{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ダウンロードしたデータセットに機械学習を適用する流れ" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 達成目標\n", "- 学習器に対して\n", " - 機械学習のタスク種別がclassification/regression/clusteringのどれに該当するかを判断することができる。\n", " - 判断したタスクに用いることができる学習器（モデル）を選択することができる。\n", " - 参考\n", " - [Choosing the right estimator](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html)\n", " - [User Guide](https://scikit-learn.org/stable/user_guide.html)\n", " - [Examplex](https://scikit-learn.org/stable/auto_examples/index.html)\n", " - 選択したモデルのAPIを参照し、手動調整が必要なハイパーパラメータを確認することができる。\n", " - 選択したモデルのAPIを参照し、評価方法(score関数)を確認することができる。\n", "- データセットに対して\n", " - ダウンロードしたデータセットの中身を目視確認し、保存されている形式を理解することができる。\n", " - 代表的な保存形式で保存されているデータセットに対して、pd.DataFrame形式として読み込むことができる。\n", " - 代表的な保存形式: csv, tsv\n", " - DataFrameから必要な行や列を指定してデータを抜き出すことができる。\n", " - 特徴ベクトルと教師データを分けたり、データセット全体を学習用・テスト用に分けることができる。\n", "- 機械学習に対して\n", " - 学習データを学習器に与えて学習させ、テストデータで学習結果の適切さを評価することができる。\n", " - 学習データとテストデータに分割することができる。[交差検定(cross-validation)](https://scikit-learn.org/stable/modules/cross_validation.html)するとベター。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 今回の流れ\n", "- Machine Learning Repositoryで公開されている代表的なデータ[Iris Data Set](https://archive.ics.uci.edu/ml/datasets/Iris)に対し、[LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html)で分類学習してみる。5分割検定で評価する。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### データセットの準備\n", "データセットのページには iris.data, iris.names が用意されていることを確認。この2つのファイルをダウンロードして、中身を覗いてみよう。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 4551 100 4551 0 0 7157 0 --:--:-- --:--:-- --:--:-- 7212\n", "5.1,3.5,1.4,0.2,Iris-setosa\n", "4.9,3.0,1.4,0.2,Iris-setosa\n", "4.7,3.2,1.3,0.2,Iris-setosa\n", "4.6,3.1,1.5,0.2,Iris-setosa\n", "5.0,3.6,1.4,0.2,Iris-setosa\n", "5.4,3.9,1.7,0.4,Iris-setosa\n", "4.6,3.4,1.4,0.3,Iris-setosa\n", "5.0,3.4,1.5,0.2,Iris-setosa\n", "4.4,2.9,1.4,0.2,Iris-setosa\n", "4.9,3.1,1.5,0.1,Iris-setosa\n" ] } ], "source": [ "# iris.data のダウンロード\n", "!curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n", "\n", "# head コマンドにより、冒頭数行を確認する\n", "!head iris.data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- どうやら iris.data には「各値がカンマで区切られて列挙されたCSV形式」でデータセットが保存されているらしい。冒頭4つが特徴量で、最後の1つはラベルデータっぽい。CSVなので恐らく [pd.read_csv](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html) を使えば DataFrame 形式で読み込めるだろう。\n", "- 次に iris.names を確認してみよう" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 2998 100 2998 0 0 4690 0 --:--:-- --:--:-- --:--:-- 4721\n", "1. Title: Iris Plants Database\n", "\tUpdated Sept 21 by C.Blake - Added discrepency information\n", "\n", "2. Sources:\n", " (a) Creator: R.A. Fisher\n", " (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n", " (c) Date: July, 1988\n", "\n", "3. Past Usage:\n", " - Publications: too many to mention!!! Here are a few.\n" ] } ], "source": [ "!curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.names\n", "!head iris.names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- どうやら iris.names はこのデータセットに関する説明が書かれているらしい。今回は使わない（説明ページで確認済み）ので、無視することにしよう。\n", "- iris.data だけで揃うようなので、これを読み込んで処理する準備をしよう。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5.13.51.40.2Iris-setosa
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..................
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" ], "text/plain": [ " 5.1 3.5 1.4 0.2 Iris-setosa\n", "0 4.9 3.0 1.4 0.2 Iris-setosa\n", "1 4.7 3.2 1.3 0.2 Iris-setosa\n", "2 4.6 3.1 1.5 0.2 Iris-setosa\n", "3 5.0 3.6 1.4 0.2 Iris-setosa\n", "4 5.4 3.9 1.7 0.4 Iris-setosa\n", ".. ... ... ... ... ...\n", "144 6.7 3.0 5.2 2.3 Iris-virginica\n", "145 6.3 2.5 5.0 1.9 Iris-virginica\n", "146 6.5 3.0 5.2 2.0 Iris-virginica\n", "147 6.2 3.4 5.4 2.3 Iris-virginica\n", "148 5.9 3.0 5.1 1.8 Iris-virginica\n", "\n", "[149 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv('iris.data')\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- pd.read_csvをそのまま使うと、1行目のデータを見出しとして処理してしまっている。これを避けるため、見出しなしとして再読み込みし直すか、もしくは別途見出しを用意して再読込し直す必要がありそうだ。ここでは見出しを用意することにしよう。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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45.03.61.40.2Iris-setosa
..................
1456.73.05.22.3Iris-virginica
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" ], "text/plain": [ " sepal length sepal width petal length petal width class\n", "0 5.1 3.5 1.4 0.2 Iris-setosa\n", "1 4.9 3.0 1.4 0.2 Iris-setosa\n", "2 4.7 3.2 1.3 0.2 Iris-setosa\n", "3 4.6 3.1 1.5 0.2 Iris-setosa\n", "4 5.0 3.6 1.4 0.2 Iris-setosa\n", ".. ... ... ... ... ...\n", "145 6.7 3.0 5.2 2.3 Iris-virginica\n", "146 6.3 2.5 5.0 1.9 Iris-virginica\n", "147 6.5 3.0 5.2 2.0 Iris-virginica\n", "148 6.2 3.4 5.4 2.3 Iris-virginica\n", "149 5.9 3.0 5.1 1.8 Iris-virginica\n", "\n", "[150 rows x 5 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_names = ['sepal length', 'sepal width', 'petal length', 'petal width', 'class']\n", "df = pd.read_csv('iris.data', names=feature_names)\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 学習に向けて特徴ベクトルと教師データを分けておく。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X.shape = (150, 4)\n", "y.shape = (150,)\n", "sepal length 5.1\n", "sepal width 3.5\n", "petal length 1.4\n", "petal width 0.2\n", "Name: 0, dtype: float64\n", "Iris-setosa\n" ] } ], "source": [ "X = df[['sepal length', 'sepal width', 'petal length', 'petal width']]\n", "y = df['class']\n", "print('X.shape = ', X.shape)\n", "print('y.shape = ', y.shape)\n", "print(X.loc[0])\n", "print(y[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 学習器の用意\n", "- [LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html)を参考にimportしておく。\n", "- ハイパーパラメータのうち C について 0.5, 1.0, 1,5 の3通りを試してみよう。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import LinearSVC\n", "Cs = [0.5, 1.0, 1.5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 交差検定を利用して学習＆評価\n", "今回は[cross_val_score](https://scikit-learn.org/stable/modules/cross_validation.html#computing-cross-validated-metrics)を使ってみよう。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C = 0.5: scores=[0.96666667 0.96666667 0.9 0.96666667 1. ], average=0.960\n", "C = 1.0: scores=[0.93333333 0.96666667 0.93333333 0.93333333 1. ], average=0.953\n", "C = 1.5: scores=[0.9 1. 0.93333333 1. 0.9 ], average=0.947\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n", "/Users/tnal/.venv/dm/lib/python3.8/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n", " warnings.warn(\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import KFold\n", "\n", "k_folds = 5 # 5分割検定\n", "\n", "for c in Cs:\n", " model = LinearSVC(C=c)\n", " scores = cross_val_score(model, X, y, cv=KFold(n_splits=k_folds, shuffle=True))\n", " average = scores.mean()\n", " print(f'C = {c}: scores={scores}, average={average:.3f}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "interpreter": { "hash": "880b2a8c90f9e6beae80b56829e3f671fedd58b6d14887184ddce26124cedfbd" }, "kernelspec": { "display_name": "Python 3.8.9 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.9" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }