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test_formula.py
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import pickle
import re
from io import BytesIO
import formulaic
import numpy as np
import pandas as pd
import pytest
from formulaic.materializers import FormulaMaterializer
from formulaic.materializers.types import EvaluatedFactor, FactorValues
from formulaic.parser.types import Factor
from scipy import sparse as sps
import tabmat as tm
from tabmat.formula import (
TabmatMaterializer,
_interact,
_InteractableCategoricalVector,
_InteractableDenseVector,
_InteractableSparseVector,
)
@pytest.fixture
def df():
df = pd.DataFrame(
{
"num_1": [1.0, 2.0, 3.0, 4.0, 5.0],
"num_2": [5.0, 4.0, 3.0, 2.0, 1.0],
"cat_1": pd.Categorical(["a", "b", "c", "b", "a"]),
"cat_2": pd.Categorical(["x", "y", "z", "x", "y"]),
"cat_3": pd.Categorical(["1", "2", "1", "2", "1"]),
"str_1": ["a", "b", "c", "b", "a"],
}
)
return df
def test_retrieval():
assert FormulaMaterializer.for_materializer("tabmat") is TabmatMaterializer
assert (
FormulaMaterializer.for_data(pd.DataFrame(), output="tabmat")
is TabmatMaterializer
)
@pytest.mark.parametrize(
"formula, expected",
[
pytest.param(
"1 + num_1",
tm.SplitMatrix(
[
tm.DenseMatrix(
np.array(
[[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0, 5.0]]
).T
)
]
),
id="numeric",
),
pytest.param(
"1 + cat_1",
tm.SplitMatrix(
[
tm.DenseMatrix(np.array([[1.0, 1.0, 1.0, 1.0, 1.0]]).T),
tm.CategoricalMatrix(
pd.Categorical(
[
"__drop__",
"cat_1[b]",
"cat_1[c]",
"cat_1[b]",
"__drop__",
],
categories=["__drop__", "cat_1[b]", "cat_1[c]"],
),
drop_first=True,
),
]
),
id="categorical",
),
pytest.param(
"{np.where(num_1 >= 2, num_1, 0)} * {np.where(num_2 <= 2, num_2, 0)}",
tm.SplitMatrix(
[
tm.DenseMatrix(np.array([[0.0, 2.0, 3.0, 4.0, 5.0]]).T),
tm.SparseMatrix(
sps.csc_matrix(
np.array(
[
[1.0, 2.0, 0.0, 0.0, 0.0],
[0.0, 2.0, 0.0, 0.0, 0.0],
]
)
).T
),
]
),
id="numeric_sparse",
),
pytest.param(
"1 + num_1 : cat_1",
tm.SplitMatrix(
[
tm.DenseMatrix(np.array([[1.0, 1.0, 1.0, 1.0, 1.0]]).T),
tm.SparseMatrix(
sps.csc_matrix(
np.array(
[
[1.0, 0.0, 0.0, 0.0, 5.0],
[0.0, 2.0, 0.0, 4.0, 0.0],
[0.0, 0.0, 3.0, 0.0, 0.0],
]
).T
)
),
]
),
id="interaction_cat_num",
),
pytest.param(
"cat_1 : cat_3 - 1",
tm.SplitMatrix(
[
tm.CategoricalMatrix(
pd.Categorical(
[
"cat_1[a]:cat_3[1]",
"cat_1[b]:cat_3[2]",
"cat_1[c]:cat_3[1]",
"cat_1[b]:cat_3[2]",
"cat_1[a]:cat_3[1]",
],
categories=[
"cat_1[a]:cat_3[1]",
"cat_1[b]:cat_3[1]",
"cat_1[c]:cat_3[1]",
"cat_1[a]:cat_3[2]",
"cat_1[c]:cat_3[2]",
"cat_1[b]:cat_3[2]",
],
),
drop_first=False,
),
]
),
id="interaction_cat_cat",
),
],
)
def test_matrix_against_expectation(df, formula, expected):
model_df = tm.from_formula(
formula, df, ensure_full_rank=True, cat_threshold=1, sparse_threshold=0.5
)
assert len(model_df.matrices) == len(expected.matrices)
for res, exp in zip(model_df.matrices, expected.matrices):
assert type(res) == type(exp)
if isinstance(res, (tm.DenseMatrix, tm.SparseMatrix)):
np.testing.assert_array_equal(res.A, res.A)
elif isinstance(res, tm.CategoricalMatrix):
assert (exp.cat == res.cat).all()
assert exp.drop_first == res.drop_first
@pytest.mark.parametrize(
"formula, expected",
[
pytest.param(
"1 + num_1",
tm.SplitMatrix(
[
tm.DenseMatrix(
np.array(
[[1.0, 1.0, 1.0, 1.0, 1.0], [1.0, 2.0, 3.0, 4.0, 5.0]]
).T
)
]
),
id="numeric",
),
pytest.param(
"1 + cat_1",
tm.SplitMatrix(
[
tm.DenseMatrix(np.array([[1.0, 1.0, 1.0, 1.0, 1.0]]).T),
tm.CategoricalMatrix(
pd.Categorical(
[
"__drop__",
"cat_1__b",
"cat_1__c",
"cat_1__b",
"__drop__",
],
categories=["__drop__", "cat_1__b", "cat_1__c"],
),
drop_first=True,
),
]
),
id="categorical",
),
pytest.param(
"1 + num_1 : cat_1",
tm.SplitMatrix(
[
tm.DenseMatrix(np.array([[1.0, 1.0, 1.0, 1.0, 1.0]]).T),
tm.SparseMatrix(
sps.csc_matrix(
np.array(
[
[1.0, 0.0, 0.0, 0.0, 5.0],
[0.0, 2.0, 0.0, 4.0, 0.0],
[0.0, 0.0, 3.0, 0.0, 0.0],
]
).T
)
),
]
),
id="interaction_cat_num",
),
pytest.param(
"cat_1 : cat_3 - 1",
tm.SplitMatrix(
[
tm.CategoricalMatrix(
pd.Categorical(
[
"cat_1__a__x__cat_3__1",
"cat_1__b__x__cat_3__2",
"cat_1__c__x__cat_3__1",
"cat_1__b__x__cat_3__2",
"cat_1__a__x__cat_3__1",
],
categories=[
"cat_1__a__x__cat_3__1",
"cat_1__b__x__cat_3__1",
"cat_1__c__x__cat_3__1",
"cat_1__a__x__cat_3__2",
"cat_1__c__x__cat_3__2",
"cat_1__b__x__cat_3__2",
],
),
drop_first=False,
),
]
),
id="interaction_cat_cat",
),
],
)
def test_matrix_against_expectation_qcl(df, formula, expected):
model_df = tm.from_formula(
formula,
df,
cat_threshold=1,
sparse_threshold=0.5,
ensure_full_rank=True,
interaction_separator="__x__",
categorical_format="{name}__{category}",
intercept_name="intercept",
)
assert len(model_df.matrices) == len(expected.matrices)
for res, exp in zip(model_df.matrices, expected.matrices):
assert type(res) == type(exp)
if isinstance(res, (tm.DenseMatrix, tm.SparseMatrix)):
np.testing.assert_array_equal(res.A, res.A)
elif isinstance(res, tm.CategoricalMatrix):
assert (exp.cat == res.cat).all()
assert exp.drop_first == res.drop_first
@pytest.mark.parametrize(
"ensure_full_rank", [True, False], ids=["full_rank", "all_levels"]
)
@pytest.mark.parametrize(
"formula",
[
pytest.param("num_1 + num_2", id="numeric"),
pytest.param("cat_1 + cat_2", id="categorical"),
pytest.param("cat_1 * cat_2 * cat_3", id="interaction"),
pytest.param("num_1 + cat_1 * num_2 * cat_2", id="mixed"),
pytest.param("{np.log(num_1)} + {num_in_scope * num_2}", id="functions"),
pytest.param("{num_1 * num_in_scope}", id="variable_in_scope"),
pytest.param("bs(num_1, 3)", id="spline"),
pytest.param(
"poly(num_1, 3, raw=True) + poly(num_2, 3, raw=False)", id="polynomial"
),
pytest.param(
"C(num_1)",
id="convert_to_categorical",
),
pytest.param(
"C(cat_1, spans_intercept=False) * cat_2 * cat_3",
id="custom_contrasts",
),
pytest.param("str_1", id="string_as_categorical"),
],
)
def test_matrix_against_pandas(df, formula, ensure_full_rank):
num_in_scope = 2 # noqa
model_df = formulaic.model_matrix(formula, df, ensure_full_rank=ensure_full_rank)
model_tabmat = tm.from_formula(
formula, df, ensure_full_rank=ensure_full_rank, include_intercept=True
)
np.testing.assert_array_equal(model_df.to_numpy(), model_tabmat.A)
@pytest.mark.parametrize(
"formula, expected_names",
[
pytest.param(
"1 + num_1 + num_2", ("Intercept", "num_1", "num_2"), id="numeric"
),
pytest.param("num_1 + num_2 - 1", ("num_1", "num_2"), id="no_intercept"),
