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fixed badges and added tests for gp reg #7

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Oct 23, 2018
Merged

fixed badges and added tests for gp reg #7

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5201478
added tests for gp reg
Emaasit Oct 23, 2018
594cf18
fixed badges
Emaasit Oct 23, 2018
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12 changes: 6 additions & 6 deletions 12 .travis.yml
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Expand Up @@ -22,12 +22,12 @@ install:
- pip install coveralls travis-sphinx==2.0.0

env:
- PYTHON_VERSION=2.7 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=2.7 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append --ignore=pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append --ignore=pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=10 --cov-append --ignore=pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=2.7 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=2.7 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py pmlearn/linear_model/tests/test_base.py pmlearn/linear_model/tests/test_logistic.py --ignore=pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"
- PYTHON_VERSION=3.6 FLOATX='float64' RUN_PYLINT="true" TESTCMD="--durations=50 --cov-append pmlearn/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_base.py --ignore=pmlearn/linear_model/tests/test_logistic.py pmlearn/gaussian_process/tests/test_gpr.py --ignore=pmlearn/mixture/tests/test_gaussian_mixture.py --ignore=pmlearn/mixture/tests/test_dirichlet_process.py --ignore=pmlearn/naive_bayes/tests/test_naive_bayes.py --ignore=pmlearn/neural_network/test_multilayer_perceptron.py"

script:
- . ./scripts/test.sh $TESTCMD
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8 changes: 4 additions & 4 deletions 8 README.rst
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Expand Up @@ -259,11 +259,11 @@ project: https://github.com/parsing-science/pymc3_models.
.. |Binder| image:: https://mybinder.org/badge.svg
:target: https://mybinder.org/v2/gh/pymc-learn/pymc-learn/master?filepath=%2Fdocs%2Fnotebooks?urlpath=lab

.. |Travis| image:: https://api.travis-ci.org/pymc-learn/pymc-learn.svg?branch=master
:target: https://travis-ci.org/pymc-learn/pymc-learn
.. |Travis| image:: https://travis-ci.com/pymc-learn/pymc-learn.svg?branch=master
:target: https://travis-ci.com/pymc-learn/pymc-learn

.. |Coverage| image:: https://coveralls.io/repos/github/pymc-learn/pymc-learn/badge.svg?branch=master
:target: https://coveralls.io/github/pymc-learn/pymc-learn?branch=master
.. |Coverage| image:: https://coveralls.io/repos/github/pymc-learn/pymc-learn/badge.svg
:target: https://coveralls.io/github/pymc-learn/pymc-learn

.. |Python27| image:: https://img.shields.io/badge/python-2.7-blue.svg
:target: https://badge.fury.io/py/pymc-learn
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222 changes: 106 additions & 116 deletions 222 pmlearn/gaussian_process/tests/test_gpr.py
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Expand Up @@ -4,56 +4,56 @@
#
# License: BSD 3 clause

# import pytest
import pytest
import numpy.testing as npt
# import pandas.testing as pdt
import pandas.testing as pdt
import shutil
import tempfile

import numpy as np
import pymc3 as pm
# from pymc3 import summary
# from sklearn.gaussian_process import \
# GaussianProcessRegressor as skGaussianProcessRegressor
# from sklearn.model_selection import train_test_split
#
#
# from pmlearn.exceptions import NotFittedError
from pymc3 import summary
from sklearn.gaussian_process import \
GaussianProcessRegressor as skGaussianProcessRegressor
from sklearn.model_selection import train_test_split


from pmlearn.exceptions import NotFittedError
from pmlearn.gaussian_process import (GaussianProcessRegressor)
# ,
# SparseGaussianProcessRegressor,
# StudentsTProcessRegressor)


class TestGaussianProcessRegressor(object):
"""
Compare the logp of GPR models in pmlearn to sklearn
"""

def setup_method(self):
"""Setup the data for testing
"""
self.num_pred = 1
self.num_training_samples = 20
self.length_scale = 0.1
self.signal_variance = 0.01
self.noise_variance = 0.01
self.X = np.random.randn(self.num_training_samples, self.num_pred)
self.y = np.random.randn(self.num_training_samples) * \
self.noise_variance
self.Xnew = np.random.randn(50, self.num_pred)
self.pnew = np.random.randn(50) * self.noise_variance
with pm.Model() as model:
cov_func = self.signal_variance**2 * \
pm.gp.cov.ExpQuad(self.num_pred, self.length_scale)
gp = pm.gp.Latent(cov_func=cov_func)
f = gp.prior("f", self.X, reparameterize=False)
p = gp.conditional("p", self.Xnew)

