Initial commit

cli/output.py

@@ -0,0 +1,79 @@
+import code
+
+import pandas as pd
+from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score
+
+
+def explore(df):
+    print("The dataset is available as `df`. Press Ctrl-D or type exit() to quit.\n")
+    code.interact(local={"df": df, "pd": pd}, banner="")
+
+
+def dataset_summary(df, n_train, n_test):
+    print("=" * 60)
+    print("DATASET")
+    print("=" * 60)
+    counts = df["label"].value_counts()
+    total = n_train + n_test
+    print(f"\n  Total messages: {len(df)}")
+    for label, n in counts.items():
+        print(f"  {label:4s}: {n:5d}  ({100 * n / len(df):.1f}%)")
+    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()
+    print(f"{'':12s} {'precision':>10} {'recall':>10} {'f1':>10}")
+    for label in ["ham", "spam"]:
+        p = precision_score(y_true, y_pred, pos_label=label, zero_division=0)
+        r = recall_score(y_true, y_pred, pos_label=label, zero_division=0)
+        f = f1_score(y_true, y_pred, pos_label=label, zero_division=0)
+        print(f"  {label:<10} {p:>10.3f} {r:>10.3f} {f:>10.3f}")
+    avg_f1 = f1_score(y_true, y_pred, average="macro", zero_division=0)
+    print(f"\n  {'average f1':10} {'':>10} {'':>10} {avg_f1:>10.3f}")
+    print()
+
+    cm = confusion_matrix(y_true, y_pred, labels=["ham", "spam"])
+    print("Confusion matrix:")
+    print(f"{'':18s} {'pred ham':>10} {'pred spam':>10}")
+    print(f"{'actual ham':18s} {cm[0][0]:>10} {cm[0][1]:>10}")
+    print(f"{'actual spam':18s} {cm[1][0]:>10} {cm[1][1]:>10}")
+    print()
+
+
+def feature_weights(clf, top_n=10):
+    if not hasattr(clf, "feature_weights"):
+        return
+    weights = clf.feature_weights(top_n=top_n)
+    if not weights:
+        return
+    print("=" * 60)
+    print(f"TOP {len(weights)} FEATURES BY WEIGHT")
+    print("=" * 60)
+    for name, w in weights:
+        direction = "spam" if w > 0 else "ham "
+        bar_len = min(int(abs(w) * 5), 25)
+        bar = ("+" if w > 0 else "-") * bar_len
+        print(f"  {name:<28} {w:+.3f}  → {direction}  {bar}")
+    print()
+
+
+def error_analysis(X, y_true, y_pred, n):
+    errors = [
+        (x, t, p)
+        for x, 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 msg, true_label, pred_label in shown:
+        display = msg if len(msg) <= 80 else msg[:77] + "..."
+        print(f"\n  true={true_label:<4}  pred={pred_label}")
+        print(f"  {display}")
+    print()