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breast-cancer-risk-estimation.ipynb
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| { | |
| "nbformat": 4, | |
| "nbformat_minor": 0, | |
| "metadata": { | |
| "colab": { | |
| "provenance": [], | |
| "collapsed_sections": [ | |
| "EO06D1IT6Jvq", | |
| "P3TyCHVX_uj-", | |
| "lV0_5q03_x_p", | |
| "sYhcvjewGBXd" | |
| ], | |
| "name": "breast-cancer-risk-estimation.ipynb", | |
| "include_colab_link": true | |
| }, | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3" | |
| }, | |
| "language_info": { | |
| "name": "python" | |
| } | |
| }, | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/sanchezcarlosjr/bfdbf294e8a89e81c005ac9f8a74a413/risk-estimation-dataset-analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Breast Cancer Risk estimation project\n", | |
| "\n", | |
| "Data collection and sharing was supported by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C). You can learn more about the BCSC at: http://www.bcsc-research.org/\n", | |
| "\n", | |
| "[Breast Cancer risk suspicion using data mining techniques at a public Mexican hospital](https://carlos-eduardo-sanchez-torres.sanchezcarlosjr.com/Data-mining-project-Breast-Cancer-risk-suspicion-using-data-mining-techniques-at-a-public-Mexican-h-7a807c1db3b641378180b0c60633c38b) by Carlos Eduardo Sanchez Torres\n", | |
| "\n", | |
| "## How do you reproduce this Jupyter?\n", | |
| "```\n", | |
| "Runtime > Run all\n", | |
| "```" | |
| ], | |
| "metadata": { | |
| "id": "EO06D1IT6Jvq" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "datasets = [\n", | |
| " 'https://www.bcsc-research.org/download_file/view/191/344',\n", | |
| " 'https://www.bcsc-research.org/download_file/view/192/344',\n", | |
| " 'https://www.bcsc-research.org/download_file/view/193/344'\n", | |
| "]\n", | |
| "datasets" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "IrYhQZ7W8tIh", | |
| "outputId": "a15a9957-0ff7-4796-c1b6-1afe5df66eaf" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['https://www.bcsc-research.org/download_file/view/191/344',\n", | |
| " 'https://www.bcsc-research.org/download_file/view/192/344',\n", | |
| " 'https://www.bcsc-research.org/download_file/view/193/344']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 1 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "7ilNPB6h6JKA" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import requests, zipfile, io\n", | |
| "for dataset in datasets:\n", | |
| " r = requests.get(dataset)\n", | |
| " z = zipfile.ZipFile(io.BytesIO(r.content))\n", | |
| " z.extractall(\".\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! ls" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "M0FulzCg83u0", | |
| "outputId": "a660900e-9ed3-44b9-f0f7-a17c570061b7" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "bcsc_risk_factors_summarized1_092020.csv\n", | |
| "bcsc_risk_factors_summarized2_092020.csv\n", | |
| "bcsc_risk_factors_summarized3_092020.csv\n", | |
| "sample_data\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "df = pd.concat(map(pd.read_csv, \n", | |
| " ['bcsc_risk_factors_summarized1_092020.csv', \n", | |
| " 'bcsc_risk_factors_summarized2_092020.csv',\n", | |
| " 'bcsc_risk_factors_summarized3_092020.csv'\n", | |
| " ]\n", | |
| " ), ignore_index=True)" | |
| ], | |
| "metadata": { | |
| "id": "93K8SGLt303D" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 488 | |
| }, | |
| "id": "PNsveNtV9GNO", | |
| "outputId": "72702ac1-ed35-4eca-f6a2-05b59a28adb1" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " year age_group_5_years race_eth first_degree_hx age_menarche \\\n", | |
| "0 2013 7 1 0 9 \n", | |
| "1 2013 7 1 0 9 \n", | |
| "2 2013 7 1 0 9 \n", | |
| "3 2013 7 1 0 9 \n", | |
| "4 2013 7 1 0 9 \n", | |
| "... ... ... ... ... ... \n", | |
| "1522335 2013 7 1 0 9 \n", | |
| "1522336 2013 7 1 0 9 \n", | |
| "1522337 2013 7 1 0 9 \n", | |
| "1522338 2013 7 1 0 9 \n", | |
| "1522339 2013 7 1 0 9 \n", | |
| "\n", | |
| " age_first_birth BIRADS_breast_density current_hrt menopaus \\\n", | |
| "0 3 1 1 2 \n", | |
| "1 3 1 1 2 \n", | |
| "2 3 1 1 2 \n", | |
| "3 3 1 1 2 \n", | |
| "4 3 1 1 2 \n", | |
| "... ... ... ... ... \n", | |
| "1522335 3 1 0 9 \n", | |
| "1522336 3 1 1 2 \n", | |
| "1522337 3 1 1 2 \n", | |
| "1522338 3 1 1 2 \n", | |
| "1522339 3 1 1 2 \n", | |
| "\n", | |
| " bmi_group biophx breast_cancer_history count \n", | |
| "0 3 0 0 7 \n", | |
| "1 3 1 0 3 \n", | |
| "2 4 0 0 6 \n", | |
| "3 4 1 0 1 \n", | |
| "4 4 1 1 1 \n", | |
| "... ... ... ... ... \n", | |
| "1522335 9 1 1 1 \n", | |
| "1522336 1 0 0 8 \n", | |
| "1522337 1 1 0 1 \n", | |
| "1522338 2 0 0 12 \n", | |
| "1522339 2 1 0 5 \n", | |
| "\n", | |
| "[1522340 rows x 13 columns]" | |
| ], | |
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| "\n", | |
| " <div id=\"df-d8b078f3-ccf4-4c03-86bd-2c9a4fb2296d\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>year</th>\n", | |
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| " <th>first_degree_hx</th>\n", | |
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| " <th>age_first_birth</th>\n", | |
| " <th>BIRADS_breast_density</th>\n", | |
| " <th>current_hrt</th>\n", | |
| " <th>menopaus</th>\n", | |
| " <th>bmi_group</th>\n", | |
| " <th>biophx</th>\n", | |
| " <th>breast_cancer_history</th>\n", | |
| " <th>count</th>\n", | |
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| " <td>1</td>\n", | |
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| " <td>2</td>\n", | |
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| " <td>0</td>\n", | |
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| " <td>0</td>\n", | |
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| " </tbody>\n", | |
| "</table>\n", | |
| "<p>1522340 rows × 13 columns</p>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8b078f3-ccf4-4c03-86bd-2c9a4fb2296d')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-d8b078f3-ccf4-4c03-86bd-2c9a4fb2296d button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-d8b078f3-ccf4-4c03-86bd-2c9a4fb2296d');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 5 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Data understanding" | |
| ], | |
| "metadata": { | |
| "id": "zSIPZnMPGGY4" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.apply(lambda x: x.unique())" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OO0VHovfjfXW", | |
| "outputId": "c16f1439-f6d1-4b3a-dbe4-6cdd0ac924ab" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "year [2013, 2014, 2015, 2016, 2017, 2005, 2006, 200...\n", | |
| "age_group_5_years [7, 8, 9, 10, 11, 12, 13, 1, 2, 3, 4, 5, 6]\n", | |
| "race_eth [1, 2, 3, 4, 5, 6, 9]\n", | |
| "first_degree_hx [0, 1, 9]\n", | |
| "age_menarche [9, 0, 1, 2]\n", | |
| "age_first_birth [3, 4, 9, 0, 1, 2]\n", | |
| "BIRADS_breast_density [1, 2, 3, 4, 9]\n", | |
| "current_hrt [1, 9, 0]\n", | |
| "menopaus [2, 1, 9, 3]\n", | |
| "bmi_group [3, 4, 9, 1, 2]\n", | |
| "biophx [0, 1, 9]\n", | |
| "breast_cancer_history [0, 1, 9]\n", | |
| "count [7, 3, 6, 1, 2, 5, 20, 9, 18, 10, 412, 19, 89,...\n", | |
| "dtype: object" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 6 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "bmi_vs_birads = pd.crosstab(df['bmi_group'],\n", | |
| " df['BIRADS_breast_density'], \n", | |
| " margins = False)\n", | |
