moved logo_skf function to del as using the MultClfs for combined data
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8 changed files with 71 additions and 1735 deletions
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Thu Jul 7 22:18:14 2022
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@author: tanu
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"""
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# Create a pipeline that standardizes the data then creates a model
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import pandas as pd
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from pandas import read_csv
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from sklearn.model_selection import KFold
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from sklearn.model_selection import cross_val_score
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from sklearn.preprocessing import StandardScaler
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from sklearn.pipeline import Pipeline
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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# load data
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url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
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names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
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dataframe = read_csv(url, names=names)
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array = dataframe.values
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X = array[:,0:8]
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X = pd.DataFrame(X)
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Y = array[:,8]
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Y = pd.DataFrame(Y)
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kfold = KFold(n_splits=1, random_state=None)
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spl_type = "check"
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fooD1 = MultModelsCl(input_df = X
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, target = Y
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, sel_cv = kfold
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, run_blind_test = False
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#, blind_test_df = df2['X_bts']
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#, blind_test_target = df2['y_bts']
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, add_cm = False
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, add_yn = False
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, tts_split_type = spl_type
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, resampling_type = 'none' # default
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, var_type = ['mixed']
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, scale_numeric = ['std']
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, return_formatted_output = True
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)
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# create pipeline
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estimators = []
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estimators.append(('standardize', StandardScaler()))
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estimators.append(('lda', LinearDiscriminantAnalysis()))
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model = Pipeline(estimators)
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# evaluate pipeline
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seed = 7
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#kfold = KFold(n_splits=10, random_state=seed)
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kfold = KFold(n_splits=10, random_state=None)
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results = cross_val_score(model, X, Y, cv=kfold)
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print(results.mean())
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results_A = round(results.mean(),2)
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results2 = cross_val_score(model, X, Y, cv=kfold, scoring = "recall")
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print(results2.mean())
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results_R = round(results2.mean(),2)
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results3 = cross_val_score(model, X, Y, cv=kfold, scoring = "precision")
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print(results3.mean())
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results_P = round(results3.mean(),2)
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results4 = cross_val_score(model, X, Y, cv=kfold, scoring = "f1")
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print(results4.mean())
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results_f1 = round(results4.mean(),2)
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results5 = cross_val_score(model, X, Y, cv=kfold, scoring = "jaccard")
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print(results5.mean())
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results_J = round(results5.mean(),2)
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results6 = cross_val_score(model, X, Y, cv=kfold, scoring = "matthews_corrcoef")
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print(results6.mean())
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results_mcc = round(results6.mean(),2)
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#%%
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import numpy as np
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from sklearn.compose import ColumnTransformer
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from sklearn.datasets import fetch_openml
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.decomposition import PCA
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from sklearn.impute import SimpleImputer, KNNImputer
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from sklearn.preprocessing import RobustScaler, OneHotEncoder
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV
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X_train, X_test, y_train, y_test = train_test_split(X, Y, stratify=Y, test_size=0.2)
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fooD2 = MultModelsCl(input_df = X_train
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, target = y_train
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, sel_cv = kfold
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, run_blind_test = True
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, blind_test_df = X_test
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, blind_test_target = y_test
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, add_cm = False
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, add_yn = False
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, tts_split_type = spl_type
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, resampling_type = 'none' # default
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, var_type = ['mixed']
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, scale_numeric = ['std']
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, return_formatted_output = True
