307 lines
11 KiB
Python
307 lines
11 KiB
Python
"""
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Lowess testing suite.
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Expected outcomes are generated by R's lowess function given the same
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arguments. The R script test_lowess_r_outputs.R can be used to
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generate the expected outcomes.
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The delta tests utilize Silverman's motorcycle collision data,
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available in R's MASS package.
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"""
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import os
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import numpy as np
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from numpy.testing import (
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assert_,
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assert_allclose,
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assert_almost_equal,
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assert_equal,
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assert_raises,
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)
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import pytest
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from statsmodels.nonparametric.smoothers_lowess import lowess
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import pandas as pd
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# Number of decimals to test equality with.
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# The default is 7.
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curdir = os.path.dirname(os.path.abspath(__file__))
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rpath = os.path.join(curdir, "results")
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class TestLowess:
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def test_import(self):
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# this does not work
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# from statsmodels.api.nonparametric import lowess as lowess1
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import statsmodels.api as sm
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lowess1 = sm.nonparametric.lowess
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assert_(lowess is lowess1)
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@pytest.mark.parametrize("use_pandas",[False, True])
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def test_flat(self, use_pandas):
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test_data = {
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"x": np.arange(20),
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"y": np.zeros(20),
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"out": np.zeros(20),
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}
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if use_pandas:
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test_data = {k: pd.Series(test_data[k]) for k in test_data}
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expected_lowess = np.array([test_data["x"], test_data["out"]]).T
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actual_lowess = lowess(test_data["y"], test_data["x"])
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assert_almost_equal(expected_lowess, actual_lowess, 7)
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def test_range(self):
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test_data = {
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"x": np.arange(20),
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"y": np.arange(20),
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"out": np.arange(20),
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}
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expected_lowess = np.array([test_data["x"], test_data["out"]]).T
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actual_lowess = lowess(test_data["y"], test_data["x"])
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assert_almost_equal(expected_lowess, actual_lowess, 7)
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@staticmethod
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def generate(name, fname, x="x", y="y", out="out", kwargs=None, decimal=7):
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kwargs = {} if kwargs is None else kwargs
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data = np.genfromtxt(
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os.path.join(rpath, fname), delimiter=",", names=True
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)
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assert_almost_equal.description = name
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if callable(kwargs):
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kwargs = kwargs(data)
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result = lowess(data[y], data[x], **kwargs)
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expect = np.array([data[x], data[out]]).T
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assert_almost_equal(result, expect, decimal)
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# TODO: Refactor as parametrized test once nose is permanently dropped
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def test_simple(self):
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self.generate("test_simple", "test_lowess_simple.csv")
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def test_iter_0(self):
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self.generate(
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"test_iter_0",
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"test_lowess_iter.csv",
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out="out_0",
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kwargs={"it": 0},
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)
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def test_iter_0_3(self):
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self.generate(
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"test_iter_0",
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"test_lowess_iter.csv",
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out="out_3",
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kwargs={"it": 3},
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)
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def test_frac_2_3(self):
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self.generate(
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"test_frac_2_3",
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"test_lowess_frac.csv",
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out="out_2_3",
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kwargs={"frac": 2.0 / 3},
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)
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def test_frac_1_5(self):
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self.generate(
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"test_frac_1_5",
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"test_lowess_frac.csv",
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out="out_1_5",
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kwargs={"frac": 1.0 / 5},
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)
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def test_delta_0(self):
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self.generate(
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"test_delta_0",
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"test_lowess_delta.csv",
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out="out_0",
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kwargs={"frac": 0.1},
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)
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def test_delta_rdef(self):
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self.generate(
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"test_delta_Rdef",
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"test_lowess_delta.csv",
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out="out_Rdef",
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kwargs=lambda data: {
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"frac": 0.1,
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"delta": 0.01 * np.ptp(data["x"]),
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},
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)
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def test_delta_1(self):
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self.generate(
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"test_delta_1",
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"test_lowess_delta.csv",
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out="out_1",
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kwargs={"frac": 0.1, "delta": 1 + 1e-10},
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decimal=10,
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)
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def test_options(self):
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rfile = os.path.join(rpath, "test_lowess_simple.csv")
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test_data = np.genfromtxt(open(rfile, "rb"), delimiter=",", names=True)
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y, x = test_data["y"], test_data["x"]
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res1_fitted = test_data["out"]
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expected_lowess = np.array([test_data["x"], test_data["out"]]).T
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# check skip sorting
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actual_lowess1 = lowess(y, x, is_sorted=True)
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assert_almost_equal(actual_lowess1, expected_lowess, decimal=13)
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# check skip sorting - DataFrame
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df = pd.DataFrame({"y": y, "x": x})
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actual_lowess1 = lowess(df["y"], df["x"], is_sorted=True)
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assert_almost_equal(actual_lowess1, expected_lowess, decimal=13)
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# check skip missing
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actual_lowess = lowess(y, x, is_sorted=True, missing="none")
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assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
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# check order/index, returns yfitted only
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actual_lowess = lowess(y[::-1], x[::-1], return_sorted=False)
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assert_almost_equal(actual_lowess, actual_lowess1[::-1, 1], decimal=13)
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# check returns yfitted only
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actual_lowess = lowess(
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y, x, return_sorted=False, missing="none", is_sorted=True
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)
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assert_almost_equal(actual_lowess, actual_lowess1[:, 1], decimal=13)
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# check integer input
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actual_lowess = lowess(np.round(y).astype(int), x, is_sorted=True)
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actual_lowess1 = lowess(np.round(y), x, is_sorted=True)
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assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
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assert_(actual_lowess.dtype is np.dtype(float))
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# this will also have duplicate x
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actual_lowess = lowess(y, np.round(x).astype(int), is_sorted=True)
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actual_lowess1 = lowess(y, np.round(x), is_sorted=True)
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assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
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assert_(actual_lowess.dtype is np.dtype(float))
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# Test specifying xvals explicitly
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perm_idx = np.arange(len(x) // 2)
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np.random.shuffle(perm_idx)
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actual_lowess2 = lowess(y, x, xvals=x[perm_idx], return_sorted=False)
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assert_almost_equal(
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actual_lowess[perm_idx, 1], actual_lowess2, decimal=13
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)
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# check with nans, this changes the arrays
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y[[5, 6]] = np.nan
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x[3] = np.nan
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mask_valid = np.isfinite(x) & np.isfinite(y)
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# actual_lowess1[[3, 5, 6], 1] = np.nan
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actual_lowess = lowess(y, x, is_sorted=True)
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actual_lowess1 = lowess(y[mask_valid], x[mask_valid], is_sorted=True)
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assert_almost_equal(actual_lowess, actual_lowess1, decimal=13)
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assert_raises(ValueError, lowess, y, x, missing="raise")
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perm_idx = np.arange(len(x))
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np.random.shuffle(perm_idx)
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yperm = y[perm_idx]
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xperm = x[perm_idx]
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actual_lowess2 = lowess(yperm, xperm, is_sorted=False)
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assert_almost_equal(actual_lowess, actual_lowess2, decimal=13)
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actual_lowess3 = lowess(
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yperm, xperm, is_sorted=False, return_sorted=False
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)
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mask_valid = np.isfinite(xperm) & np.isfinite(yperm)
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assert_equal(np.isnan(actual_lowess3), ~mask_valid)
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# get valid sorted smoothed y from actual_lowess3
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sort_idx = np.argsort(xperm)
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yhat = actual_lowess3[sort_idx]
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yhat = yhat[np.isfinite(yhat)]
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assert_almost_equal(yhat, actual_lowess2[:, 1], decimal=13)
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# Test specifying xvals explicitly, now with nans
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perm_idx = np.arange(actual_lowess.shape[0])
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actual_lowess4 = lowess(
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y, x, xvals=actual_lowess[perm_idx, 0], return_sorted=False
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)
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assert_almost_equal(
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actual_lowess[perm_idx, 1], actual_lowess4, decimal=13
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)
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def test_duplicate_xs(self):
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# see 2449
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# Generate cases with many duplicate x values
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x = [0] + [1] * 100 + [2] * 100 + [3]
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y = x + np.random.normal(size=len(x)) * 1e-8
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result = lowess(y, x, frac=50 / len(x), it=1)
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# fit values should be approximately averages of values at
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# a particular fit, which in this case are just equal to x
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assert_almost_equal(result[1:-1, 1], x[1:-1], decimal=7)
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def test_spike(self):
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# see 7700
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# Create a curve that is easy to fit at first but gets
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# harder further along.
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# This used to give an outlier bad fit at position 961
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x = np.linspace(0, 10, 1001)
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y = np.cos(x ** 2 / 5)
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result = lowess(y, x, frac=11 / len(x), it=1)
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assert_(np.all(result[:, 1] > np.min(y) - 0.1))
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assert_(np.all(result[:, 1] < np.max(y) + 0.1))
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def test_exog_predict(self):
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rfile = os.path.join(rpath, "test_lowess_simple.csv")
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test_data = np.genfromtxt(open(rfile, "rb"), delimiter=",", names=True)
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y, x = test_data["y"], test_data["x"]
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target = lowess(y, x, is_sorted=True)
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# Test specifying exog_predict explicitly
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perm_idx = np.arange(len(x) // 2)
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np.random.shuffle(perm_idx)
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actual_lowess = lowess(y, x, xvals=x[perm_idx], missing="none")
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assert_almost_equal(target[perm_idx, 1], actual_lowess, decimal=13)
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target_it0 = lowess(y, x, return_sorted=False, it=0)
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actual_lowess2 = lowess(y, x, xvals=x[perm_idx], it=0)
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assert_almost_equal(target_it0[perm_idx], actual_lowess2, decimal=13)
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# Check nans in exog_predict
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with pytest.raises(ValueError):
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lowess(y, x, xvals=np.array([np.nan, 5, 3]), missing="raise")
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# With is_sorted=True
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actual_lowess3 = lowess(y, x, xvals=x, is_sorted=True)
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assert_equal(actual_lowess3, target[:, 1])
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# check with nans, this changes the arrays
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y[[5, 6]] = np.nan
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x[3] = np.nan
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target = lowess(y, x, is_sorted=True)
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# Test specifying exog_predict explicitly, now with nans
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perm_idx = np.arange(target.shape[0])
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actual_lowess1 = lowess(y, x, xvals=target[perm_idx, 0])
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assert_almost_equal(target[perm_idx, 1], actual_lowess1, decimal=13)
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# nans and missing='drop'
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actual_lowess2 = lowess(y, x, xvals=x, missing="drop")
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all_finite = np.isfinite(x) & np.isfinite(y)
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assert_equal(actual_lowess2[all_finite], target[:, 1])
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# Dimensional check
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with pytest.raises(ValueError):
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lowess(y, x, xvals=np.array([[5], [10]]))
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def test_returns_inputs():
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# see 1960
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y = [0] * 10 + [1] * 10
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x = np.arange(20)
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result = lowess(y, x, frac=0.4)
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assert_almost_equal(result, np.column_stack((x, y)))
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def test_xvals_dtype(reset_randomstate):
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y = [0] * 10 + [1] * 10
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x = np.arange(20)
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# Previously raised ValueError: Buffer dtype mismatch
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results_xvals = lowess(y, x, frac=0.4, xvals=x[:5])
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assert_allclose(results_xvals, np.zeros(5), atol=1e-12)
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