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#!/usr/bin/env python3
"""
HuggingFace Dataset Loader - Direct Loading
Loads cryptocurrency datasets directly from Hugging Face
"""
import logging
import os
from typing import Dict, Any, Optional, List
from datetime import datetime
import pandas as pd
from pathlib import Path
logger = logging.getLogger(__name__)
# Try to import datasets
try:
from datasets import load_dataset, Dataset, DatasetDict
DATASETS_AVAILABLE = True
except ImportError:
DATASETS_AVAILABLE = False
logger.error("❌ Datasets library not available. Install with: pip install datasets")
class CryptoDatasetLoader:
"""
Direct Cryptocurrency Dataset Loader
Loads crypto datasets from Hugging Face without using pipelines
"""
def __init__(self, cache_dir: Optional[str] = None):
"""
Initialize Dataset Loader
Args:
cache_dir: Directory to cache datasets (default: ~/.cache/huggingface/datasets)
"""
if not DATASETS_AVAILABLE:
logger.warning("⚠️ Dataset Loader disabled: datasets library not available")
self.enabled = False
else:
self.enabled = True
self.cache_dir = cache_dir or os.path.expanduser("~/.cache/huggingface/datasets")
self.datasets = {}
logger.info(f"🚀 Crypto Dataset Loader initialized")
logger.info(f" Cache directory: {self.cache_dir}")
# Dataset configurations
self.dataset_configs = {
"cryptocoin": {
"dataset_id": "linxy/CryptoCoin",
"description": "CryptoCoin dataset by Linxy",
"loaded": False
},
"bitcoin_btc_usdt": {
"dataset_id": "WinkingFace/CryptoLM-Bitcoin-BTC-USDT",
"description": "Bitcoin BTC-USDT market data",
"loaded": False
},
"ethereum_eth_usdt": {
"dataset_id": "WinkingFace/CryptoLM-Ethereum-ETH-USDT",
"description": "Ethereum ETH-USDT market data",
"loaded": False
},
"solana_sol_usdt": {
"dataset_id": "WinkingFace/CryptoLM-Solana-SOL-USDT",
"description": "Solana SOL-USDT market data",
"loaded": False
},
"ripple_xrp_usdt": {
"dataset_id": "WinkingFace/CryptoLM-Ripple-XRP-USDT",
"description": "Ripple XRP-USDT market data",
"loaded": False
}
}
async def load_dataset(
self,
dataset_key: str,
split: Optional[str] = None,
streaming: bool = False
) -> Dict[str, Any]:
"""
Load a specific dataset directly
Args:
dataset_key: Key of the dataset to load
split: Dataset split to load (train, test, validation, etc.)
streaming: Whether to stream the dataset
Returns:
Status dict with dataset info
"""
if dataset_key not in self.dataset_configs:
raise ValueError(f"Unknown dataset: {dataset_key}")
config = self.dataset_configs[dataset_key]
# Check if already loaded
if dataset_key in self.datasets:
logger.info(f"✅ Dataset {dataset_key} already loaded")
config["loaded"] = True
return {
"success": True,
"dataset_key": dataset_key,
"dataset_id": config["dataset_id"],
"status": "already_loaded",
"num_rows": len(self.datasets[dataset_key]) if hasattr(self.datasets[dataset_key], "__len__") else "unknown"
}
try:
logger.info(f"📥 Loading dataset: {config['dataset_id']}")
# Load dataset directly
dataset = load_dataset(
config["dataset_id"],
split=split,
cache_dir=self.cache_dir,
streaming=streaming
)
# Store dataset
self.datasets[dataset_key] = dataset
config["loaded"] = True
# Get dataset info
if isinstance(dataset, Dataset):
num_rows = len(dataset)
columns = dataset.column_names
elif isinstance(dataset, DatasetDict):
num_rows = {split: len(dataset[split]) for split in dataset.keys()}
columns = list(dataset[list(dataset.keys())[0]].column_names)
else:
num_rows = "unknown"
columns = []
logger.info(f"✅ Dataset loaded successfully: {config['dataset_id']}")
return {
"success": True,
"dataset_key": dataset_key,
"dataset_id": config["dataset_id"],
"status": "loaded",
"num_rows": num_rows,
"columns": columns,
"streaming": streaming
}
except Exception as e:
logger.error(f"❌ Failed to load dataset {dataset_key}: {e}")
raise Exception(f"Failed to load dataset {dataset_key}: {str(e)}")
async def load_all_datasets(self, streaming: bool = False) -> Dict[str, Any]:
"""
Load all configured datasets
Args:
streaming: Whether to stream the datasets
Returns:
Status dict with all datasets
"""
results = []
success_count = 0
for dataset_key in self.dataset_configs.keys():
try:
result = await self.load_dataset(dataset_key, streaming=streaming)
results.append(result)
if result["success"]:
success_count += 1
except Exception as e:
logger.error(f"❌ Failed to load {dataset_key}: {e}")
results.append({
"success": False,
"dataset_key": dataset_key,
"error": str(e)
})
return {
"success": True,
"total_datasets": len(self.dataset_configs),
"loaded_datasets": success_count,
"failed_datasets": len(self.dataset_configs) - success_count,
"results": results,
"timestamp": datetime.utcnow().isoformat()
}
async def get_dataset_sample(
self,
dataset_key: str,
num_samples: int = 10,
split: Optional[str] = None
) -> Dict[str, Any]:
"""
Get sample rows from a dataset
Args:
dataset_key: Key of the dataset
num_samples: Number of samples to return
split: Dataset split to sample from
Returns:
Sample data
"""
# Ensure dataset is loaded
if dataset_key not in self.datasets:
await self.load_dataset(dataset_key, split=split)
try:
dataset = self.datasets[dataset_key]
# Handle different dataset types
if isinstance(dataset, DatasetDict):
# Get first split if not specified
split_to_use = split or list(dataset.keys())[0]
dataset = dataset[split_to_use]
# Get samples
samples = dataset.select(range(min(num_samples, len(dataset))))
# Convert to list of dicts
samples_list = [dict(sample) for sample in samples]
logger.info(f"✅ Retrieved {len(samples_list)} samples from {dataset_key}")
return {
"success": True,
"dataset_key": dataset_key,
"dataset_id": self.dataset_configs[dataset_key]["dataset_id"],
"num_samples": len(samples_list),
"samples": samples_list,
"columns": list(samples_list[0].keys()) if samples_list else [],
"timestamp": datetime.utcnow().isoformat()
}
except Exception as e:
logger.error(f"❌ Failed to get samples from {dataset_key}: {e}")
raise Exception(f"Failed to get samples: {str(e)}")
async def query_dataset(
self,
dataset_key: str,
filters: Optional[Dict[str, Any]] = None,
limit: int = 100
) -> Dict[str, Any]:
"""
Query dataset with filters
Args:
dataset_key: Key of the dataset
filters: Dictionary of column filters
limit: Maximum number of results
Returns:
Filtered data
"""
# Ensure dataset is loaded
if dataset_key not in self.datasets:
await self.load_dataset(dataset_key)
try:
dataset = self.datasets[dataset_key]
# Handle DatasetDict
if isinstance(dataset, DatasetDict):
dataset = dataset[list(dataset.keys())[0]]
# Apply filters if provided
if filters:
for column, value in filters.items():
dataset = dataset.filter(lambda x: x[column] == value)
# Limit results
result_dataset = dataset.select(range(min(limit, len(dataset))))
# Convert to list of dicts
results = [dict(row) for row in result_dataset]
logger.info(f"✅ Query returned {len(results)} results from {dataset_key}")
return {
"success": True,
"dataset_key": dataset_key,
"filters_applied": filters or {},
"count": len(results),
"results": results,
"timestamp": datetime.utcnow().isoformat()
}
except Exception as e:
logger.error(f"❌ Failed to query dataset {dataset_key}: {e}")
raise Exception(f"Failed to query dataset: {str(e)}")
async def get_dataset_stats(self, dataset_key: str) -> Dict[str, Any]:
"""
Get statistics about a dataset
Args:
dataset_key: Key of the dataset
Returns:
Dataset statistics
"""
# Ensure dataset is loaded
if dataset_key not in self.datasets:
await self.load_dataset(dataset_key)
try:
dataset = self.datasets[dataset_key]
# Handle DatasetDict
if isinstance(dataset, DatasetDict):
splits_info = {}
for split_name, split_dataset in dataset.items():
splits_info[split_name] = {
"num_rows": len(split_dataset),
"columns": split_dataset.column_names,
"features": str(split_dataset.features)
}
return {
"success": True,
"dataset_key": dataset_key,
"dataset_id": self.dataset_configs[dataset_key]["dataset_id"],
"type": "DatasetDict",
"splits": splits_info,
"timestamp": datetime.utcnow().isoformat()
}
else:
return {
"success": True,
"dataset_key": dataset_key,
"dataset_id": self.dataset_configs[dataset_key]["dataset_id"],
"type": "Dataset",
"num_rows": len(dataset),
"columns": dataset.column_names,
"features": str(dataset.features),
"timestamp": datetime.utcnow().isoformat()
}
except Exception as e:
logger.error(f"❌ Failed to get stats for {dataset_key}: {e}")
raise Exception(f"Failed to get dataset stats: {str(e)}")
def get_loaded_datasets(self) -> Dict[str, Any]:
"""
Get list of loaded datasets
Returns:
Dict with loaded datasets info
"""
datasets_info = []
for dataset_key, config in self.dataset_configs.items():
info = {
"dataset_key": dataset_key,
"dataset_id": config["dataset_id"],
"description": config["description"],
"loaded": dataset_key in self.datasets
}
# Add size info if loaded
if dataset_key in self.datasets:
dataset = self.datasets[dataset_key]
if isinstance(dataset, DatasetDict):
info["num_rows"] = {split: len(dataset[split]) for split in dataset.keys()}
elif hasattr(dataset, "__len__"):
info["num_rows"] = len(dataset)
else:
info["num_rows"] = "unknown"
datasets_info.append(info)
return {
"success": True,
"total_configured": len(self.dataset_configs),
"total_loaded": len(self.datasets),
"datasets": datasets_info,
"timestamp": datetime.utcnow().isoformat()
}
def unload_dataset(self, dataset_key: str) -> Dict[str, Any]:
"""
Unload a specific dataset from memory
Args:
dataset_key: Key of the dataset to unload
Returns:
Status dict
"""
if dataset_key not in self.datasets:
return {
"success": False,
"dataset_key": dataset_key,
"message": "Dataset not loaded"
}
try:
# Remove dataset
del self.datasets[dataset_key]
# Update config
self.dataset_configs[dataset_key]["loaded"] = False
logger.info(f"✅ Dataset unloaded: {dataset_key}")
return {
"success": True,
"dataset_key": dataset_key,
"message": "Dataset unloaded successfully"
}
except Exception as e:
logger.error(f"❌ Failed to unload dataset {dataset_key}: {e}")
return {
"success": False,
"dataset_key": dataset_key,
"error": str(e)
}
# Global instance - only create if datasets is available
crypto_dataset_loader = None
if DATASETS_AVAILABLE:
try:
crypto_dataset_loader = CryptoDatasetLoader()
except Exception as e:
logger.warning(f"Failed to initialize CryptoDatasetLoader: {e}")
crypto_dataset_loader = None
else:
logger.warning("CryptoDatasetLoader not available - datasets library not installed")
# Export
__all__ = ["CryptoDatasetLoader", "crypto_dataset_loader"]
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