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docs: added docstring and ahdere to pre-commit hooks
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@younesStrittmatter
younesStrittmatter committed Jul 11, 2023
1 parent df97a37 commit fbc1513
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121 changes: 80 additions & 41 deletions src/autora/experiment_runner/synthetic/psychology/luce_choice_ratio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,12 +7,12 @@


def luce_choice_ratio(
name='Luce-Choice-Ratio',
added_noise=0.01,
resolution=8,
maximum_similarity=10,
focus=0.8,
rng=np.random.default_rng()
name="Luce-Choice-Ratio",
added_noise=0.01,
resolution=8,
maximum_similarity=10,
focus=0.8,
rng=np.random.default_rng(),
):
"""
Luce-Choice-Ratio
Expand All @@ -27,11 +27,35 @@ def luce_choice_ratio(
Shepard-Luce Choice Rule according to:
- Equation (4) in Logan, G. D., & Gordon, R. D. (2001).
- and in Executive control of visual attention in dual-task situations. Psychological review, 108(2), 393.
- and in Executive control of visual attention in dual-task situations.
Psychological review, 108(2), 393.
- Equation (5) in Luce, R. D. (1963). Detection and recognition.
Examples:
First we seed numpy to get replicable results:
>>> np.random.seed(42)
We can instantiate a Shepard-Cue Choice Experiment. We use a seed to get replicable results:
>>> l_s_experiment = luce_choice_ratio(rng=42)
We can look at the name of the experiment:
>>> l_s_experiment.name
'Luce-Choice-Ratio'
To call the ground truth, we can use an attribute of the experiment:
>>> l_s_experiment.ground_truth(np.array([[1,2,3,4]]))
array([[0.21052632]])
We can also run an experiment:
>>> l_s_experiment.experiment_runner(np.array([[1,2,3,4]]))
array([[0.21016246]])
To plot the experiment use:
>>> l_s_experiment.plotter()
>>> plt.show() # doctest: +SKIP
"""
minimum_similarity = 1/maximum_similarity
minimum_similarity = 1 / maximum_similarity

params = dict(
name=name,
Expand All @@ -40,7 +64,7 @@ def luce_choice_ratio(
minimum_similarity=minimum_similarity,
resolution=resolution,
focus=focus,
rng=rng
rng=rng,
)

similarity_category_A1 = IV(
Expand All @@ -49,7 +73,7 @@ def luce_choice_ratio(
value_range=(minimum_similarity, maximum_similarity),
units="similarity",
variable_label="Similarity with Category A1",
type=ValueType.REAL
type=ValueType.REAL,
)

similarity_category_A2 = IV(
Expand All @@ -58,7 +82,7 @@ def luce_choice_ratio(
value_range=(minimum_similarity, maximum_similarity),
units="similarity",
variable_label="Similarity with Category A2",
type=ValueType.REAL
type=ValueType.REAL,
)

similarity_category_B1 = IV(
Expand All @@ -67,7 +91,7 @@ def luce_choice_ratio(
value_range=(minimum_similarity, maximum_similarity),
units="similarity",
variable_label="Similarity with Category B1",
type=ValueType.REAL
type=ValueType.REAL,
)

similarity_category_B2 = IV(
Expand All @@ -76,23 +100,24 @@ def luce_choice_ratio(
value_range=(minimum_similarity, maximum_similarity),
units="similarity",
variable_label="Similarity with Category B2",
type=ValueType.REAL
type=ValueType.REAL,
)

choose_A1 = DV(
name="choose_A1",
value_range=(0, 1),
units="probability",
variable_label="Probability of Choosing A1",
type=ValueType.PROBABILITY
type=ValueType.PROBABILITY,
)

variables = VariableCollection(
independent_variables=[similarity_category_A1,
similarity_category_A2,
similarity_category_B1,
similarity_category_B2,
],
independent_variables=[
similarity_category_A1,
similarity_category_A2,
similarity_category_B1,
similarity_category_B2,
],
dependent_variables=[choose_A1],
)

Expand All @@ -103,17 +128,17 @@ def experiment_runner(
):
Y = np.zeros((X.shape[0], 1))
for idx, x in enumerate(X):

similarity_A1 = x[0]
similarity_A2 = x[1]
similarity_B1 = x[2]
similarity_B2 = x[3]

y = (similarity_A1 * focus + np.random.normal(0, added_noise_)) / \
(similarity_A1 * focus +
similarity_A2 * focus +
similarity_B1 * (1 - focus_) +
similarity_B2 * (1 - focus_))
y = (similarity_A1 * focus + np.random.normal(0, added_noise_)) / (
similarity_A1 * focus
+ similarity_A2 * focus
+ similarity_B1 * (1 - focus_)
+ similarity_B2 * (1 - focus_)
)
# probability can't be negative or larger than 1 (the noise can make it so)
if y <= 0:
y = 0.0001
Expand All @@ -131,25 +156,31 @@ def domain():
similarity_B1 = variables.independent_variables[2].allowed_values
similarity_B2 = variables.independent_variables[3].allowed_values

X = np.array(np.meshgrid(similarity_A1,
similarity_A2,
similarity_B1,
similarity_B2,
)).T.reshape(-1, 4)

# remove all conditions from X where the focus is 0 and the similarity of A1 is 0 or the similarity of A2 is 0
X = np.array(
np.meshgrid(
similarity_A1,
similarity_A2,
similarity_B1,
similarity_B2,
)
).T.reshape(-1, 4)

# remove all conditions from X where the focus is 0 and the similarity of A1 is 0
# or the similarity of A2 is 0
X = X[~((X[:, 0] == 0) & (X[:, 1] == 0) & (X[:, 2] == 0) & (X[:, 3] == 0))]
return X

def plotter(
model=None,
):
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt

similarity_A1 = np.linspace(variables.independent_variables[0].value_range[0],
variables.independent_variables[0].value_range[1],
100)
similarity_A1 = np.linspace(
variables.independent_variables[0].value_range[0],
variables.independent_variables[0].value_range[1],
100,
)

similarity_A2 = 0.5 # 1 - similarity_A1

Expand All @@ -168,14 +199,22 @@ def plotter(
X[:, 3] = similarity_B2

y = ground_truth(X)
plt.plot(similarity_A1.reshape((len(similarity_A1), 1)), y,
label=f"Similarity to B1 = {similarity_B1} (Original)",
c=colors[col_keys[idx]])
plt.plot(
similarity_A1.reshape((len(similarity_A1), 1)),
y,
label=f"Similarity to B1 = {similarity_B1} (Original)",
c=colors[col_keys[idx]],
)

if model is not None:
y = model.predict(X)
plt.plot(similarity_A1, y, label=f"Similarity to B1 = {similarity_B1} (Recovered)",
c=colors[col_keys[idx]], linestyle="--")
plt.plot(
similarity_A1,
y,
label=f"Similarity to B1 = {similarity_B1} (Recovered)",
c=colors[col_keys[idx]],
linestyle="--",
)

x_limit = [np.min(similarity_A1), np.max(similarity_A1)]
y_limit = [0, 1]
Expand Down
4 changes: 3 additions & 1 deletion tests/test_bundled_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,9 @@
expected_value_theory,
)
from autora.experiment_runner.synthetic.economics.prospect_theory import prospect_theory
from autora.experiment_runner.synthetic.psychology.luce_choice_ratio import luce_choice_ratio
from autora.experiment_runner.synthetic.psychology.luce_choice_ratio import (
luce_choice_ratio,
)
from autora.experiment_runner.synthetic.psychophysics.weber_fechner_law import (
weber_fechner_law,
)
Expand Down

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