from sklearn.ensemble import AdaBoostClassifier
from commons import variables
import numpy as np
def learn(x, y, test_x):
# set sample weight
weight_list = []
for j in range(len(y)):
if y[j] == "0":
weight_list.append(variables.weight_0_ada)
if y[j] == "1000":
weight_list.append(variables.weight_1000_ada)
if y[j] == "1500":
weight_list.append(variables.weight_1500_ada)
if y[j] == "2000":
weight_list.append(variables.weight_2000_ada)
clf = AdaBoostClassifier(n_estimators=variables.n_estimators_ada, learning_rate=variables.learning_rate_ada).fit(x,
y,
np.asarray(
weight_list))
prediction_list = clf.predict(test_x)
prediction_list_prob = clf.predict_proba(test_x)
return prediction_list, prediction_list_prob
