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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## This notebook is to show how to load csv data and into jsonl format for the LLM data cleaner.\n",
"\n",
"First, we load the data."
]
},
{
"cell_type": "code",
"execution_count": null,
"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>sku</th>\n",
" <th>product_name (pos)</th>\n",
" <th>brand (pos)</th>\n",
" <th>product_category (pos)</th>\n",
" <th>strain_name (pos)</th>\n",
" <th>product_weight_grams (pos)</th>\n",
" <th>brand (manual review)</th>\n",
" <th>product_category (manual review)</th>\n",
" <th>sub_product_category (manual review)</th>\n",
" <th>strain_name (manual review)</th>\n",
" <th>product_weight_grams (manual review)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>bl-842922110296</td>\n",
" <td>STIIIZY - Birthday Cake Pod 1g</td>\n",
" <td>NaN</td>\n",
" <td>VAPE PENS 1G</td>\n",
" <td>NaN</td>\n",
" <td>1.0</td>\n",
" <td>STIIIZY</td>\n",
" <td>Vape</td>\n",
" <td>Vape</td>\n",
" <td>Birthday Cake</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>co-6ARLLX12</td>\n",
" <td>SMASH Hits - Hippie Slayer - Indoor - 1g</td>\n",
" <td>SMASH Hits</td>\n",
" <td>NaN</td>\n",
" <td>Hippie Slayer</td>\n",
" <td>NaN</td>\n",
" <td>SMASH Hits</td>\n",
" <td>Preroll</td>\n",
" <td>Joint</td>\n",
" <td>Hippie Slayer</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>bl-090035986141</td>\n",
" <td>Eighth Brothers - Black Jack 1g Preroll</td>\n",
" <td>NaN</td>\n",
" <td>PREROLLS</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Eighth Brothers</td>\n",
" <td>Preroll</td>\n",
" <td>Joint</td>\n",
" <td>Black Jack</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>bl-850002822274</td>\n",
" <td>GRIZZLY PEAK - Indica Bone 0.5g 7PK Prerolls</td>\n",
" <td>NaN</td>\n",
" <td>PREROLL PACKS</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>GRIZZLY PEAK</td>\n",
" <td>Preroll</td>\n",
" <td>Joint</td>\n",
" <td>NaN</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>co-76GP441T</td>\n",
" <td>Minntz - Emerald Cut - Indoor - Joint - 1g</td>\n",
" <td>Minntz</td>\n",
" <td>NaN</td>\n",
" <td>Emerald Cut</td>\n",
" <td>NaN</td>\n",
" <td>Minntz</td>\n",
" <td>Preroll</td>\n",
" <td>Joint</td>\n",
" <td>Emerald Cut</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sku product_name (pos) brand (pos) \\\n",
"0 bl-842922110296 STIIIZY - Birthday Cake Pod 1g NaN \n",
"1 co-6ARLLX12 SMASH Hits - Hippie Slayer - Indoor - 1g SMASH Hits \n",
"2 bl-090035986141 Eighth Brothers - Black Jack 1g Preroll NaN \n",
"3 bl-850002822274 GRIZZLY PEAK - Indica Bone 0.5g 7PK Prerolls NaN \n",
"4 co-76GP441T Minntz - Emerald Cut - Indoor - Joint - 1g Minntz \n",
"\n",
" product_category (pos) strain_name (pos) product_weight_grams (pos) \\\n",
"0 VAPE PENS 1G NaN 1.0 \n",
"1 NaN Hippie Slayer NaN \n",
"2 PREROLLS NaN NaN \n",
"3 PREROLL PACKS NaN NaN \n",
"4 NaN Emerald Cut NaN \n",
"\n",
" brand (manual review) product_category (manual review) \\\n",
"0 STIIIZY Vape \n",
"1 SMASH Hits Preroll \n",
"2 Eighth Brothers Preroll \n",
"3 GRIZZLY PEAK Preroll \n",
"4 Minntz Preroll \n",
"\n",
" sub_product_category (manual review) strain_name (manual review) \\\n",
"0 Vape Birthday Cake \n",
"1 Joint Hippie Slayer \n",
"2 Joint Black Jack \n",
"3 Joint NaN \n",
"4 Joint Emerald Cut \n",
"\n",
" product_weight_grams (manual review) \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 3.5 \n",
"4 1 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Load tab-delimited file into pandas dataframe\n",
"cookies = pd.read_csv('../data/Cookies-AI-Gold-Standard - Cookies-AI-Gold-Standard.csv', sep=',')\n",
"\n",
"cookies.head()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data Preparation\n",
"We transform the dataset into a pandas dataframe, with a column for prompt and completion.\n",
"\n",
"The prompt contains the \"dirty\" columns, and completion contains the \"cleaned\" columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from datasets import Dataset, DatasetDict\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# split the dataset into train, val and test datasets 80/20\n",
"cookies_train, cookies_test = train_test_split(cookies, test_size=0.20, random_state=42)\n",
"\n",
"# list of input and output columns\n",
"input_columns = ['sku','product_name (pos)','brand (pos)','product_category (pos)','strain_name (pos)','product_weight_grams (pos)']\n",
"output_columns = ['brand (manual review)','product_category (manual review)','sub_product_category (manual review)','strain_name (manual review)','product_weight_grams (manual review)']\n",
"\n",
"# functtion to convert pandas dataframe row to csv string\n",
"def row_to_csv(row):\n",
" csv_string = ','.join(str(value) for value in row.values)\n",
" return csv_string\n",
"\n",
"# create dataframe with prompt and completion columns\n",
"\n",
"# apply row_to_csv function to each row of the training dataframe\n",
"input_rows = cookies_train[input_columns ].apply(row_to_csv, axis=1)\n",
"output_rows = cookies_train[output_columns].apply(row_to_csv, axis=1)\n",
"\n",
"# create dataframe with prompt and completion columns for training dataset\n",
"prompt_df = pd.DataFrame(\n",
" zip(input_rows,\n",
" output_rows)\n",
" , columns = ['prompt','completion'])\n",
"\n",
"# save dataframe to jsonl file for training\n",
"prompt_df.to_json(\"../data/cookies_train.jsonl\", orient='records', lines=True)\n",
"\n",
"# apply row_to_csv function to each row of the test dataframe\n",
"input_test_rows = cookies_test[input_columns ].apply(row_to_csv, axis=1)\n",
"output_test_rows = cookies_test[output_columns].apply(row_to_csv, axis=1)\n",
"\n",
"# create dataframe with prompt and completion columns for test dataset\n",
"test_df = pd.DataFrame(\n",
" zip(input_test_rows,\n",
" output_test_rows)\n",
" , columns = ['prompt','completion'])\n",
"test_df.head()\n",
"\n",
"# save dataframe to jsonl file for test\n",
"test_df.to_json(\"../data/cookies_test.jsonl\", orient='records', lines=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# write a function that samples n rows from a jsonl file\n",
"def sample_jsonl(path_or_buf='../data/cookies_train.jsonl',n_samples=5): \n",
" jsonObj = pd.read_json(path_or_buf=path_or_buf, lines=True)\n",
" return jsonObj.sample(n_samples, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# write a function that adds prompt and completion samples to messages\n",
"def add_samples(messages, n_samples=None):\n",
" if n_samples is None:\n",
" return messages\n",
" samples = sample_jsonl(n_samples=n_samples)\n",
" for i in range(n_samples):\n",
" messages.append({\"role\": \"user\", \"content\": samples.iloc[i]['prompt']})\n",
" messages.append({\"role\": \"assistant\", \"content\": samples.iloc[i]['completion']})\n",
" return messages"
]
}
],
"metadata": {
"language_info": {
"name": "python"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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