File size: 4,856 Bytes
b867cf8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | {
"cells": [
{
"cell_type": "markdown",
"id": "d5c4b5c2-8c0a-4cbe-8997-1a98c14be2e4",
"metadata": {},
"source": [
"A text classification model using libraries like NLTK or SpaCy. It includes some PII data within the code (e.g., hard-coded email addresses or phone numbers for testing purposes), and include a few API tokens/secrets."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f95fa380-34d0-455d-8002-ebe5f829542c",
"metadata": {},
"outputs": [],
"source": [
"# Vulnerable libraries\n",
"!pip install django==1.11.15\n",
"!pip install flask==0.12.2\n",
"!pip install numpy==1.16.0\n",
"!pip install requests==2.19.1\n",
"!pip install scikit-learn==0.19.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25315022-9da9-4c29-8326-6532d261dd56",
"metadata": {},
"outputs": [],
"source": [
"# Non-permissible licensed libraries\n",
"import gmpy2\n",
"import oct2py\n",
"import pygsl\n",
"from PyQt5 import QtCore"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "489ad824-285c-4219-afc6-073192d54f3e",
"metadata": {},
"outputs": [],
"source": [
"# Required Libraries for our task\n",
"import nltk\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "109d2f98-4d6d-42d9-acb4-2f195af051d5",
"metadata": {},
"outputs": [],
"source": [
"# PII Data\n",
"email = \"john.doe@example.com\"\n",
"phone = \"123-456-7890\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d637e295-0953-4980-bf99-c7e7e509e876",
"metadata": {},
"outputs": [],
"source": [
"# API Keys and secrets\n",
"fb_app_secret = \"3e4a22bb7e6b2c38b7809234b3ee782b\"\n",
"db_credentials = \"username:password@localhost:5432/mydatabase\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6493567-ad7f-4b87-95e4-5068a09fca92",
"metadata": {},
"outputs": [],
"source": [
"# Download nltk data\n",
"nltk.download('punkt', download_dir='/nltk_data/')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f94e191-bfe7-4e54-9dbf-4d2484b0dbe9",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Text Classification\n",
"from sklearn.datasets import fetch_20newsgroups\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.feature_extraction.text import TfidfTransformer\n",
"from sklearn.naive_bayes import MultinomialNB"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8552e84a-e164-4519-8ce8-959c7dd277ef",
"metadata": {},
"outputs": [],
"source": [
"# Load Data\n",
"categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']\n",
"twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abc38386-e63f-4d22-81dc-1785ac8f043b",
"metadata": {},
"outputs": [],
"source": [
"# Feature Extraction\n",
"count_vect = CountVectorizer()\n",
"X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
"tfidf_transformer = TfidfTransformer()\n",
"X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ea57698-12ff-48b3-a8b6-bb8dffabbc5f",
"metadata": {},
"outputs": [],
"source": [
"# Train Model\n",
"clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a5fa6532-594c-4790-a630-83388c556591",
"metadata": {},
"outputs": [],
"source": [
"# Predict\n",
"docs_new = ['God is love', 'OpenGL on the GPU is fast']\n",
"X_new_counts = count_vect.transform(docs_new)\n",
"X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
"predicted = clf.predict(X_new_tfidf)\n",
"for doc, category in zip(docs_new, predicted):\n",
" print('%r => %s' % (doc, twenty_train.target_names[category]))"
]
}
],
"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": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|