feat: added PW3
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gabriel.marinoja
333
PW-3/ex1/ex1-bayes-stud.ipynb
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333
PW-3/ex1/ex1-bayes-stud.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Exercise 1 - Bayes classification system"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import some useful libraries\n",
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"\n",
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"import math\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import OrdinalEncoder, StandardScaler"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1a. Getting started with Bayes"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"a) Read the training data from file ex1-data-train.csv. The first two columns are x1 and x2. The last column holds the class label y."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"def read_data(file):\n",
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" dataset = pd.read_csv(file, names=['x1','x2','y'])\n",
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" print(dataset.head())\n",
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" return dataset[[\"x1\", \"x2\"]], dataset[\"y\"].values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, y_train = read_data(\"ex1-data-train.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Prepare a function to compute accuracy\n",
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"def accuracy_score(y_true, y_pred):\n",
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" return (y_true == y_pred).sum() / y_true.size"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"b) Compute the priors of both classes P(C0) and P(C1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"# TODO: Compute the priors\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"c) Compute histograms of x1 and x2 for each class (total of 4 histograms). Plot these histograms. Advice : use the numpy `histogram(a, bins=\"auto\")` function."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"# TODO: Compute histograms\n",
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"\n",
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"\n",
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"\n",
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"# TODO: plot histograms\n",
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"\n",
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"plt.figure(figsize=(16,6))\n",
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"\n",
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"plt.subplot(1, 2, 1)\n",
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"...\n",
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"plt.xlabel('Likelihood hist - Exam 1')\n",
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"\n",
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"plt.subplot(1, 2, 2)\n",
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"...\n",
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"plt.xlabel('Likelihood hist - Exam 2')\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"d) Use the histograms to compute the likelihoods p(x1|C0), p(x1|C1), p(x2|C0) and p(x2|C1). For this define a function `likelihood_hist(x, hist_values, edge_values)` that returns the likelihood of x for a given histogram (defined by its values and bin edges as returned by the numpy `histogram()` function)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"def likelihood_hist(x: float, hist_values: np.ndarray, bin_edges: np.ndarray) -> float:\n",
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" # TODO: compute likelihoods from histograms outputs\n",
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"\n",
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" return ..."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"e) Implement the classification decision according to Bayes rule and compute the overall accuracy of the system on the test set ex1-data-test.csv. :\n",
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"- using only feature x1\n",
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"- using only feature x2\n",
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"- using x1 and x2 making the naive Bayes hypothesis of feature independence, i.e. p(X|Ck) = p(x1|Ck) · p(x2|Ck)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"X_test, y_test = read_data(\"ex1-data-test.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"# TODO: predict on test set in the 3 cases described above\n",
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"\n",
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"y_pred = []\n",
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"\n",
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"...\n",
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"\n",
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"accuracy_score(y_test, y_pred)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Which system is the best ?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"TODO: answer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1b. Bayes - Univariate Gaussian distribution"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Do the same as in a) but this time using univariate Gaussian distribution to model the likelihoods p(x1|C0), p(x1|C1), p(x2|C0) and p(x2|C1). You may use the numpy functions `mean()` and `var()` to compute the mean μ and variance σ2 of the distribution. To model the likelihood of both features, you may also do the naive Bayes hypothesis of feature independence, i.e. p(X|Ck) = p(x1|Ck) · p(x2|Ck).\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"def likelihood_univariate_gaussian(x: float, mean: float, var: float) -> float:\n",
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" # TODO: compute likelihoods from histograms outputs\n",
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"\n",
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" return ..."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
|
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"pycharm": {
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"is_executing": false
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}
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},
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"outputs": [],
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"source": [
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"# TODO: Compute mean and variance for each classes and each features (8 values)\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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"# TODO: predict on test set in the 3 cases\n",
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"\n",
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"y_pred = []\n",
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"\n",
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"...\n",
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"\n",
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"accuracy_score(y_test, y_pred)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.7"
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"stem_cell": {
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"cell_type": "raw",
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"metadata": {
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"collapsed": false
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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PW-3/ex1/ex1-data-test.csv
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PW-3/ex1/ex1-data-test.csv
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100
PW-3/ex1/ex1-data-train.csv
Normal file
100
PW-3/ex1/ex1-data-train.csv
Normal file
@@ -0,0 +1,100 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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33.91550010906887,98.86943574220611,0
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35.28611281526193,47.02051394723416,0
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30.05882244669796,49.59297386723685,0
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44.66826172480893,66.45008614558913,0
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66.56089447242954,41.09209807936973,0
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40.45755098375164,97.53518548909936,1
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49.07256321908844,51.88321182073966,0
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80.27957401466998,92.11606081344084,1
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66.74671856944039,60.99139402740988,1
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32.72283304060323,43.30717306430063,0
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||||
64.0393204150601,78.03168802018232,1
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||||
72.34649422579923,96.22759296761404,1
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||||
60.45788573918959,73.09499809758037,1
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||||
58.84095621726802,75.85844831279042,1
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||||
99.82785779692128,72.36925193383885,1
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||||
47.26426910848174,88.47586499559782,1
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||||
50.45815980285988,75.80985952982456,1
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||||
60.45555629271532,42.50840943572217,0
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||||
82.22666157785568,42.71987853716458,0
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||||
88.9138964166533,69.80378889835472,1
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||||
94.83450672430196,45.69430680250754,1
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||||
67.31925746917527,66.58935317747915,1
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||||
57.23870631569862,59.51428198012956,1
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||||
80.36675600171273,90.96014789746954,1
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||||
68.46852178591112,85.59430710452014,1
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||||
42.0754545384731,78.84478600148043,0
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||||
75.47770200533905,90.42453899753964,1
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||||
78.63542434898018,96.64742716885644,1
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||||
52.34800398794107,60.76950525602592,0
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||||
94.09433112516793,77.15910509073893,1
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||||
90.44855097096364,87.50879176484702,1
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||||
55.48216114069585,35.57070347228866,0
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||||
74.49269241843041,84.84513684930135,1
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||||
89.84580670720979,45.35828361091658,1
|
||||
83.48916274498238,48.38028579728175,1
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||||
42.2617008099817,87.10385094025457,1
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||||
99.31500880510394,68.77540947206617,1
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||||
55.34001756003703,64.9319380069486,1
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||||
74.77589300092767,89.52981289513276,1
|
||||
|
535
PW-3/ex2/ex2-sys-eval-stud.ipynb
Normal file
535
PW-3/ex2/ex2-sys-eval-stud.ipynb
Normal file
@@ -0,0 +1,535 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bcf79585",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercice 2 - System evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f642cedb",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "9421a4e1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a0d67fa6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5fe90672",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Define the path of the data file"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "ecd4a4cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"path = \"ex2-system-a.csv\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "246e7392",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Read the CSV file using `read_csv`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "623096a5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dataset_a = pd.read_csv(path, sep=\";\", index_col=False, names=[\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"y_true\"])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6f764c56",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Display first rows"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "c59a1651",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>0</th>\n",
|
||||
" <th>1</th>\n",
|
||||
" <th>2</th>\n",
|
||||
" <th>3</th>\n",
|
||||
" <th>4</th>\n",
|
||||
" <th>5</th>\n",
|
||||
" <th>6</th>\n",
|
||||
" <th>7</th>\n",
|
||||
" <th>8</th>\n",
|
||||