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)
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# fitting and predicting on test
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model.fit(X_train, y_train)
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y_pred = model.predict(X_test)
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results_A = round(cross_val_score(model, X_train, y_train, cv=kfold).mean(),2)
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print(results_A)
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results_P = round(cross_val_score(model, X_train, y_train, cv=kfold, scoring = "precision").mean(),2)
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print(results_P)
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results_R = round(cross_val_score(model, X_train, y_train, cv=kfold, scoring = "recall").mean(),2)
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print(results_R)
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results_F = round(cross_val_score(model, X_train, y_train, cv=kfold, scoring = "f1").mean(),2)
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print(results_F)
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results_J = round(cross_val_score(model, X_train, y_train, cv=kfold, scoring = "jaccard").mean(),2)
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print(results_J)
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results_M = round(cross_val_score(model, X_train, y_train, cv=kfold, scoring = "matthews_corrcoef").mean(),2)
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print(results_M)
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print('\nCV example accuracy:', results_P)
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print('BTS example accuracy:', round(precision_score(y_test, y_pred),2))
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print('\nCV example accuracy:', results_J)
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print('BTS example accuracy:', round(jaccard_score(y_test, y_pred),2))
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print('\nCV example accuracy:', results_R)
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print('BTS example accuracy:', round(recall_score(y_test, y_pred),2))
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print('\nCV example accuracy:', results_F)
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print('BTS example accuracy:', round(f1_score(y_test, y_pred),2))
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print('\nCV example accuracy:', results_A)
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print('BTS example accuracy:', round(accuracy_score(y_test, y_pred),2))
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print('\nCV example accuracy:', results_M)
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print('BTS example accuracy:', round(matthews_corrcoef(y_test, y_pred),2))
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@ -92,10 +92,10 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
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, 'roc_auc' : make_scorer(roc_auc_score)
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, 'jcc' : make_scorer(jaccard_score)
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})
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# for sel_cv INSIDE FUNCTION CALL NOW
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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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# , shuffle = True, **rs)
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#rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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# , n_repeats = 3
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@ -149,25 +149,26 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
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# Run Multiple Classifiers
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############################
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target
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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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, 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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def MultModelsCl(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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, 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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, 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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, 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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@ -357,10 +358,9 @@ def MultModelsCl(input_df, target
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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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#, commented out thing,
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, cv=sel_cv
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, **njobs
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)
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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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@ -1,553 +0,0 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Fri Mar 4 15:25:33 2022
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@author: tanu
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"""
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#%%
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import os, sys
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import pandas as pd
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import numpy as np
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import pprint as pp
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from copy import deepcopy
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from sklearn import linear_model
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from sklearn import datasets
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from collections import Counter
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier
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from sklearn.naive_bayes import BernoulliNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.svm import SVC
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from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier
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from sklearn.naive_bayes import GaussianNB
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from sklearn.gaussian_process import GaussianProcessClassifier, kernels
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from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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from sklearn.svm import SVC
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from xgboost import XGBClassifier
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
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from sklearn.compose import ColumnTransformer
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from sklearn.compose import make_column_transformer
