Task Switching¶

In [1]:

# 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 task switching experiment has to be initialized with a specific formula and effects.

In [2]:

import numpy as np
from autora.experiment_runner.synthetic.neuroscience.task_switching import task_switching

s = task_switching()

import numpy as np from autora.experiment_runner.synthetic.neuroscience.task_switching import task_switching s = task_switching()

Check the docstring to get information about the model

In [3]:

help(task_switching)

help(task_switching)

Help on function task_switching in module autora.experiment_runner.synthetic.neuroscience.task_switching:

task_switching(name='Task Switching', resolution=50, priming_default=0.3, temperature=0.2, minimum_task_control=0.15, constant=1.5)
    Task Switching
    
    Args:
        name: name of the experiment
        resolution: number of allowed values for stimulus
        priming_default: default for task priming
        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

... or use the describe function:

In [4]:

from autora.experiment_runner.synthetic.utilities import describe

print(describe(s))

from autora.experiment_runner.synthetic.utilities import describe print(describe(s))

    Task Switching

    Args:
        name: name of the experiment
        resolution: number of allowed values for stimulus
        priming_default: default for task priming
        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
    

The synthetic experiement s has properties like the name of the experiment:

In [5]:

s.name

s.name

Out[5]:

'Task Switching'

... a valid variables description:

In [6]:

s.variables

s.variables

Out[6]:

VariableCollection(independent_variables=[IV(name='cur_task_strength', value_range=(0, 1), allowed_values=array([0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 , 0.22,
       0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42, 0.44,
       0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64, 0.66,
       0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86, 0.88,
       0.9 , 0.92, 0.94, 0.96, 0.98, 1.  ]), units='intensity', type=<ValueType.REAL: 'real'>, variable_label='Strength of Current Task', rescale=1, is_covariate=False), IV(name='alt_task_strength', value_range=(0, 1), allowed_values=array([0.02, 0.04, 0.06, 0.08, 0.1 , 0.12, 0.14, 0.16, 0.18, 0.2 , 0.22,
       0.24, 0.26, 0.28, 0.3 , 0.32, 0.34, 0.36, 0.38, 0.4 , 0.42, 0.44,
       0.46, 0.48, 0.5 , 0.52, 0.54, 0.56, 0.58, 0.6 , 0.62, 0.64, 0.66,
       0.68, 0.7 , 0.72, 0.74, 0.76, 0.78, 0.8 , 0.82, 0.84, 0.86, 0.88,
       0.9 , 0.92, 0.94, 0.96, 0.98, 1.  ]), units='intensity', type=<ValueType.REAL: 'real'>, variable_label='Strength of Alternative Task', rescale=1, is_covariate=False), IV(name='is_switch', value_range=(0, 1), allowed_values=[0, 1], units='indicator', type=<ValueType.PROBABILITY_SAMPLE: 'probability_sample'>, variable_label='Is Switch', rescale=1, is_covariate=False)], dependent_variables=[DV(name='cur_task_performance', value_range=(0, 1), allowed_values=None, units='performance', type=<ValueType.PROBABILITY: 'probability'>, variable_label='Accuracy of Current Task', rescale=1, is_covariate=False)], covariates=[])

... now we can generate the full domain of the data

In [7]:

x = s.domain()
x

x = s.domain() x

Out[7]:

array([[0.02, 0.02, 0.  ],
       [0.02, 0.04, 0.  ],
       [0.02, 0.06, 0.  ],
       ...,
       [1.  , 0.96, 1.  ],
       [1.  , 0.98, 1.  ],
       [1.  , 1.  , 1.  ]])

... the experiment_runner which can be called to generate experimental results:

In [8]:

experiment_data = s.run(x)
experiment_data

experiment_data = s.run(x) experiment_data

Out[8]:

	cur_task_strength	alt_task_strength	is_switch	cur_task_performance
0	0.02	0.02	0.0	0.922331
1	0.02	0.04	0.0	0.901138
2	0.02	0.06	0.0	0.887451
3	0.02	0.08	0.0	0.869731
4	0.02	0.10	0.0	0.860710
...	...	...	...	...
4995	1.00	0.92	1.0	0.536209
4996	1.00	0.94	1.0	0.531290
4997	1.00	0.96	1.0	0.533204
4998	1.00	0.98	1.0	0.519257
4999	1.00	1.00	1.0	0.516433

5000 rows × 4 columns

... a function to plot the ground truth (no noise):

In [9]:

s.plotter()

s.plotter()

... against a fitted model if it exists:

In [10]:

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(

We can wrap this functions to use with the state logic of AutoRA: First, we create the state with the variables:

In [11]:

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:

In [12]:

# 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)

      cur_task_strength  alt_task_strength  is_switch
4326               0.88               0.28          0
621                0.14               0.22          1

Wrap the runner with the experiment_runner_on_state wrapper to use it on state:

In [13]:

# 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

Out[13]:

	cur_task_strength	alt_task_strength	is_switch	cur_task_performance
4326	0.88	0.28	0	0.931829
621	0.14	0.22	1	0.714810

Wrap the regressor with the estimator_on_state wrapper:

In [16]:

theorist = LinearRegression()
theorist_on_state = estimator_on_state(theorist)

state = theorist_on_state(state)
# Access the last model:
model = state.models[-1]


print(f"rt = "
      f"{model.coef_[0][0]:.2f}*cur_task_strength "
      f"{model.coef_[0][1]:.2f}*alt_task_strength "
      f"{model.coef_[0][2]:.2f}*is_switch "
      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"rt = " f"{model.coef_[0][0]:.2f}*cur_task_strength " f"{model.coef_[0][1]:.2f}*alt_task_strength " f"{model.coef_[0][2]:.2f}*is_switch " f"{model.intercept_[0]:+.2f} ")

rt = 0.10*cur_task_strength 0.01*alt_task_strength -0.14*is_switch +0.84