Luce-Choice-Ratio¶
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# Uncomment the following line when running on Google Colab
# !pip install "autora"
# Uncomment the following line when running on Google Colab
# !pip install "autora"
The luce choice ratio experiment has to be initialized with a specific formula and effects.
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import numpy as np
from autora.experiment_runner.synthetic.psychology.luce_choice_ratio import luce_choice_ratio
s = luce_choice_ratio()
import numpy as np
from autora.experiment_runner.synthetic.psychology.luce_choice_ratio import luce_choice_ratio
s = luce_choice_ratio()
Check the docstring to get information about the model
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help(luce_choice_ratio)
help(luce_choice_ratio)
Help on function luce_choice_ratio in module autora.experiment_runner.synthetic.psychology.luce_choice_ratio:
luce_choice_ratio(name='Luce-Choice-Ratio', resolution=8, maximum_similarity=10, focus=0.8)
Luce-Choice-Ratio
Args:
name: name of the experiment
added_noise: standard deviation of normally distributed noise added to y-values
resolution: number of allowed values for stimulus DVs
maximum_similarity: upperbound for DVs
focus: parameter measuring participant focus
random_state: integer used to seed the random number generator
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.
- Equation (5) in Luce, R. D. (1963). Detection and recognition.
Examples:
We can instantiate a Shepard-Cue Choice Experiment. We use a seed to get replicable results:
>>> l_s_experiment = luce_choice_ratio()
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]]))
similarity_category_A1 ... choose_A1
0 1 ... 0.210526
<BLANKLINE>
[1 rows x 5 columns]
We can also run an experiment:
>>> l_s_experiment.run(np.array([[1,2,3,4]]), random_state=42)
similarity_category_A1 ... choose_A1
0 1 ... 0.211328
<BLANKLINE>
[1 rows x 5 columns]
To plot the experiment use:
>>> l_s_experiment.plotter()
>>> plt.show() # doctest: +SKIP
... or use the describe function:
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from autora.experiment_runner.synthetic.utilities import describe
print(describe(s))
from autora.experiment_runner.synthetic.utilities import describe
print(describe(s))
Luce-Choice-Ratio
Args:
name: name of the experiment
added_noise: standard deviation of normally distributed noise added to y-values
resolution: number of allowed values for stimulus DVs
maximum_similarity: upperbound for DVs
focus: parameter measuring participant focus
random_state: integer used to seed the random number generator
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.
- Equation (5) in Luce, R. D. (1963). Detection and recognition.
Examples:
We can instantiate a Shepard-Cue Choice Experiment. We use a seed to get replicable results:
>>> l_s_experiment = luce_choice_ratio()
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]]))
similarity_category_A1 ... choose_A1
0 1 ... 0.210526
<BLANKLINE>
[1 rows x 5 columns]
We can also run an experiment:
>>> l_s_experiment.run(np.array([[1,2,3,4]]), random_state=42)
similarity_category_A1 ... choose_A1
0 1 ... 0.211328
<BLANKLINE>
[1 rows x 5 columns]
To plot the experiment use:
>>> l_s_experiment.plotter()
>>> plt.show() # doctest: +SKIP
The synthetic experiement s has properties like the name of the experiment:
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s.name
s.name
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'Luce-Choice-Ratio'
... a valid variables description:
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s.variables
s.variables
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VariableCollection(independent_variables=[IV(name='similarity_category_A1', value_range=(0.1, 10), allowed_values=array([ 0.1 , 1.51428571, 2.92857143, 4.34285714, 5.75714286,
7.17142857, 8.58571429, 10. ]), units='similarity', type=<ValueType.REAL: 'real'>, variable_label='Similarity with Category A1', rescale=1, is_covariate=False), IV(name='similarity_category_A2', value_range=(0.1, 10), allowed_values=array([ 0.1 , 1.51428571, 2.92857143, 4.34285714, 5.75714286,
7.17142857, 8.58571429, 10. ]), units='similarity', type=<ValueType.REAL: 'real'>, variable_label='Similarity with Category A2', rescale=1, is_covariate=False), IV(name='similarity_category_B1', value_range=(0.1, 10), allowed_values=array([ 0.1 , 1.51428571, 2.92857143, 4.34285714, 5.75714286,
7.17142857, 8.58571429, 10. ]), units='similarity', type=<ValueType.REAL: 'real'>, variable_label='Similarity with Category B1', rescale=1, is_covariate=False), IV(name='similarity_category_B2', value_range=(0.1, 10), allowed_values=array([ 0.1 , 1.51428571, 2.92857143, 4.34285714, 5.75714286,
7.17142857, 8.58571429, 10. ]), units='similarity', type=<ValueType.REAL: 'real'>, variable_label='Similarity with Category B2', rescale=1, is_covariate=False)], dependent_variables=[DV(name='choose_A1', value_range=(0, 1), allowed_values=None, units='probability', type=<ValueType.PROBABILITY: 'probability'>, variable_label='Probability of Choosing A1', rescale=1, is_covariate=False)], covariates=[])
... now we can generate the full domain of the data
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x = s.domain()
x
x = s.domain()
x
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array([[ 0.1 , 0.1 , 0.1 , 0.1 ],
[ 0.1 , 1.51428571, 0.1 , 0.1 ],
[ 0.1 , 2.92857143, 0.1 , 0.1 ],
...,
[10. , 7.17142857, 10. , 10. ],
[10. , 8.58571429, 10. , 10. ],
[10. , 10. , 10. , 10. ]])
... the experiment_runner which can be called to generate experimental results:
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experiment_data = s.run(x)
experiment_data
experiment_data = s.run(x)
experiment_data
Out[10]:
| similarity_category_A1 | similarity_category_A2 | similarity_category_B1 | similarity_category_B2 | choose_A1 | |
|---|---|---|---|---|---|
| 0 | 0.1 | 0.100000 | 0.1 | 0.1 | 0.377072 |
| 1 | 0.1 | 1.514286 | 0.1 | 0.1 | 0.062468 |
| 2 | 0.1 | 2.928571 | 0.1 | 0.1 | 0.033302 |
| 3 | 0.1 | 4.342857 | 0.1 | 0.1 | 0.018954 |
| 4 | 0.1 | 5.757143 | 0.1 | 0.1 | 0.015625 |
| ... | ... | ... | ... | ... | ... |
| 4091 | 10.0 | 4.342857 | 10.0 | 10.0 | 0.518347 |
| 4092 | 10.0 | 5.757143 | 10.0 | 10.0 | 0.481514 |
| 4093 | 10.0 | 7.171429 | 10.0 | 10.0 | 0.451186 |
| 4094 | 10.0 | 8.585714 | 10.0 | 10.0 | 0.423662 |
| 4095 | 10.0 | 10.000000 | 10.0 | 10.0 | 0.400149 |
4096 rows × 5 columns
... a function to plot the ground truth (no noise):
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s.plotter()
s.plotter()
... against a fitted model if it exists:
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from sklearn.linear_model import LinearRegression
ivs = [iv.name for iv in s.variables.independent_variables]
dvs = [dv.name for dv in s.variables.dependent_variables]
X = experiment_data[ivs]
y = experiment_data[dvs]
model = LinearRegression().fit(X, y)
s.plotter(model)
from sklearn.linear_model import LinearRegression
ivs = [iv.name for iv in s.variables.independent_variables]
dvs = [dv.name for dv in s.variables.dependent_variables]
X = experiment_data[ivs]
y = experiment_data[dvs]
model = LinearRegression().fit(X, y)
s.plotter(model)
/Users/younesstrittmatter/Documents/GitHub/AutoRA/autora-synthetic/venv/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names warnings.warn( /Users/younesstrittmatter/Documents/GitHub/AutoRA/autora-synthetic/venv/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names warnings.warn( /Users/younesstrittmatter/Documents/GitHub/AutoRA/autora-synthetic/venv/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names warnings.warn(
We can wrap this functions to use with the state logic of AutoRA: First, we create the state with the variables:
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from autora.state import StandardState, on_state, experiment_runner_on_state, estimator_on_state
from autora.experimentalist.grid import grid_pool
from autora.experimentalist.random import random_sample
# We can get the variables from the runner
variables = s.variables
# With the variables, we initialize a StandardState
state = StandardState(variables)
from autora.state import StandardState, on_state, experiment_runner_on_state, estimator_on_state
from autora.experimentalist.grid import grid_pool
from autora.experimentalist.random import random_sample
# We can get the variables from the runner
variables = s.variables
# With the variables, we initialize a StandardState
state = StandardState(variables)
Wrap the experimentalists in on_state function to use them on state:
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# Wrap the functions to use on state
# Experimentalists:
pool_on_state = on_state(grid_pool, output=['conditions'])
sample_on_state = on_state(random_sample, output=['conditions'])
state = pool_on_state(state)
state = sample_on_state(state, num_samples=2)
print(state.conditions)
# Wrap the functions to use on state
# Experimentalists:
pool_on_state = on_state(grid_pool, output=['conditions'])
sample_on_state = on_state(random_sample, output=['conditions'])
state = pool_on_state(state)
state = sample_on_state(state, num_samples=2)
print(state.conditions)
similarity_category_A1 similarity_category_A2 similarity_category_B1 \
277 0.100000 5.757143 2.928571
1335 2.928571 5.757143 8.585714
similarity_category_B2
277 7.171429
1335 10.000000
Wrap the runner with the experiment_runner_on_state wrapper to use it on state:
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# Runner:
run_on_state = experiment_runner_on_state(s.run)
state = run_on_state(state)
state.experiment_data
# Runner:
run_on_state = experiment_runner_on_state(s.run)
state = run_on_state(state)
state.experiment_data
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| similarity_category_A1 | similarity_category_A2 | similarity_category_B1 | similarity_category_B2 | choose_A1 | |
|---|---|---|---|---|---|
| 277 | 0.100000 | 5.757143 | 2.928571 | 7.171429 | 0.010618 |
| 1335 | 2.928571 | 5.757143 | 8.585714 | 10.000000 | 0.219450 |
Wrap the regressor with the estimator_on_state wrapper:
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theorist = LinearRegression()
theorist_on_state = estimator_on_state(theorist)
state = theorist_on_state(state)
# Access the last model:
model = state.models[-1]
print(f"choose_A1 = "
f"{model.coef_[0][0]:.2f}*similarity_category_A1 "
f"{model.coef_[0][1]:.2f}*similarity_category_A2 "
f"{model.coef_[0][2]:.2f}*similarity_category_B1 "
f"{model.coef_[0][3]:.2f}*similarity_category_B2 "
f"{model.intercept_[0]:+.2f} ")
theorist = LinearRegression()
theorist_on_state = estimator_on_state(theorist)
state = theorist_on_state(state)
# Access the last model:
model = state.models[-1]
print(f"choose_A1 = "
f"{model.coef_[0][0]:.2f}*similarity_category_A1 "
f"{model.coef_[0][1]:.2f}*similarity_category_A2 "
f"{model.coef_[0][2]:.2f}*similarity_category_B1 "
f"{model.coef_[0][3]:.2f}*similarity_category_B2 "
f"{model.intercept_[0]:+.2f} ")
choose_A1 = 0.01*similarity_category_A1 0.00*similarity_category_A2 0.02*similarity_category_B1 0.01*similarity_category_B2 -0.15
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