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from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report
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# added
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict
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from sklearn.model_selection import train_test_split, cross_validate, cross_val_score
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.feature_selection import RFE, RFECV
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import itertools
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import seaborn as sns
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import matplotlib.pyplot as plt
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from statistics import mean, stdev, median, mode
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from imblearn.over_sampling import RandomOverSampler
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from imblearn.under_sampling import RandomUnderSampler
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from imblearn.over_sampling import SMOTE
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from sklearn.datasets import make_classification
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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from imblearn.over_sampling import SMOTENC
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from imblearn.under_sampling import EditedNearestNeighbours
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from imblearn.under_sampling import RepeatedEditedNearestNeighbours
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from sklearn.model_selection import GridSearchCV
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from sklearn.base import BaseEstimator
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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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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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, 'recall' : make_scorer(recall_score)
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, 'accuracy' : make_scorer(accuracy_score)
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, 'roc_auc' : make_scorer(roc_auc_score)
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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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###############################################################################
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score_type_ordermapD = { 'mcc' : 1
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, 'fscore' : 2
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, 'jcc' : 3
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, 'precision' : 4
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, 'recall' : 5
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, 'accuracy' : 6
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, 'roc_auc' : 7
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, 'TN' : 8
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, 'FP' : 9
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, 'FN' : 10
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, 'TP' : 11
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, 'trainingY_neg': 12
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, 'trainingY_pos': 13
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, 'blindY_neg' : 14
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, 'blindY_pos' : 15
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, 'fit_time' : 16
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, 'score_time' : 17
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}
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scoreCV_mapD = {'test_mcc' : 'MCC'
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, 'test_fscore' : 'F1'
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, 'test_precision' : 'Precision'
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, 'test_recall' : 'Recall'
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, 'test_accuracy' : 'Accuracy'
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, 'test_roc_auc' : 'ROC_AUC'
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, 'test_jcc' : 'JCC'
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}
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scoreBT_mapD = {'bts_mcc' : 'MCC'
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, 'bts_fscore' : 'F1'
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, 'bts_precision' : 'Precision'
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, 'bts_recall' : 'Recall'
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, 'bts_accuracy' : 'Accuracy'
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, 'bts_roc_auc' : 'ROC_AUC'
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, 'bts_jcc' : 'JCC'
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}
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#%%############################################################################
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############################
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# MultModelsCl()
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# Run Multiple Classifiers
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############################
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target
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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 = '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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'''
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@ param input_df: input features
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@ type: df with input features WITHOUT the target variable
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@param target: target (or output) feature
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@type: df or np.array or Series
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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-hot encoder)
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@type: list
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returns
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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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#======================================================
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# Determine categorical and numerical features
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#======================================================
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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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 == 'categorical':
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# t = [('cat', OneHotEncoder(), categorical_ix)]
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# # if var_type == 'mixed':
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# # t = [('num', MinMaxScaler(), numerical_ix)
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# # , ('cat', OneHotEncoder(), categorical_ix) ]
