def criteo_gdbtlr(X_idx, X_value, y):
import numpy as np
from sklearn.metrics import roc_auc_score, accuracy_score
from sklearn.linear_model import LogisticRegression
from lightgbm.sklearn import LGBMClassifier
X_idx = X_idx.values.tolist()
y = y.values.tolist()
num_leaves = 31
model = LGBMClassifier(num_leaves=num_leaves)
model.fit(X_idx, y)
model_path = os.path.join(pwd_path, 'gbdtlr_model1.pt')
y_pred = model.predict(X_idx, pred_leaf=True)
y_pred_gbdt = model.predict(X_idx, pred_leaf=False)
acc = model.score(X_idx, y)
print("gbdt train acc:", acc)
s = roc_auc_score(y, y_pred_gbdt)
print('gbdt auc:', s)
a = accuracy_score(y, y_pred_gbdt)
print('gbdt train acc:', a)
import pickle # pickle模块
# 保存Model(注:save文件夹要预先建立，否则会报错)
with open(model_path, 'wb') as f:
pickle.dump(model, f)
# # 读取Model
# with open('save/clf.pickle', 'rb') as f:
# clf2 = pickle.load(f)
transformed_matrix = np.zeros(
[len(y_pred), len(y_pred[0]) * num_leaves], dtype=np.int64)
for i in range(0, len(y_pred)):
temp = np.arange(len(y_pred[0])) * num_leaves - 1 + np.array(y_pred[i])
transformed_matrix[i][temp] += 1
lr_model = LogisticRegression()
lr_model.fit(transformed_matrix, y)
y_pred_lr = lr_model.predict(transformed_matrix)
print("truth_y:", y[:100], 'y_pred_lr:', y_pred_lr[:100])
s = roc_auc_score(y, y_pred_lr)
print('auc:', s)
def multi_machine_learing_models(data_train, data_cv):
print('正在训练模型！')
data_train=pd.concat([data_train,data_cv],axis=0)
y_train = data_train['label'].apply(lambda x: 0 if x == 'good' else 1)
y_test = data_cv['label'].apply(lambda x: 0 if x == 'good' else 1)
X_train = data_train.drop(['URL', 'label'], axis=1)
X_test = data_cv.drop(['URL', 'label'], axis=1)
filename_bayes = 'classifier_model\c_bayes.model'
filename_LGB = 'classifier_model\c_LGB.model'
filename_ada = 'classifier_model\c_ada.model'
filename_rf = 'classifier_model\c_rf.model'
filename_decision_tree = 'classifier_model\c_decision_tree.model'
filename_lgs = 'classifier_model\c_lgs.model'
vote = []
for i in range(len(y_test)):
vote.append(0)
bayes = BernoulliNB()
bayes.fit(X_train, y_train)
print('\nbayes模型的准确度:', bayes.score(X_test, y_test))
predict = bayes.predict(X_test)
vote = list(map(lambda x: x[0] + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(bayes, filename_bayes)
gbc = LGBMClassifier(n_estimators=200, objective='binary')
gbc.fit(X_train, y_train)
print('LGBMClassifier模型的准确度:', gbc.score(X_test, y_test))
predict = gbc.predict(X_test)
vote = list(map(lambda x: 3 * x[0] + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(gbc, filename_LGB)
ada = AdaBoostClassifier(n_estimators=100) # 迭代100次
ada.fit(X_train, y_train)
print('ada模型的准确度:', ada.score(X_test, y_test))
predict = ada.predict(X_test)
vote = list(map(lambda x: 2 * x[0] + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(ada, filename_ada)
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(X_train, y_train)
print('\nrf模型的准确度:', rf.score(X_test, y_test))
predict = rf.predict(X_test)
vote = list(map(lambda x: x[0] * 3 + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(rf, filename_rf)
decision_tree = tree.DecisionTreeClassifier()
decision_tree.fit(X_train, y_train)
print('\ndecision_tree模型的准确度:', decision_tree.score(X_test, y_test))
predict = decision_tree.predict(X_test)
vote = list(map(lambda x: x[0] * 2 + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(decision_tree, filename_decision_tree)
lgs = LogisticRegression()
lgs.fit(X_train, y_train)
print('\nLogisticRegression模型的准确度:', lgs.score(X_test, y_test))
predict = lgs.predict(X_test)
vote = list(map(lambda x: x[0] * 2 + x[1], zip(predict, vote)))
precision = metrics.precision_score(y_test, predict)
recall = metrics.recall_score(y_test, predict)
print("precison:", precision)
print("recall:", recall)
joblib.dump(lgs, filename_lgs)
print('\n投票结果：')
vote_r = []
for i in range(len(vote)):
if vote[i] >= 3:
vote_r.append(1)
else:
vote_r.append(0)
precision = metrics.precision_score(y_test, vote_r)
recall = metrics.recall_score(y_test, vote_r)
acc = metrics.accuracy_score(y_test, vote_r)
print('准确度：', acc)
print("precison:", precision)
print("recall:", recall)
