1903 lines
62 KiB
Plaintext
1903 lines
62 KiB
Plaintext
{
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"cells": [
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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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"### Prepare test set"
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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": 19,
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"metadata": {
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"slideshow": {
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"slide_type": "-"
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}
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},
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"outputs": [],
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"source": [
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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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"from collections import defaultdict\n",
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"from itertools import chain\n",
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"import random\n",
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"from tqdm import tqdm\n",
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"\n",
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"# In evaluation we do not load train set - it is not needed\n",
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"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
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"test.columns=['user', 'item', 'rating', 'timestamp']\n",
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"\n",
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"test['user_code'] = test['user'].astype(\"category\").cat.codes\n",
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"test['item_code'] = test['item'].astype(\"category\").cat.codes\n",
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"\n",
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"user_code_id = dict(enumerate(test['user'].astype(\"category\").cat.categories))\n",
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"user_id_code = dict((v, k) for k, v in user_code_id.items())\n",
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"item_code_id = dict(enumerate(test['item'].astype(\"category\").cat.categories))\n",
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"item_id_code = dict((v, k) for k, v in item_code_id.items())\n",
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"\n",
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"test_ui = sparse.csr_matrix((test['rating'], (test['user_code'], test['item_code'])))"
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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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"### Estimations metrics"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"estimations_df=pd.read_csv('Recommendations generated/ml-100k/Ready_Baseline_estimations.csv', header=None)\n",
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"estimations_df.columns=['user', 'item' ,'score']\n",
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"\n",
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"estimations_df['user_code']=[user_id_code[user] for user in estimations_df['user']]\n",
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"estimations_df['item_code']=[item_id_code[item] for item in estimations_df['item']]\n",
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"estimations=sparse.csr_matrix((estimations_df['score'], (estimations_df['user_code'], estimations_df['item_code'])), shape=test_ui.shape)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"def estimations_metrics(test_ui, estimations):\n",
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" result=[]\n",
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"\n",
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" RMSE=(np.sum((estimations.data-test_ui.data)**2)/estimations.nnz)**(1/2)\n",
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" result.append(['RMSE', RMSE])\n",
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"\n",
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" MAE=np.sum(abs(estimations.data-test_ui.data))/estimations.nnz\n",
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" result.append(['MAE', MAE])\n",
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" \n",
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" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
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" df_result.columns=list(zip(*result))[0]\n",
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" return df_result"
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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": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>RMSE</th>\n",
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" <th>MAE</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.949459</td>\n",
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" <td>0.752487</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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" RMSE MAE\n",
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"0 0.949459 0.752487"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# in case of error (in the laboratories) you might have to switch to the other version of pandas\n",
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"# try !pip3 install pandas=='1.0.3' (or pip if you use python 2) and restart the kernel\n",
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"\n",
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"estimations_metrics(test_ui, estimations)"
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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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"### Ranking metrics"
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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": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[663, 475, 62, ..., 472, 269, 503],\n",
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" [ 48, 313, 475, ..., 591, 175, 466],\n",
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" [351, 313, 475, ..., 591, 175, 466],\n",
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" ...,\n",
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" [259, 313, 475, ..., 11, 591, 175],\n",
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" [ 33, 313, 475, ..., 11, 591, 175],\n",
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" [ 77, 313, 475, ..., 11, 591, 175]])"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"reco = np.loadtxt('Recommendations generated/ml-100k/Ready_Baseline_reco.csv', delimiter=',')\n",
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"# Let's ignore scores - they are not used in evaluation: \n",
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"users=reco[:,:1]\n",
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"items=reco[:,1::2]\n",
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"# Let's use inner ids instead of real ones\n",
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"users=np.vectorize(lambda x: user_id_code.setdefault(x, -1))(users)\n",
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"items=np.vectorize(lambda x: item_id_code.setdefault(x, -1))(items) # maybe items we recommend are not in test set\n",
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"# Let's put them into one array\n",
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"reco=np.concatenate((users, items), axis=1)\n",
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"reco"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"def ranking_metrics(test_ui, reco, super_reactions=[], topK=10):\n",
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" \n",
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" nb_items=test_ui.shape[1]\n",
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" relevant_users, super_relevant_users, prec, rec, F_1, F_05, prec_super, rec_super, ndcg, mAP, MRR, LAUC, HR=\\\n",
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" 0,0,0,0,0,0,0,0,0,0,0,0,0\n",
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" \n",
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" cg = (1.0 / np.log2(np.arange(2, topK + 2)))\n",
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" cg_sum = np.cumsum(cg)\n",
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" \n",
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" for (nb_user, user) in tqdm(enumerate(reco[:,0])):\n",
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" u_rated_items=test_ui.indices[test_ui.indptr[user]:test_ui.indptr[user+1]]\n",
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" nb_u_rated_items=len(u_rated_items)\n",
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" if nb_u_rated_items>0: # skip users with no items in test set (still possible that there will be no super items)\n",
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" relevant_users+=1\n",
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" \n",
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" u_super_items=u_rated_items[np.vectorize(lambda x: x in super_reactions)\\\n",
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" (test_ui.data[test_ui.indptr[user]:test_ui.indptr[user+1]])]\n",
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" # more natural seems u_super_items=[item for item in u_rated_items if test_ui[user,item] in super_reactions]\n",
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" # but accesing test_ui[user,item] is expensive -we should avoid doing it\n",
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" if len(u_super_items)>0:\n",
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" super_relevant_users+=1\n",
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" \n",
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" user_successes=np.zeros(topK)\n",
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" nb_user_successes=0\n",
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" user_super_successes=np.zeros(topK)\n",
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" nb_user_super_successes=0\n",
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" \n",
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" # evaluation\n",
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" for (item_position,item) in enumerate(reco[nb_user,1:topK+1]):\n",
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" if item in u_rated_items:\n",
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" user_successes[item_position]=1\n",
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" nb_user_successes+=1\n",
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" if item in u_super_items:\n",
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" user_super_successes[item_position]=1\n",
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" nb_user_super_successes+=1\n",
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" \n",
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" prec_u=nb_user_successes/topK \n",
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" prec+=prec_u\n",
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" \n",
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" rec_u=nb_user_successes/nb_u_rated_items\n",
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" rec+=rec_u\n",
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" \n",
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" F_1+=2*(prec_u*rec_u)/(prec_u+rec_u) if prec_u+rec_u>0 else 0\n",
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" F_05+=(0.5**2+1)*(prec_u*rec_u)/(0.5**2*prec_u+rec_u) if prec_u+rec_u>0 else 0\n",
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" \n",
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" prec_super+=nb_user_super_successes/topK\n",
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" rec_super+=nb_user_super_successes/max(len(u_super_items),1) # to set 0 if no super items\n",
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" ndcg+=np.dot(user_successes,cg)/cg_sum[min(topK, nb_u_rated_items)-1]\n",
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" \n",
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" cumsum_successes=np.cumsum(user_successes)\n",
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" mAP+=np.dot(cumsum_successes/np.arange(1,topK+1), user_successes)/min(topK, nb_u_rated_items)\n",
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" MRR+=1/(user_successes.nonzero()[0][0]+1) if user_successes.nonzero()[0].size>0 else 0\n",
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" LAUC+=(np.dot(cumsum_successes, 1-user_successes)+\\\n",
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" (nb_user_successes+nb_u_rated_items)/2*((nb_items-nb_u_rated_items)-(topK-nb_user_successes)))/\\\n",
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" ((nb_items-nb_u_rated_items)*nb_u_rated_items)\n",
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" \n",
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" HR+=nb_user_successes>0\n",
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" \n",
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" \n",
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" result=[]\n",
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" result.append(('precision', prec/relevant_users))\n",
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" result.append(('recall', rec/relevant_users))\n",
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" result.append(('F_1', F_1/relevant_users))\n",
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" result.append(('F_05', F_05/relevant_users))\n",
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" result.append(('precision_super', prec_super/super_relevant_users))\n",
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" result.append(('recall_super', rec_super/super_relevant_users))\n",
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" result.append(('NDCG', ndcg/relevant_users))\n",
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" result.append(('mAP', mAP/relevant_users))\n",
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" result.append(('MRR', MRR/relevant_users))\n",
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" result.append(('LAUC', LAUC/relevant_users))\n",
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" result.append(('HR', HR/relevant_users))\n",
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"\n",
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" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
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" df_result.columns=list(zip(*result))[0]\n",
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" return df_result"
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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": 25,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"943it [00:00, 6210.87it/s]\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>precision</th>\n",
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" <th>recall</th>\n",
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" <th>F_1</th>\n",
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" <th>F_05</th>\n",
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" <th>precision_super</th>\n",
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" <th>recall_super</th>\n",
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" <th>NDCG</th>\n",
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" <th>mAP</th>\n",
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" <th>MRR</th>\n",
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" <th>LAUC</th>\n",
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" <th>HR</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.09141</td>\n",
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" <td>0.037652</td>\n",
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" <td>0.04603</td>\n",
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" <td>0.061286</td>\n",
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" <td>0.079614</td>\n",
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" <td>0.056463</td>\n",
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" <td>0.095957</td>\n",
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" <td>0.043178</td>\n",
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" <td>0.198193</td>\n",
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" <td>0.515501</td>\n",
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" <td>0.437964</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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" precision recall F_1 F_05 precision_super recall_super \\\n",
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"0 0.09141 0.037652 0.04603 0.061286 0.079614 0.056463 \n",
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"\n",
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" NDCG mAP MRR LAUC HR \n",
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"0 0.095957 0.043178 0.198193 0.515501 0.437964 "
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ranking_metrics(test_ui, reco, super_reactions=[4,5], topK=10)"
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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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"### Diversity metrics"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"def diversity_metrics(test_ui, reco, topK=10):\n",
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" \n",
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" frequencies=defaultdict(int)\n",
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" \n",
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" # let's assign 0 to all items in test set\n",
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" for item in list(set(test_ui.indices)):\n",
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" frequencies[item]=0\n",
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" \n",
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" # counting frequencies\n",
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" for item in reco[:,1:].flat:\n",
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" frequencies[item]+=1\n",
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" \n",
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" nb_reco_outside_test=frequencies[-1]\n",
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" del frequencies[-1]\n",
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" \n",
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" frequencies=np.array(list(frequencies.values()))\n",
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" \n",
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" nb_rec_items=len(frequencies[frequencies>0])\n",
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" nb_reco_inside_test=np.sum(frequencies)\n",
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" \n",
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" frequencies=frequencies/np.sum(frequencies)\n",
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" frequencies=np.sort(frequencies)\n",
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" \n",
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" with np.errstate(divide='ignore'): # let's put zeros put items with 0 frequency and ignore division warning\n",
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" log_frequencies=np.nan_to_num(np.log(frequencies), posinf=0, neginf=0)\n",
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" \n",
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" result=[]\n",
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" result.append(('Reco in test', nb_reco_inside_test/(nb_reco_inside_test+nb_reco_outside_test)))\n",
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" result.append(('Test coverage', nb_rec_items/test_ui.shape[1]))\n",
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" result.append(('Shannon', -np.dot(frequencies, log_frequencies)))\n",
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" result.append(('Gini', np.dot(frequencies, np.arange(1-len(frequencies), len(frequencies), 2))/(len(frequencies)-1)))\n",
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" \n",
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" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
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" df_result.columns=list(zip(*result))[0]\n",
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" return df_result"
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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": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Reco in test</th>\n",
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" <th>Test coverage</th>\n",
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" <th>Shannon</th>\n",
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" <th>Gini</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1.0</td>\n",
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" <td>0.033911</td>\n",
|
|
" <td>2.836513</td>\n",
|
|
" <td>0.991139</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
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],
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"text/plain": [
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" Reco in test Test coverage Shannon Gini\n",
|
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"0 1.0 0.033911 2.836513 0.991139"
