Initial commit

cli/output.py

@@ -0,0 +1,71 @@
+import numpy as np
+from sklearn.metrics import accuracy_score, confusion_matrix, f1_score
+
+
+def show_digit(pixels):
+    img = pixels.reshape(28, 28)
+    for row in img:
+        print(" ".join("#" if p > 0.5 else "." for p in row))
+
+
+def explore(X_train, y_train):
+    print("=" * 60)
+    print("SAMPLE DIGITS")
+    print("=" * 60)
+    for i in range(3):
+        print(f"\nLabel: {y_train[i]}")
+        show_digit(X_train[i])
+
+    unique, counts = np.unique(y_train, return_counts=True)
+    print()
+    print("=" * 60)
+    print("LABEL DISTRIBUTION")
+    print("=" * 60)
+    for digit, count in zip(unique, counts):
+        print(f"  {digit}: {count:5d}  ({100 * count / len(y_train):.1f}%)")
+    print()
+
+
+def dataset_summary(n_train, n_test):
+    print("=" * 60)
+    print("DATASET")
+    print("=" * 60)
+    total = n_train + n_test
+    print(f"\n  train: {n_train}  ({100 * n_train / total:.0f}%)   test: {n_test}  ({100 * n_test / total:.0f}%)")
+    print()
+
+
+def evaluation(y_true, y_pred, clf_name):
+    print("=" * 60)
+    print(f"RESULTS: {clf_name}")
+    print("=" * 60)
+    print()
+    accuracy = accuracy_score(y_true, y_pred)
+    avg_f1 = f1_score(y_true, y_pred, average="macro")
+    print(f"  accuracy                              {accuracy:>10.3f}")
+    print(f"  average f1                            {avg_f1:>10.3f}")
+    print()
+
+    cm = confusion_matrix(y_true, y_pred, labels=list(range(10)))
+    per_digit = cm.diagonal() / cm.sum(axis=1)
+    print("Per-digit accuracy:")
+    for digit, acc in enumerate(per_digit):
+        bar = "+" * int(acc * 30)
+        print(f"  {digit}: {acc:.3f}  {bar}")
+    print()
+
+
+def error_analysis(X, y_true, y_pred, n):
+    errors = [
+        (pixels, t, p)
+        for pixels, t, p in zip(X, y_true, y_pred)
+        if t != p
+    ]
+    shown = errors[:n]
+    print("=" * 60)
+    print(f"ERROR ANALYSIS ({len(shown)} of {len(errors)} misclassified)")
+    print("=" * 60)
+    for pixels, true_label, pred_label in shown:
+        print(f"\n  true={true_label}  pred={pred_label}")
+        show_digit(pixels)
+    print()