| "bmi_vs_birads.plot.line(ylabel='women');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 54 | |
| }, | |
| "id": "zaSPzwXFs2y2", | |
| "outputId": "77eda8d2-6e7a-416c-8f00-2163235c1eda" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Data visualization" | |
| ], | |
| "metadata": { | |
| "id": "bU7atk74_rAp" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.describe()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 364 | |
| }, | |
| "id": "hQsiOMTJ9goP", | |
| "outputId": "d8db9ff1-a5ef-4564-d868-4ceb14447ef4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " year age_group_5_years race_eth first_degree_hx \\\n", | |
| "count 1.522340e+06 1.522340e+06 1.522340e+06 1.522340e+06 \n", | |
| "mean 2.010890e+03 7.299497e+00 2.659045e+00 7.925976e-01 \n", | |
| "std 3.663113e+00 2.552748e+00 2.276457e+00 2.082614e+00 \n", | |
| "min 2.005000e+03 1.000000e+00 1.000000e+00 0.000000e+00 \n", | |
| "25% 2.008000e+03 5.000000e+00 1.000000e+00 0.000000e+00 \n", | |
| "50% 2.011000e+03 7.000000e+00 2.000000e+00 0.000000e+00 \n", | |
| "75% 2.014000e+03 9.000000e+00 3.000000e+00 1.000000e+00 \n", | |
| "max 2.017000e+03 1.300000e+01 9.000000e+00 9.000000e+00 \n", | |
| "\n", | |
| " age_menarche age_first_birth BIRADS_breast_density current_hrt \\\n", | |
| "count 1.522340e+06 1.522340e+06 1.522340e+06 1.522340e+06 \n", | |
| "mean 4.587934e+00 3.543348e+00 3.219309e+00 1.934484e+00 \n", | |
| "std 4.046724e+00 3.150968e+00 2.265064e+00 3.610054e+00 \n", | |
| "min 0.000000e+00 0.000000e+00 1.000000e+00 0.000000e+00 \n", | |
| "25% 1.000000e+00 1.000000e+00 2.000000e+00 0.000000e+00 \n", | |
| "50% 2.000000e+00 3.000000e+00 3.000000e+00 0.000000e+00 \n", | |
| "75% 9.000000e+00 4.000000e+00 3.000000e+00 1.000000e+00 \n", | |
| "max 9.000000e+00 9.000000e+00 9.000000e+00 9.000000e+00 \n", | |
| "\n", | |
| " menopaus bmi_group biophx breast_cancer_history \\\n", | |
| "count 1.522340e+06 1.522340e+06 1.522340e+06 1.522340e+06 \n", | |
| "mean 2.635728e+00 4.261685e+00 7.441419e-01 2.101958e+00 \n", | |
| "std 2.311327e+00 3.243548e+00 1.789292e+00 3.651569e+00 \n", | |
| "min 1.000000e+00 1.000000e+00 0.000000e+00 0.000000e+00 \n", | |
| "25% 2.000000e+00 2.000000e+00 0.000000e+00 0.000000e+00 \n", | |
| "50% 2.000000e+00 3.000000e+00 0.000000e+00 0.000000e+00 \n", | |
| "75% 2.000000e+00 9.000000e+00 1.000000e+00 1.000000e+00 \n", | |
| "max 9.000000e+00 9.000000e+00 9.000000e+00 9.000000e+00 \n", | |
| "\n", | |
| " count \n", | |
| "count 1.522340e+06 \n", | |
| "mean 4.459211e+00 \n", | |
| "std 1.880898e+01 \n", | |
| "min 1.000000e+00 \n", | |
| "25% 1.000000e+00 \n", | |
| "50% 1.000000e+00 \n", | |
| "75% 3.000000e+00 \n", | |
| "max 2.684000e+03 " | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-da88b8c4-67bc-41ef-a550-7a0a677d6329\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>year</th>\n", | |
| " <th>age_group_5_years</th>\n", | |
| " <th>race_eth</th>\n", | |
| " <th>first_degree_hx</th>\n", | |
| " <th>age_menarche</th>\n", | |
| " <th>age_first_birth</th>\n", | |
| " <th>BIRADS_breast_density</th>\n", | |
| " <th>current_hrt</th>\n", | |
| " <th>menopaus</th>\n", | |
| " <th>bmi_group</th>\n", | |
| " <th>biophx</th>\n", | |
| " <th>breast_cancer_history</th>\n", | |
| " <th>count</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>count</th>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " <td>1.522340e+06</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>mean</th>\n", | |
| " <td>2.010890e+03</td>\n", | |
| " <td>7.299497e+00</td>\n", | |
| " <td>2.659045e+00</td>\n", | |
| " <td>7.925976e-01</td>\n", | |
| " <td>4.587934e+00</td>\n", | |
| " <td>3.543348e+00</td>\n", | |
| " <td>3.219309e+00</td>\n", | |
| " <td>1.934484e+00</td>\n", | |
| " <td>2.635728e+00</td>\n", | |
| " <td>4.261685e+00</td>\n", | |
| " <td>7.441419e-01</td>\n", | |
| " <td>2.101958e+00</td>\n", | |
| " <td>4.459211e+00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>std</th>\n", | |
| " <td>3.663113e+00</td>\n", | |
| " <td>2.552748e+00</td>\n", | |
| " <td>2.276457e+00</td>\n", | |
| " <td>2.082614e+00</td>\n", | |
| " <td>4.046724e+00</td>\n", | |
| " <td>3.150968e+00</td>\n", | |
| " <td>2.265064e+00</td>\n", | |
| " <td>3.610054e+00</td>\n", | |
| " <td>2.311327e+00</td>\n", | |
| " <td>3.243548e+00</td>\n", | |
| " <td>1.789292e+00</td>\n", | |
| " <td>3.651569e+00</td>\n", | |
| " <td>1.880898e+01</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>min</th>\n", | |
| " <td>2.005000e+03</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25%</th>\n", | |
| " <td>2.008000e+03</td>\n", | |
| " <td>5.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>50%</th>\n", | |
| " <td>2.011000e+03</td>\n", | |
| " <td>7.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>0.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>75%</th>\n", | |
| " <td>2.014000e+03</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>4.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>2.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>1.000000e+00</td>\n", | |
| " <td>3.000000e+00</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>max</th>\n", | |
| " <td>2.017000e+03</td>\n", | |
| " <td>1.300000e+01</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>9.000000e+00</td>\n", | |
| " <td>2.684000e+03</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da88b8c4-67bc-41ef-a550-7a0a677d6329')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
| " style=\"display:none;\">\n", | |
| " \n", | |
| " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", | |
| " width=\"24px\">\n", | |
| " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n", | |
| " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n", | |
| " </svg>\n", | |
| " </button>\n", | |
| " \n", | |
| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
| " display: none;\n", | |
| " fill: #1967D2;\n", | |
| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
| " fill: #D2E3FC;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
| " }\n", | |
| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-da88b8c4-67bc-41ef-a550-7a0a677d6329 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-da88b8c4-67bc-41ef-a550-7a0a677d6329');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
| " }\n", | |
| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.hist(column='age_group_5_years');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "id": "gXCdY4c1c9nM", | |
| "outputId": "eae3b763-1fbf-42c5-a4f4-83854c6f19df" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.hist(column='BIRADS_breast_density');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "id": "Uc9bipsLeosG", | |
| "outputId": "3ade4718-8755-48d6-fb1f-d48313a00c53" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.hist(column='count');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 281 | |
| }, | |
| "id": "q4MSrrnvezIJ", | |
| "outputId": "c000c565-136d-4550-8bc5-ea97ce0b5a96" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "df.hist(figsize=(20,30), bins=20, layout=(15,4));" | |
| ], | |
| "metadata": { | |
| "id": "PtqhJT3s90NG", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 480 | |
| }, | |
| "outputId": "e927b169-69b3-417e-943e-e7e2fbdfd44d" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 1440x2160 with 60 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Age vs BIRADS" | |
| ], | |
| "metadata": { | |
| "id": "P3TyCHVX_uj-" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "age_vs_birads = pd.crosstab(df['age_group_5_years'],\n", | |
| " df['BIRADS_breast_density'], \n", | |
| " margins = False)\n", | |
| "age_vs_birads.plot.line(ylabel='women');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 54 | |
| }, | |
| "id": "jk-_Xqr9qaHZ", | |
| "outputId": "dfbc1212-8ee2-477c-91a8-d362d1838e13" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" 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| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "age_vs_birads = pd.crosstab(df['age_group_5_years'],\n", | |
| " df['BIRADS_breast_density'], \n", | |
| " margins = False)\n", | |
| "\n", | |
| "age_vs_birads" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 553 | |
| }, | |
| "id": "uME0daCwAzUk", | |
| "outputId": "0453a67a-92b4-4741-929c-d6beb0cd9b52" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "BIRADS_breast_density 1 2 3 4 9\n", | |
| "age_group_5_years \n", | |
| "1 385 1143 1786 1603 978\n", | |
| "2 1136 4433 6354 5279 2371\n", | |
| "3 3129 12258 16570 11200 5472\n", | |
| "4 10268 33373 43300 19193 13889\n", | |
| "5 16335 54536 69646 26522 22154\n", | |
| "6 26307 83441 84741 28898 29523\n", | |
| "7 31762 91292 79140 22333 29669\n", | |
| "8 24946 55741 47024 13378 20698\n", | |
| "9 24455 52526 41995 10569 17592\n", | |
| "10 20529 45428 34282 7498 13884\n", | |
| "11 15772 36559 25975 5227 10645\n", | |
| "12 10683 26290 17821 3588 7390\n", | |
| "13 6186 17005 11169 2320 4746" | |
| ], | |
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| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th>BIRADS_breast_density</th>\n", | |
| " <th>1</th>\n", | |
| " <th>2</th>\n", | |
| " <th>3</th>\n", | |
| " <th>4</th>\n", | |
| " <th>9</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>age_group_5_years</th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>385</td>\n", | |
| " <td>1143</td>\n", | |
| " <td>1786</td>\n", | |
| " <td>1603</td>\n", | |
| " <td>978</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>1136</td>\n", | |
| " <td>4433</td>\n", | |
| " <td>6354</td>\n", | |
| " <td>5279</td>\n", | |
| " <td>2371</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>3129</td>\n", | |
| " <td>12258</td>\n", | |
| " <td>16570</td>\n", | |
| " <td>11200</td>\n", | |
| " <td>5472</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>10268</td>\n", | |
| " <td>33373</td>\n", | |
| " <td>43300</td>\n", | |
| " <td>19193</td>\n", | |
| " <td>13889</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>16335</td>\n", | |
| " <td>54536</td>\n", | |
| " <td>69646</td>\n", | |
| " <td>26522</td>\n", | |
| " <td>22154</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>26307</td>\n", | |
| " <td>83441</td>\n", | |
| " <td>84741</td>\n", | |
| " <td>28898</td>\n", | |
| " <td>29523</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>31762</td>\n", | |
| " <td>91292</td>\n", | |
| " <td>79140</td>\n", | |
| " <td>22333</td>\n", | |
| " <td>29669</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>24946</td>\n", | |
| " <td>55741</td>\n", | |
| " <td>47024</td>\n", | |
| " <td>13378</td>\n", | |
| " <td>20698</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>24455</td>\n", | |
| " <td>52526</td>\n", | |
| " <td>41995</td>\n", | |
| " <td>10569</td>\n", | |
| " <td>17592</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>20529</td>\n", | |
| " <td>45428</td>\n", | |
| " <td>34282</td>\n", | |
| " <td>7498</td>\n", | |
| " <td>13884</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>15772</td>\n", | |
| " <td>36559</td>\n", | |
| " <td>25975</td>\n", | |
| " <td>5227</td>\n", | |
| " <td>10645</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>10683</td>\n", | |
| " <td>26290</td>\n", | |
| " <td>17821</td>\n", | |
| " <td>3588</td>\n", | |
| " <td>7390</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>6186</td>\n", | |
| " <td>17005</td>\n", | |
| " <td>11169</td>\n", | |
| " <td>2320</td>\n", | |
| " <td>4746</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>\n", | |
| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f83cfac-c0d8-490e-b9b4-60758374bed8')\"\n", | |
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| " <style>\n", | |
| " .colab-df-container {\n", | |
| " display:flex;\n", | |
| " flex-wrap:wrap;\n", | |
| " gap: 12px;\n", | |
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| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
| " width: 32px;\n", | |
| " }\n", | |
| "\n", | |
| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
| " fill: #174EA6;\n", | |
| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
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| " }\n", | |
| "\n", | |
| " [theme=dark] .colab-df-convert:hover {\n", | |
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| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-9f83cfac-c0d8-490e-b9b4-60758374bed8 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-9f83cfac-c0d8-490e-b9b4-60758374bed8');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
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| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from scipy.stats import chi2_contingency \n", | |
| "\n", | |
| "\n", | |
| "stat, p, degress_of_freedom, _ = chi2_contingency(age_vs_birads.to_numpy()) \n", | |
| " \n", | |
| "print(degress_of_freedom)\n", | |
| " \n", | |
| "significance_level = 0.05 # 100%-95%\n", | |
| "print(\"p value: \" + str(p)) \n", | |
| "if p <= significance_level:\n", | |
| " print('We reject null hypothesis, so we accept the alternative hypothesis --variables have a correlation.') \n", | |
| "else: \n", | |
| " print('We accept null hypothesis, variables doesn\\'t have a correlation. ') " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "clIdTPQF_ioz", | |
| "outputId": "03c185ac-f924-4a50-901a-2d8b6139a2a7" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "48\n", | |
| "p value: 0.0\n", | |
| "We reject null hypothesis, so we accept the alternative hypothesis --variables have a correlation.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Year vs BIRADS" | |
| ], | |
| "metadata": { | |
| "id": "lV0_5q03_x_p" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "data_crosstab = pd.crosstab(df['year'],\n", | |
| " df['BIRADS_breast_density'], \n", | |
| " margins = False)\n", | |
| "data_crosstab.plot.line(ylabel='women');" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 54 | |
| }, | |
| "id": "Qa_gZB9useeD", | |
| "outputId": "be73f690-22f5-4be8-c34a-28eeb46327de" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# BMI VS BIRADS" | |
| ], | |
| "metadata": { | |
| "id": "sYhcvjewGBXd" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from scipy.stats import chi2_contingency \n", | |
| "\n", | |
| "\n", | |
| "stat, p, degress_of_freedom, _ = chi2_contingency(bmi_vs_birads.to_numpy()) \n", | |
| " \n", | |
| "print(degress_of_freedom)\n", | |
| " \n", | |
| "significance_level = 0.05 # 100%-95%\n", | |
| "print(\"p value: \" + str(p)) \n", | |
| "if p <= significance_level:\n", | |
| " print('We reject null hypothesis, so we accept the alternative hypothesis --variables have a correlation.') \n", | |
