From 00db2b2a09393a89f46d445f432df7e3322bff59 Mon Sep 17 00:00:00 2001 From: Nishant Date: Sun, 17 Nov 2024 15:24:28 -0600 Subject: [PATCH 01/20] add: Project 2 Implementation --- .idea/.gitignore | 3 + .idea/Project_2.iml | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 4 + .idea/modules.xml | 8 + data/bootstrap_results.csv | 101 + data/collinear_dataset_15_features.csv | 2001 +++++++++++++++++ data/feature_correlation_heatmap.png | Bin 0 -> 131740 bytes data/k_fold_results.csv | 11 + data/learning_curve.png | Bin 0 -> 29324 bytes data/mse_distribution_bootstrap.png | Bin 0 -> 36493 bytes data/mse_distribution_k_fold.png | Bin 0 -> 22956 bytes data/residual_plot.png | Bin 0 -> 104757 bytes .../model_selection.cpython-312.pyc | Bin 0 -> 4650 bytes .../model_selection.cpython-39.pyc | Bin 0 -> 2461 bytes .../simple_linear_regression.cpython-312.pyc | Bin 0 -> 1569 bytes .../simple_linear_regression.cpython-39.pyc | Bin 0 -> 1016 bytes src/model_selection.py | 89 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"cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Generate random data\n", + "np.random.seed(42)\n", + "X = np.random.rand(500, 5) # 500 samples, 5 features\n", + "coefficients = np.array([2.5, -1.3, 0.7, 3.8, -2.1]) # True coefficients\n", + "y = X @ coefficients + np.random.randn(500) * 0.5 # Add some noise\n", + "\n", + "# Split into training and testing sets\n", + "train_size = int(0.8 * len(y))\n", + "X_train, X_test = X[:train_size], X[train_size:]\n", + "y_train, y_test = y[:train_size], y[train_size:]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cross-Validation MSE Scores: [0.3082778981135455, 0.1685034649290135, 0.1813891176082047, 0.30119000492960185, 0.20715207141827233]\n", + "Average MSE: 0.23330251139972763\n" + ] + } + ], + "source": [ + "def k_fold_cv(X, y, k=5):\n", + " np.random.seed(42)\n", + " indices = np.arange(len(y))\n", + " np.random.shuffle(indices) # Shuffle the data indices\n", + " folds = np.array_split(indices, k)\n", + " \n", + " mse_scores = []\n", + " \n", + " for i in range(k):\n", + " # Split into training and validation sets\n", + " val_indices = folds[i]\n", + " train_indices = np.hstack([folds[j] for j in range(k) if j != i])\n", + " \n", + " X_train, y_train = X[train_indices], y[train_indices]\n", + " X_val, y_val = X[val_indices], y[val_indices]\n", + " \n", + " # Train a simple linear regression model\n", + " beta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train # Normal equation\n", + " y_pred = X_val @ beta\n", + " mse = np.mean((y_val - y_pred) ** 2)\n", + " mse_scores.append(mse)\n", + " \n", + " return mse_scores, np.mean(mse_scores)\n", + "\n", + "# Perform K-fold cross-validation\n", + "cv_scores, avg_cv_score = k_fold_cv(X_train, y_train, k=5)\n", + "print(f\"Cross-Validation MSE Scores: {cv_scores}\")\n", + "print(f\"Average MSE: {avg_cv_score}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Bootstrapping MSE Scores: [0.20851088434828502, 0.23676555217996578, 0.23373954266992722, 0.22578188698517473, 0.2204746046678121] (showing first 5)\n", + "Average MSE: 0.22746837670226142\n" + ] + } + ], + "source": [ + "def bootstrap(X, y, num_iterations=100):\n", + " np.random.seed(42)\n", + " n = len(y)\n", + " mse_scores = []\n", + " \n", + " for _ in range(num_iterations):\n", + " indices = np.random.choice(np.arange(n), size=n, replace=True) # Sample with replacement\n", + " X_sample, y_sample = X[indices], y[indices]\n", + " \n", + " # Train the model on the bootstrap sample\n", + " beta = np.linalg.inv(X_sample.T @ X_sample) @ X_sample.T @ y_sample\n", + " y_pred = X_sample @ beta\n", + " mse = np.mean((y_sample - y_pred) ** 2)\n", + " mse_scores.append(mse)\n", + " \n", + " return mse_scores, np.mean(mse_scores)\n", + "\n", + "# Perform bootstrapping\n", + "bootstrap_scores, avg_bootstrap_score = bootstrap(X_train, y_train, num_iterations=100)\n", + "print(f\"Bootstrapping MSE Scores: {bootstrap_scores[:5]} (showing first 5)\")\n", + "print(f\"Average MSE: {avg_bootstrap_score}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76476555-5d18-459d-bdd6-a5137cadade6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AIC: -580.4077545069757\n" + ] + } + ], + "source": [ + "def calculate_aic(X, y):\n", + " n, p = X.shape\n", + " beta = np.linalg.inv(X.T @ X) @ X.T @ y\n", + " y_pred = X @ beta\n", + " rss = np.sum((y - y_pred) ** 2)\n", + " aic = n * np.log(rss / n) + 2 * p\n", + " return aic\n", + "\n", + "# Add intercept to X_train\n", + "X_train_intercept = np.hstack([np.ones((X_train.shape[0], 1)), X_train])\n", + "aic = calculate_aic(X_train_intercept, y_train)\n", + "print(f\"AIC: {aic}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the intercept explicitly if not already done\n", + "# Mean of y and X for calculating intercept\n", + "y_mean = np.mean(y_train)\n", + "X_mean = np.mean(X_train, axis=0)\n", + "\n", + "# Assuming 'coefficients' is a vector obtained earlier\n", + "intercept = y_mean - np.dot(X_mean, coefficients)\n", + "\n", + "# Generate predictions\n", + "y_train_pred = np.dot(X_train, coefficients) + intercept # Add intercept here\n", + "residuals = y_train - y_train_pred\n", + "\n", + "# Residuals vs Predicted Values\n", + "plt.figure(figsize=(8, 5))\n", + "plt.scatter(y_train_pred, residuals, alpha=0.7)\n", + "plt.axhline(y=0, color='red', linestyle='--')\n", + "plt.xlabel(\"Predicted Values\")\n", + "plt.ylabel(\"Residuals\")\n", + "plt.title(\"Residuals vs Predicted Values\")\n", + "plt.show()\n", + "\n", + "# Histogram/KDE of Residuals\n", + "plt.figure(figsize=(8, 5))\n", + "plt.hist(residuals, bins=20, alpha=0.7, color='blue', edgecolor='black', density=True)\n", + "plt.axvline(x=0, color='red', linestyle='--')\n", + "plt.title(\"Distribution of Residuals\")\n", + "plt.xlabel(\"Residuals\")\n", + "plt.ylabel(\"Density\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04d106c3-1e2d-48c4-a968-73fa436d81ef", + "metadata": {}, + "outputs": [], + "source": [ + "#The points appear to be randomly scattered around the red dashed line at 𝑦 = 0 y=0, which is a good sign. This indicates no clear pattern, suggesting that the model is likely capturing the relationship between predictors and the target variable reasonably well." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 95b8114eba9cb13c5852a13961eab440932c5825 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Wed, 20 Nov 2024 22:35:55 -0500 Subject: [PATCH 03/20] z --- Project2.ipynb | 6 ++++-- src/model_selection.py | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index 1a6d4eb..4dfcff6 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], @@ -18,7 +18,9 @@ "# Split into training and testing sets\n", "train_size = int(0.8 * len(y))\n", "X_train, X_test = X[:train_size], X[train_size:]\n", - "y_train, y_test = y[:train_size], y[train_size:]\n" + "y_train, y_test = y[:train_size], y[train_size:]\n", + "\n", + "#m,nmb,mn nmmmn\n" ] }, { diff --git a/src/model_selection.py b/src/model_selection.py index cc02160..314b577 100644 --- a/src/model_selection.py +++ b/src/model_selection.py @@ -13,7 +13,7 @@ def mean_squared_error(self, y_true, y_pred): return np.mean((y_true - y_pred) ** 2) def r_squared(self, y_true, y_pred): - ss_total = np.sum((y_true - np.mean(y_true)) ** 2) + ss_total = np.sum((y_true - np.mean(y_true)) ** 2) ss_residual = np.sum((y_true - y_pred) ** 2) return 1 - (ss_residual / ss_total) From 580a8361b9cc7172452ea51987ed1c03251948f4 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Wed, 20 Nov 2024 22:43:13 -0500 Subject: [PATCH 04/20] a --- Project2.ipynb | 159 +++++++++++++++++++++++++++-------------- src/model_selection.py | 141 +++++++++++++++--------------------- 2 files changed, 163 insertions(+), 137 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index 4dfcff6..1239686 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], @@ -10,22 +10,52 @@ "import numpy as np\n", "\n", "# Generate random data\n", - "np.random.seed(42)\n", - "X = np.random.rand(500, 5) # 500 samples, 5 features\n", - "coefficients = np.array([2.5, -1.3, 0.7, 3.8, -2.1]) # True coefficients\n", - "y = X @ coefficients + np.random.randn(500) * 0.5 # Add some noise\n", + "np.random.seed(42) \n", + "X = np.random.rand(5500, 10) # 500 samples, 5 features you can change the number of features and samples as you like\n", + "coefficients = np.array([2.5, -1.3, 0.7, 3.8, -2.1,2.5, -1.3, 0.7, 3.8, -2.1]) # True coefficients of the model change as you like\n", + "y = X @ coefficients + np.random.randn(5500) * 0.5 # Add some noise\n", "\n", - "# Split into training and testing sets\n", + "# Split into training and testing sets \n", "train_size = int(0.8 * len(y))\n", - "X_train, X_test = X[:train_size], X[train_size:]\n", - "y_train, y_test = y[:train_size], y[train_size:]\n", + "Xtrain, Xtest = X[:train_size], X[train_size:]\n", + "yTrain, yTest = y[:train_size], y[train_size:]\n", + "\n", + "class LinearRegression:\n", + " def __init__(self):\n", + " self.coefficients = None\n", + " self.intercept = None\n", + "\n", + " def fit(self, X, y):\n", + " X = np.insert(X, 0, 1, axis=1)\n", + " self.coefficients = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n", + " self.intercept = self.coefficients[0]\n", + " self.coefficients = self.coefficients[1:]\n", + "\n", + " def predict(self, X):\n", + " return X.dot(self.coefficients) + self.intercept\n", + "\n", + "class RidgeRegression:\n", + " def __init__(self, alpha=1.0):\n", + " self.alpha = alpha\n", + " self.coefficients = None\n", + " self.intercept = None\n", + "\n", + " def fit(self, X, y):\n", + " X = np.insert(X, 0, 1, axis=1)\n", + " I = np.eye(X.shape[1])\n", + " I[0, 0] = 0 # Don't regularize the intercept\n", + " self.coefficients = np.linalg.inv(X.T.dot(X) + self.alpha * I).dot(X.T).dot(y)\n", + " self.intercept = self.coefficients[0]\n", + " self.coefficients = self.coefficients[1:]\n", "\n", - "#m,nmb,mn nmmmn\n" + " def predict(self, X):\n", + " return X.dot(self.coefficients) + self.intercept\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 19, "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", "metadata": {}, "outputs": [ @@ -33,45 +63,54 @@ "name": "stdout", "output_type": "stream", "text": [ - "Cross-Validation MSE Scores: [0.3082778981135455, 0.1685034649290135, 0.1813891176082047, 0.30119000492960185, 0.20715207141827233]\n", - "Average MSE: 0.23330251139972763\n" + "Cross-Validation MSE Scores: [0.24731074355431615, 0.24752836260831246, 0.24481450529227203, 0.24013742706117508, 0.2325568319983055]\n", + "Average MSE: 0.24246957410287626\n" ] } ], "source": [ - "def k_fold_cv(X, y, k=5):\n", - " np.random.seed(42)\n", + "'''\n", + "X => should be a numpy array of shape (n_samples, n_features) \n", + "y => should be a numpy array of shape (n_samples,).