{ "cells": [ { "cell_type": "markdown", "id": "1ccb3bcc", "metadata": {}, "source": [ "# Matching Data" ] }, { "cell_type": "markdown", "id": "31614f02", "metadata": {}, "source": [ "In this notebook, we demonstrate the MatchingData class, which organizes population data for matching, and some plotting tools for visualizing the data. You can download this notebook to run yourself here: https://github.com/Bayer-Group/pybalance/blob/main/sphinx/demos/core_01_matching_data.ipynb." ] }, { "cell_type": "code", "execution_count": 1, "id": "4e60274e", "metadata": {}, "outputs": [], "source": [ "import os \n", "import logging \n", "logging.basicConfig(\n", " format=\"%(levelname)-4s [%(filename)s:%(lineno)d] %(message)s\",\n", " level='INFO',\n", ")\n", "\n", "import pandas as pd\n", "\n", "import pybalance\n", "from pybalance import MatchingData, MatchingHeaders, split_target_pool\n", "from pybalance.visualization import (\n", " plot_numeric_features, \n", " plot_categoric_features, \n", " plot_binary_features,\n", " plot_joint_numeric_distributions,\n", " plot_joint_numeric_categoric_distributions,\n", " plot_per_feature_loss\n", ")\n", "from pybalance.sim import get_paper_dataset_path" ] }, { "cell_type": "markdown", "id": "dda436e5", "metadata": {}, "source": [ "## Initializing MatchingData" ] }, { "cell_type": "markdown", "id": "5184f024", "metadata": {}, "source": [ "The MatchingData class is a thin wrapper around pandas DataFrame that additionally keeps track of certain metadata about the columns relevant for matching. MatchingData can be initialized from either a string or pandas DataFrame." ] }, { "cell_type": "code", "execution_count": 2, "id": "02250cfb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " Headers Numeric:
\n", " ['age', 'height', 'weight']

\n", " Headers Categoric:
\n", " ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']

\n", " Populations
\n", " ['pool', 'target']
\n", "
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ageheightweightgenderhaircolorcountrypopulationbinary_0binary_1binary_2binary_3patient_id
064.854093189.46685088.8350491.014pool0101135740
152.571993158.13494094.2151071.011pool010149288
225.828361154.69248294.2262221.003pool0010256676
370.177571160.53663294.2443561.002pool0001338287
473.779164153.55141986.1618140.001pool001172849
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27499562.547794186.00501550.9750510.001target0011579081
27499669.879934142.371386100.1383891.014target0110569939
27499756.921402130.639589108.7451821.015target0100532419
27499834.082754174.76405167.9983960.022target0001566266
27499960.981259137.41943689.8978171.005target1111544231
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275000 rows × 12 columns

\n", "
" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initialize MatchingData from path\n", "data_path = get_paper_dataset_path()\n", "m = MatchingData(data=data_path)\n", "m" ] }, { "cell_type": "code", "execution_count": 3, "id": "a45adfde", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " Headers Numeric:
\n", " ['age', 'height', 'weight']

\n", " Headers Categoric:
\n", " ['gender', 'haircolor', 'country', 'binary_0', 'binary_1', 'binary_2', 'binary_3']

\n", " Populations
\n", " ['pool', 'target']
\n", "
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ageheightweightgenderhaircolorcountrypopulationbinary_0binary_1binary_2binary_3patient_id
064.854093189.46685088.8350491.014pool0101135740
152.571993158.13494094.2151071.011pool010149288
225.828361154.69248294.2262221.003pool0010256676
370.177571160.53663294.2443561.002pool0001338287
473.779164153.55141986.1618140.001pool001172849
.......................................
27499562.547794186.00501550.9750510.001target0011579081
27499669.879934142.371386100.1383891.014target0110569939
27499756.921402130.639589108.7451821.015target0100532419
27499834.082754174.76405167.9983960.022target0001566266
27499960.981259137.41943689.8978171.005target1111544231
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275000 rows × 12 columns

\n", "
" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# initialize MatchingData from pandas DataFrame\n", "data = pd.read_parquet(data_path)\n", "m = MatchingData(data=data)\n", "m" ] }, { "cell_type": "markdown", "id": "51980126", "metadata": {}, "source": [ "MatchingData will infer which covariates to use for matching and the separation of these into numeric and categoric, unless explicitly specified. Here we specify a subset of the covariates to use for matching. Note that the unused columns are still present in the data, but will simply not be used for matching." ] }, { "cell_type": "code", "execution_count": 4, "id": "4099f276", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " Headers Numeric:
\n", " ['age', 'weight', 'height']

\n", " Headers Categoric:
\n", " ['country', 'gender', 'binary_0', 'binary_1']

