diff --git a/PW-2/ex5-regression-knn/regression-knn-stud_Charline.ipynb b/PW-2/ex5-regression-knn/regression-knn-stud_Charline.ipynb new file mode 100644 index 0000000..057988d --- /dev/null +++ b/PW-2/ex5-regression-knn/regression-knn-stud_Charline.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 44, + "id": "b94b0451", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[6.30165767]]\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Download and prepare the data\n", + "lifesat = pd.read_csv(\"lifesat.csv\")\n", + "X = lifesat[[\"GDP per capita (USD)\"]].values\n", + "y = lifesat[[\"Life satisfaction\"]].values\n", + "\n", + "# Visualize the data\n", + "lifesat.plot(kind='scatter', grid=True,\n", + " x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n", + "plt.axis([23_500, 62_500, 4, 9])\n", + "plt.show()\n", + "\n", + "# Select a linear model\n", + "model = LinearRegression()\n", + "\n", + "# Train the model\n", + "model.fit(X, y)\n", + "\n", + "# Make a prediction for Cyprus\n", + "X_new = [[37_655.2]] # Cyprus' GDP per capita in 2020\n", + "print(model.predict(X_new)) # outputs [[6.30165767]]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "94fda07f", + "metadata": {}, + "outputs": [], + "source": [ + "X_test = np.linspace(25000, 60000, 200)\n", + "X_test = [[value] for value in X_test]\n", + "y_test = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "838b0242", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data\n", + "lifesat.plot(kind='scatter', grid=True,\n", + " x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n", + "plt.axis([23_500, 62_500, 4, 9])\n", + "plt.plot(X_test, y_test, color='red')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "aa14a4ca", + "metadata": {}, + "outputs": [], + "source": [ + "class KNearestNeighborRegressor(object):\n", + " \"\"\" a kNN regressor with L2 distance \"\"\"\n", + "\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def train(self, X, y):\n", + " \"\"\"\n", + " Train the classifier. For k-nearest neighbors this is just \n", + " memorizing the training data.\n", + "\n", + " Inputs:\n", + " - X: A numpy array of shape (num_train, D) containing the training data\n", + " consisting of num_train samples each of dimension D.\n", + " - y: A numpy array of shape (N,) containing the training labels, where\n", + " y[i] is the label for X[i].\n", + " \"\"\"\n", + " self.X_train = X\n", + " self.y_train = y\n", + " \n", + " def predict(self, X, k=1):\n", + " \"\"\"\n", + " Predict labels for test data using this classifier.\n", + "\n", + " Inputs:\n", + " - X: A numpy array of shape (num_test, D) containing test data consisting\n", + " of num_test samples each of dimension D.\n", + " - k: The number of nearest neighbors that vote for the predicted labels.\n", + " - num_loops: Determines which implementation to use to compute distances\n", + " between training points and testing points.\n", + "\n", + " Returns:\n", + " - y: A numpy array of shape (num_test,) containing predicted labels for the\n", + " test data, where y[i] is the predicted label for the test point X[i]. \n", + " \"\"\"\n", + " dists = self.compute_distances(X)\n", + " \n", + " return self.predict_values(dists, k=k)\n", + "\n", + "\n", + " def compute_distances(self, X):\n", + " \"\"\"\n", + " Compute the distance between each test point in X and each training point\n", + " in self.X_train using a single loop over the test data.\n", + "\n", + " Inputs:\n", + " - X: A numpy array of shape (num_test, D) containing test data.\n", + "\n", + " Returns:\n", + " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n", + " is the Euclidean distance between the ith test point and the jth training\n", + " point.\n", + " \"\"\"\n", + " num_test = X.shape[0]\n", + " num_train = self.X_train.shape[0]\n", + " dists = np.zeros((num_test, num_train))\n", + " for i in range(num_test):\n", + " #######################################################################\n", + " # TODO: #\n", + " # Compute the l2 distance between the ith test point and all training #\n", + " # points, and store the result in dists[i, :]. #\n", + " #######################################################################\n", + " \n", + " dists[i,:] = np.sqrt(np.sum((self.X_train - X[i, :])**2, axis=1))\n", + " \n", + " #######################################################################\n", + " # END OF YOUR CODE #\n", + " #######################################################################\n", + " return dists\n", + "\n", + "\n", + "\n", + " def predict_values(self, dists, k=1):\n", + " \"\"\"\n", + " Given a matrix of distances between test points and training points,\n", + " predict a value for each test point.\n", + "\n", + " Inputs:\n", + " - dists: A numpy array of shape (num_test, num_train) where dists[i, j]\n", + " gives the distance betwen the ith test point and the jth training point.\n", + "\n", + " Returns:\n", + " - y: A numpy array of shape (num_test,) containing predicted values for the\n", + " test data, where y[i] is the predicted value for the test point X[i]. \n", + " \"\"\"\n", + " num_test = dists.shape[0]\n", + " y_pred = np.zeros(num_test)\n", + " for i in range(num_test):\n", + " # A list of length k storing the labels of the k nearest neighbors to\n", + " # the ith test point.\n", + " \n", + " \n", + " #########################################################################\n", + " # TODO: #\n", + " # Use the distance matrix to find the k nearest neighbors of the ith #\n", + " # testing point, and use self.y_train to find the labels of these #\n", + " # neighbors. Store these labels in closest_y. #\n", + " # Hint: Look up the function numpy.argsort. #\n", + " #########################################################################\n", + " \n", + " closest_y = self.y_train[np.argsort(dists[i, :])[:k]].flatten()\n", + " \n", + " #########################################################################\n", + " # TODO: #\n", + " # Now that you have found the labels of the k nearest neighbors, you #\n", + " # need to compute the average of the target values corresponding to the #\n", + " # nearest neighbors. #\n", + " #########################################################################\n", + " \n", + " closest_dists = dists[i, np.argsort(dists[i, :])[:k]]\n", + "\n", + " epsilon = 1e-8\n", + " weights = (1 / (closest_dists + epsilon))\n", + " weights = weights / np.sum(weights)\n", + "\n", + " y_pred[i] = np.sum(weights * closest_y)\n", + " \n", + " #########################################################################\n", + " # END OF YOUR CODE # \n", + " #########################################################################\n", + "\n", + " return y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "267d1168", + "metadata": {}, + "outputs": [], + "source": [ + "knn_reg = KNearestNeighborRegressor()\n", + "knn_reg.train(np.array(X), y)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "fd8203ba", + "metadata": {}, + "outputs": [], + "source": [ + "y_hat_1 = knn_reg.predict(np.array(X_test), k=1)\n", + "y_hat_3 = knn_reg.predict(np.array(X_test), k=3)\n", + "y_hat_5 = knn_reg.predict(np.array(X_test), k=5)\n", + "y_hat_7 = knn_reg.predict(np.array(X_test), k=7)\n", + "y_hat_20 = knn_reg.predict(np.array(X_test), k=20)\n", + "y_hat_27 = knn_reg.predict(np.array(X_test), k=27)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "d3704256", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Visualize the data\n", + "lifesat.plot(kind='scatter', grid=True,\n", + " x=\"GDP per capita (USD)\", y=\"Life satisfaction\")\n", + "plt.axis([23_500, 62_500, 4, 9])\n", + "plt.plot(X_test, y_test, color='red')\n", + "plt.plot(X_test, y_hat_1, color='green')\n", + "plt.plot(X_test, y_hat_3, color='blue')\n", + "plt.plot(X_test, y_hat_5, color='magenta')\n", + "plt.plot(X_test, y_hat_7, color='orange')\n", + "plt.plot(X_test, y_hat_20, color='black')\n", + "plt.plot(X_test, y_hat_27, color='grey')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "dab45709", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[7.19862495]\n", + "[7.23555601]\n" + ] + } + ], + "source": [ + "print(knn_reg.predict(np.array([[38341]]), k=7))\n", + "print(knn_reg.predict(np.array([[69669]]), k=7))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}