still the very first commit

README.md

@@ -1,3 +1,102 @@
----
-license: cc-by-nc-4.0
----
+# Dataset Card for WineSensed
+
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Fields](#data-fields)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [WineSensed Dataset](https://https://thoranna.github.io/learning_to_taste/)
+- **Repository:**
+- **Paper:** [Paper](https://arxiv.org/pdf/2308.16900.pdf)
+
+### Dataset Summary
+
+The dataset encompasses 897k images of wine labels and 824k reviews of wines
+curated from the Vivino platform. It has over 350k unique vintages, annotated
+with year, region, rating, alcohol percentage, price, and grape composition.
+We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment
+with 256 participants who were asked to rank wines based on their similarity in flavor,
+resulting in more than 5k pairwise flavor distances.
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Fields
+
+The dataset contains the file metadata.zip, consisting of the files participants.csv, which contains information connecting participants to annotations in the experiment, images_reviews_attributes.csv, which contains reviews, links to images, and wine attributes, and napping.csv, which contains the coordinates of each wine on the napping paper alongside information connecting each coordinate pair to the wine being annotated and the participant who annotated it. The chunk_<chunk num>.zip folders contain the images of the wines in the dataset in .jpg format. 
+
+#### napping.csv contains the following fields:
+
+- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in participants.csv)
+- event_name: name of the data collection event (maps to the same attribute in participants.csv)
+- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in participants.csv)
+- experiment_id: id the wine being annotated was given in the experiment
+- coor1: x-axis coordinate on the napping paper
+- coor2: y-axis coordinate on the napping paper
+- color: color of the sticker used
+
+#### participants.csv contains the following fields:
+
+- session_round_name: session number during the event_name, at most three sessions per event (maps to experiment_round in napping.csv)
+- event_name: name of data-collection event (maps to event_name in napping.csv)
+- experiment_no: which number the napping paper was in the list of papers returned for this session_round_name (maps to experiment_no in napping.csv)
+- round_id: round number (from 1-3)
+- participant_id: id the participant was given in the experiment
+
+#### images_reviews_attributes.csv contains the following fields:
+
+- vintage_id: vintage id of the wine
+- image: image link (each .jpg in chunk_<chunk num>.zip can be mapped to a corresponding image link in this column by removing the /p prefix from the link).
+- review: user review of the wine
+- experiment_id: id the wine got during data collection (each experiment_id can be mapped to the same column in napping.csv)
+- year: year the wine was produced
+- winery_id: id of the winery that produced the wine
+- wine: name of the wine
+- alcohol: the wine's alcohol percentage
+- country: the country where the wine was produced
+- region: the region where the wine was produced
+- price: price of the wine in USD (collected 05/2023)
+- rating: average rating of the wine (collected 05/2023)
+- grape: the wine's grape composition, represented as a comma-separated list ordered in descending sequence of the percentage contribution of each grape variety to the overall blend.
+
+## Dataset Creation
+
+### All Images Dataset
+
+1) Unzip all the chunk_*.zip files
+	2) Copy the script create_all_images_dataset.sh to the output_images/ directory
+	3) Execute chmod +x create_all_images_dataset.sh
+	4) Execute ./create_all_images_dataset.sh
+
+
+## Additional Information
+
+### Licensing Information
+
+LICENSE AGREEMENT
+=================
+ - WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,
+   Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence
+
+
+### Citation Information
+
+```
+ @article{bender2023learning,
+  title={Learning to Taste: A Multimodal Wine Dataset},
+  author={Bender, Thoranna and S{\o}rensen, Simon M{\o}e and Kashani, Alireza and Hjorleifsson, K Eldjarn and Hyldig, Grethe and Hauberg, S{\o}ren and Belongie, Serge and Warburg, Frederik},
+  journal={arXiv preprint arXiv:2308.16900},
+  year={2023}
+```
+

code/notebook.ipynb

@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_napping = pd.read_csv('../data/csv/napping.csv')\n",
+    "df_napping['experiment_no'] = [int(item.replace('experiment_no_', '')) for item in df_napping.experiment_no]\n",
+    "df_participant = pd.read_csv('../data/csv/participants.csv', sep = \";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1014630, 12)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_image_review_attributes = pd.read_csv('../data/csv/images_reviews_attributes.csv')\n",
+    "df_image_review_attributes.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1010152, 12)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop rows on image column if they are null\n",
+    "df_image_review_attributes = df_image_review_attributes.dropna(subset=['image'])\n",
+    "df_image_review_attributes.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def write_jsonl(df, file_path):\n",
+    "    with open(file_path, 'w') as jsonlfile:\n",
+    "        for _, row in df.iterrows():\n",
+    "            # Convert each row to a JSON string and write it as a line in JSONL\n",
+    "            json_string = row.to_json()\n",
+    "            jsonlfile.write(json_string + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# IN THE README FILE, WE HAVE THE FOLLOWING INFORMATION:\n",
+    "# to join participants with napping\n",
+    "df_napping_participants = df_participant.\\\n",
+    "    merge(df_napping, on=['event_name', 'session_round_name', 'experiment_no'])\n",
+    "\n",
+    "write_jsonl(df_napping_participants, '../data/napping_participants/napping_participants.jsonl')\n",
+    "\n",
+    "# LOAD VINTAGES\n",
+    "df_vintages = df_image_review_attributes.\\\n",
+    "    drop(columns=['image', 'review', 'experiment_id']).\\\n",
+    "    dropna(subset = ['winery_id']).\\\n",
+    "    drop_duplicates().\\\n",
+    "    astype({'year': int, 'winery_id': int})\n",
+    "write_jsonl(df_vintages, '../data/vintages/vintages_dataset.jsonl')\n",
+    "\n",
+    "# LOAD IMAGES 'small'\n",
+    "df_small = df_image_review_attributes.sample(frac = 0.1)\n",
+    "write_jsonl(df_small, '../data/small/small_dataset.jsonl')\n",
+    "# df_napping\n",
+    "# df_participant\n",
+    "\n",
+    "# LOAD IMAGES 'all'\n",
+    "write_jsonl(df_image_review_attributes, '../data/all/all_dataset.jsonl')\n",
+    "# df_napping\n",
+    "# df_participant\n",
+    "\n",
+    "# LOAD ONLY IMAGE-REVIEW of PROFILED WINES IN THE WINE TASTING EXPERIMENT\n",
+    "df_image_review_of_experiment = df_image_review_attributes.dropna(subset = ['experiment_id']).\\\n",
+    "    astype({'experiment_id': int, 'year': int})\n",
+    "write_jsonl(df_image_review_of_experiment, '../data/wt_session/wt_session.jsonl')\n",
+    "# df_napping\n",
+    "# df_participant\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\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>event_name</th>\n",
+       "      <th>session_round_name</th>\n",
+       "      <th>experiment_no</th>\n",
+       "      <th>round_id</th>\n",
+       "      <th>participant_id</th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>coor1</th>\n",
+       "      <th>coor2</th>\n",
+       "      <th>color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>experiment_vivino_31_03</td>\n",
+       "      <td>round_1_images</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>254</td>\n",
+       "      <td>106</td>\n",
+       "      <td>1728</td>\n",
+       "      <td>2882</td>\n",
+       "      <td>red</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>experiment_vivino_31_03</td>\n",
+       "      <td>round_1_images</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>254</td>\n",
+       "      <td>112</td>\n",
+       "      <td>1788</td>\n",
+       "      <td>2060</td>\n",
+       "      <td>light-purple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>experiment_vivino_31_03</td>\n",
+       "      <td>round_1_images</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>254</td>\n",
+       "      <td>109</td>\n",
+       "      <td>1004</td>\n",
+       "      <td>1972</td>\n",
+       "      <td>purple</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>experiment_vivino_31_03</td>\n",
+       "      <td>round_1_images</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>254</td>\n",
+       "      <td>111</td>\n",
+       "      <td>1015</td>\n",
+       "      <td>1760</td>\n",
+       "      <td>blue</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                event_name session_round_name  experiment_no round_id  \\\n",
+       "0  experiment_vivino_31_03     round_1_images              0        1   \n",
+       "1  experiment_vivino_31_03     round_1_images              0        1   \n",
+       "2  experiment_vivino_31_03     round_1_images              0        1   \n",
+       "3  experiment_vivino_31_03     round_1_images              0        1   \n",
+       "\n",
+       "  participant_id  experiment_id  coor1  coor2         color  \n",
+       "0            254            106   1728   2882           red  \n",
+       "1            254            112   1788   2060  light-purple  \n",
+       "2            254            109   1004   1972        purple  \n",
+       "3            254            111   1015   1760          blue  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_napping_participants.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vintage_id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>winery_id</th>\n",
+       "      <th>wine_alcohol</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>price</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>grape</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1755</th>\n",
+       "      <td>1469676</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>247919</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Italy</td>\n",
+       "      <td>Abruzzo</td>\n",
+       "      <td>22.26</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>Sangiovese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2230</th>\n",
+       "      <td>156263848</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>2080</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Napa Valley</td>\n",
+       "      <td>13.86</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Pinot Noir</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3871</th>\n",
+       "      <td>156177650</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>18224</td>\n",
+       "      <td>13.5</td>\n",
+       "      <td>Italy</td>\n",
+       "      <td>Salento</td>\n",
+       "      <td>19.46</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>Primitivo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4319</th>\n",
+       "      <td>20161520</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>215762</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Saint-Émilion Grand Cru</td>\n",
+       "      <td>23.66</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>Merlot</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      vintage_id  year  winery_id  wine_alcohol        country  \\\n",
+       "1755     1469676  2018     247919           NaN          Italy   \n",
+       "2230   156263848  2018       2080          14.0  United States   \n",
+       "3871   156177650  2018      18224          13.5          Italy   \n",
+       "4319    20161520  2015     215762          14.0         France   \n",
+       "\n",
+       "                        region  price  rating       grape  \n",
+       "1755                   Abruzzo  22.26     4.3  Sangiovese  \n",
+       "2230               Napa Valley  13.86     4.0  Pinot Noir  \n",
+       "3871                   Salento  19.46     4.0   Primitivo  \n",
+       "4319   Saint-Émilion Grand Cru  23.66     4.1      Merlot  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_vintages.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vintage_id</th>\n",
+       "      <th>image</th>\n",
+       "      <th>review</th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>winery_id</th>\n",
+       "      <th>wine_alcohol</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>price</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>grape</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>429091</th>\n",
+       "      <td>150905918</td>\n",
+       "      <td>p/SPYooSq3SrCroz8QOE19lQ.jpg</td>\n",
+       "      <td>Medium purple coloured. Medium intensity, blac...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242022</th>\n",
+       "      <td>155500756</td>\n",
+       "      <td>p/aZYgB_ioT_SeSf8NZNndaA.jpg</td>\n",
+       "      <td>LPV Lyon 2, Feb 2019: light gold, quite intens...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34614</th>\n",
+       "      <td>156712592</td>\n",
+       "      <td>p/6aNPhRyFRZmNPTD1ZgOEnQ.jpg</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124131</th>\n",
+       "      <td>159619405</td>\n",
+       "      <td>p/8vP7aXeXRIuZ_ln84WuAoA.jpg</td>\n",
+       "      <td>Intense aroma and flavors, nicely matured, ear...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        vintage_id                         image  \\\n",
+       "429091   150905918  p/SPYooSq3SrCroz8QOE19lQ.jpg   \n",
+       "242022   155500756  p/aZYgB_ioT_SeSf8NZNndaA.jpg   \n",
+       "34614    156712592  p/6aNPhRyFRZmNPTD1ZgOEnQ.jpg   \n",
+       "124131   159619405  p/8vP7aXeXRIuZ_ln84WuAoA.jpg   \n",
+       "\n",
+       "                                                   review  experiment_id  \\\n",
+       "429091  Medium purple coloured. Medium intensity, blac...            NaN   \n",
+       "242022  LPV Lyon 2, Feb 2019: light gold, quite intens...            NaN   \n",
+       "34614                                                 NaN            NaN   \n",
+       "124131  Intense aroma and flavors, nicely matured, ear...            NaN   \n",
+       "\n",
+       "        year  winery_id  wine_alcohol country region  price  rating grape  \n",
+       "429091   NaN        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "242022   NaN        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "34614    NaN        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "124131   NaN        NaN           NaN     NaN    NaN    NaN     NaN   NaN  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_small.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vintage_id</th>\n",
+       "      <th>image</th>\n",
+       "      <th>review</th>\n",
+       "      <th>experiment_id</th>\n",
+       "      <th>year</th>\n",
+       "      <th>winery_id</th>\n",
+       "      <th>wine_alcohol</th>\n",
+       "      <th>country</th>\n",
+       "      <th>region</th>\n",
+       "      <th>price</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>grape</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>150301706</td>\n",
+       "      <td>p/iVoa6qR6TSKjLeb1RoHWtQ.jpg</td>\n",
+       "      <td>Ничего особого в нем не нашел. В меру сухое, в...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>159555436</td>\n",
+       "      <td>p/e2W_085qRbCQbZJVp_tzHA.jpg</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>146958680</td>\n",
+       "      <td>p/DdLNo35SRiCMxpoKTiEXyQ.jpg</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014691</td>\n",
+       "      <td>p/vi-1ygw7RXCM6Pnwx9C6CA.jpg</td>\n",
+       "      <td>3,3/5. Белая Риоха. Бленд на основе виуры (75%...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>153305559</td>\n",
+       "      <td>p/1pjborIfR1Wdlr35jEHbtA.jpg</td>\n",
+       "      <td>Parfum! Super frumos!</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>162913950</td>\n",
+       "      <td>p/kDz5LBlFRz2wb61xaMj_Dw.jpg</td>\n",
+       "      <td>Bom vinho</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>14230455</td>\n",
+       "      <td>p/EJQLq-qLShSP-uf2Tg-G1g.jpg</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>159888939</td>\n",
+       "      <td>p/MhhKQteWSXW0gYUNnvHs6A.jpg</td>\n",
+       "      <td>V nice whitr</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3261951</td>\n",
+       "      <td>p/fdsdbl6XR2ynvoQnNYLXQQ.jpg</td>\n",
+       "      <td>Great label and ok tasting. Not the best but n...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>32363311</td>\n",
+       "      <td>p/BQkoD9sXQi-EIk3e2cG-YA.jpg</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   vintage_id                         image  \\\n",
+       "0   150301706  p/iVoa6qR6TSKjLeb1RoHWtQ.jpg   \n",
+       "1   159555436  p/e2W_085qRbCQbZJVp_tzHA.jpg   \n",
+       "2   146958680  p/DdLNo35SRiCMxpoKTiEXyQ.jpg   \n",
+       "3     2014691  p/vi-1ygw7RXCM6Pnwx9C6CA.jpg   \n",
+       "4   153305559  p/1pjborIfR1Wdlr35jEHbtA.jpg   \n",
+       "5   162913950  p/kDz5LBlFRz2wb61xaMj_Dw.jpg   \n",
