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tidying import script and moving estimators where the ML classifier func are

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Tanushree Tunstall 2022-05-28 09:44:26 +01:00
parent 2898686bf8
commit d9a1888e8c
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UQ_pnca_ML.py
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@ -14,105 +14,10 @@ print(np.__version__)
print(pd.__version__) print(pd.__version__)
import pprint as pp import pprint as pp
from copy import deepcopy from copy import deepcopy
from sklearn import linear_model
from sklearn import datasets
from collections import Counter 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
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
import json
from sklearn.impute import KNNImputer as KNN from sklearn.impute import KNNImputer as KNN
# My functions and globals
scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
, 'fscore' : make_scorer(f1_score)
, 'mcc' : make_scorer(matthews_corrcoef)
, 'precision' : make_scorer(precision_score)
, 'recall' : make_scorer(recall_score)
, 'roc_auc' : make_scorer(roc_auc_score)
, 'jcc' : make_scorer(jaccard_score)
})
rs = {'random_state': 42}
njobs = {'n_jobs': 10}
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)}
#%% #%%
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/")
# my function
#from MultClassPipe import MultClassPipeline
from MultClassPipe2 import MultClassPipeline2
from loopity_loop import MultClassPipeSKFLoop
#from MultClassPipe3 import MultClassPipeSKFCV
#from UQ_MultClassPipe4 import MultClassPipeSKFCV
from UQ_MultModelsCl import MultModelsCl
#gene = 'pncA'
#drug = 'pyrazinamide'
#gene = 'katG'
#drug = 'isoniazid'
#============== #==============
# directories # directories
#============== #==============