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# if var_type == 'mixed':
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# t = [('cat', OneHotEncoder(), categorical_ix) ]
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if type(var_type) == list:
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var_type = str(var_type[0])
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else:
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var_type = var_type
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if var_type in ['numerical','mixed']:
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if scale_numeric == ['none']:
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if scale_numeric != ['none']:
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if scale_numeric == ['min_max']:
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scaler = MinMaxScaler()
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if scale_numeric == ['min_max_neg']:
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scaler = MinMaxScaler(feature_range=(-1, 1))
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if scale_numeric == ['std']:
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scaler = StandardScaler()
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|
||||
t = [('num', scaler, numerical_ix)
|
||||
, ('cat', OneHotEncoder(), categorical_ix)]
|
||||
|
||||
|
||||
if var_type == 'categorical':
|
||||
t = [('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, 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 = {}
|
||||
|
||||
print('\n==============================================================\n'
|
||||
, '\nRunning several classification models (n):', len(models)
|
||||
,'\nList of models:')
|
||||
for m in models:
|
||||
print(m)
|
||||
print('\n================================================================\n')
|
||||
|
||||
index = 1
|
||||
for model_name, model_fn in models:
|
||||
print('\nRunning classifier:', index
|
||||
, '\nModel_name:' , model_name
|
||||
, '\nModel func:' , model_fn)
|
||||
index = index+1
|
||||
|
||||
model_pipeline = Pipeline([
|
||||
('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)
|
||||
skf_cv_modD = cross_validate(model_pipeline
|
||||
, input_df
|
||||
, target
|
||||
, cv = sel_cv
|
||||
, scoring = scoring_fn
|
||||
, return_train_score = True)
|
||||
#==============================
|
||||
# Extract mean values for CV
|
||||
#==============================
|
||||
mm_skf_scoresD[model_name] = {}
|
||||
|
||||
for key, value in skf_cv_modD.items():
|
||||
print('\nkey:', key, '\nvalue:', value)
|
||||
print('\nmean value:', np.mean(value))
|
||||
mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
|
||||
|
||||
# ADD more info: meta data related to input df
|
||||
mm_skf_scoresD[model_name]['resampling'] = resampling_type
|
||||
mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
|
||||
mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
|
||||
mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
|
||||
mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
|
||||
|
||||
#######################################################################
|
||||
#======================================================
|
||||
# Option: Add confusion matrix from cross_val_predict
|
||||
# Understand and USE with caution
|
||||
#======================================================
|
||||
if add_cm:
|
||||
cmD = {}
|
||||
|
||||
# Calculate cm
|
||||
y_pred = cross_val_predict(model_pipeline, input_df, target, cv = sel_cv, **njobs)
|
||||
#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
|
||||
tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
|
||||
|
||||
# Build cm dict
|
||||
cmD = {'TN' : tn
|
||||
, 'FP': fp
|
||||
, 'FN': fn
|
||||
, 'TP': tp}
|
||||
|
||||
# Update cv dict cmD
|
||||
mm_skf_scoresD[model_name].update(cmD)
|
||||
|
||||
#=============================================
|
||||
# Option: Add targety numbers for data
|
||||
#=============================================
|
||||
if add_yn:
|
||||
tnD = {}
|
||||
|
||||
# Build tn numbers dict
|
||||
tnD = {'n_trainingY_neg' : Counter(target)[0]
|
||||
, 'n_trainingY_pos' : Counter(target)[1] }
|
||||
|
||||
# Update cv dict with cmD and tnD
|
||||
mm_skf_scoresD[model_name].update(tnD)
|
||||
|
||||
#%%
|
||||
#=========================
|
||||
# Option: Blind test (bts)
|
||||
#=========================
|
||||
if run_blind_test:
|
||||
btD = {}
|
||||
|
||||
# Build bts numbers dict
|
||||
btD = {'n_blindY_neg' : Counter(blind_test_target)[0]
|
||||
, 'n_blindY_pos' : Counter(blind_test_target)[1]
|
||||
, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
|
||||
, 'n_test_size' : len(blind_test_df) }
|
||||
|
||||
# Update cmD+tnD dicts with btD
|
||||
mm_skf_scoresD[model_name].update(btD)
|
||||
|
||||
#--------------------------------------------------------
|
||||
# Build the final results with all scores for the model
|
||||
#--------------------------------------------------------
|
||||
#bts_predict = gscv_fs.predict(blind_test_df)
|
||||
model_pipeline.fit(input_df, target)
|
||||
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 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)
|
||||
mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#============================
|
||||
# Process the dict to have WF
|
||||
#============================
|
||||
if return_formatted_output:
|
||||
CV_BT_metaDF = ProcessMultModelsCl(mm_skf_scoresD)
|
||||
return(CV_BT_metaDF)
|
||||
else:
|
||||
return(mm_skf_scoresD)
|
||||
|
||||
#%% Process output function ###################################################
|
||||
############################
|
||||
# ProcessMultModelsCl()
|
||||
############################
|
||||
#Processes the dict from above if use_formatted_output = True
|
||||
|
||||
def ProcessMultModelsCl(inputD = {}, blind_test_data = True):
|
||||
|
||||
scoresDF = pd.DataFrame(inputD)
|
||||
|
||||
#------------------------
|
||||
# Extracting split_name
|
||||
#-----------------------
|
||||
tts_split_nameL = []
|
||||
for k,v in inputD.items():
|
||||
tts_split_nameL = tts_split_nameL + [v['tts_split']]
|
||||
|
||||
if len(set(tts_split_nameL)) == 1:
|
||||
tts_split_name = str(list(set(tts_split_nameL))[0])
|
||||
print('\nExtracting tts_split_name:', tts_split_name)
|
||||
|
||||
#----------------------
|
||||
# WF: CV results
|
||||
#----------------------
|
||||
scoresDFT = scoresDF.T
|
||||
|
||||
scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns
|
||||
# map colnames for consistency to allow concatenting
|
||||
scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns
|
||||
scoresDF_CV['source_data'] = 'CV'
|
||||
|
||||
#----------------------
|
||||
# WF: Meta data
|
||||
#----------------------
|
||||
metaDF = scoresDFT.filter(regex='^(?!test_.*$|bts_.*$|train_.*$).*'); metaDF.columns
|
||||
|
||||
print('\nTotal cols in each df:'
|
||||
, '\nCV df:', len(scoresDF_CV.columns)
|
||||
, '\nmetaDF:', len(metaDF.columns))
|
||||
|
||||
#-------------------------------------
|
||||
# Combine WF: CV + Metadata
|
||||
#-------------------------------------
|
||||
|
||||
combDF = pd.merge(scoresDF_CV, metaDF, left_index = True, right_index = True)
|
||||
print('\nAdding column: Model_name')
|
||||
combDF['Model_name'] = combDF.index
|
||||
|
||||
#----------------------
|
||||
# WF: BTS results
|
||||
#----------------------
|
||||
if blind_test_data:
|
||||
|
||||
scoresDF_BT = scoresDFT.filter(regex='^bts_.*$', axis = 1); scoresDF_BT.columns
|
||||
# map colnames for consistency to allow concatenting
|
||||
scoresDF_BT.columns = scoresDF_BT.columns.map(scoreBT_mapD); scoresDF_BT.columns
|
||||
scoresDF_BT['source_data'] = 'BT'
|
||||
|
||||
|
||||
print('\nTotal cols in bts df:'
|
||||
, '\nBT_df:', len(scoresDF_BT.columns))
|
||||
|
||||
if len(scoresDF_CV.columns) == len(scoresDF_BT.columns):
|
||||
print('\nFirst proceeding to rowbind CV and BT dfs:')
|
||||
expected_ncols_out = len(scoresDF_BT.columns) + len(metaDF.columns)
|
||||
print('\nFinal output should have:', expected_ncols_out, 'columns' )
|
||||
|
||||
#-----------------
|
||||
# Combine WF
|
||||
#-----------------
|
||||
dfs_combine_wf = [scoresDF_CV, scoresDF_BT]
|
||||
|
||||
print('\nCombinig', len(dfs_combine_wf), 'using pd.concat by row ~ rowbind'
|
||||
, '\nChecking Dims of df to combine:'
|
||||
, '\nDim of CV:', scoresDF_CV.shape
|
||||
, '\nDim of BT:', scoresDF_BT.shape)
|
||||
#print(scoresDF_CV)
|
||||
#print(scoresDF_BT)
|
||||
|
||||
dfs_nrows_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_nrows_wf = dfs_nrows_wf + [len(df)]
|
||||
dfs_nrows_wf = max(dfs_nrows_wf)
|
||||
|
||||
dfs_ncols_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_ncols_wf = dfs_ncols_wf + [len(df.columns)]
|
||||
dfs_ncols_wf = max(dfs_ncols_wf)
|
||||
print(dfs_ncols_wf)
|
||||
|
||||
expected_nrows_wf = len(dfs_combine_wf) * dfs_nrows_wf
|
||||
expected_ncols_wf = dfs_ncols_wf
|
||||
|
||||
common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf)))
|
||||
print('\nNumber of Common columns:', dfs_ncols_wf
|
||||
, '\nThese are:', common_cols_wf)
|
||||
|
||||
if len(common_cols_wf) == dfs_ncols_wf :
|
||||
combined_baseline_wf = pd.concat([df[common_cols_wf] for df in dfs_combine_wf], ignore_index=False)
|
||||
print('\nConcatenating dfs with different resampling methods [WF]:'
|
||||
, '\nSplit type:', tts_split_name
|
||||
, '\nNo. of dfs combining:', len(dfs_combine_wf))
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
if len(combined_baseline_wf) == expected_nrows_wf and len(combined_baseline_wf.columns) == expected_ncols_wf:
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
|
||||
print('\nPASS:', len(dfs_combine_wf), 'dfs successfully combined'
|
||||
, '\nnrows in combined_df_wf:', len(combined_baseline_wf)
|
||||
, '\nncols in combined_df_wf:', len(combined_baseline_wf.columns))
|
||||
else:
|
||||
print('\nFAIL: concatenating failed'
|
||||
, '\nExpected nrows:', expected_nrows_wf
|
||||
, '\nGot:', len(combined_baseline_wf)
|
||||
, '\nExpected ncols:', expected_ncols_wf
|
||||
, '\nGot:', len(combined_baseline_wf.columns))
|
||||
sys.exit('\nFIRST IF FAILS')
|
||||
##
|
||||
c1L = list(set(combined_baseline_wf.index))
|
||||
c2L = list(metaDF.index)
|
||||
|
||||
#if set(c1L) == set(c2L):
|
||||
if set(c1L) == set(c2L) and all(x in c2L for x in c1L) and all(x in c1L for x in c2L):
|
||||
print('\nPASS: proceeding to merge metadata with CV and BT dfs')
|
||||
combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True)
|
||||
print('\nAdding column: Model_name')
|
||||
combDF['Model_name'] = combDF.index
|
||||
|
||||
else:
|
||||
sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs')
|
||||
|
||||
else:
|
||||
print('\nConcatenting dfs not possible [WF],check numbers ')
|
||||
|
||||
#-------------------------------------
|
||||
# Combine WF+Metadata: Final output
|
||||
#-------------------------------------
|
||||
|
||||
# if len(combDF.columns) == expected_ncols_out:
|
||||
# print('\nPASS: Combined df has expected ncols')
|
||||
# else:
|
||||
# sys.exit('\nFAIL: Length mismatch for combined_df')
|
||||
|
||||
# print('\nAdding column: Model_name')
|
||||
# combDF['Model_name'] = combDF.index
|
||||
|
||||
print('\n========================================================='
|
||||
, '\nSUCCESS: Ran multiple classifiers'
|
||||
, '\n=======================================================')
|
||||
|
||||
#resampling_methods_wf = combined_baseline_wf[['resampling']]
|
||||
#resampling_methods_wf = resampling_methods_wf.drop_duplicates()
|
||||
#, '\n', resampling_methods_wf)
|
||||
|
||||
return combDF
|
||||
|
||||
###############################################################################
|
||||
|
|
@ -1,306 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Mar 4 15:25:33 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 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
|
||||
from sklearn.model_selection import LeaveOneGroupOut
|
||||
|
||||
#%% GLOBALS
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
|
||||
|
||||
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
|
||||
, 'fscore' : make_scorer(f1_score)
|
||||
, 'precision' : make_scorer(precision_score)
|
||||
, 'recall' : make_scorer(recall_score)
|
||||
, 'accuracy' : make_scorer(accuracy_score)
|
||||
, 'roc_auc' : make_scorer(roc_auc_score)
|
||||
, 'jcc' : make_scorer(jaccard_score)
|
||||
})
|
||||
|
||||
skf_cv = StratifiedKFold(n_splits = 10
|
||||
#, shuffle = False, random_state= None)
|
||||
, shuffle = True,**rs)
|
||||
|
||||
rskf_cv = RepeatedStratifiedKFold(n_splits = 10
|
||||
, n_repeats = 3
|
||||
, **rs)
|
||||
|
||||
logo = LeaveOneGroupOut()
|
||||
|
||||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
###############################################################################
|
||||
homedir = os.path.expanduser("~")
|
||||
sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
|
||||
sys.path
|
||||
###############################################################################
|
||||
outdir = homedir
|
||||
|
||||
from GetMLData import *
|
||||
from SplitTTS import *
|
||||
|
||||
|
||||
def remove(string):
|
||||
return(string.replace(" ", ""))
|
||||
#%%############################################################################
|
||||
############################
|
||||
# MultModelsCl()
|
||||
# Run Multiple Classifiers
|
||||
############################
|
||||
# Multiple Classification - Model Pipeline
|
||||
def MultClfs_fi(input_df, target, sel_cv
|
||||
, blind_test_df
|
||||
, blind_test_target
|
||||
, tts_split_type
|
||||
|
||||
, resampling_type = 'none' # default
|
||||
#, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
#, add_yn = True # adds target var class numbers
|
||||
, var_type = ['numerical', 'categorical','mixed']
|
||||
, run_blind_test = True
|
||||
#, return_formatted_output = True
|
||||
):
|
||||
|
||||
'''
|
||||
@ param input_df: input features
|
||||
@ type: df with input features WITHOUT the target variable
|
||||
|
||||
@param target: target (or output) feature
|
||||
@type: df or np.array or Series
|
||||
|
||||
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
|
||||
@type: int or StratifiedKfold()
|
||||
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
|
||||
@type: list
|
||||
|
||||
returns
|
||||
Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
|
||||
'''
|
||||
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_ix
|
||||
|
||||
#======================================================
|
||||
# Determine preprocessing steps ~ var_type
|
||||
#======================================================
|
||||
if var_type == 'numerical':
|
||||
t = [('num', MinMaxScaler(), numerical_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, **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 = {}
|
||||
|
||||
print('\n==============================================================\n'
|
||||
, '\nRunning several classification models (n):', len(models)
|
||||
,'\nList of models:')
|
||||
for m in models:
|
||||
print(m)
|
||||
print('\n================================================================\n')
|
||||
|
||||
index = 1
|
||||
for model_name, model_fn in models:
|
||||
print('\nRunning classifier:', index
|
||||
, '\nModel_name:' , model_name
|
||||
, '\nModel func:' , model_fn)
|
||||
index = index+1
|
||||
|
||||
model_pipeline = Pipeline([
|
||||
('prep' , col_transform)
|
||||
, ('model' , model_fn)])
|
||||
|
||||
print('\nRunning model pipeline:', model_pipeline)
|
||||
skf_cv_modD = cross_validate(model_pipeline
|
||||
, input_df
|
||||
, target
|
||||
, cv = sel_cv
|
||||
, scoring = scoring_fn)
|
||||
#==============================
|
||||
# Extract mean values for CV
|
||||
#==============================
|
||||
mm_skf_scoresD[model_name] = {}
|
||||
|
||||
for key, value in skf_cv_modD.items():
|
||||
print('\nkey:', key, '\nvalue:', value)
|
||||
print('\nmean value:', np.mean(value))
|
||||
mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
|
||||
|
||||
# ADD more info: meta data related to input df
|
||||
mm_skf_scoresD[model_name]['resampling'] = resampling_type
|
||||
mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
|
||||
mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
|
||||
mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
|
||||
mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
|
||||
|
||||
# FS
|
||||
#mnf = remove(model_name)
|
||||
#model_pipeline.fit(input_df, target)
|
||||
#print('\nFeature importance:', (model_pipeline.named_steps.model.feature_importances_))
|
||||
#allf_xgboost = model_pipeline.feature_names_in_
|
||||
#fsi_model = model_pipeline.named_steps.model.feature_importances_
|
||||
#mm_skf_scoresD[model_name]['fs_importance'] = fsi_model
|
||||
# TODO: add this as a key
|
||||
#Add
|
||||
|
||||
#pyplot.bar(range(len(model_pipeline.named_steps.model.feature_importances_)), model_pipeline.named_steps.model.feature_importances_)
|
||||
#pyplot.show()
|
||||
#plot_importance(model_pipeline.named_steps.model.feature_importances_)
|
||||
#pyplot.show()
|
||||
|
||||
|
||||
if run_blind_test:
|
||||
btD = {}
|
||||
|
||||
# Build bts numbers dict
|
||||
btD = {'n_blindY_neg' : Counter(blind_test_target)[0]
|
||||
, 'n_blindY_pos' : Counter(blind_test_target)[1]
|
||||
, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
|
||||
, 'n_test_size' : len(blind_test_df) }
|
||||
|
||||
# Update cmD+tnD dicts with btD
|
||||
mm_skf_scoresD[model_name].update(btD)
|
||||
|
||||
#--------------------------------------------------------
|
||||
# Build the final results with all scores for the model
|
||||
#--------------------------------------------------------
|
||||
#bts_predict = gscv_fs.predict(blind_test_df)
|
||||
model_pipeline.fit(input_df, target)
|
||||
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))
|
||||
|
||||
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)
|
||||
mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
|
||||
return(mm_skf_scoresD)
|
||||
#%%
|
||||
|
|
@ -1,528 +0,0 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Fri Mar 4 15:25:33 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 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
|
||||
#%% GLOBALS
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
|
||||
|
||||
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
|
||||
, 'fscore' : make_scorer(f1_score)
|
||||
, 'precision' : make_scorer(precision_score)
|
||||
, 'recall' : make_scorer(recall_score)
|
||||
, 'accuracy' : make_scorer(accuracy_score)
|
||||
, 'roc_auc' : make_scorer(roc_auc_score)
|
||||
, 'jcc' : make_scorer(jaccard_score)
|
||||
})
|
||||
|
||||
skf_cv = StratifiedKFold(n_splits = 10
|
||||
#, shuffle = False, random_state= None)
|
||||
, shuffle = True,**rs)
|
||||
|
||||
rskf_cv = RepeatedStratifiedKFold(n_splits = 10
|
||||
, n_repeats = 3
|
||||
, **rs)
|
||||
logo = LeaveOneGroupOut()
|
||||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
###############################################################################
|
||||
score_type_ordermapD = { 'mcc' : 1
|
||||
, 'fscore' : 2
|
||||
, 'jcc' : 3
|
||||
, 'precision' : 4
|
||||
, 'recall' : 5
|
||||
, 'accuracy' : 6
|
||||
, 'roc_auc' : 7
|
||||
, 'TN' : 8
|
||||
, 'FP' : 9
|
||||
, 'FN' : 10
|
||||
, 'TP' : 11
|
||||
, 'trainingY_neg': 12
|
||||
, 'trainingY_pos': 13
|
||||
, 'blindY_neg' : 14
|
||||
, 'blindY_pos' : 15
|
||||
, 'fit_time' : 16
|
||||
, 'score_time' : 17
|
||||
}
|
||||
|
||||
scoreCV_mapD = {'test_mcc' : 'MCC'
|
||||
, 'test_fscore' : 'F1'
|
||||
, 'test_precision' : 'Precision'
|
||||
, 'test_recall' : 'Recall'
|
||||
, 'test_accuracy' : 'Accuracy'
|
||||
, 'test_roc_auc' : 'ROC_AUC'
|
||||
, 'test_jcc' : 'JCC'
|
||||
}
|
||||
|
||||
scoreBT_mapD = {'bts_mcc' : 'MCC'
|
||||
, 'bts_fscore' : 'F1'
|
||||
, 'bts_precision' : 'Precision'
|
||||
, 'bts_recall' : 'Recall'
|
||||
, 'bts_accuracy' : 'Accuracy'
|
||||
, 'bts_roc_auc' : 'ROC_AUC'
|
||||
, 'bts_jcc' : 'JCC'
|
||||
}
|
||||
|
||||
#gene_group = 'gene_name'
|
||||
#%%############################################################################
|
||||
############################
|
||||
# MultModelsCl()
|
||||
# Run Multiple Classifiers
|
||||
############################
|
||||
# Multiple Classification - Model Pipeline
|
||||
def MultModelsCl_logo(input_df
|
||||
, target
|
||||
, sel_cv
|
||||
|
||||
, blind_test_df = pd.DataFrame()
|
||||
, blind_test_target = pd.Series(dtype = int)
|
||||
, tts_split_type = "none"
|
||||
, group = 'none'
|
||||
|
||||
, resampling_type = 'none' # default
|
||||
, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
, add_yn = True # adds target var class numbers
|
||||
, var_type = ['numerical', 'categorical','mixed']
|
||||
, run_blind_test = True
|
||||
, return_formatted_output = True):
|
||||
|
||||
'''
|
||||
@ param input_df: input features
|
||||
@ type: df with input features WITHOUT the target variable
|
||||
|
||||
@param target: target (or output) feature
|
||||
@type: df or np.array or Series
|
||||
|
||||
@param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass
|
||||
@type: int or StratifiedKfold()
|
||||
|
||||
@var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder)
|
||||
@type: list
|
||||
|
||||
returns
|
||||
Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training
|
||||
'''
|
||||
|
||||
# if group == 'none':
|
||||
# sel_cv = skf_cv
|
||||
# else:
|
||||
# group = 'none'
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_ix
|
||||
categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_ix
|
||||
|
||||
#======================================================
|
||||
# Determine preprocessing steps ~ var_type
|
||||
#======================================================
|
||||
if var_type == 'numerical':
|
||||
t = [('num', MinMaxScaler(), numerical_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) )
|
||||
]
|
||||
|
||||
mm_skf_scoresD = {}
|
||||
|
||||
print('\n==============================================================\n'
|
||||
, '\nRunning several classification models (n):', len(models)
