You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
988 lines
358 KiB
988 lines
358 KiB
2 years ago
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"___\n",
|
||
|
|
"\n",
|
||
|
|
"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
|
||
|
|
"___\n",
|
||
|
|
"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
|
||
|
|
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Multi-Class Logistic Regression\n",
|
||
|
|
"\n",
|
||
|
|
"Students often ask how to perform non binary classification with Logistic Regression. Fortunately, the process with scikit-learn is pretty much the same as with binary classification. To expand our understanding, we'll go through a simple data set, as well as seeing how to use LogisiticRegression with a manual GridSearchCV (instead of LogisticRegressionCV). Make sure to watch the video to understand the One-vs-all process that is occuring \"under the hood\"."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Imports"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"import seaborn as sns\n",
|
||
|
|
"import matplotlib.pyplot as plt"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Data\n",
|
||
|
|
"\n",
|
||
|
|
"We will work with the classic Iris Data Set. The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.\n",
|
||
|
|
"\n",
|
||
|
|
"Full Details: https://en.wikipedia.org/wiki/Iris_flower_data_set"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"df = pd.read_csv('../DATA/iris.csv')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"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>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": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Exploratory Data Analysis and Visualization\n",
|
||
|
|
"\n",
|
||
|
|
"Feel free to explore the data further on your own."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
|
|
"RangeIndex: 150 entries, 0 to 149\n",
|
||
|
|
"Data columns (total 5 columns):\n",
|
||
|
|
" # Column Non-Null Count Dtype \n",
|
||
|
|
"--- ------ -------------- ----- \n",
|
||
|
|
" 0 sepal_length 150 non-null float64\n",
|
||
|
|
" 1 sepal_width 150 non-null float64\n",
|
||
|
|
" 2 petal_length 150 non-null float64\n",
|
||
|
|
" 3 petal_width 150 non-null float64\n",
|
||
|
|
" 4 species 150 non-null object \n",
|
||
|
|
"dtypes: float64(4), object(1)\n",
|
||
|
|
"memory usage: 6.0+ KB\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.info()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"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>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>count</th>\n",
|
||
|
|
" <td>150.000000</td>\n",
|
||
|
|
" <td>150.000000</td>\n",
|
||
|
|
" <td>150.000000</td>\n",
|
||
|
|
" <td>150.000000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>mean</th>\n",
|
||
|
|
" <td>5.843333</td>\n",
|
||
|
|
" <td>3.054000</td>\n",
|
||
|
|
" <td>3.758667</td>\n",
|
||
|
|
" <td>1.198667</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>std</th>\n",
|
||
|
|
" <td>0.828066</td>\n",
|
||
|
|
" <td>0.433594</td>\n",
|
||
|
|
" <td>1.764420</td>\n",
|
||
|
|
" <td>0.763161</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>min</th>\n",
|
||
|
|
" <td>4.300000</td>\n",
|
||
|
|
" <td>2.000000</td>\n",
|
||
|
|
" <td>1.000000</td>\n",
|
||
|
|
" <td>0.100000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>25%</th>\n",
|
||
|
|
" <td>5.100000</td>\n",
|
||
|
|
" <td>2.800000</td>\n",
|
||
|
|
" <td>1.600000</td>\n",
|
||
|
|
" <td>0.300000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>50%</th>\n",
|
||
|
|
" <td>5.800000</td>\n",
|
||
|
|
" <td>3.000000</td>\n",
|
||
|
|
" <td>4.350000</td>\n",
|
||
|
|
" <td>1.300000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>75%</th>\n",
|
||
|
|
" <td>6.400000</td>\n",
|
||
|
|
" <td>3.300000</td>\n",
|
||
|
|
" <td>5.100000</td>\n",
|
||
|
|
" <td>1.800000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>max</th>\n",
|
||
|
|
" <td>7.900000</td>\n",
|
||
|
|
" <td>4.400000</td>\n",
|
||
|
|
" <td>6.900000</td>\n",
|
||
|
|
" <td>2.500000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" sepal_length sepal_width petal_length petal_width\n",
|
||
|
|
"count 150.000000 150.000000 150.000000 150.000000\n",
|
||
|
|
"mean 5.843333 3.054000 3.758667 1.198667\n",
|
||
|
|
"std 0.828066 0.433594 1.764420 0.763161\n",
|
||
|
|
"min 4.300000 2.000000 1.000000 0.100000\n",
|
||
|
|
"25% 5.100000 2.800000 1.600000 0.300000\n",
|
||
|
|
"50% 5.800000 3.000000 4.350000 1.300000\n",
|
||
|
|
"75% 6.400000 3.300000 5.100000 1.800000\n",
|
||
|
|
