From 100d6462613613765cc6b5e8edd9707bcbb65c8c Mon Sep 17 00:00:00 2001 From: sawradip Date: Wed, 4 Jan 2023 07:50:18 +0000 Subject: [PATCH 01/13] Fixed imports and Checked Notebooks --- notebooks/Advanced_Usage.ipynb | 368 +- notebooks/Basic_Usage.ipynb | 540 +- notebooks/CORA-geometric.ipynb | 649 -- notebooks/Gaussian_Processes.ipynb | 5356 +++++++++------- notebooks/Hugging_Face_Finetuning.ipynb | 5667 ++++++++++++++--- notebooks/Hugging_Face_Model_Checkpoint.ipynb | 2306 +++++-- notebooks/MNIST-torchvision.ipynb | 974 --- notebooks/MNIST.ipynb | 119 +- notebooks/Transfer_Learning.ipynb | 1642 +++-- .../datasets/MNIST/raw/t10k-images-idx3-ubyte | Bin 0 -> 7840016 bytes .../datasets/MNIST/raw/t10k-labels-idx1-ubyte | Bin 0 -> 10008 bytes .../MNIST/raw/train-images-idx3-ubyte | Bin 0 -> 47040016 bytes .../MNIST/raw/train-labels-idx1-ubyte | Bin 0 -> 60008 bytes ...fevents.1672760274.DESKTOP-RM8OEEP.13952.0 | Bin 0 -> 2030 bytes ...fevents.1672760896.DESKTOP-RM8OEEP.13952.1 | Bin 0 -> 40 bytes ...fevents.1672761710.DESKTOP-RM8OEEP.13952.2 | Bin 0 -> 560510 bytes 16 files changed, 10982 insertions(+), 6639 deletions(-) delete mode 100644 notebooks/CORA-geometric.ipynb delete mode 100644 notebooks/MNIST-torchvision.ipynb create mode 100644 notebooks/datasets/MNIST/raw/t10k-images-idx3-ubyte create mode 100644 notebooks/datasets/MNIST/raw/t10k-labels-idx1-ubyte create mode 100644 notebooks/datasets/MNIST/raw/train-images-idx3-ubyte create mode 100644 notebooks/datasets/MNIST/raw/train-labels-idx1-ubyte create mode 100644 notebooks/runs/Jan03_15-37-54_DESKTOP-RM8OEEP/events.out.tfevents.1672760274.DESKTOP-RM8OEEP.13952.0 create mode 100644 notebooks/runs/Jan03_15-48-16_DESKTOP-RM8OEEP/events.out.tfevents.1672760896.DESKTOP-RM8OEEP.13952.1 create mode 100644 notebooks/runs/Jan03_16-01-50_DESKTOP-RM8OEEP/events.out.tfevents.1672761710.DESKTOP-RM8OEEP.13952.2 diff --git a/notebooks/Advanced_Usage.ipynb b/notebooks/Advanced_Usage.ipynb index cbc76a56e..43f27c13f 100644 --- a/notebooks/Advanced_Usage.ipynb +++ b/notebooks/Advanced_Usage.ipynb @@ -48,9 +48,24 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If not already installed, you can install skorch by running 'pip install skorch'\n" + ] + } + ], "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch skorch" + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch' , 'torch'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" ] }, { @@ -66,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -137,7 +152,7 @@ "((1000, 20), (1000,), 0.5)" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -171,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -266,6 +281,7 @@ " def initialize(self):\n", " self.critical_epoch_ = -1\n", "\n", + " # This runs after every epoch\n", " def on_epoch_end(self, net, **kwargs):\n", " if self.critical_epoch_ > -1:\n", " return\n", @@ -273,6 +289,7 @@ " if net.history[-1, 'valid_acc'] >= self.min_accuracy:\n", " self.critical_epoch_ = len(net.history)\n", "\n", + " # This runs at the end of training\n", " def on_train_end(self, net, **kwargs):\n", " if self.critical_epoch_ < 0:\n", " msg = \"Accuracy never reached {} :(\".format(self.min_accuracy)\n", @@ -292,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -316,21 +333,21 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6954\u001b[0m \u001b[32m0.6000\u001b[0m \u001b[35m0.6844\u001b[0m 0.0176\n", - " 2 \u001b[36m0.6802\u001b[0m 0.5950 \u001b[35m0.6817\u001b[0m 0.0150\n", - " 3 0.6839 0.6000 \u001b[35m0.6792\u001b[0m 0.0178\n", - " 4 \u001b[36m0.6753\u001b[0m 0.5900 \u001b[35m0.6767\u001b[0m 0.0140\n", - " 5 0.6769 0.5950 \u001b[35m0.6742\u001b[0m 0.0172\n", - " 6 0.6774 \u001b[32m0.6050\u001b[0m \u001b[35m0.6720\u001b[0m 0.0166\n", - " 7 \u001b[36m0.6693\u001b[0m \u001b[32m0.6250\u001b[0m \u001b[35m0.6695\u001b[0m 0.0134\n", - " 8 0.6694 \u001b[32m0.6300\u001b[0m \u001b[35m0.6672\u001b[0m 0.0168\n", - " 9 0.6703 \u001b[32m0.6400\u001b[0m \u001b[35m0.6652\u001b[0m 0.0177\n", - " 10 \u001b[36m0.6523\u001b[0m \u001b[32m0.6550\u001b[0m \u001b[35m0.6623\u001b[0m 0.0151\n", - " 11 0.6641 \u001b[32m0.6650\u001b[0m \u001b[35m0.6603\u001b[0m 0.0134\n", - " 12 0.6524 0.6650 \u001b[35m0.6582\u001b[0m 0.0138\n", - " 13 \u001b[36m0.6506\u001b[0m \u001b[32m0.6700\u001b[0m \u001b[35m0.6553\u001b[0m 0.0126\n", - " 14 \u001b[36m0.6489\u001b[0m 0.6650 \u001b[35m0.6527\u001b[0m 0.0132\n", - " 15 0.6505 \u001b[32m0.6750\u001b[0m \u001b[35m0.6502\u001b[0m 0.0133\n", + " 1 \u001b[36m0.6954\u001b[0m \u001b[32m0.6000\u001b[0m \u001b[35m0.6844\u001b[0m 0.0419\n", + " 2 \u001b[36m0.6871\u001b[0m 0.5950 \u001b[35m0.6820\u001b[0m 0.0339\n", + " 3 \u001b[36m0.6826\u001b[0m \u001b[32m0.6100\u001b[0m \u001b[35m0.6793\u001b[0m 0.0359\n", + " 4 \u001b[36m0.6751\u001b[0m 0.6100 \u001b[35m0.6775\u001b[0m 0.0339\n", + " 5 0.6773 \u001b[32m0.6150\u001b[0m \u001b[35m0.6754\u001b[0m 0.0399\n", + " 6 \u001b[36m0.6722\u001b[0m 0.6150 \u001b[35m0.6733\u001b[0m 0.0549\n", + " 7 \u001b[36m0.6665\u001b[0m \u001b[32m0.6200\u001b[0m \u001b[35m0.6707\u001b[0m 0.0310\n", + " 8 \u001b[36m0.6634\u001b[0m 0.6200 \u001b[35m0.6685\u001b[0m 0.0529\n", + " 9 0.6662 0.6200 \u001b[35m0.6659\u001b[0m 0.0409\n", + " 10 0.6635 \u001b[32m0.6500\u001b[0m \u001b[35m0.6636\u001b[0m 0.0289\n", + " 11 \u001b[36m0.6605\u001b[0m \u001b[32m0.6550\u001b[0m \u001b[35m0.6616\u001b[0m 0.0309\n", + " 12 0.6605 \u001b[32m0.6600\u001b[0m \u001b[35m0.6593\u001b[0m 0.0359\n", + " 13 0.6616 \u001b[32m0.6650\u001b[0m \u001b[35m0.6568\u001b[0m 0.0469\n", + " 14 \u001b[36m0.6485\u001b[0m \u001b[32m0.6750\u001b[0m \u001b[35m0.6546\u001b[0m 0.0369\n", + " 15 \u001b[36m0.6464\u001b[0m 0.6750 \u001b[35m0.6518\u001b[0m 0.0359\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "*tweet* Accuracy never reached 0.7 :( #skorch #pytorch\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" @@ -342,14 +359,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -367,30 +384,30 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " 16 \u001b[36m0.6473\u001b[0m 0.6750 \u001b[35m0.6474\u001b[0m 0.0175\n", - " 17 \u001b[36m0.6431\u001b[0m \u001b[32m0.6800\u001b[0m \u001b[35m0.6443\u001b[0m 0.0185\n", - " 18 0.6461 \u001b[32m0.6900\u001b[0m \u001b[35m0.6418\u001b[0m 0.0162\n", - " 19 \u001b[36m0.6430\u001b[0m 0.6850 \u001b[35m0.6392\u001b[0m 0.0131\n", - " 20 \u001b[36m0.6364\u001b[0m \u001b[32m0.6950\u001b[0m \u001b[35m0.6366\u001b[0m 0.0146\n", - " 21 \u001b[36m0.6266\u001b[0m \u001b[32m0.7000\u001b[0m \u001b[35m0.6334\u001b[0m 0.0149\n", - " 22 0.6316 0.7000 \u001b[35m0.6308\u001b[0m 0.0151\n", - " 23 \u001b[36m0.6231\u001b[0m 0.7000 \u001b[35m0.6277\u001b[0m 0.0128\n", - " 24 \u001b[36m0.6094\u001b[0m 0.7000 \u001b[35m0.6242\u001b[0m 0.0160\n", - " 25 0.6250 \u001b[32m0.7050\u001b[0m \u001b[35m0.6215\u001b[0m 0.0130\n", - " 26 0.6180 \u001b[32m0.7150\u001b[0m \u001b[35m0.6187\u001b[0m 0.0139\n", - " 27 0.6186 0.7150 \u001b[35m0.6159\u001b[0m 0.0169\n", - " 28 0.6144 0.7150 \u001b[35m0.6134\u001b[0m 0.0171\n", - " 29 \u001b[36m0.5993\u001b[0m 0.7150 \u001b[35m0.6100\u001b[0m 0.0147\n", - " 30 \u001b[36m0.5976\u001b[0m 0.7150 \u001b[35m0.6071\u001b[0m 0.0138\n", + " 16 \u001b[36m0.6431\u001b[0m \u001b[32m0.6800\u001b[0m \u001b[35m0.6491\u001b[0m 0.0429\n", + " 17 \u001b[36m0.6406\u001b[0m \u001b[32m0.6850\u001b[0m \u001b[35m0.6460\u001b[0m 0.0289\n", + " 18 0.6501 \u001b[32m0.6900\u001b[0m \u001b[35m0.6437\u001b[0m 0.1586\n", + " 19 0.6450 \u001b[32m0.6950\u001b[0m \u001b[35m0.6410\u001b[0m 0.0479\n", + " 20 \u001b[36m0.6330\u001b[0m \u001b[32m0.7000\u001b[0m \u001b[35m0.6380\u001b[0m 0.0534\n", + " 21 \u001b[36m0.6306\u001b[0m \u001b[32m0.7100\u001b[0m \u001b[35m0.6352\u001b[0m 0.0329\n", + " 22 \u001b[36m0.6305\u001b[0m 0.7100 \u001b[35m0.6319\u001b[0m 0.0409\n", + " 23 0.6329 0.7100 \u001b[35m0.6295\u001b[0m 0.0329\n", + " 24 0.6322 \u001b[32m0.7150\u001b[0m \u001b[35m0.6269\u001b[0m 0.0349\n", + " 25 \u001b[36m0.6188\u001b[0m 0.7050 \u001b[35m0.6241\u001b[0m 0.0559\n", + " 26 \u001b[36m0.6163\u001b[0m 0.7000 \u001b[35m0.6206\u001b[0m 0.0349\n", + " 27 \u001b[36m0.6133\u001b[0m 0.7050 \u001b[35m0.6176\u001b[0m 0.0339\n", + " 28 0.6214 0.7050 \u001b[35m0.6150\u001b[0m 0.0349\n", + " 29 \u001b[36m0.6099\u001b[0m 0.7000 \u001b[35m0.6122\u001b[0m 0.0299\n", + " 30 0.6156 0.7000 \u001b[35m0.6095\u001b[0m 0.0399\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "*tweet* Accuracy reached 0.7 at epoch 21!!! #skorch #pytorch\n", + "*tweet* Accuracy reached 0.7 at epoch 20!!! #skorch #pytorch\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" ] }, @@ -400,25 +417,26 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# warm_start starts training from the point training stoped previously.\n", "net.fit(X, y)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -480,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -498,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -507,16 +525,16 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6932\u001b[0m \u001b[32m0.5950\u001b[0m \u001b[35m0.6749\u001b[0m 0.0161\n", - " 2 \u001b[36m0.6686\u001b[0m \u001b[32m0.6550\u001b[0m \u001b[35m0.6613\u001b[0m 0.0160\n", - " 3 \u001b[36m0.6641\u001b[0m 0.6450 \u001b[35m0.6487\u001b[0m 0.0168\n", - " 4 \u001b[36m0.6438\u001b[0m \u001b[32m0.6600\u001b[0m \u001b[35m0.6354\u001b[0m 0.0171\n", - " 5 \u001b[36m0.6293\u001b[0m \u001b[32m0.7000\u001b[0m \u001b[35m0.6190\u001b[0m 0.0171\n", - " 6 \u001b[36m0.6091\u001b[0m \u001b[32m0.7300\u001b[0m \u001b[35m0.6040\u001b[0m 0.0147\n", - " 7 \u001b[36m0.5872\u001b[0m \u001b[32m0.7500\u001b[0m \u001b[35m0.5868\u001b[0m 0.0130\n", - " 8 \u001b[36m0.5820\u001b[0m \u001b[32m0.7600\u001b[0m \u001b[35m0.5736\u001b[0m 0.0138\n", - " 9 \u001b[36m0.5778\u001b[0m \u001b[32m0.7850\u001b[0m \u001b[35m0.5595\u001b[0m 0.0129\n", - " 10 \u001b[36m0.5626\u001b[0m 0.7750 \u001b[35m0.5484\u001b[0m 0.0131\n", + " 1 \u001b[36m0.7003\u001b[0m \u001b[32m0.5150\u001b[0m \u001b[35m0.6880\u001b[0m 0.0459\n", + " 2 \u001b[36m0.6825\u001b[0m \u001b[32m0.6250\u001b[0m \u001b[35m0.6761\u001b[0m 0.0309\n", + " 3 \u001b[36m0.6632\u001b[0m \u001b[32m0.6450\u001b[0m \u001b[35m0.6665\u001b[0m 0.0549\n", + " 4 \u001b[36m0.6545\u001b[0m \u001b[32m0.6600\u001b[0m \u001b[35m0.6574\u001b[0m 0.0399\n", + " 5 \u001b[36m0.6397\u001b[0m 0.6450 \u001b[35m0.6459\u001b[0m 0.0449\n", + " 6 \u001b[36m0.6348\u001b[0m \u001b[32m0.6750\u001b[0m \u001b[35m0.6370\u001b[0m 0.0758\n", + " 7 \u001b[36m0.6239\u001b[0m \u001b[32m0.6850\u001b[0m \u001b[35m0.6276\u001b[0m 0.0239\n", + " 8 \u001b[36m0.6119\u001b[0m \u001b[32m0.6950\u001b[0m \u001b[35m0.6166\u001b[0m 0.0439\n", + " 9 \u001b[36m0.5940\u001b[0m \u001b[32m0.7250\u001b[0m \u001b[35m0.6113\u001b[0m 0.0269\n", + " 10 \u001b[36m0.5908\u001b[0m 0.7250 \u001b[35m0.6017\u001b[0m 0.0379\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", "*tweet* Accuracy reached 0.6 at epoch 2!!! #skorch #pytorch\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" @@ -528,14 +546,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -553,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -562,14 +580,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -580,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -589,18 +607,18 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 11 \u001b[36m0.5513\u001b[0m 0.7750 \u001b[35m0.5405\u001b[0m 0.0136\n", - " 12 0.5612 0.7800 \u001b[35m0.5361\u001b[0m 0.0133\n", - " 13 \u001b[36m0.5473\u001b[0m \u001b[32m0.7950\u001b[0m \u001b[35m0.5303\u001b[0m 0.0159\n", - " 14 \u001b[36m0.5304\u001b[0m 0.7900 \u001b[35m0.5241\u001b[0m 0.0162\n", - " 15 \u001b[36m0.5088\u001b[0m 0.7850 \u001b[35m0.5198\u001b[0m 0.0170\n", - " 16 0.5373 0.7800 \u001b[35m0.5168\u001b[0m 0.0170\n", - " 17 0.5377 0.7750 0.5179 0.0169\n", - " 18 0.5257 0.7700 0.5177 0.0171\n", - " 19 0.5150 0.7700 \u001b[35m0.5132\u001b[0m 0.0169\n", - " 20 0.5136 0.7450 \u001b[35m0.5116\u001b[0m 0.0167\n", + " 11 \u001b[36m0.5809\u001b[0m \u001b[32m0.7300\u001b[0m \u001b[35m0.5908\u001b[0m 0.0319\n", + " 12 \u001b[36m0.5580\u001b[0m 0.7000 \u001b[35m0.5864\u001b[0m 0.0389\n", + " 13 0.5604 0.7250 \u001b[35m0.5752\u001b[0m 0.0339\n", + " 14 \u001b[36m0.5514\u001b[0m 0.7200 \u001b[35m0.5673\u001b[0m 0.0394\n", + " 15 \u001b[36m0.5444\u001b[0m 0.7200 \u001b[35m0.5599\u001b[0m 0.0349\n", + " 16 0.5467 0.7300 \u001b[35m0.5511\u001b[0m 0.0324\n", + " 17 \u001b[36m0.5246\u001b[0m \u001b[32m0.7350\u001b[0m \u001b[35m0.5460\u001b[0m 0.0568\n", + " 18 0.5498 0.7200 \u001b[35m0.5428\u001b[0m 0.0319\n", + " 19 \u001b[36m0.5197\u001b[0m 0.7350 \u001b[35m0.5407\u001b[0m 0.0359\n", + " 20 \u001b[36m0.5159\u001b[0m 0.7350 \u001b[35m0.5355\u001b[0m 0.0289\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "*tweet* Accuracy reached 0.75 at epoch 11!!! #skorch #pytorch\n", + "*tweet* Accuracy never reached 0.75 :( #skorch #pytorch\n", "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n" ] }, @@ -610,14 +628,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -656,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -676,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -687,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -696,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -716,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -725,7 +743,7 @@ "True" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -748,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -756,19 +774,20 @@ "output_type": "stream", "text": [ "Re-initializing module.\n", + "Re-initializing criterion.\n", "Re-initializing optimizer.\n", " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6938\u001b[0m \u001b[32m0.4650\u001b[0m \u001b[35m0.6984\u001b[0m 0.0154\n", - " 2 0.6975 0.4650 \u001b[35m0.6977\u001b[0m 0.0141\n", - " 3 0.6938 0.4600 \u001b[35m0.6970\u001b[0m 0.0130\n", - " 4 \u001b[36m0.6923\u001b[0m \u001b[32m0.4700\u001b[0m \u001b[35m0.6964\u001b[0m 0.0137\n", - " 5 \u001b[36m0.6921\u001b[0m \u001b[32m0.4800\u001b[0m \u001b[35m0.6959\u001b[0m 0.0135\n", - " 6 \u001b[36m0.6878\u001b[0m \u001b[32m0.5000\u001b[0m \u001b[35m0.6954\u001b[0m 0.0138\n", - " 7 0.6901 0.4950 \u001b[35m0.6948\u001b[0m 0.0130\n", - " 8 0.6884 0.4900 \u001b[35m0.6944\u001b[0m 0.0137\n", - " 9 0.6896 0.4900 \u001b[35m0.6940\u001b[0m 0.0130\n", - " 10 \u001b[36m0.6870\u001b[0m 0.4850 \u001b[35m0.6936\u001b[0m 0.0130\n" + " 1 \u001b[36m0.6994\u001b[0m \u001b[32m0.4600\u001b[0m \u001b[35m0.7100\u001b[0m 0.0249\n", + " 2 \u001b[36m0.6960\u001b[0m \u001b[32m0.4750\u001b[0m \u001b[35m0.7072\u001b[0m 0.0379\n", + " 3 0.6983 \u001b[32m0.4900\u001b[0m \u001b[35m0.7046\u001b[0m 0.0274\n", + " 4 \u001b[36m0.6945\u001b[0m \u001b[32m0.5050\u001b[0m \u001b[35m0.7023\u001b[0m 0.0299\n", + " 5 \u001b[36m0.6859\u001b[0m 0.5050 \u001b[35m0.7000\u001b[0m 0.0349\n", + " 6 \u001b[36m0.6834\u001b[0m \u001b[32m0.5300\u001b[0m \u001b[35m0.6978\u001b[0m 0.0334\n", + " 7 \u001b[36m0.6799\u001b[0m \u001b[32m0.5450\u001b[0m \u001b[35m0.6960\u001b[0m 0.0399\n", + " 8 \u001b[36m0.6734\u001b[0m \u001b[32m0.5500\u001b[0m \u001b[35m0.6942\u001b[0m 0.0199\n", + " 9 0.6743 0.5450 \u001b[35m0.6923\u001b[0m 0.0389\n", + " 10 \u001b[36m0.6666\u001b[0m 0.5500 \u001b[35m0.6906\u001b[0m 0.0429\n" ] }, { @@ -777,14 +796,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +835,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -835,7 +854,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -882,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -891,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -901,13 +920,13 @@ " module_=ClassifierWithDict(\n", " (dense0): Linear(in_features=10, out_features=50, bias=True)\n", " (dense1): Linear(in_features=10, out_features=50, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (output): Linear(in_features=100, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -925,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -945,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +976,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": { "scrolled": true }, @@ -965,17 +984,19 @@ { "data": { "text/plain": [ - "Pipeline(memory=None,\n", - " steps=[('do-nothing', FunctionTransformer(accept_sparse=False, check_inverse=True, func=None,\n", - " inv_kw_args=None, inverse_func=None, kw_args=None,\n", - " pass_y='deprecated', validate=False)), ('net', [initialized](\n", - " module_=ClassifierWithDi... (dropout): Dropout(p=0.5)\n", + "Pipeline(steps=[('do-nothing', FunctionTransformer()),\n", + " ('net',\n", + " [initialized](\n", + " module_=ClassifierWithDict(\n", + " (dense0): Linear(in_features=10, out_features=50, bias=True)\n", + " (dense1): Linear(in_features=10, out_features=50, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (output): Linear(in_features=100, out_features=2, bias=True)\n", " ),\n", "))])" ] }, - "execution_count": 29, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -993,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1006,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1015,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1044,7 +1065,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1053,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1069,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1088,7 +1109,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1120,7 +1141,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1130,33 +1151,27 @@ "Fitting 3 folds for each of 18 candidates, totalling 54 fits\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=1)]: Done 54 out of 54 | elapsed: 11.3s finished\n" - ] - }, { "data": { "text/plain": [ - "GridSearchCV(cv=3, error_score='raise-deprecating',\n", - " estimator=Pipeline(memory=None,\n", - " steps=[('do-nothing', FunctionTransformer(accept_sparse=False, check_inverse=True, func=None,\n", - " inv_kw_args=None, inverse_func=None, kw_args=None,\n", - " pass_y='deprecated', validate=False)), ('net', [initialized](\n", - " module_=ClassifierWithDi... (dropout): Dropout(p=0.5)\n", + "GridSearchCV(cv=3,\n", + " estimator=Pipeline(steps=[('do-nothing', FunctionTransformer()),\n", + " ('net',\n", + " [initialized](\n", + " module_=ClassifierWithDict(\n", + " (dense0): Linear(in_features=10, out_features=50, bias=True)\n", + " (dense1): Linear(in_features=10, out_features=50, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (output): Linear(in_features=100, out_features=2, bias=True)\n", " ),\n", "))]),\n", - " fit_params=None, iid='warn', n_jobs=None,\n", - " param_grid={'net__module__num_units0': [10, 25, 50], 'net__module__num_units1': [10, 25, 50], 'net__lr': [0.01, 0.1]},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='accuracy', verbose=1)" + " param_grid={'net__lr': [0.01, 0.1],\n", + " 'net__module__num_units0': [10, 25, 50],\n", + " 'net__module__num_units1': [10, 25, 50]},\n", + " scoring='accuracy', verbose=1)" ] }, - "execution_count": 37, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -1167,19 +1182,19 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(0.754,\n", + "(0.7429825034615454,\n", " {'net__lr': 0.1,\n", " 'net__module__num_units0': 50,\n", " 'net__module__num_units1': 50})" ] }, - "execution_count": 38, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1218,7 +1233,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1227,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1250,7 +1265,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1279,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1310,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1345,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1358,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 47, "metadata": { "scrolled": false }, @@ -1369,16 +1384,16 @@ "text": [ " epoch train_loss valid_loss dur\n", "------- ------------ ------------ ------\n", - " 1 \u001b[36m3.8328\u001b[0m \u001b[32m3.7855\u001b[0m 0.0233\n", - " 2 \u001b[36m3.6989\u001b[0m \u001b[32m3.7111\u001b[0m 0.0244\n", - " 3 \u001b[36m3.6417\u001b[0m \u001b[32m3.6707\u001b[0m 0.0259\n", - " 4 \u001b[36m3.6101\u001b[0m \u001b[32m3.6463\u001b[0m 0.0209\n", - " 5 \u001b[36m3.5914\u001b[0m \u001b[32m3.6310\u001b[0m 0.0226\n", - " 6 \u001b[36m3.5799\u001b[0m \u001b[32m3.6212\u001b[0m 0.0242\n", - " 7 \u001b[36m3.5725\u001b[0m \u001b[32m3.6144\u001b[0m 0.0307\n", - " 8 \u001b[36m3.5672\u001b[0m \u001b[32m3.6090\u001b[0m 0.0347\n", - " 9 \u001b[36m3.5627\u001b[0m \u001b[32m3.6036\u001b[0m 0.0239\n", - " 10 \u001b[36m3.5570\u001b[0m \u001b[32m3.5963\u001b[0m 0.0264\n" + " 1 \u001b[36m3.8021\u001b[0m \u001b[32m3.7869\u001b[0m 0.0499\n", + " 2 \u001b[36m3.6940\u001b[0m \u001b[32m3.7218\u001b[0m 0.0489\n", + " 3 \u001b[36m3.6441\u001b[0m \u001b[32m3.6828\u001b[0m 0.0658\n", + " 4 \u001b[36m3.6145\u001b[0m \u001b[32m3.6578\u001b[0m 0.0399\n", + " 5 \u001b[36m3.5955\u001b[0m \u001b[32m3.6407\u001b[0m 0.0469\n", + " 6 \u001b[36m3.5824\u001b[0m \u001b[32m3.6276\u001b[0m 0.0349\n", + " 7 \u001b[36m3.5714\u001b[0m \u001b[32m3.6146\u001b[0m 0.0469\n", + " 8 \u001b[36m3.5571\u001b[0m \u001b[32m3.5906\u001b[0m 0.0349\n", + " 9 \u001b[36m3.5160\u001b[0m \u001b[32m3.4825\u001b[0m 0.0439\n", + " 10 \u001b[36m3.3439\u001b[0m \u001b[32m3.2388\u001b[0m 0.0409\n" ] }, { @@ -1405,7 +1420,7 @@ ")" ] }, - "execution_count": 45, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1439,7 +1454,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1448,7 +1463,7 @@ "(1000, 20)" ] }, - "execution_count": 46, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } @@ -1467,7 +1482,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1476,7 +1491,7 @@ "(torch.Size([1000, 20]), torch.Size([1000, 5]))" ] }, - "execution_count": 47, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } @@ -1495,7 +1510,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -1504,7 +1519,7 @@ "(torch.Size([128, 20]), torch.Size([128, 5]))" ] }, - "execution_count": 48, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -1525,16 +1540,16 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(0.8781)" + "tensor(0.8828)" ] }, - "execution_count": 49, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1553,7 +1568,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1567,7 +1582,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.7.13" + }, + "vscode": { + "interpreter": { + "hash": "bd97b8bffa4d3737e84826bc3d37be3046061822757ce35137ab82ad4c5a2016" + } } }, "nbformat": 4, diff --git a/notebooks/Basic_Usage.ipynb b/notebooks/Basic_Usage.ipynb index 064d09a0c..f8bb8a21e 100644 --- a/notebooks/Basic_Usage.ipynb +++ b/notebooks/Basic_Usage.ipynb @@ -61,16 +61,31 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If not already installed, you can install skorch by running 'pip install skorch'\n" + ] + } + ], "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch skorch" + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch' , 'torch'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -81,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -112,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -122,10 +137,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ + "# This is a toy dataset for binary classification, 1000 data points with 20 features each\n", "X, y = make_classification(1000, 20, n_informative=10, random_state=0)\n", "X, y = X.astype(np.float32), y.astype(np.int64)" ] @@ -168,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -216,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -225,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -246,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "metadata": { "scrolled": false }, @@ -255,28 +271,31 @@ "name": "stdout", "output_type": "stream", "text": [ + "Re-initializing module.\n", + "Re-initializing criterion.\n", + "Re-initializing optimizer.\n", " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6905\u001b[0m \u001b[32m0.6150\u001b[0m \u001b[35m0.6749\u001b[0m 0.0235\n", - " 2 \u001b[36m0.6648\u001b[0m \u001b[32m0.6450\u001b[0m \u001b[35m0.6633\u001b[0m 0.0213\n", - " 3 \u001b[36m0.6619\u001b[0m \u001b[32m0.6750\u001b[0m \u001b[35m0.6533\u001b[0m 0.0219\n", - " 4 \u001b[36m0.6429\u001b[0m \u001b[32m0.6800\u001b[0m \u001b[35m0.6399\u001b[0m 0.0207\n", - " 5 \u001b[36m0.6307\u001b[0m \u001b[32m0.6950\u001b[0m \u001b[35m0.6254\u001b[0m 0.0192\n", - " 6 \u001b[36m0.6291\u001b[0m \u001b[32m0.7000\u001b[0m \u001b[35m0.6134\u001b[0m 0.0202\n", - " 7 \u001b[36m0.6102\u001b[0m \u001b[32m0.7100\u001b[0m \u001b[35m0.6033\u001b[0m 0.0220\n", - " 8 \u001b[36m0.6050\u001b[0m 0.7000 \u001b[35m0.5931\u001b[0m 0.0210\n", - " 9 \u001b[36m0.5966\u001b[0m 0.7000 \u001b[35m0.5844\u001b[0m 0.0217\n", - " 10 \u001b[36m0.5636\u001b[0m 0.7100 \u001b[35m0.5689\u001b[0m 0.0226\n", - " 11 0.5757 \u001b[32m0.7200\u001b[0m \u001b[35m0.5628\u001b[0m 0.0196\n", - " 12 0.5757 0.7200 \u001b[35m0.5520\u001b[0m 0.0190\n", - " 13 \u001b[36m0.5559\u001b[0m \u001b[32m0.7300\u001b[0m \u001b[35m0.5459\u001b[0m 0.0218\n", - " 14 \u001b[36m0.5541\u001b[0m 0.7300 \u001b[35m0.5424\u001b[0m 0.0206\n", - " 15 0.5659 \u001b[32m0.7350\u001b[0m \u001b[35m0.5378\u001b[0m 0.0215\n", - " 16 \u001b[36m0.5364\u001b[0m 0.7350 \u001b[35m0.5322\u001b[0m 0.0192\n", - " 17 0.5456 0.7300 \u001b[35m0.5239\u001b[0m 0.0221\n", - " 18 0.5476 \u001b[32m0.7450\u001b[0m 0.5260 0.0188\n", - " 19 0.5499 \u001b[32m0.7500\u001b[0m 0.5249 0.0213\n", - " 20 \u001b[36m0.5273\u001b[0m 0.7350 0.5251 0.0206\n" + " 1 \u001b[36m0.6812\u001b[0m \u001b[32m0.5650\u001b[0m \u001b[35m0.6789\u001b[0m 0.1057\n", + " 2 \u001b[36m0.6710\u001b[0m \u001b[32m0.5950\u001b[0m \u001b[35m0.6712\u001b[0m 0.0489\n", + " 3 \u001b[36m0.6697\u001b[0m \u001b[32m0.6150\u001b[0m \u001b[35m0.6640\u001b[0m 0.0389\n", + " 4 \u001b[36m0.6639\u001b[0m \u001b[32m0.6350\u001b[0m \u001b[35m0.6569\u001b[0m 0.0319\n", + " 5 \u001b[36m0.6525\u001b[0m \u001b[32m0.6700\u001b[0m \u001b[35m0.6508\u001b[0m 0.0409\n", + " 6 \u001b[36m0.6489\u001b[0m 0.6650 \u001b[35m0.6445\u001b[0m 0.0299\n", + " 7 \u001b[36m0.6439\u001b[0m \u001b[32m0.6800\u001b[0m \u001b[35m0.6373\u001b[0m 0.0259\n", + " 8 \u001b[36m0.6227\u001b[0m \u001b[32m0.6850\u001b[0m \u001b[35m0.6311\u001b[0m 0.0439\n", + " 9 \u001b[36m0.6138\u001b[0m \u001b[32m0.6900\u001b[0m \u001b[35m0.6222\u001b[0m 0.0319\n", + " 10 \u001b[36m0.6045\u001b[0m 0.6900 \u001b[35m0.6123\u001b[0m 0.0269\n", + " 11 0.6094 \u001b[32m0.7050\u001b[0m \u001b[35m0.6030\u001b[0m 0.0349\n", + " 12 \u001b[36m0.5904\u001b[0m \u001b[32m0.7150\u001b[0m \u001b[35m0.5944\u001b[0m 0.0369\n", + " 13 \u001b[36m0.5694\u001b[0m \u001b[32m0.7350\u001b[0m \u001b[35m0.5780\u001b[0m 0.0269\n", + " 14 \u001b[36m0.5628\u001b[0m 0.7350 \u001b[35m0.5723\u001b[0m 0.0279\n", + " 15 \u001b[36m0.5499\u001b[0m \u001b[32m0.7450\u001b[0m \u001b[35m0.5621\u001b[0m 0.0339\n", + " 16 \u001b[36m0.5420\u001b[0m 0.7350 \u001b[35m0.5535\u001b[0m 0.0329\n", + " 17 \u001b[36m0.5339\u001b[0m 0.7300 \u001b[35m0.5451\u001b[0m 0.0319\n", + " 18 \u001b[36m0.5288\u001b[0m 0.7350 \u001b[35m0.5437\u001b[0m 0.0359\n", + " 19 0.5470 0.7400 \u001b[35m0.5403\u001b[0m 0.0299\n", + " 20 \u001b[36m0.5259\u001b[0m 0.7450 \u001b[35m0.5309\u001b[0m 0.0299\n" ] }, { @@ -285,19 +304,20 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Training the network\n", "net.fit(X, y)" ] }, @@ -332,31 +352,33 @@ } ], "source": [ + "# Making prediction for first 5 data points of X\n", "y_pred = net.predict(X[:5])\n", "y_pred" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.5349464 , 0.46505365],\n", - " [0.8685093 , 0.1314907 ],\n", - " [0.6860039 , 0.31399614],\n", - " [0.9126012 , 0.08739878],\n", - " [0.69675475, 0.30324525]], dtype=float32)" + "array([[0.5603605 , 0.4396395 ],\n", + " [0.782588 , 0.21741197],\n", + " [0.6924924 , 0.30750763],\n", + " [0.8895971 , 0.1104029 ],\n", + " [0.7074626 , 0.29253733]], dtype=float32)" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Checking probarbility of each class for first 5 data points of X\n", "y_proba = net.predict_proba(X[:5])\n", "y_proba" ] @@ -377,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -386,10 +408,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ + "# This is a toy dataset for regression, 1000 data points with 20 features each\n", "X_regr, y_regr = make_regression(1000, 20, n_informative=10, random_state=0)\n", "X_regr = X_regr.astype(np.float32)\n", "y_regr = y_regr.astype(np.float32) / 100\n", @@ -398,7 +421,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 29, "metadata": { "scrolled": true }, @@ -409,7 +432,7 @@ "((1000, 20), (1000, 1), -6.4901485, 6.154505)" ] }, - "execution_count": 15, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -419,10 +442,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "*Note*: Regression currently requires the target to be 2-dimensional, hence the need to reshape. This should be fixed with an upcoming version of pytorch." + "*Note*: Regression requires the target to be 2-dimensional, hence the need to reshape. " ] }, { @@ -441,7 +465,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -483,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -492,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -506,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -515,26 +539,26 @@ "text": [ " epoch train_loss valid_loss dur\n", "------- ------------ ------------ ------\n", - " 1 \u001b[36m4.4168\u001b[0m \u001b[32m3.0788\u001b[0m 0.0292\n", - " 2 \u001b[36m2.0120\u001b[0m \u001b[32m0.4565\u001b[0m 0.0270\n", - " 3 \u001b[36m0.3343\u001b[0m \u001b[32m0.2262\u001b[0m 0.0263\n", - " 4 \u001b[36m0.1851\u001b[0m \u001b[32m0.2223\u001b[0m 0.0257\n", - " 5 \u001b[36m0.1491\u001b[0m \u001b[32m0.1068\u001b[0m 0.0242\n", - " 6 \u001b[36m0.0946\u001b[0m 0.1207 0.0263\n", - " 7 \u001b[36m0.0739\u001b[0m \u001b[32m0.0663\u001b[0m 0.0290\n", - " 8 \u001b[36m0.0554\u001b[0m 0.0706 0.0298\n", - " 9 \u001b[36m0.0437\u001b[0m \u001b[32m0.0461\u001b[0m 0.0337\n", - " 10 \u001b[36m0.0372\u001b[0m 0.0469 0.0273\n", - " 11 \u001b[36m0.0291\u001b[0m \u001b[32m0.0343\u001b[0m 0.0263\n", - " 12 \u001b[36m0.0270\u001b[0m \u001b[32m0.0333\u001b[0m 0.0285\n", - " 13 \u001b[36m0.0207\u001b[0m \u001b[32m0.0265\u001b[0m 0.0281\n", - " 14 \u001b[36m0.0196\u001b[0m \u001b[32m0.0249\u001b[0m 0.0344\n", - " 15 \u001b[36m0.0152\u001b[0m \u001b[32m0.0215\u001b[0m 0.0286\n", - " 16 \u001b[36m0.0151\u001b[0m \u001b[32m0.0198\u001b[0m 0.0281\n", - " 17 \u001b[36m0.0120\u001b[0m \u001b[32m0.0182\u001b[0m 0.0283\n", - " 18 \u001b[36m0.0119\u001b[0m \u001b[32m0.0167\u001b[0m 0.0266\n", - " 19 \u001b[36m0.0100\u001b[0m \u001b[32m0.0159\u001b[0m 0.0266\n", - " 20 \u001b[36m0.0097\u001b[0m \u001b[32m0.0149\u001b[0m 0.0259\n" + " 1 \u001b[36m4.6036\u001b[0m \u001b[32m3.8282\u001b[0m 0.0479\n", + " 2 \u001b[36m3.6613\u001b[0m \u001b[32m1.9079\u001b[0m 0.0449\n", + " 3 \u001b[36m0.9959\u001b[0m \u001b[32m0.3900\u001b[0m 0.0509\n", + " 4 \u001b[36m0.2973\u001b[0m 0.5710 0.0349\n", + " 5 0.6202 \u001b[32m0.1553\u001b[0m 0.0309\n", + " 6 \u001b[36m0.1395\u001b[0m 0.2583 0.0339\n", + " 7 0.1801 \u001b[32m0.1075\u001b[0m 0.0289\n", + " 8 \u001b[36m0.0995\u001b[0m 0.1855 0.0439\n", + " 9 0.1232 \u001b[32m0.0853\u001b[0m 0.0279\n", + " 10 \u001b[36m0.0750\u001b[0m 0.1329 0.0339\n", + " 11 0.0854 \u001b[32m0.0672\u001b[0m 0.0419\n", + " 12 \u001b[36m0.0571\u001b[0m 0.0950 0.0529\n", + " 13 0.0590 \u001b[32m0.0543\u001b[0m 0.0339\n", + " 14 \u001b[36m0.0420\u001b[0m 0.0665 0.0419\n", + " 15 \u001b[36m0.0401\u001b[0m \u001b[32m0.0438\u001b[0m 0.0329\n", + " 16 \u001b[36m0.0308\u001b[0m 0.0472 0.0389\n", + " 17 \u001b[36m0.0274\u001b[0m \u001b[32m0.0353\u001b[0m 0.0329\n", + " 18 \u001b[36m0.0227\u001b[0m 0.0353 0.0389\n", + " 19 \u001b[36m0.0197\u001b[0m \u001b[32m0.0275\u001b[0m 0.0319\n", + " 20 \u001b[36m0.0174\u001b[0m 0.0278 0.0439\n" ] }, { @@ -549,7 +573,7 @@ ")" ] }, - "execution_count": 19, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -574,25 +598,26 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.4903931 ],\n", - " [-1.4224019 ],\n", - " [-0.77500594],\n", - " [-0.06901944],\n", - " [-0.3867012 ]], dtype=float32)" + "array([[ 0.6939189 ],\n", + " [-1.5171506 ],\n", + " [-0.501828 ],\n", + " [-0.41470915],\n", + " [-0.5944874 ]], dtype=float32)" ] }, - "execution_count": 20, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Making prediction for first 5 data points of X\n", "y_pred = net_regr.predict(X_regr[:5])\n", "y_pred" ] @@ -620,16 +645,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ + "import os\n", "import pickle" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -638,18 +664,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/thomasfan/anaconda3/lib/python3.7/site-packages/torch/serialization.py:241: UserWarning: Couldn't retrieve source code for container of type ClassifierModule. It won't be checked for correctness upon loading.\n", - " \"type \" + obj.__name__ + \". It won't be checked \"\n" - ] - } - ], + "outputs": [], "source": [ "with open(file_name, 'wb') as f:\n", " pickle.dump(net, f)" @@ -727,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -737,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -749,53 +766,56 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Re-initializing module!\n", + "Re-initializing module.\n", + "Re-initializing criterion.\n", + "Re-initializing optimizer.\n", " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7243\u001b[0m \u001b[32m0.5000\u001b[0m \u001b[35m0.7105\u001b[0m 0.0184\n", - " 2 \u001b[36m0.7057\u001b[0m 0.5000 \u001b[35m0.6996\u001b[0m 0.0207\n", - " 3 \u001b[36m0.6971\u001b[0m 0.5000 \u001b[35m0.6949\u001b[0m 0.0192\n", - " 4 \u001b[36m0.6936\u001b[0m \u001b[32m0.5050\u001b[0m \u001b[35m0.6929\u001b[0m 0.0224\n", - " 5 \u001b[36m0.6923\u001b[0m \u001b[32m0.5400\u001b[0m \u001b[35m0.6916\u001b[0m 0.0210\n", - " 6 \u001b[36m0.6905\u001b[0m 0.5000 \u001b[35m0.6906\u001b[0m 0.0189\n", - " 7 \u001b[36m0.6894\u001b[0m 0.5100 \u001b[35m0.6899\u001b[0m 0.0194\n", - " 8 \u001b[36m0.6891\u001b[0m 0.5150 \u001b[35m0.6892\u001b[0m 0.0186\n", - " 9 0.6899 0.5250 \u001b[35m0.6885\u001b[0m 0.0202\n", - " 10 \u001b[36m0.6844\u001b[0m 0.5300 \u001b[35m0.6876\u001b[0m 0.0189\n", - " 11 0.6853 \u001b[32m0.5650\u001b[0m \u001b[35m0.6865\u001b[0m 0.0199\n", - " 12 \u001b[36m0.6842\u001b[0m \u001b[32m0.5700\u001b[0m \u001b[35m0.6855\u001b[0m 0.0183\n", - " 13 \u001b[36m0.6821\u001b[0m \u001b[32m0.5850\u001b[0m \u001b[35m0.6844\u001b[0m 0.0199\n", - " 14 \u001b[36m0.6821\u001b[0m \u001b[32m0.6050\u001b[0m \u001b[35m0.6832\u001b[0m 0.0189\n", - " 15 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\u001b[32m0.7100\u001b[0m \u001b[35m0.6229\u001b[0m 0.0429\n", + " 18 \u001b[36m0.6176\u001b[0m 0.6900 \u001b[35m0.6150\u001b[0m 0.0269\n", + " 19 \u001b[36m0.6119\u001b[0m 0.6850 \u001b[35m0.6059\u001b[0m 0.0249\n", + " 20 \u001b[36m0.6097\u001b[0m 0.6950 \u001b[35m0.5988\u001b[0m 0.0269\n" ] }, { "data": { "text/plain": [ - "Pipeline(memory=None,\n", - " steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('net', [initialized](\n", + "Pipeline(steps=[('scale', StandardScaler()),\n", + " ('net',\n", + " [initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", "))])" ] }, - "execution_count": 30, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -806,20 +826,20 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[0.5064775 , 0.49352255],\n", - " [0.53243965, 0.46756038],\n", - " [0.57306874, 0.42693123],\n", - " [0.54179883, 0.45820117],\n", - " [0.5528906 , 0.44710937]], dtype=float32)" + "array([[0.41006783, 0.5899322 ],\n", + " [0.76229 , 0.23771006],\n", + " [0.5576066 , 0.44239342],\n", + " [0.73632103, 0.263679 ],\n", + " [0.6556664 , 0.34433353]], dtype=float32)" ] }, - "execution_count": 31, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -852,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -881,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -897,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -911,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -920,26 +940,26 @@ "text": [ " epoch roc_auc train_loss valid_acc valid_loss dur\n", "------- --------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6112\u001b[0m \u001b[32m0.7076\u001b[0m \u001b[35m0.5550\u001b[0m \u001b[31m0.6802\u001b[0m 0.0188\n", - " 2 \u001b[36m0.6766\u001b[0m \u001b[32m0.6750\u001b[0m \u001b[35m0.6150\u001b[0m \u001b[31m0.6626\u001b[0m 0.0204\n", - " 3 \u001b[36m0.7031\u001b[0m \u001b[32m0.6560\u001b[0m \u001b[35m0.6500\u001b[0m \u001b[31m0.6498\u001b[0m 0.0244\n", - " 4 \u001b[36m0.7201\u001b[0m \u001b[32m0.6364\u001b[0m \u001b[35m0.6650\u001b[0m \u001b[31m0.6381\u001b[0m 0.0193\n", - " 5 \u001b[36m0.7316\u001b[0m \u001b[32m0.6176\u001b[0m \u001b[35m0.6900\u001b[0m \u001b[31m0.6285\u001b[0m 0.0203\n", - " 6 \u001b[36m0.7447\u001b[0m \u001b[32m0.6094\u001b[0m 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\u001b[36m0.7745\u001b[0m \u001b[32m0.5350\u001b[0m 0.7450 \u001b[31m0.5477\u001b[0m 0.0319\n", + " 18 \u001b[36m0.7830\u001b[0m 0.5414 0.7450 \u001b[31m0.5408\u001b[0m 0.0369\n", + " 19 \u001b[36m0.7847\u001b[0m \u001b[32m0.5277\u001b[0m 0.7400 \u001b[31m0.5364\u001b[0m 0.0389\n", + " 20 0.7845 0.5554 0.7350 0.5389 0.0389\n" ] }, { @@ -948,14 +968,14 @@ "[initialized](\n", " module_=ClassifierModule(\n", " (dense0): Linear(in_features=20, out_features=10, bias=True)\n", - " (dropout): Dropout(p=0.5)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", " (dense1): Linear(in_features=10, out_features=10, bias=True)\n", " (output): Linear(in_features=10, out_features=2, bias=True)\n", " ),\n", ")" ] }, - "execution_count": 35, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } @@ -1008,14 +1028,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "module, iterator_train, iterator_valid, optimizer, criterion, callbacks, dataset\n" + "iterator_train, iterator_valid, callbacks, dataset, module, criterion, optimizer\n" ] } ], @@ -1039,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -1048,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1071,7 +1091,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1085,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -1094,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -1102,151 +1122,70 @@ "output_type": "stream", "text": [ "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n", - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False, total= 0.3s\n", - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.3s remaining: 0.0s\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False, total= 0.3s\n", - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False \n", - "[CV] lr=0.05, module__dropout=0, module__num_units=10, optimizer__nesterov=False, 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module__dropout=0.5, module__num_units=20, optimizer__nesterov=True; total time= 0.4s\n" ] }, { "data": { "text/plain": [ - "GridSearchCV(cv=3, error_score='raise-deprecating',\n", - " estimator=[uninitialized](\n", + "GridSearchCV(cv=3,\n", + " estimator=[uninitialized](\n", " module=,\n", "),\n", - " fit_params=None, iid='warn', n_jobs=None,\n", - " param_grid={'lr': [0.05, 0.1], 'module__num_units': [10, 20], 'module__dropout': [0, 0.5], 'optimizer__nesterov': [False, True]},\n", - " pre_dispatch='2*n_jobs', refit=False, return_train_score='warn',\n", - " scoring='accuracy', verbose=2)" + " param_grid={'lr': [0.05, 0.1], 'module__dropout': [0, 0.5],\n", + " 'module__num_units': [10, 20],\n", + " 'optimizer__nesterov': [False, True]},\n", + " refit=False, scoring='accuracy', verbose=2)" ] }, - "execution_count": 41, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -1257,7 +1196,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 52, "metadata": { "scrolled": true }, @@ -1266,7 +1205,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.862 {'lr': 0.05, 'module__dropout': 0, 'module__num_units': 20, 'optimizer__nesterov': False}\n" + "0.8719797641953332 {'lr': 0.1, 'module__dropout': 0, 'module__num_units': 20, 'optimizer__nesterov': True}\n" ] } ], @@ -1284,7 +1223,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [default]", + "display_name": "base", "language": "python", "name": "python3" }, @@ -1298,7 +1237,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.7.13 (default, Mar 28 2022, 08:03:21) [MSC v.1916 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "bd97b8bffa4d3737e84826bc3d37be3046061822757ce35137ab82ad4c5a2016" + } } }, "nbformat": 4, diff --git a/notebooks/CORA-geometric.ipynb b/notebooks/CORA-geometric.ipynb deleted file mode 100644 index cfe3699c1..000000000 --- a/notebooks/CORA-geometric.ipynb +++ /dev/null @@ -1,649 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "93e10022", - "metadata": {}, - "source": [ - "# torch geometric + skorch @ CORA dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f8b30bc0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fr 22. Jul 18:52:31 CEST 2022\r\n" - ] - } - ], - "source": [ - "!date" - ] - }, - { - "cell_type": "markdown", - "id": "4e144398", - "metadata": {}, - "source": [ - "This is an example for how to use skorch with [torch geometric](https://pytorch-geometric.readthedocs.io/). The code is based on the [introduction example](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html) but modified to have a proper train/valid/test split. This example is showcasing a quite small data set that does not need to employ batching to be trained efficiently. How to do batching with skorch + torch geometric will not be handled here since it is non-trivial and quite dataset specific - if you need this and are stuck, feel free to open [an issue](https://github.com/skorch-dev/skorch/issues) so that we can support you the best we can.\n", - "\n", - "Dependencies of this notebook besides skorch base installation:" - ] - }, - { - "cell_type": "raw", - "id": "3748b2a0", - "metadata": {}, - "source": [ - "!pip install torch_geometric==2.0.4" - ] - }, - { - "cell_type": "markdown", - "id": "7ce31902", - "metadata": {}, - "source": [ - "It is recommended to install the dependencies [as documented by pytorch geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)." - ] - }, - { - "cell_type": "markdown", - "id": "ff707ef1", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "efa2b5a5", - "metadata": {}, - "outputs": [], - "source": [ - "import skorch\n", - "import torch" - ] - }, - { - "cell_type": "markdown", - "id": "934fa438", - "metadata": {}, - "source": [ - "### Data Loading" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f1505f8f", - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.datasets import Planetoid\n", - "\n", - "dataset = Planetoid(root='/tmp/Cora', name='Cora')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4979ba6f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(Data(x=[2708, 1433], edge_index=[2, 10556], y=[2708], train_mask=[2708], val_mask=[2708], test_mask=[2708]),\n", - " 7)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.data, dataset.num_classes" - ] - }, - { - "cell_type": "markdown", - "id": "9cd7e104", - "metadata": {}, - "source": [ - "In order to use pytorch geometric / the cora dataset with skorch\n", - "we need to address the following things:\n", - " \n", - "1. graph convolutions cannot handle missing nodes (=> splitting node attributes but keeping edge_index intact will lead to errors)\n", - "2. cora dataset has different attributes for the different split masks (i.e. `train_mask`, `val_mask`, `test_mask`)\n", - "3. skorch expects to have (X, y) pairs for classification tasks\n", - "\n", - "To deal with (1) we will split the data into three datasets, creating three sub-graphs in the process; these complete sub-graphs can then be convolved over without errors. \n", - "We use the masks mentioned in (2) to identify the nodes and edges of the subgraphs.\n", - "\n", - "(3) will be handled by specifying our own `XYDataset` which will just have length 1 and return the dataset and the respective y values. We will therefore basically simulate a `batch_size=1` scenario." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "35aa1770", - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.data import Data\n", - "\n", - "# simulating batch_size=1 by returning the whole dataset and the\n", - "# y-values. this way, the data loader can iterate over the 'batches'\n", - "# and produce X/y values for us.\n", - "class XYDataset(torch.utils.data.Dataset):\n", - " def __init__(self, data: Data, y: torch.tensor):\n", - " self.data = data\n", - " self.y = y\n", - " \n", - " def __len__(self):\n", - " return 1\n", - " \n", - " def __getitem__(self, i):\n", - " return self.data, self.y" - ] - }, - { - "cell_type": "markdown", - "id": "7cd78cd1", - "metadata": {}, - "source": [ - "### Data Splitting" - ] - }, - { - "cell_type": "markdown", - "id": "468d8d6f", - "metadata": {}, - "source": [ - "Split the graph into train, validation and test sub-graphs.\n", - "This ensures that there will be no leakage between steps when we apply graph\n", - "convolution operators on the graph since each split has its own sub-graph.\n", - "\n", - "We use `relabel_nodes=True` to make the node indices in the edge tensor \n", - "zero-based for each sub-graph. If we would not do this the node subsets\n", - "(now zero-based after applying the mask) would not match the indices in the\n", - "edge tensor." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f3d908e4", - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.utils import subgraph\n", - "\n", - "data = dataset[0]\n", - "\n", - "edge_index_train, _ = subgraph(\n", - " subset=data.train_mask, \n", - " edge_index=data.edge_index, \n", - " relabel_nodes=True\n", - ")\n", - "ds_train = XYDataset(\n", - " Data(x=data.x[data.train_mask], edge_index=edge_index_train),\n", - " data.y[data.train_mask],\n", - ")\n", - "\n", - "edge_index_valid, _ = subgraph(\n", - " subset=data.val_mask, \n", - " edge_index=data.edge_index, \n", - " relabel_nodes=True\n", - ")\n", - "ds_valid = XYDataset(\n", - " Data(x=data.x[data.val_mask], edge_index=edge_index_valid),\n", - " data.y[data.val_mask],\n", - ")\n", - "\n", - "edge_index_test, _ = subgraph(\n", - " subset=data.test_mask, \n", - " edge_index=data.edge_index, \n", - " relabel_nodes=True\n", - ")\n", - "ds_test = XYDataset(\n", - " Data(x=data.x[data.test_mask], edge_index=edge_index_test),\n", - " data.y[data.test_mask],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "bf59f7a9", - "metadata": {}, - "source": [ - "### Data Feeding" - ] - }, - { - "cell_type": "markdown", - "id": "4d23039d", - "metadata": {}, - "source": [ - "Our \"batch\" consists of the whole dataset so if we unpack the\n", - "batch into `(X, y)` we will have `X = Data(...)` and `y = [y_true]`.\n", - "The `DataLoader` does not modify `X` but `y` gets a new batch dimension.\n", - "This will lead to a shape mismatch as `y.shape` would then be `(1, #num_samples)`. Therefore, we need our own loader that strips the first dimension to \n", - "match the predicted `y` and the labelled `y` in length.\n", - "\n", - "Note: It is possible to avoid this by stripping this dimension by overriding `get_loss` in the `NeuralNet` class. For brevity we won't do this in this example. It is possible to use [one of the many `DataLoader` classes](https://pytorch-geometric.readthedocs.io/en/latest/modules/loader.html) provided by torch geometric using the approach outlined below (just base the `RawDataloader` on one of the other classes) - chances are, though, that if you are doing this you need to deal with batching anyway which is a topic that is not handled here since it is not trivial." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "83594638", - "metadata": {}, - "outputs": [], - "source": [ - "from torch_geometric.loader import DataLoader\n", - "\n", - "class RawLoader(DataLoader):\n", - " def __iter__(self):\n", - " it = super().__iter__()\n", - " for X, y in it:\n", - " yield X, y[0]" - ] - }, - { - "cell_type": "markdown", - "id": "d51e7140", - "metadata": {}, - "source": [ - "### Modelling" - ] - }, - { - "cell_type": "markdown", - "id": "67e6ff9e", - "metadata": {}, - "source": [ - "This is the CORA example module as seen in the [torch geometric introduction](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "10de0020", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn.functional as F\n", - "from torch_geometric.nn import GCNConv\n", - "\n", - "class GCN(torch.nn.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.conv1 = GCNConv(dataset.num_node_features, 16)\n", - " self.conv2 = GCNConv(16, dataset.num_classes)\n", - "\n", - " def forward(self, data): \n", - " x, edge_index = data.x, data.edge_index\n", - "\n", - " x = self.conv1(x, edge_index)\n", - " x = F.relu(x)\n", - " x = F.dropout(x, training=self.training)\n", - " x = self.conv2(x, edge_index)\n", - "\n", - " return F.softmax(x, dim=1)" - ] - }, - { - "cell_type": "markdown", - "id": "f58e93d8", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b3f5ef3c", - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.helper import predefined_split\n", - "\n", - "torch.manual_seed(42)\n", - "\n", - "net = skorch.NeuralNetClassifier(\n", - " module=GCN,\n", - " lr=0.1,\n", - " optimizer__weight_decay=5e-4,\n", - " max_epochs=200,\n", - " train_split=skorch.helper.predefined_split(ds_valid),\n", - " batch_size=1,\n", - " iterator_train=RawLoader,\n", - " iterator_valid=RawLoader,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c2cf8bbd", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_acc valid_loss dur\n", - "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m1.9724\u001b[0m \u001b[32m0.1680\u001b[0m \u001b[35m1.9398\u001b[0m 0.0074\n", - " 2 \u001b[36m1.9625\u001b[0m \u001b[32m0.1740\u001b[0m \u001b[35m1.9376\u001b[0m 0.0052\n", - " 3 \u001b[36m1.9327\u001b[0m 0.1720 \u001b[35m1.9342\u001b[0m 0.0043\n", - " 4 \u001b[36m1.9321\u001b[0m \u001b[32m0.1760\u001b[0m \u001b[35m1.9324\u001b[0m 0.0045\n", - " 5 \u001b[36m1.9142\u001b[0m \u001b[32m0.1800\u001b[0m \u001b[35m1.9307\u001b[0m 0.0037\n", - " 6 \u001b[36m1.8923\u001b[0m 0.1800 \u001b[35m1.9290\u001b[0m 0.0036\n", - " 7 \u001b[36m1.8848\u001b[0m \u001b[32m0.1880\u001b[0m \u001b[35m1.9269\u001b[0m 0.0042\n", - " 8 1.8936 \u001b[32m0.1920\u001b[0m \u001b[35m1.9247\u001b[0m 0.0042\n", - " 9 \u001b[36m1.8783\u001b[0m \u001b[32m0.1960\u001b[0m \u001b[35m1.9219\u001b[0m 0.0044\n", - " 10 \u001b[36m1.8737\u001b[0m \u001b[32m0.2040\u001b[0m \u001b[35m1.9192\u001b[0m 0.0044\n", - " 11 \u001b[36m1.8542\u001b[0m \u001b[32m0.2060\u001b[0m \u001b[35m1.9176\u001b[0m 0.0042\n", - " 12 \u001b[36m1.8489\u001b[0m \u001b[32m0.2100\u001b[0m \u001b[35m1.9156\u001b[0m 0.0042\n", - " 13 \u001b[36m1.8314\u001b[0m \u001b[32m0.2120\u001b[0m \u001b[35m1.9121\u001b[0m 0.0074\n", - " 14 1.8334 \u001b[32m0.2220\u001b[0m \u001b[35m1.9100\u001b[0m 0.0055\n", - " 15 \u001b[36m1.8041\u001b[0m \u001b[32m0.2240\u001b[0m \u001b[35m1.9085\u001b[0m 0.0081\n", - " 16 1.8089 0.2220 \u001b[35m1.9065\u001b[0m 0.0063\n", - " 17 1.8082 0.2200 \u001b[35m1.9043\u001b[0m 0.0081\n", - " 18 \u001b[36m1.7759\u001b[0m 0.2220 \u001b[35m1.9023\u001b[0m 0.0094\n", - " 19 \u001b[36m1.7745\u001b[0m 0.2220 \u001b[35m1.8992\u001b[0m 0.0049\n", - " 20 \u001b[36m1.7630\u001b[0m \u001b[32m0.2280\u001b[0m \u001b[35m1.8970\u001b[0m 0.0069\n", - " 21 \u001b[36m1.7411\u001b[0m \u001b[32m0.2340\u001b[0m \u001b[35m1.8946\u001b[0m 0.0068\n", - " 22 1.7732 \u001b[32m0.2360\u001b[0m \u001b[35m1.8921\u001b[0m 0.0054\n", - " 23 \u001b[36m1.7407\u001b[0m \u001b[32m0.2420\u001b[0m \u001b[35m1.8893\u001b[0m 0.0061\n", - " 24 \u001b[36m1.7259\u001b[0m 0.2420 \u001b[35m1.8857\u001b[0m 0.0046\n", - " 25 \u001b[36m1.6920\u001b[0m \u001b[32m0.2520\u001b[0m \u001b[35m1.8836\u001b[0m 0.0095\n", - " 26 1.7033 \u001b[32m0.2540\u001b[0m \u001b[35m1.8805\u001b[0m 0.0070\n", - " 27 1.7080 \u001b[32m0.2580\u001b[0m \u001b[35m1.8767\u001b[0m 0.0071\n", - " 28 1.6924 \u001b[32m0.2620\u001b[0m \u001b[35m1.8741\u001b[0m 0.0064\n", - " 29 \u001b[36m1.6882\u001b[0m 0.2620 \u001b[35m1.8703\u001b[0m 0.0049\n", - " 30 \u001b[36m1.6850\u001b[0m \u001b[32m0.2660\u001b[0m \u001b[35m1.8679\u001b[0m 0.0061\n", - " 31 \u001b[36m1.6438\u001b[0m 0.2580 \u001b[35m1.8650\u001b[0m 0.0077\n", - " 32 \u001b[36m1.6345\u001b[0m \u001b[32m0.2680\u001b[0m \u001b[35m1.8618\u001b[0m 0.0088\n", - " 33 1.6816 0.2660 \u001b[35m1.8579\u001b[0m 0.0089\n", - " 34 \u001b[36m1.6169\u001b[0m 0.2620 \u001b[35m1.8559\u001b[0m 0.0065\n", - " 35 1.6373 \u001b[32m0.2720\u001b[0m \u001b[35m1.8522\u001b[0m 0.0047\n", - " 36 \u001b[36m1.6107\u001b[0m 0.2700 \u001b[35m1.8491\u001b[0m 0.0100\n", - " 37 \u001b[36m1.6035\u001b[0m \u001b[32m0.2800\u001b[0m \u001b[35m1.8449\u001b[0m 0.0069\n", - " 38 1.6060 \u001b[32m0.2840\u001b[0m \u001b[35m1.8421\u001b[0m 0.0062\n", - " 39 \u001b[36m1.5604\u001b[0m \u001b[32m0.2960\u001b[0m \u001b[35m1.8389\u001b[0m 0.0075\n", - " 40 1.5724 \u001b[32m0.3060\u001b[0m \u001b[35m1.8354\u001b[0m 0.0093\n", - " 41 \u001b[36m1.5371\u001b[0m \u001b[32m0.3160\u001b[0m \u001b[35m1.8319\u001b[0m 0.0066\n", - " 42 \u001b[36m1.5246\u001b[0m \u001b[32m0.3240\u001b[0m \u001b[35m1.8281\u001b[0m 0.0044\n", - " 43 1.5524 0.3200 \u001b[35m1.8241\u001b[0m 0.0078\n", - " 44 1.5282 \u001b[32m0.3300\u001b[0m \u001b[35m1.8211\u001b[0m 0.0068\n", - " 45 1.5356 \u001b[32m0.3380\u001b[0m \u001b[35m1.8169\u001b[0m 0.0057\n", - " 46 \u001b[36m1.5079\u001b[0m \u001b[32m0.3440\u001b[0m \u001b[35m1.8137\u001b[0m 0.0089\n", - " 47 1.5192 \u001b[32m0.3500\u001b[0m \u001b[35m1.8090\u001b[0m 0.0072\n", - " 48 \u001b[36m1.4991\u001b[0m \u001b[32m0.3540\u001b[0m \u001b[35m1.8063\u001b[0m 0.0045\n", - " 49 \u001b[36m1.4949\u001b[0m 0.3460 \u001b[35m1.8036\u001b[0m 0.0085\n", - " 50 \u001b[36m1.4892\u001b[0m \u001b[32m0.3640\u001b[0m \u001b[35m1.8000\u001b[0m 0.0051\n", - " 51 1.5165 \u001b[32m0.3760\u001b[0m \u001b[35m1.7968\u001b[0m 0.0090\n", - " 52 \u001b[36m1.4367\u001b[0m 0.3740 \u001b[35m1.7931\u001b[0m 0.0079\n", - " 53 1.4473 0.3700 \u001b[35m1.7894\u001b[0m 0.0045\n", - " 54 1.4387 \u001b[32m0.3840\u001b[0m \u001b[35m1.7855\u001b[0m 0.0052\n", - " 55 \u001b[36m1.4261\u001b[0m 0.3840 \u001b[35m1.7825\u001b[0m 0.0088\n", - " 56 1.4355 \u001b[32m0.4040\u001b[0m \u001b[35m1.7768\u001b[0m 0.0075\n", - " 57 1.4270 0.3900 \u001b[35m1.7749\u001b[0m 0.0098\n", - " 58 \u001b[36m1.4029\u001b[0m 0.4000 \u001b[35m1.7714\u001b[0m 0.0087\n", - " 59 \u001b[36m1.3793\u001b[0m 0.4040 \u001b[35m1.7679\u001b[0m 0.0080\n", - " 60 \u001b[36m1.3493\u001b[0m 0.4020 \u001b[35m1.7629\u001b[0m 0.0051\n", - " 61 1.3624 \u001b[32m0.4160\u001b[0m \u001b[35m1.7597\u001b[0m 0.0083\n", - " 62 1.3970 \u001b[32m0.4180\u001b[0m \u001b[35m1.7562\u001b[0m 0.0082\n", - " 63 1.3552 \u001b[32m0.4220\u001b[0m \u001b[35m1.7516\u001b[0m 0.0057\n", - " 64 1.3745 \u001b[32m0.4240\u001b[0m \u001b[35m1.7480\u001b[0m 0.0054\n", - " 65 1.4002 \u001b[32m0.4260\u001b[0m \u001b[35m1.7448\u001b[0m 0.0086\n", - " 66 \u001b[36m1.2924\u001b[0m \u001b[32m0.4280\u001b[0m \u001b[35m1.7405\u001b[0m 0.0071\n", - " 67 1.2954 \u001b[32m0.4300\u001b[0m \u001b[35m1.7375\u001b[0m 0.0070\n", - " 68 \u001b[36m1.2785\u001b[0m \u001b[32m0.4320\u001b[0m \u001b[35m1.7319\u001b[0m 0.0070\n", - " 69 1.3192 0.4300 \u001b[35m1.7290\u001b[0m 0.0088\n", - " 70 1.3049 \u001b[32m0.4360\u001b[0m \u001b[35m1.7246\u001b[0m 0.0057\n", - " 71 \u001b[36m1.2504\u001b[0m \u001b[32m0.4420\u001b[0m \u001b[35m1.7198\u001b[0m 0.0085\n", - " 72 1.2841 0.4340 \u001b[35m1.7165\u001b[0m 0.0077\n", - " 73 \u001b[36m1.2304\u001b[0m \u001b[32m0.4460\u001b[0m \u001b[35m1.7120\u001b[0m 0.0067\n", - " 74 1.2414 \u001b[32m0.4540\u001b[0m \u001b[35m1.7070\u001b[0m 0.0085\n", - " 75 \u001b[36m1.1753\u001b[0m 0.4520 \u001b[35m1.7020\u001b[0m 0.0076\n", - " 76 1.2608 \u001b[32m0.4580\u001b[0m \u001b[35m1.6981\u001b[0m 0.0076\n", - " 77 1.2053 0.4580 \u001b[35m1.6935\u001b[0m 0.0084\n", - " 78 1.2640 \u001b[32m0.4600\u001b[0m \u001b[35m1.6910\u001b[0m 0.0056\n", - " 79 1.2251 \u001b[32m0.4700\u001b[0m \u001b[35m1.6845\u001b[0m 0.0069\n", - " 80 1.2221 \u001b[32m0.4780\u001b[0m \u001b[35m1.6801\u001b[0m 0.0044\n", - 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}, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.fit(ds_train, None)" - ] - }, - { - "cell_type": "markdown", - "id": "fed65b00", - "metadata": {}, - "source": [ - "### Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ae562c9e", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ef5f2409", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.682" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(ds_test.y, net.predict(ds_test))" - ] - }, - { - "cell_type": "markdown", - "id": "2fb7c99b", - "metadata": {}, - "source": [ - "In conclusion this example showed you how to use a basic data graph dataset using pytorch geometric in conjunction with skorch. The final test score is lower than the ~80% accuracy in the [introduction example](https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html) which can be explained by the reduced leakage between train and validation sets due to our splitting the data into subgraphs beforehand.\n", - "\n", - "The model is now incorporated into the sklearn world (as you could already see, you can simply use sklearn metrics to evaluate the model). Thus, tools like grid and random search are available to you and it is easily possible to include a graph neural net as a feature transformer in your next ML pipeline!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/Gaussian_Processes.ipynb b/notebooks/Gaussian_Processes.ipynb index 6f15b4840..989a265f3 100644 --- a/notebooks/Gaussian_Processes.ipynb +++ b/notebooks/Gaussian_Processes.ipynb @@ -1,2436 +1,2928 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gaussian Processes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "skorch supports integration with the fantastic [GPyTorch](https://gpytorch.ai/) library. GPyTorch implements various Gaussian Process (GP) techniques on top of PyTorch." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GPyTorch adopts many patterns from PyTorch, thus making it easy to pick up for seasoned PyTorch users. Similarly, the skorch GPyTorch integration should look familiar to seasoned skorch users. However, GPs are a different beast than the more common, non-probabilistic machine learning techniques. It is important to understand the basic concepts before using them in practice." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook is not the place to learn about GPs in general, instead a basic understanding is assumed. If you're looking for an introduction to probabilistic programming and GPs, here are some pointers:\n", - "\n", - "- The GPyTorch [documentation](https://docs.gpytorch.ai/en/stable/)\n", - "- The book [Gaussian Processes for Machine Learning](http://gaussianprocess.org/gpml/chapters/) by Carl Edward Rasmussen and Christopher K. I. Williams\n", - "- The lecture series [Probabilistic Machine Learning](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) by Philipp Hennig" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we will show you how to use skorch for Gaussian Processes through GPyTorch. We assume that you are familiar with how skorch and PyTorch work and we will focus on how using GPs differs from using non-probabilistic deep learning techniques with skorch. For a discussion on when and when not to use GPyTorch with skorch, please have a look at our [documentation](https://skorch.readthedocs.io/en/latest/user/probabilistic.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you haven't already, you should install GPyTorch, since it is not installed automatically after installing skorch:\n", - "\n", - "```bash\n", - "# using pip\n", - "pip install -U gpytorch\n", - "# using conda\n", - "conda install gpytorch -c gpytorch\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch \"skorch>=0.11\" gpytorch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Table of contents" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* [Exact Gaussian Process Regression](#Exact-Gaussian-Process-Regression)\n", - " * [Simple example: sine curve](#Simple-example:-sine-curve)\n", - " * [GP regression with real world data](#Regression-with-real-world-data)\n", - "* [Stochastic Variational GP Regression](#Stochastic-Variational-GP-Regression)\n", - "* [Classification](#Classification)\n", - " * [Binary classification](#Binary-classification)\n", - " * [Multiclass classification](#Multiclass-Classification)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import math\n", - "import os\n", - "import urllib.request" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import torch\n", - "import gpytorch\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "torch.cuda.manual_seed(0)\n", - "plt.style.use('seaborn')\n", - "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Exact Gaussian Process Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GPyTorch implmenets different methods to solve GPs. The most basic form is to use exact solutions. Variational GPs are described further below." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simple example: sine curve" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The \"Hello world\" of GPs is predicting a sine curve with Gaussian noise added on top. We will start with this example." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we synthesize our data. For training, we use a sine curve with Gaussian noise added on top. For validation, we just use the sine without noise, assuming this is the underlying ground truth. To make it difficult for the model, the training data will only contain very few data points for now." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "sampling_frequency = 0.5\n", - "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", - "y_train = torch.sin(X_train) + torch.randn(len(X_train)) * 0.2" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "X_valid = torch.linspace(-10, 10, 100)\n", - "y_valid = torch.sin(X_valid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see below, there is a slight hint of periodicity in the training data but it could also just be noise." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(X_train, y_train, 'o')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As usual with PyTorch, the core of your modeling approach is to define the module. In our case, instead of subclassing `torch.nn.Module`, we subclass `gpytorch.models.ExactGP` (which itself is a subclass of `torch.nn.Module`), since we want to do exact GP. As always, we need to define our own `__init__` method (don't forget to call `super().__init__`) and our own `forward` method." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "class RbfModule(gpytorch.models.ExactGP):\n", - " def __init__(self, likelihood, noise_init=None):\n", - " # detail: We don't set train_inputs and train_targets here because skorch\n", - " # will take care of that.\n", - " super().__init__(train_inputs=None, train_targets=None, likelihood=likelihood)\n", - " self.mean_module = gpytorch.means.ConstantMean()\n", - " self.covar_module = gpytorch.kernels.RBFKernel()\n", - "\n", - " def forward(self, x):\n", - " mean_x = self.mean_module(x)\n", - " covar_x = self.covar_module(x)\n", - " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we don't want to go into too much details about GPs or GPyTorch. The important ingredients here are the _mean function_ and the _kernel function_. As the name suggests, the mean function is only there to determine the means of the Gaussian distribution. The kernel function is used to calculate the covariance matrix of the data points. Together, the means and covariance matrix are sufficient to define a Gaussian distribution.\n", - "\n", - "For the mean function `gpytorch.means.ConstantMean` will often do. Choosing the correct kernel, however, is where it gets interesting. This kernel should be chosen wisely so as to fit the problem as best as possible. The correct choice here is as crucial as choosing the correct Deep Learning architecture — when you choose an RNN for an image classification problem, you will have little luck. That being said, a good start is often to use the `RBFKernel` and then iterate from there. That's why we use the RBF for our toy example.\n", - "\n", - "The output of the `forward` method should always be a `gpytorch.distributions.MultivariateNormal` for `ExactGP`. It represents the prior latent distribution conditioned on the input data. The posterior is computed by applying a likelihood, which is `gpytorch.likelihoods.GaussianLikelihood` by default for GP regression." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### skorch `ExactGPRegressor`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's define our skorch model. For this, we import `ExactGPRegressor` and initialize it in much the same way as we would a `NeuralNet`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/vinh/work/skorch/skorch/probabilistic.py:35: SkorchWarning: The API of the Gaussian Process estimators is experimental and may change in the future\n", - " \"change in the future\", SkorchWarning)\n" - ] - } - ], - "source": [ - "from skorch.probabilistic import ExactGPRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "gpr = ExactGPRegressor(\n", - " RbfModule,\n", - " optimizer=torch.optim.Adam,\n", - " lr=0.1,\n", - " max_epochs=20,\n", - " device=DEVICE,\n", - " batch_size=-1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, we pass the `RbfModule` defined above as the first argument, as we always do. We also define the optimizer, learning rate (`lr`) and device as usual. We could pass our own `likelihood` argument, but since we use the default likelihood, we don't need to do that." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "One oddity you might have noticed is `batch_size=-1`. -1 is a placeholder that means: take all the data at once, don't use batching. The reason for this is that the exact solution requires all data to be passed at the same time, it does not work on batches. The batch size is -1 by default but we set it here explicitly to make it clear that it is so.\n", - "\n", - "If you need to use batches (say, you don't have enough GPU memory to fit all your data), you can use variational GPs, as shown later in the notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Info:\n", - " GPyTorch stores a reference to the training data (i.e X and y) on the module. This can make your model quite big if your training data is large. However, exact GPs are typically not used with large datasets - if you want to avoid this issue, take a look at the variational method described further below.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Sampling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At this point, we can already show a new feature that is available thanks to GPs. They allow us to sample from the underlying distribution, conditioned on our data, even though we have not even called `fit` on the model. To do this, we initialize the `ExactGPRegressor` by calling `initialize()` and then use the `sample` method. The first argument to `sample` is the data to condition on, in this case the training data, and the second argument is the number of samples to draw." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We plot the result next to the ground truth and the training data for comparison." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gpr.initialize()\n", - "\n", - "samples = gpr.sample(X_train, n_samples=50)\n", - "samples = samples.detach().numpy() # turn into numpy array\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "ax.plot(X_train, samples.T, color='k', alpha=0.1)\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, 'r', label='true')\n", - "ax.set_xlim([-8.1, 8.1])\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It can often be wise to plot a couple of samples _before_ starting a lengthy training process. These samples can be compared to the underlying data to see if the chosen model looks reasonable _a priori_. If the distribution of the target looks very different from the sampled distribution, it means it could not result from the assumed distribution. No matter how well you train, your model will never fit your data. In such a case, you probably need to find a better kernel function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As always, to train the model, we call the `fit` method and pass the training data and targets as arguments:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Re-initializing module.\n", - "Re-initializing criterion.\n", - "Re-initializing optimizer.\n", - " epoch train_loss dur\n", - "------- ------------ ------\n", - " 1 \u001b[36m1.2771\u001b[0m 0.0204\n", - " 2 \u001b[36m1.2650\u001b[0m 0.0097\n", - " 3 \u001b[36m1.2533\u001b[0m 0.0092\n", - " 4 \u001b[36m1.2416\u001b[0m 0.0129\n", - " 5 \u001b[36m1.2312\u001b[0m 0.0084\n", - " 6 \u001b[36m1.2211\u001b[0m 0.0138\n", - " 7 \u001b[36m1.2111\u001b[0m 0.0069\n", - " 8 \u001b[36m1.2017\u001b[0m 0.0132\n", - " 9 \u001b[36m1.1932\u001b[0m 0.0141\n", - " 10 \u001b[36m1.1850\u001b[0m 0.0088\n", - " 11 \u001b[36m1.1771\u001b[0m 0.0130\n", - " 12 \u001b[36m1.1698\u001b[0m 0.0092\n", - " 13 \u001b[36m1.1632\u001b[0m 0.0085\n", - " 14 \u001b[36m1.1570\u001b[0m 0.0047\n", - " 15 \u001b[36m1.1511\u001b[0m 0.0082\n", - " 16 \u001b[36m1.1456\u001b[0m 0.0169\n", - " 17 \u001b[36m1.1408\u001b[0m 0.0084\n", - " 18 \u001b[36m1.1363\u001b[0m 0.0101\n", - " 19 \u001b[36m1.1320\u001b[0m 0.0067\n", - " 20 \u001b[36m1.1281\u001b[0m 0.0075\n" - ] - }, - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=RbfModule(\n", - " (likelihood): GaussianLikelihood(\n", - " (noise_covar): HomoskedasticNoise(\n", - " (raw_noise_constraint): GreaterThan(1.000E-04)\n", - " )\n", - " )\n", - " (mean_module): ConstantMean()\n", - " (covar_module): RBFKernel(\n", - " (raw_lengthscale_constraint): Positive()\n", - " (distance_module): Distance()\n", - " )\n", - " ),\n", - ")" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpr.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Info:\n", - " For GP regression, skorch does not perform a train/valid split by default. This is why you only see the train loss here, not the validation loss as usual. The reason for this decision is that a random split is most often not appropriate for GP regression. E.g. when you deal with a time series, random splitting would result in data leakage. Therefore, if you want validation scores, it is probably best to implement your own train_split or use skorch.helper.predefined_split if you already have split your data beforehand (see the example further below).\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyzing the trained model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that our model is trained, we can repeat the sampling process from above. As you can see, the samples fit the data much better now." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/vinh/anaconda3/envs/skorch/lib/python3.7/site-packages/gpytorch/models/exact_gp.py:275: GPInputWarning: The input matches the stored training data. Did you forget to call model.train()?\n", - " GPInputWarning,\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "samples = gpr.sample(X_train, 50)\n", - "samples = samples.detach().numpy() # turn into numpy array\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "ax.plot(X_train, samples.T, color='k', alpha=0.1)\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, 'r', label='true')\n", - "ax.set_xlim([-8.1, 8.1])\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the model represents a probability distribution, instead of just making point predictions as is most often the case for Deep Learning, we can use the distribution to give us confidence intervals. To do this, we can pass `return_std=True` to the `predict` call. This will return one standard deviation for the given data, which we can add/subtract from our prediction to get upper/lower confidence bounds:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_pred, y_std = gpr.predict(X_valid, return_std=True)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, color='red', label='true mean')\n", - "ax.plot(X_valid, y_pred, color='blue', label='predicted mean')\n", - "ax.fill_between(X_valid, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, the confidence bounds are quite wide most of the time. Only at the training data points is the model more confident. This is exactly what we should expect: At the points where the model has seen some data, it is more confident, but between data points, it is less confident." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### More data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, we increase the sampling frequency from our sine function and train the same model again to show how a well fit model looks like." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "sampling_frequency = 2\n", - "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", - "y_train = torch.sin(X_train) + torch.randn(len(X_train)) * 0.2" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss dur\n", - "------- ------------ ------\n", - " 1 \u001b[36m1.1546\u001b[0m 0.0054\n", - " 2 \u001b[36m1.1222\u001b[0m 0.0087\n", - " 3 \u001b[36m1.0879\u001b[0m 0.0079\n", - " 4 \u001b[36m1.0535\u001b[0m 0.0060\n", - " 5 \u001b[36m1.0188\u001b[0m 0.0093\n", - " 6 \u001b[36m0.9833\u001b[0m 0.0093\n", - " 7 \u001b[36m0.9474\u001b[0m 0.0089\n", - " 8 \u001b[36m0.9114\u001b[0m 0.0097\n", - " 9 \u001b[36m0.8754\u001b[0m 0.0068\n", - " 10 \u001b[36m0.8395\u001b[0m 0.0104\n", - " 11 \u001b[36m0.8038\u001b[0m 0.0056\n", - " 12 \u001b[36m0.7684\u001b[0m 0.0058\n", - " 13 \u001b[36m0.7337\u001b[0m 0.0103\n", - " 14 \u001b[36m0.7000\u001b[0m 0.0087\n", - " 15 \u001b[36m0.6673\u001b[0m 0.0062\n", - " 16 \u001b[36m0.6357\u001b[0m 0.0097\n", - " 17 \u001b[36m0.6054\u001b[0m 0.0067\n", - " 18 \u001b[36m0.5762\u001b[0m 0.0068\n", - " 19 \u001b[36m0.5479\u001b[0m 0.0071\n", - " 20 \u001b[36m0.5200\u001b[0m 0.0072\n" - ] - }, - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=RbfModule(\n", - " (likelihood): GaussianLikelihood(\n", - " (noise_covar): HomoskedasticNoise(\n", - " (raw_noise_constraint): GreaterThan(1.000E-04)\n", - " )\n", - " )\n", - " (mean_module): ConstantMean()\n", - " (covar_module): RBFKernel(\n", - " (raw_lengthscale_constraint): Positive()\n", - " (distance_module): Distance()\n", - " )\n", - " ),\n", - ")" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpr = ExactGPRegressor(\n", - " RbfModule,optimizer=torch.optim.Adam,\n", - " lr=0.1,\n", - " max_epochs=20,\n", - " batch_size=-1,\n", - " device=DEVICE,\n", - ")\n", - "gpr.fit(X_train.reshape(-1, 1), y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_pred, y_std = gpr.predict(X_valid, return_std=True)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, color='red', label='true mean')\n", - "ax.plot(X_valid, y_pred, color='blue', label='predicted mean')\n", - "ax.fill_between(X_valid, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we can see that the confidence intervals are much narrower than above, even between data points. This means that the model is quite confident in _interpolating_ between data points. Notice, however, that the confidence bounds increase considerably at the left and right end, i.e. at values outside of the range of the training data. This means that the model is less confident in _extrapolating_ which is typically a good thing." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Confidence region" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Another way skorch implements to quickly get the confidence interval is through the `confidence_region` method. This mirrors the method by the same name in GPyTorch. By default, it returns the lower and upper bound for 2 standard deviations, but this can be changed through the `sigmas` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "lower, upper = gpr.confidence_region(X_valid, sigmas=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(upper, label='+2 std dev')\n", - "plt.plot(y_pred, label='mean')\n", - "plt.plot(lower, label='-2 std dev')\n", - "plt.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Grid search" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As always, one of the advantages of using skorch is the sklearn integration. That means that we can plug our regressor into `GridSearchCV` et al. However, there is a caveat to this, as explained below. But first, let's set up the grid search." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " 'lr': [0.01, 0.02],\n", - " 'max_epochs': [10, 20],\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "gpr = ExactGPRegressor(\n", - " RbfModule,\n", - " optimizer=torch.optim.Adam,\n", - " lr=0.1,\n", - " max_epochs=20,\n", - " batch_size=-1,\n", - " device=DEVICE,\n", - " \n", - " train_split=False,\n", - " verbose=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We turn off skorch-internal train/validation split, since the grid search already performs the data splitting for us. We also set the verbosity level to 0 to avoid too many print outputs." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "search = GridSearchCV(gpr, params, cv=3, scoring='neg_mean_squared_error', verbose=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we deal with a regression task, we choose an appropriate scoring function, in this case mean squared error. Now let's start the grid search to see the problem mentioned above:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.9s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3,\n", - " estimator=[uninitialized](\n", - " module=,\n", - "),\n", - " param_grid={'lr': [0.01, 0.02], 'max_epochs': [10, 20]},\n", - " scoring='neg_mean_squared_error', verbose=1)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The grid search finished successfully and we found the best hyper-parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.5268993576367696" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search.best_score_" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'lr': 0.01, 'max_epochs': 20}" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search.best_params_" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regression with real world data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far, we have only worked with toy data. To show how to work with real world data, we will reproduce an example from the GPyTorch docs:\n", - "\n", - "https://docs.gpytorch.ai/en/stable/examples/01_Exact_GPs/Spectral_Delta_GP_Regression.html\n", - "\n", - "This dataset contains the \"BART ridership on the 5 most commonly traveled stations in San Francisco\". \"BART\" is the \"Bay Area Rapid Transit\", i.e. public transit system in the San Francisco bay area.\n", - "\n", - "For more details, please consult the link." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First of all we need to download the data. This only needs to be done if the data has not already been downloaded." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "path = os.path.join('datasets', 'BART_sample.pt')\n", - "url = 'https://drive.google.com/uc?export=download&id=1A6LqCHPA5lHa5S3lMH8mLMNEgeku8lRG'\n", - "if not os.path.isfile(path):\n", - " print('Downloading BART sample dataset...')\n", - " urllib.request.urlretrieve(url, path)\n", - " \n", - "train_x, train_y, test_x, test_y = torch.load('../BART_sample.pt', map_location=DEVICE)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need to scale the input data a bit, following the tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "train_x_min = train_x.min()\n", - "train_x_max = train_x.max()\n", - "\n", - "X_train = train_x - train_x_min\n", - "X_valid = test_x - train_x_min" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The target data also needs to be scaled. However, contrary to the tutorial, we will use sklearn's `StandardScaler` for this. Although it performs the same transformation as in the tutorial, it allows us to easily inverse transform the targets back to their original scale, which will be useful later." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import StandardScaler" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# We need to transpose here because the target data is sequence x sample\n", - "# but we want to scale over the samples, not over the sequence.\n", - "y_scaler = StandardScaler().fit(train_y.T)\n", - "y_train = torch.from_numpy(y_scaler.transform(train_y.T).T).float()\n", - "y_valid = torch.from_numpy(y_scaler.transform(test_y.T).T).float()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([5, 1440, 1]) torch.Size([5, 1440]) torch.Size([5, 240, 1]) torch.Size([5, 240])\n" - ] - } - ], - "source": [ - "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The module is effectively the same as in the tutorial." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "class SpectralDeltaGP(gpytorch.models.ExactGP):\n", - " def __init__(self, X, y, likelihood, num_deltas, noise_init=None):\n", - " super(SpectralDeltaGP, self).__init__(X, y, likelihood)\n", - " self.mean_module = gpytorch.means.ConstantMean()\n", - " base_covar_module = gpytorch.kernels.SpectralDeltaKernel(\n", - " num_dims=X.size(-1),\n", - " num_deltas=num_deltas,\n", - " )\n", - " self.covar_module = gpytorch.kernels.ScaleKernel(base_covar_module)\n", - "\n", - " def forward(self, x):\n", - " mean_x = self.mean_module(x)\n", - " covar_x = self.covar_module(x)\n", - " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the likelihood" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we show an example of using a non default likelihood. We wrap the initialization of the likelihood inside a function because we want to use some specific methods, such as `register_prior`. This function can be passed as the `likelihood` parameter to `ExactGPRegressor`." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "def get_likelihood(\n", - " noise_constraint=gpytorch.constraints.GreaterThan(1e-11),\n", - " noise_prior=gpytorch.priors.HorseshoePrior(0.1),\n", - " noise=1e-2,\n", - "):\n", - " likelihood = gpytorch.likelihoods.GaussianLikelihood(\n", - " noise_constraint=noise_constraint,\n", - " )\n", - " likelihood.register_prior(\"noise_prior\", noise_prior, \"noise\")\n", - " likelihood.noise = noise\n", - " return likelihood" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "gpr = ExactGPRegressor(\n", - " SpectralDeltaGP,\n", - " module__num_deltas=1500,\n", - " module__X=X_train if DEVICE == 'cpu' else X_train.cuda(),\n", - " module__y=y_train if DEVICE == 'cpu' else y_train.cuda(),\n", - "\n", - " likelihood=get_likelihood,\n", - "\n", - " optimizer=torch.optim.Adam,\n", - " lr=2e-4,\n", - " max_epochs=50,\n", - " batch_size=-1,\n", - " device=DEVICE,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, as explained above, we additionally need to pass `X` and `y` to the module and we need to set the batch size to -1, since we still deal with exact GPs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Training the model" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss dur\n", - "------- ------------ ------\n", - " 1 \u001b[36m2.7906\u001b[0m 2.0871\n", - " 2 \u001b[36m2.1317\u001b[0m 1.9986\n", - " 3 \u001b[36m1.6960\u001b[0m 3.0301\n", - " 4 \u001b[36m1.4273\u001b[0m 3.0680\n", - " 5 \u001b[36m1.2657\u001b[0m 3.5300\n", - " 6 \u001b[36m1.1890\u001b[0m 2.5725\n", - " 7 \u001b[36m1.1178\u001b[0m 2.0474\n", - " 8 \u001b[36m1.0708\u001b[0m 2.0823\n", - " 9 \u001b[36m1.0580\u001b[0m 2.0268\n", - " 10 \u001b[36m1.0362\u001b[0m 2.0235\n", - " 11 \u001b[36m1.0192\u001b[0m 1.8889\n", - " 12 \u001b[36m0.9654\u001b[0m 1.8637\n", - " 13 \u001b[36m0.9529\u001b[0m 1.8471\n", - " 14 \u001b[36m0.9309\u001b[0m 1.9733\n", - " 15 \u001b[36m0.9019\u001b[0m 2.1043\n", - " 16 0.9180 1.9568\n", - " 17 \u001b[36m0.8743\u001b[0m 1.9917\n", - " 18 \u001b[36m0.8694\u001b[0m 1.9267\n", - " 19 \u001b[36m0.8587\u001b[0m 2.1423\n", - " 20 \u001b[36m0.8542\u001b[0m 1.8739\n", - " 21 \u001b[36m0.8462\u001b[0m 1.8677\n", - " 22 \u001b[36m0.8018\u001b[0m 2.0562\n", - " 23 \u001b[36m0.7965\u001b[0m 2.0123\n", - " 24 \u001b[36m0.7790\u001b[0m 2.0773\n", - " 25 0.7978 2.1713\n", - " 26 \u001b[36m0.7567\u001b[0m 2.2295\n", - " 27 \u001b[36m0.7393\u001b[0m 2.2569\n", - " 28 \u001b[36m0.7321\u001b[0m 2.1236\n", - " 29 \u001b[36m0.7017\u001b[0m 1.8720\n", - " 30 \u001b[36m0.6973\u001b[0m 1.6813\n", - " 31 0.7043 1.9117\n", - " 32 0.7009 2.1291\n", - " 33 \u001b[36m0.6733\u001b[0m 2.0217\n", - " 34 \u001b[36m0.6713\u001b[0m 2.1509\n", - " 35 \u001b[36m0.6629\u001b[0m 1.8898\n", - " 36 0.6720 1.7500\n", - " 37 \u001b[36m0.6548\u001b[0m 1.8919\n", - " 38 \u001b[36m0.6356\u001b[0m 1.8814\n", - " 39 \u001b[36m0.6187\u001b[0m 2.0170\n", - " 40 0.6403 2.2680\n", - " 41 0.6381 1.9118\n", - " 42 0.6263 2.0442\n", - " 43 0.6327 2.0453\n", - " 44 0.6357 1.8428\n", - " 45 \u001b[36m0.5957\u001b[0m 1.7891\n", - " 46 0.5961 1.8859\n", - " 47 0.6135 1.8681\n", - " 48 0.6238 1.7084\n", - " 49 0.5978 1.7232\n", - " 50 \u001b[36m0.5919\u001b[0m 1.7004\n" - ] - } - ], - "source": [ - "# This context manager ensures that we dont try to use Cholesky. This is\n", - "# in following with the tutorial.\n", - "with gpytorch.settings.max_cholesky_size(0):\n", - " gpr.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyzing the trained model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, let's plot the predictions and the standard deviations for our 5 time series and see how well the model learned." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 26.5 s, sys: 6.6 s, total: 33.1 s\n", - "Wall time: 8.29 s\n" - ] - } - ], - "source": [ - "%%time\n", - "y_pred, y_std = gpr.predict(X_valid, return_std=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we make use of our [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) to scale the targets back to their original scale. Remember to also scale the standard deviations." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = y_scaler.inverse_transform(y_pred.T).T\n", - "y_std = y_scaler.inverse_transform(y_std.T).T" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_bart(ax, X_train, y_train, X_valid, y_valid, y_pred, y_std):\n", - " lower = y_pred - y_std\n", - " upper = y_pred + y_std\n", - "\n", - " ax.plot(X_train, y_train, 'k*', label='train')\n", - " ax.plot(X_valid, y_valid, 'r*', label='valid')\n", - " # Plot predictive means as blue line\n", - " ax.plot(X_valid, y_pred, 'b')\n", - " # Shade between the lower and upper confidence bounds\n", - " ax.fill_between(X_valid, lower, upper, alpha=0.5, label='+/- 1 std dev')\n", - " ax.tick_params(axis='both', which='major', labelsize=16)\n", - " ax.tick_params(axis='both', which='minor', labelsize=16)\n", - " ax.set_ylabel('Passenger Volume', fontsize=16)\n", - " ax.set_xticks([])\n", - " return ax" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(5, figsize=(14, 28))\n", - "y_train_unscaled = y_scaler.inverse_transform(y_train.T).T\n", - "y_valid_unscaled = y_scaler.inverse_transform(y_valid.T).T\n", - "for i, ax in enumerate(axes):\n", - " ax = plot_bart(\n", - " ax,\n", - " X_train[i, -100:, 0],\n", - " y_train_unscaled[i, -100:],\n", - " X_valid[i, :, 0],\n", - " y_valid_unscaled[i],\n", - " y_pred[i],\n", - " y_std[i],\n", - " )\n", - " ax.set_title(f\"Station {i + 1}\", fontsize=18)\n", - "\n", - "ax.set_xlabel('hours', fontsize=16)\n", - "plt.xlim([1250, 1680])\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieving the covariance" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you have worked with sklearn's [`GaussianProcessRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor) in the past, you might know that it supports a way to retrieve the covariance by calling `regressor.predict(X, return_cov=True)`. This is not supported by skorch.\n", - "\n", - "It is, however, possible to use the `forward_iter` method to get the covariance indirectly. This will return the posterior distribution, which has a `covariance_matrix` attribute. Below, we show how to use this to plot the covariance of the first 20 data points of the first time series." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "posterior = next(gpr.forward_iter(X_valid))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultivariateNormal(loc: torch.Size([5, 240]))" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "posterior" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([5, 240, 240])" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "posterior.covariance_matrix.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(posterior.covariance_matrix[0, :20, :20].detach().numpy())\n", - "plt.colorbar()\n", - "plt.grid(None)\n", - "plt.axis('off');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stochastic Variational GP Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far, we have dealt with exact GP regression. As the name suggest, this method is exact instead of relying on approximations. There are a few disadvantages, however. Without going into details, exact solutions are in general only possible for Gaussian distributions, so if you want to use another distribution, you cannot use exact GPs. Also, using variational GPs with GPyTorch allows us to use batching, so it allows us to work with larger datasets." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For this part of the notebook, we rely on the following GPyTorch tutorial:\n", - "\n", - "https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/SVGP_Regression_CUDA.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we download a real world dataset (if it hasn't been downloaded already) and perform some minor preprocessing first, following the tutorial. Automatic download doesn't work (anymore), so please go to the following URL and then download the file to a `datasets` subfolder within this folder.\n", - "\n", - "https://drive.google.com/uc?export=download&id=1jhWL3YUHvXIaftia4qeAyDwVxo6j1alk" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.io import loadmat" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "path = os.path.join('datasets', 'elevators.mat')\n", - "if not os.path.isfile(path):\n", - " raise IOError(\"Please download the 'elevators.mat' file as described above\")" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "data = torch.Tensor(loadmat(path)['data'])\n", - "X = data[:, :-1]\n", - "X = X - X.min(0)[0]\n", - "X = 2 * (X / X.max(0)[0]) - 1\n", - "y = data[:, -1]\n", - "\n", - "# train/valid split\n", - "train_n = int(math.floor(0.8 * len(X)))\n", - "X_train = X[:train_n, :].contiguous()\n", - "y_train = y[:train_n].contiguous()\n", - "X_valid = X[train_n:, :].contiguous()\n", - "y_valid = y[train_n:].contiguous()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "torch.Size([13279, 18]) torch.Size([13279]) torch.Size([3320, 18]) torch.Size([3320])\n" - ] - } - ], - "source": [ - "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, we deal with a bigger dataset now than before. Thankfully, we will be able to use batching to avoid potential memory issues." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the module" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This time around, since we don't use exact GPs, we actually need to subclass from GPyTorch's `ApproximateGP`. Additionally, we need to define a variational strategy. For more details, please refer to the corresponding [GPyTorch tutorial](https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/SVGP_Regression_CUDA.html).\n", - "\n", - "Apart from that, we have to define the mean function, the kernel function, and the output distribution, as usual." - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "from gpytorch.models import ApproximateGP\n", - "from gpytorch.variational import CholeskyVariationalDistribution\n", - "from gpytorch.variational import VariationalStrategy" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [], - "source": [ - "class VariationalModule(ApproximateGP):\n", - " def __init__(self, inducing_points):\n", - " variational_distribution = CholeskyVariationalDistribution(inducing_points.size(0))\n", - " variational_strategy = VariationalStrategy(\n", - " self, inducing_points, variational_distribution, learn_inducing_locations=True,\n", - " )\n", - " super().__init__(variational_strategy)\n", - " self.mean_module = gpytorch.means.ConstantMean()\n", - " self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n", - "\n", - " def forward(self, x):\n", - " mean_x = self.mean_module(x)\n", - " covar_x = self.covar_module(x)\n", - " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the GPRegressor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we deal with non-exact GP regression, we import skorch's `GPRegressor`. On top of that, this time around we will see how to obtain validation scores using a predefined split. Specifically, we are interested in the mean absolute error. Fortunately, skorch provides all the tools to make this easy." - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.probabilistic import GPRegressor\n", - "from skorch.callbacks import EpochScoring\n", - "from skorch.dataset import Dataset\n", - "from skorch.helper import predefined_split\n", - "from sklearn.metrics import mean_absolute_error" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First we define the train/validation split using the validation data we split off earlier. Note that the input to `predefined_split` should be a `Dataset`." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [ - "train_split = predefined_split(Dataset(X_valid, y_valid))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Second, we want to calculate the mean absolute error on the validation data, which we can achieve by using skorch's `EpochScoring` and the `mean_absolute_error` metric from sklearn." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [], - "source": [ - "# the \"name\" argument is only important for printing the output\n", - "mae_callback = EpochScoring(mean_absolute_error, name='valid_mae')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we initialize the `GPRegressor` using the module, train split, and callback we just defined:" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "gpr = GPRegressor(\n", - " VariationalModule,\n", - " module__inducing_points=X_train[:500] if DEVICE == 'cpu' else X_train.cuda()[:500],\n", - "\n", - " criterion=gpytorch.mlls.VariationalELBO,\n", - " criterion__num_data=int(0.8 * len(y_train)),\n", - "\n", - " optimizer=torch.optim.Adam,\n", - " lr=0.01,\n", - " batch_size=1024,\n", - " train_split=train_split,\n", - " callbacks=[mae_callback],\n", - " device=DEVICE,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some notes:\n", - "\n", - "- Our variational strategy requires some data as \"inducing points\", which we pass to the module as `module__inducing_points`. We will use 500 data points from our training data for this.\n", - "- We use a criterion to match our variational GP, in this case `VariationalELBO` (the default).\n", - "- We can now take advantage of batching by setting `batch_size=1024` instead of -1.\n", - "- We set criterion__num_data=int(0.8 * len(y_train)), i.e. to 80% of the total data. This is because above, we split off 20% of the training data for validation. If this number if different (e.g. because you perform a grid search), you should adjust the ratio accordingly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Warning:\n", - " If you run a grid search, sklearn will split X into several folds, some of which might not contain the samples X[:500], which can lead to data leakage. In this case, you might want to set aside those inducing points completely, not using them for training at all.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_loss valid_mae dur\n", - "------- ------------ ------------ ----------- ------\n", - " 1 \u001b[36m1.0009\u001b[0m \u001b[32m0.8361\u001b[0m \u001b[35m0.1152\u001b[0m 1.5976\n", - " 2 \u001b[36m0.7821\u001b[0m \u001b[32m0.7294\u001b[0m \u001b[35m0.0883\u001b[0m 1.6898\n", - " 3 \u001b[36m0.6922\u001b[0m \u001b[32m0.6524\u001b[0m \u001b[35m0.0785\u001b[0m 1.6398\n", - " 4 \u001b[36m0.6210\u001b[0m \u001b[32m0.5857\u001b[0m \u001b[35m0.0763\u001b[0m 1.5395\n", - " 5 \u001b[36m0.5564\u001b[0m \u001b[32m0.5228\u001b[0m \u001b[35m0.0757\u001b[0m 1.5039\n", - " 6 \u001b[36m0.4937\u001b[0m \u001b[32m0.4605\u001b[0m 0.0760 1.5208\n", - " 7 \u001b[36m0.4314\u001b[0m \u001b[32m0.3982\u001b[0m 0.0759 1.5726\n", - " 8 \u001b[36m0.3690\u001b[0m \u001b[32m0.3356\u001b[0m \u001b[35m0.0754\u001b[0m 1.5846\n", - " 9 \u001b[36m0.3064\u001b[0m \u001b[32m0.2728\u001b[0m \u001b[35m0.0750\u001b[0m 1.5456\n", - " 10 \u001b[36m0.2436\u001b[0m \u001b[32m0.2098\u001b[0m \u001b[35m0.0746\u001b[0m 1.6207\n" - ] - }, - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=VariationalModule(\n", - " (variational_strategy): VariationalStrategy(\n", - " (_variational_distribution): CholeskyVariationalDistribution()\n", - " )\n", - " (mean_module): ConstantMean()\n", - " (covar_module): ScaleKernel(\n", - " (base_kernel): RBFKernel(\n", - " (raw_lengthscale_constraint): Positive()\n", - " (distance_module): Distance()\n", - " )\n", - " (raw_outputscale_constraint): Positive()\n", - " )\n", - " ),\n", - ")" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpr.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyzing the trained model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As always, we can take a look at the model prediction and the confidence intervals. We only show the first 50 predictions because the time series is really long." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred, y_std = gpr.predict(X_valid[:50], return_std=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(16, 8))\n", - "x_vec = np.arange(50)\n", - "ax.fill_between(x_vec, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", - "ax.plot(x_vec, y_pred, label='prediction', color='blue')\n", - "ax.plot(x_vec, y_valid[:50], 'ko', label='true')\n", - "ax.legend();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "GPs are most frequently used for regression, but it's possible to use them for classification as well. Since the output distribution cannot be a Gaussian in case of classification, we cannot use exact GPs for this. Instead, we again rely on variational GPs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Binary classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For binary classification, we can use skorch's `GPBinaryClassifier`, which uses `gpytorch.likelihoods.BernoulliLikelihood` by default. Bernoulli distributions can be used to model binary outcomes, which is exactly what we're interested in." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This section is loosely based on the following GPyTorch tutorial:\n", - "\n", - "https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/Non_Gaussian_Likelihoods.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, we will rely on a toy dataset based on the sine function with added noise for this section. However, instead of defining a regression target, we transform the target into a classification problem by assigning the label 1 to positive targets and 0 to negative targets." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "sampling_frequency = 2\n", - "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", - "y_train = torch.sin(X_train) + 0.5 * torch.rand(len(X_train)) - 0.25\n", - "y_train = (y_train > 0).long()" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "X_valid = torch.linspace(-10, 10, 100)\n", - "y_valid = (torch.sin(X_valid) > 0).long()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(12, 6))\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, color='red', label='true')\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the module" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "from gpytorch.variational import UnwhitenedVariationalStrategy" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As in the previous example using variational GP, we need to define a variational strategy for our module." - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "class GPClassificationModule(ApproximateGP):\n", - " def __init__(self, inducing_points):\n", - " variational_distribution = CholeskyVariationalDistribution(inducing_points.size(0))\n", - " variational_strategy = UnwhitenedVariationalStrategy(\n", - " self, inducing_points, variational_distribution, learn_inducing_locations=False,\n", - " )\n", - " super().__init__(variational_strategy)\n", - " self.mean_module = gpytorch.means.ConstantMean()\n", - " self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n", - "\n", - " def forward(self, x):\n", - " mean_x = self.mean_module(x)\n", - " covar_x = self.covar_module(x)\n", - " latent_pred = gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n", - " return latent_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the GPBinaryClassifier" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.probabilistic import GPBinaryClassifier\n", - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This time around, instead of using skorch's `GPRegressor`, we will use `GPBinaryClassifier`. The rest is pretty much the same as above. As inducing points, we use the whole training dataset, since it's only so small. For bigger datasets, you might want to choose a subset." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "gpc = GPBinaryClassifier(\n", - " GPClassificationModule,\n", - " module__inducing_points=X_train if DEVICE == 'cpu' else X_train.cuda(),\n", - " criterion__num_data=len(X_train),\n", - "\n", - " optimizer=torch.optim.Adam,\n", - " lr=0.01,\n", - " max_epochs=30,\n", - " device=DEVICE,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For classification, `GPBinaryClassifier` performs an internal, stratified train/validation split, as usual for skorch." - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_acc train_loss valid_acc valid_loss dur\n", - "------- ----------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.4444\u001b[0m \u001b[32m0.9079\u001b[0m \u001b[35m0.5714\u001b[0m \u001b[31m1.0306\u001b[0m 0.1186\n", - " 2 \u001b[36m1.0000\u001b[0m 1.0134 0.5714 \u001b[31m0.9842\u001b[0m 0.0186\n", - " 3 1.0000 0.9567 0.5714 \u001b[31m0.9721\u001b[0m 0.0196\n", - " 4 1.0000 0.9365 0.5714 0.9749 0.0229\n", - " 5 1.0000 0.9311 0.5714 \u001b[31m0.9715\u001b[0m 0.0224\n", - " 6 1.0000 0.9193 0.5714 \u001b[31m0.9549\u001b[0m 0.0229\n", - " 7 1.0000 \u001b[32m0.8940\u001b[0m 0.5714 \u001b[31m0.9396\u001b[0m 0.0204\n", - " 8 1.0000 \u001b[32m0.8700\u001b[0m 0.5714 \u001b[31m0.9385\u001b[0m 0.0171\n", - " 9 1.0000 \u001b[32m0.8603\u001b[0m 0.5714 0.9465 0.0186\n", - " 10 1.0000 \u001b[32m0.8602\u001b[0m 0.5714 0.9496 0.0167\n", - " 11 1.0000 \u001b[32m0.8555\u001b[0m 0.5714 0.9432 0.0188\n", - " 12 1.0000 \u001b[32m0.8414\u001b[0m 0.5714 \u001b[31m0.9351\u001b[0m 0.0215\n", - " 13 1.0000 \u001b[32m0.8258\u001b[0m 0.5714 \u001b[31m0.9328\u001b[0m 0.0249\n", - " 14 1.0000 \u001b[32m0.8157\u001b[0m 0.5714 0.9349 0.0221\n", - " 15 1.0000 \u001b[32m0.8101\u001b[0m 0.5714 0.9360 0.0191\n", - " 16 1.0000 \u001b[32m0.8033\u001b[0m 0.5714 0.9341 0.0227\n", - " 17 1.0000 \u001b[32m0.7935\u001b[0m 0.5714 \u001b[31m0.9319\u001b[0m 0.0210\n", - " 18 1.0000 \u001b[32m0.7835\u001b[0m 0.5714 \u001b[31m0.9318\u001b[0m 0.0251\n", - " 19 1.0000 \u001b[32m0.7758\u001b[0m 0.5714 0.9330 0.0282\n", - " 20 1.0000 \u001b[32m0.7697\u001b[0m 0.5714 0.9332 0.0352\n", - " 21 1.0000 \u001b[32m0.7627\u001b[0m 0.5714 0.9319 0.0329\n", - " 22 1.0000 \u001b[32m0.7545\u001b[0m 0.5714 \u001b[31m0.9309\u001b[0m 0.0293\n", - " 23 1.0000 \u001b[32m0.7467\u001b[0m 0.5714 0.9314 0.0269\n", - " 24 1.0000 \u001b[32m0.7405\u001b[0m 0.5714 0.9324 0.0290\n", - " 25 1.0000 \u001b[32m0.7348\u001b[0m 0.5714 0.9326 0.0238\n", - " 26 1.0000 \u001b[32m0.7284\u001b[0m 0.5714 0.9326 0.0311\n", - " 27 1.0000 \u001b[32m0.7219\u001b[0m 0.5714 0.9332 0.0290\n", - " 28 1.0000 \u001b[32m0.7161\u001b[0m 0.5714 0.9338 0.0270\n", - " 29 1.0000 \u001b[32m0.7104\u001b[0m 0.5714 0.9336 0.0204\n", - " 30 1.0000 \u001b[32m0.7042\u001b[0m 0.5714 0.9335 0.0226\n" - ] - }, - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=GPClassificationModule(\n", - " (variational_strategy): UnwhitenedVariationalStrategy(\n", - " (_variational_distribution): CholeskyVariationalDistribution()\n", - " )\n", - " (mean_module): ConstantMean()\n", - " (covar_module): ScaleKernel(\n", - " (base_kernel): RBFKernel(\n", - " (raw_lengthscale_constraint): Positive()\n", - " (distance_module): Distance()\n", - " )\n", - " (raw_outputscale_constraint): Positive()\n", - " )\n", - " ),\n", - ")" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gpc.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyzing the trained model" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "y_proba = gpc.predict_proba(X_valid)\n", - "y_proba = y_proba[:, 1] # take probability for class=1\n", - "y_pred = gpc.predict(X_valid)\n", - "\n", - "fig, ax = plt.subplots(figsize=(12, 8))\n", - "ax.plot(X_train, y_train, 'ko', label='train data')\n", - "ax.plot(X_valid, y_valid, color='red', label='true')\n", - "ax.plot(X_valid, y_proba, color='blue', label='prediction')\n", - "ax.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are the accuracy scores and training and validation data:" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9117647058823529" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_train, gpc.predict(X_train))" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.78" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_valid, gpc.predict(X_valid))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, the model performs reasonably on the dataset but the probabilities are not fantastic. Other kinds of models might do better." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Multiclass Classification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Currently, skorch does not directly support multiclass classification. We can still get there, however, by using sklearn's [`OneVsRestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html) with `GPBinaryClassifier`." - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.multiclass import OneVsRestClassifier" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Getting the data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we use a synthetic multiclass classification dataset provided by sklearn." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [], - "source": [ - "X, y = make_classification(n_samples=200, n_informative=10, n_classes=5, random_state=0)\n", - "X = X.astype(np.float32)\n", - "y = y.astype(np.int64)\n", - "X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Defining the model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use the same module as previously and again define a `GPBinaryClassifier`. We set `verbose=0` to not get flooded by print outputs, and we set `train_split=False` since we're not interested in internal validation scores." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [], - "source": [ - "gpc = GPBinaryClassifier(\n", - " GPClassificationModule,\n", - " # explicit conversion to torch tensor necessary\n", - " module__inducing_points=torch.as_tensor(X_train) if DEVICE == 'cpu' else torch.as_tensor(X_train).cuda(),\n", - " criterion__num_data=len(X_train),\n", - "\n", - " optimizer=torch.optim.Adam,\n", - " lr=0.05,\n", - " max_epochs=200,\n", - " verbose=0,\n", - " train_split=False,\n", - " device=DEVICE,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we wrap our binary classifier inside sklearn's `OneVsRestClassifier`." - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [], - "source": [ - "clf = OneVsRestClassifier(gpc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "OneVsRestClassifier(estimator=[uninitialized](\n", - " module=,\n", - " module__inducing_points=tensor([[-0.4209, -1.8626, 1.7317, ..., -1.4448, -1.1847, 1.0305],\n", - " [ 2.9429, 2.0599, 5.1854, ..., 0.3005, -0.5200, -0.8204],\n", - " [-0.2996, -1.3898, 1.9115, ..., 1.2798, 2.5717, 0.2900],\n", - " ...,\n", - " [ 1.6054, -3.4834, 2.6305, ..., -0.5219, -0.2894, -0.1725],\n", - " [-1.0977, -0.1791, -1.6726, ..., -0.9357, 1.5292, -0.0447],\n", - " [ 5.3708, -2.4126, -0.8171, ..., 0.6439, 0.1507, 1.7870]]),\n", - "))" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Analyzing the trained model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are the accuracy scores and training and validation data:" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.8266666666666667" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "KmfnvFsvamAS" + }, + "source": [ + "# Gaussian Processes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XrcDRfPBamAb" + }, + "source": [ + "skorch supports integration with the fantastic [GPyTorch](https://gpytorch.ai/) library. GPyTorch implements various Gaussian Process (GP) techniques on top of PyTorch." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kaSDDW4QamAf" + }, + "source": [ + "GPyTorch adopts many patterns from PyTorch, thus making it easy to pick up for seasoned PyTorch users. Similarly, the skorch GPyTorch integration should look familiar to seasoned skorch users. However, GPs are a different beast than the more common, non-probabilistic machine learning techniques. It is important to understand the basic concepts before using them in practice." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aMZJJ0o4amAh" + }, + "source": [ + "This notebook is not the place to learn about GPs in general, instead a basic understanding is assumed. If you're looking for an introduction to probabilistic programming and GPs, here are some pointers:\n", + "\n", + "- The GPyTorch [documentation](https://docs.gpytorch.ai/en/stable/)\n", + "- The book [Gaussian Processes for Machine Learning](http://gaussianprocess.org/gpml/chapters/) by Carl Edward Rasmussen and Christopher K. I. Williams\n", + "- The lecture series [Probabilistic Machine Learning](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd) by Philipp Hennig" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EfNQLyUHamAo" + }, + "source": [ + "Below, we will show you how to use skorch for Gaussian Processes through GPyTorch. We assume that you are familiar with how skorch and PyTorch work and we will focus on how using GPs differs from using non-probabilistic deep learning techniques with skorch. For a discussion on when and when not to use GPyTorch with skorch, please have a look at our [documentation](https://skorch.readthedocs.io/en/latest/user/probabilistic.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CimimrA2amAx" + }, + "source": [ + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4xjbC78WamA0" + }, + "source": [ + "If you haven't already, you should install GPyTorch, since it is not installed automatically after installing skorch:\n", + "\n", + "```bash\n", + "# using pip\n", + "pip install -U gpytorch\n", + "# using conda\n", + "conda install gpytorch -c gpytorch\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "M40ww18namA2" + }, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch' , 'torch', 'gpytorch'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6_K0YB9HamA5" + }, + "source": [ + "## Table of contents" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3wKPRY-eamA8" + }, + "source": [ + "* [Exact Gaussian Process Regression](#Exact-Gaussian-Process-Regression)\n", + " * [Simple example: sine curve](#Simple-example:-sine-curve)\n", + " * [GP regression with real world data](#Regression-with-real-world-data)\n", + "* [Stochastic Variational GP Regression](#Stochastic-Variational-GP-Regression)\n", + "* [Classification](#Classification)\n", + " * [Binary classification](#Binary-classification)\n", + " * [Multiclass classification](#Multiclass-Classification)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2MX9ym9TamA-" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "yPN8W_L3amBA" + }, + "outputs": [], + "source": [ + "import math\n", + "import os\n", + "import urllib.request" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "ozXCBWnDamBA" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "import gpytorch\n", + "from matplotlib import pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "2tkhHBc3amBC" + }, + "outputs": [], + "source": [ + "torch.manual_seed(0)\n", + "torch.cuda.manual_seed(0)\n", + "plt.style.use('seaborn')\n", + "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SEUB3Vp_amBC" + }, + "source": [ + "# Exact Gaussian Process Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_9-q0o85amBC" + }, + "source": [ + "GPyTorch implmenets different methods to solve GPs. The most basic form is to use exact solutions. Variational GPs are described further below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x9Mw4xyUamBD" + }, + "source": [ + "## Simple example: sine curve" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tUsiWVrgamBE" + }, + "source": [ + "The \"Hello world\" of GPs is predicting a sine curve with Gaussian noise added on top. We will start with this example." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mn5PMXgxamBE" + }, + "source": [ + "### Creating the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xQul7irHamBF" + }, + "source": [ + "First we synthesize our data. For training, we use a sine curve with Gaussian noise added on top. For validation, we just use the sine without noise, assuming this is the underlying ground truth. To make it difficult for the model, the training data will only contain very few data points for now." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "Xn_oFNxXamBG" + }, + "outputs": [], + "source": [ + "sampling_frequency = 0.5\n", + "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", + "y_train = torch.sin(X_train) + torch.randn(len(X_train)) * 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "fYjxBlwiamBH" + }, + "outputs": [], + "source": [ + "X_valid = torch.linspace(-10, 10, 100)\n", + "y_valid = torch.sin(X_valid)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "imWUx_IJamBI" + }, + "source": [ + "As you can see below, there is a slight hint of periodicity in the training data but it could also just be noise." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "FChCKGmTamBK", + "outputId": "38992cb8-3fdd-49a7-c9e1-b380ad94a38c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(X_train, y_train, 'o')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BJHFgelWamBM" + }, + "source": [ + "### Defining the module" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cw62cXRvamBN" + }, + "source": [ + "As usual with PyTorch, the core of your modeling approach is to define the module. In our case, instead of subclassing `torch.nn.Module`, we subclass `gpytorch.models.ExactGP` (which itself is a subclass of `torch.nn.Module`), since we want to do exact GP. As always, we need to define our own `__init__` method (don't forget to call `super().__init__`) and our own `forward` method." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "AOKxK3kWamBO" + }, + "outputs": [], + "source": [ + "class RbfModule(gpytorch.models.ExactGP):\n", + " def __init__(self, likelihood, noise_init=None):\n", + " # detail: We don't set train_inputs and train_targets here because skorch\n", + " # will take care of that.\n", + " super().__init__(train_inputs=None, train_targets=None, likelihood=likelihood)\n", + " self.mean_module = gpytorch.means.ConstantMean()\n", + " self.covar_module = gpytorch.kernels.RBFKernel()\n", + "\n", + " def forward(self, x):\n", + " mean_x = self.mean_module(x)\n", + " covar_x = self.covar_module(x)\n", + " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qUXeVmnJamBP" + }, + "source": [ + "Again, we don't want to go into too much details about GPs or GPyTorch. The important ingredients here are the _mean function_ and the _kernel function_. As the name suggests, the mean function is only there to determine the means of the Gaussian distribution. The kernel function is used to calculate the covariance matrix of the data points. Together, the means and covariance matrix are sufficient to define a Gaussian distribution.\n", + "\n", + "For the mean function `gpytorch.means.ConstantMean` will often do. Choosing the correct kernel, however, is where it gets interesting. This kernel should be chosen wisely so as to fit the problem as best as possible. The correct choice here is as crucial as choosing the correct Deep Learning architecture — when you choose an RNN for an image classification problem, you will have little luck. That being said, a good start is often to use the `RBFKernel` and then iterate from there. That's why we use the RBF for our toy example.\n", + "\n", + "The output of the `forward` method should always be a `gpytorch.distributions.MultivariateNormal` for `ExactGP`. It represents the prior latent distribution conditioned on the input data. The posterior is computed by applying a likelihood, which is `gpytorch.likelihoods.GaussianLikelihood` by default for GP regression." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3i7tm35vamBQ" + }, + "source": [ + "### skorch `ExactGPRegressor`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ihXuXeYjamBS" + }, + "source": [ + "Now let's define our skorch model. For this, we import `ExactGPRegressor` and initialize it in much the same way as we would a `NeuralNet`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "mRRFBtlBamBS" + }, + "outputs": [], + "source": [ + "from skorch.probabilistic import ExactGPRegressor" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "tIdAHrZOamBT" + }, + "outputs": [], + "source": [ + "gpr = ExactGPRegressor(\n", + " RbfModule,\n", + " optimizer=torch.optim.Adam,\n", + " lr=0.1,\n", + " max_epochs=20,\n", + " device=DEVICE,\n", + " batch_size=-1,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OVq9dBN5amBU" + }, + "source": [ + "As you can see, we pass the `RbfModule` defined above as the first argument, as we always do. We also define the optimizer, learning rate (`lr`) and device as usual. We could pass our own `likelihood` argument, but since we use the default likelihood, we don't need to do that." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "81DvaWchamBW" + }, + "source": [ + "One oddity you might have noticed is `batch_size=-1`. -1 is a placeholder that means: take all the data at once, don't use batching. The reason for this is that the exact solution requires all data to be passed at the same time, it does not work on batches. The batch size is -1 by default but we set it here explicitly to make it clear that it is so.\n", + "\n", + "If you need to use batches (say, you don't have enough GPU memory to fit all your data), you can use variational GPs, as shown later in the notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pavaEHohamBW" + }, + "source": [ + "
\n", + " Info:\n", + " GPyTorch stores a reference to the training data (i.e X and y) on the module. This can make your model quite big if your training data is large. However, exact GPs are typically not used with large datasets - if you want to avoid this issue, take a look at the variational method described further below.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hdBYujzjamBX" + }, + "source": [ + "### Sampling" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Toh1fq-XamBX" + }, + "source": [ + "At this point, we can already show a new feature that is available thanks to GPs. They allow us to sample from the underlying distribution, conditioned on our data, even though we have not even called `fit` on the model. To do this, we initialize the `ExactGPRegressor` by calling `initialize()` and then use the `sample` method. The first argument to `sample` is the data to condition on, in this case the training data, and the second argument is the number of samples to draw." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ohd1zk5tamBY" + }, + "source": [ + "We plot the result next to the ground truth and the training data for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "hq3vffLAamBZ", + "outputId": "e91cf327-96ab-4f65-85f1-9679443962d8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 483 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "gpr.initialize()\n", + "\n", + "samples = gpr.sample(X_train, n_samples=50)\n", + "samples = samples.detach().numpy() # turn into numpy array\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "ax.plot(X_train, samples.T, color='k', alpha=0.1)\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, 'r', label='true')\n", + "ax.set_xlim([-8.1, 8.1])\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fx2Ubr2samBa" + }, + "source": [ + "It can often be wise to plot a couple of samples _before_ starting a lengthy training process. These samples can be compared to the underlying data to see if the chosen model looks reasonable _a priori_. If the distribution of the target looks very different from the sampled distribution, it means it could not result from the assumed distribution. No matter how well you train, your model will never fit your data. In such a case, you probably need to find a better kernel function." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I2YZH6_famBb" + }, + "source": [ + "### Fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7pFdYINxamBc" + }, + "source": [ + "As always, to train the model, we call the `fit` method and pass the training data and targets as arguments:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "NfxoEZPVamBd", + "outputId": "7bdd4ed4-9c57-427e-dd87-1e7d9890699f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Re-initializing module.\n", + "Re-initializing criterion.\n", + "Re-initializing optimizer.\n", + " epoch train_loss dur\n", + "------- ------------ ------\n", + " 1 \u001b[36m1.2771\u001b[0m 0.0598\n", + " 2 \u001b[36m1.2650\u001b[0m 0.0097\n", + " 3 \u001b[36m1.2533\u001b[0m 0.0103\n", + " 4 \u001b[36m1.2416\u001b[0m 0.0099\n", + " 5 \u001b[36m1.2312\u001b[0m 0.0101\n", + " 6 \u001b[36m1.2211\u001b[0m 0.0083\n", + " 7 \u001b[36m1.2111\u001b[0m 0.0082\n", + " 8 \u001b[36m1.2017\u001b[0m 0.0074\n", + " 9 \u001b[36m1.1932\u001b[0m 0.0090\n", + " 10 \u001b[36m1.1850\u001b[0m 0.0075\n", + " 11 \u001b[36m1.1771\u001b[0m 0.0070\n", + " 12 \u001b[36m1.1698\u001b[0m 0.0069\n", + " 13 \u001b[36m1.1632\u001b[0m 0.0072\n", + " 14 \u001b[36m1.1570\u001b[0m 0.0090\n", + " 15 \u001b[36m1.1511\u001b[0m 0.0089\n", + " 16 \u001b[36m1.1456\u001b[0m 0.0072\n", + " 17 \u001b[36m1.1408\u001b[0m 0.0096\n", + " 18 \u001b[36m1.1363\u001b[0m 0.0076\n", + " 19 \u001b[36m1.1320\u001b[0m 0.0084\n", + " 20 \u001b[36m1.1281\u001b[0m 0.0100\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[initialized](\n", + " module_=RbfModule(\n", + " (likelihood): GaussianLikelihood(\n", + " (noise_covar): HomoskedasticNoise(\n", + " (raw_noise_constraint): GreaterThan(1.000E-04)\n", + " )\n", + " )\n", + " (mean_module): ConstantMean()\n", + " (covar_module): RBFKernel(\n", + " (raw_lengthscale_constraint): Positive()\n", + " (distance_module): Distance()\n", + " )\n", + " ),\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ], + "source": [ + "gpr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1WppAtp8amBe" + }, + "source": [ + "
\n", + " Info:\n", + " For GP regression, skorch does not perform a train/valid split by default. This is why you only see the train loss here, not the validation loss as usual. The reason for this decision is that a random split is most often not appropriate for GP regression. E.g. when you deal with a time series, random splitting would result in data leakage. Therefore, if you want validation scores, it is probably best to implement your own train_split or use skorch.helper.predefined_split if you already have split your data beforehand (see the example further below).\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XvVFy3BOamBf" + }, + "source": [ + "### Analyzing the trained model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zAd84-K8amBf" + }, + "source": [ + "Now that our model is trained, we can repeat the sampling process from above. As you can see, the samples fit the data much better now." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "KVgj8KFAamBf", + "outputId": "b01adcbd-a323-41aa-a4fc-fb3258026d96", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 539 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.8/dist-packages/gpytorch/models/exact_gp.py:274: GPInputWarning: The input matches the stored training data. Did you forget to call model.train()?\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "samples = gpr.sample(X_train, 50)\n", + "samples = samples.detach().numpy() # turn into numpy array\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "ax.plot(X_train, samples.T, color='k', alpha=0.1)\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, 'r', label='true')\n", + "ax.set_xlim([-8.1, 8.1])\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z5cB5_IXamBg" + }, + "source": [ + "Since the model represents a probability distribution, instead of just making point predictions as is most often the case for Deep Learning, we can use the distribution to give us confidence intervals. To do this, we can pass `return_std=True` to the `predict` call. This will return one standard deviation for the given data, which we can add/subtract from our prediction to get upper/lower confidence bounds:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "0QaLf_JWamBi", + "outputId": "17d93ef5-dd1b-4686-f9f3-2b28836e5099", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "y_pred, y_std = gpr.predict(X_valid, return_std=True)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, color='red', label='true mean')\n", + "ax.plot(X_valid, y_pred, color='blue', label='predicted mean')\n", + "ax.fill_between(X_valid, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "g7LW5NxNamBi" + }, + "source": [ + "As you can see, the confidence bounds are quite wide most of the time. Only at the training data points is the model more confident. This is exactly what we should expect: At the points where the model has seen some data, it is more confident, but between data points, it is less confident." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XRpSHZspamBk" + }, + "source": [ + "### More data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ICNtfMbVamBl" + }, + "source": [ + "Below, we increase the sampling frequency from our sine function and train the same model again to show how a well fit model looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "aVf2wNPQamBm" + }, + "outputs": [], + "source": [ + "sampling_frequency = 2\n", + "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", + "y_train = torch.sin(X_train) + torch.randn(len(X_train)) * 0.2" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false, + "id": "QC_lOcn3amBn", + "outputId": "2d8a11f6-9535-43c3-8115-9afa3b36c1ee", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " epoch train_loss dur\n", + "------- ------------ ------\n", + " 1 \u001b[36m1.1546\u001b[0m 0.0207\n", + " 2 \u001b[36m1.1222\u001b[0m 0.0089\n", + " 3 \u001b[36m1.0879\u001b[0m 0.0084\n", + " 4 \u001b[36m1.0535\u001b[0m 0.0070\n", + " 5 \u001b[36m1.0188\u001b[0m 0.0071\n", + " 6 \u001b[36m0.9833\u001b[0m 0.0080\n", + " 7 \u001b[36m0.9474\u001b[0m 0.0068\n", + " 8 \u001b[36m0.9114\u001b[0m 0.0069\n", + " 9 \u001b[36m0.8754\u001b[0m 0.0065\n", + " 10 \u001b[36m0.8395\u001b[0m 0.0070\n", + " 11 \u001b[36m0.8038\u001b[0m 0.0074\n", + " 12 \u001b[36m0.7684\u001b[0m 0.0076\n", + " 13 \u001b[36m0.7337\u001b[0m 0.0060\n", + " 14 \u001b[36m0.7000\u001b[0m 0.0074\n", + " 15 \u001b[36m0.6673\u001b[0m 0.0082\n", + " 16 \u001b[36m0.6357\u001b[0m 0.0070\n", + " 17 \u001b[36m0.6054\u001b[0m 0.0069\n", + " 18 \u001b[36m0.5762\u001b[0m 0.0068\n", + " 19 \u001b[36m0.5479\u001b[0m 0.0068\n", + " 20 \u001b[36m0.5200\u001b[0m 0.0073\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[initialized](\n", + " module_=RbfModule(\n", + " (likelihood): GaussianLikelihood(\n", + " (noise_covar): HomoskedasticNoise(\n", + " (raw_noise_constraint): GreaterThan(1.000E-04)\n", + " )\n", + " )\n", + " (mean_module): ConstantMean()\n", + " (covar_module): RBFKernel(\n", + " (raw_lengthscale_constraint): Positive()\n", + " (distance_module): Distance()\n", + " )\n", + " ),\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "gpr = ExactGPRegressor(\n", + " RbfModule,optimizer=torch.optim.Adam,\n", + " lr=0.1,\n", + " max_epochs=20,\n", + " batch_size=-1,\n", + " device=DEVICE,\n", + ")\n", + "gpr.fit(X_train.reshape(-1, 1), y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "CWrk8Wu2amBo", + "outputId": "e72a3e05-dac1-4da9-fa67-05d9aa30d155", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 17 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "y_pred, y_std = gpr.predict(X_valid, return_std=True)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, color='red', label='true mean')\n", + "ax.plot(X_valid, y_pred, color='blue', label='predicted mean')\n", + "ax.fill_between(X_valid, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5HcfpiWLamBp" + }, + "source": [ + "Here we can see that the confidence intervals are much narrower than above, even between data points. This means that the model is quite confident in _interpolating_ between data points. Notice, however, that the confidence bounds increase considerably at the left and right end, i.e. at values outside of the range of the training data. This means that the model is less confident in _extrapolating_ which is typically a good thing." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "257jXhbXamBq" + }, + "source": [ + "### Confidence region" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CBJ62VoFamBr" + }, + "source": [ + "Another way skorch implements to quickly get the confidence interval is through the `confidence_region` method. This mirrors the method by the same name in GPyTorch. By default, it returns the lower and upper bound for 2 standard deviations, but this can be changed through the `sigmas` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "RtJ-lKbYamBr" + }, + "outputs": [], + "source": [ + "lower, upper = gpr.confidence_region(X_valid, sigmas=2)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "zfnBRaBhamBr", + "outputId": "2517ecd0-1817-4488-d361-1fb4f4ad4da2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 347 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.plot(upper, label='+2 std dev')\n", + "plt.plot(y_pred, label='mean')\n", + "plt.plot(lower, label='-2 std dev')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PrV6tSTbamBv" + }, + "source": [ + "### Grid search" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L2fMNpQ1amBw" + }, + "source": [ + "As always, one of the advantages of using skorch is the sklearn integration. That means that we can plug our regressor into `GridSearchCV` et al. However, there is a caveat to this, as explained below. But first, let's set up the grid search." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "6UO2wRcVamBw" + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import GridSearchCV" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "znxpYJnQamBx" + }, + "outputs": [], + "source": [ + "params = {\n", + " 'lr': [0.01, 0.02],\n", + " 'max_epochs': [10, 20],\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": false, + "id": "TT8NfyXFamBy" + }, + "outputs": [], + "source": [ + "gpr = ExactGPRegressor(\n", + " RbfModule,\n", + " optimizer=torch.optim.Adam,\n", + " lr=0.1,\n", + " max_epochs=20,\n", + " batch_size=-1,\n", + " device=DEVICE,\n", + " \n", + " train_split=False,\n", + " verbose=0,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eEg726V8amBz" + }, + "source": [ + "We turn off skorch-internal train/validation split, since the grid search already performs the data splitting for us. We also set the verbosity level to 0 to avoid too many print outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "tprxum-5amB2" + }, + "outputs": [], + "source": [ + "search = GridSearchCV(gpr, params, cv=3, scoring='neg_mean_squared_error', verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MRJ6l0kmamB3" + }, + "source": [ + "Since we deal with a regression task, we choose an appropriate scoring function, in this case mean squared error. Now let's start the grid search to see the problem mentioned above:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true, + "id": "hgpAhWWfamB4", + "outputId": "0125b314-2462-4271-fa79-0c11a89144d7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GridSearchCV(cv=3,\n", + " estimator=[uninitialized](\n", + " module=,\n", + "),\n", + " param_grid={'lr': [0.01, 0.02], 'max_epochs': [10, 20]},\n", + " scoring='neg_mean_squared_error', verbose=1)" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ], + "source": [ + "search.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2DH_rSoVamB5" + }, + "source": [ + "The grid search finished successfully and we found the best hyper-parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "D8VaKRHWamB5", + "outputId": "5b052525-2f03-4ecd-aaa8-3813f0c27847", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "-0.5268993576367696" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "search.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "b7hdTDUEamB7", + "outputId": "1b5b511d-7738-4ffd-9631-c2033752164e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'lr': 0.01, 'max_epochs': 20}" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "search.best_params_" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YNdWWgQfamB7" + }, + "source": [ + "## Regression with real world data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dwv1UHQvamB8" + }, + "source": [ + "So far, we have only worked with toy data. To show how to work with real world data, we will reproduce an example from the GPyTorch docs:\n", + "\n", + "https://docs.gpytorch.ai/en/stable/examples/01_Exact_GPs/Spectral_Delta_GP_Regression.html\n", + "\n", + "This dataset contains the \"BART ridership on the 5 most commonly traveled stations in San Francisco\". \"BART\" is the \"Bay Area Rapid Transit\", i.e. public transit system in the San Francisco bay area.\n", + "\n", + "For more details, please consult the link." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tfJ66wzWamB9" + }, + "source": [ + "### Getting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AoZbLW6ZamB-" + }, + "source": [ + "First of all we need to download the data. This only needs to be done if the data has not already been downloaded." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "x5Oj4-8zamB_", + "outputId": "01360ff0-16e4-4d9f-cbf4-721af27d21f8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading BART sample dataset...\n" + ] + } + ], + "source": [ + "os.mkdir('datasets')\n", + "path = os.path.join('datasets', 'BART_sample.pt')\n", + "url = 'https://drive.google.com/uc?export=download&id=1A6LqCHPA5lHa5S3lMH8mLMNEgeku8lRG'\n", + "if not os.path.isfile(path):\n", + " print('Downloading BART sample dataset...')\n", + " urllib.request.urlretrieve(url, path)\n", + " \n", + "train_x, train_y, test_x, test_y = torch.load(path, map_location=DEVICE)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jnGVwNLEamB_" + }, + "source": [ + "We need to scale the input data a bit, following the tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "E05z8ufHamCA" + }, + "outputs": [], + "source": [ + "train_x_min = train_x.min()\n", + "train_x_max = train_x.max()\n", + "\n", + "X_train = train_x - train_x_min\n", + "X_valid = test_x - train_x_min" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y-LmMNNeamCA" + }, + "source": [ + "The target data also needs to be scaled. However, contrary to the tutorial, we will use sklearn's `StandardScaler` for this. Although it performs the same transformation as in the tutorial, it allows us to easily inverse transform the targets back to their original scale, which will be useful later." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "d49KbS8TamCB" + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "mF4vnCg5amCB" + }, + "outputs": [], + "source": [ + "# We need to transpose here because the target data is sequence x sample\n", + "# but we want to scale over the samples, not over the sequence.\n", + "y_scaler = StandardScaler().fit(train_y.T)\n", + "y_train = torch.from_numpy(y_scaler.transform(train_y.T).T).float()\n", + "y_valid = torch.from_numpy(y_scaler.transform(test_y.T).T).float()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "LP1WUfkZamCB", + "outputId": "195c307f-3b65-491b-ff5f-434005f6a0c1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([5, 1440, 1]) torch.Size([5, 1440]) torch.Size([5, 240, 1]) torch.Size([5, 240])\n" + ] + } + ], + "source": [ + "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m78ROdBOamCC" + }, + "source": [ + "### Defining the module" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jBTO18eRamCD" + }, + "source": [ + "The module is effectively the same as in the tutorial." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "nUWjajcAamCD" + }, + "outputs": [], + "source": [ + "class SpectralDeltaGP(gpytorch.models.ExactGP):\n", + " def __init__(self, X, y, likelihood, num_deltas, noise_init=None):\n", + " super(SpectralDeltaGP, self).__init__(X, y, likelihood)\n", + " self.mean_module = gpytorch.means.ConstantMean()\n", + " base_covar_module = gpytorch.kernels.SpectralDeltaKernel(\n", + " num_dims=X.size(-1),\n", + " num_deltas=num_deltas,\n", + " )\n", + " self.covar_module = gpytorch.kernels.ScaleKernel(base_covar_module)\n", + "\n", + " def forward(self, x):\n", + " mean_x = self.mean_module(x)\n", + " covar_x = self.covar_module(x)\n", + " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lq_CeSF-amCF" + }, + "source": [ + "### Defining the likelihood" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lI2ALbKPamCF" + }, + "source": [ + "Here we show an example of using a non default likelihood. We wrap the initialization of the likelihood inside a function because we want to use some specific methods, such as `register_prior`. This function can be passed as the `likelihood` parameter to `ExactGPRegressor`." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "PqcHWVH2amCG" + }, + "outputs": [], + "source": [ + "def get_likelihood(\n", + " noise_constraint=gpytorch.constraints.GreaterThan(1e-11),\n", + " noise_prior=gpytorch.priors.HorseshoePrior(0.1),\n", + " noise=1e-2,\n", + "):\n", + " likelihood = gpytorch.likelihoods.GaussianLikelihood(\n", + " noise_constraint=noise_constraint,\n", + " )\n", + " likelihood.register_prior(\"noise_prior\", noise_prior, \"noise\")\n", + " likelihood.noise = noise\n", + " return likelihood" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "UrnQRdjaamCH" + }, + "outputs": [], + "source": [ + "gpr = ExactGPRegressor(\n", + " SpectralDeltaGP,\n", + " module__num_deltas=1500,\n", + " module__X=X_train if DEVICE == 'cpu' else X_train.cuda(),\n", + " module__y=y_train if DEVICE == 'cpu' else y_train.cuda(),\n", + "\n", + " likelihood=get_likelihood,\n", + "\n", + " optimizer=torch.optim.Adam,\n", + " lr=2e-4,\n", + " max_epochs=50,\n", + " batch_size=-1,\n", + " device=DEVICE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6khjlhZaamCI" + }, + "source": [ + "Again, as explained above, we additionally need to pass `X` and `y` to the module and we need to set the batch size to -1, since we still deal with exact GPs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zv2WtXbtamCI" + }, + "source": [ + "### Training the model" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": false, + "id": "cfHM1NWXamCI", + "outputId": "4268d9d0-33da-4c6b-fe49-874745a6726c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " epoch train_loss dur\n", + "------- ------------ ------\n", + " 1 \u001b[36m2.8028\u001b[0m 2.6519\n", + " 2 \u001b[36m2.1384\u001b[0m 2.1296\n", + " 3 \u001b[36m1.6900\u001b[0m 2.7788\n", + " 4 \u001b[36m1.4348\u001b[0m 2.9864\n", + " 5 \u001b[36m1.2347\u001b[0m 3.1026\n", + " 6 \u001b[36m1.1455\u001b[0m 2.0706\n", + " 7 \u001b[36m1.0804\u001b[0m 2.1603\n", + " 8 \u001b[36m1.0156\u001b[0m 2.4422\n", + " 9 \u001b[36m1.0041\u001b[0m 2.3436\n", + " 10 \u001b[36m0.9777\u001b[0m 3.3862\n", + " 11 \u001b[36m0.9485\u001b[0m 2.1122\n", + " 12 \u001b[36m0.8967\u001b[0m 2.5408\n", + " 13 \u001b[36m0.8376\u001b[0m 2.2714\n", + " 14 \u001b[36m0.8271\u001b[0m 2.0843\n", + " 15 \u001b[36m0.8202\u001b[0m 2.0766\n", + " 16 \u001b[36m0.7917\u001b[0m 2.1215\n", + " 17 \u001b[36m0.7529\u001b[0m 2.6042\n", + " 18 \u001b[36m0.7299\u001b[0m 2.2852\n", + " 19 \u001b[36m0.7212\u001b[0m 2.0796\n", + " 20 \u001b[36m0.7110\u001b[0m 2.0780\n", + " 21 \u001b[36m0.7031\u001b[0m 2.1678\n", + " 22 \u001b[36m0.6786\u001b[0m 2.4351\n", + " 23 \u001b[36m0.6624\u001b[0m 2.0856\n", + " 24 \u001b[36m0.6574\u001b[0m 2.1352\n", + " 25 \u001b[36m0.6463\u001b[0m 2.1174\n", + " 26 \u001b[36m0.6381\u001b[0m 2.6208\n", + " 27 \u001b[36m0.6352\u001b[0m 2.4257\n", + " 28 \u001b[36m0.6127\u001b[0m 2.0948\n", + " 29 \u001b[36m0.6076\u001b[0m 2.1078\n", + " 30 \u001b[36m0.5876\u001b[0m 2.3149\n", + " 31 \u001b[36m0.5829\u001b[0m 2.3533\n", + " 32 \u001b[36m0.5677\u001b[0m 2.0906\n", + " 33 \u001b[36m0.5616\u001b[0m 2.1016\n", + " 34 \u001b[36m0.5592\u001b[0m 2.2556\n", + " 35 \u001b[36m0.5360\u001b[0m 2.3666\n", + " 36 0.5434 2.6180\n", + " 37 0.5394 2.1357\n", + " 38 \u001b[36m0.5351\u001b[0m 2.1147\n", + " 39 \u001b[36m0.5155\u001b[0m 2.1007\n", + " 40 \u001b[36m0.5089\u001b[0m 2.6230\n", + " 41 \u001b[36m0.4903\u001b[0m 2.2003\n", + " 42 \u001b[36m0.4885\u001b[0m 2.1141\n", + " 43 \u001b[36m0.4809\u001b[0m 2.0942\n", + " 44 \u001b[36m0.4751\u001b[0m 2.5773\n", + " 45 \u001b[36m0.4715\u001b[0m 2.6243\n", + " 46 \u001b[36m0.4624\u001b[0m 2.1212\n", + " 47 \u001b[36m0.4618\u001b[0m 2.3689\n", + " 48 \u001b[36m0.4431\u001b[0m 2.3798\n", + " 49 0.4564 2.2459\n", + " 50 0.4590 2.6121\n" + ] + } + ], + "source": [ + "# This context manager ensures that we dont try to use Cholesky. This is\n", + "# in following with the tutorial.\n", + "with gpytorch.settings.max_cholesky_size(0):\n", + " gpr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "k2gP0P5zamCJ" + }, + "source": [ + "### Analyzing the trained model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q1dCzdOzamCK" + }, + "source": [ + "Again, let's plot the predictions and the standard deviations for our 5 time series and see how well the model learned." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true, + "id": "Xa66Q973amCK", + "outputId": "5e49954c-e8ce-45e1-b4af-a0ccfc56fa98", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 9.05 s, sys: 84.9 ms, total: 9.14 s\n", + "Wall time: 9.17 s\n" + ] + } + ], + "source": [ + "%%time\n", + "y_pred, y_std = gpr.predict(X_valid, return_std=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "592pjiL_amCL" + }, + "source": [ + "Here we make use of our [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) to scale the targets back to their original scale. Remember to also scale the standard deviations." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "tZ7HYG8bamCM" + }, + "outputs": [], + "source": [ + "y_pred = y_scaler.inverse_transform(y_pred.T).T\n", + "y_std = y_scaler.inverse_transform(y_std.T).T" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "2_VwfGQPamCO" + }, + "outputs": [], + "source": [ + "def plot_bart(ax, X_train, y_train, X_valid, y_valid, y_pred, y_std):\n", + " lower = y_pred - y_std\n", + " upper = y_pred + y_std\n", + "\n", + " ax.plot(X_train, y_train, 'k*', label='train')\n", + " ax.plot(X_valid, y_valid, 'r*', label='valid')\n", + " # Plot predictive means as blue line\n", + " ax.plot(X_valid, y_pred, 'b')\n", + " # Shade between the lower and upper confidence bounds\n", + " ax.fill_between(X_valid, lower, upper, alpha=0.5, label='+/- 1 std dev')\n", + " ax.tick_params(axis='both', which='major', labelsize=16)\n", + " ax.tick_params(axis='both', which='minor', labelsize=16)\n", + " ax.set_ylabel('Passenger Volume', fontsize=16)\n", + " ax.set_xticks([])\n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false, + "id": "rDJlgYIIamCP", + "outputId": "2e2641e4-c3e6-4773-a0f8-42bc3f23fbb6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, axes = plt.subplots(5, figsize=(14, 28))\n", + "y_train_unscaled = y_scaler.inverse_transform(y_train.T).T\n", + "y_valid_unscaled = y_scaler.inverse_transform(y_valid.T).T\n", + "for i, ax in enumerate(axes):\n", + " ax = plot_bart(\n", + " ax,\n", + " X_train[i, -100:, 0],\n", + " y_train_unscaled[i, -100:],\n", + " X_valid[i, :, 0],\n", + " y_valid_unscaled[i],\n", + " y_pred[i],\n", + " y_std[i],\n", + " )\n", + " ax.set_title(f\"Station {i + 1}\", fontsize=18)\n", + "\n", + "ax.set_xlabel('hours', fontsize=16)\n", + "plt.xlim([1250, 1680])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zNAzOJ0kamCR" + }, + "source": [ + "### Retrieving the covariance" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KPVt-tKlamCR" + }, + "source": [ + "If you have worked with sklearn's [`GaussianProcessRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor) in the past, you might know that it supports a way to retrieve the covariance by calling `regressor.predict(X, return_cov=True)`. This is not supported by skorch.\n", + "\n", + "It is, however, possible to use the `forward_iter` method to get the covariance indirectly. This will return the posterior distribution, which has a `covariance_matrix` attribute. Below, we show how to use this to plot the covariance of the first 20 data points of the first time series." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "id": "XamLxsJ4amCT" + }, + "outputs": [], + "source": [ + "posterior = next(gpr.forward_iter(X_valid))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "y_IofA0AamCT", + "outputId": "2e849906-3923-40c5-bdb0-f8e8b025c3d6", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "MultivariateNormal(loc: torch.Size([5, 240]))" + ] + }, + "metadata": {}, + "execution_count": 43 + } + ], + "source": [ + "posterior" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "UbJbAhdKamCT", + "outputId": "c3748289-d833-4e47-955c-29f7343cd86b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "torch.Size([5, 240, 240])" + ] + }, + "metadata": {}, + "execution_count": 44 + } + ], + "source": [ + "posterior.covariance_matrix.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "jWO3m32zamCT", + "outputId": "cf7a689a-c5e4-4214-f8f5-a61f906d6294", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 330 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.imshow(posterior.covariance_matrix[0, :20, :20].detach().numpy())\n", + "plt.colorbar()\n", + "plt.grid(None)\n", + "plt.axis('off');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hxy7pLUpamCV" + }, + "source": [ + "## Stochastic Variational GP Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Top4loQQamCW" + }, + "source": [ + "So far, we have dealt with exact GP regression. As the name suggest, this method is exact instead of relying on approximations. There are a few disadvantages, however. Without going into details, exact solutions are in general only possible for Gaussian distributions, so if you want to use another distribution, you cannot use exact GPs. Also, using variational GPs with GPyTorch allows us to use batching, so it allows us to work with larger datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EAcBzCU9amCW" + }, + "source": [ + "For this part of the notebook, we rely on the following GPyTorch tutorial:\n", + "\n", + "https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/SVGP_Regression_CUDA.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2T_7uI1XamCW" + }, + "source": [ + "### Getting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_tOOflQBamCX" + }, + "source": [ + "Again, we download a real world dataset (if it hasn't been downloaded already) and perform some minor preprocessing first, following the tutorial. Automatic download doesn't work (anymore), so please go to the following URL and then download the file to a `datasets` subfolder within this folder.\n", + "\n", + "https://drive.google.com/uc?export=download&id=1jhWL3YUHvXIaftia4qeAyDwVxo6j1alk" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "id": "fwkWqkTmamCX" + }, + "outputs": [], + "source": [ + "\n", + "from scipy.io import loadmat" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "Nsbvwm4LamCY" + }, + "outputs": [], + "source": [ + "path = os.path.join('datasets', 'elevators.mat')\n", + "\n", + "if not os.path.isfile(path):\n", + " raise IOError(\"Please download the 'elevators.mat' file as described above\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "id": "sQfG8u2AamCY" + }, + "outputs": [], + "source": [ + "data = torch.Tensor(loadmat(path)['data'])\n", + "X = data[:, :-1]\n", + "X = X - X.min(0)[0]\n", + "X = 2 * (X / X.max(0)[0]) - 1\n", + "y = data[:, -1]\n", + "\n", + "# train/valid split\n", + "train_n = int(math.floor(0.8 * len(X)))\n", + "X_train = X[:train_n, :].contiguous()\n", + "y_train = y[:train_n].contiguous()\n", + "X_valid = X[train_n:, :].contiguous()\n", + "y_valid = y[train_n:].contiguous()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "id": "XX2w8_vjamCZ", + "outputId": "439f19bd-f1f4-401a-9c5e-f2d226c18f9b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.Size([13279, 18]) torch.Size([13279]) torch.Size([3320, 18]) torch.Size([3320])\n" + ] + } + ], + "source": [ + "print(X_train.shape, y_train.shape, X_valid.shape, y_valid.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NhU76xframCa" + }, + "source": [ + "As you can see, we deal with a bigger dataset now than before. Thankfully, we will be able to use batching to avoid potential memory issues." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "folrRp0DamCa" + }, + "source": [ + "### Defining the module" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YhZM8r3SamCb" + }, + "source": [ + "This time around, since we don't use exact GPs, we actually need to subclass from GPyTorch's `ApproximateGP`. Additionally, we need to define a variational strategy. For more details, please refer to the corresponding [GPyTorch tutorial](https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/SVGP_Regression_CUDA.html).\n", + "\n", + "Apart from that, we have to define the mean function, the kernel function, and the output distribution, as usual." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "J9B57SHramCc" + }, + "outputs": [], + "source": [ + "from gpytorch.models import ApproximateGP\n", + "from gpytorch.variational import CholeskyVariationalDistribution\n", + "from gpytorch.variational import VariationalStrategy" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "id": "qh6FfMPRamCc" + }, + "outputs": [], + "source": [ + "class VariationalModule(ApproximateGP):\n", + " def __init__(self, inducing_points):\n", + " variational_distribution = CholeskyVariationalDistribution(inducing_points.size(0))\n", + " variational_strategy = VariationalStrategy(\n", + " self, inducing_points, variational_distribution, learn_inducing_locations=True,\n", + " )\n", + " super().__init__(variational_strategy)\n", + " self.mean_module = gpytorch.means.ConstantMean()\n", + " self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n", + "\n", + " def forward(self, x):\n", + " mean_x = self.mean_module(x)\n", + " covar_x = self.covar_module(x)\n", + " return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2Ygc1WsgamCd" + }, + "source": [ + "### Defining the GPRegressor" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gZ73biMTamCd" + }, + "source": [ + "Since we deal with non-exact GP regression, we import skorch's `GPRegressor`. On top of that, this time around we will see how to obtain validation scores using a predefined split. Specifically, we are interested in the mean absolute error. Fortunately, skorch provides all the tools to make this easy." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "id": "TmXlOyy9amCf" + }, + "outputs": [], + "source": [ + "from skorch.probabilistic import GPRegressor\n", + "from skorch.callbacks import EpochScoring\n", + "from skorch.dataset import Dataset\n", + "from skorch.helper import predefined_split\n", + "from sklearn.metrics import mean_absolute_error" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NsqNJpLcamCg" + }, + "source": [ + "First we define the train/validation split using the validation data we split off earlier. Note that the input to `predefined_split` should be a `Dataset`." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "id": "O_uAmj-JamCh" + }, + "outputs": [], + "source": [ + "train_split = predefined_split(Dataset(X_valid, y_valid))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qeqaflOhamCj" + }, + "source": [ + "Second, we want to calculate the mean absolute error on the validation data, which we can achieve by using skorch's `EpochScoring` and the `mean_absolute_error` metric from sklearn." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "id": "r9cMEoEIamCk" + }, + "outputs": [], + "source": [ + "# the \"name\" argument is only important for printing the output\n", + "mae_callback = EpochScoring(mean_absolute_error, name='valid_mae')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3o7ixv49amCl" + }, + "source": [ + "Next we initialize the `GPRegressor` using the module, train split, and callback we just defined:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "id": "XGz1waizamCl" + }, + "outputs": [], + "source": [ + "gpr = GPRegressor(\n", + " VariationalModule,\n", + " module__inducing_points=X_train[:500] if DEVICE == 'cpu' else X_train.cuda()[:500],\n", + "\n", + " criterion=gpytorch.mlls.VariationalELBO,\n", + " criterion__num_data=int(0.8 * len(y_train)),\n", + "\n", + " optimizer=torch.optim.Adam,\n", + " lr=0.01,\n", + " batch_size=1024,\n", + " train_split=train_split,\n", + " callbacks=[mae_callback],\n", + " device=DEVICE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IS6msHZeamCm" + }, + "source": [ + "Some notes:\n", + "\n", + "- Our variational strategy requires some data as \"inducing points\", which we pass to the module as `module__inducing_points`. We will use 500 data points from our training data for this.\n", + "- We use a criterion to match our variational GP, in this case `VariationalELBO` (the default).\n", + "- We can now take advantage of batching by setting `batch_size=1024` instead of -1.\n", + "- We set criterion__num_data=int(0.8 * len(y_train)), i.e. to 80% of the total data. This is because above, we split off 20% of the training data for validation. If this number if different (e.g. because you perform a grid search), you should adjust the ratio accordingly." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KtTYgLZJamCn" + }, + "source": [ + "
\n", + " Warning:\n", + " If you run a grid search, sklearn will split X into several folds, some of which might not contain the samples X[:500], which can lead to data leakage. In this case, you might want to set aside those inducing points completely, not using them for training at all.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wdwxcGejamCn" + }, + "source": [ + "### Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "scrolled": false, + "id": "zhDfV5l_amCn", + "outputId": "95963623-a06c-4056-e2fb-d1c8d61ba75f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " epoch train_loss valid_loss valid_mae dur\n", + "------- ------------ ------------ ----------- ------\n", + " 1 \u001b[36m1.0025\u001b[0m \u001b[32m0.8360\u001b[0m \u001b[35m0.1151\u001b[0m 3.9791\n", + " 2 \u001b[36m0.7809\u001b[0m \u001b[32m0.7282\u001b[0m \u001b[35m0.0874\u001b[0m 3.7204\n", + " 3 \u001b[36m0.6913\u001b[0m \u001b[32m0.6517\u001b[0m \u001b[35m0.0787\u001b[0m 3.8067\n", + " 4 \u001b[36m0.6203\u001b[0m \u001b[32m0.5851\u001b[0m \u001b[35m0.0766\u001b[0m 4.4642\n", + " 5 \u001b[36m0.5557\u001b[0m \u001b[32m0.5221\u001b[0m \u001b[35m0.0763\u001b[0m 4.3604\n", + " 6 \u001b[36m0.4931\u001b[0m \u001b[32m0.4599\u001b[0m 0.0764 4.4972\n", + " 7 \u001b[36m0.4310\u001b[0m \u001b[32m0.3977\u001b[0m 0.0764 6.2739\n", + " 8 \u001b[36m0.3687\u001b[0m \u001b[32m0.3354\u001b[0m 0.0765 3.7686\n", + " 9 \u001b[36m0.3062\u001b[0m \u001b[32m0.2728\u001b[0m 0.0765 3.7224\n", + " 10 \u001b[36m0.2434\u001b[0m \u001b[32m0.2099\u001b[0m \u001b[35m0.0761\u001b[0m 3.6770\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[initialized](\n", + " module_=VariationalModule(\n", + " (variational_strategy): VariationalStrategy(\n", + " (_variational_distribution): CholeskyVariationalDistribution()\n", + " )\n", + " (mean_module): ConstantMean()\n", + " (covar_module): ScaleKernel(\n", + " (base_kernel): RBFKernel(\n", + " (raw_lengthscale_constraint): Positive()\n", + " (distance_module): Distance()\n", + " )\n", + " (raw_outputscale_constraint): Positive()\n", + " )\n", + " ),\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 63 + } + ], + "source": [ + "gpr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vhu_Sfi3amCn" + }, + "source": [ + "### Analyzing the trained model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U_BF_EQVamCn" + }, + "source": [ + "As always, we can take a look at the model prediction and the confidence intervals. We only show the first 50 predictions because the time series is really long." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "id": "rSSxDNiramCn" + }, + "outputs": [], + "source": [ + "y_pred, y_std = gpr.predict(X_valid[:50], return_std=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "id": "qj0wqrfAamCp", + "outputId": "696bd5c6-e8ec-43fb-8dcf-cbc2c213da87", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 483 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(16, 8))\n", + "x_vec = np.arange(50)\n", + "ax.fill_between(x_vec, y_pred - y_std, y_pred + y_std, alpha=0.5, label='+/- 1 std dev')\n", + "ax.plot(x_vec, y_pred, label='prediction', color='blue')\n", + "ax.plot(x_vec, y_valid[:50], 'ko', label='true')\n", + "ax.legend();" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jaE2UMLMamCp" + }, + "source": [ + "# Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N9De-hMLamCp" + }, + "source": [ + "GPs are most frequently used for regression, but it's possible to use them for classification as well. Since the output distribution cannot be a Gaussian in case of classification, we cannot use exact GPs for this. Instead, we again rely on variational GPs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bfIvhx_xamCp" + }, + "source": [ + "## Binary classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Dxha0ItcamCp" + }, + "source": [ + "For binary classification, we can use skorch's `GPBinaryClassifier`, which uses `gpytorch.likelihoods.BernoulliLikelihood` by default. Bernoulli distributions can be used to model binary outcomes, which is exactly what we're interested in." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jq9MDpgAamCp" + }, + "source": [ + "This section is loosely based on the following GPyTorch tutorial:\n", + "\n", + "https://docs.gpytorch.ai/en/stable/examples/04_Variational_and_Approximate_GPs/Non_Gaussian_Likelihoods.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y-8ZC6PHamCp" + }, + "source": [ + "### Getting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JLO6U8ZeamCr" + }, + "source": [ + "Again, we will rely on a toy dataset based on the sine function with added noise for this section. However, instead of defining a regression target, we transform the target into a classification problem by assigning the label 1 to positive targets and 0 to negative targets." + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "id": "Qk0mqUHeamCr" + }, + "outputs": [], + "source": [ + "sampling_frequency = 2\n", + "X_train = torch.arange(-8, 9, 1 / sampling_frequency).float()\n", + "y_train = torch.sin(X_train) + 0.5 * torch.rand(len(X_train)) - 0.25\n", + "y_train = (y_train > 0).long()" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "id": "Gs4kxDBSamCr" + }, + "outputs": [], + "source": [ + "X_valid = torch.linspace(-10, 10, 100)\n", + "y_valid = (torch.sin(X_valid) > 0).long()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "id": "rCRIWgi3amCs", + "outputId": "0896af3e-e097-446a-9ab4-0c732a693972", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 392 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 68 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, color='red', label='true')\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hNsn4hmvamCt" + }, + "source": [ + "### Defining the module" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "id": "q5W2G5v6amCt" + }, + "outputs": [], + "source": [ + "from gpytorch.variational import UnwhitenedVariationalStrategy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_BgQvIUcamCu" + }, + "source": [ + "As in the previous example using variational GP, we need to define a variational strategy for our module." + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "id": "o4zQnJmnamCu" + }, + "outputs": [], + "source": [ + "class GPClassificationModule(ApproximateGP):\n", + " def __init__(self, inducing_points):\n", + " variational_distribution = CholeskyVariationalDistribution(inducing_points.size(0))\n", + " variational_strategy = UnwhitenedVariationalStrategy(\n", + " self, inducing_points, variational_distribution, learn_inducing_locations=False,\n", + " )\n", + " super().__init__(variational_strategy)\n", + " self.mean_module = gpytorch.means.ConstantMean()\n", + " self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())\n", + "\n", + " def forward(self, x):\n", + " mean_x = self.mean_module(x)\n", + " covar_x = self.covar_module(x)\n", + " latent_pred = gpytorch.distributions.MultivariateNormal(mean_x, covar_x)\n", + " return latent_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mBcH5MSDamCv" + }, + "source": [ + "### Defining the GPBinaryClassifier" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "id": "Zf3ojE5DamCw" + }, + "outputs": [], + "source": [ + "from skorch.probabilistic import GPBinaryClassifier\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xt9q59sfamCw" + }, + "source": [ + "This time around, instead of using skorch's `GPRegressor`, we will use `GPBinaryClassifier`. The rest is pretty much the same as above. As inducing points, we use the whole training dataset, since it's only so small. For bigger datasets, you might want to choose a subset." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "id": "aFgQVuBeamCw" + }, + "outputs": [], + "source": [ + "gpc = GPBinaryClassifier(\n", + " GPClassificationModule,\n", + " module__inducing_points=X_train if DEVICE == 'cpu' else X_train.cuda(),\n", + " criterion__num_data=len(X_train),\n", + "\n", + " optimizer=torch.optim.Adam,\n", + " lr=0.01,\n", + " max_epochs=30,\n", + " device=DEVICE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aFHc2bHzamCx" + }, + "source": [ + "### Fitting" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4uCgeXaCamCx" + }, + "source": [ + "For classification, `GPBinaryClassifier` performs an internal, stratified train/validation split, as usual for skorch." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "scrolled": false, + "id": "FnQ20I-TamCx", + "outputId": "aa4495ec-dcee-441a-9edd-21509d849ca7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " epoch train_acc train_loss valid_acc valid_loss dur\n", + "------- ----------- ------------ ----------- ------------ ------\n", + " 1 \u001b[36m0.6296\u001b[0m \u001b[32m0.9078\u001b[0m \u001b[35m0.5714\u001b[0m \u001b[31m1.0316\u001b[0m 0.0365\n", + " 2 \u001b[36m1.0000\u001b[0m 1.0146 0.5714 \u001b[31m0.9822\u001b[0m 0.0257\n", + " 3 1.0000 0.9550 0.5714 \u001b[31m0.9725\u001b[0m 0.0216\n", + " 4 1.0000 0.9369 0.5714 0.9728 0.0224\n", + " 5 1.0000 0.9291 0.5714 \u001b[31m0.9709\u001b[0m 0.0213\n", + " 6 1.0000 0.9189 0.5714 \u001b[31m0.9554\u001b[0m 0.0257\n", + " 7 1.0000 \u001b[32m0.8954\u001b[0m 0.5714 \u001b[31m0.9384\u001b[0m 0.0261\n", + " 8 1.0000 \u001b[32m0.8704\u001b[0m 0.5714 \u001b[31m0.9358\u001b[0m 0.0209\n", + " 9 1.0000 \u001b[32m0.8598\u001b[0m 0.5714 0.9447 0.0224\n", + " 10 1.0000 0.8606 0.5714 0.9492 0.0218\n", + " 11 1.0000 \u001b[32m0.8569\u001b[0m 0.5714 0.9434 0.0265\n", + " 12 1.0000 \u001b[32m0.8430\u001b[0m 0.5714 \u001b[31m0.9355\u001b[0m 0.0224\n", + " 13 1.0000 \u001b[32m0.8270\u001b[0m 0.5714 \u001b[31m0.9330\u001b[0m 0.0203\n", + " 14 1.0000 \u001b[32m0.8165\u001b[0m 0.5714 0.9350 0.0235\n", + " 15 1.0000 \u001b[32m0.8108\u001b[0m 0.5714 0.9364 0.0245\n", + " 16 1.0000 \u001b[32m0.8046\u001b[0m 0.5714 0.9346 0.0226\n", + " 17 1.0000 \u001b[32m0.7954\u001b[0m 0.5714 \u001b[31m0.9320\u001b[0m 0.0252\n", + " 18 1.0000 \u001b[32m0.7856\u001b[0m 0.5714 \u001b[31m0.9314\u001b[0m 0.0235\n", + " 19 1.0000 \u001b[32m0.7778\u001b[0m 0.5714 0.9321 0.0216\n", + " 20 1.0000 \u001b[32m0.7713\u001b[0m 0.5714 0.9322 0.0263\n", + " 21 1.0000 \u001b[32m0.7642\u001b[0m 0.5714 0.9314 0.0337\n", + " 22 1.0000 \u001b[32m0.7563\u001b[0m 0.5714 \u001b[31m0.9311\u001b[0m 0.0226\n", + " 23 1.0000 \u001b[32m0.7489\u001b[0m 0.5714 0.9318 0.0227\n", + " 24 1.0000 \u001b[32m0.7429\u001b[0m 0.5714 0.9324 0.0216\n", + " 25 1.0000 \u001b[32m0.7368\u001b[0m 0.5714 0.9322 0.0210\n", + " 26 1.0000 \u001b[32m0.7302\u001b[0m 0.5714 0.9321 0.0222\n", + " 27 1.0000 \u001b[32m0.7238\u001b[0m 0.5714 0.9328 0.0240\n", + " 28 1.0000 \u001b[32m0.7182\u001b[0m 0.5714 0.9333 0.0288\n", + " 29 1.0000 \u001b[32m0.7127\u001b[0m 0.5714 0.9330 0.0289\n", + " 30 1.0000 \u001b[32m0.7065\u001b[0m 0.5714 0.9328 0.0283\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[initialized](\n", + " module_=GPClassificationModule(\n", + " (variational_strategy): UnwhitenedVariationalStrategy(\n", + " (_variational_distribution): CholeskyVariationalDistribution()\n", + " )\n", + " (mean_module): ConstantMean()\n", + " (covar_module): ScaleKernel(\n", + " (base_kernel): RBFKernel(\n", + " (raw_lengthscale_constraint): Positive()\n", + " (distance_module): Distance()\n", + " )\n", + " (raw_outputscale_constraint): Positive()\n", + " )\n", + " ),\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 73 + } + ], + "source": [ + "gpc.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N5kYHR9uamCy" + }, + "source": [ + "### Analyzing the trained model" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "scrolled": false, + "id": "PmrRKM5DamCy", + "outputId": "cf97e2b9-70af-45ac-a9a3-8d0a15c9576c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 501 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 74 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ], + "source": [ + "y_proba = gpc.predict_proba(X_valid)\n", + "y_proba = y_proba[:, 1] # take probability for class=1\n", + "y_pred = gpc.predict(X_valid)\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 8))\n", + "ax.plot(X_train, y_train, 'ko', label='train data')\n", + "ax.plot(X_valid, y_valid, color='red', label='true')\n", + "ax.plot(X_valid, y_proba, color='blue', label='prediction')\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ouxqfTq5amCy" + }, + "source": [ + "Here are the accuracy scores and training and validation data:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "id": "6f5TrQXWamCy", + "outputId": "dea06f4b-402a-4ff3-8dea-5bce69a2e4cd", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8529411764705882" + ] + }, + "metadata": {}, + "execution_count": 75 + } + ], + "source": [ + "accuracy_score(y_train, gpc.predict(X_train))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "id": "1M6Yx90xamCz", + "outputId": "505072b8-65a6-4e7f-fe9b-263f5952818d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.75" + ] + }, + "metadata": {}, + "execution_count": 76 + } + ], + "source": [ + "accuracy_score(y_valid, gpc.predict(X_valid))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XAtvkyQTamCz" + }, + "source": [ + "As you can see, the model performs reasonably on the dataset but the probabilities are not fantastic. Other kinds of models might do better." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "07QX2tffamC0" + }, + "source": [ + "## Multiclass Classification" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q7yizJRvamC1" + }, + "source": [ + "Currently, skorch does not directly support multiclass classification. We can still get there, however, by using sklearn's [`OneVsRestClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html) with `GPBinaryClassifier`." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "id": "bEJzfNqUamC1" + }, + "outputs": [], + "source": [ + "from sklearn.multiclass import OneVsRestClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dTJMajaHamC1" + }, + "source": [ + "### Getting the data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DGZlA2e2amC2" + }, + "source": [ + "Here we use a synthetic multiclass classification dataset provided by sklearn." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "id": "Iup1O6DFamC3" + }, + "outputs": [], + "source": [ + "from sklearn.datasets import make_classification\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "id": "Z7k0R_bQamC3" + }, + "outputs": [], + "source": [ + "X, y = make_classification(n_samples=200, n_informative=10, n_classes=5, random_state=0)\n", + "X = X.astype(np.float32)\n", + "y = y.astype(np.int64)\n", + "X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sCXOqAjaamC4" + }, + "source": [ + "### Defining the model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Hl5ForDfamC4" + }, + "source": [ + "We use the same module as previously and again define a `GPBinaryClassifier`. We set `verbose=0` to not get flooded by print outputs, and we set `train_split=False` since we're not interested in internal validation scores." + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "id": "xGNZR_fxamC4" + }, + "outputs": [], + "source": [ + "gpc = GPBinaryClassifier(\n", + " GPClassificationModule,\n", + " # explicit conversion to torch tensor necessary\n", + " module__inducing_points=torch.as_tensor(X_train) if DEVICE == 'cpu' else torch.as_tensor(X_train).cuda(),\n", + " criterion__num_data=len(X_train),\n", + "\n", + " optimizer=torch.optim.Adam,\n", + " lr=0.05,\n", + " max_epochs=200,\n", + " verbose=0,\n", + " train_split=False,\n", + " device=DEVICE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F52l_eU0amC5" + }, + "source": [ + "Next we wrap our binary classifier inside sklearn's `OneVsRestClassifier`." + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "id": "M60SjjDCamDF" + }, + "outputs": [], + "source": [ + "clf = OneVsRestClassifier(gpc)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0kJCA70camDG" + }, + "source": [ + "### Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "id": "qRKcSanxamDG", + "outputId": "0aac9a83-4931-48a0-acbc-787c08d8d0a8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "OneVsRestClassifier(estimator=[uninitialized](\n", + " module=,\n", + " module__inducing_points=tensor([[-0.4209, -1.8626, 1.7317, ..., -1.4448, -1.1847, 1.0305],\n", + " [ 2.9429, 2.0599, 5.1854, ..., 0.3005, -0.5200, -0.8204],\n", + " [-0.2996, -1.3898, 1.9115, ..., 1.2798, 2.5717, 0.2900],\n", + " ...,\n", + " [ 1.6054, -3.4834, 2.6305, ..., -0.5219, -0.2894, -0.1725],\n", + " [-1.0977, -0.1791, -1.6726, ..., -0.9357, 1.5292, -0.0447],\n", + " [ 5.3708, -2.4126, -0.8171, ..., 0.6439, 0.1507, 1.7870]]),\n", + "))" + ] + }, + "metadata": {}, + "execution_count": 82 + } + ], + "source": [ + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bZrG_KvnamDH" + }, + "source": [ + "### Analyzing the trained model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BcejsWtmamDH" + }, + "source": [ + "Here are the accuracy scores and training and validation data:" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "id": "j_KU-ckhamDH", + "outputId": "420f628c-0828-494e-d3c8-ebf698a99be7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8266666666666667" + ] + }, + "metadata": {}, + "execution_count": 83 + } + ], + "source": [ + "clf.score(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": { + "id": "ne2vnlLIamDI", + "outputId": "9b7400c0-1fac-46b3-b1ac-6b96ee9d4b88", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.66" + ] + }, + "metadata": {}, + "execution_count": 84 + } + ], + "source": [ + "clf.score(X_valid, y_valid)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jKIxMOcJamDI" + }, + "source": [ + "The multiclass classifier does quite well on the training data but the score on the validation data leaves something to be desired. Given that we have 5 classes, a naive model would only be 20% accurate, so the given score is markedly better than that, but it's still not spectacular." + ] } - ], - "source": [ - "clf.score(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.66" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.7" + }, + "colab": { + "provenance": [] } - ], - "source": [ - "clf.score(X_valid, y_valid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The multiclass classifier does quite well on the training data but the score on the validation data leaves something to be desired. Given that we have 5 classes, a naive model would only be 20% accurate, so the given score is markedly better than that, but it's still not spectacular." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/Hugging_Face_Finetuning.ipynb b/notebooks/Hugging_Face_Finetuning.ipynb index 4bdba332e..c0498b9c8 100644 --- a/notebooks/Hugging_Face_Finetuning.ipynb +++ b/notebooks/Hugging_Face_Finetuning.ipynb @@ -1,949 +1,4726 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "dea00c9e-ab6a-4a56-8e91-782b06002f6c", - "metadata": {}, - "source": [ - "# Fine-tuning a BERT model with skorch and Hugging Face" - ] - }, - { - "cell_type": "markdown", - "id": "11b4d0cc-40c5-48a9-bd52-fdd522498acf", - "metadata": {}, - "source": [ - "In this notebook, we follow the fine-tuning guideline from [Hugging Face documentation](https://huggingface.co/docs/transformers/training). Please check it out if we you want to know more about BERT and fine-tuning. Here, we assume that you're familiar with the general ideas.\n", - "\n", - "You will learn how to:\n", - "- integrate the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library with skorch\n", - "- use skorch to fine-tune a BERT model on a text classification task\n", - "- use skorch with the [Hugging Face accelerate](https://huggingface.co/docs/accelerate/index) library for automatic mixed precision (AMP) training" - ] - }, - { - "cell_type": "markdown", - "id": "922bfcd7", - "metadata": {}, - "source": [ - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub
" - ] - }, - { - "cell_type": "markdown", - "id": "9737e20e", - "metadata": {}, - "source": [ - "The first part of the notebook requires hugginface `transformers` as an additional dependency. If you have not already installed it, you can do so like this:\n", - "\n", - "`python -m pip install transformers`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "465f48cc", - "metadata": {}, - "outputs": [], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch \"skorch>=0.12\" transformers" - ] - }, - { - "cell_type": "markdown", - "id": "f9e0f846-ea36-4a07-835d-c2b8c69c27fd", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5164c02c-9d4f-4b1c-bf72-0e8f21a23b89", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import torch\n", - "from sklearn.datasets import fetch_20newsgroups\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.pipeline import Pipeline\n", - "from skorch import NeuralNetClassifier\n", - "from skorch.callbacks import LRScheduler, ProgressBar\n", - "from skorch.hf import HuggingfacePretrainedTokenizer\n", - "from torch import nn\n", - "from torch.optim.lr_scheduler import LambdaLR\n", - "from transformers import AutoModelForSequenceClassification\n", - "from transformers import AutoTokenizer" - ] - }, - { - "cell_type": "markdown", - "id": "380c4fee-8c1d-42e2-933f-27570d1c7ea3", - "metadata": {}, - "source": [ - "## Parameters" - ] - }, - { - "cell_type": "markdown", - "id": "83c77bdf-9929-47b7-8e19-39b623fd4a52", - "metadata": {}, - "source": [ - "Change the values below if you want to try out different model architectures and hyper-parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e6f17a28-52e3-4f0c-8c3e-308ecc9288f3", - "metadata": {}, - "outputs": [], - "source": [ - "# Choose a tokenizer and BERT model that work together\n", - "TOKENIZER = \"distilbert-base-uncased\"\n", - "PRETRAINED_MODEL = \"distilbert-base-uncased\"\n", - "\n", - "# model hyper-parameters\n", - "OPTMIZER = torch.optim.AdamW\n", - "LR = 5e-5\n", - "MAX_EPOCHS = 3\n", - "CRITERION = nn.CrossEntropyLoss\n", - "BATCH_SIZE = 8\n", - "\n", - "# device\n", - "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'" - ] - }, - { - "cell_type": "markdown", - "id": "556ed4ac-27e8-4b80-bcc6-2431f1d9ec12", - "metadata": {}, - "source": [ - "## Data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "8ecfd881-f589-493a-a028-68becdf240ea", - "metadata": {}, - "outputs": [], - "source": [ - "dataset = fetch_20newsgroups()" - ] - }, - { - "cell_type": "markdown", - "id": "27e33d0d-5bfb-49ce-82e7-d0e05d10c0e6", - "metadata": {}, - "source": [ - "For this notebook, we're making use the 20 newsgroups dataset. It is a text classification dataset with 20 classes. A decent score would be to reach 89% accuracy out of sample. For more details, read the description below:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a9d84f34-b506-40c1-a9da-2d40c0a2feca", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ".. _20newsgroups_dataset:\n", - "\n", - "The 20 newsgroups text dataset\n", - "------------------------------\n", - "\n", - "The 20 newsgroups dataset comprises around 18000 newsgroups posts on\n", - "20 topics split in two subsets: one for training (or development)\n", - "and the other one for testing (or for performance evaluation). The split\n", - "between the train and test set is based upon a messages posted before\n", - "and after a specific date.\n", - "\n", - "This module contains two loaders. The first one,\n", - ":func:`sklearn.datasets.fetch_20newsgroups`,\n", - "returns a list of the raw texts that can be fed to text feature\n", - "extractors such as :class:`~sklearn.feature_extraction.text.CountVectorizer`\n", - "with custom parameters so as to extract feature vectors.\n", - "The second one, :func:`sklearn.datasets.fetch_20newsgroups_vectorized`,\n", - "returns ready-to-use features, i.e., it is not necessary to use a feature\n", - "extractor.\n", - "\n", - "**Data Set Characteristics:**\n", - "\n", - " ================= ==========\n", - " Classes 20\n", - " Samples total 18846\n", - " Dimensionality 1\n", - " Features text\n", - " ================= ==========\n", - "\n", - "\n" - ] - } - ], - "source": [ - "print(dataset.DESCR.split('Usage')[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cec3f23b-7906-4804-a517-db17001c22e6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['alt.atheism',\n", - " 'comp.graphics',\n", - " 'comp.os.ms-windows.misc',\n", - " 'comp.sys.ibm.pc.hardware',\n", - " 'comp.sys.mac.hardware',\n", - " 'comp.windows.x',\n", - " 'misc.forsale',\n", - " 'rec.autos',\n", - " 'rec.motorcycles',\n", - " 'rec.sport.baseball',\n", - " 'rec.sport.hockey',\n", - " 'sci.crypt',\n", - " 'sci.electronics',\n", - " 'sci.med',\n", - " 'sci.space',\n", - " 'soc.religion.christian',\n", - " 'talk.politics.guns',\n", - " 'talk.politics.mideast',\n", - " 'talk.politics.misc',\n", - " 'talk.religion.misc']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.target_names" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "061eb1d4-4805-4a7f-91fe-b64b43939905", - "metadata": {}, - "outputs": [], - "source": [ - "X = dataset.data\n", - "y = dataset.target" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "b491705e-95b0-4699-86b0-71128e7d54e3", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test, = train_test_split(X, y, stratify=y, random_state=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d4ec04ba-57fd-47bf-9528-1dc56fcad7ea", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['From: hilmi-er@dsv.su.se (Hilmi Eren)\\nSubject: Re: ARMENIA SAYS IT COULD SHOOT DOWN TURKISH PLANES (Henrik)\\nLines: 53\\nNntp-Posting-Host: alban.dsv.su.se\\nReply-To: hilmi-er@dsv.su.se (Hilmi Eren)\\nOrganization: Dept. of Computer and Systems Sciences, Stockholm University\\n\\n\\n \\n|> henrik@quayle.kpc.com writes:\\n\\n\\n|>\\tThe Armenians in Nagarno-Karabagh are simply DEFENDING their RIGHTS\\n|> to keep their homeland and it is the AZERIS that are INVADING their \\n|> territorium...\\n\\t\\n\\n\\tHomeland? First Nagarno-Karabagh was Armenians homeland today\\n\\tFizuli, Lacin and several villages (in Azerbadjan)\\n\\tare their homeland. Can\\'t you see the\\n\\tthe \"Great Armenia\" dream in this? With facist methods like\\n\\tkilling, raping and bombing villages. The last move was the \\n\\tblast of a truck with 60 kurdish refugees, trying to\\n\\tescape the from Lacin, a city that was \"given\" to the Kurds\\n\\tby the Armenians. \\n\\n\\n|> However, I hope that the Armenians WILL force a TURKISH airplane \\n|> to LAND for purposes of SEARCHING for ARMS similar to the one\\n|> that happened last SUMMER. Turkey searched an AMERICAN plane\\n|> (carrying humanitarian aid) bound to ARMENIA.