From 9f231f2afc2648954c8bec03561884e515ee660e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Zofia=20Fra=C5=9B?= Date: Tue, 22 Jun 2021 16:04:08 +0200 Subject: [PATCH] =?UTF-8?q?Prze=C5=9Blij=20pliki=20do=20''?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- rnn_fras-Copy1.ipynb | 546 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 546 insertions(+) create mode 100644 rnn_fras-Copy1.ipynb diff --git a/rnn_fras-Copy1.ipynb b/rnn_fras-Copy1.ipynb new file mode 100644 index 0000000..ef6e1d0 --- /dev/null +++ b/rnn_fras-Copy1.ipynb @@ -0,0 +1,546 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Zadanie domowe\n", + "\n", + "\n", + "- sklonować repozytorium https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003\n", + "- stworzyć model seq labelling bazujący na sieci neuronowej opisanej w punkcie niżej (można bazować na tym jupyterze lub nie).\n", + "- model sieci to GRU (o dowolnych parametrach) + CRF w pytorchu korzystając z modułu CRF z poprzednich zajęć- - stworzyć predykcje w plikach dev-0/out.tsv oraz test-A/out.tsv\n", + "- wynik fscore sprawdzony za pomocą narzędzia geval (patrz poprzednie zadanie) powinien wynosić conajmniej 0.65\n", + "- proszę umieścić predykcję oraz skrypty generujące (w postaci tekstowej a nie jupyter) w repo, a w MS TEAMS umieścić link do swojego repo\n", + "termin 22.06, 60 punktów, za najlepszy wynik- 100 punktów\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import torch\n", + "from torchtext.vocab import Vocab\n", + "from collections import Counter\n", + "from tqdm.notebook import tqdm\n", + "import lzma\n", + "import itertools\n", + "from torchcrf import CRF" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def read_data(filename):\n", + " all_data = lzma.open(filename).read().decode('UTF-8').split('\\n')\n", + " return [line.split('\\t') for line in all_data][:-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def data_process(dt):\n", + " return [torch.tensor([vocab['']] + [vocab[token] for token in document] + [vocab['']], dtype = torch.long) for document in dt]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def labels_process(dt):\n", + " return [ torch.tensor([0] + document + [0], dtype = torch.long) for document in dt]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def build_vocab(dataset):\n", + " counter = Counter()\n", + " for document in dataset:\n", + " counter.update(document)\n", + " return Vocab(counter, specials=['', '', '', ''])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "train_data = read_data('train/train.tsv.xz')\n", + "\n", + "tokens, ner_tags = [], []\n", + "for i in train_data:\n", + " ner_tags.append(i[0].split())\n", + " tokens.append(i[1].split())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "vocab = build_vocab(tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "train_tokens_ids = data_process(tokens)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['B-LOC', 'B-MISC', 'B-ORG', 'B-PER', 'I-LOC', 'I-MISC', 'I-ORG', 'I-PER', 'O']\n" + ] + } + ], + "source": [ + "ner_tags_set = list(set(itertools.chain(*ner_tags)))\n", + "ner_tags_set.sort()\n", + "print(ner_tags_set)\n", + "train_labels = labels_process([[ner_tags_set.index(token) for token in doc] for doc in ner_tags])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "num_tags = max([max(x) for x in train_labels]) + 1 " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "class GRU(torch.nn.Module):\n", + "\n", + " def __init__(self):\n", + " super(GRU, self).__init__()\n", + " self.emb = torch.nn.Embedding(len(vocab.itos),100)\n", + " self.dropout = torch.nn.Dropout(0.2)\n", + " self.rec = torch.nn.GRU(100, 256, 2, batch_first = True, bidirectional = True)\n", + " self.fc1 = torch.nn.Linear(2* 256 , 9)\n", + " \n", + " def forward(self, x):\n", + " emb = torch.relu(self.emb(x))\n", + " emb = self.dropout(emb)\n", + " gru_output, h_n = self.rec(emb)\n", + " out_weights = self.fc1(gru_output)\n", + " return out_weights" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def get_scores(y_true, y_pred):\n", + " acc_score = 0\n", + " tp = 0\n", + " fp = 0\n", + " selected_items = 0\n", + " relevant_items = 0 \n", + "\n", + " for p,t in zip(y_pred, y_true):\n", + " if p == t:\n", + " acc_score +=1\n", + "\n", + " if p > 0 and p == t:\n", + " tp +=1\n", + "\n", + " if p > 0:\n", + " selected_items += 1\n", + "\n", + " if t > 0 :\n", + " relevant_items +=1\n", + " \n", + " if selected_items == 0:\n", + " precision = 1.0\n", + " else:\n", + " precision = tp / selected_items\n", + " \n", + " if relevant_items == 0:\n", + " recall = 1.0\n", + " else:\n", + " recall = tp / relevant_items\n", + " \n", + " if precision + recall == 0.0 :\n", + " f1 = 0.0\n", + " else:\n", + " f1 = 2* precision * recall / (precision + recall)\n", + "\n", + " return precision, recall, f1" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "def eval_model(dataset_tokens, dataset_labels, model):\n", + " Y_true = []\n", + " Y_pred = []\n", + " gru.eval()\n", + " crf.eval()\n", + " for i in tqdm(range(len(dataset_labels))):\n", + " batch_tokens = dataset_tokens[i]\n", + " tags = list(dataset_labels[i].numpy())\n", + " emissions = ff(batch_tokens).unsqueeze(1)\n", + " Y_pred += crf.decode(emissions)[0]\n", + " Y_true += tags\n", + " return get_scores(Y_true, Y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "gru = GRU()\n", + "crf = CRF(num_tags)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "params = list(gru.parameters()) + list(crf.parameters())\n", + "criterion = torch.nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(params)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_EPOCHS = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "946d7c19bfdd4671a7c4f5fbff7cc735", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=945.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "ename": "ValueError", + "evalue": "emissions must have dimension of 3, got 4", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mpredicted_tags\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgru\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_tokens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mcrf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_tags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1049\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1050\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1051\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1052\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.8/site-packages/torchcrf/__init__.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, emissions, tags, mask, reduction)\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0mreduction\u001b[0m \u001b[0;32mis\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mnone\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0motherwise\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \"\"\"\n\u001b[0;32m---> 90\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memissions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 91\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreduction\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sum'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'token_mean'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'invalid reduction: {reduction}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.8/site-packages/torchcrf/__init__.py\u001b[0m in \u001b[0;36m_validate\u001b[0;34m(self, emissions, tags, mask)\u001b[0m\n\u001b[1;32m 145\u001b[0m mask: Optional[torch.ByteTensor] = None) -> None:\n\u001b[1;32m 146\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0memissions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'emissions must have dimension of 3, got {emissions.dim()}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0memissions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_tags\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m raise ValueError(\n", + "\u001b[0;31mValueError\u001b[0m: emissions must have dimension of 3, got 4" + ] + } + ], + "source": [ + "for i in range(NUM_EPOCHS):\n", + " gru.train()\n", + " crf.train()\n", + " for i in tqdm(range(len(train_labels))):\n", + " batch_tokens = train_tokens_ids[i].unsqueeze(0)\n", + " tags = train_labels[i].unsqueeze(1)\n", + " predicted_tags = gru(batch_tokens)\n", + " optimizer.zero_grad()\n", + " loss = -crf(predicted_tags.unsqueeze(1),tags.squeeze(1))\n", + " loss.backward()\n", + " optimizer.step()\n", + " gru.eval()\n", + " crf.eval()\n", + " print(eval_model(train_tokens_ids, train_labels, gru))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## dev-0" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "def predict_labels(dataset_tokens, model):\n", + " Y_true = []\n", + " Y_pred = []\n", + " result = []\n", + " for i in tqdm(range(len(dataset_tokens))):\n", + " batch_tokens = dataset_tokens[i].unsqueeze(0)\n", + " Y_batch_pred_weights = model(batch_tokens).squeeze(0)\n", + " Y_batch_pred = torch.argmax(Y_batch_pred_weights,1)\n", + " Y_pred += list(Y_batch_pred.numpy())\n", + " result += [list(Y_batch_pred.numpy())]\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "with open('dev-0/in.tsv', \"r\", encoding=\"utf-8\") as f:\n", + " dev_0_data = [line.rstrip() for line in f]\n", + " \n", + "dev_0_data = [i.split() for i in dev_0_data]\n", + "dev_0_tokens_ids = data_process(dev_0_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "with open('dev-0/expected.tsv', \"r\", encoding=\"utf-8\") as f:\n", + " dev_0_labels = [line.rstrip() for line in f]\n", + " \n", + "dev_0_labels = [i.split() for i in dev_0_labels]\n", + "dev_0_labels = labels_process([[ner_tags_set.index(token) for token in doc] for doc in dev_0_labels])" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2e335038c15f4e68bbbd184e5f8dded2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=215.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "tmp = predict_labels(dev_0_tokens_ids, gru)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "r = [[ner_tags_set[i] for i in tmp2] for tmp2 in tmp]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "# for doc in r:\n", + "# if doc[0] != 'O':\n", + "# doc[0] = 'B' + doc[0][1:]\n", + "# for i in range(len(doc))[:-1]:\n", + "# if doc[i] == 'O':\n", + "# if doc[i + 1] != 'O':\n", + "# doc[i + 1] = 'B' + doc[i + 1][1:]\n", + "# elif doc[i + 1] != 'O':\n", + "# if doc[i][1:] == doc[i + 1][1:]:\n", + "# doc[i + 1] = 'I' + doc[i + 1][1:]\n", + "# else:\n", + "# doc[i + 1] = 'B' + doc[i + 1][1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "f = open(\"dev-0/out.tsv\", \"a\")\n", + "for i in r:\n", + " f.write(' '.join(i) + '\\n')\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## test-A" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "with open('test-A/in.tsv', \"r\", encoding=\"utf-8\") as f:\n", + " test_A_data = [line.rstrip() for line in f]\n", + " \n", + "test_A_data = [i.split() for i in test_A_data]\n", + "test_A_tokens_ids = data_process(test_A_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "83cd31864a29458f81be3d79cf43d1ca", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=215.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "tmp = predict_labels(dev_0_tokens_ids, gru)\n", + "r = [[ner_tags_set[i] for i in tmp2] for tmp2 in tmp]\n", + "for doc in r:\n", + " if doc[0] != 'O':\n", + " doc[0] = 'B' + doc[0][1:]\n", + " for i in range(len(doc))[:-1]:\n", + " if doc[i] == 'O':\n", + " if doc[i + 1] != 'O':\n", + " doc[i + 1] = 'B' + doc[i + 1][1:]\n", + " elif doc[i + 1] != 'O':\n", + " if doc[i][1:] == doc[i + 1][1:]:\n", + " doc[i + 1] = 'I' + doc[i + 1][1:]\n", + " else:\n", + " doc[i + 1] = 'B' + doc[i + 1][1:]" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "f = open(\"test-A/out.tsv\", \"a\")\n", + "for i in r:\n", + " f.write(' '.join(i) + '\\n')\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}