 42}
+njobs = {'n_jobs': 10}
+skf_cv = StratifiedKFold(n_splits = 10
+                          #, shuffle = False, random_state= None)
+                           , shuffle = True,**rs)
+
+rskf_cv = RepeatedStratifiedKFold(n_splits = 10
+                                  , n_repeats=3
+                                 #, shuffle = False, random_state= None)
+                                 #, shuffle = True
+                                 ,**rs)
+#my_mcc = make_scorer({'mcc':make_scorer(matthews_corrcoef})
+mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
+
+#%%
+homedir = os.path.expanduser("~")
+os.chdir(homedir + "/git/ML_AI_training/")
+
+# my function
+#from MultClassPipe import MultClassPipeline
+from MultClassPipe2 import MultClassPipeline2
+from loopity_loop import MultClassPipeSKFLoop
+from MultClassPipe3 import MultClassPipeSKFCV
+
+gene = 'pncA'
+drug = 'pyrazinamide'
+
+#==============
+# directories
+#==============
+datadir = homedir + '/git/Data/'
+indir   = datadir + drug + '/input/'
+outdir  = datadir + drug + '/output/'
+
+#=======
+# input
+#=======
+infile_ml1 = outdir + gene.lower() + '_merged_df3.csv' 
+#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
+
+my_df = pd.read_csv(infile_ml1, index_col = 0)
+my_df.dtypes
+my_df_cols = my_df.columns
+
+geneL_basic = ['pnca']
+
+# -- CHECK script -- imports.py
+#%% get cols
+mycols = my_df.columns
+mycols
+
+# change from numberic to 
+num_type = ['int64', 'float64']
+cat_type = ['object', 'bool']
+
+# TODO:
+# Treat active site aa pos as category and not numerical: This needs to be part of merged_df3!
+#if my_df['active_aa_pos'].dtype in num_type:
+#    my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
+#    my_df['active_aa_pos'].dtype
+
+# -- CHECK script -- imports.py
+#%%============================================================================
+#%% IMPUTE values for OR
+
+#%% Combine mmCSM_lig Data
+
+#%% Combine PROVEAN data
+
+#%% Combine ED logo data
+
+#%% Masking columns (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
+
+# get logic from upstream!
+my_df_ml = my_df.copy()
+
+my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
+my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
+my_df_ml.groupby(['mutationinformation'])['ligand_distance'].apply(lambda x: (x>10)).value_counts()
+
+my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
+(my_df_ml['ligand_affinity_change'] == 0).sum()
+
+#%%============================================================================
+# Separate blind test set
+my_df_ml[drug].isna().sum()
+
+blind_test_df = my_df_ml[my_df_ml[drug].isna()]
+blind_test_df.shape
+
+training_df =  my_df_ml[my_df_ml[drug].notna()]
+training_df.shape
+
+# Target1: dst
+training_df[drug].value_counts()
+training_df['dst_mode'].value_counts()
+
+#%% Build X
+common_cols_stabiltyN = ['ligand_distance'
+           , 'ligand_affinity_change'
+           , 'duet_stability_change'
+           , 'ddg_foldx'
+           , 'deepddg'
+           , 'ddg_dynamut2']
+
+foldX_cols = ['contacts'
+#, 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
+#, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
+#, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
+#, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
+#, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
+#, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
+]
+
+X_strFN =  ['rsa'
+           #, 'asa'
+           , 'kd_values'
+           , 'rd_values']    
+
+X_evolFN =  ['consurf_score'
+           , 'snap2_score']
+
+# quick inspection which lineage to use:
+#foo = my_df_ml[['lineage', 'lineage_count_all', 'lineage_count_unique']]
+    
+X_genomicFN =  ['maf'
+#            , 'or_mychisq'
+#            , 'or_logistic'
+#            , 'or_fisher'
+#            , 'pval_fisher'
+             #, 'lineage'
+             , 'lineage_count_all'
+             , 'lineage_count_unique'
+]
+
+#%% Construct numerical and categorical column names
+
+# numerical feature names
+numerical_FN = common_cols_stabiltyN + foldX_cols + X_strFN + X_evolFN + X_genomicFN 
+
+#categorical feature names
+categorical_FN = ['ss_class'
+             , 'wt_prop_water'
+            # , 'lineage_labels' # misleading if using merged_df3
+             , 'mut_prop_water'
+             , 'wt_prop_polarity'
+             , 'mut_prop_polarity'
+             , 'wt_calcprop'
+             , 'mut_calcprop'
+             #, 'active_aa_pos'
+             ]
+
+#%% extracting dfs based on numerical, categorical column names
+#----------------------------------
+# WITHOUT the target var included
+#----------------------------------
+num_df = training_df[numerical_FN]
+num_df.shape
+
+cat_df = training_df[categorical_FN]
+cat_df.shape
+
+all_df = training_df[numerical_FN + categorical_FN]
+all_df.shape
+
+#------------------------------
+# WITH the target var included:
+    #'wtgt': with target
+#------------------------------
+# drug and dst_mode should be the same thing
+num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
+num_df_wtgt.shape
+
+cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
+cat_df_wtgt.shape
+
+all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
+all_df_wtgt.shape
+#%%================================================================
+#%% Apply ML
+#TODO: Apply oversampling!
+
+#%% Data
+#X = all_df_wtgt[numerical_FN+categorical_FN]
+X = all_df_wtgt[numerical_FN]
+y = all_df_wtgt['dst_mode']
+
+#Blind test data {same format}
+X_bts = blind_test_df[numerical_FN]
+y_bts = blind_test_df['dst_mode']
+
+X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']] 
+
+# Quick check
+(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
+#%% MultClassPipeSKFCV: function call()
+mm_skf_scoresD = MultClassPipeSKFCV(input_df = X
+                                        , target = y
+                                        , var_type = 'numerical'
+                                        , skf_cv = skf_cv)
+
+
+mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
+mm_skf_scores_df_all
+mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
+mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
+print(mm_skf_scores_df_train)
+print(mm_skf_scores_df_test)
+