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ktyl f2b4eaadb9 week 1
2025-01-24 13:21:11 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Lecture 3: Introduction to Scikit-Learn"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"![](https://www.tensorflow.org/images/colab_logo_32px.png)\n",
"[Run in colab](https://colab.research.google.com/drive/1TZW7xcheEHt7DdDraOZUiSG92rqF3TGF)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
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"shell.execute_reply": "2024-01-10T00:13:23.223286Z"
},
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last executed: 2024-01-10 00:13:23\n"
]
}
],
"source": [
"import datetime\n",
"now = datetime.datetime.now()\n",
"print(\"Last executed: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Scikit-Learn overview\n",
"\n",
"[Scikit-Learn](http://scikit-learn.org/stable/) is an extremely popular python machine learning package.\n",
"\n",
"Provides implementations of a number of different machine learning algorithms."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Clean, uniform and streamlined API.\n",
"- Useful and complete online documentation.\n",
"- Straightforward to switch models or algorithms."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Two main general concepts:\n",
"- Data representation\n",
"- Estimator API"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data representations"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Scikit-Learn includes a number of example data-sets"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:23.262339Z",
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"iopub.status.idle": "2024-01-10T00:13:23.802820Z",
"shell.execute_reply": "2024-01-10T00:13:23.802100Z"
}
},
"outputs": [],
"source": [
"from sklearn import datasets"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:23.806483Z",
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"shell.execute_reply": "2024-01-10T00:13:23.809598Z"
}
},
"outputs": [],
"source": [
"# Type datasets.<TAB> to see more\n",
"#datasets."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Data as a table\n",
"\n",
"Best way to think about data in Scikit-Learn is in terms of tables of data.\n",
"\n",
"Using the [`seaborn`](http://seaborn.pydata.org/) library we can read example data-sets as a Pandas `DataFrame`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:23.813758Z",
"iopub.status.busy": "2024-01-10T00:13:23.813178Z",
"iopub.status.idle": "2024-01-10T00:13:25.297828Z",
"shell.execute_reply": "2024-01-10T00:13:25.297118Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"iris = sns.load_dataset('iris')\n",
"type(iris)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.301227Z",
"iopub.status.busy": "2024-01-10T00:13:25.300607Z",
"iopub.status.idle": "2024-01-10T00:13:25.313145Z",
"shell.execute_reply": "2024-01-10T00:13:25.312527Z"
}
},
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Iris data\n",
"\n",
"Here we consider the [Iris flower data](https://en.wikipedia.org/wiki/Iris_flower_data_set).\n",
"\n",
"- Introduced by statistician and biologist Ronald Fisher in 1936 paper.\n",
"\n",
"- Consists of 50 samples of three different species of Iris (Iris Setosa, Iris Virginica and Iris Versicolor).\n",
"\n",
"- Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.316231Z",
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"shell.execute_reply": "2024-01-10T00:13:25.326456Z"
}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>145</th>\n",
" <td>6.7</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>6.3</td>\n",
" <td>2.5</td>\n",
" <td>5.0</td>\n",
" <td>1.9</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.2</td>\n",
" <td>2.0</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>6.2</td>\n",
" <td>3.4</td>\n",
" <td>5.4</td>\n",
" <td>2.3</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>5.9</td>\n",
" <td>3.0</td>\n",
" <td>5.1</td>\n",
" <td>1.8</td>\n",
" <td>virginica</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"145 6.7 3.0 5.2 2.3 virginica\n",
"146 6.3 2.5 5.0 1.9 virginica\n",
"147 6.5 3.0 5.2 2.0 virginica\n",
"148 6.2 3.4 5.4 2.3 virginica\n",
"149 5.9 3.0 5.1 1.8 virginica"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Parts of a flower\n",
"\n",
"Measured flower [petals](https://en.wikipedia.org/wiki/Petal) and [sepals](https://en.wikipedia.org/wiki/Sepal).\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/Mature_flower_diagram.png\" width=\"1000px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Image credit: [Mariana Ruiz](https://en.wikipedia.org/wiki/Sepal#/media/File:Mature_flower_diagram.svg)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"#### Images of different species\n",
"\n",
"<!--\n",
"<table border=\"0\" cellpadding=\"0\">\n",
" <tr>\n",
" <td><center><img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_setosa.jpg\" width=\"60%\"/></center></td>\n",
" <td><center><img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_versicolor.jpg\" width=\"70%\"/></center></td>\n",
" <td><center><img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_virginica.jpg\" width=\"50%\"/></center></td>\n",
" </tr>\n",
" <tr>\n",
" <td><center>Iris Setosa</center></td>\n",
" <td><center>Iris Versicolor</center></td>\n",
" <td><center>Iris Virginica</center></td> \n",
" </tr>\n",
"</table>\n",
"-->\n",
"\n",
"##### Iris Setosa\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_setosa.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
"\n",
"##### Iris Versicolor\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_versicolor.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
"\n",
"##### Iris Virginica\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/iris_virginica.jpg\" width=\"300\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Image source](https://github.com/jakevdp/sklearn_tutorial)]\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Features matrix\n",
"\n",
"Recall data represented to learning algorithm as \"*features*\".\n",
"\n",
"Each row corresponds to an observed (*sampled*) flower, with a number of *features*."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.330360Z",
"iopub.status.busy": "2024-01-10T00:13:25.329898Z",
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"shell.execute_reply": "2024-01-10T00:13:25.340725Z"
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"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
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"</div>"
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"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"In this example we extract a feature matrix, removing species (which we want to predict)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.344521Z",
"iopub.status.busy": "2024-01-10T00:13:25.343963Z",
"iopub.status.idle": "2024-01-10T00:13:25.356078Z",
"shell.execute_reply": "2024-01-10T00:13:25.355443Z"
}
},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris = iris.drop('species', axis='columns')\n",
"X_iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.358964Z",
"iopub.status.busy": "2024-01-10T00:13:25.358716Z",
"iopub.status.idle": "2024-01-10T00:13:25.365488Z",
"shell.execute_reply": "2024-01-10T00:13:25.364851Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(X_iris)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Target array\n",
"\n",
"Consider 1D *target array* containing labels or targets that we want to predict.\n",
"\n",
"May be numerical values or discrete classes/labels."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"In this example we want to predict the flower species from other measurements."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.368660Z",
"iopub.status.busy": "2024-01-10T00:13:25.368188Z",
"iopub.status.idle": "2024-01-10T00:13:25.374927Z",
"shell.execute_reply": "2024-01-10T00:13:25.374241Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 setosa\n",
"1 setosa\n",
"2 setosa\n",
"3 setosa\n",
"4 setosa\n",
"Name: species, dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_iris = iris['species']\n",
"y_iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.377983Z",
"iopub.status.busy": "2024-01-10T00:13:25.377595Z",
"iopub.status.idle": "2024-01-10T00:13:25.381761Z",
"shell.execute_reply": "2024-01-10T00:13:25.381237Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(y_iris)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Features matrix and target vector\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture03_Images/data-layout.png\" alt=\"data-layout\" width=\"500\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Image source](https://github.com/jakevdp/sklearn_tutorial)]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.384689Z",
"iopub.status.busy": "2024-01-10T00:13:25.384254Z",
"iopub.status.idle": "2024-01-10T00:13:25.390554Z",
"shell.execute_reply": "2024-01-10T00:13:25.390008Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150, 4)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.393600Z",
"iopub.status.busy": "2024-01-10T00:13:25.393051Z",
"iopub.status.idle": "2024-01-10T00:13:25.399664Z",
"shell.execute_reply": "2024-01-10T00:13:25.399032Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150,)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_iris.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Visualizing the data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:25.402904Z",
"iopub.status.busy": "2024-01-10T00:13:25.402329Z",
"iopub.status.idle": "2024-01-10T00:13:29.947973Z",
"shell.execute_reply": "2024-01-10T00:13:29.947247Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 730x600 with 20 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import seaborn as sns; sns.set()\n",
"sns.pairplot(iris, hue='species', height=1.5);"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"inclass_exercise"
]
},
"source": [
"\n",
"How well do you expect classification to perform with these features and why?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
},
"tags": [
