From e80f4c7acb76686102d6ef9feb446f683deecda6 Mon Sep 17 00:00:00 2001 From: louni-g Date: Wed, 6 Dec 2023 12:07:31 +0100 Subject: [PATCH] :tada: (Spider): first commit and EDA --- lib/spider/README.md | 0 lib/spider/notebooks/eda.ipynb | 273 +++++ lib/spider/poetry.lock | 2056 ++++++++++++++++++++++++++++++++ lib/spider/pyproject.toml | 65 + 4 files changed, 2394 insertions(+) create mode 100644 lib/spider/README.md create mode 100644 lib/spider/notebooks/eda.ipynb create mode 100644 lib/spider/poetry.lock create mode 100644 lib/spider/pyproject.toml diff --git a/lib/spider/README.md b/lib/spider/README.md new file mode 100644 index 0000000..e69de29 diff --git a/lib/spider/notebooks/eda.ipynb b/lib/spider/notebooks/eda.ipynb new file mode 100644 index 0000000..31987aa --- /dev/null +++ b/lib/spider/notebooks/eda.ipynb @@ -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": {}, + 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'py310', + 'py311', + 'py39' +] + +[tool.coverage.report] +exclude_lines = [ + "if __name__ == .__main__.:", + "pass", + "pragma: no cover", + "raise NotImplementedError", + "raise TypeError", + "raise ValueError" +] + +[tool.isort] +known_local_folder = ['spider'] +line_length = 100 +profile = 'black' + +[tool.poetry] +authors = ["Arkhn's NLP Team "] +description = "" +license = "Apache-2.0" +name = "spider" +readme = "README.md" +repository = "https://github.com/arkhn/ai-lembic/tree/main/lib/spider" +version = "0.1.0" + +[tool.poetry.dependencies] +jupyterlab = "^4.0.9" +pandas = "^2.1.3" +plotly = "^5.18.0" +python = "~3.11" + +[tool.pytest.ini_options] +addopts = "--import-mode=importlib --cov-report term-missing:skip-covered" +markers = [ + "serial", + "slow: marks tests as slow (deselect with '-m \"not slow\"')" +]