From 3f225003a043b01f951bc91e049c4899d22fdf3b Mon Sep 17 00:00:00 2001 From: Tomasz Grzybowski Date: Sat, 2 Jul 2022 12:26:40 +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 --- kenlm.ipynb | 283 +++ kenlm.py | 117 + n-gram.py | 78 + neural_network.ipynb | 5298 ++++++++++++++++++++++++++++++++++++++++++ neural_network.py | 192 ++ 5 files changed, 5968 insertions(+) create mode 100644 kenlm.ipynb create mode 100644 kenlm.py create mode 100644 n-gram.py create mode 100644 neural_network.ipynb create mode 100644 neural_network.py diff --git a/kenlm.ipynb b/kenlm.ipynb new file mode 100644 index 0000000..6f57391 --- /dev/null +++ b/kenlm.ipynb @@ -0,0 +1,283 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "kenlm.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GQG8KfEo5BwV", + "outputId": "7899949c-5bc3-4d13-acb2-88aa47f46655" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install https://github.com/kpu/kenlm/archive/master.zip" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GsoWSBmH5DT3", + "outputId": "f67d798f-54f8-4c90-bdef-590424b49dd5" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting https://github.com/kpu/kenlm/archive/master.zip\n", + " Using cached https://github.com/kpu/kenlm/archive/master.zip (550 kB)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install english_words" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rwNPsafM6KSb", + "outputId": "b4e21df6-cf55-4f7a-843c-a87f1acc6082" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting english_words\n", + " Downloading english-words-1.1.0.tar.gz (1.1 MB)\n", + "\u001b[K |████████████████████████████████| 1.1 MB 5.4 MB/s \n", + "\u001b[?25hBuilding wheels for collected packages: english-words\n", + " Building wheel for english-words (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for english-words: filename=english_words-1.1.0-py3-none-any.whl size=1106680 sha256=9959ed5d02a4c06063019ede18eebf1ef1be2562a62aa85f86a13d6a3fe1e34b\n", + " Stored in directory: /root/.cache/pip/wheels/25/3d/4c/12a119ce90b46b4f90f9ddf41d719ecabb40faec6103379fc8\n", + "Successfully built english-words\n", + "Installing collected packages: english-words\n", + "Successfully installed english-words-1.1.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import nltk\n", + "nltk.download(\"punkt\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "02yP2lJ9_4dT", + "outputId": "5de6ad9b-41e0-4577-9af3-4ceefe85f3d0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + " lmplz_command = f\"{KENLM_LMPLZ_PATH} -o 4 < train.txt > model.arpa\"\n", + " build_binary_command = f\"{KENLM_BUILD_BINARY_PATH} model.arpa model.binary\"\n", + " os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, lmplz_command))\n", + " os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, build_binary_command))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YC397rhc7-CW", + "outputId": "53adb185-9cbf-4ace-8556-7335776313d6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "256" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tt_ucItY484I", + "outputId": "e2839c64-b3b9-42fb-c2cf-dc7dc60ad8ab" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:51: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version.\n", + "\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import csv\n", + "import regex as re\n", + "import kenlm\n", + "from english_words import english_words_alpha_set\n", + "from nltk import word_tokenize\n", + "from math import log10\n", + "from pathlib import Path\n", + "import os\n", + "import numpy as np\n", + "\n", + "\n", + "KENLM_BUILD_PATH = Path(\"gdrive/My Drive/gonito/kenlm/build\")\n", + "KENLM_LMPLZ_PATH = KENLM_BUILD_PATH / \"bin\" / \"lmplz\"\n", + "KENLM_BUILD_BINARY_PATH = KENLM_BUILD_PATH / \"bin\" / \"build_binary\"\n", + "SUDO_PASSWORD = \"\"\n", + "PREDICTION = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'\n", + "\n", + "\n", + "def clean(text):\n", + " text = str(text).lower().replace(\"-\\\\n\", \"\").replace(\"\\\\n\", \" \")\n", + " return re.sub(r\"\\p{P}\", \"\", text)\n", + "\n", + "\n", + "def create_train_data():\n", + " data = pd.read_csv(\"gdrive/My Drive/gonito/train/in.tsv.xz\", sep=\"\\t\", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=50000)\n", + " train_labels = pd.read_csv(\"gdrive/My Drive/gonito/train/expected.tsv\", sep=\"\\t\", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=50000)\n", + "\n", + " train_data = data[[6, 7]]\n", + " train_data = pd.concat([train_data, train_labels], axis=1)\n", + "\n", + " return train_data[6] + train_data[0] + train_data[7]\n", + "\n", + "\n", + "def create_train_file(filename=\"gdrive/My Drive/gonito/train.txt\"):\n", + " with open(filename, \"w\") as f:\n", + " for line in create_train_data():\n", + " f.write(clean(line) + \"\\n\")\n", + " \n", + "\n", + "def train_model():\n", + " lmplz_command = f\"{KENLM_LMPLZ_PATH} -o 4 < train.txt > model.arpa\"\n", + " build_binary_command = f\"{KENLM_BUILD_BINARY_PATH} model.arpa model.binary\"\n", + " os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, lmplz_command))\n", + " os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, build_binary_command))\n", + " \n", + "\n", + "def softmax(x):\n", + " e_x = np.exp(x - np.max(x))\n", + " return e_x / e_x.sum(axis=0)\n", + "\n", + "def predict(model, before, after):\n", + " best_scores = []\n", + " for word in english_words_alpha_set:\n", + " text = ' '.join([before, word, after])\n", + " text_score = model.score(text, bos=False, eos=False)\n", + " if len(best_scores) < 12:\n", + " best_scores.append((word, text_score))\n", + " else:\n", + " worst_score = None\n", + " for score in best_scores:\n", + " if not worst_score:\n", + " worst_score = score\n", + " else:\n", + " if worst_score[1] > score[1]:\n", + " worst_score = score\n", + " if worst_score[1] < text_score:\n", + " best_scores.remove(worst_score)\n", + " best_scores.append((word, text_score))\n", + " probs = sorted(best_scores, key=lambda tup: tup[1], reverse=True)\n", + " pred_str = ''\n", + " for word, prob in probs:\n", + " pred_str += f'{word}:{prob} '\n", + " pred_str += f':{log10(0.99)}'\n", + " return pred_str\n", + "\n", + "def make_prediction(model, path, result_path):\n", + " data = pd.read_csv(path, sep='\\t', header=None, quoting=csv.QUOTE_NONE)\n", + " with open(result_path, 'w', encoding='utf-8') as file_out:\n", + " for _, row in data.iterrows():\n", + " before, after = word_tokenize(clean(str(row[6]))), word_tokenize(clean(str(row[7])))\n", + " if len(before) < 2 or len(after) < 2:\n", + " pred = PREDICTION\n", + " else:\n", + " pred = predict(model, before[-1], after[0])\n", + " file_out.write(pred + '\\n')\n", + "\n", + "\n", + "create_train_file()\n", + "train_model()\n", + "model = kenlm.Model('gdrive/My Drive/gonito/model.binary')\n", + "make_prediction(model, \"gdrive/My Drive/gonito/dev-0/in.tsv.xz\", \"gdrive/My Drive/gonito/dev-0/out.tsv\")\n", + "make_prediction(model, \"gdrive/My Drive/gonito/test-A/in.tsv.xz\", \"gdrive/My Drive/gonito/test-A/out.tsv\")" + ] + } + ] +} \ No newline at end of file diff --git a/kenlm.py b/kenlm.py new file mode 100644 index 0000000..00aa7e7 --- /dev/null +++ b/kenlm.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +"""kenlm.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1ov9aRonhHahzGcs1BIMjVHEldjHg4yTs +""" + +from google.colab import drive +drive.mount('/content/gdrive') + +!pip install https://github.com/kpu/kenlm/archive/master.zip + +!pip install english_words + +import nltk +nltk.download("punkt") + +lmplz_command = f"{KENLM_LMPLZ_PATH} -o 4 < train.txt > model.arpa" + build_binary_command = f"{KENLM_BUILD_BINARY_PATH} model.arpa model.binary" + os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, lmplz_command)) + os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, build_binary_command)) + +import pandas as pd +import csv +import regex as re +import kenlm +from english_words import english_words_alpha_set +from nltk import word_tokenize +from math import log10 +from pathlib import Path +import os +import numpy as np + + +KENLM_BUILD_PATH = Path("gdrive/My Drive/gonito/kenlm/build") +KENLM_LMPLZ_PATH = KENLM_BUILD_PATH / "bin" / "lmplz" +KENLM_BUILD_BINARY_PATH = KENLM_BUILD_PATH / "bin" / "build_binary" +SUDO_PASSWORD = "" +PREDICTION = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77' + + +def clean(text): + text = str(text).lower().replace("-\\n", "").replace("\\n", " ") + return re.sub(r"\p{P}", "", text) + + +def create_train_data(): + data = pd.read_csv("gdrive/My Drive/gonito/train/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=50000) + train_labels = pd.read_csv("gdrive/My Drive/gonito/train/expected.tsv", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE, nrows=50000) + + train_data = data[[6, 7]] + train_data = pd.concat([train_data, train_labels], axis=1) + + return train_data[6] + train_data[0] + train_data[7] + + +def create_train_file(filename="gdrive/My Drive/gonito/train.txt"): + with open(filename, "w") as f: + for line in create_train_data(): + f.write(clean(line) + "\n") + + +def train_model(): + lmplz_command = f"{KENLM_LMPLZ_PATH} -o 4 < train.txt > model.arpa" + build_binary_command = f"{KENLM_BUILD_BINARY_PATH} model.arpa model.binary" + os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, lmplz_command)) + os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, build_binary_command)) + + +def softmax(x): + e_x = np.exp(x - np.max(x)) + return e_x / e_x.sum(axis=0) + +def predict(model, before, after): + best_scores = [] + for word in english_words_alpha_set: + text = ' '.join([before, word, after]) + text_score = model.score(text, bos=False, eos=False) + if len(best_scores) < 12: + best_scores.append((word, text_score)) + else: + worst_score = None + for score in best_scores: + if not worst_score: + worst_score = score + else: + if worst_score[1] > score[1]: + worst_score = score + if worst_score[1] < text_score: + best_scores.remove(worst_score) + best_scores.append((word, text_score)) + probs = sorted(best_scores, key=lambda tup: tup[1], reverse=True) + pred_str = '' + for word, prob in probs: + pred_str += f'{word}:{prob} ' + pred_str += f':{log10(0.99)}' + return pred_str + +def make_prediction(model, path, result_path): + data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE) + with open(result_path, 'w', encoding='utf-8') as file_out: + for _, row in data.iterrows(): + before, after = word_tokenize(clean(str(row[6]))), word_tokenize(clean(str(row[7]))) + if len(before) < 2 or len(after) < 2: + pred = PREDICTION + else: + pred = predict(model, before[-1], after[0]) + file_out.write(pred + '\n') + + +create_train_file() +train_model() +model = kenlm.Model('gdrive/My Drive/gonito/model.binary') +make_prediction(model, "gdrive/My Drive/gonito/dev-0/in.tsv.xz", "gdrive/My Drive/gonito/dev-0/out.tsv") +make_prediction(model, "gdrive/My Drive/gonito/test-A/in.tsv.xz", "gdrive/My Drive/gonito/test-A/out.tsv") \ No newline at end of file diff --git a/n-gram.py b/n-gram.py new file mode 100644 index 0000000..6e19890 --- /dev/null +++ b/n-gram.py @@ -0,0 +1,78 @@ +import pandas as pd +import csv +import regex as re +from nltk import bigrams, word_tokenize +from collections import Counter, defaultdict +import string +import unicodedata + +DEFAULT_PREDICTION = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1" + +data = pd.read_csv("train/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) +train_labels = pd.read_csv("train/expected.tsv", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) + +train_data = data[[6, 7]] +train_data = pd.concat([train_data, train_labels], axis=1) + +train_data["final"] = train_data[6] + train_data[0] + train_data[7] + +model = defaultdict(lambda: defaultdict(lambda: 0)) + + +def clean(text): + text = str(text).lower().replace("-\\n", "").replace("\\n", " ") + return re.sub(r"\p{P}", "", text) + +for _, row in train_data.iterrows(): + words = word_tokenize(clean(row["final"])) + for w1, w2 in bigrams(words, pad_left=True, pad_right=True): + if w1 and w2: + model[w1][w2] += 1 +for w1 in model: + total_count = float(sum(model[w1].values())) + for w2 in model[w1]: + model[w1][w2] /= total_count + + +def predict(word): + predictions = dict(model[word]) + most_common = dict(Counter(predictions).most_common(5)) + + total_prob = 0.0 + str_prediction = "" + + for word, prob in most_common.items(): + total_prob += prob + str_prediction += f"{word}:{prob} " + + if not total_prob: + return DEFAULT_PREDICTION + + if 1 - total_prob >= 0.01: + str_prediction += f":{1-total_prob}" + else: + str_prediction += f":0.01" + + return str_prediction + + +data = pd.read_csv("dev-0/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) +with open("dev-0/out.tsv", "w", encoding="UTF-8") as file: + for _, row in data.iterrows(): + words = word_tokenize(clean(row[6])) + if len(words) < 3: + prediction = DEFAULT_PREDICTION + else: + prediction = predict(words[-1]) + file.write(prediction + "\n") + + +data = pd.read_csv("test-A/in.tsv.xz", sep="\t", error_bad_lines=False, header=None, quoting=csv.QUOTE_NONE) +with open("test-A/out.tsv", "w", encoding="UTF-8") as file: + for _, row in data.iterrows(): + words = word_tokenize(clean(row[6])) + if len(words) < 3: + prediction = DEFAULT_PREDICTION + else: + prediction = predict(words[-1]) + file.write(prediction + "\n") diff --git a/neural_network.ipynb b/neural_network.ipynb new file mode 100644 index 0000000..3600f89 --- /dev/null +++ b/neural_network.ipynb @@ -0,0 +1,5298 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "neural_network.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ECxQCLFdh2dg", + "outputId": "8402ab56-979b-4a4e-c331-d905cabccd23" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "code", + "source": [ + "root_path = 'gdrive/My Drive/gonito/'" + ], + "metadata": { + "id": "uWXe1O7FjKVZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "torch.cuda.is_available()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Lo2e9lxajQGy", + "outputId": "883c9239-c4fb-4607-85ef-6a0cbd6dad3e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "True" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import csv\n", + "torch.cuda.empty_cache()\n", + "from torch.utils.data import DataLoader\n", + "import pandas as pd\n", + "from os.path import exists\n", + "from torchtext.vocab import build_vocab_from_iterator\n", + "import itertools\n", + "import regex as re\n", + "from csv import QUOTE_NONE\n", + "from torch import nn\n", + "\n", + "ENCODING = \"utf-8\"\n", + "\n", + "REP = re.compile(r\"[{}\\[\\]\\&%^$*#\\(\\)@\\t\\n0123456789]+\")\n", + "REM = re.compile(r\"'s|[\\-]\\\\n|\\-\\\\n|\\p{P}\")\n", + "\n", + "def read_csv(fname):\n", + " return pd.read_csv(fname, sep=\"\\t\", on_bad_lines='skip', header=None, quoting=QUOTE_NONE, encoding=ENCODING)\n", + "\n", + "def clean_text(text):\n", + " res = str(text).lower().strip()\n", + " res = res.replace(\"’\", \"'\")\n", + " res = REM.sub(\"\", res)\n", + " res = REP.sub(\" \", res)\n", + " res = res.replace(\"'t\", \" not\")\n", + " res = res.replace(\"'s\", \" is\")\n", + " res = res.replace(\"'ll\", \" will\")\n", + " res = res.replace(\"won't\", \"will not\")\n", + " res = res.replace(\"isn't\", \"is not\")\n", + " res = res.replace(\"aren't\", \"are not\")\n", + " res = res.replace(\"'ve'\", \"have\")\n", + " return res.replace(\"'m\", \" am\")\n", + "\n", + "def get_words_from_line(line, specials = True):\n", + " line = line.rstrip()\n", + " if specials:\n", + " yield ''\n", + " for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n", + " yield m.group(0).lower()\n", + " if specials:\n", + " yield ''\n", + "\n", + "\n", + "def get_word_lines_from_data(d):\n", + " for line in d:\n", + " yield get_words_from_line(line)\n", + "\n", + "\n", + "\n", + "\n", + "class Bigrams(torch.utils.data.IterableDataset):\n", + " def __init__(self, data, vocabulary_size):\n", + " self.vocab = build_vocab_from_iterator(\n", + " get_word_lines_from_data(data),\n", + " max_tokens = vocabulary_size,\n", + " specials = [''])\n", + " self.vocab.set_default_index(self.vocab[''])\n", + " self.vocabulary_size = vocabulary_size\n", + " self.data = data\n", + "\n", + " @staticmethod\n", + " def look_ahead_iterator(gen):\n", + " w1 = None\n", + " for item in gen:\n", + " if w1 is not None:\n", + " yield (w1, item)\n", + " w1 = item\n", + "\n", + " def __iter__(self):\n", + " return self.look_ahead_iterator(\n", + " (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_data(self.data))))\n", + "\n", + "class SimpleBigramNeuralLanguageModel(torch.nn.Module):\n", + " def __init__(self, vocabulary_size, embedding_size):\n", + " super(SimpleBigramNeuralLanguageModel, self).__init__()\n", + " self.model = nn.Sequential(\n", + " nn.Embedding(vocabulary_size, embedding_size),\n", + " nn.Linear(embedding_size, vocabulary_size),\n", + " nn.Softmax(),\n", + " )\n", + "\n", + " def forward(self, x):\n", + " return self.model(x)\n", + "\n", + "\n", + "\n", + "\n", + "data = read_csv(\"gdrive/My Drive/gonito/train/in.tsv.xz\")\n", + "train_words = read_csv(\"gdrive/My Drive/gonito/train/expected.tsv\")\n", + "\n", + "train_data = data[[6, 7]]\n", + "train_data = pd.concat([train_data, train_words], axis=1)\n", + "train_data = train_data[6] + train_data[0] + train_data[7]\n", + "train_data = train_data.apply(clean_text)\n", + "\n", + "vocab_size = 30000\n", + "embed_size = 150\n", + "\n", + "train_dataset = Bigrams(train_data, vocab_size)\n", + "\n", + "\n", + "\n", + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n", + "print(device)\n", + "if(not exists('model1.bin')):\n", + " data = DataLoader(train_dataset, batch_size=8000)\n", + " optimizer = torch.optim.Adam(model.parameters())\n", + " criterion = torch.nn.NLLLoss()\n", + "\n", + " model.train()\n", + " step = 0\n", + " for i in range(2):\n", + " print(f\"EPOCH {i}=========================\")\n", + " for x, y in data:\n", + " x = x.to(device)\n", + " y = y.to(device)\n", + " optimizer.zero_grad()\n", + " ypredicted = model(x)\n", + " loss = criterion(torch.log(ypredicted), y)\n", + " if step % 100 == 0:\n", + " print(step, loss)\n", + " step += 1\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " torch.save(model.state_dict(), 'model1.bin')\n", + "else:\n", + " print(\"Loading model1\")\n", + " model.load_state_dict(torch.load('model1.bin'))\n", + "\n", + "\n", + "\n", + "vocab = train_dataset.vocab\n", + "\n", + "def predict(tokens):\n", + " ixs = torch.tensor(vocab.forward(tokens)).to(device)\n", + " out = model(ixs)\n", + " top = torch.topk(out[0], 8)\n", + " top_indices = top.indices.tolist()\n", + " top_probs = top.values.tolist()\n", + " top_words = vocab.lookup_tokens(top_indices)\n", + " result = \"\"\n", + " for word, prob in list(zip(top_words, top_probs)):\n", + " result += f\"{word}:{prob} \"\n", + " # result += f':0.01'\n", + " return result\n", + "\n", + "DEFAULT_PREDICTION = \"a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1\"\n", + "\n", + "def predict_file(result_path, data):\n", + " with open(result_path, \"w+\", encoding=\"UTF-8\") as f:\n", + " for row in data:\n", + " result = {}\n", + " before = None\n", + " for before in get_words_from_line(clean_text(str(row)), False):\n", + " pass\n", + " before = [before]\n", + " print(before)\n", + " if(len(before) < 1):\n", + " result = DEFAULT_PREDICTION\n", + " else:\n", + " result = predict(before)\n", + " result = result.strip()\n", + " f.write(result + \"\\n\")\n", + " print(result)\n", + "\n", + "dev_data = pd.read_csv(\"gdrive/My Drive/gonito/dev-0/in.tsv.xz\", sep='\\t', header=None, quoting=csv.QUOTE_NONE)[6]\n", + "dev_data = dev_data.apply(clean_text)\n", + "predict_file(\"gdrive/My Drive/gonito/dev-0/out.tsv\", dev_data)\n", + "\n", + "test_data = pd.read_csv(\"gdrive/My Drive/gonito/test-A/in.tsv.xz\", sep='\\t', header=None, quoting=csv.QUOTE_NONE)[6]\n", + "test_data = test_data.apply(clean_text)\n", + "predict_file(\"gdrive/My Drive/gonito/test-A/out.tsv\", test_data)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GDsznRxrjNSi", + "outputId": "036e6f73-c657-4eaa-b13c-8af613572de7" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "cuda\n", + "EPOCH 0=========================\n", + "0 tensor(10.4703, device='cuda:0', grad_fn=)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/container.py:141: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n", + " input = module(input)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43mStrumieniowane dane wyjściowe obcięte do 5000 ostatnich wierszy.\u001b[0m\n", + "['employed']\n", + "in:0.18340028822422028 by:0.09708617627620697 :0.07454902678728104 to:0.057051483541727066 as:0.04201669245958328 at:0.03614005073904991 and:0.033467430621385574 on:0.02761789597570896\n", + "['the']\n", + ":0.22579598426818848 same:0.007832455448806286 state:0.007307393476366997 first:0.006077633239328861 city:0.005409764591604471 people:0.0050051286816596985 most:0.004963664337992668 united:0.004868470132350922\n", + "['man']\n", + ":0.1252487599849701 who:0.11225556582212448 of:0.04771805554628372 and:0.043321795761585236 in:0.04039822518825531 is:0.02757207676768303 to:0.02686801366508007 was:0.02203497476875782\n", + "['acre']\n", + ":0.1225854679942131 of:0.0909235030412674 and:0.07578513026237488 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r:0.012387360446155071 a:0.009343990124762058 b:0.00762265594676137 w:0.007346999365836382 t:0.006951986812055111 c:0.006841892842203379\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/neural_network.py b/neural_network.py new file mode 100644 index 0000000..d5309b8 --- /dev/null +++ b/neural_network.py @@ -0,0 +1,192 @@ +# -*- coding: utf-8 -*- +"""neural_network.ipynb + +Automatically generated by Colaboratory. + +Original file is located at + https://colab.research.google.com/drive/1c4GrMHn9isBMqMEfYsJftgWbWqqLO1Np +""" + +from google.colab import drive +drive.mount('/content/gdrive') + +root_path = 'gdrive/My Drive/gonito/' + +import torch +torch.cuda.is_available() + +import torch +import csv +torch.cuda.empty_cache() +from torch.utils.data import DataLoader +import pandas as pd +from os.path import exists +from torchtext.vocab import build_vocab_from_iterator +import itertools +import regex as re +from csv import QUOTE_NONE +from torch import nn + +ENCODING = "utf-8" + +REP = re.compile(r"[{}\[\]\&%^$*#\(\)@\t\n0123456789]+") +REM = re.compile(r"'s|[\-]\\n|\-\\n|\p{P}") + +def read_csv(fname): + return pd.read_csv(fname, sep="\t", on_bad_lines='skip', header=None, quoting=QUOTE_NONE, encoding=ENCODING) + +def clean_text(text): + res = str(text).lower().strip() + res = res.replace("’", "'") + res = REM.sub("", res) + res = REP.sub(" ", res) + res = res.replace("'t", " not") + res = res.replace("'s", " is") + res = res.replace("'ll", " will") + res = res.replace("won't", "will not") + res = res.replace("isn't", "is not") + res = res.replace("aren't", "are not") + res = res.replace("'ve'", "have") + return res.replace("'m", " am") + +def get_words_from_line(line, specials = True): + line = line.rstrip() + if specials: + yield '' + for m in re.finditer(r'[\p{L}0-9\*]+|\p{P}+', line): + yield m.group(0).lower() + if specials: + yield '' + + +def get_word_lines_from_data(d): + for line in d: + yield get_words_from_line(line) + + + + +class Bigrams(torch.utils.data.IterableDataset): + def __init__(self, data, vocabulary_size): + self.vocab = build_vocab_from_iterator( + get_word_lines_from_data(data), + max_tokens = vocabulary_size, + specials = ['']) + self.vocab.set_default_index(self.vocab['']) + self.vocabulary_size = vocabulary_size + self.data = data + + @staticmethod + def look_ahead_iterator(gen): + w1 = None + for item in gen: + if w1 is not None: + yield (w1, item) + w1 = item + + def __iter__(self): + return self.look_ahead_iterator( + (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_data(self.data)))) + +class SimpleBigramNeuralLanguageModel(torch.nn.Module): + def __init__(self, vocabulary_size, embedding_size): + super(SimpleBigramNeuralLanguageModel, self).__init__() + self.model = nn.Sequential( + nn.Embedding(vocabulary_size, embedding_size), + nn.Linear(embedding_size, vocabulary_size), + nn.Softmax(), + ) + + def forward(self, x): + return self.model(x) + + + + +data = read_csv("gdrive/My Drive/gonito/train/in.tsv.xz") +train_words = read_csv("gdrive/My Drive/gonito/train/expected.tsv") + +train_data = data[[6, 7]] +train_data = pd.concat([train_data, train_words], axis=1) +train_data = train_data[6] + train_data[0] + train_data[7] +train_data = train_data.apply(clean_text) + +vocab_size = 30000 +embed_size = 150 + +train_dataset = Bigrams(train_data, vocab_size) + + + +device = 'cuda' if torch.cuda.is_available() else 'cpu' +model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device) +print(device) +if(not exists('model1.bin')): + data = DataLoader(train_dataset, batch_size=8000) + optimizer = torch.optim.Adam(model.parameters()) + criterion = torch.nn.NLLLoss() + + model.train() + step = 0 + for i in range(2): + print(f"EPOCH {i}=========================") + for x, y in data: + x = x.to(device) + y = y.to(device) + optimizer.zero_grad() + ypredicted = model(x) + loss = criterion(torch.log(ypredicted), y) + if step % 100 == 0: + print(step, loss) + step += 1 + loss.backward() + optimizer.step() + + torch.save(model.state_dict(), 'model1.bin') +else: + print("Loading model1") + model.load_state_dict(torch.load('model1.bin')) + + + +vocab = train_dataset.vocab + +def predict(tokens): + ixs = torch.tensor(vocab.forward(tokens)).to(device) + out = model(ixs) + top = torch.topk(out[0], 8) + top_indices = top.indices.tolist() + top_probs = top.values.tolist() + top_words = vocab.lookup_tokens(top_indices) + result = "" + for word, prob in list(zip(top_words, top_probs)): + result += f"{word}:{prob} " + # result += f':0.01' + return result + +DEFAULT_PREDICTION = "a:0.2 the:0.2 to:0.2 of:0.1 and:0.1 of:0.1 :0.1" + +def predict_file(result_path, data): + with open(result_path, "w+", encoding="UTF-8") as f: + for row in data: + result = {} + before = None + for before in get_words_from_line(clean_text(str(row)), False): + pass + before = [before] + print(before) + if(len(before) < 1): + result = DEFAULT_PREDICTION + else: + result = predict(before) + result = result.strip() + f.write(result + "\n") + print(result) + +dev_data = pd.read_csv("gdrive/My Drive/gonito/dev-0/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6] +dev_data = dev_data.apply(clean_text) +predict_file("gdrive/My Drive/gonito/dev-0/out.tsv", dev_data) + +test_data = pd.read_csv("gdrive/My Drive/gonito/test-A/in.tsv.xz", sep='\t', header=None, quoting=csv.QUOTE_NONE)[6] +test_data = test_data.apply(clean_text) +predict_file("gdrive/My Drive/gonito/test-A/out.tsv", test_data) \ No newline at end of file