├── .gitignore ├── .ipynb_checkpoints └── Pseudo-Labeling-checkpoint.ipynb ├── Keras └── feature_extract_and_xgboost.ipynb ├── MachineLearning ├── .ipynb_checkpoints │ └── nile_RF-checkpoint.ipynb ├── Readme.md ├── ch03_rasbt.ipynb ├── ch04_rasbt.ipynb ├── ch05_rasbt.ipynb ├── ch06_rasbt.ipynb ├── ch07_rasbt.ipynb ├── ch10_rasbt.ipynb ├── ch11_rasbt.ipynb ├── images │ ├── 03_05.png │ ├── 03_09.png │ ├── 03_11.png │ ├── 04_15.png │ ├── 06_01.png │ ├── 06_02.png │ ├── 06_03.png │ ├── 06_04.png │ ├── 06_05.png │ ├── 06_06.png │ ├── 06_07.png │ ├── 06_08.png │ ├── 06_10.png │ ├── 07_06.png │ ├── 07_07.png │ ├── 07_08.png │ ├── 07_09.png │ ├── 07_10.png │ ├── 07_11.png │ └── 11_11.png └── pandas_tutorial.ipynb ├── PseudoLabel ├── Accurate-Pseudo-Label.ipynb ├── Cross-Pseudo-Label.ipynb ├── Distillation-Pseudo-Label.ipynb ├── Make-Pseudo-Label.ipynb └── Train-Pseudo-Label.ipynb ├── examples └── entity_embeddings.ipynb ├── hikkoshi ├── Untitled.ipynb ├── Visualize.ipynb ├── dow_embedding.png ├── entity_embeddings_example.ipynb ├── month_embedding.png ├── subspace_method_example.ipynb └── train.ipynb └── 輪講 ├── 353.png ├── 356.png ├── 366.png ├── EP.ipynb ├── alg35.png ├── alg36.png ├── ep.pdf └── ep.py /.gitignore: -------------------------------------------------------------------------------- 1 | *.tsv 2 | *.csv 3 | */.ipynb_checkpoints/ 4 | .DS_Store 5 | PseudoLabel/data/ 6 | PseudoLabel/log/ 7 | PseudoLabel/save/ 8 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Pseudo-Labeling-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import torch\n", 12 | "import torch.nn as nn\n", 13 | "import torch.nn.functional as F\n", 14 | "import torch.optim as optim\n", 15 | "from torchvision import datasets, transforms\n", 16 | "from torch.autograd import Variable\n", 17 | "\n", 18 | "batch_size = 64\n", 19 | "test_batch_size = batch_size * 2\n", 20 | "epochs = 20\n", 21 | "lr = 0.01\n", 22 | "seed = 2017\n", 23 | "log_interval = 1000\n", 24 | "\n", 25 | "torch.manual_seed(args.seed)\n", 26 | "\n", 27 | "kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n", 28 | "train_loader = torch.utils.data.DataLoader(\n", 29 | " datasets.MNIST('../data', train=True, download=True,\n", 30 | " transform=transforms.Compose([\n", 31 | " transforms.ToTensor(),\n", 32 | " transforms.Normalize((0.1307,), (0.3081,))\n", 33 | " ])),\n", 34 | " batch_size=batch_size, shuffle=True, **kwargs)\n", 35 | "test_loader = torch.utils.data.DataLoader(\n", 36 | " datasets.MNIST('../data', train=False, transform=transforms.Compose([\n", 37 | " transforms.ToTensor(),\n", 38 | " transforms.Normalize((0.1307,), (0.3081,))\n", 39 | " ])),\n", 40 | " batch_size=args.test_batch_size, shuffle=True, **kwargs)\n", 41 | "\n", 42 | "\n", 43 | "class Net(nn.Module):\n", 44 | " def __init__(self):\n", 45 | " super(Net, self).__init__()\n", 46 | " self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n", 47 | " self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n", 48 | " self.conv2_drop = nn.Dropout2d()\n", 49 | " self.fc1 = nn.Linear(320, 50)\n", 50 | " self.fc2 = nn.Linear(50, 10)\n", 51 | "\n", 52 | " def forward(self, x):\n", 53 | " x = F.relu(F.max_pool2d(self.conv1(x), 2))\n", 54 | " x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", 55 | " x = x.view(-1, 320)\n", 56 | " x = F.relu(self.fc1(x))\n", 57 | " x = F.dropout(x, training=self.training)\n", 58 | " x = self.fc2(x)\n", 59 | " return F.log_softmax(x)\n", 60 | "\n", 61 | "model = Net()\n", 62 | "if args.cuda:\n", 63 | " model.cuda()\n", 64 | "\n", 65 | "optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)\n", 66 | "\n", 67 | "def train(epoch):\n", 68 | " model.train()\n", 69 | " for batch_idx, (data, target) in enumerate(train_loader):\n", 70 | " if args.cuda:\n", 71 | " data, target = data.cuda(), target.cuda()\n", 72 | " data, target = Variable(data), Variable(target)\n", 73 | " optimizer.zero_grad()\n", 74 | " output = model(data)\n", 75 | " loss = F.nll_loss(output, target)\n", 76 | " loss.backward()\n", 77 | " optimizer.step()\n", 78 | " if batch_idx % args.log_interval == 0:\n", 79 | " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", 80 | " epoch, batch_idx * len(data), len(train_loader.dataset),\n", 81 | " 100. * batch_idx / len(train_loader), loss.data[0]))\n", 82 | "\n", 83 | "def test():\n", 84 | " model.eval()\n", 85 | " test_loss = 0\n", 86 | " correct = 0\n", 87 | " for data, target in test_loader:\n", 88 | " if args.cuda:\n", 89 | " data, target = data.cuda(), target.cuda()\n", 90 | " data, target = Variable(data, volatile=True), Variable(target)\n", 91 | " output = model(data)\n", 92 | " test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss\n", 93 | " pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n", 94 | " correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n", 95 | "\n", 96 | " test_loss /= len(test_loader.dataset)\n", 97 | " print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n", 98 | " test_loss, correct, len(test_loader.dataset),\n", 99 | " 100. * correct / len(test_loader.dataset)))\n", 100 | "\n", 101 | "\n", 102 | "for epoch in range(1, args.epochs + 1):\n", 103 | " train(epoch)\n", 104 | " test()" 105 | ] 106 | } 107 | ], 108 | "metadata": { 109 | "kernelspec": { 110 | "display_name": "Python 3", 111 | "language": "python", 112 | "name": "python3" 113 | }, 114 | "language_info": { 115 | "codemirror_mode": { 116 | "name": "ipython", 117 | "version": 3 118 | }, 119 | "file_extension": ".py", 120 | "mimetype": "text/x-python", 121 | "name": "python", 122 | "nbconvert_exporter": "python", 123 | "pygments_lexer": "ipython3", 124 | "version": "3.6.3" 125 | }, 126 | "toc": { 127 | "nav_menu": {}, 128 | "number_sections": true, 129 | "sideBar": true, 130 | "skip_h1_title": false, 131 | "toc_cell": false, 132 | "toc_position": {}, 133 | "toc_section_display": "block", 134 | "toc_window_display": false 135 | }, 136 | "varInspector": { 137 | "cols": { 138 | "lenName": 16, 139 | "lenType": 16, 140 | "lenVar": 40 141 | }, 142 | "kernels_config": { 143 | "python": { 144 | "delete_cmd_postfix": "", 145 | "delete_cmd_prefix": "del ", 146 | "library": "var_list.py", 147 | "varRefreshCmd": "print(var_dic_list())" 148 | }, 149 | "r": { 150 | "delete_cmd_postfix": ") ", 151 | "delete_cmd_prefix": "rm(", 152 | "library": "var_list.r", 153 | "varRefreshCmd": "cat(var_dic_list()) " 154 | } 155 | }, 156 | "types_to_exclude": [ 157 | "module", 158 | "function", 159 | "builtin_function_or_method", 160 | "instance", 161 | "_Feature" 162 | ], 163 | "window_display": false 164 | } 165 | }, 166 | "nbformat": 4, 167 | "nbformat_minor": 2 168 | } 169 | -------------------------------------------------------------------------------- /MachineLearning/.ipynb_checkpoints/nile_RF-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 65, 6 | "metadata": { 7 | "collapsed": false, 8 | "scrolled": true 9 | }, 10 | "outputs": [ 11 | { 12 | "data": { 13 | "image/png": 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mmYGS4dBIjzMDjQg2MIDqV64uRtGBR157qCJwPHlgGEXZ3WBAOE/W0wwMWn2z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\nuczA6fOT5AvFpqoXAOgNtEhebjZMT2fr5uszYIw5jdsqGNz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14 | "text/plain": [ 15 | "" 16 | ] 17 | }, 18 | "metadata": {}, 19 | "output_type": "display_data" 20 | }, 21 | { 22 | "name": "stdout", 23 | "output_type": "stream", 24 | "text": [ 25 | "0.245969448342\n" 26 | ] 27 | }, 28 | { 29 | "data": { 30 | "text/plain": [ 31 | "{'bootstrap': True,\n", 32 | " 'criterion': 'mse',\n", 33 | " 'max_depth': None,\n", 34 | " 'max_features': 'auto',\n", 35 | " 'max_leaf_nodes': None,\n", 36 | " 'min_impurity_split': 1e-07,\n", 37 | " 'min_samples_leaf': 1,\n", 38 | " 'min_samples_split': 2,\n", 39 | " 'min_weight_fraction_leaf': 0.0,\n", 40 | " 'n_estimators': 100,\n", 41 | " 'n_jobs': 1,\n", 42 | " 'oob_score': True,\n", 43 | " 'random_state': 1,\n", 44 | " 'verbose': 0,\n", 45 | " 'warm_start': False}" 46 | ] 47 | }, 48 | "execution_count": 65, 49 | "metadata": {}, 50 | "output_type": "execute_result" 51 | }, 52 | { 53 | "data": { 54 | "text/plain": [ 55 | "" 56 | ] 57 | }, 58 | "metadata": {}, 59 | "output_type": "display_data" 60 | }, 61 | { 62 | "data": { 63 | "image/png": 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1E9uJ7bh88VunicgS/+bKIYC6QGwlwRCTVjGFbrYUJmbFNXJxbvfPfihUTKo1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\nhD0mF2K7FCQervncoyG/TFkNyh39cgfszKW3PcLRWR+Brkal4HZItSt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64 | "text/plain": [ 65 | "" 66 | ] 67 | }, 68 | "metadata": {}, 69 | "output_type": "display_data" 70 | } 71 | ], 72 | "source": [ 73 | "%matplotlib inline\n", 74 | "import statsmodels as sm\n", 75 | "import pandas as pd\n", 76 | "import matplotlib.pyplot as plt\n", 77 | "import seaborn as sns\n", 78 | "import numpy as np\n", 79 | "from sklearn.ensemble import RandomForestRegressor\n", 80 | "\n", 81 | "def load_data():\n", 82 | " df_read = sm.datasets.nile.load_pandas().data\n", 83 | " s_date = pd.Series(\n", 84 | " [pd.to_datetime(str(int(y_str))) for y_str in df_read['year']]\n", 85 | " )\n", 86 | " df = df_read.set_index(s_date)\n", 87 | " df = df.drop('year', axis=1)\n", 88 | "\n", 89 | " return df\n", 90 | "\n", 91 | "df_nile = load_data()\n", 92 | "\n", 93 | "# Plot Time Series Data\n", 94 | "plt.figure()\n", 95 | "ax = df_nile['volume'].plot(figsize=(8,4), grid=True)\n", 96 | "ax.set_xlabel('year')\n", 97 | "ax.set_ylabel('volume x 10^8 m^3')\n", 98 | "plt.show()\n", 99 | "\n", 100 | "df_nile['lag1'] = df_nile['volume'].shift(1) \n", 101 | "df_nile['lag2'] = df_nile['volume'].shift(2)\n", 102 | "df_nile['lag3'] = df_nile['volume'].shift(3)\n", 103 | "\n", 104 | "df_nile = df_nile.dropna()\n", 105 | "\n", 106 | "x_train = df_nile[['lag1', 'lag2', 'lag3']][:67].values\n", 107 | "x_test = df_nile[['lag1', 'lag2', 'lag3']][67:].values\n", 108 | "\n", 109 | "y_train = df_nile['volume'][:67].values\n", 110 | "y_test = df_nile['volume'][67:].values\n", 111 | "\n", 112 | "# bootstrap:Bootstrapするかどうか\n", 113 | "# n_estimators:木の本数\n", 114 | "# criterion:評価関数 \n", 115 | "# max_features:説明変数の数(推奨値は分類：√n 回帰：n/3 特徴量がいっぱいあるときはもっと増やしたほうが良い) \n", 116 | "# max_leaf_nodes:最大ノード数（推奨値は分類：１　回帰５）\n", 117 | "# random_state:乱数のシード\n", 118 | "# oob_score: R^2をout of bags使って評価するかどうか\n", 119 | "rf = RandomForestRegressor(\n", 120 | " bootstrap=True,\n", 121 | " n_estimators=100,\n", 122 | " criterion='mse', \n", 123 | " max_features='auto',\n", 124 | " random_state=1,\n", 125 | " oob_score=True,\n", 126 | ")\n", 127 | "\n", 128 | "\n", 129 | "rf.fit(x_train, y_train)\n", 130 | "train_pred = rf.predict(x_train)\n", 131 | "test_pred = rf.predict(x_test)\n", 132 | "\n", 133 | "pred = np.concatenate((train_pred, test_pred))\n", 134 | "df_nile['pred'] = pred\n", 135 | "\n", 136 | "plt.figure()\n", 137 | "df_nile[['volume', 'pred']].plot()\n", 138 | "print(rf.oob_score_)\n", 139 | "\n", 140 | "# 実はテスト用のデータの特徴量に、訓練データの特徴量が時系列でスライドしてる分混じってしまっている" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 63, 146 | "metadata": { 147 | "collapsed": false 148 | }, 149 | "outputs": [ 150 | { 151 | "name": "stdout", 152 | "output_type": "stream", 153 | "text": [ 154 | "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", 155 | " max_features=3, max_leaf_nodes=None, min_impurity_split=1e-07,\n", 156 | " min_samples_leaf=1, min_samples_split=2,\n", 157 | " min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n", 158 | " oob_score=False, random_state=1, verbose=0, warm_start=False)\n" 159 | ] 160 | } 161 | ], 162 | "source": [ 163 | "# parameter tuning by grid search\n", 164 | "from sklearn.grid_search import GridSearchCV \n", 165 | "\n", 166 | "parameters = {\n", 167 | " 'n_estimators' : [10, 50, 100, 150, 200, 250, 300],\n", 168 | " 'max_features' : [1, 2, 3],\n", 169 | " 'random_state' : [1],\n", 170 | "}\n", 171 | "\n", 172 | "rf_tuned = GridSearchCV(RandomForestRegressor(), parameters)\n", 173 | "rf_tuned.fit(x_train, y_train)\n", 174 | "\n", 175 | "# クロスバリデーションによって得られた最適なパラメータの表示\n", 176 | "print(rf_tuned.best_estimator_)" 177 | ] 178 | } 179 | ], 180 | "metadata": { 181 | "kernelspec": { 182 | "display_name": "Python 3", 183 | "language": "python", 184 | "name": "python3" 185 | }, 186 | "language_info": { 187 | "codemirror_mode": { 188 | "name": "ipython", 189 | "version": 3 190 | }, 191 | "file_extension": ".py", 192 | "mimetype": "text/x-python", 193 | "name": "python", 194 | "nbconvert_exporter": "python", 195 | "pygments_lexer": "ipython3", 196 | "version": "3.6.0" 197 | } 198 | }, 199 | "nbformat": 4, 200 | "nbformat_minor": 2 201 | } 202 | -------------------------------------------------------------------------------- /MachineLearning/Readme.md: -------------------------------------------------------------------------------- 1 | # 「Python 機械学習プログラミング」の学習まとめレポジトリ 2 | 3 | * まとめノートのファイル名 => 'ch0x_rasbt.ipynb' 4 | * pandasの使い方を断片的にまとめたノート => 'pandas_tutorial.ipynb' 5 | * ch01,02,08,09,12,13については内容的にまとめを作っていません。 6 | 7 | ## ch03 8 | * ロジスティック回帰 9 | * SVM 10 | * ガウシアンカーネル 11 | * 決定木 12 | 13 | ## ch04 14 | * pandasを使ったデータの扱い 15 | * 正則化 16 | * 特徴量選択（ランダムフォレストのfeature_importance_） 17 | 18 | ## ch05 19 | * 主成分分析 20 | * 線形判別分析 21 | * カーネル主成分分析 22 | 23 | ## ch06 24 | * 機械学習手法のパイプライン化 25 | * クロスバリデーション 26 | * 学習曲線、検証曲線 27 | * グリッドサーチ 28 | * 分類機の性能指標 29 | 30 | ## ch07 31 | * バギング 32 | * ブースティング 33 | * アダブースト 34 | 35 | ## ch10 36 | * 特徴量間の相関性 37 | * R2スコア 38 | * 重回帰 39 | * 多項式回帰 40 | * 決定木回帰、ランダムフォレスト回帰 41 | 42 | ## ch11 43 | * k-means 44 | * 階層的クラスタリング 45 | * DBSCAN 46 | -------------------------------------------------------------------------------- /MachineLearning/ch03_rasbt.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# ロジスティック回帰\n", 8 | "名前に反して分類手法の一つであり、人気手法の一つ\n", 9 | "\n", 10 | "### 長所\n", 11 | "* 出力が0〜1の確率で表されるため、天気予報などの「どのくらいの確率で現象Aが起こるか」を知りたいタスクで便利\n", 12 | "* シンプルで解釈性にも優れる\n", 13 | "\n", 14 | "### 短所\n", 15 | "* ロジスティック回帰は与えられた訓練データから、パラメータの条件付き尤度最大化を行っているため、外れ値に影響されやすい\n", 16 | "* 他クラスの場合は一対他で比較する必要がある\n", 17 | "\n", 18 | "# サポートベクタマシン（SVM）\n", 19 | "人気手法の一つであり、ディープラーニングが出てくるまでは無双していた\n", 20 | "### 長所\n", 21 | "* 比較的実装が容易であり、軽量\n", 22 | "* データが線形分離不可能な場合（XORなど）で、非線形分離したい時に使う **カーネル法** と相性が良い\n", 23 | "* 訓練時にSVMにとって興味があるのが「サポートベクタ（決定境界付近のデータ）」のみであるので、外れ値に強い\n", 24 | "\n", 25 | "### 短所\n", 26 | "* パラメータチューニングが必要\n", 27 | "\n", 28 | "#### 補足：カーネル法について\n", 29 | "非線形な射影関数を用いて、非線形分離を行うことが可能である。\n", 30 | "$\\phi(x)$をデータ$x$の射影とすると、訓練時に解く式に登場する$x$を$\\phi(x)$で置き換えるだけで良い。\n", 31 | "しかし、SVMは最適化する際に$\\phi(x)$の内積を計算する必要があるが、陽に$\\phi(x)$を扱うと計算が大変である。\n", 32 | "よって以下のカーネル関数を代わりに計算する。\n", 33 | "$$k(x, x') = \\phi(x)^T \\phi(x')$$\n", 34 | "\n", 35 | "よく使われるカーネル関数はガウスカーネルと呼ばれる無限次元の基底を持つ射影関数である。式を下に示す。\n", 36 | "\n", 37 | "$${k(x, x') = exp(-\\frac {||x - x'||^2} {2\\sigma^2} )}$$\n", 38 | "\n", 39 | "実際には定数項をまとめて下のように簡略化される\n", 40 | "\n", 41 | "$${k(x, x') = exp(-\\gamma {||x - x'||^2} )}$$\n", 42 | "\n", 43 | "カーネルは、大雑把に言うと「2サンプル間の類似度」を表している。\n", 44 | "\n", 45 | "実際にカーネルSVMを実行してみる。" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 7, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "image/png": 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CDDt2nyu83dt5AsgUfDMKhRUGDAIKEAcVKHCbNxtzTkeO2H++tTVX6TYyYszR6Jif8bIT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\n4ffiu5veiYsb38K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58 | "text/plain": [ 59 | "" 60 | ] 61 | }, 62 | "metadata": {}, 63 | "output_type": "display_data" 64 | } 65 | ], 66 | "source": [ 67 | "import matplotlib.pyplot as plt\n", 68 | "import numpy as np\n", 69 | "from sklearn.svm import SVC\n", 70 | "\n", 71 | "plt.xlim([-3, 3])\n", 72 | "plt.ylim([-3, 3])\n", 73 | "\n", 74 | "# 'rbf' はガウスカーネルのこと。線形分離したいときは'linear'\n", 75 | "# gamma：大きいほど複雑な決定境界になる C：大きいほどハードマージン\n", 76 | "svm = SVC(kernel='rbf', random_state=0, gamma=0.10, C=10.0)\n", 77 | "\n", 78 | "svm.fit(X_xor, y_xor)\n", 79 | "plot_decision_regions(X_xor, y_xor,\n", 80 | " classifier=svm)\n", 81 | "plt.legend(loc='upper left')\n", 82 | "plt.tight_layout()\n", 83 | "plt.show()" 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "metadata": {}, 89 | "source": [ 90 | "## ロジスティック回帰とSVMの比較" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 10, 96 | "metadata": { 97 | "collapsed": false 98 | }, 99 | "outputs": [ 100 | { 101 | "data": { 102 | "text/html": [ 103 | "
ロジスティック回帰SVM
" 104 | ], 105 | "text/plain": [ 106 | "" 107 | ] 108 | }, 109 | "metadata": {}, 110 | "output_type": "display_data" 111 | } 112 | ], 113 | "source": [ 114 | "from IPython.display import HTML, display\n", 115 | "display(HTML(\"
ロジスティック回帰SVM
\"))" 116 | ] 117 | }, 118 | { 119 | "cell_type": "markdown", 120 | "metadata": {}, 121 | "source": [ 122 | "# 決定木\n", 123 | "ジニ係数（ジニ不純度）：データの混ざり具合\n", 124 | "\n", 125 | "### 長所\n", 126 | "* 特徴量のスケーリングが必要ない\n", 127 | "* モデルの解釈が容易（どの特徴量がどのように影響を及ぼしているか分かりやすい）\n", 128 | "\n", 129 | "### 短所\n", 130 | "* 過学習しやすい。（しかし、低バイアス高バリアンスの性質を活かしたアンサンブル学習器の「ランダムフォレスト」がとても強力）" 131 | ] 132 | }, 133 | { 134 | "cell_type": "markdown", 135 | "metadata": {}, 136 | "source": [ 137 | "---\n", 138 | "### その他コード" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 4, 144 | "metadata": { 145 | "collapsed": true 146 | }, 147 | "outputs": [], 148 | "source": [ 149 | "from matplotlib.colors import ListedColormap\n", 150 | "import matplotlib.pyplot as plt\n", 151 | "import warnings\n", 152 | "\n", 153 | "\n", 154 | "def versiontuple(v):\n", 155 | " return tuple(map(int, (v.split(\".\"))))\n", 156 | "\n", 157 | "\n", 158 | "def plot_decision_regions(X, y, classifier, test_idx=None, resolution=0.02):\n", 159 | "\n", 160 | " # setup marker generator and color map\n", 161 | " markers = ('s', 'x', 'o', '^', 'v')\n", 162 | " colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')\n", 163 | " cmap = ListedColormap(colors[:len(np.unique(y))])\n", 164 | "\n", 165 | " # plot the decision surface\n", 166 | " x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n", 167 | " x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n", 168 | " xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n", 169 | " np.arange(x2_min, x2_max, resolution))\n", 170 | " Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)\n", 171 | " Z = Z.reshape(xx1.shape)\n", 172 | " plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)\n", 173 | " plt.xlim(xx1.min(), xx1.max())\n", 174 | " plt.ylim(xx2.min(), xx2.max())\n", 175 | "\n", 176 | " for idx, cl in enumerate(np.unique(y)):\n", 177 | " plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],\n", 178 | " alpha=0.8, c=cmap(idx),\n", 179 | " marker=markers[idx], label=cl)\n", 180 | "\n", 181 | " # highlight test samples\n", 182 | " if test_idx:\n", 183 | " # plot all samples\n", 184 | " if not versiontuple(np.__version__) >= versiontuple('1.9.0'):\n", 185 | " X_test, y_test = X[list(test_idx), :], y[list(test_idx)]\n", 186 | " warnings.warn('Please update to NumPy 1.9.0 or newer')\n", 187 | " else:\n", 188 | " X_test, y_test = X[test_idx, :], y[test_idx]\n", 189 | "\n", 190 | " plt.scatter(X_test[:, 0],\n", 191 | " X_test[:, 1],\n", 192 | " c='',\n", 193 | " alpha=1.0,\n", 194 | " linewidths=1,\n", 195 | " marker='o',\n", 196 | " s=55, label='test set')" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": 6, 202 | "metadata": { 203 | "collapsed": false 204 | }, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "text/plain": [ 209 | "" 210 | ] 211 | }, 212 | "execution_count": 6, 213 | "metadata": {}, 214 | "output_type": "execute_result" 215 | } 216 | ], 217 | "source": [ 218 | "np.random.seed(0)\n", 219 | "X_xor = np.random.randn(200, 2)\n", 220 | "y_xor = np.logical_xor(X_xor[:, 0] > 0,\n", 221 | " X_xor[:, 1] > 0)\n", 222 | "y_xor = np.where(y_xor, 1, -1)\n", 223 | "\n", 224 | "plt.scatter(X_xor[y_xor == 1, 0],\n", 225 | " X_xor[y_xor == 1, 1],\n", 226 | " c='b', marker='x',\n", 227 | " label='1')\n", 228 | "plt.scatter(X_xor[y_xor == -1, 0],\n", 229 | " X_xor[y_xor == -1, 1],\n", 230 | " c='r',\n", 231 | " marker='s',\n", 232 | " label='-1')\n" 233 | ] 234 | } 235 | ], 236 | "metadata": { 237 | "kernelspec": { 238 | "display_name": "Python 3", 239 | "language": "python", 240 | "name": "python3" 241 | }, 242 | "language_info": { 243 | "codemirror_mode": { 244 | "name": "ipython", 245 | "version": 3 246 | }, 247 | "file_extension": ".py", 248 | "mimetype": "text/x-python", 249 | "name": "python", 250 | "nbconvert_exporter": "python", 251 | "pygments_lexer": "ipython3", 252 | "version": "3.6.0" 253 | } 254 | }, 255 | "nbformat": 4, 256 | "nbformat_minor": 2 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" n_dimension : int\n", 22 | " Number of dimension of subspace.\n", 23 | " Attributes\n", 24 | " ----------\n", 25 | " subspaces_ : array, shape (n_classes, n_features, n_features)\n", 26 | " Subspace of N classes.\n", 27 | " \n", 28 | " classes_ : array, shape (n_classes,)\n", 29 | " Unique labels of Classes.\n", 30 | " \"\"\"\n", 31 | " \n", 32 | " def __init__(self, n_dimension):\n", 33 | " self.n_dimension = n_dimension\n", 34 | " \n", 35 | " def fit(self, X, y):\n", 36 | " \"\"\"Fit the model with X.\n", 37 | " X should be normalized because this method uses PCA.\n", 38 | " \n", 39 | " Parameters\n", 40 | " ----------\n", 41 | " X : array-like, shape (n_samples, n_features)\n", 42 | " Training data, where n_samples in the number of samples\n", 43 | " and n_features is the number of features.\n", 44 | " y : array_like, shape (n_samples,)\n", 45 | " Label data, where n_samples in the number of samples\n", 46 | " Returns\n", 47 | " -------\n", 48 | " self : object\n", 49 | " Returns the instance itself.\n", 50 | " \"\"\"\n", 51 | " self.classes_ = np.unique(y)\n", 52 | " self.subspaces_ = []\n", 53 | " for class_name in self.classes_:\n", 54 | " idx = np.argwhere(y == class_name).squeeze()\n", 55 | " X_class_i = X[idx, :]\n", 56 | "\n", 57 | " pca = PCA(n_components=self.n_dimension)\n", 58 | " pca.fit(X_class_i)\n", 59 | "\n", 60 | " # define projection matrix (equal to subspace)\n", 61 | " P = pca.components_.T @ pca.components_\n", 62 | " self.subspaces_.append(P)\n", 63 | " \n", 64 | " self.subspaces_ = np.array(self.subspaces_)\n", 65 | " \n", 66 | " return self\n", 67 | "\n", 68 | " def predict(self, X):\n", 69 | " \"\"\"\n", 70 | " Perform classification on samples in X.\n", 71 | " Parameters\n", 72 | " ----------\n", 73 | " X : array-like, shape (n_samples, n_features)\n", 74 | " Returns\n", 75 | " -------\n", 76 | " y_pred : array, shape (n_samples,)\n", 77 | " Class labels for samples in X.\n", 78 | " \"\"\"\n", 79 | "\n", 80 | " #======#\n", 81 | "# n, d = X.shape\n", 82 | "# preds = []\n", 83 | "# for x in X:\n", 84 | "# similarities = []\n", 85 | "# for P in self.subspaces_:\n", 86 | "# similarities.append(x.T @ P @ x)\n", 87 | "# preds.append(np.argmax(similarities))\n", 88 | "# return self.classes_[preds]\n", 89 | "\n", 90 | " #======#\n", 91 | " # n: n_samples, d: n_features\n", 92 | " n, d = X.shape\n", 93 | "\n", 94 | " similarities = X.reshape((n, 1, 1, d)) @ \\\n", 95 | " np.expand_dims(self.subspaces_, axis=0) @ \\\n", 96 | " X.reshape((n, 1, d, 1))\n", 97 | " similarities = similarities.squeeze()\n", 98 | " \n", 99 | " return self.classes_[np.argmax(similarities, axis=1)]\n", 100 | " \n", 101 | " def score(self, X, y):\n", 102 | " return np.mean(self.predict(X) == y)\n", 103 | "\n", 104 | " def get_params(self, deep=True):\n", 105 | " return {'n_dimension': self.n_dimension}\n", 106 | "\n", 107 | " def set_params(self, **parameters):\n", 108 | " for parameter, value in parameters.items():\n", 109 | " setattr(self,parameter, value)\n", 110 | " return self" 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "metadata": {}, 116 | "source": [ 117 | "# Prepare the data" 118 | ] 119 | }, 120 | { 121 | "cell_type": "code", 122 | "execution_count": 3, 123 | "metadata": { 124 | "collapsed": false 125 | }, 126 | "outputs": [], 127 | "source": [ 128 | "import numpy as np\n", 129 | "from sklearn.datasets import load_digits\n", 130 | "from sklearn.model_selection import train_test_split\n", 131 | "\n", 132 | "digits_data = load_digits()\n", 133 | "x = digits_data.data\n", 134 | "y = digits_data.target\n", 135 | "\n", 136 | "x -= np.mean(x, axis=0)\n", 137 | "\n", 138 | "X_train, X_test, y_train, y_test = train_test_split(\n", 139 | " x, y, test_size=0.33, random_state=42)" 140 | ] 141 | }, 142 | { 143 | "cell_type": "code", 144 | "execution_count": 4, 145 | "metadata": { 146 | "collapsed": false 147 | }, 148 | "outputs": [ 149 | { 150 | "name": "stdout", 151 | "output_type": "stream", 152 | "text": [ 153 | "(1203, 64) (1203,)\n", 154 | "(594, 64) (594,)\n" 155 | ] 156 | } 157 | ], 158 | "source": [ 159 | "print(X_train.shape, y_train.shape)\n", 160 | "print(X_test.shape, y_test.shape)" 161 | ] 162 | }, 163 | { 164 | "cell_type": "markdown", 165 | "metadata": {}, 166 | "source": [ 167 | "# Training & Inference" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 20, 173 | "metadata": { 174 | "collapsed": false, 175 | "scrolled": true 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "text/plain": [ 181 | "0.80808080808080807" 182 | ] 183 | }, 184 | "execution_count": 20, 185 | "metadata": {}, 186 | "output_type": "execute_result" 187 | } 188 | ], 189 | "source": [ 190 | "sm = SM(n_dimension=10)\n", 191 | "sm.fit(X_train, y_train)\n", 192 | "y_pred = sm.predict(X_test)\n", 193 | "sm.score(X_test, y_test)" 194 | ] 195 | }, 196 | { 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "# Grid Search" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 91, 206 | "metadata": { 207 | "collapsed": false 208 | }, 209 | "outputs": [ 210 | { 211 | "data": { 212 | "text/plain": [ 213 | "{'n_dimension': 12}" 214 | ] 215 | }, 216 | "execution_count": 91, 217 | "metadata": {}, 218 | "output_type": "execute_result" 219 | } 220 | ], 221 | "source": [ 222 | "from sklearn.model_selection import GridSearchCV\n", 223 | "\n", 224 | "parameters = {'n_dimension': [i for i in range(X_train.shape[1])]}\n", 225 | "gs = GridSearchCV(sm, parameters, cv=5)\n", 226 | "gs.fit(X_train, y_train)\n", 227 | "\n", 228 | "gs.best_params_" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | "execution_count": 26, 234 | "metadata": { 235 | "collapsed": false 236 | }, 237 | "outputs": [ 238 | { 239 | "data": { 240 | "text/plain": [ 241 | "0.8232323232323232" 242 | ] 243 | }, 244 | "execution_count": 26, 245 | "metadata": {}, 246 | "output_type": "execute_result" 247 | } 248 | ], 249 | "source": [ 250 | "sm = SM(**gs.best_params_)\n", 251 | "sm.fit(X_train, y_train)\n", 252 | "y_pred = sm.predict(X_test)\n", 253 | "sm.score(X_test, y_test)" 254 | ] 255 | }, 256 | { 257 | "cell_type": "markdown", 258 | "metadata": {}, 259 | "source": [ 260 | "# Validation Curve" 261 | ] 262 | }, 263 | { 264 | "cell_type": "code", 265 | "execution_count": 89, 266 | "metadata": { 267 | "collapsed": false, 268 | "scrolled": true 269 | }, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "image/png": 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rr3r/7aRpKsuvpKTXDy2ESHISoPpIQ4Ma1lu+XP3IZ86MHZzCYVUyKJ75pawsyM522gyB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\nYcPmZj++tdXP29y8sc0XXwyzZ8OwYXDGGXD88e1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274 | "text/plain": [ 275 | "" 276 | ] 277 | }, 278 | "metadata": {}, 279 | "output_type": "display_data" 280 | } 281 | ], 282 | "source": [ 283 | "%matplotlib inline\n", 284 | "from matplotlib import pyplot as plt\n", 285 | "import seaborn as sns\n", 286 | "from sklearn.learning_curve import validation_curve\n", 287 | "\n", 288 | "param_range = gs.get_params()['param_grid']['n_dimension']\n", 289 | "\n", 290 | "test_mean = [gs.grid_scores_[i][1] for i in range(len(gs.get_params()['param_grid']['n_dimension']))]\n", 291 | "test_std = np.std(\n", 292 | " [gs.grid_scores_[i][2] for i in