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removed MultModelsCl.py and ProcessMultModelsCl.py as these are merged into a single script for convenience

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Tanushree Tunstall 2022-06-24 13:25:51 +01:00
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scripts/ml/MultModelsCl.py
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#!/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
#%% GLOBALS
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
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)
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
#====================
# Import ProcessFunc
#====================
from ProcessMultModelsCl import *
#%%
############################
# MultModelsCl()
# Run Multiple Classifiers
############################
# Multiple Classification - Model Pipeline
def MultModelsCl(input_df, target, skf_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']
, 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 ) )
# , ('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)
skf_cv_modD = cross_validate(model_pipeline
, input_df
, target
, cv = skf_cv
, scoring = scoring_fn
, return_train_score = True)
#######################################################################
#======================================================
# Option: Add confusion matrix from cross_val_predict
# Understand and USE with caution
# 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."
# https://stackoverflow.com/questions/65645125/producing-a-confusion-matrix-with-cross-validate
#======================================================
if add_cm:
#-----------------------------------------------------------
# Initialise dict of Confusion Matrix (cm)
#-----------------------------------------------------------
cmD = {}
# Calculate cm
y_pred = cross_val_predict(model_pipeline, input_df, target, cv = skf_cv, **njobs)
#_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally
tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel()
# Build dict
cmD = {'TN' : tn
, 'FP': fp
, 'FN': fn
, 'TP': tp}
#---------------------------------
# Update cv dict with cmD and tbtD
#----------------------------------
skf_cv_modD.update(cmD)
else:
skf_cv_modD = skf_cv_modD
#######################################################################
#=============================================
# Option: Add targety numbers for data
#=============================================
if add_yn:
#-----------------------------------------------------------
# Initialise dict of target numbers: training and blind (tbt)
#-----------------------------------------------------------
tbtD = {}
# training y
tyn = Counter(target)
tyn_neg = tyn[0]
tyn_pos = tyn[1]
# blind test y
btyn = Counter(blind_test_target)
btyn_neg = btyn[0]
btyn_pos = btyn[1]
# Build dict
tbtD = {'n_trainingY_neg' : tyn_neg
, 'n_trainingY_pos' : tyn_pos
, 'n_blindY_neg' : btyn_neg
, 'n_blindY_pos' : btyn_pos}
#---------------------------------
# Update cv dict with cmD and tbtD
#----------------------------------
skf_cv_modD.update(tbtD)
else:
skf_cv_modD = skf_cv_modD
#######################################################################
#==============================
# 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)
#return(mm_skf_scoresD)
#%%
#=========================
# Blind test: BTS results
#=========================
# 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))
# Diff b/w train and bts test scores
# train_test_diff_MCC = cvtrain_mcc - bts_mcc_score
# print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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)
#%%
# ADD more info: meta data related to input and blind and resampling
# target numbers: training
yc1 = Counter(target)
yc1_ratio = yc1[0]/yc1[1]
# target numbers: test
yc2 = Counter(blind_test_target)
yc2_ratio = yc2[0]/yc2[1]
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(yc1_ratio, 2)
mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df)
mm_skf_scoresD[model_name]['n_testY_ratio'] = round(yc2_ratio,2)
mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
#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)

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scripts/ml/ProcessMultModelsCl.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 23 20:39:20 2022
@author: tanu
"""
import os, sys
import pandas as pd
import numpy as np
import re
##############################################################################
#%% FUNCTION: Process output dict from MultModelsCl
############################
# ProcessMultModelsCl()
############################
#Processes the dict from above if use_formatted_output = True
def ProcessMultModelsCl(inputD = {}):
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: only CV and BTS
#-----------------------
scoresDFT = scoresDF.T
scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns
# map colnames for consistency to allow concatenting
scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns
scoresDF_CV['source_data'] = 'CV'
scoresDF_BT = scoresDFT.filter(regex='^bts_.*$', 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'
# dfs_combine_wf = [baseline_BT, smnc_BT, ros_BT, rus_BT, rouC_BT,
# baseline_CV, smnc_CV, ros_CV, rus_CV, rouC_CV]
#baseline_all = baseline_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0)
#metaDF = scoresDFT.filter(regex='training_size|blind_test_size|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling', axis = 1); scoresDF_BT.columns
#metaDF = scoresDFT.filter(regex='n_.*$|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling|tts.*', axis = 1); metaDF.columns
metaDF = scoresDFT.filter(regex='^(?!test_.*$|bts_.*$|train_.*$).*'); metaDF.columns
print('\nTotal cols in each df:'
, '\nCV df:', len(scoresDF_CV.columns)
, '\nBT_df:', len(scoresDF_BT.columns)
, '\nmetaDF:', len(metaDF.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')
else:
print('\nConcatenting dfs not possible [WF],check numbers ')
#-------------------------------------
# Combine WF+Metadata: Final output
#-------------------------------------
# checking indices for the dfs to combine:
c1 = list(set(combined_baseline_wf.index))
c2 = list(metaDF.index)
if c1 == c2:
print('\nPASS: proceeding to merge metadata with CV and BT dfs')
combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True)
else:
sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs')
if len(combDF.columns) == expected_ncols_out:
print('\nPASS: Combined df has expected ncols')
else:
sys.exit('\nFAIL: Length mismatch for combined_df')
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