scikit-fda

library0.10.1pypypi✓ verified 83d ago

scikit-fda is a Python package for functional data analysis (FDA). Version 0.10.1 requires Python >=3.10. It provides tools for representation, preprocessing, and statistical analysis of functional data, following scikit-learn like API. Releases are irregular, roughly 1-2 per year.

ai-ml data

pip install scikit-fda

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · SCIKIT-FDA

scikit-fda

ai-mlpythonv0.10.1

Install

30.1s avg

Import

6929ms

Disk

782MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.10.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 30.1s · import 5.543s · 748MB

782MB installed

● package 782MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

FDataGrid

✓ from skfda import FDataGrid

✗ from skfda.representation import FDataGrid

In older versions, FDataGrid was under skfda.representation.grid, but since 0.9 it is directly importable from skfda.

FPCA

✓ from skfda.preprocessing.dim_reduction import FPCA

No common wrong import; use full path.

regularize

✓ from skfda.preprocessing.registration import least_squares_warping

✗ from skfda.registration import regularize

The registration module was reorganized; now use least_squares_warping directly.

Creates a simple FDataGrid object and performs functional principal component analysis (FPCA).

import numpy as np from skfda import FDataGrid from skfda.preprocessing.dim_reduction import FPCA # Generate functional data: 10 curves, each with 50 points t = np.linspace(0, 1, 50) data_matrix = np.random.randn(10, 50) # 10 samples, 50 time points fd = FDataGrid(data_matrix, grid_points=t) # Perform FPCA fpca = FPCA(n_components=3) fpca.fit(fd) scores = fpca.transform(fd) print(scores.shape)

Debug

Known issues

breakingIn version 0.9, the top-level imports were reorganized. Importing from `skfda.representation.grid` or `skfda.representation.basis` directly is deprecated. Use `from skfda import FDataGrid` and `from skfda.representation.basis import FDataBasis`.

fix

Update imports to the new paths as shown in the docs.

affects: <0.9

deprecatedThe `regularize` function from `skfda.preprocessing.registration` was deprecated in 0.9. Use `least_squares_warping` instead.

fix

Replace `regularize` calls with `least_squares_warping` from the same module.

affects: >=0.9,<1.0

gotchaFDataGrid expects data_matrix of shape (n_samples, n_points) by default, not (n_points, n_samples). Common mistake: transposed data leads to weird errors.

fix

Ensure data_matrix has shape (n_samples, n_points) or specify `sample_points` argument accordingly.

affects: All

gotchaWhen using basis expansion (e.g., `FDataBasis`), the coefficients array shape must match the basis. Dimensions mismatch leads to obscure NumPy errors.

fix

Check that the shape of coefficients is (n_samples, n_basis) for unidimensional basis.

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'skfda.representation'

Incorrect import path; in older docs, some examples use deep nested paths that no longer exist.

fix

Use the new top-level imports: `from skfda import FDataGrid` instead of `from skfda.representation.grid import FDataGrid`.

ValueError: Data must be 2D array with shape (n_samples, n_points) or (n_samples, n_points, n_dimensions)

Data passed to FDataGrid has wrong shape (e.g., transposed).

fix

Reshape data to (n_samples, n_points). For multivariate functional data, shape should be (n_samples, n_points, n_dimensions).

AttributeError: 'FData' object has no attribute 'regularize'

The `regularize` method was removed in newer versions (>=0.9).

fix

Use `least_squares_warping` from `skfda.preprocessing.registration` instead.

Upgrade

Version history

0.10.1latest on PyPI · released Apr 4, 2025

Audit

Dependencies

scikit-learnrequiredCore dependency for base classes and utilities

numpyrequiredNumerical operations

scipyrequiredInterpolation, integration, and optimization

matplotliboptionalPlotting functionality

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/GAA-UAM/scikit-fda ↗

packagehttps://pypi.org/project/scikit-fda/ ↗

docshttps://fda.readthedocs.io ↗