\n", + "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", + "seed => is an integer that sets the random seed for reproducibility.\n", + "\n", + "\n", + "'''\n", + "def kFoldCv(X, y,model, k=5,seed=1):\n", + "\n", + " np.random.seed(seed)\n", + " # Shuffling the data indices\n", " indices = np.arange(len(y))\n", - " np.random.shuffle(indices) # Shuffle the data indices\n", + " np.random.shuffle(indices) \n", " folds = np.array_split(indices, k)\n", " \n", - " mse_scores = []\n", - " \n", + " mseScores = []\n", + " # it will loop through the folds\n", " for i in range(k):\n", - " # Split into training and validation sets\n", - " val_indices = folds[i]\n", - " train_indices = np.hstack([folds[j] for j in range(k) if j != i])\n", - " \n", - " X_train, y_train = X[train_indices], y[train_indices]\n", - " X_val, y_val = X[val_indices], y[val_indices]\n", - " \n", - " # Train a simple linear regression model\n", - " beta = np.linalg.inv(X_train.T @ X_train) @ X_train.T @ y_train # Normal equation\n", - " y_pred = X_val @ beta\n", - " mse = np.mean((y_val - y_pred) ** 2)\n", - " mse_scores.append(mse)\n", - " \n", - " return mse_scores, np.mean(mse_scores)\n", + " # split the data into training and validation sets\n", + " valIndices = folds[i]\n", + " trainIndices = np.hstack([folds[j] for j in range(k) if j != i])\n", + " Xtrain, yTrain = X[trainIndices], y[trainIndices]\n", + " Xval, yVal = X[valIndices], y[valIndices]\n", + " # Fit the regression model on the training data\n", + " model.fit(Xtrain, yTrain)\n", + " # Predict on the validation set and calculate the MSE\n", + " yPred = model.predict(Xval)\n", + " mse = np.mean((yVal - yPred) ** 2)\n", + " mseScores.append(mse)\n", + " return mseScores, np.mean(mseScores)\n", + "\n", "\n", "# Perform K-fold cross-validation\n", - "cv_scores, avg_cv_score = k_fold_cv(X_train, y_train, k=5)\n", + "cv_scores, avg_cv_score = kFoldCv(Xtrain, yTrain,model=LinearRegression(), k=5,seed=1)\n", "print(f\"Cross-Validation MSE Scores: {cv_scores}\")\n", "print(f\"Average MSE: {avg_cv_score}\")\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 20, "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", "metadata": {}, "outputs": [ @@ -79,46 +118,60 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bootstrapping MSE Scores: [0.20851088434828502, 0.23676555217996578, 0.23373954266992722, 0.22578188698517473, 0.2204746046678121] (showing first 5)\n", - "Average MSE: 0.22746837670226142\n" + "Bootstrapping MSE Scores: [0.24076741491007278, 0.23159162671654032, 0.24750871435905714, 0.2506853089469749, 0.24118386552327975] (showing first 5)\n", + "Average MSE: 0.23972558425857263\n" ] } ], "source": [ - "def bootstrap(X, y, num_iterations=100):\n", - " np.random.seed(42)\n", + "'''\n", + "X => should be a numpy array of shape (n_samples, n_features)\n", + "y => should be a numpy array of shape (n_samples,).\n", + "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", + "numIterations => is an integer that sets the number of bootstrap iterations.\n", + "seed => is an integer that sets the random seed for reproducibility.\n", + "'''\n", + "# Bootstrapping function\n", + "def bootstrap(X, y,model, numIterations=100, seed=1):\n", + " np.random.seed(seed)\n", " n = len(y)\n", " mse_scores = []\n", - " \n", - " for _ in range(num_iterations):\n", - " indices = np.random.choice(np.arange(n), size=n, replace=True) # Sample with replacement\n", - " X_sample, y_sample = X[indices], y[indices]\n", - " \n", - " # Train the model on the bootstrap sample\n", - " beta = np.linalg.inv(X_sample.T @ X_sample) @ X_sample.T @ y_sample\n", - " y_pred = X_sample @ beta\n", - " mse = np.mean((y_sample - y_pred) ** 2)\n", + " # Loop through the number of iterations\n", + " for _ in range(numIterations):\n", + " # Sample with replacement from the data\n", + " indices = np.random.choice(np.arange(n), size=n, replace=True) \n", + " Xsample, ySample = X[indices], y[indices]\n", + " # Train the regression model on the sampled data\n", + " model.fit(Xsample, ySample)\n", + " yPred = model.predict(Xsample)\n", + " mse = np.mean((ySample - yPred) ** 2)\n", " mse_scores.append(mse)\n", " \n", " return mse_scores, np.mean(mse_scores)\n", "\n", + "\n", + "\n", "# Perform bootstrapping\n", - "bootstrap_scores, avg_bootstrap_score = bootstrap(X_train, y_train, num_iterations=100)\n", + "bootstrap_scores, avg_bootstrap_score = bootstrap(Xtrain, yTrain,model=LinearRegression(), numIterations=100, seed=1)\n", "print(f\"Bootstrapping MSE Scores: {bootstrap_scores[:5]} (showing first 5)\")\n", "print(f\"Average MSE: {avg_bootstrap_score}\")\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "id": "76476555-5d18-459d-bdd6-a5137cadade6", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "AIC: -580.4077545069757\n" + "ename": "NameError", + "evalue": "name 'X_train' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[21], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m aic\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# Add intercept to X_train\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m X_train_intercept \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mhstack([np\u001b[38;5;241m.\u001b[39mones((\u001b[43mX_train\u001b[49m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m)), X_train])\n\u001b[0;32m 11\u001b[0m aic \u001b[38;5;241m=\u001b[39m calculate_aic(X_train_intercept, y_train)\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAIC: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maic\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined" ] } ], @@ -139,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ diff --git a/src/model_selection.py b/src/model_selection.py index 314b577..b8feba5 100644 --- a/src/model_selection.py +++ b/src/model_selection.py @@ -1,89 +1,62 @@ import numpy as np -class ModelSelection: - def __init__(self, model, X, y): - self.model = model - self.X = X - self.y = y - def mean_absolute_error(self, y_true, y_pred): - return np.mean(np.abs(y_true - y_pred)) +# by default it sets k=5 for folds Xtrain and yTrain are the training data +''' +X => should be a numpy array of shape (n_samples, n_features) +y => should be a numpy array of shape (n_samples,). +model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression. +seed => is an integer that sets the random seed for reproducibility. + + +''' +def kFoldCv(X, y,model, k=5,seed=1): + + np.random.seed(seed) + # Shuffling the data indices + indices = np.arange(len(y)) + np.random.shuffle(indices) + folds = np.array_split(indices, k) + + mseScores = [] + # it will loop through the folds + for i in range(k): + # split the data into training and validation sets + valIndices = folds[i] + trainIndices = np.hstack([folds[j] for j in range(k) if j != i]) + Xtrain, yTrain = X[trainIndices], y[trainIndices] + Xval, yVal = X[valIndices], y[valIndices] + # Fit the regression model on the training data + model.fit(Xtrain, yTrain) + # Predict on the validation set and calculate the MSE + yPred = model.predict(Xval) + mse = np.mean((yVal - yPred) ** 2) + mseScores.append(mse) + return mseScores, np.mean(mseScores) + +''' +X => should be a numpy array of shape (n_samples, n_features) +y => should be a numpy array of shape (n_samples,). +model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression. +numIterations => is an integer that sets the number of bootstrap iterations. +seed => is an integer that sets the random seed for reproducibility. +''' +# Bootstrapping function +def bootstrap(X, y,model, numIterations=100, seed=1): + np.random.seed(seed) + n = len(y) + mse_scores = [] + # Loop through the number of iterations + for _ in range(numIterations): + # Sample with replacement from the data + indices = np.random.choice(np.arange(n), size=n, replace=True) + Xsample, ySample = X[indices], y[indices] + # Train the regression model on the sampled data + model.fit(Xsample, ySample) + yPred = model.predict(Xsample) + mse = np.mean((ySample - yPred) ** 2) + mse_scores.append(mse) + + return mse_scores, np.mean(mse_scores) - def mean_squared_error(self, y_true, y_pred): - return np.mean((y_true - y_pred) ** 2) - def r_squared(self, y_true, y_pred): - ss_total = np.sum((y_true - np.mean(y_true)) ** 2) - ss_residual = np.sum((y_true - y_pred) ** 2) - return 1 - (ss_residual / ss_total) - - def k_fold_cross_validation(self, k=5): - fold_size = len(self.X) // k - r2_scores = [] - mae_scores = [] - mse_scores = [] - - for i in range(k): - # Split data into k folds - start = i * fold_size - end = (i + 1) * fold_size if i != k - 1 else len(self.X) - - X_train = np.concatenate([self.X[:start], self.X[end:]]) - y_train = np.concatenate([self.y[:start], self.y[end:]]) - X_val = self.X[start:end] - y_val = self.y[start:end] - - # Train model - self.model.fit(X_train, y_train) - y_pred = self.model.predict(X_val) - - # Evaluate performance - r2 = self.r_squared(y_val, y_pred) - mae = self.mean_absolute_error(y_val, y_pred) - mse = self.mean_squared_error(y_val, y_pred) - - r2_scores.append(r2) - mae_scores.append(mae) - mse_scores.append(mse) - - return { - 'mean_r2': np.mean(r2_scores), - 'mean_mae': np.mean(mae_scores), - 'mean_mse': np.mean(mse_scores), - 'r2_scores': r2_scores, - 'mae_scores': mae_scores, - 'mse_scores': mse_scores - } - - def bootstrap(self, n_iterations=100): - r2_scores = [] - mae_scores = [] - mse_scores = [] - - for i in range(n_iterations): - # Resample data with replacement - indices = np.random.choice(len(self.X), size=len(self.X), replace=True) - X_resampled = self.X[indices] - y_resampled = self.y[indices] - - # Train model - self.model.fit(X_resampled, y_resampled) - y_pred = self.model.predict(self.X) - - # Evaluate performance - r2 = self.r_squared(self.y, y_pred) - mae = self.mean_absolute_error(self.y, y_pred) - mse = self.mean_squared_error(self.y, y_pred) - - r2_scores.append(r2) - mae_scores.append(mae) - mse_scores.append(mse) - - return { - 'mean_r2': np.mean(r2_scores), - 'mean_mae': np.mean(mae_scores), - 'mean_mse': np.mean(mse_scores), - 'r2_scores': r2_scores, - 'mae_scores': mae_scores, - 'mse_scores': mse_scores - } From 9db22e94a76bdc5d60aacc11ff413a3b8b05449a Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Wed, 20 Nov 2024 22:55:03 -0500 Subject: [PATCH 05/20] a --- Project2.ipynb | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index 1239686..0be40c3 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 49, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], @@ -55,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 50, "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", "metadata": {}, "outputs": [ @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 51, "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", "metadata": {}, "outputs": [ @@ -159,46 +159,45 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 52, "id": "76476555-5d18-459d-bdd6-a5137cadade6", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'X_train' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[21], line 10\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m aic\n\u001b[0;32m 9\u001b[0m \u001b[38;5;66;03m# Add intercept to X_train\u001b[39;00m\n\u001b[1;32m---> 10\u001b[0m X_train_intercept \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mhstack([np\u001b[38;5;241m.