polars-sgt 0.3.0

Sequence Graph Transform (SGT) for Polars - Transform sequential data into weighted n-gram representations

polars-sgt

Sequence Graph Transform for Polars

Transform sequential data into powerful n-gram representations with Polars.

polars-sgt brings Sequence Graph Transform (SGT) to Polars, enabling you to:

• ✅ Transform sequences into weighted n-gram features
• ✅ Grouped Analysis: Apply SGT across subsets (e.g., by direction, metric) and merge into a single wide DataFrame
• ✅ Billion-Row Scale: Optimized Rust implementation with O(1) time weight lookups
• ✅ Temporal Dynamics: Capture patterns with multiple decay functions across all n-gram transitions
• ✅ Flexible: Support for datetime, date, duration, and numeric time columns
• ✅ Lazy & Parallel: Fully compatible with Polars lazy evaluation and Rayon-backed parallel processing

What is SGT?

Sequence Graph Transform converts sequential data (like user clickstreams, sensor readings, or transaction histories) into weighted n-gram representations. Unlike traditional n-grams, SGT captures:

• Sequential patterns: Multi-transition dependencies (Unigrams, bigrams, trigrams...)
• Temporal dynamics: Weights decay based on time gaps between events.
• Normalized features: L1/L2 normalization for machine-learning-ready feature spaces.

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Performance at Scale

Optimized for processing billions of rows:

• O(1) Weight Calculation: Uses cumulative product prefix arrays to calculate multi-transition time weights in constant time.
• Zero-Cost Abstraction: Written in Rust with Rayon for automatic multi-core utilization.
• Memory Efficient: Leverages Polars' arrow-backed memory management.

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Installation

pip install polars-sgt

Quick Start

1. High-Level API: sgt_transform_df

The sgt_transform_df function is the easiest way to generate SGT features. It handles unnesting, exploding, and pivoting into a wide format automatically.

Single Group (Default)

import polars as pl
import polars_sgt as sgt

df = pl.DataFrame({
    "user_id": ["A", "A", "A", "B", "B"],
    "action": ["login", "view", "purchase", "login", "view"],
    "time": [1, 2, 10, 1, 5],
})

# Generate wide-format features merged into one DataFrame
features = sgt.sgt_transform_df(
    df, 
    sequence_id_col="user_id", 
    state_col="action", 
    time_col="time",
    kappa=2
)

Grouped Sequence Analysis

Calculate separate SGT features for different groups (e.g., event types or directions) and merge them into one wide DataFrame.

# Calculate SGT features for each 'direction' and 'metric'
result = sgt.sgt_transform_df(
    df,
    sequence_id_col="user_id",
    state_col="action",
    time_col="time",
    group_cols=["direction", "metric"],
    kappa=3,
    time_penalty="exponential",
    alpha=0.7,
    group_name="analysis"
)
# Columns: ['user_id', 'analysis-buy-p_login', 'analysis-sell-p_login', ...]

2. Expression API: sgt_transform

For more control or integration into complex pipelines, use the expression-based API.

# Basic expression usage (returns a struct)
result = df.select(
    sgt.sgt_transform(
        "user_id",
        "action",
        time_col="time",
        kappa=2,
        time_penalty="exponential",
        alpha=0.1,
        mode="l1"
    ).alias("sgt_features")
)

# Extract and explode
features = result.select([
    pl.col("sgt_features").struct.field("sequence_id"),
    pl.col("sgt_features").struct.field("ngram_keys").alias("ngrams"),
    pl.col("sgt_features").struct.field("value").alias("weights"),
]).explode(["ngrams", "weights"])

With DateTime Columns

from datetime import datetime

df = pl.DataFrame({
    "session_id": ["A", "A", "A", "A"],
    "event": ["start", "click", "scroll", "exit"],
    "time": [
        datetime(2024, 1, 1, 10, 0),
        datetime(2024, 1, 1, 10, 5),
        datetime(2024, 1, 1, 10, 7),
        datetime(2024, 1, 1, 10, 15),
    ],
})

result = df.select(
    sgt.sgt_transform(
        "session_id",
        "event",
        time_col="time",
        deltatime="m",  # unit: minutes
        kappa=3,
    )
)

Lazy Evaluation & Streaming

result = (
    pl.scan_csv("large_sequences.csv")
    .with_columns(pl.col("timestamp").str.to_datetime())
    .select(
        sgt.sgt_transform(
            "user_id",
            "action",
            time_col="timestamp",
            kappa=2,
            deltatime="h",
        )
    )
    .collect(engine="streaming")
)

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API Reference

sgt.sgt_transform_df

The recommended high-level entry point. Returns a wide-format DataFrame.

• df: Input DataFrame or LazyFrame.
• sequence_id_col: Column(s) identifying sequences.
• state_col: Column containing states/events.
• time_col: Optional timestamp column.
• group_cols: Optional column(s) to group by before SGT.
• kappa: Maximum n-gram size.
• mode: Normalization ("l1", "l2", "none").
• time_penalty: Decay function ("inverse", "exponential", "linear", "power", "none").

sgt.sgt_transform (Expression)

Returns a struct with sequence_id, ngram_keys, and value.

df.select(
    sgt.sgt_transform("user", "action", kappa=2).alias("sgt")
).unnest("sgt")

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Author & Acknowledgments

Author: Zedd ([e-mail])

Special Thanks: Built upon polars-xdt by Marco Gorelli.

License

MIT