Skip to content

Instantly share code, notes, and snippets.

@dsaint31x

dsaint31x/dl_torch_binary_classification.ipynb

Created May 23, 2024 13:06
Show Gist options
  • Save dsaint31x/79fea70bc5c9fdea7aab4b1ad38dc273 to your computer and use it in GitHub Desktop.
Save dsaint31x/79fea70bc5c9fdea7aab4b1ad38dc273 to your computer and use it in GitHub Desktop.
Download ZIP
dl_torch_binary_classification.ipynb
Display the source blob
Display the rendered blob
Raw
dl_torch_binary_classification.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"toc_visible": true,
"authorship_tag": "ABX9TyM4UdfbFGgwsIDIyiK9q47n",
"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/dsaint31x/79fea70bc5c9fdea7aab4b1ad38dc273/dl_torch_binary_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Dataset"
],
"metadata": {
"id": "XwRyZEthq7FW"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oLUs_h-Ysn6i",
"outputId": "6c45fb9d-b7bb-4c4a-c77f-f7b7d4981c3c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'sklearn.utils._bunch.Bunch'>\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['DESCR',\n",
" 'data',\n",
" 'data_module',\n",
" 'feature_names',\n",
" 'filename',\n",
" 'frame',\n",
" 'target',\n",
" 'target_names']"
]
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"from sklearn.datasets import load_breast_cancer\n",
"\n",
"cancer = load_breast_cancer()\n",
"print(type(cancer))\n",
"dir(cancer)"
]
},
{
"cell_type": "markdown",
"source": [
"# Exploratory Data Analysis (EDA)\n",
"\n",
"탐색적 데이터 분석.\n",
"\n",
"여기선 df를 이용해 간단히 살펴본다."
],
"metadata": {
"id": "BigXS9S_E9n7"
}
},
{
"cell_type": "code",
"source": [
"from IPython import display\n",
"display.Markdown(cancer.DESCR)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "TWQMre-us0bD",
"outputId": "e7d0f1bc-ca78-48b8-f2d9-04be799f03f8"
},
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<IPython.core.display.Markdown object>"
],
"text/markdown": ".. _breast_cancer_dataset:\n\nBreast cancer wisconsin (diagnostic) dataset\n--------------------------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 569\n\n :Number of Attributes: 30 numeric, predictive attributes and the class\n\n :Attribute Information:\n - radius (mean of distances from center to points on the perimeter)\n - texture (standard deviation of gray-scale values)\n - perimeter\n - area\n - smoothness (local variation in radius lengths)\n - compactness (perimeter^2 / area - 1.0)\n - concavity (severity of concave portions of the contour)\n - concave points (number of concave portions of the contour)\n - symmetry\n - fractal dimension (\"coastline approximation\" - 1)\n\n The mean, standard error, and \"worst\" or largest (mean of the three\n worst/largest values) of these features were computed for each image,\n resulting in 30 features. For instance, field 0 is Mean Radius, field\n 10 is Radius SE, field 20 is Worst Radius.\n\n - class:\n - WDBC-Malignant\n - WDBC-Benign\n\n :Summary Statistics:\n\n ===================================== ====== ======\n Min Max\n ===================================== ====== ======\n radius (mean): 6.981 28.11\n texture (mean): 9.71 39.28\n perimeter (mean): 43.79 188.5\n area (mean): 143.5 2501.0\n smoothness (mean): 0.053 0.163\n compactness (mean): 0.019 0.345\n concavity (mean): 0.0 0.427\n concave points (mean): 0.0 0.201\n symmetry (mean): 0.106 0.304\n fractal dimension (mean): 0.05 0.097\n radius (standard error): 0.112 2.873\n texture (standard error): 0.36 4.885\n perimeter (standard error): 0.757 21.98\n area (standard error): 6.802 542.2\n smoothness (standard error): 0.002 0.031\n compactness (standard error): 0.002 0.135\n concavity (standard error): 0.0 0.396\n concave points (standard error): 0.0 0.053\n symmetry (standard error): 0.008 0.079\n fractal dimension (standard error): 0.001 0.03\n radius (worst): 7.93 36.04\n texture (worst): 12.02 49.54\n perimeter (worst): 50.41 251.2\n area (worst): 185.2 4254.0\n smoothness (worst): 0.071 0.223\n compactness (worst): 0.027 1.058\n concavity (worst): 0.0 1.252\n concave points (worst): 0.0 0.291\n symmetry (worst): 0.156 0.664\n fractal dimension (worst): 0.055 0.208\n ===================================== ====== ======\n\n :Missing Attribute Values: None\n\n :Class Distribution: 212 - Malignant, 357 - Benign\n\n :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n\n :Donor: Nick Street\n\n :Date: November, 1995\n\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\nhttps://goo.gl/U2Uwz2\n\nFeatures are computed from a digitized image of a fine needle\naspirate (FNA) of a breast mass. They describe\ncharacteristics of the cell nuclei present in the image.\n\nSeparating plane described above was obtained using\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\nConstruction Via Linear Programming.\" Proceedings of the 4th\nMidwest Artificial Intelligence and Cognitive Science Society,\npp. 97-101, 1992], a classification method which uses linear\nprogramming to construct a decision tree. Relevant features\nwere selected using an exhaustive search in the space of 1-4\nfeatures and 1-3 separating planes.\n\nThe actual linear program used to obtain the separating plane\nin the 3-dimensional space is that described in:\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\nProgramming Discrimination of Two Linearly Inseparable Sets\",\nOptimization Methods and Software 1, 1992, 23-34].\n\nThis database is also available through the UW CS ftp server:\n\nftp ftp.cs.wisc.edu\ncd math-prog/cpo-dataset/machine-learn/WDBC/\n\n.. topic:: References\n\n - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n San Jose, CA, 1993.\n - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n July-August 1995.\n - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n 163-171."
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
"df['label'] = cancer.target\n",
"\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 290
},
"id": "dhnsMaE0Dl3s",
"outputId": "7fb68cb1-8b08-4fc9-ba4e-85cfbbd1e3e3"
},
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.3001 0.14710 0.2419 \n",
"1 0.07864 0.0869 0.07017 0.1812 \n",
"2 0.15990 0.1974 0.12790 0.2069 \n",
"3 0.28390 0.2414 0.10520 0.2597 \n",
"4 0.13280 0.1980 0.10430 0.1809 \n",
"\n",
" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
"0 0.07871 ... 17.33 184.60 2019.0 \n",
"1 0.05667 ... 23.41 158.80 1956.0 \n",
"2 0.05999 ... 25.53 152.50 1709.0 \n",
"3 0.09744 ... 26.50 98.87 567.7 \n",
"4 0.05883 ... 16.67 152.20 1575.0 \n",
"\n",
" worst smoothness worst compactness worst concavity worst concave points \\\n",
"0 0.1622 0.6656 0.7119 0.2654 \n",
"1 0.1238 0.1866 0.2416 0.1860 \n",
"2 0.1444 0.4245 0.4504 0.2430 \n",
"3 0.2098 0.8663 0.6869 0.2575 \n",
"4 0.1374 0.2050 0.4000 0.1625 \n",
"\n",
" worst symmetry worst fractal dimension label \n",
"0 0.4601 0.11890 0 \n",
"1 0.2750 0.08902 0 \n",
"2 0.3613 0.08758 0 \n",
"3 0.6638 0.17300 0 \n",
"4 0.2364 0.07678 0 \n",
"\n",
"[5 rows x 31 columns]"
],
"text/html": [
"\n",
" <div id=\"df-d92927d4-f91e-47ec-9c5d-2c350377216e\" 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>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>...</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>17.99</td>\n",
" <td>10.38</td>\n",
" <td>122.80</td>\n",
" <td>1001.0</td>\n",
" <td>0.11840</td>\n",
" <td>0.27760</td>\n",
" <td>0.3001</td>\n",
" <td>0.14710</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>...</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.1622</td>\n",
" <td>0.6656</td>\n",
" <td>0.7119</td>\n",
" <td>0.2654</td>\n",
" <td>0.4601</td>\n",
" <td>0.11890</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>20.57</td>\n",
" <td>17.77</td>\n",
" <td>132.90</td>\n",
" <td>1326.0</td>\n",
" <td>0.08474</td>\n",
" <td>0.07864</td>\n",
" <td>0.0869</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.1238</td>\n",
" <td>0.1866</td>\n",
" <td>0.2416</td>\n",
" <td>0.1860</td>\n",
" <td>0.2750</td>\n",
" <td>0.08902</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>19.69</td>\n",
" <td>21.25</td>\n",
" <td>130.00</td>\n",
" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
" <td>0.1974</td>\n",
" <td>0.12790</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>...</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.1444</td>\n",
" <td>0.4245</td>\n",
" <td>0.4504</td>\n",
" <td>0.2430</td>\n",
" <td>0.3613</td>\n",
" <td>0.08758</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.42</td>\n",
" <td>20.38</td>\n",
" <td>77.58</td>\n",
" <td>386.1</td>\n",
" <td>0.14250</td>\n",
" <td>0.28390</td>\n",
" <td>0.2414</td>\n",
" <td>0.10520</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>...</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.2098</td>\n",
" <td>0.8663</td>\n",
" <td>0.6869</td>\n",
" <td>0.2575</td>\n",
" <td>0.6638</td>\n",
" <td>0.17300</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>20.29</td>\n",
" <td>14.34</td>\n",
" <td>135.10</td>\n",
" <td>1297.0</td>\n",
" <td>0.10030</td>\n",
" <td>0.13280</td>\n",
" <td>0.1980</td>\n",
" <td>0.10430</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>...</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.1374</td>\n",
" <td>0.2050</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d92927d4-f91e-47ec-9c5d-2c350377216e')\"\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 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\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",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\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-d92927d4-f91e-47ec-9c5d-2c350377216e 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-d92927d4-f91e-47ec-9c5d-2c350377216e');\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",
"\n",
"\n",
"<div id=\"df-51de0bfb-e645-428b-9507-70a9cc6f0e83\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51de0bfb-e645-428b-9507-70a9cc6f0e83')\"\n",
" title=\"Suggest charts\"\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",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-51de0bfb-e645-428b-9507-70a9cc6f0e83 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df"
