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