\u001b[39mones((\u001b[43mX_train\u001b[49m\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;241m1\u001b[39m)), X_train])\n\u001b[0;32m 11\u001b[0m aic \u001b[38;5;241m=\u001b[39m calculate_aic(X_train_intercept, y_train)\n\u001b[0;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAIC: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maic\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mNameError\u001b[0m: name 'X_train' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "AIC: -6247.928876908957\n" ] } ], "source": [ "def calculate_aic(X, y):\n", " n, p = X.shape\n", + " # Fit the model\n", " beta = np.linalg.inv(X.T @ X) @ X.T @ y\n", " y_pred = X @ beta\n", + " # Calculate the residual sum of squares\n", " rss = np.sum((y - y_pred) ** 2)\n", + " # Calculate the AIC\n", " aic = n * np.log(rss / n) + 2 * p\n", " return aic\n", "\n", "# Add intercept to X_train\n", - "X_train_intercept = np.hstack([np.ones((X_train.shape[0], 1)), X_train])\n", - "aic = calculate_aic(X_train_intercept, y_train)\n", + "XtrainIntercept = np.hstack([np.ones((Xtrain.shape[0], 1)), Xtrain])\n", + "aic = calculate_aic(XtrainIntercept, yTrain)\n", "print(f\"AIC: {aic}\")\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -218,21 +217,23 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "# Compute the intercept explicitly if not already done\n", "# Mean of y and X for calculating intercept\n", - "y_mean = np.mean(y_train)\n", - "X_mean = np.mean(X_train, axis=0)\n", + "y_mean = np.mean(yTrain)\n", + "X_mean = np.mean(Xtrain, axis=0)\n", "\n", "# Assuming 'coefficients' is a vector obtained earlier\n", "intercept = y_mean - np.dot(X_mean, coefficients)\n", "\n", "# Generate predictions\n", - "y_train_pred = np.dot(X_train, coefficients) + intercept # Add intercept here\n", - "residuals = y_train - y_train_pred\n", + "yTrain_pred = np.dot(Xtrain, coefficients) + intercept # Add intercept here\n", + "residuals = yTrain - yTrain_pred\n", "\n", "# Residuals vs Predicted Values\n", "plt.figure(figsize=(8, 5))\n", - "plt.scatter(y_train_pred, residuals, alpha=0.7)\n", + "plt.scatter(yTrain_pred, residuals, alpha=0.7)\n", "plt.axhline(y=0, color='red', linestyle='--')\n", "plt.xlabel(\"Predicted Values\")\n", "plt.ylabel(\"Residuals\")\n", @@ -251,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "04d106c3-1e2d-48c4-a968-73fa436d81ef", "metadata": {}, "outputs": [], From 1dd0c681f72c8667628c9aed200754da28382a6f Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:01:43 -0500 Subject: [PATCH 06/20] update --- Project2.ipynb | 33 ++-- README.md | 82 +++++++-- .../model_selection.cpython-312.pyc | Bin 4650 -> 0 bytes .../model_selection.cpython-39.pyc | Bin 2461 -> 0 bytes .../simple_linear_regression.cpython-312.pyc | Bin 1569 -> 0 bytes .../simple_linear_regression.cpython-39.pyc | Bin 1016 -> 0 bytes src/modelSelection.py | 85 +++++++++ src/model_selection.py | 62 ------- src/pythonModelTest.py | 20 +++ src/simple_linear_regression.py | 19 -- .../test_model_selection.cpython-312.pyc | Bin 5026 -> 0 bytes ...odel_selection.cpython-39-pytest-7.1.2.pyc | Bin 1361 -> 0 bytes .../test_model_selection.cpython-39.pyc | Bin 4336 -> 0 bytes tests/test_model_selection.py | 165 ------------------ 14 files changed, 186 insertions(+), 280 deletions(-) delete mode 100644 src/__pycache__/model_selection.cpython-312.pyc delete mode 100644 src/__pycache__/model_selection.cpython-39.pyc delete mode 100644 src/__pycache__/simple_linear_regression.cpython-312.pyc delete mode 100644 src/__pycache__/simple_linear_regression.cpython-39.pyc create mode 100644 src/modelSelection.py delete mode 100644 src/model_selection.py create mode 100644 src/pythonModelTest.py delete mode 100644 src/simple_linear_regression.py delete mode 100644 tests/__pycache__/test_model_selection.cpython-312.pyc delete mode 100644 tests/__pycache__/test_model_selection.cpython-39-pytest-7.1.2.pyc delete mode 100644 tests/__pycache__/test_model_selection.cpython-39.pyc delete mode 100644 tests/test_model_selection.py diff --git a/Project2.ipynb b/Project2.ipynb index 0be40c3..de8a588 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 49, + "execution_count": 65, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], @@ -20,6 +20,8 @@ "Xtrain, Xtest = X[:train_size], X[train_size:]\n", "yTrain, yTest = y[:train_size], y[train_size:]\n", "\n", + "\n", + "\n", "class LinearRegression:\n", " def __init__(self):\n", " self.coefficients = None\n", @@ -55,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 66, "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", "metadata": {}, "outputs": [ @@ -74,9 +76,8 @@ "y => should be a numpy array of shape (n_samples,).\n", "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", "seed => is an integer that sets the random seed for reproducibility.\n", - "\n", - "\n", "'''\n", + "\n", "def kFoldCv(X, y,model, k=5,seed=1):\n", "\n", " np.random.seed(seed)\n", @@ -110,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 67, "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", "metadata": {}, "outputs": [ @@ -159,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 68, "id": "76476555-5d18-459d-bdd6-a5137cadade6", "metadata": {}, "outputs": [ @@ -167,16 +168,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "AIC: -6247.928876908957\n" + "AIC: -6249.928876908958\n" ] } ], "source": [ - "def calculate_aic(X, y):\n", + "def calculate_aic(X, y,model):\n", " n, p = X.shape\n", " # Fit the model\n", - " beta = np.linalg.inv(X.T @ X) @ X.T @ y\n", - " y_pred = X @ beta\n", + " model.fit(X, y)\n", + " y_pred = model.predict(X)\n", + " \n", " # Calculate the residual sum of squares\n", " rss = np.sum((y - y_pred) ** 2)\n", " # Calculate the AIC\n", @@ -184,14 +186,13 @@ " return aic\n", "\n", "# Add intercept to X_train\n", - "XtrainIntercept = np.hstack([np.ones((Xtrain.shape[0], 1)), Xtrain])\n", - "aic = calculate_aic(XtrainIntercept, yTrain)\n", + "aic = calculate_aic(Xtrain, yTrain,model=LinearRegression())\n", "print(f\"AIC: {aic}\")\n" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 69, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ @@ -252,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 70, "id": "04d106c3-1e2d-48c4-a968-73fa436d81ef", "metadata": {}, "outputs": [], @@ -263,7 +264,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -277,7 +278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/README.md b/README.md index f746e56..ed7980c 100644 --- a/README.md +++ b/README.md @@ -1,29 +1,75 @@ -# Project 2 +# Project Overview -Select one of the following two options: +This project implements and evaluates different model selection techniques to compare the performance of regression models. The following methods are covered: -## Boosting Trees +- **Cross-Validation (K-Fold):** Evaluates model performance by splitting the data into multiple folds. +- **Bootstrapping:** Estimates variability in performance using multiple resamples of the dataset. +- **Akaike Information Criterion (AIC):** Balances model fit and complexity by penalizing overly complex models. -Implement the gradient-boosting tree algorithm (with the usual fit-predict interface) as described in Sections 10.9-10.10 of Elements of Statistical Learning (2nd Edition). Answer the questions below as you did for Project 1. +These methods are tested on synthetic data using Linear and Ridge Regression models implemented from scratch. -Put your README below. Answer the following questions. +## How to Run the Code -* What does the model you have implemented do and when should it be used? -* How did you test your model to determine if it is working reasonably correctly? -* What parameters have you exposed to users of your implementation in order to tune performance? (Also perhaps provide some basic usage examples.) -* Are there specific inputs that your implementation has trouble with? Given more time, could you work around these or is it fundamental? +### Prerequisites -## Model Selection +Install Python and required libraries: `numpy`, `matplotlib`. -Implement generic k-fold cross-validation and bootstrapping model selection methods. +### Steps to Execute -In your README, answer the following questions: +1. Open the terminal at the project folder: +2. Command: `jupyter notebook Project2.ipynb` +3. Run all cells sequentially +4. View model evaluation outputs (e.g., MSE scores, AIC values) +5. View the plots for insights into model performance and residuals -* Do your cross-validation and bootstrapping model selectors agree with a simpler model selector like AIC in simple cases (like linear regression)? -* In what cases might the methods you've written fail or give incorrect or undesirable results? -* What could you implement given more time to mitigate these cases or help users of your methods? -* What parameters have you exposed to your users in order to use your model selectors. +## Outputs -See sections 7.10-7.11 of Elements of Statistical Learning and the lecture notes. Pay particular attention to Section 7.10.2. +1. **Cross-Validation:** + - MSE Scores: [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] + - Average MSE: 0.2425 +2. **Bootstrapping:** + - MSE Scores (first 5): [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] + - Average MSE: 0.2397 +3. **AIC:** + - Value: -6247.93 +4. **Visualizations:** + - Residual distribution plot + - Error comparison across methods + - Bootstrapping confidence intervals -As usual, above-and-beyond efforts will be considered for bonus points. +## Key Questions and Answers + +1. **Do Cross-Validation and Bootstrapping agree with AIC?** + - Yes, all three methods largely agree in this implementation. + - Example: + - Cross-validation and bootstrapping indicate a lower MSE for the selected model. + - AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. + +2. **In what cases might these methods fail?** + - **Cross-Validation:** Fails for small datasets where splits lead to insufficient training data. + - **Bootstrapping:** Assumes data is i.i.d.; fails for dependent datasets like time series. + - **AIC:** Assumes normally distributed residuals; results may mislead for non-linear models. + +3. **What could mitigate these issues?** + - Implement stratified k-fold for imbalanced datasets. + - Use block bootstrapping for dependent data. + - Extend AIC to include models with corrected versions like AICc. + +4. **What parameters are exposed?** + - **Cross-Validation:** + - `k` (number of folds), default: 5 + - **Bootstrapping:** + - `num_iterations` (default: 100), seed for reproducibility + - **Regression Models:** + - Regularization parameter `alpha` (for Ridge), default: 1.0 + +## Additional Features + +- Synthetic data generator for flexible testing +- Residual analysis and visualization tools for insights +- Extendable framework for adding new model selectors + +## Compatibility + +- Tested on Windows, macOS, and Linux +- Requires `numpy`, `matplotlib`, `Jupyter` diff 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np.random.shuffle(indices) + folds = np.array_split(indices, k) + + mseScores = [] + # it will loop through the folds + for i in range(k): + # split the data into training and validation sets + valIndices = folds[i] + trainIndices = np.hstack([folds[j] for j in range(k) if j != i]) + Xtrain, yTrain = X[trainIndices], y[trainIndices] + Xval, yVal = X[valIndices], y[valIndices] + # Fit the regression model on the training data + model.fit(Xtrain, yTrain) + # Predict on the validation set and calculate the MSE + yPred = model.predict(Xval) + mse = np.mean((yVal - yPred) ** 2) + mseScores.append(mse) + return mseScores, np.mean(mseScores) + + + + # Bootstrapping function + def bootstrap(X, y,model, numIterations=100, seed=1): + ''' + X => should be a numpy array of shape (n_samples, n_features) + y => should be a numpy array of shape (n_samples,). + model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression. + numIterations => is an integer that sets the number of bootstrap iterations. + seed => is an integer that sets the random seed for reproducibility. + ''' + np.random.seed(seed) + n = len(y) + mse_scores = [] + # Loop through the number of iterations + for _ in range(numIterations): + # Sample with replacement from the data + indices = np.random.choice(np.arange(n), size=n, replace=True) + Xsample, ySample = X[indices], y[indices] + # Train the regression model on the sampled data + model.fit(Xsample, ySample) + yPred = model.predict(Xsample) + # Calculate the MSE + mse = np.mean((ySample - yPred) ** 2) + # Append the MSE to the list of MSEs + mse_scores.append(mse) + + return mse_scores, np.mean(mse_scores) + + + def calculateAic(X, y,model): + ''' + X => should be a numpy array of shape (n_samples, n_features) + y => should be a numpy array of shape (n_samples,). + model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression + ''' + n, p = X.shape + # Fit the model + model.fit(X, y) + y_pred = model.predict(X) + + # Calculate the residual sum of squares + rss = np.sum((y - y_pred) ** 2) + # Calculate the AIC + aic = n * np.log(rss / n) + 2 * p + return aic + + + + diff --git a/src/model_selection.py b/src/model_selection.py deleted file mode 100644 index b8feba5..0000000 --- a/src/model_selection.py +++ /dev/null @@ -1,62 +0,0 @@ -import numpy as np - - -# by default it sets k=5 for folds Xtrain and yTrain are the training data -''' -X => should be a numpy array of shape (n_samples, n_features) -y => should be a numpy array of shape (n_samples,). -model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression. -seed => is an integer that sets the random seed for reproducibility. - - -''' -def kFoldCv(X, y,model, k=5,seed=1): - - np.random.seed(seed) - # Shuffling the data indices - indices = np.arange(len(y)) - np.random.shuffle(indices) - folds = np.array_split(indices, k) - - mseScores = [] - # it will loop through the folds - for i in range(k): - # split the data into training and validation sets - valIndices = folds[i] - trainIndices = np.hstack([folds[j] for j in range(k) if j != i]) - Xtrain, yTrain = X[trainIndices], y[trainIndices] - Xval, yVal = X[valIndices], y[valIndices] - # Fit the regression model on the training data - model.fit(Xtrain, yTrain) - # Predict on the validation set and calculate the MSE - yPred = model.predict(Xval) - mse = np.mean((yVal - yPred) ** 2) - mseScores.append(mse) - return mseScores, np.mean(mseScores) - -''' -X => should be a numpy array of shape (n_samples, n_features) -y => should be a numpy array of shape (n_samples,). -model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression. -numIterations => is an integer that sets the number of bootstrap iterations. -seed => is an integer that sets the random seed for reproducibility. -''' -# Bootstrapping function -def bootstrap(X, y,model, numIterations=100, seed=1): - np.random.seed(seed) - n = len(y) - mse_scores = [] - # Loop through the number of iterations - for _ in range(numIterations): - # Sample with replacement from the data - indices = np.random.choice(np.arange(n), size=n, replace=True) - Xsample, ySample = X[indices], y[indices] - # Train the regression model on the sampled data - model.fit(Xsample, ySample) - yPred = model.predict(Xsample) - mse = np.mean((ySample - yPred) ** 2) - mse_scores.append(mse) - - return mse_scores, np.mean(mse_scores) - - diff --git a/src/pythonModelTest.py b/src/pythonModelTest.py new file mode 100644 index 0000000..39e05c3 --- /dev/null +++ b/src/pythonModelTest.py @@ -0,0 +1,20 @@ +import numpy as np +from sklearn.linear_model import LinearRegression +from modelSelection import ModelEvaluations + +X = np.random.rand(100, 3) +y = X @ np.array([2, -1, 3]) + np.random.normal(0, 0.5, 100) + +# Create an instance of the LinearRegression model +model = LinearRegression() + +kFoldMseScores, kFoldAvgMse = ModelEvaluations.kFoldCv(X, y, model, k=5) +print("k-Fold MSE Scores:", kFoldMseScores) +print("Average MSE from k-Fold:", kFoldAvgMse) + +bootstrapMseScores, bootstrapAvgMse = ModelEvaluations.bootstrap(X, y, model, numIterations=100) +print("Bootstrap MSE Scores:", bootstrapMseScores) +print("Average MSE from Bootstrapping:", bootstrapAvgMse) + +aicValue = ModelEvaluations.calculateAic(X, y, model) +print("AIC Value:", aicValue) diff --git a/src/simple_linear_regression.py b/src/simple_linear_regression.py deleted file mode 100644 index b99a3fe..0000000 --- a/src/simple_linear_regression.py +++ /dev/null @@ -1,19 +0,0 @@ -import numpy as np - -class SimpleLinearRegression: - def __init__(self): - self.coefficients = None - self.intercept = None - - def fit(self, X, y): - # Add a column of ones for the intercept term - X = np.c_[np.ones(X.shape[0]), X] - # Compute the coefficients using the normal 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zd~wjR*Rc5t3J=t0z3TX62p^+7m#>!@bv@RsF(#TZIVnVrG;*fnPN?&39vW54Uk~G0 zO~CvxVXbk=Fv_0oh6!pj*Q!96G|i&zfN7GX=&D#UEKF9c4Ft|OO9?v$2cS&+H;km+ zX2T_hc$^qMj0(xr+qB7jjIDgNO2B%f9ViyZ7@1XizL;& z4y3C|Hu?t1d}N`lUbKCZsMd>%#J5dBV$y0AycC>=F?`9Z_>j0})xzjt+#Ih&bjvP~ z^njI*COGzA$xFsNM*b8m^hXX!IoMh??5Eqi;7S_8NEz%$5NaBqt zS0u3=5t)dnyBNprDr^|yc_3}LClm?qi6NazH7*4j;RVab-YfYDYinIpam#z(d{x8b zT})NYf&1d$8T@ZJB-Q$tQN@?kFY0I14n3n9s-bu4S)>N_A(ZKtbdQ==dr&H_W^gBo zG=93K_W!$EyQCXxTzvmZ#`=GL*T|@8y<6|l2emXx_M=wu34O_wVV Date: Thu, 21 Nov 2024 22:04:11 -0500 Subject: [PATCH 07/20] a --- README.md | 79 ++++++++++++++++++++++++++----------------------------- 1 file changed, 38 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index ed7980c..78c4234 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,43 @@ -# Project Overview +## How to run the code -This project implements and evaluates different model selection techniques to compare the performance of regression models. The following methods are covered: +### Using a Notebook File -- **Cross-Validation (K-Fold):** Evaluates model performance by splitting the data into multiple folds. -- **Bootstrapping:** Estimates variability in performance using multiple resamples of the dataset. -- **Akaike Information Criterion (AIC):** Balances model fit and complexity by penalizing overly complex models. +#### Prerequisites +- Install Python and required libraries: `numpy`, `matplotlib`. -These methods are tested on synthetic data using Linear and Ridge Regression models implemented from scratch. +#### Steps to Execute +1. Open the terminal at the project folder. +2. Run the command: `jupyter notebook Project2.ipynb` +3. Run all cells sequentially. +4. View the model evaluation outputs (e.g., MSE scores, AIC values) and plots for insights into model performance and residuals. -## How to Run the Code +### Using Python Class -### Prerequisites +#### Prerequisites +- Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. -Install Python and required libraries: `numpy`, `matplotlib`. +#### Steps to Execute +1. Go to the `src` folder. +2. Execute the command: `python .\pythonModelTest.py` +3. View the model evaluation outputs and plots for insights into model performance and residuals. -### Steps to Execute +## Outputs -1. Open the terminal at the project folder: -2. Command: `jupyter notebook Project2.ipynb` -3. Run all cells sequentially -4. View model evaluation outputs (e.g., MSE scores, AIC values) -5. View the plots for insights into model performance and residuals +### Cross-Validation +- **MSE Scores:** [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] +- **Average MSE:** 0.2425 -## Outputs +### Bootstrapping +- **MSE Scores (first 5):** [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] +- **Average MSE:** 0.2397 + +### AIC +- **Value:** -6247.93 -1. **Cross-Validation:** - - MSE Scores: [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] - - Average MSE: 0.2425 -2. **Bootstrapping:** - - MSE Scores (first 5): [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] - - Average MSE: 0.2397 -3. **AIC:** - - Value: -6247.93 -4. **Visualizations:** - - Residual distribution plot - - Error comparison across methods - - Bootstrapping confidence intervals +### Visualizations +- Residual distribution plot. +- Error comparison across methods. +- Bootstrapping confidence intervals. ## Key Questions and Answers @@ -56,20 +58,15 @@ Install Python and required libraries: `numpy`, `matplotlib`. - Extend AIC to include models with corrected versions like AICc. 4. **What parameters are exposed?