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test_target_spec.py
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test_target_spec.py
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import numpy as np
import pytest
from psyneulink.core.components.functions.distributionfunctions import NormalDist
from psyneulink.core.components.mechanisms.processing.transfermechanism import TransferMechanism
from psyneulink.core.components.process import Process
from psyneulink.core.components.system import System
from psyneulink.core.globals.environment import RunError
from psyneulink.core.globals.keywords import ENABLED
class TestSimpleLearningPathway:
def test_dict_target_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP],
)
# S.run(inputs={A: 1.0},
# targets={B: 2.0})
S.run(inputs={A: 1.0},
targets={B: [2.0]})
S.run(inputs={A: 1.0},
targets={B: [[2.0]]})
def test_dict_target_spec_length2(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B",
default_variable=[[0.0, 0.0]])
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP])
S.run(inputs={A: 1.0},
targets={B: [2.0, 3.0]})
S.run(inputs={A: 1.0},
targets={B: [[2.0, 3.0]]})
def test_list_target_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP])
# S.run(inputs={A: 1.0},
# targets=2.0)
S.run(inputs={A: 1.0},
targets=[2.0])
S.run(inputs={A: 1.0},
targets=[[2.0]])
input_dictionary = {A: [[[1.0]], [[2.0]], [[3.0]], [[4.0]], [[5.0]]]}
target_dictionary = {B: [[1.0], [2.0], [3.0], [4.0], [5.0]]}
S.run(inputs=input_dictionary,
targets=target_dictionary)
target_list = [[1.0], [2.0], [3.0], [4.0], [5.0]]
S.run(inputs=input_dictionary,
targets=target_list)
def test_list_target_spec_length2(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B",
default_variable=[[0.0, 0.0]])
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP],
)
S.run(inputs={A: 1.0},
targets=[2.0, 3.0])
S.run(inputs={A: 1.0},
targets=[[2.0, 3.0]])
def test_function_target_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B",
default_variable=np.array([[0.0, 0.0]]))
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP])
def target_function():
val_1 = NormalDist(mean=3.0)()
val_2 = NormalDist(mean=3.0)()
target_value = np.array([val_1, val_2])
return target_value
S.run(inputs={A: [[[1.0]], [[2.0]], [[3.0]]]},
targets={B: target_function})
class TestMultilayerLearning:
def test_dict_target_spec(self):
A = TransferMechanism(name="multilayer-mech-A")
B = TransferMechanism(name="multilayer-mech-B")
C = TransferMechanism(name="multilayer-mech-C")
P = Process(name="multilayer-process",
pathway=[A, B, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[P]
)
S.run(inputs={A: 1.0},
targets={C: 2.0})
S.run(inputs={A: 1.0},
targets={C: [2.0]})
S.run(inputs={A: 1.0},
targets={C: [[2.0]]})
def test_dict_target_spec_length2(self):
A = TransferMechanism(name="multilayer-mech-A")
B = TransferMechanism(name="multilayer-mech-B")
C = TransferMechanism(name="multilayer-mech-C",
default_variable=[[0.0, 0.0]])
P = Process(name="multilayer-process",
pathway=[A, B, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[P])
S.run(inputs={A: 1.0},
targets={C: [2.0, 3.0]})
S.run(inputs={A: 1.0},
targets={C: [[2.0, 3.0]]})
def test_function_target_spec(self):
A = TransferMechanism(name="multilayer-mech-A")
B = TransferMechanism(name="multilayer-mech-B")
C = TransferMechanism(name="multilayer-mech-C")
P = Process(name="multilayer-process",
pathway=[A, B, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[P])
def target_function():
val_1 = NormalDist(mean=3.0)()
return val_1
S.run(inputs={A: 1.0},
targets={C: target_function})
class TestDivergingLearningPathways:
def test_dict_target_spec(self):
A = TransferMechanism(name="diverging-learning-pathways-mech-A")
B = TransferMechanism(name="diverging-learning-pathways-mech-B")
C = TransferMechanism(name="diverging-learning-pathways-mech-C")
D = TransferMechanism(name="diverging-learning-pathways-mech-D")
E = TransferMechanism(name="diverging-learning-pathways-mech-E")
P1 = Process(name="learning-pathway-1",
pathway=[A, B, C],
learning=ENABLED)
P2 = Process(name="learning-pathway-2",
pathway=[A, D, E],
learning=ENABLED)
S = System(name="learning-system",
processes=[P1, P2]
)
S.run(inputs={A: 1.0},
targets={C: 2.0,
E: 4.0})
S.run(inputs={A: 1.0},
targets={C: [2.0],
E: [4.0]})
S.run(inputs={A: 1.0},
targets={C: [[2.0]],
E: [[4.0]]})
def test_dict_target_spec_length2(self):
A = TransferMechanism(name="diverging-learning-pathways-mech-A")
B = TransferMechanism(name="diverging-learning-pathways-mech-B")
C = TransferMechanism(name="diverging-learning-pathways-mech-C",
default_variable=[[0.0, 0.0]])
D = TransferMechanism(name="diverging-learning-pathways-mech-D")
E = TransferMechanism(name="diverging-learning-pathways-mech-E",
default_variable=[[0.0, 0.0]])
P1 = Process(name="learning-pathway-1",
pathway=[A, B, C],
learning=ENABLED)
P2 = Process(name="learning-pathway-2",
pathway=[A, D, E],
learning=ENABLED)
S = System(name="learning-system",
processes=[P1, P2]
)
S.run(inputs={A: 1.0},
targets={C: [2.0, 3.0],
E: [4.0, 5.0]})
S.run(inputs={A: 1.0},
targets={C: [[2.0, 3.0]],
E: [[4.0, 5.0]]})
def test_dict_list_and_function(self):
A = TransferMechanism(name="diverging-learning-pathways-mech-A")
B = TransferMechanism(name="diverging-learning-pathways-mech-B")
C = TransferMechanism(name="diverging-learning-pathways-mech-C")
D = TransferMechanism(name="diverging-learning-pathways-mech-D")
E = TransferMechanism(name="diverging-learning-pathways-mech-E")
P1 = Process(name="learning-pathway-1",
pathway=[A, B, C],
learning=ENABLED)
P2 = Process(name="learning-pathway-2",
pathway=[A, D, E],
learning=ENABLED)
S = System(name="learning-system",
processes=[P1, P2]
)
def target_function():
val_1 = NormalDist(mean=3.0)()
return val_1
S.run(inputs={A: 1.0},
targets={C: 2.0,
E: target_function})
S.run(inputs={A: 1.0},
targets={C: [2.0],
E: target_function})
S.run(inputs={A: 1.0},
targets={C: [[2.0]],
E: target_function})
class TestConvergingLearningPathways:
def test_dict_target_spec(self):
A = TransferMechanism(name="converging-learning-pathways-mech-A")
B = TransferMechanism(name="converging-learning-pathways-mech-B")
C = TransferMechanism(name="converging-learning-pathways-mech-C")
D = TransferMechanism(name="converging-learning-pathways-mech-D")
E = TransferMechanism(name="converging-learning-pathways-mech-E")
P1 = Process(name="learning-pathway-1",
pathway=[A, B, C],
learning=ENABLED)
P2 = Process(name="learning-pathway-2",
pathway=[D, E, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[P1, P2]
)
S.run(inputs={A: 1.0,
D: 1.0},
targets={C: 2.0})
S.run(inputs={A: 1.0,
D: 1.0},
targets={C: [2.0]})
S.run(inputs={A: 1.0,
D: 1.0},
targets={C: [[2.0]]})
def test_dict_target_spec_length2(self):
A = TransferMechanism(name="converging-learning-pathways-mech-A")
B = TransferMechanism(name="converging-learning-pathways-mech-B")
C = TransferMechanism(name="converging-learning-pathways-mech-C",
default_variable=[[0.0, 0.0]])
D = TransferMechanism(name="converging-learning-pathways-mech-D")
E = TransferMechanism(name="converging-learning-pathways-mech-E")
P1 = Process(name="learning-pathway-1",
pathway=[A, B, C],
learning=ENABLED)
P2 = Process(name="learning-pathway-2",
pathway=[D, E, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[P1, P2]
)
S.run(inputs={A: 1.0,
D: 1.0},
targets={C: [2.0, 3.0]})
S.run(inputs={A: 1.0,
D: 1.0},
targets={C: [[2.0, 3.0]]})
class TestInvalidTargetSpecs:
def test_3_targets_4_inputs(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP],
)
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]], [[2.0]], [[3.0]], [[4.0]]]},
targets={B: [[1.0], [2.0], [3.0]]})
assert 'Number of target values specified (3) for each learning sequence' in str(error_text.value) and \
'must equal the number of input values specified (4)' in str(error_text.value)
def test_2_target_mechanisms_1_dict_entry(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
C = TransferMechanism(name="learning-process-mech-C")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
LP2 = Process(name="learning-process2",
pathway=[A, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP, LP2],
)
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]]]},
targets={B: [[1.0]]})
assert 'missing from specification of targets for run' in str(error_text.value)
def test_1_target_mechanisms_2_dict_entries(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
C = TransferMechanism(name="learning-process-mech-C")
LP = Process(name="learning-process",
pathway=[A, B, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP],
)
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]]]},
targets={B: [[1.0]],
C: [[1.0]]})
assert 'does not project to a target Mechanism in' in str(error_text.value)
def test_2_target_mechanisms_list_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
C = TransferMechanism(name="learning-process-mech-C")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
LP2 = Process(name="learning-process2",
pathway=[A, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP, LP2],
)
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]]]},
targets=[[1.0]])
assert 'Target values for' in str(error_text.value) and \
'must be specified in a dictionary' in str(error_text.value)
def test_2_target_mechanisms_fn_spec(self):
A = TransferMechanism(name="learning-process-mech-A")
B = TransferMechanism(name="learning-process-mech-B")
C = TransferMechanism(name="learning-process-mech-C")
LP = Process(name="learning-process",
pathway=[A, B],
learning=ENABLED)
LP2 = Process(name="learning-process2",
pathway=[A, C],
learning=ENABLED)
S = System(name="learning-system",
processes=[LP, LP2],
)
def target_function():
val_1 = NormalDist(mean=3.0)()
val_2 = NormalDist(mean=3.0)()
return [val_1, val_2]
with pytest.raises(RunError) as error_text:
S.run(inputs={A: [[[1.0]]]},
targets=target_function)
assert 'Target values for' in str(error_text.value) and \
'must be specified in a dictionary' in str(error_text.value)