self.latent_logp = model.logp({"f": self.y, "p": self.pnew})
self.plogp = p.logp({"f": self.y, "p": self.pnew})

self.test_gpr = GaussianProcessRegressor(kernel=cov_func)
self.num_training_samples = 300

self.length_scale = 1.0
self.signal_variance = 0.1
self.noise_variance = 0.1

X = np.linspace(start=0, stop=10,
num=self.num_training_samples)[:, None]

cov_func = self.signal_variance ** 2 * pm.gp.cov.ExpQuad(
1, self.length_scale)
mean_func = pm.gp.mean.Zero()

f_true = np.random.multivariate_normal(
mean_func(X).eval(),
cov_func(X).eval() + 1e-8 * np.eye(self.num_training_samples),
1).flatten()
y = f_true + \
self.noise_variance * np.random.randn(self.num_training_samples)

self.X_train, self.X_test, self.y_train, self.y_test = \
train_test_split(X, y, test_size=0.3)

self.advi_gpr = GaussianProcessRegressor()

self.test_dir = tempfile.mkdtemp()

def teardown_method(self):
Expand All @@ -67,93 +67,83 @@ def test_advi_fit_returns_correct_model(self):
# This print statement ensures PyMC3 output won't overwrite
# the test name
print('')
self.test_gpr.fit(self.X, self.y)

npt.assert_equal(self.num_pred, self.test_gpr.num_pred)
npt.assert_almost_equal(self.signal_variance,
int(self.test_GPR.summary['mean']['signal_variance__0']),
0)
self.assertAlmostEqual(self.length_scale,
int(self.test_GPR.summary['mean']['length_scale__0_0']),
0)
self.assertAlmostEqual(self.noise_variance,
int(self.test_GPR.summary['mean']['noise_variance__0']),
0)
self.advi_gpr.fit(self.X_train, self.y_train,
inference_args={"n": 25000})

# def test_nuts_fit_returns_correct_model(self):
# # This print statement ensures PyMC3 output won't overwrite the test name
# print('')
# self.test_nuts_GPR.fit(self.X_train, self.y_train, inference_type='nuts')
#
# self.assertEqual(self.num_pred, self.test_nuts_GPR.num_pred)
# self.assertAlmostEqual(self.signal_variance,
# int(self.test_nuts_GPR.summary['mean']['signal_variance__0']),
# 0)
# self.assertAlmostEqual(self.length_scale,
# int(self.test_nuts_GPR.summary['mean']['length_scale__0_0']),
# 0)
# self.assertAlmostEqual(self.noise_variance,
# int(self.test_nuts_GPR.summary['mean']['noise_variance__0']),
# 0)
npt.assert_equal(self.num_pred, self.advi_gpr.num_pred)
npt.assert_almost_equal(
self.signal_variance,
self.advi_gpr.summary['mean']['signal_variance__0'],
0)
npt.assert_almost_equal(
self.length_scale,
self.advi_gpr.summary['mean']['length_scale__0_0'],
0)
npt.assert_almost_equal(
self.noise_variance,
self.advi_gpr.summary['mean']['noise_variance__0'],
0)


# class GaussianProcessRegressorPredictTestCase(GaussianProcessRegressorTestCase):
# def test_predict_returns_predictions(self):
# print('')
# self.test_GPR.fit(self.X_train, self.y_train)
# preds = self.test_GPR.predict(self.X_test)
# self.assertEqual(self.y_test.shape, preds.shape)
#
# def test_predict_returns_mean_predictions_and_std(self):
# print('')
# self.test_GPR.fit(self.X_train, self.y_train)
# preds, stds = self.test_GPR.predict(self.X_test, return_std=True)
# self.assertEqual(self.y_test.shape, preds.shape)
# self.assertEqual(self.y_test.shape, stds.shape)
#
# def test_predict_raises_error_if_not_fit(self):
# print('')
# with self.assertRaises(NotFittedError) as no_fit_error:
# test_GPR = GaussianProcessRegressor()
# test_GPR.predict(self.X_train)
#
# expected = 'Run fit on the model before predict.'
# self.assertEqual(str(no_fit_error.exception), expected)
class TestGaussianProcessRegressorPredict(TestGaussianProcessRegressor):
def test_predict_returns_predictions(self):
print('')
self.advi_gpr.fit(self.X_train, self.y_train,
inference_args={"n": 25000})
preds = self.advi_gpr.predict(self.X_test)
npt.assert_equal(self.y_test.shape, preds.shape)

def test_predict_returns_mean_predictions_and_std(self):
print('')
self.advi_gpr.fit(self.X_train, self.y_train,
inference_args={"n": 25000})
preds, stds = self.advi_gpr.predict(self.X_test, return_std=True)
npt.assert_equal(self.y_test.shape, preds.shape)
npt.assert_equal(self.y_test.shape, stds.shape)

def test_predict_raises_error_if_not_fit(self):
print('')
with pytest.raises(NotFittedError):
advi_gpr = GaussianProcessRegressor()
advi_gpr.predict(self.X_train)


class TestGaussianProcessRegressorScore(TestGaussianProcessRegressor):
def test_score_matches_sklearn_performance(self):
print('')
sk_gpr = skGaussianProcessRegressor()
sk_gpr.fit(self.X_train, self.y_train)
sk_gpr_score = sk_gpr.score(self.X_test, self.y_test)

self.advi_gpr.fit(self.X_train, self.y_train,
inference_args={"n": 25000})
advi_gpr_score = self.advi_gpr.score(self.X_test, self.y_test)

npt.assert_almost_equal(sk_gpr_score, advi_gpr_score, 1)


class TestGaussianProcessRegressorSaveAndLoad(TestGaussianProcessRegressor):
def test_save_and_load_work_correctly(self):
print('')
self.advi_gpr.fit(self.X_train, self.y_train,
inference_args={"n": 25000})
score1 = self.advi_gpr.score(self.X_test, self.y_test)
self.advi_gpr.save(self.test_dir)

gpr2 = GaussianProcessRegressor()
gpr2.load(self.test_dir)

npt.assert_equal(self.advi_gpr.inference_type, gpr2.inference_type)
npt.assert_equal(self.advi_gpr.num_pred, gpr2.num_pred)
npt.assert_equal(self.advi_gpr.num_training_samples,
gpr2.num_training_samples)
pdt.assert_frame_equal(summary(self.advi_gpr.trace),
summary(gpr2.trace))

score2 = gpr2.score(self.X_test, self.y_test)
npt.assert_almost_equal(score1, score2, 0)


# class GaussianProcessRegressorScoreTestCase(GaussianProcessRegressorTestCase):
# def test_score_matches_sklearn_performance(self):
# print('')
# skGPR = skGaussianProcessRegressor()
# skGPR.fit(self.X_train, self.y_train)
# skGPR_score = skGPR.score(self.X_test, self.y_test)
#
# self.test_GPR.fit(self.X_train, self.y_train)
# test_GPR_score = self.test_GPR.score(self.X_test, self.y_test)
#
# self.assertAlmostEqual(skGPR_score, test_GPR_score, 1)
#
#
# class GaussianProcessRegressorSaveAndLoadTestCase(GaussianProcessRegressorTestCase):
# def test_save_and_load_work_correctly(self):
# print('')
# self.test_GPR.fit(self.X_train, self.y_train)
# score1 = self.test_GPR.score(self.X_test, self.y_test)
# self.test_GPR.save(self.test_dir)
#
# GPR2 = GaussianProcessRegressor()
# GPR2.load(self.test_dir)
#
# self.assertEqual(self.test_GPR.inference_type, GPR2.inference_type)
# self.assertEqual(self.test_GPR.num_pred, GPR2.num_pred)
# self.assertEqual(self.test_GPR.num_training_samples, GPR2.num_training_samples)
# pd.testing.assert_frame_equal(summary(self.test_GPR.trace),
# summary(GPR2.trace))
#
# score2 = GPR2.score(self.X_test, self.y_test)
# self.assertAlmostEqual(score1, score2, 1)
#
#
# class StudentsTProcessRegressorTestCase(unittest.TestCase):
#
# def setUp(self):
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