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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Booking.com Multi-Destinations Trips Dataset demo"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load train set"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1166835, 9)\n"
]
},
{
"data": {
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"<div>\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_id</th>\n",
" <th>checkin</th>\n",
" <th>checkout</th>\n",
" <th>city_id</th>\n",
" <th>device_class</th>\n",
" <th>affiliate_id</th>\n",
" <th>booker_country</th>\n",
" <th>hotel_country</th>\n",
" <th>utrip_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000027</td>\n",
" <td>2016-08-13</td>\n",
" <td>2016-08-14</td>\n",
" <td>8183</td>\n",
" <td>desktop</td>\n",
" <td>7168</td>\n",
" <td>Elbonia</td>\n",
" <td>Gondal</td>\n",
" <td>1000027_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1000027</td>\n",
" <td>2016-08-14</td>\n",
" <td>2016-08-16</td>\n",
" <td>15626</td>\n",
" <td>desktop</td>\n",
" <td>7168</td>\n",
" <td>Elbonia</td>\n",
" <td>Gondal</td>\n",
" <td>1000027_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1000027</td>\n",
" <td>2016-08-16</td>\n",
" <td>2016-08-18</td>\n",
" <td>60902</td>\n",
" <td>desktop</td>\n",
" <td>7168</td>\n",
" <td>Elbonia</td>\n",
" <td>Gondal</td>\n",
" <td>1000027_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000027</td>\n",
" <td>2016-08-18</td>\n",
" <td>2016-08-21</td>\n",
" <td>30628</td>\n",
" <td>desktop</td>\n",
" <td>253</td>\n",
" <td>Elbonia</td>\n",
" <td>Gondal</td>\n",
" <td>1000027_1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1000033</td>\n",
" <td>2016-04-09</td>\n",
" <td>2016-04-11</td>\n",
" <td>38677</td>\n",
" <td>mobile</td>\n",
" <td>359</td>\n",
" <td>Gondal</td>\n",
" <td>Cobra Island</td>\n",
" <td>1000033_1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id checkin checkout city_id device_class affiliate_id \\\n",
"0 1000027 2016-08-13 2016-08-14 8183 desktop 7168 \n",
"1 1000027 2016-08-14 2016-08-16 15626 desktop 7168 \n",
"2 1000027 2016-08-16 2016-08-18 60902 desktop 7168 \n",
"3 1000027 2016-08-18 2016-08-21 30628 desktop 253 \n",
"4 1000033 2016-04-09 2016-04-11 38677 mobile 359 \n",
"\n",
" booker_country hotel_country utrip_id \n",
"0 Elbonia Gondal 1000027_1 \n",
"1 Elbonia Gondal 1000027_1 \n",
"2 Elbonia Gondal 1000027_1 \n",
"3 Elbonia Gondal 1000027_1 \n",
"4 Gondal Cobra Island 1000033_1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_set = pd.read_csv('train_set.csv').sort_values(by=['utrip_id','checkin'])\n",
"\n",
"print(train_set.shape)\n",
"train_set.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load testset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(378667, 9)\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>checkin</th>\n",
" <th>checkout</th>\n",
" <th>device_class</th>\n",
" <th>affiliate_id</th>\n",
" <th>booker_country</th>\n",
" <th>utrip_id</th>\n",
" <th>city_id</th>\n",
" <th>hotel_country</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000066</td>\n",
" <td>2016-07-21</td>\n",
" <td>2016-07-23</td>\n",
" <td>desktop</td>\n",
" <td>9924</td>\n",
" <td>Gondal</td>\n",
" <td>1000066_2</td>\n",
" <td>56430</td>\n",
" <td>Urkesh</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1000066</td>\n",
" <td>2016-07-23</td>\n",
" <td>2016-07-25</td>\n",
" <td>desktop</td>\n",
" <td>9924</td>\n",
" <td>Gondal</td>\n",
" <td>1000066_2</td>\n",
" <td>41971</td>\n",
" <td>Urkesh</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1000066</td>\n",
" <td>2016-07-25</td>\n",
" <td>2016-07-28</td>\n",
" <td>desktop</td>\n",
" <td>9924</td>\n",
" <td>Gondal</td>\n",
" <td>1000066_2</td>\n",
" <td>5797</td>\n",
" <td>Urkesh</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000066</td>\n",
" <td>2016-07-28</td>\n",
" <td>2016-07-31</td>\n",
" <td>mobile</td>\n",
" <td>2436</td>\n",
" <td>Gondal</td>\n",
" <td>1000066_2</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1000270</td>\n",
" <td>2016-02-08</td>\n",
" <td>2016-02-09</td>\n",
" <td>mobile</td>\n",
" <td>9452</td>\n",
" <td>The Devilfire Empire</td>\n",
" <td>1000270_1</td>\n",
" <td>50075</td>\n",
" <td>The Devilfire Empire</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user_id checkin checkout device_class affiliate_id \\\n",
"0 1000066 2016-07-21 2016-07-23 desktop 9924 \n",
"1 1000066 2016-07-23 2016-07-25 desktop 9924 \n",
"2 1000066 2016-07-25 2016-07-28 desktop 9924 \n",
"3 1000066 2016-07-28 2016-07-31 mobile 2436 \n",
"4 1000270 2016-02-08 2016-02-09 mobile 9452 \n",
"\n",
" booker_country utrip_id city_id hotel_country \n",
"0 Gondal 1000066_2 56430 Urkesh \n",
"1 Gondal 1000066_2 41971 Urkesh \n",
"2 Gondal 1000066_2 5797 Urkesh \n",
"3 Gondal 1000066_2 0 NaN \n",
"4 The Devilfire Empire 1000270_1 50075 The Devilfire Empire "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_set = pd.read_csv('test_set.csv').sort_values(by=['utrip_id','checkin'])\n",
"print(test_set.shape)\n",
"\n",
"test_set.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate Dummy Predictions - use top 4 cities in the trainset as benchmark recommendation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"topcities = train_set.city_id.value_counts().index[:4]\n",
"\n",
"test_trips = (test_set[['utrip_id']].drop_duplicates()).reset_index().drop('index', axis=1)\n",
"cities_prediction = pd.DataFrame([topcities]*test_trips.shape[0]\n",
" , columns= ['city_id_1','city_id_2','city_id_3','city_id_4'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Submission file according to the format"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(70662, 5)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>utrip_id</th>\n",
" <th>city_id_1</th>\n",
" <th>city_id_2</th>\n",
" <th>city_id_3</th>\n",
" <th>city_id_4</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000066_2</td>\n",
" <td>47499</td>\n",
" <td>23921</td>\n",
" <td>36063</td>\n",
" <td>17013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1000270_1</td>\n",
" <td>47499</td>\n",
" <td>23921</td>\n",
" <td>36063</td>\n",
" <td>17013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1000441_1</td>\n",
" <td>47499</td>\n",
" <td>23921</td>\n",
" <td>36063</td>\n",
" <td>17013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>100048_1</td>\n",
" <td>47499</td>\n",
" <td>23921</td>\n",
" <td>36063</td>\n",
" <td>17013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1000543_1</td>\n",
" <td>47499</td>\n",
" <td>23921</td>\n",
" <td>36063</td>\n",
" <td>17013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" utrip_id city_id_1 city_id_2 city_id_3 city_id_4\n",
"0 1000066_2 47499 23921 36063 17013\n",
"1 1000270_1 47499 23921 36063 17013\n",
"2 1000441_1 47499 23921 36063 17013\n",
"3 100048_1 47499 23921 36063 17013\n",
"4 1000543_1 47499 23921 36063 17013"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"submission = pd.concat([test_trips,cities_prediction], axis =1)\n",
"print(submission.shape)\n",
"submission.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"submission.to_csv('submission.csv',index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read submission file and ground truth"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"ground_truth = pd.read_csv('ground_truth.csv',index_col=[0])\n",
"submission = pd.read_csv('submission.csv',index_col=[0])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(70662, 2)\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>city_id</th>\n",
" <th>hotel_country</th>\n",
" </tr>\n",
" <tr>\n",
" <th>utrip_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1038944_1</th>\n",
" <td>54085</td>\n",
" <td>Sokovia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1068715_1</th>\n",
" <td>29319</td>\n",
" <td>Cobra Island</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1075528_1</th>\n",
" <td>55763</td>\n",
" <td>Bozatta</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1110462_4</th>\n",
" <td>11930</td>\n",
" <td>Alvonia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1132565_1</th>\n",
" <td>58659</td>\n",
" <td>Axphain</td>\n",
" </tr>\n",
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],
"text/plain": [
" city_id hotel_country\n",
"utrip_id \n",
"1038944_1 54085 Sokovia\n",
"1068715_1 29319 Cobra Island\n",
"1075528_1 55763 Bozatta\n",
"1110462_4 11930 Alvonia\n",
"1132565_1 58659 Axphain"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(ground_truth.shape)\n",
"ground_truth.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate - use accuracy at 4 to evaluate the prediction"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def evaluate_accuracy_at_4(submission,ground_truth):\n",
" '''checks if the true city is within the four recommended cities'''\n",
" data = submission.join(ground_truth,on='utrip_id')\n",
"\n",
" hits = ((data['city_id']==data['city_id_1'])|(data['city_id']==data['city_id_2'])|\n",
" (data['city_id']==data['city_id_3'])|(data['city_id']==data['city_id_4']))*1\n",
" return hits.mean()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.05271574537941185"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evaluate_accuracy_at_4(submission,ground_truth)"
]
}
],
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|