** - - **Cross-Validation:** - - `k` (number of folds), default: 5 - - **Bootstrapping:** - - `num_iterations` (default: 100), seed for reproducibility - - **Regression Models:** - - Regularization parameter `alpha` (for Ridge), default: 1.0 + - **Cross-Validation:** `k` (number of folds), default: 5. + - **Bootstrapping:** `num_iterations` (default: 100), seed for reproducibility. + - **Regression Models:** Regularization parameter `alpha` (for Ridge), default: 1.0. ## Additional Features - -- Synthetic data generator for flexible testing -- Residual analysis and visualization tools for insights -- Extendable framework for adding new model selectors +- Synthetic data generator for flexible testing. +- Residual analysis and visualization tools for insights. +- Extendable framework for adding new model selectors. ## Compatibility - -- Tested on Windows, macOS, and Linux -- Requires `numpy`, `matplotlib`, `Jupyter` +- Tested on Windows. +- Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn`. From d732c8e1e0e1d0e8dc9150954de734c392221bfd Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:06:27 -0500 Subject: [PATCH 08/20] a --- README.md | 165 +++++++++++++++++++++++++++++++----------------------- 1 file changed, 94 insertions(+), 71 deletions(-) diff --git a/README.md b/README.md index 78c4234..0fb5afe 100644 --- a/README.md +++ b/README.md @@ -1,72 +1,95 @@ -## How to run the code +TEAM: +Rakesh Reddy - A20525389 +Geeta Hade - A20580824 +Nishant Khandhar - A20581012 +Amogh Vastrad - A20588808 + + +How to run the code : + + +For using a notebook file do these steps + + +Prerequisites +Install Python and required libraries: numpy, matplotlib. +Steps to Execute + +1. Open the terminal at project folder: +2. Command : jupyter notebook Project2.ipynb +3. Run all cells sequentially +4. model evaluations outputs (e.g MSE scores, AIC values) +Views the plots for insight for insights into model performance and residuals + + +For using Python class + +Prerequisites +Install Python and required libraries: numpy, matplotlib, sklearn. +Steps to Execute +Go to src folder +Then execute py py .\pythonModelTest.py + +Views the plots for insight for insights into model performance and residuals + + + +Outputs +1. Cross-Validation: +◦ MSE Scores: [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] +◦ Average MSE: 0.2425 +2. Bootstrapping: +◦ MSE Scores (first 5): [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] +◦ Average MSE: 0.2397 + +2. AIC: +◦ Value: -6247.93 +◦ + +4. Visualizations: +◦ Residual distribution plot. +◦ Error comparison across methods. +◦ Bootstrapping confidence intervals. + +Key Questions and Answers +1. Do Cross-Validation and Bootstrapping agree with AIC? +Yes, all three methods largely agree in this implementation. +Example: +• Cross-validation and bootstrapping indicate a lower MSE for the selected model. +• AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. + +2. In what cases might these methods fail? +• Cross-Validation: +Fails for small datasets where splits lead to insufficient training data. +• Bootstrapping: +Assumes data is i.i.d.; fails for dependent datasets like time series. +• AIC: +Assumes normally distributed residuals; results may mislead for non-linear models. + +3. What could mitigate these issues? +• Implement stratified k-fold for imbalanced datasets. +• Use block bootstrapping for dependent data. +• Extend AIC to include models with corrected versions like AICc. + +4. What parameters are exposed? +• Cross-Validation: +◦ k (number of folds), default: 5. +• Bootstrapping: +◦ num_iterations (default: 100), seed for reproducibility. +• Regression Models: +◦ Regularization parameter alpha (for Ridge), default: 1.0. + + + +Additional Features +• Synthetic data generator for flexible testing. +• Residual analysis and visualization tools for insights. +• Extendable framework for adding new model selectors. + + +Compatibility +• Tested on Windows +• Requires numpy, matplotlib, Jupyter, sklearn + + -### Using a Notebook File - -#### Prerequisites -- Install Python and required libraries: `numpy`, `matplotlib`. - -#### Steps to Execute -1. Open the terminal at the project folder. -2. Run the command: `jupyter notebook Project2.ipynb` -3. Run all cells sequentially. -4. View the model evaluation outputs (e.g., MSE scores, AIC values) and plots for insights into model performance and residuals. - -### Using Python Class - -#### Prerequisites -- Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. - -#### Steps to Execute -1. Go to the `src` folder. -2. Execute the command: `python .\pythonModelTest.py` -3. View the model evaluation outputs and plots for insights into model performance and residuals. - -## Outputs - -### Cross-Validation -- **MSE Scores:** [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] -- **Average MSE:** 0.2425 - -### Bootstrapping -- **MSE Scores (first 5):** [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] -- **Average MSE:** 0.2397 - -### AIC -- **Value:** -6247.93 - -### Visualizations -- Residual distribution plot. -- Error comparison across methods. -- Bootstrapping confidence intervals. - -## Key Questions and Answers - -1. **Do Cross-Validation and Bootstrapping agree with AIC?** - - Yes, all three methods largely agree in this implementation. - - Example: - - Cross-validation and bootstrapping indicate a lower MSE for the selected model. - - AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. - -2. **In what cases might these methods fail?** - - **Cross-Validation:** Fails for small datasets where splits lead to insufficient training data. - - **Bootstrapping:** Assumes data is i.i.d.; fails for dependent datasets like time series. - - **AIC:** Assumes normally distributed residuals; results may mislead for non-linear models. - -3. **What could mitigate these issues?** - - Implement stratified k-fold for imbalanced datasets. - - Use block bootstrapping for dependent data. - - Extend AIC to include models with corrected versions like AICc. - -4. **What parameters are exposed?** - - **Cross-Validation:** `k` (number of folds), default: 5. - - **Bootstrapping:** `num_iterations` (default: 100), seed for reproducibility. - - **Regression Models:** Regularization parameter `alpha` (for Ridge), default: 1.0. - -## Additional Features -- Synthetic data generator for flexible testing. -- Residual analysis and visualization tools for insights. -- Extendable framework for adding new model selectors. - -## Compatibility -- Tested on Windows. -- Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn`. From beed05a63f21bfe5d604d667b6a11752021cd89e Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:14:11 -0500 Subject: [PATCH 09/20] a --- Project2.ipynb | 84 ++++++++++---------- README.md | 170 ++++++++++++++++++----------------------- src/modelSelection.py | 30 +++----- src/pythonModelTest.py | 6 +- 4 files changed, 131 insertions(+), 159 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index de8a588..c7870f1 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,26 +2,26 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 83, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", - "# Generate random data\n", + "# generate random data\n", "np.random.seed(42) \n", "X = np.random.rand(5500, 10) # 500 samples, 5 features you can change the number of features and samples as you like\n", - "coefficients = np.array([2.5, -1.3, 0.7, 3.8, -2.1,2.5, -1.3, 0.7, 3.8, -2.1]) # True coefficients of the model change as you like\n", + "coefficients = np.array([2.5, -1.3, 0.7, 3.8, -2.1,2.5, -1.3, 0.7, 3.8, -2.1]) # true coefficients of the model change as you like\n", "y = X @ coefficients + np.random.randn(5500) * 0.5 # Add some noise\n", "\n", - "# Split into training and testing sets \n", + "# split into training and testing sets \n", "train_size = int(0.8 * len(y))\n", "Xtrain, Xtest = X[:train_size], X[train_size:]\n", "yTrain, yTest = y[:train_size], y[train_size:]\n", "\n", "\n", - "\n", + "# Linear regression Model\n", "class LinearRegression:\n", " def __init__(self):\n", " self.coefficients = None\n", @@ -35,7 +35,7 @@ "\n", " def predict(self, X):\n", " return X.dot(self.coefficients) + self.intercept\n", - "\n", + "# Ridge Regression Model\n", "class RidgeRegression:\n", " def __init__(self, alpha=1.0):\n", " self.alpha = alpha\n", @@ -45,7 +45,7 @@ " def fit(self, X, y):\n", " X = np.insert(X, 0, 1, axis=1)\n", " I = np.eye(X.shape[1])\n", - " I[0, 0] = 0 # Don't regularize the intercept\n", + " I[0, 0] = 0 # we don't regularize the intercept\n", " self.coefficients = np.linalg.inv(X.T.dot(X) + self.alpha * I).dot(X.T).dot(y)\n", " self.intercept = self.coefficients[0]\n", " self.coefficients = self.coefficients[1:]\n", @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 84, "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", "metadata": {}, "outputs": [ @@ -74,14 +74,13 @@ "'''\n", "X => should be a numpy array of shape (n_samples, n_features) \n", "y => should be a numpy array of shape (n_samples,).\n", - "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", + "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", "seed => is an integer that sets the random seed for reproducibility.\n", "'''\n", "\n", - "def kFoldCv(X, y,model, k=5,seed=1):\n", - "\n", + "def kFoldCv(X, y, model, k=5, seed=1):\n", " np.random.seed(seed)\n", - " # Shuffling the data indices\n", + " # shuffling the data indices\n", " indices = np.arange(len(y))\n", " np.random.shuffle(indices) \n", " folds = np.array_split(indices, k)\n", @@ -94,24 +93,23 @@ " trainIndices = np.hstack([folds[j] for j in range(k) if j != i])\n", " Xtrain, yTrain = X[trainIndices], y[trainIndices]\n", " Xval, yVal = X[valIndices], y[valIndices]\n", - " # Fit the regression model on the training data\n", + " # fit the regression model on the training data\n", " model.fit(Xtrain, yTrain)\n", - " # Predict on the validation set and calculate the MSE\n", + " # predict on the validation set and calculate the MSE\n", " yPred = model.predict(Xval)\n", " mse = np.mean((yVal - yPred) ** 2)\n", " mseScores.append(mse)\n", " return mseScores, np.mean(mseScores)\n", "\n", - "\n", - "# Perform K-fold cross-validation\n", - "cv_scores, avg_cv_score = kFoldCv(Xtrain, yTrain,model=LinearRegression(), k=5,seed=1)\n", + "# perform K-fold cross-validation\n", + "cv_scores, avg_cv_score = kFoldCv(Xtrain, yTrain, model=LinearRegression(), k=5, seed=1)\n", "print(f\"Cross-Validation MSE Scores: {cv_scores}\")\n", "print(f\"Average MSE: {avg_cv_score}\")\n" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 85, "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", "metadata": {}, "outputs": [ @@ -128,21 +126,21 @@ "'''\n", "X => should be a numpy array of shape (n_samples, n_features)\n", "y => should be a numpy array of shape (n_samples,).\n", - "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", + "model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression.\n", "numIterations => is an integer that sets the number of bootstrap iterations.\n", "seed => is an integer that sets the random seed for reproducibility.\n", "'''\n", - "# Bootstrapping function\n", - "def bootstrap(X, y,model, numIterations=100, seed=1):\n", + "# bootstrapping function\n", + "def bootstrap(X, y, model, numIterations=100, seed=1):\n", " np.random.seed(seed)\n", " n = len(y)\n", " mse_scores = []\n", - " # Loop through the number of iterations\n", + " # loop through the number of iterations\n", " for _ in range(numIterations):\n", - " # Sample with replacement from the data\n", + " # sample with replacement from the data\n", " indices = np.random.choice(np.arange(n), size=n, replace=True) \n", " Xsample, ySample = X[indices], y[indices]\n", - " # Train the regression model on the sampled data\n", + " # train the regression model on the sampled data\n", " model.fit(Xsample, ySample)\n", " yPred = model.predict(Xsample)\n", " mse = np.mean((ySample - yPred) ** 2)\n", @@ -150,17 +148,15 @@ " \n", " return mse_scores, np.mean(mse_scores)\n", "\n", - "\n", - "\n", - "# Perform bootstrapping\n", - "bootstrap_scores, avg_bootstrap_score = bootstrap(Xtrain, yTrain,model=LinearRegression(), numIterations=100, seed=1)\n", + "# perform bootstrapping\n", + "bootstrap_scores, avg_bootstrap_score = bootstrap(Xtrain, yTrain, model=LinearRegression(), numIterations=100, seed=1)\n", "print(f\"Bootstrapping MSE Scores: {bootstrap_scores[:5]} (showing first 5)\")\n", "print(f\"Average MSE: {avg_bootstrap_score}\")\n" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 86, "id": "76476555-5d18-459d-bdd6-a5137cadade6", "metadata": {}, "outputs": [ @@ -173,26 +169,26 @@ } ], "source": [ - "def calculate_aic(X, y,model):\n", + "def calculate_aic(X, y, model):\n", " n, p = X.shape\n", - " # Fit the model\n", + " # fit the model\n", " model.fit(X, y)\n", " y_pred = model.predict(X)\n", " \n", - " # Calculate the residual sum of squares\n", + " # calculate the residual sum of squares\n", " rss = np.sum((y - y_pred) ** 2)\n", - " # Calculate the AIC\n", + " # calculate the AIC\n", " aic = n * np.log(rss / n) + 2 * p\n", " return aic\n", "\n", - "# Add intercept to X_train\n", - "aic = calculate_aic(Xtrain, yTrain,model=LinearRegression())\n", + "# add intercept to X_train\n", + "aic = calculate_aic(Xtrain, yTrain, model=LinearRegression())\n", "print(f\"AIC: {aic}\")\n" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 87, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ @@ -220,19 +216,19 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "# Compute the intercept explicitly if not already done\n", - "# Mean of y and X for calculating intercept\n", + "# compute the intercept explicitly if not already done\n", + "# mean of y and X for calculating intercept\n", "y_mean = np.mean(yTrain)\n", "X_mean = np.mean(Xtrain, axis=0)\n", "\n", - "# Assuming 'coefficients' is a vector obtained earlier\n", + "# assuming 'coefficients' is a vector obtained earlier\n", "intercept = y_mean - np.dot(X_mean, coefficients)\n", "\n", - "# Generate predictions\n", - "yTrain_pred = np.dot(Xtrain, coefficients) + intercept # Add intercept here\n", + "# generate predictions\n", + "yTrain_pred = np.dot(Xtrain, coefficients) + intercept # add intercept here\n", "residuals = yTrain - yTrain_pred\n", "\n", - "# Residuals vs Predicted Values\n", + "# residuals vs predicted values\n", "plt.figure(figsize=(8, 5))\n", "plt.scatter(yTrain_pred, residuals, alpha=0.7)\n", "plt.axhline(y=0, color='red', linestyle='--')\n", @@ -241,7 +237,7 @@ "plt.title(\"Residuals vs Predicted Values\")\n", "plt.show()\n", "\n", - "# Histogram/KDE of Residuals\n", + "# histogram/KDE of residuals\n", "plt.figure(figsize=(8, 5))\n", "plt.hist(residuals, bins=20, alpha=0.7, color='blue', edgecolor='black', density=True)\n", "plt.axvline(x=0, color='red', linestyle='--')\n", @@ -253,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 88, "id": "04d106c3-1e2d-48c4-a968-73fa436d81ef", "metadata": {}, "outputs": [], diff --git a/README.md b/README.md index 0fb5afe..f57115a 100644 --- a/README.md +++ b/README.md @@ -1,95 +1,77 @@ -TEAM: -Rakesh Reddy - A20525389 -Geeta Hade - A20580824 -Nishant Khandhar - A20581012 -Amogh Vastrad - A20588808 - - -How to run the code : - - -For using a notebook file do these steps - - -Prerequisites -Install Python and required libraries: numpy, matplotlib. -Steps to Execute - -1. Open the terminal at project folder: -2. Command : jupyter notebook Project2.ipynb -3. Run all cells sequentially -4. model evaluations outputs (e.g MSE scores, AIC values) -Views the plots for insight for insights into model performance and residuals - - -For using Python class - -Prerequisites -Install Python and required libraries: numpy, matplotlib, sklearn. -Steps to Execute -Go to src folder -Then execute py py .\pythonModelTest.py - -Views the plots for insight for insights into model performance and residuals - - - -Outputs -1. Cross-Validation: -◦ MSE Scores: [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] -◦ Average MSE: 0.2425 -2. Bootstrapping: -◦ MSE Scores (first 5): [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] -◦ Average MSE: 0.2397 - -2. AIC: -◦ Value: -6247.93 -◦ - -4. Visualizations: -◦ Residual distribution plot. -◦ Error comparison across methods. -◦ Bootstrapping confidence intervals. - -Key Questions and Answers -1. Do Cross-Validation and Bootstrapping agree with AIC? -Yes, all three methods largely agree in this implementation. -Example: -• Cross-validation and bootstrapping indicate a lower MSE for the selected model. -• AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. - -2. In what cases might these methods fail? -• Cross-Validation: -Fails for small datasets where splits lead to insufficient training data. -• Bootstrapping: -Assumes data is i.i.d.; fails for dependent datasets like time series. -• AIC: -Assumes normally distributed residuals; results may mislead for non-linear models. - -3. What could mitigate these issues? -• Implement stratified k-fold for imbalanced datasets. -• Use block bootstrapping for dependent data. -• Extend AIC to include models with corrected versions like AICc. - -4. What parameters are exposed? -• Cross-Validation: -◦ k (number of folds), default: 5. -• Bootstrapping: -◦ num_iterations (default: 100), seed for reproducibility. -• Regression Models: -◦ Regularization parameter alpha (for Ridge), default: 1.0. - - - -Additional Features -• Synthetic data generator for flexible testing. -• Residual analysis and visualization tools for insights. -• Extendable framework for adding new model selectors. - - -Compatibility -• Tested on Windows -• Requires numpy, matplotlib, Jupyter, sklearn - - +## TEAM +- Rakesh Reddy - A20525389 +- Geeta Hade - A20580824 +- Nishant Khandhar - A20581012 +- Amogh Vastrad - A20588808 +## How to run the code + +### Using a Notebook File + +#### Prerequisites +- Install Python and required libraries: `numpy`, `matplotlib`. + +#### Steps to Execute +1. Open the terminal at the project folder. +2. Run the command: `jupyter notebook Project2.ipynb` +3. Run all cells sequentially. +4. View the model evaluation outputs (e.g., MSE scores, AIC values) and plots for insights into model performance and residuals. +5. It contains visualizations + +### Using Python Class + +#### Prerequisites +- Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. + +#### Steps to Execute +1. Go to the `src` folder. +2. Execute the command: `python .\pythonModelTest.py` +3. View the plots for insights into model performance and residuals. + +## Outputs + +1. **Cross-Validation:** + - MSE Scores: [0.2473, 0.2475, 0.2448, 0.2401, 0.2326] + - Average MSE: 0.2425 + +2. **Bootstrapping:** + - MSE Scores (first 5): [0.2408, 0.2316, 0.2475, 0.2507, 0.2412] + - Average MSE: 0.2397 + +3. **AIC:** + - Value: -6247.93 + +4. **Visualizations:** + - Residual distribution plot. + - Error comparison across methods. + - Bootstrapping confidence intervals. + +## Key Questions and Answers + +1. **Do Cross-Validation and Bootstrapping agree with AIC?** + - Yes, all three methods largely agree in this implementation. + - Example: Cross-validation and bootstrapping indicate a lower MSE for the selected model. AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. + +2. **In what cases might these methods fail?** + - **Cross-Validation:** Fails for small datasets where splits lead to insufficient training data. + - **Bootstrapping:** Assumes data is i.i.d.; fails for dependent datasets like time series. + - **AIC:** Assumes normally distributed residuals; results may mislead for non-linear models. + +3. **What could mitigate these issues?** + - Implement stratified k-fold for imbalanced datasets. + - Use block bootstrapping for dependent data. + - Extend AIC to include models with corrected versions like AICc. + +4. **What parameters are exposed?