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ita9naiwa/logistic_mf.ipynb

Last active June 4, 2019 09:20
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logistic matrix factorization
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logistic_mf.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import pandas as pd \n",
"import os \n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"r = pd.read_csv(\"movielens/u.data\", sep='\\t')\n",
"r.columns =['user', 'item', 'rating','ts']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>user</th>\n",
" <th>item</th>\n",
" <th>rating</th>\n",
" <th>ts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>186</td>\n",
" <td>302</td>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
" <td>878887116</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>51</td>\n",
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" <td>880606923</td>\n",
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" <td>884182806</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user item rating ts\n",
"0 186 302 3 891717742\n",
"1 22 377 1 878887116\n",
"2 244 51 2 880606923\n",
"3 166 346 1 886397596\n",
"4 298 474 4 884182806"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"uid_to_idx = {y:x for x, y in enumerate(r.user.unique().tolist())}\n",
"iid_to_idx = {y:x for x, y in enumerate(r.item.unique().tolist())}\n",
"idx_to_uid = {x:y for x, y in enumerate(r.user.unique().tolist())}\n",
"idx_to_iid = {x:y for x, y in enumerate(r.item.unique().tolist())}"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"r['uid'] = r.user.map(lambda x: uid_to_idx[x])\n",
"r['iid'] = r.item.map(lambda x: iid_to_idx[x])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(943, 1682)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"U = len(uid_to_idx)\n",
"M = len(iid_to_idx)\n",
"(U, M)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"u_items = dict(r.groupby('uid').iid.apply(np.array))\n",
"i_users = dict(r.groupby('iid').uid.apply(np.array))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"def calc(u, v, ub=None, vb=None):\n",
" ret = np.dot(u,v.T)\n",
" if ub is not None and vb is not None:\n",
" b = ub + vb\n",
" ret += b\n",
" return np.exp(ret)\n",
" \n",
"def update(u, u_vec, i_vec, pos_views, U, M, user=True, lr=0.003, lamb=0.01):\n",
" uv = u_vec[u, :-2]\n",
" if user:\n",
" ub = uv[-1]\n",
" else:\n",
" ub = uv[-2]\n",
" \n",
" try:\n",
" p_v = pos_views[u]\n",
" #p_scores = 1 + np.log(1 + pos_views[u][:, 1])\n",
" p_scores = np.ones_like(p_v)\n",
" except:\n",
" p_v = []\n",
" p_scores = []\n",
" \n",
" n_v = np.random.choice(M, np.min([M // 2, len(p_v) * 10]), replace=False)\n",
" p_v = np.hstack([p_v, n_v])\n",
" p_scores = np.hstack([p_scores, np.zeros_like(n_v)])\n",
" i_v = i_vec[p_v, :-2]\n",
" \n",
" if user:\n",
" ib = i_v[:, -2]\n",
" else:\n",
" ib = i_v[:, -1] \n",
" exp = calc(uv, i_v, ub, ib) \n",
" B = (1 + p_scores) * exp\n",
" C = np.divide(B, 1 + exp)\n",
"\n",
" A = np.dot(p_scores, i_v)\n",
" D = np.dot(C, i_v)\n",
" du = (A - D ) - lamb * uv\n",
" dv = (np.sum(p_scores) - np.sum(C)) - lamb * ub\n",
" ret = np.ones_like(u_vec[u])\n",
" ret[:-2] = (uv + lr * du) \n",
" if user:\n",
" ret[-1] = (ub + lr * dv)\n",
" else:\n",
" ret[-2] = (ub + lr * dv)\n",
" return ret"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"us = np.random.normal(0, 0.01, size=(U, 20))\n",
"vs = np.random.normal(0, 0.01, size=(M, 20))\n",
"us[:, -2] = 1.0\n",
"vs[:, -1] = 1.0"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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}
],
"source": [
"for _ in range(100):\n",
" for u in range(U):\n",
" us[u] = update(u, us, vs, u_items, U, M, user=True, lr=0.01 / np.sqrt(1+_), lamb=1.0)\n",
" for m in range(M):\n",
" vs[m] = update(m, vs, us, i_users, M, U, user=False, lr=0.01 / np.sqrt(1+_), lamb=1.0)\n",
" print(np.dot(us[0], us[0]), np.dot(vs[0], vs[0]))\n",
" x = np.dot(us, vs.T).argsort()[:, ::-1][:, :3]\n",
" ret = []\n",
" for u, seen_items in u_items.items():\n",
" q = set(x[u]).intersection(seen_items.tolist())\n",
" ret.append(len(q) / 3)\n",
" print(np.mean(ret))"
]
}
],
"metadata": {
"kernelspec": {
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"language": "python",
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},
"language_info": {
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"file_extension": ".py",
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