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unitTests.py
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unitTests.py
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from __future__ import print_function
from ZIFA import ZIFA, block_ZIFA
import numpy as np
import random
from copy import deepcopy
from sklearn.decomposition import FactorAnalysis
from example import generateSimulatedDimensionalityReductionData
from scipy.stats import pearsonr
def unitTests():
"""
Just test ZIFA and block ZIFA under a variety of conditions to make sure projected dimensions don't change.
"""
random.seed(35)
np.random.seed(32)
n = 200
d = 20
k = 2
sigma = .3
n_clusters = 3
decay_coef = .1
X, Y, Z, ids = generateSimulatedDimensionalityReductionData(n_clusters, n, d, k, sigma, decay_coef)
Zhat, params = ZIFA.fitModel(Y, k)
assert np.allclose(Zhat[-1, :], [ 1.50067515, 0.04742477])
assert np.allclose(params['A'][0, :], [ 0.66884415, -0.17173555])
assert np.allclose(params['decay_coef'], 0.10458794970222711)
assert np.allclose(params['sigmas'][0], 0.30219903)
Zhat, params = block_ZIFA.fitModel(Y, k)
assert np.allclose(Zhat[-1, :], [1.49712162, 0.05823952]) # this is slightly different (though highly correlated) because ZIFA runs one extra half-step of EM
assert np.allclose(params['A'][0, :], [ 0.66884415, -0.17173555])
assert np.allclose(params['decay_coef'], 0.10458794970222711)
assert np.allclose(params['sigmas'][0], 0.30219903)
Zhat, params = block_ZIFA.fitModel(Y, k, n_blocks = 3)
assert np.allclose(Zhat[-1, :], [ 9.84455438e-01, 4.50924335e-02])
n = 50
d = 60
k = 3
sigma = .3
n_clusters = 3
decay_coef = .1
X, Y, Z, ids = generateSimulatedDimensionalityReductionData(n_clusters, n, d, k, sigma, decay_coef)
Zhat, params = block_ZIFA.fitModel(Y, k, n_blocks = 3)
assert np.allclose(Zhat[-1, :], [-1.69609638,-0.5475882, 0.08008015])
X, Y, Z, ids = generateSimulatedDimensionalityReductionData(n_clusters, n, d, k, sigma, decay_coef)
Zhat, params = ZIFA.fitModel(Y, k)
print(Zhat[-1, :])
assert np.allclose(Zhat[-1, :], [-0.63075905, -0.77361427, -0.11544281])
print('Tests passed!')
if __name__ == '__main__':
unitTests()