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README.md

@@ -1,14 +1,52 @@
 ---
 title: Sequential Fraud Detection
-emoji: 👁
+emoji: 🛡️
 colorFrom: indigo
 colorTo: gray
 sdk: gradio
 sdk_version: 6.22.0
-python_version: '3.13'
 app_file: app.py
-pinned: false
+pinned: true
 license: mit
+short_description: A GRU scoring card transaction sequences, live
+tags:
+  - fraud-detection
+  - pytorch
+  - deep-learning
+  - fintech
+  - gru
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sequential Fraud Detection
+
+Most credit card fraud models score each transaction in isolation. Fraud is a behavioural
+signal: what matters is that *this card* has never behaved this way before.
+
+This demo runs a GRU over each card's last 10 transactions and compares it against a
+LightGBM model given the identical features without sequence context. On a held out time
+period of 555,719 transactions the GRU reaches **0.965 PR-AUC** against **0.899**, and at the
+cost minimising threshold it misses **31** of 2,145 frauds where LightGBM misses **112**.
+
+**Every score in the first tab is a real PyTorch forward pass**, computed when you click.
+Nothing is looked up.
+
+## The two tabs
+
+**Score a transaction.** Pick a scenario, including the interesting one where the sequence
+model catches fraud the flat model misses, and see the card's recent history alongside both
+models' verdicts.
+
+**Cost explorer.** A missed fraud costs the transaction amount; a false positive costs a
+manual review. Move the review cost and watch the optimal threshold move with it. The
+decision threshold is a business parameter, not a modelling constant.
+
+## Limits worth stating
+
+The data is simulated (Sparkov via Kaggle). Rule generated fraud is far more learnable than
+the adversarial kind, so 0.965 PR-AUC is not a production number; the relative comparison
+between models is the meaningful output. The cost minimising threshold is also selected on
+the test set, which makes the dollar figures optimistic.
+
+Trained on CPU: 22,577 parameters, 4 epochs, 7.1 minutes on a laptop with no GPU.
+
+[Full code and methodology on GitHub](https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection)

app.py

@@ -0,0 +1,556 @@
+"""
+Sequential Fraud Detection, live demo.
+
+Two things happen here that a static repository cannot show:
+
+  1. The GRU actually runs. Every score on the first tab is a real forward pass
+     over that card's last 10 transactions, computed when you click.
+  2. The cost model is interactive. The decision threshold is not a modelling
+     constant, it is a business parameter, and the second tab lets you move the
+     inputs and watch the optimum move with them.
+
+Repository: https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import gradio as gr
+import lightgbm as lgb
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import torch
+
+from model import GRUFraudModel
+
+ASSETS = Path(__file__).parent / "assets"
+
+# --------------------------------------------------------------------- loading
+
+with open(ASSETS / "config.json", encoding="utf-8") as f:
+    CFG = json.load(f)
+
+SEQ_LEN = CFG["seq_len"]
+N_NUMERIC = CFG["n_numeric"]
+N_FEAT = N_NUMERIC - 1
+THR_GRU = CFG["threshold_gru"]
+THR_LGB = CFG["threshold_lgb"]
+
+_w = np.load(ASSETS / "demo_windows.npz")
+WIN_NUM = _w["win_num"]
+WIN_CAT = _w["win_cat"]
+
+META = pd.read_csv(ASSETS / "demo_meta.csv")
+DISPLAY = pd.read_csv(ASSETS / "demo_display.csv")
+
+_s = np.load(ASSETS / "test_scores.npz")
+S_GRU = _s["gru"].astype(np.float64)
+S_LGB = _s["lgb"].astype(np.float64)
+Y_TEST = _s["y"].astype(np.float64)
+AMT_TEST = _s["amt"].astype(np.float64)
+
+N_TEST = len(Y_TEST)
+N_FRAUD = int(Y_TEST.sum())
+TOTAL_FRAUD_AMT = float((Y_TEST * AMT_TEST).sum())
+
+GRU = GRUFraudModel(n_numeric=N_NUMERIC, n_categories=CFG["n_categories"])
+GRU.load_state_dict(torch.load(ASSETS / "gru.pth", map_location="cpu", weights_only=True))
+GRU.eval()
+torch.set_num_threads(2)
+
+BOOSTER = lgb.Booster(model_file=str(ASSETS / "lightgbm.txt"))
+
+RNG = np.random.default_rng()
+
+
+# ------------------------------------------------------------------ inference
+
+
+def score_gru(sample_id: int) -> float:
+    """A genuine forward pass over the card's last 10 transactions."""
+    with torch.no_grad():
+        logit = GRU(
+            torch.from_numpy(WIN_NUM[sample_id : sample_id + 1]),
+            torch.from_numpy(WIN_CAT[sample_id : sample_id + 1]),
+        )
+        return float(torch.sigmoid(logit).item())
+
+
+def score_lgb(sample_id: int) -> float:
+    """LightGBM sees only the final timestep: no sequence, same features."""
+    flat = np.concatenate(
+        [
+            WIN_NUM[sample_id, -1, :N_FEAT].astype(np.float64),
+            [float(WIN_CAT[sample_id, -1])],
+        ]
+    ).reshape(1, -1)
+    return float(BOOSTER.predict(flat)[0])
+
+
+# ----------------------------------------------------------- cost machinery
+
+
+def _cumulative(scores: np.ndarray) -> dict:
+    order = np.argsort(-scores, kind="stable")
+    ys = Y_TEST[order]
+    return {
+        "s_desc": scores[order],
+        "tp": np.concatenate([[0.0], np.cumsum(ys)]),
+        "famt": np.concatenate([[0.0], np.cumsum(ys * AMT_TEST[order])]),
+    }
+
+
+CUM = {"GRU": _cumulative(S_GRU), "LightGBM": _cumulative(S_LGB)}
+K_AXIS = np.arange(N_TEST + 1, dtype=np.float64)
+
+
+def cost_curve(model: str, review_cost: float) -> np.ndarray:
+    c = CUM[model]
+    return review_cost * (K_AXIS - c["tp"]) + (TOTAL_FRAUD_AMT - c["famt"])
+
+
+def optimal_threshold(model: str, review_cost: float) -> tuple[float, float]:
+    curve = cost_curve(model, review_cost)
+    k = int(np.argmin(curve))
+    thr = 1.0 if k == 0 else float(CUM[model]["s_desc"][k - 1])
+    return thr, float(curve[k])
+
+
+def confusion_at(model: str, threshold: float, review_cost: float) -> dict:
+    c = CUM[model]
+    k = int(np.searchsorted(-c["s_desc"], -threshold, side="right"))
+    tp = float(c["tp"][k])
+    fp = k - tp
+    fn = N_FRAUD - tp
+    tn = N_TEST - k - fn
+    cost = review_cost * fp + (TOTAL_FRAUD_AMT - float(c["famt"][k]))
+    return {
+        "alerts": k,
+        "tp": int(tp),
+        "fp": int(fp),
+        "fn": int(fn),
+        "tn": int(tn),
+        "cost": cost,
+        "recall": tp / N_FRAUD if N_FRAUD else 0.0,
+        "precision": tp / k if k else 0.0,
+        "alert_rate": k / N_TEST,
+    }
+
+
+# ------------------------------------------------------------------- tab one
+
+
+def _history_table(sample_id: int) -> str:
+    rows = DISPLAY[DISPLAY.sample_id == sample_id].sort_values("position")
+    body = []
+    for _, r in rows.iterrows():
+        target = bool(r.is_target)
+        cls = "target" if target else ""
+        marker = "▶" if target else ""
+        gap = "first seen" if r.hours_since_prev >= 719 else f"{r.hours_since_prev:,.1f} h"
+        body.append(
+            f"<tr class='{cls}'>"
+            f"<td class='marker'>{marker}</td>"
+            f"<td>{r.timestamp}</td>"
+            f"<td class='num'>${r.amount:,.2f}</td>"
+            f"<td>{r.category}</td>"
+            f"<td class='num'>{r.distance_km:,.0f} km</td>"
+            f"<td class='num'>{gap}</td>"
