/usr/lib/python2.7/dist-packages/patsy/splines.py is in python-patsy 0.4.1+git34-ga5b54c2-1.
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# Copyright (C) 2012-2013 Nathaniel Smith <njs@pobox.com>
# See file LICENSE.txt for license information.
# R-compatible spline basis functions
# These are made available in the patsy.* namespace
__all__ = ["bs"]
import numpy as np
from patsy.util import have_pandas, no_pickling, assert_no_pickling
from patsy.state import stateful_transform
if have_pandas:
import pandas
def _eval_bspline_basis(x, knots, degree):
try:
from scipy.interpolate import splev
except ImportError: # pragma: no cover
raise ImportError("spline functionality requires scipy")
# 'knots' are assumed to be already pre-processed. E.g. usually you
# want to include duplicate copies of boundary knots; you should do
# that *before* calling this constructor.
knots = np.atleast_1d(np.asarray(knots, dtype=float))
assert knots.ndim == 1
knots.sort()
degree = int(degree)
x = np.atleast_1d(x)
if x.ndim == 2 and x.shape[1] == 1:
x = x[:, 0]
assert x.ndim == 1
# XX FIXME: when points fall outside of the boundaries, splev and R seem
# to handle them differently. I don't know why yet. So until we understand
# this and decide what to do with it, I'm going to play it safe and
# disallow such points.
if np.min(x) < np.min(knots) or np.max(x) > np.max(knots):
raise NotImplementedError("some data points fall outside the "
"outermost knots, and I'm not sure how "
"to handle them. (Patches accepted!)")
# Thanks to Charles Harris for explaining splev. It's not well
# documented, but basically it computes an arbitrary b-spline basis
# given knots and degree on some specificed points (or derivatives
# thereof, but we don't use that functionality), and then returns some
# linear combination of these basis functions. To get out the basis
# functions themselves, we use linear combinations like [1, 0, 0], [0,
# 1, 0], [0, 0, 1].
# NB: This probably makes it rather inefficient (though I haven't checked
# to be sure -- maybe the fortran code actually skips computing the basis
# function for coefficients that are zero).
# Note: the order of a spline is the same as its degree + 1.
# Note: there are (len(knots) - order) basis functions.
n_bases = len(knots) - (degree + 1)
basis = np.empty((x.shape[0], n_bases), dtype=float)
for i in range(n_bases):
coefs = np.zeros((n_bases,))
coefs[i] = 1
basis[:, i] = splev(x, (knots, coefs, degree))
return basis
def _R_compat_quantile(x, probs):
#return np.percentile(x, 100 * np.asarray(probs))
probs = np.asarray(probs)
quantiles = np.asarray([np.percentile(x, 100 * prob)
for prob in probs.ravel(order="C")])
return quantiles.reshape(probs.shape, order="C")
def test__R_compat_quantile():
def t(x, prob, expected):
assert np.allclose(_R_compat_quantile(x, prob), expected)
t([10, 20], 0.5, 15)
t([10, 20], 0.3, 13)
t([10, 20], [0.3, 0.7], [13, 17])
t(list(range(10)), [0.3, 0.7], [2.7, 6.3])
class BS(object):
"""bs(x, df=None, knots=None, degree=3, include_intercept=False, lower_bound=None, upper_bound=None)
Generates a B-spline basis for ``x``, allowing non-linear fits. The usual
usage is something like::
y ~ 1 + bs(x, 4)
to fit ``y`` as a smooth function of ``x``, with 4 degrees of freedom
given to the smooth.
:arg df: The number of degrees of freedom to use for this spline. The
return value will have this many columns. You must specify at least one
of ``df`` and ``knots``.
:arg knots: The interior knots to use for the spline. If unspecified, then
equally spaced quantiles of the input data are used. You must specify at
least one of ``df`` and ``knots``.
:arg degree: The degree of the spline to use.
:arg include_intercept: If ``True``, then the resulting
spline basis will span the intercept term (i.e., the constant
function). If ``False`` (the default) then this will not be the case,
which is useful for avoiding overspecification in models that include
multiple spline terms and/or an intercept term.
:arg lower_bound: The lower exterior knot location.
:arg upper_bound: The upper exterior knot location.
A spline with ``degree=0`` is piecewise constant with breakpoints at each
knot, and the default knot positions are quantiles of the input. So if you
find yourself in the situation of wanting to quantize a continuous
variable into ``num_bins`` equal-sized bins with a constant effect across
each bin, you can use ``bs(x, num_bins - 1, degree=0)``. (The ``- 1`` is
because one degree of freedom will be taken by the intercept;
alternatively, you could leave the intercept term out of your model and
use ``bs(x, num_bins, degree=0, include_intercept=True)``.
A spline with ``degree=1`` is piecewise linear with breakpoints at each
knot.
The default is ``degree=3``, which gives a cubic b-spline.
This is a stateful transform (for details see
:ref:`stateful-transforms`). If ``knots``, ``lower_bound``, or
``upper_bound`` are not specified, they will be calculated from the data
and then the chosen values will be remembered and re-used for prediction
from the fitted model.
Using this function requires scipy be installed.
.. note:: This function is very similar to the R function of the same
name. In cases where both return output at all (e.g., R's ``bs`` will
raise an error if ``degree=0``, while patsy's will not), they should
produce identical output given identical input and parameter settings.
.. warning:: I'm not sure on what the proper handling of points outside
the lower/upper bounds is, so for now attempting to evaluate a spline
basis at such points produces an error. Patches gratefully accepted.
.. versionadded:: 0.2.0
"""
def __init__(self):
self._tmp = {}
self._degree = None
self._all_knots = None
def memorize_chunk(self, x, df=None, knots=None, degree=3,
include_intercept=False,
lower_bound=None, upper_bound=None):
args = {"df": df,
"knots": knots,
"degree": degree,
"include_intercept": include_intercept,
"lower_bound": lower_bound,
"upper_bound": upper_bound,
}
self._tmp["args"] = args
# XX: check whether we need x values before saving them
x = np.atleast_1d(x)
if x.ndim == 2 and x.shape[1] == 1:
x = x[:, 0]
if x.ndim > 1:
raise ValueError("input to 'bs' must be 1-d, "
"or a 2-d column vector")
# There's no better way to compute exact quantiles than memorizing
# all data.
self._tmp.setdefault("xs", []).append(x)
def memorize_finish(self):
tmp = self._tmp
args = tmp["args"]
del self._tmp
if args["degree"] < 0:
raise ValueError("degree must be greater than 0 (not %r)"
% (args["degree"],))
if int(args["degree"]) != args["degree"]:
raise ValueError("degree must be an integer (not %r)"
% (self._degree,))
# These are guaranteed to all be 1d vectors by the code above
x = np.concatenate(tmp["xs"])
if args["df"] is None and args["knots"] is None:
raise ValueError("must specify either df or knots")
order = args["degree"] + 1
if args["df"] is not None:
n_inner_knots = args["df"] - order
if not args["include_intercept"]:
n_inner_knots += 1
if n_inner_knots < 0:
raise ValueError("df=%r is too small for degree=%r and "
"include_intercept=%r; must be >= %s"
% (args["df"], args["degree"],
args["include_intercept"],
# We know that n_inner_knots is negative;
# if df were that much larger, it would
# have been zero, and things would work.
args["df"] - n_inner_knots))
if args["knots"] is not None:
if len(args["knots"]) != n_inner_knots:
raise ValueError("df=%s with degree=%r implies %s knots, "
"but %s knots were provided"
% (args["df"], args["degree"],
n_inner_knots, len(args["knots"])))
else:
# Need to compute inner knots
knot_quantiles = np.linspace(0, 1, n_inner_knots + 2)[1:-1]
inner_knots = _R_compat_quantile(x, knot_quantiles)
if args["knots"] is not None:
inner_knots = args["knots"]
if args["lower_bound"] is not None:
lower_bound = args["lower_bound"]
else:
lower_bound = np.min(x)
if args["upper_bound"] is not None:
upper_bound = args["upper_bound"]
else:
upper_bound = np.max(x)
if lower_bound > upper_bound:
raise ValueError("lower_bound > upper_bound (%r > %r)"
% (lower_bound, upper_bound))
inner_knots = np.asarray(inner_knots)
if inner_knots.ndim > 1:
raise ValueError("knots must be 1 dimensional")
if np.any(inner_knots < lower_bound):
raise ValueError("some knot values (%s) fall below lower bound "
"(%r)"
% (inner_knots[inner_knots < lower_bound],
lower_bound))
if np.any(inner_knots > upper_bound):
raise ValueError("some knot values (%s) fall above upper bound "
"(%r)"
% (inner_knots[inner_knots > upper_bound],
upper_bound))
all_knots = np.concatenate(([lower_bound, upper_bound] * order,
inner_knots))
all_knots.sort()
self._degree = args["degree"]
self._all_knots = all_knots
def transform(self, x, df=None, knots=None, degree=3,
include_intercept=False,
lower_bound=None, upper_bound=None):
basis = _eval_bspline_basis(x, self._all_knots, self._degree)
if not include_intercept:
basis = basis[:, 1:]
if have_pandas:
if isinstance(x, (pandas.Series, pandas.DataFrame)):
basis = pandas.DataFrame(basis)
basis.index = x.index
return basis
__getstate__ = no_pickling
bs = stateful_transform(BS)
def test_bs_compat():
from patsy.test_state import check_stateful
from patsy.test_splines_bs_data import (R_bs_test_x,
R_bs_test_data,
R_bs_num_tests)
lines = R_bs_test_data.split("\n")
tests_ran = 0
start_idx = lines.index("--BEGIN TEST CASE--")
while True:
if not lines[start_idx] == "--BEGIN TEST CASE--":
break
start_idx += 1
stop_idx = lines.index("--END TEST CASE--", start_idx)
block = lines[start_idx:stop_idx]
test_data = {}
for line in block:
key, value = line.split("=", 1)
test_data[key] = value
# Translate the R output into Python calling conventions
kwargs = {
"degree": int(test_data["degree"]),
# integer, or None
"df": eval(test_data["df"]),
# np.array() call, or None
"knots": eval(test_data["knots"]),
}
if test_data["Boundary.knots"] != "None":
lower, upper = eval(test_data["Boundary.knots"])
kwargs["lower_bound"] = lower
kwargs["upper_bound"] = upper
kwargs["include_intercept"] = (test_data["intercept"] == "TRUE")
# Special case: in R, setting intercept=TRUE increases the effective
# dof by 1. Adjust our arguments to match.
# if kwargs["df"] is not None and kwargs["include_intercept"]:
# kwargs["df"] += 1
output = np.asarray(eval(test_data["output"]))
if kwargs["df"] is not None:
assert output.shape[1] == kwargs["df"]
# Do the actual test
check_stateful(BS, False, R_bs_test_x, output, **kwargs)
tests_ran += 1
# Set up for the next one
start_idx = stop_idx + 1
assert tests_ran == R_bs_num_tests
test_bs_compat.slow = 1
# This isn't checked by the above, because R doesn't have zero degree
# b-splines.
def test_bs_0degree():
x = np.logspace(-1, 1, 10)
result = bs(x, knots=[1, 4], degree=0, include_intercept=True)
assert result.shape[1] == 3
expected_0 = np.zeros(10)
expected_0[x < 1] = 1
assert np.array_equal(result[:, 0], expected_0)
expected_1 = np.zeros(10)
expected_1[(x >= 1) & (x < 4)] = 1
assert np.array_equal(result[:, 1], expected_1)
expected_2 = np.zeros(10)
expected_2[x >= 4] = 1
assert np.array_equal(result[:, 2], expected_2)
# Check handling of points that exactly fall on knots. They arbitrarily
# get included into the larger region, not the smaller. This is consistent
# with Python's half-open interval convention -- each basis function is
# constant on [knot[i], knot[i + 1]).
assert np.array_equal(bs([0, 1, 2], degree=0, knots=[1],
include_intercept=True),
[[1, 0],
[0, 1],
[0, 1]])
result_int = bs(x, knots=[1, 4], degree=0, include_intercept=True)
result_no_int = bs(x, knots=[1, 4], degree=0, include_intercept=False)
assert np.array_equal(result_int[:, 1:], result_no_int)
def test_bs_errors():
from nose.tools import assert_raises
x = np.linspace(-10, 10, 20)
# error checks:
# out of bounds
assert_raises(NotImplementedError, bs, x, 3, lower_bound=0)
assert_raises(NotImplementedError, bs, x, 3, upper_bound=0)
# must specify df or knots
assert_raises(ValueError, bs, x)
# df/knots match/mismatch (with and without intercept)
# match:
bs(x, df=10, include_intercept=False, knots=[0] * 7)
bs(x, df=10, include_intercept=True, knots=[0] * 6)
bs(x, df=10, include_intercept=False, knots=[0] * 9, degree=1)
bs(x, df=10, include_intercept=True, knots=[0] * 8, degree=1)
# too many knots:
assert_raises(ValueError,
bs, x, df=10, include_intercept=False, knots=[0] * 8)
assert_raises(ValueError,
bs, x, df=10, include_intercept=True, knots=[0] * 7)
assert_raises(ValueError,
bs, x, df=10, include_intercept=False, knots=[0] * 10,
degree=1)
assert_raises(ValueError,
bs, x, df=10, include_intercept=True, knots=[0] * 9,
degree=1)
# too few knots:
assert_raises(ValueError,
bs, x, df=10, include_intercept=False, knots=[0] * 6)
assert_raises(ValueError,
bs, x, df=10, include_intercept=True, knots=[0] * 5)
assert_raises(ValueError,
bs, x, df=10, include_intercept=False, knots=[0] * 8,
degree=1)
assert_raises(ValueError,
bs, x, df=10, include_intercept=True, knots=[0] * 7,
degree=1)
# df too small
assert_raises(ValueError,
bs, x, df=1, degree=3)
assert_raises(ValueError,
bs, x, df=3, degree=5)
# bad degree
assert_raises(ValueError,
bs, x, df=10, degree=-1)
assert_raises(ValueError,
bs, x, df=10, degree=1.5)
# upper_bound < lower_bound
assert_raises(ValueError,
bs, x, 3, lower_bound=1, upper_bound=-1)
# multidimensional input
assert_raises(ValueError,
bs, np.column_stack((x, x)), 3)
# unsorted knots are okay, and get sorted
assert np.array_equal(bs(x, knots=[1, 4]), bs(x, knots=[4, 1]))
# 2d knots
assert_raises(ValueError,
bs, x, knots=[[0], [20]])
# knots > upper_bound
assert_raises(ValueError,
bs, x, knots=[0, 20])
assert_raises(ValueError,
bs, x, knots=[0, 4], upper_bound=3)
# knots < lower_bound
assert_raises(ValueError,
bs, x, knots=[-20, 0])
assert_raises(ValueError,
bs, x, knots=[-4, 0], lower_bound=-3)
# differences between bs and ns (since the R code is a pile of copy-paste):
# - degree is always 3
# - different number of interior knots given df (b/c fewer dof used at edges I
# guess)
# - boundary knots always repeated exactly 4 times (same as bs with degree=3)
# - complications at the end to handle boundary conditions
# the 'rcs' function uses slightly different conventions -- in particular it
# picks boundary knots that are not quite at the edges of the data, which
# makes sense for a natural spline.
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