{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![MLU Logo](../data/MLU_Logo.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Accelerator - Tabular Data - Lecture 1\n", "\n", "\n", "## K Nearest Neighbors Model \n", "\n", "In this notebook, we build a [__K Nearest Neighbors Classifier__](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) to predict the __Outcome Type__ field of our review dataset.\n", "\n", "1. Read the dataset\n", "2. Exploratory Data Analysis\n", "3. Select features to build the model\n", "4. Training and test datasets\n", "5. Data processing with Pipeline\n", "6. Train a classifier\n", "7. Test the classifier\n", "8. Improvement ideas\n", "\n", "__Austin Animal Center Dataset__:\n", "\n", "In this exercise, we are working with pet adoption data from __Austin Animal Center__. We have two datasets that cover intake and outcome of animals. Intake data is available from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Intakes/wter-evkm) and outcome is from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Outcomes/9t4d-g238). \n", "\n", "In order to work with a single table, we joined the intake and outcome tables using the \"Animal ID\" column and created a single __review.csv__ file. We also didn't consider animals with multiple entries to the facility to keep our dataset simple. If you want to see the original datasets and the merged data with multiple entries, they are available under data/review folder: Austin_Animal_Center_Intakes.csv, Austin_Animal_Center_Outcomes.csv and Austin_Animal_Center_Intakes_Outcomes.csv.\n", "\n", "__Dataset schema:__ \n", "- __Pet ID__ - Unique ID of pet\n", "- __Outcome Type__ - State of pet at the time of recording the outcome (0 = not placed, 1 = placed). This is the field to predict.\n", "- __Sex upon Outcome__ - Sex of pet at outcome\n", "- __Name__ - Name of pet \n", "- __Found Location__ - Found location of pet before entered the center\n", "- __Intake Type__ - Circumstances bringing the pet to the center\n", "- __Intake Condition__ - Health condition of pet when entered the center\n", "- __Pet Type__ - Type of pet\n", "- __Sex upon Intake__ - Sex of pet when entered the center\n", "- __Breed__ - Breed of pet \n", "- __Color__ - Color of pet \n", "- __Age upon Intake Days__ - Age of pet when entered the center (days)\n", "- __Age upon Outcome Days__ - Age of pet at outcome (days)\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.3.1 is available.\n", "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/pytorch_p39/bin/python -m pip install --upgrade pip' command.\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install -q -r ../requirements.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read the dataset\n", "(Go to top)\n", "\n", "Let's read the dataset into a dataframe, using Pandas." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The shape of the dataset is: (95485, 13)\n" ] } ], "source": [ "import pandas as pd\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", " \n", "df = pd.read_csv('../data/review/review_dataset.csv')\n", "\n", "print('The shape of the dataset is:', df.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Exploratory Data Analysis\n", "(Go to top)\n", "\n", "We will look at number of rows, columns and some simple statistics of the dataset." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pet IDOutcome TypeSex upon OutcomeNameFound LocationIntake TypeIntake ConditionPet TypeSex upon IntakeBreedColorAge upon Intake DaysAge upon Outcome Days
0A7940111.0Neutered MaleChunkAustin (TX)Owner SurrenderNormalCatNeutered MaleDomestic Shorthair MixBrown Tabby/White730730
1A7763591.0Neutered MaleGizmo7201 Levander Loop in Austin (TX)StrayNormalDogIntact MaleChihuahua Shorthair MixWhite/Brown365365
2A6747540.0Intact MaleNaN12034 Research in Austin (TX)StrayNursingCatIntact MaleDomestic Shorthair MixOrange Tabby66
3A6897241.0Neutered Male*Donatello2300 Waterway Bnd in Austin (TX)StrayNormalCatIntact MaleDomestic Shorthair MixBlack6060
4A6809691.0Neutered Male*Zeus4701 Staggerbrush Rd in Austin (TX)StrayNursingCatIntact MaleDomestic Shorthair MixWhite/Orange Tabby760
\n", "
" ], "text/plain": [ " Pet ID Outcome Type Sex upon Outcome Name \\\n", "0 A794011 1.0 Neutered Male Chunk \n", "1 A776359 1.0 Neutered Male Gizmo \n", "2 A674754 0.0 Intact Male NaN \n", "3 A689724 1.0 Neutered Male *Donatello \n", "4 A680969 1.0 Neutered Male *Zeus \n", "\n", " Found Location Intake Type Intake Condition \\\n", "0 Austin (TX) Owner Surrender Normal \n", "1 7201 Levander Loop in Austin (TX) Stray Normal \n", "2 12034 Research in Austin (TX) Stray Nursing \n", "3 2300 Waterway Bnd in Austin (TX) Stray Normal \n", "4 4701 Staggerbrush Rd in Austin (TX) Stray Nursing \n", "\n", " Pet Type Sex upon Intake Breed Color \\\n", "0 Cat Neutered Male Domestic Shorthair Mix Brown Tabby/White \n", "1 Dog Intact Male Chihuahua Shorthair Mix White/Brown \n", "2 Cat Intact Male Domestic Shorthair Mix Orange Tabby \n", "3 Cat Intact Male Domestic Shorthair Mix Black \n", "4 Cat Intact Male Domestic Shorthair Mix White/Orange Tabby \n", "\n", " Age upon Intake Days Age upon Outcome Days \n", "0 730 730 \n", "1 365 365 \n", "2 6 6 \n", "3 60 60 \n", "4 7 60 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print the first five rows\n", "# NaN means missing data\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 95485 entries, 0 to 95484\n", "Data columns (total 13 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Pet ID 95485 non-null object \n", " 1 Outcome Type 95485 non-null float64\n", " 2 Sex upon Outcome 95484 non-null object \n", " 3 Name 59138 non-null object \n", " 4 Found Location 95485 non-null object \n", " 5 Intake Type 95485 non-null object \n", " 6 Intake Condition 95485 non-null object \n", " 7 Pet Type 95485 non-null object \n", " 8 Sex upon Intake 95484 non-null object \n", " 9 Breed 95485 non-null object \n", " 10 Color 95485 non-null object \n", " 11 Age upon Intake Days 95485 non-null int64 \n", " 12 Age upon Outcome Days 95485 non-null int64 \n", "dtypes: float64(1), int64(2), object(10)\n", "memory usage: 9.5+ MB\n" ] } ], "source": [ "# Let's see the data types and non-null values for each column\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Outcome TypeAge upon Intake DaysAge upon Outcome Days
count95485.00000095485.00000095485.000000
mean0.564005703.436959717.757313
std0.4958891052.2521971055.023160
min0.0000000.0000000.000000
25%0.00000030.00000060.000000
50%1.000000365.000000365.000000
75%1.000000730.000000730.000000
max1.0000009125.0000009125.000000
\n", "
" ], "text/plain": [ " Outcome Type Age upon Intake Days Age upon Outcome Days\n", "count 95485.000000 95485.000000 95485.000000\n", "mean 0.564005 703.436959 717.757313\n", "std 0.495889 1052.252197 1055.023160\n", "min 0.000000 0.000000 0.000000\n", "25% 0.000000 30.000000 60.000000\n", "50% 1.000000 365.000000 365.000000\n", "75% 1.000000 730.000000 730.000000\n", "max 1.000000 9125.000000 9125.000000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This prints basic statistics for numerical columns\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's separate model features and model target. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['Pet ID', 'Outcome Type', 'Sex upon Outcome', 'Name', 'Found Location',\n", " 'Intake Type', 'Intake Condition', 'Pet Type', 'Sex upon Intake',\n", " 'Breed', 'Color', 'Age upon Intake Days', 'Age upon Outcome Days'],\n", " dtype='object')\n" ] } ], "source": [ "print(df.columns)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model features: Index(['Pet ID', 'Sex upon Outcome', 'Name', 'Found Location', 'Intake Type',\n", " 'Intake Condition', 'Pet Type', 'Sex upon Intake', 'Breed', 'Color',\n", " 'Age upon Intake Days', 'Age upon Outcome Days'],\n", " dtype='object')\n", "Model target: Outcome Type\n" ] } ], "source": [ "model_features = df.columns.drop('Outcome Type')\n", "model_target = 'Outcome Type'\n", "\n", "print('Model features: ', model_features)\n", "print('Model target: ', model_target)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can explore the features set further, figuring out first what features are numerical or categorical. Beware that some integer-valued features could actually be categorical features, and some categorical features could be text features. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical columns: Index(['Age upon Intake Days', 'Age upon Outcome Days'], dtype='object')\n", "\n", "Categorical columns: Index(['Pet ID', 'Sex upon Outcome', 'Name', 'Found Location', 'Intake Type',\n", " 'Intake Condition', 'Pet Type', 'Sex upon Intake', 'Breed', 'Color'],\n", " dtype='object')\n" ] } ], "source": [ "import numpy as np\n", "numerical_features_all = df[model_features].select_dtypes(include=np.number).columns\n", "print('Numerical columns:',numerical_features_all)\n", "\n", "print('')\n", "\n", "categorical_features_all = df[model_features].select_dtypes(include='object').columns\n", "print('Categorical columns:',categorical_features_all)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Target distribution\n", "\n", "Let's check our target distribution." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "df[model_target].value_counts().plot.bar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the target plots we can identify whether or not we are dealing with imbalanced datasets - this means one result type is dominating the other one(s). \n", "\n", "Handling class imbalance is highly recommended, as the model performance can be greatly impacted. In particular the model may not work well for the infrequent classes, as there are not enough samples to learn patterns from, and so it would be hard for the classifier to identify and match those patterns. \n", "\n", "We might want to downsample the dominant class or upsample the rare the class, to help with learning its patterns. However, we should only fix the imbalance in training set, without changing the validation and test sets, as these should follow the original distribution. We will perform this task after train/test split. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Select features to build the model\n", "(Go to top)\n", "\n", "We only consider the __numerical features__ to build the model for this first sample solution. \n", "\n", "Let's examine the numerical features." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age upon Intake Days\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Age upon Outcome Days\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "for c in numerical_features_all:\n", " print(c)\n", " df[c].plot.hist(bins=5)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If for some histograms the values are heavily placed in the first bin, it is good to check for outliers, either checking the min-max values of those particular features and/or explore value ranges." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age upon Intake Days\n", "min: 0 max: 9125\n", "Age upon Outcome Days\n", "min: 0 max: 9125\n" ] } ], "source": [ "for c in numerical_features_all:\n", " print(c)\n", " print('min:', df[c].min(), 'max:', df[c].max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With __value_counts()__ function, we can increase the number of histogram bins to 10 for more bins for a more refined view of the numerical features." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age upon Intake Days\n", "(-9.126, 912.5] 74835\n", "(912.5, 1825.0] 10647\n", "(1825.0, 2737.5] 3471\n", "(2737.5, 3650.0] 3998\n", "(3650.0, 4562.5] 1234\n", "(4562.5, 5475.0] 1031\n", "(5475.0, 6387.5] 183\n", "(6387.5, 7300.0] 79\n", "(7300.0, 8212.5] 5\n", "(8212.5, 9125.0] 2\n", "Name: Age upon Intake Days, dtype: int64\n", "Age upon Outcome Days\n", "(-9.126, 912.5] 74642\n", "(912.5, 1825.0] 10699\n", "(1825.0, 2737.5] 3465\n", "(2737.5, 3650.0] 4080\n", "(3650.0, 4562.5] 1263\n", "(4562.5, 5475.0] 1061\n", "(5475.0, 6387.5] 187\n", "(6387.5, 7300.0] 81\n", "(7300.0, 8212.5] 5\n", "(8212.5, 9125.0] 2\n", "Name: Age upon Outcome Days, dtype: int64\n" ] } ], "source": [ "for c in numerical_features_all: \n", " print(c)\n", " print(df[c].value_counts(bins=10, sort=False))\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If any outliers are identified as very likely wrong values, dropping them could improve the numerical values histograms, and later overall model performance. While a good rule of thumb is that anything not in the range of (Q1 - 1.5 IQR) and (Q3 + 1.5 IQR) is an outlier, other rules for removing 'outliers' should be considered as well. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check missing values for these numerical features." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Age upon Intake Days 0\n", "Age upon Outcome Days 0\n", "dtype: int64\n" ] } ], "source": [ "print(df[numerical_features_all].isna().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If any missing values, as a quick fix, we can apply mean imputation. This will replace the missing values with the mean value of the corresponding column.\n", "\n", "__Note__: The statistically correct way to perform mean/mode imputation before training an ML model is to compute the column-wise means on the training data only, and then use these values to impute missing data in both the train and test sets. So, you'll need to split your dataset first.\n", "\n", "Also, more exploratory data analysis might reveal other important hidden atributes and/or relationships of the model features considered. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Training and test datasets\n", "(Go to top)\n", "\n", "We split our dataset into training (90%) and test (10%) subsets using sklearn's [__train_test_split()__](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "train_data, test_data = train_test_split(df, test_size=0.1, shuffle=True, random_state=23)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Target balancing" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set shape: (85936, 13)\n", "Class 0 samples in the training set: 37499\n", "Class 1 samples in the training set: 48437\n", "Class 0 samples in the test set: 4132\n", "Class 1 samples in the test set: 5417\n" ] } ], "source": [ "print('Training set shape:', train_data.shape)\n", "\n", "print('Class 0 samples in the training set:', sum(train_data[model_target] == 0))\n", "print('Class 1 samples in the training set:', sum(train_data[model_target] == 1))\n", "\n", "print('Class 0 samples in the test set:', sum(test_data[model_target] == 0))\n", "print('Class 1 samples in the test set:', sum(test_data[model_target] == 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Important note:__ We want to fix the imbalance only in training set. We shouldn't change the validation and test sets, as these should follow the original distribution." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from sklearn.utils import shuffle\n", "\n", "class_0_no = train_data[train_data[model_target] == 0]\n", "class_1_no = train_data[train_data[model_target] == 1]\n", "\n", "upsampled_class_0_no = class_0_no.sample(n=len(class_1_no), replace=True, random_state=42)\n", "\n", "train_data = pd.concat([class_1_no, upsampled_class_0_no])\n", "train_data = shuffle(train_data)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set shape: (96874, 13)\n", "Class 1 samples in the training set: 48437\n", "Class 0 samples in the training set: 48437\n" ] } ], "source": [ "print('Training set shape:', train_data.shape)\n", "\n", "print('Class 1 samples in the training set:', sum(train_data[model_target] == 1))\n", "print('Class 0 samples in the training set:', sum(train_data[model_target] == 0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Data processing with Pipeline\n", "(Go to top)\n", "\n", "In a typical machine learning workflow you will need to apply data transformations, like imputation and scaling shown here, at least twice. First on the training dataset with __.fit()__ and __.transform()__, when preparing the data to training the model. And again, on any new data you want to predict on, with _-.transform()__. Scikit-learn [Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) is a tool that simplifies this process by enforcing the implementation and order of data processing steps. \n", "\n", "We build a pipeline to impute the missing values with the mean using sklearn's SimpleImputer, scale the numerical features to have similar orders of magnitude by bringing them into the 0-1 range with sklearn's MinMaxScaler, and finally train an estimator [Decision Tree Classifier](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) on the imputed and scaled dataset. \n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', MinMaxScaler()),\n",
       "                ('estimator', KNeighborsClassifier(n_neighbors=3))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', MinMaxScaler()),\n", " ('estimator', KNeighborsClassifier(n_neighbors=3))])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.impute import SimpleImputer\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.pipeline import Pipeline\n", "\n", "### PIPELINE ###\n", "################\n", "\n", "# Pipeline desired data transformers, along with an estimator at the end\n", "# For each step specify: a name, the actual transformer/estimator with its parameters\n", "classifier = Pipeline([\n", " ('imputer', SimpleImputer(strategy='mean')),\n", " ('scaler', MinMaxScaler()),\n", " ('estimator', KNeighborsClassifier(n_neighbors = 3))\n", "])\n", "\n", "# Visualize the pipeline\n", "# This will come in handy especially when building more complex pipelines, stringing together multiple preprocessing steps\n", "from sklearn import set_config\n", "set_config(display='diagram')\n", "classifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 6. Train a classifier\n", "(Go to top)\n", "\n", "We train our classifier with __.fit()__ on our training dataset. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', MinMaxScaler()),\n",
       "                ('estimator', KNeighborsClassifier(n_neighbors=3))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', MinMaxScaler()),\n", " ('estimator', KNeighborsClassifier(n_neighbors=3))])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get train data to train the classifier\n", "X_train = train_data[numerical_features_all]\n", "y_train = train_data[model_target]\n", "\n", "# Fit the classifier to training data\n", "# Train data going through the Pipeline it's first imputed, then scaled, and finally used to fit the estimator\n", "classifier.fit(X_train, y_train)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 7. Test the classifier\n", "(Go to top)\n", "\n", "Let's evaluate the performance of the trained classifier. We use __.predict()__ this time. \n", "\n", "Let's first see how the model works on the training dataset." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model performance on the train set:\n", "[[36481 11956]\n", " [22753 25684]]\n", " precision recall f1-score support\n", "\n", " 0.0 0.62 0.75 0.68 48437\n", " 1.0 0.68 0.53 0.60 48437\n", "\n", " accuracy 0.64 96874\n", " macro avg 0.65 0.64 0.64 96874\n", "weighted avg 0.65 0.64 0.64 96874\n", "\n", "Train accuracy: 0.6417098499081281\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score\n", "\n", "# Use the fitted model to make predictions on the train dataset\n", "# Train data going through the Pipeline it's first imputed (with means from the train), scaled (with the min/max from the train data), and finally used to make predictions\n", "train_predictions = classifier.predict(X_train)\n", "\n", "print('Model performance on the train set:')\n", "print(confusion_matrix(y_train, train_predictions))\n", "print(classification_report(y_train, train_predictions))\n", "print(\"Train accuracy:\", accuracy_score(y_train, train_predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And now, let's evaluate the performance of the classifier on the test set." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model performance on the test set:\n", "[[3104 1028]\n", " [2518 2899]]\n", " precision recall f1-score support\n", "\n", " 0.0 0.55 0.75 0.64 4132\n", " 1.0 0.74 0.54 0.62 5417\n", "\n", " accuracy 0.63 9549\n", " macro avg 0.65 0.64 0.63 9549\n", "weighted avg 0.66 0.63 0.63 9549\n", "\n", "Test accuracy: 0.6286522148916117\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score\n", "\n", "# Get test data to test the classifier\n", "X_test = test_data[numerical_features_all]\n", "y_test = test_data[model_target]\n", "\n", "# Use the fitted model to make predictions on the test dataset\n", "# Test data going through the Pipeline it's first imputed (with means from the train), scaled (with the min/max from the train data), and finally used to make predictions\n", "test_predictions = classifier.predict(X_test)\n", "\n", "print('Model performance on the test set:')\n", "print(confusion_matrix(y_test, test_predictions))\n", "print(classification_report(y_test, test_predictions))\n", "print(\"Test accuracy:\", accuracy_score(y_test, test_predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 8. Improvement ideas\n", "(Go to top)\n", "\n", "* Tune K parameter: You can use the [__train_test_split()__](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function on the training set, create a validation set, and search for optimum K value using the validation performance.\n" ] } ], "metadata": { "kernelspec": { "display_name": "conda_pytorch_p39", "language": "python", "name": "conda_pytorch_p39" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.13" } }, "nbformat": 4, "nbformat_minor": 4 }