Adaptive Design Optimization on Experimental Tasks

## Project description

# ADOpy

**ADOpy** is a Python implementation of Adaptive Design Optimization (ADO; Myung, Cavagnaro, & Pitt, 2013), which computes optimal designs dynamically in an experiment. Its modular structure permit easy integration into existing experimentation code.

ADOpy supports Python 3.5 or above and relies on NumPy, SciPy, and Pandas.

### Features

**Grid-based computation of optimal designs using only three classes**:`adopy.Task`

,`adopy.Model`

, and`adopy.Engine`

.**Easily customizable for your own tasks and models****Pre-implemented Task and Model classes including**:- Psychometric function estimation for 2AFC tasks (
`adopy.tasks.psi`

) - Delay discounting task (
`adopy.tasks.ddt`

) - Choice under risk and ambiguity task (
`adopy.tasks.cra`

)

- Psychometric function estimation for 2AFC tasks (
**Example code for experiments using PsychoPy**(link)

### Resources

## (not so) Quick-start guide

### Step 0. Install ADOpy on the terminal

# Install the stable version from PyPI pip install adopy # Or install the developmental version from GitHub git clone https://github.com/adopy/adopy.git cd adopy git checkout develop pip install .

### Step 1. Define a task using `adopy.Task`

Assume that a user want to use ADOpy for an *arbitrary* task with two design
variables (`x1`

and `x2`

) where participants can make a binary choice on each
trial. Then, the task can be defined with `adopy.Task`

as described below:

from adopy import Task task = Task(name='My New Experiment', # Name of the task (optional) designs = ['x1', 'x2'], # Labels of design variables responses = [0, 1]) # Possible responses

### Step 2. Define a model using `adopy.Model`

To predict partipants' choices, here we assume a logistic regression model
that calculates the probability to make a positive response using three model
parameters (`b0`

, `b1`

, and `b2`

):

How to compute the probabilty `p`

should be defined as a function:

import numpy as np def calculate_prob(x1, x2, b0, b1, b2): """A function to compute the probability of a positive response.""" logit = b0 + x1 * b1 + x1 * b2 p_obs = 1. / (1 + np.exp(-logit)) return p_obs

Using the information and the function, the model can be defined with
`adopy.Model`

:

from adopy import Model model = Model(name='My Logistic Model', # Name of the model (optional) params=['b0', 'b1', 'b2'], # Labels of model parameters func=calculate_prob) # A probability function

### Step 3. Define grids for design variables and model parameters

Since ADOpy uses grid search to search the design space and parameter space, you must define a grid for design variables and model parameters. The grid can be defined using the labels (of design variables or model parameters) as its key and an array of the corresponding grid points as its value.

import numpy as np grid_design = { 'x1': np.linspace(0, 50, 100), # 100 grid points within [0, 50] 'x2': np.linspace(-20, 30, 100), # 100 grid points within [-20, 30] } grid_param = { 'b0': np.linspace(-5, 5, 100), # 100 grid points within [-5, 5] 'b1': np.linspace(-5, 5, 100), # 100 grid points within [-5, 5] 'b2': np.linspace(-5, 5, 100), # 100 grid points within [-5, 5] }

### Step 4. Initialize an engine using `adopy.Engine`

Using the objects created so far, an engine should be initialized using
`adopy.Engine`

. It contains built-in functions to compute an optimal design
using ADO.

from adopy import Engine engine = Engine(model=model, # a Model object task=task, # a Task object grid_design=grid_design, # a grid for design variables grid_param=grid_param) # a grid for model parameters

### Step 5. Compute a design using the engine

# Compute an optimal design using ADO design = engine.get_design() design = engine.get_design('optimal') # Compute a randomly chosen design, as is typically done in non-ADO experiments design = engine.get_design('random')

### Step 6. Collect an observation in your experiment

# Get a response from a participant using your own code response = ...

### Step 7. Update the engine with the observation

# Update the engine with the design and the corresponding response engine.update(design, response)

### Step 8. Repeat Step 5 through Step 7 until the experiment is over

NUM_TRIAL = 100 # number of trials for trial in range(NUM_TRIAL): # Compute an optimal design for the current trial design = engine.get_design('optimal') # Get a response using the optimal design response = ... # Using users' own codes # Update the engine engine.update(design, response)

## Citation

If you use ADOpy, please cite this package along with the specific version. It greatly encourages contributors to continue supporting ADOpy.

Yang, J., Ahn, W.-Y., Pitt., M. A., & Myung, J. I. (2019).

ADOpy: A Python Package for Adaptive Design Optimization. Retrieved from https://adopy.org

## References

- Myung, J. I., Cavagnaro, D. R., and Pitt, M. A. (2013).
A tutorial on adaptive design optimization.
*Journal of Mathematical Psychology, 57*, 53–67.

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