def test_apogee_bcnn(self):
random_xdata = np.random.normal(0, 1, (200, 7514))
random_ydata = np.random.normal(0, 1, (200, 7))
# ApogeeBCNN
print("======ApogeeBCNN======")
bneuralnet = ApogeeBCNN()
bneuralnet.targetname = [
'teff', 'logg', 'M', 'alpha', 'C1', 'Ti', 'Ti2'
]
bneuralnet.max_epochs = 1
bneuralnet.callbacks = ErrorOnNaN()
bneuralnet.train(random_xdata, random_ydata)
output_shape = bneuralnet.output_shape
input_shape = bneuralnet.input_shape
# prevent memory issue on Tavis CI
bneuralnet.mc_num = 3
prediction, prediction_err = bneuralnet.test(random_xdata)
print(bneuralnet.evaluate(random_xdata, random_ydata))
bneuralnet.plot_dense_stats()
bneuralnet.plot_model()
jacobian = bneuralnet.jacobian(random_xdata[:3], mean_output=True)
np.testing.assert_array_equal(prediction.shape, random_ydata.shape)
bneuralnet.save(name='apogee_bcnn')
# just to make sure it can load it back without error
bneuralnet_loaded = load_folder("apogee_bcnn")
bneuralnet_loaded.plot_dense_stats()
bneuralnet_loaded.callbacks = ErrorOnNaN()
# prevent memory issue on Tavis CI
bneuralnet_loaded.mc_num = 2
pred, pred_err = bneuralnet_loaded.test(random_xdata)
bneuralnet_loaded.aspcap_residue_plot(pred, pred, pred_err['total'])
bneuralnet_loaded.jacobian_aspcap(jacobian)
bneuralnet_loaded.save()
# Fine-tuning test
bneuralnet_loaded.max_epochs = 1
bneuralnet_loaded.train(random_xdata, random_ydata)
pred, pred_err = bneuralnet_loaded.test_old(random_xdata)
def test_apogee_bcnn(self):
"""
Test ApogeeBCNN models
- training, testing, evaluation
- Apogee plotting functions
"""
# Data preparation
random_xdata = np.random.normal(0, 1, (200, 7514))
random_ydata = np.random.normal(0, 1, (200, 7))
# ApogeeBCNN
print("======ApogeeBCNN======")
bneuralnet = ApogeeBCNN()
# deliberately chosen targetname to test targetname conversion too
bneuralnet.targetname = ['teff', 'logg', 'M', 'alpha', 'C1', 'Ti', 'Ti2']
bneuralnet.max_epochs = 1 # for quick result
bneuralnet.callbacks = ErrorOnNaN() # Raise error and fail the test if Nan
bneuralnet.train(random_xdata, random_ydata)
output_shape = bneuralnet.output_shape
input_shape = bneuralnet.input_shape
# prevent memory issue on Tavis CI so set mc_num=2
bneuralnet.mc_num = 2
prediction, prediction_err = bneuralnet.test(random_xdata)
# assert all of them not equal becaues of MC Dropout
self.assertEqual(
np.all(bneuralnet.evaluate(random_xdata, random_ydata) != bneuralnet.evaluate(random_xdata, random_ydata)),
True)
jacobian = bneuralnet.jacobian(random_xdata[:2], mean_output=True)
np.testing.assert_array_equal(prediction.shape, random_ydata.shape)
bneuralnet.save(name='apogee_bcnn')
# just to make sure it can load it back without error
bneuralnet_loaded = load_folder("apogee_bcnn")
# prevent memory issue on Tavis CI
bneuralnet_loaded.mc_num = 2
pred, pred_err = bneuralnet_loaded.test(random_xdata)
bneuralnet_loaded.aspcap_residue_plot(pred, pred, pred_err['total'])
bneuralnet_loaded.jacobian_aspcap(jacobian)
bneuralnet_loaded.save()
# Fine-tuning test
bneuralnet_loaded.max_epochs = 1
bneuralnet_loaded.callbacks = ErrorOnNaN()
bneuralnet_loaded.train(random_xdata, random_ydata)
pred, pred_err = bneuralnet_loaded.test_old(random_xdata)
def test_apogee_bcnn(self):
"""
Test ApogeeBCNN models
- training, testing, evaluation
- Apogee plotting functions
"""
# ApogeeBCNN
print("======ApogeeBCNN======")
bneuralnet = ApogeeBCNN()
# deliberately chosen targetname to test targetname conversion too
bneuralnet.targetname = ['logg', 'feh']
bneuralnet.max_epochs = 5 # for quick result
bneuralnet.callbacks = ErrorOnNaN(
) # Raise error and fail the test if Nan
bneuralnet.train(xdata, ydata)
output_shape = bneuralnet.output_shape
input_shape = bneuralnet.input_shape
bneuralnet.mc_num = 2
prediction, prediction_err = bneuralnet.test(xdata)
mape = np.median(
np.abs(prediction[bneuralnet.val_idx] - ydata[bneuralnet.val_idx])
/ ydata[bneuralnet.val_idx],
axis=0)
self.assertEqual(np.all(0.15 > mape),
True) # assert less than 15% error
self.assertEqual(
np.all(0.25 > np.median(prediction_err['total'], axis=0)),
True) # assert entropy
# assert all of them not equal becaues of MC Dropout
self.assertEqual(
np.all(
bneuralnet.evaluate(xdata, ydata) != bneuralnet.evaluate(
xdata, ydata)), True)
jacobian = bneuralnet.jacobian(xdata[:2], mean_output=True)
np.testing.assert_array_equal(prediction.shape, ydata.shape)
bneuralnet.save(name='apogee_bcnn')
bneuralnet.train_on_batch(xdata[:64],
ydata[:64]) # single batch fine-tuning test
# just to make sure it can load it back without error
bneuralnet_loaded = load_folder("apogee_bcnn")
# prevent memory issue on Tavis CI
bneuralnet_loaded.mc_num = 2
pred, pred_err = bneuralnet_loaded.test(xdata)
bneuralnet_loaded.save()
# Fine-tuning test
bneuralnet_loaded.max_epochs = 5
bneuralnet_loaded.callbacks = ErrorOnNaN()
bneuralnet_loaded.train(xdata, ydata)