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test.py
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import numpy as np
import sklearn.mixture
import torch
from gmm import GaussianMixture
import unittest
class CpuCheck(unittest.TestCase):
"""
Basic tests for CPU.
"""
def testPredictClasses(self):
"""
Assert that torch.FloatTensor is handled correctly.
"""
x = torch.randn(400, 2)
n_components = np.random.randint(1, 100)
model = GaussianMixture(n_components, x.size(1))
model.fit(x)
y = model.predict(x)
# check that dimensionality of class memberships is (n)
self.assertEqual(torch.Tensor(x.size(0)).size(), y.size())
def testPredictProbabilities(self):
"""
Assert that torch.FloatTensor is handled correctly when returning class probabilities.
"""
x = torch.randn(400, 2)
n_components = np.random.randint(1, 100)
model = GaussianMixture(n_components, x.size(1))
model.fit(x)
# check that y_p has dimensions (n, k)
y_p = model.predict(x, probs=True)
self.assertEqual(torch.Tensor(x.size(0), n_components).size(), y_p.size())
def testEmMatchesDiagSkLearn(self):
"""
Assert that log-probabilities (E-step) and parameter updates (M-step) approximately match those of sklearn.
"""
d = 20
n_components = np.random.randint(1, 100)
# (n, k, d)
x = torch.randn(400, 1, d)
# (n, d)
x_np = np.squeeze(x.data.numpy())
var_init = torch.ones(1, n_components, d) - .4
model = GaussianMixture(n_components, d, var_init=var_init, covariance_type="diag")
model_sk = sklearn.mixture.GaussianMixture(n_components,
covariance_type="diag",
init_params="random",
means_init=np.squeeze(model.mu.data.numpy()),
precisions_init=np.squeeze(1. / np.sqrt(var_init.data.numpy())))
model_sk._initialize_parameters(x_np, np.random.RandomState())
log_prob_sk = model_sk._estimate_log_prob(x_np)
log_prob = model._estimate_log_prob(x)
# Test whether log-probabilities are approximately equal
np.testing.assert_almost_equal(np.squeeze(log_prob.data.numpy()),
log_prob_sk,
decimal=2,
verbose=True)
_, log_resp_sk = model_sk._e_step(x_np)
_, log_resp = model._e_step(x)
# Test whether E-steps are approximately equal
np.testing.assert_almost_equal(np.squeeze(log_resp.data.numpy()),
log_resp_sk,
decimal=0,
verbose=True)
model_sk._m_step(x_np, log_prob_sk)
pi_sk = model_sk.weights_
mu_sk = model_sk.means_
var_sk = model_sk.means_
pi, mu, var = model._m_step(x, log_prob)
# Test whether pi ..
np.testing.assert_almost_equal(np.squeeze(pi.data.numpy()),
pi_sk,
decimal=1,
verbose=True)
# .. mu ..
np.testing.assert_almost_equal(np.squeeze(mu.data.numpy()),
mu_sk,
decimal=1,
verbose=True)
# .. and var are approximately equal
np.testing.assert_almost_equal(np.squeeze(var.data.numpy()),
var_sk,
decimal=1,
verbose=True)
def testEmMatchesFullSkLearn(self):
"""
Assert that log-probabilities (E-step) and parameter updates (M-step) approximately match those of sklearn.
"""
d = 20
n_components = np.random.randint(1, 100)
# (n, k, d)
x = torch.randn(400, 1, d)
# (n, d)
x_np = np.squeeze(x.data.numpy())
var_init = torch.eye(d,dtype=torch.float64).reshape(1, 1, d, d).repeat(1,n_components,1, 1)
model = GaussianMixture(n_components, d, init_params="random", var_init=var_init, covariance_type="full")
model_sk = sklearn.mixture.GaussianMixture(n_components,
covariance_type="full",
init_params="random",
means_init=np.squeeze(model.mu.data.numpy()),
precisions_init=np.squeeze(np.linalg.inv(var_init)))
model_sk._initialize_parameters(x_np, np.random.RandomState())
log_prob_sk = model_sk._estimate_log_prob(x_np)
log_prob = model._estimate_log_prob(x)
# Test whether log-probabilities are approximately equal
np.testing.assert_almost_equal(np.squeeze(log_prob.data.numpy()),
log_prob_sk,
decimal=2,
verbose=True)
_, log_resp_sk = model_sk._e_step(x_np)
_, log_resp = model._e_step(x)
# Test whether E-steps are approximately equal
np.testing.assert_almost_equal(np.squeeze(log_resp.data.numpy()),
log_resp_sk,
decimal=0,
verbose=True)
model_sk._m_step(x_np, log_resp_sk)
pi_sk = model_sk.weights_
mu_sk = model_sk.means_
var_sk = model_sk.covariances_
pi, mu, var = model._m_step(x, log_resp)
# Test whether pi ..
np.testing.assert_almost_equal(np.squeeze(pi.data.numpy()),
pi_sk,
decimal=1,
verbose=True)
# .. mu ..
np.testing.assert_almost_equal(np.squeeze(mu.data.numpy()),
mu_sk,
decimal=1,
verbose=True)
# .. and var are approximately equal
np.testing.assert_almost_equal(np.squeeze(var.data.numpy()),
var_sk,
decimal=1,
verbose=True)
class GpuCheck(unittest.TestCase):
"""
Basic tests for GPU.
"""
def testPredictClasses(self):
"""
Assert that torch.cuda.FloatTensor is handled correctly.
"""
x = torch.randn(400, 2).cuda()
n_components = np.random.randint(1, 100)
model = GaussianMixture(n_components, x.size(1), covariance_type="diag").cuda()
model.fit(x)
y = model.predict(x)
# check that dimensionality of class memberships is (n)
self.assertEqual(torch.Tensor(x.size(0)).size(), y.size())
def testPredictProbabilities(self):
"""
Assert that torch.cuda.FloatTensor is handled correctly when returning class probabilities.
"""
x = torch.randn(400, 2).cuda()
n_components = np.random.randint(1, 100)
model = GaussianMixture(n_components, x.size(1), covariance_type="diag").cuda()
model.fit(x)
# check that y_p has dimensions (n, k)
y_p = model.predict(x, probs=True)
self.assertEqual(torch.Tensor(x.size(0), n_components).size(), y_p.size())
if __name__ == "__main__":
unittest.main()