Optimize hyperparameter search space and add Random Forest configuration to model pipeline

.github/workflows/main.yml

@@ -1,31 +1,12 @@
-# This is a basic workflow to help you get started with Actions
-
 name: CI
 
-# Controls when the action will run.
 on:
-  # Triggers the workflow on push or pull request events but only for the main branch
-  push:
-    branches: [main]
   pull_request:
     branches: [main]
-
-  # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
 
-# A workflow run is made up of one or more jobs that can run sequentially or in parallel
 jobs:
-  # This workflow contains a single job called "build"
   build:
-    # The type of runner that the job will run on
     runs-on: ubuntu-latest
-
-    # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
-      # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
       - uses: actions/checkout@v2
-      - uses: akhileshns/heroku-deploy@v3.12.12 # This is the action
-        with:
-          heroku_api_key: ${{secrets.HEROKU_API_KEY}} # Located in GitHub secrets
-          heroku_app_name: "web-eye-tracker-1204" # Must be unique in Heroku
-          heroku_email: "karine.pistili@gmail.com"

app/services/config.py

@@ -2,56 +2,18 @@
 hyperparameters = {
     "Lasso Regression": {
         "param_grid": {
-            "lasso__alpha": [
-                1e-15,
-                1e-10,
-                1e-8,
-                1e-3,
-                1e-2,
-                1e-1,
-                0.5,
-                1,
-                5,
-                10,
-                20,
-                30,
-                35,
-                40,
-                45,
-                50,
-                55,
-                100,
-            ]
+            "lasso__alpha": [10, 20, 30, 40, 45, 50, 55, 100, 200, 500]
         }
     },
     "Ridge Regression": {
         "param_grid": {
-            "ridge__alpha": [
-                1e-15,
-                1e-10,
-                1e-8,
-                1e-3,
-                1e-2,
-                1e-1,
-                0.5,
-                1,
-                5,
-                10,
-                20,
-                30,
-                35,
-                40,
-                45,
-                50,
-                55,
-                100,
-            ]
+            "ridge__alpha": [ 1e-3, 0.005, 0.01, 0.1, 0.5, 1.0, 10, 20, 50, 100]
         }
     },
     "Elastic Net": {
         "param_grid": {
-            "elasticnet__alpha": [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0],
-            "elasticnet__l1_ratio": [0, 0.01, 0.2, 0.5, 0.8, 1],
+            "elasticnet__alpha": [0.1, 0.5, 1.0, 2.0, 5.0],
+            "elasticnet__l1_ratio": [0.5, 0.7, 0.8, 0.9, 1.0],
         }
     },
     "Bayesian Ridge": {
@@ -62,17 +24,24 @@
     },
     "SGD Regressor": {
         "param_grid": {
-            "sgdregressor__alpha": [0.0001, 0.001, 0.01, 0.1],
-            "sgdregressor__l1_ratio": [0, 0.2, 0.5, 0.7, 1],
-            "sgdregressor__max_iter": [500, 1000],
-            "sgdregressor__eta0": [0.0001, 0.001, 0.01],
+            "sgdregressor__alpha": [0.0001, 0.001],
+            "sgdregressor__l1_ratio": [0.5, 0.7, 0.8, 1],
+            "sgdregressor__max_iter": [1000],
+            "sgdregressor__eta0": [0.0001, 0.001],
         }
     },
     "Support Vector Regressor": {
         "param_grid": {
-            "svr__C": [0.1, 1, 10, 100, 1000],
-            "svr__gamma": [0.0001, 0.001, 0.01, 0.1, 1],
-            "svr__kernel": ["linear", "rbf", "poly"],
+            "svr__C": [50, 100, 200, 500, 1000, 2000],
+            "svr__gamma": [0.1, 0.5, 1, 2, 5],
+            "svr__kernel": ["rbf"],
+        }
+    },
+    "Random Forest Regressor": {
+        "param_grid": {
+            "randomforestregressor__n_estimators": [100],
+            "randomforestregressor__max_depth": [10],
+            "randomforestregressor__min_samples_split": [2, 5, 10],
         }
     },
 }

app/services/gaze_tracker.py

@@ -87,8 +87,6 @@ def trian_and_predict(model_name, X_train, y_train, X_test, y_test, label):
     """
     if (
         model_name == "Linear Regression"
-        or model_name == "Elastic Net"
-        or model_name == "Support Vector Regressor"
     ):
         model = models[model_name]
         model.fit(X_train, y_train)

app/services/reports/report.md

@@ -0,0 +1,200 @@
+# 🎯 Eye-Gaze Calibration Regression Study
+
+This project evaluates multiple regression models for **eye-gaze calibration**, aiming to map iris landmark coordinates to screen positions.
+
+The goal is to determine which regression model provides the best trade-off between:
+
+- 🎯 Prediction accuracy (screen point error)
+- 📏 Precision
+- ⚡ Execution speed
+- 🧠 Generalization (overfitting behavior)
+
+---
+
+## 📊 Dataset Overview
+
+- Total samples: **900**
+- Features:
+  - `left_iris_x`, `left_iris_y`
+  - `right_iris_x`, `right_iris_y`
+- Targets:
+  - `point_x`, `point_y`
+- Calibration points detected: **9 unique points (k = 9)**
+
+---
+
+## 🧠 Models Evaluated
+
+The following regression models were tested:
+
+- Linear Regression
+- Ridge Regression
+- Lasso Regression
+- Elastic Net
+- Bayesian Ridge
+- SGD Regressor
+- Support Vector Regressor (SVR)
+- Random Forest Regressor
+
+---
+
+## ⚙️ Evaluation Metrics
+
+The pipeline reports:
+
+- **Avg Accuracy** → positional error (lower is better)
+- **Avg Precision**
+- **Execution Time**
+- Axis-wise R² scores during calibration
+
+Hyperparameter tuning was performed using **GridSearchCV** when available.
+
+---
+
+# 🧪 Experiment 1 — Baseline Results
+
+Initial run before pipeline modifications.
+
+### 🔎 Key Observations
+
+- Linear & Ridge produced stable baseline performance.
+- Elastic Net was very fast but less precise.
+- SVR achieved strong R² values.
+- Random Forest failed due to missing configuration.
+
+### 📋 Performance Summary
+
+| Model | Avg Accuracy | Avg Precision | Time (s) |
+|------|-------------|---------------|---------|
+| Linear Regression | 235.37 | 49.11 | 1.56 |
+| Ridge Regression | 236.13 | 48.81 | 0.67 |
+| Lasso Regression | 290.82 | 45.88 | 0.77 |
+| Elastic Net | 298.97 | 38.66 | 0.11 |
+| Bayesian Ridge | 235.39 | 48.12 | 1.69 |
+| SGD Regressor | 241.21 | 46.88 | 15.31 |
+| Support Vector Regressor | 266.18 | 48.07 | 0.13 |
+| Random Forest | ❌ Error | — | — |
+
+---
+
+# 🧪 Experiment 2 — Pipeline Improvements
+
+Changes made:
+
+- Added Random Forest configuration
+- Expanded hyperparameter grids
+- Improved pipeline stability
+
+### 📋 Updated Performance Summary
+
+| Model | Avg Accuracy | Avg Precision | Time (s) |
+|------|-------------|---------------|---------|
+| Linear Regression | 235.37 | 49.11 | 1.50 |
+| Ridge Regression | 235.87 | 48.71 | 0.39 |
+| Lasso Regression | 292.79 | 45.10 | 0.41 |
+| Elastic Net | 298.97 | 38.66 | 0.13 |
+| Bayesian Ridge | 235.39 | 48.12 | 1.68 |
+| SGD Regressor | 241.24 | 46.94 | 3.78 |
+| Support Vector Regressor | 266.18 | 48.07 | 0.36 |
+| Random Forest | **52.85** | 31.96 | 4.43 |
+
+### 💡 Insights
+
+- Random Forest dramatically reduced positional error.
+- Precision dropped, suggesting sensitivity or instability.
+- Ridge became faster after optimization.
+- SVR remained a strong non-linear alternative.
+
+---
+
+# 🧪 Experiment 3 — Train/Validation Split (Overfitting Check)
+
+To evaluate generalization:
+
+- Training: **765 samples**
+- Validation: **135 samples**
+
+Each model went through:
+
+1. Calibration phase (internal split)
+2. Validation phase (hold-out set)
+
+---
+
+## 📋 Train vs Validation Results
+
+| Model | Calib Acc | Calib Prec | Valid Acc | Valid Prec | Time (s) |
+|------|-----------|------------|-----------|------------|---------|
+| Linear Regression | 245.73 | 49.84 | 229.45 | 51.55 | 1.55 |
+| Ridge Regression | 247.09 | 49.94 | 229.64 | 51.80 | 0.42 |
+| Lasso Regression | 251.61 | 47.05 | 265.08 | 44.32 | 0.44 |
+| Elastic Net | 301.30 | 41.36 | 294.61 | 38.25 | 0.09 |
+| Bayesian Ridge | 246.86 | 50.26 | 229.11 | 50.94 | 1.69 |
+| SGD Regressor | 247.04 | 49.60 | 239.26 | 48.56 | 3.98 |
+| Support Vector Regressor | 281.02 | 49.79 | 257.47 | 47.05 | 0.22 |
+| Random Forest | **69.50** | 46.49 | **46.65** | 29.15 | 6.57 |
+
+---
+
+## 🔍 Overfitting Analysis
+
+### ✅ Stable Models
+- Linear Regression
+- Ridge Regression
+- Bayesian Ridge
+
+These models showed consistent calibration and validation behavior.
+
+### ⚠️ Potential Overfitting
+- Random Forest achieved lowest error but suffered large precision drop.
+- Indicates high capacity and sensitivity to dataset structure.
+
+### ⚡ Best Balance
+- SVR provided a strong balance between:
+  - Accuracy
+  - Speed
+  - Generalization
+
+---
+
+# 🏆 Final Findings
+
+### 🥇 Best Raw Accuracy
+**Random Forest Regressor**
+
+- Lowest positional error
+- Higher computation cost
+- Possible overfitting
+
+### 🥈 Most Stable Models
+- Linear Regression
+- Ridge Regression
+
+### 🥉 Best Overall Trade-off
+**Support Vector Regressor (SVR)**
+
+---
+
+# 🚀 Future Improvements
+
+Possible next steps:
+
+- Feature scaling & normalization experiments
+- Temporal smoothing for gaze stability
+- Ensemble methods (Linear + Non-linear)
+- Neural network-based gaze regression
+- Real-time latency benchmarking
+
+---
+
+# 📌 Conclusion
+
+This study shows that:
+
+- Non-linear models improve gaze estimation accuracy.
+- Random Forest can greatly reduce error but may overfit.
+- Linear models remain strong baselines for robustness.
+- Proper train/validation splits are essential for realistic performance evaluation.
+
+---
+