"solution",
"inclass_exercise"
]
},
"source": [
"Fairly well since the different classes are reasonably well separated in feature space."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Scikit-Learn's Estimator API"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Scikit-Learn API design principles\n",
"\n",
"- Consistency: All objects share a common interface.\n",
"- Inspection: All specified parameter values exposed as public attributes.\n",
"- Limited object hierarchy: Only algorithms are represented by Python classes; data-sets/parameters represented in standard formats.\n",
"- Composition: Many machine learning tasks can be expressed as sequences of more fundamental algorithms.\n",
"- Sensible defaults: Library defines appropriate default value."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Impact of design principles\n",
"\n",
"- Makes Scikit-Learn easy to use, once the basic principles are understood. \n",
"- Every machine learning algorithm in Scikit-Learn implemented via the Estimator API.\n",
"- Provides a consistent interface for a wide range of machine learning applications."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Typical Scikit-Learn Estimator API steps\n",
"\n",
"1. Choose a class of model (import appropriate estimator class).\n",
"2. Choose model hyperparameters (instantiate class with desired values).\n",
"3. Arrange data into a features matrix and target vector.\n",
"4. Fit the model to data (calling `fit` method of model instance).\n",
"5. Apply model to new data:\n",
" - Supervised learning: often predict targets for unknown data using the `predict` method.\n",
" - For unsupervised learning: often transform or infer properties of the data using the `transform` or `predict` method."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Linear regression as machine learning"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:29.951745Z",
"iopub.status.busy": "2024-01-10T00:13:29.951355Z",
"iopub.status.idle": "2024-01-10T00:13:30.270629Z",
"shell.execute_reply": "2024-01-10T00:13:30.269923Z"
}
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"n_samples = 50\n",
"rng = np.random.RandomState(42)\n",
"x = 10 * rng.rand(n_samples)\n",
"y = 2 * x - 1 + rng.randn(n_samples)\n",
"plt.scatter(x, y);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 1. Choose a class of model\n",
"\n",
"Every class of model is represented by a Python class."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.274187Z",
"iopub.status.busy": "2024-01-10T00:13:30.273534Z",
"iopub.status.idle": "2024-01-10T00:13:30.331758Z",
"shell.execute_reply": "2024-01-10T00:13:30.331059Z"
}
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 2. Choose model hyperparameters\n",
"\n",
"Make instance of model with defined hyperparameters (e.g. y-intersect, regularization)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.335535Z",
"iopub.status.busy": "2024-01-10T00:13:30.335036Z",
"iopub.status.idle": "2024-01-10T00:13:30.343050Z",
"shell.execute_reply": "2024-01-10T00:13:30.342450Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression(fit_intercept=True)\n",
"model"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 3. Arrange data into a features matrix and target vector"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.347095Z",
"iopub.status.busy": "2024-01-10T00:13:30.345723Z",
"iopub.status.idle": "2024-01-10T00:13:30.352782Z",
"shell.execute_reply": "2024-01-10T00:13:30.352163Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50, 1)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = x.reshape(n_samples,1)\n",
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.355875Z",
"iopub.status.busy": "2024-01-10T00:13:30.355272Z",
"iopub.status.idle": "2024-01-10T00:13:30.361812Z",
"shell.execute_reply": "2024-01-10T00:13:30.361206Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(50,)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.shape "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 4. Fit the model to data\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.364747Z",
"iopub.status.busy": "2024-01-10T00:13:30.364379Z",
"iopub.status.idle": "2024-01-10T00:13:30.371856Z",
"shell.execute_reply": "2024-01-10T00:13:30.371202Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"All model parameters that were learned during the `fit()` process have *trailing underscores*."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.374971Z",
"iopub.status.busy": "2024-01-10T00:13:30.374528Z",
"iopub.status.idle": "2024-01-10T00:13:30.378642Z",
"shell.execute_reply": "2024-01-10T00:13:30.378102Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"-0.9033107255311146"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.381546Z",
"iopub.status.busy": "2024-01-10T00:13:30.380989Z",
"iopub.status.idle": "2024-01-10T00:13:30.387913Z",
