From 885e7dd9a457e39c1af193efcd0dad2151ddee8f Mon Sep 17 00:00:00 2001 From: benjas <909336740@qq.com> Date: Fri, 11 Dec 2020 23:42:19 +0800 Subject: [PATCH] =?UTF-8?q?Create=20=E6=A8=A1=E5=9E=8B=E8=A7=A3=E9=87=8A-c?= =?UTF-8?q?heckpoint.ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../模型解释-checkpoint.ipynb | 886 ++++++++++++++++++ 1 file changed, 886 insertions(+) create mode 100644 机器学习竞赛实战_优胜解决方案/特征工程建模/.ipynb_checkpoints/模型解释-checkpoint.ipynb diff --git a/机器学习竞赛实战_优胜解决方案/特征工程建模/.ipynb_checkpoints/模型解释-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/特征工程建模/.ipynb_checkpoints/模型解释-checkpoint.ipynb new file mode 100644 index 0000000..759fd4e --- /dev/null +++ b/机器学习竞赛实战_优胜解决方案/特征工程建模/.ipynb_checkpoints/模型解释-checkpoint.ipynb @@ -0,0 +1,886 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Permutation Importance" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateTeamOpponentGoal ScoredBall Possession %AttemptsOn-TargetOff-TargetBlockedCorners...Yellow CardYellow & RedRedMan of the Match1st GoalRoundPSOGoals in PSOOwn goalsOwn goal Time
014-06-2018RussiaSaudi Arabia540137336...000Yes12.0Group StageNo0NaNNaN
114-06-2018Saudi ArabiaRussia06060332...000NoNaNGroup StageNo0NaNNaN
215-06-2018EgyptUruguay04383320...200NoNaNGroup StageNo0NaNNaN
315-06-2018UruguayEgypt157144645...000Yes89.0Group StageNo0NaNNaN
415-06-2018MoroccoIran064133645...100NoNaNGroup StageNo01.090.0
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5 rows × 27 columns

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" + ], + "text/plain": [ + " Date Team Opponent Goal Scored Ball Possession % \\\n", + "0 14-06-2018 Russia Saudi Arabia 5 40 \n", + "1 14-06-2018 Saudi Arabia Russia 0 60 \n", + "2 15-06-2018 Egypt Uruguay 0 43 \n", + "3 15-06-2018 Uruguay Egypt 1 57 \n", + "4 15-06-2018 Morocco Iran 0 64 \n", + "\n", + " Attempts On-Target Off-Target Blocked Corners ... Yellow Card \\\n", + "0 13 7 3 3 6 ... 0 \n", + "1 6 0 3 3 2 ... 0 \n", + "2 8 3 3 2 0 ... 2 \n", + "3 14 4 6 4 5 ... 0 \n", + "4 13 3 6 4 5 ... 1 \n", + "\n", + " Yellow & Red Red Man of the Match 1st Goal Round PSO \\\n", + "0 0 0 Yes 12.0 Group Stage No \n", + "1 0 0 No NaN Group Stage No \n", + "2 0 0 No NaN Group Stage No \n", + "3 0 0 Yes 89.0 Group Stage No \n", + "4 0 0 No NaN Group Stage No \n", + "\n", + " Goals in PSO Own goals Own goal Time \n", + "0 0 NaN NaN \n", + "1 0 NaN NaN \n", + "2 0 NaN NaN \n", + "3 0 NaN NaN \n", + "4 0 1.0 90.0 \n", + "\n", + "[5 rows x 27 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv('data/FIFA 2018 Statistics.csv') # 足球赛事数据集\n", + "data.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "怎么知道哪些特征影响多大\n", + "\n", + "破坏特征：比如把Goal Scored上下的顺序打乱，其它不变，然后比较破坏前和后的区别\n", + "\n", + "如果前 ≈ 后，说明这个特征对结果没影响\n", + "如果后 ＜ 前，说明这个特征对结果影响起正作用\n", + "如果后 ＞ 前，说明这个特征对结果影响起负作用（一般不会出现）" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 True\n", + "1 False\n", + "2 False\n", + "3 True\n", + "4 False\n", + "Name: Man of the Match, dtype: bool" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = (data['Man of the Match'] == 'Yes') # 转换标签\n", + "y[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Goal ScoredBall Possession %AttemptsOn-TargetOff-TargetBlockedCornersOffsidesFree KicksSavesPass Accuracy %PassesDistance Covered (Kms)Fouls CommittedYellow CardYellow & RedRedGoals in PSO
0540137336311078306118220000
106060332125286511105100000
20438332017378395112122000
