434766 plusalpha
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dev-0/out.tsv
15502
dev-0/out.tsv
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run.ipynb
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run.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from collections import defaultdict, Counter\n",
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"from nltk import trigrams, word_tokenize\n",
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"import csv\n",
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"import regex as re\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import time\n",
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"\n",
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"in_file = 'train/in.tsv.xz'\n",
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"out_file = 'train/expected.tsv'\n",
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"\n",
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"X_train = pd.read_csv(in_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=30000, on_bad_lines='skip')\n",
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"Y_train = pd.read_csv(out_file, sep='\\t', header=None, quoting=csv.QUOTE_NONE, nrows=30000, on_bad_lines='skip')\n",
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"\n",
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"X_train = X_train[[6, 7]]\n",
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"X_train = pd.concat([X_train, Y_train], axis=1)\n",
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"X_train['row'] = X_train[6] + X_train[0] + X_train[7]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train(X_train, Y_train, alpha):\n",
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" model = defaultdict(lambda: defaultdict(lambda: 0))\n",
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" vocabulary = set()\n",
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" for _, (_, row) in enumerate(X_train.iterrows()):\n",
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" text = preprocess(str(row['row']))\n",
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" words = word_tokenize(text)\n",
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" for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):\n",
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" if w1 and w2 and w3:\n",
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" model[(w1, w3)][w2] += 1\n",
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" vocabulary.add(w1)\n",
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" vocabulary.add(w2)\n",
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" vocabulary.add(w3)\n",
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"\n",
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" for _, w13 in enumerate(model):\n",
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" count = float(sum(model[w13].values()))\n",
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" denominator = count + alpha * len(vocabulary)\n",
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" for w2 in model[w13]:\n",
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" nominator = model[w13][w2] + alpha\n",
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" model[w13][w2] = nominator / denominator \n",
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" return model\n",
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"\n",
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"def preprocess(row):\n",
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" row = re.sub(r'\\p{P}', '', row.lower().replace('-\\\\n', '').replace('\\\\n', ' '))\n",
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" return row\n",
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"\n",
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"def predict_word(before, after, model):\n",
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" output = ''\n",
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" p = 0.0\n",
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" Y_pred = dict(Counter(dict(model[before, after])).most_common(7))\n",
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" for key, value in Y_pred.items():\n",
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" p += value\n",
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" output += f'{key}:{value} '\n",
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" if p == 0.0:\n",
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" output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'\n",
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" return output\n",
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" output += f':{max(1 - p, 0.01)}'\n",
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" return output\n",
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"\n",
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"def word_gap_prediction(file, model):\n",
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" X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip')\n",
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" with open(f'{file}/out.tsv', 'w', encoding='utf-8') as output_file:\n",
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" for _, row in X_test.iterrows():\n",
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" before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))\n",
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" if len(before) < 2 or len(after) < 2:\n",
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" output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'\n",
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" else:\n",
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" output = predict_word(before[-1], after[0],model)\n",
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" output_file.write(output + '\\n')\n",
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" \n",
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"def alpha_tuning(alphas):\n",
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" for alpha in alphas:\n",
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" model = train(X_train, Y_train, alpha)\n",
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" word_gap_prediction('dev-0',model)\n",
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" time.sleep(10)\n",
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" print(\"Alpha = \",alpha)\n",
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" print(\"dev-0 score\")\n",
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" !./geval -t dev-0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"alphas = np.round(np.arange(0.1, 0.6, 0.1).tolist(),2)\n",
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"alphas2 = np.round(alphas * 0.01,3)\n",
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"alphas3 = np.round(alphas * 0.001,4)\n",
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"alphas4 = np.round(alphas * 0.0001,5)\n",
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"alphas5 = np.round(alphas * 0.00001,6)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Alpha = 0.1\n",
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"dev-0 score\n",
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"789.71\n",
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"Alpha = 0.2\n",
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"dev-0 score\n",
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"819.57\n",
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"Alpha = 0.3\n",
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"dev-0 score\n",
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"833.52\n",
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"Alpha = 0.4\n",
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"dev-0 score\n",
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"841.93\n",
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"Alpha = 0.5\n",
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"dev-0 score\n",
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"847.66\n"
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]
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}
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],
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"source": [
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"alpha_tuning(alphas)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Alpha = 0.001\n",
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"dev-0 score\n",
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"472.05\n",
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"Alpha = 0.002\n",
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"dev-0 score\n",
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"519.17\n",
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"Alpha = 0.003\n",
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"dev-0 score\n",
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"548.93\n",
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"Alpha = 0.004\n",
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"dev-0 score\n",
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"570.68\n",
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"Alpha = 0.005\n",
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"dev-0 score\n",
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"587.76\n"
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]
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}
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],
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"source": [
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"alpha_tuning(alphas2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Alpha = 0.0001\n",
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"dev-0 score\n",
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"367.28\n",
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"Alpha = 0.0002\n",
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"dev-0 score\n",
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"389.51\n",
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"Alpha = 0.0003\n",
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"dev-0 score\n",
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"406.30\n",
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"Alpha = 0.0004\n",
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"dev-0 score\n",
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"419.89\n",
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"Alpha = 0.0005\n",
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"dev-0 score\n",
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"431.39\n"
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]
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}
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],
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"source": [
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"alpha_tuning(alphas3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Alpha = 1e-05\n",
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"dev-0 score\n",
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"350.33\n",
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"Alpha = 2e-05\n",
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"dev-0 score\n",
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"346.35\n",
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"Alpha = 3e-05\n",
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"dev-0 score\n",
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"347.66\n",
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"Alpha = 4e-05\n",
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"dev-0 score\n",
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"350.20\n",
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"Alpha = 5e-05\n",
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"dev-0 score\n",
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"353.09\n"
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]
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}
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],
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"source": [
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"alpha_tuning(alphas4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Alpha = 1e-06\n",
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"dev-0 score\n",
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"422.25\n",
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"Alpha = 2e-06\n",
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"dev-0 score\n",
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"390.96\n",
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"Alpha = 3e-06\n",
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"dev-0 score\n",
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"376.49\n",
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"Alpha = 4e-06\n",
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"dev-0 score\n",
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"367.96\n",
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"Alpha = 5e-06\n",
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"dev-0 score\n",
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"362.34\n"
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]
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}
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],
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"source": [
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"alpha_tuning(alphas5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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38
run.py
38
run.py
@ -3,39 +3,45 @@ from nltk import trigrams, word_tokenize
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import csv
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import regex as re
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import pandas as pd
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import numpy as np
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import time
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in_file = 'train/in.tsv.xz'
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out_file = 'train/expected.tsv'
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X_train = pd.read_csv(in_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
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Y_train = pd.read_csv(out_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=10000, error_bad_lines=False)
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X_train = pd.read_csv(in_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=70000, on_bad_lines='skip')
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Y_train = pd.read_csv(out_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=70000, on_bad_lines='skip')
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X_train = X_train[[6, 7]]
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X_train = pd.concat([X_train, Y_train], axis=1)
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X_train['row'] = X_train[6] + X_train[0] + X_train[7]
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def train(X_train, Y_train):
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def train(X_train, Y_train, alpha):
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model = defaultdict(lambda: defaultdict(lambda: 0))
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vocabulary = set()
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for _, (_, row) in enumerate(X_train.iterrows()):
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text = preprocess(str(row['row']))
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words = word_tokenize(text)
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for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
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if w1 and w2 and w3:
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model[(w1, w3)][w2] += 1
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vocabulary.add(w1)
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vocabulary.add(w2)
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vocabulary.add(w3)
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for _, w13 in enumerate(model):
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count = sum(model[w13].values())
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count = float(sum(model[w13].values()))
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denominator = count + alpha * len(vocabulary)
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for w2 in model[w13]:
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model[w13][w2] += 0.25
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model[w13][w2] /= float(count + 0.25 + len(w2))
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nominator = model[w13][w2] + alpha
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model[w13][w2] = nominator / denominator
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return model
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def preprocess(row):
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row = re.sub(r'\p{P}', '', row.lower().replace('-\\n', '').replace('\\n', ' '))
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return row
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def predict_word(before, after):
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def predict_word(before, after, model):
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output = ''
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p = 0.0
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Y_pred = dict(Counter(dict(model[before, after])).most_common(7))
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@ -48,17 +54,19 @@ def predict_word(before, after):
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output += f':{max(1 - p, 0.01)}'
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return output
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def word_gap_prediction(file):
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X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', header=None, quoting=csv.QUOTE_NONE, error_bad_lines=False)
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def word_gap_prediction(file, model):
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X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', header=None, quoting=csv.QUOTE_NONE, on_bad_lines='skip')
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with open(f'{file}/out.tsv', 'w', encoding='utf-8') as output_file:
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for _, row in X_test.iterrows():
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before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
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if len(before) < 3 or len(after) < 3:
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if len(before) < 2 or len(after) < 2:
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output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'
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else:
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output = predict_word(before[-1], after[0])
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output = predict_word(before[-1], after[0],model)
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output_file.write(output + '\n')
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model = train(X_train, Y_train)
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word_gap_prediction('dev-0')
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word_gap_prediction('test-A')
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alpha = 0.00002
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model = train(X_train, Y_train, alpha)
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word_gap_prediction('dev-0', model)
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word_gap_prediction('test-A',model)
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10302
test-A/out.tsv
10302
test-A/out.tsv
File diff suppressed because it is too large
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Reference in New Issue
Block a user