bm-eval-metrics 1.5.4

Python package providing easy-to-use evaluation metrics and utilities for Machine Learning

itertoolkit

Functions creating iterators and cached data pipelines for efficient looping.

itertoolkit is an itertoolkit-inspired wrapper focused on practical data processing. It keeps the lazy, composable style of iterator algebra, then adds cache-aware helpers so repeated list and data-structure transformations run faster.

The goal is simple:

• Keep memory usage low with lazy iterators.
• Speed up repeated workloads with caching.
• Make iterator pipelines readable and reusable.

Installation

pip install itertoolkit

Quick Start

from itertoolkit import count, islice

# Example: base itertoolkit stream
stream = (x * x for x in count(1))
print(list(islice(stream, 5)))  # [1, 4, 9, 16, 25]

# Example: cached computation workflow (concept)
# result = itertoolkit.cached_map(expensive_fn, dataset, cache_key="v1")

Why It Is Faster

itertoolkit performance comes from combining:

• Lazy iteration, so intermediate materialization is avoided.
• Cache-first wrappers, so repeated transformations are reused.
• Composable pipelines, so complex loops stay compact and optimized.

In repeated analytics or feature-building jobs, the first pass computes and stores results, and later passes can fetch from cache instead of recomputing every step.

Core Iterator Families

General iterators

Iterator concept	Input	Output shape	Typical use
Running reduction	iterable, func	incremental totals	rolling stats
Batching	iterable, n	tuples of size n	chunk processing
Chaining	multiple iterables	one continuous stream	merging sources
Selection	data + selectors	filtered stream	mask-based filtering
Windowing	iterable	adjacent pairs/windows	transition analysis
Truncation	predicate/slice	bounded output	safe handling of infinite streams

Combinatoric iterators

Iterator concept	Output
Cartesian products	all pairings across inputs
Permutations	order-sensitive tuples
Combinations	order-insensitive unique tuples
Combinations with replacement	tuples allowing repeated values

Pipeline Pattern

Use this pattern when processing large lists, tables, graphs, or text records:

1. Start from one or more iterables.
2. Chain filtering, mapping, grouping, and batching.
3. Add cache boundaries around expensive stages.
4. Materialize only where needed (list, tuple, DataFrame, model input).

from itertoolkit import chain

sources = [[1, 2, 3], [4, 5], [6]]
pipeline = (x * 10 for x in chain.from_iterable(sources) if x % 2 == 0)
print(list(pipeline))  # [20, 40, 60]

Caching Strategy

Recommended caching behavior for data-heavy workloads:

• Key by transformation signature and input fingerprint.
• Keep deterministic steps cacheable.
• Invalidate cache on function/version changes.
• Persist long-running results between sessions.

This makes repeated preprocessing and feature extraction significantly cheaper.

License

MIT