mirror of
https://github.com/NicolasBohn/NexQuant.git
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feat: add describe_data_folder_v2 (#738)
* add describe_data_folder_v2 * fix ci * fix * add packages
This commit is contained in:
Yuante Li
@@ -1,17 +1,17 @@
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import json
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import os
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from pathlib import Path
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from typing import Dict
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import pandas as pd
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from PIL import Image, TiffTags
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from rdagent.app.data_science.conf import DS_RD_SETTING
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from rdagent.components.coder.data_science.conf import get_ds_env
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from rdagent.core.experiment import FBWorkspace
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from rdagent.core.scenario import Scenario
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from rdagent.log import rdagent_logger as logger
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from rdagent.oai.llm_utils import APIBackend
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from rdagent.scenarios.data_science.scen.utils import (
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describe_data_folder,
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describe_data_folder_v2,
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)
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from rdagent.scenarios.kaggle.kaggle_crawler import (
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crawl_descriptions,
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leaderboard_scores,
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@@ -19,211 +19,6 @@ from rdagent.scenarios.kaggle.kaggle_crawler import (
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from rdagent.utils.agent.tpl import T
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def read_csv_head(file_path, indent=0, lines=5, max_col_width=100):
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"""
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Reads the first few rows of a CSV file and formats them with indentation and optional truncation.
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Parameters:
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file_path (str): Path to the CSV file.
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indent (int): Number of spaces to prepend to each line for indentation.
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lines (int): Number of rows to read from the CSV file.
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max_col_width (int): Maximum width of each column's content.
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Returns:
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str: A formatted string of the first few rows of the CSV file.
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"""
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try:
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# Read the CSV file with specified rows
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df = pd.read_csv(file_path, nrows=lines)
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if df.empty:
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return " " * indent + "(No data in the file)"
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# Truncate column contents to a maximum width
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truncated_df = df.copy()
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for col in truncated_df.columns:
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truncated_df[col] = (
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truncated_df[col]
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.astype(str)
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.apply(lambda x: (x[:max_col_width] + "...") if len(x) > max_col_width else x)
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)
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# Convert DataFrame to a string representation
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df_string_lines = truncated_df.to_string(index=False).split("\n")
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# Add indentation to each line
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indented_lines = [" " * indent + line for line in df_string_lines]
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return "\n".join(indented_lines)
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except FileNotFoundError:
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return f"Error: File not found at path '{file_path}'."
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except pd.errors.EmptyDataError:
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return f"Error: The file at '{file_path}' is empty."
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except Exception as e:
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return f"Error reading CSV: {e}"
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def get_dir_snapshot(folder_path):
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"""
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[note]
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- Returns a set of file extensions within the subfolder (excluding subfolder names)
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- Compares only the types of files contained, not specific file names or quantities
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"""
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exts = set()
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try:
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with os.scandir(folder_path) as it:
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for entry in it:
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if entry.is_file():
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file_ext = os.path.splitext(entry.name)[1]
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exts.add(file_ext)
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except Exception as e:
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logger.error(f"Error scanning directory: {e}")
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return frozenset(exts)
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def describe_data_folder(folder_path, indent=0, max_files=2, partial_expand_subfolders=2, is_top_level=True):
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"""
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folder_path : Current directory path
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indent : Current indentation
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max_files : Maximum number of files of the same type to display
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partial_expand_subfolders: When all subfolders have the same internal file types, only expand this many subfolders, the rest are omitted
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is_top_level : Indicates if the current folder is the top-level folder
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"""
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result = []
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files_count = {}
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files_details = {}
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for root, dirs, files in os.walk(folder_path):
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dirs.sort()
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files.sort()
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if not dirs:
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for file in files:
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file_path = os.path.join(root, file)
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file_type = os.path.splitext(file)[1][1:]
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file_size = os.path.getsize(file_path)
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if file_type not in files_count:
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files_count[file_type] = 0
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files_details[file_type] = []
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files_count[file_type] += 1
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# At top level, collect all CSV and Markdown files without restrictions
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# In deeper levels, follow the max_files restriction
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if is_top_level and file_type in ["csv", "md"]:
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files_details[file_type].append((file, file_size, file_path))
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elif len(files_details[file_type]) < max_files:
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files_details[file_type].append((file, file_size, file_path))
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break
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# Collect "type snapshots" of subfolders
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snapshots = []
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for d in dirs:
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subfolder_path = os.path.join(root, d)
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snapshot = get_dir_snapshot(subfolder_path)
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snapshots.append(snapshot)
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# Determine if all subfolders have the same file type distribution
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first_snapshot = snapshots[0]
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all_same_structure = all(s == first_snapshot for s in snapshots)
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if all_same_structure:
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for i, d in enumerate(dirs):
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if i < partial_expand_subfolders:
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result.append(" " * indent + f"- Folder: {d}")
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subfolder_path = os.path.join(root, d)
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result.append(
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describe_data_folder(
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folder_path=subfolder_path,
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indent=indent + 2,
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max_files=max_files,
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partial_expand_subfolders=partial_expand_subfolders,
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is_top_level=False,
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)
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)
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else:
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remaining = len(dirs) - i
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result.append(" " * indent + f"... ({remaining} more subfolders)")
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break
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else:
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for d in dirs:
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result.append(" " * indent + f"- Folder: {d}")
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subfolder_path = os.path.join(root, d)
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result.append(
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describe_data_folder(
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folder_path=subfolder_path,
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indent=indent + 2,
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max_files=max_files,
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partial_expand_subfolders=partial_expand_subfolders,
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is_top_level=False,
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)
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)
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for file in files:
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file_path = os.path.join(root, file)
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file_type = os.path.splitext(file)[1][1:]
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file_size = os.path.getsize(file_path)
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if file_type not in files_count:
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files_count[file_type] = 0
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files_details[file_type] = []
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files_count[file_type] += 1
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# At top level, collect all CSV and Markdown files without restrictions
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# In deeper levels, follow the max_files restriction
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if is_top_level and file_type in ["csv", "md"]:
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files_details[file_type].append((file, file_size, file_path))
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elif not is_top_level and len(files_details[file_type]) <= max_files:
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files_details[file_type].append((file, file_size, file_path))
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break
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# Print the folder and its contents
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for file_type, count in files_count.items():
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if count > max_files and file_type not in ["csv", "md", "txt"]:
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result.append(" " * indent + f"{count} {file_type}s:")
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for file, size, path in files_details[file_type]:
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result.append(" " * (indent + 2) + f"- {file} ({size} bytes)")
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result.append(" " * (indent + 2) + "... (file limit reached)")
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else:
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for file, size, path in files_details[file_type]:
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if file_type == "csv":
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df = pd.read_csv(path)
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result.append(
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" " * indent + f"- {file} ({size} bytes, with {df.shape[0]} rows and {df.shape[1]} columns)"
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)
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result.append(" " * (indent + 2) + f"- Head of {file}:")
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csv_head = read_csv_head(path, indent + 4)
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result.append(csv_head)
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continue
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result.append(" " * indent + f"- {file} ({size} bytes)")
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if file_type == "md":
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result.append(" " * (indent + 2) + f"- Content of {file}:")
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if file == "description.md":
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result.append(" " * (indent + 4) + f"Please refer to the background of the scenario context.")
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continue
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with open(path, "r", encoding="utf-8") as f:
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result.append(" " * (indent + 4) + f.read())
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if file_type == "tif":
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result.append(" " * (indent + 2) + f"- Metadata of {file}:")
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with Image.open(path) as img:
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for tag, value in img.tag_v2.items():
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tag_name = TiffTags.TAGS_V2.get(tag, f"Unknown Tag {tag}")
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result.append(" " * (indent + 4) + f"{tag_name}: {value}")
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if file_type in ["json", "txt"]:
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result.append(" " * (indent + 2) + f"- Content of {file}:")
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with open(path, "r", encoding="utf-8") as f:
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for i, line in enumerate(f):
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if i < 2:
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result.append(
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" " * (indent + 4) + line.strip()[:100] + ("..." if len(line.strip()) > 100 else "")
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)
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else:
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break
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return "\n".join(result) + "\n"
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class DataScienceScen(Scenario):
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"""Data Science Scenario"""
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@@ -333,7 +128,7 @@ class DataScienceScen(Scenario):
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return stdout
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def _get_data_folder_description(self) -> str:
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return describe_data_folder(Path(DS_RD_SETTING.local_data_path) / self.competition)
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return describe_data_folder_v2(Path(DS_RD_SETTING.local_data_path) / self.competition)
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class KaggleScen(DataScienceScen):
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@@ -364,3 +159,5 @@ class KaggleScen(DataScienceScen):
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if __name__ == "__main__":
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print(describe_data_folder(Path("/data/userdata/share/mle_kaggle") / "stanford-covid-vaccine"))
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print(describe_data_folder_v2(Path("/data/userdata/share/mle_kaggle") / "stanford-covid-vaccine"))
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@@ -0,0 +1,382 @@
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import os
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import pandas as pd
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from PIL import Image, TiffTags
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from rdagent.log import rdagent_logger as logger
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""" data folder description version 1 """
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def read_csv_head(file_path, indent=0, lines=5, max_col_width=100):
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"""
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Reads the first few rows of a CSV file and formats them with indentation and optional truncation.
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Parameters:
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file_path (str): Path to the CSV file.
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indent (int): Number of spaces to prepend to each line for indentation.
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lines (int): Number of rows to read from the CSV file.
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max_col_width (int): Maximum width of each column's content.
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Returns:
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str: A formatted string of the first few rows of the CSV file.
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"""
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try:
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# Read the CSV file with specified rows
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df = pd.read_csv(file_path, nrows=lines)
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if df.empty:
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return " " * indent + "(No data in the file)"
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# Truncate column contents to a maximum width
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truncated_df = df.copy()
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for col in truncated_df.columns:
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truncated_df[col] = (
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truncated_df[col]
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.astype(str)
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.apply(lambda x: (x[:max_col_width] + "...") if len(x) > max_col_width else x)
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)
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# Convert DataFrame to a string representation
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df_string_lines = truncated_df.to_string(index=False).split("\n")
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# Add indentation to each line
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indented_lines = [" " * indent + line for line in df_string_lines]
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return "\n".join(indented_lines)
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except FileNotFoundError:
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return f"Error: File not found at path '{file_path}'."
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except pd.errors.EmptyDataError:
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return f"Error: The file at '{file_path}' is empty."
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except Exception as e:
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return f"Error reading CSV: {e}"
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def get_dir_snapshot(folder_path):
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"""
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[note]
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- Returns a set of file extensions within the subfolder (excluding subfolder names)
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- Compares only the types of files contained, not specific file names or quantities
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"""
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exts = set()
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try:
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with os.scandir(folder_path) as it:
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for entry in it:
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if entry.is_file():
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file_ext = os.path.splitext(entry.name)[1]
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exts.add(file_ext)
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except Exception as e:
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logger.error(f"Error scanning directory: {e}")
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return frozenset(exts)
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def describe_data_folder(folder_path, indent=0, max_files=2, partial_expand_subfolders=2, is_top_level=True):
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"""
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folder_path : Current directory path
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indent : Current indentation
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max_files : Maximum number of files of the same type to display
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partial_expand_subfolders: When all subfolders have the same internal file types, only expand this many subfolders, the rest are omitted
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is_top_level : Indicates if the current folder is the top-level folder
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"""
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result = []
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files_count = {}
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files_details = {}
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for root, dirs, files in os.walk(folder_path):
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dirs.sort()
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files.sort()
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if not dirs:
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for file in files:
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file_path = os.path.join(root, file)
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file_type = os.path.splitext(file)[1][1:]
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file_size = os.path.getsize(file_path)
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if file_type not in files_count:
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files_count[file_type] = 0
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files_details[file_type] = []
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files_count[file_type] += 1
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# At top level, collect all CSV and Markdown files without restrictions
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# In deeper levels, follow the max_files restriction
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if is_top_level and file_type in ["csv", "md"]:
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files_details[file_type].append((file, file_size, file_path))
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elif len(files_details[file_type]) < max_files:
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files_details[file_type].append((file, file_size, file_path))
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break
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# Collect "type snapshots" of subfolders
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snapshots = []
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for d in dirs:
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subfolder_path = os.path.join(root, d)
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snapshot = get_dir_snapshot(subfolder_path)
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snapshots.append(snapshot)
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# Determine if all subfolders have the same file type distribution
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first_snapshot = snapshots[0]
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all_same_structure = all(s == first_snapshot for s in snapshots)
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if all_same_structure:
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for i, d in enumerate(dirs):
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if i < partial_expand_subfolders:
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result.append(" " * indent + f"- Folder: {d}")
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subfolder_path = os.path.join(root, d)
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result.append(
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describe_data_folder(
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folder_path=subfolder_path,
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indent=indent + 2,
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max_files=max_files,
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partial_expand_subfolders=partial_expand_subfolders,
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is_top_level=False,
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)
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)
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else:
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remaining = len(dirs) - i
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result.append(" " * indent + f"... ({remaining} more subfolders)")
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break
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else:
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for d in dirs:
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result.append(" " * indent + f"- Folder: {d}")
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subfolder_path = os.path.join(root, d)
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result.append(
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describe_data_folder(
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folder_path=subfolder_path,
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indent=indent + 2,
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max_files=max_files,
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partial_expand_subfolders=partial_expand_subfolders,
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is_top_level=False,
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)
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)
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for file in files:
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file_path = os.path.join(root, file)
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file_type = os.path.splitext(file)[1][1:]
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file_size = os.path.getsize(file_path)
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|
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if file_type not in files_count:
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files_count[file_type] = 0
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files_details[file_type] = []
|
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files_count[file_type] += 1
|
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|
||||
# At top level, collect all CSV and Markdown files without restrictions
|
||||
# In deeper levels, follow the max_files restriction
|
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if is_top_level and file_type in ["csv", "md"]:
|
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files_details[file_type].append((file, file_size, file_path))
|
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elif not is_top_level and len(files_details[file_type]) <= max_files:
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files_details[file_type].append((file, file_size, file_path))
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break
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|
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# Print the folder and its contents
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for file_type, count in files_count.items():
|
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if count > max_files and file_type not in ["csv", "md", "txt"]:
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result.append(" " * indent + f"{count} {file_type}s:")
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for file, size, path in files_details[file_type]:
|
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result.append(" " * (indent + 2) + f"- {file} ({size} bytes)")
|
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result.append(" " * (indent + 2) + "... (file limit reached)")
|
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else:
|
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for file, size, path in files_details[file_type]:
|
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if file_type == "csv":
|
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df = pd.read_csv(path)
|
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result.append(
|
||||
" " * indent + f"- {file} ({size} bytes, with {df.shape[0]} rows and {df.shape[1]} columns)"
|
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)
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result.append(" " * (indent + 2) + f"- Head of {file}:")
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csv_head = read_csv_head(path, indent + 4)
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result.append(csv_head)
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continue
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result.append(" " * indent + f"- {file} ({size} bytes)")
|
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if file_type == "md":
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result.append(" " * (indent + 2) + f"- Content of {file}:")
|
||||
if file == "description.md":
|
||||
result.append(" " * (indent + 4) + f"Please refer to the background of the scenario context.")
|
||||
continue
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
result.append(" " * (indent + 4) + f.read())
|
||||
if file_type == "tif":
|
||||
result.append(" " * (indent + 2) + f"- Metadata of {file}:")
|
||||
with Image.open(path) as img:
|
||||
for tag, value in img.tag_v2.items():
|
||||
tag_name = TiffTags.TAGS_V2.get(tag, f"Unknown Tag {tag}")
|
||||
result.append(" " * (indent + 4) + f"{tag_name}: {value}")
|
||||
if file_type in ["json", "txt"]:
|
||||
result.append(" " * (indent + 2) + f"- Content of {file}:")
|
||||
with open(path, "r", encoding="utf-8") as f:
|
||||
for i, line in enumerate(f):
|
||||
if i < 2:
|
||||
result.append(
|
||||
" " * (indent + 4) + line.strip()[:100] + ("..." if len(line.strip()) > 100 else "")
|
||||
)
|
||||
else:
|
||||
break
|
||||
|
||||
return "\n".join(result) + "\n"
|
||||
|
||||
|
||||
""" data folder description version 2 """
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
import humanize
|
||||
import pandas as pd
|
||||
from genson import SchemaBuilder
|
||||
from pandas.api.types import is_numeric_dtype
|
||||
|
||||
# these files are treated as code (e.g. markdown wrapped)
|
||||
code_files = {".py", ".sh", ".yaml", ".yml", ".md", ".html", ".xml", ".log", ".rst"}
|
||||
# we treat these files as text (rather than binary) files
|
||||
plaintext_files = {".txt", ".csv", ".json", ".tsv"} | code_files
|
||||
|
||||
|
||||
def get_file_len_size(f: Path) -> tuple[int, str]:
|
||||
"""
|
||||
Calculate the size of a file (#lines for plaintext files, otherwise #bytes)
|
||||
Also returns a human-readable string representation of the size.
|
||||
"""
|
||||
if f.suffix in plaintext_files:
|
||||
num_lines = sum(1 for _ in open(f))
|
||||
return num_lines, f"{num_lines} lines"
|
||||
else:
|
||||
s = f.stat().st_size
|
||||
return s, humanize.naturalsize(s)
|
||||
|
||||
|
||||
def file_tree(path: Path, depth=0) -> str:
|
||||
"""Generate a tree structure of files in a directory"""
|
||||
result = []
|
||||
files = [p for p in Path(path).iterdir() if not p.is_dir()]
|
||||
dirs = [p for p in Path(path).iterdir() if p.is_dir()]
|
||||
max_n = 4 if len(files) > 30 else 8
|
||||
for p in sorted(files)[:max_n]:
|
||||
result.append(f"{' '*depth*4}{p.name} ({get_file_len_size(p)[1]})")
|
||||
if len(files) > max_n:
|
||||
result.append(f"{' '*depth*4}... and {len(files)-max_n} other files")
|
||||
|
||||
for p in sorted(dirs):
|
||||
result.append(f"{' '*depth*4}{p.name}/")
|
||||
result.append(file_tree(p, depth + 1))
|
||||
|
||||
return "\n".join(result)
|
||||
|
||||
|
||||
def _walk(path: Path):
|
||||
"""Recursively walk a directory (analogous to os.walk but for pathlib.Path)"""
|
||||
for p in sorted(Path(path).iterdir()):
|
||||
if p.is_dir():
|
||||
yield from _walk(p)
|
||||
continue
|
||||
yield p
|
||||
|
||||
|
||||
def preview_csv(p: Path, file_name: str, simple=True) -> str:
|
||||
"""Generate a textual preview of a csv file
|
||||
|
||||
Args:
|
||||
p (Path): the path to the csv file
|
||||
file_name (str): the file name to use in the preview
|
||||
simple (bool, optional): whether to use a simplified version of the preview. Defaults to True.
|
||||
|
||||
Returns:
|
||||
str: the textual preview
|
||||
"""
|
||||
df = pd.read_csv(p)
|
||||
|
||||
out = []
|
||||
|
||||
out.append(f"-> {file_name} has {df.shape[0]} rows and {df.shape[1]} columns.")
|
||||
|
||||
if simple:
|
||||
cols = df.columns.tolist()
|
||||
sel_cols = 15
|
||||
cols_str = ", ".join(cols[:sel_cols])
|
||||
res = f"The columns are: {cols_str}"
|
||||
if len(cols) > sel_cols:
|
||||
res += f"... and {len(cols)-sel_cols} more columns"
|
||||
out.append(res)
|
||||
else:
|
||||
out.append("Here is some information about the columns:")
|
||||
for col in sorted(df.columns):
|
||||
dtype = df[col].dtype
|
||||
name = f"{col} ({dtype})"
|
||||
|
||||
nan_count = df[col].isnull().sum()
|
||||
|
||||
if dtype == "bool":
|
||||
v = df[col][df[col].notnull()].mean()
|
||||
out.append(f"{name} is {v*100:.2f}% True, {100-v*100:.2f}% False")
|
||||
elif df[col].nunique() < 10:
|
||||
out.append(f"{name} has {df[col].nunique()} unique values: {df[col].unique().tolist()}")
|
||||
elif is_numeric_dtype(df[col]):
|
||||
out.append(f"{name} has range: {df[col].min():.2f} - {df[col].max():.2f}, {nan_count} nan values")
|
||||
elif dtype == "object":
|
||||
out.append(
|
||||
f"{name} has {df[col].nunique()} unique values. Some example values: {df[col].value_counts().head(4).index.tolist()}"
|
||||
)
|
||||
|
||||
return "\n".join(out)
|
||||
|
||||
|
||||
def preview_json(p: Path, file_name: str):
|
||||
"""Generate a textual preview of a json file using a generated json schema"""
|
||||
builder = SchemaBuilder()
|
||||
with open(p) as f:
|
||||
first_line = f.readline().strip()
|
||||
|
||||
try:
|
||||
first_object = json.loads(first_line)
|
||||
|
||||
if not isinstance(first_object, dict):
|
||||
raise json.JSONDecodeError("The first line isn't JSON", first_line, 0)
|
||||
|
||||
# if the the next line exists and is not empty, then it is a JSONL file
|
||||
second_line = f.readline().strip()
|
||||
if second_line:
|
||||
f.seek(0) # so reset and read line by line
|
||||
for line in f:
|
||||
builder.add_object(json.loads(line.strip()))
|
||||
# if it is empty, then it's a single JSON object file
|
||||
else:
|
||||
builder.add_object(first_object)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
# if first line isn't JSON, then it's prettified and we can read whole file
|
||||
f.seek(0)
|
||||
builder.add_object(json.load(f))
|
||||
|
||||
return f"-> {file_name} has auto-generated json schema:\n" + builder.to_json(indent=2)
|
||||
|
||||
|
||||
def describe_data_folder_v2(base_path, include_file_details=True, simple=False):
|
||||
"""
|
||||
Generate a textual preview of a directory, including an overview of the directory
|
||||
structure and previews of individual files
|
||||
"""
|
||||
tree = f"```\n{file_tree(base_path)}```"
|
||||
out = [tree]
|
||||
|
||||
if include_file_details:
|
||||
for fn in _walk(base_path):
|
||||
file_name = str(fn.relative_to(base_path))
|
||||
|
||||
if fn.suffix == ".csv":
|
||||
out.append(preview_csv(fn, file_name, simple=simple))
|
||||
elif fn.suffix == ".json":
|
||||
out.append(preview_json(fn, file_name))
|
||||
elif fn.suffix in plaintext_files:
|
||||
if get_file_len_size(fn)[0] < 30:
|
||||
with open(fn) as f:
|
||||
content = f.read()
|
||||
if fn.suffix in code_files:
|
||||
content = f"```\n{content}\n```"
|
||||
out.append(f"-> {file_name} has content:\n\n{content}")
|
||||
|
||||
result = "\n\n".join(out)
|
||||
|
||||
# if the result is very long we generate a simpler version
|
||||
if len(result) > 6_000 and not simple:
|
||||
return describe_data_folder_v2(base_path, include_file_details=include_file_details, simple=True)
|
||||
# if still too long, we truncate
|
||||
if len(result) > 6_000 and simple:
|
||||
return result[:6_000] + "\n... (truncated)"
|
||||
|
||||
return result
|
||||
@@ -51,3 +51,7 @@ nbformat
|
||||
setuptools-scm
|
||||
seaborn
|
||||
azure.ai.inference
|
||||
|
||||
# data folder desc
|
||||
humanize
|
||||
genson
|
||||
|
||||
Reference in New Issue
Block a user