Spaces:
Sleeping
Sleeping
File size: 7,405 Bytes
45a5c11 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.metrics.pairwise import cosine_similarity"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cast</th>\n",
" <th>director</th>\n",
" <th>keywords</th>\n",
" <th>overview</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" <th>year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>movieId</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>117529</th>\n",
" <td>['Chris Pratt', 'Bryce Dallas Howard', 'Irrfan...</td>\n",
" <td>Colin Trevorrow</td>\n",
" <td>['monster', 'dna', 'tyrannosaurus rex', 'veloc...</td>\n",
" <td>Twenty-two years after the events of Jurassic ...</td>\n",
" <td>Jurassic World</td>\n",
" <td>['Action', 'Adventure', 'Drama', 'Sci-Fi', 'Th...</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cast director \\\n",
"movieId \n",
"117529 ['Chris Pratt', 'Bryce Dallas Howard', 'Irrfan... Colin Trevorrow \n",
"\n",
" keywords \\\n",
"movieId \n",
"117529 ['monster', 'dna', 'tyrannosaurus rex', 'veloc... \n",
"\n",
" overview title \\\n",
"movieId \n",
"117529 Twenty-two years after the events of Jurassic ... Jurassic World \n",
"\n",
" genres year \n",
"movieId \n",
"117529 ['Action', 'Adventure', 'Drama', 'Sci-Fi', 'Th... 2015 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../data/reduced/movies_m10_rich_pre.csv', index_col='movieId').iloc[:,2:]\n",
"df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"columns = ['cast', 'director', 'keywords', 'overview', 'title', 'genres', 'year']"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def clean_data(x):\n",
" if isinstance(x, list):\n",
" return [str.lower(i for i in x)]\n",
" else:\n",
" if isinstance(x, str):\n",
" return str.lower(x)\n",
" else:\n",
" return ''"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def get_soup(x):\n",
" return (''.join(x['cast']) + ' ' + x['director'] + ' ' + ''.join(x['keywords']) + ' ' + x['overview'] +\n",
" ' ' + x['title'] + ' ' + ''.join(x['genres']))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"for column in columns:\n",
" df[column] = df[column].apply(clean_data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df['soup'] = df.apply(get_soup, axis=1)\n",
"df.reset_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"count = CountVectorizer(stop_words='english')\n",
"\n",
"count_matrix = count.fit_transform(df['soup'])\n",
"\n",
"cosim = cosine_similarity(count_matrix, count_matrix)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def get_recommendations(df, title, cosim):\n",
" index = df[df['title'] == title+' '].index.values[0]\n",
"\n",
" sim_scores = list(enumerate(cosim[index]))\n",
" sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
" sim_scores = sim_scores[1:11]\n",
"\n",
" movie_indexes = [i[0] for i in sim_scores]\n",
"\n",
" return df['title'].iloc[movie_indexes]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"title = str.lower('Toy Story 2')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1146 toy story \n",
"117 toy story 3 \n",
"1576 small soldiers \n",
"168 being john malkovich \n",
"1283 toys \n",
"1952 stand by me \n",
"1002 everything you always wanted to know about sex...\n",
"239 monsters, inc. \n",
"1548 antz \n",
"1691 big \n",
"Name: title, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_recommendations(df, title, cosim)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"df['soup'].to_csv('soup.csv')"
]
}
],
"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": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|