Create lgb.py

competition/科大讯飞AI开发者大赛2021/科大讯飞商店销量预测/lgb.py

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+import pandas as pd
+import numpy as np
+
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import KFold
+
+import lightgbm as lgb
+
+import warnings
+warnings.filterwarnings('ignore')
+
+train = pd.read_csv('train.csv')
+test = pd.read_csv('test.csv')
+sample_submit = pd.read_csv('sample_submit.csv')
+
+df = pd.concat([train, test], axis=0, ignore_index=True)
+
+def lag_feature_adv(df, lags, col):
+    '''
+    历史N周平移特征
+    '''
+    tmp = df[['week','shop_id','item_id',col]]
+    for i in lags:
+        shifted = tmp.copy()
+        shifted.columns = ['week','shop_id','item_id', col+'_lag_'+str(i)+'_adv']
+        shifted['week'] += i
+        df = pd.merge(df, shifted, on=['week','shop_id','item_id'], how='left')
+        df[col+'_lag_'+str(i)+'_adv'] = df[col+'_lag_'+str(i)+'_adv']
+    return df
+
+df = lag_feature_adv(df, [1, 2, 3], 'weekly_sales')
+
+x_train = df[df.week < 33].drop(['weekly_sales'], axis=1)
+y_train = df[df.week < 33]['weekly_sales']
+x_test = df[df.week == 33].drop(['weekly_sales'], axis=1)
+
+
+def cv_model(clf, train_x, train_y, test_x, clf_name='lgb'):
+    folds = 5
+    seed = 1024
+    kf = KFold(n_splits=folds, shuffle=True, random_state=seed)
+
+    train = np.zeros(train_x.shape[0])
+    test = np.zeros(test_x.shape[0])
+
+    categorical_feature = ['shop_id','item_id','item_category_id']
+    cv_scores = []
+
+    for i, (train_index, valid_index) in enumerate(kf.split(train_x, train_y)):
+        print('************************************ {} ************************************'.format(str(i+1)))
+        trn_x, trn_y, val_x, val_y = train_x.iloc[train_index], train_y[train_index], train_x.iloc[valid_index], train_y[valid_index]
+
+        train_matrix = clf.Dataset(trn_x, label=trn_y)
+        valid_matrix = clf.Dataset(val_x, label=val_y)
+
+        params = {
+            'boosting_type': 'gbdt',
+            'objective': 'mse',
+            'metric': 'mse',
+            'min_child_weight': 5,
+            'num_leaves': 2 ** 7,
+            'lambda_l2': 10,
+            'feature_fraction': 0.9,
+            'bagging_fraction': 0.9,
+            'bagging_freq': 4,
+            'learning_rate': 0.05,
+            'seed': 1024,
+            'n_jobs':-1,
+            'silent': True,
+            'verbose': -1,
+        }
+
+        model = clf.train(params, train_matrix, 5000, valid_sets=[train_matrix, valid_matrix], 
+                          categorical_feature = categorical_feature,
+                          verbose_eval=500,early_stopping_rounds=200)
+        val_pred = model.predict(val_x, num_iteration=model.best_iteration)
+        test_pred = model.predict(test_x, num_iteration=model.best_iteration)
+
+        train[valid_index] = val_pred
+        test += test_pred / kf.n_splits
+        cv_scores.append(mean_squared_error(val_y, val_pred))
+
+        print(cv_scores)
+
+    print("%s_scotrainre_list:" % clf_name, cv_scores)
+    print("%s_score_mean:" % clf_name, np.mean(cv_scores))
+    print("%s_score_std:" % clf_name, np.std(cv_scores))
+    return train, test
+
+lgb_train, lgb_test = cv_model(lgb, x_train, y_train, x_test)
+
+
+sample_submit['weekly_sales'] = lgb_test
+sample_submit['weekly_sales'] = sample_submit['weekly_sales'].apply(lambda x:x if x>0 else 0).values
+sample_submit.to_csv('baseline_result.csv', index=False)