pytest.param(
"1 + cat_1", ("Intercept", "cat_1[b]", "cat_1[c]"), id="categorical"
),
pytest.param(
"1 + cat_2 * cat_3",
(
"Intercept",
"cat_2[y]",
"cat_2[z]",
"cat_3[2]",
"cat_2[y]:cat_3[2]",
"cat_2[z]:cat_3[2]",
),
id="interaction",
),
pytest.param(
"poly(num_1, 3) - 1",
("poly(num_1, 3)[1]", "poly(num_1, 3)[2]", "poly(num_1, 3)[3]"),
id="polynomial",
),
pytest.param(
"1 + {np.log(num_1 ** 2)}",
("Intercept", "np.log(num_1 ** 2)"),
id="functions",
),
],
)
def test_names_against_expectation(df, formula, expected_names):
model_tabmat = tm.from_formula(formula, df, ensure_full_rank=True)
assert model_tabmat.model_spec.column_names == expected_names
assert model_tabmat.column_names == list(expected_names)
@pytest.mark.parametrize(
"formula, expected_names",
[
pytest.param(
"1 + cat_1", ("intercept", "cat_1__b", "cat_1__c"), id="categorical"
),
pytest.param(
"1 + cat_2 * cat_3",
(
"intercept",
"cat_2__y",
"cat_2__z",
"cat_3__2",
"cat_2__y__x__cat_3__2",
"cat_2__z__x__cat_3__2",
),
id="interaction",
),
pytest.param(
"poly(num_1, 3) - 1",
("poly(num_1, 3)[1]", "poly(num_1, 3)[2]", "poly(num_1, 3)[3]"),
id="polynomial",
),
pytest.param(
"1 + {np.log(num_1 ** 2)}",
("intercept", "np.log(num_1 ** 2)"),
id="functions",
),
],
)
def test_names_against_expectation_qcl(df, formula, expected_names):
model_tabmat = tm.from_formula(
formula,
df,
ensure_full_rank=True,
categorical_format="{name}__{category}",
interaction_separator="__x__",
intercept_name="intercept",
)
assert model_tabmat.model_spec.column_names == expected_names
assert model_tabmat.column_names == list(expected_names)
@pytest.mark.parametrize(
"formula, expected_names",
[
pytest.param("1 + cat_1", ("1", "cat_1", "cat_1"), id="categorical"),
pytest.param(
"1 + cat_2 * cat_3",
(
"1",
"cat_2",
"cat_2",
"cat_3",
"cat_2:cat_3",
"cat_2:cat_3",
),
id="interaction",
),
pytest.param(
"poly(num_1, 3) - 1",
("poly(num_1, 3)", "poly(num_1, 3)", "poly(num_1, 3)"),
id="polynomial",
),
pytest.param(
"1 + {np.log(num_1 ** 2)}",
("1", "np.log(num_1 ** 2)"),
id="functions",
),
],
)
def test_term_names_against_expectation(df, formula, expected_names):
model_tabmat = tm.from_formula(
formula,
df,
ensure_full_rank=True,
intercept_name="intercept",
)
assert model_tabmat.term_names == list(expected_names)
@pytest.mark.parametrize(
"categorical_format",
["{name}[{category}]", "{name}__{category}", "{name}<<{category}>>"],
ids=["brackets", "double_underscore", "custom"],
)
def test_all_names_against_from_pandas(df, categorical_format):
mat_from_pandas = tm.from_pandas(
df, drop_first=False, object_as_cat=True, categorical_format=categorical_format
)
mat_from_formula = tm.from_formula(
"num_1 + num_2 + cat_1 + cat_2 + cat_3 + str_1 - 1",
data=df,
ensure_full_rank=False,
categorical_format=categorical_format,
)
assert mat_from_formula.column_names == mat_from_pandas.column_names
assert mat_from_formula.term_names == mat_from_pandas.term_names
@pytest.mark.parametrize(
"ensure_full_rank", [True, False], ids=["full_rank", "all_levels"]
)
@pytest.mark.parametrize(
"formula",
[
pytest.param("1 + num_1 + num_2", id="numeric"),
pytest.param("1 + cat_1 + cat_2", id="categorical"),
pytest.param("1 + cat_1 * cat_2 * cat_3", id="interaction"),
pytest.param("1 + num_1 + cat_1 * num_2 * cat_2", id="mixed"),
pytest.param("1 + {np.log(num_1)} + {num_in_scope * num_2}", id="functions"),
pytest.param("1 + {num_1 * num_in_scope}", id="variable_in_scope"),
pytest.param("1 + bs(num_1, 3)", id="spline"),
pytest.param(
"1 + poly(num_1, 3, raw=True) + poly(num_2, 3, raw=False)", id="polynomial"
),
pytest.param(
"1 + C(num_1)",
id="convert_to_categorical",
),
pytest.param(
"1 + C(cat_1, spans_intercept=False) * cat_2 * cat_3",
id="custom_contrasts",
),
],
)
def test_names_against_pandas(df, formula, ensure_full_rank):
num_in_scope = 2 # noqa
model_df = formulaic.model_matrix(formula, df, ensure_full_rank=ensure_full_rank)
model_tabmat = tm.from_formula(
formula,
df,
ensure_full_rank=ensure_full_rank,
categorical_format="{name}[T.{category}]",
)
assert model_tabmat.model_spec.column_names == model_df.model_spec.column_names
assert model_tabmat.model_spec.column_names == tuple(model_df.columns)
assert model_tabmat.column_names == list(model_df.columns)
@pytest.mark.parametrize(
"ensure_full_rank", [True, False], ids=["full_rank", "all_levels"]
)
@pytest.mark.parametrize(
"formula, formula_with_intercept, formula_wo_intercept",
[
("num_1", "1 + num_1", "num_1 - 1"),
("cat_1", "1 + cat_1", "cat_1 - 1"),
(
"num_1 * cat_1 * cat_2",
"1 + num_1 * cat_1 * cat_2",
"num_1 * cat_1 * cat_2 - 1",
),
],
ids=["numeric", "categorical", "mixed"],
)
def test_include_intercept(
df, formula, formula_with_intercept, formula_wo_intercept, ensure_full_rank
):
model_no_include = tm.from_formula(
formula, df, include_intercept=False, ensure_full_rank=ensure_full_rank
)
model_no_intercept = tm.from_formula(
formula_wo_intercept,
df,
include_intercept=True,
ensure_full_rank=ensure_full_rank,
)
np.testing.assert_array_equal(model_no_include.A, model_no_intercept.A)
assert (
model_no_include.model_spec.column_names
== model_no_intercept.model_spec.column_names
)
model_include = tm.from_formula(
formula, df, include_intercept=True, ensure_full_rank=ensure_full_rank
)
model_intercept = tm.from_formula(
formula_with_intercept,
df,
include_intercept=False,
ensure_full_rank=ensure_full_rank,
)
np.testing.assert_array_equal(model_include.A, model_intercept.A)
assert (
model_no_include.model_spec.column_names
== model_no_intercept.model_spec.column_names
)
@pytest.mark.parametrize(
"formula",
[
pytest.param("str_1 : cat_1", id="implicit"),
pytest.param("C(str_1) : C(cat_1, spans_intercept=False)", id="explicit"),
],
)
@pytest.mark.parametrize(
"ensure_full_rank", [True, False], ids=["full_rank", "all_levels"]
)
def test_C_state(df, formula, ensure_full_rank):
model_tabmat = tm.from_formula(
"str_1 : cat_1 + 1", df, cat_threshold=0, ensure_full_rank=ensure_full_rank
)
model_tabmat_2 = model_tabmat.model_spec.get_model_matrix(df[:2])
np.testing.assert_array_equal(model_tabmat.A[:2, :], model_tabmat_2.A)
np.testing.assert_array_equal(
model_tabmat.matrices[1].cat.categories,
model_tabmat_2.matrices[1].cat.categories,
)
VECTORS = [
_InteractableDenseVector(np.array([1, 2, 3, 4, 5], dtype=np.float64)).set_name(
"dense"
),
_InteractableSparseVector(
sps.csc_matrix(np.array([[1, 0, 0, 0, 2]], dtype=np.float64).T)
).set_name("sparse"),
_InteractableCategoricalVector.from_categorical(
pd.Categorical(["a", "b", "c", "b", "a"]), reduced_rank=True
).set_name("cat_reduced"),
_InteractableCategoricalVector.from_categorical(
pd.Categorical(["a", "b", "c", "b", "a"]), reduced_rank=False
).set_name("cat_full"),
]
@pytest.mark.parametrize(
"left", VECTORS, ids=["dense", "sparse", "cat_full", "cat_reduced"]
)
@pytest.mark.parametrize(
"right", VECTORS, ids=["dense", "sparse", "cat_full", "cat_reduced"]
)
@pytest.mark.parametrize("reverse", [False, True], ids=["not_reversed", "reversed"])
def test_interactable_vectors(left, right, reverse):
n = left.to_tabmat().shape[0]
left_np = left.to_tabmat().A.reshape((n, -1))
right_np = right.to_tabmat().A.reshape((n, -1))
if reverse:
left_np, right_np = right_np, left_np
if isinstance(left, _InteractableCategoricalVector) and isinstance(
right, _InteractableCategoricalVector
):
result_np = np.zeros((n, left_np.shape[1] * right_np.shape[1]))
for i in range(right_np.shape[1]):
for j in range(left_np.shape[1]):
result_np[:, i * left_np.shape[1] + j] = left_np[:, j] * right_np[:, i]
else:
result_np = left_np * right_np
result_vec = _interact(left, right, reverse=reverse)
# Test types
if isinstance(left, _InteractableCategoricalVector) or isinstance(
right, _InteractableCategoricalVector
):
assert isinstance(result_vec, _InteractableCategoricalVector)
elif isinstance(left, _InteractableSparseVector) or isinstance(
right, _InteractableSparseVector
):
assert isinstance(result_vec, _InteractableSparseVector)
else:
assert isinstance(result_vec, _InteractableDenseVector)
# Test values
np.testing.assert_array_equal(
result_vec.to_tabmat().A.squeeze(), result_np.squeeze()
)
# Test names
if not reverse:
assert result_vec.name == left.name + ":" + right.name
else:
assert result_vec.name == right.name + ":" + left.name
@pytest.mark.parametrize("cat_missing_method", ["zero", "convert"])
@pytest.mark.parametrize(
"cat_missing_name",
["__missing__", "(MISSING)"],
)
def test_cat_missing_handling(cat_missing_method, cat_missing_name):
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", None, "b", "a"]),
}
)
mat_from_pandas = tm.from_pandas(
df,
cat_threshold=0,
cat_missing_method=cat_missing_method,
cat_missing_name=cat_missing_name,
)
mat_from_formula = tm.from_formula(
"cat_1 - 1",
df,
cat_threshold=0,
cat_missing_method=cat_missing_method,
cat_missing_name=cat_missing_name,
)
assert mat_from_pandas.column_names == mat_from_formula.column_names
assert mat_from_pandas.term_names == mat_from_formula.term_names
np.testing.assert_array_equal(mat_from_pandas.A, mat_from_formula.A)
mat_from_formula_new = mat_from_formula.model_spec.get_model_matrix(df)
assert mat_from_pandas.column_names == mat_from_formula_new.column_names
assert mat_from_pandas.term_names == mat_from_formula_new.term_names
np.testing.assert_array_equal(mat_from_pandas.A, mat_from_formula_new.A)
def test_cat_missing_C():
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", None, "b", "a"]),
"cat_2": pd.Categorical(["1", "2", None, "1", "2"]),
}
)
formula = (
"C(cat_1, missing_method='convert', missing_name='M') "
"+ C(cat_2, missing_method='zero')"
)
expected_names = [
"C(cat_1, missing_method='convert', missing_name='M')[a]",
"C(cat_1, missing_method='convert', missing_name='M')[b]",
"C(cat_1, missing_method='convert', missing_name='M')[M]",
"C(cat_2, missing_method='zero')[1]",
"C(cat_2, missing_method='zero')[2]",
]
result = tm.from_formula(formula, df)
assert result.column_names == expected_names
assert result.model_spec.get_model_matrix(df).column_names == expected_names
np.testing.assert_equal(result.model_spec.get_model_matrix(df).A, result.A)
np.testing.assert_equal(
result.model_spec.get_model_matrix(df[:2]).A, result.A[:2, :]
)
@pytest.mark.parametrize(
"cat_missing_method", ["zero", "convert"], ids=["zero", "convert"]
)
def test_cat_missing_unseen(cat_missing_method):
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", None, "b", "a"]),
}
)
df_unseen = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", None]),
}
)
result_seen = tm.from_formula(
"cat_1 - 1", df, cat_missing_method=cat_missing_method
)
result_unseen = result_seen.model_spec.get_model_matrix(df_unseen)
assert result_seen.column_names == result_unseen.column_names
if cat_missing_method == "convert":
expected_array = np.array([[1, 0, 0], [0, 0, 1]], dtype=np.float64)
elif cat_missing_method == "zero":
expected_array = np.array([[1, 0], [0, 0]], dtype=np.float64)
np.testing.assert_array_equal(result_unseen.A, expected_array)
def test_cat_missing_interactions():
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", None, "b", "a"]),
"cat_2": pd.Categorical(["1", "2", None, "1", "2"]),
}
)
formula = "C(cat_1, missing_method='convert') : C(cat_2, missing_method='zero') - 1"
expected_names = [
"C(cat_1, missing_method='convert')[a]:C(cat_2, missing_method='zero')[1]",
"C(cat_1, missing_method='convert')[b]:C(cat_2, missing_method='zero')[1]",
"C(cat_1, missing_method='convert')[(MISSING)]:"
+ "C(cat_2, missing_method='zero')[1]",
"C(cat_1, missing_method='convert')[a]:C(cat_2, missing_method='zero')[2]",
"C(cat_1, missing_method='convert')[b]:C(cat_2, missing_method='zero')[2]",
"C(cat_1, missing_method='convert')[(MISSING)]:"
+ "C(cat_2, missing_method='zero')[2]",
]
assert tm.from_formula(formula, df).column_names == expected_names
@pytest.mark.parametrize(
"cat_missing_method", ["zero", "convert", "fail"], ids=["zero", "convert", "fail"]
)
def test_unseen_category(cat_missing_method):
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b"]),
}
)
df_unseen = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", "c"]),
}
)
result_seen = tm.from_formula(
"cat_1 - 1", df, cat_missing_method=cat_missing_method
)
with pytest.raises(ValueError, match="contains unseen categories"):
result_seen.model_spec.get_model_matrix(df_unseen)
@pytest.mark.parametrize("cat_missing_method", ["zero", "convert", "fail"])
def test_unseen_missing(cat_missing_method):
df = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b"]),
}
)
df_unseen = pd.DataFrame(
{
"cat_1": pd.Categorical(["a", "b", pd.NA]),
}
)
result_seen = tm.from_formula(
"cat_1 - 1", df, cat_missing_method=cat_missing_method
)
if cat_missing_method == "convert":
with pytest.raises(ValueError, match="contains unseen categories"):
result_seen.model_spec.get_model_matrix(df_unseen)
elif cat_missing_method == "fail":
with pytest.raises(
ValueError, match="Categorical data can't have missing values"
):
result_seen.model_spec.get_model_matrix(df_unseen)
elif cat_missing_method == "zero":
result_unseen = result_seen.model_spec.get_model_matrix(df_unseen)
assert result_unseen.A.shape == (3, 2)
np.testing.assert_array_equal(
result_unseen.A, np.array([[1, 0], [0, 1], [0, 0]])
)
assert result_unseen.column_names == ["cat_1[a]", "cat_1[b]"]
# Tests from formulaic's test suite
# ---------------------------------
FORMULAIC_TESTS = {
# '<formula>': (<full_rank_names>, <names>, <full_rank_null_names>, <null_rows>)
"a": (["Intercept", "a"], ["Intercept", "a"], ["Intercept", "a"], 2),
"A": (
["Intercept", "A[b]", "A[c]"],
["Intercept", "A[a]", "A[b]", "A[c]"],
["Intercept", "A[c]"],
2,
),
"C(A)": (
["Intercept", "C(A)[b]", "C(A)[c]"],
["Intercept", "C(A)[a]", "C(A)[b]", "C(A)[c]"],
["Intercept", "C(A)[c]"],
2,
),
"A:a": (
["Intercept", "A[a]:a", "A[b]:a", "A[c]:a"],
["Intercept", "A[a]:a", "A[b]:a", "A[c]:a"],
["Intercept", "A[a]:a"],
1,
),
"A:B": (
[
"Intercept",
"B[b]",
"B[c]",
"A[b]:B[a]",
"A[c]:B[a]",
"A[b]:B[b]",
"A[c]:B[b]",
"A[b]:B[c]",
"A[c]:B[c]",
],
[
"Intercept",
"A[a]:B[a]",
"A[b]:B[a]",
"A[c]:B[a]",
"A[a]:B[b]",
"A[b]:B[b]",
"A[c]:B[b]",
"A[a]:B[c]",
"A[b]:B[c]",
"A[c]:B[c]",
],
["Intercept"],
1,
),
}
class TestFormulaicTests:
@pytest.fixture
def data(self):
return pd.DataFrame(
{"a": [1, 2, 3], "b": [1, 2, 3], "A": ["a", "b", "c"], "B": ["a", "b", "c"]}
)
@pytest.fixture
def data_with_nulls(self):
return pd.DataFrame(
{"a": [1, 2, None], "A": ["a", None, "c"], "B": ["a", "b", None]}
)
@pytest.fixture
def materializer(self, data):
return TabmatMaterializer(data)
@pytest.mark.parametrize("formula,tests", FORMULAIC_TESTS.items())
def test_get_model_matrix(self, materializer, formula, tests):
mm = materializer.get_model_matrix(formula, ensure_full_rank=True)
assert isinstance(mm, tm.MatrixBase)
assert mm.shape == (3, len(tests[0]))
assert list(mm.model_spec.column_names) == tests[0]
mm = materializer.get_model_matrix(formula, ensure_full_rank=False)
assert isinstance(mm, tm.MatrixBase)
assert mm.shape == (3, len(tests[1]))
assert list(mm.model_spec.column_names) == tests[1]
def test_get_model_matrix_edge_cases(self, materializer):
mm = materializer.get_model_matrix(("a",), ensure_full_rank=True)
assert isinstance(mm, formulaic.ModelMatrices)
assert isinstance(mm[0], tm.MatrixBase)
mm = materializer.get_model_matrix("a ~ A", ensure_full_rank=True)
assert isinstance(mm, formulaic.ModelMatrices)
assert "lhs" in mm.model_spec
assert "rhs" in mm.model_spec
mm = materializer.get_model_matrix(("a ~ A",), ensure_full_rank=True)
assert isinstance(mm, formulaic.ModelMatrices)
assert isinstance(mm[0], formulaic.ModelMatrices)
def test_get_model_matrix_invalid_output(self, materializer):
with pytest.raises(
formulaic.errors.FormulaMaterializationError,
match=r"Nominated output .* is invalid\. Available output types are: ",
):
materializer.get_model_matrix(
"a", ensure_full_rank=True, output="invalid_output"
)
@pytest.mark.parametrize("formula,tests", FORMULAIC_TESTS.items())
def test_na_handling(self, data_with_nulls, formula, tests):
mm = TabmatMaterializer(data_with_nulls).get_model_matrix(formula)
assert isinstance(mm, tm.MatrixBase)
assert mm.shape == (tests[3], len(tests[2]))
assert list(mm.model_spec.column_names) == tests[2]
if formula != "a":
pytest.skip("Tabmat does not allo NAs in categoricals")
mm = TabmatMaterializer(data_with_nulls).get_model_matrix(
formula, na_action="ignore"
)
assert isinstance(mm, tm.MatrixBase)
assert mm.shape == (3, len(tests[0]) + (-1 if "A" in formula else 0))
def test_state(self, materializer):
mm = materializer.get_model_matrix("center(a) - 1")
assert isinstance(mm, tm.MatrixBase)
assert list(mm.model_spec.column_names) == ["center(a)"]
assert np.allclose(mm.getcol(0).unpack().squeeze(), [-1, 0, 1])
mm2 = TabmatMaterializer(pd.DataFrame({"a": [4, 5, 6]})).get_model_matrix(
mm.model_spec
)
assert isinstance(mm2, tm.MatrixBase)
assert list(mm2.model_spec.column_names) == ["center(a)"]
assert np.allclose(mm2.getcol(0).unpack().squeeze(), [2, 3, 4])
mm3 = mm.model_spec.get_model_matrix(pd.DataFrame({"a": [4, 5, 6]}))
assert isinstance(mm3, tm.MatrixBase)
assert list(mm3.model_spec.column_names) == ["center(a)"]
assert np.allclose(mm3.getcol(0).unpack().squeeze(), [2, 3, 4])
def test_factor_evaluation_edge_cases(self, materializer):
# Test that categorical kinds are set if type would otherwise be numerical
ev_factor = materializer._evaluate_factor(
Factor("a", eval_method="lookup", kind="categorical"),
formulaic.model_spec.ModelSpec(formula=[]),
drop_rows=set(),
)
assert ev_factor.metadata.kind.value == "categorical"
# Test that other kind mismatches result in an exception
materializer.factor_cache = {}
with pytest.raises(
formulaic.errors.FactorEncodingError,
match=re.escape(
"Factor `A` is expecting values of kind 'numerical', "
"but they are actually of kind 'categorical'."
),
):
materializer._evaluate_factor(
Factor("A", eval_method="lookup", kind="numerical"),
formulaic.model_spec.ModelSpec(formula=[]),
drop_rows=set(),
)
# For an encoding has already been determined, an exception
# should be raised if the new encoding does not match
materializer.factor_cache = {}
with pytest.raises(
formulaic.errors.FactorEncodingError,
match=re.escape(
"The model specification expects factor `a` to have values of kind "
"`categorical`, but they are actually of kind `numerical`."
),
):
materializer._evaluate_factor(
Factor("a", eval_method="lookup", kind="numerical"),
formulaic.model_spec.ModelSpec(
formula=[], encoder_state={"a": ("categorical", {})}
),
drop_rows=set(),
)
def test__is_categorical(self, materializer):
assert materializer._is_categorical([1, 2, 3]) is False
assert materializer._is_categorical(pd.Series(["a", "b", "c"])) is True
assert materializer._is_categorical(pd.Categorical(["a", "b", "c"])) is True
assert materializer._is_categorical(FactorValues({}, kind="categorical"))
def test_encoding_edge_cases(self, materializer):
# Verify that constant encoding works well
encoded_factor = materializer._encode_evaled_factor(
factor=EvaluatedFactor(
factor=Factor("10", eval_method="literal", kind="constant"),
values=FactorValues(10, kind="constant"),
),
spec=formulaic.model_spec.ModelSpec(formula=[]),
drop_rows=[],