| "else: \n", | |
| " print('We accept null hypothesis, variables doesn\\'t have a correlation. ') " | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "xi99qhh6GFbT", | |
| "outputId": "197ed055-d6b1-4704-c0a8-cc5a17cc5ed4" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "16\n", | |
| "p value: 0.0\n", | |
| "We reject null hypothesis, so we accept the alternative hypothesis --variables have a correlation.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Insights" | |
| ], | |
| "metadata": { | |
| "id": "ePUfN1_Dbfh_" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "BIRADS breast density values are 1,2,3, and 9. Histogram \\ref{fig:birads-breast-density} has mean and standard deviation $3.21\\pm 2.26$ density, so the largest percentage of the population of women with BIRADS breast density 2 (heterogeneously dense) is $33.76\\%$. The margin of sampling error is $\\pm 0.0751\\%$ with a $95\\%$ level of confidence." | |
| ], | |
| "metadata": { | |
| "id": "NjVhj5etIe53" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "intervals=[len(df[df['BIRADS_breast_density'] == i])/len(df) for i in range(0, 10)]\n", | |
| "intervals" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uH28eXyjlCSK", | |
| "outputId": "d67ea58b-0306-42ba-867c-6ba8719ad04a" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[0.0,\n", | |
| " 0.12605134201295373,\n", | |
| " 0.33765453183914235,\n", | |
| " 0.3151746653178659,\n", | |
| " 0.10353009183231078,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.11758936899772718]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 18 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import math\n", | |
| "list(map(lambda p: 1.96*math.sqrt(p*(1-p)/len(df)), intervals))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "S3U9wFwLlNga", | |
| "outputId": "5fdb36c6-e259-4365-a46f-5d8f2b45cdc5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[0.0,\n", | |
| " 0.0005272504253476445,\n", | |
| " 0.0007512401972661614,\n", | |
| " 0.0007380160408339917,\n", | |
| " 0.0004839510090113622,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.0,\n", | |
| " 0.0005117049731053823]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 19 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Age" | |
| ], | |
| "metadata": { | |
| "id": "376tCxbEqgJm" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "intervals=[len(df[df['age_group_5_years'] == i])/len(df) for i in range(1, 14)]\n", | |
| "intervals" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "WZlH5_lWqg54", | |
| "outputId": "5e0601ef-78a0-48d9-c78c-1d2351901859" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[0.0038723281264369325,\n", | |
| " 0.012857180393341829,\n", | |
| " 0.031943586846565154,\n", | |
| " 0.07884112616104155,\n", | |
| " 0.12427775661153224,\n", | |
| " 0.16613240143463354,\n", | |
| " 0.16697715359249576,\n", | |
| " 0.10627520790362205,\n", | |
| " 0.09665186489220542,\n", | |
| " 0.07989082596529028,\n", | |
| " 0.06186397256854579,\n", | |
| " 0.043204540378627636,\n", | |
| " 0.02721205512566181]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 20 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import math\n", | |
| "errors=list(map(lambda p: 1.96*math.sqrt(p*(1-p)/len(df)), intervals))\n", | |
| "errors" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "SGM41HsIrhrU", | |
| "outputId": "9f4d8368-b327-44a1-c194-8bdd515f0973" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "[9.866061531027916e-05,\n", | |
| " 0.00017896294013899404,\n", | |
| " 0.00027934597313382956,\n", | |
| " 0.0004280987123977315,\n", | |
| " 0.0005240589410676474,\n", | |
| " 0.0005912565471220409,\n", | |
| " 0.0005924575318247272,\n", | |
| " 0.0004895737529554526,\n", | |
| " 0.00046938903176779354,\n", | |
| " 0.00043069356006051864,\n", | |
| " 0.0003826943509727114,\n", | |
| " 0.00032297915169511105,\n", | |
| " 0.0002584580140954284]" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 21 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "errors[6] == max(errors)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "uSPTlHKSr8xL", | |
| "outputId": "ebdcb9d5-64f1-4d88-a418-06c65d6ab9f0" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "True" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 22 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Forest tree" | |
| ], | |
| "metadata": { | |
| "id": "wIsngnWfNieK" | |
| } | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "## Gini" | |
| ], | |
| "metadata": { | |
| "id": "vvw_F85wOK7i" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas\n", | |
| "from sklearn import tree\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.tree import DecisionTreeClassifier\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "features = list(df.columns.drop(['year', 'BIRADS_breast_density']))\n", | |
| "\n", | |
| "X = df[features]\n", | |
| "Y = df['BIRADS_breast_density']\n", | |
| "X_train, test_x, y_train, test_y = train_test_split(X,Y, test_size = 0.2)\n", | |
| "dtree = DecisionTreeClassifier()\n", | |
| "dtree = dtree.fit(X_train, y_train)" | |
| ], | |
| "metadata": { | |
| "id": "O_P3cAvmxSge" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np\n", | |
| "feature_importance = dtree.tree_.compute_feature_importances(normalize=True)\n", | |
| "df_features = pd.DataFrame(np.array([feature_importance, features]).T, columns=['Importance', 'Feature']).infer_objects().sort_values(by='Importance', ascending=False)[['Feature', 'Importance']]\n", | |
| "df_features" | |
| ], | |
| "metadata": { | |
| "id": "yAh_Q0bS8HWN", | |
| "outputId": "717997a4-0899-4324-e43c-d05f74ab4e88", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 394 | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Feature Importance\n", | |
| "0 age_group_5_years 0.1967111632125812\n", | |
| "10 count 0.18027246491611515\n", | |
| "4 age_first_birth 0.14883589011403126\n", | |
| "3 age_menarche 0.08886141856450767\n", | |
| "1 race_eth 0.07540218609781281\n", | |
| "6 menopaus 0.06707746683299348\n", | |
| "7 bmi_group 0.06060052421418999\n", | |
| "8 biophx 0.05585093727319101\n", | |
| "2 first_degree_hx 0.05318650635534024\n", | |
| "5 current_hrt 0.04121993664716855\n", | |
| "9 breast_cancer_history 0.03198150577206851" | |
| ], | |
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| " <div>\n", | |
| "<style scoped>\n", | |
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| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
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| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Feature</th>\n", | |
| " <th>Importance</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>age_group_5_years</td>\n", | |
| " <td>0.1967111632125812</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>count</td>\n", | |
| " <td>0.18027246491611515</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>age_first_birth</td>\n", | |
| " <td>0.14883589011403126</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>age_menarche</td>\n", | |
| " <td>0.08886141856450767</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>race_eth</td>\n", | |
| " <td>0.07540218609781281</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>menopaus</td>\n", | |
| " <td>0.06707746683299348</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>bmi_group</td>\n", | |
| " <td>0.06060052421418999</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>biophx</td>\n", | |
| " <td>0.05585093727319101</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>first_degree_hx</td>\n", | |
| " <td>0.05318650635534024</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>current_hrt</td>\n", | |
| " <td>0.04121993664716855</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>breast_cancer_history</td>\n", | |
| " <td>0.03198150577206851</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
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| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5714dd4-d5ce-46cb-9893-739bbd4e7ac0')\"\n", | |
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| " const element = document.querySelector('#df-b5714dd4-d5ce-46cb-9893-739bbd4e7ac0');\n", | |
| " const dataTable =\n", | |
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| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
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| "metadata": {}, | |
| "execution_count": 24 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import graphviz \n", | |
| "dot_data = tree.export_graphviz(dtree, out_file=None, max_depth=1) \n", | |
| "graph = graphviz.Source(dot_data) \n", | |
| "graph" | |
| ], | |
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| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
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| "outputId": "f0334222-a691-4ba5-e0cc-d0ee97c83930" | |
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| "execution_count": null, | |
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| { | |
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| }, | |
| "metadata": {}, | |
| "execution_count": 25 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn import metrics\n", | |
| "\n", | |
| "import seaborn as sns\n", | |
| "\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "confusion_matrix = metrics.confusion_matrix(test_y, dtree.predict(test_x))\n", | |
| "\n", | |
| "matrix_df = pd.DataFrame(confusion_matrix)\n", | |
| "\n", | |
| "ax = plt.axes()\n", | |
| "\n", | |
| "sns.set(font_scale=1.3)\n", | |
| "\n", | |
| "plt.figure(figsize=(10,7))\n", | |
| "\n", | |
| "sns.heatmap(matrix_df, annot=True, fmt=\"g\", ax=ax, cmap=\"magma\")\n", | |
| "\n", | |
| "ax.set_title('Confusion Matrix - Decision Tree')\n", | |
| "\n", | |
| "ax.set_xlabel(\"Predicted label\", fontsize =15)\n", | |
| "\n", | |
| "ax.set_ylabel(\"True Label\", fontsize=15)\n", | |
| "\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 321 | |
| }, | |
| "id": "lgNxqG7W5K7H", | |
| "outputId": "70ed07b2-f88f-491f-e278-042e6bda0205" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
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| |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 720x504 with 0 Axes>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "matrix_df" | |
| ], | |
| "metadata": { | |
| "id": "cwehDaIIFopE", | |
| "outputId": "141df580-3cf8-4c56-9264-f470de8edde3", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 206 | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " 0 1 2 3 4\n", | |
| "0 13262 11801 7779 2991 2486\n", | |
| "1 13078 51686 28292 4321 5614\n", | |
| "2 11420 37529 36155 5334 5341\n", | |
| "3 5019 8279 8746 7296 2052\n", | |
| "4 6318 11676 9238 3182 5573" | |
| ], | |
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| " <div id=\"df-d1dac063-834d-4479-8e54-faec2cb72295\">\n", | |
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| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
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| " <th>3</th>\n", | |
| " <th>4</th>\n", | |
| " </tr>\n", | |
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| " <tbody>\n", | |
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| " <td>2486</td>\n", | |
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| " <tr>\n", | |
| " <th>1</th>\n", | |
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| " <td>37529</td>\n", | |
| " <td>36155</td>\n", | |
| " <td>5334</td>\n", | |
| " <td>5341</td>\n", | |
| " </tr>\n", | |
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| " <th>3</th>\n", | |
| " <td>5019</td>\n", | |
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| " <td>7296</td>\n", | |
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| " <th>4</th>\n", | |
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| " <td>11676</td>\n", | |
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| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1dac063-834d-4479-8e54-faec2cb72295')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
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| " .colab-df-container {\n", | |
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| " gap: 12px;\n", | |
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| "\n", | |
| " .colab-df-convert {\n", | |
| " background-color: #E8F0FE;\n", | |
| " border: none;\n", | |
| " border-radius: 50%;\n", | |
| " cursor: pointer;\n", | |
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| " height: 32px;\n", | |
| " padding: 0 0 0 0;\n", | |
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| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
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| " fill: #FFFFFF;\n", | |
| " }\n", | |
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| "\n", | |
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| " buttonEl.style.display =\n", | |
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| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-d1dac063-834d-4479-8e54-faec2cb72295');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
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| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
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| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.metrics import accuracy_score\n", | |
| "accuracy_score(test_y, dtree.predict(test_x))" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "b2KjsYGdMHuT", | |
| "outputId": "7478821c-2664-4a28-856c-16eae37f3459" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.3743316210570569" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 28 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# Gradient Boosting Classifier" | |
| ], | |
| "metadata": { | |
| "id": "-yQHap1kLqPI" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas\n", | |
| "from sklearn import tree\n", | |
| "from sklearn.model_selection import train_test_split\n", | |
| "from sklearn.tree import DecisionTreeClassifier\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "features = list(df.columns.drop(['year', 'BIRADS_breast_density']))\n", | |
| "\n", | |
| "X = df[features]\n", | |
| "Y = df['BIRADS_breast_density']\n", | |
| "X_train, test_x, y_train, test_y = train_test_split(X,Y, test_size = 0.2)" | |
| ], | |
| "metadata": { | |
| "id": "vDcUEROlLwmS" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.datasets import make_hastie_10_2\n", | |
| "from sklearn.ensemble import GradientBoostingClassifier\n", | |
| "clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0).fit(X_train, y_train)" | |
| ], | |
| "metadata": { | |
| "id": "DGDA0sa-L6iT" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import numpy as np\n", | |
| "feature_importance = clf.feature_importances_\n", | |
| "df_features = pd.DataFrame(np.array([feature_importance, features]).T, columns=['Importance', 'Feature']).infer_objects().sort_values(by='Importance', ascending=False)[['Feature', 'Importance']]\n", | |
| "df_features" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 394 | |
| }, | |
| "id": "2Z_cZCnvMThr", | |
| "outputId": "93cbf1c1-2001-4494-8213-f8891377b5ae" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| " Feature Importance\n", | |
| "7 bmi_group 0.20963663426558438\n", | |
| "0 age_group_5_years 0.15974443095662982\n", | |
| "1 race_eth 0.11017433253232582\n", | |
| "3 age_menarche 0.10202748751286943\n", | |
| "4 age_first_birth 0.10093116910937996\n", | |
| "10 count 0.09655565791616803\n", | |
| "9 breast_cancer_history 0.07362735151157536\n", | |
| "6 menopaus 0.05191713473405378\n", | |
| "8 biophx 0.032926011265828455\n", | |
| "2 first_degree_hx 0.03233503894451464\n", | |
| "5 current_hrt 0.030124751251070506" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div id=\"df-3cfd8fca-fd10-4770-b1c0-6c0abc007114\">\n", | |
| " <div class=\"colab-df-container\">\n", | |
| " <div>\n", | |
| "<style scoped>\n", | |
| " .dataframe tbody tr th:only-of-type {\n", | |
| " vertical-align: middle;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe tbody tr th {\n", | |
| " vertical-align: top;\n", | |
| " }\n", | |
| "\n", | |
| " .dataframe thead th {\n", | |
| " text-align: right;\n", | |
| " }\n", | |
| "</style>\n", | |
| "<table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: right;\">\n", | |
| " <th></th>\n", | |
| " <th>Feature</th>\n", | |
| " <th>Importance</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>bmi_group</td>\n", | |
| " <td>0.20963663426558438</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>age_group_5_years</td>\n", | |
| " <td>0.15974443095662982</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>race_eth</td>\n", | |
| " <td>0.11017433253232582</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>age_menarche</td>\n", | |
| " <td>0.10202748751286943</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>age_first_birth</td>\n", | |
| " <td>0.10093116910937996</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>count</td>\n", | |
| " <td>0.09655565791616803</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>breast_cancer_history</td>\n", | |
| " <td>0.07362735151157536</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>menopaus</td>\n", | |
| " <td>0.05191713473405378</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>biophx</td>\n", | |
| " <td>0.032926011265828455</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>first_degree_hx</td>\n", | |
| " <td>0.03233503894451464</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>current_hrt</td>\n", | |
| " <td>0.030124751251070506</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
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| " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3cfd8fca-fd10-4770-b1c0-6c0abc007114')\"\n", | |
| " title=\"Convert this dataframe to an interactive table.\"\n", | |
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| " \n", | |
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| " .colab-df-convert:hover {\n", | |
| " background-color: #E2EBFA;\n", | |
| " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", | |
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| "\n", | |
| " [theme=dark] .colab-df-convert {\n", | |
| " background-color: #3B4455;\n", | |
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| " [theme=dark] .colab-df-convert:hover {\n", | |
| " background-color: #434B5C;\n", | |
| " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", | |
| " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", | |
| " fill: #FFFFFF;\n", | |
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| " </style>\n", | |
| "\n", | |
| " <script>\n", | |
| " const buttonEl =\n", | |
| " document.querySelector('#df-3cfd8fca-fd10-4770-b1c0-6c0abc007114 button.colab-df-convert');\n", | |
| " buttonEl.style.display =\n", | |
| " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", | |
| "\n", | |
| " async function convertToInteractive(key) {\n", | |
| " const element = document.querySelector('#df-3cfd8fca-fd10-4770-b1c0-6c0abc007114');\n", | |
| " const dataTable =\n", | |
| " await google.colab.kernel.invokeFunction('convertToInteractive',\n", | |
| " [key], {});\n", | |
| " if (!dataTable) return;\n", | |
| "\n", | |
| " const docLinkHtml = 'Like what you see? Visit the ' +\n", | |
| " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", | |
| " + ' to learn more about interactive tables.';\n", | |
| " element.innerHTML = '';\n", | |
| " dataTable['output_type'] = 'display_data';\n", | |
| " await google.colab.output.renderOutput(dataTable, element);\n", | |
| " const docLink = document.createElement('div');\n", | |
| " docLink.innerHTML = docLinkHtml;\n", | |
| " element.appendChild(docLink);\n", | |
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| " </script>\n", | |
| " </div>\n", | |
| " </div>\n", | |
| " " | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 31 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn import metrics\n", | |
| "import seaborn as sns\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "confusion_matrix = metrics.confusion_matrix(test_y, clf.predict(test_x))\n", | |
| "matrix_df = pd.DataFrame(confusion_matrix)" | |
| ], | |
| "metadata": { | |
| "id": "yW3MYMGIMqjR" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "ax = plt.axes()\n", | |
| "sns.set(font_scale=1.3)\n", | |
| "plt.figure(figsize=(10,7))\n", | |
| "sns.heatmap(matrix_df, annot=True, fmt=\"g\", ax=ax, cmap=\"magma\")\n", | |
| "\n", | |
| "ax.set_title('Confusion Matrix - Decision Tree')\n", | |
| "ax.set_xlabel(\"Predicted label\", fontsize =15)\n", | |
| "ax.set_ylabel(\"True Label\", fontsize=15)\n", | |
| "\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "id": "dXf2nc8RNMKc", | |
| "outputId": "2c74cf2b-73ba-4d9f-b3cb-16ef814a000b", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 328 | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 720x504 with 0 Axes>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.metrics import accuracy_score\n", | |
| "accuracy_score(test_y, clf.predict(test_x))" | |
| ], | |
| "metadata": { | |
| "id": "le7jxZTAMkNW", | |
| "outputId": "f4888b3f-0dcc-4fea-8f14-b5bf9d113fdd", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| } | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "0.39366041751514114" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 34 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# TODO: Neuronal networks" | |
| ], | |
| "metadata": { | |
| "id": "jVchPMk7RSwt" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from sklearn.neural_network import MLPClassifier\n", | |
| "clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2))\n", | |
| "clf.fit(X_train, y_train)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "q6RHpV08RiwO", | |
| "outputId": "63cbaede-feaf-4b33-e14b-e72df67f5ec3" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "MLPClassifier(alpha=1e-05, hidden_layer_sizes=(5, 2), solver='lbfgs')" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 35 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import tensorflow as tf" | |
| ], | |
| "metadata": { | |
| "id": "bC5JOcmMRrnN" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = tf.keras.Sequential([\n", | |
| "tf.keras.layers.Dense(units=1,input_shape=[X_train.shape[1]])])\n", | |
| " \n", | |
| "# after you create your model it's\n", | |
| "# always a good habit to print out it's summary\n", | |
| "model.summary()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3IUWc-lLSO72", | |
| "outputId": "478ff28d-e11d-4d6f-91ff-4404cfb10c00" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Model: \"sequential\"\n", | |
| "_________________________________________________________________\n", | |
| " Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| " dense (Dense) (None, 1) 12 \n", | |
| " \n", | |
| "=================================================================\n", | |
| "Total params: 12\n", | |
| "Trainable params: 12\n", | |
| "Non-trainable params: 0\n", | |
| "_________________________________________________________________\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = tf.keras.Sequential([\n", | |
| " tf.keras.layers.Dense(units=64, activation='relu',\n", | |
| " input_shape=[X_train.shape[1]]),\n", | |
| " tf.keras.layers.Dense(units=64, activation='relu'),\n", | |
| " tf.keras.layers.Dense(units=1)\n", | |
| "])\n", | |
| "model.summary()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "cPZKWsUXSSsn", | |
| "outputId": "1152fbf6-b362-4a3d-8ec9-f23f4c040c10" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Model: \"sequential_1\"\n", | |
| "_________________________________________________________________\n", | |
| " Layer (type) Output Shape Param # \n", | |
| "=================================================================\n", | |
| " dense_1 (Dense) (None, 64) 768 \n", | |
| " \n", | |
| " dense_2 (Dense) (None, 64) 4160 \n", | |
| " \n", | |
| " dense_3 (Dense) (None, 1) 65 \n", | |
| " \n", | |
| "=================================================================\n", | |
| "Total params: 4,993\n", | |
| "Trainable params: 4,993\n", | |
| "Non-trainable params: 0\n", | |
| "_________________________________________________________________\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model.compile(optimizer='adam', \n", | |
| " \n", | |
| " # MAE error is good for\n", | |
| " # numerical predictions\n", | |
| " loss='mae') " | |
| ], | |
| "metadata": { | |
| "id": "4OH2K8q_Smtu" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "losses = model.fit(X_train, y_train,\n", | |
| " \n", | |
| " validation_data=(test_x, test_y),\n", | |
| " \n", | |
| " # it will use 'batch_size' number\n", | |
| " # of examples per example\n", | |
| " batch_size=256,\n", | |
| " epochs=15, # total epoch\n", | |
| " \n", | |
| " )" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "v-ypOPuwSqOu", | |
| "outputId": "33099d50-54cc-4560-c855-dbb38937e18e" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Epoch 1/15\n", | |
| "4758/4758 [==============================] - 16s 3ms/step - loss: 1.3723 - val_loss: 1.3625\n", | |
| "Epoch 2/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3478 - val_loss: 1.3439\n", | |
| "Epoch 3/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3382 - val_loss: 1.3383\n", | |
| "Epoch 4/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3310 - val_loss: 1.3309\n", | |
| "Epoch 5/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3272 - val_loss: 1.3288\n", | |
| "Epoch 6/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3245 - val_loss: 1.3265\n", | |
| "Epoch 7/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3229 - val_loss: 1.3272\n", | |
| "Epoch 8/15\n", | |
| "4758/4758 [==============================] - 14s 3ms/step - loss: 1.3218 - val_loss: 1.3256\n", | |
| "Epoch 9/15\n", | |
| "4758/4758 [==============================] - 14s 3ms/step - loss: 1.3212 - val_loss: 1.3256\n", | |
| "Epoch 10/15\n", | |
| "4758/4758 [==============================] - 15s 3ms/step - loss: 1.3203 - val_loss: 1.3237\n", | |
| "Epoch 11/15\n", | |
| "4758/4758 [==============================] - 16s 3ms/step - loss: 1.3197 - val_loss: 1.3223\n", | |
| "Epoch 12/15\n", | |
| "4758/4758 [==============================] - 14s 3ms/step - loss: 1.3190 - val_loss: 1.3246\n", | |
| "Epoch 13/15\n", | |
| "4758/4758 [==============================] - 13s 3ms/step - loss: 1.3186 - val_loss: 1.3218\n", | |
| "Epoch 14/15\n", | |
| "4758/4758 [==============================] - 14s 3ms/step - loss: 1.3181 - val_loss: 1.3211\n", | |
| "Epoch 15/15\n", | |
| "4758/4758 [==============================] - 14s 3ms/step - loss: 1.3176 - val_loss: 1.3222\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "loss_df = pd.DataFrame(losses.history)\n", | |
| " \n", | |
| "# history stores the loss/val\n", | |
| "# loss in each epoch\n", | |
| " \n", | |
| "# loss_df is a dataframe which\n", | |
| "# contains the losses so we can\n", | |
| "# plot it to visualize our model training\n", | |
| "loss_df.loc[:,['loss','val_loss']].plot()" | |
| ], | |
| "metadata": { | |
| "id": "dGD5Sv33StJt", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 112 | |
| }, | |
| "outputId": "24ccf962-b025-4a1a-95f3-46815cdc5663" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f6d0f5c9e20>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 41 | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model.save('model')" | |
| ], | |
| "metadata": { | |
| "id": "LG3TbwU3T_Qv" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "y_pred = model.predict(test_x)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "QcAhlbdPFQa4", | |
| "outputId": "e5da72eb-d073-4ff4-b66e-9aa6a19d638b" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "9515/9515 [==============================] - 13s 1ms/step\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "confusion_mtx = tf.math.confusion_matrix(test_y, y_pred)\n", | |
| "plt.figure(figsize=(10, 8))\n", | |
| "sns.heatmap(confusion_mtx,\n", | |
| " annot=True, fmt='g')\n", | |
| "plt.xlabel('Prediction')\n", | |
| "plt.ylabel('Label')\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 54 | |
| }, | |
| "id": "VSINW_x3Fcsa", | |
| "outputId": "e1812d14-0998-4f2c-ca27-11ef79cdc1aa" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 720x576 with 2 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "# DICOM" | |
| ], | |
| "metadata": { | |
| "id": "4QMKPakQKTfY" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "! pip install pydicom" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "k489Rt6uKXfc", | |
| "outputId": "a0d8a402-49a6-4e35-d875-38fbd17e1163" | |
| }, | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", | |
| "Collecting pydicom\n", | |
| " Downloading pydicom-2.3.1-py3-none-any.whl (2.0 MB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m26.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hInstalling collected packages: pydicom\n", | |
| "Successfully installed pydicom-2.3.1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from pydicom import dcmread\n", | |
| "from pydicom.data import get_testdata_file\n", | |
| "\n", | |
| "print(__doc__)\n", | |
| "\n", | |
| "path = get_testdata_file('MR_small.dcm')\n", | |
| "ds = dcmread(path)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "aY6utuRSK9DH", | |
| "outputId": "570dcbe2-d2e1-419a-bcec-5d39665a1e0c" | |
| }, | |
| "execution_count": 6, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Automatically created module for IPython interactive environment\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "type(ds.PixelData)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "MNTiSI1zLJu9", | |
| "outputId": "537e6848-5226-429f-91a6-b6a05c2dff2e" | |
| }, | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "bytes" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 7 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "ds" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "JUCCFt3gLj0p", | |
| "outputId": "9fe95d39-f16b-46b6-d7ec-a7bed67b7c0e" | |
| }, | |
| "execution_count": 8, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "Dataset.file_meta -------------------------------\n", | |
| "(0002, 0000) File Meta Information Group Length UL: 190\n", | |
| "(0002, 0001) File Meta Information Version OB: b'\\x00\\x01'\n", | |
| "(0002, 0002) Media Storage SOP Class UID UI: MR Image Storage\n", | |
| "(0002, 0003) Media Storage SOP Instance UID UI: 1.3.6.1.4.1.5962.1.1.4.1.1.20040826185059.5457\n", | |
| "(0002, 0010) Transfer Syntax UID UI: Explicit VR Little Endian\n", | |
| "(0002, 0012) Implementation Class UID UI: 1.3.6.1.4.1.5962.2\n", | |
| "(0002, 0013) Implementation Version Name SH: 'DCTOOL100'\n", | |
| "(0002, 0016) Source Application Entity Title AE: 'CLUNIE1'\n", | |
| "-------------------------------------------------\n", | |
| "(0008, 0008) Image Type CS: ['DERIVED', 'SECONDARY', 'OTHER']\n", | |
| "(0008, 0012) Instance Creation Date DA: '20040826'\n", | |
| "(0008, 0013) Instance Creation Time TM: '185434'\n", | |
| "(0008, 0014) Instance Creator UID UI: 1.3.6.1.4.1.5962.3\n", | |
| "(0008, 0016) SOP Class UID UI: MR Image Storage\n", | |
| "(0008, 0018) SOP Instance UID UI: 1.3.6.1.4.1.5962.1.1.4.1.1.20040826185059.5457\n", | |
| "(0008, 0020) Study Date DA: '20040826'\n", | |
| "(0008, 0021) Series Date DA: ''\n", | |
| "(0008, 0022) Acquisition Date DA: ''\n", | |
| "(0008, 0030) Study Time TM: '185059'\n", | |
| "(0008, 0031) Series Time TM: ''\n", | |
| "(0008, 0032) Acquisition Time TM: ''\n", | |
| "(0008, 0050) Accession Number SH: ''\n", | |
| "(0008, 0060) Modality CS: 'MR'\n", | |
| "(0008, 0070) Manufacturer LO: 'TOSHIBA_MEC'\n", | |
| "(0008, 0080) Institution Name LO: 'TOSHIBA'\n", | |
| "(0008, 0090) Referring Physician's Name PN: ''\n", | |
| "(0008, 0201) Timezone Offset From UTC SH: '-0400'\n", | |
| "(0008, 1010) Station Name SH: '000000000'\n", | |
| "(0008, 1060) Name of Physician(s) Reading Study PN: '----'\n", | |
| "(0008, 1070) Operators' Name PN: '----'\n", | |
| "(0008, 1090) Manufacturer's Model Name LO: 'MRT50H1'\n", | |
| "(0010, 0010) Patient's Name PN: 'CompressedSamples^MR1'\n", | |
| "(0010, 0020) Patient ID LO: '4MR1'\n", | |
| "(0010, 0030) Patient's Birth Date DA: ''\n", | |
| "(0010, 0040) Patient's Sex CS: 'F'\n", | |
| "(0010, 1020) Patient's Size DS: None\n", | |
| "(0010, 1030) Patient's Weight DS: '80.0'\n", | |
| "(0018, 0010) Contrast/Bolus Agent LO: ''\n", | |
| "(0018, 0020) Scanning Sequence CS: 'SE'\n", | |
| "(0018, 0021) Sequence Variant CS: 'NONE'\n", | |
| "(0018, 0022) Scan Options CS: ''\n", | |
| "(0018, 0023) MR Acquisition Type CS: '3D'\n", | |
| "(0018, 0050) Slice Thickness DS: '0.8'\n", | |
| "(0018, 0080) Repetition Time DS: '4000.0'\n", | |
| "(0018, 0081) Echo Time DS: '240.0'\n", | |
| "(0018, 0083) Number of Averages DS: '1.0'\n", | |
| "(0018, 0084) Imaging Frequency DS: '63.924339'\n", | |
| "(0018, 0085) Imaged Nucleus SH: 'H'\n", | |
| "(0018, 0086) Echo Number(s) IS: '1'\n", | |
| "(0018, 0091) Echo Train Length IS: None\n", | |
| "(0018, 1000) Device Serial Number LO: '-0000200'\n", | |
| "(0018, 1020) Software Versions LO: 'V3.51*P25'\n", | |
| "(0018, 1314) Flip Angle DS: '90.0'\n", | |
| "(0018, 5100) Patient Position CS: 'HFS'\n", | |
| "(0020, 000d) Study Instance UID UI: 1.3.6.1.4.1.5962.1.2.4.20040826185059.5457\n", | |
| "(0020, 000e) Series Instance UID UI: 1.3.6.1.4.1.5962.1.3.4.1.20040826185059.5457\n", | |
| "(0020, 0010) Study ID SH: '4MR1'\n", | |
| "(0020, 0011) Series Number IS: '1'\n", | |
| "(0020, 0012) Acquisition Number IS: '0'\n", | |
| "(0020, 0013) Instance Number IS: '1'\n", | |
| "(0020, 0032) Image Position (Patient) DS: [-83.9063, -91.2000, 6.6406]\n", | |
| "(0020, 0037) Image Orientation (Patient) DS: [1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000]\n", | |
| "(0020, 0052) Frame of Reference UID UI: 1.3.6.1.4.1.5962.1.4.4.1.20040826185059.5457\n", | |
| "(0020, 0060) Laterality CS: ''\n", | |
| "(0020, 1040) Position Reference Indicator LO: ''\n", | |
| "(0020, 1041) Slice Location DS: '0.0'\n", | |
| "(0020, 4000) Image Comments LT: 'Uncompressed'\n", | |
| "(0028, 0002) Samples per Pixel US: 1\n", | |
| "(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'\n", | |
| "(0028, 0010) Rows US: 64\n", | |
| "(0028, 0011) Columns US: 64\n", | |
| "(0028, 0030) Pixel Spacing DS: [0.3125, 0.3125]\n", | |
| "(0028, 0100) Bits Allocated US: 16\n", | |
| "(0028, 0101) Bits Stored US: 16\n", | |
| "(0028, 0102) High Bit US: 15\n", | |
| "(0028, 0103) Pixel Representation US: 1\n", | |
| "(0028, 0106) Smallest Image Pixel Value SS: 0\n", | |
| "(0028, 0107) Largest Image Pixel Value SS: 4000\n", | |
| "(0028, 1050) Window Center DS: '600.0'\n", | |
| "(0028, 1051) Window Width DS: '1600.0'\n", | |
| "(7fe0, 0010) Pixel Data OW: Array of 8192 elements\n", | |
| "(fffc, fffc) Data Set Trailing Padding OB: Array of 126 elements" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 8 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.imshow(ds.pixel_array, cmap=\"gray\")\n", | |
| "plt.show()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 268 | |
| }, | |
| "id": "HEjWhVjJLqsK", | |
| "outputId": "bd990a6e-e84f-40c0-f2ed-1cf5cbe6481f" | |
| }, | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ], | |
| "image/png": 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\n" | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import cv2\n", | |
| "import pydicom\n", | |
| "import numpy as np\n", | |
| "from google.colab.patches import cv2_imshow\n", | |
| "\n", | |
| "# Extract the pixel data from the DICOM file\n", | |
| "pixel_data = ds.pixel_array\n", | |
| "\n", | |
| "# Convert the pixel data to a 16-bit format suitable for OpenCV\n", | |
| "image_16bit = np.array(pixel_data, dtype=np.uint16)\n", | |
| "\n", | |
| "# Convert the 16-bit image array to an 8-bit image array suitable for OpenCV\n", | |
| "image_8bit = cv2.normalize(image_16bit, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U)\n", | |
| "\n", | |
| "# Show the image using OpenCV\n", | |
| "cv2_imshow(image_8bit)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 81 | |
| }, | |
| "id": "1M5s1tidMr0H", | |
| "outputId": "40f85578-0e93-4107-8dcf-3ea7292fe20b" | |
| }, | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<PIL.Image.Image image mode=L size=64x64 at 0x7FB433328DF0>" | |
| ], | |
| "image/png": "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\n" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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