def RecommendByDT(train_data, train_data_y, test_data, test_data_y, recommendNum=5):
grid_parameters = [
{'min_samples_leaf': [2, 4, 8, 16, 32, 64], 'max_depth': [2, 4, 6, 8]}] # 调节参数
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import GridSearchCV
clf = DecisionTreeClassifier()
clf = GridSearchCV(clf, param_grid=grid_parameters, n_jobs=-1)
clf.fit(train_data, train_data_y)
predictions = clf.predict_proba(test_data)
print(clf.best_params_)
"""预测结果转化为data array"""
predictions = DataProcessUtils.convertMultilabelProbaToDataArray(predictions)
print(predictions)
recommendList = DataProcessUtils.getListFromProbable(predictions, range(1, train_data_y.shape[1] + 1),
recommendNum)
answerList = test_data_y
print(predictions)
print(test_data_y)
print(recommendList)
print(answerList)
return [recommendList, answerList]
def RecommendByRF(train_data, train_data_y, test_data, test_data_y, recommendNum=5):
"""多标签分类 随机森林"""
clf = RandomForestClassifier(n_estimators=50, max_depth=5, n_jobs=-1)
"""对弱分类器数量做调参数量"""
# param_test1 = {'n_estimators': range(200, 250, 10)}
# clf = GridSearchCV(estimator=clf, param_grid=param_test1)
# print(clf.best_params_)
# print(clf.best_params_, clf.best_score_)
"""对决策树的参数做调参"""
# param_test2 = {'max_depth': range(6, 8, 1), 'min_samples_split': range(18, 22, 1)}
# clf = GridSearchCV(estimator=clf, param_grid=param_test1, cv=5, n_jobs=5)
clf.fit(train_data, train_data_y)
predictions = clf.predict_proba(test_data)
# print(clf.best_params_)
# print(clf.best_score_)
# print(clf.cv_results_)
"""预测结果转化为data array"""
predictions = DataProcessUtils.convertMultilabelProbaToDataArray(predictions)
print(predictions)
recommendList = DataProcessUtils.getListFromProbable(predictions, range(1, train_data_y.shape[1] + 1),
recommendNum)
answerList = test_data_y
print(predictions)
print(test_data_y)
print(recommendList)
print(answerList)
return [recommendList, answerList]
def RecommendByKN(train_data, train_data_y, test_data, test_data_y, recommendNum=5):
"""ML KNeighbors"""
clf = KNeighborsClassifier()
clf.fit(train_data, train_data_y)
predictions = clf.predict_proba(test_data)
"""预测结果转化为data array"""
predictions = DataProcessUtils.convertMultilabelProbaToDataArray(predictions)
print(predictions)
recommendList = DataProcessUtils.getListFromProbable(predictions, range(1, train_data_y.shape[1] + 1),
recommendNum)
answerList = test_data_y
print(predictions)
print(test_data_y)
print(recommendList)
print(answerList)
return [recommendList, answerList]
def RecommendByETS(train_data, train_data_y, test_data, test_data_y, recommendNum=5):
"""多标签分类 """
clf = ExtraTreesClassifier(n_jobs=3, n_estimators=250)
param_test2 = {'max_depth': range(10, 40, 10), 'min_samples_split': range(15, 30, 5)}
clf = GridSearchCV(estimator=clf, param_grid=param_test2, iid=False, cv=10, n_jobs=2)
clf.fit(train_data, train_data_y)
predictions = clf.predict_proba(test_data)
"""预测结果转化为data array"""
predictions = DataProcessUtils.convertMultilabelProbaToDataArray(predictions)
print(predictions)
recommendList = DataProcessUtils.getListFromProbable(predictions, range(1, train_data_y.shape[1] + 1),
recommendNum)
answerList = test_data_y
print(predictions)
print(test_data_y)
print(recommendList)
print(answerList)
return [recommendList, answerList]