Upload StaminaSenseModel.ipynb with huggingface_hub

StaminaSenseModel.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Preprocessing Stage"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Preprocessing Stage\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# Load the dataset and baseline data\n",
+        "df = pd.read_csv(\"data.csv\")\n",
+        "baseline_df = pd.read_csv(\"baseline.csv\")\n",
+        "\n",
+        "required_cols = [\n",
+        "    \"ID\",\n",
+        "    \"Age\",\n",
+        "    \"Weight\",\n",
+        "    \"Height\",\n",
+        "    \"AVRR\",\n",
+        "    \"SDNN\",\n",
+        "    \"RMSSD\",\n",
+        "    \"PNN50\",\n",
+        "    \"Coefficient_of_Variation\",\n",
+        "    \"Fatigue_Level\",\n",
+        "]\n",
+        "\n",
+        "numeric_cols = [c for c in required_cols if c not in [\"Fatigue_Level\", \"ID\"]]\n",
+        "for c in numeric_cols:\n",
+        "    df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
+        "\n",
+        "df[\"ID\"] = pd.to_numeric(df[\"ID\"], errors=\"coerce\")\n",
+        "df[\"Fatigue_Level\"] = pd.to_numeric(df[\"Fatigue_Level\"], errors=\"coerce\")\n",
+        "\n",
+        "reshaped_df = df[required_cols].dropna().copy()\n",
+        "reshaped_df[\"Fatigue_Level\"] = reshaped_df[\"Fatigue_Level\"].astype(int)\n",
+        "reshaped_df[\"ID\"] = reshaped_df[\"ID\"].astype(int)\n",
+        "\n",
+        "# Normalize HRV features using per-athlete baseline (percent change)\n",
+        "# This matches what the backend sends: (value - baseline) / baseline\n",
+        "hrv_features = ['AVRR', 'SDNN', 'RMSSD', 'PNN50', 'Coefficient_of_Variation']\n",
+        "baseline_dict = baseline_df.set_index('ID')[hrv_features].to_dict('index')\n",
+        "\n",
+        "for feature in hrv_features:\n",
+        "    reshaped_df[feature] = reshaped_df.apply(\n",
+        "        lambda row: (row[feature] - baseline_dict[row['ID']][feature]) / baseline_dict[row['ID']][feature]\n",
+        "        if row['ID'] in baseline_dict and baseline_dict[row['ID']][feature] != 0\n",
+        "        else 0,\n",
+        "        axis=1\n",
+        "    )\n",
+        "\n",
+        "print(\"Dataset after baseline normalization (percent change):\")\n",
+        "print(reshaped_df.head(10))\n",
+        "print(reshaped_df.shape)\n",
+        "\n",
+        "# Engineer additional features from HRV percent-change values\n",
+        "reshaped_df['RMSSD_SDNN_ratio'] = reshaped_df['RMSSD'] / (reshaped_df['SDNN'].abs() + 0.001)\n",
+        "reshaped_df['HRV_index'] = (reshaped_df['SDNN'] + reshaped_df['RMSSD']) / 2\n",
+        "reshaped_df['Stress_index'] = reshaped_df['AVRR'] / (reshaped_df['SDNN'].abs() + 0.001)\n",
+        "reshaped_df['Parasympathetic'] = reshaped_df['RMSSD'] * reshaped_df['PNN50']\n",
+        "reshaped_df['AVRR_PNN50'] = reshaped_df['AVRR'] * reshaped_df['PNN50']\n",
+        "reshaped_df['CV_SDNN'] = reshaped_df['Coefficient_of_Variation'] * reshaped_df['SDNN']\n",
+        "reshaped_df['RMSSD_sq'] = reshaped_df['RMSSD'] ** 2\n",
+        "reshaped_df['SDNN_sq'] = reshaped_df['SDNN'] ** 2\n",
+        "\n",
+        "print(f\"\\nEngineered features added. Total columns: {len(reshaped_df.columns)}\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Feature Setup"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "from sklearn.model_selection import cross_val_predict, StratifiedKFold\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score,\n",
+        "    precision_score,\n",
+        "    recall_score,\n",
+        "    f1_score,\n",
+        "    mean_absolute_error,\n",
+        "    mean_squared_error,\n",
+        "    r2_score,\n",
+        "    confusion_matrix,\n",
+        "    classification_report\n",
+        ")\n",
+        "import copy\n",
+        "\n",
+        "target_col = 'Fatigue_Level'\n",
+        "base_feature_cols = [\n",
+        "    'AVRR', 'SDNN', 'RMSSD', 'PNN50', 'Coefficient_of_Variation',\n",
+        "    'Age', 'Weight', 'Height'\n",
+        "]\n",
+        "engineered_cols = [\n",
+        "    'RMSSD_SDNN_ratio', 'HRV_index', 'Stress_index', 'Parasympathetic',\n",
+        "    'AVRR_PNN50', 'CV_SDNN', 'RMSSD_sq', 'SDNN_sq'\n",
+        "]\n",
+        "feature_cols = base_feature_cols + engineered_cols\n",
+        "\n",
+        "X = reshaped_df[feature_cols]\n",
+        "y = reshaped_df[target_col]\n",
+        "\n",
+        "# Train on ALL data (demo mode)\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(X)\n",
+        "\n",
+        "print(f\"Total samples: {len(X)}\")\n",
+        "print(f\"Features: {len(feature_cols)}\")\n",
+        "print(f\"Class distribution:\\n{y.value_counts().sort_index()}\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Model Comparison (10-Fold Stratified Cross-Validation)\n",
+        "\n",
+        "We compare multiple supervised classifiers using 10-fold stratified CV for reliable accuracy estimates.\n",
+        "Each sample is tested exactly once while trained on the other 90%."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "models = {\n",
+        "    'Random Forest': RandomForestClassifier(\n",
+        "        n_estimators=300, max_depth=None, min_samples_leaf=1,\n",
+        "        random_state=42, class_weight='balanced'\n",
+        "    ),\n",
+        "    'Gradient Boosting': GradientBoostingClassifier(\n",
+        "        n_estimators=300, max_depth=4, learning_rate=0.1,\n",
+        "        subsample=0.8, random_state=42\n",
+        "    ),\n",
+        "    'Extra Trees': ExtraTreesClassifier(\n",
+        "        n_estimators=300, max_depth=None,\n",
+        "        class_weight='balanced', random_state=42\n",
+        "    ),\n",
+        "    'SVM (RBF)': SVC(\n",
+        "        kernel='rbf', C=100.0, gamma='scale',\n",
+        "        probability=True, random_state=42\n",
+        "    ),\n",
+        "    'KNN': KNeighborsClassifier(n_neighbors=3, weights='distance'),\n",
+        "    'Logistic Regression': LogisticRegression(\n",
+        "        max_iter=5000, class_weight='balanced', random_state=42\n",
+        "    ),\n",
+        "}\n",
+        "\n",
+        "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)\n",
+        "\n",
+        "print(\"=\" * 70)\n",
+        "print(f\"MODEL PERFORMANCE (10-Fold Stratified CV, {len(X)} samples)\")\n",
+        "print(\"=\" * 70)\n",
+        "print(f\"{'Model':<25} {'Accuracy':>10} {'Precision':>10} {'Recall':>10} {'F1-Score':>10}\")\n",
+        "print(\"-\" * 70)\n",
+        "\n",
+        "cv_results = {}\n",
+        "for name, m in models.items():\n",
+        "    m_cv = copy.deepcopy(m)\n",
+        "    y_pred = cross_val_predict(m_cv, X_scaled, y, cv=cv)\n",
+        "    acc = accuracy_score(y, y_pred)\n",
+        "    prec = precision_score(y, y_pred, average='weighted', zero_division=0)\n",
+        "    rec = recall_score(y, y_pred, average='weighted', zero_division=0)\n",
+        "    f1_val = f1_score(y, y_pred, average='weighted', zero_division=0)\n",
+        "    cv_results[name] = {'accuracy': acc, 'precision': prec, 'recall': rec, 'f1': f1_val}\n",
+        "    print(f\"{name:<25} {acc:>10.4f} {prec:>10.4f} {rec:>10.4f} {f1_val:>10.4f}\")\n",
+        "\n",
+        "print(\"=\" * 70)\n",
+        "\n",
+        "best_model_name = 'Random Forest'\n",
+        "print(f\"\\nBest CV model: {best_model_name} at {cv_results[best_model_name]['accuracy']:.1%}\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Visualize Model Comparison"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "cv_model_names = list(cv_results.keys())\n",
+        "cv_accuracies = [cv_results[m]['accuracy'] for m in cv_model_names]\n",
+        "cv_f1_scores = [cv_results[m]['f1'] for m in cv_model_names]\n",
+        "\n",
+        "x_cv = np.arange(len(cv_model_names))\n",
+        "width = 0.35\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(12, 6))\n",
+        "bars1 = ax.bar(x_cv - width/2, cv_accuracies, width, label='Accuracy', color='steelblue')\n",
+        "bars2 = ax.bar(x_cv + width/2, cv_f1_scores, width, label='F1 Score', color='coral')\n",
+        "\n",
+        "for bar in bars1:\n",
+        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
+        "            f'{bar.get_height():.1%}', ha='center', va='bottom', fontsize=9)\n",
+        "for bar in bars2:\n",
+        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
+        "            f'{bar.get_height():.1%}', ha='center', va='bottom', fontsize=9)\n",
+        "\n",
+        "ax.set_xlabel('Model')\n",
+        "ax.set_ylabel('Score')\n",
+        "ax.set_title('Model Comparison (10-Fold Stratified Cross-Validation)')\n",
+        "ax.set_xticks(x_cv)\n",
+        "ax.set_xticklabels(cv_model_names, rotation=45, ha='right')\n",
+        "ax.legend()\n",
+        "ax.set_ylim(0, 0.95)\n",
+        "ax.grid(axis='y', alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Train Final Model on ALL Data (Demo Mode)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "model = RandomForestClassifier(\n",
+        "    n_estimators=300, max_depth=None, min_samples_leaf=1,\n",
+        "    random_state=42, class_weight='balanced'\n",
+        ")\n",
+        "model.fit(X_scaled, y)\n",
+        "\n",
+        "train_preds = model.predict(X_scaled)\n",
+        "train_acc = accuracy_score(y, train_preds)\n",
+        "print(f\"Training accuracy (all data): {train_acc:.4f}\")\n",
+        "\n",
+        "if hasattr(model, 'feature_importances_'):\n",
+        "    print(\"\\nFeature Importance:\")\n",
+        "    feature_importance = pd.DataFrame({\n",
+        "        'feature': feature_cols,\n",
+        "        'importance': model.feature_importances_\n",
+        "    }).sort_values('importance', ascending=False)\n",
+        "    print(feature_importance.to_string(index=False))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Feature Importance Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "if hasattr(model, 'feature_importances_'):\n",
+        "    fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "    feature_importance_sorted = feature_importance.sort_values('importance', ascending=True)\n",
+        "    ax.barh(feature_importance_sorted['feature'], feature_importance_sorted['importance'])\n",
+        "    ax.set_xlabel('Importance')\n",
+        "    ax.set_ylabel('Feature')\n",
+        "    ax.set_title('Feature Importance (Random Forest)')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Confusion Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "conf_matrix = confusion_matrix(y, train_preds)\n",
+        "print(f\"Confusion Matrix (training data):\")\n",
+        "print(conf_matrix)\n",
+        "print(f\"\\n{classification_report(y, train_preds, zero_division=0)}\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import seaborn as sns\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8, 6))\n",
+        "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', ax=ax,\n",
+        "            xticklabels=[1,2,3,4,5],\n",
+        "            yticklabels=[1,2,3,4,5])\n",
+        "ax.set_xlabel('Predicted Fatigue Level')\n",
+        "ax.set_ylabel('True Fatigue Level')\n",
+        "ax.set_title(f'Confusion Matrix - {best_model_name} (Training Data)')\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Prediction Function for Live Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "def _build_feature_row(raw_data):\n",
+        "    \"\"\"Compute engineered features from base HRV percent-change values.\"\"\"\n",
+        "    row = dict(raw_data)\n",
+        "    row['RMSSD_SDNN_ratio'] = row['RMSSD'] / (abs(row['SDNN']) + 0.001)\n",
+        "    row['HRV_index'] = (row['SDNN'] + row['RMSSD']) / 2\n",
+        "    row['Stress_index'] = row['AVRR'] / (abs(row['SDNN']) + 0.001)\n",
+        "    row['Parasympathetic'] = row['RMSSD'] * row['PNN50']\n",
+        "    row['AVRR_PNN50'] = row['AVRR'] * row['PNN50']\n",
+        "    row['CV_SDNN'] = row['Coefficient_of_Variation'] * row['SDNN']\n",
+        "    row['RMSSD_sq'] = row['RMSSD'] ** 2\n",
+        "    row['SDNN_sq'] = row['SDNN'] ** 2\n",
+        "    return row\n",
+        "\n",
+        "\n",
+        "def predict_fatigue_level(new_data_row, scaler, model):\n",
+        "    row = _build_feature_row(new_data_row)\n",
+        "    input_df = pd.DataFrame([row])[feature_cols]\n",
+        "    scaled_input = scaler.transform(input_df)\n",
+        "    prediction = model.predict(scaled_input)[0]\n",
+        "    return int(prediction)\n",
+        "\n",
+        "\n",
+        "def predict_fatigue_with_confidence(new_data_row, scaler, model):\n",
+        "    row = _build_feature_row(new_data_row)\n",
+        "    input_df = pd.DataFrame([row])[feature_cols]\n",
+        "    scaled_input = scaler.transform(input_df)\n",
+        "    prediction = model.predict(scaled_input)[0]\n",
+        "\n",
+        "    if hasattr(model, 'predict_proba'):\n",
+        "        proba = model.predict_proba(scaled_input)[0]\n",
+        "        classes = model.classes_\n",
+        "        prob_dict = {int(cls): float(prob) for cls, prob in zip(classes, proba)}\n",
+        "    else:\n",
+        "        prob_dict = {int(prediction): 1.0}\n",
+        "\n",
+        "    return int(prediction), prob_dict"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Save Model Artifacts"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "import joblib\n",
+        "import json\n",
+        "\n",
+        "joblib.dump(scaler, \"scaler.joblib\")\n",
+        "joblib.dump(model, \"fatigue_model.joblib\")\n",
+        "\n",
+        "cv_acc = cv_results[best_model_name]['accuracy']\n",
+        "cv_f1 = cv_results[best_model_name]['f1']\n",
+        "model_metadata = {\n",
+        "    'model_type': best_model_name,\n",
+        "    'feature_cols': feature_cols,\n",
+        "    'target_col': target_col,\n",
+        "    'cv_accuracy': float(cv_acc),\n",
+        "    'cv_f1_score': float(cv_f1),\n",
+        "    'training_accuracy': float(train_acc),\n",
+        "    'n_samples': len(X),\n",
+        "    'training_mode': 'all_data_demo'\n",
+        "}\n",
+        "\n",
+        "with open(\"model_metadata.json\", \"w\") as f:\n",
+        "    json.dump(model_metadata, f, indent=2)\n",
+        "\n",
+        "print(\"Model artifacts saved:\")\n",
+        "print(\"  - scaler.joblib\")\n",
+        "print(\"  - fatigue_model.joblib\")\n",
+        "print(\"  - model_metadata.json\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Test Predictions with Sample Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "print(\"=\" * 60)\n",
+        "print(\"SAMPLE PREDICTIONS\")\n",
+        "print(\"=\" * 60)\n",
+        "\n",
+        "new_data = {\n",
+        "    \"AVRR\": 0.05,\n",
+        "    \"SDNN\": 0.10,\n",
+        "    \"RMSSD\": 0.15,\n",
+        "    \"PNN50\": 0.10,\n",
+        "    \"Coefficient_of_Variation\": 0.05,\n",
+        "    \"Age\": 22,\n",
+        "    \"Height\": 183,\n",
+        "    \"Weight\": 180\n",
+        "}\n",
+        "\n",
+        "pred, probs = predict_fatigue_with_confidence(new_data, scaler, model)\n",
+        "print(f\"\\nTest 1 - Near baseline (expected low fatigue):\")\n",
+        "print(f\"  Predicted Fatigue Level: {pred}\")\n",
+        "print(f\"  Confidence scores: {probs}\")\n",
+        "\n",
+        "new_data = {\n",
+        "    \"AVRR\": -0.20,\n",
+        "    \"SDNN\": -0.15,\n",
+        "    \"RMSSD\": -0.10,\n",
+        "    \"PNN50\": -0.30,\n",
+        "    \"Coefficient_of_Variation\": 0.10,\n",
+        "    \"Age\": 22,\n",
+        "    \"Height\": 183,\n",
+        "    \"Weight\": 180\n",
+        "}\n",
+        "\n",
+        "pred, probs = predict_fatigue_with_confidence(new_data, scaler, model)\n",
+        "print(f\"\\nTest 2 - Moderate decline from baseline:\")\n",
+        "print(f\"  Predicted Fatigue Level: {pred}\")\n",
+        "print(f\"  Confidence scores: {probs}\")\n",
+        "\n",
+        "new_data = {\n",
+        "    \"AVRR\": -0.40,\n",
+        "    \"SDNN\": -0.50,\n",
+        "    \"RMSSD\": -0.45,\n",
+        "    \"PNN50\": -0.60,\n",
+        "    \"Coefficient_of_Variation\": -0.20,\n",
+        "    \"Age\": 21,\n",
+        "    \"Height\": 163,\n",
+        "    \"Weight\": 97\n",
+        "}\n",
+        "\n",
+        "pred, probs = predict_fatigue_with_confidence(new_data, scaler, model)\n",
+        "print(f\"\\nTest 3 - Significant decline from baseline:\")\n",
+        "print(f\"  Predicted Fatigue Level: {pred}\")\n",
+        "print(f\"  Confidence scores: {probs}\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "StaminaSense (venv)",
+      "language": "python",
+      "name": "staminasense"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.13.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}