Created
September 2, 2016 20:40
-
-
Save bwengals/694583587299eeb253dd4d05580f001b to your computer and use it in GitHub Desktop.
Patsy b-splines with an option to return the derivative
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# This file is part of Patsy | |
# 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, der): | |
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), der) | |
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 | |
self._der = None | |
def memorize_chunk(self, x, df=None, knots=None, degree=3, | |
include_intercept=False, | |
lower_bound=None, upper_bound=None, der=0): | |
args = {"df": df, | |
"knots": knots, | |
"degree": degree, | |
"include_intercept": include_intercept, | |
"lower_bound": lower_bound, | |
"upper_bound": upper_bound, | |
"der": der, | |
} | |
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 | |
self._der = args["der"] | |
def transform(self, x, df=None, knots=None, degree=3, | |
include_intercept=False, | |
lower_bound=None, upper_bound=None, der=0): | |
basis = _eval_bspline_basis(x, self._all_knots, self._degree, self._der) | |
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. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment