{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Lectures 5 and 6: Class demo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports, Announcements, LOs" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# import the libraries\n", "import os\n", "import sys\n", "sys.path.append(os.path.join(os.path.abspath(\"../\"), \"code\"))\n", "from plotting_functions import *\n", "from utils import *\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "%matplotlib inline\n", "\n", "pd.set_option(\"display.max_colwidth\", 200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do you recall [the restaurants survey](https://ubc.ca1.qualtrics.com/jfe/form/SV_73VuZiuwM1eDVrw) you completed at the start of the course?\n", "\n", "Let's use that data for this demo. You'll find a [wrangled version](https://github.com/UBC-CS/cpsc330-2023W1/blob/main/lectures/data/cleaned_restaurant_data.csv) in the course repository." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('../data/cleaned_restaurant_data.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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north_americaeat_out_freqagen_peoplepricefood_typenoise_levelgood_servercommentsrestaurant_nametarget
0Yes3.02910.0120.0ItalianmediumYesAmbienceNaNdislike
1Yes2.0233.020.0Canadian/Americanno musicNofood tastes badNaNdislike
2Yes2.02120.015.0ChinesemediumYesbad foodNaNdislike
3No2.02414.018.0OthermediumNoOverall vibe on the restaurantNaNdislike
4Yes5.02330.020.0ChinesemediumYesA bad dayNaNdislike
....................................
959No10.022NaNNaNNaNNaNNaNNaNNaNlike
960Yes1.020NaNNaNNaNNaNNaNNaNNaNlike
961No1.02240.050.0ChinesemediumYesThe self service sauce table is very clean and the sauces were always filled up.Haidilaolike
962Yes3.021NaNNaNNaNNaNNaNNaNNaNlike
963Yes3.02720.022.0OthermediumYesLots of meat that was very soft and tasty. Hearty and amazing broth. Good noodle thickness and consistencyUno Beef Noodlelike
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964 rows × 11 columns

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" ], "text/plain": [ " north_america eat_out_freq age n_people price food_type \\\n", "0 Yes 3.0 29 10.0 120.0 Italian \n", "1 Yes 2.0 23 3.0 20.0 Canadian/American \n", "2 Yes 2.0 21 20.0 15.0 Chinese \n", "3 No 2.0 24 14.0 18.0 Other \n", "4 Yes 5.0 23 30.0 20.0 Chinese \n", ".. ... ... ... ... ... ... \n", "959 No 10.0 22 NaN NaN NaN \n", "960 Yes 1.0 20 NaN NaN NaN \n", "961 No 1.0 22 40.0 50.0 Chinese \n", "962 Yes 3.0 21 NaN NaN NaN \n", "963 Yes 3.0 27 20.0 22.0 Other \n", "\n", " noise_level good_server \\\n", "0 medium Yes \n", "1 no music No \n", "2 medium Yes \n", "3 medium No \n", "4 medium Yes \n", ".. ... ... \n", "959 NaN NaN \n", "960 NaN NaN \n", "961 medium Yes \n", "962 NaN NaN \n", "963 medium Yes \n", "\n", " comments \\\n", "0 Ambience \n", "1 food tastes bad \n", "2 bad food \n", "3 Overall vibe on the restaurant \n", "4 A bad day \n", ".. ... \n", "959 NaN \n", "960 NaN \n", "961 The self service sauce table is very clean and the sauces were always filled up. \n", "962 NaN \n", "963 Lots of meat that was very soft and tasty. Hearty and amazing broth. Good noodle thickness and consistency \n", "\n", " restaurant_name target \n", "0 NaN dislike \n", "1 NaN dislike \n", "2 NaN dislike \n", "3 NaN dislike \n", "4 NaN dislike \n", ".. ... ... \n", "959 NaN like \n", "960 NaN like \n", "961 Haidilao like \n", "962 NaN like \n", "963 Uno Beef Noodle like \n", "\n", "[964 rows x 11 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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eat_out_freqagen_peopleprice
count964.000000964.0000006.960000e+02696.000000
mean2.58518723.9751041.439254e+041472.179152
std2.2464864.5567163.790481e+0537903.575636
min0.00000010.000000-2.000000e+000.000000
25%1.00000021.0000001.000000e+0118.000000
50%2.00000022.0000002.000000e+0125.000000
75%3.00000026.0000003.000000e+0140.000000
max15.00000046.0000001.000000e+071000000.000000
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" ], "text/plain": [ " eat_out_freq age n_people price\n", "count 964.000000 964.000000 6.960000e+02 696.000000\n", "mean 2.585187 23.975104 1.439254e+04 1472.179152\n", "std 2.246486 4.556716 3.790481e+05 37903.575636\n", "min 0.000000 10.000000 -2.000000e+00 0.000000\n", "25% 1.000000 21.000000 1.000000e+01 18.000000\n", "50% 2.000000 22.000000 2.000000e+01 25.000000\n", "75% 3.000000 26.000000 3.000000e+01 40.000000\n", "max 15.000000 46.000000 1.000000e+07 1000000.000000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are there any unusual values in this data that you notice?\n", "Let's get rid of these outliers. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(942, 11)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upperbound_price = 200\n", "lowerbound_people = 1\n", "df = df[~(df['price'] > 200)]\n", "restaurant_df = df[~(df['n_people'] < lowerbound_people)]\n", "restaurant_df.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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eat_out_freqagen_peopleprice
count942.000000942.000000674.000000674.000000
mean2.59805723.99256924.97329434.023279
std2.2577874.58257022.01666029.018622
min0.00000010.0000001.0000000.000000
25%1.00000021.00000010.00000018.000000
50%2.00000022.00000020.00000025.000000
75%3.00000026.00000030.00000040.000000
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" ], "text/plain": [ " eat_out_freq age n_people price\n", "count 942.000000 942.000000 674.000000 674.000000\n", "mean 2.598057 23.992569 24.973294 34.023279\n", "std 2.257787 4.582570 22.016660 29.018622\n", "min 0.000000 10.000000 1.000000 0.000000\n", "25% 1.000000 21.000000 10.000000 18.000000\n", "50% 2.000000 22.000000 20.000000 25.000000\n", "75% 3.000000 26.000000 30.000000 40.000000\n", "max 15.000000 46.000000 200.000000 200.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restaurant_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data splitting \n", "\n", "We aim to predict whether a restaurant is liked or disliked." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Separate `X` and `y`. \n", "\n", "X = restaurant_df.drop(columns=['target'])\n", "y = restaurant_df['target']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm perturbing this data just to demonstrate a few concepts. Don't do it in real life. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "X.at[459, 'food_type'] = 'Quebecois'\n", "X['price'] = X['price'] * 100" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Split the data\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### EDA " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_train.hist(bins=20, figsize=(12, 8));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do you see anything interesting in these plots? " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "food_type\n", "Other 189\n", "Canadian/American 131\n", "Chinese 102\n", "Indian 36\n", "Italian 32\n", "Thai 20\n", "Fusion 18\n", "Mexican 17\n", "fusion 3\n", "Quebecois 1\n", "Name: count, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['food_type'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Error in data collection? Probably \"Fusion\" and \"fusion\" categories should be combined?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "X_train['food_type'] = X_train['food_type'].replace(\"fusion\", \"Fusion\")\n", "X_test['food_type'] = X_test['food_type'].replace(\"fusion\", \"Fusion\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "food_type\n", "Other 189\n", "Canadian/American 131\n", "Chinese 102\n", "Indian 36\n", "Italian 32\n", "Fusion 21\n", "Thai 20\n", "Mexican 17\n", "Quebecois 1\n", "Name: count, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['food_type'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, usually we should spend lots of time in EDA, but let's stop here so that we have time to learn about transformers and pipelines. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dummy Classifier" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fit_timescore_timetest_scoretrain_score
00.0008700.0009420.5165560.514950
10.0005750.0003920.5165560.514950
20.0009650.0004850.5165560.514950
30.0005370.0003660.5133330.515755
40.0004980.0003560.5133330.515755
\n", "
" ], "text/plain": [ " fit_time score_time test_score train_score\n", "0 0.000870 0.000942 0.516556 0.514950\n", "1 0.000575 0.000392 0.516556 0.514950\n", "2 0.000965 0.000485 0.516556 0.514950\n", "3 0.000537 0.000366 0.513333 0.515755\n", "4 0.000498 0.000356 0.513333 0.515755" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.dummy import DummyClassifier\n", "\n", "dummy = DummyClassifier()\n", "scores = cross_validate(dummy, X_train, y_train, return_train_score=True)\n", "pd.DataFrame(scores)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have a relatively balanced distribution of both 'like' and 'dislike' classes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's try KNN on this data\n", "\n", "Do you think KNN would work directly on `X_train` and `y_train`?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# Preprocessing and pipeline\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn = KNeighborsClassifier()\n", "# knn.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to preprocess the data before passing it to ML models. What are the different types of features in the data? " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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north_americaeat_out_freqagen_peoplepricefood_typenoise_levelgood_servercommentsrestaurant_name
80No2.02130.02200.0ChinesehighNoThe environment was very not clean. The food tasted awful.NaN
934Yes4.02130.03000.0Canadian/AmericanlowYesThe building and the room gave a very comfy feeling. Immediately after sitting down it felt like we were right at home.NaN
911No4.02040.02500.0Canadian/AmericanmediumYesI was hungryChambar
459Yes5.021NaNNaNQuebecoisNaNNaNNaNNaN
62Yes2.02420.03000.0IndianhighYesbad tasteeast is east
\n", "
" ], "text/plain": [ " north_america eat_out_freq age n_people price food_type \\\n", "80 No 2.0 21 30.0 2200.0 Chinese \n", "934 Yes 4.0 21 30.0 3000.0 Canadian/American \n", "911 No 4.0 20 40.0 2500.0 Canadian/American \n", "459 Yes 5.0 21 NaN NaN Quebecois \n", "62 Yes 2.0 24 20.0 3000.0 Indian \n", "\n", " noise_level good_server \\\n", "80 high No \n", "934 low Yes \n", "911 medium Yes \n", "459 NaN NaN \n", "62 high Yes \n", "\n", " comments \\\n", "80 The environment was very not clean. The food tasted awful. \n", "934 The building and the room gave a very comfy feeling. Immediately after sitting down it felt like we were right at home. \n", "911 I was hungry \n", "459 NaN \n", "62 bad taste \n", "\n", " restaurant_name \n", "80 NaN \n", "934 NaN \n", "911 Chambar \n", "459 NaN \n", "62 east is east " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What all transformations we need to apply before training a machine learning model? \n", "- Can we group features based on what type of transformations we would like to apply?" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['north_america', 'eat_out_freq', 'age', 'n_people', 'price',\n", " 'food_type', 'noise_level', 'good_server', 'comments',\n", " 'restaurant_name'],\n", " dtype='object')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.columns" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "noise_level\n", "medium 232\n", "low 186\n", "high 75\n", "no music 37\n", "crazy loud 18\n", "Name: count, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['noise_level'].value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "numeric_feats = ['age', 'n_people', 'price'] # Continuous and quantitative features\n", "categorical_feats = ['north_america', 'food_type'] # Discrete and qualitative features\n", "binary_feats = ['good_server'] # Categorical features with only two possible values \n", "ordinal_feats = ['noise_level'] # Some natural ordering in the categories \n", "noise_cats = ['no music', 'low', 'medium', 'high', 'crazy loud']\n", "drop_feats = ['comments', 'restaurant_name'] # Let's drop them for now. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's begin with numeric features. What if we just use numeric features to train a KNN model? Would it work? " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# knn.fit(X_train[numeric_feats], y_train)\n", "X_train_num = X_train[numeric_feats]\n", "X_test_num = X_test[numeric_feats]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to deal with NaN values. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### sklearn's `SimpleImputer` " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Impute numeric features using SimpleImputer\n", "from sklearn.impute import SimpleImputer\n", "\n", "imputer = SimpleImputer(strategy=\"median\")\n", "imputer.fit(X_train_num)\n", "X_train_num_imp = imputer.transform(X_train_num)\n", "X_test_num_imp = imputer.transform(X_test_num)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier()
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On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "KNeighborsClassifier()" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn.fit(X_train_num_imp, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No more errors. It worked! Let's try cross validation. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5418278145695364" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(knn, X_train_num_imp, y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have slightly improved results in comparison to the dummy model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discussion questions \n", "\n", "- What's the difference between sklearn estimators and transformers? \n", "- Can you think of a better way to impute missing values? \n", "\n", "



" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do we need to scale the data? " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age n_people price\n", "80 21 30.0 2200.0\n", "934 21 30.0 3000.0\n", "911 20 40.0 2500.0\n", "459 21 NaN NaN\n", "62 24 20.0 3000.0\n", ".. ... ... ...\n", "106 27 10.0 1500.0\n", "333 24 12.0 800.0\n", "393 20 5.0 1500.0\n", "376 20 NaN NaN\n", "525 20 50.0 3000.0\n", "\n", "[753 rows x 3 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train[numeric_feats]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Scale the imputed data \n", "\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "scaler.fit(X_train_num_imp)\n", "X_train_num_imp_scaled = scaler.transform(X_train_num_imp)\n", "X_test_num_imp_scaled = scaler.transform(X_test_num_imp)" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "### What are some alternative methods for scaling?\n", "- [MinMaxScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html): Transform each feature to a desired range\n", "- [RobustScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.RobustScaler.html): Scale features using median and quantiles. Robust to outliers. \n", "- [Normalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html): Works on rows rather than columns. Normalize examples individually to unit norm.\n", "- [MaxAbsScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html): A scaler that scales each feature by its maximum absolute value.\n", " - What would happen when you apply `StandardScaler` to sparse data? \n", "- You can also apply custom scaling on columns using [`FunctionTransformer`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html). For example, when a column follows the power law distribution (a handful of your values have many data points whereas most other values have few data points) log scaling is helpful. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- For now, let's focus on `StandardScaler`. Let's carry out cross-validation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.55629139, 0.49006623, 0.56953642, 0.54 , 0.53333333])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(knn, X_train_num_imp_scaled, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, we don't see a big difference with `StandardScaler`. But usually, scaling is a good idea. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- This worked but are we doing anything wrong here? \n", "- What's the problem with calling `cross_val_score` with preprocessed data? \n", "- How would you do it properly?\n", "



" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# Create a pipeline \n", "pipe_knn = make_pipeline(\n", " SimpleImputer(strategy = \"median\"),\n", " StandardScaler(),\n", " KNeighborsClassifier()\n", ")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5245916114790287" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(pipe_knn, X_train_num, y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What all things are happening under the hood? \n", "- Why is this a better approach? " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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m5pZ4zIsvTy4+t8PevXs1cOBAXXXVVapRo4ZuvfVWbdu2zW0/Jc2lkZqa6pxf4tNPP1XXrl1Vo0YN1a1bV8OGDdMPP/xQ4vswd+5cXX/99apevboaNWqkSZMm6eeff5bD4VCPHj0u+z42bdpUSUlJbu9j8XkucnJyNHLkSF1zzTUKDQ1VbGysRo4cqQMHDlx2/0UuXLigv/zlL7r22mtVvXp1XXvttZo5c6YKCwtLXP+7777TiBEj1KxZM1WvXl3R0dFKSEjQI488UuZjAoAvrV+/Xg6HQyNHjizx9YMHD6patWrq1auXc9mWLVs0btw4tW3bVlFRUQoPD1d8fLyee+45nT9/vkzHvdQtPJ9//rmSkpKc55f77ruv1O9yT86RTZs21YIFCyRJzZo1c55Lip+HSpvjKT8/X9OmTVPr1q1VvXp11alTR3379tWXX37ptm7x8/C7776rhIQEhYeHq0GDBvrDH/6gM2fOXPb9KTrfrl27VpKctV5c24IFC9SlSxfVrFlTNWvWVJcuXZwZy2rHjh3q16+fIiMjFRUVpT59+igzM9OjfSDwlGUMK0nnzp3TSy+9pISEBNWoUUORkZHq1q2bli5d6rbPn376SVOmTFGbNm1Us2ZNRUVFqXXr1hoxYoTzZ7xHjx6aPn26JKlnz56lfvZLcuTIEU2YMEEtW7ZUeHi46tSpo/j4eI0dO1YnTpxwWbesdZflewOBjyueAPi1Hj16KDs7WwsWLFBSUpLLSap27dou644fP15fffWV+vbtq379+unqq6+WZP5S8OKLL6pXr17q3LmzQkJC9J///EcpKSn6+OOPlZGRoaioqDLVk52drc6dO6tNmzZ64IEHtGfPHn300Ufq2bOnsrKynMe8nLS0NC1btkx33nmnkpOTtX79ei1cuFB79uzR559/7rLulClTNGPGDDVo0EC/+93vFBwcrPfee0+7du0q07Ek6aGHHtLWrVvd3seieS6+++47de3aVbm5ubrzzjt1/fXXa8eOHZo3b56WLVumL774Qtdee+1lj/O73/1O8+bNU7NmzfTggw/q559/1ksvvVTiLxaHDx9Wp06ddPr0afXt21f33XefTp06pe+++06vvPKKXnzxxTLnAwBf6datm5o2bar3339fr776qqpXr+7y+uLFi1VYWKihQ4c6l/3zn/9UWlqaunfvrj59+ig/P1+fffaZJk+erM2bN+v9998vdz2ffvqp7rjjDgUFBem+++5Tw4YN9emnnyoxMVFXXXWV2/qenCMfeughpaamatu2bZowYYLzPHy5X2jPnj2rXr166auvvlJCQoIeeugh5ebm6p133tG///1vvfPOO7r77rvdtnv11Ve1cuVK9e/fXz169NCqVav0yiuv6IcfftDixYsvecz27dtr6tSpSk1N1f79+zV16lRJrmOHhx9+WC+//LKuueYajRw5Ug6HQ++//76GDx+ubdu26aWXXrrkMSQpMzNTiYmJOnXqlO6++261bNlSmzZtUmJiotq1a3fZ7RG4yjKGPXv2rHr37q3PPvtMN954o0aOHKnz589r+fLl6t+/v1555RWNGzdOkmQYhm6//XZ9/fXXSkxMVO/evRUUFKTs7Gx98MEHGjZsmBo1auT8w+u6des0bNgw58/nxePmi+Xn5ysxMVHZ2dm67bbbNGDAAJ07d0579+5VamqqJk2apFq1anlcd3m/NxBgDADwc2vXrjUkGVOnTi3x9WHDhhmSjNjYWGP//v1ur3///ffGyZMn3ZYvWLDAkGQ8/fTTLsunTp1qSDLWrl3rXLZv3z5DkiHJeO6551zW//Of/2xIMmbOnOmyPCkpybj4a3b+/PmGJCM4ONj4/PPPncsLCgqMHj16GJKMjRs3Opd/++23RrVq1YzGjRsbeXl5zuUnT540rr/+ekOSkZSUVOL7crFLvY+33HKLIcmYO3euy/K5c+cakoxevXq5LC96z/ft2+e2/3bt2hmnTp1yLj948KARHR1tSDKGDRvmXD5r1ixDkvH3v//drZ6jR4+WKRMA+IMnnnjCkGS8++67bq/Fx8cb4eHhxokTJ5zLsrOzjYKCApf1CgsLjQceeMCQ5HJ+MIySzyclfadfuHDBaN68ueFwOIwNGza47Pv+++93nseK8/QcWdL3f3FNmjQxmjRp4rLsqaeeMiQZv/nNb4zCwkLn8m3bthlhYWHGVVdd5fL+FJ2Ho6KijF27djmX5+fnG9ddd53hcDiMQ4cOlXj8i5X03hmGYaxfv96QZMTFxRnHjx93Lj9+/LjRunVrQ5LLe1g0Dih+Hiu+/0WLFrksnzx5svP9Lu29QuC73Bj28ccfNyQZ06ZNc/nZOHHihNGxY0cjNDTU+Vn/5ptvDEnGgAED3Pbz888/u/wclzSWvZylS5cakoyHH37Y7bUTJ04YZ8+eLVfdhnH57w0EPm61AxAw/vSnP5U4r1JMTIxq1qzptnzo0KGqVauWVq9eXeZjNGvWTH/6059clhXdXrF58+Yy7+f+++9XYmKi89+rVaumYcOGue3nrbfe0oULF/TII4+obt26zuU1a9bUn//85zIf71IOHDigNWvWqE2bNho9erTLa6NHj1ZcXJw+/fTTy95yt3DhQknmFVo1atRwLr/mmms0YcKEUrcLDw93W+Zvk+UCwKUUXc20aNEil+Xbtm3T9u3b1b9/f0VGRjqXN2nSRNWqVXNZ1+Fw6MEHH5Qkj85LxX3++efau3ev+vXrp65du7rs+9lnn3U7puTdc2RpUlNTFRISoueee87lFvQbbrhBw4cP148//qiPPvrIbbsJEyaoVatWzn8PDw/X4MGDZRiGtmzZUuGaJPN2qOJXPUdFRTmvjip+O3pJcnJytG7dOt1www36zW9+4/La448/ftkrTFC1FRYWKiUlRddee62mTJni8rMRGRmpKVOm6Ny5c1qyZInLdiWNm8LCwkr8OS6PkvYfGRmp0NDQCtWNqo1b7QAEjE6dOpX62pIlSzR37lxlZGToxx9/1IULF5yvFU08Whbt2rVTUJBrzz42NlaSdPz48TLvJyEhwW1ZSfspmjvq5ptvdlu/pGXl8Z///EeSlJSU5DYnlcPhUPfu3ZWVlaVt27apUaNGpe6nqNZu3bq5vVbSsn79+umxxx7Tgw8+qE8++US9e/dW165ddd1111UkDgBUulatWqljx45auXKljh07pjp16kiS3njjDUlyuc1OMudGmT17tt5++23t2rVLp06dkmEYztc9OS8Vd6nv4SZNmqhRo0YlPs7cW+fIkpw4cUJ79+5VXFyc8zxXXI8ePTR37lxt3bpVQ4YMcXmtrOfK8ig695U0z0zRsq1bt15yH0Xvd/EmX5GaNWuqffv2fvM4e/ifb7/9Vj/++KMaNmzonJOpuKNHj0qSc2qFuLg4xcfH680339SBAwd01113qVu3bkpISCixqeyp7t27q379+po5c6a2bt2qvn37qmvXroqPj3cZH3paNyDReAIQQEqbX+nFF1/UxIkTVa9ePd12222KjY11/jXn5Zdf1tmzZ8t8jJLmggoONr9Kiw/UvbWfookc69Wr57Z+WeeTupyiY5S2v6InAf3000+X3M9PP/2koKCgEq9WKmnfzZo108aNGzV9+nStXLlS7733niTzF7gZM2bonnvu8SgHAPjS0KFDlZ6ernfffVe///3vVVhYqLfeeksxMTG67bbbXNYdOHCg0tLSdN111+m+++5TTEyMQkJCdPz4cf3973/36LxUXNH3dExMTImvX3311W6NJ2+eI0tSkXOMt865pdUVFBRU6vk1KCioTOc96dLvN1CaY8eOSTInp9+xY0ep650+fVqS+dlfs2aNpk2bpiVLljgfxBIdHa3x48friSeeqFADKioqShs3btTUqVOVlpamFStWSDKbvZMnT9bYsWPLVTcg0XgCEEAuvlpHkgoKCjRjxgw1bNhQW7dudRlgGoah559/vjJL9FjRJI5Hjx5VkyZNXF77/vvvvXqM0vZXtLxovdJERUWpsLBQeXl5bgP50vZ9ww036P3339f58+e1ZcsWrVy5UrNmzXJOiFv8dkQA8GeDBg3SI488okWLFun3v/+91qxZo8OHD2vChAnOZolk3k6dlpam22+/XcuXL3f5RfGrr77S3//+93LXUNSoKemJrZL7d3FlnCO9dY7xtlq1aqmwsFBHjx51axzl5uaqsLCwTOe9ovVL4q3zNAJT0efr17/+tf71r3+VaZvo6GjNnj1br7zyinbt2qU1a9bolVde0dSpUxUSEqLJkydXqKaiJ9BduHBB27dv17///W/NmjVLDz74oK666ioNHjy4XHUDzPEEwO8VDcrL89fNvLw8/fTTT+rSpYtbMyQ9Pb1Mj2T2paIn4pT0VLiSlpVH0ZPt1q9f73Krh2T+4rFhwwaX9UpTVGvR+sWVtKy4kJAQdenSRdOnT9esWbNkGIaWLVtWxgQA4HtFVzZ9+eWX2rdvn3O+p4tvH9uzZ48kqW/fvm5XJ1zuu/JyLvU9vH//fre5+spzjvT0nFyrVi01b95cu3fv1qFDh9xeX7dunaTLn2O87cYbb5SkEm+FK2tNRe/3xU+jlaRTp05d9lY9BL5L/bzExcWpVq1aSk9P1/nz5z3ar8PhUFxcnHO6AklaunRpmY5b1rrbt2+vSZMm6a233nLZf3nqrmg9uPLReALg94rmyjh48KDH28bExCg8PFwZGRnKz893Lv/xxx81fvx4r9Vol0GDBikoKEgvvfSSfvjhB+fy06dP65lnnvHKMRo3bqyePXtqx44dmjdvnstr8+bN044dO3TLLbdccn4nSfrtb38rSXrqqadcLq8+dOhQiX/B37x5c4l/JS76C3FJk1sCgD8bOnSoDMPQ66+/riVLlqh169bq2LGjyzpFV69e3KzYsWOHZs6cWaHjd+3aVc2aNdOyZctc9m8Yhh5//HG3X/rKc44szzl52LBhOn/+vCZPnuzyB47MzEzNnz9fUVFRuuuuu8q8P28oeqDH9OnTnbcDSuYteEXz1hStU5rGjRure/fu+uabb7R48WKX15599tkKz0OFK9+lfl6Cg4OVnJys/fv3a+LEiSU2cTIzM51jpX379mnnzp1u65Q0birPz2lmZqb2799/2f17Wnd560Fg4VY7AH6vdevWatiwod5++21FREQoNjZWDodDycnJJc7/UFxQUJDGjh2rF198Ue3atdOdd96pEydOaOXKlWrSpIkaNmxYSSnKp1WrVnrsscf07LPPKj4+Xvfcc4+Cg4O1ZMkSxcfHKzMz022y8/JISUlR165dNXr0aKWlpalNmzbauXOnli5dqnr16iklJeWy++jRo4dGjBih+fPnKz4+XgMGDNDZs2f1zjvvqEuXLm5XMC1evFhz5sxRjx49dO2116pWrVrauXOnVqxYoejoaD3wwAMVzgUAlal///6qVauWXnjhBZ0/f95tUnHJfBBGp06d9O677+rIkSPq0qWLcnJytHTpUvXt27dCt64EBQXpH//4h/r06aNbb73VedvymjVrdOTIEd1www365ptvXNb39Bx5yy236K9//avGjBmje+65RzVq1FDjxo11//33l1rXpEmTtHz5cr3xxhvKyspSr169dPToUb3zzjs6f/68Fi5c6PLUv8rQvXt3jR8/Xq+88oratm2rX//61zIMQ0uWLNGBAwf0hz/8Qd27d7/sfl599VUlJibqt7/9rT788EO1bNlSmzdv1qZNm9StW7cKX8WGK9vlxrDTp09XRkaGZs2apeXLlyspKUn16tXToUOHtH37dm3btk0bN25UTEyMtm3bpgEDBuimm25S27ZtVb9+fR06dEgffvihqlWr5pzzSZJ69uwph8OhJ554Qrt27VJUVJSioqKUnJxcaq2rV6/WI488osTERLVu3Vp169bV3r17tXTpUoWHh2vcuHHOdT2pWyrf9wYCjAEAV4CvvvrKSEpKMiIjIw1JhiRj3759hmEYxrBhw1z+/WLnzp0znnnmGaNly5ZGWFiY0bhxY+OPf/yjcfLkSaNJkyZGkyZNXNafOnWqIclYu3atc9m+ffsMScawYcNKPIYkIykpyWVZUlKScfHX7Pz58w1Jxvz58932sXbtWkOSMXXqVLfX5syZY8TFxRmhoaFGbGysMXHiROPAgQOGJKN///4l1uTJ/g3DMLKzs40RI0YYDRo0MIKDg40GDRoYI0aMMLKzs93WLe09LygoMGbOnGk0b97cCA0NNZo3b248++yzxu7du93ev6+++soYM2aM0bZtW6N27dpGeHi40bJlS+MPf/iDkZOTU6ZMAOBvRowYYUgyHA5Hid+fhmEYubm5xgMPPGA0bNjQqF69uhEfH2+8+uqrxt69e0s815R0PrnUd/r69euN7t27G+Hh4UadOnWMe+65x9i/f3+J+/H0HGkYhvH8888bLVu2NEJCQtzOf6Vtc+rUKePJJ580rrvuOiM0NNSoXbu2cccddxgbNmxwW7ek83CRS51HS1JS5uLmzZtn3HTTTUZERIQRERFh3HTTTca8efPc1rvUOGD79u1Gnz59jJo1axqRkZHGHXfcYWzfvv2y4xNUDZcawxqGOXaaO3eukZiYaNSqVcv5c9i7d28jJSXFOHXqlGEYhnHgwAHjscceM7p06WLExMQYoaGhRuPGjY2BAwcaX3/9tdtxU1NTjfj4eCMsLMyQVOLPZXE7d+40JkyYYNx4441G3bp1jbCwMKN58+bG8OHDjZ07d7qtX9a6i1zqewOBz2EYF03oAQC4IqxevVq//OUvNWnSJP3lL3/xdTkAAAAA4IY5ngDAzx09etRtXo7jx487n1xS2fNiAAAAAEBZMccTAPi5xYsX669//atuueUWNWzYUEeOHNGqVauUm5ur4cOH6xe/+IWvSwQAAACAEtF4AgA/d/PNN6tDhw5avXq1jh07pmrVqikuLk5PPvmkxo4d6+vyAAAAAKBUzPEEAAAAAAAAWzDHEwAAAAAAAGxB4wkAAAAAAAC2oPEEAAAAAAAAW9B4AgAAAAAAgC1oPAEAAAAAAMAWNJ4AAAAAAABgCxpPAAAAAAAAsAWNJwAAAAAAANji/wF8JRHjdSUtnQAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_improper_processing(\"kNN\")" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_proper_processing(\"kNN\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "



" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Categorical features\n", "\n", "Let's assess the scores using categorical features." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "food_type\n", "Other 189\n", "Canadian/American 131\n", "Chinese 102\n", "Indian 36\n", "Italian 32\n", "Fusion 21\n", "Thai 20\n", "Mexican 17\n", "Quebecois 1\n", "Name: count, dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['food_type'].value_counts()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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north_americafood_type
80NoChinese
934YesCanadian/American
911NoCanadian/American
459YesQuebecois
62YesIndian
.........
106NoChinese
333NoOther
393YesCanadian/American
376YesNaN
525Don't want to shareChinese
\n", "

753 rows × 2 columns

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" ], "text/plain": [ " north_america food_type\n", "80 No Chinese\n", "934 Yes Canadian/American\n", "911 No Canadian/American\n", "459 Yes Quebecois\n", "62 Yes Indian\n", ".. ... ...\n", "106 No Chinese\n", "333 No Other\n", "393 Yes Canadian/American\n", "376 Yes NaN\n", "525 Don't want to share Chinese\n", "\n", "[753 rows x 2 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train[categorical_feats]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "north_america\n", "Yes 415\n", "No 330\n", "Don't want to share 8\n", "Name: count, dtype: int64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['north_america'].value_counts()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "food_type\n", "Other 189\n", "Canadian/American 131\n", "Chinese 102\n", "Indian 36\n", "Italian 32\n", "Fusion 21\n", "Thai 20\n", "Mexican 17\n", "Quebecois 1\n", "Name: count, dtype: int64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train['food_type'].value_counts()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "X_train_cat = X_train[categorical_feats]\n", "X_test_cat = X_test[categorical_feats]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# One-hot encoding of categorical features \n", "from sklearn.preprocessing import OneHotEncoder\n", "# Define and fit OneHotEncoder\n", "ohe = OneHotEncoder(sparse_output=False)\n", "ohe.fit(X_train_cat)\n", "X_train_cat_ohe = ohe.transform(X_train_cat) # transform the train set\n", "X_test_cat_ohe = ohe.transform(X_test_cat) # transform the test set" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0., 1., 0., ..., 0., 0., 0.],\n", " [0., 0., 1., ..., 0., 0., 0.],\n", " [0., 1., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 1., ..., 0., 0., 0.],\n", " [0., 0., 1., ..., 0., 0., 1.],\n", " [1., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train_cat_ohe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- It's a sparse matrix. \n", "- Why? What would happen if we pass `sparse_output=False`? Why we might want to do that? " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " north_america_Don't want to share north_america_No north_america_Yes \\\n", "0 0.0 1.0 0.0 \n", "1 0.0 0.0 1.0 \n", "2 0.0 1.0 0.0 \n", "3 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 \n", ".. ... ... ... \n", "748 0.0 1.0 0.0 \n", "749 0.0 1.0 0.0 \n", "750 0.0 0.0 1.0 \n", "751 0.0 0.0 1.0 \n", "752 1.0 0.0 0.0 \n", "\n", " food_type_Canadian/American food_type_Chinese food_type_Fusion \\\n", "0 0.0 1.0 0.0 \n", "1 1.0 0.0 0.0 \n", "2 1.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", ".. ... ... ... \n", "748 0.0 1.0 0.0 \n", "749 0.0 0.0 0.0 \n", "750 1.0 0.0 0.0 \n", "751 0.0 0.0 0.0 \n", "752 0.0 1.0 0.0 \n", "\n", " food_type_Indian food_type_Italian food_type_Mexican food_type_Other \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 1.0 0.0 0.0 0.0 \n", ".. ... ... ... ... \n", "748 0.0 0.0 0.0 0.0 \n", "749 0.0 0.0 0.0 1.0 \n", "750 0.0 0.0 0.0 0.0 \n", "751 0.0 0.0 0.0 0.0 \n", "752 0.0 0.0 0.0 0.0 \n", "\n", " food_type_Quebecois food_type_Thai food_type_nan \n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 1.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", ".. ... ... ... \n", "748 0.0 0.0 0.0 \n", "749 0.0 0.0 0.0 \n", "750 0.0 0.0 0.0 \n", "751 0.0 0.0 1.0 \n", "752 0.0 0.0 0.0 \n", "\n", "[753 rows x 13 columns]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the OHE feature names \n", "ohe_feats = ohe.get_feature_names_out(categorical_feats).tolist()\n", "pd.DataFrame(X_train_cat_ohe, columns = ohe_feats)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.53642384, 0.53642384, 0.50993377, 0.51333333, 0.47333333])" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(knn, X_train_cat_ohe, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are we breaking the golden rule here? Let's do this properly with a pipeline. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "# Code to create a pipeline for OHE and KNN\n", "pipe_ohe_knn = make_pipeline(OneHotEncoder(), KNeighborsClassifier())" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:842: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: \n", "Traceback (most recent call last):\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 140, in __call__\n", " score = scorer(estimator, *args, **routed_params.get(name).score)\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/metrics/_scorer.py\", line 527, in __call__\n", " return estimator.score(*args, **kwargs)\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/pipeline.py\", line 749, in score\n", " Xt = transform.transform(Xt)\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/utils/_set_output.py\", line 140, in wrapped\n", " data_to_wrap = f(self, X, *args, **kwargs)\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 1016, in transform\n", " X_int, X_mask = self._transform(\n", " File \"/Users/kvarada/miniconda3/envs/cpsc330/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py\", line 199, in _transform\n", " raise ValueError(msg)\n", "ValueError: Found unknown categories ['Quebecois'] in column 1 during transform\n", "\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([ nan, 0.53642384, 0.56291391, 0.54666667, 0.56 ])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(pipe_ohe_knn, X_train_cat, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- What's wrong here? \n", "- How can we fix this? " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Fix the OHE\n", "\n", "pipe_ohe_knn = make_pipeline(OneHotEncoder(handle_unknown=\"ignore\"), KNeighborsClassifier())" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5537836644591612" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(pipe_ohe_knn, X_train_cat, y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Right now we are working with numeric and categorical features separately. But ideally when we create a model, we need to use all these features together. \n", "\n", "**Enter column transformer!**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How can we vertically stack \n", "- preprocessed numeric features\n", "- preprocessed binary features, and \n", "- preprocessed categorical features?\n", "\n", "Let's define a column transformer. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "from sklearn.compose import make_column_transformer\n", "\n", "numeric_transformer = make_pipeline(SimpleImputer(strategy=\"median\"), StandardScaler())\n", "binary_transformer = make_pipeline(SimpleImputer(strategy=\"most_frequent\"), OneHotEncoder(drop=\"if_binary\"))\n", "categorical_transformer = make_pipeline(SimpleImputer(strategy=\"most_frequent\"), OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False))\n", "\n", "preprocessor = make_column_transformer(\n", " (numeric_transformer, numeric_feats), \n", " (binary_transformer, binary_feats), \n", " (categorical_transformer, categorical_feats), \n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How does the transformed data look like? " ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "transformed = preprocessor.fit_transform(X_train)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
ColumnTransformer(transformers=[('pipeline-1',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='median')),\n",
       "                                                 ('standardscaler',\n",
       "                                                  StandardScaler())]),\n",
       "                                 ['age', 'n_people', 'price']),\n",
       "                                ('pipeline-2',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(drop='if_binary'))]),\n",
       "                                 ['good_server']),\n",
       "                                ('pipeline-3',\n",
       "                                 Pipeline(steps=[('simpleimputer',\n",
       "                                                  SimpleImputer(strategy='most_frequent')),\n",
       "                                                 ('onehotencoder',\n",
       "                                                  OneHotEncoder(handle_unknown='ignore',\n",
       "                                                                sparse_output=False))]),\n",
       "                                 ['north_america', 'food_type'])])
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": [ "ColumnTransformer(transformers=[('pipeline-1',\n", " Pipeline(steps=[('simpleimputer',\n", " SimpleImputer(strategy='median')),\n", " ('standardscaler',\n", " StandardScaler())]),\n", " ['age', 'n_people', 'price']),\n", " ('pipeline-2',\n", " Pipeline(steps=[('simpleimputer',\n", " SimpleImputer(strategy='most_frequent')),\n", " ('onehotencoder',\n", " OneHotEncoder(drop='if_binary'))]),\n", " ['good_server']),\n", " ('pipeline-3',\n", " Pipeline(steps=[('simpleimputer',\n", " SimpleImputer(strategy='most_frequent')),\n", " ('onehotencoder',\n", " OneHotEncoder(handle_unknown='ignore',\n", " sparse_output=False))]),\n", " ['north_america', 'food_type'])])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocessor" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\"north_america_Don't want to share\",\n", " 'north_america_No',\n", " 'north_america_Yes',\n", " 'food_type_Canadian/American',\n", " 'food_type_Chinese',\n", " 'food_type_Fusion',\n", " 'food_type_Indian',\n", " 'food_type_Italian',\n", " 'food_type_Mexican',\n", " 'food_type_Other',\n", " 'food_type_Quebecois',\n", " 'food_type_Thai']" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting feature names from a column transformer\n", "ohe_feat_names = preprocessor.named_transformers_['pipeline-3']['onehotencoder'].get_feature_names_out(categorical_feats).tolist()\n", "ohe_feat_names" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "# feat_names = numeric_feats + binary_feats + ohe_feat_names" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# pd.DataFrame(preprocessor.fit_transform(X_train), columns = feat_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have new columns for the categorical features. Let's create a pipeline with the preprocessor and SVC. " ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6825960264900662" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc_num_cat_pipe = make_pipeline(preprocessor, SVC())\n", "cross_val_score(svc_num_cat_pipe, X_train, y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are getting better results! \n", "


" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Incorporating text features \n", "\n", "We haven't incorporated the comments feature into our pipeline yet, even though it holds significant value in indicating whether the restaurant was liked or not." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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north_americaeat_out_freqagen_peoplepricefood_typenoise_levelgood_servercommentsrestaurant_name
80No2.02130.02200.0ChinesehighNoThe environment was very not clean. The food tasted awful.NaN
934Yes4.02130.03000.0Canadian/AmericanlowYesThe building and the room gave a very comfy feeling. Immediately after sitting down it felt like we were right at home.NaN
911No4.02040.02500.0Canadian/AmericanmediumYesI was hungryChambar
459Yes5.021NaNNaNQuebecoisNaNNaNNaNNaN
62Yes2.02420.03000.0IndianhighYesbad tasteeast is east
.................................
106No3.02710.01500.0ChinesemediumYesFood wasn't great.NaN
333No1.02412.0800.0OthermediumYesNaNNaN
393Yes4.0205.01500.0Canadian/AmericanlowNoNaNNaN
376Yes5.020NaNNaNNaNNaNNaNNaNNaN
525Don't want to share4.02050.03000.0ChinesehighYesNaNHaidilao
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753 rows × 10 columns

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" ], "text/plain": [ " north_america eat_out_freq age n_people price \\\n", "80 No 2.0 21 30.0 2200.0 \n", "934 Yes 4.0 21 30.0 3000.0 \n", "911 No 4.0 20 40.0 2500.0 \n", "459 Yes 5.0 21 NaN NaN \n", "62 Yes 2.0 24 20.0 3000.0 \n", ".. ... ... ... ... ... \n", "106 No 3.0 27 10.0 1500.0 \n", "333 No 1.0 24 12.0 800.0 \n", "393 Yes 4.0 20 5.0 1500.0 \n", "376 Yes 5.0 20 NaN NaN \n", "525 Don't want to share 4.0 20 50.0 3000.0 \n", "\n", " food_type noise_level good_server \\\n", "80 Chinese high No \n", "934 Canadian/American low Yes \n", "911 Canadian/American medium Yes \n", "459 Quebecois NaN NaN \n", "62 Indian high Yes \n", ".. ... ... ... \n", "106 Chinese medium Yes \n", "333 Other medium Yes \n", "393 Canadian/American low No \n", "376 NaN NaN NaN \n", "525 Chinese high Yes \n", "\n", " comments \\\n", "80 The environment was very not clean. The food tasted awful. \n", "934 The building and the room gave a very comfy feeling. Immediately after sitting down it felt like we were right at home. \n", "911 I was hungry \n", "459 NaN \n", "62 bad taste \n", ".. ... \n", "106 Food wasn't great. \n", "333 NaN \n", "393 NaN \n", "376 NaN \n", "525 NaN \n", "\n", " restaurant_name \n", "80 NaN \n", "934 NaN \n", "911 Chambar \n", "459 NaN \n", "62 east is east \n", ".. ... \n", "106 NaN \n", "333 NaN \n", "393 NaN \n", "376 NaN \n", "525 Haidilao \n", "\n", "[753 rows x 10 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create bag-of-words representation of the `comments` feature. But first we need to impute the rows where there are no comments. There is a small complication if we want to put `SimpleImputer` and `CountVectorizer` in a pipeline. \n", "- `SimpleImputer` takes a 2D array as input and produced 2D array as output. \n", "- `CountVectorizer` takes a 1D array as input. \n", "\n", "To deal with this, we will use sklearn's `FunctionTransformer` to convert the 2D output of `SimpleImputer` into a 1D array which can be passed to `CountVectorizer` as input. " ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6467019867549668" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import FunctionTransformer\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "\n", "reshape_for_countvectorizer = FunctionTransformer(lambda X: X.squeeze(), validate=False)\n", "text_transformer = make_pipeline(SimpleImputer(strategy=\"constant\", fill_value=\"missing\"), \n", " reshape_for_countvectorizer, \n", " CountVectorizer(max_features=100, stop_words=\"english\"))\n", "text_pipe = make_pipeline(text_transformer, SVC())\n", "cross_val_score(text_pipe, X_train[['comments']], y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty good scores just with text features! Do we get better scores if we combine all features? Let's define a column transformer which carries out \n", "- imputation and scaling on numeric features\n", "- imputation and one-hot encoding with `drop=\"if_binary\"` on binary features\n", "- imputation and one-hot encoding with `handle_unknown=\"ignore\"` on categorical features\n", "- imputation, reshaping, and bag-of-words transformation on the text feature" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "text_feat = ['comments']\n", "preprocessor = make_column_transformer(\n", " (numeric_transformer, numeric_feats),\n", " (binary_transformer, binary_feats), \n", " (categorical_transformer, categorical_feats),\n", " (text_transformer, text_feat)\n", ")" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<753x116 sparse matrix of type ''\n", "\twith 5670 stored elements in Compressed Sparse Row format>" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preprocessor.fit_transform(X_train)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6972185430463577" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc_num_cat_text_pipe = make_pipeline(preprocessor, SVC())\n", "cross_val_score(svc_num_cat_text_pipe, X_train, y_train).mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "No big improvement when we combine all features. " ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "cpsc330", "language": "python", "name": "cpsc330" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 4 }