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Files for data deduplication in text for the paper #50

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123 changes: 123 additions & 0 deletions galvasr2/codelabs/create_dataset-process.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from datasketch import MinHash, MinHashLSH, MinHashLSHForest\n",
"from nltk import ngrams\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import itertools\n",
"import random\n",
"\n",
"pd.options.display.float_format = '{:20,.2f}'.format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"df = pd.read_csv('good.csv', error_bad_lines=False)\n",
"df['len_text'] = df['text'].str.split().str.len()\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"texts = list(df['text'].values[0:2000])\n",
"texts = ' '.join(texts).split('.')\n",
"texts = [sentence for sentence in texts if len(sentence.split())>10]\n",
"total_texts = list(df['text'].values)\n",
"def duplicate_function(text):\n",
" number = random.uniform(0, 1)\n",
" if number > 0.75:\n",
" text = text.split('. ')\n",
" for i in range(int(len(text) * 0.1)):\n",
" text.insert(random.randint(0, len(text)), random.choice(texts))\n",
" result = pd.Series(['. '.join(text), 1])\n",
" return result\n",
" else:\n",
" result = pd.Series([random.choice(total_texts), 0])\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"df[['new_text', 'deduplicate']] = df['text'].apply(duplicate_function)\n",
"df['len_text'] = df['text'].str.split().str.len()\n",
"df['len_new_text'] = df['new_text'].str.split().str.len()\n",
"df['diff_similarity'] = np.abs(1 - (df['len_new_text'] / df['len_text'])) * 100\n",
"df = df[df['text'] != df['new_text']]\n",
"df_1 = df[['text', 'deduplicate', 'diff_similarity']]\n",
"df_1[['deduplicate', 'diff_similarity']] = 0\n",
"df_2 = df[df['deduplicate'] == 1][['new_text', 'deduplicate', 'diff_similarity']]\n",
"df_2.rename(columns={'new_text':'text'}, inplace=True)\n",
"df = pd.concat([df_1, df_2])\n",
"df = df.reset_index().rename(columns={'index':'original'})\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('Dataset_duplicate_test.csv', index=None)"
]
},
{
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
160 changes: 160 additions & 0 deletions galvasr2/codelabs/data-deduplication-tuning.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"pd.options.display.float_format = '{:20,.2f}'.format\n",
"from datasketch import MinHash, MinHashLSH, MinHashLSHForest, MinHashLSHEnsemble\n",
"from nltk import ngrams\n",
"from tqdm import tqdm\n",
"import numpy as np\n",
"import itertools\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
"import seaborn as sn\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('Dataset_duplicate_train.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%%time\n",
"# that accepts MinHash objects with 128 permutations functions\n",
"data = df['text'].values\n",
"num_perm = 128\n",
"lsh = MinHashLSH(threshold=0.8, num_perm=num_perm)\n",
" \n",
"# Create MinHash objects\n",
"minhashes = {}\n",
"error = []\n",
"for c, i in enumerate(tqdm(data)):\n",
" try:\n",
" if c%5000 == 0:\n",
" print(c)\n",
" minhash = MinHash(num_perm=num_perm)\n",
" for d in ngrams(i, 16):\n",
" minhash.update(\"\".join(d).encode('utf-8'))\n",
" lsh.insert(c, minhash)\n",
" minhashes[c] = minhash\n",
" except:\n",
" error.append(c)\n",
" pass \n",
"\n",
" duplicate = []\n",
"for i in range(len(minhashes.keys())):\n",
" try:\n",
" result = lsh.query(minhashes[i])\n",
" if len(result) > 1:\n",
" result.sort()\n",
" duplicate.append(result)\n",
" except:\n",
" pass\n",
"duplicate.sort()\n",
"duplicate = list(duplicate for duplicate, _ in itertools.groupby(duplicate))\n",
"delete = []\n",
"for value in duplicate:\n",
" delete.append(value[1:])\n",
"delete = list(itertools.chain(*delete))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['predict'] = np.where(df.index.isin(delete), 1, 0)\n",
"plt.figure(figsize = (10,7))\n",
"ax = plt.gca()\n",
"ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
"labels = ['deduplicate', 'no-deduplicate']\n",
"print(roc_auc_score(df['deduplicate'], df['predict']))\n",
"sn.heatmap(confusion_matrix(df['deduplicate'], df['predict']), annot=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('Dataset_duplicate_test.csv')\n",
"df_result = deduplicate_model(df_test, num_perm=128, ngram=16, threshold=0.6)\n",
"roc_auc_score(df_result['deduplicate'], df_result['predict'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plt.figure(figsize = (10,7))\n",
"ax = plt.gca()\n",
"ax.get_xaxis().get_major_formatter().set_scientific(False)\n",
"labels = ['deduplicate', 'no-deduplicate']\n",
"sn.heatmap(confusion_matrix(df_result['deduplicate'], df_result['predict']), annot=True);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"print(classification_report(df['deduplicate'], df['predict']))"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}