added plots

ml_plots.py

@@ -1,6 +1,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+import matplotlib.cm as cm
+import scipy.stats as stats
+import matplotlib.gridspec as gridspec
+
+from sklearn.linear_model import LinearRegression
 import os
 import time
 
@@ -72,4 +77,87 @@ def plot_predictions(true_values, predicted_values, colors, title='True vs Predi
     # save plot
     if save:
         save_plot(plt, title)
-    return plt
+    return plt
+
+def plot_residuals(labels, preds, colors, title='Residuals Plot', save=True):
+    residuals = np.array(preds) - np.array(labels)
+    
+    fig = plt.figure(figsize=(14, 6))
+    gs = gridspec.GridSpec(1, 2, width_ratios=[4, 1])
+
+    # Main plot
+    ax0 = plt.subplot(gs[0])
+    ax0.scatter(labels, residuals, label='Residuals', color=colors['blue'], alpha=0.5)
+    
+    # Fit linear regression model to residuals
+    labels_reshaped = np.array(labels).reshape(-1, 1)
+    model = LinearRegression()
+    model.fit(labels_reshaped, residuals)
+    trend_line = model.predict(labels_reshaped)
+    
+    # Plot trend line
+    ax0.plot(labels, trend_line, color=colors['red'], label='Trend Line', linewidth=2)
+    
+    ax0.set_xlabel('True Values')
+    ax0.set_ylabel('Residuals')
+    ax0.axhline(y=0, color='k', linestyle='--')
+    ax0.set_title(title)
+    ax0.legend()
+
+    # Side plot for distribution of true values
+    ax1 = plt.subplot(gs[1], sharey=ax0)
+    ax1.hist(residuals, bins=30, alpha=0.5, color=colors['blue'], orientation='horizontal')
+    ax1.set_xlabel('Frequency')
+    ax1.set_title('Distribution of residuals')
+    ax1.yaxis.tick_right()
+    ax1.yaxis.set_label_position("right")
+
+    plt.tight_layout()
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+def plot_qq(labels, preds, colors, title='Q-Q Plot of Residuals', save=True):
+    residuals = np.array(preds) - np.array(labels)
+
+    # Generate a Normal Q-Q plot
+    fig = plt.figure(figsize=(8, 6))
+    ax = fig.add_subplot(111)
+    stats.probplot(residuals, dist="norm", plot=ax)
+    
+    # Set colors
+    line = ax.get_lines()
+    line[0].set_color(colors['blue'])  # Data points
+    line[1].set_color(colors['red'])   # Fit line
+    
+    plt.title(title)
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+def plot_val_preds(val_preds, val_labels, colors, title='Histogram of Validation Predictions', save=True):
+    plt.figure(figsize=(10, 6))
+    plt.hist(val_labels, bins=20, alpha=0.5, label='True Values', color=colors['green'],)
+
+    cmap = cm.get_cmap('coolwarm', len(val_preds))  # Use 'coolwarm' colormap for gradient from red to blue
+    for epoch, preds in val_preds.items():
+        color = cmap(len(val_preds) - epoch )  # Get color from colormap
+        plt.hist(preds, bins=20, alpha=0.5, label=f'Epoch {epoch}', color=color)
+
+    plt.xlabel('Predicted Values')
+    plt.ylabel('Frequency')
+    plt.title(title)
+    plt.legend()
+    plt.grid(axis='y', linestyle='--', alpha=0.7)
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+
+####################################################################################################
+############### Comparison Plots ###################################################################
+####################################################################################################
+