Update core/plot.py

core/plot.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 import pandas as pd
+import numpy as np
 
 
 def plot_forecast(result):
@@ -32,18 +33,15 @@ def plot_forecast(result):
 
 def plot_future_forecast(df, result, future_df):
     fig, ax = plt.subplots(figsize=(10, 6))
-    # Plot historical data
     ax.plot(df['Date'], df['value'], label="Historical Data", color="blue", linewidth=2)
     
     if "latest_prediction" in result:
         last_date = df['Date'].iloc[-1]
         horizon = len(result["latest_prediction"])
         future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=horizon, freq='B')
-        # Plot predictions
         ax.plot(future_dates, result["latest_prediction"], label="Forecast", color="orange", linestyle="--", linewidth=2)
         for i, val in enumerate(result["latest_prediction"]):
             ax.text(future_dates[i], val, f"{val:.2f}", color="orange", fontsize=8, ha='center', va='bottom')
-        # Plot actual future values if available
         if not future_df.empty and "future_actuals" in result:
             actual_future_dates = future_df['Date']
             actual_future_values = future_df['value']
@@ -60,23 +58,51 @@ def plot_future_forecast(df, result, future_df):
     return fig
 
 
-def plot_metrics_r2(result):
-    fig, ax = plt.subplots(figsize=(6, 4))
-    metrics = {k: v for k, v in result['metrics'].items() if k in ['R2', 'MAPE']}
+def plot_metrics_precision(result):
+    fig, ax = plt.subplots(figsize=(8, 5))
+    metrics = {k: v for k, v in result['metrics'].items() if k in ['R2', 'Explained Variance', 'MDA (%)'] and v is not None}
+    if not metrics:
+        ax.text(0.5, 0.5, "No valid precision metrics available", ha='center', va='center')
+        ax.set_title("Precision Metrics (Model Accuracy)")
+        return fig
+    
     sns.barplot(x=list(metrics.keys()), y=list(metrics.values()), ax=ax, palette="Blues_d")
-    ax.set_title("R² and MAPE Metrics")
-    ax.set_ylim(-1, 1)
-    ax.grid(True)
+    # Add labels on bars
+    for i, v in enumerate(metrics.values()):
+        ax.text(i, v + 0.01 * max(metrics.values(), default=1), f"{v:.4f}", ha='center', va='bottom', fontsize=10)
+    
+    # Dynamic y-axis scaling
+    max_val = max(metrics.values(), default=1)
+    min_val = min(metrics.values(), default=0)
+    ax.set_ylim(min(min_val - 0.1 * abs(min_val), -0.1), max_val + 0.2 * max_val)
+    
+    ax.set_title("Precision Metrics (Model Accuracy)")
+    ax.set_ylabel("Value")
+    ax.grid(True, axis='y')
     plt.tight_layout()
     return fig
 
 
-def plot_metrics_errors(result):
-    fig, ax = plt.subplots(figsize=(6, 4))
-    metrics = {k: v for k, v in result['metrics'].items() if k in ['RMSE', 'MAE']}
+def plot_metrics_risk(result):
+    fig, ax = plt.subplots(figsize=(8, 5))
+    metrics = {k: v for k, v in result['metrics'].items() if k in ['RMSE', 'MAE', 'MAPE (%)', 'MASE'] and v is not None}
+    if not metrics:
+        ax.text(0.5, 0.5, "No valid risk metrics available", ha='center', va='center')
+        ax.set_title("Risk Metrics (Error Magnitude)")
+        return fig
+    
     sns.barplot(x=list(metrics.keys()), y=list(metrics.values()), ax=ax, palette="Reds_d")
-    ax.set_title("RMSE and MAE Metrics")
-    ax.grid(True)
+    # Add labels on bars
+    for i, v in enumerate(metrics.values()):
+        ax.text(i, v + 0.01 * max(metrics.values(), default=1), f"{v:.4f}", ha='center', va='bottom', fontsize=10)
+    
+    # Dynamic y-axis scaling
+    max_val = max(metrics.values(), default=1)
+    ax.set_ylim(0, max_val + 0.2 * max_val)
+    
+    ax.set_title("Risk Metrics (Error Magnitude)")
+    ax.set_ylabel("Value")
+    ax.grid(True, axis='y')
     plt.tight_layout()
     return fig