113 lines
4.4 KiB
Python
113 lines
4.4 KiB
Python
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import os
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.ensemble import RandomForestRegressor
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def analyze_feature_importance(self):
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"""分析特征重要性"""
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try:
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self._log_step("Analyzing feature importance...")
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if not (hasattr(self, 'data') and self.data is not None and len(self.data.columns) > 1):
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self._log_step("Not enough data for feature importance analysis", "warning")
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return None, "Not enough data for feature importance analysis", None
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X = self.data
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y = self.data.iloc[:, 0] # 使用第一列作为目标变量
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model = RandomForestRegressor(n_estimators=50, random_state=42) # 减少树的数量
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model.fit(X, y)
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feature_importance = pd.Series(model.feature_importances_, index=X.columns)
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feature_importance = feature_importance.sort_values(ascending=False)
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fi_df = feature_importance.reset_index()
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fi_df.columns = ['feature', 'importance']
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summary = f"Feature importance analysis completed, top feature: {fi_df.iloc[0]['feature']}"
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if not self.generate_plots:
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self._log_step("Feature importance analysis completed (data only)", "success")
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return None, summary, fi_df
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plt.figure(figsize=(8, 6))
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feature_importance.head(10).plot(kind='bar')
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plt.title('Feature Importance Analysis')
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plt.ylabel('Importance Score')
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plt.tight_layout()
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img_path = os.path.join(self.temp_dir.name, 'feature_importance.png')
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plt.savefig(img_path, dpi=150, bbox_inches='tight')
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plt.close()
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self._log_step("Feature importance analysis completed", "success")
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return img_path, summary, fi_df
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except Exception as e:
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self._log_step(f"Feature importance analysis failed: {e}", "error")
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return None, f"Feature importance analysis failed: {e}", None
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def perform_var_analysis(self):
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"""执行向量自回归分析"""
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try:
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self._log_step("Performing VAR analysis...")
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if not (hasattr(self, 'data') and self.data is not None and len(self.data.columns) > 1):
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self._log_step("Not enough data for VAR analysis", "warning")
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return None, "数据不足，无法进行VAR分析", None
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from statsmodels.tsa.api import VAR
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numeric_data = self.data.select_dtypes(include=[np.number])
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if len(numeric_data.columns) < 2:
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self._log_step("Not enough numeric columns for VAR analysis", "warning")
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return None, "数值变量不足，无法进行VAR分析", None
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var_data = numeric_data.iloc[:, : min(3, len(numeric_data.columns))]
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model = VAR(var_data)
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results = model.fit(maxlags=2, ic='aic')
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lag_order = results.k_ar
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forecast = results.forecast(var_data.values[-lag_order:], steps=10)
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forecast_df = pd.DataFrame(data=forecast, columns=[f"{col}_forecast" for col in var_data.columns])
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summary = f"VAR分析完成，使用滞后阶数: {results.k_ar}，生成了10期预测"
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if not self.generate_plots:
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self._log_step("VAR analysis completed (data only)", "success")
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return None, summary, forecast_df
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plt.figure(figsize=(12, 8))
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for i, col in enumerate(var_data.columns):
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plt.plot(range(len(var_data)), var_data[col].values, label=f'{col} (actual)', alpha=0.7)
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plt.plot(
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range(len(var_data), len(var_data) + 10),
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forecast[:, i],
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label=f'{col} (forecast)',
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linestyle='--',
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)
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plt.axvline(x=len(var_data), color='red', linestyle=':', alpha=0.7, label='Forecast Start')
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plt.xlabel('Time')
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plt.ylabel('Value')
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plt.title('Vector Autoregression (VAR) Forecast')
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plt.legend()
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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img_path = os.path.join(self.temp_dir.name, 'var_analysis.png')
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plt.savefig(img_path, dpi=150, bbox_inches='tight')
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plt.close()
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self._log_step("VAR analysis completed", "success")
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return img_path, summary, forecast_df
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except Exception as e:
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self._log_step(f"VAR analysis failed: {e}", "error")
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return None, f"VAR分析失败: {e}", None
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