tidy script to run my versions of multiple modles with blind tests and also with oversampled data
This commit is contained in:
parent
b6f0308e42
commit
2898686bf8
3 changed files with 433 additions and 0 deletions
268
UQ_MultModelsCl.py
Normal file
268
UQ_MultModelsCl.py
Normal file
|
|
@ -0,0 +1,268 @@
|
|||
#!/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.linear_model import LogisticRegression, LinearRegression
|
||||
from sklearn.naive_bayes import BernoulliNB
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.tree import DecisionTreeClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
from xgboost import XGBClassifier
|
||||
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
|
||||
|
||||
from sklearn.compose import ColumnTransformer
|
||||
from sklearn.compose import make_column_transformer
|
||||
|
||||
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
|
||||
from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score
|
||||
from sklearn.metrics import make_scorer
|
||||
from sklearn.metrics import classification_report
|
||||
|
||||
from sklearn.metrics import average_precision_score
|
||||
|
||||
from sklearn.model_selection import cross_validate
|
||||
from sklearn.model_selection import train_test_split
|
||||
from sklearn.model_selection import StratifiedKFold
|
||||
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.pipeline import make_pipeline
|
||||
|
||||
from sklearn.feature_selection import RFE
|
||||
from sklearn.feature_selection import RFECV
|
||||
import itertools
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
print(np.__version__)
|
||||
print(pd.__version__)
|
||||
from statistics import mean, stdev, median, mode
|
||||
|
||||
from imblearn.over_sampling import RandomOverSampler
|
||||
from imblearn.over_sampling import SMOTE
|
||||
from imblearn.pipeline import Pipeline
|
||||
from sklearn.datasets import make_classification
|
||||
from sklearn.model_selection import cross_validate
|
||||
from sklearn.model_selection import RepeatedStratifiedKFold
|
||||
from sklearn.ensemble import AdaBoostClassifier
|
||||
from imblearn.combine import SMOTEENN
|
||||
from imblearn.under_sampling import EditedNearestNeighbours
|
||||
|
||||
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
|
||||
from sklearn.neural_network import MLPClassifier
|
||||
|
||||
from sklearn.linear_model import RidgeClassifier, SGDClassifier, PassiveAggressiveClassifier
|
||||
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
|
||||
from sklearn.svm import SVC
|
||||
from xgboost import XGBClassifier
|
||||
from sklearn.naive_bayes import MultinomialNB
|
||||
from sklearn.naive_bayes import GaussianNB
|
||||
|
||||
from sklearn.linear_model import SGDClassifier
|
||||
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
|
||||
from sklearn.utils import all_estimators
|
||||
|
||||
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
|
||||
|
||||
#%%
|
||||
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)
|
||||
, 'jaccard' : make_scorer(jaccard_score)
|
||||
})
|
||||
|
||||
#%%
|
||||
# Multiple Classification - Model Pipeline
|
||||
def MultModelsCl(input_df, target, skf_cv
|
||||
, blind_test_input_df
|
||||
, blind_test_target
|
||||
, var_type = ['numerical', 'categorical','mixed']):
|
||||
|
||||
'''
|
||||
@ 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
|
||||
lr = LogisticRegression(**rs)
|
||||
lrcv = LogisticRegressionCV(**rs)
|
||||
gnb = GaussianNB()
|
||||
nb = BernoulliNB()
|
||||
knn = KNeighborsClassifier()
|
||||
svc = SVC(**rs)
|
||||
mlp = MLPClassifier(max_iter = 500, **rs)
|
||||
dt = DecisionTreeClassifier(**rs)
|
||||
ets = ExtraTreesClassifier(**rs)