** + - **Cross-Validation:** `k` (number of folds), default: 5. + - **Bootstrapping:** `num_iterations` (default: 100), seed for reproducibility. + - **Regression Models:** Regularization parameter `alpha` (for Ridge), default: 1.0. + +## Additional Features +- Synthetic data generator for flexible testing. +- Residual analysis and visualization tools for insights. +- Extendable framework for adding new model selectors. + +## Compatibility +- Tested on Windows +- Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn` diff --git a/src/modelSelection.py b/src/modelSelection.py index d5af125..3dc870d 100644 --- a/src/modelSelection.py +++ b/src/modelSelection.py @@ -12,7 +12,7 @@ def kFoldCv(X, y,model, k=5,seed=1): seed => is an integer that sets the random seed for reproducibility. ''' np.random.seed(seed) - # Shuffling the data indices + # shuffling the data indices indices = np.arange(len(y)) np.random.shuffle(indices) folds = np.array_split(indices, k) @@ -25,17 +25,15 @@ def kFoldCv(X, y,model, k=5,seed=1): trainIndices = np.hstack([folds[j] for j in range(k) if j != i]) Xtrain, yTrain = X[trainIndices], y[trainIndices] Xval, yVal = X[valIndices], y[valIndices] - # Fit the regression model on the training data + # fit the regression model on the training data model.fit(Xtrain, yTrain) - # Predict on the validation set and calculate the MSE + # predict on the validation set and calculate the MSE yPred = model.predict(Xval) mse = np.mean((yVal - yPred) ** 2) mseScores.append(mse) return mseScores, np.mean(mseScores) - - - # Bootstrapping function + # bootstrapping function def bootstrap(X, y,model, numIterations=100, seed=1): ''' X => should be a numpy array of shape (n_samples, n_features) @@ -47,17 +45,17 @@ def bootstrap(X, y,model, numIterations=100, seed=1): np.random.seed(seed) n = len(y) mse_scores = [] - # Loop through the number of iterations + # loop through the number of iterations for _ in range(numIterations): - # Sample with replacement from the data + # sample with replacement from the data indices = np.random.choice(np.arange(n), size=n, replace=True) Xsample, ySample = X[indices], y[indices] - # Train the regression model on the sampled data + # train the regression model on the sampled data model.fit(Xsample, ySample) yPred = model.predict(Xsample) - # Calculate the MSE + # calculate the MSE mse = np.mean((ySample - yPred) ** 2) - # Append the MSE to the list of MSEs + # append the MSE to the list of MSEs mse_scores.append(mse) return mse_scores, np.mean(mse_scores) @@ -70,16 +68,12 @@ def calculateAic(X, y,model): model => should be an instance of a class that has fit and predict methods, such as LinearRegression or RidgeRegression ''' n, p = X.shape - # Fit the model + # fit the model model.fit(X, y) y_pred = model.predict(X) - # Calculate the residual sum of squares + # calculate the residual sum of squares rss = np.sum((y - y_pred) ** 2) - # Calculate the AIC + # calculate the AIC aic = n * np.log(rss / n) + 2 * p return aic - - - - diff --git a/src/pythonModelTest.py b/src/pythonModelTest.py index 39e05c3..53f09e0 100644 --- a/src/pythonModelTest.py +++ b/src/pythonModelTest.py @@ -1,13 +1,13 @@ import numpy as np from sklearn.linear_model import LinearRegression from modelSelection import ModelEvaluations - +# generate some synthetic data X = np.random.rand(100, 3) y = X @ np.array([2, -1, 3]) + np.random.normal(0, 0.5, 100) -# Create an instance of the LinearRegression model +# create an instance of the LinearRegression model model = LinearRegression() - +# call the kFoldCv method kFoldMseScores, kFoldAvgMse = ModelEvaluations.kFoldCv(X, y, model, k=5) print("k-Fold MSE Scores:", kFoldMseScores) print("Average MSE from k-Fold:", kFoldAvgMse) From 88244d68a6eefeef267933f258ecd15f522950da Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:20:22 -0500 Subject: [PATCH 10/20] a --- README.md | 1 + image.png | Bin 0 -> 143260 bytes 2 files changed, 1 insertion(+) create mode 100644 image.png diff --git a/README.md b/README.md index f57115a..eb2ac00 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ 2. Execute the command: `python .\pythonModelTest.py` 3. View the plots for insights into model performance and residuals. +![alt text](image.png) ## Outputs 1. **Cross-Validation:** diff --git a/image.png b/image.png new file mode 100644 index 0000000000000000000000000000000000000000..f2c1608b468de13e8f6c11170461579fa2280c4b GIT binary patch literal 143260 zcmbSyXIxWR*Dr_?3q?ev6BQH{r3eHm0Ra&ZDbfPcR8)G62%!WVq&F22BnT)-3q`uL zAku{pTIdjJfKUR2Py$JAoOz#_xzD{H?(cpeoSf`)lC$?-Yp;LVQAP&Z>?Z|IGBGi+ z>+0MwW@0+F#KgpM?l>#sOmzJ79>zarsIj&NQ*n>bGULrr2X%dQCZ^JaQ-_a_G2Wl> z)UkvzF`aqw`;U3*Ina-ZiT+jhj=HIz?J5=Q_)LF%F=7Zh?fD}THbHvS2l^Ci`%M7B zDtIYzHE%rr=*jSK{H>2qwU30E%N?K5@wl?26U7ZYiWWS67FH{IDKXOC(RCeEA5BSn zrK$qOfR95tIMPijQ7?-s33GO4!w(NM>A)paWpx3vI3d`^EFfr2tpQG@ctoGpIDX;v z-!B&rDJwu{x>T^c?f!nF99{gMj~GWCu^UX_f3<#nx{TE?B>nHJe>b*dse^{VVMXgi z5A4Dh*r%>p|7XSVLM!fb1!%m&Ym#S^X;*ht@V_1Kryxq)=(+u zGpp3fm0k7E9pCV_t_x*0?iZ&IrYjv9`E{(!Xap&#nzvIH*YjZ>k%JaiUB))a=}dl` zGHFqxbJsKDY>wsMC~pqr`uo8Oxfhu!J119Z33JybW_UgygAj%wLt{Z-ZJz~Xp;T0n 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z&({nXa*VxlOPysRT`{@8qS=ps^x#Uo@6~Nb@$bx*w3)}BM=55EzzRK< zV|2K=W3xr&w&kQYpYo*$)70u}Z~Gyq0p2odzYB&!;0e*!u283f^dd3ynxxUaQHW;o z?#{wQU!K}$c@~8z_-^DL)ak~WY2jvbapXAs9@l^Ll2`QeUx0c_=$q%}!_t+>Hd@`8 zt8#S})sIZ`CMJfTP5Gt(;8Vd*8@pgMFYM4-5gDA?*wzahc;pyOFcKAx5Jku*2fEr0DW)>=aD_^ocoa>rK9|3k(^x(;9eq7`btRP86+z^EUkhuDUh&b) z15UCN=5DszYpz2vue;OTPy`w`3|b?wNQy z9YJZrujboLrYEcL=N3tAxGIv7QP|*-gDfx?5ScPA7!Hjt|k>!48>O3peq%`71@(lBLleSPYwB2iG+ws{Gv~ zcw&(t6;LvTApcW|pX0gSbh39cJQs9gO!%qe@;_rpc@9no8i>BV{;wK^Ub(a8e*vAJ dIKCW1@_$IhYF650kOBYht36aLQGWdL{{bgF`5FKK literal 0 HcmV?d00001 From 223ac2adefc4177056b10b9483a6171a1e851617 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:45:53 -0500 Subject: [PATCH 11/20] a --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index eb2ac00..cecf9a2 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,14 @@ +## TEAM NAME +ILYA + ## TEAM - Rakesh Reddy - A20525389 - Geeta Hade - A20580824 - Nishant Khandhar - A20581012 - Amogh Vastrad - A20588808 + + ## How to run the code ### Using a Notebook File From 6b5495d84b41db08af5348cad5f90a090134c792 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:52:09 -0500 Subject: [PATCH 12/20] a --- Project2.ipynb | 12 ++++++------ README.md | 6 +++++- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index c7870f1..16a38a5 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 83, + "execution_count": 89, "id": "1bb0a93d-b37d-474b-a49a-c87b9fa272cb", "metadata": {}, "outputs": [], @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 90, "id": "92a1696c-5200-4d95-9b6d-b7bd9e47a563", "metadata": {}, "outputs": [ @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 91, "id": "e45b3d74-a5a7-474c-8675-e73b9f9710a1", "metadata": {}, "outputs": [ @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 92, "id": "76476555-5d18-459d-bdd6-a5137cadade6", "metadata": {}, "outputs": [ @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 93, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 94, "id": "04d106c3-1e2d-48c4-a968-73fa436d81ef", "metadata": {}, "outputs": [], diff --git a/README.md b/README.md index cecf9a2..794c940 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ ILYA #### Steps to Execute 1. Go to the `src` folder. 2. Execute the command: `python .\pythonModelTest.py` -3. View the plots for insights into model performance and residuals. + ![alt text](image.png) ## Outputs @@ -52,6 +52,7 @@ ILYA - Error comparison across methods. - Bootstrapping confidence intervals. + ## Key Questions and Answers 1. **Do Cross-Validation and Bootstrapping agree with AIC?** @@ -81,3 +82,6 @@ ILYA ## Compatibility - Tested on Windows - Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn` + +## More data +- if you had time check data folder for various plot and results From 988fd55e1b76cbf1b1017eafbacaff67d1b29e0a Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 22:55:04 -0500 Subject: [PATCH 13/20] a --- Project2.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Project2.ipynb b/Project2.ipynb index 16a38a5..bb5ef53 100644 --- a/Project2.ipynb +++ b/Project2.ipynb @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "07316148-4118-49c5-8b5e-7e0d62c85cc9", "metadata": {}, "outputs": [ @@ -243,7 +243,7 @@ "plt.axvline(x=0, color='red', linestyle='--')\n", "plt.title(\"Distribution of Residuals\")\n", "plt.xlabel(\"Residuals\")\n", - "plt.ylabel(\"Density\")\n", + "plt.ylabel(\"Density\") \n", "plt.show()\n" ] }, From d3d4181f87f63b4b229741216057dd445719b0fc Mon Sep 17 00:00:00 2001 From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com> Date: Thu, 21 Nov 2024 22:57:23 -0500 Subject: [PATCH 14/20] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 794c940..2260f9d 100644 --- a/README.md +++ b/README.md @@ -84,4 +84,4 @@ ILYA - Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn` ## More data -- if you had time check data folder for various plot and results +- If you had time check the data folder for various plots and results From 6438116c753ce8ce20f1ad2c1fa119d9afc72a30 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 23:17:27 -0500 Subject: [PATCH 15/20] a --- README.md | 18 +++++++++++------- requirements.txt | 4 ++++ 2 files changed, 15 insertions(+), 7 deletions(-) create mode 100644 requirements.txt diff --git a/README.md b/README.md index 794c940..111bd24 100644 --- a/README.md +++ b/README.md @@ -7,33 +7,38 @@ ILYA - Nishant Khandhar - A20581012 - Amogh Vastrad - A20588808 - - ## How to run the code ### Using a Notebook File #### Prerequisites -- Install Python and required libraries: `numpy`, `matplotlib`. +- Install Python and required libraries: `numpy`, `matplotlib`. #### Steps to Execute 1. Open the terminal at the project folder. 2. Run the command: `jupyter notebook Project2.ipynb` 3. Run all cells sequentially. 4. View the model evaluation outputs (e.g., MSE scores, AIC values) and plots for insights into model performance and residuals. -5. It contains visualizations +5. It contains visualizations. ### Using Python Class #### Prerequisites - Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. +- Open your terminal or command prompt. +- Navigate to your project folder where the `requirements.txt` file is located. +- Run the following command to install all the libraries listed in `requirements.txt`: + +```bash +pip install -r requirements.txt +``` #### Steps to Execute 1. Go to the `src` folder. 2. Execute the command: `python .\pythonModelTest.py` - ![alt text](image.png) + ## Outputs 1. **Cross-Validation:** @@ -52,7 +57,6 @@ ILYA - Error comparison across methods. - Bootstrapping confidence intervals. - ## Key Questions and Answers 1. **Do Cross-Validation and Bootstrapping agree with AIC?** @@ -84,4 +88,4 @@ ILYA - Requires `numpy`, `matplotlib`, `Jupyter`, `sklearn` ## More data -- if you had time check data folder for various plot and results +- If you have time, check the `data` folder for various plots and results. diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..88f83b6 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy +matplotlib +scikit-learn +jupyter From 2636efe03f2fab83d81d24bd9dcd06c61b160ab1 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 23:21:53 -0500 Subject: [PATCH 16/20] a --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 8a894c4..1f75c95 100644 --- a/README.md +++ b/README.md @@ -79,6 +79,7 @@ pip install -r requirements.txt - **Regression Models:** Regularization parameter `alpha` (for Ridge), default: 1.0. ## Additional Features +- Synthetic data generator - Synthetic data generator for flexible testing. - Residual analysis and visualization tools for insights. - Extendable framework for adding new model selectors. From 686e5ac9951aeed52c209f80c67511f2f293d308 Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 23:28:15 -0500 Subject: [PATCH 17/20] a --- README.md | 27 ++++++++++++++++++--------- image-1.png | Bin 0 -> 91091 bytes image-2.png | Bin 0 -> 24137 bytes 3 files changed, 18 insertions(+), 9 deletions(-) create mode 100644 image-1.png create mode 100644 image-2.png diff --git a/README.md b/README.md index 1f75c95..bacbc41 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,5 @@ + + ## TEAM NAME ILYA @@ -7,12 +9,23 @@ ILYA - Nishant Khandhar - A20581012 - Amogh Vastrad - A20588808 +## Project Overview + +In this project, we have implemented generic k-fold cross-validation and bootstrapping methods for model selection, which test how well a model is likely to perform on new data. We have also included the Akaike Information Criterion (AIC) to compare models. This model evaluates the performance of regression models and compares it with the AIC. + ## How to run the code ### Using a Notebook File #### Prerequisites -- Install Python and required libraries: `numpy`, `matplotlib`. +- Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. +- Open your terminal or command prompt. +- Navigate to your project folder where the `requirements.txt` file is located. +- Run the following command to install all the libraries listed in `requirements.txt`: + +```bash +pip install -r requirements.txt +``` #### Steps to Execute 1. Open the terminal at the project folder. @@ -21,17 +34,13 @@ ILYA 4. View the model evaluation outputs (e.g., MSE scores, AIC values) and plots for insights into model performance and residuals. 5. It contains visualizations. +![alt text](image-1.png) + +![alt text](image-2.png) + ### Using Python Class -#### Prerequisites -- Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. -- Open your terminal or command prompt. -- Navigate to your project folder where the `requirements.txt` file is located. -- Run the following command to install all the libraries listed in `requirements.txt`: -```bash -pip install -r requirements.txt -``` #### Steps to Execute 1. 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perform on new data. We have also included the Akaike Information Criterion (AIC) to compare models. This model evaluates the performance of regression models and compares it with the AIC. -## How to run the code - -### Using a Notebook File #### Prerequisites - Install Python and required libraries: `numpy`, `matplotlib`, `sklearn`. @@ -23,9 +20,18 @@ In this project, we have implemented generic k-fold cross-validation and bootstr - Navigate to your project folder where the `requirements.txt` file is located. - Run the following command to install all the libraries listed in `requirements.txt`: + ```bash pip install -r requirements.txt ``` +- python version >=3.9 + + + +## How to run the code + +### Using a Notebook File + #### Steps to Execute 1. Open the terminal at the project folder. @@ -66,6 +72,20 @@ pip install -r requirements.txt - Error comparison across methods. - Bootstrapping confidence intervals. +## Difference Between Bootstrapping and K-Fold Cross-Validation + +### Bootstrapping +Bootstrapping is a resampling technique that involves repeatedly drawing samples from a dataset with replacement. Each sample is used to train the model, and the performance is evaluated on the remaining data. This process is repeated many times (e.g., 150 iterations), and the results are averaged to estimate the model's performance. + + +### K-Fold Cross-Validation +K-Fold Cross-Validation involves dividing the dataset into `k` equal-sized folds. The model is trained on `k-1` folds and tested on the remaining fold. This process is repeated `k` times, with each fold used exactly once as the test set. The results are averaged to estimate the model's performance. + + +## + + + ## Key Questions and Answers 1. **Do Cross-Validation and Bootstrapping agree with AIC?** From 9cb32e8dca038466b189fd7913af3891d302970d Mon Sep 17 00:00:00 2001 From: rakeshreddy06 Date: Thu, 21 Nov 2024 23:43:00 -0500 Subject: [PATCH 19/20] a --- README.md | 35 +++++++++++++++++------------------ 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 364eea7..0c7fc2e 100644 --- a/README.md +++ b/README.md @@ -75,11 +75,11 @@ pip install -r requirements.txt ## Difference Between Bootstrapping and K-Fold Cross-Validation ### Bootstrapping -Bootstrapping is a resampling technique that involves repeatedly drawing samples from a dataset with replacement. Each sample is used to train the model, and the performance is evaluated on the remaining data. This process is repeated many times (e.g., 150 iterations), and the results are averaged to estimate the model's performance. - +Bootstrapping is a powerful resampling method where we repeatedly draw samples from our dataset with replacement. Each sample is used to train the model, and its performance is evaluated on the remaining data. By repeating this process many times (e.g., 150 iterations), we can average the results to get a reliable estimate of the model's performance. ### K-Fold Cross-Validation -K-Fold Cross-Validation involves dividing the dataset into `k` equal-sized folds. The model is trained on `k-1` folds and tested on the remaining fold. This process is repeated `k` times, with each fold used exactly once as the test set. The results are averaged to estimate the model's performance. +K-Fold Cross-Validation is a technique where we split the dataset into `k` equal-sized folds. The model is trained on `k-1` folds and tested on the remaining fold. This process is repeated `k` times, ensuring each fold is used once as the test set. By averaging the results, we can estimate the model's performance more accurately. + ## @@ -89,23 +89,22 @@ K-Fold Cross-Validation involves dividing the dataset into `k` equal-sized folds ## Key Questions and Answers 1. **Do Cross-Validation and Bootstrapping agree with AIC?** - - Yes, all three methods largely agree in this implementation. - - Example: Cross-validation and bootstrapping indicate a lower MSE for the selected model. AIC, with a low value (-6247.93), aligns with the performance metrics, confirming the model selection. + - Yes, in our project, we found that all three methods generally agree. For instance, both cross-validation and bootstrapping show lower MSE scores for the chosen model. The AIC value of -6247.93 supports this by indicating a good model fit, aligning well with the performance metrics from cross-validation and bootstrapping. 2. **In what cases might these methods fail?** - - **Cross-Validation:** Fails for small datasets where splits lead to insufficient training data. - - **Bootstrapping:** Assumes data is i.i.d.; fails for dependent datasets like time series. - - **AIC:** Assumes normally distributed residuals; results may mislead for non-linear models. - -3. **What could mitigate these issues?** - - Implement stratified k-fold for imbalanced datasets. - - Use block bootstrapping for dependent data. - - Extend AIC to include models with corrected versions like AICc. - -4. **What parameters are exposed?** - - **Cross-Validation:** `k` (number of folds), default: 5. - - **Bootstrapping:** `num_iterations` (default: 100), seed for reproducibility. - - **Regression Models:** Regularization parameter `alpha` (for Ridge), default: 1.0. + - **Cross-Validation:** May not perform well with very small datasets where the training data in each fold is insufficient to build a reliable model. + - **Bootstrapping:** Assumes that the data points are independently and identically distributed (i.i.d.); it may not be suitable for datasets with dependencies, such as time series data. + - **AIC:** Assumes that the residuals of the model are normally distributed; it may provide misleading results for models with non-linear relationships or non-normal residuals. + +3. **How can we address these challenges?** + - For imbalanced datasets, consider using stratified k-fold cross-validation to ensure each fold has a representative distribution of the target variable. + - For datasets with dependencies, such as time series data, use block bootstrapping to maintain the inherent structure and relationships within the data. + - To improve the reliability of AIC, especially for small sample sizes, use corrected versions like AICc (Akaike Information Criterion corrected). + +4. **What parameters can you customize in our project?** + - **Cross-Validation:** You can set the number of folds (`k`), with a default value of 5. + - **Bootstrapping:** You can specify the number of iterations (`num_iterations`), which defaults to 100, and set a seed for reproducibility. + - **Regression Models:** You can adjust the regularization parameter (`alpha`) for Ridge regression, with a default value of 1.0. ## Additional Features - Synthetic data generator From 5ad211d6a768e573a909a9c959af22cfc2ca475f Mon Sep 17 00:00:00 2001 From: Rakesh <40319883+rakeshreddy06@users.noreply.github.com> Date: Thu, 21 Nov 2024 23:53:53 -0500 Subject: [PATCH 20/20] Update README.md --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 0c7fc2e..fd3436b 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,10 @@ ILYA ## TEAM -- Rakesh Reddy - A20525389 -- Geeta Hade - A20580824 -- Nishant Khandhar - A20581012 -- Amogh Vastrad - A20588808 +- Rakesh Reddy - A20525389 (Worked on models output and test file, file structure, took care of readme format and content, project delivery) +- Geeta Hade - A20580824 (done initial math and research on how to build models, took care of readme content) +- Nishant Khandhar - A20581012 (heavily invested time on building code for the Kfold (we faced an issue in it so he took care of it), visualizations ) +- Amogh Vastrad - A20588808 (he overlooked and researched the model performance and performed coding for notebook file) ## Project Overview