TSFX

TSFX -- Time Series Feature eXtraction

About

TSFX is a Python library for extracting features from time series data. Inspired by the great TSFresh library, TSFX aims to provide a similar feature set focused on performance on large datasets. In order to achieve this, TSFX is built on top of the Polars DataFrame library, and feature extractors are implemented in Rust.

Installation

For most common platforms and architectures, pre-built wheels are available. You can install the package directly from PyPI:

pip install tsfx

Installing from Source (Unsupported Targets)

If a pre-built wheel is not available for your specific platform, pip will automatically attempt to build the package from the source distribution (sdist).

Because tsfx is built with Maturin and its core features are implemented in Rust, you will need a Rust compiler to build it from source. Important: Building from source requires a nightly Rust toolchain. The exact nightly is pinned in rust-toolchain.toml to match the Polars version TSFX is built against.

To set up your environment for a source build:

1. Install Rust (if you haven't already) using rustup:

   curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
   

2. Nightly toolchain: with rustup, the pinned nightly from rust-toolchain.toml is installed and used automatically when building in the source directory. No rustup default nightly is needed.

3. Install the package:

   pip install tsfx
   

   pip will now use your nightly Rust compiler to build the package for your target.

Usage

Below is a simple example of extracting features from a time series dataset:

import polars as pl
from tsfx import (
    ExtractionSettings,
    FeatureSetting,
    extract_features,
)

df = pl.DataFrame(
    {
        "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
        "val": [1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
        "value": [4.0, 5.0, 6.0, 6.0, 5.0, 4.0, 4.0, 5.0, 6.0],
    },
).lazy()
settings = ExtractionSettings(
    grouping_cols=["id"],
    feature_setting=FeatureSetting.Efficient,
    value_cols=["val", "value"],
)
gdf = extract_features(df, settings)
gdf = gdf.sort(by="id")
with pl.Config(set_tbl_width_chars=80):
    print(gdf)

which produces the following output:

shape: (3, 1_498)
┌─────┬────────┬─────────┬─────────┬───┬─────────┬─────────┬─────────┬─────────┐
│ id  ┆ length ┆ val__su ┆ val__me ┆ … ┆ value__ ┆ value__ ┆ value__ ┆ value__ │
│ --- ┆ ---    ┆ m_value ┆ an      ┆   ┆ friedri ┆ friedri ┆ friedri ┆ max_lan │
│ str ┆ u32    ┆ s       ┆ ---     ┆   ┆ ch_coef ┆ ch_coef ┆ ch_coef ┆ gevin_f │
│     ┆        ┆ ---     ┆ f64     ┆   ┆ ficient ┆ ficient ┆ ficient ┆ ixed_po │
│     ┆        ┆ f64     ┆         ┆   ┆ s_…     ┆ s_…     ┆ s_…     ┆ in…     │
│     ┆        ┆         ┆         ┆   ┆ ---     ┆ ---     ┆ ---     ┆ ---     │
│     ┆        ┆         ┆         ┆   ┆ f64     ┆ f64     ┆ f64     ┆ f64     │
╞═════╪════════╪═════════╪═════════╪═══╪═════════╪═════════╪═════════╪═════════╡
│ a   ┆ 3      ┆ 6.0     ┆ 2.0     ┆ … ┆ 0.00399 ┆ 0.08720 ┆ 0.71646 ┆ 9.92847 │
│     ┆        ┆         ┆         ┆   ┆ 6       ┆ 7       ┆ 2       ┆ 5       │
│ b   ┆ 3      ┆ 6.0     ┆ 2.0     ┆ … ┆ -0.0028 ┆ -0.0718 ┆ -0.7111 ┆ 12.0654 │
│     ┆        ┆         ┆         ┆   ┆ 71      ┆ 12      ┆ 93      ┆ 07      │
│ c   ┆ 3      ┆ 6.0     ┆ 2.0     ┆ … ┆ 0.00399 ┆ 0.08720 ┆ 0.71646 ┆ 9.92847 │
│     ┆        ┆         ┆         ┆   ┆ 6       ┆ 7       ┆ 2       ┆ 5       │
└─────┴────────┴─────────┴─────────┴───┴─────────┴─────────┴─────────┴─────────┘

Extracting over a time window

An additional feature of TSFX is the ability to extract features over a time window. Below is an example of extracting features over a 3 year window:

import polars as pl
from tsfx import (
    DynamicGroupBySettings,
    ExtractionSettings,
    FeatureSetting,
    extract_features,
)
tdf = pl.DataFrame(
    {
        "id": ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
        "time": [
            "2001-01-01",
            "2002-01-01",
            "2003-01-01",
            "2001-01-01",
            "2002-01-01",
            "2003-01-01",
            "2001-01-01",
            "2002-01-01",
            "2003-01-01",
        ],
        "val": [1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
        "value": [4.0, 5.0, 6.0, 6.0, 5.0, 4.0, 4.0, 5.0, 6.0],
    },
).lazy()

dyn_settings = DynamicGroupBySettings(
    time_col="time",
    every="3y",
    period="3y",
    offset="0y",
    datetime_format="%Y-%m-%d",
)
settings = ExtractionSettings(
    grouping_cols=["id"],
    value_cols=["val", "value"],
    feature_setting=FeatureSetting.Efficient,
    dynamic_settings=dyn_settings,
)
gdf = extract_features(tdf, settings)
gdf = gdf.sort(by="id")
print(gdf)

which produces the following output:

shape: (6, 1_499)
┌─────┬────────────┬────────┬─────────────┬───┬─────────────┬────────────┬────────────┬────────────┐
│ id  ┆ time       ┆ length ┆ val__sum_va ┆ … ┆ value__frie ┆ value__fri ┆ value__fri ┆ value__max │
│ --- ┆ ---        ┆ ---    ┆ lues        ┆   ┆ drich_coeff ┆ edrich_coe ┆ edrich_coe ┆ _langevin_ │
│ str ┆ date       ┆ u32    ┆ ---         ┆   ┆ icients_…   ┆ fficients_ ┆ fficients_ ┆ fixed_poin │
│     ┆            ┆        ┆ f64         ┆   ┆ ---         ┆ …          ┆ …          ┆ …          │
│     ┆            ┆        ┆             ┆   ┆ f64         ┆ ---        ┆ ---        ┆ ---        │
│     ┆            ┆        ┆             ┆   ┆             ┆ f64        ┆ f64        ┆ f64        │
╞═════╪════════════╪════════╪═════════════╪═══╪═════════════╪════════════╪════════════╪════════════╡
│ a   ┆ 2000-01-01 ┆ 2      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
│ a   ┆ 2003-01-01 ┆ 1      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
│ b   ┆ 2000-01-01 ┆ 2      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
│ b   ┆ 2003-01-01 ┆ 1      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
│ c   ┆ 2000-01-01 ┆ 2      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
│ c   ┆ 2003-01-01 ┆ 1      ┆ 3.0         ┆ … ┆ NaN         ┆ NaN        ┆ NaN        ┆ NaN        │
└─────┴────────────┴────────┴─────────────┴───┴─────────────┴────────────┴────────────┴────────────┘

For more examples, see the examples directory.

Contributing

We welcome any kind of contribution to TSFX (bug reports, documentation enhancements, pull requests, etc.). More information can be found in the CONTRIBUTING.md instruction file.

Feature Coverage Compared to TSFresh

Acknowledgement

The tsfx package was developed within the Vinnova projects DFusion, TolkAI, and SIFT.