/usr/share/pyshared/patsy/user_util.py is in python-patsy 0.2.1-3.
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# Copyright (C) 2012 Nathaniel Smith <njs@pobox.com>
# See file COPYING 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 numpy as np
from patsy import PatsyError
from patsy.compat import itertools_product
from patsy.categorical import C
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 xrange(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(categorical.values())
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
from nose.tools import assert_raises
assert_raises(PatsyError, demo_data, "a", "b", "__123")
assert_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):
return 0
def memorize_chunk(self, state, which_pass, env): # 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
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)
from nose.tools import assert_raises
assert_raises(ValueError, LookupFactor, "nc", contrast="CONTRAST")
assert_raises(ValueError, LookupFactor, "nc", levels=(1, 2))
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