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]
|
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},
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"execution_count": 27,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
|
"# in case of errors try !pip3 install numpy==1.18.4 (or pip if you use python 2) and restart the kernel\n",
|
|
"\n",
|
|
"import evaluation_measures as ev\n",
|
|
"import imp\n",
|
|
"imp.reload(ev)\n",
|
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"\n",
|
|
"x=diversity_metrics(test_ui, reco, topK=10)\n",
|
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"x"
|
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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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"# To be used in other notebooks"
|
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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": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"943it [00:00, 6361.52it/s]\n"
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
|
" <th>RMSE</th>\n",
|
|
" <th>MAE</th>\n",
|
|
" <th>precision</th>\n",
|
|
" <th>recall</th>\n",
|
|
" <th>F_1</th>\n",
|
|
" <th>F_05</th>\n",
|
|
" <th>precision_super</th>\n",
|
|
" <th>recall_super</th>\n",
|
|
" <th>NDCG</th>\n",
|
|
" <th>mAP</th>\n",
|
|
" <th>MRR</th>\n",
|
|
" <th>LAUC</th>\n",
|
|
" <th>HR</th>\n",
|
|
" <th>H2R</th>\n",
|
|
" <th>Reco in test</th>\n",
|
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" <th>Test coverage</th>\n",
|
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" <th>Shannon</th>\n",
|
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" <th>Gini</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>0</th>\n",
|
|
" <td>0.949459</td>\n",
|
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" <td>0.752487</td>\n",
|
|
" <td>0.09141</td>\n",
|
|
" <td>0.037652</td>\n",
|
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" <td>0.04603</td>\n",
|
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" <td>0.061286</td>\n",
|
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" <td>0.079614</td>\n",
|
|
" <td>0.056463</td>\n",
|
|
" <td>0.095957</td>\n",
|
|
" <td>0.043178</td>\n",
|
|
" <td>0.198193</td>\n",
|
|
" <td>0.515501</td>\n",
|
|
" <td>0.437964</td>\n",
|
|
" <td>0.239661</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.033911</td>\n",
|
|
" <td>2.836513</td>\n",
|
|
" <td>0.991139</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
|
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"text/plain": [
|
|
" RMSE MAE precision recall F_1 F_05 \\\n",
|
|
"0 0.949459 0.752487 0.09141 0.037652 0.04603 0.061286 \n",
|
|
"\n",
|
|
" precision_super recall_super NDCG mAP MRR LAUC \\\n",
|
|
"0 0.079614 0.056463 0.095957 0.043178 0.198193 0.515501 \n",
|
|
"\n",
|
|
" HR H2R Reco in test Test coverage Shannon Gini \n",
|
|
"0 0.437964 0.239661 1.0 0.033911 2.836513 0.991139 "
|
|
]
|
|
},
|
|
"execution_count": 28,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import evaluation_measures as ev\n",
|
|
"import imp\n",
|
|
"imp.reload(ev)\n",
|
|
"\n",
|
|
"estimations_df=pd.read_csv('Recommendations generated/ml-100k/Ready_Baseline_estimations.csv', header=None)\n",
|
|
"reco=np.loadtxt('Recommendations generated/ml-100k/Ready_Baseline_reco.csv', delimiter=',')\n",
|
|
"\n",
|
|
"ev.evaluate(test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None),\n",
|
|
" estimations_df=estimations_df, \n",
|
|
" reco=reco,\n",
|
|
" super_reactions=[4,5])\n",
|
|
"#also you can just type ev.evaluate_all(estimations_df, reco) - I put above values as default"
|
|
]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 29,
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"metadata": {},
|
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"943it [00:00, 6381.68it/s]\n",
|
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"943it [00:00, 5946.17it/s]\n",
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"943it [00:00, 6749.72it/s]\n",
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"943it [00:00, 6013.22it/s]\n",
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"943it [00:00, 5717.96it/s]\n"
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]
|
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}
|
|
],
|
|
"source": [
|
|
"import evaluation_measures as ev\n",
|
|
"import imp\n",
|
|
"imp.reload(ev)\n",
|
|
"\n",
|
|
"dir_path=\"Recommendations generated/ml-100k/\"\n",
|
|
"super_reactions=[4,5]\n",
|
|
"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
|
|
"\n",
|
|
"df=ev.evaluate_all(test, dir_path, super_reactions)\n",
|
|
"#also you can just type ev.evaluate_all() - I put above values as default"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Model</th>\n",
|
|
" <th>RMSE</th>\n",
|
|
" <th>MAE</th>\n",
|
|
" <th>precision</th>\n",
|
|
" <th>recall</th>\n",
|
|
" <th>F_1</th>\n",
|
|
" <th>F_05</th>\n",
|
|
" <th>precision_super</th>\n",
|
|
" <th>recall_super</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopPop</td>\n",
|
|
" <td>2.508258</td>\n",
|
|
" <td>2.217909</td>\n",
|
|
" <td>0.188865</td>\n",
|
|
" <td>0.116919</td>\n",
|
|
" <td>0.118732</td>\n",
|
|
" <td>0.141584</td>\n",
|
|
" <td>0.130472</td>\n",
|
|
" <td>0.137473</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Baseline</td>\n",
|
|
" <td>0.949459</td>\n",
|
|
" <td>0.752487</td>\n",
|
|
" <td>0.091410</td>\n",
|
|
" <td>0.037652</td>\n",
|
|
" <td>0.046030</td>\n",
|
|
" <td>0.061286</td>\n",
|
|
" <td>0.079614</td>\n",
|
|
" <td>0.056463</td>\n",
|
|
" </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",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Random</td>\n",
|
|
" <td>1.524954</td>\n",
|
|
" <td>1.223352</td>\n",
|
|
" <td>0.045599</td>\n",
|
|
" <td>0.021181</td>\n",
|
|
" <td>0.024585</td>\n",
|
|
" <td>0.031518</td>\n",
|
|
" <td>0.027897</td>\n",
|
|
" <td>0.021931</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopRated</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.032025</td>\n",
|
|
" <td>0.012674</td>\n",
|
|
" <td>0.015714</td>\n",
|
|
" <td>0.021183</td>\n",
|
|
" <td>0.028433</td>\n",
|
|
" <td>0.018573</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_BaselineUI</td>\n",
|
|
" <td>0.967585</td>\n",
|
|
" <td>0.762740</td>\n",
|
|
" <td>0.000954</td>\n",
|
|
" <td>0.000170</td>\n",
|
|
" <td>0.000278</td>\n",
|
|
" <td>0.000463</td>\n",
|
|
" <td>0.000644</td>\n",
|
|
" <td>0.000189</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 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 1.524954 1.223352 0.045599 0.021181 0.024585 \n",
|
|
"0 Self_TopRated NaN NaN 0.032025 0.012674 0.015714 \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 0.031518 0.027897 0.021931 \n",
|
|
"0 0.021183 0.028433 0.018573 \n",
|
|
"0 0.000463 0.000644 0.000189 "
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
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],
|
|
"source": [
|
|
"df.iloc[:,:9]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"outputs": [
|
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{
|
|
"data": {
|
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" }\n",
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|
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|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Model</th>\n",
|
|
" <th>NDCG</th>\n",
|
|
" <th>mAP</th>\n",
|
|
" <th>MRR</th>\n",
|
|
" <th>LAUC</th>\n",
|
|
" <th>HR</th>\n",
|
|
" <th>H2R</th>\n",
|
|
" <th>Reco in test</th>\n",
|
|
" <th>Test coverage</th>\n",
|
|
" <th>Shannon</th>\n",
|
|
" <th>Gini</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopPop</td>\n",
|
|
" <td>0.214651</td>\n",
|
|
" <td>0.111707</td>\n",
|
|
" <td>0.400939</td>\n",
|
|
" <td>0.555546</td>\n",
|
|
" <td>0.765642</td>\n",
|
|
" <td>0.492047</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.038961</td>\n",
|
|
" <td>3.159079</td>\n",
|
|
" <td>0.987317</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Baseline</td>\n",
|
|
" <td>0.095957</td>\n",
|
|
" <td>0.043178</td>\n",
|
|
" <td>0.198193</td>\n",
|
|
" <td>0.515501</td>\n",
|
|
" <td>0.437964</td>\n",
|
|
" <td>0.239661</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.033911</td>\n",
|
|
" <td>2.836513</td>\n",
|
|
" <td>0.991139</td>\n",
|
|
" </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>0.142100</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.025974</td>\n",
|
|
" <td>2.711772</td>\n",
|
|
" <td>0.992003</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Random</td>\n",
|
|
" <td>0.048111</td>\n",
|
|
" <td>0.017381</td>\n",
|
|
" <td>0.119005</td>\n",
|
|
" <td>0.507096</td>\n",
|
|
" <td>0.330859</td>\n",
|
|
" <td>0.091198</td>\n",
|
|
" <td>0.988123</td>\n",
|
|
" <td>0.181818</td>\n",
|
|
" <td>5.100792</td>\n",
|
|
" <td>0.906866</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopRated</td>\n",
|
|
" <td>0.022741</td>\n",
|
|
" <td>0.005328</td>\n",
|
|
" <td>0.031602</td>\n",
|
|
" <td>0.502764</td>\n",
|
|
" <td>0.237540</td>\n",
|
|
" <td>0.065748</td>\n",
|
|
" <td>0.697031</td>\n",
|
|
" <td>0.014430</td>\n",
|
|
" <td>2.220811</td>\n",
|
|
" <td>0.995173</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_BaselineUI</td>\n",
|
|
" <td>0.000752</td>\n",
|
|
" <td>0.000168</td>\n",
|
|
" <td>0.001677</td>\n",
|
|
" <td>0.496424</td>\n",
|
|
" <td>0.009544</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.600530</td>\n",
|
|
" <td>0.005051</td>\n",
|
|
" <td>1.803126</td>\n",
|
|
" <td>0.996380</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Model NDCG mAP MRR LAUC HR \\\n",
|
|
"0 Self_TopPop 0.214651 0.111707 0.400939 0.555546 0.765642 \n",
|
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"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",
|
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"0 Ready_Random 0.048111 0.017381 0.119005 0.507096 0.330859 \n",
|
|
"0 Self_TopRated 0.022741 0.005328 0.031602 0.502764 0.237540 \n",
|
|
"0 Self_BaselineUI 0.000752 0.000168 0.001677 0.496424 0.009544 \n",
|
|
"\n",
|
|
" H2R Reco in test Test coverage Shannon Gini \n",
|
|
"0 0.492047 1.000000 0.038961 3.159079 0.987317 \n",
|
|
"0 0.239661 1.000000 0.033911 2.836513 0.991139 \n",
|
|
"0 0.142100 1.000000 0.025974 2.711772 0.992003 \n",
|
|
"0 0.091198 0.988123 0.181818 5.100792 0.906866 \n",
|
|
"0 0.065748 0.697031 0.014430 2.220811 0.995173 \n",
|
|
"0 0.000000 0.600530 0.005051 1.803126 0.996380 "
|
|
]
|
|
},
|
|
"execution_count": 31,
|
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"metadata": {},
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"output_type": "execute_result"
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}
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],
|
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"source": [
|
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"df.iloc[:,np.append(0,np.arange(9, df.shape[1]))]"
|
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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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"# Check metrics on toy dataset"
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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": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"3it [00:00, 2328.44it/s]\n"
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]
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},
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"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>Model</th>\n",
|
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" <th>RMSE</th>\n",
|
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" <th>MAE</th>\n",
|
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" <th>precision</th>\n",
|
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" <th>recall</th>\n",
|
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" <th>F_1</th>\n",
|
|
" <th>F_05</th>\n",
|
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" <th>precision_super</th>\n",
|
|
" <th>recall_super</th>\n",
|
|
" <th>NDCG</th>\n",
|
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" <th>mAP</th>\n",
|
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" <th>MRR</th>\n",
|
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" <th>LAUC</th>\n",
|
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" <th>HR</th>\n",
|
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" <th>H2R</th>\n",
|
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" <th>Reco in test</th>\n",
|
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" <th>Test coverage</th>\n",
|
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" <th>Shannon</th>\n",
|
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" <th>Gini</th>\n",
|
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" </tr>\n",
|
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" </thead>\n",
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" <tbody>\n",
|
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" <tr>\n",
|
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" <th>0</th>\n",
|
|
" <td>Self_BaselineUI</td>\n",
|
|
" <td>1.612452</td>\n",
|
|
" <td>1.4</td>\n",
|
|
" <td>0.444444</td>\n",
|
|
" <td>0.888889</td>\n",
|
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" <td>0.555556</td>\n",
|
|
" <td>0.478632</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>0.75</td>\n",
|
|
" <td>0.676907</td>\n",
|
|
" <td>0.574074</td>\n",
|
|
" <td>0.611111</td>\n",
|
|
" <td>0.638889</td>\n",
|
|
" <td>1.0</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>0.888889</td>\n",
|
|
" <td>0.8</td>\n",
|
|
" <td>1.386294</td>\n",
|
|
" <td>0.25</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
|
],
|
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"text/plain": [
|
|
" Model RMSE MAE precision recall F_1 F_05 \\\n",
|
|
"0 Self_BaselineUI 1.612452 1.4 0.444444 0.888889 0.555556 0.478632 \n",
|
|
"\n",
|
|
" precision_super recall_super NDCG mAP MRR LAUC HR \\\n",
|
|
"0 0.333333 0.75 0.676907 0.574074 0.611111 0.638889 1.0 \n",
|
|
"\n",
|
|
" H2R Reco in test Test coverage Shannon Gini \n",
|
|
"0 0.333333 0.888889 0.8 1.386294 0.25 "
|
|
]
|
|
},
|
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"metadata": {},
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"output_type": "display_data"
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},
|
|
{
|
|
"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Training data:\n"
|
|
]
|
|
},
|
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{
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"data": {
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"text/plain": [
|
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"matrix([[3, 4, 0, 0, 5, 0, 0, 4],\n",
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" [0, 1, 2, 3, 0, 0, 0, 0],\n",
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" [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
|
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
|
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},
|
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{
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Test data:\n"
|
|
]
|
|
},
|
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{
|
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"data": {
|
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"text/plain": [
|
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"matrix([[0, 0, 0, 0, 0, 0, 3, 0],\n",
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" [0, 0, 0, 0, 5, 0, 0, 0],\n",
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" [5, 0, 4, 0, 0, 0, 0, 2]], dtype=int64)"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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"text": [
|
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"Recommendations:\n"
|
|
]
|
|
},
|
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>0</th>\n",
|
|
" <th>1</th>\n",
|
|
" <th>2</th>\n",
|
|
" <th>3</th>\n",
|
|
" <th>4</th>\n",
|
|
" <th>5</th>\n",
|
|
" <th>6</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>10</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>60</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>40</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 0 1 2 3 4 5 6\n",
|
|
"0 0 30 5.0 20 4.0 60 4.0\n",
|
|
"1 10 40 3.0 60 2.0 70 2.0\n",
|
|
"2 20 40 5.0 20 4.0 70 4.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Estimations:\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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" }\n",
|
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"</style>\n",
|
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"<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>est_score</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>0</td>\n",
|
|
" <td>60</td>\n",
|
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" <td>4.0</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>1</th>\n",
|
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" <td>10</td>\n",
|
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" <td>40</td>\n",
|
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" <td>3.0</td>\n",
|
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" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>20</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>20</td>\n",
|
|
" <td>70</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
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],
|
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"text/plain": [
|
|
" user item est_score\n",
|
|
"0 0 60 4.0\n",
|
|
"1 10 40 3.0\n",
|
|
"2 20 0 3.0\n",
|
|
"3 20 20 4.0\n",
|
|
"4 20 70 4.0"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import evaluation_measures as ev\n",
|
|
"import imp\n",
|
|
"import helpers\n",
|
|
"imp.reload(ev)\n",
|
|
"\n",
|
|
"dir_path=\"Recommendations generated/toy-example/\"\n",
|
|
"super_reactions=[4,5]\n",
|
|
"test=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None)\n",
|
|
"\n",
|
|
"display(ev.evaluate_all(test, dir_path, super_reactions, topK=3))\n",
|
|
"#also you can just type ev.evaluate_all() - I put above values as default\n",
|
|
"\n",
|
|
"toy_train_read=pd.read_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
|
|
"toy_test_read=pd.read_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
|
|
"reco=pd.read_csv('Recommendations generated/toy-example/Self_BaselineUI_reco.csv', header=None)\n",
|
|
"estimations=pd.read_csv('Recommendations generated/toy-example/Self_BaselineUI_estimations.csv', names=['user', 'item', 'est_score'])\n",
|
|
"toy_train_ui, toy_test_ui, toy_user_code_id, toy_user_id_code, \\\n",
|
|
"toy_item_code_id, toy_item_id_code = helpers.data_to_csr(toy_train_read, toy_test_read)\n",
|
|
"\n",
|
|
"print('Training data:')\n",
|
|
"display(toy_train_ui.todense())\n",
|
|
"\n",
|
|
"print('Test data:')\n",
|
|
"display(toy_test_ui.todense())\n",
|
|
"\n",
|
|
"print('Recommendations:')\n",
|
|
"display(reco)\n",
|
|
"\n",
|
|
"print('Estimations:')\n",
|
|
"display(estimations)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Sample recommendations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Here is what user rated high:\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" 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>rating</th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>genres</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3054</th>\n",
|
|
" <td>4</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>26266</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Air Force One (1997)</td>\n",
|
|
" <td>Action, Thriller</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>69162</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Desperate Measures (1998)</td>\n",
|
|
" <td>Crime, Drama, Thriller</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>58881</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Wedding Singer, The (1998)</td>\n",
|
|
" <td>Comedy, Romance</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>56335</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Ulee's Gold (1997)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>48926</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Lost Highway (1997)</td>\n",
|
|
" <td>Mystery</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>46942</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Cop Land (1997)</td>\n",
|
|
" <td>Crime, Drama, Mystery</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>28827</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Blues Brothers 2000 (1998)</td>\n",
|
|
" <td>Action, Comedy, Musical</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>21562</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>In & Out (1997)</td>\n",
|
|
" <td>Comedy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19981</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Liar Liar (1997)</td>\n",
|
|
" <td>Comedy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>71647</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Assignment, The (1997)</td>\n",
|
|
" <td>Thriller</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>24585</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>One Flew Over the Cuckoo's Nest (1975)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>7469</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>Starship Troopers (1997)</td>\n",
|
|
" <td>Action, Adventure, Sci-Fi, War</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>31546</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>Seven (Se7en) (1995)</td>\n",
|
|
" <td>Crime, Thriller</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>32233</th>\n",
|
|
" <td>4</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>Event Horizon (1997)</td>\n",
|
|
" <td>Action, Mystery, Sci-Fi, Thriller</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
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"text/plain": [
|
|
" user rating title \\\n",
|
|
"3054 4 5 Star Wars (1977) \n",
|
|
"26266 4 5 Air Force One (1997) \n",
|
|
"69162 4 5 Desperate Measures (1998) \n",
|
|
"58881 4 5 Wedding Singer, The (1998) \n",
|
|
"56335 4 5 Ulee's Gold (1997) \n",
|
|
"48926 4 5 Lost Highway (1997) \n",
|
|
"46942 4 5 Cop Land (1997) \n",
|
|
"28827 4 5 Blues Brothers 2000 (1998) \n",
|
|
"21562 4 5 In & Out (1997) \n",
|
|
"19981 4 5 Liar Liar (1997) \n",
|
|
"71647 4 5 Assignment, The (1997) \n",
|
|
"24585 4 4 One Flew Over the Cuckoo's Nest (1975) \n",
|
|
"7469 4 4 Starship Troopers (1997) \n",
|
|
"31546 4 4 Seven (Se7en) (1995) \n",
|
|
"32233 4 4 Event Horizon (1997) \n",
|
|
"\n",
|
|
" genres \n",
|
|
"3054 Action, Adventure, Romance, Sci-Fi, War \n",
|
|
"26266 Action, Thriller \n",
|
|
"69162 Crime, Drama, Thriller \n",
|
|
"58881 Comedy, Romance \n",
|
|
"56335 Drama \n",
|
|
"48926 Mystery \n",
|
|
"46942 Crime, Drama, Mystery \n",
|
|
"28827 Action, Comedy, Musical \n",
|
|
"21562 Comedy \n",
|
|
"19981 Comedy \n",
|
|
"71647 Thriller \n",
|
|
"24585 Drama \n",
|
|
"7469 Action, Adventure, Sci-Fi, War \n",
|
|
"31546 Crime, Thriller \n",
|
|
"32233 Action, Mystery, Sci-Fi, Thriller "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
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},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
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"text": [
|
|
"Here is what we recommend:\n"
|
|
]
|
|
},
|
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{
|
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"data": {
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"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>user</th>\n",
|
|
" <th>rec_nb</th>\n",
|
|
" <th>title</th>\n",
|
|
" <th>genres</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>Great Day in Harlem, A (1994)</td>\n",
|
|
" <td>Documentary</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>945</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>Tough and Deadly (1995)</td>\n",
|
|
" <td>Action, Drama, Thriller</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1887</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>Aiqing wansui (1994)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2829</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>Delta of Venus (1994)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3771</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>5</td>\n",
|
|
" <td>Someone Else's America (1995)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4713</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>Saint of Fort Washington, The (1993)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>5655</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>7</td>\n",
|
|
" <td>Celestial Clockwork (1994)</td>\n",
|
|
" <td>Comedy</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>6597</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>Some Mother's Son (1996)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>8491</th>\n",
|
|
" <td>4.0</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>7538</th>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>10</td>\n",
|
|
" <td>Prefontaine (1997)</td>\n",
|
|
" <td>Drama</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" user rec_nb title \\\n",
|
|
"3 4.0 1 Great Day in Harlem, A (1994) \n",
|
|
"945 4.0 2 Tough and Deadly (1995) \n",
|
|
"1887 4.0 3 Aiqing wansui (1994) \n",
|
|
"2829 4.0 4 Delta of Venus (1994) \n",
|
|
"3771 4.0 5 Someone Else's America (1995) \n",
|
|
"4713 4.0 6 Saint of Fort Washington, The (1993) \n",
|
|
"5655 4.0 7 Celestial Clockwork (1994) \n",
|
|
"6597 4.0 8 Some Mother's Son (1996) \n",
|
|
"8491 4.0 9 Maya Lin: A Strong Clear Vision (1994) \n",
|
|
"7538 4.0 10 Prefontaine (1997) \n",
|
|
"\n",
|
|
" genres \n",
|
|
"3 Documentary \n",
|
|
"945 Action, Drama, Thriller \n",
|
|
"1887 Drama \n",
|
|
"2829 Drama \n",
|
|
"3771 Drama \n",
|
|
"4713 Drama \n",
|
|
"5655 Comedy \n",
|
|
"6597 Drama \n",
|
|
"8491 Documentary \n",
|
|
"7538 Drama "
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"train=pd.read_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None, names=['user', 'item', 'rating', 'timestamp'])\n",
|
|
"items=pd.read_csv('./Datasets/ml-100k/movies.csv')\n",
|
|
"\n",
|
|
"user=random.choice(list(set(train['user'])))\n",
|
|
"\n",
|
|
"train_content=pd.merge(train, items, left_on='item', right_on='id')\n",
|
|
"\n",
|
|
"print('Here is what user rated high:')\n",
|
|
"display(train_content[train_content['user']==user][['user', 'rating', 'title', 'genres']]\\\n",
|
|
" .sort_values(by='rating', ascending=False)[:15])\n",
|
|
"\n",
|
|
"reco = np.loadtxt('Recommendations generated/ml-100k/Self_BaselineUI_reco.csv', delimiter=',')\n",
|
|
"items=pd.read_csv('./Datasets/ml-100k/movies.csv')\n",
|
|
"\n",
|
|
"# Let's ignore scores - they are not used in evaluation: \n",
|
|
"reco_users=reco[:,:1]\n",
|
|
"reco_items=reco[:,1::2]\n",
|
|
"# Let's put them into one array\n",
|
|
"reco=np.concatenate((reco_users, reco_items), axis=1)\n",
|
|
"\n",
|
|
"# Let's rebuild it user-item dataframe\n",
|
|
"recommended=[]\n",
|
|
"for row in reco:\n",
|
|
" for rec_nb, entry in enumerate(row[1:]):\n",
|
|
" recommended.append((row[0], rec_nb+1, entry))\n",
|
|
"recommended=pd.DataFrame(recommended, columns=['user','rec_nb', 'item'])\n",
|
|
"\n",
|
|
"recommended_content=pd.merge(recommended, items, left_on='item', right_on='id')\n",
|
|
"\n",
|
|
"print('Here is what we recommend:')\n",
|
|
"recommended_content[recommended_content['user']==user][['user', 'rec_nb', 'title', 'genres']].sort_values(by='rec_nb')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# project task 3: implement some other evaluation measure"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# it may be your idea, modification of what we have already implemented \n",
|
|
"# (for example Hit2 rate which would count as a success users whoreceived at least 2 relevant recommendations) \n",
|
|
"# or something well-known\n",
|
|
"# expected output: modification of evaluation_measures.py such that evaluate_all will also display your measure"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 37,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"943it [00:00, 6201.84it/s]\n",
|
|
"943it [00:00, 6111.62it/s]\n",
|
|
"943it [00:00, 6907.71it/s]\n",
|
|
"943it [00:00, 5865.19it/s]\n",
|
|
"943it [00:00, 5220.21it/s]\n",
|
|
"943it [00:00, 5230.33it/s]\n"
|
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]
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},
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>Model</th>\n",
|
|
" <th>RMSE</th>\n",
|
|
" <th>MAE</th>\n",
|
|
" <th>precision</th>\n",
|
|
" <th>recall</th>\n",
|
|
" <th>F_1</th>\n",
|
|
" <th>F_05</th>\n",
|
|
" <th>precision_super</th>\n",
|
|
" <th>recall_super</th>\n",
|
|
" <th>NDCG</th>\n",
|
|
" <th>mAP</th>\n",
|
|
" <th>MRR</th>\n",
|
|
" <th>LAUC</th>\n",
|
|
" <th>HR</th>\n",
|
|
" <th>H2R</th>\n",
|
|
" <th>Reco in test</th>\n",
|
|
" <th>Test coverage</th>\n",
|
|
" <th>Shannon</th>\n",
|
|
" <th>Gini</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopPop</td>\n",