+            f"<td class='num'>{r.amt_vs_card_mean:,.2f}x</td>"
+            f"</tr>"
+        )
+    return (
+        "<div class='tablewrap'><table class='hist'>"
+        "<thead><tr><th></th><th>Timestamp</th><th>Amount</th><th>Category</th>"
+        "<th>Distance</th><th>Since previous</th><th>vs card average</th></tr></thead>"
+        f"<tbody>{''.join(body)}</tbody></table></div>"
+        "<p class='caption'>The highlighted row is the transaction being scored. "
+        "The rows above it are the context the GRU reads.</p>"
+    )
+
+
+def _score_panel(name: str, prob: float, threshold: float, subtitle: str) -> str:
+    flagged = prob >= threshold
+    pct = prob * 100
+    tone = "flag" if flagged else "clear"
+    verdict = "FLAG FOR REVIEW" if flagged else "ALLOW"
+    return (
+        f"<div class='scorecard {tone}'>"
+        f"<div class='sc-name'>{name}</div>"
+        f"<div class='sc-sub'>{subtitle}</div>"
+        f"<div class='sc-prob'>{pct:.1f}<span>%</span></div>"
+        f"<div class='sc-bar'><div class='sc-fill' style='width:{min(pct, 100):.1f}%'></div>"
+        f"<div class='sc-thr' style='left:{threshold * 100:.1f}%'></div></div>"
+        f"<div class='sc-verdict'>{verdict}</div>"
+        f"<div class='sc-thrlab'>threshold {threshold:.3f}</div>"
+        f"</div>"
+    )
+
+
+def _outcome_banner(is_fraud: int, p_gru: float, p_lgb: float) -> str:
+    gru_flag = p_gru >= THR_GRU
+    lgb_flag = p_lgb >= THR_LGB
+    truth = "FRAUDULENT" if is_fraud else "LEGITIMATE"
+
+    if is_fraud and gru_flag and not lgb_flag:
+        tone, msg = "good", "The sequence model caught it. The flat model did not."
+    elif is_fraud and gru_flag:
+        tone, msg = "good", "Both models caught it."
+    elif is_fraud and not gru_flag:
+        tone, msg = "bad", "Both models missed this one. It is one of the 31 the GRU lets through."
+    elif not is_fraud and gru_flag:
+        tone, msg = "warn", "A false alarm. This costs a review, not a chargeback."
+    else:
+        tone, msg = "good", "Correctly cleared, with no analyst time spent."
+
+    return (
+        f"<div class='banner {tone}'>"
+        f"<span class='b-label'>Ground truth</span>"
+        f"<span class='b-truth'>{truth}</span>"
+        f"<span class='b-msg'>{msg}</span></div>"
+    )
+
+
+def load_case(scenario: str):
+    pool = META[META.scenario == scenario]
+    if pool.empty:
+        pool = META
+    row = pool.iloc[int(RNG.integers(len(pool)))]
+    sid = int(row.sample_id)
+
+    p_gru = score_gru(sid)
+    p_lgb = score_lgb(sid)
+
+    header = (
+        f"<div class='caseheader'>"
+        f"<div><span class='ch-label'>Card</span><span class='ch-val'>{row.card}</span></div>"
+        f"<div><span class='ch-label'>Transaction</span>"
+        f"<span class='ch-val'>${row.amount:,.2f}</span></div>"
+        f"<div><span class='ch-label'>History available</span>"
+        f"<span class='ch-val'>{int(row.history_length)} of {SEQ_LEN} steps</span></div>"
+        f"</div>"
+    )
+    panels = (
+        "<div class='panelrow'>"
+        + _score_panel("GRU", p_gru, THR_GRU, "reads the last 10 transactions")
+        + _score_panel("LightGBM", p_lgb, THR_LGB, "reads this transaction only")
+        + "</div>"
+    )
+    banner = _outcome_banner(int(row.is_fraud), p_gru, p_lgb)
+    return header, _history_table(sid), panels, banner
+
+
+# ------------------------------------------------------------------- tab two
+
+
+def _metric_tile(label: str, value: str, sub: str = "") -> str:
+    return (
+        f"<div class='tile'><div class='t-label'>{label}</div>"
+        f"<div class='t-value'>{value}</div><div class='t-sub'>{sub}</div></div>"
+    )
+
+
+def _style_fig(fig: go.Figure) -> go.Figure:
+    """
+    Transparent background and neutral greys.
+
+    A plotly_white template renders as a white rectangle on a dark page, which
+    looks broken. Letting the page show through, with axis text in a mid grey
+    that has contrast against both, works in either mode.
+    """
+    fig.update_layout(
+        paper_bgcolor="rgba(0,0,0,0)",
+        plot_bgcolor="rgba(0,0,0,0)",
+        font=dict(color="#94a3b8", size=12),
+        legend=dict(orientation="h", yanchor="bottom", y=1.0, x=0),
+        margin=dict(l=68, r=20, t=34, b=50),
+    )
+    grid = "rgba(148,163,184,0.20)"
+    line = "rgba(148,163,184,0.35)"
+    fig.update_xaxes(gridcolor=grid, zerolinecolor=line, linecolor=line)
+    fig.update_yaxes(gridcolor=grid, zerolinecolor=line, linecolor=line)
+    return fig
+
+
+def explore(review_cost: float, threshold: float):
+    gru = confusion_at("GRU", threshold, review_cost)
+    opt_thr, opt_cost = optimal_threshold("GRU", review_cost)
+    opt_thr_l, opt_cost_l = optimal_threshold("LightGBM", review_cost)
+    naive = confusion_at("GRU", 0.5, review_cost)
+
+    tiles = (
+        "<div class='tilehead'>GRU, at your chosen threshold</div>"
+        "<div class='tilerow'>"
+        + _metric_tile("Alerts raised", f"{gru['alerts']:,}",
+                       f"{gru['alert_rate'] * 100:.2f}% of transactions")
+        + _metric_tile("Fraud caught", f"{gru['tp']:,}",
+                       f"{gru['recall'] * 100:.1f}% recall")
+        + _metric_tile("Fraud missed", f"{gru['fn']:,}", "escaped review")
+        + _metric_tile("False alarms", f"{gru['fp']:,}",
+                       f"{gru['precision'] * 100:.1f}% precision")
+        + _metric_tile("Total cost", f"${gru['cost']:,.0f}",
+                       f"vs ${naive['cost']:,.0f} at threshold 0.5")
+        + "</div>"
+        + f"<div class='optnote'>At ${review_cost:,.2f} per review, each model's own cost "
+          f"minimising threshold is <b>{opt_thr:.3f}</b> for the GRU "
+          f"(<b>${opt_cost:,.0f}</b>) and <b>{opt_thr_l:.3f}</b> for LightGBM "
+          f"(<b>${opt_cost_l:,.0f}</b>). Sequence context is worth "
+          f"<b>${opt_cost_l - opt_cost:,.0f}</b>.</div>"
+    )
+
+    fig = go.Figure()
+    for name, colour in (("GRU", "#4f46e5"), ("LightGBM", "#f59e0b")):
+        curve = cost_curve(name, review_cost)
+        s = CUM[name]["s_desc"]
+        step = max(1, len(s) // 1500)
+        fig.add_trace(
+            go.Scatter(
+                x=s[::step], y=curve[1:][::step], mode="lines", name=name,
+                line=dict(color=colour, width=2),
+                hovertemplate="threshold %{x:.3f}<br>cost $%{y:,.0f}<extra></extra>",
+            )
+        )
+    fig.add_vline(x=threshold, line_dash="dash", line_color="#94a3b8",
+                  annotation_text="your threshold", annotation_position="top")
+    fig.add_trace(
+        go.Scatter(x=[opt_thr], y=[opt_cost], mode="markers", name="GRU optimum",
+                   marker=dict(color="#4f46e5", size=12, symbol="circle"),
+                   hovertemplate="optimum %{x:.3f}<br>$%{y:,.0f}<extra></extra>")
+    )
+    fig.update_layout(
+        yaxis_type="log",
+        xaxis_title="Decision threshold",
+        yaxis_title="Total cost, dollars (log scale)",
+        height=420,
+        hovermode="x unified",
+    )
+    # Decade ticks only. Plotly's default log minor ticks produce a cluttered
+    # axis of 2s and 3s next to the 100k labels.
+    fig.update_yaxes(dtick=1, tickprefix="$", tickformat="~s")
+    return tiles, _style_fig(fig)
+
+
+def snap_to_optimal(review_cost: float):
+    thr, _ = optimal_threshold("GRU", review_cost)
+    return thr
+
+
+# ----------------------------------------------------------------- tab three
+
+
+def pr_figure() -> go.Figure:
+    fig = go.Figure()
+    for name, colour in (("GRU", "#4f46e5"), ("LightGBM", "#f59e0b")):
+        c = CUM[name]
+        k = np.arange(1, N_TEST + 1)
+        recall = c["tp"][1:] / N_FRAUD
+        precision = c["tp"][1:] / k
+        step = max(1, N_TEST // 2000)
+        fig.add_trace(
+            go.Scatter(x=recall[::step], y=precision[::step], mode="lines", name=name,
+                       line=dict(color=colour, width=2)))
+    fig.add_hline(y=N_FRAUD / N_TEST, line_dash="dot", line_color="#94a3b8",
+                  annotation_text="random classifier")
+    fig.update_layout(
+        xaxis_title="Recall", yaxis_title="Precision",
+        yaxis_range=[0, 1.02], height=400,
+    )
+    return _style_fig(fig)
+
+
+M = CFG["metrics"]
+ABOUT = f"""
+### What this is
+
+Most credit card fraud models score each transaction in isolation. But fraud is a
+behavioural signal: what matters is that *this card* has never behaved this way before.
+This model builds a per card sequence of recent transactions and feeds it to a GRU.
+
+The comparison is against a LightGBM model given the **identical features** for the
+transaction being scored, but no sequence context. That isolates what the sequence adds.
+
+### Results on the held out time period
+
+{CFG['n_test']:,} transactions, {CFG['n_fraud']:,} fraudulent ({CFG['n_fraud'] / CFG['n_test'] * 100:.3f} percent).
+
+| Model | PR-AUC | ROC-AUC | Recall @ 0.1% | Recall @ 1% |
+|---|---|---|---|---|
+| **GRU (sequence)** | **{M['gru']['pr_auc']:.4f}** | {M['gru']['roc_auc']:.4f} | {M['gru']['recall_at_0.001']:.3f} | **{M['gru']['recall_at_0.01']:.3f}** |
+| LightGBM (no sequence) | {M['lightgbm']['pr_auc']:.4f} | {M['lightgbm']['roc_auc']:.4f} | {M['lightgbm']['recall_at_0.001']:.3f} | {M['lightgbm']['recall_at_0.01']:.3f} |
+| Logistic regression | {M['logreg']['pr_auc']:.4f} | {M['logreg']['roc_auc']:.4f} | {M['logreg']['recall_at_0.001']:.3f} | {M['logreg']['recall_at_0.01']:.3f} |
+
+Accuracy is deliberately absent. Predicting "legitimate" for every transaction scores
+**{CFG['trivial_accuracy'] * 100:.3f} percent** while catching nothing.
+
+### How it was built
+
+- **Chronological split by timestamp.** Every test row is strictly later than every training row.
+- **Causal features only.** A card's average spend is an expanding mean over prior rows, so a
+  transaction never contributes to its own baseline.
+- **Windows sliced on demand.** One flat array per split rather than materialising 1.29M
+  sequences, which keeps the whole thing inside 16 GB.
+- **Trained on CPU.** 22,577 parameters, 4 epochs, 7.1 minutes on a laptop with no GPU.
+
+### Honest limits
+
+The data is simulated (Sparkov), and rule generated fraud is far more learnable than the
+adversarial kind, so a PR-AUC of 0.965 is not a production number. The cost minimising
+threshold is also selected on the test set, which makes those dollar figures optimistic.
+Both points are covered in more detail in the repository.
+
+[Full code, methodology and limitations on GitHub](https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection)
+"""
+
+# Every colour below is either a Gradio theme variable or a translucent overlay,
+# so the demo reads correctly in both light and dark mode. Hardcoding light
+# greys here puts dark text on a dark background for anyone whose browser
+# prefers dark, which is most people.
+CSS = """
+.gradio-container { max-width: 1180px !important; }
+#hero { padding: 4px 0 2px 0; }
+#hero h1 { font-size: 2rem; font-weight: 700; margin: 0 0 6px 0; letter-spacing: -0.02em;
+  color: var(--body-text-color); }
+#hero p { color: var(--body-text-color-subdued); margin: 0; font-size: 1.02rem; }
+
+.caseheader { display:flex; gap:34px; padding:14px 18px;
+  background: var(--background-fill-secondary);
+  border:1px solid var(--border-color-primary); border-radius:10px;
+  margin-bottom:6px; flex-wrap:wrap; }
+.caseheader > div { display:flex; flex-direction:column; }
+.ch-label { font-size:0.7rem; text-transform:uppercase; letter-spacing:0.07em;
+  color: var(--body-text-color-subdued); }
+.ch-val { font-size:1.12rem; font-weight:650; color: var(--body-text-color); }
+
+.tablewrap { overflow-x:auto; border:1px solid var(--border-color-primary); border-radius:10px; }
+table.hist { width:100%; border-collapse:collapse; font-size:0.87rem; }
+table.hist th { background: var(--background-fill-secondary); text-align:left;
+  padding:9px 11px; font-weight:600; color: var(--body-text-color-subdued);
+  border-bottom:1px solid var(--border-color-primary); white-space:nowrap;
+  font-size:0.78rem; text-transform:uppercase; letter-spacing:0.04em; }
+table.hist td { padding:8px 11px; border-bottom:1px solid var(--border-color-primary);
+  color: var(--body-text-color); white-space:nowrap; }
+table.hist td.num { text-align:right; font-variant-numeric:tabular-nums; }
+table.hist td.marker { color:#818cf8; width:18px; }
+table.hist tr.target td { background: rgba(99,102,241,0.18); font-weight:700; }
+.caption { color: var(--body-text-color-subdued); font-size:0.8rem; margin:7px 2px 0 2px; }
+
+.panelrow { display:flex; gap:16px; flex-wrap:wrap; }
+.scorecard { flex:1; min-width:250px; border:1px solid var(--border-color-primary);
+  border-radius:12px; padding:16px 18px; background: var(--background-fill-primary); }
+.scorecard.flag { border-color: rgba(239,68,68,0.45); background: rgba(239,68,68,0.09); }
+.scorecard.clear { border-color: rgba(34,197,94,0.45); background: rgba(34,197,94,0.09); }
+.sc-name { font-size:1.02rem; font-weight:700; color: var(--body-text-color); }
+.sc-sub { font-size:0.78rem; color: var(--body-text-color-subdued); margin-bottom:10px; }
+.sc-prob { font-size:2.5rem; font-weight:700; line-height:1; color: var(--body-text-color);
+  font-variant-numeric:tabular-nums; }
+.sc-prob span { font-size:1.1rem; color: var(--body-text-color-subdued); margin-left:2px; }
+.sc-bar { position:relative; height:8px; background: rgba(148,163,184,0.30);
+  border-radius:4px; margin:12px 0 10px 0; }
+.sc-fill { position:absolute; height:100%; border-radius:4px; background:#6366f1; }
+.scorecard.flag .sc-fill { background:#ef4444; }
+.scorecard.clear .sc-fill { background:#22c55e; }
+.sc-thr { position:absolute; top:-3px; width:2px; height:14px; background: var(--body-text-color); }
+.sc-verdict { font-size:0.82rem; font-weight:700; letter-spacing:0.06em; }
+.scorecard.flag .sc-verdict { color:#ef4444; }
+.scorecard.clear .sc-verdict { color:#22c55e; }
+.sc-thrlab { font-size:0.72rem; color: var(--body-text-color-subdued); margin-top:3px; }
+
+.banner { display:flex; align-items:center; gap:14px; padding:13px 18px; border-radius:10px;
+  margin-top:4px; flex-wrap:wrap; }
+.banner.good { background: rgba(34,197,94,0.10); border:1px solid rgba(34,197,94,0.40); }
+.banner.bad { background: rgba(239,68,68,0.10); border:1px solid rgba(239,68,68,0.40); }
+.banner.warn { background: rgba(245,158,11,0.12); border:1px solid rgba(245,158,11,0.40); }
+.b-label { font-size:0.7rem; text-transform:uppercase; letter-spacing:0.07em;
+  color: var(--body-text-color-subdued); }
+.b-truth { font-weight:750; font-size:0.95rem; letter-spacing:0.03em;
+  color: var(--body-text-color); }
+.b-msg { color: var(--body-text-color); opacity:0.85; font-size:0.9rem; }
+
+.tilehead { font-size:0.72rem; text-transform:uppercase; letter-spacing:0.07em;
+  color: var(--body-text-color-subdued); margin:2px 2px 7px 2px; }
+.tilerow { display:flex; gap:12px; flex-wrap:wrap; margin-bottom:10px; }
+.tile { flex:1; min-width:150px; border:1px solid var(--border-color-primary);
+  border-radius:10px; padding:13px 15px; background: var(--background-fill-primary); }
+.t-label { font-size:0.7rem; text-transform:uppercase; letter-spacing:0.07em;
+  color: var(--body-text-color-subdued); }
+.t-value { font-size:1.6rem; font-weight:700; color: var(--body-text-color);
+  font-variant-numeric:tabular-nums; line-height:1.2; }
+.t-sub { font-size:0.76rem; color: var(--body-text-color-subdued); }
+.optnote { padding:12px 16px; background: rgba(99,102,241,0.12);
+  border:1px solid rgba(99,102,241,0.40); border-radius:10px;
+  color: var(--body-text-color); font-size:0.92rem; }
+"""
+
+THEME = gr.themes.Base(
+    primary_hue=gr.themes.colors.indigo,
+    secondary_hue=gr.themes.colors.slate,
+    neutral_hue=gr.themes.colors.slate,
+)
+
+with gr.Blocks(title="Sequential Fraud Detection") as demo:
+    gr.HTML(
+        "<div id='hero'><h1>Sequential Fraud Detection</h1>"
+        "<p>A GRU reads each card's last 10 transactions. A LightGBM baseline sees the same "
+        "features without the sequence. Every score below is computed live.</p></div>"
+    )
+
+    with gr.Tabs():
+        with gr.Tab("Score a transaction"):
+            with gr.Row():
+                scenario = gr.Dropdown(
+                    choices=CFG["scenario_order"],
+                    value=CFG["scenario_order"][0],
+                    label="Pick a scenario",
+                    scale=3,
+                )
+                shuffle = gr.Button("Load another case", variant="primary", scale=1)
+
+            case_header = gr.HTML()
+            history = gr.HTML()
+            panels = gr.HTML()
+            banner = gr.HTML()
+
+            scenario.change(load_case, scenario, [case_header, history, panels, banner])
+            shuffle.click(load_case, scenario, [case_header, history, panels, banner])
+            demo.load(load_case, scenario, [case_header, history, panels, banner])
+
+        with gr.Tab("Cost explorer"):
+            gr.Markdown(
+                "A missed fraud costs the full transaction amount. A false positive costs a "
+                "manual review. Move the inputs and watch where the optimum goes: as reviews "
+                "get more expensive, the threshold rises and you alert less."
+            )
+            with gr.Row():
+                review_cost = gr.Slider(1, 50, value=CFG["default_review_cost"], step=0.5,
+                                        label="Cost of one manual review ($)")
+                threshold = gr.Slider(0.0, 1.0, value=THR_GRU, step=0.001,
+                                      label="Decision threshold")
+            snap = gr.Button("Snap to the cost minimising threshold")
+
+            tiles = gr.HTML()
+            cost_plot = gr.Plot(show_label=False)
+
+            for control in (review_cost, threshold):
+                control.change(explore, [review_cost, threshold], [tiles, cost_plot])
+            snap.click(snap_to_optimal, review_cost, threshold)
+            demo.load(explore, [review_cost, threshold], [tiles, cost_plot])
+
+        with gr.Tab("How it works"):
+            gr.Markdown(ABOUT)
+            gr.Plot(pr_figure(), show_label=False)
+
+
+if __name__ == "__main__":
+    # Sanity check: live inference must reproduce the scores computed during
+    # the original evaluation run. A mismatch means the export is misaligned.
+    ids = META.sample_id.values[:25]
+    d_gru = max(abs(score_gru(int(i)) - float(META.score_gru[i])) for i in ids)
+    d_lgb = max(abs(score_lgb(int(i)) - float(META.score_lgb[i])) for i in ids)
+    print(f"[check] max score drift  GRU {d_gru:.2e}  LightGBM {d_lgb:.2e}")
+
+    # Gradio 6 takes theme and css at launch time rather than on the Blocks
+    # constructor. The Space pins this same version, so behaviour matches.
+    demo.launch(theme=THEME, css=CSS)

assets/config.json

@@ -0,0 +1,41 @@
+{
+  "seq_len": 10,
+  "n_numeric": 11,
+  "n_categories": 14,
+  "threshold_gru": 0.7138904333114624,
+  "threshold_lgb": 0.5880337498766687,
+  "default_review_cost": 5.0,
+  "n_test": 555719,
+  "n_fraud": 2145,
+  "total_fraud_amount": 1133324.75,
+  "trivial_accuracy": 0.9961401224136353,
+  "train_rows": 1296675,
+  "train_frauds": 7506,
+  "scenario_order": [
+    "Fraud caught by the GRU, missed by LightGBM",
+    "Fraud caught by both models",
+    "Fraud missed by the GRU",
+    "False alarm raised by the GRU",
+    "Legitimate, correctly cleared"
+  ],
+  "metrics": {
+    "gru": {
+      "pr_auc": 0.9652347549329161,
+      "roc_auc": 0.9992351169901056,
+      "recall_at_0.001": 0.2592074592074592,
+      "recall_at_0.01": 0.9892773892773893
+    },
+    "lightgbm": {
+      "pr_auc": 0.8985085792471785,
+      "roc_auc": 0.9974172780171616,
+      "recall_at_0.001": 0.25874125874125875,
+      "recall_at_0.01": 0.9501165501165502
+    },
+    "logreg": {
+      "pr_auc": 0.21899742390107182,
+      "roc_auc": 0.919594279505511,
+      "recall_at_0.001": 0.11701631701631701,
+      "recall_at_0.01": 0.5538461538461539
+    }
+  }
+}

assets/demo_meta.csv

@@ -0,0 +1,232 @@
+sample_id,scenario,card,is_fraud,score_gru,score_lgb,amount,history_length
+0,"Fraud caught by the GRU, missed by LightGBM",card ending 0731,1,0.9999901,0.58049184,7.15,10
+1,"Fraud caught by the GRU, missed by LightGBM",card ending 0710,1,0.99999774,0.07683075,7.23,10
+2,"Fraud caught by the GRU, missed by LightGBM",card ending 1577,1,0.83218426,0.5303817,7.83,10
+3,"Fraud caught by the GRU, missed by LightGBM",card ending 2529,1,0.99983704,0.2679808,9.03,10
+4,"Fraud caught by the GRU, missed by LightGBM",card ending 0168,1,0.99998915,0.46935344,9.1,10
+5,"Fraud caught by the GRU, missed by LightGBM",card ending 9669,1,0.9999827,0.23812468,7.74,10
+6,"Fraud caught by the GRU, missed by LightGBM",card ending 9817,1,0.99994063,0.003563515,26.34,10
+7,"Fraud caught by the GRU, missed by LightGBM",card ending 3035,1,0.9972844,0.2954881,801.38,10
+8,"Fraud caught by the GRU, missed by LightGBM",card ending 2988,1,0.9999976,0.0026371698,229.83,10
+9,"Fraud caught by the GRU, missed by LightGBM",card ending 0127,1,0.9999708,0.3038156,18.72,10
+10,"Fraud caught by the GRU, missed by LightGBM",card ending 0167,1,0.99998975,0.10531434,21.55,10
+11,"Fraud caught by the GRU, missed by LightGBM",card ending 6071,1,0.99999845,0.46532136,21.88,10
+12,"Fraud caught by the GRU, missed by LightGBM",card ending 2620,1,0.99944407,0.09026146,95.52,10
+13,"Fraud caught by the GRU, missed by LightGBM",card ending 3773,1,0.99996734,0.5627058,10.23,10
+14,"Fraud caught by the GRU, missed by LightGBM",card ending 3773,1,0.9999467,0.24042638,874.71,10
+15,"Fraud caught by the GRU, missed by LightGBM",card ending 9724,1,0.9999778,0.00018027819,219.46,10
+16,"Fraud caught by the GRU, missed by LightGBM",card ending 2311,1,0.9977331,0.19513614,107.46,10
+17,"Fraud caught by the GRU, missed by LightGBM",card ending 8828,1,0.9988404,0.06663319,21.19,10
+18,"Fraud caught by the GRU, missed by LightGBM",card ending 1404,1,0.9997193,0.32762414,137.61,10
+19,"Fraud caught by the GRU, missed by LightGBM",card ending 9816,1,0.9994796,0.31162152,16.69,10
+20,"Fraud caught by the GRU, missed by LightGBM",card ending 3698,1,0.9998939,0.43868282,22.55,10
+21,"Fraud caught by the GRU, missed by LightGBM",card ending 7959,1,0.94743615,0.053698935,5.6,10
+22,"Fraud caught by the GRU, missed by LightGBM",card ending 2095,1,0.99940634,0.0029671884,218.12,10
+23,"Fraud caught by the GRU, missed by LightGBM",card ending 0073,1,0.99999297,0.54631144,10.18,10
+24,"Fraud caught by the GRU, missed by LightGBM",card ending 9543,1,0.9994685,0.32986715,139.84,10
+25,"Fraud caught by the GRU, missed by LightGBM",card ending 3197,1,0.9999287,0.016789751,102.42,10
+26,"Fraud caught by the GRU, missed by LightGBM",card ending 9375,1,0.99993527,0.22732805,24.84,10
+27,"Fraud caught by the GRU, missed by LightGBM",card ending 9446,1,0.9999646,0.33600897,949.25,10
+28,"Fraud caught by the GRU, missed by LightGBM",card ending 8561,1,0.7230288,0.024998164,241.45,10
+29,"Fraud caught by the GRU, missed by LightGBM",card ending 8268,1,0.99992776,0.16690575,23.36,10
+30,"Fraud caught by the GRU, missed by LightGBM",card ending 8268,1,0.9998417,0.0011061538,232.25,10
+31,"Fraud caught by the GRU, missed by LightGBM",card ending 9806,1,0.9999981,0.56434464,6.67,10
+32,"Fraud caught by the GRU, missed by LightGBM",card ending 8423,1,0.9998785,0.0022294042,17.51,10
+33,"Fraud caught by the GRU, missed by LightGBM",card ending 3829,1,0.99996555,0.0075690732,216.01,10
+34,"Fraud caught by the GRU, missed by LightGBM",card ending 3829,1,0.99970335,0.021006443,234.54,10
+35,"Fraud caught by the GRU, missed by LightGBM",card ending 7162,1,0.9422511,0.0007981081,10.53,1
+36,"Fraud caught by the GRU, missed by LightGBM",card ending 9660,1,0.99995136,0.0044562295,16.75,10
+37,"Fraud caught by the GRU, missed by LightGBM",card ending 1180,1,0.99995935,0.2331846,9.94,10
+38,"Fraud caught by the GRU, missed by LightGBM",card ending 9750,1,0.92301565,0.11316425,1029.47,10
+39,"Fraud caught by the GRU, missed by LightGBM",card ending 0532,1,0.9999695,0.17258067,18.86,10
+40,"Fraud caught by the GRU, missed by LightGBM",card ending 0532,1,0.99971026,0.23307104,10.46,10
+41,"Fraud caught by the GRU, missed by LightGBM",card ending 8276,1,0.999998,0.011943006,236.42,10
+42,"Fraud caught by the GRU, missed by LightGBM",card ending 9997,1,0.99991167,0.256229,7.12,10
+43,"Fraud caught by the GRU, missed by LightGBM",card ending 5522,1,0.9998301,0.013598998,168.06,10
+44,"Fraud caught by the GRU, missed by LightGBM",card ending 2306,1,0.99999404,0.4513615,16.91,10
+45,"Fraud caught by the GRU, missed by LightGBM",card ending 4121,1,0.99993837,0.014097717,9.89,10
+46,"Fraud caught by the GRU, missed by LightGBM",card ending 9027,1,0.9993548,0.0077013858,52.82,10
+47,"Fraud caught by the GRU, missed by LightGBM",card ending 6016,1,0.99965966,0.43362147,101.04,10
+48,"Fraud caught by the GRU, missed by LightGBM",card ending 5730,1,0.99998176,0.3565554,9.65,10
+49,"Fraud caught by the GRU, missed by LightGBM",card ending 5369,1,0.9999312,0.58591455,21.11,10
+50,Fraud caught by both models,card ending 3109,1,0.9999857,0.99878836,8.45,10
+51,Fraud caught by both models,card ending 9986,1,0.9999591,0.99986035,315.39,10
+52,Fraud caught by both models,card ending 7795,1,0.99991953,0.9993055,988.01,10
+53,Fraud caught by both models,card ending 7795,1,0.99993825,0.9994888,859.65,10
+54,Fraud caught by both models,card ending 2597,1,0.99997354,0.9966072,916.04,10
+55,Fraud caught by both models,card ending 0710,1,0.99999964,0.999809,301.37,10
+56,Fraud caught by both models,card ending 5974,1,0.99999976,0.99975175,897.8,10
+57,Fraud caught by both models,card ending 3689,1,0.9959369,0.9997346,722.27,10
+58,Fraud caught by both models,card ending 3689,1,0.99996936,0.99966264,969.05,10
+59,Fraud caught by both models,card ending 8158,1,0.9998592,0.90191764,641.44,10
+60,Fraud caught by both models,card ending 5985,1,0.99981207,0.98782736,114.9,10
+61,Fraud caught by both models,card ending 0127,1,0.9919852,0.9975527,855.54,10
+62,Fraud caught by both models,card ending 7183,1,0.98905444,0.9995207,319.23,10
+63,Fraud caught by both models,card ending 7661,1,0.9999988,0.99849623,1009.4,10
+64,Fraud caught by both models,card ending 0192,1,0.9999728,0.9880377,780.45,10
+65,Fraud caught by both models,card ending 3109,1,0.9999682,0.999895,789.22,10
+66,Fraud caught by both models,card ending 6333,1,0.9999944,0.98842525,1019.51,10
+67,Fraud caught by both models,card ending 3058,1,0.99999857,0.99954045,950.19,10
+68,Fraud caught by both models,card ending 2684,1,0.9975304,0.9878894,1043.4,10
+69,Fraud caught by both models,card ending 2684,1,0.99853396,0.98807144,127.17,10
+70,Fraud caught by both models,card ending 8609,1,0.9999846,0.9974542,313.62,10
+71,Fraud caught by both models,card ending 6425,1,0.9999471,0.9995023,8.65,10
+72,Fraud caught by both models,card ending 6425,1,0.9999515,0.9998004,314.74,10
+73,Fraud caught by both models,card ending 6555,1,0.9999876,0.9994462,1024.42,10
+74,Fraud caught by both models,card ending 9497,1,0.999969,0.99967915,10.64,10
+75,Fraud caught by both models,card ending 9543,1,0.9998535,0.9995925,1111.35,10
+76,Fraud caught by both models,card ending 9330,1,0.99999595,0.9927563,250.32,10
+77,Fraud caught by both models,card ending 2582,1,0.99999297,0.99987465,312.5,10
+78,Fraud caught by both models,card ending 2582,1,0.99995697,0.9998308,306.79,10
+79,Fraud caught by both models,card ending 2582,1,0.9999763,0.9998508,332.24,10
+80,Fraud caught by both models,card ending 7350,1,0.99997413,0.9998084,727.32,10
+81,Fraud caught by both models,card ending 8089,1,0.999998,0.9997776,332.59,2
+82,Fraud caught by both models,card ending 5425,1,0.9999975,0.9998568,348.61,10
+83,Fraud caught by both models,card ending 2580,1,0.99994457,0.98760927,1003.86,10
+84,Fraud caught by both models,card ending 3890,1,0.999099,0.9928431,22.11,10
+85,Fraud caught by both models,card ending 8268,1,0.9999881,0.9938878,1001.99,10
+86,Fraud caught by both models,card ending 3829,1,0.9979564,0.9988224,829.36,10
+87,Fraud caught by both models,card ending 9716,1,0.9884985,0.99763703,739.85,1
+88,Fraud caught by both models,card ending 1412,1,0.99950457,0.998244,18.9,10
+89,Fraud caught by both models,card ending 1241,1,0.99998593,0.9998611,725.34,10
+90,Fraud caught by both models,card ending 0532,1,0.9999294,0.9979735,709.07,10
+91,Fraud caught by both models,card ending 6057,1,0.99999154,0.99513406,1034.66,10
+92,Fraud caught by both models,card ending 8276,1,0.9999776,0.9970941,214.51,10
+93,Fraud caught by both models,card ending 8648,1,0.999652,0.8172538,15.99,10
+94,Fraud caught by both models,card ending 4802,1,0.9994924,0.99964267,309.7,10
+95,Fraud caught by both models,card ending 1314,1,0.9999496,0.99935716,758.6,10
+96,Fraud caught by both models,card ending 2306,1,0.99999475,0.99945134,750.68,10
+97,Fraud caught by both models,card ending 2193,1,0.99993896,0.99701965,267.74,10
+98,Fraud caught by both models,card ending 9245,1,0.9998385,0.9600009,18.85,10
+99,Fraud caught by both models,card ending 6883,1,0.99975866,0.99970233,960.14,10
+100,Fraud missed by the GRU,card ending 2387,1,0.16554838,0.9301583,8.08,10
+101,Fraud missed by the GRU,card ending 2621,1,0.5525818,0.99771875,305.1,10
+102,Fraud missed by the GRU,card ending 2455,1,0.0011045086,0.007584502,19.02,10
+103,Fraud missed by the GRU,card ending 2455,1,0.0015910426,0.30770463,9.25,10
+104,Fraud missed by the GRU,card ending 2455,1,0.6191622,0.998649,1130.56,10
+105,Fraud missed by the GRU,card ending 7768,1,0.4106906,0.90742165,260.44,10
+106,Fraud missed by the GRU,card ending 3689,1,0.43455544,0.99966955,274.08,10
+107,Fraud missed by the GRU,card ending 9584,1,0.0056192502,0.03374157,8.49,10
+108,Fraud missed by the GRU,card ending 9584,1,0.26173532,0.99981177,710.45,10
+109,Fraud missed by the GRU,card ending 9584,1,0.1275148,0.9977829,48.95,10
+110,Fraud missed by the GRU,card ending 0127,1,0.030776095,0.6068254,133.43,10
+111,Fraud missed by the GRU,card ending 2290,1,0.6597321,0.9996876,283.58,10
+112,Fraud missed by the GRU,card ending 2311,1,0.00012275545,0.0031429573,50.59,10
+113,Fraud missed by the GRU,card ending 4065,1,0.18986869,0.9817645,837.53,10
+114,Fraud missed by the GRU,card ending 3386,1,0.172274,0.07801823,111.17,10
+115,Fraud missed by the GRU,card ending 0662,1,0.5255446,0.70071393,119.72,10
+116,Fraud missed by the GRU,card ending 7879,1,0.025860542,0.60389924,21.38,10
+117,Fraud missed by the GRU,card ending 6568,1,0.604364,0.36144868,140.13,10
+118,Fraud missed by the GRU,card ending 0341,1,0.09775736,0.01590944,46.36,10
+119,Fraud missed by the GRU,card ending 5374,1,0.0062118103,0.0067008287,21.09,10
+120,Fraud missed by the GRU,card ending 4931,1,0.00041542365,7.388653e-05,17.45,10
+121,Fraud missed by the GRU,card ending 6057,1,0.20173854,0.8969215,966.98,10
+122,Fraud missed by the GRU,card ending 7745,1,0.012767666,3.5899473e-05,5.26,10
+123,Fraud missed by the GRU,card ending 0186,1,0.061968446,0.67524207,19.05,10
+124,Fraud missed by the GRU,card ending 0186,1,0.038256023,0.95467687,149.42,10
+125,Fraud missed by the GRU,card ending 0186,1,0.0202298,0.7926477,8.41,10
+126,Fraud missed by the GRU,card ending 0186,1,0.013994445,0.9801244,17.77,10
+127,Fraud missed by the GRU,card ending 0915,1,0.06988248,0.835683,17.71,10
+128,Fraud missed by the GRU,card ending 0915,1,0.4762789,0.22455584,132.33,10
+129,Fraud missed by the GRU,card ending 9027,1,0.022901328,0.60879016,52.45,10
+130,Fraud missed by the GRU,card ending 6408,1,0.6043448,0.06975564,107.67,10
+131,False alarm raised by the GRU,card ending 6219,0,0.85642076,0.222954,21.71,10
+132,False alarm raised by the GRU,card ending 0592,0,0.9562068,0.9891122,15.62,10
+133,False alarm raised by the GRU,card ending 0168,0,0.99896085,0.000475615,21.17,10
+134,False alarm raised by the GRU,card ending 9857,0,0.71894515,0.23299862,8.36,10
+135,False alarm raised by the GRU,card ending 9857,0,0.76245147,1.9675595e-05,21.18,10
+136,False alarm raised by the GRU,card ending 1492,0,0.99582773,0.73138404,24.55,10
+137,False alarm raised by the GRU,card ending 3023,0,0.7675419,0.10415603,1380.66,10
+138,False alarm raised by the GRU,card ending 2315,0,0.9180552,0.000408209,156.99,10
+139,False alarm raised by the GRU,card ending 8427,0,0.996307,0.2102755,6.33,10
+140,False alarm raised by the GRU,card ending 5770,0,0.9905577,0.20583712,2.03,10
+141,False alarm raised by the GRU,card ending 9449,0,0.98763496,0.44082472,524.65,10
+142,False alarm raised by the GRU,card ending 0895,0,0.99955565,0.18312402,19.31,10
+143,False alarm raised by the GRU,card ending 7270,0,0.99845445,0.0012007295,53.68,10
+144,False alarm raised by the GRU,card ending 0336,0,0.96198565,0.0001142461,30.95,10
+145,False alarm raised by the GRU,card ending 4907,0,0.9772613,0.05348462,1722.76,10
+146,False alarm raised by the GRU,card ending 4333,0,0.99542135,0.0034557006,35.2,10
+147,False alarm raised by the GRU,card ending 2492,0,0.8759521,0.027181638,506.95,10
+148,False alarm raised by the GRU,card ending 3960,0,0.9686354,0.21529111,895.63,10
+149,False alarm raised by the GRU,card ending 1233,0,0.99328494,0.8937972,6.92,10
+150,False alarm raised by the GRU,card ending 4120,0,0.9995865,0.00012935966,84.55,10
+151,False alarm raised by the GRU,card ending 4120,0,0.9847552,0.00047943555,210.84,10
+152,False alarm raised by the GRU,card ending 9419,0,0.8189587,6.085334e-05,3.67,10
+153,False alarm raised by the GRU,card ending 3625,0,0.9877529,0.544954,7.68,10
+154,False alarm raised by the GRU,card ending 1552,0,0.90577024,1.9137733e-05,4.93,10
+155,False alarm raised by the GRU,card ending 9773,0,0.9941491,0.17576227,914.1,10
+156,False alarm raised by the GRU,card ending 3522,0,0.8205868,0.012814419,1012.41,10
+157,False alarm raised by the GRU,card ending 6548,0,0.9071076,0.05155802,376.85,10
+158,False alarm raised by the GRU,card ending 9494,0,0.72276217,0.0066942046,1232.74,10
+159,False alarm raised by the GRU,card ending 0035,0,0.7807672,0.00034983282,32.69,10
+160,False alarm raised by the GRU,card ending 4285,0,0.7865164,0.00022320634,172.9,10
+161,False alarm raised by the GRU,card ending 0341,0,0.94774354,0.0006213321,25.42,10
+162,False alarm raised by the GRU,card ending 5478,0,0.99317884,0.040101014,20.18,10
+163,False alarm raised by the GRU,card ending 5751,0,0.9223014,0.92138296,1855.84,10
+164,False alarm raised by the GRU,card ending 7671,0,0.9948413,0.051075343,2606.86,10
+165,False alarm raised by the GRU,card ending 4630,0,0.9174663,0.04582901,1310.91,10
+166,False alarm raised by the GRU,card ending 4931,0,0.8713996,0.022155289,1348.88,10
+167,False alarm raised by the GRU,card ending 6294,0,0.72085,0.9251436,2274.48,10
+168,False alarm raised by the GRU,card ending 6400,0,0.7788446,0.019208128,18.77,10
+169,False alarm raised by the GRU,card ending 9750,0,0.8714391,0.087344326,18.41,10
+170,False alarm raised by the GRU,card ending 8276,0,0.8704047,0.16692759,985.13,10
+171,False alarm raised by the GRU,card ending 5903,0,0.82258725,0.0012041178,5.45,10
+172,False alarm raised by the GRU,card ending 9997,0,0.7182004,0.7337843,11.11,10
+173,False alarm raised by the GRU,card ending 8160,0,0.7933024,0.00012148686,65.88,10
+174,False alarm raised by the GRU,card ending 4500,0,0.99545586,0.9975607,19.73,10
+175,False alarm raised by the GRU,card ending 8216,0,0.7510039,0.0001805572,43.94,10
+176,False alarm raised by the GRU,card ending 0186,0,0.727426,0.62168807,22.59,10
+177,False alarm raised by the GRU,card ending 5434,0,0.9461209,0.13520549,10.79,10
+178,False alarm raised by the GRU,card ending 1302,0,0.79255116,0.005438155,6.26,10
+179,False alarm raised by the GRU,card ending 2777,0,0.99631643,0.26869652,23.98,10
+180,False alarm raised by the GRU,card ending 9432,0,0.9905041,0.01538513,13.34,10
+181,"Legitimate, correctly cleared",card ending 6709,0,8.893039e-06,4.6896497e-05,101.21,10
+182,"Legitimate, correctly cleared",card ending 5126,0,4.690343e-05,1.6899228e-05,9.05,10
+183,"Legitimate, correctly cleared",card ending 7322,0,6.79495e-05,0.00012251438,51.55,10
+184,"Legitimate, correctly cleared",card ending 2499,0,1.1658574e-05,0.0041574873,23.49,10
+185,"Legitimate, correctly cleared",card ending 2499,0,1.8802855e-05,0.00016973798,91.8,10
+186,"Legitimate, correctly cleared",card ending 8703,0,0.008520837,0.003156836,11.22,10
+187,"Legitimate, correctly cleared",card ending 8794,0,0.00029792296,5.9022426e-07,3.94,10
+188,"Legitimate, correctly cleared",card ending 6504,0,0.00018796635,0.0029864553,1.7,10
+189,"Legitimate, correctly cleared",card ending 4871,0,0.0004225552,0.008189974,267.25,10
+190,"Legitimate, correctly cleared",card ending 5320,0,0.0009930147,0.0029659308,35.39,10
+191,"Legitimate, correctly cleared",card ending 8506,0,7.513256e-05,5.612957e-05,86.48,10
+192,"Legitimate, correctly cleared",card ending 9178,0,0.00018110925,8.3055365e-06,83.26,10
+193,"Legitimate, correctly cleared",card ending 6888,0,0.00020197514,0.00044528677,520.62,10
+194,"Legitimate, correctly cleared",card ending 7660,0,1.2356696e-05,3.6854017e-06,6.42,10
+195,"Legitimate, correctly cleared",card ending 5802,0,2.5458876e-05,3.687818e-05,60.47,10
+196,"Legitimate, correctly cleared",card ending 0030,0,0.0004964879,2.1163083e-05,76.16,10
+197,"Legitimate, correctly cleared",card ending 3780,0,6.929844e-06,3.1729007e-05,89.56,10
+198,"Legitimate, correctly cleared",card ending 2314,0,0.00823937,0.00092781946,37.16,10
+199,"Legitimate, correctly cleared",card ending 8499,0,2.368849e-06,0.00015747253,106.33,10
+200,"Legitimate, correctly cleared",card ending 6537,0,0.00012586365,0.0010625104,2.54,10
+201,"Legitimate, correctly cleared",card ending 1125,0,1.2372806e-06,6.0120063e-05,71.87,10
+202,"Legitimate, correctly cleared",card ending 7633,0,1.1300421e-05,1.4568015e-05,135.9,10
+203,"Legitimate, correctly cleared",card ending 3058,0,7.087911e-06,7.715945e-07,4.82,10
+204,"Legitimate, correctly cleared",card ending 1789,0,2.5901396e-05,1.5876813e-05,2.16,10
+205,"Legitimate, correctly cleared",card ending 6919,0,3.9364128e-05,0.00016834475,78.84,10
+206,"Legitimate, correctly cleared",card ending 7350,0,3.409065e-05,1.2461516e-05,15.34,10
+207,"Legitimate, correctly cleared",card ending 4278,0,0.0019649353,6.125634e-05,5.25,10
+208,"Legitimate, correctly cleared",card ending 5774,0,0.0005637887,9.8339355e-05,26.35,10
+209,"Legitimate, correctly cleared",card ending 4745,0,6.3677367e-06,0.00031404654,40.64,10
+210,"Legitimate, correctly cleared",card ending 3463,0,0.0018401808,2.4977902e-07,1.58,10
+211,"Legitimate, correctly cleared",card ending 4947,0,2.3110211e-05,0.0028097834,15.45,10
+212,"Legitimate, correctly cleared",card ending 5002,0,1.3803499e-05,5.791114e-05,86.63,10
+213,"Legitimate, correctly cleared",card ending 0341,0,8.223805e-06,0.00014266005,114.35,10
+214,"Legitimate, correctly cleared",card ending 6900,0,3.785761e-05,6.4106007e-06,4.17,10
+215,"Legitimate, correctly cleared",card ending 0989,0,0.00022232455,4.787023e-06,2.22,10
+216,"Legitimate, correctly cleared",card ending 5790,0,3.1901227e-06,1.8629911e-05,84.42,10
+217,"Legitimate, correctly cleared",card ending 9043,0,0.00062586844,0.001228773,11.7,10
+218,"Legitimate, correctly cleared",card ending 9010,0,8.922933e-06,0.00018945723,85.89,10
+219,"Legitimate, correctly cleared",card ending 5522,0,1.5236932e-05,0.0003380949,113.86,10
+220,"Legitimate, correctly cleared",card ending 9333,0,6.0215778e-05,3.928301e-05,62.8,10
+221,"Legitimate, correctly cleared",card ending 5248,0,0.00013961516,7.062526e-07,212.23,10
+222,"Legitimate, correctly cleared",card ending 2105,0,3.7629125e-06,1.040725e-05,8.26,10
+223,"Legitimate, correctly cleared",card ending 7171,0,0.0014116894,0.0013639225,8.22,10
+224,"Legitimate, correctly cleared",card ending 9027,0,0.0009955311,0.000100095225,3.59,10
+225,"Legitimate, correctly cleared",card ending 7611,0,4.2383122e-05,2.2544796e-06,90.79,10
+226,"Legitimate, correctly cleared",card ending 1852,0,1.5716314e-06,0.0009727804,65.45,10
+227,"Legitimate, correctly cleared",card ending 1773,0,0.00035255958,6.711299e-05,3.44,10
+228,"Legitimate, correctly cleared",card ending 1815,0,3.370073e-05,2.2217191e-05,1.32,10
+229,"Legitimate, correctly cleared",card ending 9245,0,4.040741e-05,6.0880648e-05,90.42,10
+230,"Legitimate, correctly cleared",card ending 4250,0,0.00010561431,1.0367631e-05,58.19,10

assets/demo_windows.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ed5b3a0ed9dcb4b4389f6246a5e740eb3e2f2b3321e893b6610e26dee8d2e0b
+size 46154

assets/gru.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fea103c036220dce52ac9b974be1cc8e798fce565541501d973ef53a85a81a15
+size 93581

assets/test_scores.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8f45794458e58cad3eb31c02856271c9d3d7a80a17a6a7352f7b70d1545b34b
+size 5658831

model.py

@@ -0,0 +1,48 @@
+"""
+The GRU, copied verbatim from the training repository so the Space is
+self-contained. Architecture must match the checkpoint exactly.
+
+Source: https://github.com/adwitiyashukla/DL-based-sequential-fraud-detection
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import nn
+
+
+class GRUFraudModel(nn.Module):
+    """
+    Per timestep the model sees a learned embedding of the merchant category
+    and a linear projection of the standardised numeric features. Those are
+    concatenated and fed to a single layer GRU. The hidden state at the final
+    timestep, which is the transaction being scored, goes through dropout and a
+    linear head to one logit.
+    """
+
+    def __init__(
+        self,
+        n_numeric: int,
+        n_categories: int,
+        emb_dim: int = 16,
+        proj_dim: int = 32,
+        hidden: int = 64,
+        dropout: float = 0.2,
+    ) -> None:
+        super().__init__()
+        self.cat_emb = nn.Embedding(n_categories + 1, emb_dim, padding_idx=0)
+        self.num_proj = nn.Linear(n_numeric, proj_dim)
+        self.gru = nn.GRU(
+            input_size=proj_dim + emb_dim,
+            hidden_size=hidden,
+            num_layers=1,
+            batch_first=True,
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.head = nn.Linear(hidden, 1)
+
+    def forward(self, x_num: torch.Tensor, x_cat: torch.Tensor) -> torch.Tensor:
+        emb = self.cat_emb(x_cat)
+        proj = torch.relu(self.num_proj(x_num))
+        out, _ = self.gru(torch.cat([proj, emb], dim=-1))
+        return self.head(self.dropout(out[:, -1, :])).squeeze(-1)

requirements.txt

@@ -0,0 +1,13 @@
+# gradio itself is installed by the Space runtime, so it is not listed here.
+#
+# torch is pinned to the CPU build. The default PyPI wheel bundles CUDA, which
+# is roughly 2.5 GB of dead weight on a CPU Space and slows every cold start.
+# The "+cpu" local tag only resolves from PyTorch's own index, hence the extra
+# index URL below.
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+torch==2.13.0+cpu
+numpy
+pandas
+lightgbm
+plotly