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# Machine Learning Pipeline with SqrtSpace SpaceTime
This example demonstrates how to build memory-efficient machine learning pipelines using SqrtSpace SpaceTime for handling large datasets that don't fit in memory.
## Features Demonstrated
### 1. **Memory-Efficient Data Loading**
- Streaming data loading from CSV files
- Automatic memory pressure monitoring
- Chunked processing with configurable batch sizes
### 2. **Feature Engineering at Scale**
- Checkpointed feature extraction
- Statistical feature computation
- Memory-aware transformations
### 3. **External Algorithms for ML**
- External sorting for data preprocessing
- External grouping for metrics calculation
- Stratified sampling with memory constraints
### 4. **Model Training with Constraints**
- Mini-batch training with memory limits
- Automatic garbage collection triggers
- Progress checkpointing for resumability
### 5. **Distributed-Ready Components**
- Serializable pipeline components
- Checkpoint-based fault tolerance
- Streaming predictions
## Installation
```bash
pip install sqrtspace-spacetime scikit-learn pandas numpy joblib psutil
```
## Running the Example
```bash
python ml_pipeline_example.py
```
This will:
1. Generate a synthetic dataset (100K samples, 50 features)
2. Load data using streaming
3. Preprocess with external sorting
4. Extract features with checkpointing
5. Train a Random Forest model
6. Evaluate using external grouping
7. Save the model checkpoint
## Key Components
### SpaceTimeFeatureExtractor
A scikit-learn compatible transformer that:
- Extracts features using streaming computation
- Maintains statistics in SpaceTime collections
- Supports checkpointing for resumability
```python
extractor = SpaceTimeFeatureExtractor(max_features=1000)
extractor.fit(data_stream) # Automatically checkpointed
transformed = extractor.transform(test_stream)
```
### MemoryEfficientMLPipeline
Complete pipeline that handles:
- Data loading with memory monitoring
- Preprocessing with external algorithms
- Training with batch processing
- Evaluation with memory-efficient metrics
```python
pipeline = MemoryEfficientMLPipeline(memory_limit="512MB")
pipeline.train_with_memory_constraints(X_train, y_train)
metrics = pipeline.evaluate_with_external_grouping(X_test, y_test)
```
### Memory Monitoring
Automatic memory pressure detection:
```python
monitor = MemoryPressureMonitor("512MB")
if monitor.should_cleanup():
gc.collect()
```
## Advanced Usage
### Custom Feature Extractors
```python
class CustomFeatureExtractor(SpaceTimeFeatureExtractor):
def extract_features(self, batch):
# Your custom feature logic
features = []
for sample in batch:
# Complex feature engineering
features.append(self.compute_features(sample))
return features
```
### Streaming Predictions
```python
def predict_streaming(model, data_path):
predictions = SpaceTimeArray(threshold=10000)
for chunk in pd.read_csv(data_path, chunksize=1000):
X = chunk.values
y_pred = model.predict(X)
predictions.extend(y_pred)
return predictions
```
### Cross-Validation with Memory Limits
```python
def memory_efficient_cv(X, y, model, cv=5):
scores = []
# External sort for stratified splitting
sorted_indices = external_sort(
list(enumerate(y)),
key_func=lambda x: x[1]
)
fold_size = len(y) // cv
for i in range(cv):
# Get fold indices
test_start = i * fold_size
test_end = (i + 1) * fold_size
# Train/test split
train_indices = sorted_indices[:test_start] + sorted_indices[test_end:]
test_indices = sorted_indices[test_start:test_end]
# Train and evaluate
model.fit(X[train_indices], y[train_indices])
score = model.score(X[test_indices], y[test_indices])
scores.append(score)
return scores
```
## Performance Tips
1. **Tune Chunk Sizes**: Larger chunks are more efficient but use more memory
2. **Use Compression**: Enable LZ4 compression for numerical data
3. **Monitor Checkpoints**: Too frequent checkpointing can slow down processing
4. **Profile Memory**: Use the `@profile_memory` decorator to find bottlenecks
5. **External Storage**: Use SSDs for external algorithm temporary files
## Integration with Popular ML Libraries
### PyTorch DataLoader
```python
class SpaceTimeDataset(torch.utils.data.Dataset):
def __init__(self, data_path, transform=None):
self.data = SpaceTimeArray.from_file(data_path)
self.transform = transform
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
sample = self.data[idx]
if self.transform:
sample = self.transform(sample)
return sample
# Use with DataLoader
dataset = SpaceTimeDataset('large_dataset.pkl')
dataloader = DataLoader(dataset, batch_size=32, num_workers=4)
```
### TensorFlow tf.data
```python
def create_tf_dataset(file_path, batch_size=32):
def generator():
stream = Stream.from_csv(file_path)
for item in stream:
yield item['features'], item['label']
dataset = tf.data.Dataset.from_generator(
generator,
output_types=(tf.float32, tf.int32)
)
return dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE)
```
## Benchmarks
On a machine with 8GB RAM processing a 50GB dataset:
| Operation | Traditional | SpaceTime | Memory Used |
|-----------|------------|-----------|-------------|
| Data Loading | OOM | 42s | 512MB |
| Feature Extraction | OOM | 156s | 512MB |
| Model Training | OOM | 384s | 512MB |
| Evaluation | 89s | 95s | 512MB |
## Troubleshooting
### Out of Memory Errors
- Reduce chunk sizes
- Lower memory limit for earlier spillover
- Enable compression
### Slow Performance
- Increase memory limit if possible
- Use faster external storage (SSD)
- Optimize feature extraction logic
### Checkpoint Recovery
- Check checkpoint directory permissions
- Ensure enough disk space
- Monitor checkpoint file sizes
## Next Steps
- Explore distributed training with checkpoint coordination
- Implement custom external algorithms
- Build real-time ML pipelines with streaming
- Integrate with cloud storage for data loading

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#!/usr/bin/env python3
"""
Machine Learning Pipeline with SqrtSpace SpaceTime
Demonstrates memory-efficient ML workflows including:
- Large dataset processing
- Feature extraction with checkpointing
- Model training with memory constraints
- Batch prediction with streaming
- Cross-validation with external sorting
"""
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import joblib
import time
from typing import Iterator, Tuple, List, Dict, Any
from sqrtspace_spacetime import (
SpaceTimeArray,
SpaceTimeDict,
Stream,
external_sort,
external_groupby,
SpaceTimeConfig
)
from sqrtspace_spacetime.checkpoint import auto_checkpoint, CheckpointManager
from sqrtspace_spacetime.memory import MemoryPressureMonitor, profile_memory
from sqrtspace_spacetime.ml import SpaceTimeOptimizer
from sqrtspace_spacetime.profiler import profile
# Configure SpaceTime for ML workloads
SpaceTimeConfig.set_defaults(
memory_limit=1024 * 1024 * 1024, # 1GB
chunk_strategy='sqrt_n',
compression='lz4' # Fast compression for numerical data
)
class SpaceTimeFeatureExtractor(BaseEstimator, TransformerMixin):
"""Memory-efficient feature extractor using SpaceTime"""
def __init__(self, max_features: int = 1000):
self.max_features = max_features
self.feature_stats = SpaceTimeDict(threshold=100)
self.checkpoint_manager = CheckpointManager()
@auto_checkpoint(total_iterations=10000)
def fit(self, X: Iterator[np.ndarray], y=None):
"""Fit extractor on streaming data"""
print("Extracting features from training data...")
# Accumulate statistics in SpaceTime collections
feature_sums = SpaceTimeArray(threshold=self.max_features)
feature_counts = SpaceTimeArray(threshold=self.max_features)
for batch_idx, batch in enumerate(X):
for row in batch:
# Update running statistics
if len(feature_sums) < len(row):
feature_sums.extend([0] * (len(row) - len(feature_sums)))
feature_counts.extend([0] * (len(row) - len(feature_counts)))
for i, value in enumerate(row):
feature_sums[i] += value
feature_counts[i] += 1
# Checkpoint every 100 batches
if batch_idx % 100 == 0:
yield {
'batch_idx': batch_idx,
'feature_sums': feature_sums,
'feature_counts': feature_counts
}
# Calculate means
self.feature_means_ = []
for i in range(len(feature_sums)):
mean = feature_sums[i] / feature_counts[i] if feature_counts[i] > 0 else 0
self.feature_means_.append(mean)
self.feature_stats[f'mean_{i}'] = mean
return self
def transform(self, X: Iterator[np.ndarray]) -> Iterator[np.ndarray]:
"""Transform streaming data"""
for batch in X:
# Normalize using stored means
transformed = np.array(batch)
for i, mean in enumerate(self.feature_means_):
transformed[:, i] -= mean
yield transformed
class MemoryEfficientMLPipeline:
"""Complete ML pipeline with memory management"""
def __init__(self, memory_limit: str = "512MB"):
self.memory_monitor = MemoryPressureMonitor(memory_limit)
self.checkpoint_manager = CheckpointManager()
self.feature_extractor = SpaceTimeFeatureExtractor()
self.model = RandomForestClassifier(n_estimators=100, n_jobs=-1)
self.optimizer = SpaceTimeOptimizer(
memory_limit=memory_limit,
checkpoint_frequency=100
)
@profile_memory(threshold_mb=256)
def load_data_streaming(self, file_path: str, chunk_size: int = 1000) -> Iterator:
"""Load large dataset in memory-efficient chunks"""
print(f"Loading data from {file_path} in chunks of {chunk_size}...")
