Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+import seaborn as sns
 from datasets import load_dataset
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_absolute_error, r2_score
@@ -22,7 +23,7 @@ LOCAL_BENCHMARK_CSV = "noise_benchmark_results.csv"
 TARGET_COL = "ideal_expval_Z_global"
 
 EXCLUDE_COLS = {
-    "sample_id", "sample_seed", "split",
+    "sample_id", "sample_seed", "split", "circuit_qasm", "circuit_qasm_transpiled",
     "ideal_expval_Z_global", "ideal_expval_X_global", "ideal_expval_Y_global",
     "noisy_expval_Z_global", "noisy_expval_X_global", "noisy_expval_Y_global",
     "error_Z_global", "error_X_global", "error_Y_global",
@@ -31,15 +32,6 @@ EXCLUDE_COLS = {
     "sign_ideal_Y_global", "sign_noisy_Y_global",
 }
 
-MODEL_PARAMS = dict(
-    n_estimators=80,
-    max_depth=10,
-    min_samples_leaf=2,
-    random_state=42,
-    n_jobs=-1,
-)
-
-# Global cache to avoid redundant downloads
 dataset_cache = {}
 
 # =========================================================
@@ -48,7 +40,6 @@ dataset_cache = {}
 def get_df(dataset_key):
     if dataset_key not in dataset_cache:
         repo_id = DATASET_MAP[dataset_key]
-        print(f"Downloading {repo_id}...")
         ds = load_dataset(repo_id)
         dataset_cache[dataset_key] = pd.DataFrame(ds["train"])
     return dataset_cache[dataset_key]
@@ -60,152 +51,105 @@ def get_numeric_feature_cols(df: pd.DataFrame) -> list[str]:
 # =========================================================
 # TAB FUNCTIONS
 # =========================================================
-def update_explorer(dataset_name):
+def update_explorer(dataset_name, split_name):
     df = get_df(dataset_name)
     splits = df["split"].unique().tolist() if "split" in df.columns else ["all"]
-    return gr.update(choices=splits, value=splits[0]), df.head(10)
-
-def filter_explorer_by_split(dataset_name, split_name):
-    df = get_df(dataset_name)
-    if "split" in df.columns:
-        return df[df["split"] == split_name].head(10)
-    return df.head(10)
+    
+    filtered = df[df["split"] == split_name].head(10) if "split" in df.columns else df.head(10)
+    qasm_sample = filtered["circuit_qasm"].iloc[0] if "circuit_qasm" in filtered.columns else "// QASM not found"
+    
+    return gr.update(choices=splits), filtered, qasm_sample
 
 def run_model_demo(dataset_name):
     df = get_df(dataset_name)
     feature_cols = get_numeric_feature_cols(df)
-    
-    # Ensure target exists, fallback to noisy if clean is missing (though unlikely in your schema)
     target = TARGET_COL if TARGET_COL in df.columns else df.filter(like="expval").columns[0]
     
     work_df = df.dropna(subset=feature_cols + [target]).reset_index(drop=True)
-    X = work_df[feature_cols]
-    y = work_df[target]
-
+    X, y = work_df[feature_cols], work_df[target]
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-    model = RandomForestRegressor(**MODEL_PARAMS)
+    model = RandomForestRegressor(n_estimators=50, max_depth=10, n_jobs=-1, random_state=42)
     model.fit(X_train, y_train)
     preds = model.predict(X_test)
 
-    r2 = r2_score(y_test, preds)
-    mae = mean_absolute_error(y_test, preds)
-
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(18, 5))
 
-    # Parity Plot
-    ax1.scatter(y_test, preds, alpha=0.5, color='#636EFA')
-    lims = [min(y_test.min(), preds.min()), max(y_test.max(), preds.max())]
-    ax1.plot(lims, lims, 'r--', alpha=0.75, zorder=3)
-    ax1.set_xlabel("Ground Truth")
-    ax1.set_ylabel("Predictions")
-    ax1.set_title(f"Prediction Accuracy\nR² = {r2:.4f}")
+    # 1. Parity Plot
+    ax1.scatter(y_test, preds, alpha=0.4, color='#636EFA')
+    ax1.plot([y.min(), y.max()], [y.min(), y.max()], 'r--', lw=2)
+    ax1.set_title(f"Parity Plot (R²={r2_score(y_test, preds):.3f})")
 
-    # Feature Importance
+    # 2. Feature Importance
     importances = model.feature_importances_
     indices = np.argsort(importances)[-10:]
-    ax2.barh(range(len(indices)), importances[indices], color='#EF553B')
-    ax2.set_yticks(range(len(indices)))
+    ax2.barh(range(10), importances[indices], color='#EF553B')
     ax2.set_yticklabels([feature_cols[i] for i in indices])
-    ax2.set_title("Top 10 Structural Features")
+    ax2.set_title("Top 10 Features")
+
+    # 3. Residuals (Error Distribution)
+    sns.histplot(y_test - preds, kde=True, ax=ax3, color='#00CC96')
+    ax3.set_title("Residuals Distribution")
     
     plt.tight_layout()
-    
-    summary = f"""
-    ### Model Performance: {dataset_name}
-    - **R² Score:** {r2:.4f}
-    - **Mean Absolute Error (MAE):** {mae:.4f}
-    
-    *This baseline demonstrates that structural circuit metrics (entropy, gate counts, etc.) hold predictive power for quantum expectation values.*
-    """
-    return fig, summary
+    return fig, f"### Baseline Analysis for {dataset_name}\nMAE: {mean_absolute_error(y_test, preds):.4f}"
 
 def load_benchmark():
     path = Path(LOCAL_BENCHMARK_CSV)
-    if not path.exists():
-        return pd.DataFrame([{"info": "Benchmark file not found"}]), None, None
-    
+    if not path.exists(): return None, None, None
     df = pd.read_csv(path)
-    
-    # R2 Plot
     fig_r2, ax = plt.subplots(figsize=(8, 4))
-    ax.bar(df["dataset"], df["r2"], color='skyblue')
-    ax.set_title("Cross-Dataset Robustness (R² Score)")
-    ax.set_ylabel("R²")
-    plt.xticks(rotation=15)
-    plt.tight_layout()
-
-    # MAE Plot
-    fig_mae, ax = plt.subplots(figsize=(8, 4))
-    ax.bar(df["dataset"], df["mae"], color='salmon')
-    ax.set_title("Cross-Dataset Error (MAE)")
-    ax.set_ylabel("MAE")
+    ax.bar(df["dataset"], df["r2"], color=['#636EFA', '#EF553B', '#00CC96', '#AB63FA'])
     plt.xticks(rotation=15)
     plt.tight_layout()
-
-    return df, fig_r2, fig_mae
+    return df, fig_r2, "Benchmark comparison completed."
 
 # =========================================================
 # INTERFACE
 # =========================================================
 with gr.Blocks(title="QSBench Unified Explorer", theme=gr.themes.Soft()) as demo:
-    gr.Markdown(
-        """
-        # 🌌 QSBench: Quantum Synthetic Benchmark Explorer
-        **Unified interface for Core, Noise-Affected, and Hardware-Transpiled Quantum Datasets.**
-        
-        Browse the demo datasets from the QSBench family, run baseline ML models, and analyze noise robustness across different distributions.
-        """
-    )
+    gr.Markdown("# 🌌 QSBench: Quantum Synthetic Benchmark Explorer\n**Professional-grade datasets for Noise-Aware QML and Hardware Optimization.**")
 
     with gr.Tabs():
-        # TAB 1: DATA EXPLORER
         with gr.TabItem("🔎 Dataset Explorer"):
             with gr.Row():
-                ds_selector = gr.Dropdown(choices=list(DATASET_MAP.keys()), value="Core (Clean)", label="Select Dataset Pack")
+                ds_selector = gr.Dropdown(choices=list(DATASET_MAP.keys()), value="Core (Clean)", label="Dataset Pack")
                 split_selector = gr.Dropdown(choices=["train", "test", "validation"], value="train", label="Split")
             
-            data_table = gr.Dataframe(label="Sample Data (First 10 rows)", interactive=False)
+            data_table = gr.Dataframe(interactive=False)
+            qasm_view = gr.Code(label="Circuit QASM Preview (First row of selection)", language="wasm")
             
-            ds_selector.change(update_explorer, inputs=[ds_selector], outputs=[split_selector, data_table])
-            split_selector.change(filter_explorer_by_split, inputs=[ds_selector, split_selector], outputs=[data_table])
+            ds_selector.change(update_explorer, [ds_selector, split_selector], [split_selector, data_table, qasm_view])
+            split_selector.change(update_explorer, [ds_selector, split_selector], [split_selector, data_table, qasm_view])
 
-        # TAB 2: ML BASELINE
         with gr.TabItem("🤖 ML Baseline Demo"):
-            gr.Markdown("Select a dataset and train a Random Forest regressor to predict expectation values from circuit metadata.")
-            model_ds_selector = gr.Dropdown(choices=list(DATASET_MAP.keys()), value="Core (Clean)", label="Target Dataset")
+            model_ds_selector = gr.Dropdown(choices=list(DATASET_MAP.keys()), value="Core (Clean)", label="Select Target Pack")
             train_btn = gr.Button("Train Baseline Model", variant="primary")
-            
-            with gr.Row():
-                plot_output = gr.Plot(label="Model Metrics")
-                text_output = gr.Markdown(label="Stats")
-            
-            train_btn.click(run_model_demo, inputs=[model_ds_selector], outputs=[plot_output, text_output])
-
-        # TAB 3: BENCHMARKING
-        with gr.TabItem("📊 Noise Robustness Benchmark"):
-            gr.Markdown("Analysis of model performance degradation under distribution shifts (Clean → Noisy → Hardware).")
-            bench_btn = gr.Button("Load Precomputed Benchmark Results")
-            bench_table = gr.Dataframe(interactive=False)
-            with gr.Row():
-                r2_plot = gr.Plot()
-                mae_plot = gr.Plot()
-            
-            bench_btn.click(load_benchmark, outputs=[bench_table, r2_plot, mae_plot])
-
-    gr.Markdown(
-        """
-        ---
-        ### About QSBench
-        QSBench is a collection of high-quality synthetic datasets designed for **Quantum Machine Learning** research. 
-        It provides paired ideal/noisy data, structural circuit metrics, and transpilation metadata.
+            plot_output = gr.Plot()
+            text_output = gr.Markdown()
+            train_btn.click(run_model_demo, [model_ds_selector], [plot_output, text_output])
+
+        with gr.TabItem("📊 Cross-Dataset Benchmark"):
+            bench_btn = gr.Button("Analyze Robustness Across All Packs")
+            bench_table = gr.Dataframe()
+            bench_plot = gr.Plot()
+            bench_btn.click(load_benchmark, outputs=[bench_table, bench_plot, text_output])
+
+    gr.Markdown("""
+    ---
+    ### 🔬 Data Integrity & Research Value
+    The demo files provided here serve as a **structural validation** for researchers. 
+    - **Demographic**: 8-10 Qubits, Depth 6-8.
+    - **Features**: Includes gate entropy, Meyer-Wallach entanglement, and transpilation metrics.
+    
+    To achieve state-of-the-art results in error mitigation or noise modeling, access to the full dataset family (up to 200,000 samples) is recommended to ensure statistical significance and model generalization.
         
         🔗 [Website](https://qsbench.github.io) | 🤗 [Hugging Face](https://huggingface.co/QSBench) | 🛠️ [GitHub](https://github.com/QSBench/QSBench-Demo)
         """
     )
 
     # Initial load
-    demo.load(update_explorer, inputs=[ds_selector], outputs=[split_selector, data_table])
+    demo.load(update_explorer, [ds_selector, split_selector], [split_selector, data_table, qasm_view])
 
 if __name__ == "__main__":
     demo.launch()