range(len(gs.get_params()['param_grid']['n_dimension']))], \n", 293 | " axis=1)\n", 294 | "\n", 295 | "plt.plot(param_range, test_mean, \n", 296 | " color='blue', linestyle='--', \n", 297 | " label='validation accuracy')\n", 298 | "\n", 299 | "plt.fill_between(param_range, \n", 300 | " test_mean + test_std,\n", 301 | " test_mean - test_std, \n", 302 | " alpha=0.15, color='blue')\n", 303 | "\n", 304 | "plt.legend(loc='best')\n", 305 | "plt.xlabel('Subspace Dimension')\n", 306 | "plt.ylabel('Accuracy')\n", 307 | "plt.tight_layout()\n", 308 | "# plt.savefig('./figures/validation_curve.png', dpi=300)\n", 309 | "plt.show()" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": 21, 315 | "metadata": { 316 | "collapsed": true 317 | }, 318 | "outputs": [], 319 | "source": [ 320 | "import numpy as np\n", 321 | "from sklearn.decomposition import KernelPCA\n", 322 | "from sklearn.base import BaseEstimator\n", 323 | "\n", 324 | "class KSM(BaseEstimator):\n", 325 | " \"\"\"Subspace Method (SM)\n", 326 | " Classification method using Subspace.\n", 327 | " \n", 328 | " Parameters\n", 329 | " ----------\n", 330 | " n_dimension : int\n", 331 | " Number of dimension of subspace.\n", 332 | " Attributes\n", 333 | " ----------\n", 334 | " subspaces_ : array, shape (n_classes, n_features, n_features)\n", 335 | " Subspace of N classes.\n", 336 | " \n", 337 | " classes_ : array, shape (n_classes,)\n", 338 | " Unique labels of Classes.\n", 339 | " \"\"\"\n", 340 | " \n", 341 | " def __init__(self, n_dimension, gamma):\n", 342 | " self.n_dimension = n_dimension\n", 343 | " self.gamma = gamma\n", 344 | " \n", 345 | " def fit(self, X, y):\n", 346 | " \"\"\"Fit the model with X.\n", 347 | " X should be normalized because this method uses PCA.\n", 348 | " \n", 349 | " Parameters\n", 350 | " ----------\n", 351 | " X : array-like, shape (n_samples, n_features)\n", 352 | " Training data, where n_samples in the number of samples\n", 353 | " and n_features is the number of features.\n", 354 | " y : array_like, shape (n_samples,)\n", 355 | " Label data, where n_samples in the number of samples\n", 356 | " Returns\n", 357 | " -------\n", 358 | " self : object\n", 359 | " Returns the instance itself.\n", 360 | " \"\"\"\n", 361 | " self.classes_ = np.unique(y)\n", 362 | " self.subspaces_ = []\n", 363 | " for class_name in self.classes_:\n", 364 | " idx = np.argwhere(y == class_name).squeeze()\n", 365 | " X_class_i = X[idx, :]\n", 366 | "\n", 367 | " pca = KernelPCA(n_components=self.n_dimension, kernel='rbf', gamma=self.gamma)\n", 368 | " pca.fit(X_class_i.T)\n", 369 | "\n", 370 | " # define projection matrix (equal to subspace)\n", 371 | " P = pca.alphas_ @ pca.alphas_.T\n", 372 | " self.subspaces_.append(P)\n", 373 | " \n", 374 | " self.subspaces_ = np.array(self.subspaces_)\n", 375 | " \n", 376 | " return self\n", 377 | "\n", 378 | " def predict(self, X):\n", 379 | " \"\"\"\n", 380 | " Perform classification on samples in X.\n", 381 | " Parameters\n", 382 | " ----------\n", 383 | " X : array-like, shape (n_samples, n_features)\n", 384 | " Returns\n", 385 | " -------\n", 386 | " y_pred : array, shape (n_samples,)\n", 387 | " Class labels for samples in X.\n", 388 | " \"\"\"\n", 389 | "\n", 390 | " #======#\n", 391 | "# n, d = X.shape\n", 392 | "# preds = []\n", 393 | "# for x in X:\n", 394 | "# similarities = []\n", 395 | "# for P in self.subspaces_:\n", 396 | "# similarities.append(x.T @ P @ x)\n", 397 | "# preds.append(np.argmax(similarities))\n", 398 | "# return self.classes_[preds]\n", 399 | "\n", 400 | " #======#\n", 401 | " # n: n_samples, d: n_features\n", 402 | " n, d = X.shape\n", 403 | "\n", 404 | " similarities = X.reshape((n, 1, 1, d)) @ \\\n", 405 | " np.expand_dims(self.subspaces_, axis=0) @ \\\n", 406 | " X.reshape((n, 1, d, 1))\n", 407 | " similarities = similarities.squeeze()\n", 408 | " \n", 409 | " return self.classes_[np.argmax(similarities, axis=1)]\n", 410 | " \n", 411 | " def score(self, X, y):\n", 412 | " return np.mean(self.predict(X) == y)\n", 413 | "\n", 414 | " def get_params(self, deep=True):\n", 415 | " return {'n_dimension': self.n_dimension}\n", 416 | "\n", 417 | " def set_params(self, **parameters):\n", 418 | " for parameter, value in parameters.items():\n", 419 | " setattr(self,parameter, value)\n", 420 | " return self" 421 | ] 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 33, 426 | "metadata": { 427 | "collapsed": false 428 | }, 429 | "outputs": [ 430 | { 431 | "data": { 432 | "text/plain": [ 433 | "0.97138047138047134" 434 | ] 435 | }, 436 | "execution_count": 33, 437 | "metadata": {}, 438 | "output_type": "execute_result" 439 | } 440 | ], 441 | "source": [ 