{ "cells": [ { "cell_type": "markdown", "id": "fantastic-invitation", "metadata": {}, "source": [ "# Machine Learning with Python\n", "\n", "The aim of this lab is to implement a complete ML project in python. We will go through the steps of data exploration, cleaning and transformation followed by model training and selection.\n", "\n", "The lab consists of two parts. The first part is an interactive tutorial adapted from [Aurélien Géron's excellent ML book](https://github.com/ageron/handson-ml2) with some modifications to make it a classification tutorial instead of a regression one. We will learn to classify neighborhoods by median house value. The tutorial will contains conceptual questions as well as fill-in code that we'll give you a few minutes to write.\n", "\n", "In the second part of this lab, you will pick a dataset of your own choosing and go through the steps mentioned above. You should try at least 3 different classes of models, and one of them should be an `xgboost` classifier or regressor. We recommend that you pick a dataset from [Kaggle](https://www.kaggle.com/). We will be very flexible in grading this lab; we just need to see that you've taken the right steps. You should submit your notebook to gradescope. Submission instructions will be given on Piazza." ] }, { "cell_type": "markdown", "id": "external-blend", "metadata": {}, "source": [ "## Setup\n", "Run the following code to install libraries and download required files." ] }, { "cell_type": "code", "execution_count": 1, "id": "european-guyana", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/pycparser-2.21-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/cffi-1.15.1-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n", "\u001b[0mLooking in indexes: https://aws:****@cmt-797462016303.d.codeartifact.us-east-1.amazonaws.com/pypi/python-read/simple/\n", "\u001b[33mWARNING: 401 Error, Credentials not correct for https://cmt-797462016303.d.codeartifact.us-east-1.amazonaws.com/pypi/python-read/simple/sklearn/\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[31mERROR: Could not find a version that satisfies the requirement sklearn (from versions: none)\u001b[0m\u001b[31m\n", "\u001b[0m\u001b[31mERROR: No matching distribution found for sklearn\u001b[0m\u001b[31m\n", "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/six-1.16.0-py3.11.egg-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "import sys\n", "assert sys.version_info >= (3, 9) # You might later get errors if you have a lower version.\n", "\n", "# Install necessary libraries\n", "!pip install sklearn matplotlib numpy pandas xgboost\n", "\n", "# Do all necessary imports\n", "import sklearn\n", "import numpy as np\n", "import pandas as pd\n", "import os\n", "%matplotlib inline\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import xgboost\n", "mpl.rc('axes', labelsize=14)\n", "mpl.rc('xtick', labelsize=12)\n", "mpl.rc('ytick', labelsize=12)" ] }, { "cell_type": "code", "execution_count": 3, "id": "modified-depth", "metadata": {}, "outputs": [], "source": [ "# Download necessary files\n", "import tarfile\n", "import urllib.request\n", "\n", "def perform_downloads():\n", " # Download data file\n", " root_url = \"https://raw.githubusercontent.com/ageron/handson-ml2/master/\"\n", " housing_url = f\"{root_url}datasets/housing/housing.tgz\"\n", " tgz_path = \"housing.tgz\"\n", " urllib.request.urlretrieve(housing_url, tgz_path)\n", " housing_tgz = tarfile.open(tgz_path)\n", " housing_tgz.extractall(path=\"./\")\n", " housing_tgz.close()\n", " \n", "\n", "def to_classification():\n", " # Original dataset was for a regression tasks.\n", " # Transform it to classification task.\n", " classes = ['LOW', 'MEDIUM', 'HIGH']\n", " housing = pd.read_csv('housing.csv')\n", " housing[\"house_value_class\"] = pd.cut(housing[\"median_house_value\"], bins=[0.0, 200000, 400000, np.inf], labels=classes)\n", " housing.drop('median_house_value', axis=1, inplace=True)\n", " housing.to_csv(\"housing_classification.csv\", index=False)\n", "\n", " \n", " \n", "perform_downloads()\n", "to_classification()" ] }, { "cell_type": "markdown", "id": "metallic-rally", "metadata": {}, "source": [ "## Preliminary\n", "Before we begin, we split our data into a training and a testing set. All our learning will be done on the training set. The testing set is going to used only once after training.**Q: Why is a good idea?**\n", "\n", "You could manually implement the splitting logic, but Scikit-Learn has builtin functions that do this for us. There are many splitting methodologies, but the simplest one that works well for us is as follows" ] }, { "cell_type": "code", "execution_count": 4, "id": "young-candidate", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data size=20640; training size=16512; testing size=4128\n" ] } ], "source": [ "# Import splitting function\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Read data\n", "data = pd.read_csv(\"housing_classification.csv\")\n", "\n", "# Split data. 80% is used as training; 20% as testing. The data will be randomly split\n", "train_set, test_set = train_test_split(data, test_size=0.2, random_state=37)\n", "\n", "print(f\"data size={len(data)}; training size={len(train_set)}; testing size={len(test_set)}\")" ] }, { "cell_type": "markdown", "id": "unnecessary-words", "metadata": {}, "source": [ "## Data Exploration" ] }, { "cell_type": "code", "execution_count": 5, "id": "trained-style", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityhouse_value_class
9694-121.6336.6824.02591.0739.03243.0702.02.1766<1H OCEANLOW
18859-122.0941.3252.04019.0824.01728.0706.02.2462INLANDLOW
11074-117.8633.8034.01793.0480.01722.0441.02.8235<1H OCEANLOW
10858-117.8633.7136.0191.042.0208.037.03.3750<1H OCEANLOW
16462-121.2738.1111.03163.0794.02106.0762.02.4482INLANDLOW
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" ], "text/plain": [ " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", "9694 -121.63 36.68 24.0 2591.0 739.0 \n", "18859 -122.09 41.32 52.0 4019.0 824.0 \n", "11074 -117.86 33.80 34.0 1793.0 480.0 \n", "10858 -117.86 33.71 36.0 191.0 42.0 \n", "16462 -121.27 38.11 11.0 3163.0 794.0 \n", "\n", " population households median_income ocean_proximity house_value_class \n", "9694 3243.0 702.0 2.1766 <1H OCEAN LOW \n", "18859 1728.0 706.0 2.2462 INLAND LOW \n", "11074 1722.0 441.0 2.8235 <1H OCEAN LOW \n", "10858 208.0 37.0 3.3750 <1H OCEAN LOW \n", "16462 2106.0 762.0 2.4482 INLAND LOW " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read data, and take look at the attributes\n", "housing = train_set # Rename for convenience\n", "housing.head()" ] }, { "cell_type": "markdown", "id": "occupational-therapy", "metadata": {}, "source": [ "Each row contains housing data aggregated by *block group*, which is how the US officially reports housing data. Our goal is to predict the housing value class (low, medium or high) given information about a block. Let's take a look at the columns within the dataframe." ] }, { "cell_type": "code", "execution_count": 6, "id": "israeli-natural", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 16512 entries, 9694 to 1935\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 longitude 16512 non-null float64\n", " 1 latitude 16512 non-null float64\n", " 2 housing_median_age 16512 non-null float64\n", " 3 total_rooms 16512 non-null float64\n", " 4 total_bedrooms 16345 non-null float64\n", " 5 population 16512 non-null float64\n", " 6 households 16512 non-null float64\n", " 7 median_income 16512 non-null float64\n", " 8 ocean_proximity 16512 non-null object \n", " 9 house_value_class 16512 non-null object \n", "dtypes: float64(8), object(2)\n", "memory usage: 1.4+ MB\n" ] } ], "source": [ "# Info gives a quick summary\n", "housing.info()" ] }, { "cell_type": "markdown", "id": "promotional-worcester", "metadata": {}, "source": [ "First, note that the `total_bedrooms` columns contains missing values. \n", "**Q: What techniques can we use to handle them?** \n", "\n", "---\n", "\n", "We also note that only the target class `house_value_class` and `ocean_proximity` are categorical. In the following code, find the distinct values of `ocean_proximity`." ] }, { "cell_type": "code", "execution_count": 8, "id": "refined-youth", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['LOW' 'MEDIUM' 'HIGH']\n", "['<1H OCEAN' 'INLAND' 'NEAR BAY' 'NEAR OCEAN' 'ISLAND']\n" ] } ], "source": [ "# TODO: Distinct valus of house_value_class\n", "print(housing.house_value_class.unique())\n", "\n", "# TODO: Distinct values of ocean_proximity.\n", "print(housing.ocean_proximity.unique())" ] }, { "cell_type": "markdown", "id": "previous-geneva", "metadata": {}, "source": [ "**Q: What's the standard way of dealing with categorical features in ML algorithms?**\n", "\n", "----\n", "\n", "As for the numerical attributes, we can quickly summarize their distribution as follows:" ] }, { "cell_type": "code", "execution_count": 9, "id": "timely-china", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_income
count16512.00000016512.00000016512.00000016512.00000016345.00000016512.00000016512.00000016512.000000
mean-119.56852235.62685428.6277862644.478622539.3028451428.389777500.4216933.877276
std2.0089382.13758912.5891422187.314665422.0623831131.002342382.4234111.908253
min-124.35000032.5400001.0000008.0000001.0000003.0000001.0000000.499900
25%-121.80000033.93000018.0000001445.000000295.000000787.000000279.0000002.566350
50%-118.49000034.25000029.0000002128.000000435.0000001167.000000410.0000003.541700
75%-118.00000037.71000037.0000003160.000000648.0000001728.000000607.0000004.750000
max-114.31000041.95000052.00000037937.0000005471.00000035682.0000005189.00000015.000100
\n", "
" ], "text/plain": [ " longitude latitude housing_median_age total_rooms \\\n", "count 16512.000000 16512.000000 16512.000000 16512.000000 \n", "mean -119.568522 35.626854 28.627786 2644.478622 \n", "std 2.008938 2.137589 12.589142 2187.314665 \n", "min -124.350000 32.540000 1.000000 8.000000 \n", "25% -121.800000 33.930000 18.000000 1445.000000 \n", "50% -118.490000 34.250000 29.000000 2128.000000 \n", "75% -118.000000 37.710000 37.000000 3160.000000 \n", "max -114.310000 41.950000 52.000000 37937.000000 \n", "\n", " total_bedrooms population households median_income \n", "count 16345.000000 16512.000000 16512.000000 16512.000000 \n", "mean 539.302845 1428.389777 500.421693 3.877276 \n", "std 422.062383 1131.002342 382.423411 1.908253 \n", "min 1.000000 3.000000 1.000000 0.499900 \n", "25% 295.000000 787.000000 279.000000 2.566350 \n", "50% 435.000000 1167.000000 410.000000 3.541700 \n", "75% 648.000000 1728.000000 607.000000 4.750000 \n", "max 5471.000000 35682.000000 5189.000000 15.000100 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "housing.describe()" ] }, { "cell_type": "markdown", "id": "difficult-tribe", "metadata": {}, "source": [ "We see that some columns (e.g., `longititude` vs `population`) have very different ranges of values. \n", "**Q: What's the standard way to deal with such differences in numerical data distributions?**\n", "\n", "----\n", "\n", "While these numbers are informative, plots are usually a lot more intuitive. Using [pandas.DataFrame.hist](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html), show a histogram for all the numerical columns. \n", "**Q: Briefly describe what stands out most to you?**" ] }, { "cell_type": "code", "execution_count": 54, "id": "creative-context", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ],\n", " [,\n", " , ]],\n", " dtype=object)" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# TODO: Plot histograms (< 2min)\n", "# Hint: Set figure size to (15, 15), and the number of bins to 50 for good formatting.\n", "housing.hist(bins=50, figsize=(15,15))" ] }, { "cell_type": "markdown", "id": "continued-increase", "metadata": {}, "source": [ "It's also possible to visualize multiple attributes together. Using [pandas.DataFrame.plot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html), plot the relationship between total number of rooms and population. \n", "\n", "**Q: What do you notice?**" ] }, { "cell_type": "code", "execution_count": 10, "id": "wooden-negotiation", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# TODO: plot population vs total_rooms\n", "# Hint: Use a scatter plot.\n", "housing.plot(kind=\"scatter\", x=\"population\", y=\"total_rooms\", alpha=0.2)" ] }, { "cell_type": "markdown", "id": "occupational-biology", "metadata": {}, "source": [ "Take a look at the following code. \n", "\n", "**Q: What pattern does the following code show?** Hint: [Look at this map.](https://www.google.com/maps/place/California/@36.4998513,-121.7064629,6.55z/data=!4m5!3m4!1s0x808fb9fe5f285e3d:0x8b5109a227086f55!8m2!3d36.778261!4d-119.4179324)" ] }, { "cell_type": "code", "execution_count": 11, "id": "offshore-answer", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# TODO by TAs.\n", "housing.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.2, s=housing[\"population\"]/100, label=\"population\", figsize=(10,7))" ] }, { "cell_type": "markdown", "id": "touched-smith", "metadata": {}, "source": [ "We are now done with data exploration. We know what the data looks like, and what preparation we need to do before moving on to training." ] }, { "cell_type": "markdown", "id": "packed-export", "metadata": {}, "source": [ "## Data Preparation" ] }, { "cell_type": "code", "execution_count": 12, "id": "restricted-remedy", "metadata": {}, "outputs": [], "source": [ "# First separate features from target variables.\n", "features = housing.drop(\"house_value_class\", axis=1)\n", "target = housing[\"house_value_class\"]" ] }, { "cell_type": "markdown", "id": "multiple-batman", "metadata": {}, "source": [ "We need the following transformation to make our data work with ML algorithms:\n", "1. One hot encoding of the categorical features. \n", "2. Median imputation on numerical features (you could try other methods).\n", "3. Standardize numerical features to 0/1 range (required for many models).\n", "4. Encode class labels into ordinals (0, 1, 2, ...). This is what the ML libraries we'll use expect.\n", "\n", "`sklearn` has builtin functions for all of these. It also has convenience functions to put them all together.\n", "\n", "----\n", "\n", "One hot encoding works as follows." ] }, { "cell_type": "code", "execution_count": 16, "id": "undefined-graph", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1., 0., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.],\n", " [1., 0., 0., 0., 0.],\n", " ...,\n", " [0., 1., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.],\n", " [0., 1., 0., 0., 0.]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# One hot encoding\n", "from sklearn.preprocessing import OneHotEncoder\n", "\n", "# Get columns to transform.\n", "categorical_col_names = ['ocean_proximity'] # stored as a separate variable for later.\n", "categorical = features[categorical_col_names]\n", "\n", "# Do one hot encoding.\n", "encoder = OneHotEncoder()\n", "categorical_one_hot = encoder.fit_transform(categorical)\n", "categorical_one_hot.toarray()" ] }, { "cell_type": "markdown", "id": "professional-cooking", "metadata": {}, "source": [ "As for imputation, `sklearn` has a [variety of implementations](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute) (corresponding to the techniques we described in class). We will stick to simple median imputation, but feel free to try something different in your custom project." ] }, { "cell_type": "code", "execution_count": 17, "id": "growing-penny", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Num missing before imputation: 167\n", "Num missing after imputation: 0\n" ] } ], "source": [ "from sklearn.impute import SimpleImputer\n", "\n", "# Get columns to transform.\n", "numerical_col_names = [c for c in features.columns if c != 'ocean_proximity'] # stored as a separate variable for later.\n", "numerical = features[numerical_col_names]\n", "\n", "# Print missing count\n", "print(\"Num missing before imputation:\", np.count_nonzero(np.isnan(numerical)))\n", "\n", "# Do transformation.\n", "imputer = SimpleImputer(strategy=\"median\")\n", "imputed_numerical = imputer.fit_transform(numerical)\n", "\n", "# Print missing count. Should be 0.\n", "print(\"Num missing after imputation:\", np.count_nonzero(np.isnan(imputed_numerical)))" ] }, { "cell_type": "markdown", "id": "economic-curve", "metadata": {}, "source": [ "Take a look at the documentation for [sklearn.preprocessing.StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) and use it to normalize numerical columns in `imputed_numerical`." ] }, { "cell_type": "code", "execution_count": 18, "id": "mineral-objective", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-1.02618438e+00, 4.92694304e-01, -3.67612514e-01, ...,\n", " 1.60447517e+00, 5.27123615e-01, -8.91248289e-01],\n", " [-1.25516802e+00, 2.66342955e+00, 1.85659377e+00, ...,\n", " 2.64914832e-01, 5.37583542e-01, -8.54774030e-01],\n", " [ 8.50485909e-01, -8.54658609e-01, 4.26746875e-01, ...,\n", " 2.59609643e-01, -1.55386650e-01, -5.52236821e-01],\n", " ...,\n", " [-1.14067620e+00, 9.69881794e-01, -1.24140784e+00, ...,\n", " 2.61776635e+00, 2.32884611e+00, 1.47019796e+00],\n", " [ 8.90309151e-01, -4.89750528e-01, -1.79745941e+00, ...,\n", " 4.18633403e+00, 3.34868904e+00, 1.58497574e-03],\n", " [-7.07598442e-01, 1.42367774e+00, -1.63858754e+00, ...,\n", " 5.09837745e-01, 5.82038234e-01, 8.19635528e-01]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO: Do scaling (~2 mins)\n", "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "numerical_scaled = scaler.fit_transform(imputed_numerical)\n", "numerical_scaled" ] }, { "cell_type": "markdown", "id": "rocky-arabic", "metadata": {}, "source": [ "`sklearn` has a simple API to chain all of these transformation together. Take a look at the documentation for [sklearn.pipeline.Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) and [sklearn.compose.ColumnTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html)." ] }, { "cell_type": "code", "execution_count": 19, "id": "developing-texture", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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We will then give a few minutes to try out gradient boosted trees.\n", "\n", "Training a model is simple with `sklearn`." ] }, { "cell_type": "code", "execution_count": 21, "id": "funded-mother", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SGDClassifier()" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import SGDClassifier\n", "\n", "# Train model\n", "lin_sgd = SGDClassifier(loss='hinge')\n", "\n", "lin_sgd.fit(features_prepped, target_prepped)" ] }, { "cell_type": "markdown", "id": "beneficial-whale", "metadata": {}, "source": [ "Performing predictions is equally easy." ] }, { "cell_type": "code", "execution_count": 22, "id": "cardiac-indication", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['MEDIUM', 'LOW', 'LOW', 'LOW', 'MEDIUM', 'LOW', 'LOW', 'MEDIUM',\n", " 'MEDIUM', 'MEDIUM'], dtype=object)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get some random input\n", "sample = housing.sample(n=10, random_state=1)\n", "# Tranform input\n", "sample_prepped = feature_transformer.transform(sample)\n", "# Make prediction\n", "prediction = lin_sgd.predict(sample_prepped)\n", "# Decode prediction\n", "label_encoder.inverse_transform(prediction)" ] }, { "cell_type": "markdown", "id": "contrary-enterprise", "metadata": {}, "source": [ "Let's validate the efficacy of the model. `sklearn` provides builtin cross-validation. ****" ] }, { "cell_type": "code", "execution_count": 23, "id": "worldwide-garden", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 76.48% (1.47%)\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score, KFold\n", "\n", "# Do cross validation\n", "# The cv parameter is the number of cross-validation splits.\n", "lin_sgd = SGDClassifier(loss='hinge')\n", "kfold = KFold(n_splits=10, shuffle=True, random_state=37)\n", "scores = cross_val_score(lin_sgd, features_prepped, target_prepped, cv=kfold)\n", "print(\"Accuracy: %.2f%% (%.2f%%)\" % (scores.mean()*100, scores.std()*100))" ] }, { "cell_type": "markdown", "id": "unnecessary-specific", "metadata": {}, "source": [ "We get a validation accuracy of ~$76\\%$. This may or may not be ok depending on your application. Let's try `xgboost.XGBClassifier` and see if it does better." ] }, { "cell_type": "code", "execution_count": 24, "id": "traditional-latter", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 85.58% (0.73%)\n" ] } ], "source": [ "from xgboost import XGBClassifier\n", "\n", "# You'll need at least these flags \n", "xgb_cls = XGBClassifier(eval_metric='mlogloss', use_label_encoder=False)\n", "\n", "# TODO: Add fitting and validation. Validation can be somewhat slow.\n", "kfold = KFold(n_splits=10, shuffle=True, random_state=37)\n", "scores = cross_val_score(xgb_cls, features_prepped, target_prepped, cv=kfold)\n", "print(\"Accuracy: %.2f%% (%.2f%%)\" % (scores.mean()*100, scores.std()*100))\n" ] }, { "cell_type": "markdown", "id": "center-worth", "metadata": {}, "source": [ "You should have obtained an accuracy better than before. \n", "**Q: Why do think `xgboost` performs better than linear SGD?**\n", "\n", "---\n", "\n", "**Given a model, how should we tune it to improve training performance?**" ] }, { "cell_type": "markdown", "id": "rental-indianapolis", "metadata": {}, "source": [ "## Testing\n", "We can now test our model to see how well it performs in the \"real-world\". **Q: Why can't we compare our models in the testing phase?**.\n", "\n", "Fill-in the following code." ] }, { "cell_type": "code", "execution_count": 25, "id": "legislative-coupon", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score\n", "\n", "# Train\n", "xgb_cls = XGBClassifier(eval_metric='mlogloss', use_label_encoder=False)\n", "xgb_cls.fit(features_prepped, target_prepped)\n", "\n", "# TODO: Implementing testing code here.\n", "test_target = test_set['house_value_class']\n", "test_features = test_set.drop('house_value_class', axis=1)\n", "\n", "test_target_prepped = label_encoder.transform(test_target)\n", "test_features_prepped = feature_transformer.transform(test_features)\n", "\n", "test_predictions = xgb_cls.predict(test_features_prepped)\n", "accuracy = accuracy_score(test_target_prepped, test_predictions)" ] }, { "cell_type": "code", "execution_count": 26, "id": "connected-roller", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.8604651162790697" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy" ] }, { "cell_type": "markdown", "id": "monetary-forge", "metadata": {}, "source": [ "Thanks to k-fold cross validation, you should see that your test error is fairly similar to your validation error. This allows us to predict performance on unseen data, as long its distribution is reasonably similar to the training data." ] }, { "cell_type": "code", "execution_count": null, "id": "round-player", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.0" } }, "nbformat": 4, "nbformat_minor": 5 }