原始版本

yugou.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+plt.rcParams["font.sans-serif"] = "SimHei"  # 解决中文乱码问题
+# 清理内存
+import gc
+import seaborn as sns
+import random
+
+# # from sklearn.model_selection import train_test_split
+# from sklearn.linear_model import LogisticRegression
+# from sklearn.preprocessing import LabelEncoder
+# from sklearn.metrics import accuracy_score
+# from sklearn import model_selection
+# from sklearn.neighbors import KNeighborsRegressor
+
+df_train = pd.read_csv(r'data/data_format1/train_format1.csv')
+df_test = pd.read_csv(r'data/data_format1/test_format1.csv')
+user_info = pd.read_csv(r'data/data_format1/user_info_format1.csv')
+user_log = pd.read_csv(r'data/data_format1/user_log_format1.csv')
+
+print(df_test.shape, df_train.shape)
+print(user_info.shape, user_log.shape)
+
+# 填充缺失值
+user_info['age_range'].replace(0.0, np.nan, inplace=True)
+user_info['gender'].replace(2.0, np.nan, inplace=True)
+user_info['age_range'].replace(np.nan, -1, inplace=True)
+user_info['gender'].replace(np.nan, -1, inplace=True)
+
+# 聚合特征
+seller_group = user_log.groupby(["seller_id", "action_type"]).count()[["user_id"]].reset_index().rename(
+    columns={'user_id': 'count'})
+
+# del user_log
+# gc.collect()
+
+# age_range,gender特征添加
+df_train = pd.merge(df_train, user_info, on="user_id", how="left")
+df_train.head()
+
+total_logs_temp = user_log.groupby([user_log["user_id"], user_log["seller_id"]]).count().reset_index()[
+    ["user_id", "seller_id", "item_id"]]
+total_logs_temp.rename(columns={"seller_id": "merchant_id", "item_id": "total_logs"}, inplace=True)
+df_train = pd.merge(df_train, total_logs_temp, on=["user_id", "merchant_id"], how="left")
+
+# 添加unique_item_ids特征
+unique_item_ids_temp = \
+    user_log.groupby([user_log["user_id"], user_log["seller_id"], user_log["item_id"]]).count().reset_index()[
+        ["user_id", "seller_id", "item_id"]]
+unique_item_ids_temp1 = unique_item_ids_temp.groupby(
+    [unique_item_ids_temp["user_id"], unique_item_ids_temp["seller_id"]]).count().reset_index()
+unique_item_ids_temp1.rename(columns={"seller_id": "merchant_id", "item_id": "unique_item_ids"}, inplace=True)
+df_train = pd.merge(df_train, unique_item_ids_temp1, on=["user_id", "merchant_id"], how="left")
+
+#
+categories_temp = \
+    user_log.groupby([user_log["user_id"], user_log["seller_id"], user_log["cat_id"]]).count().reset_index()[
+        ["user_id", "seller_id", "cat_id"]]
+categories_temp1 = categories_temp.groupby(
+    [categories_temp["user_id"], categories_temp["seller_id"]]).count().reset_index()
+categories_temp1.rename(columns={"seller_id": "merchant_id", "cat_id": "categories"}, inplace=True)
+df_train = pd.merge(df_train, categories_temp1, on=["user_id", "merchant_id"], how="left")
+
+df_train.head(10)
+# 添加browse_days特征
+browse_days_temp = \
+    user_log.groupby([user_log["user_id"], user_log["seller_id"], user_log["time_stamp"]]).count().reset_index()[
+        ["user_id", "seller_id", "time_stamp"]]
+browse_days_temp1 = browse_days_temp.groupby(
+    [browse_days_temp["user_id"], browse_days_temp["seller_id"]]).count().reset_index()
+browse_days_temp1.rename(columns={"seller_id": "merchant_id", "time_stamp": "browse_days"}, inplace=True)
+df_train = pd.merge(df_train, browse_days_temp1, on=["user_id", "merchant_id"], how="left")
+
+# 添加one_clicks、shopping_carts、purchase_times、favourite_times特征
+one_clicks_temp = \
+    user_log.groupby([user_log["user_id"], user_log["seller_id"], user_log["action_type"]]).count().reset_index()[
+        ["user_id", "seller_id", "action_type", "item_id"]]
+one_clicks_temp.rename(columns={"seller_id": "merchant_id", "item_id": "times"}, inplace=True)
+one_clicks_temp["one_clicks"] = one_clicks_temp["action_type"] == 0
+one_clicks_temp["one_clicks"] = one_clicks_temp["one_clicks"] * one_clicks_temp["times"]
+one_clicks_temp["shopping_carts"] = one_clicks_temp["action_type"] == 1
+one_clicks_temp["shopping_carts"] = one_clicks_temp["shopping_carts"] * one_clicks_temp["times"]
+one_clicks_temp["purchase_times"] = one_clicks_temp["action_type"] == 2
+one_clicks_temp["purchase_times"] = one_clicks_temp["purchase_times"] * one_clicks_temp["times"]
+one_clicks_temp["favourite_times"] = one_clicks_temp["action_type"] == 3
+one_clicks_temp["favourite_times"] = one_clicks_temp["favourite_times"] * one_clicks_temp["times"]
+four_features = one_clicks_temp.groupby(
+    [one_clicks_temp["user_id"], one_clicks_temp["merchant_id"]]).sum().reset_index()
+four_features = four_features.drop(["action_type", "times"], axis=1)
+df_train = pd.merge(df_train, four_features, on=["user_id", "merchant_id"], how="left")
+
+# 缺失值处理
+df_train.isnull().sum(axis=0)
+df_train = df_train.fillna(method='ffill')
+
+# 模型构建与调参
+Y = df_train['label']
+X = df_train.drop(['user_id', 'merchant_id', 'label'], axis=1)
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.25, random_state=10)
+
+
+
+mlp = MLPClassifier(solver='lbfgs', activation='relu', alpha=0.1, random_state=0, hidden_layer_sizes=[10, 10]).fit(
+    X_train, y_train)
+Predict = mlp.predict(X_test)
+Predict_proba = mlp.predict_proba(X_test)
+print(Predict_proba[:])
+Score = accuracy_score(y_test, Predict)
+print(Score)
+# df_train = pd.merge(df_train, browse_days_temp1, on=["user_id", "merchant_id"], how="left")