|
||||
,'\nList of models:')
|
||||
for m in models:
|
||||
print(m)
|
||||
print('\n================================================================\n')
|
||||
|
||||
index = 1
|
||||
for model_name, model_fn in models:
|
||||
print('\nRunning classifier:', index
|
||||
, '\nModel_name:' , model_name
|
||||
, '\nModel func:' , model_fn)
|
||||
index = index+1
|
||||
|
||||
model_pipeline = Pipeline([
|
||||
('prep' , col_transform)
|
||||
, ('model' , model_fn)])
|
||||
|
||||
print('\nRunning model pipeline:', model_pipeline)
|
||||
cv_modD = cross_validate(model_pipeline
|
||||
, input_df
|
||||
, target
|
||||
, cv = sel_cv
|
||||
, groups = group
|
||||
, scoring = scoring_fn
|
||||
, return_train_score = True)
|
||||
#==============================
|
||||
# Extract mean values for CV
|
||||
#==============================
|
||||
mm_skf_scoresD[model_name] = {}
|
||||
|
||||
for key, value in cv_modD.items():
|
||||
print('\nkey:', key, '\nvalue:', value)
|
||||
print('\nmean value:', np.mean(value))
|
||||
mm_skf_scoresD[model_name][key] = round(np.mean(value),2)
|
||||
|
||||
# ADD more info: meta data related to input df
|
||||
mm_skf_scoresD[model_name]['resampling'] = resampling_type
|
||||
mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
|
||||
mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
|
||||
mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
|
||||
mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
|
||||
|
||||
#######################################################################
|
||||
#======================================================
|
||||
# Option: Add confusion matrix from cross_val_predict
|
||||
# Understand and USE with caution
|
||||
#======================================================
|
||||
if add_cm:
|
||||
cmD = {}
|
||||
|
||||
# Calculate cm
|
||||
y_pred = cross_val_predict(model_pipeline, input_df, target, cv = sel_cv, groups = group, **njobs)
|
||||
#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
|
||||
tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
|
||||
|
||||
# Build cm dict
|
||||
cmD = {'TN' : tn
|
||||
, 'FP': fp
|
||||
, 'FN': fn
|
||||
, 'TP': tp}
|
||||
|
||||
# Update cv dict cmD
|
||||
mm_skf_scoresD[model_name].update(cmD)
|
||||
|
||||
#=============================================
|
||||
# Option: Add targety numbers for data
|
||||
#=============================================
|
||||
if add_yn:
|
||||
tnD = {}
|
||||
|
||||
# Build tn numbers dict
|
||||
tnD = {'n_trainingY_neg' : Counter(target)[0]
|
||||
, 'n_trainingY_pos' : Counter(target)[1] }
|
||||
|
||||
# Update cv dict with cmD and tnD
|
||||
mm_skf_scoresD[model_name].update(tnD)
|
||||
|
||||
#%%
|
||||
#=========================
|
||||
# Option: Blind test (bts)
|
||||
#=========================
|
||||
if run_blind_test:
|
||||
btD = {}
|
||||
|
||||
# Build bts numbers dict
|
||||
btD = {'n_blindY_neg' : Counter(blind_test_target)[0]
|
||||
, 'n_blindY_pos' : Counter(blind_test_target)[1]
|
||||
, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
|
||||
, 'n_test_size' : len(blind_test_df) }
|
||||
|
||||
# Update cmD+tnD dicts with btD
|
||||
mm_skf_scoresD[model_name].update(btD)
|
||||
|
||||
#--------------------------------------------------------
|
||||
# Build the final results with all scores for the model
|
||||
#--------------------------------------------------------
|
||||
#bts_predict = gscv_fs.predict(blind_test_df)
|
||||
model_pipeline.fit(input_df, target)
|
||||
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))
|
||||
|
||||
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)
|
||||
mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
|
||||
mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#============================
|
||||
# Process the dict to have WF
|
||||
#============================
|
||||
if return_formatted_output:
|
||||
CV_BT_metaDF = ProcessMultModelsCl(mm_skf_scoresD)
|
||||
return(CV_BT_metaDF)
|
||||
else:
|
||||
return(mm_skf_scoresD)
|
||||
|
||||
#%% Process output function ###################################################
|
||||
############################
|
||||
# ProcessMultModelsCl()
|
||||
############################
|
||||
#Processes the dict from above if use_formatted_output = True
|
||||
|
||||
def ProcessMultModelsCl(inputD = {}
|
||||
, blind_test_data = True):
|
||||
|
||||
scoresDF = pd.DataFrame(inputD)
|
||||
|
||||
#------------------------
|
||||
# Extracting split_name
|
||||
#-----------------------
|
||||
tts_split_nameL = []
|
||||
for k,v in inputD.items():
|
||||
tts_split_nameL = tts_split_nameL + [v['tts_split']]
|
||||
|
||||
if len(set(tts_split_nameL)) == 1:
|
||||
tts_split_name = str(list(set(tts_split_nameL))[0])
|
||||
print('\nExtracting tts_split_name:', tts_split_name)
|
||||
|
||||
#----------------------
|
||||
# WF: CV results
|
||||
#----------------------
|
||||
scoresDFT = scoresDF.T
|
||||
|
||||
scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns
|
||||
# map colnames for consistency to allow concatenting
|
||||
scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns
|
||||
scoresDF_CV['source_data'] = 'CV'
|
||||
|
||||
#----------------------
|
||||
# WF: Meta data
|
||||
#----------------------
|
||||
metaDF = scoresDFT.filter(regex='^(?!test_.*$|bts_.*$|train_.*$).*'); metaDF.columns
|
||||
|
||||
print('\nTotal cols in each df:'
|
||||
, '\nCV df:', len(scoresDF_CV.columns)
|
||||
, '\nmetaDF:', len(metaDF.columns))
|
||||
|
||||
#-------------------------------------
|
||||
# Combine WF: CV + Metadata
|
||||
#-------------------------------------
|
||||
|
||||
combDF = pd.merge(scoresDF_CV, metaDF, left_index = True, right_index = True)
|
||||
print('\nAdding column: Model_name')
|
||||
combDF['Model_name'] = combDF.index
|
||||
|
||||
#----------------------
|
||||
# WF: BTS results
|
||||
#----------------------
|
||||
if blind_test_data:
|
||||
|
||||
scoresDF_BT = scoresDFT.filter(regex='^bts_.*$', axis = 1); scoresDF_BT.columns
|
||||
# map colnames for consistency to allow concatenting
|
||||
scoresDF_BT.columns = scoresDF_BT.columns.map(scoreBT_mapD); scoresDF_BT.columns
|
||||
scoresDF_BT['source_data'] = 'BT'
|
||||
|
||||
|
||||
print('\nTotal cols in bts df:'
|
||||
, '\nBT_df:', len(scoresDF_BT.columns))
|
||||
|
||||
if len(scoresDF_CV.columns) == len(scoresDF_BT.columns):
|
||||
print('\nFirst proceeding to rowbind CV and BT dfs:')
|
||||
expected_ncols_out = len(scoresDF_BT.columns) + len(metaDF.columns)
|
||||
print('\nFinal output should have:', expected_ncols_out, 'columns' )
|
||||
|
||||
#-----------------
|
||||
# Combine WF
|
||||
#-----------------
|
||||
dfs_combine_wf = [scoresDF_CV, scoresDF_BT]
|
||||
|
||||
print('\nCombinig', len(dfs_combine_wf), 'using pd.concat by row ~ rowbind'
|
||||
, '\nChecking Dims of df to combine:'
|
||||
, '\nDim of CV:', scoresDF_CV.shape
|
||||
, '\nDim of BT:', scoresDF_BT.shape)
|
||||
#print(scoresDF_CV)
|
||||
#print(scoresDF_BT)
|
||||
|
||||
dfs_nrows_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_nrows_wf = dfs_nrows_wf + [len(df)]
|
||||
dfs_nrows_wf = max(dfs_nrows_wf)
|
||||
|
||||
dfs_ncols_wf = []
|
||||
for df in dfs_combine_wf:
|
||||
dfs_ncols_wf = dfs_ncols_wf + [len(df.columns)]
|
||||
dfs_ncols_wf = max(dfs_ncols_wf)
|
||||
print(dfs_ncols_wf)
|
||||
|
||||
expected_nrows_wf = len(dfs_combine_wf) * dfs_nrows_wf
|
||||
expected_ncols_wf = dfs_ncols_wf
|
||||
|
||||
common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf)))
|
||||
print('\nNumber of Common columns:', dfs_ncols_wf
|
||||
, '\nThese are:', common_cols_wf)
|
||||
|
||||
if len(common_cols_wf) == dfs_ncols_wf :
|
||||
combined_baseline_wf = pd.concat([df[common_cols_wf] for df in dfs_combine_wf], ignore_index=False)
|
||||
print('\nConcatenating dfs with different resampling methods [WF]:'
|
||||
, '\nSplit type:', tts_split_name
|
||||
, '\nNo. of dfs combining:', len(dfs_combine_wf))
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
if len(combined_baseline_wf) == expected_nrows_wf and len(combined_baseline_wf.columns) == expected_ncols_wf:
|
||||
#print('\n================================================^^^^^^^^^^^^')
|
||||
|
||||
print('\nPASS:', len(dfs_combine_wf), 'dfs successfully combined'
|
||||
, '\nnrows in combined_df_wf:', len(combined_baseline_wf)
|
||||
, '\nncols in combined_df_wf:', len(combined_baseline_wf.columns))
|
||||
else:
|
||||
print('\nFAIL: concatenating failed'
|
||||
, '\nExpected nrows:', expected_nrows_wf
|
||||
, '\nGot:', len(combined_baseline_wf)
|
||||
, '\nExpected ncols:', expected_ncols_wf
|
||||
, '\nGot:', len(combined_baseline_wf.columns))
|
||||
sys.exit('\nFIRST IF FAILS')
|
||||
##
|
||||
c1L = list(set(combined_baseline_wf.index))
|
||||
c2L = list(metaDF.index)
|
||||
|
||||
#if set(c1L) == set(c2L):
|
||||
if set(c1L) == set(c2L) and all(x in c2L for x in c1L) and all(x in c1L for x in c2L):
|
||||
print('\nPASS: proceeding to merge metadata with CV and BT dfs')
|
||||
combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True)
|
||||
print('\nAdding column: Model_name')
|
||||
combDF['Model_name'] = combDF.index
|
||||
|
||||
else:
|
||||
sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs')
|
||||
|
||||
else:
|
||||
# print('\nConcatenting dfs not possible [WF],check numbers ')
|
||||
print('\nOnly combining CV and metadata')
|
||||
|
||||
#-------------------------------------
|
||||
# Combine WF+Metadata: Final output
|
||||
#-------------------------------------
|
||||
|
||||
# if len(combDF.columns) == expected_ncols_out:
|
||||
# print('\nPASS: Combined df has expected ncols')
|
||||
# else:
|
||||
# sys.exit('\nFAIL: Length mismatch for combined_df')
|
||||
|
||||
# print('\nAdding column: Model_name')
|
||||
# combDF['Model_name'] = combDF.index
|
||||
|
||||
print('\n========================================================='
|
||||
, '\nSUCCESS: Ran multiple classifiers'
|
||||
, '\n=======================================================')
|
||||
|
||||
#resampling_methods_wf = combined_baseline_wf[['resampling']]
|
||||
#resampling_methods_wf = resampling_methods_wf.drop_duplicates()
|
||||
#, '\n', resampling_methods_wf)
|
||||
|
||||
return combDF
|
||||
|
||||
###############################################################################
|
||||
|
|
@ -77,9 +77,11 @@ import re
|
|||
import itertools
|
||||
from sklearn.model_selection import LeaveOneGroupOut
|
||||
from sklearn.decomposition import PCA
|
||||
from sklearn.naive_bayes import ComplementNB
|
||||
from sklearn.dummy import DummyClassifier
|
||||
|
||||
#%% GLOBALS
|
||||
#rs = {'random_state': 42}
|
||||
#rs = {'random_state': 42} # INSIDE FUNCTION CALL NOW
|
||||
#njobs = {'n_jobs': os.cpu_count() } # the number of jobs should equal the number of CPU cores
|
||||
|
||||
scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
|
||||
|
|
@ -90,8 +92,7 @@ scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef)
|
|||
, 'roc_auc' : make_scorer(roc_auc_score)
|
||||
, 'jcc' : make_scorer(jaccard_score)
|
||||
})
|
||||
|
||||
# for sel_cv
|
||||
# for sel_cv INSIDE FUNCTION CALL NOW
|
||||
#skf_cv = StratifiedKFold(n_splits = 10
|
||||
# #, shuffle = False, random_state= None)
|
||||
# , shuffle = True, **rs)
|
||||
|
|
@ -149,25 +150,25 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
|
|||
############################
|
||||
# Multiple Classification - Model Pipeline
|
||||
def MultModelsCl_logo_skf(input_df
|
||||
, target
|
||||
, sel_cv
|
||||
, tts_split_type
|
||||
, resampling_type
|
||||
#, group = None
|
||||
|
||||
, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
, add_yn = True # adds target var class numbers
|
||||
, var_type = ['numerical', 'categorical','mixed']
|
||||
, scale_numeric = ['min_max', 'std', 'min_max_neg', 'none']
|
||||
, target
|
||||
, sel_cv
|
||||
, tts_split_type
|
||||
, resampling_type
|
||||
#, group = None
|
||||
|
||||
, add_cm = True # adds confusion matrix based on cross_val_predict
|
||||
, add_yn = True # adds target var class numbers
|
||||
, var_type = ['numerical', 'categorical','mixed']
|
||||
, scale_numeric = ['min_max', 'std', 'min_max_neg', 'none']
|
||||
|
||||
, run_blind_test = True
|
||||
, blind_test_df = pd.DataFrame()
|
||||
, blind_test_target = pd.Series(dtype = int)
|
||||
, return_formatted_output = True
|
||||
, run_blind_test = True
|
||||
, blind_test_df = pd.DataFrame()
|
||||
, blind_test_target = pd.Series(dtype = int)
|
||||
, return_formatted_output = True
|
||||
|
||||
, random_state = 42
|
||||
, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
|
||||
):
|
||||
, random_state = 42
|
||||
, n_jobs = os.cpu_count() # the number of jobs should equal the number of CPU cores
|
||||
):
|
||||
|
||||
'''
|
||||
@ param input_df: input features
|
||||
|
|
@ -189,7 +190,15 @@ def MultModelsCl_logo_skf(input_df
|
|||
#%% Func globals
|
||||
rs = {'random_state': random_state}
|
||||
njobs = {'n_jobs': n_jobs}
|
||||
|
||||
skf_cv = StratifiedKFold(n_splits = 10
|
||||
#, shuffle = False, random_state= None)
|
||||
, shuffle = True,**rs)
|
||||
|
||||
rskf_cv = RepeatedStratifiedKFold(n_splits = 10
|
||||
, n_repeats = 3
|
||||
, **rs)
|
||||
logo = LeaveOneGroupOut()
|
||||
|
||||
# select CV type:
|
||||
# if group == None:
|
||||
|
|
@ -252,8 +261,10 @@ def MultModelsCl_logo_skf(input_df
|
|||
#======================================================
|
||||
# Specify multiple Classification Models
|
||||
#======================================================
|
||||
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
||||
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
|
||||
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
||||
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
|
||||
#, ('Bernoulli NB' , BernoulliNB() ) # pks Naive Bayes, CAUTION
|
||||
, ('Complement NB' , ComplementNB() )
|
||||
, ('Decision Tree' , DecisionTreeClassifier(**rs) )
|
||||
, ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
, ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
|
|
@ -265,23 +276,23 @@ def MultModelsCl_logo_skf(input_df
|
|||
, ('Logistic Regression' , LogisticRegression(**rs) )
|
||||
, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
|
||||
, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
|
||||
, ('Multinomial' , MultinomialNB() )
|
||||
, ('Naive Bayes' , BernoulliNB() )
|
||||
, ('Multinomial NB' , MultinomialNB() )
|
||||
, ('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) )
|
||||
, 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) )
|
||||
, ('Dummy Classifier' , DummyClassifier(strategy = 'most_frequent') )
|
||||
]
|
||||
|
||||
mm_skf_scoresD = {}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue