Merge pull request #33 from AutoResearch/31-add-doctests-to-runner

bug: add minimum examples to the runners, fix bugs

src/autora/experiment_runner/synthetic/economics/expected_value_theory.py

@@ -86,6 +86,11 @@ def expected_value_theory(
         resolution:
         minimum_value:
         maximum_value:
+        Examples:
+            >>> s = expected_value_theory()
+            >>> s.run(np.array([[1,2,.1,.9]]), random_state=42)
+               V_A  P_A  V_B  P_B  choose_A
+            0  1.0  2.0  0.1  0.9  0.999938
     """
 
     params = dict(

src/autora/experiment_runner/synthetic/economics/prospect_theory.py

@@ -40,6 +40,11 @@ def prospect_theory(
     Probability function according to:
         A. Tversky, D. Kahneman, Advances in prospect theory: Cumulative representation of
         uncertainty. J. Risk Uncertain. 5, 297–323 (1992). doi:10.1007/BF00122574
+    Examples:
+        >>> s = prospect_theory()
+        >>> s.run(np.array([[.9,.1,.1,.9]]), random_state=42)
+           V_A  P_A  V_B  P_B  choose_A
+        0  0.9  0.1  0.1  0.9  0.709777
 
     """
 

src/autora/experiment_runner/synthetic/neuroscience/task_switching.py

@@ -26,6 +26,11 @@ def task_switching(
         temperature: temperature for softmax when computing performance of current task
         constant: constant for task activation
         minimum_task_control: minimum task control
+    Examples:
+        >>> s = task_switching()
+        >>> s.run(np.array([[.5,.7,0]]), random_state=42)
+           cur_task_strength  alt_task_strength  is_switch  cur_task_performance
+        0                0.5                0.7        0.0              0.685351
     """
 
     params = dict(
@@ -107,13 +112,13 @@ def run(
                 cur_task_strength
                 + priming_default * (1 - is_switch)
                 + cur_task_control
-                + rng.random.normal(0, added_noise)
+                + rng.normal(0, added_noise)
             )
 
             alt_task_input = (
                 alt_task_strength
                 + priming_default * (is_switch)
-                + rng.random.normal(0, added_noise)
+                + rng.normal(0, added_noise)
             )
 
             cur_task_activation = 1 - np.exp(-constant * cur_task_input)
@@ -125,8 +130,10 @@ def run(
             )
 
             Y[idx] = cur_task_performance
-
-        return Y
+        experiment_data = pd.DataFrame(conditions)
+        experiment_data.columns = [v.name for v in variables.independent_variables]
+        experiment_data[variables.dependent_variables[0].name] = Y
+        return experiment_data
 
     ground_truth = partial(run, added_noise=0.0)
 

src/autora/experiment_runner/synthetic/psychology/exp_learning.py

@@ -28,6 +28,11 @@ def exp_learning(
         minimum_trial: upper bound for exponential constant
         name: name of the experiment
         resolution: number of allowed values for stimulus
+        Examples:
+        >>> s = exp_learning()
+        >>> s.run(np.array([[.2,.1]]), random_state=42)
+           P_asymptotic  trial  performance
+        0           0.2    0.1     0.205444
     """
 
     maximum_trial = resolution
@@ -98,11 +103,14 @@ def run(
             y = (
                 p_asymptotic
                 - (p_asymptotic - p_initial_exp) * np.exp(-lr * trial_exp)
-                + rng.random.normal(0, added_noise)
+                + rng.normal(0, added_noise)
             )
             Y[idx] = y
 
-        return Y
+        experiment_data = pd.DataFrame(conditions)
+        experiment_data.columns = [v.name for v in variables.independent_variables]
+        experiment_data[variables.dependent_variables[0].name] = Y
+        return experiment_data
 
     ground_truth = partial(run, added_noise=0.0)
 

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

@@ -24,6 +24,11 @@ def stevens_power_law(
         modality_constant: power constant
         proportionality_constant: constant multiplier
         maximum_stimulus_intensity: maximum value for stimulus
+    Examples:
+        >>> s = stevens_power_law()
+        >>> s.run(np.array([[.9]]), random_state=42)
+             S  perceived_intensity
+        0  0.9             0.922213
     """
 
     params = dict(
@@ -69,10 +74,12 @@ def run(
         for idx, x in enumerate(X):
             y = proportionality_constant * x[
                 0
-            ] ** modality_constant + rng.random.normal(0, added_noise)
+            ] ** modality_constant + rng.normal(0, added_noise)
             Y[idx] = y
-
-        return Y
+        experiment_data = pd.DataFrame(conditions)
+        experiment_data.columns = [v.name for v in variables.independent_variables]
+        experiment_data[variables.dependent_variables[0].name] = Y
+        return experiment_data
 
     ground_truth = partial(run, added_noise=0.0)