" <th>9</th>\n",
|
||||
" <th>y_true</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>5.348450e-08</td>\n",
|
||||
" <td>7.493480e-10</td>\n",
|
||||
" <td>8.083470e-07</td>\n",
|
||||
" <td>2.082290e-05</td>\n",
|
||||
" <td>5.222360e-10</td>\n",
|
||||
" <td>2.330260e-08</td>\n",
|
||||
" <td>5.241270e-12</td>\n",
|
||||
" <td>9.999650e-01</td>\n",
|
||||
" <td>4.808590e-07</td>\n",
|
||||
" <td>0.000013</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>1.334270e-03</td>\n",
|
||||
" <td>3.202960e-05</td>\n",
|
||||
" <td>8.504280e-01</td>\n",
|
||||
" <td>1.669090e-03</td>\n",
|
||||
" <td>1.546460e-07</td>\n",
|
||||
" <td>2.412940e-04</td>\n",
|
||||
" <td>1.448280e-01</td>\n",
|
||||
" <td>1.122810e-11</td>\n",
|
||||
" <td>1.456330e-03</td>\n",
|
||||
" <td>0.000011</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>3.643050e-06</td>\n",
|
||||
" <td>9.962760e-01</td>\n",
|
||||
" <td>2.045910e-03</td>\n",
|
||||
" <td>4.210530e-04</td>\n",
|
||||
" <td>2.194020e-05</td>\n",
|
||||
" <td>1.644130e-05</td>\n",
|
||||
" <td>2.838160e-04</td>\n",
|
||||
" <td>3.722960e-04</td>\n",
|
||||
" <td>5.150120e-04</td>\n",
|
||||
" <td>0.000044</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>9.998200e-01</td>\n",
|
||||
" <td>2.550390e-10</td>\n",
|
||||
" <td>1.112010e-05</td>\n",
|
||||
" <td>1.653200e-05</td>\n",
|
||||
" <td>5.375730e-10</td>\n",
|
||||
" <td>8.999750e-05</td>\n",
|
||||
" <td>9.380920e-06</td>\n",
|
||||
" <td>4.464470e-05</td>\n",
|
||||
" <td>2.418440e-06</td>\n",
|
||||
" <td>0.000006</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>2.092460e-08</td>\n",
|
||||
" <td>7.464220e-08</td>\n",
|
||||
" <td>3.560820e-05</td>\n",
|
||||
" <td>5.496200e-07</td>\n",
|
||||
" <td>9.988960e-01</td>\n",
|
||||
" <td>3.070920e-08</td>\n",
|
||||
" <td>2.346150e-04</td>\n",
|
||||
" <td>9.748010e-07</td>\n",
|
||||
" <td>1.071610e-06</td>\n",
|
||||
" <td>0.000831</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 0 1 2 3 4 \\\n",
|
||||
"0 5.348450e-08 7.493480e-10 8.083470e-07 2.082290e-05 5.222360e-10 \n",
|
||||
"1 1.334270e-03 3.202960e-05 8.504280e-01 1.669090e-03 1.546460e-07 \n",
|
||||
"2 3.643050e-06 9.962760e-01 2.045910e-03 4.210530e-04 2.194020e-05 \n",
|
||||
"3 9.998200e-01 2.550390e-10 1.112010e-05 1.653200e-05 5.375730e-10 \n",
|
||||
"4 2.092460e-08 7.464220e-08 3.560820e-05 5.496200e-07 9.988960e-01 \n",
|
||||
"\n",
|
||||
" 5 6 7 8 9 y_true \n",
|
||||
"0 2.330260e-08 5.241270e-12 9.999650e-01 4.808590e-07 0.000013 7 \n",
|
||||
"1 2.412940e-04 1.448280e-01 1.122810e-11 1.456330e-03 0.000011 2 \n",
|
||||
"2 1.644130e-05 2.838160e-04 3.722960e-04 5.150120e-04 0.000044 1 \n",
|
||||
"3 8.999750e-05 9.380920e-06 4.464470e-05 2.418440e-06 0.000006 0 \n",
|
||||
"4 3.070920e-08 2.346150e-04 9.748010e-07 1.071610e-06 0.000831 4 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dataset_a.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "41f040b0",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Store some useful statistics (class names + number of classes)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "fd0adce4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class_names = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
|
||||
"nb_classes = len(class_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5a0ab85a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exercise's steps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "66ae582e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"a) Write a function to take classification decisions on such outputs according to Bayes’rule."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "3c36b377",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def bayes_classification(df):\n",
|
||||
" \"\"\"\n",
|
||||
" Take classification decisions according to Bayes rule.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" df : Pandas DataFrame of shape (n_samples, n_features + ground truth)\n",
|
||||
" Dataset.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" preds : Numpy array of shape (n_samples,)\n",
|
||||
" Class labels for each data sample.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b5e8140b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"b) What is the overall error rate of the system ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f3b21bfb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: compute and print the error rate of the system"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a4f0fa5f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"c) Compute and report the confusion matrix of the system."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "bb106415",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def confusion_matrix(y_true, y_pred, n_classes):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute the confusion matrix.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" y_true : Numpy array of shape (n_samples,)\n",
|
||||
" Ground truth.\n",
|
||||
" y_pred : Numpy array of shape (n_samples,)\n",
|
||||
" Predictions.\n",
|
||||
" n_classes : Integer\n",
|
||||
" Number of classes.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" cm : Numpy array of shape (n_classes, n_classes)\n",
|
||||
" Confusion matrix.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "1b38e3a8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: compute and print the confusion matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ed8db908",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"d) What are the worst and best classes in terms of precision and recall ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "0e229ce0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def precision_per_class(cm):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute the precision per class.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" cm : Numpy array of shape (n_classes, n_classes)\n",
|
||||
" Confusion matrix.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" precisions : Numpy array of shape (n_classes,)\n",
|
||||
" Precision per class.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "95325772",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def recall_per_class(cm):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute the recall per class.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" cm : Numpy array of shape (n_classes, n_classes)\n",
|
||||
" Confusion matrix.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" recalls : Numpy array of shape (n_classes,)\n",
|
||||
" Recall per class.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "a0fb19e3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: find and print the worst and best classes in terms of precision"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "42c3edd8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: find and print the worst and best classes in terms of recall"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7ac6fe5d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"e) In file `ex1-system-b.csv` you find the output of a second system B. What is the best system between (a) and (b) in terms of error rate and F1."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "b98c2545",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: load the data of the system B"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "050091b9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def system_accuracy(cm):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute the system accuracy.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" cm : Numpy array of shape (n_classes, n_classes)\n",
|
||||
" Confusion matrix.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" accuracy : Float\n",
|
||||
" Accuracy of the system.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "adc0f138",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def system_f1_score(cm):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute the system F1 score.\n",
|
||||
" \n",
|
||||
" Parameters\n",
|
||||
" ----------\n",
|
||||
" cm : Numpy array of shape (n_classes, n_classes)\n",
|
||||
" Confusion matrix.\n",
|
||||
" \n",
|
||||
" Returns\n",
|
||||
" -------\n",
|
||||
" f1_score : Float\n",
|
||||
" F1 score of the system.\n",
|
||||
" \"\"\"\n",
|
||||
" # Your code here\n",
|
||||
" pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "f1385c87",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: compute and print the accuracy and the F1 score of the system A"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "50c64d08",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Your code here: compute and print the accuracy and the F1 score of the system B"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
10000
PW-3/ex2/ex2-system-a.csv
Normal file
10000
PW-3/ex2/ex2-system-a.csv
Normal file
File diff suppressed because it is too large
Load Diff
10000
PW-3/ex2/ex2-system-b.csv
Normal file
10000
PW-3/ex2/ex2-system-b.csv
Normal file
File diff suppressed because it is too large
Load Diff
172
PW-3/ex3/ex3-review-questions-stud.ipynb
Normal file
172
PW-3/ex3/ex3-review-questions-stud.ipynb
Normal file
@@ -0,0 +1,172 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ad0d40d6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercice 3 - Review questions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3e556a9d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**a) Assuming an univariate input *x*, what is the complexity at inference time of a Bayesian classifier based on histogram computation of the likelihood ?**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8d2fb7ef",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "99632770",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**b) Bayesian models are said to be generative as they can be used to generate new samples. Taking the implementation of the exercise 1.a, explain the steps to generate new samples using the system you have put into place.**\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "88ab64b2",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e2f611fe",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"***Optional*: Provide an implementation in a function generateSample(priors, histValues, edgeValues, n)**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "14aba0f7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "ed8c4f6b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**c) What is the minimum overall accuracy of a 2-class system relying only on priors and that is built on a training set that includes 5 times more samples in class A than in class B?**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4bb03365",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "58450ff6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**d) Let’s look back at the PW02 exercise 3 of last week. We have built a knn classification systems for images of digits on the MNIST database.**\n",
|
||||
"\n",
|
||||
"**How would you build a Bayesian classification for the same task ? Comment on the prior probabilities and on the likelihood estimators. More specifically, what kind of likelihood estimator could we use in this case ?**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d2bf1500",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a3ca9715",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"***Optional:* implement it and report performance !**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "4de72736",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pass"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b812b46f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**e) Read [europe-border-control-ai-lie-detector](https://theintercept.com/2019/07/26/europe-border-control-ai-lie-detector/). The described system is \"a virtual policeman designed to strengthen European borders\". It can be seen as a 2-class problem, either you are a suspicious traveler or you are not. If you are declared as suspicious by the system, you are routed to a human border agent who analyses your case in a more careful way.**\n",
|
||||
"\n",
|
||||
"1. What kind of errors can the system make ? Explain them in your own words.\n",
|
||||
"2. Is one error more critical than the other ? Explain why.\n",
|
||||
"3. According to the previous points, which metric would you recommend to tune your MLsystem ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1adf1760",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "195a1f73-c0f7-4707-9551-c71bfa379960",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**f) When a deep learning architecture is trained using an unbalanced training set, we usually observe a problem of bias, i.e. the system favors one class over another one. Using the Bayes equation, explain what is the origin of the problem.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fa5ffd45-0645-4093-9a1b-0a7aeaeece0e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"TODO"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.9.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user