def train_knn(X_train, y):
"""
Train the KNN classifier with the parameters defined
with benchmark_knn.py
Args:
X_train (pd.DataFrame) : training set
y (np.array) : target values
"""
features = ['PropertyField37','SalesField5','PersonalField9','Field7','PersonalField2',
'PersonalField1','SalesField4','PersonalField10A','SalesField1B', 'PersonalField10B',
'PersonalField12']
train = X_train.loc[:, features]
train.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
clf = neighbors.KNeighborsClassifier(50)
clf.fit(train.values, y)
res = clf.predict_proba(train.values)[:,1]
fh.save_model('knn', clf)
def train_xgb(X_train, y):
"""
Train the XGBoost classifier with the parameters defined
with benchmark_xgb.py
Args:
X_train (pd.DataFrame) : training set
y (np.array) : target values
"""
clf = xgb.XGBClassifier(n_estimators=25,
nthread=-1,
max_depth=15,
learning_rate=0.025,
silent=False,
subsample=1,
colsample_bytree=0.9)
xgb_model = clf.fit(X_train, y, eval_metric="auc")
fh.save_model('xgb', clf)