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Data loading and preprocessing for the Hugging Face model ecosystem dataset.
"""
import pandas as pd
from datasets import load_dataset
from typing import Optional, Dict, List
import numpy as np
class ModelDataLoader:
"""Load and preprocess model data from Hugging Face dataset."""
def __init__(self, dataset_name: str = "modelbiome/ai_ecosystem"):
self.dataset_name = dataset_name
self.df: Optional[pd.DataFrame] = None
def load_data(self, sample_size: Optional[int] = None, split: str = "train",
prioritize_base_models: bool = True) -> pd.DataFrame:
"""
Load dataset from Hugging Face Hub with methodological sampling.
Args:
sample_size: If provided, sample this many rows using stratified approach
split: Dataset split to load
prioritize_base_models: If True, prioritize base models (no parent) in sampling
Returns:
DataFrame with model data
"""
dataset = load_dataset(self.dataset_name, split=split)
df_full = dataset.to_pandas()
if sample_size and len(df_full) > sample_size:
if prioritize_base_models:
# Methodological sampling: prioritize base models
df_full = self._stratified_sample(df_full, sample_size)
else:
# Random sampling (old approach)
dataset = dataset.shuffle(seed=42).select(range(sample_size))
df_full = dataset.to_pandas()
self.df = df_full
return self.df
def _stratified_sample(self, df: pd.DataFrame, sample_size: int) -> pd.DataFrame:
"""
Stratified sampling prioritizing base models and popular models.
Strategy:
1. Include ALL base models (no parent) if they fit in sample_size
2. Add popular models (high downloads/likes)
3. Fill remaining with diverse models across libraries/tasks
Args:
df: Full DataFrame
sample_size: Target sample size
Returns:
Sampled DataFrame
"""
# Identify base models (no parent)
# parent_model is stored as string representation of list: '[]' for base models
base_models = df[
df['parent_model'].isna() |
(df['parent_model'] == '') |
(df['parent_model'] == '[]') |
(df['parent_model'] == 'null')
]
# Start with base models
if len(base_models) <= sample_size:
# All base models fit - include them all
sampled = base_models.copy()
remaining_size = sample_size - len(sampled)
# Get non-base models
non_base = df[~df.index.isin(sampled.index)]
if remaining_size > 0 and len(non_base) > 0:
# Add popular derived models and diverse samples
# Sort by downloads + likes for popularity
non_base['popularity_score'] = (
non_base.get('downloads', 0).fillna(0) +
non_base.get('likes', 0).fillna(0) * 100 # Weight likes more
)
# Take top 50% by popularity, 50% stratified by library
popular_size = min(remaining_size // 2, len(non_base))
diverse_size = remaining_size - popular_size
# Popular models
popular_models = non_base.nlargest(popular_size, 'popularity_score')
sampled = pd.concat([sampled, popular_models])
# Diverse sampling across libraries
if diverse_size > 0:
remaining = non_base[~non_base.index.isin(popular_models.index)]
if len(remaining) > 0:
# Stratify by library if possible
if 'library_name' in remaining.columns:
libraries = remaining['library_name'].value_counts()
diverse_samples = []
per_library = max(1, diverse_size // len(libraries))
for library in libraries.index:
lib_models = remaining[remaining['library_name'] == library]
n_sample = min(per_library, len(lib_models))
diverse_samples.append(lib_models.sample(n=n_sample, random_state=42))
diverse_df = pd.concat(diverse_samples).head(diverse_size)
else:
diverse_df = remaining.sample(n=min(diverse_size, len(remaining)), random_state=42)
sampled = pd.concat([sampled, diverse_df])
sampled = sampled.drop(columns=['popularity_score'], errors='ignore')
else:
# Too many base models - sample from them strategically
# Prioritize popular base models
base_models = base_models.copy() # Avoid SettingWithCopyWarning
base_models['popularity_score'] = (
base_models.get('downloads', 0).fillna(0) +
base_models.get('likes', 0).fillna(0) * 100
)
sampled = base_models.nlargest(sample_size, 'popularity_score')
sampled = sampled.drop(columns=['popularity_score'], errors='ignore')
return sampled.reset_index(drop=True)
def preprocess_for_embedding(self, df: Optional[pd.DataFrame] = None) -> pd.DataFrame:
"""
Preprocess data for embedding generation.
Combines text fields into a single representation.
Args:
df: DataFrame to process (uses self.df if None)
Returns:
DataFrame with combined text field
"""
if df is None:
df = self.df.copy()
else:
df = df.copy()
text_fields = ['tags', 'pipeline_tag', 'library_name', 'modelCard']
for field in text_fields:
if field in df.columns:
df[field] = df[field].fillna('')
# Build combined text from available fields
df['combined_text'] = (
df.get('tags', '').astype(str) + ' ' +
df.get('pipeline_tag', '').astype(str) + ' ' +
df.get('library_name', '').astype(str)
)
# Add modelCard if available (only in withmodelcards dataset)
if 'modelCard' in df.columns:
df['combined_text'] = df['combined_text'] + ' ' + df['modelCard'].astype(str).str[:500]
return df
def filter_data(
self,
df: Optional[pd.DataFrame] = None,
min_downloads: Optional[int] = None,
min_likes: Optional[int] = None,
libraries: Optional[List[str]] = None,
pipeline_tags: Optional[List[str]] = None,
search_query: Optional[str] = None
) -> pd.DataFrame:
"""
Filter dataset based on criteria.
Args:
df: DataFrame to filter (uses self.df if None)
min_downloads: Minimum download count
min_likes: Minimum like count
libraries: List of library names to include
pipeline_tags: List of pipeline tags to include
search_query: Text search in model_id or tags
Returns:
Filtered DataFrame
"""
if df is None:
df = self.df.copy()
else:
df = df.copy()
if min_downloads is not None:
downloads_col = df.get('downloads', pd.Series([0] * len(df), index=df.index))
df = df[downloads_col >= min_downloads]
if min_likes is not None:
likes_col = df.get('likes', pd.Series([0] * len(df), index=df.index))
df = df[likes_col >= min_likes]
if libraries:
library_col = df.get('library_name', pd.Series([''] * len(df), index=df.index))
df = df[library_col.isin(libraries)]
if pipeline_tags:
pipeline_col = df.get('pipeline_tag', pd.Series([''] * len(df), index=df.index))
df = df[pipeline_col.isin(pipeline_tags)]
if search_query:
query_lower = search_query.lower()
model_id_col = df.get('model_id', '').astype(str).str.lower()
tags_col = df.get('tags', '').astype(str).str.lower()
mask = model_id_col.str.contains(query_lower, na=False) | tags_col.str.contains(query_lower, na=False)
df = df[mask]
return df
def get_unique_values(self, column: str) -> List[str]:
"""Get unique non-null values from a column."""
if self.df is None:
return []
values = self.df[column].dropna().unique().tolist()
return sorted([str(v) for v in values if v and str(v) != 'nan'])
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