finished first 2 lectures
This commit is contained in:
Robert
parent
18d5c09409
commit
e36414e7ce
@ -13,12 +13,15 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# if you don't have some library installed try using pip (or pip3) to install it - you can do it from the notebook\n",
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"# example: !pip install tqdm\n",
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"# also on labs it's better to use python3 kernel - ipython3 notebook\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import scipy.sparse as sparse\n",
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"import time\n",
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"import random\n",
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"import evaluation_measures as ev\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"import os\n",
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@ -161,7 +164,7 @@
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"text": [
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"We have 943 users, 1682 items and 100000 ratings.\n",
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"\n",
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"Average number of ratings per user is 106.04. \n",
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"Average number of ratings per user is 106.0445. \n",
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"\n",
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"Average number of ratings per item is 59.453.\n",
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"\n",
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@ -170,13 +173,13 @@
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}
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],
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"source": [
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"users, items, ratings=len(set(df['user'])), len(set(df['item'])), len(df)\n",
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"users, items, ratings=df['user'].nunique(), df['item'].nunique(), len(df)\n",
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"\n",
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"print('We have {} users, {} items and {} ratings.\\n'.format(users, items, ratings))\n",
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"print(f'We have {users} users, {items} items and {ratings} ratings.\\n')\n",
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"\n",
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"print('Average number of ratings per user is {}. \\n'.format(round(ratings/users,2)))\n",
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"print('Average number of ratings per item is {}.\\n'.format(round(ratings/items,4)))\n",
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"print('Data sparsity (% of missing entries) is {}%.'.format(round(100*ratings/(users*items),4)))"
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"print(f'Average number of ratings per user is {round(ratings/users,4)}. \\n')\n",
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"print(f'Average number of ratings per item is {round(ratings/items,4)}.\\n')\n",
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"print(f'Data sparsity (% of missing entries) is {round(100*ratings/(users*items),4)}%.')"
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]
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},
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{
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@ -636,7 +639,6 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.makedirs('./Datasets/toy-example/', exist_ok = True)"
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]
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},
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@ -239,11 +239,9 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Number of ratings: 8 \n",
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"Number of users: 3 \n",
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"Number of items: 4 \n",
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"\n"
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"Number of ratings: 8\n",
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"Number of users: 3\n",
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"Number of items: 4\n"
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]
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}
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],
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@ -251,8 +249,9 @@
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"print('Ratings matrix with missing entries replaced by zeros:')\n",
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"display(sample_csr.todense())\n",
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"\n",
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"print('\\nNumber of ratings: {} \\nNumber of users: {} \\nNumber of items: {} \\n'\n",
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" .format(sample_csr.nnz, sample_csr.shape[0], sample_csr.shape[1]))"
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"print(f'Number of ratings: {sample_csr.nnz}')\n",
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"print(f'Number of users: {sample_csr.shape[0]}')\n",
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"print(f'Number of items: {sample_csr.shape[1]}')"
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]
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},
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{
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@ -278,7 +277,7 @@
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"print('Regarding items:', sample_csr.indices)\n",
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"\n",
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"for i in range(sample_csr.shape[0]):\n",
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" print('Where ratings from {} to {} belongs to user {}.'.format(sample_csr.indptr[i], sample_csr.indptr[i+1]-1, i))"
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" print(f'Where ratings from {sample_csr.indptr[i]} to {sample_csr.indptr[i+1]-1} belongs to user {i}.')"
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]
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},
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{
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@ -307,7 +306,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"885 ns ± 165 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"1.44 µs ± 184 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n",
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"Inefficient way to access items rated by user:\n"
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]
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},
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@ -325,7 +324,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"153 µs ± 9.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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"172 µs ± 14.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
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]
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}
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],
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@ -482,7 +481,7 @@
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"display(sparse.diags(row_means).todense())\n",
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"\n",
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"print(\"\"\"Let's apply them in nonzero entries:\"\"\")\n",
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"to_subtract=sparse.diags(row_means)*sample_csr.power(0)\n",
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"to_subtract=sparse.diags(row_means)*(sample_csr>0)\n",
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"display(to_subtract.todense())\n",
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"\n",
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"print(\"Finally after subtraction:\")\n",
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@ -573,26 +572,26 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"TopPop=[]\n",
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"train_iu=train_ui.transpose().tocsr()\n",
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"scaling_factor=train_ui.max()/max(np.diff(train_iu.indptr))\n",
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"top_pop = []\n",
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"train_iu = train_ui.transpose().tocsr()\n",
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"scaling_factor = train_ui.max()/max(np.diff(train_iu.indptr))\n",
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"\n",
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"for i in range(train_iu.shape[0]):\n",
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" TopPop.append((i, (train_iu.indptr[i+1]-train_iu.indptr[i])*scaling_factor))\n",
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" top_pop.append((i, (train_iu.indptr[i+1]-train_iu.indptr[i])*scaling_factor))\n",
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" \n",
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"TopPop.sort(key=lambda x: x[1], reverse=True)\n",
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"#TopPop is an array of pairs (item, rescaled_popularity) sorted descending from the most popular\n",
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"top_pop.sort(key=lambda x: x[1], reverse=True)\n",
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"#top_pop is an array of pairs (item, rescaled_popularity) sorted descending from the most popular\n",
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"\n",
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"k=10\n",
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"result=[]\n",
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"k = 10\n",
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"result = []\n",
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"\n",
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"for u in range(train_ui.shape[0]):\n",
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" user_rated=train_ui.indices[train_ui.indptr[u]:train_ui.indptr[u+1]]\n",
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" rec_user=[]\n",
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" item_pos=0\n",
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" user_rated = train_ui.indices[train_ui.indptr[u]:train_ui.indptr[u+1]]\n",
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" rec_user = []\n",
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" item_pos = 0\n",
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" while len(rec_user)<10:\n",
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" if TopPop[item_pos][0] not in user_rated:\n",
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" rec_user.append((item_code_id[TopPop[item_pos][0]], TopPop[item_pos][1]))\n",
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" if top_pop[item_pos][0] not in user_rated:\n",
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" rec_user.append((item_code_id[top_pop[item_pos][0]], top_pop[item_pos][1]))\n",
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" item_pos+=1\n",
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" result.append([user_code_id[u]]+list(chain(*rec_user)))\n",
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"\n",
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@ -613,7 +612,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Self made global average"
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"# Self made top rated"
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]
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},
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{
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@ -622,11 +621,15 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"GlobalAvg=[]\n",
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"avg=np.sum(train_ui)/train_ui.nnz\n",
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"top_rated = []\n",
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"global_avg = sum(train_iu.data)/train_ui.nnz\n",
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"\n",
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"for i in range(train_iu.shape[0]):\n",
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" GlobalAvg.append((i, avg))\n",
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" ratings = train_iu.data[train_iu.indptr[i]: train_iu.indptr[i+1]]\n",
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" avg = np.mean(ratings) if len(ratings)>0 else global_avg\n",
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" top_rated.append((i, avg))\n",
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" \n",
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"top_rated.sort(key=lambda x: x[1], reverse=True)\n",
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" \n",
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"k=10\n",
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"result=[]\n",
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" rec_user=[]\n",
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" item_pos=0\n",
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" while len(rec_user)<10:\n",
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" if GlobalAvg[item_pos][0] not in user_rated:\n",
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" rec_user.append((item_code_id[GlobalAvg[item_pos][0]], GlobalAvg[item_pos][1]))\n",
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" if top_rated[item_pos][0] not in user_rated:\n",
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" rec_user.append((item_code_id[top_rated[item_pos][0]], top_rated[item_pos][1]))\n",
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" item_pos+=1\n",
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" result.append([user_code_id[u]]+list(chain(*rec_user)))\n",
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"\n",
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"(pd.DataFrame(result)).to_csv('Recommendations generated/ml-100k/Self_GlobalAvg_reco.csv', index=False, header=False)\n",
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"(pd.DataFrame(result)).to_csv('Recommendations generated/ml-100k/Self_TopRated_reco.csv', index=False, header=False)\n",
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"\n",
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"\n",
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"# estimations - score is a bit artificial since that method is not designed for scoring, but for ranking\n",
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"\n",
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"estimations=[]\n",
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"d = dict(top_rated)\n",
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"\n",
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"for user, item in zip(*test_ui.nonzero()):\n",
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" estimations.append([user_code_id[user], item_code_id[item], avg])\n",
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"(pd.DataFrame(estimations)).to_csv('Recommendations generated/ml-100k/Self_GlobalAvg_estimations.csv', index=False, header=False)"
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" estimations.append([user_code_id[user], item_code_id[item], d[item]])\n",
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"(pd.DataFrame(estimations)).to_csv('Recommendations generated/ml-100k/Self_TopRated_estimations.csv', index=False, header=False)"
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]
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},
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{
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@ -706,50 +709,50 @@
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1</td>\n",
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" <td>5</td>\n",
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" <td>3.529975</td>\n",
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" <td>10</td>\n",
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" <td>3.529975</td>\n",
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" <td>25</td>\n",
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" <td>3.529975</td>\n",
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" <td>32</td>\n",
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" <td>3.529975</td>\n",
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" <td>33</td>\n",
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" <td>814</td>\n",
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" <td>5.0</td>\n",
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" <td>1122</td>\n",
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" <td>5.0</td>\n",
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" <td>1189</td>\n",
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" <td>5.0</td>\n",
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" <td>1201</td>\n",
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" <td>5.0</td>\n",
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" <td>1293</td>\n",
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" <td>...</td>\n",
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" <td>44</td>\n",
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" <td>3.529975</td>\n",
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" <td>46</td>\n",
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" <td>3.529975</td>\n",
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" <td>50</td>\n",
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" <td>3.529975</td>\n",
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" <td>52</td>\n",
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" <td>3.529975</td>\n",
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" <td>55</td>\n",
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" <td>3.529975</td>\n",
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" <td>1306</td>\n",
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" <td>5.0</td>\n",
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" <td>1467</td>\n",
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" <td>5.0</td>\n",
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" <td>1491</td>\n",
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" <td>5.0</td>\n",
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" <td>1500</td>\n",
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" <td>5.0</td>\n",
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" <td>1536</td>\n",
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" <td>5.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>3.529975</td>\n",
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" <td>2</td>\n",
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" <td>3.529975</td>\n",
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" <td>3</td>\n",
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" <td>3.529975</td>\n",
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" <td>4</td>\n",
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" <td>3.529975</td>\n",
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" <td>5</td>\n",
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" <td>119</td>\n",
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" <td>5.0</td>\n",
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" <td>814</td>\n",
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" <td>5.0</td>\n",
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" <td>1122</td>\n",
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" <td>5.0</td>\n",
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" <td>1189</td>\n",
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" <td>5.0</td>\n",
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" <td>1201</td>\n",
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" <td>...</td>\n",
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" <td>6</td>\n",
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" <td>3.529975</td>\n",
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" <td>7</td>\n",
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" <td>3.529975</td>\n",
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" <td>8</td>\n",
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" <td>3.529975</td>\n",
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" <td>9</td>\n",
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" <td>3.529975</td>\n",
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" <td>11</td>\n",
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" <td>3.529975</td>\n",
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" <td>1293</td>\n",
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" <td>5.0</td>\n",
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" <td>1306</td>\n",
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" <td>5.0</td>\n",
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" <td>1467</td>\n",
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" <td>5.0</td>\n",
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" <td>1491</td>\n",
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" <td>5.0</td>\n",
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" <td>1500</td>\n",
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" <td>5.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2 3 4 5 6 7 8 9 ... 11 \\\n",
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"0 1 5 3.529975 10 3.529975 25 3.529975 32 3.529975 33 ... 44 \n",
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"1 2 1 3.529975 2 3.529975 3 3.529975 4 3.529975 5 ... 6 \n",
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" 0 1 2 3 4 5 6 7 8 9 ... 11 12 13 \\\n",
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"0 1 814 5.0 1122 5.0 1189 5.0 1201 5.0 1293 ... 1306 5.0 1467 \n",
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"1 2 119 5.0 814 5.0 1122 5.0 1189 5.0 1201 ... 1293 5.0 1306 \n",
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"\n",
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" 12 13 14 15 16 17 18 19 20 \n",
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"0 3.529975 46 3.529975 50 3.529975 52 3.529975 55 3.529975 \n",
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"1 3.529975 7 3.529975 8 3.529975 9 3.529975 11 3.529975 \n",
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" 14 15 16 17 18 19 20 \n",
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"0 5.0 1491 5.0 1500 5.0 1536 5.0 \n",
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"1 5.0 1467 5.0 1491 5.0 1500 5.0 \n",
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"\n",
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"[2 rows x 21 columns]"
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]
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"pd.DataFrame(result)[:2]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Project task 1 - self made top rated"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"# project task 1: implement TopRated\n",
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"# Implement recommender system which will recommend movies (which user hasn't seen) with the highest average rating\n",
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"# The output should be saved in 'Recommendations generated/ml-100k/Self_TopRated_reco.csv'\n",
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"# and 'Recommendations generated/ml-100k/Self_TopRated_estimations.csv'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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" \n",
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" max_row_mean=np.max(row_means)\n",
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" row_means[row_means==0]=max_row_mean+1\n",
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" to_subtract_rows=sparse.diags(row_means)*result.power(0)\n",
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" to_subtract_rows=sparse.diags(row_means)*(result>0)\n",
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" to_subtract_rows.sort_indices() # needed to have valid .data\n",
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" \n",
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" subtract=to_subtract_rows.data\n",
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# project task 2: implement self-made BaselineIU"
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"# project task 1: implement self-made BaselineIU"
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]
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Implement recommender system which will recommend movies (which user hasn't seen) which is similar to BaselineUI\n",
|
||||
"# but first subtract col means then row means\n",
|
||||
"# but first subtract column means then row means\n",
|
||||
"# The output should be saved in 'Recommendations generated/ml-100k/Self_BaselineIU_reco.csv'\n",
|
||||
"# and 'Recommendations generated/ml-100k/Self_BaselineIU_estimations.csv'"
|
||||
]
|
||||
@ -1089,7 +1073,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -1146,7 +1130,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
@ -1163,7 +1147,7 @@
|
||||
"0.7524871012820799"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
@ -1193,24 +1177,24 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"RMSE: 1.5317\n",
|
||||
"MAE: 1.2304\n"
|
||||
"RMSE: 1.5147\n",
|
||||
"MAE: 1.2155\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1.2303840461147084"
|
||||
"1.2154990549993152"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
|
||||
Binary file not shown.
@ -273,7 +273,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"943it [00:00, 7666.87it/s]\n"
|
||||
"943it [00:00, 6497.15it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -477,7 +477,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"943it [00:00, 7370.69it/s]\n"
|
||||
"943it [00:00, 5143.71it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -585,11 +585,11 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"943it [00:00, 7772.74it/s]\n",
|
||||
"943it [00:00, 5607.69it/s]\n",
|
||||
"943it [00:00, 4737.64it/s]\n",
|
||||
"943it [00:00, 4986.41it/s]\n",
|
||||
"943it [00:00, 3513.77it/s]\n"
|
||||
"943it [00:00, 3573.64it/s]\n",
|
||||
"943it [00:00, 5141.54it/s]\n",
|
||||
"943it [00:00, 2827.19it/s]\n",
|
||||
"943it [00:00, 2513.13it/s]\n",
|
||||
"943it [00:00, 3555.67it/s]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -670,27 +670,27 @@
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Self_GlobalAvg</td>\n",
|
||||
" <td>1.125760</td>\n",
|
||||
" <td>0.943534</td>\n",
|
||||
" <td>0.061188</td>\n",
|
||||
" <td>0.025968</td>\n",
|
||||
" <td>0.031383</td>\n",
|
||||
" <td>0.041343</td>\n",
|
||||
" <td>0.040558</td>\n",
|
||||
" <td>0.032107</td>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>1.525959</td>\n",
|
||||
" <td>1.225122</td>\n",
|
||||
" <td>0.047402</td>\n",
|
||||
" <td>0.020629</td>\n",
|
||||
" <td>0.024471</td>\n",
|
||||
" <td>0.032042</td>\n",
|
||||
" <td>0.027682</td>\n",
|
||||
" <td>0.019353</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>1.531724</td>\n",
|
||||
" <td>1.230384</td>\n",
|
||||
" <td>0.049417</td>\n",
|
||||
" <td>0.022558</td>\n",
|
||||
" <td>0.025490</td>\n",
|
||||
" <td>0.033242</td>\n",
|
||||
" <td>0.030365</td>\n",
|
||||
" <td>0.022626</td>\n",
|
||||
" <td>Self_TopRated</td>\n",
|
||||
" <td>1.030712</td>\n",
|
||||
" <td>0.820904</td>\n",
|
||||
" <td>0.000954</td>\n",
|
||||
" <td>0.000188</td>\n",
|
||||
" <td>0.000298</td>\n",
|
||||
" <td>0.000481</td>\n",
|
||||
" <td>0.000644</td>\n",
|
||||
" <td>0.000223</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
@ -712,15 +712,15 @@
|
||||
" Model RMSE MAE precision recall F_1 \\\n",
|
||||
"0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n",
|
||||
"0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n",
|
||||
"0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n",
|
||||
"0 Ready_Random 1.531724 1.230384 0.049417 0.022558 0.025490 \n",
|
||||
"0 Ready_Random 1.525959 1.225122 0.047402 0.020629 0.024471 \n",
|
||||
"0 Self_TopRated 1.030712 0.820904 0.000954 0.000188 0.000298 \n",
|
||||
"0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n",
|
||||
"\n",
|
||||
" F_05 precision_super recall_super \n",
|
||||
"0 0.141584 0.130472 0.137473 \n",
|
||||
"0 0.061286 0.079614 0.056463 \n",
|
||||
"0 0.041343 0.040558 0.032107 \n",
|
||||
"0 0.033242 0.030365 0.022626 \n",
|
||||
"0 0.032042 0.027682 0.019353 \n",
|
||||
"0 0.000481 0.000644 0.000223 \n",
|
||||
"0 0.000463 0.000644 0.000189 "
|
||||
]
|
||||
},
|
||||
@ -800,29 +800,29 @@
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Self_GlobalAvg</td>\n",
|
||||
" <td>0.067695</td>\n",
|
||||
" <td>0.027470</td>\n",
|
||||
" <td>0.171187</td>\n",
|
||||
" <td>0.509546</td>\n",
|
||||
" <td>0.384942</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>0.025974</td>\n",
|
||||
" <td>2.711772</td>\n",
|
||||
" <td>0.992003</td>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>0.051593</td>\n",
|
||||
" <td>0.019428</td>\n",
|
||||
" <td>0.129062</td>\n",
|
||||
" <td>0.506826</td>\n",
|
||||
" <td>0.336161</td>\n",
|
||||
" <td>0.987593</td>\n",
|
||||
" <td>0.175325</td>\n",
|
||||
" <td>5.087656</td>\n",
|
||||
" <td>0.908118</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>0.054166</td>\n",
|
||||
" <td>0.021656</td>\n",
|
||||
" <td>0.128378</td>\n",
|
||||
" <td>0.507802</td>\n",
|
||||
" <td>0.325557</td>\n",
|
||||
" <td>0.988865</td>\n",
|
||||
" <td>0.190476</td>\n",
|
||||
" <td>5.100033</td>\n",
|
||||
" <td>0.907724</td>\n",
|
||||
" <td>Self_TopRated</td>\n",
|
||||
" <td>0.001043</td>\n",
|
||||
" <td>0.000335</td>\n",
|
||||
" <td>0.003348</td>\n",
|
||||
" <td>0.496433</td>\n",
|
||||
" <td>0.009544</td>\n",
|
||||
" <td>0.699046</td>\n",
|
||||
" <td>0.005051</td>\n",
|
||||
" <td>1.945910</td>\n",
|
||||
" <td>0.995669</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
@ -845,15 +845,15 @@
|
||||
" Model NDCG mAP MRR LAUC HR \\\n",
|
||||
"0 Self_TopPop 0.214651 0.111707 0.400939 0.555546 0.765642 \n",
|
||||
"0 Ready_Baseline 0.095957 0.043178 0.198193 0.515501 0.437964 \n",
|
||||
"0 Self_GlobalAvg 0.067695 0.027470 0.171187 0.509546 0.384942 \n",
|
||||
"0 Ready_Random 0.054166 0.021656 0.128378 0.507802 0.325557 \n",
|
||||
"0 Ready_Random 0.051593 0.019428 0.129062 0.506826 0.336161 \n",
|
||||
"0 Self_TopRated 0.001043 0.000335 0.003348 0.496433 0.009544 \n",
|
||||
"0 Self_BaselineUI 0.000752 0.000168 0.001677 0.496424 0.009544 \n",
|
||||
"\n",
|
||||
" Reco in test Test coverage Shannon Gini \n",
|
||||
"0 1.000000 0.038961 3.159079 0.987317 \n",
|
||||
"0 1.000000 0.033911 2.836513 0.991139 \n",
|
||||
"0 1.000000 0.025974 2.711772 0.992003 \n",
|
||||
"0 0.988865 0.190476 5.100033 0.907724 \n",
|
||||
"0 0.987593 0.175325 5.087656 0.908118 \n",
|
||||
"0 0.699046 0.005051 1.945910 0.995669 \n",
|
||||
"0 0.600530 0.005051 1.803126 0.996380 "
|
||||
]
|
||||
},
|
||||
@ -882,7 +882,7 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"3it [00:00, 1941.81it/s]\n"
|
||||
"3it [00:00, 1191.68it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1246,148 +1246,148 @@
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2985</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <th>50941</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Star Wars (1977)</td>\n",
|
||||
" <td>Action, Adventure, Romance, Sci-Fi, War</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25980</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Dead Man Walking (1995)</td>\n",
|
||||
" <td>It's a Wonderful Life (1946)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9357</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <th>9531</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Last Supper, The (1995)</td>\n",
|
||||
" <td>Drama, Thriller</td>\n",
|
||||
" <td>Wizard of Oz, The (1939)</td>\n",
|
||||
" <td>Adventure, Children's, Drama, Musical</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>17306</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <th>27182</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Leaving Las Vegas (1995)</td>\n",
|
||||
" <td>Drama, Romance</td>\n",
|
||||
" <td>Empire Strikes Back, The (1980)</td>\n",
|
||||
" <td>Action, Adventure, Drama, Romance, Sci-Fi, War</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>36474</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <th>23944</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Swingers (1996)</td>\n",
|
||||
" <td>Comedy, Drama</td>\n",
|
||||
" <td>Apocalypse Now (1979)</td>\n",
|
||||
" <td>Drama, War</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>65139</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Welcome to the Dollhouse (1995)</td>\n",
|
||||
" <td>Comedy, Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>61975</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Private Parts (1997)</td>\n",
|
||||
" <td>Comedy, Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>56522</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Waiting for Guffman (1996)</td>\n",
|
||||
" <td>Comedy</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41414</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Donnie Brasco (1997)</td>\n",
|
||||
" <td>Crime, Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>36617</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Lone Star (1996)</td>\n",
|
||||
" <td>Drama, Mystery</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24501</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>People vs. Larry Flynt, The (1996)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>20210</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <th>20285</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Return of the Jedi (1983)</td>\n",
|
||||
" <td>Action, Adventure, Romance, Sci-Fi, War</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8230</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Beautiful Girls (1996)</td>\n",
|
||||
" <th>37504</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Aladdin (1992)</td>\n",
|
||||
" <td>Animation, Children's, Comedy, Musical</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>68312</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Babe (1995)</td>\n",
|
||||
" <td>Children's, Comedy, Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16362</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Apollo 13 (1995)</td>\n",
|
||||
" <td>Action, Drama, Thriller</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15168</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Indiana Jones and the Last Crusade (1989)</td>\n",
|
||||
" <td>Action, Adventure</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>29402</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Psycho (1960)</td>\n",
|
||||
" <td>Horror, Romance, Thriller</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>40755</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Jean de Florette (1986)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19781</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Liar Liar (1997)</td>\n",
|
||||
" <td>Comedy</td>\n",
|
||||
" <th>41950</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Die Hard (1988)</td>\n",
|
||||
" <td>Action, Thriller</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>39387</th>\n",
|
||||
" <td>789</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Sleepers (1996)</td>\n",
|
||||
" <td>Crime, Drama</td>\n",
|
||||
" <th>58932</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Enchanted April (1991)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>43013</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>2001: A Space Odyssey (1968)</td>\n",
|
||||
" <td>Drama, Mystery, Sci-Fi, Thriller</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>65664</th>\n",
|
||||
" <td>661</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Star Trek: The Wrath of Khan (1982)</td>\n",
|
||||
" <td>Action, Adventure, Sci-Fi</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" user rating title \\\n",
|
||||
"2985 789 5 Star Wars (1977) \n",
|
||||
"25980 789 5 Dead Man Walking (1995) \n",
|
||||
"9357 789 5 Last Supper, The (1995) \n",
|
||||
"17306 789 5 Leaving Las Vegas (1995) \n",
|
||||
"36474 789 5 Swingers (1996) \n",
|
||||
"65139 789 4 Welcome to the Dollhouse (1995) \n",
|
||||
"61975 789 4 Private Parts (1997) \n",
|
||||
"56522 789 4 Waiting for Guffman (1996) \n",
|
||||
"41414 789 4 Donnie Brasco (1997) \n",
|
||||
"36617 789 4 Lone Star (1996) \n",
|
||||
"24501 789 4 People vs. Larry Flynt, The (1996) \n",
|
||||
"20210 789 4 Return of the Jedi (1983) \n",
|
||||
"8230 789 3 Beautiful Girls (1996) \n",
|
||||
"19781 789 3 Liar Liar (1997) \n",
|
||||
"39387 789 3 Sleepers (1996) \n",
|
||||
" user rating title \\\n",
|
||||
"50941 661 5 It's a Wonderful Life (1946) \n",
|
||||
"9531 661 5 Wizard of Oz, The (1939) \n",
|
||||
"27182 661 5 Empire Strikes Back, The (1980) \n",
|
||||
"23944 661 5 Apocalypse Now (1979) \n",
|
||||
"20285 661 5 Return of the Jedi (1983) \n",
|
||||
"37504 661 5 Aladdin (1992) \n",
|
||||
"68312 661 5 Babe (1995) \n",
|
||||
"16362 661 5 Apollo 13 (1995) \n",
|
||||
"15168 661 5 Indiana Jones and the Last Crusade (1989) \n",
|
||||
"29402 661 5 Psycho (1960) \n",
|
||||
"40755 661 5 Jean de Florette (1986) \n",
|
||||
"41950 661 5 Die Hard (1988) \n",
|
||||
"58932 661 5 Enchanted April (1991) \n",
|
||||
"43013 661 5 2001: A Space Odyssey (1968) \n",
|
||||
"65664 661 5 Star Trek: The Wrath of Khan (1982) \n",
|
||||
"\n",
|
||||
" genres \n",
|
||||
"2985 Action, Adventure, Romance, Sci-Fi, War \n",
|
||||
"25980 Drama \n",
|
||||
"9357 Drama, Thriller \n",
|
||||
"17306 Drama, Romance \n",
|
||||
"36474 Comedy, Drama \n",
|
||||
"65139 Comedy, Drama \n",
|
||||
"61975 Comedy, Drama \n",
|
||||
"56522 Comedy \n",
|
||||
"41414 Crime, Drama \n",
|
||||
"36617 Drama, Mystery \n",
|
||||
"24501 Drama \n",
|
||||
"20210 Action, Adventure, Romance, Sci-Fi, War \n",
|
||||
"8230 Drama \n",
|
||||
"19781 Comedy \n",
|
||||
"39387 Crime, Drama "
|
||||
" genres \n",
|
||||
"50941 Drama \n",
|
||||
"9531 Adventure, Children's, Drama, Musical \n",
|
||||
"27182 Action, Adventure, Drama, Romance, Sci-Fi, War \n",
|
||||
"23944 Drama, War \n",
|
||||
"20285 Action, Adventure, Romance, Sci-Fi, War \n",
|
||||
"37504 Animation, Children's, Comedy, Musical \n",
|
||||
"68312 Children's, Comedy, Drama \n",
|
||||
"16362 Action, Drama, Thriller \n",
|
||||
"15168 Action, Adventure \n",
|
||||
"29402 Horror, Romance, Thriller \n",
|
||||
"40755 Drama \n",
|
||||
"41950 Action, Thriller \n",
|
||||
"58932 Drama \n",
|
||||
"43013 Drama, Mystery, Sci-Fi, Thriller \n",
|
||||
"65664 Action, Adventure, Sci-Fi "
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
@ -1429,71 +1429,71 @@
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>787</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>659</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>Great Day in Harlem, A (1994)</td>\n",
|
||||
" <td>Documentary</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1729</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>1601</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>Tough and Deadly (1995)</td>\n",
|
||||
" <td>Action, Drama, Thriller</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2671</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>2543</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>Aiqing wansui (1994)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3613</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>3485</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>Delta of Venus (1994)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4555</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>4427</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>Someone Else's America (1995)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5497</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>5369</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>6</td>\n",
|
||||
" <td>Saint of Fort Washington, The (1993)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6439</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>6311</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>Celestial Clockwork (1994)</td>\n",
|
||||
" <td>Comedy</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7380</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>7253</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" <td>Some Mother's Son (1996)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9276</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>9148</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>9</td>\n",
|
||||
" <td>Maya Lin: A Strong Clear Vision (1994)</td>\n",
|
||||
" <td>Documentary</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8322</th>\n",
|
||||
" <td>789.0</td>\n",
|
||||
" <th>8194</th>\n",
|
||||
" <td>661.0</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>Prefontaine (1997)</td>\n",
|
||||
" <td>Drama</td>\n",
|
||||
@ -1504,28 +1504,28 @@
|
||||
],
|
||||
"text/plain": [
|
||||
" user rec_nb title \\\n",
|
||||
"787 789.0 1 Great Day in Harlem, A (1994) \n",
|
||||
"1729 789.0 2 Tough and Deadly (1995) \n",
|
||||
"2671 789.0 3 Aiqing wansui (1994) \n",
|
||||
"3613 789.0 4 Delta of Venus (1994) \n",
|
||||
"4555 789.0 5 Someone Else's America (1995) \n",
|
||||
"5497 789.0 6 Saint of Fort Washington, The (1993) \n",
|
||||
"6439 789.0 7 Celestial Clockwork (1994) \n",
|
||||
"7380 789.0 8 Some Mother's Son (1996) \n",
|
||||
"9276 789.0 9 Maya Lin: A Strong Clear Vision (1994) \n",
|
||||
"8322 789.0 10 Prefontaine (1997) \n",
|
||||
"659 661.0 1 Great Day in Harlem, A (1994) \n",
|
||||
"1601 661.0 2 Tough and Deadly (1995) \n",
|
||||
"2543 661.0 3 Aiqing wansui (1994) \n",
|
||||
"3485 661.0 4 Delta of Venus (1994) \n",
|
||||
"4427 661.0 5 Someone Else's America (1995) \n",
|
||||
"5369 661.0 6 Saint of Fort Washington, The (1993) \n",
|
||||
"6311 661.0 7 Celestial Clockwork (1994) \n",
|
||||
"7253 661.0 8 Some Mother's Son (1996) \n",
|
||||
"9148 661.0 9 Maya Lin: A Strong Clear Vision (1994) \n",
|
||||
"8194 661.0 10 Prefontaine (1997) \n",
|
||||
"\n",
|
||||
" genres \n",
|
||||
"787 Documentary \n",
|
||||
"1729 Action, Drama, Thriller \n",
|
||||
"2671 Drama \n",
|
||||
"3613 Drama \n",
|
||||
"4555 Drama \n",
|
||||
"5497 Drama \n",
|
||||
"6439 Comedy \n",
|
||||
"7380 Drama \n",
|
||||
"9276 Documentary \n",
|
||||
"8322 Drama "
|
||||
"659 Documentary \n",
|
||||
"1601 Action, Drama, Thriller \n",
|
||||
"2543 Drama \n",
|
||||
"3485 Drama \n",
|
||||
"4427 Drama \n",
|
||||
"5369 Drama \n",
|
||||
"6311 Comedy \n",
|
||||
"7253 Drama \n",
|
||||
"9148 Documentary \n",
|
||||
"8194 Drama "
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
@ -1595,11 +1595,11 @@
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"943it [00:00, 4479.94it/s]\n",
|
||||
"943it [00:00, 4036.40it/s]\n",
|
||||
"943it [00:00, 4598.99it/s]\n",
|
||||
"943it [00:00, 5170.18it/s]\n",
|
||||
"943it [00:00, 4778.23it/s]\n"
|
||||
"943it [00:00, 4220.01it/s]\n",
|
||||
"943it [00:00, 3015.35it/s]\n",
|
||||
"943it [00:00, 2308.31it/s]\n",
|
||||
"943it [00:00, 3461.11it/s]\n",
|
||||
"943it [00:00, 3442.41it/s]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -1688,45 +1688,45 @@
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Self_GlobalAvg</td>\n",
|
||||
" <td>1.125760</td>\n",
|
||||
" <td>0.943534</td>\n",
|
||||
" <td>0.061188</td>\n",
|
||||
" <td>0.025968</td>\n",
|
||||
" <td>0.031383</td>\n",
|
||||
" <td>0.041343</td>\n",
|
||||
" <td>0.040558</td>\n",
|
||||
" <td>0.032107</td>\n",
|
||||
" <td>0.067695</td>\n",
|
||||
" <td>0.027470</td>\n",
|
||||
" <td>0.171187</td>\n",
|
||||
" <td>0.509546</td>\n",
|
||||
" <td>0.384942</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" <td>0.025974</td>\n",
|
||||
" <td>2.711772</td>\n",
|
||||
" <td>0.992003</td>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>1.525959</td>\n",
|
||||
" <td>1.225122</td>\n",
|
||||
" <td>0.047402</td>\n",
|
||||
" <td>0.020629</td>\n",
|
||||
" <td>0.024471</td>\n",
|
||||
" <td>0.032042</td>\n",
|
||||
" <td>0.027682</td>\n",
|
||||
" <td>0.019353</td>\n",
|
||||
" <td>0.051593</td>\n",
|
||||
" <td>0.019428</td>\n",
|
||||
" <td>0.129062</td>\n",
|
||||
" <td>0.506826</td>\n",
|
||||
" <td>0.336161</td>\n",
|
||||
" <td>0.987593</td>\n",
|
||||
" <td>0.175325</td>\n",
|
||||
" <td>5.087656</td>\n",
|
||||
" <td>0.908118</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Ready_Random</td>\n",
|
||||
" <td>1.531724</td>\n",
|
||||
" <td>1.230384</td>\n",
|
||||
" <td>0.049417</td>\n",
|
||||
" <td>0.022558</td>\n",
|
||||
" <td>0.025490</td>\n",
|
||||
" <td>0.033242</td>\n",
|
||||
" <td>0.030365</td>\n",
|
||||
" <td>0.022626</td>\n",
|
||||
" <td>0.054166</td>\n",
|
||||
" <td>0.021656</td>\n",
|
||||
" <td>0.128378</td>\n",
|
||||
" <td>0.507802</td>\n",
|
||||
" <td>0.325557</td>\n",
|
||||
" <td>0.988865</td>\n",
|
||||
" <td>0.190476</td>\n",
|
||||
" <td>5.100033</td>\n",
|
||||
" <td>0.907724</td>\n",
|
||||
" <td>Self_TopRated</td>\n",
|
||||
" <td>1.030712</td>\n",
|
||||
" <td>0.820904</td>\n",
|
||||
" <td>0.000954</td>\n",
|
||||
" <td>0.000188</td>\n",
|
||||
" <td>0.000298</td>\n",
|
||||
" <td>0.000481</td>\n",
|
||||
" <td>0.000644</td>\n",
|
||||
" <td>0.000223</td>\n",
|
||||
" <td>0.001043</td>\n",
|
||||
" <td>0.000335</td>\n",
|
||||
" <td>0.003348</td>\n",
|
||||
" <td>0.496433</td>\n",
|
||||
" <td>0.009544</td>\n",
|
||||
" <td>0.699046</td>\n",
|
||||
" <td>0.005051</td>\n",
|
||||
" <td>1.945910</td>\n",
|
||||
" <td>0.995669</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
@ -1757,22 +1757,22 @@
|
||||
" Model RMSE MAE precision recall F_1 \\\n",
|
||||
"0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n",
|
||||
"0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n",
|
||||
"0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n",
|
||||
"0 Ready_Random 1.531724 1.230384 0.049417 0.022558 0.025490 \n",
|
||||
"0 Ready_Random 1.525959 1.225122 0.047402 0.020629 0.024471 \n",
|
||||
"0 Self_TopRated 1.030712 0.820904 0.000954 0.000188 0.000298 \n",
|
||||
"0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n",
|
||||
"\n",
|
||||
" F_05 precision_super recall_super NDCG mAP MRR \\\n",
|
||||
"0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n",
|
||||
"0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n",
|
||||
"0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n",
|
||||
"0 0.033242 0.030365 0.022626 0.054166 0.021656 0.128378 \n",
|
||||
"0 0.032042 0.027682 0.019353 0.051593 0.019428 0.129062 \n",
|
||||
"0 0.000481 0.000644 0.000223 0.001043 0.000335 0.003348 \n",
|
||||
"0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n",
|
||||
"\n",
|
||||
" LAUC HR Reco in test Test coverage Shannon Gini \n",
|
||||
"0 0.555546 0.765642 1.000000 0.038961 3.159079 0.987317 \n",
|
||||
"0 0.515501 0.437964 1.000000 0.033911 2.836513 0.991139 \n",
|
||||
"0 0.509546 0.384942 1.000000 0.025974 2.711772 0.992003 \n",
|
||||
"0 0.507802 0.325557 0.988865 0.190476 5.100033 0.907724 \n",
|
||||
"0 0.506826 0.336161 0.987593 0.175325 5.087656 0.908118 \n",
|
||||
"0 0.496433 0.009544 0.699046 0.005051 1.945910 0.995669 \n",
|
||||
"0 0.496424 0.009544 0.600530 0.005051 1.803126 0.996380 "
|
||||
]
|
||||
},
|
||||
|
||||
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