442 | "sm = KSM(n_dimension=10, gamma=1e-4)\n", 443 | "sm.fit(X_train, y_train)\n", 444 | "y_pred = sm.predict(X_test)\n", 445 | "sm.score(X_test, y_test)" 446 | ] 447 | }, 448 | { 449 | "cell_type": "code", 450 | "execution_count": null, 451 | "metadata": { 452 | "collapsed": true 453 | }, 454 | "outputs": [], 455 | "source": [] 456 | } 457 | ], 458 | "metadata": { 459 | "kernelspec": { 460 | "display_name": "Python 3", 461 | "language": "python", 462 | "name": "python3" 463 | }, 464 | "language_info": { 465 | "codemirror_mode": { 466 | "name": "ipython", 467 | "version": 3 468 | }, 469 | "file_extension": ".py", 470 | "mimetype": "text/x-python", 471 | "name": "python", 472 | "nbconvert_exporter": "python", 473 | "pygments_lexer": "ipython3", 474 | "version": "3.6.0" 475 | } 476 | }, 477 | "nbformat": 4, 478 | "nbformat_minor": 2 479 | } 480 | -------------------------------------------------------------------------------- /輪講/353.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lyakaap/notebooks/c339731f0eb26342250688f0d7f1486f7f1f6951/輪講/353.png -------------------------------------------------------------------------------- /輪講/356.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lyakaap/notebooks/c339731f0eb26342250688f0d7f1486f7f1f6951/輪講/356.png -------------------------------------------------------------------------------- /輪講/366.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /輪講/ep.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import numpy.linalg as la 4 | 5 | class EP: 6 | 7 | def __init__(self, n): 8 | self.sigma_tilde = np.ones((n, 1)) * 0.0003 9 | self.tau_tilde = 1.0 / self.sigma_tilde 10 | self.S_tilde = np.diag(self.tau_tilde.reshape(-1)) 11 | self.mu_tilde = np.ones((n, 1)) 12 | self.nu_tilde = self.S_tilde @ self.mu_tilde 13 | 14 | def fit(self, K, y, n_iter=5): 15 | Sigma = K 16 | mu = np.zeros((len(K), 1)) 17 | for j in range(n_iter): 18 | for i in range(len(K)): 19 | 20 | # eq. (3.56) 21 | tau_cavity = 1.0 / Sigma[i, i] - self.tau_tilde[i, 0] 22 | nu_cavity = mu[i, 0] / Sigma[i, i] - self.nu_tilde[i, 0] 23 | mu_cavity = nu_cavity / tau_cavity 24 | sigma_cavity = 1.0 / tau_cavity 25 | 26 | # eq. (3.58) 27 | zi = y[i, 0] * mu_cavity / np.sqrt(1.0 + sigma_cavity) 28 | Zi_hat = 1.0 / (1.0 + np.exp(-zi)) 29 | nd = np.exp(- 0.5 * zi**2) / np.sqrt(2.0 * np.pi) 30 | mu_hat = mu_cavity + (y[i, 0] * sigma_cavity * nd) / (Zi_hat * np.sqrt(1.0 + sigma_cavity)) 31 | sigma_hat = sigma_cavity - sigma_cavity**2 * nd * (zi + nd / Zi_hat) / (1.0 + sigma_cavity) / Zi_hat 32 | 33 | # eq. (3.59) 34 | delta_tau = 1.0 / sigma_hat - tau_cavity - self.tau_tilde[i, 0] 35 | self.tau_tilde[i, 0] = self.tau_tilde[i, 0] + delta_tau 36 | self.nu_tilde[i, 0] = 1.0 / sigma_hat * mu_hat - nu_cavity 37 | 38 | # eq. (3.53), (3.70) 39 | si = Sigma[:, i] 40 | Sigma = Sigma - (1.0 / (1.0 / delta_tau + Sigma[i, i])) * si @ si.T 41 | mu = Sigma @ self.nu_tilde 42 | 43 | # eq. (3.53), (3.68) 44 | S_root = self.S_tilde**0.5 45 | L = la.cholesky(np.eye(len(K)) + S_root @ K @ S_root) 46 | V = la.solve(L.T, S_root) @ K 47 | Sigma = K - V.T @ V 48 | mu = Sigma @ self.nu_tilde 49 | 50 | return(self.nu_tilde, self.tau_tilde) 51 | 52 | def predict(self, X, K, y, k, x_test): 53 | S_root = self.S_tilde**0.5 54 | L = la.cholesky(np.eye(len(K)) + S_root @ K @ S_root) 55 | 56 | # eq. (3.60) 57 | z = S_root @ (la.solve(L.T, (la.solve(L, S_root @ K @ self.nu_tilde)))) 58 | kx = np.array([k(X[:, i], x_test) for i in range(X.shape[1])]).reshape(-1, 1) 59 | f_bar_test = kx.T @ (self.nu_tilde - z) 60 | 61 | # eq. (3.61) 62 | v = la.solve(L, S_root @ kx) 63 | var_f_test = k(x_test, x_test) - v.T @ v 64 | 65 | # eq. (3.63) 66 | pi_bar_test = 1.0 / (1.0 + np.exp(-(f_bar_test / np.sqrt(1 + var_f_test)))) 67 | 68 | return pi_bar_test 69 | 70 | ## classification by using EP 71 | 72 | # The squared exponential covariance function k(x, x') 73 | sigmaf2 = 2500.0 74 | l2 = 8.0 75 | k = lambda x, x_dash: sigmaf2 * np.exp(-0.5 * (la.norm(x - x_dash))**2 / l2) 76 | 77 | # iris dataset(only 2 class) 78 | from sklearn.datasets import load_iris 79 | iris = load_iris() 80 | df = pd.DataFrame(iris.data, columns=iris.feature_names) 81 | 82 | y = np.array(iris.target[iris.target != 2]) 83 | y[y == 0] = -1 84 | y = y.reshape(-1, 1) 85 | n = y.shape[0] 86 | x = df.ix[:n - 1, [0, 2]] 87 | K = np.array([[k(x.ix[i], x.ix[j]) for i in range(n)] for j in range(n)]) 88 | 89 | ep = EP(n=n) 90 | ep.fit(K=K, y=y) 91 | pred = ep.predict(X=x.T.values, K=K, y=y, k=k, x_test=x.ix[0].reshape(-1, 1)) 92 | print(pred) --------------------------------------------------------------------------------