{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "VqZ9BtJnwear" }, "source": [ "# Задание 1. Bootstrap" ] }, { "cell_type": "markdown", "metadata": { "id": "2jZ6d3Owweau" }, "source": [ "В этом задании используйте датасет [Breast Cancer 🛠️[doc]](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html) — классический датасет для задачи бинарной классификации. Обучите модели:\n", "\n", " - `DecisionTreeClassifier`\n", " - `RandomForestClassifier`\n", " - `LGBMClassifier`\n", " - `SVC`\n", " - `BaggingClassifier` с базовым класификатором `SVC`.\n", "\n", "Параметры моделей можете оставить по умолчанию или задать сами.\n", "\n", "Для каждой модели посчитайте [корреляцию Мэтьюса 📚[wiki]](https://en.wikipedia.org/wiki/Phi_coefficient) — метрику для оценки качества бинарной классификации, в частности, устойчивую к дисбалансу классов, (`sklearn.metrics.matthews_corrcoef` [🛠️[doc]](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html) для предсказанного ею класса и реального. Подробнее почитать про его пользу можно в статье:\n", "\n", "[[article] 🎓 The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation](https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-6413-7)\n", "\n", "С помощью bootstrap-подхода постройте 90% доверительные интервалы для качества полученных моделей. Используйте функцию `bootstrap_metric()` из лекции.\n", "\n", "Постройте [боксплоты 🛠️[doc]](https://seaborn.pydata.org/generated/seaborn.boxplot.html) для качества полученных моделей." ] }, { "cell_type": "markdown", "metadata": { "id": "Nan68aZIweaw" }, "source": [ "Установка и импорт необходимых библиотек:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EobqrBMqweax", "outputId": "44e71e9b-ca42-4128-f9bb-d8c982586514" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.6/242.6 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install -q dask[dataframe]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rNK2PHixweaz" }, "outputs": [], "source": [ "import lightgbm\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import sklearn.datasets\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import matthews_corrcoef\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier" ] }, { "cell_type": "markdown", "metadata": { "id": "c914Tk78wea0" }, "source": [ "Загрузка датасета:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "nA-lRHUTwea0", "jupyter": { "outputs_hidden": true }, "outputId": "6735dae2-ad75-4dc4-b9c3-d3cc248e6a44" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. _breast_cancer_dataset:\n", "\n", "Breast 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", "\n", "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n", "https://goo.gl/U2Uwz2\n", "\n", "Features are computed from a digitized image of a fine needle\n", "aspirate (FNA) of a breast mass. They describe\n", "characteristics of the cell nuclei present in the image.\n", "\n", "Separating plane described above was obtained using\n", "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n", "Construction Via Linear Programming.\" Proceedings of the 4th\n", "Midwest Artificial Intelligence and Cognitive Science Society,\n", "pp. 97-101, 1992], a classification method which uses linear\n", "programming to construct a decision tree. Relevant features\n", "were selected using an exhaustive search in the space of 1-4\n", "features and 1-3 separating planes.\n", "\n", "The actual linear program used to obtain the separating plane\n", "in the 3-dimensional space is that described in:\n", "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n", "Programming Discrimination of Two Linearly Inseparable Sets\",\n", "Optimization Methods and Software 1, 1992, 23-34].\n", "\n", "This database is also available through the UW CS ftp server:\n", "\n", "ftp ftp.cs.wisc.edu\n", "cd math-prog/cpo-dataset/machine-learn/WDBC/\n", "\n", ".. dropdown:: 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.\n", "\n" ] } ], "source": [ "breast_cancer = sklearn.datasets.load_breast_cancer()\n", "print(breast_cancer.DESCR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PMzugGlGwea0" }, "outputs": [], "source": [ "x = breast_cancer.data\n", "y = breast_cancer.target\n", "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=42)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TxFinYhZ_PIS", "outputId": "930146d8-42b0-48a3-e400-5ec67724ad28" }, "outputs": [ { "data": { "text/plain": [ "array([[1.799e+01, 1.038e+01, 1.228e+02, 1.001e+03, 1.184e-01, 2.776e-01,\n", " 3.001e-01, 1.471e-01, 2.419e-01, 7.871e-02, 1.095e+00, 9.053e-01,\n", " 8.589e+00, 1.534e+02, 6.399e-03, 4.904e-02, 5.373e-02, 1.587e-02,\n", " 3.003e-02, 6.193e-03, 2.538e+01, 1.733e+01, 1.846e+02, 2.019e+03,\n", " 1.622e-01, 6.656e-01, 7.119e-01, 2.654e-01, 4.601e-01, 1.189e-01],\n", " [2.057e+01, 1.777e+01, 1.329e+02, 1.326e+03, 8.474e-02, 7.864e-02,\n", " 8.690e-02, 7.017e-02, 1.812e-01, 5.667e-02, 5.435e-01, 7.339e-01,\n", " 3.398e+00, 7.408e+01, 5.225e-03, 1.308e-02, 1.860e-02, 1.340e-02,\n", " 1.389e-02, 3.532e-03, 2.499e+01, 2.341e+01, 1.588e+02, 1.956e+03,\n", " 1.238e-01, 1.866e-01, 2.416e-01, 1.860e-01, 2.750e-01, 8.902e-02],\n", " [1.969e+01, 2.125e+01, 1.300e+02, 1.203e+03, 1.096e-01, 1.599e-01,\n", " 1.974e-01, 1.279e-01, 2.069e-01, 5.999e-02, 7.456e-01, 7.869e-01,\n", " 4.585e+00, 9.403e+01, 6.150e-03, 4.006e-02, 3.832e-02, 2.058e-02,\n", " 2.250e-02, 4.571e-03, 2.357e+01, 2.553e+01, 1.525e+02, 1.709e+03,\n", " 1.444e-01, 4.245e-01, 4.504e-01, 2.430e-01, 3.613e-01, 8.758e-02]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GJ6iCg4Awea1" }, "outputs": [], "source": [ "# Your code here\n", "DTC = DecisionTreeClassifier(random_state = 42)\n", "DTC.fit(x_train, y_train)\n", "dtc_pred = DTC.predict(x_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E_tUhJkWA74e", "outputId": "bcad98fe-9205-4206-9d73-6bed18cc0316" }, "outputs": [ { "data": { "text/plain": [ "0.8963356530877563" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matthews_corrcoef(y_pred=dtc_pred, y_true=y_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "rN_GrSMbA7DU" }, "outputs": [], "source": [ "RFC = RandomForestClassifier(max_depth=None,)\n", "RFC.fit(x_train, y_train)\n", "rfc_pred = RFC.predict(x_test)\n", "\n", "LGMBC = lightgbm.LGBMClassifier(n_estimators=2000,\n", " learning_rate=0.1,\n", " max_depth=-1,\n", " num_leaves=2**5,\n", " random_state=42,\n", " min_child_weight=13,\n", " n_jobs=-1,\n", " force_col_wise=True,\n", " verbose=-1,)\n", "LGMBC.fit(X=x_train, y=y_train)\n", "lgbmc_pred = LGMBC.predict(x_test)\n", "\n", "svc = SVC()\n", "svc.fit(x_train, y_train)\n", "svc_pred = svc.predict(X=x_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C0itI8YNBdZD" }, "outputs": [], "source": [ "BagC_SVC = BaggingClassifier(estimator=SVC())\n", "BagC_SVC.fit(x_train, y_train)\n", "bagc_pred = BagC_SVC.predict(x_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hEvTzHOdCh3A" }, "outputs": [], "source": [ "def bootstrap_metric(y_true, y_pred, metric_fn, samples_cnt=1000, random_state=42):\n", " np.random.seed(random_state)\n", " b_metric = np.zeros(samples_cnt)\n", " for i in range(samples_cnt):\n", " poses = np.random.choice(y_true.shape[0], size=y_true.shape[0], replace=True)\n", "\n", " y_true_boot = y_true[poses]\n", " y_pred_boot = y_pred[poses]\n", " m_val = metric_fn(y_true_boot, y_pred_boot)\n", " b_metric[i] = m_val\n", "\n", " return b_metric" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "o5Rps5hoB4Jg" }, "outputs": [], "source": [ "boot_dtc = bootstrap_metric(y_test, dtc_pred, metric_fn=matthews_corrcoef)\n", "boot_rfc = bootstrap_metric(y_test, rfc_pred, metric_fn=matthews_corrcoef)\n", "boot_lgbmc = bootstrap_metric(y_test, lgbmc_pred, metric_fn=matthews_corrcoef)\n", "boot_bagc = bootstrap_metric(y_test, bagc_pred, metric_fn=matthews_corrcoef)\n", "boot_svc = bootstrap_metric(y_test, svc_pred, metric_fn=matthews_corrcoef)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 536 }, "id": "yXCp7ZejDAQ2", "outputId": "b25060af-c33e-4210-aba5-9e2244193b91" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "