Update utils.py

utils.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import numpy as np
+from itertools import combinations
+
+
+def generate_features(df, candidate_numbers=None):
+    """
+    Generate simple frequency/recency features for each number 1–50
+    """
+    numbers = list(range(1, 51)) if candidate_numbers is None else candidate_numbers
+    draws = df[["n1", "n2", "n3", "n4", "n5", "n6"]].values.tolist()
+
+    features = []
+    labels = []
+
+    # Flatten draws
+    for idx, row in df.iterrows():
+        draw_nums = set(row[["n1", "n2", "n3", "n4", "n5", "n6"]].tolist())
+        for n in numbers:
+            # Label: 1 if number in this draw, else 0
+            label = 1 if n in draw_nums else 0
+            labels.append(label)
+
+            # Features
+            last_seen = -1
+            for back, prev in enumerate(draws[:idx][::-1], 1):
+                if n in prev:
+                    last_seen = back
+                    break
+
+            freq_50 = sum([1 for prev in draws[max(0, idx-50):idx] if n in prev])
+            freq_200 = sum([1 for prev in draws[max(0, idx-200):idx] if n in prev])
+
+            features.append([n, freq_50, freq_200, last_seen if last_seen != -1 else 999])
+
+    X = pd.DataFrame(features, columns=["number", "freq50", "freq200", "recency"])
+    y = np.array(labels)
+    return X, y
+
+
+def pick_top15(all_numbers_df):
+    """
+    Return top 15 numbers sorted by score
+    """
+    top15 = all_numbers_df.sort_values("score", ascending=False).head(15)["number"].tolist()
+    return sorted(top15)
+
+
+def generate_system15_csv(top15, output_path="system15.csv"):
+    """
+    Generate all 5005 combinations of 15 numbers into a CSV
+    """
+    combos = list(combinations(top15, 6))
+    df = pd.DataFrame(combos, columns=[f"n{i}" for i in range(1, 7)])
+    df.to_csv(output_path, index=False)
+    return output_path