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workshops_recommender_systems/P2. Evaluation.ipynb
Robert 45e88a01aa Changes in slides
2020-05-21 22:53:34 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare test set"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import scipy.sparse as sparse\n",
"from collections import defaultdict\n",
"from itertools import chain\n",
"import random\n",
"from tqdm import tqdm\n",
"\n",
"# In evaluation we do not load train set - it is not needed\n",
"test=pd.read_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None)\n",
"test.columns=['user', 'item', 'rating', 'timestamp']\n",
"\n",
"test['user_code'] = test['user'].astype(\"category\").cat.codes\n",
"test['item_code'] = test['item'].astype(\"category\").cat.codes\n",
"\n",
"user_code_id = dict(enumerate(test['user'].astype(\"category\").cat.categories))\n",
"user_id_code = dict((v, k) for k, v in user_code_id.items())\n",
"item_code_id = dict(enumerate(test['item'].astype(\"category\").cat.categories))\n",
"item_id_code = dict((v, k) for k, v in item_code_id.items())\n",
"\n",
"test_ui = sparse.csr_matrix((test['rating'], (test['user_code'], test['item_code'])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimations metrics"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"estimations_df=pd.read_csv('Recommendations generated/ml-100k/Ready_Baseline_estimations.csv', header=None)\n",
"estimations_df.columns=['user', 'item' ,'score']\n",
"\n",
"estimations_df['user_code']=[user_id_code[user] for user in estimations_df['user']]\n",
"estimations_df['item_code']=[item_id_code[item] for item in estimations_df['item']]\n",
"estimations=sparse.csr_matrix((estimations_df['score'], (estimations_df['user_code'], estimations_df['item_code'])), shape=test_ui.shape)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def estimations_metrics(test_ui, estimations):\n",
" result=[]\n",
"\n",
" RMSE=(np.sum((estimations.data-test_ui.data)**2)/estimations.nnz)**(1/2)\n",
" result.append(['RMSE', RMSE])\n",
"\n",
" MAE=np.sum(abs(estimations.data-test_ui.data))/estimations.nnz\n",
" result.append(['MAE', MAE])\n",
" \n",
" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
" df_result.columns=list(zip(*result))[0]\n",
" return df_result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>RMSE</th>\n",
" <th>MAE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.949459</td>\n",
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"text/plain": [
" RMSE MAE\n",
"0 0.949459 0.752487"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# in case of error (in the laboratories) you might have to switch to the other version of pandas\n",
"# try !pip3 install pandas=='1.0.3' (or pip if you use python 2) and restart the kernel\n",
"\n",
"estimations_metrics(test_ui, estimations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ranking metrics"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[663, 475, 62, ..., 472, 269, 503],\n",
" [ 48, 313, 475, ..., 591, 175, 466],\n",
" [351, 313, 475, ..., 591, 175, 466],\n",
" ...,\n",
" [259, 313, 475, ..., 11, 591, 175],\n",
" [ 33, 313, 475, ..., 11, 591, 175],\n",
" [ 77, 313, 475, ..., 11, 591, 175]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"reco = np.loadtxt('Recommendations generated/ml-100k/Ready_Baseline_reco.csv', delimiter=',')\n",
"# Let's ignore scores - they are not used in evaluation: \n",
"users=reco[:,:1]\n",
"items=reco[:,1::2]\n",
"# Let's use inner ids instead of real ones\n",
"users=np.vectorize(lambda x: user_id_code.setdefault(x, -1))(users)\n",
"items=np.vectorize(lambda x: item_id_code.setdefault(x, -1))(items) # maybe items we recommend are not in test set\n",
"# Let's put them into one array\n",
"reco=np.concatenate((users, items), axis=1)\n",
"reco"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def ranking_metrics(test_ui, reco, super_reactions=[], topK=10):\n",
" \n",
" nb_items=test_ui.shape[1]\n",
" relevant_users, super_relevant_users, prec, rec, F_1, F_05, prec_super, rec_super, ndcg, mAP, MRR, LAUC, HR=\\\n",
" 0,0,0,0,0,0,0,0,0,0,0,0,0\n",
" \n",
" cg = (1.0 / np.log2(np.arange(2, topK + 2)))\n",
" cg_sum = np.cumsum(cg)\n",
" \n",
" for (nb_user, user) in tqdm(enumerate(reco[:,0])):\n",
" u_rated_items=test_ui.indices[test_ui.indptr[user]:test_ui.indptr[user+1]]\n",
" nb_u_rated_items=len(u_rated_items)\n",
" if nb_u_rated_items>0: # skip users with no items in test set (still possible that there will be no super items)\n",
" relevant_users+=1\n",
" \n",
" u_super_items=u_rated_items[np.vectorize(lambda x: x in super_reactions)\\\n",
" (test_ui.data[test_ui.indptr[user]:test_ui.indptr[user+1]])]\n",
" # more natural seems u_super_items=[item for item in u_rated_items if test_ui[user,item] in super_reactions]\n",
" # but accesing test_ui[user,item] is expensive -we should avoid doing it\n",
" if len(u_super_items)>0:\n",
" super_relevant_users+=1\n",
" \n",
" user_successes=np.zeros(topK)\n",
" nb_user_successes=0\n",
" user_super_successes=np.zeros(topK)\n",
" nb_user_super_successes=0\n",
" \n",
" # evaluation\n",
" for (item_position,item) in enumerate(reco[nb_user,1:topK+1]):\n",
" if item in u_rated_items:\n",
" user_successes[item_position]=1\n",
" nb_user_successes+=1\n",
" if item in u_super_items:\n",
" user_super_successes[item_position]=1\n",
" nb_user_super_successes+=1\n",
" \n",
" prec_u=nb_user_successes/topK \n",
" prec+=prec_u\n",
" \n",
" rec_u=nb_user_successes/nb_u_rated_items\n",
" rec+=rec_u\n",
" \n",
" F_1+=2*(prec_u*rec_u)/(prec_u+rec_u) if prec_u+rec_u>0 else 0\n",
" 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",
" \n",
" prec_super+=nb_user_super_successes/topK\n",
" rec_super+=nb_user_super_successes/max(len(u_super_items),1) # to set 0 if no super items\n",
" ndcg+=np.dot(user_successes,cg)/cg_sum[min(topK, nb_u_rated_items)-1]\n",
" \n",
" cumsum_successes=np.cumsum(user_successes)\n",
" mAP+=np.dot(cumsum_successes/np.arange(1,topK+1), user_successes)/min(topK, nb_u_rated_items)\n",
" MRR+=1/(user_successes.nonzero()[0][0]+1) if user_successes.nonzero()[0].size>0 else 0\n",
" LAUC+=(np.dot(cumsum_successes, 1-user_successes)+\\\n",
" (nb_user_successes+nb_u_rated_items)/2*((nb_items-nb_u_rated_items)-(topK-nb_user_successes)))/\\\n",
" ((nb_items-nb_u_rated_items)*nb_u_rated_items)\n",
" \n",
" HR+=nb_user_successes>0\n",
" \n",
" \n",
" result=[]\n",
" result.append(('precision', prec/relevant_users))\n",
" result.append(('recall', rec/relevant_users))\n",
" result.append(('F_1', F_1/relevant_users))\n",
" result.append(('F_05', F_05/relevant_users))\n",
" result.append(('precision_super', prec_super/super_relevant_users))\n",
" result.append(('recall_super', rec_super/super_relevant_users))\n",
" result.append(('NDCG', ndcg/relevant_users))\n",
" result.append(('mAP', mAP/relevant_users))\n",
" result.append(('MRR', MRR/relevant_users))\n",
" result.append(('LAUC', LAUC/relevant_users))\n",
" result.append(('HR', HR/relevant_users))\n",
"\n",
" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
" df_result.columns=list(zip(*result))[0]\n",
" return df_result"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"943it [00:00, 6311.05it/s]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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>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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.09141</td>\n",
" <td>0.037652</td>\n",
" <td>0.04603</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" precision recall F_1 F_05 precision_super recall_super \\\n",
"0 0.09141 0.037652 0.04603 0.061286 0.079614 0.056463 \n",
"\n",
" NDCG mAP MRR LAUC HR \n",
"0 0.095957 0.043178 0.198193 0.515501 0.437964 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ranking_metrics(test_ui, reco, super_reactions=[4,5], topK=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Diversity metrics"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def diversity_metrics(test_ui, reco, topK=10):\n",
" \n",
" frequencies=defaultdict(int)\n",
" \n",
" # let's assign 0 to all items in test set\n",
" for item in list(set(test_ui.indices)):\n",
" frequencies[item]=0\n",
" \n",
" # counting frequencies\n",
" for item in reco[:,1:].flat:\n",
" frequencies[item]+=1\n",
" \n",
" nb_reco_outside_test=frequencies[-1]\n",
" del frequencies[-1]\n",
" \n",
" frequencies=np.array(list(frequencies.values()))\n",
" \n",
" nb_rec_items=len(frequencies[frequencies>0])\n",
" nb_reco_inside_test=np.sum(frequencies)\n",
" \n",
" frequencies=frequencies/np.sum(frequencies)\n",
" frequencies=np.sort(frequencies)\n",
" \n",
" with np.errstate(divide='ignore'): # let's put zeros put items with 0 frequency and ignore division warning\n",
" log_frequencies=np.nan_to_num(np.log(frequencies), posinf=0, neginf=0)\n",
" \n",
" result=[]\n",
" result.append(('Reco in test', nb_reco_inside_test/(nb_reco_inside_test+nb_reco_outside_test)))\n",
" result.append(('Test coverage', nb_rec_items/test_ui.shape[1]))\n",
" result.append(('Shannon', -np.dot(frequencies, log_frequencies)))\n",
" result.append(('Gini', np.dot(frequencies, np.arange(1-len(frequencies), len(frequencies), 2))/(len(frequencies)-1)))\n",
" \n",
" df_result=(pd.DataFrame(list(zip(*result))[1])).T\n",
" df_result.columns=list(zip(*result))[0]\n",
" return df_result"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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>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>"
],
"text/plain": [
" Reco in test Test coverage Shannon Gini\n",
"0 1.0 0.033911 2.836513 0.991139"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"\n",
"x=diversity_metrics(test_ui, reco, topK=10)\n",
"x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# To be used in other notebooks"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"943it [00:00, 7479.62it/s]\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\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>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>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>0.949459</td>\n",
" <td>0.752487</td>\n",
" <td>0.09141</td>\n",
" <td>0.037652</td>\n",
" <td>0.04603</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>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>"
],
"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 Reco in test Test coverage Shannon Gini \n",
"0 0.437964 1.0 0.033911 2.836513 0.991139 "
]
},
"execution_count": 10,
"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"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"943it [00:00, 7637.21it/s]\n",
"943it [00:00, 6445.83it/s]\n",
"943it [00:00, 7342.27it/s]\n",
"943it [00:00, 7577.08it/s]\n",
"943it [00:00, 7295.64it/s]\n"
]
}
],
"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",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<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>1.517787</td>\n",
" <td>1.217953</td>\n",
" <td>0.047826</td>\n",
" <td>0.017861</td>\n",
" <td>0.022711</td>\n",
" <td>0.031080</td>\n",
" <td>0.028219</td>\n",
" <td>0.016982</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 1.517787 1.217953 0.047826 0.017861 0.022711 \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 0.031080 0.028219 0.016982 \n",
"0 0.000463 0.000644 0.000189 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,:9]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>NDCG</th>\n",
" <th>mAP</th>\n",
" <th>MRR</th>\n",
" <th>LAUC</th>\n",
" <th>HR</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>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>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>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>0.051154</td>\n",
" <td>0.019551</td>\n",
" <td>0.125693</td>\n",
" <td>0.505448</td>\n",
" <td>0.318134</td>\n",
" <td>0.986426</td>\n",
" <td>0.186869</td>\n",
" <td>5.091730</td>\n",
" <td>0.908288</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>0.600530</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",
"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 0.051154 0.019551 0.125693 0.505448 0.318134 \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 0.986426 0.186869 5.091730 0.908288 \n",
"0 0.600530 0.005051 1.803126 0.996380 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[:,np.append(0,np.arange(9, df.shape[1]))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check metrics on toy dataset"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"3it [00:00, 2446.61it/s]\n"
]
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\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>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>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_BaselineUI</td>\n",
" <td>1.612452</td>\n",
" <td>1.4</td>\n",
" <td>0.444444</td>\n",
" <td>0.888889</td>\n",
" <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>0.888889</td>\n",
" <td>0.8</td>\n",
" <td>1.386294</td>\n",
" <td>0.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
" Reco in test Test coverage Shannon Gini \n",
