fastapriori
Fast association rule mining — even at very low support thresholds.
A compiled Rust engine with an inverted-index architecture. Counts pair co-occurrences exhaustively at k=2 (constant runtime in min_support) and uses anchor-and-extend with Apriori pruning at k>=3. Across eight real-world datasets (9.8K to 3.2M transactions, up to 49K items), algo="fast" wins 100% of k=2 configurations vs efficient-apriori (median 61x, up to 969x) and 100% vs the like-for-like compiled Apriori baseline (median 3.9x, up to 92.8x).
→ See the GitHub repository for benchmarks, the decision rule, performance plots, and the algorithm description.
Installation
pip install fastapriori
Pre-built wheels are shipped for Linux x86_64/aarch64, macOS arm64, and Windows x64 across Python 3.9–3.13. Other platforms build from source and need the Rust toolchain.
Quick Start
import pandas as pd
from fastapriori import find_associations
# Transactional data: one row per (transaction, item) pair
df = pd.DataFrame({
"txn_id": [1, 1, 1, 2, 2, 3, 3, 3],
"item": ["A", "B", "C", "A", "B", "B", "C", "D"],
})
# k=2 (default) — pairwise associations with seven metrics
pairs = find_associations(
df,
transaction_col="txn_id",
item_col="item",
min_support=0.01,
min_confidence=0.1,
)
# k=3 — triplet associations
triplets = find_associations(
df,
transaction_col="txn_id",
item_col="item",
k=3,
min_support=0.01,
)
k=2 output columns
item_A, item_B, instances, support, confidence, lift, conviction, leverage, cosine, jaccard.
k>=3 output columns
antecedent_1 … antecedent_{k-1}, consequent, instances, support, confidence, lift.
API Reference
find_associations()
find_associations(
df,
transaction_col,
item_col,
k=2, # itemset size (2 to 50)
min_support=None, # minimum support (float or None)
min_confidence=0.0, # minimum P(B|A)
min_lift=0.0, # minimum lift (k=2 only)
min_conviction=0.0, # minimum conviction (k=2 only)
min_leverage=None, # minimum leverage (k=2 only)
min_cosine=0.0, # minimum cosine similarity (k=2 only)
min_jaccard=0.0, # minimum Jaccard similarity (k=2 only)
max_items_per_txn=None, # cap outlier transactions (k>=3)
item_weights=None, # dict for custom ranking used by max_items_per_txn
low_memory="auto", # pre-filter infrequent items to reduce memory
show_progress=False, # tqdm progress bar
backend="auto", # "auto", "rust", "python", "polars", "pandas"
algo="fast", # "fast" (default), "classic", or "auto"
sorted_by="support", # sort column (or None to skip)
verbose=False, # print dataset stats and density warnings
)
algo:
"fast"(default) — inverted-index count-all. Constant runtime at k=2; wins 91–97% of real-world configurations vs the compiled Apriori control across k=3..9."classic"— Rust port of Apriori (join + prune + short-circuit). Requiresmin_support. Useful for dense, correlated data at k>=4."auto"— routes to"fast".
backend:
"auto"(default) — Rust if the compiled extension is available, otherwise Python."python"— polars for k=2 (falling back to pandas), counter_chain for k>=3.
Helper functions
from fastapriori import (
get_top_associations, # top-N items associated with a given item
filter_associations, # filter results to associations involving specific items
to_heatmap, # pivot results into an item x item matrix
plot_heatmap, # matplotlib heatmap (requires matplotlib)
to_graph, # networkx.DiGraph (requires networkx)
)
Practical Workflow: Run Once, Filter Many Times
Because fast k=2 runtime is constant in min_support, you can compute the full co-occurrence table once with min_support=None and then filter interactively — no re-computation:
full = find_associations(df, "txn_id", "item")
strong = full[(full["lift"] > 2) & (full["confidence"] > 0.3)]
substitutes = full[full["lift"] < 1]
rare = full[full["instances"] == 1] # anomaly / error detection
Memory and Limitations
- The pair counter scales as O(m²) in the unique-item count. Instacart (~50k items) peaks at ~1.5 GB; Chainstore (~46k items) at ~0.94 GB. Use
low_memory=True(with amin_support) to pre-filter infrequent items for an additional 5–10x reduction on large catalogs. - At k>=3 with dense baskets,
max_items_per_txncaps the C(d_max, k-1) blow-up at the cost of lower-bound counts. - Single-machine only — no distributed (Spark / Dask) version.
License
MIT
1 maintainer
Metadata
1 maintainer
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