+       "6    14230455  p/EJQLq-qLShSP-uf2Tg-G1g.jpg   \n",
+       "7   159888939  p/MhhKQteWSXW0gYUNnvHs6A.jpg   \n",
+       "8     3261951  p/fdsdbl6XR2ynvoQnNYLXQQ.jpg   \n",
+       "9    32363311  p/BQkoD9sXQi-EIk3e2cG-YA.jpg   \n",
+       "\n",
+       "                                              review  experiment_id  year  \\\n",
+       "0  Ничего особого в нем не нашел. В меру сухое, в...            NaN   NaN   \n",
+       "1                                                NaN            NaN   NaN   \n",
+       "2                                                NaN            NaN   NaN   \n",
+       "3  3,3/5. Белая Риоха. Бленд на основе виуры (75%...            NaN   NaN   \n",
+       "4                              Parfum! Super frumos!            NaN   NaN   \n",
+       "5                                          Bom vinho            NaN   NaN   \n",
+       "6                                                NaN            NaN   NaN   \n",
+       "7                                       V nice whitr            NaN   NaN   \n",
+       "8  Great label and ok tasting. Not the best but n...            NaN   NaN   \n",
+       "9                                                NaN            NaN   NaN   \n",
+       "\n",
+       "   winery_id  wine_alcohol country region  price  rating grape  \n",
+       "0        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "1        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "2        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "3        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "4        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "5        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "6        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "7        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "8        NaN           NaN     NaN    NaN    NaN     NaN   NaN  \n",
+       "9        NaN           NaN     NaN    NaN    NaN     NaN   NaN  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_image_review_attributes.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ALL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame([item.replace('p/', '') for item in df_image_review_attributes.image if item is not None]).\\\n",
+    "    to_csv('../data/csv/all.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SMALL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read small_dataset.jsonl \n",
+    "with open('../data/small/small_dataset.jsonl') as json_file:\n",
+    "    data = json_file.readlines()\n",
+    "    data = [json.loads(line) for line in data] # convert string to dict format\n",
+    "\n",
+    "import pandas as pd\n",
+    "small_df = pd.DataFrame(data)\n",
+    "\n",
+    "\n",
+    "# write the image column to a csv file\n",
+    "pd.DataFrame([item.replace('p/', '') for item in small_df.image if item is not None]).\\\n",
+    "    to_csv('../data/csv/small_images.csv', index=False)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## WT_SESSION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read small_dataset.jsonl \n",
+    "with open('../data/jsonl/wt_session.jsonl') as json_file:\n",
+    "    data = json_file.readlines()\n",
+    "    data = [json.loads(line) for line in data] # convert string to dict format\n",
+    "\n",
+    "import pandas as pd\n",
+    "small_df = pd.DataFrame(data)\n",
+    "\n",
+    "# write the image column to a csv file\n",
+    "pd.DataFrame([item.replace('p/', '') for item in small_df.image if item is not None]).\\\n",
+    "    to_csv('../data/csv/wt_session.jsonl_images.csv', index=False)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# read small_dataset.jsonl \n",
+    "with open('../data/jsonl/wt_session.jsonl') as json_file:\n",
+    "    data = json_file.readlines()\n",
+    "    data = [json.loads(line) for line in data] # convert string to dict format\n",
+    "\n",
+    "import pandas as pd\n",
+    "wt_session = pd.DataFrame(data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop rows on image column if they are null\n",
+    "wt_session_no_nall = wt_session.dropna(subset=['image'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(45339, 12)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wt_session_no_nall.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert small_df_no_nall into jsonl format\n",
+    "write_jsonl(wt_session_no_nall, '../data/wt_session/wt_session_no_null.jsonl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.DataFrame([item.replace('p/', '') for item in wt_session_no_nall.image])[0].to_csv('../data/wt_session/wt_session_no_null_images.list', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "litegrave",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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+{"vintage_id":156306216,"year":2018,"winery_id":21107,"wine_alcohol":14.5,"country":"Spain","region":" Ribera del Duero","price":23.66,"rating":4.0,"grape":"Tempranillo"}
+{"vintage_id":156355599,"year":2018,"winery_id":119751,"wine_alcohol":14.0,"country":"Italy","region":" Primitivo di Manduria","price":13.86,"rating":4.3,"grape":"Primitivo"}
+{"vintage_id":156402693,"year":2018,"winery_id":97304,"wine_alcohol":14.5,"country":"France","region":" Saint-\u00c9milion Grand Cru","price":41.86,"rating":4.1,"grape":"Merlot"}
+{"vintage_id":156466136,"year":2018,"winery_id":38437,"wine_alcohol":null,"country":"Spain","region":" Cari\u00f1ena","price":13.24,"rating":4.0,"grape":"Garnacha"}
+{"vintage_id":156527258,"year":2018,"winery_id":16294,"wine_alcohol":14.0,"country":"Australia","region":" South Australia","price":11.06,"rating":4.0,"grape":"Shiraz\/Syrah"}
+{"vintage_id":156659797,"year":2018,"winery_id":245531,"wine_alcohol":15.0,"country":"Spain","region":" Rioja","price":12.46,"rating":3.9,"grape":"Tempranillo"}
+{"vintage_id":156840139,"year":2016,"winery_id":15240,"wine_alcohol":null,"country":"Italy","region":" Toscana","price":16.83,"rating":3.8,"grape":"Merlot"}
+{"vintage_id":156877904,"year":2017,"winery_id":215762,"wine_alcohol":15.0,"country":"Spain","region":" Toro","price":13.87,"rating":3.7,"grape":"Tinta de toro"}
+{"vintage_id":156994738,"year":2018,"winery_id":232197,"wine_alcohol":13.5,"country":"Italy","region":" Terre Siciliane","price":23.58,"rating":4.0,"grape":"Cabernet Sauvignon"}
+{"vintage_id":157154427,"year":2018,"winery_id":11186,"wine_alcohol":13.5,"country":"Spain","region":" Priorat","price":24.41,"rating":4.3,"grape":"Garnacha"}
+{"vintage_id":157217859,"year":2016,"winery_id":14122,"wine_alcohol":13.3,"country":"France","region":" Bourgogne","price":22.68,"rating":3.8,"grape":"Gamay"}
+{"vintage_id":157718233,"year":2018,"winery_id":234859,"wine_alcohol":null,"country":"France","region":" Bordeaux Sup\u00e9rieur","price":9.28,"rating":4.1,"grape":"Merlot"}
+{"vintage_id":158193257,"year":2018,"winery_id":47170,"wine_alcohol":12.5,"country":"Spain","region":" Jumilla","price":10.65,"rating":3.9,"grape":"Shiraz\/Syrah"}
+{"vintage_id":158240201,"year":2018,"winery_id":47170,"wine_alcohol":12.5,"country":"France","region":" Terrasses du Larzac","price":13.46,"rating":4.0,"grape":"Grenache"}
+{"vintage_id":158475988,"year":2018,"winery_id":1252,"wine_alcohol":null,"country":"Italy","region":" Salento","price":11.9,"rating":4.2,"grape":"Negroamaro"}
+{"vintage_id":158565485,"year":2018,"winery_id":35080,"wine_alcohol":null,"country":"Italy","region":" Toscana","price":15.43,"rating":3.9,"grape":"Sangiovese"}
+{"vintage_id":158645398,"year":2017,"winery_id":77461,"wine_alcohol":null,"country":"Italy","region":" Toscana","price":13.86,"rating":4.2,"grape":"Sangiovese"}
+{"vintage_id":159498249,"year":2019,"winery_id":1757,"wine_alcohol":13.5,"country":"Italy","region":" Primitivo di Manduria","price":17.26,"rating":4.3,"grape":"Primitivo"}
+{"vintage_id":159636021,"year":2019,"winery_id":3707,"wine_alcohol":null,"country":"Italy","region":" Toscana","price":9.66,"rating":4.0,"grape":"Sangiovese"}
+{"vintage_id":159983662,"year":2018,"winery_id":65495,"wine_alcohol":null,"country":"Italy","region":" Lazio","price":13.99,"rating":3.8,"grape":"Shiraz\/Syrah"}
+{"vintage_id":160083243,"year":2018,"winery_id":29109,"wine_alcohol":null,"country":"Spain","region":" Ribera del Duero","price":12.46,"rating":3.7,"grape":"Tempranillo"}
+{"vintage_id":160102339,"year":2019,"winery_id":11110,"wine_alcohol":null,"country":"Italy","region":" Veneto","price":18.58,"rating":4.1,"grape":"Cabernet Sauvignon"}
+{"vintage_id":160561247,"year":2019,"winery_id":4875,"wine_alcohol":13.0,"country":"United States","region":" Monterey County","price":20.99,"rating":3.8,"grape":"Pinot Noir"}
+{"vintage_id":160803615,"year":2018,"winery_id":20497,"wine_alcohol":18.0,"country":"France","region":" Bordeaux Sup\u00e9rieur","price":16.59,"rating":4.0,"grape":"Merlot"}
+{"vintage_id":160839600,"year":2019,"winery_id":18338,"wine_alcohol":13.5,"country":"Spain","region":" Vino de Espa\u00f1a","price":9.66,"rating":4.1,"grape":"Syrah\/Shiraz"}
+{"vintage_id":161074726,"year":2018,"winery_id":27010,"wine_alcohol":14.5,"country":"Italy","region":" Montepulciano d'Abruzzo","price":16.69,"rating":3.8,"grape":"Montepulciano"}
+{"vintage_id":161362798,"year":2019,"winery_id":291112,"wine_alcohol":null,"country":"Italy","region":" Puglia","price":23.66,"rating":4.1,"grape":"Zinfandel"}
+{"vintage_id":161375238,"year":2019,"winery_id":251144,"wine_alcohol":14.5,"country":"France","region":" Montagne-Saint-\u00c9milion","price":16.66,"rating":4.0,"grape":"Merlot"}
+{"vintage_id":161452776,"year":2019,"winery_id":51044,"wine_alcohol":15.0,"country":"Italy","region":" Salento","price":16.07,"rating":4.0,"grape":"Merlot"}
+{"vintage_id":161949992,"year":2019,"winery_id":235719,"wine_alcohol":13.0,"country":"Italy","region":" Puglia","price":9.66,"rating":3.9,"grape":"Primitivo"}
+{"vintage_id":162166597,"year":2021,"winery_id":31643,"wine_alcohol":13.5,"country":"Italy","region":" Abruzzo","price":23.66,"rating":4.3,"grape":"Montepulciano"}
+{"vintage_id":162364183,"year":2016,"winery_id":26446,"wine_alcohol":14.0,"country":"France","region":" Vin de France","price":11.36,"rating":3.6,"grape":"Merlot"}
+{"vintage_id":162811260,"year":2019,"winery_id":270586,"wine_alcohol":14.5,"country":"France","region":" Cairanne","price":13.86,"rating":4.0,"grape":"Grenache"}
+{"vintage_id":162964968,"year":2020,"winery_id":47647,"wine_alcohol":null,"country":"Italy","region":" Salento","price":8.26,"rating":4.1,"grape":"Primitivo"}
+{"vintage_id":163144848,"year":2020,"winery_id":42128,"wine_alcohol":13.5,"country":"Italy","region":" Aglianico del Vulture","price":8.33,"rating":3.8,"grape":"Aglianico"}
+{"vintage_id":163185284,"year":2020,"winery_id":1378,"wine_alcohol":15.0,"country":"Italy","region":" Puglia","price":10.63,"rating":4.1,"grape":"Primitivo"}
+{"vintage_id":163214556,"year":2020,"winery_id":25492,"wine_alcohol":null,"country":"Spain","region":" Ribera del Duero","price":12.46,"rating":4.0,"grape":"Tempranillo"}
+{"vintage_id":163510898,"year":2020,"winery_id":287723,"wine_alcohol":null,"country":"Italy","region":" Puglia","price":9.72,"rating":4.1,"grape":"Negromaro"}
+{"vintage_id":163688366,"year":2016,"winery_id":1457,"wine_alcohol":14.0,"country":"Italy","region":" Toscana","price":16.1,"rating":3.7,"grape":"Merlot"}
+{"vintage_id":163966765,"year":2019,"winery_id":19731,"wine_alcohol":13.5,"country":"France","region":" C\u00f4tes-du-Rh\u00f4ne","price":16.69,"rating":4.2,"grape":"Shiraz\/Syrah"}
+{"vintage_id":164648889,"year":2016,"winery_id":5027,"wine_alcohol":13.5,"country":"Italy","region":" Chianti","price":10.93,"rating":3.2,"grape":"Sangiovese"}
+{"vintage_id":164881304,"year":2020,"winery_id":251125,"wine_alcohol":null,"country":"Italy","region":" Salento","price":18.06,"rating":4.2,"grape":"Montemajor"}
+{"vintage_id":165802879,"year":2019,"winery_id":27203,"wine_alcohol":null,"country":"United States","region":" California","price":10.5,"rating":3.9,"grape":"Zinfandel"}
+{"vintage_id":166112946,"year":2020,"winery_id":246946,"wine_alcohol":null,"country":"Spain","region":" Vino de Espa\u00f1a","price":8.26,"rating":4.1,"grape":"Monastrell"}
+{"vintage_id":166703132,"year":2020,"winery_id":173735,"wine_alcohol":14.5,"country":"Italy","region":" Emilia-Romagna","price":10.22,"rating":4.0,"grape":"Bonarda"}
+{"vintage_id":167430286,"year":2020,"winery_id":6900,"wine_alcohol":14.0,"country":"Italy","region":" Puglia","price":6.57,"rating":4.1,"grape":"Negromaro"}
+{"vintage_id":168712016,"year":2019,"winery_id":7875,"wine_alcohol":null,"country":"United States","region":" Lodi","price":8.84,"rating":3.8,"grape":"Zinfandel"}
+{"vintage_id":173955418,"year":2020,"winery_id":171548,"wine_alcohol":14.0,"country":"Italy","region":" Puglia","price":13.71,"rating":4.0,"grape":"Merlot"}
+{"vintage_id":173955418,"year":2020,"winery_id":236594,"wine_alcohol":14.5,"country":"Italy","region":" Puglia","price":13.71,"rating":4.0,"grape":"Merlot"}

data/vintages/vintages_dataset.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c1694c1566c68c3fb8d58b42b820c7b3fd317ae10e3ac7f3d182dcd56e2992ce
+size 3581

data/wt_session/wt_session.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd99db5ac52f76d63bb86f8955cc706e2ac7fa6b0f57c5cc37a5be4a8129c475
+size 1230403208

docs/instructions.md

@@ -0,0 +1,33 @@
+## Instructions
+
+### 1. **Subset the Valid Files**
+
+Run the following script to subset the valid files:
+
+```bash
+./scripts/create_filtered_images_dataset.sh data/all_images_big data/all/ data/all_d/all.list
+```
+
+Use the following command to compress the files:
+
+```bash
+tar -cvzf all.tar.gz all/
+```
+
+### 2. Push Large Files
+```bash
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.jsonl filter=lfs diff=lfs merge=lfs -text
+```
+
+Execute the following command in the root directory of the git repository:
+
+```bash
+git lfs install
+huggingface-cli lfs-enable-largefiles .
+git add .
+git commit -m "<commit message>"
+git push
+```
+
+You can copy the above Markdown-formatted instructions and view them in any Markdown viewer to see the enhanced readability.

pyproject.toml

@@ -0,0 +1,21 @@
+[tool.poetry]
+name = "l2t"
+version = "0.1.0"
+description = ""
+authors = ["Alireza Kashani <alireza.kashanipour@gmail.com>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.10,<3.13"
+pandas = "^2.1.1"
+ipykernel = "^6.25.2"
+datasets = "^2.14.5"
+openpyxl = "^3.1.2"
+matplotlib = "^3.8.0"
+openai = "^0.28.1"
+huggingface-hub = "^0.18.0"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

python-script.py

@@ -0,0 +1,312 @@
+import datasets
+import json
+
+logger = datasets.logging.get_logger(__name__)
+
+USERNAME = "Dakhoo"
+REPO_NAME = "small-dataset-img-test"
+
+_CITATION = """\
+ @article{bender2023learning,
+title={Learning to Taste: A Multimodal Wine Dataset},
+author={Bender, Thoranna and S{\o}rensen, Simon M{\o}e and Kashani, Alireza and Hjorleifsson, K Eldjarn and Hyldig, Grethe and Hauberg, S{\o}ren and Belongie, Serge and Warburg, Frederik},
+journal={arXiv preprint arXiv:2308.16900},
+year={2023}
+}
+"""
+
+_DESCRIPTION = (
+    "The dataset encompasses 897k images of wine labels and 824k reviews of wines "
+    "curated from the Vivino platform. It has over 350k unique vintages, annotated "
+    "with year, region, rating, alcohol percentage, price, and grape composition. "
+    "We obtained fine-grained flavor annotations on a subset by conducting a wine-tasting experiment "
+    "with 256 participants who were asked to rank wines based on their similarity in flavor,  "
+    "resulting in more than 5k pairwise flavor distances."
+)
+
+_HOMEPAGE = "https://https://thoranna.github.io/learning_to_taste/"
+
+_LICENSE = """\
+LICENSE AGREEMENT
+=================
+ - WineSensed by Thoranna Bender, Simon Søresen, Alireza Kashani, Kristjan Eldjarn, Grethe Hyldig,
+   Søren Hauberg, Serge Belongie, Frederik Warburg is licensed under a CC BY-NC-ND 4.0 Licence
+"""
+
+reviews = ['Deliciously fragrant xxx',
+                'Barolo & Brunello Tasting with Janne',
+                'Oak',
+                'Muito bom. Foi uma agradável surpresa. Óptimo sabor e guloso a acompanhar o almoço. Recomendo. ',
+                'Flauw zwoele smaak zonder al teveel afdronk. Voor de prijs oké zonder meer. Ik ben geen fan. ',
+                'Very different, very pink. Quite fruity can feel at the back sides of tongue ',
+                'Honey, apricot, tinned peaches in syrup. Oily, silky texture. Sweetness is well balanced with acidity. ',
+                'Amazing fruit and great finish. ',
+                'Dry, floral nose with fruit on the back',
+                'This Riesling Kabinett was good. Had a few minor problems, but cant complain to much at $13 ',
+                'Such an unusual drop, honey, spice notes. Drank it chilled. Nose like the skin on a sauccison...!',
+                'Very sweet and light bubbly red wine ',
+                'Great value. Really enjoyable wine and went down a treat with a steak 👌🏻',
+                '',
+                'Quite refreshing with a light citrus taste.',
+                'Pours in dark amber colour with excellant lacing. Aroma of raisins, caramel. Highly sweet, medium sour, light bitterness, taste of nutts, raisins. Full bodied, thick feel, long lasting aftertaste',
+                'Light, dry, grapefruit flavor, delicious ']
+
+
+_REPO = f"https://huggingface.co/datasets/{USERNAME}/{REPO_NAME}/resolve/main"
+_REPO = f"/Users/alka/Devel/L2T-NeurIPS-2023"
+
+class WineSensedConfig(datasets.BuilderConfig):
+    """BuilderConfig for WineSensed."""
+
+    def __init__(self, data_url, metadata_urls, **kwargs):
+        """BuilderConfig for WineSensed.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          matadata_urls: dictionary with 'train' containing the metadata URLs
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(WineSensedConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+        self.metadata_urls = metadata_urls
+
+class WineSensed(datasets.GeneratorBasedBuilder):
+    """WineSensed Images dataset"""
+
+    BUILDER_CONFIGS = [
+        WineSensedConfig(
+            name="vintages",
+            description="All tasted vintages along with their attributions.",
+            data_url=f"{_REPO}/data/vintages/vintages_dataset.tar.gz",
+            metadata_urls={
+                "train": f"{_REPO}/data/vintages/train.txt",
+            },
+        ),
+        WineSensedConfig(
+            name="napping_participants",
+            description="Napping and Participants datasets",
+            data_url=f"{_REPO}/data/napping_participants/napping_participants.tar.gz",
+            metadata_urls={
+                "train": f"{_REPO}/data/napping_participants/train.txt",
+            },
+        ),
+        WineSensedConfig(
+            name="small",
+            description="Small dataset.",
+            data_url=f"{_REPO}/data/small/small.tar.gz",
+            metadata_urls={
+                "train": f"{_REPO}/data/small/small_dataset.jsonl",
+            },
+        ),
+        WineSensedConfig(
+            name="wt_session",
+            description="Image-Review dataset.",
+            data_url=f"{_REPO}/data/wt_session/wt_session.tar.gz",
+            metadata_urls={
+                "train": f"{_REPO}/data/wt_session/wt_session.jsonl",
+            },
+        ),
+        WineSensedConfig(
+            name="all",
+            description="All images.",
+            data_url=f"{_REPO}/data/all/all.tar.gz",
+            metadata_urls={
+                "train": f"{_REPO}/data/all/all_dataset.jsonl",
+            },
+        ),
+    ]
+
+    def _info(self):
+
+        if self.config.name == 'vintages':
+            features = datasets.Features(
+                {
+                    "vintage_id": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "winery_id": datasets.Value("string"),
+                    "wine_alcohol": datasets.Value("string"),
+                    "country": datasets.Value("string"),
+                    "region": datasets.Value("string"),
+                    "price": datasets.Value("string"),
+                    "rating": datasets.Value("string"),
+                    "grape": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == 'napping_participants':
+            features = datasets.Features(
+                {								
+                    "event_name": datasets.Value("string"),
+                    "session_round_name": datasets.Value("string"),
+                    "experiment_no": datasets.Value("string"),
+                    "round_id": datasets.Value("string"),
+                    "participant_id": datasets.Value("string"),
+                    "experiment_id": datasets.Value("string"),
+                    "coor1": datasets.Value("string"),
+                    "coor2": datasets.Value("string"),
+                    "color": datasets.Value("string"),
+                }
+            )
+        else:
+            features = datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "vintage_id": datasets.Value("string"),
+                    "year": datasets.Value("string"),
+                    "winery_id": datasets.Value("string"),
+                    "wine_alcohol": datasets.Value("string"),
+                    "country": datasets.Value("string"),
+                    "region": datasets.Value("string"),
+                    "price": datasets.Value("string"),
+                    "rating": datasets.Value("string"),
+                    "grape": datasets.Value("string"),
+                    "review": datasets.Value("string"),
+                    "event_name": datasets.Value("string"),
+                    "session_round_name": datasets.Value("string"),
+                    "experiment_no": datasets.Value("string"),
+                    "round_id": datasets.Value("string"),
+                    "participant_id": datasets.Value("string"),
+                    "experiment_id": datasets.Value("string"),
+                    "coor1": datasets.Value("string"),
+                    "coor2": datasets.Value("string"),
+                    "color": datasets.Value("string"),
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION + self.config.description,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(self.config.data_url)
+        metadata_paths = dl_manager.download(self.config.metadata_urls)
+        record_iters = dl_manager.iter_archive(archive_path)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "records": record_iters,
+                    "metadata_path": metadata_paths["train"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, records, metadata_path):
+        """Generate images and metadata for splits."""
+        # Process the JSONL file to extract all metadata
+        if self.config.name == 'vintages':
+            for idx, (filepath, image) in enumerate(records):
+                file_jsonl = image.read()
+                jsonl_string = file_jsonl.decode('utf-8')
+                json_objects = jsonl_string.strip().split('\n')
+
+                id = 0
+                for json_object in json_objects:
+                    data_dict = json.loads(json_object)
+                    yield id, {
+                        "vintage_id": data_dict['vintage_id'],
+                        "year": data_dict['year'],
+                        "winery_id": data_dict['winery_id'],
+                        "wine_alcohol": data_dict['wine_alcohol'],
+                        "country": data_dict['country'],
+                        "region": data_dict['region'],
+                        "price": data_dict['price'],
+                        "rating": data_dict['rating'],
+                        "grape": data_dict['grape'],
+                    }
+                    id += 1 
+
+        elif self.config.name == 'napping_participants':
+            for idx, (filepath, image) in enumerate(records):
+                file_jsonl = image.read()
+                jsonl_string = file_jsonl.decode('utf-8')
+                json_objects = jsonl_string.strip().split('\n')
+
+                id = 0
+                for json_object in json_objects:
+                    data_dict = json.loads(json_object)
+                    yield id, {
+                        "event_name": data_dict['event_name'],
+                        "session_round_name": data_dict['session_round_name'],
+                        "experiment_no": data_dict['experiment_no'],
+                        "round_id": data_dict['round_id'],
+                        "participant_id": data_dict['participant_id'],
+                        "experiment_id": data_dict['experiment_id'],
+                        "coor1": data_dict['coor1'],
+                        "coor2": data_dict['coor2'],
+                        "color": data_dict['color'],
+                    }
+                    id += 1 
+
+        else:
+            metadata_dict = self._process_images_jsonl_file(metadata_path)
+
+            for idx, (filepath, image) in enumerate(records):
+                yield idx, {
+                    "image": {"path": filepath, "bytes": image.read()},
+                    "vintage_id": metadata_dict.get(filepath.split('/')[1], {}).get('vintage_id', None),
+                    "year": metadata_dict.get(filepath.split('/')[1], {}).get('year', None),
+                    "winery_id": metadata_dict.get(filepath.split('/')[1], {}).get('winery_id', None),
+                    "wine_alcohol": metadata_dict.get(filepath.split('/')[1], {}).get('wine_alcohol', None),
+                    "country": metadata_dict.get(filepath.split('/')[1], {}).get('country', None),
+                    "region": metadata_dict.get(filepath.split('/')[1], {}).get('region', None),
+                    "price": metadata_dict.get(filepath.split('/')[1], {}).get('price', None),
+                    "rating": metadata_dict.get(filepath.split('/')[1], {}).get('rating', None),
+                    "grape": metadata_dict.get(filepath.split('/')[1], {}).get('grape', None),
+                    "review": metadata_dict.get(filepath.split('/')[1], {}).get('review', None),
+                    "event_name": metadata_dict.get(filepath.split('/')[1], {}).get('event_name', None),
+                    "session_round_name": metadata_dict.get(filepath.split('/')[1], {}).get('session_round_name', None),
+                    "experiment_no": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_no', None),
+                    "round_id": metadata_dict.get(filepath.split('/')[1], {}).get('round_id', None),
+                    "participant_id": metadata_dict.get(filepath.split('/')[1], {}).get('participant_id', None),
+                    "experiment_id": metadata_dict.get(filepath.split('/')[1], {}).get('experiment_id', None),
+                    "coor1": metadata_dict.get(filepath.split('/')[1], {}).get('coor1', None),
+                    "coor2": metadata_dict.get(filepath.split('/')[1], {}).get('coor2', None),
+                    "color": metadata_dict.get(filepath.split('/')[1], {}).get('color', None),
+                }
+
+    def _process_images_jsonl_file(self, jsonl_file_path):
+        """A utility function defined within the WineSensed class.
+        This function reads and processes a JSONL (JSON Lines) file containing metadata about images and reviews.
+        It iterates through the lines in the JSONL file, parsing each line as JSON data.
+        For each JSON object in the file, it extracts relevant information such as image paths, reviews, vintage IDs, and more.
+        The extracted information is stored in a dictionary called metadata_dict, which is returned by the function. """
+        metadata_dict = {}
+
+        with open(jsonl_file_path, 'r', encoding="utf-8") as jsonl_file:
+            for line in jsonl_file:
+                try:
+                    data = json.loads(line)
+                    image = data.get('image', None)
+
+                    # Check if 'image' is present in the JSON object
+                    if image is not None:
+                        metadata_dict[image] = {
+                            "review": data.get('review', None),
+                            "vintage_id": data.get('vintage_id', None),
+                            "experiment_id": data.get('experiment_id', None),
+                            "year": data.get('year', None),
+                            "winery_id": data.get('winery_id', None),
+                            "wine_alcohol": data.get('wine_alcohol', None),
+                            "country": data.get('country', None),
+                            "region": data.get('region', None),
+                            "price": data.get('price', None),
+                            "rating": data.get('rating', None),
+                            "grape": data.get('grape', None),
+                            "event_name": data.get('event_name', None),
+                            "session_round_name": data.get('session_round_name', None),
+                            "experiment_no": data.get('experiment_no', None),
+                            "round_id": data.get('round_id', None),
+                            "participant_id": data.get('participant_id', None),
+                            "experiment_id": data.get('experiment_id', None),
+                            "coor1": data.get('coor1', None),
+                            "coor2": data.get('coor2', None),
+                            "color": data.get('color', None),
+                        }
+                except json.JSONDecodeError as e:
+                    print(f"Error parsing JSON: {e}")
+
+        return metadata_dict

scripts/create_all_images_dataset.sh

@@ -0,0 +1,8 @@
+#!/bin/bash
+
+# Create the target directory if it doesn't exist
+mkdir -p ../all
+
+# Use find and xargs to move jpg files from all chunk directories
+find ./chunk_* -name "*.jpg" -print0 | xargs -0 mv -t ../all/
+

scripts/create_filtered_images_dataset.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+
+# Check if the correct number of command-line arguments are provided
+if [ "$#" -ne 3 ]; then
+    echo "Usage: $0 <source_directory> <destination_directory> <image_list_file>"
+    exit 1
+fi
+
+# Get the source directory, destination directory, and image list file from command-line arguments
+source_dir="$1"
+destination_dir="$2"
+image_list_file="$3"
+
+# Check if the source directory exists
+if [ ! -d "$source_dir" ]; then
+    echo "Source directory '$source_dir' does not exist."
+    exit 1
+fi
+
+# Create the destination directory if it doesn't exist
+mkdir -p "$destination_dir"
+
+# Read each image name from the text file and copy the corresponding image to the destination directory
+while IFS= read -r image_name; do
+    echo "Processing: $image_name"
+    if [ -e "$source_dir/$image_name" ]; then
+        cp "$source_dir/$image_name" "$destination_dir/"
+	echo "Copied: $image_name"
+    else
+        echo "Image '$image_name' not found in the source directory."
+    fi
+done < "$image_list_file"
+
+echo "Images copied to the destination directory."
+