Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

🎉 (Spider): first commit and EDA #2

Merged
merged 1 commit into from
Dec 6, 2023
Merged

🎉 (Spider): first commit and EDA #2

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Empty file added lib/spider/README.md
View file
Open in desktop
Empty file.
273 changes: 273 additions & 0 deletions lib/spider/notebooks/eda.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,273 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4480527f3256e044",
"metadata": {},
"source": [
"Dataset archive downloaded from https://drive.google.com/uc?export=download&id=1TqleXec_OykOYFREKKtschzY29dUcVAQ"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2408797a0a334bd7",
"metadata": {},
"outputs": [],
"source": [
"!unzip ../data/spider.zip"
]
},
{
"cell_type": "markdown",
"id": "ee86f2a07265fa2a",
"metadata": {},
"source": [
"# Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e39ff161-ce51-4341-8cae-f8b8f4be2828",
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"import pandas as pd\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92764af2-bf21-4336-a516-1439e8098d06",
"metadata": {},
"outputs": [],
"source": [
"dataset_path = Path(\"../data/spider\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c03202bb-8953-4cd8-82ef-ca5965ed303c",
"metadata": {},
"outputs": [],
"source": [
"train_spider = pd.read_json(dataset_path / \"train_spider.json\")\n",
"train_others = pd.read_json(dataset_path / \"train_others.json\")\n",
"dev = pd.read_json(dataset_path / \"dev.json\")\n",
"train_spider.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ccf9ef9acfa640f4",
"metadata": {},
"outputs": [],
"source": [
"print(\"train_spider: \", len(train_spider))\n",
"print(\"train_others: \", len(train_others))\n",
"print(\"dev: \", len(dev))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d4da79a493c04a28",
"metadata": {},
"outputs": [],
"source": [
"for df, name in zip([train_spider, train_others, dev], [\"train_spider\", \"train_others\", \"dev\"]):\n",
" fig = px.histogram([len(x) for x in df.question_toks])\n",
" fig.update_layout(\n",
" showlegend=False,\n",
" xaxis_title=\"Number of tokens\",\n",
" title={\n",
" 'text': f\"Distribution of the number of tokens in {name} questions\",\n",
" 'y':0.95,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" }\n",
" )\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cad7e8f256c3d994",
"metadata": {},
"outputs": [],
"source": [
"for df, name in zip([train_spider, train_others, dev], [\"train_spider\", \"train_others\", \"dev\"]):\n",
" fig = px.histogram([len(x) for x in df.query_toks])\n",
" fig.update_layout(\n",
" showlegend=False,\n",
" xaxis_title=\"Number of tokens\",\n",
" title={\n",
" 'text': f\"Distribution of the number of tokens in {name} queries\",\n",
" 'y':0.95,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" }\n",
" )\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4059d45661d6945c",
"metadata": {},
"outputs": [],
"source": [
"for df, name in zip([train_spider, train_others, dev], [\"train_spider\", \"train_others\", \"dev\"]):\n",
" fig = px.histogram(df, x=\"db_id\").update_xaxes(categoryorder=\"category ascending\")\n",
" fig.update_layout(\n",
" showlegend=False,\n",
" title={\n",
" 'text': f\"Databases used in {name}\",\n",
" 'y':0.95,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" }\n",
" )\n",
" fig.show()"
]
},
{
"cell_type": "markdown",
"id": "f8809e118ffb551a",
"metadata": {},
"source": [
"# Databases "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b19828abc0d7607",
"metadata": {},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "319829d9d8b70877",
"metadata": {},
"outputs": [],
"source": [
"databases_path = list((dataset_path / \"database\").glob(\"*/*.sqlite\"))\n",
"print(\"Databases:\", len(databases_path))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab57cd2ef258da5d",
"metadata": {},
"outputs": [],
"source": [
"n_tables = {}\n",
"for db_path in databases_path:\n",
" conn = sqlite3.connect(str(db_path))\n",
" cursor = conn.cursor()\n",
" cursor.execute(\"SELECT count(*) FROM sqlite_master WHERE type='table';\")\n",
" n_tables[db_path.name] = cursor.fetchone()[0]\n",
"\n",
"fig = px.histogram(n_tables.values())\n",
"fig.update_layout(\n",
" showlegend=False,\n",
" xaxis_title=\"Number of tables\",\n",
" title={\n",
" 'text': f\"Distribution of the number of tables in the databases\",\n",
" 'y':0.95,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c4f8f376952b51d",
"metadata": {},
"outputs": [],
"source": [
"print(f\"Average number of tables: {sum(n_tables.values()) / len(n_tables.values())}\")\n",
"print(f\"Max number of tables: {max(n_tables, key=n_tables.get)} with {n_tables[max(n_tables, key=n_tables.get)]} tables\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5e0ca0825a65980",
"metadata": {},
"outputs": [],
"source": [
"n_columns = []\n",
"for db_path in databases_path:\n",
" conn = sqlite3.connect(str(db_path))\n",
" cursor = conn.cursor()\n",
" cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
" tables = cursor.fetchall()\n",
" for table in tables:\n",
" cursor.execute(f\"SELECT COUNT(*) FROM pragma_table_info('{table[0]}')\")\n",
" n_columns.append(cursor.fetchone()[0])\n",
"\n",
"fig = px.histogram(n_columns)\n",
"fig.update_layout(\n",
" showlegend=False,\n",
" xaxis_title=\"Number of columns\",\n",
" title={\n",
" 'text': f\"Distribution of the number of columns in the databases' tables\",\n",
" 'y':0.95,\n",
" 'x':0.5,\n",
" 'xanchor': 'center',\n",
" 'yanchor': 'top'\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb8ba5e9a53f92d4",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "spider-py3.11",
"language": "python",
"name": "spider-py3.11"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Loading