saving and organising work to call form cmd line
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5 changed files with 232 additions and 106 deletions
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@ -6,89 +6,93 @@ 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 copy import deepcopy
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from sklearn import linear_model
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from sklearn.linear_model import LogisticRegression, LinearRegression
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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
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
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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 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 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 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
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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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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.discriminant_analysis import QuadraticDiscriminantAnalysis
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from sklearn.neural_network import MLPClassifier
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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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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.naive_bayes import GaussianNB
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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.utils import all_estimators
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from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
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#%%
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#%% GLOBALS
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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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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, 'jaccard' : make_scorer(jaccard_score)
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})
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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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#%%
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# Multiple Classification - Model Pipeline
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def MultModelsCl(input_df, target, skf_cv
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@ -111,9 +115,9 @@ def MultModelsCl(input_df, target, skf_cv
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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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# determine categorical and numerical features
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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
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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@ -133,7 +137,7 @@ def MultModelsCl(input_df, target, skf_cv
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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#%% Specify multiple Classification models
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# Specify multiple Classification models
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lr = LogisticRegression(**rs)
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lrcv = LogisticRegressionCV(**rs)
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gnb = GaussianNB()
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