}
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"df.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 384
},
"id": "HtdJ8b29ETZb",
"outputId": "35e49ec5-f0f0-444c-8ab6-52aa48d87768"
},
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean radius mean texture mean perimeter mean area \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 14.127292 19.289649 91.969033 654.889104 \n",
"std 3.524049 4.301036 24.298981 351.914129 \n",
"min 6.981000 9.710000 43.790000 143.500000 \n",
"25% 11.700000 16.170000 75.170000 420.300000 \n",
"50% 13.370000 18.840000 86.240000 551.100000 \n",
"75% 15.780000 21.800000 104.100000 782.700000 \n",
"max 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" mean smoothness mean compactness mean concavity mean concave points \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" mean symmetry mean fractal dimension ... worst texture \\\n",
"count 569.000000 569.000000 ... 569.000000 \n",
"mean 0.181162 0.062798 ... 25.677223 \n",
"std 0.027414 0.007060 ... 6.146258 \n",
"min 0.106000 0.049960 ... 12.020000 \n",
"25% 0.161900 0.057700 ... 21.080000 \n",
"50% 0.179200 0.061540 ... 25.410000 \n",
"75% 0.195700 0.066120 ... 29.720000 \n",
"max 0.304000 0.097440 ... 49.540000 \n",
"\n",
" worst perimeter worst area worst smoothness worst compactness \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 107.261213 880.583128 0.132369 0.254265 \n",
"std 33.602542 569.356993 0.022832 0.157336 \n",
"min 50.410000 185.200000 0.071170 0.027290 \n",
"25% 84.110000 515.300000 0.116600 0.147200 \n",
"50% 97.660000 686.500000 0.131300 0.211900 \n",
"75% 125.400000 1084.000000 0.146000 0.339100 \n",
"max 251.200000 4254.000000 0.222600 1.058000 \n",
"\n",
" worst concavity worst concave points worst symmetry \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.272188 0.114606 0.290076 \n",
"std 0.208624 0.065732 0.061867 \n",
"min 0.000000 0.000000 0.156500 \n",
"25% 0.114500 0.064930 0.250400 \n",
"50% 0.226700 0.099930 0.282200 \n",
"75% 0.382900 0.161400 0.317900 \n",
"max 1.252000 0.291000 0.663800 \n",
"\n",
" worst fractal dimension label \n",
"count 569.000000 569.000000 \n",
"mean 0.083946 0.627417 \n",
"std 0.018061 0.483918 \n",
"min 0.055040 0.000000 \n",
"25% 0.071460 0.000000 \n",
"50% 0.080040 1.000000 \n",
"75% 0.092080 1.000000 \n",
"max 0.207500 1.000000 \n",
"\n",
"[8 rows x 31 columns]"
],
"text/html": [
"\n",
" <div id=\"df-4cd7c5c3-e363-4144-90ad-32da4fe0050c\" 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>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
" <th>...</th>\n",
" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
" <th>worst symmetry</th>\n",
" <th>worst fractal dimension</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>...</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>14.127292</td>\n",
" <td>19.289649</td>\n",
" <td>91.969033</td>\n",
" <td>654.889104</td>\n",
" <td>0.096360</td>\n",
" <td>0.104341</td>\n",
" <td>0.088799</td>\n",
" <td>0.048919</td>\n",
" <td>0.181162</td>\n",
" <td>0.062798</td>\n",
" <td>...</td>\n",
" <td>25.677223</td>\n",
" <td>107.261213</td>\n",
" <td>880.583128</td>\n",
" <td>0.132369</td>\n",
" <td>0.254265</td>\n",
" <td>0.272188</td>\n",
" <td>0.114606</td>\n",
" <td>0.290076</td>\n",
" <td>0.083946</td>\n",
" <td>0.627417</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.524049</td>\n",
" <td>4.301036</td>\n",
" <td>24.298981</td>\n",
" <td>351.914129</td>\n",
" <td>0.014064</td>\n",
" <td>0.052813</td>\n",
" <td>0.079720</td>\n",
" <td>0.038803</td>\n",
" <td>0.027414</td>\n",
" <td>0.007060</td>\n",
" <td>...</td>\n",
" <td>6.146258</td>\n",
" <td>33.602542</td>\n",
" <td>569.356993</td>\n",
" <td>0.022832</td>\n",
" <td>0.157336</td>\n",
" <td>0.208624</td>\n",
" <td>0.065732</td>\n",
" <td>0.061867</td>\n",
" <td>0.018061</td>\n",
" <td>0.483918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.981000</td>\n",
" <td>9.710000</td>\n",
" <td>43.790000</td>\n",
" <td>143.500000</td>\n",
" <td>0.052630</td>\n",
" <td>0.019380</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.106000</td>\n",
" <td>0.049960</td>\n",
" <td>...</td>\n",
" <td>12.020000</td>\n",
" <td>50.410000</td>\n",
" <td>185.200000</td>\n",
" <td>0.071170</td>\n",
" <td>0.027290</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.156500</td>\n",
" <td>0.055040</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>11.700000</td>\n",
" <td>16.170000</td>\n",
" <td>75.170000</td>\n",
" <td>420.300000</td>\n",
" <td>0.086370</td>\n",
" <td>0.064920</td>\n",
" <td>0.029560</td>\n",
" <td>0.020310</td>\n",
" <td>0.161900</td>\n",
" <td>0.057700</td>\n",
" <td>...</td>\n",
" <td>21.080000</td>\n",
" <td>84.110000</td>\n",
" <td>515.300000</td>\n",
" <td>0.116600</td>\n",
" <td>0.147200</td>\n",
" <td>0.114500</td>\n",
" <td>0.064930</td>\n",
" <td>0.250400</td>\n",
" <td>0.071460</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>13.370000</td>\n",
" <td>18.840000</td>\n",
" <td>86.240000</td>\n",
" <td>551.100000</td>\n",
" <td>0.095870</td>\n",
" <td>0.092630</td>\n",
" <td>0.061540</td>\n",
" <td>0.033500</td>\n",
" <td>0.179200</td>\n",
" <td>0.061540</td>\n",
" <td>...</td>\n",
" <td>25.410000</td>\n",
" <td>97.660000</td>\n",
" <td>686.500000</td>\n",
" <td>0.131300</td>\n",
" <td>0.211900</td>\n",
" <td>0.226700</td>\n",
" <td>0.099930</td>\n",
" <td>0.282200</td>\n",
" <td>0.080040</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>15.780000</td>\n",
" <td>21.800000</td>\n",
" <td>104.100000</td>\n",
" <td>782.700000</td>\n",
" <td>0.105300</td>\n",
" <td>0.130400</td>\n",
" <td>0.130700</td>\n",
" <td>0.074000</td>\n",
" <td>0.195700</td>\n",
" <td>0.066120</td>\n",
" <td>...</td>\n",
" <td>29.720000</td>\n",
" <td>125.400000</td>\n",
" <td>1084.000000</td>\n",
" <td>0.146000</td>\n",
" <td>0.339100</td>\n",
" <td>0.382900</td>\n",
" <td>0.161400</td>\n",
" <td>0.317900</td>\n",
" <td>0.092080</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>28.110000</td>\n",
" <td>39.280000</td>\n",
" <td>188.500000</td>\n",
" <td>2501.000000</td>\n",
" <td>0.163400</td>\n",
" <td>0.345400</td>\n",
" <td>0.426800</td>\n",
" <td>0.201200</td>\n",
" <td>0.304000</td>\n",
" <td>0.097440</td>\n",
" <td>...</td>\n",
" <td>49.540000</td>\n",
" <td>251.200000</td>\n",
" <td>4254.000000</td>\n",
" <td>0.222600</td>\n",
" <td>1.058000</td>\n",
" <td>1.252000</td>\n",
" <td>0.291000</td>\n",
" <td>0.663800</td>\n",
" <td>0.207500</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 31 columns</p>\n",
"</div>\n",
" <div class=\"colab-df-buttons\">\n",
"\n",
" <div class=\"colab-df-container\">\n",
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4cd7c5c3-e363-4144-90ad-32da4fe0050c')\"\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 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\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",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\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-4cd7c5c3-e363-4144-90ad-32da4fe0050c 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-4cd7c5c3-e363-4144-90ad-32da4fe0050c');\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",
"\n",
"\n",
"<div id=\"df-a8c3e45a-e1ee-4840-b774-7f38a099272e\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a8c3e45a-e1ee-4840-b774-7f38a099272e')\"\n",
" title=\"Suggest charts\"\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",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-a8c3e45a-e1ee-4840-b774-7f38a099272e button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"df.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VQ3w6RP0EbIC",
"outputId": "486327e5-209a-44e3-ca13-8a2b3ec141a7"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 31 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 mean radius 569 non-null float64\n",
" 1 mean texture 569 non-null float64\n",
" 2 mean perimeter 569 non-null float64\n",
" 3 mean area 569 non-null float64\n",
" 4 mean smoothness 569 non-null float64\n",
" 5 mean compactness 569 non-null float64\n",
" 6 mean concavity 569 non-null float64\n",
" 7 mean concave points 569 non-null float64\n",
" 8 mean symmetry 569 non-null float64\n",
" 9 mean fractal dimension 569 non-null float64\n",
" 10 radius error 569 non-null float64\n",
" 11 texture error 569 non-null float64\n",
" 12 perimeter error 569 non-null float64\n",
" 13 area error 569 non-null float64\n",
" 14 smoothness error 569 non-null float64\n",
" 15 compactness error 569 non-null float64\n",
" 16 concavity error 569 non-null float64\n",
" 17 concave points error 569 non-null float64\n",
" 18 symmetry error 569 non-null float64\n",
" 19 fractal dimension error 569 non-null float64\n",
" 20 worst radius 569 non-null float64\n",
" 21 worst texture 569 non-null float64\n",
" 22 worst perimeter 569 non-null float64\n",
" 23 worst area 569 non-null float64\n",
" 24 worst smoothness 569 non-null float64\n",
" 25 worst compactness 569 non-null float64\n",
" 26 worst concavity 569 non-null float64\n",
" 27 worst concave points 569 non-null float64\n",
" 28 worst symmetry 569 non-null float64\n",
" 29 worst fractal dimension 569 non-null float64\n",
" 30 label 569 non-null int64 \n",
"dtypes: float64(30), int64(1)\n",
"memory usage: 137.9 KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"df['label'].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RJ6EnxywFQ4M",
"outputId": "b466befc-fe2c-431b-8513-fdc2fd287dcc"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"label\n",
"1 357\n",
"0 212\n",
"Name: count, dtype: int64"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"np.unique(cancer.target_names) # 0: benign, 1: malignant"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nR5npuudGSV6",
"outputId": "c580b433-a942-4c5c-8f14-b01db2bfc77d"
},
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['benign', 'malignant'], dtype='<U9')"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"source": [
"# Data Processing"
],
"metadata": {
"id": "lb5YoeHDq4hl"
}
},
{
"cell_type": "code",
"source": [
"x_raw = cancer.data\n",
"y_raw = cancer.target\n",
"\n",
"print(f'{type(x_raw)=}:{x_raw.shape=}')\n",
"print(f'{type(y_raw)=}:{y_raw.shape=}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HFn8uURBtCP5",
"outputId": "397aefc8-384f-4138-be72-ed9f624e36ce"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"type(x_raw)=<class 'numpy.ndarray'>:x_raw.shape=(569, 30)\n",
"type(y_raw)=<class 'numpy.ndarray'>:y_raw.shape=(569,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"cancer.feature_names"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1frbC4zpuCkL",
"outputId": "a0f0d84a-40c2-4826-85b4-49a5fb252d4c"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
" 'mean smoothness', 'mean compactness', 'mean concavity',\n",
" 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
" 'radius error', 'texture error', 'perimeter error', 'area error',\n",
" 'smoothness error', 'compactness error', 'concavity error',\n",
" 'concave points error', 'symmetry error',\n",
" 'fractal dimension error', 'worst radius', 'worst texture',\n",
" 'worst perimeter', 'worst area', 'worst smoothness',\n",
" 'worst compactness', 'worst concavity', 'worst concave points',\n",
" 'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"from torch.utils.data import Dataset\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"x_train, x_test, y_train, y_test = train_test_split(\n",
" x_raw, y_raw, test_size = .1,\n",
" stratify=y_raw,\n",
")\n",
"\n",
"x_train, x_val, y_train, y_val = train_test_split(\n",
" x_train, y_train, test_size = .1,\n",
" stratify=y_train,\n",
")\n",
"\n",
"print(f'{len(x_train)=}\\n {len(x_val)=}\\n {len(x_test)=}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DcTbgjUDuW1Q",
"outputId": "75aa1b4c-35b6-41bd-d442-a7d20d99f4e8"
},
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"len(x_train)=460\n",
" len(x_val)=52\n",
" len(x_test)=57\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"test_r = np.unique(y_test , return_counts = True)\n",
"train_r = np.unique(y_train, return_counts = True)\n",
"val_r = np.unique(y_val, return_counts = True)\n",
"\n",
"print(\"test's cancer ratio : \", np.round( test_r[1][1]/np.sum( test_r[1]),2))\n",
"print(\"train's cancer ratio: \", np.round(train_r[1][1]/np.sum(train_r[1]),2))\n",
"print(\"val's cancer ratio. : \", np.round( val_r[1][1]/np.sum( val_r[1]),2))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Sm5GtCYGu8Up",
"outputId": "04a04f41-f88b-4c21-9fd2-16d043ff3110"
},
"execution_count": 12,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"test's cancer ratio : 0.63\n",
"train's cancer ratio: 0.63\n",
"val's cancer ratio. : 0.63\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"print(f'{x_train.shape=}\\n{ x_val.shape= }\\n {x_test.shape= }')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rYQVGnODvNsV",
"outputId": "6b44f77b-55ae-47bf-da8f-561b38bd6a75"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"x_train.shape=(460, 30)\n",
" x_val.shape= (52, 30)\n",
" x_test.shape= (57, 30)\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Dataset and DataLoader"
],
"metadata": {
"id": "gCnQPW_ajg5R"
}
},
{
"cell_type": "code",
"source": [
"from torch.utils.data import Dataset\n",
"import torch\n",
"import torch.nn as nn\n",
"\n",
"class BCDataset (Dataset):\n",
"\n",
" def __init__(self, r_vec, r_label):\n",
" self.data = torch.tensor(r_vec).float()\n",
" self.label = torch.tensor(r_label).float().unsqueeze(1)\n",
"\n",
" def __len__(self):\n",
" return len(self.data)\n",
"\n",
" def __getitem__(self, idx):\n",
" return self.data[idx],self.label[idx]\n",
"\n",
"train_ds = BCDataset(x_train,y_train)\n",
"val_ds = BCDataset(x_val, y_val)\n",
"test_ds = BCDataset(x_test, y_test)"
],
"metadata": {
"id": "xCk7YXHRyQ3f"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "code",
"source": [
"next(iter(train_ds))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pWDO_6s_K-ch",
"outputId": "ff5fd13a-1dd1-4d0e-bbeb-c04e79935287"
},
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(tensor([1.3200e+01, 1.5820e+01, 8.4070e+01, 5.3730e+02, 8.5110e-02, 5.2510e-02,\n",
" 1.4610e-03, 3.2610e-03, 1.6320e-01, 5.8940e-02, 1.9030e-01, 5.7350e-01,\n",
" 1.2040e+00, 1.5500e+01, 3.6320e-03, 7.8610e-03, 1.1280e-03, 2.3860e-03,\n",
" 1.3440e-02, 2.5850e-03, 1.4410e+01, 2.0450e+01, 9.2000e+01, 6.3690e+02,\n",
" 1.1280e-01, 1.3460e-01, 1.1200e-02, 2.5000e-02, 2.6510e-01, 8.3850e-02]),\n",
" tensor([1.]))"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"from torch.utils.data import DataLoader\n",
"\n",
"train_loader = DataLoader(\n",
" dataset = train_ds,\n",
" batch_size = 32,\n",
" shuffle = True,\n",
")\n",
"valid_loader = DataLoader(\n",
" dataset = val_ds,\n",
" batch_size = 32,\n",
" shuffle = True,\n",
")\n",
"test_loader = DataLoader(\n",
" dataset = test_ds,\n",
" batch_size = 32,\n",
" shuffle = False,\n",
")"
],
"metadata": {
"id": "1JnWNey3Q-tO"
},
"execution_count": 17,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Training Loop Implementation"
],
"metadata": {
"id": "Gk4lP3FpjlOt"
}
},
{
"cell_type": "code",
"source": [
"from tqdm import tqdm\n",
"\n",
"def train_loop (\n",
" n_epoch,\n",
" train_ds, val_ds,\n",
" model, optimizer, loss_fnc,\n",
" device = 'cpu',):\n",
"\n",
" log_hist = torch.zeros([0,3]).to(device)\n",
" for epoch in tqdm(range(n_epoch)):\n",
" # print(f'\\r{epoch:6>}', end='')\n",
"\n",
" model = model.to(device)\n",
" model.train()\n",
"\n",
" for x_tensor, y_tensor in train_ds:\n",
"\n",
" x_tensor.to(device)\n",
" y_tensor.to(device)\n",
"\n",
" y_pred = model(x_tensor)\n",
" loss_train = loss_fnc(y_pred, y_tensor)\n",
"\n",
" if torch.isinf(loss_train):\n",
" print(f'Error: loss is infinity!')\n",
" break\n",
"\n",
" optimizer.zero_grad()\n",
" loss_train.backward()\n",
" optimizer.step()\n",
"\n",
" with torch.no_grad():\n",
" for x_tensor, y_tensor in val_ds:\n",
" x_tensor = x_tensor.to(device)\n",
" y_tensor = y_tensor.to(device)\n",
" model.eval()\n",
" pred = model(x_tensor)\n",
" loss_val = loss_fnc(pred, y_tensor)\n",
"\n",
"\n",
" if epoch % 100 == 0:\n",
" tmp = torch.tensor([epoch, loss_train.item(), loss_val.item()]).to(log_hist.device)\n",
" # print(tmp)\n",
" log_hist = torch.concat( (log_hist, tmp.reshape(1,-1)), dim=0 )\n",
" print(f' Epoch / loss {loss_train.item():.4f} / val_loss {loss_val.item():.4f}')\n",
"\n",
" if epoch == n_epoch:\n",
" print(f' Epoch / loss {loss_train.item():.4f} / val_loss {loss_val.item():.4f}')\n",
"\n",
" return model,log_hist\n"
],
"metadata": {
"id": "ANWVidl1LB8C"
},
"execution_count": 33,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# Model Implementation"
],
"metadata": {
"id": "ONhcGYTmjo06"
}
},
{
"cell_type": "code",
"source": [
"class SimpleModel(nn.Module):\n",
"\n",
" def __init__(self, n_feature):\n",
" super().__init__()\n",
"\n",
" self.l0 = nn.Linear(n_feature, 16)\n",
" self.a0 = nn.ReLU()\n",
" self.l1 = nn.Linear(16,16)\n",
" self.a1 = nn.ReLU()\n",
" self.lf = nn.Linear(16,1)\n",
" self.out = nn.Sigmoid()\n",
"\n",
" def forward(self, x):\n",
" x = self.l0(x)\n",
" x = self.a0(x)\n",
" x = self.l1(x)\n",
" x = self.a1(x)\n",
" x = self.lf(x)\n",
" x = self.out(x)\n",
"\n",
" return x"
],
"metadata": {
"id": "3jQjVqvBO7x1"
},
"execution_count": 34,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### Test Model"
],
"metadata": {
"id": "aTCpXoHTjx5z"
}
},
{
"cell_type": "code",
"source": [
"# input vector의 number of components확인.\n",
"n_features = x_raw.shape[-1]\n",
"print(f'{n_features =}')\n",
"\n",
"# random한 input vector 5개에 대한\n",
"# 모델의 예측 결과를 확인하여\n",
"# 모델의 i/o 의 shape 확인.\n",
"model = SimpleModel(n_features)\n",
"x = torch.randn( (5, n_features) )\n",
"print(f'{x.shape =}')\n",
"print(f'{model(x).shape=}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2PyGzAoIP4XY",
"outputId": "88b34990-a125-4291-dd9b-f797821508d8"
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"n_features =30\n",
"x.shape =torch.Size([5, 30])\n",
"model(x).shape=torch.Size([5, 1])\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Training"
],
"metadata": {
"id": "3HP3z8YHjXVB"
}
},
{
"cell_type": "code",
"source": [
"# device 설정.\n",
"device = (\n",
" \"cuda\" if torch.cuda.is_available()\n",
" else \"mps\"\n",
" if torch.backends.mps.is_available()\n",
" else \"cpu\"\n",
" )\n",
"print(f\"{device=}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4_8JdhNbERd-",
"outputId": "60a9f3fc-ed0c-415a-c827-095a8798fef4"
},
"execution_count": 36,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"device='cpu'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# number of epochs\n",
"n_epoch = 1000\n",
"# learning ratio\n",
"lr = 1e-4\n",
"\n",
"# loss function 설정\n",
"loss_fnc = nn.BCELoss()\n",
"# 모델 생성 및 초기화\n",
"model = SimpleModel(n_features)\n",
"# optimizer 생성 및 초기화\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
"\n",
"m, h = train_loop (\n",
" n_epoch,\n",
" train_loader, valid_loader,\n",
" model, optimizer, loss_fnc,\n",
" device = device)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3ffpgj5jMbsZ",
"outputId": "91d918f2-f1d0-45a4-b5e4-8faa346a1211"
},
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
" 1%| | 10/1000 [00:00<00:19, 49.60it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.9343 / val_loss 1.1734\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 11%|█ | 107/1000 [00:02<00:22, 39.82it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.0846 / val_loss 0.0781\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 21%|██ | 209/1000 [00:04<00:14, 55.17it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.2112 / val_loss 0.1641\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 31%|███ | 311/1000 [00:06<00:12, 55.47it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.2714 / val_loss 0.2213\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 41%|████ | 407/1000 [00:08<00:10, 56.20it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.1352 / val_loss 0.2115\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 51%|█████ | 509/1000 [00:10<00:08, 56.50it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.1822 / val_loss 0.1590\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 61%|██████ | 611/1000 [00:11<00:06, 56.33it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.1287 / val_loss 0.1117\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 71%|███████ | 707/1000 [00:13<00:05, 54.96it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.0594 / val_loss 0.0785\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 81%|████████ | 806/1000 [00:16<00:05, 37.92it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.2125 / val_loss 0.1328\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" 91%|█████████ | 908/1000 [00:18<00:01, 55.32it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" Epoch / loss 0.0636 / val_loss 0.1116\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 1000/1000 [00:19<00:00, 50.33it/s]\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"# Learning Curve Check"
],
"metadata": {
"id": "vcWQDO8vjQW9"
}
},
{
"cell_type": "code",
"source": [
"h"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZM1WKxeSItRM",
"outputId": "35f7774c-ca60-4b45-bcfb-2a6a85bd08f7"
},
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"tensor([[0.0000e+00, 9.3433e-01, 1.1734e+00],\n",
" [1.0000e+02, 8.4551e-02, 7.8147e-02],\n",
" [2.0000e+02, 2.1115e-01, 1.6410e-01],\n",
" [3.0000e+02, 2.7140e-01, 2.2133e-01],\n",
" [4.0000e+02, 1.3520e-01, 2.1155e-01],\n",
" [5.0000e+02, 1.8224e-01, 1.5905e-01],\n",
" [6.0000e+02, 1.2867e-01, 1.1170e-01],\n",
" [7.0000e+02, 5.9447e-02, 7.8507e-02],\n",
" [8.0000e+02, 2.1250e-01, 1.3276e-01],\n",
" [9.0000e+02, 6.3619e-02, 1.1159e-01]])"
]
},
"metadata": {},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"h0 = h.detach().numpy()\n",
"print(h0.shape)\n",
"plt.plot(h0[:,0], h0[:,1], label='train')\n",
"plt.plot(h0[:,0], h0[:,2], label='valid')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 448
},
"id": "2PbbyHveHWp0",
"outputId": "ec67dd02-d333-46d9-9f15-d7f669a886ff"
},
"execution_count": 30,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(10, 3)\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": "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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# Test"
],
"metadata": {
"id": "tq0zTWUvjLxO"
}
},
{
"cell_type": "code",
"source": [
"f_m = model.cpu()\n",
"f_m.eval()\n",
"with torch.no_grad():\n",
" is_first = True\n",
" for x_tensor, y_tensor in test_loader:\n",
" x_tensor = x_tensor.cpu()\n",
" y_tensor = y_tensor.cpu()\n",
" pred = f_m(x_tensor)\n",
"\n",
" # print(y_tensor.numpy().shape)\n",
" if is_first:\n",
" test_pred_prob = pred.numpy().copy()\n",
" test_label = y_tensor.numpy().copy()\n",
" is_first = False\n",
" else:\n",
" test_label = np.concatenate((test_label, y_tensor.numpy()), axis=0)\n",
" test_pred_prob = np.concatenate((test_pred_prob, pred.numpy()), axis=0)\n",
"\n",
"print(test_label.shape, test_label.dtype)\n",
"print(test_pred_prob.shape, test_pred_prob.dtype)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bJEygiJETGIt",
"outputId": "da11d3b9-844c-4743-8463-df6958378fc5"
},
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(57, 1) float32\n",
"(57, 1) float32\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import (\n",
" confusion_matrix,\n",
" precision_score,\n",
" recall_score,\n",
" f1_score,\n",
" fbeta_score,\n",
")\n",
"pred = np.round(test_pred_prob)\n",
"label = test_label\n",
"\n",
"print('Confusion Matrix\\n',\n",
" str(confusion_matrix(label,pred))\n",
" )\n",
"print(f'Precision:{precision_score(label,pred):.2f}')\n",
"print(f'Recall. :{recall_score(label,pred):.2f}')\n",
"print(f'F1-Score :{f1_score(label,pred):.2f}')\n",
"print(f'F2-Score :{fbeta_score(label,pred,beta=2):.2f}')\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "BhUC7lATWE4g",
"outputId": "8903c18a-82a9-41e0-b511-01d9f8148909"
},
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Confusion Matrix\n",
" [[18 3]\n",
" [ 3 33]]\n",
"Precision:0.92\n",
"Recall. :0.92\n",
"F1-Score :0.92\n",
"F2-Score :0.92\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pred"
],
"metadata": {
"id": "IQlQpO-CYcHV",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "bccced75-ce35-488a-c0b5-9c8359bd788c"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([7.7657259e-01, 6.2944692e-01, 8.5690951e-01, 2.7190824e-04,\n",
" 1.3909681e-01, 9.3183416e-01, 9.6883911e-01, 3.0913796e-09,\n",
" 9.4301140e-01, 9.1904014e-01, 9.2052245e-01, 7.9076892e-01,\n",
" 4.7718093e-01, 5.0635308e-01, 6.9300316e-02, 9.8726141e-01,\n",
" 9.7495699e-01, 9.0912378e-01, 8.5156107e-01, 9.7413176e-01,\n",
" 7.8029285e-07, 9.4359112e-01, 1.7913450e-01, 9.8232216e-01,\n",
" 9.5713109e-01, 1.1772829e-09, 9.7428089e-01, 7.1198255e-01,\n",
" 5.2952123e-07, 8.3453077e-01, 9.5434213e-01, 9.1411507e-01,\n",
" 7.0542717e-01, 5.4176518e-05, 4.3255496e-10, 9.2647159e-01,\n",
" 9.7760856e-01, 1.9108060e-05, 9.6683151e-01, 1.3417931e-01,\n",
" 9.7265130e-01, 8.4110308e-01, 1.3005364e-04, 8.0472642e-01,\n",
" 9.3157888e-07, 2.6140261e-01, 5.3463671e-17, 9.7554988e-01,\n",
" 2.5391018e-01, 9.2323446e-01, 9.6894068e-01, 8.7520927e-01,\n",
" 1.8549596e-01, 9.3754983e-01, 1.3416738e-05, 1.0306122e-08,\n",
" 1.7757897e-03], dtype=float32)"
]
},
"metadata": {},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "NLurYYLLXM2s"
},
"execution_count": null,
"outputs": []
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment