{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "\n", "**15-448: Machine Learning in a Nutshell**, *CMU-Qatar* Spring'20\n", "\n", "**Gianni A. Di Caro**, www.giannidicaro.com\n", "\n", "Disclaimer: This notebook was prepared for teaching purposes. It can include material from different web sources. I'll happy to explicitly acknowledge a source if required. \n", "\n", "***" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Case study: the diabetes dataset from `sklearn`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/giannidicaro/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n", " from collections import Mapping, defaultdict\n" ] } ], "source": [ "from sklearn import datasets\n", "diabetes = datasets.load_diabetes()\n", "targets = diabetes.target\n", "features = diabetes.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspect the data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(442, 10)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", " 0.01990842, -0.01764613],\n", " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", " -0.06832974, -0.09220405],\n", " [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n", " 0.00286377, -0.02593034],\n", " ...,\n", " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n", " -0.04687948, 0.01549073],\n", " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", " 0.04452837, -0.02593034],\n", " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", " -0.00421986, 0.00306441]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data': array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", " 0.01990842, -0.01764613],\n", " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", " -0.06832974, -0.09220405],\n", " [ 0.08529891, 0.05068012, 0.04445121, ..., -0.00259226,\n", " 0.00286377, -0.02593034],\n", " ...,\n", " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.01107952,\n", " -0.04687948, 0.01549073],\n", " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", " 0.04452837, -0.02593034],\n", " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", " -0.00421986, 0.00306441]]),\n", " 'target': array([151., 75., 141., 206., 135., 97., 138., 63., 110., 310., 101.,\n", " 69., 179., 185., 118., 171., 166., 144., 97., 168., 68., 49.,\n", " 68., 245., 184., 202., 137., 85., 131., 283., 129., 59., 341.,\n", " 87., 65., 102., 265., 276., 252., 90., 100., 55., 61., 92.,\n", " 259., 53., 190., 142., 75., 142., 155., 225., 59., 104., 182.,\n", " 128., 52., 37., 170., 170., 61., 144., 52., 128., 71., 163.,\n", " 150., 97., 160., 178., 48., 270., 202., 111., 85., 42., 170.,\n", " 200., 252., 113., 143., 51., 52., 210., 65., 141., 55., 134.,\n", " 42., 111., 98., 164., 48., 96., 90., 162., 150., 279., 92.,\n", " 83., 128., 102., 302., 198., 95., 53., 134., 144., 232., 81.,\n", " 104., 59., 246., 297., 258., 229., 275., 281., 179., 200., 200.,\n", " 173., 180., 84., 121., 161., 99., 109., 115., 268., 274., 158.,\n", " 107., 83., 103., 272., 85., 280., 336., 281., 118., 317., 235.,\n", " 60., 174., 259., 178., 128., 96., 126., 288., 88., 292., 71.,\n", " 197., 186., 25., 84., 96., 195., 53., 217., 172., 131., 214.,\n", " 59., 70., 220., 268., 152., 47., 74., 295., 101., 151., 127.,\n", " 237., 225., 81., 151., 107., 64., 138., 185., 265., 101., 137.,\n", " 143., 141., 79., 292., 178., 91., 116., 86., 122., 72., 129.,\n", " 142., 90., 158., 39., 196., 222., 277., 99., 196., 202., 155.,\n", " 77., 191., 70., 73., 49., 65., 263., 248., 296., 214., 185.,\n", " 78., 93., 252., 150., 77., 208., 77., 108., 160., 53., 220.,\n", " 154., 259., 90., 246., 124., 67., 72., 257., 262., 275., 177.,\n", " 71., 47., 187., 125., 78., 51., 258., 215., 303., 243., 91.,\n", " 150., 310., 153., 346., 63., 89., 50., 39., 103., 308., 116.,\n", " 145., 74., 45., 115., 264., 87., 202., 127., 182., 241., 66.,\n", " 94., 283., 64., 102., 200., 265., 94., 230., 181., 156., 233.,\n", " 60., 219., 80., 68., 332., 248., 84., 200., 55., 85., 89.,\n", " 31., 129., 83., 275., 65., 198., 236., 253., 124., 44., 172.,\n", " 114., 142., 109., 180., 144., 163., 147., 97., 220., 190., 109.,\n", " 191., 122., 230., 242., 248., 249., 192., 131., 237., 78., 135.,\n", " 244., 199., 270., 164., 72., 96., 306., 91., 214., 95., 216.,\n", " 263., 178., 113., 200., 139., 139., 88., 148., 88., 243., 71.,\n", " 77., 109., 272., 60., 54., 221., 90., 311., 281., 182., 321.,\n", " 58., 262., 206., 233., 242., 123., 167., 63., 197., 71., 168.,\n", " 140., 217., 121., 235., 245., 40., 52., 104., 132., 88., 69.,\n", " 219., 72., 201., 110., 51., 277., 63., 118., 69., 273., 258.,\n", " 43., 198., 242., 232., 175., 93., 168., 275., 293., 281., 72.,\n", " 140., 189., 181., 209., 136., 261., 113., 131., 174., 257., 55.,\n", " 84., 42., 146., 212., 233., 91., 111., 152., 120., 67., 310.,\n", " 94., 183., 66., 173., 72., 49., 64., 48., 178., 104., 132.,\n", " 220., 57.]),\n", " 'DESCR': 'Diabetes dataset\\n================\\n\\nNotes\\n-----\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\nData Set Characteristics:\\n\\n :Number of Instances: 442\\n\\n :Number of Attributes: First 10 columns are numeric predictive values\\n\\n :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n :Attributes:\\n :Age:\\n :Sex:\\n :Body mass index:\\n :Average blood pressure:\\n :S1:\\n :S2:\\n :S3:\\n :S4:\\n :S5:\\n :S6:\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttp://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\\n',\n", " 'feature_names': ['age',\n", " 'sex',\n", " 'bmi',\n", " 'bp',\n", " 's1',\n", " 's2',\n", " 's3',\n", " 's4',\n", " 's5',\n", " 's6']}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Diabetes dataset\n", "================\n", "\n", "Notes\n", "-----\n", "\n", "Ten baseline variables, age, sex, body mass index, average blood\n", "pressure, and six blood serum measurements were obtained for each of n =\n", "442 diabetes patients, as well as the response of interest, a\n", "quantitative measure of disease progression one year after baseline.\n", "\n", "Data Set Characteristics:\n", "\n", " :Number of Instances: 442\n", "\n", " :Number of Attributes: First 10 columns are numeric predictive values\n", "\n", " :Target: Column 11 is a quantitative measure of disease progression one year after baseline\n", "\n", " :Attributes:\n", " :Age:\n", " :Sex:\n", " :Body mass index:\n", " :Average blood pressure:\n", " :S1:\n", " :S2:\n", " :S3:\n", " :S4:\n", " :S5:\n", " :S6:\n", "\n", "Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\n", "\n", "Source URL:\n", "http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\n", "\n", "For more information see:\n", "Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\n", "(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)\n", "\n" ] } ], "source": [ "# data is organized into a Bunch data structure which is a dictionary\n", "# \n", "print(diabetes['DESCR'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_names = diabetes['feature_names']\n", "feature_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Store the dataset into a Pandas DataFrame" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# first, add the features, with the feature names\n", "diabetes_df = pd.DataFrame(features, columns=feature_names)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# then, add the targets, using a custom name \n", "diabetes_df['Progression index'] = targets" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agesexbmibps1s2s3s4s5s6Progression index
00.0380760.0506800.0616960.021872-0.044223-0.034821-0.043401-0.0025920.019908-0.017646151.0
1-0.001882-0.044642-0.051474-0.026328-0.008449-0.0191630.074412-0.039493-0.068330-0.09220475.0
20.0852990.0506800.044451-0.005671-0.045599-0.034194-0.032356-0.0025920.002864-0.025930141.0
3-0.089063-0.044642-0.011595-0.0366560.0121910.024991-0.0360380.0343090.022692-0.009362206.0
40.005383-0.044642-0.0363850.0218720.0039350.0155960.008142-0.002592-0.031991-0.046641135.0
....................................
4370.0417080.0506800.0196620.059744-0.005697-0.002566-0.028674-0.0025920.0311930.007207178.0
438-0.0055150.050680-0.015906-0.0676420.0493410.079165-0.0286740.034309-0.0181180.044485104.0
4390.0417080.050680-0.0159060.017282-0.037344-0.013840-0.024993-0.011080-0.0468790.015491132.0
440-0.045472-0.0446420.0390620.0012150.0163180.015283-0.0286740.0265600.044528-0.025930220.0
441-0.045472-0.044642-0.073030-0.0814140.0837400.0278090.173816-0.039493-0.0042200.00306457.0
\n", "

442 rows × 11 columns

\n", "
" ], "text/plain": [ " age sex bmi bp s1 s2 s3 \\\n", "0 0.038076 0.050680 0.061696 0.021872 -0.044223 -0.034821 -0.043401 \n", "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163 0.074412 \n", "2 0.085299 0.050680 0.044451 -0.005671 -0.045599 -0.034194 -0.032356 \n", "3 -0.089063 -0.044642 -0.011595 -0.036656 0.012191 0.024991 -0.036038 \n", "4 0.005383 -0.044642 -0.036385 0.021872 0.003935 0.015596 0.008142 \n", ".. ... ... ... ... ... ... ... \n", "437 0.041708 0.050680 0.019662 0.059744 -0.005697 -0.002566 -0.028674 \n", "438 -0.005515 0.050680 -0.015906 -0.067642 0.049341 0.079165 -0.028674 \n", "439 0.041708 0.050680 -0.015906 0.017282 -0.037344 -0.013840 -0.024993 \n", "440 -0.045472 -0.044642 0.039062 0.001215 0.016318 0.015283 -0.028674 \n", "441 -0.045472 -0.044642 -0.073030 -0.081414 0.083740 0.027809 0.173816 \n", "\n", " s4 s5 s6 Progression index \n", "0 -0.002592 0.019908 -0.017646 151.0 \n", "1 -0.039493 -0.068330 -0.092204 75.0 \n", "2 -0.002592 0.002864 -0.025930 141.0 \n", "3 0.034309 0.022692 -0.009362 206.0 \n", "4 -0.002592 -0.031991 -0.046641 135.0 \n", ".. ... ... ... ... \n", "437 -0.002592 0.031193 0.007207 178.0 \n", "438 0.034309 -0.018118 0.044485 104.0 \n", "439 -0.011080 -0.046879 0.015491 132.0 \n", "440 0.026560 0.044528 -0.025930 220.0 \n", "441 -0.039493 -0.004220 0.003064 57.0 \n", "\n", "[442 rows x 11 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_df.round(4) # display all numbers with at most 4 decimals\n", "diabetes_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As it is said in DESCR all values in the datase have been **mean-centered** and **normalized**. \n", "\n", "While this is good for processing the data making date **units-free**, it makes hard to understand what the data are about." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The transformation for the entries $x_i$ of a feature $x$ has been the following:\n", "\n", "$$znorm(x_i) = \\frac{x_i - \\mu(x)}{\\sigma(x)\\cdot \\sqrt{n}}$$\n", " \n", "where $n$ is the total number of data records, $x_i$ is the $i$-th data entry for feature $x$, $\\mu(x)$ is the mean over all values of feature $x$, and $\\sigma(x)$ is the standard deviation over all values of $x$.\n", "\n", "- The $z$ part of the transformation, $z(x_i) = \\frac{x_i - \\mu(x)}{\\sigma(x)}$, makes the transformed entries being \n", "centered about the mean, such that each feature has **zero mean,** $\\mu(z(x))=0$, and has standard deviation equal to 1.

\n", "\n", "Check the Notebook of the lecture on Outliers and Data scaling!\n", " \n", "https://web2.qatar.cmu.edu/~gdicaro/15488/lectures/488-S20-11-Outliers_Scaling.ipynb\n", " \n", " \n", "- The further scaling by $\\sqrt{n}$ doesn't affect the mean, but it makes the **standard deviation** of each feature becoming \n", " $$\\sigma(znorm(x)) = 1/\\sqrt{n},$$ \n", " \n", " and the *feature vector* having **norm equal to 1**: $\\sum_{i=1}^n znorm(x_i)^2 = 1$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check out these properties in the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's set the number of decimal digits when pandas print out things:\n", "\n", "https://pandas.pydata.org/pandas-docs/stable/user_guide/options.html" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#pd.set_option('precision', 3) # 3 decimal digits\n", "pd.options.display.float_format = '{:.3f}'.format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregate statistics about the data" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age -0.000\n", "sex 0.000\n", "bmi -0.000\n", "bp 0.000\n", "s1 -0.000\n", "s2 0.000\n", "s3 -0.000\n", "s4 0.000\n", "s5 -0.000\n", "s6 -0.000\n", "Progression index 152.133\n", "dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_df.mean()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0.005\n", "sex -0.045\n", "bmi -0.007\n", "bp -0.006\n", "s1 -0.004\n", "s2 -0.004\n", "s3 -0.007\n", "s4 -0.003\n", "s5 -0.002\n", "s6 -0.001\n", "Progression index 140.500\n", "dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_df.median()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0.048\n", "sex 0.048\n", "bmi 0.048\n", "bp 0.048\n", "s1 0.048\n", "s2 0.048\n", "s3 0.048\n", "s4 0.048\n", "s5 0.048\n", "s6 0.048\n", "Progression index 77.093\n", "dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_df.std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which is the same as $1/\\sqrt{n}$:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "age 0.048\n", "sex 0.048\n", "bmi 0.048\n", "bp 0.048\n", "s1 0.048\n", "s2 0.048\n", "s3 0.048\n", "s4 0.048\n", "s5 0.048\n", "s6 0.048\n", "Progression index 0.048\n", "dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(1/np.sqrt(diabetes_df.count()) )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# This function calls matplotlib.pyplot.hist(), on each series in \n", "# the DataFrame, resulting in one histogram per column.\n", "#\n", "#https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html\n", "\n", "diabetes_df.hist(bins=10, figsize=(13,13))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Issue: we can't reconstruct the original data!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we don't have the original data, we don't know the $\\mu(x)$ and $\\sigma(x)$ that have been used in $znorm(x_i) = \\frac{x_i - \\mu(x)}{\\sigma(x)\\cdot \\sqrt{n}},$ such that we can't reconstruct the original $x_i$ data.\n", "\n", "Note that when using the `preprocessing` module in `sklearn` every transformation can be stored in a **transform object**, such that the same transformation can be applied to other data, or can be reversed!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case we don't have access to the transform object, such that we have to find the original data on the Internet using the URL in the DESCR field :-)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read the original dataset, unscaled and unnormalized" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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442 rows × 11 columns

\n", "
" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "0 59 2 32.100 101.000 157 93.200 38.000 4.000 4.860 87 151\n", "1 48 1 21.600 87.000 183 103.200 70.000 3.000 3.892 69 75\n", "2 72 2 30.500 93.000 156 93.600 41.000 4.000 4.673 85 141\n", "3 24 1 25.300 84.000 198 131.400 40.000 5.000 4.890 89 206\n", "4 50 1 23.000 101.000 192 125.400 52.000 4.000 4.290 80 135\n", ".. ... ... ... ... ... ... ... ... ... ... ...\n", "437 60 2 28.200 112.000 185 113.800 42.000 4.000 4.984 93 178\n", "438 47 2 24.900 75.000 225 166.000 42.000 5.000 4.443 102 104\n", "439 60 2 24.900 99.670 162 106.600 43.000 3.770 4.127 95 132\n", "440 36 1 30.000 95.000 201 125.200 42.000 4.790 5.130 85 220\n", "441 36 1 19.600 71.000 250 133.200 97.000 3.000 4.595 92 57\n", "\n", "[442 rows x 11 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig = pd.read_csv('../datasets/diabetes.tsv', sep='\\t')\n", "diabetes_orig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregate statistics (now they are more meaningful!)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 48.518\n", "SEX 1.468\n", "BMI 26.376\n", "BP 94.647\n", "S1 189.140\n", "S2 115.439\n", "S3 49.788\n", "S4 4.070\n", "S5 4.641\n", "S6 91.260\n", "Y 152.133\n", "dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.mean()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 50.000\n", "SEX 1.000\n", "BMI 25.700\n", "BP 93.000\n", "S1 186.000\n", "S2 113.000\n", "S3 48.000\n", "S4 4.000\n", "S5 4.620\n", "S6 91.000\n", "Y 140.500\n", "dtype: float64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.median()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 59.000\n", "SEX 2.000\n", "BMI 29.275\n", "BP 105.000\n", "S1 209.750\n", "S2 134.500\n", "S3 57.750\n", "S4 5.000\n", "S5 4.997\n", "S6 98.000\n", "Y 211.500\n", "Name: 0.75, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.quantile(0.75)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 13.109\n", "SEX 0.500\n", "BMI 4.418\n", "BP 13.831\n", "S1 34.608\n", "S2 30.413\n", "S3 12.934\n", "S4 1.290\n", "S5 0.522\n", "S6 11.496\n", "Y 77.093\n", "dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.std()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 442\n", "SEX 442\n", "BMI 442\n", "BP 442\n", "S1 442\n", "S2 442\n", "S3 442\n", "S4 442\n", "S5 442\n", "S6 442\n", "Y 442\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.count()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
count442.000442.000442.000442.000442.000442.000442.000442.000442.000442.000442.000
mean48.5181.46826.37694.647189.140115.43949.7884.0704.64191.260152.133
std13.1090.5004.41813.83134.60830.41312.9341.2900.52211.49677.093
min19.0001.00018.00062.00097.00041.60022.0002.0003.25858.00025.000
25%38.2501.00023.20084.000164.25096.05040.2503.0004.27783.25087.000
50%50.0001.00025.70093.000186.000113.00048.0004.0004.62091.000140.500
75%59.0002.00029.275105.000209.750134.50057.7505.0004.99798.000211.500
max79.0002.00042.200133.000301.000242.40099.0009.0906.107124.000346.000
\n", "
" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 \\\n", "count 442.000 442.000 442.000 442.000 442.000 442.000 442.000 442.000 442.000 \n", "mean 48.518 1.468 26.376 94.647 189.140 115.439 49.788 4.070 4.641 \n", "std 13.109 0.500 4.418 13.831 34.608 30.413 12.934 1.290 0.522 \n", "min 19.000 1.000 18.000 62.000 97.000 41.600 22.000 2.000 3.258 \n", "25% 38.250 1.000 23.200 84.000 164.250 96.050 40.250 3.000 4.277 \n", "50% 50.000 1.000 25.700 93.000 186.000 113.000 48.000 4.000 4.620 \n", "75% 59.000 2.000 29.275 105.000 209.750 134.500 57.750 5.000 4.997 \n", "max 79.000 2.000 42.200 133.000 301.000 242.400 99.000 9.090 6.107 \n", "\n", " S6 Y \n", "count 442.000 442.000 \n", "mean 91.260 152.133 \n", "std 11.496 77.093 \n", "min 58.000 25.000 \n", "25% 83.250 87.000 \n", "50% 91.000 140.500 \n", "75% 98.000 211.500 \n", "max 124.000 346.000 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histogram visualization" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "diabetes_orig.hist(bins=10, figsize=(13,13))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply the normalization transform\n", "\n", "Let's make the exercise to transform the data to the **normalized** form!" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
00.0380.0510.0620.022-0.044-0.035-0.043-0.0030.020-0.018-0.001
1-0.002-0.045-0.051-0.026-0.008-0.0190.074-0.039-0.068-0.092-0.048
20.0850.0510.044-0.006-0.046-0.034-0.032-0.0030.003-0.026-0.007
3-0.089-0.045-0.012-0.0370.0120.025-0.0360.0340.023-0.0090.033
40.005-0.045-0.0360.0220.0040.0160.008-0.003-0.032-0.047-0.011
....................................
4370.0420.0510.0200.060-0.006-0.003-0.029-0.0030.0310.0070.016
438-0.0060.051-0.016-0.0680.0490.079-0.0290.034-0.0180.044-0.030
4390.0420.051-0.0160.017-0.037-0.014-0.025-0.011-0.0470.015-0.012
440-0.045-0.0450.0390.0010.0160.015-0.0290.0270.044-0.0260.042
441-0.045-0.045-0.073-0.0810.0840.0280.174-0.039-0.0040.003-0.059
\n", "

442 rows × 11 columns

\n", "
" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 \\\n", "0 0.038 0.051 0.062 0.022 -0.044 -0.035 -0.043 -0.003 0.020 -0.018 \n", "1 -0.002 -0.045 -0.051 -0.026 -0.008 -0.019 0.074 -0.039 -0.068 -0.092 \n", "2 0.085 0.051 0.044 -0.006 -0.046 -0.034 -0.032 -0.003 0.003 -0.026 \n", "3 -0.089 -0.045 -0.012 -0.037 0.012 0.025 -0.036 0.034 0.023 -0.009 \n", "4 0.005 -0.045 -0.036 0.022 0.004 0.016 0.008 -0.003 -0.032 -0.047 \n", ".. ... ... ... ... ... ... ... ... ... ... \n", "437 0.042 0.051 0.020 0.060 -0.006 -0.003 -0.029 -0.003 0.031 0.007 \n", "438 -0.006 0.051 -0.016 -0.068 0.049 0.079 -0.029 0.034 -0.018 0.044 \n", "439 0.042 0.051 -0.016 0.017 -0.037 -0.014 -0.025 -0.011 -0.047 0.015 \n", "440 -0.045 -0.045 0.039 0.001 0.016 0.015 -0.029 0.027 0.044 -0.026 \n", "441 -0.045 -0.045 -0.073 -0.081 0.084 0.028 0.174 -0.039 -0.004 0.003 \n", "\n", " Y \n", "0 -0.001 \n", "1 -0.048 \n", "2 -0.007 \n", "3 0.033 \n", "4 -0.011 \n", ".. ... \n", "437 0.016 \n", "438 -0.030 \n", "439 -0.012 \n", "440 0.042 \n", "441 -0.059 \n", "\n", "[442 rows x 11 columns]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# this is the normalization applied to center and rescale the data\n", "# first apply the z-trasnform\n", "# then, normalize to unit vector\n", "#\n", "def center_and_normalize(df):\n", " return (df - df.mean()) / (df.std() * np.sqrt(df.count())) \n", "\n", "center_and_normalize(diabetes_orig)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normalize using `sklearn`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same result can be obatined by using `sklearn`" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from sklearn import preprocessing" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.80050009, 1.06548848, 1.29708846, ..., 0.41853093,\n", " -0.37098854, -0.01471948],\n", " [-0.03956713, -0.93853666, -1.08218016, ..., -1.43658851,\n", " -1.93847913, -1.00165882],\n", " [ 1.79330681, 1.06548848, 0.93453324, ..., 0.06015558,\n", " -0.54515416, -0.14457991],\n", " ...,\n", " [ 0.87686984, 1.06548848, -0.33441002, ..., -0.98564884,\n", " 0.32567395, -0.26145431],\n", " [-0.9560041 , -0.93853666, 0.82123474, ..., 0.93616291,\n", " -0.54515416, 0.88131756],\n", " [-0.9560041 , -0.93853666, -1.53537419, ..., -0.08875225,\n", " 0.06442552, -1.23540761]])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# z-transform the data\n", "#\n", "z = preprocessing.scale(diabetes_orig)\n", "z" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.03807591, 0.05068012, 0.06169621, ..., 0.01990749,\n", " -0.01764613, -0.00070013],\n", " [-0.00188202, -0.04464164, -0.05147406, ..., -0.06833155,\n", " -0.09220405, -0.04764405],\n", " [ 0.08529891, 0.05068012, 0.04445121, ..., 0.00286131,\n", " -0.02593034, -0.00687697],\n", " ...,\n", " [ 0.04170844, 0.05068012, -0.01590626, ..., -0.04688253,\n", " 0.01549073, -0.01243611],\n", " [-0.04547248, -0.04464164, 0.03906215, ..., 0.04452873,\n", " -0.02593034, 0.04192 ],\n", " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.00422151,\n", " 0.00306441, -0.05876235]])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# normalize the z-transformed data, column-wise, by setting axis=0\n", "# makes all the vectors having unit-norm\n", "#\n", "z_n = preprocessing.normalize(z, norm='l2', axis=0)\n", "z_n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9999999999999996" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# check that each feature vector has unit L2 norm\n", "#\n", "np.sum(z_n[:,0]**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Missing entries: check and impute" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far we had no issues for missing entries, but pandas are robust to missing data!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's (re)read the file with diabetes data from a *corrupted* source:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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442 rows × 11 columns

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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "0 59 2 32.100 101.000 157 93.200 38.000 4.000 4.860 87 151.000\n", "1 48 1 21.600 87.000 183 103.200 70.000 3.000 3.892 69 75.000\n", "2 72 2 30.500 93.000 156 93.600 41.000 4.000 4.673 85 141.000\n", "3 24 1 25.300 84.000 198 131.400 40.000 5.000 4.890 89 206.000\n", "4 50 1 23.000 101.000 192 125.400 52.000 4.000 4.290 80 135.000\n", ".. ... ... ... ... ... ... ... ... ... ... ...\n", "437 60 2 28.200 112.000 185 113.800 42.000 4.000 4.984 93 178.000\n", "438 47 2 24.900 75.000 225 166.000 42.000 5.000 4.443 102 104.000\n", "439 60 2 24.900 99.670 162 106.600 43.000 3.770 4.127 95 132.000\n", "440 36 1 30.000 95.000 201 125.200 42.000 4.790 5.130 85 220.000\n", "441 36 1 19.600 71.000 250 133.200 97.000 3.000 4.595 92 57.000\n", "\n", "[442 rows x 11 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig = pd.read_csv('../datasets/diabetes2.tsv', sep='\\t')\n", "diabetes_orig" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of non-numeric entries: 0\n" ] } ], "source": [ "# skelearn dataset: no missing entries\n", "total_nan = np.sum(np.sum(diabetes_df.isnull()))\n", "print('Total number of non-numeric entries: {}'.format(total_nan))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total non-numeric entries: 3\n" ] } ], "source": [ "# corrupted csv dataset: missing entries!\n", "total_nan = np.sum(np.sum(diabetes_orig.isnull()))\n", "print('Total non-numeric entries: {}'.format(total_nan))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset has missing values issues, that need to be fixed according to some **imputation strategy.** \n", "\n", "A nice set of examples of using pandas' methods for dealing with missing data:\n", "https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drop all rows with at least one missing entry" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.dropna(axis=0, how='any')" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total nan: 0, rows 439, cols 11\n", "Total nan: 3, rows 442, cols 11\n" ] } ], "source": [ "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(df.isnull())), \n", " df.shape[0], df.shape[1]))\n", "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(diabetes_orig.isnull())),\n", " diabetes_orig.shape[0], diabetes_orig.shape[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Keep only the rows/columns with at least `thresh` non-missing values\n", "\n", "Drop all row/columns with less than $n$ valid entries" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# keep only the columns with at least thresh entries non null\n", "df = diabetes_orig.dropna(axis=1, thresh=2)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total nan: 3, rows 442, cols 11\n", "Total nan: 3, rows 442, cols 11\n" ] } ], "source": [ "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(df.isnull())), \n", " df.shape[0], df.shape[1]))\n", "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(diabetes_orig.isnull())),\n", " diabetes_orig.shape[0], diabetes_orig.shape[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nothing changed, since the changing threshold was never reached" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.dropna(axis=1, how='any')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total nan: 0, rows 442, cols 8\n", "Total nan: 3, rows 442, cols 11\n" ] } ], "source": [ "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(df.isnull())), \n", " df.shape[0], df.shape[1]))\n", "print('Total nan: {}, rows {}, cols {}'.format(np.sum(np.sum(diabetes_orig.isnull())),\n", " diabetes_orig.shape[0], diabetes_orig.shape[1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Replace with an arbitrary value" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.replace(to_replace = np.nan, value = -99) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check if the replacing value, -99 in this case, is now present in the data frame." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE False\n", "SEX False\n", "BMI False\n", "BP True\n", "S1 False\n", "S2 False\n", "S3 False\n", "S4 True\n", "S5 False\n", "S6 False\n", "Y True\n", "dtype: bool" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.eq(-99).any(axis=0)\n", "\n", "#Pandas' any() method checks whether any value in the caller object \n", "#(Dataframe or series) is not 0, and returns True for that. \n", "#If all values are 0, it will return False." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "437 False\n", "438 False\n", "439 False\n", "440 False\n", "441 False\n", "Length: 442, dtype: bool" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.eq(-99).any(axis=1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "15 34 2 24.700 118.000 254 184.200 39.000 7.000 5.037 81 -99.000\n", "18 38 1 25.400 84.000 162 103.000 42.000 -99.000 4.443 87 97.000\n", "26 19 1 19.200 -99.000 124 54.000 57.000 2.000 4.174 90 137.000" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.eq(-99).any(1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Replace with statistical estimators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fill with the column mean" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.fillna(diabetes_orig.mean())" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 48.518\n", "SEX 1.468\n", "BMI 26.376\n", "BP 94.664\n", "S1 189.140\n", "S2 115.439\n", "S3 49.788\n", "S4 4.070\n", "S5 4.641\n", "S6 91.260\n", "Y 152.091\n", "dtype: float64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the 15 data row, the value of the `Y` column was missing. Let's check that the value is now what we expect (i.e., -99). \n", "\n", "We will make the check on the same date entry for all the other imputation strategies below. " ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 34.000\n", "SEX 2.000\n", "BMI 24.700\n", "BP 118.000\n", "S1 254.000\n", "S2 184.200\n", "S3 39.000\n", "S4 7.000\n", "S5 5.037\n", "S6 81.000\n", "Y 152.091\n", "Name: 15, dtype: float64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[15, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Replacements can also be done **in-place**." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.copy()\n", "df.fillna(df.mean(), inplace=True)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 34.000\n", "SEX 2.000\n", "BMI 24.700\n", "BP 118.000\n", "S1 254.000\n", "S2 184.200\n", "S3 39.000\n", "S4 7.000\n", "S5 5.037\n", "S6 81.000\n", "Y 152.091\n", "Name: 15, dtype: float64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[15, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fill with the column median" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.fillna(diabetes_orig.median())" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 50.000\n", "SEX 1.000\n", "BMI 25.700\n", "BP 93.000\n", "S1 186.000\n", "S2 113.000\n", "S3 48.000\n", "S4 4.000\n", "S5 4.620\n", "S6 91.000\n", "Y 140.000\n", "dtype: float64" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.median()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGE 34.000\n", "SEX 2.000\n", "BMI 24.700\n", "BP 118.000\n", "S1 254.000\n", "S2 184.200\n", "S3 39.000\n", "S4 7.000\n", "S5 5.037\n", "S6 81.000\n", "Y 140.000\n", "Name: 15, dtype: float64" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[15, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fill with the column mode" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.fillna(diabetes_orig.mode())" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "0 53.000 1.000 23.500 83.000 162 114.800 46.000 3.000 4.443 92.000 72.000\n", "1 nan nan 24.100 93.000 184 125.800 nan nan nan nan 200.000" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.mode(axis=0, dropna=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `mode()` function shows all the mode(s) of a column (or a row, depending on the selected axis). If there are multiple modes (i.e., multiple values with the same highest number of presences), they are all displayed, over multiple rows. If for a column there's only one mode, the values on the other result rows are indicated as `nan`." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6 6\n" ] } ], "source": [ "# verify that for Y col, 72 and 200 appear the same number of times\n", "print(np.sum(diabetes_orig['Y'].eq(72)), np.sum(diabetes_orig['Y'].eq(200)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fill with time-series approaches" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Propagate the last valid observation backward" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.fillna(method='backfill')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "14 61 1 24.000 91.000 202 115.400 72.000 3.000 4.290 73 118.000\n", "15 34 2 24.700 118.000 254 184.200 39.000 7.000 5.037 81 166.000\n", "16 47 1 30.300 109.000 207 100.200 70.000 3.000 5.215 98 166.000" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[14:17, :]" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6Y
1461124.00091.000202115.40072.0003.0004.29073118.000
1534224.700118.000254184.20039.0007.0005.03781nan
1647130.300109.000207100.20070.0003.0005.21598166.000
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "14 61 1 24.000 91.000 202 115.400 72.000 3.000 4.290 73 118.000\n", "15 34 2 24.700 118.000 254 184.200 39.000 7.000 5.037 81 nan\n", "16 47 1 30.300 109.000 207 100.200 70.000 3.000 5.215 98 166.000" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diabetes_orig.iloc[14:17, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forward-propagate the last valid observation " ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.fillna(method='ffill')" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AGESEXBMIBPS1S2S3S4S5S6Y
1461124.00091.000202115.40072.0003.0004.29073118.000
1534224.700118.000254184.20039.0007.0005.03781118.000
1647130.300109.000207100.20070.0003.0005.21598166.000
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "14 61 1 24.000 91.000 202 115.400 72.000 3.000 4.290 73 118.000\n", "15 34 2 24.700 118.000 254 184.200 39.000 7.000 5.037 81 118.000\n", "16 47 1 30.300 109.000 207 100.200 70.000 3.000 5.215 98 166.000" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[14:17, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Interpolate" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "df = diabetes_orig.interpolate(method='linear')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AGESEXBMIBPS1S2S3S4S5S6Y
1461124.00091.000202115.40072.0003.0004.29073118.000
1534224.700118.000254184.20039.0007.0005.03781142.000
1647130.300109.000207100.20070.0003.0005.21598166.000
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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n", "14 61 1 24.000 91.000 202 115.400 72.000 3.000 4.290 73 118.000\n", "15 34 2 24.700 118.000 254 184.200 39.000 7.000 5.037 81 142.000\n", "16 47 1 30.300 109.000 207 100.200 70.000 3.000 5.215 98 166.000" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[14:17, :]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "142.0" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# simple linear interpolation\n", "(118 + 166) / 2\n", "\n", "# A number of different interpolation options\n", "# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.interpolate.html?highlight=interpolate#pandas.DataFrame.interpolate" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Check and manage outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Are **outliers** present in the data?\n", "\n", "We can use the methods from the last lecture to check it out!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the z-score: if any of the features have a z-score outside of the bounds of 3$\\sigma$, the corresponding data entry will be tagged as containing an outlier. \n", "\n", "Indeed, this is a very simplified way to identify outliers in a multivariate setting." ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.80050009, 1.06548848, 1.29708846, ..., -0.05449919,\n", " 0.41853093, -0.37098854],\n", " [-0.03956713, -0.93853666, -1.08218016, ..., -0.83030083,\n", " -1.43658851, -1.93847913],\n", " [ 1.79330681, 1.06548848, 0.93453324, ..., -0.05449919,\n", " 0.06015558, -0.54515416],\n", " ...,\n", " [ 0.87686984, 1.06548848, -0.33441002, ..., -0.23293356,\n", " -0.98564884, 0.32567395],\n", " [-0.9560041 , -0.93853666, 0.82123474, ..., 0.55838411,\n", " 0.93616291, -0.54515416],\n", " [-0.9560041 , -0.93853666, -1.53537419, ..., -0.83030083,\n", " -0.08875225, 0.06442552]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy import stats\n", "\n", "diabetes_orig = pd.read_csv('../datasets/diabetes.tsv', sep='\\t')\n", "\n", "features = diabetes_orig.iloc[:, 0:-1]\n", "\n", "z = stats.zscore(features)\n", "z" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AGESEXBMIBPS1S2S3S4S5S6
00.8001.0641.2960.459-0.929-0.731-0.911-0.0540.418-0.371
1-0.040-0.937-1.081-0.553-0.177-0.4021.563-0.829-1.435-1.936
21.7911.0640.933-0.119-0.958-0.718-0.679-0.0540.060-0.545
3-1.870-0.937-0.243-0.7700.2560.525-0.7570.7200.476-0.197
40.113-0.937-0.7640.4590.0830.3280.171-0.054-0.672-0.979
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4370.8761.0640.4131.255-0.120-0.054-0.602-0.0540.6550.151
438-0.1161.064-0.334-1.4201.0361.662-0.6020.720-0.3800.934
4390.8761.064-0.3340.363-0.784-0.291-0.525-0.233-0.9850.325
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441-0.955-0.937-1.534-1.7101.7590.5843.650-0.829-0.0890.064
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442 rows × 10 columns

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" ], "text/plain": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6\n", "0 0.800 1.064 1.296 0.459 -0.929 -0.731 -0.911 -0.054 0.418 -0.371\n", "1 -0.040 -0.937 -1.081 -0.553 -0.177 -0.402 1.563 -0.829 -1.435 -1.936\n", "2 1.791 1.064 0.933 -0.119 -0.958 -0.718 -0.679 -0.054 0.060 -0.545\n", "3 -1.870 -0.937 -0.243 -0.770 0.256 0.525 -0.757 0.720 0.476 -0.197\n", "4 0.113 -0.937 -0.764 0.459 0.083 0.328 0.171 -0.054 -0.672 -0.979\n", ".. ... ... ... ... ... ... ... ... ... ...\n", "437 0.876 1.064 0.413 1.255 -0.120 -0.054 -0.602 -0.054 0.655 0.151\n", "438 -0.116 1.064 -0.334 -1.420 1.036 1.662 -0.602 0.720 -0.380 0.934\n", "439 0.876 1.064 -0.334 0.363 -0.784 -0.291 -0.525 -0.233 -0.985 0.325\n", "440 -0.955 -0.937 0.820 0.026 0.343 0.321 -0.602 0.558 0.935 -0.545\n", "441 -0.955 -0.937 -1.534 -1.710 1.759 0.584 3.650 -0.829 -0.089 0.064\n", "\n", "[442 rows x 10 columns]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# just a double check that stats.zscore() does the job :-)\n", "def z_transform(df):\n", " return (df - df.mean()) / df.std()\n", "\n", "z_transform(features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spot and get the data examples with outlier features values:" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6\n", "58 60 1 20.400 105.000 198 78.400 99.000 2.000 4.635 79\n", "123 50 2 29.600 94.330 300 242.400 33.000 9.090 4.812 109\n", "123 50 2 29.600 94.330 300 242.400 33.000 9.090 4.812 109\n", "123 50 2 29.600 94.330 300 242.400 33.000 9.090 4.812 109\n", "216 52 2 29.700 109.000 228 162.800 31.000 8.000 5.142 103\n", "230 38 2 33.000 78.000 301 215.000 50.000 6.020 5.193 108\n", "230 38 2 33.000 78.000 301 215.000 50.000 6.020 5.193 108\n", "256 35 1 41.300 81.000 168 102.800 37.000 5.000 4.949 94\n", "260 60 1 25.600 78.000 195 95.400 91.000 2.000 3.761 87\n", "261 62 1 22.500 125.000 215 99.000 98.000 2.000 4.500 95\n", "269 51 1 23.400 87.000 220 108.800 93.000 2.000 4.511 82\n", "322 55 2 32.100 112.670 207 92.400 25.000 8.280 6.105 111\n", "336 43 1 34.300 84.000 256 172.600 33.000 8.000 5.529 104\n", "367 46 2 42.200 99.000 211 137.000 44.000 5.000 5.011 99\n", "441 36 1 19.600 71.000 250 133.200 97.000 3.000 4.595 92 \n", " (15, 10)\n" ] } ], "source": [ "outliers = features[((z > 3) | (z < -3))]\n", "\n", "print(outliers, '\\n', outliers.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's use the interquartile range." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "IQRs:\n", "AGE 20.750\n", "SEX 1.000\n", "BMI 6.075\n", "BP 21.000\n", "S1 45.500\n", "S2 38.450\n", "S3 17.500\n", "S4 2.000\n", "S5 0.721\n", "S6 14.750\n", "dtype: float64\n", "\n", "Cut-off:\n", "AGE 20.750\n", "SEX 1.000\n", "BMI 6.075\n", "BP 21.000\n", "S1 45.500\n", "S2 38.450\n", "S3 17.500\n", "S4 2.000\n", "S5 0.721\n", "S6 14.750\n", "dtype: float64\n", "\n", "\n", "Lower cut-offs:\n", "AGE 17.500\n", "SEX 0.000\n", "BMI 17.125\n", "BP 63.000\n", "S1 118.750\n", "S2 57.600\n", "S3 22.750\n", "S4 1.000\n", "S5 3.556\n", "S6 68.500\n", "dtype: float64\n", "\n", "Upper cut-offs:\n", "AGE 79.750\n", "SEX 3.000\n", "BMI 35.350\n", "BP 126.000\n", "S1 255.250\n", "S2 172.950\n", "S3 75.250\n", "S4 7.000\n", "S5 5.718\n", "S6 112.750\n", "dtype: float64\n", "\n", "125 outliers have been found using IQR cut-offs\n" ] } ], "source": [ "# calculate interquartile range \n", "\n", "q25, q75 = features.quantile(0.25), features.quantile(0.75)\n", "\n", "iqr = q75 - q25\n", "print('IQRs:\\n{}\\n'.format(iqr))\n", "\n", "# calculate the cut-off for the outliers\n", "cut_off = iqr * 1.0\n", "print('Cut-off:\\n{}\\n'.format(cut_off))\n", "\n", "lower, upper = q25 - cut_off, q75 + cut_off\n", "print('\\nLower cut-offs:\\n{}\\n'\n", " '\\nUpper cut-offs:\\n{}'.format(lower, upper))\n", "\n", "# identify outliers\n", "outliers = features[ (features < lower) | (features > upper) ]\n", "\n", "np.set_printoptions(precision=3)\n", "print('\\n{} outliers have been found'\n", " ' using IQR cut-offs'.format(np.sum(outliers.count())))\n", "\n", "#print(outliers[0:50])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlations among data\n", "\n", "The **correlation matrix** is an important tool to understand the correlation between the different features. \n", "\n", "**Correlation coefficients** (often indicated with $r$ or $\\rho$) are used in statistics to measure how strong a relationship is between two variables.\n", "\n", "The values are in [-1,1].\n", "\n", "- 1 indicates a strong positive relationship.\n", "- -1 indicates a strong negative relationship.\n", "- 0 indicates no relationship at all.\n", "\n", "For **linear correlation,** as expressed by **Pearson's coefficient,** \n", "a correlation coefficient of 1 means that for every positive increase in one variable, there is a positive increase of a fixed proportion in the other (e.g., shoe sizes go up in (almost) perfect correlation with foot length).\n", "\n", "A correlation coefficient of -1 means that for every positive increase in one variable, there is a negative decrease of a fixed proportion in the other (e.g., the amount of gas in a tank decreases in (almost) perfect correlation with speed).\n", "\n", "Zero means that for every increase, there isn't a positive or negative increase. The two variables just aren't related.\n", "\n", "\n", "The **absolute value** of the correlation coefficient gives us the relationship strength. The larger the number, the stronger the relationship. " ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "attachments": { "image.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": {}, "source": [ "![image.png](attachment:image.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `corr()` methods offers the possibility to compute correlations according to different statistical estimators setting the named argument `method`.\n", "\n", "- `pearson` : standard correlation coefficient, Pearson's method\n", "\n", "- `kendall` : Kendall Tau correlation coefficient\n", "\n", "- `spearman` : Spearman rank correlation" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " AGE SEX BMI BP S1 S2 S3 S4 S5 S6 \\\n", "AGE 1.000 0.174 0.185 0.335 0.260 0.219 -0.075 0.204 0.271 0.302 \n", "SEX 0.174 1.000 0.088 0.241 0.035 0.143 -0.379 0.332 0.150 0.208 \n", "BMI 0.185 0.088 1.000 0.395 0.250 0.261 -0.367 0.414 0.446 0.389 \n", "BP 0.335 0.241 0.395 1.000 0.242 0.186 -0.179 0.258 0.393 0.390 \n", "S1 0.260 0.035 0.250 0.242 1.000 0.897 0.052 0.542 0.516 0.326 \n", "S2 0.219 0.143 0.261 0.186 0.897 1.000 -0.196 0.660 0.318 0.291 \n", "S3 -0.075 -0.379 -0.367 -0.179 0.052 -0.196 1.000 -0.738 -0.399 -0.274 \n", "S4 0.204 0.332 0.414 0.258 0.542 0.660 -0.738 1.000 0.618 0.417 \n", "S5 0.271 0.150 0.446 0.393 0.516 0.318 -0.399 0.618 1.000 0.465 \n", "S6 0.302 0.208 0.389 0.390 0.326 0.291 -0.274 0.417 0.465 1.000 \n", "Y 0.188 0.043 0.586 0.441 0.212 0.174 -0.395 0.430 0.566 0.382 \n", "\n", " Y \n", "AGE 0.188 \n", "SEX 0.043 \n", "BMI 0.586 \n", "BP 0.441 \n", "S1 0.212 \n", "S2 0.174 \n", "S3 -0.395 \n", "S4 0.430 \n", "S5 0.566 \n", "S6 0.382 \n", "Y 1.000 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 67, "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": [ "import seaborn as sns\n", "\n", "corr = diabetes_orig.corr()\n", "print(corr)\n", "sns.heatmap(corr, \n", " xticklabels=corr.columns, \n", " yticklabels=corr.columns)\n" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " age sex bmi bp s1 s2 s3 s4 \\\n", "age 1.000 0.174 0.185 0.335 0.260 0.219 -0.075 0.204 \n", "sex 0.174 1.000 0.088 0.241 0.035 0.143 -0.379 0.332 \n", "bmi 0.185 0.088 1.000 0.395 0.250 0.261 -0.367 0.414 \n", "bp 0.335 0.241 0.395 1.000 0.242 0.186 -0.179 0.258 \n", "s1 0.260 0.035 0.250 0.242 1.000 0.897 0.052 0.542 \n", "s2 0.219 0.143 0.261 0.186 0.897 1.000 -0.196 0.660 \n", "s3 -0.075 -0.379 -0.367 -0.179 0.052 -0.196 1.000 -0.738 \n", "s4 0.204 0.332 0.414 0.258 0.542 0.660 -0.738 1.000 \n", "s5 0.271 0.150 0.446 0.393 0.516 0.318 -0.399 0.618 \n", "s6 0.302 0.208 0.389 0.390 0.326 0.291 -0.274 0.417 \n", "Progression index 0.188 0.043 0.586 0.441 0.212 0.174 -0.395 0.430 \n", "\n", " s5 s6 Progression index \n", "age 0.271 0.302 0.188 \n", "sex 0.150 0.208 0.043 \n", "bmi 0.446 0.389 0.586 \n", "bp 0.393 0.390 0.441 \n", "s1 0.516 0.326 0.212 \n", "s2 0.318 0.291 0.174 \n", "s3 -0.399 -0.274 -0.395 \n", "s4 0.618 0.417 0.430 \n", "s5 1.000 0.465 0.566 \n", "s6 0.465 1.000 0.382 \n", "Progression index 0.566 0.382 1.000 \n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 68, "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": [ "corr = diabetes_df.corr()\n", "print(corr)\n", "sns.heatmap(corr, \n", " xticklabels=corr.columns, \n", " yticklabels=corr.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a lot of information from the correlation matrix and the visual heatmap representation. \n", "\n", "The **correlation between each feature and the target values** are particularly important to identify redundant features.\n", "\n", "**If two features are highly correlated,** one can be likely removed without affecting the ML performance since they are predictors of each other." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.2" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }