update

params_search.py

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+import pandas as pd
+import lightgbm as lgb
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import mean_absolute_percentage_error, r2_score
+from rov_train import process_data
+from config import set_config
+
+config_, env = set_config()
+
+train_filename = config_.TRAIN_DATA_FILENAME
+X, Y, videos, fea = process_data(filename=train_filename)
+x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.33)
+
+# params = {
+#           'boosting_type': 'gbdt',
+#           'objective': 'regression',
+#           'metric': 'mape',
+#           'nthread': 4,
+#           'learning_rate': 0.1,
+#           'num_leaves': 30,
+#           'max_depth': 5,
+#           'subsample': 0.8,
+#           'colsample_bytree': 0.8,
+#     }
+# data_train = lgb.Dataset(x_train, y_train)
+# cv_results = lgb.cv(
+#     params,
+#     data_train,
+#     num_boost_round=1000,
+#     nfold=5,
+#     stratified=False,
+#     shuffle=True,
+#     metrics='mape',
+#     early_stopping_rounds=50,
+#     seed=0
+# )
+# print('cv_results: ', cv_results)
+# print('best n_estimators: ', len(cv_results['mape-mean']))
+# print('best cv score: ', pd.Series(cv_results['mape-mean']).min())
+
+# best n_estimators:  1000
+# best cv score:  0.1578205729958749
+
+
+# params_test1 = {'max_depth': range(3, 8, 1), 'num_leaves': range(5, 100, 10)}
+# gsearch1 = GridSearchCV(
+#     estimator=lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.1,
+#                                 n_estimators=1000, max_depth=6, bagging_fraction=0.8, feature_fraction=0.8),
+#     param_grid=params_test1, scoring='r2', cv=5, n_jobs=-1)
+# gsearch1.fit(x_train, y_train)
+# print(gsearch1.cv_results_, gsearch1.best_params_, gsearch1.best_score_)
+
+# {'max_depth': 3, 'num_leaves': 5} 0.9263654009553877
+
+
+# params_test2 = {'max_bin': range(5, 256, 10), 'min_data_in_leaf': range(1, 102, 10)}
+#
+# gsearch2 = GridSearchCV(
+#     estimator=lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.1,
+#                                 n_estimators=1000, max_depth=3, num_leaves=5, bagging_fraction=0.8,
+#                                 feature_fraction=0.8),
+#     param_grid=params_test2, scoring='r2', cv=5, n_jobs=-1)
+# gsearch2.fit(x_train, y_train)
+# print(gsearch2.cv_results_, gsearch2.best_params_, gsearch2.best_score_)
+
+# {'max_bin': 125, 'min_data_in_leaf': 21} 0.7918455469932647
+
+
+# params_test3 = {'feature_fraction': [0.6, 0.7, 0.8, 0.9, 1.0],
+#                 'bagging_fraction': [0.6, 0.7, 0.8, 0.9, 1.0],
+#                 'bagging_freq': range(0, 81, 10)
+#                 }
+#
+# gsearch3 = GridSearchCV(
+#     estimator=lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.1,
+#                                 n_estimators=1000, max_depth=3, num_leaves=5, max_bin=125, min_data_in_leaf=21),
+#     param_grid=params_test3, scoring='r2', cv=5, n_jobs=-1)
+# gsearch3.fit(x_train, y_train)
+# print(gsearch3.cv_results_, gsearch3.best_params_, gsearch3.best_score_)
+
+# {'bagging_fraction': 0.6, 'bagging_freq': 0, 'feature_fraction': 0.9} 0.9003939890191465
+
+
+# params_test4 = {'lambda_l1': [1e-5, 1e-3, 1e-1, 0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0],
+#                 'lambda_l2': [1e-5, 1e-3, 1e-1, 0.0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]
+#                 }
+#
+# gsearch4 = GridSearchCV(
+#     estimator=lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.1,
+#                                 n_estimators=1000, max_depth=3, num_leaves=5, max_bin=125, min_data_in_leaf=21,
+#                                 bagging_fraction=0.6, bagging_freq=0, feature_fraction=0.9),
+#     param_grid=params_test4, scoring='r2', cv=5, n_jobs=-1)
+# gsearch4.fit(x_train, y_train)
+# print(gsearch4.cv_results_, gsearch4.best_params_, gsearch4.best_score_)
+
+# {'lambda_l1': 0.7, 'lambda_l2': 0.9} 0.7538021411131314
+
+
+# params_test5 = {'min_split_gain': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}
+# gsearch5 = GridSearchCV(
+#     estimator=lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.1,
+#                                 n_estimators=1000, max_depth=3, num_leaves=5, max_bin=125, min_data_in_leaf=21,
+#                                 bagging_fraction=0.6, bagging_freq=0, feature_fraction=0.9,
+#                                 lambda_l1=0.7, lambda_l2=0.9),
+#     param_grid=params_test5, scoring='r2', cv=5, n_jobs=-1)
+# gsearch5.fit(x_train, y_train)
+# print(gsearch5.cv_results_, gsearch5.best_params_, gsearch5.best_score_)
+#
+# {'min_split_gain': 0.0} 0.8903744931054016
+
+
+# model = lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.01,
+#                           n_estimators=7000, max_depth=3, num_leaves=5, max_bin=125, min_data_in_leaf=21,
+#                           bagging_fraction=0.6, bagging_freq=0, feature_fraction=0.9,
+#                           lambda_l1=0.7, lambda_l2=0.9, min_split_gain=0)
+# model.fit(x_train, y_train)
+# y_pre = model.predict(x_test)
+# print('mape: ', mean_absolute_percentage_error(y_test, y_pre))
+# print('r2: ', r2_score(y_test, y_pre))
+
+# mape:  0.7407816559793686
+# r2:  0.9069208480190998
+
+
+# model = lgb.LGBMRegressor()
+# model.fit(x_train, y_train)
+# y_pre = model.predict(x_test)
+# print('mape: ', mean_absolute_percentage_error(y_test, y_pre))
+# print('r2: ', r2_score(y_test, y_pre))
+
+# mape:  0.211698833035155
+# r2:  0.893979193478978
+
+
+model = lgb.LGBMRegressor(boosting_type='gbdt', objective='regression', metrics='mape', learning_rate=0.08,
+                          n_estimators=3000, max_depth=3, num_leaves=5, max_bin=125, min_data_in_leaf=21,
+                          bagging_fraction=0.6, bagging_freq=0, feature_fraction=0.9,
+                          lambda_l1=0.7, lambda_l2=0.9, min_split_gain=0)
+model.fit(x_train, y_train)
+y_pre = model.predict(x_test)
+print('mape: ', mean_absolute_percentage_error(y_test, y_pre))
+print('r2: ', r2_score(y_test, y_pre))