"max 7.900000 4.400000 6.900000 2.500000"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.describe()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"setosa 50\n",
|
||
|
|
"versicolor 50\n",
|
||
|
|
"virginica 50\n",
|
||
|
|
"Name: species, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['species'].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='species', ylabel='count'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.countplot(df['species'])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='sepal_length', ylabel='sepal_width'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.scatterplot(x='sepal_length',y='sepal_width',data=df,hue='species')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:xlabel='petal_length', ylabel='petal_width'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.scatterplot(x='petal_length',y='petal_width',data=df,hue='species')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.PairGrid at 0x2a1a26a4908>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 804.75x720 with 20 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.pairplot(df,hue='species')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<AxesSubplot:>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sns.heatmap(df.corr(),annot=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"Easily discover new plot types with a google search! Searching for \"3d matplotlib scatter plot\" quickly takes you to: https://matplotlib.org/3.1.1/gallery/mplot3d/scatter3d.html"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array(['setosa', 'versicolor', 'virginica'], dtype=object)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['species'].unique()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from mpl_toolkits.mplot3d import Axes3D \n",
|
||
|
|
"fig = plt.figure()\n",
|
||
|
|
"ax = fig.add_subplot(111, projection='3d')\n",
|
||
|
|
"colors = df['species'].map({'setosa':0, 'versicolor':1, 'virginica':2})\n",
|
||
|
|
"ax.scatter(df['sepal_width'],df['petal_width'],df['petal_length'],c=colors);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Train | Test Split and Scaling"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X = df.drop('species',axis=1)\n",
|
||
|
|
"y = df['species']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"from sklearn.preprocessing import StandardScaler"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 77,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 78,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scaler = StandardScaler()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 79,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"scaled_X_train = scaler.fit_transform(X_train)\n",
|
||
|
|
"scaled_X_test = scaler.transform(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Multi-Class Logistic Regression Model"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 80,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.linear_model import LogisticRegression"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 81,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.model_selection import GridSearchCV"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 96,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Depending on warnings you may need to adjust max iterations allowed \n",
|
||
|
|
"# Or experiment with different solvers\n",
|
||
|
|
"log_model = LogisticRegression(solver='saga',multi_class=\"ovr\",max_iter=5000)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### GridSearch for Best Hyper-Parameters\n",
|
||
|
|
"\n",
|
||
|
|
"Main parameter choices are regularization penalty choice and regularization C value."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 97,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Penalty Type\n",
|
||
|
|
"penalty = ['l1', 'l2']\n",
|
||
|
|
"\n",
|
||
|
|
"# Use logarithmically spaced C values (recommended in official docs)\n",
|
||
|
|
"C = np.logspace(0, 4, 10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 98,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"grid_model = GridSearchCV(log_model,param_grid={'C':C,'penalty':penalty})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"GridSearchCV(estimator=LogisticRegression(max_iter=5000, multi_class='ovr',\n",
|
||
|
|
" solver='saga'),\n",
|
||
|
|
" param_grid={'C': array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,\n",
|
||
|
|
" 5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,\n",
|
||
|
|
" 3.59381366e+03, 1.00000000e+04]),\n",
|
||
|
|
" 'penalty': ['l1', 'l2']})"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"grid_model.fit(scaled_X_train,y_train)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 100,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"{'C': 7.742636826811269, 'penalty': 'l1'}"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 100,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"grid_model.best_params_"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Model Performance on Classification Tasks"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 101,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,plot_confusion_matrix"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 102,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"y_pred = grid_model.predict(scaled_X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 103,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"0.9736842105263158"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 103,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"accuracy_score(y_test,y_pred)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 104,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([[10, 0, 0],\n",
|
||
|
|
" [ 0, 17, 0],\n",
|
||
|
|
" [ 0, 1, 10]], dtype=int64)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 104,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"confusion_matrix(y_test,y_pred)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2a1a83ac0c8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 108,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_confusion_matrix(grid_model,scaled_X_test,y_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 109,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2a1a843ac48>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 109,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Scaled so highest value=1\n",
|
||
|
|
"plot_confusion_matrix(grid_model,scaled_X_test,y_test,normalize='true')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 110,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" precision recall f1-score support\n",
|
||
|
|
"\n",
|
||
|
|
" setosa 1.00 1.00 1.00 10\n",
|
||
|
|
" versicolor 0.94 1.00 0.97 17\n",
|
||
|
|
" virginica 1.00 0.91 0.95 11\n",
|
||
|
|
"\n",
|
||
|
|
" accuracy 0.97 38\n",
|
||
|
|
" macro avg 0.98 0.97 0.97 38\n",
|
||
|
|
"weighted avg 0.98 0.97 0.97 38\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(classification_report(y_test,y_pred))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Evaluating Curves and AUC\n",
|
||
|
|
"\n",
|
||
|
|
"**Make sure to watch the video on this! We need to manually create the plots for a Multi-Class situation. Fortunately, Scikit-learn's documentation already has plenty of examples on this.**\n",
|
||
|
|
"\n",
|
||
|
|
"Source: https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 116,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.metrics import roc_curve, auc"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 122,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def plot_multiclass_roc(clf, X_test, y_test, n_classes, figsize=(5,5)):\n",
|
||
|
|
" y_score = clf.decision_function(X_test)\n",
|
||
|
|
"\n",
|
||
|
|
" # structures\n",
|
||
|
|
" fpr = dict()\n",
|
||
|
|
" tpr = dict()\n",
|
||
|
|
" roc_auc = dict()\n",
|
||
|
|
"\n",
|
||
|
|
" # calculate dummies once\n",
|
||
|
|
" y_test_dummies = pd.get_dummies(y_test, drop_first=False).values\n",
|
||
|
|
" for i in range(n_classes):\n",
|
||
|
|
" fpr[i], tpr[i], _ = roc_curve(y_test_dummies[:, i], y_score[:, i])\n",
|
||
|
|
" roc_auc[i] = auc(fpr[i], tpr[i])\n",
|
||
|
|
"\n",
|
||
|
|
" # roc for each class\n",
|
||
|
|
" fig, ax = plt.subplots(figsize=figsize)\n",
|
||
|
|
" ax.plot([0, 1], [0, 1], 'k--')\n",
|
||
|
|
" ax.set_xlim([0.0, 1.0])\n",
|
||
|
|
" ax.set_ylim([0.0, 1.05])\n",
|
||
|
|
" ax.set_xlabel('False Positive Rate')\n",
|
||
|
|
" ax.set_ylabel('True Positive Rate')\n",
|
||
|
|
" ax.set_title('Receiver operating characteristic example')\n",
|
||
|
|
" for i in range(n_classes):\n",
|
||
|
|
" ax.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f) for label %i' % (roc_auc[i], i))\n",
|
||
|
|
" ax.legend(loc=\"best\")\n",
|
||
|
|
" ax.grid(alpha=.4)\n",
|
||
|
|
" sns.despine()\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 123,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 1152x720 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot_multiclass_roc(grid_model, scaled_X_test, y_test, n_classes=3, figsize=(16, 10))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"------\n",
|
||
|
|
"------"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"anaconda-cloud": {},
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|