Update category_models: add logistic regression model

src/long_articles/category_models.py

@@ -95,6 +95,77 @@ class CategoryRegressionV1:
 
         return params, t_stats, p_values
 
+class CategoryLR:
+    def __init__(self):
+        self.features = [
+            'CateOddities', 'CateFamily', 'CateHeartwarm',
+            'CateHistory', 'CateHealth', 'CateLifeKnowledge', 'CateGossip',
+            'CatePolitics', 'CateMilitary', 'CateMovie', 'CateSociety',
+            'view_count_rate', 'bias'
+        ]
+
+    def preprocess_data(self, df):
+        for cate in category_name_map:
+            colname = category_name_map[cate]
+            df[colname] = df['category'] == cate
+            df[colname] = df[colname].astype(int)
+
+        df['ClassY'] = df['read_avg_rate'] > 1
+        df['bias'] = 1.0
+        return df
+
+    def build_and_print(self, df, account_name):
+        if account_name is not None:
+            sub_df = df[df['account_name'] == account_name]
+        else:
+            sub_df = df
+        sample_count = len(sub_df)
+        positive_count = len(sub_df.query('ClassY == 1'))
+        if sample_count < 10 or positive_count * (sample_count - positive_count) == 0:
+            return
+        sample_count = len(sub_df)
+        params, t_stats, p_values = self.run_logistic_regression(sub_df)
+        row = f'{account_name}\t{sample_count}'
+        for param, p_value in zip(params, p_values):
+            row += f'\t{param:.3f}'
+        print(row)
+
+    def build(self, df):
+        p_value_column_names = '\t'.join(self.features)
+        print('account\tsamples\t{}'.format(p_value_column_names))
+        # self.build_and_print(df, None)
+        for account_name in df['account_name'].unique():
+            self.build_and_print(df, account_name)
+
+    def get_param_names(self):
+        return ['bias'] + self.features
+
+    def run_logistic_regression(self, df):
+        X = df[self.features]  # 特征列
+        y = df['ClassY']  # 目标变量
+
+        # 将数据集分为训练集和测试集
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # 创建线性回归模型
+        logreg = LogisticRegression()
+
+        # 训练模型
+        logreg.fit(X_train, y_train)
+
+        # 预测测试集
+        y_pred = logreg.predict(X_test)
+
+        # 评估模型性能
+        accuracy = accuracy_score(y_test, y_pred)
+        conf_matrix = confusion_matrix(y_test, y_pred)
+        class_report = classification_report(y_test, y_pred)
+
+        # print(f"Accuracy: {accuracy}")
+        # print(f"Confusion Matrix: \n{conf_matrix}")
+        # print(f"Classification Report: \n{class_report}")
+        return logreg.coef_[0], None, [0] * len(logreg.coef_[0])
+
 def main():
     df = pd.read_excel('20241101_read_rate_samples.xlsx')  # 如果数据来自CSV文件
     df['read_avg'] = df['阅读均值']
@@ -103,7 +174,7 @@ def main():
     df['similarity'] = df['Similarity']
     filter_condition = 'read_avg > 500 ' \
         'and read_avg_rate > 0 and read_avg_rate < 3 ' \
-        'and dt > 20240914 and similarity > 0' 
+        'and dt > 20240914 and similarity > 0'
     df = df.query(filter_condition).copy()
 
     m_cate = CategoryRegressionV1()