883 lines
30 KiB
Plaintext
883 lines
30 KiB
Plaintext
{
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}
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},
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"from sklearn.metrics.pairwise import cosine_similarity\n",
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"import numpy as np"
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],
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"id": "5a823fabad3c186f",
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"outputs": [],
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"execution_count": 2
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},
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}
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},
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"cell_type": "code",
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"source": [
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"def split_by_user(data, test_size=0.2, random_state=42):\n",
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" unique_users = data['userId'].unique()\n",
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" np.random.seed(random_state)\n",
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" test_users = np.random.choice(unique_users, size=int(len(unique_users) * test_size), replace=False)\n",
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" test_data = data[data['userId'].isin(test_users)]\n",
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" train_data = data[~data['userId'].isin(test_users)]\n",
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" return train_data, test_data"
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],
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"id": "d694dbe819b591ac",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-12-07T01:55:39.715567Z",
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}
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},
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"cell_type": "code",
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"source": [
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"movies = pd.read_csv('ml-latest-small/movies.csv')\n",
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"ratings = pd.read_csv('ml-latest-small/ratings.csv')"
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],
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"id": "1bc78bafbae06c89",
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"outputs": [],
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"execution_count": 4
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},
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{
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}
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},
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"cell_type": "code",
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"source": "movies['genres'] = movies['genres'].str.split('|')",
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"id": "2543aa8216425342",
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"outputs": [],
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"execution_count": 5
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},
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}
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},
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"cell_type": "code",
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"source": "data = pd.merge(ratings, movies, on=\"movieId\")",
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"id": "b3fa37255dccb066",
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"outputs": [],
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"execution_count": 6
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" userId movieId rating timestamp title \\\n",
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"4 1 50 5.0 964982931 Usual Suspects, The (1995) \n",
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"... ... ... ... ... ... \n",
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"100832 610 168248 5.0 1493850091 John Wick: Chapter Two (2017) \n",
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"100833 610 168250 5.0 1494273047 Get Out (2017) \n",
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"100834 610 168252 5.0 1493846352 Logan (2017) \n",
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"... ... \n",
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"100832 [Action, Crime, Thriller] \n",
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"100833 [Horror] \n",
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" <th></th>\n",
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" <th>userId</th>\n",
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" <td>Split (2017)</td>\n",
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"outputs": [],
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"execution_count": 8
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{
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"data": {
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" userId movieId rating timestamp title \\\n",
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||
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"1 1 3 4.0 964981247 Grumpier Old Men (1995) \n",
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"3 1 47 5.0 964983815 Seven (a.k.a. Se7en) (1995) \n",
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"4 1 50 5.0 964982931 Usual Suspects, The (1995) \n",
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||
"... ... ... ... ... ... \n",
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"100831 610 166534 4.0 1493848402 Split (2017) \n",
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"100832 610 168248 5.0 1493850091 John Wick: Chapter Two (2017) \n",
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"100833 610 168250 5.0 1494273047 Get Out (2017) \n",
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"100834 610 168252 5.0 1493846352 Logan (2017) \n",
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"100835 610 170875 3.0 1493846415 The Fate of the Furious (2017) \n",
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"\n",
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"... ... \n",
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"100833 [Horror] \n",
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||
" </tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
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|
||
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|
||
"execution_count": 9
|
||
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|
||
{
|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" userId movieId rating timestamp \\\n",
|
||
"261 3 31 0.5 1306463578 \n",
|
||
"262 3 527 0.5 1306464275 \n",
|
||
"263 3 647 0.5 1306463619 \n",
|
||
"264 3 688 0.5 1306464228 \n",
|
||
"265 3 720 0.5 1306463595 \n",
|
||
"... ... ... ... ... \n",
|
||
"99529 609 892 3.0 847221080 \n",
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||
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||
"99531 609 1059 3.0 847221054 \n",
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||
"99532 609 1150 4.0 847221054 \n",
|
||
"99533 609 1161 4.0 847221080 \n",
|
||
"\n",
|
||
" title \\\n",
|
||
"261 Dangerous Minds (1995) \n",
|
||
"262 Schindler's List (1993) \n",
|
||
"263 Courage Under Fire (1996) \n",
|
||
"264 Operation Dumbo Drop (1995) \n",
|
||
"265 Wallace & Gromit: The Best of Aardman Animatio... \n",
|
||
"... ... \n",
|
||
"99529 Twelfth Night (1996) \n",
|
||
"99530 Jude (1996) \n",
|
||
"99531 William Shakespeare's Romeo + Juliet (1996) \n",
|
||
"99532 Return of Martin Guerre, The (Retour de Martin... \n",
|
||
"99533 Tin Drum, The (Blechtrommel, Die) (1979) \n",
|
||
"\n",
|
||
" genres \n",
|
||
"261 [Drama] \n",
|
||
"262 [Drama, War] \n",
|
||
"263 [Action, Crime, Drama, War] \n",
|
||
"264 [Action, Adventure, Comedy, War] \n",
|
||
"265 [Adventure, Animation, Comedy] \n",
|
||
"... ... \n",
|
||
"99529 [Comedy, Drama, Romance] \n",
|
||
"99530 [Drama] \n",
|
||
"99531 [Drama, Romance] \n",
|
||
"99532 [Drama] \n",
|
||
"99533 [Drama, War] \n",
|
||
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|
||
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|
||
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|
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||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>Dangerous Minds (1995)</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
" <td>1306463619</td>\n",
|
||
" <td>Courage Under Fire (1996)</td>\n",
|
||
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|
||
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|
||
" <tr>\n",
|
||
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|
||
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|
||
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|
||
" <td>0.5</td>\n",
|
||
" <td>1306464228</td>\n",
|
||
" <td>Operation Dumbo Drop (1995)</td>\n",
|
||
" <td>[Action, Adventure, Comedy, War]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>265</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>720</td>\n",
|
||
" <td>0.5</td>\n",
|
||
" <td>1306463595</td>\n",
|
||
" <td>Wallace & Gromit: The Best of Aardman Animatio...</td>\n",
|
||
" <td>[Adventure, Animation, Comedy]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99529</th>\n",
|
||
" <td>609</td>\n",
|
||
" <td>892</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>847221080</td>\n",
|
||
" <td>Twelfth Night (1996)</td>\n",
|
||
" <td>[Comedy, Drama, Romance]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99530</th>\n",
|
||
" <td>609</td>\n",
|
||
" <td>1056</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>847221080</td>\n",
|
||
" <td>Jude (1996)</td>\n",
|
||
" <td>[Drama]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99531</th>\n",
|
||
" <td>609</td>\n",
|
||
" <td>1059</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>847221054</td>\n",
|
||
" <td>William Shakespeare's Romeo + Juliet (1996)</td>\n",
|
||
" <td>[Drama, Romance]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99532</th>\n",
|
||
" <td>609</td>\n",
|
||
" <td>1150</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>847221054</td>\n",
|
||
" <td>Return of Martin Guerre, The (Retour de Martin...</td>\n",
|
||
" <td>[Drama]</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>99533</th>\n",
|
||
" <td>609</td>\n",
|
||
" <td>1161</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>847221080</td>\n",
|
||
" <td>Tin Drum, The (Blechtrommel, Die) (1979)</td>\n",
|
||
" <td>[Drama, War]</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>27659 rows × 6 columns</p>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
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|
||
],
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"metadata": {
|
||
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|
||
"end_time": "2024-12-07T01:56:24.904397Z",
|
||
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|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"expanded_train_data = train_data.explode('genres')\n",
|
||
"train_data = train_data.copy()\n",
|
||
"test_data = test_data.copy()\n",
|
||
"\n",
|
||
"train_user_genre_rating = (\n",
|
||
" expanded_train_data.groupby(['userId', 'genres'])['rating']\n",
|
||
" .mean()\n",
|
||
" .reset_index()\n",
|
||
" .rename(columns={'rating': 'avg_genre_rating'})\n",
|
||
")\n",
|
||
"\n",
|
||
"train_user_preferences = train_user_genre_rating.pivot(index='userId', columns='genres', values='avg_genre_rating').fillna(0)\n",
|
||
"\n",
|
||
"def train_genre_match_calc(row):\n",
|
||
" user_id = row['userId']\n",
|
||
" genres = row['genres']\n",
|
||
"\n",
|
||
" if isinstance(genres, str):\n",
|
||
" genres = [genres]\n",
|
||
"\n",
|
||
" user_pref = train_user_genre_rating[train_user_genre_rating['userId'] == user_id]\n",
|
||
" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
|
||
"\n",
|
||
" if not genre_scores.empty:\n",
|
||
" return genre_scores.mean()\n",
|
||
" else:\n",
|
||
" return 0\n",
|
||
"\n",
|
||
"train_data['genreMatch'] = train_data.apply(train_genre_match_calc, axis=1)\n",
|
||
"\n",
|
||
"\n",
|
||
"expanded_test_data = test_data.explode('genres')\n",
|
||
"\n",
|
||
"test_user_genre_rating = (\n",
|
||
" expanded_test_data.groupby(['userId', 'genres'])['rating']\n",
|
||
" .mean()\n",
|
||
" .reset_index()\n",
|
||
" .rename(columns={'rating': 'avg_genre_rating'})\n",
|
||
")\n",
|
||
"\n",
|
||
"test_user_preferences = test_user_genre_rating.pivot(index='userId', columns='genres', values='avg_genre_rating').fillna(0)\n",
|
||
"\n",
|
||
"def test_genre_match_calc(row):\n",
|
||
" user_id = row['userId']\n",
|
||
" genres = row['genres']\n",
|
||
"\n",
|
||
" if isinstance(genres, str):\n",
|
||
" genres = [genres]\n",
|
||
"\n",
|
||
" user_pref = test_user_genre_rating[test_user_genre_rating['userId'] == user_id]\n",
|
||
" genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
|
||
"\n",
|
||
" if not genre_scores.empty:\n",
|
||
" return genre_scores.mean()\n",
|
||
" else:\n",
|
||
" return 0\n",
|
||
"\n",
|
||
"test_data['genreMatch'] = test_data.apply(test_genre_match_calc, axis=1)"
|
||
],
|
||
"id": "88296c8c47cdbf60",
|
||
"outputs": [],
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.150918Z",
|
||
"start_time": "2024-12-07T01:56:24.905400Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_user_movie = train_data.pivot(index='userId', columns='movieId', values='rating')\n",
|
||
"train_user_movie_filled = train_user_movie.fillna(0)\n",
|
||
"train_user_similarity = cosine_similarity(train_user_movie_filled)\n",
|
||
"train_user_similarity_df = pd.DataFrame(train_user_similarity, index=train_user_movie.index, columns=train_user_movie.index)\n",
|
||
"\n",
|
||
"def train_average(user_id, movie_id, top_n=5):\n",
|
||
" similar_users = train_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
|
||
" similar_ratings = train_user_movie.loc[similar_users, movie_id]\n",
|
||
"\n",
|
||
" return similar_ratings.dropna().mean()\n",
|
||
"\n",
|
||
"train_data['similarUsers'] = train_data.apply(\n",
|
||
" lambda row: train_average(row['userId'], row['movieId']), axis=1\n",
|
||
")\n",
|
||
"\n",
|
||
"test_user_movie = test_data.pivot(index='userId', columns='movieId', values='rating')\n",
|
||
"test_user_movie_filled = test_user_movie.fillna(0)\n",
|
||
"test_user_similarity = cosine_similarity(test_user_movie_filled)\n",
|
||
"test_user_similarity_df = pd.DataFrame(test_user_similarity, index=test_user_movie.index, columns=test_user_movie.index)\n",
|
||
"\n",
|
||
"def test_average(user_id, movie_id, top_n=5):\n",
|
||
" similar_users = test_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
|
||
" similar_ratings = test_user_movie.loc[similar_users, movie_id]\n",
|
||
"\n",
|
||
" return similar_ratings.dropna().mean()\n",
|
||
"\n",
|
||
"test_data['similarUsers'] = test_data.apply(\n",
|
||
" lambda row: test_average(row['userId'], row['movieId']), axis=1\n",
|
||
")\n"
|
||
],
|
||
"id": "e931f4041a1802fb",
|
||
"outputs": [],
|
||
"execution_count": 12
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.194857Z",
|
||
"start_time": "2024-12-07T01:56:54.151920Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_ratings = train_data[['userId', 'movieId', 'similarUsers']]\n",
|
||
"train_data = pd.merge(train_data, train_ratings, on=['userId', 'movieId'], how='left')\n",
|
||
"\n",
|
||
"test_ratings = test_data[['userId', 'movieId', 'similarUsers']]\n",
|
||
"test_data = pd.merge(test_data, test_ratings, on=['userId', 'movieId'], how='left')"
|
||
],
|
||
"id": "ce65ce417e7f5207",
|
||
"outputs": [],
|
||
"execution_count": 13
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.224008Z",
|
||
"start_time": "2024-12-07T01:56:54.195860Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_popularity = train_data.groupby('movieId').size().reset_index(name='popularity')\n",
|
||
"train_data = pd.merge(train_data, train_popularity, on='movieId', how='left')\n",
|
||
"\n",
|
||
"test_popularity = test_data.groupby('movieId').size().reset_index(name='popularity')\n",
|
||
"test_data = pd.merge(test_data, test_popularity, on='movieId', how='left')"
|
||
],
|
||
"id": "aa798201db531188",
|
||
"outputs": [],
|
||
"execution_count": 14
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.819973Z",
|
||
"start_time": "2024-12-07T01:56:54.225011Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_data.to_csv('datasets/train_all.csv', index=False)\n",
|
||
"test_data.to_csv('datasets/test_all.csv', index=False)"
|
||
],
|
||
"id": "f7b5130c72ad35af",
|
||
"outputs": [],
|
||
"execution_count": 15
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.846938Z",
|
||
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|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"def get_top_movies(data):\n",
|
||
" top = (data.sort_values(by=['userId', 'rating', 'popularity'], ascending=[True, False, False]).groupby('userId').head(5))\n",
|
||
" return top\n",
|
||
"\n",
|
||
"train_top = get_top_movies(train_data)\n",
|
||
"test_top = get_top_movies(test_data)"
|
||
],
|
||
"id": "20dba13e7a3d105b",
|
||
"outputs": [],
|
||
"execution_count": 16
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2024-12-07T01:56:54.873768Z",
|
||
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|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"train_top.to_csv('datasets/train_top.csv', index=False)\n",
|
||
"test_top.to_csv('datasets/test_top.csv', index=False)"
|
||
],
|
||
"id": "be9f6106c5e4b04a",
|
||
"outputs": [],
|
||
"execution_count": 17
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
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|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
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|
||
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|
||
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|
||
"nbformat_minor": 5
|
||
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|