\\n|>\\n\\n\\tDon\\'t speak about things you don\\'t know: 8 American Cargo planes\\n\\twere heading to Armenia. When the Turkish authorities\\n\\tannounced that they were going to search these cargo \\n\\tplanes 3 of these planes returned to it\\'s base in Germany.\\n\\t5 of these planes were searched in Turkey. The content of\\n\\tof the other 3 planes? Not hard to guess, is it? It was sure not\\n\\thumanitarian aid.....\\n\\n\\tSearch Turkish planes? You don\\'t know what you are talking about.\\n\\tTurkey\\'s government has announced that it\\'s giving weapons\\n\\tto Azerbadjan since Armenia started to attack Azerbadjan\\n\\tit self, not the Karabag province. So why search a plane for weapons\\n\\tsince it\\'s content is announced to be weapons? \\n\\n\\n\\n\\n\\n\\nHilmi Eren\\nDept. of Computer and Systems Sciences, Stockholm University\\nSweden\\nHilmi-er@dsv.su.se\\n\\n\\n\\n\\n',\n", - " 'Subject: VHS movie for sale\\nFrom: koutd@hirama.hiram.edu (DOUGLAS KOU)\\nOrganization: Hiram College\\nNntp-Posting-Host: hirama.hiram.edu\\nLines: 13\\n\\nVHS movie for sale.\\n\\nDance with Wovies\\t($12.00)\\n\\nThe tape is new and just open, buyer pay shipping cost.\\nIf you are interested, please send your offer to\\nkoutd@hirama.hiram.edu\\n\\nthanks,\\n\\nDouglas Kou\\nHiram College\\n\\n']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train[:2]" - ] - }, - { - "cell_type": "markdown", - "id": "ad5a8f51-98bc-4f09-b14d-5708a1788b80", - "metadata": {}, - "source": [ - "## Prepare the training" - ] - }, - { - "cell_type": "markdown", - "id": "9f7c0f71-4c62-4c3c-b411-02075d81169f", - "metadata": {}, - "source": [ - "We want to use a linear learning rate schedule that linearly decreases the learning rate during training." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "acde3950-a9dc-434f-b76c-587e98fa8d35", - "metadata": {}, - "outputs": [], - "source": [ - "num_training_steps = MAX_EPOCHS * (len(X_train) // BATCH_SIZE + 1)\n", - "\n", - "def lr_schedule(current_step):\n", - " factor = float(num_training_steps - current_step) / float(max(1, num_training_steps))\n", - " assert factor > 0\n", - " return factor" - ] - }, - { - "cell_type": "markdown", - "id": "3207018e-f281-4fff-b394-b35d41694bad", - "metadata": {}, - "source": [ - "Next we wrap the BERT module itself inside a simple `nn.Module`. The only real work for us here is to load the pretrained model and to return the _logits_ from the model output. The rest of the outputs is not needed." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "57e0924d-65d4-4fd0-af48-338acf40ec53", - "metadata": {}, - "outputs": [], - "source": [ - "class BertModule(nn.Module):\n", - " def __init__(self, name, num_labels):\n", - " super().__init__()\n", - " self.name = name\n", - " self.num_labels = num_labels\n", - " \n", - " self.reset_weights()\n", - " \n", - " def reset_weights(self):\n", - " self.bert = AutoModelForSequenceClassification.from_pretrained(\n", - " self.name, num_labels=self.num_labels\n", - " )\n", - " \n", - " def forward(self, **kwargs):\n", - " pred = self.bert(**kwargs)\n", - " return pred.logits" - ] - }, - { - "cell_type": "markdown", - "id": "ac6234ba", - "metadata": {}, - "source": [ - "### Tokenizer" - ] - }, - { - "cell_type": "markdown", - "id": "37f08038", - "metadata": {}, - "source": [ - "We make use of `HuggingfacePretrainedTokenizer`, which is a wrapper that skorch provides to use the tokenizers from Hugging Face. In this instance, we use a tokenizer that was pretrained in conjunction with BERT. The tokenizer is automatically downloaded if not already present. More on Hugging Face tokenizers can be found [here](https://huggingface.co/docs/tokenizers/index)." - ] - }, - { - "cell_type": "markdown", - "id": "87512ddc-8e1e-4db1-b203-f26e6e8f3730", - "metadata": {}, - "source": [ - "## Training" - ] - }, - { - "cell_type": "markdown", - "id": "82675bea-7f6e-470b-a468-e803a94cfcf3", - "metadata": {}, - "source": [ - "### Putting it all togther" - ] - }, - { - "cell_type": "markdown", - "id": "6a626328-b4a1-445c-9fad-5347263e889e", - "metadata": {}, - "source": [ - "Now we can put together all the parts from above. There is nothing special going on here, we simply use an sklearn `Pipeline` to chain the `HuggingfacePretrainedTokenizer` and the neural net. Using skorch's `NeuralNetClassifier`, we make sure to pass the `BertModule` as the first argument and to set the number of labels based on `y_train`. The criterion is `CrossEntropyLoss` because we return the logits. Moreover, we make use of the learning rate schedule we defined above, and we add the `ProgressBar` callback to monitor our progress." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "b798511a-c389-4a77-a77a-35ecc7c30e45", - "metadata": {}, - "outputs": [], - "source": [ - "pipeline = Pipeline([\n", - " ('tokenizer', HuggingfacePretrainedTokenizer(TOKENIZER)),\n", - " ('net', NeuralNetClassifier(\n", - " BertModule,\n", - " module__name=PRETRAINED_MODEL,\n", - " module__num_labels=len(set(y_train)),\n", - " optimizer=OPTMIZER,\n", - " lr=LR,\n", - " max_epochs=MAX_EPOCHS,\n", - " criterion=CRITERION,\n", - " batch_size=BATCH_SIZE,\n", - " iterator_train__shuffle=True,\n", - " device=DEVICE,\n", - " callbacks=[\n", - " LRScheduler(LambdaLR, lr_lambda=lr_schedule, step_every='batch'),\n", - " ProgressBar(),\n", - " ],\n", - " )),\n", - "])" - ] - }, - { - "cell_type": "markdown", - "id": "be19feb6-aeaf-43f7-8409-1baebe9a4c77", - "metadata": {}, - "source": [ - "Since we are using skorch, we could now take this pipeline to run a grid search or other kind of hyper-parameter sweep to figure out the best hyper-parameters for this model. E.g. we could try out a different BERT model or a different `max_length`." - ] - }, - { - "cell_type": "markdown", - "id": "744c78c9-6941-4de9-a43c-d4f154bdd13e", - "metadata": {}, - "source": [ - "### Fitting" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3159ab90-1c5e-411f-937a-5ac9addbedbc", - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "torch.cuda.manual_seed(0)\n", - "torch.cuda.manual_seed_all(0)\n", - "np.random.seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "9e8d0e55-dd20-4f13-ac3f-4ff9b807cf33", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n", - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias']\n", - "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n", - "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1062 [00:00[initialized](\n", - " module_=BertModule(\n", - " (bert): DistilBertForSequenceClassification(\n", - " (distilbert): DistilBertModel(\n", - " (embeddings): Embeddings(\n", - " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", - " (position_embeddin...\n", - " (lin1): Linear(in_features=768, out_features=3072, bias=True)\n", - " (lin2): Linear(in_features=3072, out_features=768, bias=True)\n", - " (activation): GELUActivation()\n", - " )\n", - " (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", - " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", - " (dropout): Dropout(p=0.2, inplace=False)\n", - " )\n", - " ),\n", - "))])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time pipeline.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "4385305a-af65-4c19-b5a3-809f933e7794", - "metadata": {}, - "source": [ - "### Evaluation" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "cc50f549-ce59-4440-a1e4-c5b30c4e8e4b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19.4 s, sys: 23.6 ms, total: 19.5 s\n", - "Wall time: 15 s\n" - ] - } - ], - "source": [ - "%%time\n", - "with torch.inference_mode():\n", - " y_pred = pipeline.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "eb04fc96-baa3-4592-a236-a022c96d1e2a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9006716154118063" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_test, y_pred)" - ] - }, - { - "cell_type": "markdown", - "id": "9799b81f-57d5-4239-ba7b-82cd2f76ed0d", - "metadata": {}, - "source": [ - "We can be happy with the results. We set ourselves the goal to reach or exceed 89% accuracy on the test set and we managed to do that." - ] - }, - { - "cell_type": "markdown", - "id": "707bd2da-89ef-4684-a45c-7ccc44fb7596", - "metadata": {}, - "source": [ - "## Training with automatic mixed precision (AMP)" - ] - }, - { - "cell_type": "markdown", - "id": "a8376427-6d9d-421b-9078-3357f041726d", - "metadata": {}, - "source": [ - "For this to work, you need:\n", - "- A GPU that is capable of mixed precision training\n", - "- The [accelerate library](https://huggingface.co/docs/accelerate/index), which you can install as: `python -m pip install 'accelerate>=0.11'`.\n", - "- skorch version 0.12 or installed from the current master branch (`python -m pip install git+https://github.com/skorch-dev/skorch.git`)\n", - "\n", - "Again, we assume that you're familiar with the general concept of mixed precision training. For more information on how skorch integrates with accelerate, please consult the [skorch docs](https://skorch.readthedocs.io/en/latest/user/huggingface.html#accelerate)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "a6981f1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - } - ], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install 'accelerate>=0.11'" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c47aa1a6-f466-4a2c-84ab-034e4d6bdbcd", - "metadata": {}, - "outputs": [], - "source": [ - "from accelerate import Accelerator\n", - "from skorch.hf import AccelerateMixin" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "63dfa5a1-c75b-4b61-93b6-cedc203ce9a6", - "metadata": {}, - "outputs": [], - "source": [ - "class AcceleratedNet(AccelerateMixin, NeuralNetClassifier):\n", - " \"\"\"NeuralNetClassifier with accelerate support\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "2eeac984-cc13-48e7-979f-9e9f208211df", - "metadata": {}, - "outputs": [], - "source": [ - "accelerator = Accelerator(mixed_precision='fp16')" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "783df165-369c-448b-8867-12a8eb85daa2", - "metadata": {}, - "outputs": [], - "source": [ - "pipeline2 = Pipeline([\n", - " ('tokenizer', HuggingfacePretrainedTokenizer(TOKENIZER)),\n", - " ('net', AcceleratedNet( # <= changed\n", - " BertModule,\n", - " accelerator=accelerator, # <= changed\n", - " module__name=PRETRAINED_MODEL,\n", - " module__num_labels=len(set(y_train)),\n", - " optimizer=OPTMIZER,\n", - " lr=LR,\n", - " max_epochs=MAX_EPOCHS,\n", - " criterion=CRITERION,\n", - " batch_size=BATCH_SIZE,\n", - " iterator_train__shuffle=True,\n", - " # device=DEVICE, # <= changed\n", - " callbacks=[\n", - " LRScheduler(LambdaLR, lr_lambda=lr_schedule, step_every='batch'),\n", - " ProgressBar(),\n", - " ],\n", - " )),\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "123f7967-e2d5-4bff-877d-cef395af9f14", - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "torch.cuda.manual_seed(0)\n", - "torch.cuda.manual_seed_all(0)\n", - "np.random.seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "4f7e22db-eced-4360-ab2d-92ea9d1dbe79", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias']\n", - "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight']\n", - "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1062 [00:00[initialized](\n", - " module_=BertModule(\n", - " (bert): DistilBertForSequenceClassification(\n", - " (distilbert): DistilBertModel(\n", - " (embeddings): Embeddings(\n", - " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", - " (position_embeddings): Embedding(...\n", - " (lin1): Linear(in_features=768, out_features=3072, bias=True)\n", - " (lin2): Linear(in_features=3072, out_features=768, bias=True)\n", - " (activation): GELUActivation()\n", - " )\n", - " (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", - " )\n", - " )\n", - " )\n", - " )\n", - " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", - " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", - " (dropout): Dropout(p=0.2, inplace=False)\n", - " )\n", - " ),\n", - "))])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pipeline2.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8fda6530-49b4-4423-b6fa-cf941dbf91db", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 11.7 s, sys: 4.97 ms, total: 11.7 s\n", - "Wall time: 7.27 s\n" - ] + "cells": [ + { + "cell_type": "markdown", + "id": "dea00c9e-ab6a-4a56-8e91-782b06002f6c", + "metadata": { + "id": "dea00c9e-ab6a-4a56-8e91-782b06002f6c" + }, + "source": [ + "# Fine-tuning a BERT model with skorch and Hugging Face" + ] + }, + { + "cell_type": "markdown", + "id": "11b4d0cc-40c5-48a9-bd52-fdd522498acf", + "metadata": { + "id": "11b4d0cc-40c5-48a9-bd52-fdd522498acf" + }, + "source": [ + "In this notebook, we follow the fine-tuning guideline from [Hugging Face documentation](https://huggingface.co/docs/transformers/training). Please check it out if we you want to know more about BERT and fine-tuning. Here, we assume that you're familiar with the general ideas.\n", + "\n", + "You will learn how to:\n", + "- integrate the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library with skorch\n", + "- use skorch to fine-tune a BERT model on a text classification task\n", + "- use skorch with the [Hugging Face accelerate](https://huggingface.co/docs/accelerate/index) library for automatic mixed precision (AMP) training" + ] + }, + { + "cell_type": "markdown", + "id": "922bfcd7", + "metadata": { + "id": "922bfcd7" + }, + "source": [ + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, + { + "cell_type": "markdown", + "id": "9737e20e", + "metadata": { + "id": "9737e20e" + }, + "source": [ + "The first part of the notebook requires hugginface `transformers` as an additional dependency. If you have not already installed it, you can do so like this:\n", + "\n", + "`python -m pip install transformers`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "465f48cc", + "metadata": { + "id": "465f48cc" + }, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch', 'transformers'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" + ] + }, + { + "cell_type": "markdown", + "id": "f9e0f846-ea36-4a07-835d-c2b8c69c27fd", + "metadata": { + "id": "f9e0f846-ea36-4a07-835d-c2b8c69c27fd" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5164c02c-9d4f-4b1c-bf72-0e8f21a23b89", + "metadata": { + "id": "5164c02c-9d4f-4b1c-bf72-0e8f21a23b89" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "from sklearn.datasets import fetch_20newsgroups\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from skorch import NeuralNetClassifier\n", + "from skorch.callbacks import LRScheduler, ProgressBar\n", + "from skorch.hf import HuggingfacePretrainedTokenizer\n", + "from torch import nn\n", + "from torch.optim.lr_scheduler import LambdaLR\n", + "from transformers import AutoModelForSequenceClassification\n", + "from transformers import AutoTokenizer" + ] + }, + { + "cell_type": "markdown", + "id": "380c4fee-8c1d-42e2-933f-27570d1c7ea3", + "metadata": { + "id": "380c4fee-8c1d-42e2-933f-27570d1c7ea3" + }, + "source": [ + "## Parameters" + ] + }, + { + "cell_type": "markdown", + "id": "83c77bdf-9929-47b7-8e19-39b623fd4a52", + "metadata": { + "id": "83c77bdf-9929-47b7-8e19-39b623fd4a52" + }, + "source": [ + "Change the values below if you want to try out different model architectures and hyper-parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e6f17a28-52e3-4f0c-8c3e-308ecc9288f3", + "metadata": { + "id": "e6f17a28-52e3-4f0c-8c3e-308ecc9288f3" + }, + "outputs": [], + "source": [ + "# Choose a tokenizer and BERT model that work together\n", + "TOKENIZER = \"distilbert-base-uncased\"\n", + "PRETRAINED_MODEL = \"distilbert-base-uncased\"\n", + "\n", + "# model hyper-parameters\n", + "OPTMIZER = torch.optim.AdamW\n", + "LR = 5e-5\n", + "MAX_EPOCHS = 3\n", + "CRITERION = nn.CrossEntropyLoss\n", + "BATCH_SIZE = 8\n", + "\n", + "# device\n", + "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'" + ] + }, + { + "cell_type": "markdown", + "id": "556ed4ac-27e8-4b80-bcc6-2431f1d9ec12", + "metadata": { + "id": "556ed4ac-27e8-4b80-bcc6-2431f1d9ec12" + }, + "source": [ + "## Data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8ecfd881-f589-493a-a028-68becdf240ea", + "metadata": { + "id": "8ecfd881-f589-493a-a028-68becdf240ea" + }, + "outputs": [], + "source": [ + "dataset = fetch_20newsgroups()" + ] + }, + { + "cell_type": "markdown", + "id": "27e33d0d-5bfb-49ce-82e7-d0e05d10c0e6", + "metadata": { + "id": "27e33d0d-5bfb-49ce-82e7-d0e05d10c0e6" + }, + "source": [ + "For this notebook, we're making use the 20 newsgroups dataset. It is a text classification dataset with 20 classes. A decent score would be to reach 89% accuracy out of sample. For more details, read the description below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a9d84f34-b506-40c1-a9da-2d40c0a2feca", + "metadata": { + "id": "a9d84f34-b506-40c1-a9da-2d40c0a2feca", + "outputId": "26a0e0cc-2e89-436f-d97f-5a469cbd095e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + ".. _20newsgroups_dataset:\n", + "\n", + "The 20 newsgroups text dataset\n", + "------------------------------\n", + "\n", + "The 20 newsgroups dataset comprises around 18000 newsgroups posts on\n", + "20 topics split in two subsets: one for training (or development)\n", + "and the other one for testing (or for performance evaluation). The split\n", + "between the train and test set is based upon a messages posted before\n", + "and after a specific date.\n", + "\n", + "This module contains two loaders. The first one,\n", + ":func:`sklearn.datasets.fetch_20newsgroups`,\n", + "returns a list of the raw texts that can be fed to text feature\n", + "extractors such as :class:`~sklearn.feature_extraction.text.CountVectorizer`\n", + "with custom parameters so as to extract feature vectors.\n", + "The second one, :func:`sklearn.datasets.fetch_20newsgroups_vectorized`,\n", + "returns ready-to-use features, i.e., it is not necessary to use a feature\n", + "extractor.\n", + "\n", + "**Data Set Characteristics:**\n", + "\n", + " ================= ==========\n", + " Classes 20\n", + " Samples total 18846\n", + " Dimensionality 1\n", + " Features text\n", + " ================= ==========\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(dataset.DESCR.split('Usage')[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cec3f23b-7906-4804-a517-db17001c22e6", + "metadata": { + "id": "cec3f23b-7906-4804-a517-db17001c22e6", + "outputId": "4047ade6-5e96-4028-add2-776b27a8b938", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['alt.atheism',\n", + " 'comp.graphics',\n", + " 'comp.os.ms-windows.misc',\n", + " 'comp.sys.ibm.pc.hardware',\n", + " 'comp.sys.mac.hardware',\n", + " 'comp.windows.x',\n", + " 'misc.forsale',\n", + " 'rec.autos',\n", + " 'rec.motorcycles',\n", + " 'rec.sport.baseball',\n", + " 'rec.sport.hockey',\n", + " 'sci.crypt',\n", + " 'sci.electronics',\n", + " 'sci.med',\n", + " 'sci.space',\n", + " 'soc.religion.christian',\n", + " 'talk.politics.guns',\n", + " 'talk.politics.mideast',\n", + " 'talk.politics.misc',\n", + " 'talk.religion.misc']" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ], + "source": [ + "dataset.target_names" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "061eb1d4-4805-4a7f-91fe-b64b43939905", + "metadata": { + "id": "061eb1d4-4805-4a7f-91fe-b64b43939905" + }, + "outputs": [], + "source": [ + "X = dataset.data\n", + "y = dataset.target" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b491705e-95b0-4699-86b0-71128e7d54e3", + "metadata": { + "id": "b491705e-95b0-4699-86b0-71128e7d54e3" + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test, = train_test_split(X, y, stratify=y, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d4ec04ba-57fd-47bf-9528-1dc56fcad7ea", + "metadata": { + "id": "d4ec04ba-57fd-47bf-9528-1dc56fcad7ea", + "outputId": "b78f511b-dd02-4763-9833-92ce41298235", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['From: hilmi-er@dsv.su.se (Hilmi Eren)\\nSubject: Re: ARMENIA SAYS IT COULD SHOOT DOWN TURKISH PLANES (Henrik)\\nLines: 53\\nNntp-Posting-Host: alban.dsv.su.se\\nReply-To: hilmi-er@dsv.su.se (Hilmi Eren)\\nOrganization: Dept. of Computer and Systems Sciences, Stockholm University\\n\\n\\n \\n|> henrik@quayle.kpc.com writes:\\n\\n\\n|>\\tThe Armenians in Nagarno-Karabagh are simply DEFENDING their RIGHTS\\n|> to keep their homeland and it is the AZERIS that are INVADING their \\n|> territorium...\\n\\t\\n\\n\\tHomeland? First Nagarno-Karabagh was Armenians homeland today\\n\\tFizuli, Lacin and several villages (in Azerbadjan)\\n\\tare their homeland. Can\\'t you see the\\n\\tthe \"Great Armenia\" dream in this? With facist methods like\\n\\tkilling, raping and bombing villages. The last move was the \\n\\tblast of a truck with 60 kurdish refugees, trying to\\n\\tescape the from Lacin, a city that was \"given\" to the Kurds\\n\\tby the Armenians. \\n\\n\\n|> However, I hope that the Armenians WILL force a TURKISH airplane \\n|> to LAND for purposes of SEARCHING for ARMS similar to the one\\n|> that happened last SUMMER. Turkey searched an AMERICAN plane\\n|> (carrying humanitarian aid) bound to ARMENIA.\\n|>\\n\\n\\tDon\\'t speak about things you don\\'t know: 8 American Cargo planes\\n\\twere heading to Armenia. When the Turkish authorities\\n\\tannounced that they were going to search these cargo \\n\\tplanes 3 of these planes returned to it\\'s base in Germany.\\n\\t5 of these planes were searched in Turkey. The content of\\n\\tof the other 3 planes? Not hard to guess, is it? It was sure not\\n\\thumanitarian aid.....\\n\\n\\tSearch Turkish planes? You don\\'t know what you are talking about.\\n\\tTurkey\\'s government has announced that it\\'s giving weapons\\n\\tto Azerbadjan since Armenia started to attack Azerbadjan\\n\\tit self, not the Karabag province. So why search a plane for weapons\\n\\tsince it\\'s content is announced to be weapons? \\n\\n\\n\\n\\n\\n\\nHilmi Eren\\nDept. of Computer and Systems Sciences, Stockholm University\\nSweden\\nHilmi-er@dsv.su.se\\n\\n\\n\\n\\n',\n", + " 'Subject: VHS movie for sale\\nFrom: koutd@hirama.hiram.edu (DOUGLAS KOU)\\nOrganization: Hiram College\\nNntp-Posting-Host: hirama.hiram.edu\\nLines: 13\\n\\nVHS movie for sale.\\n\\nDance with Wovies\\t($12.00)\\n\\nThe tape is new and just open, buyer pay shipping cost.\\nIf you are interested, please send your offer to\\nkoutd@hirama.hiram.edu\\n\\nthanks,\\n\\nDouglas Kou\\nHiram College\\n\\n']" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "X_train[:2]" + ] + }, + { + "cell_type": "markdown", + "id": "ad5a8f51-98bc-4f09-b14d-5708a1788b80", + "metadata": { + "id": "ad5a8f51-98bc-4f09-b14d-5708a1788b80" + }, + "source": [ + "## Prepare the training" + ] + }, + { + "cell_type": "markdown", + "id": "9f7c0f71-4c62-4c3c-b411-02075d81169f", + "metadata": { + "id": "9f7c0f71-4c62-4c3c-b411-02075d81169f" + }, + "source": [ + "We want to use a linear learning rate schedule that linearly decreases the learning rate during training." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "acde3950-a9dc-434f-b76c-587e98fa8d35", + "metadata": { + "id": "acde3950-a9dc-434f-b76c-587e98fa8d35" + }, + "outputs": [], + "source": [ + "num_training_steps = MAX_EPOCHS * (len(X_train) // BATCH_SIZE + 1)\n", + "\n", + "def lr_schedule(current_step):\n", + " factor = float(num_training_steps - current_step) / float(max(1, num_training_steps))\n", + " assert factor > 0\n", + " return factor" + ] + }, + { + "cell_type": "markdown", + "id": "3207018e-f281-4fff-b394-b35d41694bad", + "metadata": { + "id": "3207018e-f281-4fff-b394-b35d41694bad" + }, + "source": [ + "Next we wrap the BERT module itself inside a simple `nn.Module`. The only real work for us here is to load the pretrained model and to return the _logits_ from the model output. The rest of the outputs is not needed." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "57e0924d-65d4-4fd0-af48-338acf40ec53", + "metadata": { + "id": "57e0924d-65d4-4fd0-af48-338acf40ec53" + }, + "outputs": [], + "source": [ + "class BertModule(nn.Module):\n", + " def __init__(self, name, num_labels):\n", + " super().__init__()\n", + " self.name = name\n", + " self.num_labels = num_labels\n", + " \n", + " self.reset_weights()\n", + " \n", + " def reset_weights(self):\n", + " self.bert = AutoModelForSequenceClassification.from_pretrained(\n", + " self.name, num_labels=self.num_labels\n", + " )\n", + " \n", + " def forward(self, **kwargs):\n", + " pred = self.bert(**kwargs)\n", + " return pred.logits" + ] + }, + { + "cell_type": "markdown", + "id": "ac6234ba", + "metadata": { + "id": "ac6234ba" + }, + "source": [ + "### Tokenizer" + ] + }, + { + "cell_type": "markdown", + "id": "37f08038", + "metadata": { + "id": "37f08038" + }, + "source": [ + "We make use of `HuggingfacePretrainedTokenizer`, which is a wrapper that skorch provides to use the tokenizers from Hugging Face. In this instance, we use a tokenizer that was pretrained in conjunction with BERT. The tokenizer is automatically downloaded if not already present. More on Hugging Face tokenizers can be found [here](https://huggingface.co/docs/tokenizers/index)." + ] + }, + { + "cell_type": "markdown", + "id": "87512ddc-8e1e-4db1-b203-f26e6e8f3730", + "metadata": { + "id": "87512ddc-8e1e-4db1-b203-f26e6e8f3730" + }, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "id": "82675bea-7f6e-470b-a468-e803a94cfcf3", + "metadata": { + "id": "82675bea-7f6e-470b-a468-e803a94cfcf3" + }, + "source": [ + "### Putting it all togther" + ] + }, + { + "cell_type": "markdown", + "id": "6a626328-b4a1-445c-9fad-5347263e889e", + "metadata": { + "id": "6a626328-b4a1-445c-9fad-5347263e889e" + }, + "source": [ + "Now we can put together all the parts from above. There is nothing special going on here, we simply use an sklearn `Pipeline` to chain the `HuggingfacePretrainedTokenizer` and the neural net. Using skorch's `NeuralNetClassifier`, we make sure to pass the `BertModule` as the first argument and to set the number of labels based on `y_train`. The criterion is `CrossEntropyLoss` because we return the logits. Moreover, we make use of the learning rate schedule we defined above, and we add the `ProgressBar` callback to monitor our progress." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b798511a-c389-4a77-a77a-35ecc7c30e45", + "metadata": { + "id": "b798511a-c389-4a77-a77a-35ecc7c30e45" + }, + "outputs": [], + "source": [ + "pipeline = Pipeline([\n", + " ('tokenizer', HuggingfacePretrainedTokenizer(TOKENIZER)),\n", + " ('net', NeuralNetClassifier(\n", + " BertModule,\n", + " module__name=PRETRAINED_MODEL,\n", + " module__num_labels=len(set(y_train)),\n", + " optimizer=OPTMIZER,\n", + " lr=LR,\n", + " max_epochs=MAX_EPOCHS,\n", + " criterion=CRITERION,\n", + " batch_size=BATCH_SIZE,\n", + " iterator_train__shuffle=True,\n", + " device=DEVICE,\n", + " callbacks=[\n", + " LRScheduler(LambdaLR, lr_lambda=lr_schedule, step_every='batch'),\n", + " ProgressBar(),\n", + " ],\n", + " )),\n", + "])" + ] + }, + { + "cell_type": "markdown", + "id": "be19feb6-aeaf-43f7-8409-1baebe9a4c77", + "metadata": { + "id": "be19feb6-aeaf-43f7-8409-1baebe9a4c77" + }, + "source": [ + "Since we are using skorch, we could now take this pipeline to run a grid search or other kind of hyper-parameter sweep to figure out the best hyper-parameters for this model. E.g. we could try out a different BERT model or a different `max_length`." + ] + }, + { + "cell_type": "markdown", + "id": "744c78c9-6941-4de9-a43c-d4f154bdd13e", + "metadata": { + "id": "744c78c9-6941-4de9-a43c-d4f154bdd13e" + }, + "source": [ + "### Fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3159ab90-1c5e-411f-937a-5ac9addbedbc", + "metadata": { + "id": "3159ab90-1c5e-411f-937a-5ac9addbedbc" + }, + "outputs": [], + "source": [ + "torch.manual_seed(0)\n", + "torch.cuda.manual_seed(0)\n", + "torch.cuda.manual_seed_all(0)\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9e8d0e55-dd20-4f13-ac3f-4ff9b807cf33", + "metadata": { + "id": "9e8d0e55-dd20-4f13-ac3f-4ff9b807cf33", + "outputId": "471958da-131c-4fe1-d35b-32f63b394735", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 860, + "referenced_widgets": [ + "719ea1322c734fb194a675705a6787da", + "cd1ac6a1143a4d14a75858645a2d99b2", + "725e76227ca64eaebe5baa32acff8260", + "fbfa8a6774bf4a1b90982c6e1865609f", + "e9bdd4bc9f334a4781750f5d3b5012f0", + "5629de3939ad41acaa3adc46beaea3e4", + "8e314a52777840f59361285caacfa8a2", + "26c94982ab4542c3a5a2c33df89ce891", + "d6f6c51e8b594288b4dc4b2a2d6d3323", + "a150b90caffc42acabcaa0ae1dd83622", + "2a2eb191115149fb82301cc0b4505a42", + "e7a328327f5344398d9c693daff67901", + "fbdc3706cb6b46c2b17dec22dba0a659", + "eb8c1baa89c24bf8bee1e9e7994c08a6", + "fc1014a76cbc45b081d13ea2474458d9", + "1617cd968ff043b599cbe273ff4bb735", + "1b93ee76c1c14233a2987b8260033eaf", + "8607a0158e3f42e09255df8e3270b0db", + "8243077289ba46a58f50bb876cae1627", + "46a90028e8154d08a5c3b85ecc5d99b4", + "e8cd1c662c084776b199b6cc140a3e8f", + "8e148f11b6be44f69d3578860fda20ad", + "6514fa1669f648e39ae04a569831c2e0", + "46d9bdfc5fe6412aa8c0d643db86a38e", + "3da3225eaefc41689a930f4e75d6db2b", + "ff10fe7f1eab41309b9e219e0e82141b", + "a4d53971fbd14cdca32b7e67e6b676e4", + "429123c690ed4bf88a0a14919eb00f32", + "a4ec577f603f4163bec954a293f7efdb", + "8bab042f93134d71b223c320ff24ee59", + "d835bd1c83624548943b93ed1245246d", + "b7967a15efa54f2fb3b572ceaa0be324", + "65d1b166622b4656af40f26dd91ef1fb", + "10f641492b0e44eaa009564cd934ca02", + "470c7d90191849898f6625f6bed0979d", + "3d23a964d0554ffa82ffa8489a3b9c34", + "0b27281e9e9542b59a37696b464f58e7", + "95acb8e094bb42158d47a8105aa8fb15", + "83ddb2ce51d04cb583cba6aad61de497", + "f094f664b7e2422f951578d270bd7149", + "fced7272508d4094aaae3cee5ee87f29", + "8f9e9b77a92142638ce017701c8a0ea7", + "9666c8b02b49412c8433e97b3fc91f17", + "567e8ee1733740b497873ca71b3e890b", + "7c5200869ffe4ab08f0436465d64f00e", + "cb0d43fddc564a77906ab8671f88ad1e", + "dbe316e4c2d44dea932356be2cfb2a6e", + "0cbc08bf820545fa806afa30b3073e1a", + "022e6eda3b5a4d3d89c1ab40e5f6926b", + "4f1bf966c208460da23d629b5ba51d3c", + "91ec3bf507e543ad810c55007cac9187", + "7380f557b22b4aba9c14a680dcf7c24b", + "4528e1f883cc4f369dadb2bd5e37a754", + "1d49739fc4d549fba022ca27609e1857", + "5baac70b0ddf47b8bfa414f141d114cb" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/28.0 [00:00[initialized](\n", + " module_=BertModule(\n", + " (bert): DistilBertForSequenceClassification(\n", + " (distilbert): DistilBertModel(\n", + " (embeddings): Embeddings(\n", + " (word_embeddings): Embedding(30522, 768, padding_idx=0)\n", + " (position_embeddin...\n", + " (lin1): Linear(in_features=768, out_features=3072, bias=True)\n", + " (lin2): Linear(in_features=3072, out_features=768, bias=True)\n", + " (activation): GELUActivation()\n", + " )\n", + " (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n", + " )\n", + " )\n", + " )\n", + " )\n", + " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", + " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " ),\n", + "))])" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "%time pipeline.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "id": "4385305a-af65-4c19-b5a3-809f933e7794", + "metadata": { + "id": "4385305a-af65-4c19-b5a3-809f933e7794" + }, + "source": [ + "### Evaluation" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cc50f549-ce59-4440-a1e4-c5b30c4e8e4b", + "metadata": { + "id": "cc50f549-ce59-4440-a1e4-c5b30c4e8e4b", + "outputId": "93bfe489-c465-4079-e31c-25f2dae19f08", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 26.7 s, sys: 92.2 ms, total: 26.8 s\n", + "Wall time: 24.5 s\n" + ] + } + ], + "source": [ + "%%time\n", + "with torch.inference_mode():\n", + " y_pred = pipeline.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "eb04fc96-baa3-4592-a236-a022c96d1e2a", + "metadata": { + "id": "eb04fc96-baa3-4592-a236-a022c96d1e2a", + "outputId": "36024a79-05ff-4638-b9ab-b544ca0fc0eb", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.8985507246376812" + ] + }, + "metadata": {}, + "execution_count": 16 + } + ], + "source": [ + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "9799b81f-57d5-4239-ba7b-82cd2f76ed0d", + "metadata": { + "id": "9799b81f-57d5-4239-ba7b-82cd2f76ed0d" + }, + "source": [ + "We can be happy with the results. We set ourselves the goal to reach or exceed 89% accuracy on the test set and we managed to do that." + ] + }, + { + "cell_type": "markdown", + "id": "707bd2da-89ef-4684-a45c-7ccc44fb7596", + "metadata": { + "id": "707bd2da-89ef-4684-a45c-7ccc44fb7596" + }, + "source": [ + "## Training with automatic mixed precision (AMP)" + ] + }, + { + "cell_type": "markdown", + "id": "a8376427-6d9d-421b-9078-3357f041726d", + "metadata": { + "id": "a8376427-6d9d-421b-9078-3357f041726d" + }, + "source": [ + "For this to work, you need:\n", + "- A GPU that is capable of mixed precision training\n", + "- The [accelerate library](https://huggingface.co/docs/accelerate/index), which you can install as: `python -m pip install 'accelerate>=0.11'`.\n", + "- skorch version 0.12 or installed from the current master branch (`python -m pip install git+https://github.com/skorch-dev/skorch.git`)\n", + "\n", + "Again, we assume that you're familiar with the general concept of mixed precision training. For more information on how skorch integrates with accelerate, please consult the [skorch docs](https://skorch.readthedocs.io/en/latest/user/huggingface.html#accelerate)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a6981f1d", + "metadata": { + "id": "a6981f1d", + "outputId": "5a04b60c-2164-45a1-eab1-adeb0d20bf74", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "CompletedProcess(args=['python', '-m', 'pip', 'install', 'accelerate>=0.11'], returncode=0)" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "import subprocess\n", + "\n", + "subprocess.run(['python', '-m', 'pip', 'install', 'accelerate>=0.11'])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c47aa1a6-f466-4a2c-84ab-034e4d6bdbcd", + "metadata": { + "id": "c47aa1a6-f466-4a2c-84ab-034e4d6bdbcd" + }, + "outputs": [], + "source": [ + "from accelerate import Accelerator\n", + "from skorch.hf import AccelerateMixin" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "63dfa5a1-c75b-4b61-93b6-cedc203ce9a6", + "metadata": { + "id": "63dfa5a1-c75b-4b61-93b6-cedc203ce9a6" + }, + "outputs": [], + "source": [ + "class AcceleratedNet(AccelerateMixin, NeuralNetClassifier):\n", + " \"\"\"NeuralNetClassifier with accelerate support\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2eeac984-cc13-48e7-979f-9e9f208211df", + "metadata": { + "id": "2eeac984-cc13-48e7-979f-9e9f208211df" + }, + "outputs": [], + "source": [ + "accelerator = Accelerator(mixed_precision='fp16')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "783df165-369c-448b-8867-12a8eb85daa2", + "metadata": { + "id": "783df165-369c-448b-8867-12a8eb85daa2" + }, + "outputs": [], + "source": [ + "pipeline2 = Pipeline([\n", + " ('tokenizer', HuggingfacePretrainedTokenizer(TOKENIZER)),\n", + " ('net', AcceleratedNet( # <= changed\n", + " BertModule,\n", + " accelerator=accelerator, # <= changed\n", + " module__name=PRETRAINED_MODEL,\n", + " module__num_labels=len(set(y_train)),\n", + " optimizer=OPTMIZER,\n", + " lr=LR,\n", + " max_epochs=MAX_EPOCHS,\n", + " criterion=CRITERION,\n", + " batch_size=BATCH_SIZE,\n", + " iterator_train__shuffle=True,\n", + " # device=DEVICE, # <= changed\n", + " callbacks=[\n", + " LRScheduler(LambdaLR, lr_lambda=lr_schedule, step_every='batch'),\n", + " ProgressBar(),\n", + " ],\n", + " )),\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "123f7967-e2d5-4bff-877d-cef395af9f14", + "metadata": { + "id": "123f7967-e2d5-4bff-877d-cef395af9f14" + }, + "outputs": [], + "source": [ + "torch.manual_seed(0)\n", + "torch.cuda.manual_seed(0)\n", + "torch.cuda.manual_seed_all(0)\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4f7e22db-eced-4360-ab2d-92ea9d1dbe79", + "metadata": { + "id": 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" )\n", + " )\n", + " )\n", + " (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n", + " (classifier): Linear(in_features=768, out_features=20, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " )\n", + " ),\n", + "))])" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "pipeline2.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8fda6530-49b4-4423-b6fa-cf941dbf91db", + "metadata": { + "id": "8fda6530-49b4-4423-b6fa-cf941dbf91db", + "outputId": "d63d2c2d-64b3-49cc-dfcb-75c35c76475a", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "CPU times: user 26.6 s, sys: 244 ms, total: 26.9 s\n", + "Wall time: 24.4 s\n" + ] + } + ], + "source": [ + "%%time\n", + "with torch.inference_mode():\n", + " y_pred = pipeline2.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "ab4ce9f3-85ab-4b6a-af6c-b7926320d4b0", + "metadata": { + "id": "ab4ce9f3-85ab-4b6a-af6c-b7926320d4b0", + "outputId": "70bcb323-71fd-4599-f7b9-6224a2197fa8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.9070342877341817" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "c4163616-3b62-46b5-976d-355378209fa7", + "metadata": { + "id": "c4163616-3b62-46b5-976d-355378209fa7" + }, + "source": [ + "Using AMP, we could reduce our training and prediction time by half, while attaining the same scores." + ] } - ], - "source": [ - "%%time\n", - "with torch.inference_mode():\n", - " y_pred = pipeline2.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ab4ce9f3-85ab-4b6a-af6c-b7926320d4b0", - "metadata": {}, - "outputs": [ - { - "data": { - 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"display_name": "Python 3 (ipykernel)", - "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": "3.9.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/notebooks/Hugging_Face_Model_Checkpoint.ipynb b/notebooks/Hugging_Face_Model_Checkpoint.ipynb index f18055a7b..a29740345 100644 --- a/notebooks/Hugging_Face_Model_Checkpoint.ipynb +++ b/notebooks/Hugging_Face_Model_Checkpoint.ipynb @@ -1,711 +1,1603 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Creating checkpoints on the Hugging Face Hub" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This short notebook explains how you can create a model checkpoint on [Hugging Face Hub](https://huggingface.co/docs/hub/repositories)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import torch\n", - "from sklearn.datasets import make_classification\n", - "from sklearn.model_selection import train_test_split\n", - "from torch import nn" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch import NeuralNetClassifier\n", - "from skorch.callbacks import TrainEndCheckpoint\n", - "from skorch.hf import HfHubStorage" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from huggingface_hub import Repository, create_repo, HfApi" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If not installed already, please install the [Hugging Face Hub](https://huggingface.co/docs/huggingface_hub/index) library:\n", - "\n", - "`$ python -m pip install huggingface_hub`\n", - "\n", - "Also, you need `skorch>=0.12` or installed from the master branch on GitHub." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub
" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch \"skorch>=0.12\" huggingface_hub" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Settings" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# set the token as an environment variable called HF_TOKEN, e.g. `HF_TOKEN=hf_...`\n", - "# the token can be found at: https://huggingface.co/settings/tokens\n", - "TOKEN = os.environ['HF_TOKEN']\n", - "# choose name for the whole model and for the model weights\n", - "# typically, you only need one of the two, we use both for demonstration purposes\n", - "MODEL_NAME = 'skorch-model.pkl'\n", - "WEIGHTS_NAME = 'weights.pt'\n", - "# choose a repo name within your user account or organization\n", - "REPO_NAME = 'BenjaminB/test-skorch'" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "np.random.seed(0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Create data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use a toy dataset for this demo." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "X, y = make_classification(10000, 20, n_informative=10, random_state=0)\n", - "X, y = X.astype(np.float32), y.astype(np.int64)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Define model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### The module" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "class ClassifierModule(nn.Module):\n", - " def __init__(\n", - " self,\n", - " num_units=30,\n", - " nonlin=nn.ReLU(),\n", - " dropout=0.5,\n", - " ):\n", - " super(ClassifierModule, self).__init__()\n", - " self.num_units = num_units\n", - " self.nonlin = nonlin\n", - " self.dropout = dropout\n", - "\n", - " self.dense0 = nn.Linear(20, num_units)\n", - " self.nonlin = nonlin\n", - " self.dropout = nn.Dropout(dropout)\n", - " self.dense1 = nn.Linear(num_units, num_units)\n", - " self.output = nn.Linear(num_units, 2)\n", - " self.softmax = nn.Softmax(dim=-1)\n", - "\n", - " def forward(self, X, **kwargs):\n", - " X = self.nonlin(self.dense0(X))\n", - " X = self.dropout(X)\n", - " X = self.nonlin(self.dense1(X))\n", - " X = self.softmax(self.output(X))\n", - " return X" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a repository on Hugging Face Hub" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assuming the repo doesn't exist yet, create a new one using this function:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://huggingface.co/BenjaminB/test-skorch'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "skorch_repo = create_repo(\n", - " REPO_NAME,\n", - " private=True, # set to False if it should be public\n", - " token=TOKEN,\n", - " exist_ok=True,\n", - ")\n", - "skorch_repo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create a `HfHubStorage` instance to use with the `TrainEndCheckpoint` callback" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ingredient we need to save models on the hub is the `skorch.hf.HfHubStorage`. This object can be used instead of a filename when you use `skorch.callbacks.TrainEndCheckpoint` (or `skorch.callbacks.Checkpoint`, but more on that later). Therefore, you can continue to use your existing checkpoints, only that models are stored on Hugging Face Hub instead of locally." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a first step, we need to create a `HfApi` instance, which is used by the `HfHubStorage` to perform the upload." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "hf_api = HfApi()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we create a `hub_pickle_storer`, which is used by the checkpoint callback to write the whole skorch model as a pickle file to the indicated repository. We indicate the file path, repository name, and the Hugging Face token. Optionally, we can also set `verbose=1` to print a message when a file has been uploaded." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "hub_pickle_storer = HfHubStorage(\n", - " hf_api,\n", - " path_in_repo=MODEL_NAME,\n", - " repo_id=REPO_NAME,\n", - " token=TOKEN,\n", - " verbose=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instead of writing the whole skorch model to the Hub, we can also decide to only write specific components, e.g. the `module`. This saves the `state_dict` of the module to the Hub using `torch.save` under the hood.\n", - "\n", - "Also, by default, the parameters are stored in an in-memory buffer. If you want to avoid that memory overhead, it is possible to save it on disk using the `local_storage` argument. Below, we choose to store the model weights in a file called `my-model-weights.pt`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "hub_params_storer = HfHubStorage(\n", - " hf_api,\n", - " path_in_repo=WEIGHTS_NAME,\n", - " repo_id=REPO_NAME,\n", - " token=TOKEN,\n", - " verbose=1,\n", - " local_storage='my-model-weights.pt',\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The other attributes (optimizer, criterion, training history) are not saved for this demo. That's why we set their values to `None` when initializing the `TrainEndCheckpoint` below." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "checkpoint = TrainEndCheckpoint(\n", - " f_pickle=hub_pickle_storer,\n", - " f_params=hub_params_storer,\n", - " f_optimizer=None,\n", - " f_criterion=None,\n", - " f_history=None,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, let's create our net and fit it with the data. The checkpoint callback will automatically store the parameters on the Hugging Face Hub at the end of training." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "net = NeuralNetClassifier(\n", - " ClassifierModule,\n", - " lr=0.1,\n", - " device='cpu',\n", - " iterator_train__shuffle=True,\n", - " callbacks=[checkpoint],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_acc valid_loss dur\n", - "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.6627\u001b[0m \u001b[32m0.7573\u001b[0m \u001b[35m0.5772\u001b[0m 0.1184\n", - " 2 \u001b[36m0.5550\u001b[0m \u001b[32m0.8593\u001b[0m \u001b[35m0.4154\u001b[0m 0.0889\n", - " 3 \u001b[36m0.4622\u001b[0m \u001b[32m0.8973\u001b[0m \u001b[35m0.3253\u001b[0m 0.1337\n", - " 4 \u001b[36m0.4119\u001b[0m \u001b[32m0.9073\u001b[0m \u001b[35m0.2840\u001b[0m 0.1137\n", - " 5 \u001b[36m0.3739\u001b[0m \u001b[32m0.9113\u001b[0m \u001b[35m0.2569\u001b[0m 0.0892\n", - " 6 \u001b[36m0.3489\u001b[0m \u001b[32m0.9213\u001b[0m \u001b[35m0.2368\u001b[0m 0.0978\n", - " 7 \u001b[36m0.3331\u001b[0m \u001b[32m0.9240\u001b[0m \u001b[35m0.2328\u001b[0m 0.1332\n", - " 8 \u001b[36m0.3115\u001b[0m \u001b[32m0.9287\u001b[0m \u001b[35m0.2187\u001b[0m 0.1163\n", - " 9 0.3117 \u001b[32m0.9300\u001b[0m \u001b[35m0.2087\u001b[0m 0.1244\n", - " 10 \u001b[36m0.2983\u001b[0m \u001b[32m0.9320\u001b[0m 0.2102 0.1537\n", - "Uploaded file to https://huggingface.co/BenjaminB/test-skorch/blob/main/weights.pt\n", - "Uploaded file to https://huggingface.co/BenjaminB/test-skorch/blob/main/skorch-model.pkl\n" - ] - }, - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=ClassifierModule(\n", - " (nonlin): ReLU()\n", - " (dense0): Linear(in_features=20, out_features=30, bias=True)\n", - " (dropout): Dropout(p=0.5, inplace=False)\n", - " (dense1): Linear(in_features=30, out_features=30, bias=True)\n", - " (output): Linear(in_features=30, out_features=2, bias=True)\n", - " (softmax): Softmax(dim=-1)\n", - " ),\n", - ")" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As you can see, both the weights of the PyTorch module and the whole skorch model were saved on Hub. Visit the printed URLs to see them on the Hub." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As a next step, think about adding a [Model Card](https://huggingface.co/docs/hub/models-cards) to your repository to provide further information about the model." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - " Info: Using the HfHubStorage with Checkpoint:
\n", - "\n", - "\n", - "Right now, we use `TrainEndCheckpoint`, which uploads the model only once, at the end of training. Instead, we could use `Checkpoint`, which uploads the model each time that the monitored metric improves. You should note, however, that at the moment, the upload is _synchronous_, i.e. we wait for the upload to finish. So if uploading the model takes a long time compared to training the model, your training process could be slowed down considerably, depending on how often the model improves.\n", - "\n", - "If you still decide to use `Checkpoint`, you might want to keep a version of each upload file, instead of the latest one overwriting the previous one. This is possible by choosing a templated model name, e.g. `'skorch-model-{}.pkl'`. This way, the first upload will create the file `'skorch-model-0.pkl'`, the second one creates the file `'skorch-model-1.pkl'`, etc.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import pickle\n", - "from huggingface_hub import hf_hub_download\n", - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading the whole model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The skorch model is just a normal pickle file and can be loaded like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://huggingface.co/BenjaminB/test-skorch/blob/main/skorch-model.pkl'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hub_pickle_storer.latest_url_" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "379867c1205f405e8b082753ceb86fd6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/43.2k [00:00=0.12` or installed from the master branch on GitHub." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4hXLIgZ8fyZI" + }, + "source": [ + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tcT0N4L1fyZL" + }, + "source": [ + "## Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "ILZLjJSKfyZL" + }, + "outputs": [], + "source": [ + "# set the token as an environment variable called HF_TOKEN, e.g. `HF_TOKEN=hf_...`\n", + "# the token can be found at: https://huggingface.co/settings/tokens\n", + "TOKEN = os.environ['HF_TOKEN']\n", + "# choose name for the whole model and for the model weights\n", + "# typically, you only need one of the two, we use both for demonstration purposes\n", + "MODEL_NAME = 'skorch-model.pkl'\n", + "WEIGHTS_NAME = 'weights.pt'\n", + "# choose a repo name within your user account or organization\n", + "REPO_NAME = 'sawradip/demo-skorch'" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "6pNJfb8qfyZN" + }, + "outputs": [], + "source": [ + "torch.manual_seed(0)\n", + "np.random.seed(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DjmH2ZxVfyZN" + }, + "source": [ + "## Create data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N-g1bkuvfyZO" + }, + "source": [ + "We use a toy dataset for this demo." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "XyXgoQKVfyZO" + }, + "outputs": [], + "source": [ + "X, y = make_classification(10000, 20, n_informative=10, random_state=0)\n", + "X, y = X.astype(np.float32), y.astype(np.int64)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "ZLQioMN7fyZP" + }, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CujSNR4sfyZQ" + }, + "source": [ + "## Define model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0AdbR0JFfyZQ" + }, + "source": [ + "### The module" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "RNLyLu5zfyZR" + }, + "outputs": [], + "source": [ + "class ClassifierModule(nn.Module):\n", + " def __init__(\n", + " self,\n", + " num_units=30,\n", + " nonlin=nn.ReLU(),\n", + " dropout=0.5,\n", + " ):\n", + " super(ClassifierModule, self).__init__()\n", + " self.num_units = num_units\n", + " self.nonlin = nonlin\n", + " self.dropout = dropout\n", + "\n", + " self.dense0 = nn.Linear(20, num_units)\n", + " self.nonlin = nonlin\n", + " self.dropout = nn.Dropout(dropout)\n", + " self.dense1 = nn.Linear(num_units, num_units)\n", + " self.output = nn.Linear(num_units, 2)\n", + " self.softmax = nn.Softmax(dim=-1)\n", + "\n", + " def forward(self, X, **kwargs):\n", + " X = self.nonlin(self.dense0(X))\n", + " X = self.dropout(X)\n", + " X = self.nonlin(self.dense1(X))\n", + " X = self.softmax(self.output(X))\n", + " return X" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MYENk5rJfyZS" + }, + "source": [ + "### Create a repository on Hugging Face Hub" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WPQWKSjGfyZS" + }, + "source": [ + "Assuming the repo doesn't exist yet, create a new one using this function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "EOdGpJnjfyZS", + "outputId": "29d64713-6bfb-4b59-be8e-3c7a7cbfcd41", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'https://huggingface.co/sawradip/demo-skorch'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 11 + } + ], + "source": [ + "skorch_repo = create_repo(\n", + " REPO_NAME,\n", + " private=False, # set to False if it should be public\n", + " token=TOKEN,\n", + " exist_ok=True,\n", + ")\n", + "skorch_repo" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bcE6tTqefyZT" + }, + "source": [ + "### Create a `HfHubStorage` instance to use with the `TrainEndCheckpoint` callback" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wf_KURmdfyZU" + }, + "source": [ + "The ingredient we need to save models on the hub is the `skorch.hf.HfHubStorage`. This object can be used instead of a filename when you use `skorch.callbacks.TrainEndCheckpoint` (or `skorch.callbacks.Checkpoint`, but more on that later). Therefore, you can continue to use your existing checkpoints, only that models are stored on Hugging Face Hub instead of locally." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "melwPiVmfyZU" + }, + "source": [ + "As a first step, we need to create a `HfApi` instance, which is used by the `HfHubStorage` to perform the upload." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "8fQMbifAfyZU" + }, + "outputs": [], + "source": [ + "hf_api = HfApi()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yhEB4_OafyZV" + }, + "source": [ + "Then, we create a `hub_pickle_storer`, which is used by the checkpoint callback to write the whole skorch model as a pickle file to the indicated repository. We indicate the file path, repository name, and the Hugging Face token. Optionally, we can also set `verbose=1` to print a message when a file has been uploaded." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "Nm-DFF9MfyZV" + }, + "outputs": [], + "source": [ + "hub_pickle_storer = HfHubStorage(\n", + " hf_api,\n", + " path_in_repo=MODEL_NAME,\n", + " repo_id=REPO_NAME,\n", + " token=TOKEN,\n", + " verbose=1,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3Wq9b5t0fyZV" + }, + "source": [ + "Instead of writing the whole skorch model to the Hub, we can also decide to only write specific components, e.g. the `module`. This saves the `state_dict` of the module to the Hub using `torch.save` under the hood.\n", + "\n", + "Also, by default, the parameters are stored in an in-memory buffer. If you want to avoid that memory overhead, it is possible to save it on disk using the `local_storage` argument. Below, we choose to store the model weights in a file called `my-model-weights.pt`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "Dxkol4BYfyZW" + }, + "outputs": [], + "source": [ + "hub_params_storer = HfHubStorage(\n", + " hf_api,\n", + " path_in_repo=WEIGHTS_NAME,\n", + " repo_id=REPO_NAME,\n", + " token=TOKEN,\n", + " verbose=1,\n", + " local_storage='my-model-weights.pt',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SRGCCOJpfyZW" + }, + "source": [ + "The other attributes (optimizer, criterion, training history) are not saved for this demo. That's why we set their values to `None` when initializing the `TrainEndCheckpoint` below." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "EorN5u8EfyZW" + }, + "outputs": [], + "source": [ + "checkpoint = TrainEndCheckpoint(\n", + " f_pickle=hub_pickle_storer,\n", + " f_params=hub_params_storer,\n", + " f_optimizer=None,\n", + " f_criterion=None,\n", + " f_history=None,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JzfV1QJCfyZX" + }, + "source": [ + "Finally, let's create our net and fit it with the data. The checkpoint callback will automatically store the parameters on the Hugging Face Hub at the end of training." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "UP0zP3WKfyZX" + }, + "outputs": [], + "source": [ + "net = NeuralNetClassifier(\n", + " ClassifierModule,\n", + " lr=0.1,\n", + " device='cpu',\n", + " iterator_train__shuffle=True,\n", + " callbacks=[checkpoint],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": false, + "id": "N85J0ljOfyZY", + "outputId": "2071d886-b838-4e9a-a9c6-b3f355379bf8", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " epoch train_loss valid_acc valid_loss dur\n", + "------- ------------ ----------- ------------ ------\n", + " 1 \u001b[36m0.6627\u001b[0m \u001b[32m0.7573\u001b[0m \u001b[35m0.5772\u001b[0m 0.3237\n", + " 2 \u001b[36m0.5550\u001b[0m \u001b[32m0.8593\u001b[0m \u001b[35m0.4154\u001b[0m 0.1127\n", + " 3 \u001b[36m0.4622\u001b[0m \u001b[32m0.8973\u001b[0m \u001b[35m0.3253\u001b[0m 0.0997\n", + " 4 \u001b[36m0.4119\u001b[0m \u001b[32m0.9073\u001b[0m \u001b[35m0.2840\u001b[0m 0.1122\n", + " 5 \u001b[36m0.3739\u001b[0m \u001b[32m0.9113\u001b[0m \u001b[35m0.2569\u001b[0m 0.1006\n", + " 6 \u001b[36m0.3489\u001b[0m \u001b[32m0.9213\u001b[0m \u001b[35m0.2368\u001b[0m 0.1105\n", + " 7 \u001b[36m0.3331\u001b[0m \u001b[32m0.9240\u001b[0m \u001b[35m0.2328\u001b[0m 0.1032\n", + " 8 \u001b[36m0.3115\u001b[0m \u001b[32m0.9287\u001b[0m \u001b[35m0.2187\u001b[0m 0.0994\n", + " 9 0.3117 \u001b[32m0.9300\u001b[0m \u001b[35m0.2087\u001b[0m 0.1355\n", + " 10 \u001b[36m0.2983\u001b[0m \u001b[32m0.9320\u001b[0m 0.2102 0.1012\n", + "Uploaded file to https://huggingface.co/sawradip/demo-skorch/blob/main/weights.pt\n", + "Uploaded file to https://huggingface.co/sawradip/demo-skorch/blob/main/skorch-model.pkl\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[initialized](\n", + " module_=ClassifierModule(\n", + " (nonlin): ReLU()\n", + " (dense0): Linear(in_features=20, out_features=30, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (dense1): Linear(in_features=30, out_features=30, bias=True)\n", + " (output): Linear(in_features=30, out_features=2, bias=True)\n", + " (softmax): Softmax(dim=-1)\n", + " ),\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ], + "source": [ + "net.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R4rU2NFSfyZY" + }, + "source": [ + "As you can see, both the weights of the PyTorch module and the whole skorch model were saved on Hub. Visit the printed URLs to see them on the Hub." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wxmWH3hofyZZ" + }, + "source": [ + "As a next step, think about adding a [Model Card](https://huggingface.co/docs/hub/models-cards) to your repository to provide further information about the model." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MQ5GGidHfyZZ" + }, + "source": [ + "
\n", + " Info: Using the HfHubStorage with Checkpoint:
\n", + "\n", + "\n", + "Right now, we use `TrainEndCheckpoint`, which uploads the model only once, at the end of training. Instead, we could use `Checkpoint`, which uploads the model each time that the monitored metric improves. You should note, however, that at the moment, the upload is _synchronous_, i.e. we wait for the upload to finish. So if uploading the model takes a long time compared to training the model, your training process could be slowed down considerably, depending on how often the model improves.\n", + "\n", + "If you still decide to use `Checkpoint`, you might want to keep a version of each upload file, instead of the latest one overwriting the previous one. This is possible by choosing a templated model name, e.g. `'skorch-model-{}.pkl'`. This way, the first upload will create the file `'skorch-model-0.pkl'`, the second one creates the file `'skorch-model-1.pkl'`, etc.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZSzd6Fy6fyZa" + }, + "source": [ + "## Loading" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "B-8aWvf1fyZb" + }, + "outputs": [], + "source": [ + "import pickle\n", + "from huggingface_hub import hf_hub_download\n", + "from sklearn.metrics import accuracy_score" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Rb5IrDldfyZb" + }, + "source": [ + "### Loading the whole model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cfYchdAGfyZb" + }, + "source": [ + "The skorch model is just a normal pickle file and can be loaded like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "TaoiDcUqfyZb", + "outputId": "122ed97b-d016-46c8-cbb1-c1df406db94a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'https://huggingface.co/sawradip/demo-skorch/blob/main/skorch-model.pkl'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 19 + } + ], + "source": [ + "hub_pickle_storer.latest_url_" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "vigihCj-fyZc", + "outputId": "d61d71f3-16a9-4ed6-e039-91acad08742d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "e0c5544113084c248aa0358a5e506bad", + "7e92b933ac6b48b1b48035f8f573c219", + "f90bf80b70b946aa962ed237a7c3ea06", + "8a60084abf2c48dfba63e79e8072a844", + "9a8d2e1a118b4eb8944eb03a842d4718", + "e7d85e270eb34c2daf701bfd415bd09f", + "eb32f92d8ddc4c05931b038e66659663", + "ef8ef95adc93431b829f1fd87f9c5788", + "afddf4dd005c4ad69ba467fa3a7ac1a1", + "a49f08b689064a01aca835113e7c895e", + "588262b260bf4fc5a21e0c641a34150b" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading: 0%| | 0.00/43.2k [00:00\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/MNIST-torchvision.ipynb), we recommend you enable a free GPU by going:\n", - "\n", - "> **Runtime**   →   **Change runtime type**   →   **Hardware Accelerator: GPU**\n", - "\n", - "If you are running in colab, you should install the dependencies and download the dataset by running the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch scikit-learn==0.21.* skorch" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from itertools import islice\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "import torch\n", - "import torchvision\n", - "from torchvision.datasets import MNIST\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "USE_TENSORBOARD = True # whether to use TensorBoard\n", - "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n", - "MNIST_FLAT_DIM = 28 * 28" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading Data\n", - "\n", - "Use torchvision's data repository to provide MNIST data in form of a torch `Dataset`. Originally, the `MNIST` dataset provides 28x28 `PIL` images. To use them with PyTorch, we convert those to tensors by adding the `ToTensor` transform." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "mnist_train = MNIST('datasets', train=True, download=True, transform=torchvision.transforms.Compose([\n", - " torchvision.transforms.ToTensor(),\n", - "]))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "mnist_test = MNIST('datasets', train=False, download=True, transform=torchvision.transforms.Compose([\n", - " torchvision.transforms.ToTensor(),\n", - "]))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Taking a look at the data\n", - "\n", - "Each entry in the `mnist_train` and `mnist_test` Dataset instances consists of a 28 x 28 images and the corresponding label (numbers between 0 and 9). The image data is already normalized to the range [0; 1]. Let's take a look at the first 5 images of the training set:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "X_example, y_example = zip(*islice(iter(mnist_train), 5))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(tensor(0.), tensor(1.))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_example[0].min(), X_example[0].max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Print a selection of training images and their labels" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_example(X, y, n=5):\n", - " \"\"\"Plot the images in X and their labels in rows of `n` elements.\"\"\"\n", - " fig = plt.figure()\n", - " rows = len(X) // n + 1\n", - " for i, (img, y) in enumerate(zip(X, y)):\n", - " ax = fig.add_subplot(rows, n, i + 1)\n", - " ax.imshow(img.reshape(28, 28))\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.set_title(y)\n", - " plt.tight_layout()\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_example(torch.stack(X_example), y_example, n=5);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Preparing a validation split\n", - "\n", - "skorch can split the data for us automatically but since we are using `Dataset`s for their lazy-loading property there is no way skorch can do a stratified split automatically without exploring the data completely first (which it doesn't). \n", - "\n", - "If we want skorch to do a validation split for us we need to retrieve the `y` values from the dataset and pass these values to `net.fit` later on:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "y_train = np.array([y for x, y in iter(mnist_train)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build Neural Network with PyTorch\n", - "\n", - "Simple, fully connected neural network with one hidden layer. Input layer has 784 dimensions (28x28), hidden layer has 98 (= 784 / 8) and output layer 10 neurons, representing digits 0 - 9." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "from torch import nn\n", - "import torch.nn.functional as F" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A simple neural network classifier with linear layers and a final softmax in PyTorch:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "class ClassifierModule(nn.Module):\n", - " def __init__(\n", - " self,\n", - " input_dim=MNIST_FLAT_DIM,\n", - " hidden_dim=98,\n", - " output_dim=10,\n", - " dropout=0.5,\n", - " ):\n", - " super(ClassifierModule, self).__init__()\n", - " self.dropout = nn.Dropout(dropout)\n", - "\n", - " self.hidden = nn.Linear(input_dim, hidden_dim)\n", - " self.output = nn.Linear(hidden_dim, output_dim)\n", - "\n", - " def forward(self, X, **kwargs):\n", - " X = X.reshape(-1, self.hidden.in_features)\n", - " X = F.relu(self.hidden(X))\n", - " X = self.dropout(X)\n", - " X = F.softmax(self.output(X), dim=-1)\n", - " return X" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "skorch allows to use PyTorch with an sklearn API. We will train the classifier using the classic sklearn `.fit()`:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch import NeuralNetClassifier\n", - "from skorch.dataset import CVSplit" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We might also add tensorboard logging. For that, skorch offers the `TensorBoard` callback, which automatically logs useful information to tensorboard\n", - "\n", - "**Note**: Using tensorboard requires installing the following Python packages: `tensorboard, future, pillow`\n", - "\n", - "After this, to start tensorboard, run:\n", - "\n", - "`$ tensorboard --logdir runs`\n", - "\n", - "in the directory you are running this notebook in." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "callbacks = []\n", - "if USE_TENSORBOARD:\n", - " from torch.utils.tensorboard import SummaryWriter\n", - " from skorch.callbacks import TensorBoard\n", - " writer = SummaryWriter()\n", - " callbacks.append(TensorBoard(writer))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "\n", - "net = NeuralNetClassifier(\n", - " ClassifierModule,\n", - " max_epochs=10,\n", - " iterator_train__num_workers=4,\n", - " iterator_valid__num_workers=4,\n", - " lr=0.1,\n", - " device=DEVICE,\n", - " callbacks=callbacks,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_acc valid_loss dur\n", - "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.7908\u001b[0m \u001b[32m0.9005\u001b[0m \u001b[35m0.3620\u001b[0m 2.3784\n", - " 2 \u001b[36m0.4249\u001b[0m \u001b[32m0.9213\u001b[0m \u001b[35m0.2846\u001b[0m 2.2981\n", - " 3 \u001b[36m0.3557\u001b[0m \u001b[32m0.9303\u001b[0m \u001b[35m0.2411\u001b[0m 2.2295\n", - " 4 \u001b[36m0.3192\u001b[0m \u001b[32m0.9376\u001b[0m \u001b[35m0.2147\u001b[0m 2.2887\n", - " 5 \u001b[36m0.2877\u001b[0m \u001b[32m0.9434\u001b[0m \u001b[35m0.1970\u001b[0m 2.2926\n", - " 6 \u001b[36m0.2676\u001b[0m \u001b[32m0.9471\u001b[0m \u001b[35m0.1809\u001b[0m 2.3752\n", - " 7 \u001b[36m0.2534\u001b[0m \u001b[32m0.9494\u001b[0m \u001b[35m0.1704\u001b[0m 2.3644\n", - " 8 \u001b[36m0.2413\u001b[0m \u001b[32m0.9521\u001b[0m \u001b[35m0.1602\u001b[0m 2.5879\n", - " 9 \u001b[36m0.2295\u001b[0m \u001b[32m0.9557\u001b[0m \u001b[35m0.1519\u001b[0m 2.3586\n", - " 10 \u001b[36m0.2189\u001b[0m \u001b[32m0.9572\u001b[0m \u001b[35m0.1464\u001b[0m 2.3270\n" - ] - } - ], - "source": [ - "net.fit(mnist_train, y=y_train);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import accuracy_score" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = net.predict(mnist_test)\n", - "y_test = np.array([y for x, y in iter(mnist_test)])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.958" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_test, y_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An accuracy of about 96% for a network with only one hidden layer is not too bad.\n", - "\n", - "Let's take a look at some predictions that went wrong.\n", - "\n", - "We compute the index of elements that are misclassified and plot a few of those to get an idea\n", - "of what went wrong." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "error_mask = y_pred != y_test" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have the mask we need a way to access the images from the `mnist_test` dataset. Luckily, skorch provides a helper class that lets us slice arbitrary `Dataset` objects, `SlicedDataset`:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.helper import SliceDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "mnist_test_sliceable = SliceDataset(mnist_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "X_pred = torch.stack(list(mnist_test_sliceable[error_mask]))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_example(X_pred[:5], y_pred[error_mask][:5]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If tensorboard was enabled, here is how the metrics could look like:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![tensorboard scalars](../assets/tensorboard_scalars.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Convolutional Network\n", - "\n", - "Next we want to turn it up a notch and use a convolutional neural network which is far better\n", - "suited for images than simple densely connected layers.\n", - "\n", - "PyTorch expects a 4 dimensional tensor as input for its 2D convolution layer. The dimensions represent:\n", - "\n", - "* Batch size\n", - "* Number of channels\n", - "* Height\n", - "* Width\n", - "\n", - "MNIST data only has one channel since there is no color information. As stated above, each MNIST vector represents a 28x28 pixel image. Hence, the resulting shape for the input tensor needs to be `(x, 1, 28, 28)` where `x` is the batch size and automatically provided by the data loader.\n", - "\n", - "Luckily, our data is already formated that way:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 28, 28])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_example[0].shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let us define the convolutional neural network module using PyTorch:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "class Cnn(nn.Module):\n", - " def __init__(self, dropout=0.5):\n", - " super(Cnn, self).__init__()\n", - " self.conv1 = nn.Conv2d(1, 32, kernel_size=3)\n", - " self.conv2 = nn.Conv2d(32, 64, kernel_size=3)\n", - " self.conv2_drop = nn.Dropout2d(p=dropout)\n", - " self.fc1 = nn.Linear(1600, 100) # 1600 = number channels * width * height\n", - " self.fc2 = nn.Linear(100, 10)\n", - " self.fc1_drop = nn.Dropout(p=dropout)\n", - "\n", - " def forward(self, x):\n", - " x = torch.relu(F.max_pool2d(self.conv1(x), 2))\n", - " x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n", - " \n", - " # flatten over channel, height and width = 1600\n", - " x = x.view(-1, x.size(1) * x.size(2) * x.size(3))\n", - " \n", - " x = torch.relu(self.fc1_drop(self.fc1(x)))\n", - " x = torch.softmax(self.fc2(x), dim=-1)\n", - " return x" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also want to extend tensorboard logging by two more features:\n", - "\n", - "1. Add the predictions for the misclassified images to tensorboard.\n", - " \n", - " To do this, we subclass the `TensorBoard` callback and call `self.writer.add_figure` with our produced images. When subclassing, don't forget to call `super()` or the other logged metrics won't show.\n", - "\n", - "\n", - "2. Add a graph of the module\n", - " \n", - " To do this, we use the summary writer's ability to add a traced graph of our module to tensorboard by calling `add_graph`. We also make sure to only call this on the very first batch by inspecting the `self.first_batch_` attribute on `TensorBoard`." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "callbacks = []\n", - "if USE_TENSORBOARD:\n", - " from torch.utils.tensorboard import SummaryWriter\n", - " from skorch.callbacks import TensorBoard\n", - " writer = SummaryWriter()\n", - "\n", - " class MyTensorBoard(TensorBoard):\n", - " def __init__(self, *args, X, **kwargs):\n", - " self.X = X\n", - " super().__init__(*args, **kwargs)\n", - "\n", - " def add_graph(self, module, X):\n", - " \"\"\"\"Add a graph to tensorboard\n", - "\n", - " This requires to run the module with a sample from the\n", - " dataset.\n", - "\n", - " \"\"\"\n", - " self.writer.add_graph(module, X.to(DEVICE))\n", - "\n", - " def on_batch_begin(self, net, X, **kwargs):\n", - " if self.first_batch_:\n", - " # only add graph on very first batch\n", - " self.add_graph(net.module_, X)\n", - " \n", - " def add_figure(self, net):\n", - " # show how difficult images were classified\n", - " epoch = net.history[-1, 'epoch']\n", - " y_pred = net.predict(self.X)\n", - " fig = plot_example(self.X, y_pred)\n", - " self.writer.add_figure('difficult images', fig, global_step=epoch)\n", - "\n", - " def on_epoch_end(self, net, **kwargs):\n", - " self.add_figure(net)\n", - " super().on_epoch_end(net, **kwargs) # call super last\n", - "\n", - " X_difficult = torch.stack(list(mnist_test_sliceable[error_mask][:15]))\n", - " callbacks.append(MyTensorBoard(writer, X=X_difficult))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As before we can wrap skorch's `NeuralNetClassifier` around our module and start training it like every other sklearn model using `.fit`:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "torch.manual_seed(0)\n", - "\n", - "cnn = NeuralNetClassifier(\n", - " Cnn,\n", - " max_epochs=10,\n", - " lr=0.0002,\n", - " optimizer=torch.optim.Adam,\n", - " device=DEVICE,\n", - " iterator_train__num_workers=4,\n", - " iterator_valid__num_workers=4,\n", - " callbacks=callbacks,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_acc valid_loss dur\n", - "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.9300\u001b[0m \u001b[32m0.9297\u001b[0m \u001b[35m0.2459\u001b[0m 2.9154\n", - " 2 \u001b[36m0.3148\u001b[0m \u001b[32m0.9541\u001b[0m \u001b[35m0.1518\u001b[0m 2.9141\n", - " 3 \u001b[36m0.2208\u001b[0m \u001b[32m0.9663\u001b[0m \u001b[35m0.1160\u001b[0m 3.0988\n", - " 4 \u001b[36m0.1779\u001b[0m \u001b[32m0.9701\u001b[0m \u001b[35m0.0990\u001b[0m 2.9270\n", - " 5 \u001b[36m0.1549\u001b[0m \u001b[32m0.9743\u001b[0m \u001b[35m0.0890\u001b[0m 3.0307\n", - " 6 \u001b[36m0.1406\u001b[0m \u001b[32m0.9759\u001b[0m \u001b[35m0.0800\u001b[0m 2.9676\n", - " 7 \u001b[36m0.1282\u001b[0m \u001b[32m0.9780\u001b[0m \u001b[35m0.0734\u001b[0m 2.9617\n", - " 8 \u001b[36m0.1143\u001b[0m \u001b[32m0.9795\u001b[0m \u001b[35m0.0691\u001b[0m 2.9718\n", - " 9 \u001b[36m0.1071\u001b[0m \u001b[32m0.9807\u001b[0m \u001b[35m0.0640\u001b[0m 3.0400\n", - " 10 \u001b[36m0.1043\u001b[0m \u001b[32m0.9816\u001b[0m \u001b[35m0.0610\u001b[0m 2.9902\n" - ] - } - ], - "source": [ - "cnn.fit(mnist_train, y=y_train);" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_cnn = cnn.predict(mnist_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9856" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_test, y_pred_cnn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An accuracy of >98% should suffice for this example!\n", - "\n", - "Let's see how we fare on the examples that went wrong before:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.7261904761904762" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "accuracy_score(y_test[error_mask], y_pred_cnn[error_mask])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Great success! The majority of the previously misclassified images are now correctly identified." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On tensorboard, in the \"IMAGES\" section, we can see how well the CNN classified the difficult images, and how that changed over the epochs:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"tensorboard" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the \"GRAPHS\" section, we can see the graph of our module." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"tensorboard" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid searching parameter configurations\n", - "\n", - "Finally we want to show an example of how to use sklearn grid search when using torch `Dataset` instances.\n", - "\n", - "When doing k-fold validation grid search we have the same problem as before that sklearn is only able to do (stratified) splits when the data is sliceable. While skorch knows how to deal with PyTorch `Dataset` objects and only needs `y` to be known beforehand, sklearn doesn't know how to deal with `Dataset`s and needs a wrapper that makes them sliceable.\n", - "\n", - "Fortunately, we already know that skorch provides such a helper: `SliceDataset`.\n", - "\n", - "What is left to do is to define our parameter search space and run the grid search with a sliceable instance of `mnist_train`:" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import GridSearchCV" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[initialized](\n", - " module_=Cnn(\n", - " (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n", - " (conv2_drop): Dropout2d(p=0.5)\n", - " (fc1): Linear(in_features=1600, out_features=100, bias=True)\n", - " (fc2): Linear(in_features=100, out_features=10, bias=True)\n", - " (fc1_drop): Dropout(p=0.5)\n", - " ),\n", - ")" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cnn.set_params(max_epochs=2, verbose=False, train_split=False, callbacks=[])" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "params = {\n", - " 'module__dropout': [0, 0.5, 0.8],\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The parameter we are interested in here is the dropout rate. We want to see which of the values (no dropout, 50%, 80%) is the best choice for our network.\n", - "\n", - "Additionally:\n", - "\n", - "- We use only two epochs (`max_epochs=2`) for each `.fit` (only to reduce execution time, normally we wouldn't change this and possibly add an `EarlyStopping` callback).\n", - "- Disable the network print output (`verbose=False`)\n", - "- Disable the internal train/validation split (`train_split=False`) since the grid search will do k-fold validation anyway\n", - "- Turn off tensorboard logging (`callbacks=[]`)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "cnn.initialize();" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "gs = GridSearchCV(cnn, param_grid=params, scoring='accuracy', verbose=1, cv=3)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "mnist_train_sliceable = SliceDataset(mnist_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n", - "[Parallel(n_jobs=1)]: Done 9 out of 9 | elapsed: 1.1min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=3, error_score='raise-deprecating',\n", - " estimator=[initialized](\n", - " module_=Cnn(\n", - " (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n", - " (conv2_drop): Dropout2d(p=0.5)\n", - " (fc1): Linear(in_features=1600, out_features=100, bias=True)\n", - " (fc2): Linear(in_features=100, out_features=10, bias=True)\n", - " (fc1_drop): Dropout(p=0.5)\n", - " ),\n", - "),\n", - " fit_params=None, iid='warn', n_jobs=None,\n", - " param_grid={'module__dropout': [0, 0.5, 0.8]},\n", - " pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n", - " scoring='accuracy', verbose=1)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gs.fit(mnist_train_sliceable, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After running the grid search we now know the best configuration in our search space:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'module__dropout': 0}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gs.best_params_" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/MNIST.ipynb b/notebooks/MNIST.ipynb index 2fb66a5a1..8aa8845d6 100644 --- a/notebooks/MNIST.ipynb +++ b/notebooks/MNIST.ipynb @@ -30,9 +30,24 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "If not already installed, you can install skorch by running 'pip install skorch'\n" + ] + } + ], "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch scikit-learn==0.20.* skorch" + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch' , 'torch'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" ] }, { @@ -215,9 +230,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] }, "metadata": {}, @@ -364,26 +379,26 @@ "text": [ " epoch train_loss valid_acc valid_loss dur\n", "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.8299\u001b[0m \u001b[32m0.8893\u001b[0m \u001b[35m0.4037\u001b[0m 0.8951\n", - " 2 \u001b[36m0.4331\u001b[0m \u001b[32m0.9113\u001b[0m \u001b[35m0.3075\u001b[0m 0.7862\n", - " 3 \u001b[36m0.3619\u001b[0m \u001b[32m0.9240\u001b[0m \u001b[35m0.2614\u001b[0m 0.8314\n", - " 4 \u001b[36m0.3237\u001b[0m \u001b[32m0.9305\u001b[0m \u001b[35m0.2379\u001b[0m 0.7739\n", - " 5 \u001b[36m0.2914\u001b[0m \u001b[32m0.9371\u001b[0m \u001b[35m0.2173\u001b[0m 0.7721\n", - " 6 \u001b[36m0.2739\u001b[0m \u001b[32m0.9413\u001b[0m \u001b[35m0.1979\u001b[0m 0.7949\n", - " 7 \u001b[36m0.2569\u001b[0m \u001b[32m0.9449\u001b[0m \u001b[35m0.1859\u001b[0m 0.7691\n", - " 8 \u001b[36m0.2420\u001b[0m \u001b[32m0.9461\u001b[0m \u001b[35m0.1813\u001b[0m 0.7871\n", - " 9 \u001b[36m0.2337\u001b[0m \u001b[32m0.9496\u001b[0m \u001b[35m0.1708\u001b[0m 0.7730\n", - " 10 \u001b[36m0.2195\u001b[0m \u001b[32m0.9532\u001b[0m \u001b[35m0.1604\u001b[0m 0.7992\n", - " 11 \u001b[36m0.2151\u001b[0m \u001b[32m0.9547\u001b[0m \u001b[35m0.1514\u001b[0m 0.7955\n", - " 12 \u001b[36m0.2065\u001b[0m \u001b[32m0.9560\u001b[0m \u001b[35m0.1476\u001b[0m 0.7734\n", - " 13 \u001b[36m0.2015\u001b[0m \u001b[32m0.9563\u001b[0m \u001b[35m0.1455\u001b[0m 0.8316\n", - " 14 \u001b[36m0.1943\u001b[0m \u001b[32m0.9587\u001b[0m \u001b[35m0.1389\u001b[0m 0.7913\n", - " 15 \u001b[36m0.1883\u001b[0m 0.9578 \u001b[35m0.1381\u001b[0m 0.8092\n", - " 16 \u001b[36m0.1848\u001b[0m \u001b[32m0.9596\u001b[0m \u001b[35m0.1323\u001b[0m 0.7907\n", - " 17 \u001b[36m0.1838\u001b[0m \u001b[32m0.9606\u001b[0m \u001b[35m0.1312\u001b[0m 0.7806\n", - " 18 \u001b[36m0.1776\u001b[0m \u001b[32m0.9623\u001b[0m \u001b[35m0.1261\u001b[0m 0.7657\n", - " 19 \u001b[36m0.1738\u001b[0m \u001b[32m0.9625\u001b[0m \u001b[35m0.1250\u001b[0m 0.7690\n", - " 20 \u001b[36m0.1704\u001b[0m \u001b[32m0.9627\u001b[0m \u001b[35m0.1238\u001b[0m 0.7627\n" + " 1 \u001b[36m0.8309\u001b[0m \u001b[32m0.8865\u001b[0m \u001b[35m0.4049\u001b[0m 1.4432\n", + " 2 \u001b[36m0.4323\u001b[0m \u001b[32m0.9124\u001b[0m \u001b[35m0.3047\u001b[0m 1.5334\n", + " 3 \u001b[36m0.3606\u001b[0m \u001b[32m0.9215\u001b[0m \u001b[35m0.2682\u001b[0m 1.6298\n", + " 4 \u001b[36m0.3191\u001b[0m \u001b[32m0.9297\u001b[0m \u001b[35m0.2424\u001b[0m 1.4204\n", + " 5 \u001b[36m0.2969\u001b[0m \u001b[32m0.9360\u001b[0m \u001b[35m0.2192\u001b[0m 1.4114\n", + " 6 \u001b[36m0.2760\u001b[0m \u001b[32m0.9395\u001b[0m \u001b[35m0.2040\u001b[0m 1.6088\n", + " 7 \u001b[36m0.2596\u001b[0m \u001b[32m0.9440\u001b[0m \u001b[35m0.1911\u001b[0m 1.4049\n", + " 8 \u001b[36m0.2463\u001b[0m \u001b[32m0.9477\u001b[0m \u001b[35m0.1775\u001b[0m 1.4742\n", + " 9 \u001b[36m0.2356\u001b[0m \u001b[32m0.9499\u001b[0m \u001b[35m0.1697\u001b[0m 1.4681\n", + " 10 \u001b[36m0.2273\u001b[0m \u001b[32m0.9520\u001b[0m \u001b[35m0.1652\u001b[0m 1.6725\n", + " 11 \u001b[36m0.2198\u001b[0m \u001b[32m0.9534\u001b[0m \u001b[35m0.1568\u001b[0m 1.4685\n", + " 12 \u001b[36m0.2076\u001b[0m \u001b[32m0.9549\u001b[0m \u001b[35m0.1529\u001b[0m 1.3873\n", + " 13 \u001b[36m0.2037\u001b[0m \u001b[32m0.9558\u001b[0m \u001b[35m0.1489\u001b[0m 1.5479\n", + " 14 \u001b[36m0.1985\u001b[0m 0.9549 \u001b[35m0.1468\u001b[0m 1.5329\n", + " 15 \u001b[36m0.1960\u001b[0m \u001b[32m0.9580\u001b[0m \u001b[35m0.1391\u001b[0m 1.4183\n", + " 16 \u001b[36m0.1886\u001b[0m \u001b[32m0.9581\u001b[0m \u001b[35m0.1388\u001b[0m 1.4396\n", + " 17 \u001b[36m0.1814\u001b[0m 0.9579 \u001b[35m0.1371\u001b[0m 1.8017\n", + " 18 0.1851 \u001b[32m0.9606\u001b[0m \u001b[35m0.1313\u001b[0m 1.4541\n", + " 19 \u001b[36m0.1770\u001b[0m \u001b[32m0.9611\u001b[0m \u001b[35m0.1279\u001b[0m 1.4681\n", + " 20 \u001b[36m0.1768\u001b[0m 0.9604 0.1311 1.5211\n" ] } ], @@ -424,7 +439,7 @@ { "data": { "text/plain": [ - "0.9627428571428571" + "0.9612571428571428" ] }, "execution_count": 23, @@ -461,9 +476,9 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -600,18 +615,18 @@ "name": "stdout", "output_type": "stream", "text": [ - " epoch train_loss valid_acc valid_loss dur\n", - "------- ------------ ----------- ------------ ------\n", - " 1 \u001b[36m0.4298\u001b[0m \u001b[32m0.9729\u001b[0m \u001b[35m0.0898\u001b[0m 4.9916\n", - " 2 \u001b[36m0.1577\u001b[0m \u001b[32m0.9799\u001b[0m \u001b[35m0.0646\u001b[0m 4.8633\n", - " 3 \u001b[36m0.1261\u001b[0m \u001b[32m0.9824\u001b[0m \u001b[35m0.0564\u001b[0m 4.9402\n", - " 4 \u001b[36m0.1120\u001b[0m \u001b[32m0.9848\u001b[0m \u001b[35m0.0507\u001b[0m 4.8320\n", - " 5 \u001b[36m0.1006\u001b[0m \u001b[32m0.9855\u001b[0m \u001b[35m0.0446\u001b[0m 4.8227\n", - " 6 \u001b[36m0.0924\u001b[0m \u001b[32m0.9862\u001b[0m \u001b[35m0.0415\u001b[0m 4.8191\n", - " 7 \u001b[36m0.0844\u001b[0m \u001b[32m0.9886\u001b[0m \u001b[35m0.0375\u001b[0m 4.8168\n", - " 8 \u001b[36m0.0828\u001b[0m 0.9854 0.0414 4.8333\n", - " 9 \u001b[36m0.0779\u001b[0m 0.9885 \u001b[35m0.0368\u001b[0m 4.8199\n", - " 10 \u001b[36m0.0768\u001b[0m \u001b[32m0.9891\u001b[0m \u001b[35m0.0350\u001b[0m 4.8129\n" + " epoch train_loss valid_acc valid_loss dur\n", + "------- ------------ ----------- ------------ -------\n", + " 1 \u001b[36m0.4334\u001b[0m \u001b[32m0.9726\u001b[0m \u001b[35m0.0896\u001b[0m 30.7984\n", + " 2 \u001b[36m0.1611\u001b[0m \u001b[32m0.9799\u001b[0m \u001b[35m0.0654\u001b[0m 31.1465\n", + " 3 \u001b[36m0.1271\u001b[0m \u001b[32m0.9818\u001b[0m \u001b[35m0.0570\u001b[0m 31.0072\n", + " 4 \u001b[36m0.1122\u001b[0m \u001b[32m0.9839\u001b[0m \u001b[35m0.0494\u001b[0m 31.8891\n", + " 5 \u001b[36m0.0996\u001b[0m \u001b[32m0.9851\u001b[0m \u001b[35m0.0462\u001b[0m 31.8246\n", + " 6 \u001b[36m0.0935\u001b[0m \u001b[32m0.9870\u001b[0m \u001b[35m0.0445\u001b[0m 32.5026\n", + " 7 \u001b[36m0.0877\u001b[0m \u001b[32m0.9878\u001b[0m \u001b[35m0.0403\u001b[0m 30.7359\n", + " 8 \u001b[36m0.0797\u001b[0m 0.9877 \u001b[35m0.0394\u001b[0m 30.9183\n", + " 9 \u001b[36m0.0792\u001b[0m 0.9878 \u001b[35m0.0365\u001b[0m 30.8700\n", + " 10 \u001b[36m0.0721\u001b[0m \u001b[32m0.9885\u001b[0m 0.0378 32.2872\n" ] } ], @@ -636,7 +651,7 @@ { "data": { "text/plain": [ - "0.9889714285714286" + "0.9868" ] }, "execution_count": 34, @@ -665,7 +680,7 @@ { "data": { "text/plain": [ - "0.7745398773006135" + "0.7477876106194691" ] }, "execution_count": 35, @@ -686,14 +701,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -703,11 +718,18 @@ "source": [ "plot_example(X_test[error_mask], y_pred_cnn[error_mask])" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "base", "language": "python", "name": "python3" }, @@ -721,7 +743,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.13" + }, + "vscode": { + "interpreter": { + "hash": "bd97b8bffa4d3737e84826bc3d37be3046061822757ce35137ab82ad4c5a2016" + } } }, "nbformat": 4, diff --git a/notebooks/Transfer_Learning.ipynb b/notebooks/Transfer_Learning.ipynb index 07fa3413d..9a4acb8ec 100644 --- a/notebooks/Transfer_Learning.ipynb +++ b/notebooks/Transfer_Learning.ipynb @@ -1,419 +1,1233 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Transfer Learning with skorch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this tutorial, you will learn how to train a neural network using transfer learning with the `skorch` API. Transfer learning uses a pretrained model to initialize a network. This tutorial converts the pure PyTorch approach described in [PyTorch's Transfer Learning Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) to `skorch`.\n", - "\n", - "We will be using `torchvision` for this tutorial. Instructions on how to install `torchvision` for your platform can be found at https://pytorch.org.\n", - "\n", - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on GitHub
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Transfer_Learning.ipynb), we recommend you enable a free GPU by going:\n", - "\n", - "> **Runtime**   →   **Change runtime type**   →   **Hardware Accelerator: GPU**\n", - "\n", - "If you are running in colab, you should install the dependencies and download the dataset by running the following cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "! [ ! -z \"$COLAB_GPU\" ] && pip install torch torchvision pillow==4.1.1 skorch\n", - "! [ ! -z \"$COLAB_GPU\" ] && mkdir -p datasets\n", - "! [ ! -z \"$COLAB_GPU\" ] && wget -nc --no-check-certificate https://download.pytorch.org/tutorial/hymenoptera_data.zip -P datasets\n", - "! [ ! -z \"$COLAB_GPU\" ] && unzip -u datasets/hymenoptera_data.zip -d datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from urllib import request\n", - "from zipfile import ZipFile\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import numpy as np\n", - "from torchvision import datasets, models, transforms\n", - "\n", - "from skorch import NeuralNetClassifier\n", - "from skorch.helper import predefined_split\n", - "\n", - "torch.manual_seed(360);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preparations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Before we begin, lets download the data needed for this tutorial:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data has been downloaded and extracted to datasets.\n" - ] - } - ], - "source": [ - "def download_and_extract_data(dataset_dir='datasets'):\n", - " data_zip = os.path.join(dataset_dir, 'hymenoptera_data.zip')\n", - " data_path = os.path.join(dataset_dir, 'hymenoptera_data')\n", - " url = \"https://download.pytorch.org/tutorial/hymenoptera_data.zip\"\n", - "\n", - " if not os.path.exists(data_path):\n", - " if not os.path.exists(data_zip):\n", - " print(\"Starting to download data...\")\n", - " data = request.urlopen(url, timeout=15).read()\n", - " with open(data_zip, 'wb') as f:\n", - " f.write(data)\n", - "\n", - " print(\"Starting to extract data...\")\n", - " with ZipFile(data_zip, 'r') as zip_f:\n", - " zip_f.extractall(dataset_dir)\n", - " \n", - " print(\"Data has been downloaded and extracted to {}.\".format(dataset_dir))\n", - " \n", - "download_and_extract_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Problem" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are going to train a neural network to classify **ants** and **bees**. The dataset consist of 120 training images and 75 validiation images for each class. First we create the training and validiation datasets:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "data_dir = 'datasets/hymenoptera_data'\n", - "train_transforms = transforms.Compose([\n", - " transforms.RandomResizedCrop(224),\n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], \n", - " [0.229, 0.224, 0.225])\n", - "])\n", - "val_transforms = transforms.Compose([\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], \n", - " [0.229, 0.224, 0.225])\n", - "])\n", - "\n", - "train_ds = datasets.ImageFolder(\n", - " os.path.join(data_dir, 'train'), train_transforms)\n", - "val_ds = datasets.ImageFolder(\n", - " os.path.join(data_dir, 'val'), val_transforms)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The train dataset includes data augmentation techniques such as cropping to size 224 and horizontal flips.The train and validiation datasets are normalized with mean: `[0.485, 0.456, 0.406]`, and standard deviation: `[0.229, 0.224, 0.225]`. These values are the means and standard deviations of the ImageNet images. We used these values because the pretrained model was trained on ImageNet." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading pretrained model" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use a pretrained `ResNet18` neural network model with its final layer replaced with a fully connected layer:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class PretrainedModel(nn.Module):\n", - " def __init__(self, output_features):\n", - " super().__init__()\n", - " model = models.resnet18(pretrained=True)\n", - " num_ftrs = model.fc.in_features\n", - " model.fc = nn.Linear(num_ftrs, output_features)\n", - " self.model = model\n", - " \n", - " def forward(self, x):\n", - " return self.model(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we are training a binary classifier, the output of the final fully connected layer has size 2." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using skorch's API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we will create a `skorch.NeuralNetClassifier` to solve our classification problem. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Callbacks" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we create a `LRScheduler` callback which is a learning rate scheduler that uses `torch.optim.lr_scheduler.StepLR` to scale learning rates by `gamma=0.1` every 7 steps:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.callbacks import LRScheduler\n", - "\n", - "lrscheduler = LRScheduler(\n", - " policy='StepLR', step_size=7, gamma=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we create a `Checkpoint` callback which saves the best model by by monitoring the validation accuracy. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.callbacks import Checkpoint\n", - "\n", - "checkpoint = Checkpoint(\n", - " f_params='best_model.pt', monitor='valid_acc_best')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, we create a `Freezer` to freeze all weights besides the final layer named `model.fc`:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from skorch.callbacks import Freezer\n", - "\n", - "freezer = Freezer(lambda x: not x.startswith('model.fc'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### skorch.NeuralNetClassifier" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With all the preparations out of the way, we can now define our `NeuralNetClassifier`:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "net = NeuralNetClassifier(\n", - " PretrainedModel, \n", - " criterion=nn.CrossEntropyLoss,\n", - " lr=0.001,\n", - " batch_size=4,\n", - " max_epochs=25,\n", - " module__output_features=2,\n", - " optimizer=optim.SGD,\n", - " optimizer__momentum=0.9,\n", - " iterator_train__shuffle=True,\n", - " iterator_train__num_workers=4,\n", - " iterator_valid__shuffle=True,\n", - " iterator_valid__num_workers=4,\n", - " train_split=predefined_split(val_ds),\n", - " callbacks=[lrscheduler, checkpoint, freezer],\n", - " device='cuda' # comment to train on cpu\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That is quite a few parameters! Lets walk through each one:\n", - "\n", - "1. `model_ft`: Our `ResNet18` neural network\n", - "2. `criterion=nn.CrossEntropyLoss`: loss function\n", - "3. `lr`: Initial learning rate\n", - "4. `batch_size`: Size of a batch\n", - "5. `max_epochs`: Number of epochs to train\n", - "6. `module__output_features`: Used by `__init__` in our `PretrainedModel` class to set the number of classes.\n", - "7. `optimizer`: Our optimizer\n", - "8. `optimizer__momentum`: The initial momentum\n", - "9. `iterator_{train,valid}__{shuffle,num_workers}`: Parameters that are passed to the dataloader.\n", - "10. `train_split`: A wrapper around `val_ds` to use our validation dataset.\n", - "11. `callbacks`: Our callbacks \n", - "12. `device`: Set to `cuda` to train on gpu.\n", - "\n", - "Now we are ready to train our neural network:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "id": "sLMSHWM-oiyf" + }, + "source": [ + "# Transfer Learning with skorch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2KJVmPqYoiyj" + }, + "source": [ + "In this tutorial, you will learn how to train a neural network using transfer learning with the `skorch` API. Transfer learning uses a pretrained model to initialize a network. This tutorial converts the pure PyTorch approach described in [PyTorch's Transfer Learning Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) to `skorch`.\n", + "\n", + "We will be using `torchvision` for this tutorial. Instructions on how to install `torchvision` for your platform can be found at https://pytorch.org.\n", + "\n", + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - " epoch train_loss valid_acc valid_loss cp dur\n", - "------- ------------ ----------- ------------ ---- ------\n", - " 1 \u001b[36m0.8220\u001b[0m \u001b[32m0.9150\u001b[0m \u001b[35m0.2294\u001b[0m + 1.7953\n", - " 2 \u001b[36m0.4949\u001b[0m \u001b[32m0.9346\u001b[0m \u001b[35m0.2116\u001b[0m + 0.9276\n", - " 3 \u001b[36m0.4873\u001b[0m 0.8105 0.4593 0.9309\n", - " 4 0.5291 \u001b[32m0.9477\u001b[0m \u001b[35m0.1725\u001b[0m + 0.9292\n", - " 5 \u001b[36m0.4530\u001b[0m 0.9216 0.2275 0.9046\n", - " 6 \u001b[36m0.3869\u001b[0m 0.9412 \u001b[35m0.1697\u001b[0m 0.9121\n", - " 7 \u001b[36m0.2903\u001b[0m \u001b[32m0.9608\u001b[0m 0.1778 + 0.9504\n", - " 8 0.3000 0.9477 0.1769 0.9169\n", - " 9 0.4068 0.9542 0.1830 0.9312\n", - " 10 0.5076 0.9281 0.1953 1.0024\n", - " 11 0.3271 0.9346 0.1911 0.9144\n", - " 12 0.3728 0.9281 0.2180 0.8806\n", - " 13 \u001b[36m0.2847\u001b[0m 0.9477 0.1847 0.9466\n", - " 14 0.3526 0.9216 0.2333 0.9141\n", - " 15 0.3254 0.9281 0.1802 0.8951\n", - " 16 0.3407 0.9477 0.1888 0.8973\n", - " 17 \u001b[36m0.2498\u001b[0m 0.9346 0.1931 0.9159\n", - " 18 0.4421 0.9477 0.1848 0.9186\n", - " 19 0.3548 0.9216 0.2010 0.8960\n", - " 20 0.3037 0.9281 0.2188 0.9178\n", - " 21 0.3454 0.9542 0.1837 0.9184\n", - " 22 0.3227 0.9412 0.1732 0.9115\n", - " 23 0.2595 0.9542 0.1765 0.9040\n", - " 24 0.3164 0.9477 0.1794 0.9101\n", - " 25 0.2607 0.9412 0.1934 0.9493\n" - ] + "cell_type": "markdown", + "metadata": { + "id": "uFUihT98oiyp" + }, + "source": [ + "**Note**: If you are running this in [a colab notebook](https://colab.research.google.com/github/skorch-dev/skorch/blob/master/notebooks/Transfer_Learning.ipynb), we recommend you enable a free GPU by going:\n", + "\n", + "> **Runtime**   →   **Change runtime type**   →   **Hardware Accelerator: GPU**\n", + "\n", + "If you are running in colab, you should install the dependencies and download the dataset by running the following cell:" + ] + }, + { + "cell_type": "code", + "source": [ + "import subprocess\n", + "\n", + "# Installation\n", + "try:\n", + " import os\n", + " import google.colab\n", + " subprocess.run(['python', '-m', 'pip', 'install', 'skorch', 'torchvision'])\n", + " subprocess.run(['mkdir', '-p', 'datasets'])\n", + " subprocess.run(['wget', '-nc', '--no-check-certificate', 'https://download.pytorch.org/tutorial/hymenoptera_data.zip', '-P', 'datasets'])\n", + " subprocess.run(['unzip', '-u', 'datasets/hymenoptera_data.zip', '-d' 'datasets'])\n", + "except ImportError:\n", + " print(\"If not already installed, you can install skorch by running 'pip install skorch'\")" + ], + "metadata": { + "id": "i1U_Mi4gon2r" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "8EctsAZCoiyy" + }, + "outputs": [], + "source": [ + "import os\n", + "from urllib import request\n", + "from zipfile import ZipFile\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import numpy as np\n", + "from torchvision import datasets, models, transforms\n", + "\n", + "from skorch import NeuralNetClassifier\n", + "from skorch.helper import predefined_split\n", + "\n", + "torch.manual_seed(360);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_vaY9ew5oiy1" + }, + "source": [ + "## Preparations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ybj2whoboiy3" + }, + "source": [ + "Before we begin, lets download the data needed for this tutorial:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "vXrGCvunoiy4", + "outputId": "c5dafcbd-c8d6-4428-ff88-9a8205dc6219", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Data has been downloaded and extracted to datasets.\n" + ] + } + ], + "source": [ + "def download_and_extract_data(dataset_dir='datasets'):\n", + " data_zip = os.path.join(dataset_dir, 'hymenoptera_data.zip')\n", + " data_path = os.path.join(dataset_dir, 'hymenoptera_data')\n", + " url = \"https://download.pytorch.org/tutorial/hymenoptera_data.zip\"\n", + "\n", + " if not os.path.exists(data_path):\n", + " if not os.path.exists(data_zip):\n", + " print(\"Starting to download data...\")\n", + " data = request.urlopen(url, timeout=15).read()\n", + " with open(data_zip, 'wb') as f:\n", + " f.write(data)\n", + "\n", + " print(\"Starting to extract data...\")\n", + " with ZipFile(data_zip, 'r') as zip_f:\n", + " zip_f.extractall(dataset_dir)\n", + " \n", + " print(\"Data has been downloaded and extracted to {}.\".format(dataset_dir))\n", + " \n", + "download_and_extract_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V0K5OAKPoiy7" + }, + "source": [ + "## The Problem" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m52cP6kaoiy8" + }, + "source": [ + "We are going to train a neural network to classify **ants** and **bees**. The dataset consist of 120 training images and 75 validiation images for each class. First we create the training and validiation datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "y3lg-xiaoiy9" + }, + "outputs": [], + "source": [ + "data_dir = 'datasets/hymenoptera_data'\n", + "train_transforms = transforms.Compose([\n", + " transforms.RandomResizedCrop(224),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])\n", + "])\n", + "val_transforms = transforms.Compose([\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])\n", + "])\n", + "\n", + "train_ds = datasets.ImageFolder(\n", + " os.path.join(data_dir, 'train'), train_transforms)\n", + "val_ds = datasets.ImageFolder(\n", + " os.path.join(data_dir, 'val'), val_transforms)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qfbGs4wfoiy_" + }, + "source": [ + "The train dataset includes data augmentation techniques such as cropping to size 224 and horizontal flips.The train and validiation datasets are normalized with mean: `[0.485, 0.456, 0.406]`, and standard deviation: `[0.229, 0.224, 0.225]`. These values are the means and standard deviations of the ImageNet images. We used these values because the pretrained model was trained on ImageNet." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "APIEJMARoizA" + }, + "source": [ + "## Loading pretrained model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TUNjvYdwoizA" + }, + "source": [ + "We use a pretrained `ResNet18` neural network model with its final layer replaced with a fully connected layer:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "cegvloOPoizB" + }, + "outputs": [], + "source": [ + "class PretrainedModel(nn.Module):\n", + " def __init__(self, output_features):\n", + " super().__init__()\n", + " model = models.resnet18(pretrained=True)\n", + " num_ftrs = model.fc.in_features\n", + " model.fc = nn.Linear(num_ftrs, output_features)\n", + " self.model = model\n", + " \n", + " def forward(self, x):\n", + " return self.model(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "al8ehonloizD" + }, + "source": [ + "Since we are training a binary classifier, the output of the final fully connected layer has size 2." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vNmUTqT6oizD" + }, + "source": [ + "## Using skorch's API" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j2hJ-CUUoizE" + }, + "source": [ + "In this section, we will create a `skorch.NeuralNetClassifier` to solve our classification problem. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ob1FqKNloizF" + }, + "source": [ + "### Callbacks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aHVRYecRoizF" + }, + "source": [ + "First, we create a `LRScheduler` callback which is a learning rate scheduler that uses `torch.optim.lr_scheduler.StepLR` to scale learning rates by `gamma=0.1` every 7 steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "ZJEUie2VoizG" + }, + "outputs": [], + "source": [ + "from skorch.callbacks import LRScheduler\n", + "\n", + "lrscheduler = LRScheduler(\n", + " policy='StepLR', step_size=7, gamma=0.1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bli6ngl5oizG" + }, + "source": [ + "Next, we create a `Checkpoint` callback which saves the best model by by monitoring the validation accuracy. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "rGPmqqhWoizH" + }, + "outputs": [], + "source": [ + "from skorch.callbacks import Checkpoint\n", + "\n", + "checkpoint = Checkpoint(\n", + " f_params='best_model.pt', monitor='valid_acc_best')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XQS39O_GoizJ" + }, + "source": [ + "Lastly, we create a `Freezer` to freeze all weights besides the final layer named `model.fc`:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "kXOe41FBoizJ" + }, + "outputs": [], + "source": [ + "from skorch.callbacks import Freezer\n", + "\n", + "freezer = Freezer(lambda x: not x.startswith('model.fc'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4_JqX5wEoizK" + }, + "source": [ + "### skorch.NeuralNetClassifier" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2ahqnHCZoizL" + }, + "source": [ + "With all the preparations out of the way, we can now define our `NeuralNetClassifier`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "xQCwRTaZoizL" + }, + "outputs": [], + "source": [ + "net = NeuralNetClassifier(\n", + " PretrainedModel, \n", + " criterion=nn.CrossEntropyLoss,\n", + " lr=0.001,\n", + " batch_size=4,\n", + " max_epochs=25,\n", + " module__output_features=2,\n", + " optimizer=optim.SGD,\n", + " optimizer__momentum=0.9,\n", + " iterator_train__shuffle=True,\n", + " iterator_train__num_workers=4,\n", + " iterator_valid__shuffle=True,\n", + " iterator_valid__num_workers=4,\n", + " train_split=predefined_split(val_ds),\n", + " callbacks=[lrscheduler, checkpoint, freezer],\n", + " device='cuda' # comment to train on cpu\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZggANL3DoizL" + }, + "source": [ + "That is quite a few parameters! Lets walk through each one:\n", + "\n", + "1. `model_ft`: Our `ResNet18` neural network\n", + "2. `criterion=nn.CrossEntropyLoss`: loss function\n", + "3. `lr`: Initial learning rate\n", + "4. `batch_size`: Size of a batch\n", + "5. `max_epochs`: Number of epochs to train\n", + "6. `module__output_features`: Used by `__init__` in our `PretrainedModel` class to set the number of classes.\n", + "7. `optimizer`: Our optimizer\n", + "8. `optimizer__momentum`: The initial momentum\n", + "9. `iterator_{train,valid}__{shuffle,num_workers}`: Parameters that are passed to the dataloader.\n", + "10. `train_split`: A wrapper around `val_ds` to use our validation dataset.\n", + "11. `callbacks`: Our callbacks \n", + "12. `device`: Set to `cuda` to train on gpu.\n", + "\n", + "Now we are ready to train our neural network:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "sYjoTUdyoizM", + "outputId": "4b54bfcd-151b-4ed0-8b66-f7b39928b11f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "dd4c65ce447b4f5ea9d0e53f43650222", + "c2f60728c2fc472aaa17feeec8ae48e7", + "525533ef389a4d18b90d359c03eecb0b", + "9c31c5be96794ec1a1e67f02f7fcb36f", + "f11b7a2e494c4c378e871547d407bbfd", + "46e9162e6ba84baca4f25b7d3ef9b958", + "1ebfd87d7ac3436393b8dce7af4bc6bf", + "4048cb86d50447998a648ff8c5afac51", + "17f514e59ea541aa973b0eb74ba3a092", + "798a71bd22b2484aa30abc089bc20f93", + "e94947a1a00f4516b13a72fe08e48d7c" + ] + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.8/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n", + "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0.00/44.7M [00:00zIU(h{_j&{lKCW8%w@&U^?SN ze7Ud$D;nkcPy(SJN!;ratU(5OM*^Wq2K3;P#&iq<+Mq|PaBIN6o~Q95@=g0UXf3Xm59bsvNtIR<5^-Y)HWR-mRKxuR<1S~ z#rEUE7TjpaM zF?#f9su-0^pq-Q%J~S@b>lW5*ibrFwHecqcF`t3MC=Jq2@jroj(b_DNRK z5UvSBqkqUpl^+OFTwMUzpL(rW_N*PVV&Heqg@0p1oSBtncpWxx8klcmh}9FW(lxQ4 zAtiSk88+0Xf$g~q*e(u2w=izi>Ji3j=I{_2qGqM`XRZx{+|cs+$>H*jxBH#h8Ycd2 zTW-nP{9a2g_BP}HPH(OIdb(Sbab&=#r#fri20P|?(!hFOv6uDDmC3sJP{o&5d?WUz z(VRGD$2O$Z)`bhZFt67s@v32G7Cy{<4rrtDm5%vEX7VL$fd`7AHd!$(Xsn`&`2}oL z)*E8lGxvF;k5Uvnii(ZOna<#T{OzP-hHR);*_5Jyi+q};D7g)$`FyhzID}RyW~@xe zAjT=m8wPEELVMB9GX0=u(sp8sl5A(0{>a$DCT%Bn@|kIeUugdaWPq&FE%J~M=Wna! 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