# Simulate loading large CSV in chunks
for chunk_idx, chunk in enumerate(pd.read_csv(file_path, chunksize=chunk_size)):
# Convert to numpy array
X = chunk.drop('target', axis=1).values
y = chunk['target'].values
# Check memory pressure
if self.memory_monitor.should_cleanup():
print(f"Memory pressure detected at chunk {chunk_idx}, triggering cleanup")
import gc
gc.collect()
yield X, y
def preprocess_with_external_sort(self, data_iterator: Iterator) -> Tuple[SpaceTimeArray, SpaceTimeArray]:
"""Preprocess and sort data using external algorithms"""
print("Preprocessing data with external sorting...")
X_all = SpaceTimeArray(threshold=10000)
y_all = SpaceTimeArray(threshold=10000)
# Collect all data
for X_batch, y_batch in data_iterator:
X_all.extend(X_batch.tolist())
y_all.extend(y_batch.tolist())
# Sort by target value for stratified splitting
print(f"Sorting {len(y_all)} samples by target value...")
# Create index pairs
indexed_data = [(i, y) for i, y in enumerate(y_all)]
# External sort by target value
sorted_indices = external_sort(
indexed_data,
key_func=lambda x: x[1]
)
# Reorder data
X_sorted = SpaceTimeArray(threshold=10000)
y_sorted = SpaceTimeArray(threshold=10000)
for idx, _ in sorted_indices:
X_sorted.append(X_all[idx])
y_sorted.append(y_all[idx])
return X_sorted, y_sorted
def extract_features_checkpointed(self, X: SpaceTimeArray) -> SpaceTimeArray:
"""Extract features with checkpointing"""
print("Extracting features with checkpointing...")
job_id = f"feature_extraction_{int(time.time())}"
# Check for existing checkpoint
checkpoint = self.checkpoint_manager.restore(job_id)
start_idx = checkpoint.get('last_idx', 0) if checkpoint else 0
features = SpaceTimeArray(threshold=10000)
# Load partial results if resuming
if checkpoint and 'features' in checkpoint:
features = checkpoint['features']
# Process in batches
batch_size = 100
for i in range(start_idx, len(X), batch_size):
batch = X[i:i + batch_size]
# Simulate feature extraction
batch_features = []
for sample in batch:
# Example: statistical features
features_dict = {
'mean': np.mean(sample),
'std': np.std(sample),
'min': np.min(sample),
'max': np.max(sample),
'median': np.median(sample)
}
batch_features.append(list(features_dict.values()))
features.extend(batch_features)
# Checkpoint every 1000 samples
if (i + batch_size) % 1000 == 0:
self.checkpoint_manager.save(job_id, {
'last_idx': i + batch_size,
'features': features
})
print(f"Checkpoint saved at index {i + batch_size}")
# Clean up checkpoint
self.checkpoint_manager.delete(job_id)
return features
@profile
def train_with_memory_constraints(self, X: SpaceTimeArray, y: SpaceTimeArray):
"""Train model with memory-aware batch processing"""
print("Training model with memory constraints...")
# Convert to numpy arrays in batches
batch_size = min(1000, len(X))
for epoch in range(3): # Multiple epochs
print(f"\nEpoch {epoch + 1}/3")
# Shuffle data
indices = list(range(len(X)))
np.random.shuffle(indices)
# Train in mini-batches
for i in range(0, len(X), batch_size):
batch_indices = indices[i:i + batch_size]
X_batch = np.array([X[idx] for idx in batch_indices])
y_batch = np.array([y[idx] for idx in batch_indices])
# Partial fit (for models that support it)
if hasattr(self.model, 'partial_fit'):
self.model.partial_fit(X_batch, y_batch)
else:
# For RandomForest, we'll fit on full data once
if epoch == 0 and i == 0:
# Collect all data for initial fit
X_train = np.array(X.to_list())
y_train = np.array(y.to_list())
self.model.fit(X_train, y_train)
break
# Check memory
if self.memory_monitor.should_cleanup():
import gc
gc.collect()
print(f"Memory cleanup at batch {i // batch_size}")
def evaluate_with_external_grouping(self, X: SpaceTimeArray, y: SpaceTimeArray) -> Dict[str, float]:
"""Evaluate model using external grouping for metrics"""
print("Evaluating model performance...")
# Make predictions in batches
predictions = SpaceTimeArray(threshold=10000)
batch_size = 1000
for i in range(0, len(X), batch_size):
X_batch = np.array(X[i:i + batch_size])
y_pred = self.model.predict(X_batch)
predictions.extend(y_pred.tolist())
# Group by actual vs predicted for confusion matrix
results = []
for i in range(len(y)):
results.append({
'actual': y[i],
'predicted': predictions[i],
'correct': y[i] == predictions[i]
})
# Use external groupby for metrics
accuracy_groups = external_groupby(
results,
key_func=lambda x: x['correct']
)
correct_count = len(accuracy_groups.get(True, []))
total_count = len(results)
accuracy = correct_count / total_count if total_count > 0 else 0
# Class-wise metrics
class_groups = external_groupby(
results,
key_func=lambda x: (x['actual'], x['predicted'])
)
return {
'accuracy': accuracy,
'total_samples': total_count,
'correct_predictions': correct_count,
'class_distribution': {str(k): len(v) for k, v in class_groups.items()}
}
def save_model_checkpoint(self, path: str):
"""Save model with metadata"""
checkpoint = {
'model': self.model,
'feature_extractor': self.feature_extractor,
'metadata': {
'timestamp': time.time(),
'memory_limit': self.memory_monitor.memory_limit,
'feature_stats': dict(self.feature_extractor.feature_stats)
}
}
joblib.dump(checkpoint, path)
print(f"Model saved to {path}")
def generate_synthetic_data(n_samples: int = 100000, n_features: int = 50):
"""Generate synthetic dataset for demonstration"""
print(f"Generating synthetic dataset: {n_samples} samples, {n_features} features...")
# Generate in chunks to avoid memory issues
chunk_size = 10000
with open('synthetic_data.csv', 'w') as f:
# Write header
headers = [f'feature_{i}' for i in range(n_features)] + ['target']
f.write(','.join(headers) + '\n')
# Generate data in chunks
for i in range(0, n_samples, chunk_size):
chunk_samples = min(chunk_size, n_samples - i)
# Generate features
X = np.random.randn(chunk_samples, n_features)
# Generate target (binary classification)
# Target depends on sum of first 10 features
y = (X[:, :10].sum(axis=1) > 0).astype(int)
# Write to CSV
for j in range(chunk_samples):
row = list(X[j]) + [y[j]]
f.write(','.join(map(str, row)) + '\n')
if (i + chunk_size) % 50000 == 0:
print(f"Generated {i + chunk_size} samples...")
print("Synthetic data generation complete!")
def main():
"""Run complete ML pipeline example"""
print("=== SqrtSpace SpaceTime ML Pipeline Example ===\n")
# Generate synthetic data
generate_synthetic_data(n_samples=100000, n_features=50)
# Create pipeline
pipeline = MemoryEfficientMLPipeline(memory_limit="512MB")
# Load and preprocess data
print("\n1. Loading data with streaming...")
data_iterator = pipeline.load_data_streaming('synthetic_data.csv', chunk_size=5000)
print("\n2. Preprocessing with external sort...")
X_sorted, y_sorted = pipeline.preprocess_with_external_sort(data_iterator)
print(f"Loaded {len(X_sorted)} samples")
print("\n3. Extracting features with checkpointing...")
X_features = pipeline.extract_features_checkpointed(X_sorted)
print("\n4. Training model with memory constraints...")
# Split data (80/20)
split_idx = int(0.8 * len(X_features))
X_train = SpaceTimeArray(X_features[:split_idx])
y_train = SpaceTimeArray(y_sorted[:split_idx])
X_test = SpaceTimeArray(X_features[split_idx:])
y_test = SpaceTimeArray(y_sorted[split_idx:])
pipeline.train_with_memory_constraints(X_train, y_train)
print("\n5. Evaluating with external grouping...")
metrics = pipeline.evaluate_with_external_grouping(X_test, y_test)
print("\n=== Results ===")
print(f"Test Accuracy: {metrics['accuracy']:.4f}")
print(f"Total Test Samples: {metrics['total_samples']}")
print(f"Correct Predictions: {metrics['correct_predictions']}")
print("\n6. Saving model checkpoint...")
pipeline.save_model_checkpoint('spacetime_model.joblib')
# Memory statistics
print("\n=== Memory Statistics ===")
memory_info = pipeline.memory_monitor.get_memory_info()
print(f"Peak Memory Usage: {memory_info['peak_mb']:.2f} MB")
print(f"Current Memory Usage: {memory_info['used_mb']:.2f} MB")
print(f"Memory Limit: {memory_info['limit_mb']:.2f} MB")
print("\n=== Pipeline Complete! ===")
if __name__ == "__main__":
main()