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models.py
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models.py
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from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, GradientBoostingRegressor
from sklearn.model_selection import GridSearchCV, cross_val_predict
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
def logit(X_train, y_train, X_test):
model = LogisticRegression()
fit = model.fit(X_train,y_train)
y_hat_probit = fit.predict_proba(X_test)[:,1]
return y_hat_probit
def RF(X_train, y_train,X_test):
parameters = {'class_weight':['balanced', None],
'max_depth': [10,12,14],
'max_features': [9,11,13]
}
gscv = GridSearchCV(RandomForestClassifier(), parameters)
fit = gscv.fit(X_train, y_train)
print('Best parameters for RF: {}'.format(fit.best_params_))
y_hat_RF = fit.predict_proba(X_test)[:,1]
return y_hat_RF
def GBC(X_train, y_train,X_test):
parameters = {'learning_rate':[0.1],
'n_estimators': [300,400]
}
decisionTree = GradientBoostingClassifier()
gscv = GridSearchCV(decisionTree, parameters,scoring = 'roc_auc')
fit = gscv.fit(X_train, y_train)
print('Best parameters for GBC: {}'.format(fit.best_params_))
y_hat_GBC = fit.predict_proba(X_test)[:,1]
return y_hat_GBC
def ABC(X_train, y_train,X_test):
parameters = {'learning_rate':[0.1],
'n_estimators': [200,150]
}
decisionTree = AdaBoostClassifier(DecisionTreeClassifier(max_depth=3))
gscv = GridSearchCV(decisionTree, parameters,scoring = 'roc_auc')
fit = gscv.fit(X_train, y_train)
print('Best parameters for ABC: {}'.format(fit.best_params_))
y_hat_ABC = fit.predict_proba(X_test)[:,1]
return y_hat_ABC
def GBC_Logit(X_train,y_train,X_test):
X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,
y_train,
test_size=0.5)
grd = GradientBoostingClassifier(n_estimators=200,learning_rate=0.1)
grd_enc = OneHotEncoder()
grd_lm = LogisticRegression()
grd.fit(X_train, y_train)
grd_enc.fit(grd.apply(X_train)[:, :, 0])
grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)[:, :, 0]), y_train_lr)
y_hat_GBC_log = grd_lm.predict_proba(
grd_enc.transform(grd.apply(X_test)[:, :, 0]))[:, 1]
return y_hat_GBC_log
def RF_Logit(X_train,y_train,X_test):
X_train, X_train_lr, y_train, y_train_lr = train_test_split(X_train,
y_train,
test_size=0.5)
grd = RandomForestClassifier(max_depth=10,max_features=9)
grd_enc = OneHotEncoder()
grd_lm = LogisticRegression()
grd.fit(X_train, y_train)
grd_enc.fit(grd.apply(X_train))
grd_lm.fit(grd_enc.transform(grd.apply(X_train_lr)), y_train_lr)
y_hat_RF_log = grd_lm.predict_proba(
grd_enc.transform(grd.apply(X_test)))[:, 1]
return y_hat_RF_log