\n", " Populations
\n", " ['pool', 'target']
\n", "
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ageheightweightgenderhaircolorcountrypopulationbinary_0binary_1binary_2binary_3patient_id
064.854093189.46685088.8350491.014pool0101135740
152.571993158.13494094.2151071.011pool010149288
225.828361154.69248294.2262221.003pool0010256676
370.177571160.53663294.2443561.002pool0001338287
473.779164153.55141986.1618140.001pool001172849
.......................................
27499562.547794186.00501550.9750510.001target0011579081
27499669.879934142.371386100.1383891.014target0110569939
27499756.921402130.639589108.7451821.015target0100532419
27499834.082754174.76405167.9983960.022target0001566266
27499960.981259137.41943689.8978171.005target1111544231
\n", "

275000 rows × 12 columns

\n", "
" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "headers = MatchingHeaders(\n", " categoric=['country', 'gender', 'binary_0', 'binary_1'],\n", " numeric=['age', 'weight', 'height']\n", ")\n", "m_restricted_features = MatchingData(\n", " data=data, \n", " headers=headers\n", ")\n", "m_restricted_features" ] }, { "cell_type": "markdown", "id": "d101d154", "metadata": {}, "source": [ "## Exploring MatchingData" ] }, { "cell_type": "markdown", "id": "39ecd310", "metadata": {}, "source": [ "The describe*() methods can be used to generate summary tables of the matching covariates." ] }, { "cell_type": "code", "execution_count": 5, "id": "2122eccd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pooltarget
population sizeN250000.0025000.00
gender0.0120054.0012956.00
1.0129946.0012044.00
haircolor0.0100096.004924.00
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country0.00.002490.00
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binary_00.0225028.0017535.00
1.024972.007465.00
binary_10.0175673.0012527.00
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binary_20.0125113.0017472.00
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binary_30.049933.0012562.00
1.0200067.0012438.00
agemean55.2748.33
std13.1814.39
min18.0118.01
q2546.3837.29
median57.1548.74
q7566.1059.85
max75.0075.00
heightmean159.13153.68
std19.8416.45
min125.00125.00
q25142.09140.29
median158.74152.75
q75175.87165.95
max195.00195.00
weightmean88.3082.25
std16.3218.89
min50.0050.00
q2576.3966.14
median88.8581.69
q75100.8897.41
max120.00120.00
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" ], "text/plain": [ " pool target\n", "population size N 250000.00 25000.00\n", "gender 0.0 120054.00 12956.00\n", " 1.0 129946.00 12044.00\n", "haircolor 0.0 100096.00 4924.00\n", " 1.0 75185.00 10055.00\n", " 2 74719.00 10021.00\n", "country 0.0 0.00 2490.00\n", " 1.0 25033.00 5045.00\n", " 2 49534.00 4981.00\n", " 3 75337.00 2474.00\n", " 4 74934.00 5010.00\n", " 5 25162.00 5000.00\n", "binary_0 0.0 225028.00 17535.00\n", " 1.0 24972.00 7465.00\n", "binary_1 0.0 175673.00 12527.00\n", " 1.0 74327.00 12473.00\n", "binary_2 0.0 125113.00 17472.00\n", " 1.0 124887.00 7528.00\n", "binary_3 0.0 49933.00 12562.00\n", " 1.0 200067.00 12438.00\n", "age mean 55.27 48.33\n", " std 13.18 14.39\n", " min 18.01 18.01\n", " q25 46.38 37.29\n", " median 57.15 48.74\n", " q75 66.10 59.85\n", " max 75.00 75.00\n", "height mean 159.13 153.68\n", " std 19.84 16.45\n", " min 125.00 125.00\n", " q25 142.09 140.29\n", " median 158.74 152.75\n", " q75 175.87 165.95\n", " max 195.00 195.00\n", "weight mean 88.30 82.25\n", " std 16.32 18.89\n", " min 50.00 50.00\n", " q25 76.39 66.14\n", " median 88.85 81.69\n", " q75 100.88 97.41\n", " max 120.00 120.00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.describe(normalize=False)" ] }, { "cell_type": "markdown", "id": "50da290d", "metadata": {}, "source": [ "You can access fields on the underlying data similarly to how you would in pandas." ] }, { "cell_type": "code", "execution_count": 6, "id": "5401a9cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " population gender\n", "0 pool 1.0\n", "1 pool 1.0\n", "2 pool 1.0\n", "3 pool 1.0\n", "4 pool 0.0\n", "... ... ...\n", "274995 target 0.0\n", "274996 target 1.0\n", "274997 target 1.0\n", "274998 target 0.0\n", "274999 target 1.0\n", "\n", "[275000 rows x 2 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[['population', 'gender']]" ] }, { "cell_type": "code", "execution_count": 7, "id": "191d7c65", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age height weight gender haircolor country \\\n", "4 73.779164 153.551419 86.161814 0.0 0 1 \n", "7 67.404918 132.383184 67.107753 0.0 0 5 \n", "11 61.489148 140.780034 73.662572 0.0 0 1 \n", "12 73.718093 133.743721 58.879321 0.0 1 4 \n", "13 70.707782 156.629048 70.681391 0.0 0 2 \n", "... ... ... ... ... ... ... \n", "274990 19.063519 167.704149 59.876565 0.0 2 4 \n", "274992 58.745450 146.747313 70.291448 0.0 2 4 \n", "274993 55.736083 132.434020 92.264209 0.0 1 4 \n", "274995 62.547794 186.005015 50.975051 0.0 0 1 \n", "274998 34.082754 174.764051 67.998396 0.0 2 2 \n", "\n", " population binary_0 binary_1 binary_2 binary_3 patient_id \n", "4 pool 0 0 1 1 72849 \n", "7 pool 0 0 0 1 171211 \n", "11 pool 0 0 0 1 20695 \n", "12 pool 0 0 1 0 58718 \n", "13 pool 0 1 1 1 352801 \n", "... ... ... ... ... ... ... \n", "274990 target 0 1 1 1 536365 \n", "274992 target 0 0 0 1 535279 \n", "274993 target 0 1 0 0 595582 \n", "274995 target 0 0 1 1 579081 \n", "274998 target 0 0 0 1 566266 \n", "\n", "[133010 rows x 12 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[m['gender'] == 0]" ] }, { "cell_type": "markdown", "id": "b63f8c72", "metadata": {}, "source": [ "Often our matching data consists of exactly two populations, a reference population, \n", "which we call the \"target\" and a population to be matched, which we call the \"pool\". \n", "It is sometimes convenient to split these two populations and the function \n", "split_target_pool does just that. The function will assign the smaller population to the\n", "target, unless explicitly given the name of the target population. Note that the returned\n", "values are pandas DataFrame objects and not MatchingData objects." ] }, { "cell_type": "code", "execution_count": 8, "id": "e630b0f4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age height weight gender haircolor country \\\n", "250000 57.266010 159.759575 94.325267 0.0 1 4 \n", "250001 53.152645 145.515410 95.988094 0.0 1 2 \n", "250002 34.079212 166.272208 73.090671 0.0 1 2 \n", "250003 45.494927 144.336677 96.678251 1.0 2 5 \n", "250004 18.036012 174.843524 60.586475 0.0 1 2 \n", "\n", " population binary_0 binary_1 binary_2 binary_3 patient_id \n", "250000 target 0 1 0 1 512966 \n", "250001 target 1 0 1 1 540606 \n", "250002 target 0 0 1 1 578266 \n", "250003 target 1 1 1 1 559858 \n", "250004 target 1 1 0 1 588368 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target, pool = split_target_pool(m)\n", "target.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "83f90e8a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age height weight gender haircolor country \\\n", "250000 57.266010 159.759575 94.325267 0.0 1 4 \n", "250001 53.152645 145.515410 95.988094 0.0 1 2 \n", "250002 34.079212 166.272208 73.090671 0.0 1 2 \n", "250003 45.494927 144.336677 96.678251 1.0 2 5 \n", "250004 18.036012 174.843524 60.586475 0.0 1 2 \n", "\n", " population binary_0 binary_1 binary_2 binary_3 patient_id \n", "250000 target 0 1 0 1 512966 \n", "250001 target 1 0 1 1 540606 \n", "250002 target 0 0 1 1 578266 \n", "250003 target 1 1 1 1 559858 \n", "250004 target 1 1 0 1 588368 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target, pool = split_target_pool(m, target_name='pool')\n", "pool.head()" ] }, { "cell_type": "markdown", "id": "13d05376", "metadata": {}, "source": [ "## Visualizing MatchingData" ] }, { "cell_type": "markdown", "id": "8dda104d", "metadata": {}, "source": [ "Some built-in tools help you get a quick visual snapshot of the data. Many of these plotting routines are thin wrappers around seaborn plotting routines with extra logic relevant to matching situations (e.g. where one of the populations is a reference population or where variables should be treated as numeric / categoric). In most cases, the user can pass along any keyword arguments that are understood by the underlying seaborn routine." ] }, { "cell_type": "code", "execution_count": 10, "id": "029b22de", "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 11, "id": "888c6bc9-73c1-499b-bc0e-82801e46dbbb", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " Headers Numeric:
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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the standardized mean difference for each feature\n", "from pybalance.utils import BetaBalance\n", "bc = BetaBalance(m, standardize_difference=True)\n", "fig = plot_per_feature_loss(m, bc)" ] }, { "cell_type": "code", "execution_count": 13, "id": "042b9a5a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plot_categoric_features(m, cumulative=False, palette='colorblind', include_binary=False)" ] }, { "cell_type": "code", "execution_count": 14, "id": "62efef9a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plot_numeric_features(m, bins=10, cumulative=False, palette='colorblind')" ] }, { "cell_type": "code", "execution_count": 15, "id": "19711749", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/gmema/src/matching-fork/venv/lib/python3.12/site-packages/pybalance/visualization/distributions.py:460: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. Otherwise, ticks may be mislabeled.\n", " plt.gca().set_yticklabels([\"\"] * len(labels), minor=True)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plot_binary_features(m, palette='colorblind', orient_horizontal=False, standardize_difference=False)" ] } ], "metadata": { "kernelspec": { "display_name": "pybal2", "language": "python", "name": "pybal2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.1" } }, "nbformat": 4, "nbformat_minor": 5 }