|
||||
|
||||
rf = RandomForestClassifier(**rs, n_estimators = 1000 )
|
||||
rf2 = RandomForestClassifier(
|
||||
min_samples_leaf = 5
|
||||
, n_estimators = 1000
|
||||
, bootstrap = True
|
||||
, oob_score = True
|
||||
, **njobs
|
||||
, **rs
|
||||
, max_features = 'auto')
|
||||
xgb = XGBClassifier(**rs, verbosity = 0, use_label_encoder =False)
|
||||
|
||||
lda = LinearDiscriminantAnalysis()
|
||||
|
||||
mnb = MultinomialNB()
|
||||
|
||||
pa = PassiveAggressiveClassifier(**rs, **njobs)
|
||||
|
||||
sgd = SGDClassifier(**rs, **njobs)
|
||||
|
||||
abc = AdaBoostClassifier(**rs)
|
||||
bc = BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True)
|
||||
et = ExtraTreeClassifier(**rs)
|
||||
gpc = GaussianProcessClassifier(**rs)
|
||||
gbc = GradientBoostingClassifier(**rs)
|
||||
qda = QuadraticDiscriminantAnalysis()
|
||||
rc = RidgeClassifier(**rs)
|
||||
rccv = RidgeClassifierCV(cv = 10)
|
||||
|
||||
models = [('Logistic Regression' , lr)
|
||||
, ('Logistic RegressionCV' , lrcv)
|
||||
, ('Gaussian NB' , gnb)
|
||||
, ('Naive Bayes' , nb)
|
||||
, ('K-Nearest Neighbors' , knn)
|
||||
, ('SVM' , svc)
|
||||
, ('MLP' , mlp)
|
||||
, ('Decision Tree' , dt)
|
||||
, ('Extra Trees' , ets)
|
||||
, ('Extra Tree' , et)
|
||||
, ('Random Forest' , rf)
|
||||
, ('Random Forest2' , rf2)
|
||||
, ('Naive Bayes' , nb)
|
||||
, ('XGBoost' , xgb)
|
||||
, ('LDA' , lda)
|
||||
, ('Multinomial' , mnb)
|
||||
, ('Passive Aggresive' , pa)
|
||||
, ('Stochastic GDescent' , sgd)
|
||||
, ('AdaBoost Classifier' , abc)
|
||||
, ('Bagging Classifier' , bc)
|
||||
, ('Gaussian Process' , gpc)
|
||||
, ('Gradient Boosting' , gbc)
|
||||
, ('QDA' , qda)
|
||||
, ('Ridge Classifier' , rc)
|
||||
, ('Ridge ClassifierCV' , rccv)
|
||||
]
|
||||
|
||||
mm_skf_scoresD = {}
|
||||
|
||||
for model_name, model_fn in models:
|
||||
print('\nModel_name:', model_name
|
||||
, '\nModel func:' , model_fn
|
||||
, '\nList of models:', models)
|
||||
|
||||
model_pipeline = Pipeline([
|
||||
('prep' , col_transform)
|
||||
, ('model' , model_fn)])
|
||||
|
||||
print('Running model pipeline:', model_pipeline)
|
||||
skf_cv_mod = cross_validate(model_pipeline
|
||||
, input_df
|
||||
, target
|
||||
, cv = skf_cv
|
||||
, scoring = scoring_fn
|
||||
, return_train_score = True)
|
||||
mm_skf_scoresD[model_name] = {}
|
||||
for key, value in skf_cv_mod.items():
|
||||
print('\nkey:', key, '\nvalue:', value)
|
||||
print('\nmean value:', mean(value))
|
||||
mm_skf_scoresD[model_name][key] = round(mean(value),2)
|
||||
#pp.pprint(mm_skf_scoresD)
|
||||
#cvtrain_mcc = mm_skf_scoresD[model_name]['test_mcc']
|
||||
|
||||
#return(mm_skf_scoresD)
|
||||
#%%
|
||||
#=========================
|
||||
# Blind test: BTS results
|
||||
#=========================
|
||||
# Build the final results with all scores for a feature selected model
|
||||
#bts_predict = gscv_fs.predict(blind_test_input_df)
|
||||
model_pipeline.fit(input_df, target)
|
||||
bts_predict = model_pipeline.predict(blind_test_input_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)
|
||||
|
||||
|
||||
# # create a dict with all scores
|
||||
# lr_btsD = { 'model_name': model_name
|
||||
# , 'bts_mcc':None
|
||||
# , 'bts_fscore':None
|
||||
# , 'bts_precision':None
|
||||
# , 'bts_recall':None
|
||||
# , 'bts_accuracy':None
|
||||
# , 'bts_roc_auc':None
|
||||
# , 'bts_jaccard':None}
|
||||
|
||||
|
||||
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_jaccard'] = round(jaccard_score(blind_test_target, bts_predict),2)
|
||||
#mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC
|
||||
|
||||
return(mm_skf_scoresD)
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue