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228add1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """
Visualization module.
Generates training curves, confusion matrices, and per-class metric charts.
"""
from pathlib import Path
from typing import Optional
import numpy as np
import matplotlib
matplotlib.use('Agg') # Non-interactive backend
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
# Style defaults
plt.style.use('seaborn-v0_8-whitegrid')
COLORS = {
'train': '#2196F3',
'val': '#FF5722',
'primary': '#4CAF50',
'secondary': '#FFC107',
}
def plot_training_curves(
history: dict,
save_path: str | Path = None,
title: str = 'Training Curves',
figsize: tuple = (14, 5),
) -> None:
"""Plot loss and accuracy curves for training and validation."""
fig, axes = plt.subplots(1, 2, figsize=figsize)
epochs = range(1, len(history['train_loss']) + 1)
# Loss
axes[0].plot(epochs, history['train_loss'], color=COLORS['train'], label='Train', linewidth=2)
axes[0].plot(epochs, history['val_loss'], color=COLORS['val'], label='Validation', linewidth=2)
axes[0].set_title(f'{title} - Loss', fontsize=13, fontweight='bold')
axes[0].set_xlabel('Epoch')
axes[0].set_ylabel('Loss')
axes[0].legend()
axes[0].grid(True, alpha=0.3)
# Accuracy
axes[1].plot(epochs, history['train_accuracy'], color=COLORS['train'], label='Train', linewidth=2)
axes[1].plot(epochs, history['val_accuracy'], color=COLORS['val'], label='Validation', linewidth=2)
axes[1].set_title(f'{title} - Accuracy', fontsize=13, fontweight='bold')
axes[1].set_xlabel('Epoch')
axes[1].set_ylabel('Accuracy')
axes[1].legend()
axes[1].grid(True, alpha=0.3)
axes[1].yaxis.set_major_formatter(mticker.PercentFormatter(1.0))
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
print(f" Saved training curves to {save_path}")
plt.close()
def plot_confusion_matrix(
y_true: list[int],
y_pred: list[int],
class_names: list[str],
save_path: str | Path = None,
title: str = 'Confusion Matrix',
figsize: tuple = None,
normalize: bool = True,
) -> None:
"""Plot confusion matrix heatmap."""
from sklearn.metrics import confusion_matrix as sk_cm
cm = sk_cm(y_true, y_pred)
n_classes = len(class_names)
if figsize is None:
size = max(10, n_classes * 0.5)
figsize = (size, size)
if normalize:
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
cm_display = cm_normalized
fmt = '.2f'
else:
cm_display = cm
fmt = 'd'
fig, ax = plt.subplots(figsize=figsize)
sns.heatmap(
cm_display,
annot=True,
fmt=fmt,
cmap='Blues',
xticklabels=class_names,
yticklabels=class_names,
ax=ax,
linewidths=0.5,
cbar_kws={'shrink': 0.8},
)
ax.set_title(title, fontsize=14, fontweight='bold', pad=15)
ax.set_xlabel('Predicted', fontsize=12)
ax.set_ylabel('True', fontsize=12)
# Rotate labels
plt.xticks(rotation=45, ha='right', fontsize=8)
plt.yticks(rotation=0, fontsize=8)
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
print(f" Saved confusion matrix to {save_path}")
plt.close()
def plot_per_class_f1(
per_class_metrics: dict,
class_names: list[str],
save_path: str | Path = None,
title: str = 'Per-Class F1 Score',
figsize: tuple = None,
) -> None:
"""Plot horizontal bar chart of per-class F1 scores."""
f1_scores = []
names = []
for name in class_names:
if name in per_class_metrics:
f1_scores.append(per_class_metrics[name]['f1'])
names.append(name)
# Sort by F1 score
sorted_pairs = sorted(zip(names, f1_scores), key=lambda x: x[1])
names, f1_scores = zip(*sorted_pairs) if sorted_pairs else ([], [])
n = len(names)
if figsize is None:
figsize = (10, max(6, n * 0.35))
fig, ax = plt.subplots(figsize=figsize)
colors = [plt.cm.RdYlGn(score) for score in f1_scores]
bars = ax.barh(range(n), f1_scores, color=colors, edgecolor='white', linewidth=0.5)
ax.set_yticks(range(n))
ax.set_yticklabels(names, fontsize=9)
ax.set_xlabel('F1 Score', fontsize=11)
ax.set_title(title, fontsize=13, fontweight='bold')
ax.set_xlim(0, 1.05)
# Add value labels
for bar, score in zip(bars, f1_scores):
ax.text(bar.get_width() + 0.01, bar.get_y() + bar.get_height() / 2,
f'{score:.2f}', va='center', fontsize=8)
ax.axvline(x=np.mean(list(f1_scores)), color='red', linestyle='--',
alpha=0.7, label=f'Mean: {np.mean(list(f1_scores)):.2f}')
ax.legend(fontsize=10)
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
print(f" Saved per-class F1 chart to {save_path}")
plt.close()
def plot_sample_predictions(
images: list,
true_labels: list[str],
pred_labels: list[str],
confidences: list[float],
save_path: str | Path = None,
title: str = 'Sample Predictions',
n_samples: int = 16,
) -> None:
"""Plot grid of sample predictions with true vs predicted labels."""
n = min(n_samples, len(images))
cols = 4
rows = (n + cols - 1) // cols
fig, axes = plt.subplots(rows, cols, figsize=(cols * 3.5, rows * 3.5))
if rows == 1:
axes = [axes]
axes = [ax for row in axes for ax in (row if hasattr(row, '__iter__') else [row])]
for i in range(n):
ax = axes[i]
img = images[i]
if hasattr(img, 'numpy'):
img = img.numpy()
if img.shape[0] == 3:
img = np.transpose(img, (1, 2, 0))
img = np.clip(img, 0, 1)
ax.imshow(img)
correct = true_labels[i] == pred_labels[i]
color = 'green' if correct else 'red'
ax.set_title(
f"True: {true_labels[i]}\nPred: {pred_labels[i]} ({confidences[i]:.0%})",
fontsize=8, color=color, fontweight='bold',
)
ax.axis('off')
for i in range(n, len(axes)):
axes[i].axis('off')
fig.suptitle(title, fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
if save_path:
Path(save_path).parent.mkdir(parents=True, exist_ok=True)
plt.savefig(save_path, dpi=150, bbox_inches='tight')
plt.close()
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