-
Notifications
You must be signed in to change notification settings - Fork 17
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
remove unittest code from the tests function
- Loading branch information
1 parent
f72ca61
commit 099a858
Showing
11 changed files
with
1,296 additions
and
1,375 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,8 +1,5 @@ | ||
# 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 | ||
|
@@ -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) |
Oops, something went wrong.