Hacktoberfest 2026: the issues maintainers tagged for October, open and beginner-friendly. Browse Hacktoberfest issues

Is there a way to get dmatrix to drop all-zero columns?

Open
  • Is something wrong?
#161 1 comment 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
data

Research direction

Start with the reproducible script in the issue and inspect patsy.dmatrices and the resulting DesignInfo objects. Determine how zero or dependent columns could be identified while preserving the formula metadata needed by statsmodels. Done should include a defined behavior for reduced design matrices and coverage for the demonstrated categorical-interaction case.

Written by the indexing model from the issue text.

Description

I have an experiment design that does not include all combinations of its categorical variables, and ran into some difficulties getting a full-rank design matrix for statsmodels. I included a simplified version below.

import numpy as np
import numpy.linalg as la
import pandas as pd
import patsy

index_vals = tuple("abc")
level_names = list("ABD")
n_samples = 2


def describe_design_matrix(design_matrix):
    print("Shape:", design_matrix.shape)
    print("Rank: ", la.matrix_rank(design_matrix))
    print(
        "Approximate condition number: {0:.2g}".format(
            np.divide(*la.svd(design_matrix)[1][[0, -1]])
        )
    )


ds_simple = pd.DataFrame(
    index=pd.MultiIndex.from_product(
        [index_vals] * len(level_names) + [range(n_samples)],
        names=level_names + ["sample"],
    ),
    columns=["y"],
    data=np.random.randn(len(index_vals) ** len(level_names) * n_samples),
).reset_index()

print("All sampled")
simple_X = patsy.dmatrices("y ~ (A + B + D) ** 3", ds_simple)[1]
describe_design_matrix(simple_X)

print("Only some sampled")
simple_X = patsy.dmatrices(
    "y ~ (A + B + D) ** 3", ds_simple.query("A != 'a' or B == 'a'")
)[1]
describe_design_matrix(simple_X)

print("Reduced X")
simple_X = patsy.dmatrices(
    "y ~ (A + B + D) ** 3",
    ds_simple.query("A != 'a' or B == 'a'"),
    return_type="dataframe",
)[1]
reduced_X = simple_X.loc[
    :, [col for col in simple_X.columns if not col.startswith("A[T.b]:B")]
]
describe_design_matrix(reduced_X)

print("Only some sampled: alternate method")
simple_X = patsy.dmatrices(
    "y ~ (C(A, Treatment('b')) + B + D) ** 3", ds_simple.query("A != 'a' or B == 'a'")
)[1]
describe_design_matrix(simple_X)
print("Number of nonzero elements:", (simple_X != 0).sum(axis=0))
print("Number of all-zero columns:", np.count_nonzero((simple_X != 0).sum(axis=0) == 0))

print("Reduced X: alternate method")
simple_X = patsy.dmatrices(
    "y ~ (C(A, Treatment('b')) + B + D) ** 3",
    ds_simple.query("A != 'a' or B == 'a'"),
    return_type="dataframe",
)[1]
reduced_X = simple_X.loc[
    :,
    [
        col
        for col in simple_X.columns
        if not col.startswith("C(A, Treatment('b'))[T.a]:B")
    ],
]
describe_design_matrix(reduced_X)

produces as output

All sampled
Shape: (54, 27)
Rank:  27
Approximate condition number: 52
Only some sampled
Shape: (42, 27)
Rank:  21
Approximate condition number: 3.8e+16
Reduced X
Shape: (42, 21)
Rank:  21
Approximate condition number: 37
Only some sampled: alternate method
Shape: (42, 27)
Rank:  21
Approximate condition number: 3.4e+16
Number of nonzero elements: [42  6 18 12 12 14 14  0  6  0  6  2  6  2  6  4  4  4  4  0  2  0  2  0
  2  0  2]
Number of all-zero columns: 6
Reduced X: alternate method
Shape: (42, 21)
Rank:  21
Approximate condition number: 39

I don't mind spending the time to find the representation that produces all-zero columns, but there doesn't seem to be a way within patsy to say "I know some of these columns are going to be all zeros" or "These columns will be linear dependent on others". Since some statsmodels functions require the formula information from patsy.DesignInfo objects, I wanted to see what could be done within patsy.

matthewwardrop/formulaic#19 is a related issue, with some discussion of how to generalize the "Reduced X" method in the script.

Dominant language
Python
Stars
990
Forks
106
Avg merge
7d 34m
Merged PRs (30d)
1

Getting set up

We have not checked this project's setup files yet. Start from its README, and see our first-contribution guide for the general steps.

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from pydata/patsy

All issues in pydata/patsy

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.