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remove unittest code from the tests function
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@LegrandNico
LegrandNico committed Aug 22, 2024
1 parent f72ca61 commit 099a858
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380 changes: 187 additions & 193 deletions tests/test_binary.py
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Original file line number Diff line number Diff line change
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# Author: Nicolas Legrand <[email protected]>

import unittest
from unittest import TestCase

import jax.numpy as jnp
from jax.lax import scan
from jax.tree_util import Partial
Expand All @@ -26,193 +23,190 @@
from pyhgf.utils import beliefs_propagation


class Testbinary(TestCase):
def test_gaussian_density(self):
surprise = gaussian_density(
x=jnp.array([1.0, 1.0]),
mean=jnp.array([0.0, 0.0]),
precision=jnp.array([1.0, 1.0]),
)
assert jnp.all(jnp.isclose(surprise, 0.24197073))

def test_sgm(self):
assert jnp.all(jnp.isclose(sigmoid(jnp.array([0.3, 0.3])), 0.5744425))

def test_binary_surprise(self):
surprise = binary_surprise(
x=jnp.array([1.0]),
expected_mean=jnp.array([0.2]),
)
assert jnp.all(jnp.isclose(surprise, 1.609438))

def test_update_binary_input_parents(self):
##########################
# three level binary HGF #
##########################
input_node_parameters = {
"expected_precision": jnp.inf,
"eta0": 0.0,
"eta1": 1.0,
"surprise": 0.0,
"time_step": 0.0,
"values": 0.0,
"observed": 1,
"volatility_coupling_parents": None,
"value_coupling_parents": (1.0,),
}
node_parameters_1 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": (1.0,),
"value_coupling_parents": (1.0,),
"volatility_coupling_parents": None,
"volatility_coupling_children": None,
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"binary_expected_precision": jnp.nan,
"temp": {
"value_prediction_error": 0.0,
},
}
node_parameters_2 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": (1.0,),
"value_coupling_parents": None,
"volatility_coupling_parents": (1.0,),
"volatility_coupling_children": None,
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"temp": {
"effective_precision": 1.0,
"value_prediction_error": 0.0,
"volatility_prediction_error": 0.0,
},
}
node_parameters_3 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": None,
"value_coupling_parents": None,
"volatility_coupling_parents": None,
"volatility_coupling_children": (1.0,),
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"temp": {
"effective_precision": 1.0,
"value_prediction_error": 0.0,
"volatility_prediction_error": 0.0,
},
}

edges = (
AdjacencyLists(0, (1,), None, None, None, (None,)),
AdjacencyLists(1, (2,), None, (0,), None, (None,)),
AdjacencyLists(2, None, (3,), (1,), None, (None,)),
AdjacencyLists(2, None, None, None, (2,), (None,)),
)
attributes = {
0: input_node_parameters,
1: node_parameters_1,
2: node_parameters_2,
3: node_parameters_3,
}

# create update sequence
sequence1 = 3, continuous_node_prediction
sequence2 = 2, continuous_node_prediction
sequence3 = 1, binary_state_node_prediction
sequence4 = 0, binary_input_prediction_error_infinite_precision
sequence5 = 1, binary_node_update_infinite
sequence6 = 1, binary_state_node_prediction_error
sequence7 = 2, continuous_node_update
sequence8 = 2, continuous_node_prediction_error
sequence9 = 3, continuous_node_update
update_sequence = (
sequence1,
sequence2,
sequence3,
sequence4,
sequence5,
sequence6,
sequence7,
sequence8,
sequence9,
)
data = jnp.ones(1)
time_steps = jnp.ones(1)
observed = jnp.ones(1)
inputs = Inputs(0, 1)

# apply sequence
new_attributes, _ = beliefs_propagation(
structure=(inputs, edges),
attributes=attributes,
update_sequence=update_sequence,
input_data=(data, time_steps, observed),
)
for idx, val in zip(
["mean", "expected_mean", "binary_expected_precision"],
[1.0, 0.7310586, 5.0861616],
):
assert jnp.isclose(new_attributes[1][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[1.8515793, 1.0, 0.31581485, 0.11920292],
):
assert jnp.isclose(new_attributes[2][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[0.5050575, 1.0, 0.47702926, 0.26894143],
):
assert jnp.isclose(new_attributes[3][idx], val)

# use scan
u, _ = load_data("binary")

# Create the data (value and time steps vectors) - only use the 5 first trials
# as the priors are ill defined here
data = jnp.array([u[:5]]).T
time_steps = jnp.ones((len(u[:5]), 1))
observed = jnp.ones((len(u[:5]), 1))
inputs = Inputs(0, 1)

# create the function that will be scaned
scan_fn = Partial(
beliefs_propagation,
update_sequence=update_sequence,
structure=(inputs, edges),
)

# Run the entire for loop
last, _ = scan(scan_fn, attributes, (data, time_steps, observed))
for idx, val in zip(
["mean", "expected_mean", "binary_expected_precision"],
[0.0, 0.95616907, 23.860779],
):
assert jnp.isclose(last[1][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[-2.1582031, 3.0825963, 0.18244718, 0.1405374],
):
assert jnp.isclose(last[2][idx], val)
for idx, val in zip(
["expected_mean", "expected_precision"], [-0.30260748, 0.14332297]
):
assert jnp.isclose(last[3][idx], val)


if __name__ == "__main__":
unittest.main(argv=["first-arg-is-ignored"], exit=False)
def test_gaussian_density():
surprise = gaussian_density(
x=jnp.array([1.0, 1.0]),
mean=jnp.array([0.0, 0.0]),
precision=jnp.array([1.0, 1.0]),
)
assert jnp.all(jnp.isclose(surprise, 0.24197073))

def test_sgm():
assert jnp.all(jnp.isclose(sigmoid(jnp.array([0.3, 0.3])), 0.5744425))


def test_binary_surprise():
surprise = binary_surprise(
x=jnp.array([1.0]),
expected_mean=jnp.array([0.2]),
)
assert jnp.all(jnp.isclose(surprise, 1.609438))


def test_update_binary_input_parents():
##########################
# three level binary HGF #
##########################
input_node_parameters = {
"expected_precision": jnp.inf,
"eta0": 0.0,
"eta1": 1.0,
"surprise": 0.0,
"time_step": 0.0,
"values": 0.0,
"observed": 1,
"volatility_coupling_parents": None,
"value_coupling_parents": (1.0,),
}
node_parameters_1 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": (1.0,),
"value_coupling_parents": (1.0,),
"volatility_coupling_parents": None,
"volatility_coupling_children": None,
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"binary_expected_precision": jnp.nan,
"temp": {
"value_prediction_error": 0.0,
},
}
node_parameters_2 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": (1.0,),
"value_coupling_parents": None,
"volatility_coupling_parents": (1.0,),
"volatility_coupling_children": None,
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"temp": {
"effective_precision": 1.0,
"value_prediction_error": 0.0,
"volatility_prediction_error": 0.0,
},
}
node_parameters_3 = {
"expected_precision": 1.0,
"precision": 1.0,
"expected_mean": 1.0,
"value_coupling_children": None,
"value_coupling_parents": None,
"volatility_coupling_parents": None,
"volatility_coupling_children": (1.0,),
"autoconnection_strength": 1.0,
"mean": 1.0,
"observed": 1,
"tonic_volatility": 1.0,
"tonic_drift": 0.0,
"temp": {
"effective_precision": 1.0,
"value_prediction_error": 0.0,
"volatility_prediction_error": 0.0,
},
}

edges = (
AdjacencyLists(0, (1,), None, None, None),
AdjacencyLists(1, (2,), None, (0,), None),
AdjacencyLists(2, None, (3,), (1,), None),
AdjacencyLists(2, None, None, None, (2,)),
)
attributes = {
0: input_node_parameters,
1: node_parameters_1,
2: node_parameters_2,
3: node_parameters_3,
}

# create update sequence
sequence1 = 3, continuous_node_prediction
sequence2 = 2, continuous_node_prediction
sequence3 = 1, binary_state_node_prediction
sequence4 = 0, binary_input_prediction_error_infinite_precision
sequence5 = 1, binary_node_update_infinite
sequence6 = 1, binary_state_node_prediction_error
sequence7 = 2, continuous_node_update
sequence8 = 2, continuous_node_prediction_error
sequence9 = 3, continuous_node_update
update_sequence = (
sequence1,
sequence2,
sequence3,
sequence4,
sequence5,
sequence6,
sequence7,
sequence8,
sequence9,
)
data = jnp.ones(1)
time_steps = jnp.ones(1)
observed = jnp.ones(1)
inputs = Inputs(0, 1)

# apply sequence
new_attributes, _ = beliefs_propagation(
structure=(inputs, edges),
attributes=attributes,
update_sequence=update_sequence,
input_data=(data, time_steps, observed),
)
for idx, val in zip(
["mean", "expected_mean", "binary_expected_precision"],
[1.0, 0.7310586, 5.0861616],
):
assert jnp.isclose(new_attributes[1][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[1.8515793, 1.0, 0.31581485, 0.11920292],
):
assert jnp.isclose(new_attributes[2][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[0.5050575, 1.0, 0.47702926, 0.26894143],
):
assert jnp.isclose(new_attributes[3][idx], val)

# use scan
u, _ = load_data("binary")

# Create the data (value and time steps vectors) - only use the 5 first trials
# as the priors are ill defined here
data = jnp.array([u[:5]]).T
time_steps = jnp.ones((len(u[:5]), 1))
observed = jnp.ones((len(u[:5]), 1))
inputs = Inputs(0, 1)

# create the function that will be scaned
scan_fn = Partial(
beliefs_propagation,
update_sequence=update_sequence,
structure=(inputs, edges),
)

# Run the entire for loop
last, _ = scan(scan_fn, attributes, (data, time_steps, observed))
for idx, val in zip(
["mean", "expected_mean", "binary_expected_precision"],
[0.0, 0.95616907, 23.860779],
):
assert jnp.isclose(last[1][idx], val)
for idx, val in zip(
["mean", "expected_mean", "precision", "expected_precision"],
[-2.1582031, 3.0825963, 0.18244718, 0.1405374],
):
assert jnp.isclose(last[2][idx], val)
for idx, val in zip(
["expected_mean", "expected_precision"], [-0.30260748, 0.14332297]
):
assert jnp.isclose(last[3][idx], val)
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