tidying script to run from cmd and via ssh
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4 changed files with 271 additions and 76 deletions
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@ -117,15 +117,15 @@ print(len(X_enn)) #53
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#------------------------------
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# Determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix = X.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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num_featuresL = list(numerical_ix)
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numerical_colind = input_df.columns.get_indexer(list(numerical_ix) )
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numerical_colind = X.columns.get_indexer(list(numerical_ix) )
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numerical_colind
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix = X.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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categorical_colind = input_df.columns.get_indexer(list(categorical_ix))
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categorical_colind = X.columns.get_indexer(list(categorical_ix))
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categorical_colind
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k_sm = 5 # 5 is deafult
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@ -10,77 +10,57 @@ Created on Sun Mar 6 13:41:54 2022
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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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print(np.__version__)
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print(pd.__version__)
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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, LinearRegression
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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
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.ensemble import GradientBoostingClassifier
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from sklearn.ensemble import BaggingClassifier
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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
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from sklearn.gaussian_process import kernels
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from sklearn.gaussian_process.kernels import RBF
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from sklearn.gaussian_process.kernels import DotProduct
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from sklearn.gaussian_process.kernels import Matern
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from sklearn.gaussian_process.kernels import RationalQuadratic
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from sklearn.gaussian_process.kernels import WhiteKernel
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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 QuadraticDiscriminantAnalysis
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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.linear_model import RidgeClassifier, SGDClassifier, PassiveAggressiveClassifier
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from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
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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.linear_model import SGDClassifier
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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 confusion_matrix, accuracy_score, precision_score, recall_score
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from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score
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from sklearn.metrics import jaccard_score
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from sklearn.metrics import make_scorer
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from sklearn.metrics import classification_report
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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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from sklearn.metrics import average_precision_score
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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.model_selection import cross_validate
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import StratifiedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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from sklearn.pipeline import Pipeline
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_selection import RFE, RFECV
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from sklearn.feature_selection import RFE
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from sklearn.feature_selection import RFECV
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import itertools
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#import seaborn as sns
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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print(np.__version__)
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print(pd.__version__)
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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 imblearn.pipeline import Pipeline
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from sklearn.datasets import make_classification
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from sklearn.model_selection import cross_validate, cross_val_score
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from sklearn.model_selection import RepeatedStratifiedKFold
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from sklearn.ensemble import AdaBoostClassifier
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from imblearn.combine import SMOTEENN
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from imblearn.combine import SMOTETomek
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@ -124,8 +104,8 @@ os.chdir(homedir + "/git/ML_AI_training/")
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from MultClassPipe2 import MultClassPipeline2
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from loopity_loop import MultClassPipeSKFLoop
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#from MultClassPipe3 import MultClassPipeSKFCV
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from UQ_MultClassPipe4 import MultClassPipeSKFCV
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#from UQ_MultClassPipe4 import MultClassPipeSKFCV
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from UQ_MultModelsCl import MultModelsCl
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#gene = 'pncA'
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#drug = 'pyrazinamide'
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@ -6,17 +6,7 @@
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# autosklearn --> pipleine --> components --> classification
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# https://github.com/automl/auto-sklearn/tree/master/autosklearn/pipeline/components/classification
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# TOADD:
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# LDA
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/lda.py
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# Multinomial_nb
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/multinomial_nb.py
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# passive_aggressive
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/passive_aggressive.py
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# SGD
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/sgd.py
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# ADDED 27/05/2022: Extra Tree + LRCV and RCCV
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######https://scikit-learn.org/stable/supervised_learning.html
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########################################################################
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@ -57,7 +47,7 @@ param_grid_abc = [
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#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/extra_trees.py
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#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
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#======================
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estimator = ExtraTreesClassifier**rs)
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estimator = ExtraTreesClassifier(**rs)
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# Define pipleline with steps
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pipe_abc = Pipeline([
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@ -85,6 +75,40 @@ param_grid_abc = [
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}
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]
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#======================
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# Extra TreeClassifier()
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https://scikit-learn.org/stable/modules/generated/sklearn.tree.ExtraTreeClassifier.html
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#======================
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estimator = ExtraTreeClassifier(**rs)
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# Define pipleline with steps
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pipe_abc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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# , ('clf', ExtraTreesClassifier(**rs))])
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, ('clf', estimator)
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])
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# Define hyperparmeter space to search for
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param_grid_abc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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# 'clf': [ExtraTreeClassifier(**rs)],
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'clf__max_depth': [None],
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'clf__criterion': ['gini', 'entropy'],
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'clf__max_features': [None, 'sqrt', 'log2', 0.5, 1],
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'clf__min_samples_leaf': [1, 5, 10, 15, 20],
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'clf__min_samples_split': [2, 5, 10, 15, 20]
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}
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]
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#===========================
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# DecisionTreeClassifier()
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/decision_tree.py
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@ -304,8 +328,8 @@ param_grid_gbc = [
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#########################################################################
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#===========================
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# GaussianNB()
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/gaussian_nb.py
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https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
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#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/gaussian_nb.py
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#https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
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#===========================
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# Define estimator
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estimator = GaussianNB()
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@ -439,12 +463,58 @@ param_grid_lr = [
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'clf__solver': ['liblinear']
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}
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]
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#########################################################################
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#===========================
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# LogisticRegressionCV () *
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# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html
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#===========================
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# Define estimator
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estimator = LogisticRegressionCV(cv = 10, **rs)
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# Define pipleline with steps
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pipe_lr = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(LogisticRegression(**rs), cv = rskf_cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)])
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# Define hyperparmeter space to search for
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param_grid_lr = [
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{'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [rskf_cv]
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},
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{
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# 'clf': [LogisticRegressionCV(cv = 10, **rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['none', 'l1', 'l2', 'elasticnet'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['saga']
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},
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{
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# 'clf': [LogisticRegressionCV(cv = 10, **rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l2', 'none'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['newton-cg', 'lbfgs', 'sag']
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},
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{
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# 'clf': [LogisticRegressionCV(cv = 10, **rs)],
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'clf__C': np.logspace(0, 4, 10),
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'clf__penalty': ['l1', 'l2'],
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'clf__max_iter': list(range(100,800,100)),
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'clf__solver': ['liblinear']
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}
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]
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#########################################################################
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#==================
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# MLPClassifier()
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https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/mlp.py
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https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
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#https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/mlp.py
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#https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
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#==================
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# Define estimator
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estimator = MLPClassifier(**rs)
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@ -531,6 +601,35 @@ param_grid_rc = [
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'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
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}
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]
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#######################################################################
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#====================
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# RidgeClassifier() *
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https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifierCV.html
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#====================
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# Define estimator
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estimator = RidgeClassifierCV(cv = 10, **rs)
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# Define pipleline with steps
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pipe_rc = Pipeline([
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('pre', MinMaxScaler())
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, ('fs', RFECV(DecisionTreeClassifier(**rs), cv = cv, scoring = 'matthews_corrcoef'))
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# , ('fs', RFECV(estimator, cv = cv, scoring = 'matthews_corrcoef'))
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, ('clf', estimator)
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])
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param_grid_rc = [
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{
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'fs__min_features_to_select' : [1,2]
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# , 'fs__cv': [cv]
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},
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{
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#'clf' : [RidgeClassifierCV(cv = 10, **rs)],
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'clf__alpha': [0.1, 0.2, 0.5, 0.8, 1.0]
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}
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]
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#######################################################################
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#========
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# SVC()
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@ -27,17 +27,42 @@ from sklearn.model_selection import train_test_split, cross_validate, cross_val_
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# Metric
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from sklearn.metrics import mean_squared_error, make_scorer, roc_auc_score, f1_score, matthews_corrcoef, accuracy_score, balanced_accuracy_score, confusion_matrix, classification_report
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# other vars
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
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, 'fscore' : make_scorer(f1_score)
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, 'mcc' : make_scorer(matthews_corrcoef)
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, 'precision' : make_scorer(precision_score)
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, 'recall' : make_scorer(recall_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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#%% YC
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#def run_all_ML(input_pd, target_label, bts_input, bts_target, var_type):
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def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'):
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def run_all_ML(input_pd, target_label, blind_test_input_df, blind_test_target, preprocess = True, var_type = 'numerical'):
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#y = input_pd[target_label]
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#X = input_pd.drop(target_label,axis=1)
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y = target_label
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X = input_pd
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# determine categorical and numerical features
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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# Determine categorical and numerical features
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numerical_ix = input_pd.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 = input_pd.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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# Determine preprocessing steps ~ var_type
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@ -54,16 +79,20 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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result_pd = pd.DataFrame()
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result_bts_pd = pd.DataFrame()
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#results_btsD = {}
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results_all = {}
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for name, algorithm in all_estimators(type_filter="classifier"):
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try:
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estmator = algorithm()
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temp_pd = pd.DataFrame()
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temp_cm = pd.DataFrame()
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# orig
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pipe = Pipeline([
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("model" , algorithm())
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])
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# # orig
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# pipe = Pipeline([
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# ("model" , algorithm())
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# ])
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# turn on and off preprocessing
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if preprocess == True:
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@ -76,8 +105,14 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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("model" , algorithm())
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])
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# cross val scores
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y_pred = cross_val_predict(pipe, X, y, cv = 10, **njobs)
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# CHANGE to cross_validate: ONLY THEN CAN YOU TRUST
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# y_pred = cross_validate(pipe, X, y
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# , cv = 10
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# , scoring = scoring_fn
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# , **njobs)
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y_pred = cross_val_predict(pipe, X, y, cv = 10, n_jobs=10)
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_mcc = round(matthews_corrcoef(y_pred, y), 3)
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_bacc = round(balanced_accuracy_score(y_pred, y), 3)
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_f1 = round(f1_score(y_pred, y), 3)
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@ -88,7 +123,88 @@ def run_all_ML(input_pd, target_label, preprocess = True, var_type = 'numerical'
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columns=['estimator', 'TP', 'TN', 'FP', 'FN',
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'roc_auc', 'matthew', 'bacc', 'f1']),\
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ignore_index=True)
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#=========================
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# Blind test: BTS results
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#=========================
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#Build the final results with all scores for a feature selected model
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pipe.fit(input_pd, target_label)
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bts_predict = pipe.predict(blind_test_input_df)
|
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|
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
|
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print('\nMCC on Blind test:' , bts_mcc_score)
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#print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
|
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|
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_mccBTS = round(matthews_corrcoef(bts_predict, blind_test_target), 3)
|
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_baccBTS = round(balanced_accuracy_score(bts_predict, blind_test_target), 3)
|
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_f1BTS = round(f1_score(bts_predict, blind_test_target), 3)
|
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_roc_aucBTS = round(roc_auc_score(bts_predict, blind_test_target), 3)
|
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_tnBTS, _fpBTS, _fnBTS, _tpBTS = confusion_matrix(bts_predict, blind_test_target).ravel()
|
||||
|
||||
result_bts_pd = result_bts_pd.append(pd.DataFrame(np.column_stack([name
|
||||
, _tpBTS, _tnBTS
|
||||
, _fpBTS, _fnBTS
|
||||
, _roc_aucBTS
|
||||
, _mccBTS
|
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, _baccBTS, _f1BTS]),\
|
||||
columns=['estimator', 'TP', 'TN', 'FP', 'FN',
|
||||
'roc_auc', 'matthew', 'bacc', 'f1']),\
|
||||
ignore_index=True)
|
||||
|
||||
|
||||
results_all['CrossValResultsDF'] = result_pd
|
||||
results_all['BlindTestResultsDF'] = result_bts_pd
|
||||
|
||||
except Exception as e:
|
||||
print("Got an error while running {}".format(name))
|
||||
print("XXXGot an error while running {}".format(name))
|
||||
print(e)
|
||||
return(result_pd)
|
||||
|
||||
|
||||
#return(result_pd)
|
||||
return(results_all)
|
||||
|
||||
|
||||
#%% CALL function
|
||||
#run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
|
||||
YC_resD2 = run_all_ML(input_pd=X, target_label=y, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
|
||||
YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
|
||||
CVResultsDF = YC_resD2['CrossValResultsDF']
|
||||
CVResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF = YC_resD2['BlindTestResultsDF']
|
||||
BTSResultsDF.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# from sklearn.utils import all_estimators
|
||||
# for name, algorithm in all_estimators(type_filter="classifier"):
|
||||
# clf = algorithm()
|
||||
# print('Name:', name, '\nAlgo:', clf)
|
||||
|
||||
# Random Oversampling
|
||||
YC_resD_ros = run_all_ML(input_pd=X_ros, target_label=y_ros, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_ros = YC_resD_ros['CrossValResultsDF']
|
||||
CVResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_ros = YC_resD_ros['BlindTestResultsDF']
|
||||
BTSResultsDF_ros.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# Random Undersampling
|
||||
YC_resD_rus = run_all_ML(input_pd=X_rus, target_label=y_rus, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_rus = YC_resD_rus['CrossValResultsDF']
|
||||
CVResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_rus = YC_resD_rus['BlindTestResultsDF']
|
||||
BTSResultsDF_rus.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# Random Oversampling+Undersampling
|
||||
YC_resD_rouC = run_all_ML(input_pd=X_rouC, target_label=y_rouC, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_rouC = YC_resD_rouC['CrossValResultsDF']
|
||||
CVResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_rouC = YC_resD_rouC['BlindTestResultsDF']
|
||||
BTSResultsDF_rouC.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
# SMOTE NC
|
||||
YC_resD_smnc = run_all_ML(input_pd=X_smnc, target_label=y_smnc, blind_test_input_df=X_bts, blind_test_target=y_bts, preprocess = True, var_type = 'mixed')
|
||||
CVResultsDF_smnc = YC_resD_smnc['CrossValResultsDF']
|
||||
CVResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
BTSResultsDF_smnc = YC_resD_smnc['BlindTestResultsDF']
|
||||
BTSResultsDF_smnc.sort_values(by=['matthew'], ascending=False, inplace=True)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue