Add Stevens' Power Law to synthetic models

src/autora/experiment_runner/synthetic/psychophysics/stevens_power_law.py

@@ -0,0 +1,122 @@
+from functools import partial
+from typing import Optional
+
+import numpy as np
+
+from autora.experiment_runner.synthetic.utilities import SyntheticExperimentCollection
+from autora.variable import DV, IV, ValueType, VariableCollection
+
+
+def stevens_power_law(
+    name="Stevens' Power Law",
+    resolution=100,
+    proportionality_constant=1.0,
+    modality_constant=0.8,
+    maximum_stimulus_intensity=5.0,
+    random_state: Optional[int] = None,
+):
+    """
+    Stevens' Power Law
+
+    Args:
+        name: name of the experiment
+        resolution: number of allowed values for stimulus
+        modality_constant: power constant
+        proportionality_constant: constant multiplier
+        maximum_stimulus_intensity: maximum value for stimulus
+        random_state: integer used to seed the random number generator
+
+    """
+
+    params = dict(
+        name=name,
+        resolution=resolution,
+        proportionality_constant=proportionality_constant,
+        modality_constant=modality_constant,
+        maximum_stimulus_intensity=maximum_stimulus_intensity,
+        random_state=random_state,
+    )
+
+    iv1 = IV(
+        name="S",
+        allowed_values=np.linspace(1 / resolution, maximum_stimulus_intensity, resolution),
+        value_range=(1 / resolution, maximum_stimulus_intensity),
+        units="intensity",
+        variable_label="Stimulus Intensity",
+        type=ValueType.REAL
+    )
+
+    dv1 = DV(
+        name="perceived_intensity",
+        value_range=(0, maximum_stimulus_intensity),
+        units="sensation",
+        variable_label="Perceived Intensity",
+        type=ValueType.REAL
+    )
+
+    variables = VariableCollection(
+        independent_variables=[iv1],
+        dependent_variables=[dv1],
+    )
+
+    rng = np.random.default_rng(random_state)
+
+    def experiment_runner(
+        conditions: Union[pd.DataFrame, np.ndarray, np.recarray],
+        observation_noise: float = 0.01,
+    ):
+        X = np.array(conditions)
+        Y = np.zeros((X.shape[0], 1))
+        for idx, x in enumerate(X):
+            y = proportionality_constant * x[0] ** modality_constant + rng.random.normal(0, std)
+            Y[idx] = y
+
+        return Y
+
+    ground_truth = partial(experiment_runner, observation_noise =0.0)
+
+    def domain():
+        s_values = variables.independent_variables[0].allowed_values
+
+        X = np.array(np.meshgrid(s_values)).T.reshape(-1, 1)
+        return X
+
+    def plotter(
+        model=None,
+    ):
+        import matplotlib.pyplot as plt
+        import matplotlib.colors as mcolors
+
+        colors = mcolors.TABLEAU_COLORS
+        col_keys = list(colors.keys())
+        X = domain()
+        y = ground_truth(X)
+        plt.plot(X, y, label="Original", c=colors[col_keys[0]])
+        if model is not None:
+            y = model.predict(X)
+            plt.plot(X, y, label=f"Recovered", c=colors[col_keys[0]], linestyle="--")
+        x_limit = [0, variables.independent_variables[0].value_range[1]]
+        y_limit = [0, 4]
+        x_label = "Stimulus Intensity"
+        y_label = "Perceived Stimulus Intensity"
+
+        plt.xlim(x_limit)
+        plt.ylim(y_limit)
+        plt.xlabel(x_label, fontsize="large")
+        plt.ylabel(y_label, fontsize="large")
+        plt.legend(loc=2, fontsize="medium")
+        plt.title("Stevens' Power Law", fontsize="x-large")
+        plt.show()
+
+    collection = SyntheticExperimentCollection(
+        name=name,
+        description=stevens_power_law.__doc__,
+        variables=variables,
+        experiment_runner=experiment_runner,
+        ground_truth=ground_truth,
+        domain=domain,
+        plotter=plotter,
+        params=params,
+        factory_function=stevens_power_law,
+    )
+    return collection

tests/test_bundled_models.py

@@ -14,6 +14,11 @@
 from autora.experiment_runner.synthetic.psychophysics.weber_fechner_law import (
     weber_fechner_law,
 )
+
+from autora.experiment_runner.synthetic.psychophysics.stevens_power_law import (
+    stevens_power_law,
+)
+
 from autora.experiment_runner.synthetic.utilities import describe, register, retrieve
 
 all_bundled_models = [
@@ -22,6 +27,7 @@
     ("luce_choice_ratio", luce_choice_ratio),
     ("template_experiment", template_experiment),
     ("weber_fechner_law", weber_fechner_law),
+    ("stevens_power_law", stevens_power_law),
 ]
 
 all_bundled_model_names = [b[0] for b in all_bundled_models]