patsy/patsy/user_util.py at master · Python-Repository-Hub/patsy · GitHub

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# This file is part of Patsy

# See file LICENSE.txt for license information.

# Miscellaneous utilities that are useful to users (as compared to

# patsy.util, which is misc. utilities useful for implementing patsy).

# These are made available in the patsy.* namespace

__all__ = ["balanced", "demo_data", "LookupFactor"]

import itertools

import numpy as np

from patsy import PatsyError

from patsy.categorical import C

from patsy.util import no_pickling, assert_no_pickling

def balanced(**kwargs):

"""balanced(factor_name=num_levels, [factor_name=num_levels, ..., repeat=1])

Create simple balanced factorial designs for testing.

Given some factor names and the number of desired levels for each,

generates a balanced factorial design in the form of a data

dictionary. For example:

.. ipython::

In [1]: balanced(a=2, b=3)

Out[1]:

{'a': ['a1', 'a1', 'a1', 'a2', 'a2', 'a2'],

'b': ['b1', 'b2', 'b3', 'b1', 'b2', 'b3']}

By default it produces exactly one instance of each combination of levels,

but if you want multiple replicates this can be accomplished via the

`repeat` argument:

.. ipython::

In [2]: balanced(a=2, b=2, repeat=2)

Out[2]:

{'a': ['a1', 'a1', 'a2', 'a2', 'a1', 'a1', 'a2', 'a2'],

'b': ['b1', 'b2', 'b1', 'b2', 'b1', 'b2', 'b1', 'b2']}

"""

repeat = kwargs.pop("repeat", 1)

levels = []

names = sorted(kwargs)

for name in names:

level_count = kwargs[name]

levels.append(["%s%s" % (name, i) for i in range(1, level_count + 1)])

# zip(*...) does an "unzip"

values = zip(*itertools.product(*levels))

data = {}

for name, value in zip(names, values):

data[name] = list(value) * repeat

return data

def test_balanced():

data = balanced(a=2, b=3)

assert data["a"] == ["a1", "a1", "a1", "a2", "a2", "a2"]

assert data["b"] == ["b1", "b2", "b3", "b1", "b2", "b3"]

data = balanced(a=2, b=3, repeat=2)

assert data["a"] == ["a1", "a1", "a1", "a2", "a2", "a2",

"a1", "a1", "a1", "a2", "a2", "a2"]

assert data["b"] == ["b1", "b2", "b3", "b1", "b2", "b3",

"b1", "b2", "b3", "b1", "b2", "b3"]

def demo_data(*names, **kwargs):

"""demo_data(*names, nlevels=2, min_rows=5)

Create simple categorical/numerical demo data.

Pass in a set of variable names, and this function will return a simple

data set using those variable names.

Names whose first letter falls in the range "a" through "m" will be made

categorical (with `nlevels` levels). Those that start with a "p" through

"z" are numerical.

We attempt to produce a balanced design on the categorical variables,

repeating as necessary to generate at least `min_rows` data

points. Categorical variables are returned as a list of strings.

Numerical data is generated by sampling from a normal distribution. A

fixed random seed is used, so that identical calls to demo_data() will

produce identical results. Numerical data is returned in a numpy array.

Example:

.. ipython:

In [1]: patsy.demo_data("a", "b", "x", "y")

Out[1]:

{'a': ['a1', 'a1', 'a2', 'a2', 'a1', 'a1', 'a2', 'a2'],

'b': ['b1', 'b2', 'b1', 'b2', 'b1', 'b2', 'b1', 'b2'],

'x': array([ 1.76405235, 0.40015721, 0.97873798, 2.2408932 ,

1.86755799, -0.97727788, 0.95008842, -0.15135721]),

'y': array([-0.10321885, 0.4105985 , 0.14404357, 1.45427351,

0.76103773, 0.12167502, 0.44386323, 0.33367433])}

"""

nlevels = kwargs.pop("nlevels", 2)

min_rows = kwargs.pop("min_rows", 5)

if kwargs:

raise TypeError("unexpected keyword arguments %r" % (kwargs,))

numerical = set()

categorical = {}

for name in names:

if name[0] in "abcdefghijklmn":

categorical[name] = nlevels

elif name[0] in "pqrstuvwxyz":

numerical.add(name)

else:

raise PatsyError("bad name %r" % (name,))

balanced_design_size = np.prod(list(categorical.values()), dtype=int)

repeat = int(np.ceil(min_rows * 1.0 / balanced_design_size))

num_rows = repeat * balanced_design_size

data = balanced(repeat=repeat, **categorical)

r = np.random.RandomState(0)

for name in sorted(numerical):

data[name] = r.normal(size=num_rows)

return data

def test_demo_data():

d1 = demo_data("a", "b", "x")

assert sorted(d1.keys()) == ["a", "b", "x"]

assert d1["a"] == ["a1", "a1", "a2", "a2", "a1", "a1", "a2", "a2"]

assert d1["b"] == ["b1", "b2", "b1", "b2", "b1", "b2", "b1", "b2"]

assert d1["x"].dtype == np.dtype(float)

assert d1["x"].shape == (8,)

d2 = demo_data("x", "y")

assert sorted(d2.keys()) == ["x", "y"]

assert len(d2["x"]) == len(d2["y"]) == 5

assert len(demo_data("x", min_rows=10)["x"]) == 10

assert len(demo_data("a", "b", "x", min_rows=10)["x"]) == 12

assert len(demo_data("a", "b", "x", min_rows=10, nlevels=3)["x"]) == 18

import pytest

pytest.raises(PatsyError, demo_data, "a", "b", "__123")

pytest.raises(TypeError, demo_data, "a", "b", asdfasdf=123)

class LookupFactor(object):

"""A simple factor class that simply looks up a named entry in the given

data.

Useful for programatically constructing formulas, and as a simple example

of the factor protocol. For details see

:ref:`expert-model-specification`.

Example::

dmatrix(ModelDesc([], [Term([LookupFactor("x")])]), {"x": [1, 2, 3]})

:arg varname: The name of this variable; used as a lookup key in the

passed in data dictionary/DataFrame/whatever.

:arg force_categorical: If True, then treat this factor as

categorical. (Equivalent to using :func:`C` in a regular formula, but

of course you can't do that with a :class:`LookupFactor`.

:arg contrast: If given, the contrast to use; see :func:`C`. (Requires

``force_categorical=True``.)

:arg levels: If given, the categorical levels; see :func:`C`. (Requires

``force_categorical=True``.)

:arg origin: Either ``None``, or the :class:`Origin` of this factor for use

in error reporting.

.. versionadded:: 0.2.0

The ``force_categorical`` and related arguments.

"""

def __init__(self, varname,

force_categorical=False, contrast=None, levels=None,

origin=None):

self._varname = varname

self._force_categorical = force_categorical

self._contrast = contrast

self._levels = levels

self.origin = origin

if not self._force_categorical:

if contrast is not None:

raise ValueError("contrast= requires force_categorical=True")

if levels is not None:

raise ValueError("levels= requires force_categorical=True")

def name(self):

return self._varname

def __repr__(self):

return "%s(%r)" % (self.__class__.__name__, self._varname)

def __eq__(self, other):

return (isinstance(other, LookupFactor)

and self._varname == other._varname

and self._force_categorical == other._force_categorical

and self._contrast == other._contrast

and self._levels == other._levels)

def __ne__(self, other):

return not self == other

def __hash__(self):

return hash((LookupFactor, self._varname,

self._force_categorical, self._contrast, self._levels))

def memorize_passes_needed(self, state, eval_env):

return 0

def memorize_chunk(self, state, which_pass, data): # pragma: no cover

assert False

def memorize_finish(self, state, which_pass): # pragma: no cover

assert False

def eval(self, memorize_state, data):

value = data[self._varname]

if self._force_categorical:

value = C(value, contrast=self._contrast, levels=self._levels)

return value

__getstate__ = no_pickling

def test_LookupFactor():

l_a = LookupFactor("a")

assert l_a.name() == "a"

assert l_a == LookupFactor("a")

assert l_a != LookupFactor("b")

assert hash(l_a) == hash(LookupFactor("a"))

assert hash(l_a) != hash(LookupFactor("b"))

assert l_a.eval({}, {"a": 1}) == 1

assert l_a.eval({}, {"a": 2}) == 2

assert repr(l_a) == "LookupFactor('a')"

assert l_a.origin is None

l_with_origin = LookupFactor("b", origin="asdf")

assert l_with_origin.origin == "asdf"

l_c = LookupFactor("c", force_categorical=True,

contrast="CONTRAST", levels=(1, 2))

box = l_c.eval({}, {"c": [1, 1, 2]})

assert box.data == [1, 1, 2]

assert box.contrast == "CONTRAST"

assert box.levels == (1, 2)

import pytest

pytest.raises(ValueError, LookupFactor, "nc", contrast="CONTRAST")

pytest.raises(ValueError, LookupFactor, "nc", levels=(1, 2))

assert_no_pickling(LookupFactor("a"))

patsy/patsy/user_util.py at master · Python-Repository-Hub/patsy · GitHub

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