added option to add confusion matrix and target numbers in the mult function
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135efcee41
3 changed files with 144 additions and 140 deletions
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@ -138,94 +138,75 @@ def MultModelsCl(input_df, target, skf_cv
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, remainder='passthrough')
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# Specify multiple Classification models
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lr = LogisticRegression(**rs)
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lrcv = LogisticRegressionCV(**rs)
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gnb = GaussianNB()
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nb = BernoulliNB()
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knn = KNeighborsClassifier()
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svc = SVC(**rs)
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mlp = MLPClassifier(max_iter = 500, **rs)
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dt = DecisionTreeClassifier(**rs)
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ets = ExtraTreesClassifier(**rs)
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et = ExtraTreeClassifier(**rs)
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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 = 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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xgb = XGBClassifier(**rs, verbosity = 0, use_label_encoder =False)
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lda = LinearDiscriminantAnalysis()
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mnb = MultinomialNB()
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pa = PassiveAggressiveClassifier(**rs, **njobs)
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sgd = SGDClassifier(**rs, **njobs)
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abc = AdaBoostClassifier(**rs)
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bc = BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True)
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gpc = GaussianProcessClassifier(**rs)
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gbc = GradientBoostingClassifier(**rs)
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qda = QuadraticDiscriminantAnalysis()
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rc = RidgeClassifier(**rs)
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rccv = RidgeClassifierCV(cv = 10)
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models = [('Logistic Regression' , lr)
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, ('Logistic RegressionCV' , lrcv)
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, ('Gaussian NB' , gnb)
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, ('Naive Bayes' , nb)
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, ('K-Nearest Neighbors' , knn)
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, ('SVC' , svc)
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, ('MLP' , mlp)
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, ('Decision Tree' , dt)
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, ('Extra Trees' , ets)
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, ('Extra Tree' , et)
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, ('Random Forest' , rf)
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, ('Random Forest2' , rf2)
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, ('XGBoost' , xgb)
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, ('LDA' , lda)
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, ('Multinomial' , mnb)
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, ('Passive Aggresive' , pa)
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, ('Stochastic GDescent' , sgd)
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, ('AdaBoost Classifier' , abc)
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, ('Bagging Classifier' , bc)
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, ('Gaussian Process' , gpc)
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, ('Gradient Boosting' , gbc)
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, ('QDA' , qda)
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, ('Ridge Classifier' , rc)
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, ('Ridge ClassifierCV' , rccv)
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models = [('Logistic Regression' , LogisticRegression(**rs) )
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, ('Logistic RegressionCV' , LogisticRegressionCV(**rs) )
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, ('Gaussian NB' , GaussianNB() )
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, ('Naive Bayes' , BernoulliNB() )
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, ('K-Nearest Neighbors' , KNeighborsClassifier() )
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, ('SVC' , SVC(**rs) )
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, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
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, ('Decision Tree' , DecisionTreeClassifier(**rs) )
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, ('Extra Trees' , ExtraTreesClassifier(**rs) )
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, ('Extra Tree' , ExtraTreeClassifier(**rs) )
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, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
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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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, ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) )
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, ('LDA' , LinearDiscriminantAnalysis() )
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, ('Multinomial' , MultinomialNB() )
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, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
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, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
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, ('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
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, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) )
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, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
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, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
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, ('QDA' , QuadraticDiscriminantAnalysis() )
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, ('Ridge Classifier' , RidgeClassifier(**rs) )
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, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 10) )
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]
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mm_skf_scoresD = {}
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print('\n==============================================================\n'
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, '\nRunning several classification models (n):', len(models)
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,'\nList of models:')
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for m in models:
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print(m)
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print('\n================================================================\n')
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index = 1
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for model_name, model_fn in models:
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print('\nModel_name:', model_name
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, '\nModel func:' , model_fn
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, '\nList of models:', models)
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print('\nRunning classifier:', index
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, '\nModel_name:' , model_name
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, '\nModel func:' , model_fn)
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index = index+1
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model_pipeline = Pipeline([
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('prep' , col_transform)
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, ('model' , model_fn)])
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print('Running model pipeline:', model_pipeline)
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skf_cv_mod = cross_validate(model_pipeline
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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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, input_df
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, target
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, cv = skf_cv
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, scoring = scoring_fn
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, return_train_score = True)
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#==============================
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# Extract mean values for CV
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#==============================
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mm_skf_scoresD[model_name] = {}
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for key, value in skf_cv_mod.items():
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for key, value in skf_cv_modD.items():
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print('\nkey:', key, '\nvalue:', value)
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print('\nmean value:', mean(value))
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mm_skf_scoresD[model_name][key] = round(mean(value),2)
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#pp.pprint(mm_skf_scoresD)
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#cvtrain_mcc = mm_skf_scoresD[model_name]['test_mcc']
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#return(mm_skf_scoresD)
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#%%
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@ -101,6 +101,9 @@ jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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def MultModelsCl_dissected(input_df, target, skf_cv
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, blind_test_input_df
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, blind_test_target
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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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, feature_groups = ['']
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, var_type = ['numerical', 'categorical','mixed']):
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'''
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@ -201,52 +204,88 @@ def MultModelsCl_dissected(input_df, target, skf_cv
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, scoring = scoring_fn
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, return_train_score = True)
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#----------
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# check 1
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#----------
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foo_df = pd.DataFrame.from_dict(skf_cv_modD, orient ='index')
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#foo_df = pd.DataFrame.from_dict(skf_cv_modD)
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#===================
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# Confusion matrix: Not an easy problem to solve! STILL DOING it, USE with caution
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#######################################################################
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#======================================================
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# Option 1: Add confusion matrix from cross_val_predict
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# Understand and USE with caution
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# cross_val_score, cross_val_predict, "Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from cross_validate and cross_val_score unless all tests sets have equal size and the metric decomposes over samples."
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# https://stackoverflow.com/questions/65645125/producing-a-confusion-matrix-with-cross-validate
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#===================
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y_pred = cross_val_predict(model_pipeline, input_df, target, cv = 10, **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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# create a dict of confusion matrix that can be appended to the one above
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# cmD = {'TN' : np.array(tn)
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# , 'FP': np.array(fp)
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# , 'FN': np.array(fn)
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# , 'TP': np.array(tp)}
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#======================================================
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if add_cm:
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cmD = {'TN' : tn
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, 'FP': fp
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, 'FN': fn
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, 'TP': tp}
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skf_cv_modD.update(cmD)
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#-----------------------------------------------------------
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# Initialise dict of Confusion Matrix (cm)
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#-----------------------------------------------------------
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cmD = {}
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#----------
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# check 2
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#----------
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#foo2_df = pd.DataFrame.from_dict(skf_cv_modD, orient ='index')
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#foo_df = pd.DataFrame.from_dict(skf_cv_modD)
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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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#_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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# Build dict
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cmD = {'TN' : tn
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, 'FP': fp
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, 'FN': fn
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, 'TP': tp}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(cmD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#=============================================
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# Option 2: Add targety numbers for data
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#=============================================
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if add_yn:
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#-----------------------------------------------------------
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# Initialise dict of target numbers: training and blind (tbt)
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#-----------------------------------------------------------
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tbtD = {}
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# training y
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tyn = Counter(target)
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tyn_neg = tyn[0]
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tyn_pos = tyn[1]
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# blind test y
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btyn = Counter(blind_test_target)
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btyn_neg = btyn[0]
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btyn_pos = btyn[1]
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# Build dict
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tbtD = {'trainingY_neg' : tyn_neg
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, 'trainingY_pos' : tyn_pos
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, 'blindY_neg' : btyn_neg
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, 'blindY_pos' : btyn_pos}
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#---------------------------------
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# Update cv dict with cmD and tbtD
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#----------------------------------
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skf_cv_modD.update(tbtD)
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else:
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skf_cv_modD = skf_cv_modD
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#######################################################################
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#==============================
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# Extract mean values for CV
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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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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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#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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# Build the final results with all scores for the model
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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(blind_test_input_df)
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@ -255,22 +294,6 @@ def MultModelsCl_dissected(input_df, target, skf_cv
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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_MCC = cvtrain_mcc - 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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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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@ -104,29 +104,29 @@ else:
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print('\n#####################################################################\n')
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###############################################################################
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#==================
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# Baseline models
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#==================
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mm_skf_scoresD = MultModelsCl(input_df = X
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, target = y
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, var_type = 'mixed'
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, skf_cv = skf_cv
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, blind_test_input_df = X_bts
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, blind_test_target = y_bts)
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# ###############################################################################
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# #==================
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# # Baseline models
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# #==================
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# mm_skf_scoresD = MultModelsCl(input_df = X
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# , target = y
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# , var_type = 'mixed'
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# , skf_cv = skf_cv
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# , blind_test_input_df = X_bts
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# , blind_test_target = y_bts)
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baseline_all = pd.DataFrame(mm_skf_scoresD)
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baseline_all = baseline_all.T
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#baseline_train = baseline_all.filter(like='train_', axis=1)
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baseline_CT = baseline_all.filter(like='test_', axis=1)
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baseline_CT.sort_values(by=['test_mcc'], ascending=False, inplace=True)
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# baseline_all = pd.DataFrame(mm_skf_scoresD)
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# baseline_all = baseline_all.T
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# #baseline_train = baseline_all.filter(like='train_', axis=1)
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# baseline_CT = baseline_all.filter(like='test_', axis=1)
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# baseline_CT.sort_values(by=['test_mcc'], ascending=False, inplace=True)
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baseline_BT = baseline_all.filter(like='bts_', axis=1)
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baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# baseline_BT = baseline_all.filter(like='bts_', axis=1)
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# baseline_BT.sort_values(by = ['bts_mcc'], ascending = False, inplace = True)
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# Write csv
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baseline_CT.to_csv(outdir_ml + gene.lower() + '_baseline_CT_allF.csv')
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baseline_BT.to_csv(outdir_ml + gene.lower() + '_baseline_BT_allF.csv')
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# # Write csv
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# baseline_CT.to_csv(outdir_ml + gene.lower() + '_baseline_CT_allF.csv')
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# baseline_BT.to_csv(outdir_ml + gene.lower() + '_baseline_BT_allF.csv')
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# #%% SMOTE NC: Oversampling [Numerical + categorical]
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