315714464511338658911160000
4064133645014286433101221000
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" + ], + "text/plain": [ + " Goal Scored Ball Possession % Attempts On-Target Off-Target Blocked \\\n", + "0 5 40 13 7 3 3 \n", + "1 0 60 6 0 3 3 \n", + "2 0 43 8 3 3 2 \n", + "3 1 57 14 4 6 4 \n", + "4 0 64 13 3 6 4 \n", + "\n", + " Corners Offsides Free Kicks Saves Pass Accuracy % Passes \\\n", + "0 6 3 11 0 78 306 \n", + "1 2 1 25 2 86 511 \n", + "2 0 1 7 3 78 395 \n", + "3 5 1 13 3 86 589 \n", + "4 5 0 14 2 86 433 \n", + "\n", + " Distance Covered (Kms) Fouls Committed Yellow Card Yellow & Red Red \\\n", + "0 118 22 0 0 0 \n", + "1 105 10 0 0 0 \n", + "2 112 12 2 0 0 \n", + "3 111 6 0 0 0 \n", + "4 101 22 1 0 0 \n", + "\n", + " Goals in PSO \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_names = [i for i in data.columns if data[i].dtype in [np.int64]]\n", + "X = data[feature_names]\n", + "X.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "D:\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n", + " \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n" + ] + } + ], + "source": [ + "train_X, val_X,train_y,val_y = train_test_split(X, y,random_state=1)\n", + "my_model = RandomForestClassifier(random_state=0).fit(train_X,train_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
WeightFeature
\n", + " 0.0750\n", + " \n", + " ± 0.1159\n", + " \n", + " \n", + " Goal Scored\n", + "
\n", + " 0.0625\n", + " \n", + " ± 0.0791\n", + " \n", + " \n", + " Corners\n", + "
\n", + " 0.0437\n", + " \n", + " ± 0.0500\n", + " \n", + " \n", + " Distance Covered (Kms)\n", + "
\n", + " 0.0375\n", + " \n", + " ± 0.0729\n", + " \n", + " \n", + " On-Target\n", + "
\n", + " 0.0375\n", + " \n", + " ± 0.0468\n", + " \n", + " \n", + " Free Kicks\n", + "
\n", + " 0.0187\n", + " \n", + " ± 0.0306\n", + " \n", + " \n", + " Blocked\n", + "
\n", + " 0.0125\n", + " \n", + " ± 0.0750\n", + " \n", + " \n", + " Pass Accuracy %\n", + "
\n", + " 0.0125\n", + " \n", + " ± 0.0500\n", + " \n", + " \n", + " Yellow Card\n", + "
\n", + " 0.0063\n", + " \n", + " ± 0.0468\n", + " \n", + " \n", + " Saves\n", + "
\n", + " 0.0063\n", + " \n", + " ± 0.0250\n", + " \n", + " \n", + " Offsides\n", + "
\n", + " 0.0063\n", + " \n", + " ± 0.1741\n", + " \n", + " \n", + " Off-Target\n", + "
\n", + " 0.0000\n", + " \n", + " ± 0.1046\n", + " \n", + " \n", + " Passes\n", + "
\n", + " 0\n", + " \n", + " ± 0.0000\n", + " \n", + " \n", + " Red\n", + "
\n", + " 0\n", + " \n", + " ± 0.0000\n", + " \n", + " \n", + " Yellow & Red\n", + "
\n", + " 0\n", + " \n", + " ± 0.0000\n", + " \n", + " \n", + " Goals in PSO\n", + "
\n", + " -0.0312\n", + " \n", + " ± 0.0884\n", + " \n", + " \n", + " Fouls Committed\n", + "
\n", + " -0.0375\n", + " \n", + " ± 0.0919\n", + " \n", + " \n", + " Attempts\n", + "
\n", + " -0.0500\n", + " \n", + " ± 0.0500\n", + " \n", + " \n", + " Ball Possession %\n", + "
\n", + " \n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import eli5 # pip install eli5\n", + "from eli5.sklearn import PermutationImportance\n", + "\n", + "perm = PermutationImportance(my_model,random_state=1).fit(val_X, val_y)\n", + "\n", + "eli5.show_weights(perm, feature_names=val_X.columns.tolist())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Partial Dependence Plots\n", + "\n", + "特征重要性展示了每个特征发挥的作用情况，partial dependence plots 可以展示一个特征怎么影响预测结果\n", + "\n", + "模型建立完成后进行使用，概述如下：\n", + "