"shell.execute_reply": "2024-01-10T00:13:30.387091Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([1.9776566])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.coef_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Intercept and slope are close to the model used to generate the data (-1 and 2 respectively)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 5. Predict targets for unknown data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.391306Z",
"iopub.status.busy": "2024-01-10T00:13:30.390667Z",
"iopub.status.idle": "2024-01-10T00:13:30.396006Z",
"shell.execute_reply": "2024-01-10T00:13:30.395397Z"
}
},
"outputs": [],
"source": [
"n_fit = 50\n",
"xfit = np.linspace(-1, 11, n_fit)\n",
"Xfit = xfit.reshape(n_fit,1)\n",
"yfit = model.predict(Xfit)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.398979Z",
"iopub.status.busy": "2024-01-10T00:13:30.398541Z",
"iopub.status.idle": "2024-01-10T00:13:30.618415Z",
"shell.execute_reply": "2024-01-10T00:13:30.617751Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y)\n",
"plt.plot(xfit, yfit, 'r');"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Supervised learning: classification\n",
"\n",
"Consider Iris data-set and predict species."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Split data into training and test sets (hint: [`train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) is a convenient scikit-learn function for this task)."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.621937Z",
"iopub.status.busy": "2024-01-10T00:13:30.621464Z",
"iopub.status.idle": "2024-01-10T00:13:30.627694Z",
"shell.execute_reply": "2024-01-10T00:13:30.627089Z"
}
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X_iris, y_iris, test_size=0.5, random_state=1)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.630352Z",
"iopub.status.busy": "2024-01-10T00:13:30.630134Z",
"iopub.status.idle": "2024-01-10T00:13:30.641703Z",
"shell.execute_reply": "2024-01-10T00:13:30.641083Z"
}
},
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>6.4</td>\n",
" <td>2.9</td>\n",
" <td>4.3</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>6.5</td>\n",
" <td>3.0</td>\n",
" <td>5.5</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>5.0</td>\n",
" <td>2.3</td>\n",
" <td>3.3</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>6.3</td>\n",
" <td>3.3</td>\n",
" <td>6.0</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>5.5</td>\n",
" <td>2.5</td>\n",
" <td>4.0</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"74 6.4 2.9 4.3 1.3\n",
"116 6.5 3.0 5.5 1.8\n",
"93 5.0 2.3 3.3 1.0\n",
"100 6.3 3.3 6.0 2.5\n",
"89 5.5 2.5 4.0 1.3"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.head() "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"Use a Gaussian Naive Bayes (`GaussianNB`) model to predict Iris species. Then evaluate performance on test data.\n",
"\n",
"(Hint: choose, instantiate, fit and predict.) \n",
"\n",
"See Scikit-Learn documentation on [`GaussianNB`](http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html).\n",
"\n",
"Evaluate performance using simple [`accuracy_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score).\n",
"\n",
"(Do not set any priors.)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.644869Z",
"iopub.status.busy": "2024-01-10T00:13:30.644398Z",
"iopub.status.idle": "2024-01-10T00:13:30.654902Z",
"shell.execute_reply": "2024-01-10T00:13:30.654270Z"
},
"tags": [
"solution"
]
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
"model = GaussianNB() # 2. instantiate model\n",
"model.fit(X_train, y_train) # 3. fit model to data\n",
"y_model = model.predict(X_test) # 4. predict on new data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Evaluate performance on test data."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.658096Z",
"iopub.status.busy": "2024-01-10T00:13:30.657663Z",
"iopub.status.idle": "2024-01-10T00:13:30.665168Z",
"shell.execute_reply": "2024-01-10T00:13:30.664579Z"
},
"tags": [
"solution"
]
},
"outputs": [
{
"data": {
"text/plain": [
"0.96"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test, y_model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Unsupervised learning: dimensionality reduction\n",
"\n",
"Reduce dimensionality of Iris data for visualisation or to discover structure.\n",
"\n",
"Recall the original Iris data has four features."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.667996Z",
"iopub.status.busy": "2024-01-10T00:13:30.667639Z",
"iopub.status.idle": "2024-01-10T00:13:30.676274Z",
"shell.execute_reply": "2024-01-10T00:13:30.675749Z"
}
},
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"0 5.1 3.5 1.4 0.2\n",
"1 4.9 3.0 1.4 0.2\n",
"2 4.7 3.2 1.3 0.2\n",
"3 4.6 3.1 1.5 0.2\n",
"4 5.0 3.6 1.4 0.2"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.679207Z",
"iopub.status.busy": "2024-01-10T00:13:30.678646Z",
"iopub.status.idle": "2024-01-10T00:13:30.685276Z",
"shell.execute_reply": "2024-01-10T00:13:30.684661Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150, 4)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_iris.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Compute principle component analysis (`PCA`), with 2 components, and apply transform. Plot data in PCA space. \n",
"\n",
"(Hint: choose, instantiate, fit and transform.)\n",
"\n",
"See Scikit-Learn documentation on [`PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html).\n",
"\n",
"See Seaborn documentation on [`lmplot`](https://seaborn.pydata.org/generated/seaborn.lmplot.html)."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.688494Z",
"iopub.status.busy": "2024-01-10T00:13:30.687909Z",
"iopub.status.idle": "2024-01-10T00:13:30.703068Z",
"shell.execute_reply": "2024-01-10T00:13:30.702344Z"
},
"tags": [
"solution"
]
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA # 1. Choose the model class\n",
"model = PCA(n_components=2) # 2. Instantiate the model with hyperparameters\n",
"model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n",
"X_2D = model.transform(X_iris) # 4. Transform the data to two dimensions "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.706164Z",
"iopub.status.busy": "2024-01-10T00:13:30.705697Z",
"iopub.status.idle": "2024-01-10T00:13:30.720075Z",
"shell.execute_reply": "2024-01-10T00:13:30.719309Z"
},
"tags": [
"solution"
]
},
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" <th>PCA1</th>\n",
" <th>PCA2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" <td>-2.684126</td>\n",
" <td>0.319397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" <td>-2.714142</td>\n",
" <td>-0.177001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" <td>-2.888991</td>\n",
" <td>-0.144949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" <td>-2.745343</td>\n",
" <td>-0.318299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" <td>-2.728717</td>\n",
" <td>0.326755</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species PCA1 \\\n",
"0 5.1 3.5 1.4 0.2 setosa -2.684126 \n",
"1 4.9 3.0 1.4 0.2 setosa -2.714142 \n",
"2 4.7 3.2 1.3 0.2 setosa -2.888991 \n",
"3 4.6 3.1 1.5 0.2 setosa -2.745343 \n",
"4 5.0 3.6 1.4 0.2 setosa -2.728717 \n",
"\n",
" PCA2 \n",
"0 0.319397 \n",
"1 -0.177001 \n",
"2 -0.144949 \n",
"3 -0.318299 \n",
"4 0.326755 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris['PCA1'] = X_2D[:, 0]\n",
"iris['PCA2'] = X_2D[:, 1]\n",
"iris.head() "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:30.723401Z",
"iopub.status.busy": "2024-01-10T00:13:30.723006Z",
"iopub.status.idle": "2024-01-10T00:13:31.391220Z",
"shell.execute_reply": "2024-01-10T00:13:31.390526Z"
},
"tags": [
"solution"
]
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 630x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(data=iris, x=\"PCA1\", y=\"PCA2\", hue='species', fit_reg=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"How well do you expect classification to perform using PCA components as features and why?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
},
"tags": [
"solution"
]
},
"source": [
"Very well since the different classes are well separated in PCA feature space."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Unsupervised learning: clustering\n",
"\n",
"Attempt to find \"groups\" in Iris data without given labels or training data.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
" \n",
"Cluster Iris data into 3 components using Gaussian Mixture Model (GMM). Plot the 3 components separately in PCA space.\n",
"\n",
"(Hint: choose, instantiate, fit and predict.)\n",
"\n",
"See Scikit-Learn documentation on [`GaussianMixture`](http://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html)."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:31.394705Z",
"iopub.status.busy": "2024-01-10T00:13:31.394206Z",
"iopub.status.idle": "2024-01-10T00:13:31.493375Z",
"shell.execute_reply": "2024-01-10T00:13:31.492329Z"
},
"tags": [
"solution"
]
},
"outputs": [],
"source": [
"from sklearn.mixture import GaussianMixture # 1. Choose the model class\n",
"model = GaussianMixture(n_components=3) # 2. Instantiate the model with hyperparameters\n",
"model.fit(X_iris) # 3. Fit to data. Notice y is not specified!\n",
"y_gmm = model.predict(X_iris) # 4. Determine cluster labels"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:13:31.497402Z",
"iopub.status.busy": "2024-01-10T00:13:31.496762Z",
"iopub.status.idle": "2024-01-10T00:13:33.256040Z",
"shell.execute_reply": "2024-01-10T00:13:33.255265Z"
},
"tags": [
"solution"
]
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1630x500 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iris['cluster'] = y_gmm\n",
"sns.lmplot(data=iris, x=\"PCA1\", y=\"PCA2\", hue='species',\n",
" col='cluster', fit_reg=False);"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
},
"tags": [
"solution"
]
},
"source": [
"The GMM has done a reasonably good job of separating the different classes. Setosa is perfectly separated in one cluster, while there remains some mixing between versicolor and viginica."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": [
"exercise_pointer"
]
},
"source": [
"**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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