train_docker.py

"""Regenerate ML artifacts inside Docker to ensure pickle compatibility."""

import numpy as np
import pandas as pd
import json
import joblib
import os
import sys
import warnings

warnings.filterwarnings("ignore")
np.random.seed(42)

_script_dir = os.path.dirname(os.path.abspath(__file__)) if "__file__" in dir() else os.getcwd()
sys.path.insert(0, _script_dir)
from app.services.generators import GENERATORS
from app.services.feature_engine import engineer_features
from app.config import FEATURE_COLS, START_DATE, DAYS

from sklearn.preprocessing import RobustScaler
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.metrics import silhouette_score
from xgboost import XGBClassifier
import umap

ARTIFACTS_DIR = os.path.join(_script_dir, "app", "artifacts")
os.makedirs(ARTIFACTS_DIR, exist_ok=True)

# 1. Generate dataset
print("Generating dataset...")
counts = {
    "normal_salaried_employee": 600, "normal_freelancer": 350, "normal_student": 450,
    "normal_retiree": 350, "normal_small_business": 300, "normal_high_net_worth": 200,
    "normal_young_professional": 400, "normal_family_household": 350,
    "mule_rapid_passthrough": 130, "mule_structuring_smurfing": 100,
    "mule_funnel_collector": 90, "mule_dormant_burst": 110,
    "mule_recruit_escalation": 120, "mule_round_trip": 100,
    "mule_crypto_cashout": 120, "mule_layering_chain": 110,
    "mule_micro_structuring": 130, "mule_ghost_payroll": 140,
    "mule_onboarding_burst": 120, "mule_device_mule": 110,
}
all_records = []
for btype, count in counts.items():
    print(f"  {btype}: {count}")
    all_records += GENERATORS[btype](count)

df = pd.DataFrame(all_records)
df["timestamp"] = pd.to_datetime(df["timestamp"])
df = df.sort_values("timestamp").reset_index(drop=True)
df["day_of_week"] = df["timestamp"].dt.dayofweek
df["hour"] = df["timestamp"].dt.hour
df["is_weekend"] = df["day_of_week"].isin([5, 6]).astype(int)
df["category"] = df["label"].apply(lambda x: "mule" if x.startswith("mule_") else "normal")
print(f"  Total: {len(df):,} txns, {df['account_id'].nunique():,} accounts")

# 2. Feature engineering
print("Engineering features...")
features_df = engineer_features(df)
label_cat = df.groupby("account_id").agg(label=("label", "first"), category=("category", "first"))
features_df = features_df.join(label_cat)
feature_cols = [c for c in FEATURE_COLS if c in features_df.columns]
X = features_df[feature_cols].fillna(0).values

# 3. Scaler
print("Fitting scaler...")
scaler = RobustScaler()
X_scaled = scaler.fit_transform(X)
X_scaled = np.nan_to_num(X_scaled, nan=0.0, posinf=0.0, neginf=0.0)
joblib.dump(scaler, os.path.join(ARTIFACTS_DIR, "scaler.joblib"))

# 4. PCA
print("Fitting PCA...")
pca2 = PCA(n_components=2)
pca2.fit(X_scaled)
joblib.dump(pca2, os.path.join(ARTIFACTS_DIR, "pca2.joblib"))

# 5. UMAP
print("Fitting UMAP...")
reducer = umap.UMAP(n_components=2, n_neighbors=30, min_dist=0.3, random_state=42)
X_umap = reducer.fit_transform(X_scaled)
joblib.dump(reducer, os.path.join(ARTIFACTS_DIR, "umap_reducer.joblib"))

# 6. KMeans
print("Fitting KMeans...")
K_range = range(2, 16)
sil_scores = []
for k in K_range:
    km = KMeans(n_clusters=k, n_init=10, random_state=42)
    labs = km.fit_predict(X_scaled)
    sil_scores.append(silhouette_score(X_scaled, labs))
best_k = list(K_range)[np.argmax(sil_scores)]
print(f"  Best k = {best_k}")
kmeans = KMeans(n_clusters=best_k, n_init=10, random_state=42)
kmeans.fit(X_scaled)
joblib.dump(kmeans, os.path.join(ARTIFACTS_DIR, "kmeans.joblib"))

# 7. XGBoost classifier
print("Training XGBoost classifier...")
y_binary = (features_df["category"] == "mule").astype(int).values
classifier = XGBClassifier(
    n_estimators=300, max_depth=5, learning_rate=0.1,
    subsample=0.8, colsample_bytree=0.8,
    scale_pos_weight=sum(y_binary == 0) / max(sum(y_binary == 1), 1),
    random_state=42, use_label_encoder=False, eval_metric="logloss",
)
classifier.fit(X, y_binary)
print(f"  Train accuracy: {classifier.score(X, y_binary):.3f}")
joblib.dump(classifier, os.path.join(ARTIFACTS_DIR, "classifier.joblib"))
joblib.dump(classifier, os.path.join(ARTIFACTS_DIR, "surrogate_model.joblib"))

bg_indices = np.random.RandomState(42).choice(len(X), size=min(200, len(X)), replace=False)
np.save(os.path.join(ARTIFACTS_DIR, "shap_background.npy"), X[bg_indices])

# 8. Cluster metadata
print("Computing metadata...")
cluster_labels = kmeans.predict(X_scaled)
features_df["cluster"] = cluster_labels
features_df["umap_1"] = X_umap[:, 0]
features_df["umap_2"] = X_umap[:, 1]

normal_mask = features_df["category"] == "normal"
normal_centroid = X_scaled[normal_mask.values].mean(axis=0).tolist()
normal_distances = np.linalg.norm(X_scaled[normal_mask.values] - np.array(normal_centroid), axis=1)
max_normal_distance = float(np.percentile(normal_distances, 95))

clusters_meta = {}
for c in range(best_k):
    c_mask = features_df["cluster"] == c
    c_data = features_df[c_mask]
    mule_pct = float((c_data["category"] == "mule").mean())
    clusters_meta[str(c)] = {"size": int(c_mask.sum()), "mule_pct": round(mule_pct, 4),
                              "dominant": "mule" if mule_pct > 0.5 else "normal"}

cluster_metadata = {"best_k": best_k, "clusters": clusters_meta,
                    "normal_centroid_scaled": normal_centroid,
                    "max_normal_distance": max_normal_distance, "feature_cols": feature_cols}
with open(os.path.join(ARTIFACTS_DIR, "cluster_metadata.json"), "w") as f:
    json.dump(cluster_metadata, f, indent=2)

# 9. Baseline
normal_features = features_df[normal_mask][feature_cols]
baseline = {"means": normal_features.mean().to_dict(), "stds": normal_features.std().fillna(0).to_dict(),
            "mins": features_df[feature_cols].min().to_dict(), "maxs": features_df[feature_cols].max().to_dict()}
for key in baseline:
    baseline[key] = {k: float(v) for k, v in baseline[key].items()}
with open(os.path.join(ARTIFACTS_DIR, "baseline_features.json"), "w") as f:
    json.dump(baseline, f, indent=2)

# 10. UMAP coordinates
umap_points = [{"x": round(float(row["umap_1"]), 4), "y": round(float(row["umap_2"]), 4),
                "category": row["category"], "label": row["label"]}
               for _, row in features_df.iterrows()]
with open(os.path.join(ARTIFACTS_DIR, "existing_umap_coords.json"), "w") as f:
    json.dump(umap_points, f)

# 11. Transactions CSV
print("Saving CSV...")
df.to_csv(os.path.join(ARTIFACTS_DIR, "synthetic_transactions.csv"), index=False)

print("Done!")