tanu/ML_AI_training
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Tanushree Tunstall 2022-03-18 17:50:24 +00:00
parent ab1508e9fb
commit 824c2f041c
2 changed files with 6 additions and 5 deletions
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4
hyperparams.py
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@ -91,10 +91,6 @@ gs_gb = GridSearchCV(estimator=XGBClassifier(n_jobs=-1),
n_jobs=4, n_jobs=4,
refit='recall') refit='recall')
#%% #%%
#%%
gs_mlp = MLPClassifier(max_iter=600) gs_mlp = MLPClassifier(max_iter=600)
clf = GridSearchCV(gs_mlp, parameter_space, n_jobs= 10, cv = 10) clf = GridSearchCV(gs_mlp, parameter_space, n_jobs= 10, cv = 10)

7
imports.py
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@ -9,7 +9,7 @@ import os, sys
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import pprint as pp import pprint as pp
#from copy import deepcopy from copy import deepcopy
from sklearn import linear_model from sklearn import linear_model
from sklearn.linear_model import LogisticRegression, LinearRegression from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.naive_bayes import BernoulliNB from sklearn.naive_bayes import BernoulliNB
@ -30,6 +30,8 @@ from sklearn.compose import make_column_transformer
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score 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 from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef
from sklearn.metrics import jaccard_score
from sklearn.metrics import make_scorer from sklearn.metrics import make_scorer
from sklearn.metrics import classification_report from sklearn.metrics import classification_report
@ -71,6 +73,7 @@ scoring_fn = ({'accuracy' : make_scorer(accuracy_score)
, 'precision' : make_scorer(precision_score) , 'precision' : make_scorer(precision_score)
, 'recall' : make_scorer(recall_score) , 'recall' : make_scorer(recall_score)
, 'roc_auc' : make_scorer(roc_auc_score) , 'roc_auc' : make_scorer(roc_auc_score)
, 'jcc' : make_scorer(jaccard_score)
}) })
rs = {'random_state': 42} rs = {'random_state': 42}
@ -78,6 +81,8 @@ njobs = {'n_jobs': 10}
skf_cv = StratifiedKFold(n_splits = 10 skf_cv = StratifiedKFold(n_splits = 10
#, shuffle = False, random_state= None) #, shuffle = False, random_state= None)
, shuffle = True,**rs) , shuffle = True,**rs)
#my_mcc = make_scorer({'mcc':make_scorer(matthews_corrcoef})
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
#%% #%%
homedir = os.path.expanduser("~") homedir = os.path.expanduser("~")