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test_net_common.py
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test_net_common.py
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from physDBD import ConvertMomentsToNMomentsLayer, DeathRxnLayer, \
BirthRxnLayer, EatRxnLayer, ConvertNMomentsTEtoMomentsTE
# Depreciation warnings
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
import copy
import os
import shutil
import numpy as np
import tensorflow as tf
class Vals:
nv = 3
nh = 2
batch_size = 2
i_death = 0
i_birth = 0
i_predator = 1
i_prey = 0
_mu = np.array([19., 45., 62., 4., 8.])
_cov = np.array([
[30., 67., 107., 10., 9.],
[67., 162., 241., 20., 27.],
[107., 241., 402., 40., 27.],
[10., 20., 40., 5., 0.],
[9., 27., 27., 0., 9.]
])
_ncov = np.array([
[391., 922., 1285., 86., 161.],
[922., 2187., 3031., 200., 387.],
[1285., 3031., 4246., 288., 523.],
[86., 200., 288., 21., 32.],
[161., 387., 523., 32., 73.]
])
_mu_TE = np.array([3.0, 5.0, 2.0, 1.0, 0.8])
_ncov_TE = np.array([
[12.0, 6.0, 3.0, 2.0, 1.0],
[6.0, 18.0, 4.0, 3.0, 1.0],
[3.0, 4.0, 16.0, 2.0, 1.0],
[2.0, 3.0, 2.0, 8.0, 0.5],
[1.0, 1.0, 1.0, 0.5, 6.0]
])
@classmethod
def mu_TE(cls):
return np.tile(cls._mu_TE, (cls.batch_size,1))
@classmethod
def ncov_TE(cls):
return np.tile(cls._ncov_TE, (cls.batch_size,1,1))
@classmethod
def mu(cls):
return np.tile(cls._mu, (cls.batch_size,1))
@classmethod
def cov(cls):
return np.tile(cls._cov, (cls.batch_size,1,1))
@classmethod
def ncov(cls):
return np.tile(cls._ncov, (cls.batch_size,1,1))
@tf.keras.utils.register_keras_serializable(package="physDBD")
class SingleLayerModel(tf.keras.Model):
def __init__(self, lyr, **kwargs):
super(SingleLayerModel, self).__init__(name='')
self.lyr = lyr
def get_config(self):
return {
"lyr": self.lyr
}
@classmethod
def from_config(cls, config):
return cls(**config)
def call(self, input_tensor, training=False):
return self.lyr(input_tensor)
class TestNet:
def assert_equal_dicts(self, x_out, x_out_true):
# Convert x_out_true covh to covh_diag as needed
y_out_true = copy.copy(x_out_true)
for key,val in x_out_true.items():
if key == "covh":
y_out_true["covh_diag"] = np.diag(val)
if "covh" in y_out_true:
del y_out_true["covh"]
for key, val_true in y_out_true.items():
val = x_out[key]
self.assert_equal_arrs(val,val_true)
def assert_equal_arrs(self, x_out, x_out_true):
tol = 1.e-4
assert np.max(abs(x_out-x_out_true)) < tol
def save_load_model(self, lyr, x_in):
# Test save; call the model once to build it first
model = SingleLayerModel(lyr)
x_out = model(x_in)
model.save("saved_models/model", save_traces=False)
# Test load
model_rel = tf.keras.models.load_model("saved_models/model")
print(model_rel)
# Check types match!
# Otherwise we may have: tensorflow.python.keras.saving.saved_model.load.XYZ instead of XYZ
assert type(model_rel) is type(model)
# Remove
if os.path.isdir("saved_models"):
shutil.rmtree("saved_models")
def test_moments_to_nmoments(self):
v = Vals()
lyr = ConvertMomentsToNMomentsLayer()
# Input
x_in = {
"mu": tf.constant(v.mu(), dtype="float32"),
"cov": tf.constant(v.cov(), dtype="float32")
}
# Output
x_out = lyr(x_in)
print(x_out)
x_out_true = {
"mu": np.array([19., 45., 62., 4., 8.]),
"ncov": np.array([
[391., 922., 1285., 86., 161.],
[922., 2187., 3031., 200., 387.],
[1285., 3031., 4246., 288., 523.],
[86., 200., 288., 21., 32.],
[161., 387., 523., 32., 73.]
])
}
self.assert_equal_dicts(x_out,x_out_true)
self.save_load_model(lyr, x_in)
def test_death_rxn(self):
v = Vals()
lyr = DeathRxnLayer(nv=v.nv,nh=v.nh,i_sp=v.i_death)
# Input
x_in = {
"mu": tf.constant(v.mu(), dtype="float32"),
"ncov": tf.constant(v.ncov(), dtype="float32")
}
# Output
x_out = lyr(x_in)
print(x_out)
x_out_true = {
"muTE": np.array([-19., 0., 0., 0., 0.]),
"ncovTE": np.array([
[-763., -922., -1285., -86., -161.],
[-922., 0., 0., 0., 0.],
[-1285., 0., 0., 0., 0.],
[-86., 0., 0., 0., 0.],
[-161., 0., 0., 0., 0.]
])
}
self.assert_equal_dicts(x_out,x_out_true)
self.save_load_model(lyr, x_in)
def test_birth_rxn(self):
v = Vals()
lyr = BirthRxnLayer(nv=v.nv,nh=v.nh,i_sp=v.i_birth)
# Input
x_in = {
"mu": tf.constant(v.mu(), dtype="float32"),
"ncov": tf.constant(v.ncov(), dtype="float32")
}
# Output
x_out = lyr(x_in)
print(x_out)
x_out_true = {
"muTE": np.array([19., 0., 0., 0., 0.]),
"ncovTE": np.array([
[801., 922., 1285., 86., 161.],
[922., 0., 0., 0., 0.],
[1285., 0., 0., 0., 0.],
[86., 0., 0., 0., 0.],
[161., 0., 0., 0., 0.]
])
}
self.assert_equal_dicts(x_out,x_out_true)
self.save_load_model(lyr, x_in)
def test_eat_rxn(self):
v = Vals()
lyr = EatRxnLayer(nv=v.nv,nh=v.nh,i_prey=v.i_prey,i_hunter=v.i_predator)
# Input
x_in = {
"mu": tf.constant(v.mu(), dtype="float32"),
"ncov": tf.constant(v.ncov(), dtype="float32")
}
# Output
x_out = lyr(x_in)
print(x_out)
x_out_true = {
"muTE": np.array([-922., 922., 0., 0., 0.]),
"ncovTE": np.array([
[-39360., -28364., -66558., -4518., -8294.],
[-28364., 96088., 66558., 4518., 8294.],
[-66558., 66558., 0., 0., 0.],
[-4518., 4518., 0., 0., 0.],
[-8294., 8294., 0., 0., 0.]
])
}
self.assert_equal_dicts(x_out,x_out_true)
self.save_load_model(lyr, x_in)
def test_convert_nmomentsTE_to_momentsTE(self):
v = Vals()
lyr = ConvertNMomentsTEtoMomentsTE()
# Input
x_in = {
"mu": tf.constant(v.mu(), dtype="float32"),
"muTE": tf.constant(v.mu_TE(), dtype="float32"),
"ncovTE": tf.constant(v.ncov_TE(), dtype="float32")
}
# Output
x_out = lyr(x_in)
print(x_out)
x_out_true = {
"muTE": np.array([3., 5., 2., 1., 0.8]),
"covTE": np.array([
[-102., -224., -221., -29., -38.2],
[-224., -432., -396., -62., -75.],
[-221., -396., -232., -68., -64.6],
[-29., -62., -68., 0., -10.7],
[-38.2, -75., -64.6, -10.7, -6.8]
])
}
self.assert_equal_dicts(x_out,x_out_true)
self.save_load_model(lyr, x_in)