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added UQ_MultClassPipe4.py and UQ_imbalance.py

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Tanushree Tunstall 2022-05-26 07:39:47 +01:00
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UQ_MultClassPipe4.py Normal file
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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.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.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
#%%
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
scoring_fn = ({ 'fscore' : make_scorer(f1_score)
, 'mcc' : make_scorer(matthews_corrcoef)
, '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 MultClassPipeSKFCV(input_df, target, skf_cv, 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 = [('cat', OneHotEncoder(), categorical_ix)
, ('num', MinMaxScaler(), numerical_ix)]
col_transform = ColumnTransformer(transformers = t
, remainder='passthrough')
#%% Specify multiple Classification models
log_reg = LogisticRegression(**rs)
nb = BernoulliNB()
knn = KNeighborsClassifier()
svm = SVC(**rs)
mlp = MLPClassifier(max_iter = 500, **rs)
dt = DecisionTreeClassifier(**rs)
et = ExtraTreesClassifier(**rs)
rf = RandomForestClassifier(**rs, n_estimators = 1000 )
rf2 = RandomForestClassifier(
min_samples_leaf = 5
, n_estimators = 100 #10
, 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)
models = [('Logistic Regression', log_reg)
, ('Naive Bayes' , nb)
, ('K-Nearest Neighbors', knn)
, ('SVM' , svm)
, ('MLP' , mlp)
# , ('Decision Tree' , dt)
# , ('Extra Trees' , et)
# , ('Random Forest' , rf)
# , ('Naive Bayes' , nb)
# , ('Random Forest2' , rf2)
# , ('XGBoost' , xgb)
# , ('LDA' , lda)
# , ('MultinomialNB' , mnb)
# , ('PassiveAggresive' , pa)
# , ('StochasticGDescent' , sgd)
]
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)
#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(X_bts)
model_pipeline.fit(input_df, target)
bts_predict = model_pipeline.predict(X_bts)
print('\nMCC on Blind test:' , round(matthews_corrcoef(y_bts, bts_predict),2))
print('\nAccuracy on Blind test:', round(accuracy_score(y_bts, bts_predict),2))
bts_mcc_score = round(matthews_corrcoef(y_bts, bts_predict),2)
# Diff b/w train and bts test scores
# train_test_diff = train_bscore - 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}
lr_btsD
lr_btsD['bts_mcc'] = bts_mcc_score
lr_btsD['bts_fscore'] = round(f1_score(y_bts, bts_predict),2)
lr_btsD['bts_precision'] = round(precision_score(y_bts, bts_predict),2)
lr_btsD['bts_recall'] = round(recall_score(y_bts, bts_predict),2)
lr_btsD['bts_accuracy'] = round(accuracy_score(y_bts, bts_predict),2)
lr_btsD['bts_roc_auc'] = round(roc_auc_score(y_bts, bts_predict),2)
lr_btsD['bts_jaccard'] = round(jaccard_score(y_bts, bts_predict),2)
lr_btsD
return(lr_btsD)

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu May 26 05:19:25 2022
@author: tanu
"""
#%% https://www.kite.com/blog/python/smote-python-imbalanced-learn-for-oversampling/
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.svm import SVC
from imblearn.over_sampling import SMOTE
#%%############################################################################
def train_SVM(df):
# select the feature columns
X = df.loc[:, df.columns != 'label']
# select the label column
y = df.label
# train an SVM with linear kernel
clf = SVC(kernel='linear')
clf.fit(X, y)
return clf
def plot_svm_boundary(clf, df, title):
fig, ax = plt.subplots()
X0, X1 = df.iloc[:, 0], df.iloc[:, 1]
x_min, x_max = X0.min() - 1, X0.max() + 1
y_min, y_max = X1.min() - 1, X1.max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
out = ax.contourf(xx, yy, Z, cmap=plt.cm.coolwarm, alpha=0.8)
ax.scatter(X0, X1, c=df.label, cmap=plt.cm.coolwarm, s=20, edgecolors='k')
ax.set_ylabel('y')
ax.set_xlabel('x')
ax.set_title(title)
plt.show()
#%%############################################################################
# SMOTE number of neighbors
#k = 1 (pnca, extra trees baseline is 0.49,numerical only)
k = 1
sm = SMOTE(sampling_strategy = 'auto', k_neighbors = k, **rs)
X_sm, y_sm = sm.fit_resample(X, y)
print(len(X_sm)) #228
print(Counter(y))
y_sm_df = y_sm.to_frame()
y_sm_df.value_counts().plot(kind = 'bar')
oversample = RandomOverSampler(sampling_strategy='minority')
X_ros, y_ros = oversample.fit_resample(X, y)
print(len(X_ros)) #228
undersample = RandomUnderSampler(sampling_strategy='majority')
X_rus, y_rus = undersample.fit_resample(X, y)
print(len(X_rus)) #142
sm_enn = SMOTEENN(enn=EditedNearestNeighbours(sampling_strategy='all'))
X_enn, y_enn = sm_enn.fit_resample(X, y)
print(len(X_enn)) #53