"0 0.888889 0.8 1.386294 0.25 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data:\n"
]
},
{
"data": {
"text/plain": [
"matrix([[3, 4, 0, 0, 5, 0, 0, 4],\n",
" [0, 1, 2, 3, 0, 0, 0, 0],\n",
" [0, 0, 0, 5, 0, 3, 4, 0]], dtype=int64)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test data:\n"
]
},
{
"data": {
"text/plain": [
"matrix([[0, 0, 0, 0, 0, 0, 3, 0],\n",
" [0, 0, 0, 0, 5, 0, 0, 0],\n",
" [5, 0, 4, 0, 0, 0, 0, 2]], dtype=int64)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Recommendations:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\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>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": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user</th>\n",
" <th>item</th>\n",
" <th>est_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>4.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>40</td>\n",
" <td>3.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>3.0</td>\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",
"</div>"
],
"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": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is what user rated high:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user</th>\n",
" <th>rating</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>7917</th>\n",
" <td>100</td>\n",
" <td>5</td>\n",
" <td>As Good As It Gets (1997)</td>\n",
" <td>Comedy, Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40494</th>\n",
" <td>100</td>\n",
" <td>5</td>\n",
" <td>Titanic (1997)</td>\n",
" <td>Action, Drama, Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23127</th>\n",
" <td>100</td>\n",
" <td>5</td>\n",
" <td>Apt Pupil (1998)</td>\n",
" <td>Drama, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45163</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Tomorrow Never Dies (1997)</td>\n",
" <td>Action, Romance, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28194</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Conspiracy Theory (1997)</td>\n",
" <td>Action, Mystery, Romance, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46108</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>L.A. Confidential (1997)</td>\n",
" <td>Crime, Film-Noir, Mystery, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43730</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Amistad (1997)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42366</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Postman, The (1997)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37000</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Contact (1997)</td>\n",
" <td>Drama, Sci-Fi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66556</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Apostle, The (1997)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67009</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Big Bang Theory, The (1994)</td>\n",
" <td>Crime</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67332</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Good Will Hunting (1997)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29564</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Kundun (1997)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26441</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Air Force One (1997)</td>\n",
" <td>Action, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69735</th>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" <td>Wag the Dog (1997)</td>\n",
" <td>Comedy, Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user rating title \\\n",
"7917 100 5 As Good As It Gets (1997) \n",
"40494 100 5 Titanic (1997) \n",
"23127 100 5 Apt Pupil (1998) \n",
"45163 100 4 Tomorrow Never Dies (1997) \n",
"28194 100 4 Conspiracy Theory (1997) \n",
"46108 100 4 L.A. Confidential (1997) \n",
"43730 100 4 Amistad (1997) \n",
"42366 100 4 Postman, The (1997) \n",
"37000 100 4 Contact (1997) \n",
"66556 100 4 Apostle, The (1997) \n",
"67009 100 4 Big Bang Theory, The (1994) \n",
"67332 100 4 Good Will Hunting (1997) \n",
"29564 100 4 Kundun (1997) \n",
"26441 100 4 Air Force One (1997) \n",
"69735 100 4 Wag the Dog (1997) \n",
"\n",
" genres \n",
"7917 Comedy, Drama \n",
"40494 Action, Drama, Romance \n",
"23127 Drama, Thriller \n",
"45163 Action, Romance, Thriller \n",
"28194 Action, Mystery, Romance, Thriller \n",
"46108 Crime, Film-Noir, Mystery, Thriller \n",
"43730 Drama \n",
"42366 Drama \n",
"37000 Drama, Sci-Fi \n",
"66556 Drama \n",
"67009 Crime \n",
"67332 Drama \n",
"29564 Drama \n",
"26441 Action, Thriller \n",
"69735 Comedy, Drama "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is what we recommend:\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user</th>\n",
" <th>rec_nb</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>100.0</td>\n",
" <td>1</td>\n",
" <td>Great Day in Harlem, A (1994)</td>\n",
" <td>Documentary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1041</th>\n",
" <td>100.0</td>\n",
" <td>2</td>\n",
" <td>Tough and Deadly (1995)</td>\n",
" <td>Action, Drama, Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1983</th>\n",
" <td>100.0</td>\n",
" <td>3</td>\n",
" <td>Aiqing wansui (1994)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2925</th>\n",
" <td>100.0</td>\n",
" <td>4</td>\n",
" <td>Delta of Venus (1994)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3867</th>\n",
" <td>100.0</td>\n",
" <td>5</td>\n",
" <td>Someone Else's America (1995)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4809</th>\n",
" <td>100.0</td>\n",
" <td>6</td>\n",
" <td>Saint of Fort Washington, The (1993)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5750</th>\n",
" <td>100.0</td>\n",
" <td>7</td>\n",
" <td>Celestial Clockwork (1994)</td>\n",
" <td>Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6693</th>\n",
" <td>100.0</td>\n",
" <td>8</td>\n",
" <td>Some Mother's Son (1996)</td>\n",
" <td>Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8587</th>\n",
" <td>100.0</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>7633</th>\n",
" <td>100.0</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",
"98 100.0 1 Great Day in Harlem, A (1994) \n",
"1041 100.0 2 Tough and Deadly (1995) \n",
"1983 100.0 3 Aiqing wansui (1994) \n",
"2925 100.0 4 Delta of Venus (1994) \n",
"3867 100.0 5 Someone Else's America (1995) \n",
"4809 100.0 6 Saint of Fort Washington, The (1993) \n",
"5750 100.0 7 Celestial Clockwork (1994) \n",
"6693 100.0 8 Some Mother's Son (1996) \n",
"8587 100.0 9 Maya Lin: A Strong Clear Vision (1994) \n",
"7633 100.0 10 Prefontaine (1997) \n",
"\n",
" genres \n",
"98 Documentary \n",
"1041 Action, Drama, Thriller \n",
"1983 Drama \n",
"2925 Drama \n",
"3867 Drama \n",
"4809 Drama \n",
"5750 Comedy \n",
"6693 Drama \n",
"8587 Documentary \n",
"7633 Drama "
]
},
"execution_count": 15,
"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": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"943it [00:00, 6211.85it/s]\n",
"943it [00:00, 7728.29it/s]\n",
"943it [00:00, 7446.93it/s]\n",
"943it [00:00, 6379.07it/s]\n",
"943it [00:00, 6994.80it/s]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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>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>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>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>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>1.517787</td>\n",
" <td>1.217953</td>\n",
" <td>0.047826</td>\n",
" <td>0.017861</td>\n",
" <td>0.022711</td>\n",
" <td>0.031080</td>\n",
" <td>0.028219</td>\n",
" <td>0.016982</td>\n",
" <td>0.051154</td>\n",
" <td>0.019551</td>\n",
" <td>0.125693</td>\n",
" <td>0.505448</td>\n",
" <td>0.318134</td>\n",
" <td>0.986426</td>\n",
" <td>0.186869</td>\n",
" <td>5.091730</td>\n",
" <td>0.908288</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",
" <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>0.600530</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 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 1.517787 1.217953 0.047826 0.017861 0.022711 \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 0.031080 0.028219 0.016982 0.051154 0.019551 0.125693 \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 0.505448 0.318134 0.986426 0.186869 5.091730 0.908288 \n",
"0 0.496424 0.009544 0.600530 0.005051 1.803126 0.996380 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"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",
"ev.evaluate_all(test, dir_path, super_reactions)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"version": 3
},
"file_extension": ".py",
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