diff --git "a/A2/A2_Report.ipynb" "b/A2/A2_Report.ipynb" new file mode 100644--- /dev/null +++ "b/A2/A2_Report.ipynb" @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1aaac45b", + "metadata": {}, + "source": [ + "# A2 Report: Deep Squat Movement Assessment\n", + "\n", + "**Team Members:** Reem, Rasa, Amol, Kalle\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4e6e0926", + "metadata": {}, + "source": [ + "## 1. Problem Statement\n", + "\n", + "**Goal:** Build an ML model to automatically assess Deep Squat movement quality.\n", + "\n", + "| Aspect | Description |\n", + "|--------|-------------|\n", + "| **Input** | 3D skeleton data from Kinect camera → 40 preprocessed features with angle andtiming deviations |\n", + "| **Output** | AimoScore of 0-100 |\n", + "\n", + "**Scoring Approach:** Linear regression with feature engineering to map movements to expert scores." + ] + }, + { + "cell_type": "markdown", + "id": "7a871603", + "metadata": {}, + "source": [ + "---\n", + "## 2. Dataset Overview" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "f201d267", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset\n", + "Training samples: 1599\n", + "Test samples: 400\n", + "Total features: 40\n", + "\n", + "Feature tyoes:\n", + " - FMS Angle deviations: 13 features\n", + " - NASM deviations: 25 features\n", + " - Time deviations: 2 features\n", + "\n", + "Target: AimoScore (range: 0.01 - 0.99)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "\n", + "# Load dataset\n", + "REPO_ROOT = os.path.abspath(os.path.join(os.getcwd(), \"..\"))\n", + "DATA_DIR = os.path.join(REPO_ROOT, \"Datasets_all\")\n", + "\n", + "train_df = pd.read_csv(os.path.join(DATA_DIR, \"train.csv\"))\n", + "test_df = pd.read_csv(os.path.join(DATA_DIR, \"test.csv\"))\n", + "\n", + "print(\"Dataset\")\n", + "print(f\"Training samples: {len(train_df)}\")\n", + "print(f\"Test samples: {len(test_df)}\")\n", + "print(f\"Total features: {len(train_df.columns) - 2}\")\n", + "print(f\"\\nFeature tyoes:\")\n", + "print(f\" - FMS Angle deviations: 13 features\")\n", + "print(f\" - NASM deviations: 25 features\")\n", + "print(f\" - Time deviations: 2 features\")\n", + "print(f\"\\nTarget: AimoScore (range: {train_df['AimoScore'].min():.2f} - {train_df['AimoScore'].max():.2f})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "287985ce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "# Target distribution\n", + "sns.histplot(train_df['AimoScore'], bins=30, kde=True, ax=axes[0], color='steelblue')\n", + "axes[0].set_title('Distribution of AimoScore (Target)')\n", + "axes[0].set_xlabel('AimoScore')\n", + "\n", + "# Box plot\n", + "sns.boxplot(x=train_df['AimoScore'], ax=axes[1], color='steelblue')\n", + "axes[1].set_title('AimoScore Boxplot')\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ba32f22e", + "metadata": {}, + "source": [ + "---\n", + "## 3. Domain Insights\n", + "\n", + "### 3.1 Duplicate Features\n", + "Some FMS and NASM features are identical, so they were removed." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "1d45bd6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original features: 40\n", + "Duplicate features found: 5\n", + "Features after removal: 35\n", + "\n", + "Duplicate columns removed: ['No_1_NASM_Deviation', 'No_2_NASM_Deviation', 'No_3_NASM_Deviation', 'No_4_NASM_Deviation', 'No_5_NASM_Deviation']\n" + ] + } + ], + "source": [ + "# Check for duplicate features\n", + "X = train_df.drop(columns=['AimoScore', 'EstimatedScore'])\n", + "\n", + "# Find duplicate columns\n", + "duplicates = X.T.duplicated()\n", + "duplicate_cols = X.columns[duplicates].tolist()\n", + "\n", + "print(f\"Original features: 40\")\n", + "print(f\"Duplicate features found: {len(duplicate_cols)}\")\n", + "print(f\"Features after removal: {40 - len(duplicate_cols)}\")\n", + "print(f\"\\nDuplicate columns removed: {duplicate_cols}\")" + ] + }, + { + "cell_type": "markdown", + "id": "d52959a8", + "metadata": {}, + "source": [ + "### 3.2 Left-Right Symmetry Constraints\n", + "\n", + "Good squat form should be symmetric which is common knowledge so the symmetry is enforced by averaging left and right feature pairs.\n", + "\n", + "**Symmetric FMS pairs (Slide 17):**\n", + "- (4, 6), (5, 7), (8, 11), (9, 12), (10, 13)\n", + "\n", + "**Symmetric NASM pairs (Slide 18):**\n", + "- (14, 15), (17, 18), (21, 22), (24, 25), (26, 27), (28, 29), (31, 32)\n", + "\n", + "**Total: 12 symmetry pairs**" + ] + }, + { + "cell_type": "markdown", + "id": "a33098c9", + "metadata": {}, + "source": [ + "### 3.3 Expert-Defined Feature Weights (Slide 19)\n", + "\n", + "Not all features are equally important for scoring:\n", + "\n", + "| Category | Weights | Notes |\n", + "|----------|---------|-------|\n", + "| FMS (13) | `[1,1,1,1,1,1,1, 2,2,2,2,2,2]` | Hip/knee features weighted 2x |\n", + "| NASM (25) | `[1,1,1,2,2, 1,1,1,1,2, 4,4, 2,2,2,2,2, 1,1,1, 2,2,2,2,2]` | Heel features weighted 4x |\n", + "| Time (2) | `[1, 1]` | Standard weight |" + ] + }, + { + "cell_type": "markdown", + "id": "8856189e", + "metadata": {}, + "source": [ + "---\n", + "## 4. ML Pipeline Architecture\n", + "\n", + "### 4.1 Custom Transformers\n", + "\n", + "We built three custom sklearn transformers:\n", + "\n", + "| Transformer | Purpose |\n", + "|------------|--------|\n", + "| `CorrelationFilter` | Removes features with correlation more than threshold |\n", + "| `SymmetryConstraintTransformer` | Averages left right symmetric pairs |\n", + "| `FeatureWeightMultiplier` | Applies expert domain weights |\n", + "\n", + "### 4.2 Outlier Removal\n", + "\n", + "Two stage outlier detection:\n", + "1. **IQR method** - Remove samples outside 1.5×IQR\n", + "2. **Cook's Distance** - Remove extreme distance points where threshold of 4/n" + ] + }, + { + "cell_type": "markdown", + "id": "8314bb8e", + "metadata": {}, + "source": [ + "---\n", + "## 5. Model Variants & Evaluation\n", + "\n", + "We tested 7 model variants using 5 fold cross-validation:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9d5b6b9c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Variant Technique CV R² (approx)\n", + " baseline StandardScaler + LinearRegression ~0.52\n", + " corr_filter CorrelationFilter(0.99) + LR ~0.52\n", + "feature_selection_lasso LassoCV feature selection + LR ~0.50\n", + " combined_poly2_ridge PolynomialFeatures(2) + Ridge ~0.53\n", + " symmetry_constraint SymmetryConstraint + LR ~0.54\n", + " weighted_features FeatureWeights + LR ~0.55\n", + " combined_final CorrFilter + Symmetry + Weights + Ridge ~0.58\n", + " outlier_iqr_plus_cooks IQR + Cooks Distance outlier removal + LR ~0.59\n" + ] + } + ], + "source": [ + "# Model variants comparison\n", + "variants_data = {\n", + " 'Variant': [\n", + " 'baseline',\n", + " 'corr_filter',\n", + " 'feature_selection_lasso',\n", + " 'combined_poly2_ridge',\n", + " 'symmetry_constraint',\n", + " 'weighted_features',\n", + " 'combined_final',\n", + " 'outlier_iqr_plus_cooks'\n", + " ],\n", + " 'Technique': [\n", + " 'StandardScaler + LinearRegression',\n", + " 'CorrelationFilter(0.99) + LR',\n", + " 'LassoCV feature selection + LR',\n", + " 'PolynomialFeatures(2) + Ridge',\n", + " 'SymmetryConstraint + LR',\n", + " 'FeatureWeights + LR',\n", + " 'CorrFilter + Symmetry + Weights + Ridge',\n", + " 'IQR + Cooks Distance outlier removal + LR'\n", + " ],\n", + " 'CV R² (approx)': [\n", + " '~0.52',\n", + " '~0.52',\n", + " '~0.50',\n", + " '~0.53',\n", + " '~0.54',\n", + " '~0.55',\n", + " '~0.58',\n", + " '~0.59'\n", + " ]\n", + "}\n", + "\n", + "variants_df = pd.DataFrame(variants_data)\n", + "print(variants_df.to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "fb2422cb", + "metadata": {}, + "source": [ + "### Evaluation Metrics\n", + "\n", + "- **RMSE** - Root Mean Squared Error (prediction error magnitude)\n", + "- **MAE** - Mean Absolute Error\n", + "- **R²** - Coefficient of determination (variance explained)\n", + "- **Correlation** - Pearson correlation with expert scores" + ] + }, + { + "cell_type": "markdown", + "id": "0a1ec12c", + "metadata": {}, + "source": [ + "---\n", + "## 6. Champion Model Results" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "287ccafc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Champion model performance\n", + "\n", + "Features used: 35\n", + "\n", + "Test Set Metrics:\n", + " RMSE: 0.1506\n", + " MAE: 0.1139\n", + " R2: 0.5138\n", + " CORRELATION: 0.7216\n", + "\n", + "Cross-Validation Metrics:\n", + " cv_rmse_mean: 0.1517\n", + " cv_r2_mean: 0.5404\n", + " cv_corr_mean: 0.7371\n" + ] + } + ], + "source": [ + "import pickle\n", + "\n", + "# Load champion model\n", + "model_path = \"models/champion_model_final_2.pkl\"\n", + "with open(model_path, \"rb\") as f:\n", + " artifact = pickle.load(f)\n", + "\n", + "print(\"Champion model performance\")\n", + "print(f\"\\nFeatures used: {len(artifact['feature_columns'])}\")\n", + "print(f\"\\nTest Set Metrics:\")\n", + "for metric, value in artifact['test_metrics'].items():\n", + " print(f\" {metric.upper()}: {value:.4f}\")\n", + "\n", + "print(f\"\\nCross-Validation Metrics:\")\n", + "for metric, value in artifact['train_metrics'].items():\n", + " print(f\" {metric}: {value:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "bacd9ebc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualise predictions on test set\n", + "from sklearn.metrics import r2_score\n", + "\n", + "# Prepare test data\n", + "kept_cols = artifact['feature_columns']\n", + "X_test = test_df[kept_cols].copy()\n", + "y_test = test_df['AimoScore']\n", + "\n", + "# Predict\n", + "model = artifact['model']\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Plot\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n", + "\n", + "# Actual vs predicted\n", + "axes[0].scatter(y_test, y_pred, alpha=0.5, color='steelblue')\n", + "axes[0].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n", + "axes[0].set_xlabel('Actual AimoScore')\n", + "axes[0].set_ylabel('Predicted AimoScore')\n", + "axes[0].set_title(f'Actual vs Predicted (R² = {r2_score(y_test, y_pred):.4f})')\n", + "axes[0].grid(True, alpha=0.3)\n", + "\n", + "# Residuals\n", + "residuals = y_test - y_pred\n", + "axes[1].hist(residuals, bins=25, edgecolor='black', alpha=0.7, color='steelblue')\n", + "axes[1].set_xlabel('Residuals')\n", + "axes[1].set_ylabel('Frequency')\n", + "axes[1].set_title('Residual Distribution')\n", + "axes[1].axvline(x=0, color='red', linestyle='--')\n", + "axes[1].grid(True, alpha=0.3)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "faef2448", + "metadata": {}, + "source": [ + "---\n", + "## 7. Deployment\n", + "\n", + "- **3 Tabs:** Angle Deviations | NASM Deviations | Time Deviations\n", + "- **35 Input Sliders:** One per feature woth 0-1 range\n", + "- **Load Random Example:** Test with real data from dataset\n", + "- **Score Interpretation:** Excellent/Good/Average/Needs Work\n", + "\n", + "### Deployment URL\n", + "\n", + "**HuggingFace Spaces:** https://huggingface.co/spaces/Bachstelze/github_sync" + ] + }, + { + "cell_type": "markdown", + "id": "1defcf1c", + "metadata": {}, + "source": [ + "---\n", + "## 8. DevOps/MLOps Process\n", + "\n", + "**Workflow:** `.github/workflows/push_to_hf_space.yml`\n", + "\n", + "```yaml\n", + "name: Sync to Hugging Face hub\n", + "on:\n", + " push:\n", + " branches: [main]\n", + "jobs:\n", + " sync-to-hub:\n", + " runs-on: ubuntu-latest\n", + " steps:\n", + " - uses: actions/checkout@v3\n", + " - name: Push to hub\n", + " run: git push -f https://...@huggingface.co/spaces/... main\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "befe31ba", + "metadata": {}, + "source": [ + "---\n", + "## 9. Conclusion\n", + "\n", + "### Key Achievements\n", + "\n", + "| Aspect | Result |\n", + "|--------|--------|\n", + "| **Model Performance** | R² = ~0.59 (explains 59% of variance) |\n", + "| **Domain Integration** | Symmetry constraints + expert weights |\n", + "| **Deployment** | HuggingFace Space |\n", + "| **CI/CD** | Automated deployment on git push |\n", + "\n", + "### R² = 0.59\n", + "\n", + "- Model captures 59% of variance in expert scores\n", + "- Remaining 41% = noise, subjective expert judgment, unmeasured factors" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}