def getSVC(df, random_split=None):
X, Y = to_array(df.drop("validation", axis=1))
scaler = StandardScaler()
X = scaler.fit_transform(X)
tr_ind = df[df["validation"] == 0].index.values.astype(int)
val_ind = df[df["validation"] == 1].index.values.astype(int)
custom_CV_iterator = [(tr_ind, val_ind)]
print("Create a Random Forest Classifier")
print("__Parameter searching...")
# TODOs: cross-validation for best hyper parameter
clf = GridSearchCV(SVC(probability=False),
param_grid=TUNED_PARAMS,
scoring='roc_auc',
n_jobs=10,
verbose=5,
cv=custom_CV_iterator)
clf.fit(X, Y)
print("Best score: {}".format(clf.best_score_))
print("Best parameters: {}".format(clf.best_params_))
return clf, scaler
if __name__ == "__main__":
output_fname = sys.argv[1]
df, test_df = preprocess()
model, scaler = getSVC(df)
write_ans(model, test_df, ofname=output_fname, scaler=scaler)
'max_depth': [7, 9, None],
'max_features': [0.05, 0.1, 0.3]
}]
def getRF(df, random_split=None):
X, Y = to_array(df.drop("validation", axis=1))
tr_ind = df[df["validation"] == 0].index.values.astype(int)
val_ind = df[df["validation"] == 1].index.values.astype(int)
custom_CV_iterator = [(tr_ind, val_ind)]
print("Create a Random Forest Classifier")
print("__Parameter searching...")
# TODOs: cross-validation for best hyper parameter
clf = GridSearchCV(RandomForestClassifier(n_estimators=10000, n_jobs=2),
param_grid=TUNED_PARAMS,
scoring='roc_auc',
n_jobs=10,
verbose=5,
cv=custom_CV_iterator)
clf.fit(X, Y)
print("Best score: {}".format(clf.best_score_))
print("Best parameters: {}".format(clf.best_params_))
return clf
if __name__ == "__main__":
output_fname = sys.argv[1]
df, test_df = preprocess()
model = getRF(df)
write_ans(model, test_df, ofname=output_fname)
def getSVC(df, random_split=None):
X, Y = to_array(df.drop("validation", axis=1))
scaler = StandardScaler()
X = scaler.fit_transform(X)
tr_ind = df[df["validation"]==0].index.values.astype(int)
val_ind = df[df["validation"]==1].index.values.astype(int)
custom_CV_iterator = [(tr_ind, val_ind)]
print("Create a Random Forest Classifier")
print("__Parameter searching...")
# TODOs: cross-validation for best hyper parameter
clf = GridSearchCV(SVC(probability=False),
param_grid=TUNED_PARAMS,
scoring='roc_auc',
n_jobs=10,
verbose=5,
cv=custom_CV_iterator
)
clf.fit(X, Y)
print("Best score: {}".format(clf.best_score_))
print("Best parameters: {}".format(clf.best_params_))
return clf, scaler
if __name__ == "__main__":
output_fname = sys.argv[1]
df, test_df = preprocess()
model, scaler = getSVC(df)
write_ans(model, test_df, ofname=output_fname, scaler=scaler)
]
def getRF(df, random_split=None):
X, Y = to_array(df.drop("validation", axis=1))
tr_ind = df[df["validation"]==0].index.values.astype(int)
val_ind = df[df["validation"]==1].index.values.astype(int)
custom_CV_iterator = [(tr_ind, val_ind)]
print("Create a Random Forest Classifier")
print("__Parameter searching...")
# TODOs: cross-validation for best hyper parameter
clf = GridSearchCV(RandomForestClassifier(n_estimators=10000, n_jobs=2),
param_grid=TUNED_PARAMS,
scoring='roc_auc',
n_jobs=10,
verbose=5,
cv=custom_CV_iterator
)
clf.fit(X, Y)
print("Best score: {}".format(clf.best_score_))
print("Best parameters: {}".format(clf.best_params_))
return clf
if __name__ == "__main__":
output_fname = sys.argv[1]
df, test_df = preprocess()
model = getRF(df)
write_ans(model, test_df, ofname=output_fname)