fancyimpute

library0.7.0pypypi✓ verified 85d ago

Matrix completion and feature imputation algorithms for Python. Current version is 0.7.0 (last release 2020). The library provides iterative imputation methods like SoftImpute, IterativeImputer, KNN, and NuclearNormMinimization. It is in maintenance mode with no recent updates.

ai-ml data

pip install fancyimpute

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · FANCYIMPUTE

fancyimpute

ai-mlpythonv0.7.0

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

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

SimpleFill

✓ from fancyimpute import SimpleFill

correct import

KNN

✓ from fancyimpute import KNN

correct import

SoftImpute

✓ from fancyimpute import SoftImpute

correct import

IterativeImputer

✓ from fancyimpute import IterativeImputer

✗ from fancyimpute.iterative_imputer import IterativeImputer

wrong: internal submodule not exposed

NuclearNormMinimization

✓ from fancyimpute import NuclearNormMinimization

correct import

Basic usage: impute missing values using SoftImpute.

import numpy as np from fancyimpute import SoftImpute # Create data with missing values X = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]]) # impute using SoftImpute X_filled = SoftImpute().fit_transform(X) print(X_filled)

Debug

Known issues

deprecatedfancyimpute is no longer actively maintained (last release 2020). Consider using sklearn.impute.IterativeImputer for iterative imputation or other modern alternatives.

fix

Use sklearn.impute.IterativeImputer or other up-to-date libraries.

affects: >=0.7.0

gotchaInput data must be a 2D numpy array with NaN values for missing entries. If you use a pandas DataFrame, it will be converted but columns may be reordered.

fix

Ensure data is a 2D numpy array with NaN for missing values. Convert pandas DataFrame with .values.

affects: all

gotchaSome solvers (e.g., NuclearNormMinimization) require cvxpy, which is an optional dependency. If not installed, import will fail.

fix

Install cvxpy: pip install cvxpy

affects: all

breakingIn version 0.5.0, the API changed: fit() returns a fitted model, and transform() or fit_transform() must be used to impute. Older versions used fit() to return imputed data.

fix

Use fit_transform() or fit() then transform() instead of relying on fit() returning imputed data.

affects: 0.5.0

Errors

Common errors & fixes

ImportError: No module named 'fancyimpute'

fancyimpute not installed or installed in wrong environment.

fix

Run: pip install fancyimpute

ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

Data contains infinity or extremely large values.

fix

Replace infinity with NaN using np.isinf, or scale data.

ModuleNotFoundError: No module named 'cvxpy'

Attempting to use NuclearNormMinimization without cvxpy installed.

fix

Install cvxpy: pip install cvxpy

AttributeError: 'SoftImpute' object has no attribute 'fit_transform'

Old version of fancyimpute (<0.5.0) where fit() returned imputed matrix.

fix

Upgrade fancyimpute: pip install --upgrade fancyimpute

Upgrade

Version history

0.7.0latest on PyPI · released Oct 21, 2021

Audit

Dependencies

numpyrequiredcore dependency for array operations

scipyrequiredsparse matrices and linear algebra

scikit-learnrequiredused for KNN and IterativeImputer

cvxpyoptionalused for convex optimization (NuclearNormMinimization)

Agent activity

9 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/iskandr/fancyimpute ↗

packagehttps://pypi.org/project/fancyimpute/ ↗

homepagehttps://pypi.org/project/fancyimpute/ ↗