import prepare_data as prepare
import evaluate
from sklearn.ensemble import RandomForestClassifier
from sklearn.multiclass import OneVsRestClassifier
train_data, validation_data, test_data, basic_users_info = prepare.get_data()
label_encoder = {}
train_x, train_y = prepare.get_exclude_ndf_x(train_data, basic_users_info, label_encoder)
validation_x, validation_y = prepare.get_exclude_ndf_x(validation_data, basic_users_info, label_encoder)
rf = OneVsRestClassifier(RandomForestClassifier(n_estimators=11, criterion='gini')).fit(train_x, train_y)
validation_predict = rf.predict(validation_x)
validation_predict_proba = rf.predict_proba(validation_x)
# print validation_predict_proba
class_order = rf.classes_
predict_list = evaluate.candidate_classes(validation_predict_proba, class_order)
#print predict_list
ndcg = evaluate.ndcg(predict_list, validation_data)
print(ndcg)
test_x = prepare.get_exclude_ndf_test_x(test_data, basic_users_info, label_encoder)
test_predict = rf.predict_proba(test_x)
test_predict_list = evaluate.candidate_classes(test_predict, class_order)
prepare.get_test_predict(test_data, test_predict_list)
import evaluate
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.multiclass import OneVsRestClassifier
train_data, validation_data, test_data, basic_users_info = prepare.get_data()
label_encoder = {}
train_x, train_y = prepare.get_exclude_ndf_x(train_data, basic_users_info,
label_encoder)
validation_x, validation_y = prepare.get_exclude_ndf_x(validation_data,
basic_users_info,
label_encoder)
#for i in range(1, 50):
rf = OneVsRestClassifier(
GradientBoostingClassifier(learning_rate=0.1,
n_estimators=11)).fit(train_x, train_y)
validation_predict_proba = rf.predict_proba(validation_x)
class_order = rf.classes_
predict_list = evaluate.candidate_classes(validation_predict_proba,
class_order)
ndcg = evaluate.ndcg(predict_list, validation_data)
print ndcg
#ndcg_list.append(ndcg)
#print i, " ", ndcg
test_x = prepare.get_exclude_ndf_test_x(test_data, basic_users_info,
label_encoder)
test_predict = rf.predict_proba(test_x)
test_predict_list = evaluate.candidate_classes(test_predict, class_order)
prepare.get_test_predict(test_data, test_predict_list)