|
|
" <td>2.508258</td>\n",
|
|
" <td>2.217909</td>\n",
|
|
" <td>0.188865</td>\n",
|
|
" <td>0.116919</td>\n",
|
|
" <td>0.118732</td>\n",
|
|
" <td>0.141584</td>\n",
|
|
" <td>0.130472</td>\n",
|
|
" <td>0.137473</td>\n",
|
|
" <td>0.214651</td>\n",
|
|
" <td>0.111707</td>\n",
|
|
" <td>0.400939</td>\n",
|
|
" <td>0.555546</td>\n",
|
|
" <td>0.765642</td>\n",
|
|
" <td>0.492047</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.038961</td>\n",
|
|
" <td>3.159079</td>\n",
|
|
" <td>0.987317</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Baseline</td>\n",
|
|
" <td>0.949459</td>\n",
|
|
" <td>0.752487</td>\n",
|
|
" <td>0.091410</td>\n",
|
|
" <td>0.037652</td>\n",
|
|
" <td>0.046030</td>\n",
|
|
" <td>0.061286</td>\n",
|
|
" <td>0.079614</td>\n",
|
|
" <td>0.056463</td>\n",
|
|
" <td>0.095957</td>\n",
|
|
" <td>0.043178</td>\n",
|
|
" <td>0.198193</td>\n",
|
|
" <td>0.515501</td>\n",
|
|
" <td>0.437964</td>\n",
|
|
" <td>0.239661</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.033911</td>\n",
|
|
" <td>2.836513</td>\n",
|
|
" <td>0.991139</td>\n",
|
|
" </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>0.142100</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.025974</td>\n",
|
|
" <td>2.711772</td>\n",
|
|
" <td>0.992003</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Ready_Random</td>\n",
|
|
" <td>1.524954</td>\n",
|
|
" <td>1.223352</td>\n",
|
|
" <td>0.045599</td>\n",
|
|
" <td>0.021181</td>\n",
|
|
" <td>0.024585</td>\n",
|
|
" <td>0.031518</td>\n",
|
|
" <td>0.027897</td>\n",
|
|
" <td>0.021931</td>\n",
|
|
" <td>0.048111</td>\n",
|
|
" <td>0.017381</td>\n",
|
|
" <td>0.119005</td>\n",
|
|
" <td>0.507096</td>\n",
|
|
" <td>0.330859</td>\n",
|
|
" <td>0.091198</td>\n",
|
|
" <td>0.988123</td>\n",
|
|
" <td>0.181818</td>\n",
|
|
" <td>5.100792</td>\n",
|
|
" <td>0.906866</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>Self_TopRated</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.032025</td>\n",
|
|
" <td>0.012674</td>\n",
|
|
" <td>0.015714</td>\n",
|
|
" <td>0.021183</td>\n",
|
|
" <td>0.028433</td>\n",
|
|
" <td>0.018573</td>\n",
|
|
" <td>0.022741</td>\n",
|
|
" <td>0.005328</td>\n",
|
|
" <td>0.031602</td>\n",
|
|
" <td>0.502764</td>\n",
|
|
" <td>0.237540</td>\n",
|
|
" <td>0.065748</td>\n",
|
|
" <td>0.697031</td>\n",
|
|
" <td>0.014430</td>\n",
|
|
" <td>2.220811</td>\n",
|
|
" <td>0.995173</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
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" <th>0</th>\n",
|
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" <td>Self_BaselineUI</td>\n",
|
|
" <td>0.967585</td>\n",
|
|
" <td>0.762740</td>\n",
|
|
" <td>0.000954</td>\n",
|
|
" <td>0.000170</td>\n",
|
|
" <td>0.000278</td>\n",
|
|
" <td>0.000463</td>\n",
|
|
" <td>0.000644</td>\n",
|
|
" <td>0.000189</td>\n",
|
|
" <td>0.000752</td>\n",
|
|
" <td>0.000168</td>\n",
|
|
" <td>0.001677</td>\n",
|
|
" <td>0.496424</td>\n",
|
|
" <td>0.009544</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.600530</td>\n",
|
|
" <td>0.005051</td>\n",
|
|
" <td>1.803126</td>\n",
|
|
" <td>0.996380</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
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"</div>"
|
|
],
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"text/plain": [
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" Model RMSE MAE precision recall F_1 \\\n",
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"0 Self_TopPop 2.508258 2.217909 0.188865 0.116919 0.118732 \n",
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"0 Ready_Baseline 0.949459 0.752487 0.091410 0.037652 0.046030 \n",
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"0 Self_GlobalAvg 1.125760 0.943534 0.061188 0.025968 0.031383 \n",
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"0 Ready_Random 1.524954 1.223352 0.045599 0.021181 0.024585 \n",
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"0 Self_TopRated NaN NaN 0.032025 0.012674 0.015714 \n",
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"0 Self_BaselineUI 0.967585 0.762740 0.000954 0.000170 0.000278 \n",
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"\n",
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" F_05 precision_super recall_super NDCG mAP MRR \\\n",
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"0 0.141584 0.130472 0.137473 0.214651 0.111707 0.400939 \n",
|
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"0 0.061286 0.079614 0.056463 0.095957 0.043178 0.198193 \n",
|
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"0 0.041343 0.040558 0.032107 0.067695 0.027470 0.171187 \n",
|
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"0 0.031518 0.027897 0.021931 0.048111 0.017381 0.119005 \n",
|
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"0 0.021183 0.028433 0.018573 0.022741 0.005328 0.031602 \n",
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"0 0.000463 0.000644 0.000189 0.000752 0.000168 0.001677 \n",
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"\n",
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" LAUC HR H2R Reco in test Test coverage Shannon \\\n",
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"0 0.555546 0.765642 0.492047 1.000000 0.038961 3.159079 \n",
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"0 0.515501 0.437964 0.239661 1.000000 0.033911 2.836513 \n",
|
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"0 0.509546 0.384942 0.142100 1.000000 0.025974 2.711772 \n",
|
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"0 0.507096 0.330859 0.091198 0.988123 0.181818 5.100792 \n",
|
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"0 0.502764 0.237540 0.065748 0.697031 0.014430 2.220811 \n",
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"0 0.496424 0.009544 0.000000 0.600530 0.005051 1.803126 \n",
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"\n",
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" Gini \n",
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"0 0.987317 \n",
|
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"0 0.991139 \n",
|
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"0 0.992003 \n",
|
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"0 0.906866 \n",
|
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"0 0.995173 \n",
|
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"0 0.996380 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
|
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"dir_path=\"Recommendations generated/ml-100k/\"\n",
|
|
"super_reactions=[4,5]\n",
|
|
"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
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"\n",
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"display(ev.evaluate_all(test, dir_path, super_reactions))\n",
|
|
"#H2R too easy not to do this one"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
|
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"version": "3.6.5"
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}
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},
|
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"nbformat": 4,
|
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"nbformat_minor": 4
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}
|