convert_to_PTSM#

convert_to_PTSM(data, safe_magnitudes, risky_magnitudes, risky_probability, response, **kwargs)[source]#

Convert a pandas DataFrame into a format compatible with the PTSM wrapper. This function takes in a DataFrame and the column names for the human response, reward, stimulus, and participant identifier, and returns a new DataFrame that is structured in a way that can be used with the PTSM wrapper. The function also checks to ensure that the specified columns exist in the input data and raises informative error messages if any of the columns are missing. Additionally, it can handle cases where there are multiple columns for stimulus, human response, or reward by creating new column names that match the number of columns provided.

Parameters:
datapandas.DataFrame

The pandas DataFrame to convert.

safe_magnitudesstr or list

The column name for the safe magnitudes (safe options).

risky_magnitudesstr or list

The column name for the risky magnitudes (risky options).

risky_probabilitystr or list

The column name for the probabilities of the risky options.

responsestr or list

The column name for the human response (choices).

kwargsdict, optional

Any other keyword arguments to pass to the pandas DataFrame.

Returns:
pandas.DataFrame

The pandas.DataFrame compatible with the PTSM wrapper, containing the safe magnitudes, risky magnitudes, risky probabilities, and human response columns.

See also

cpm.applications.decision_making.PTSM

The PTSM wrapper that this function is designed to be compatible with.

cpm.applications.decision_making.PTSM1992

The PTSM1992 wrapper that this function is designed to be compatible with.

cpm.applications.decision_making.PTSM2025

The PTSM2025 wrapper that this function is designed to be compatible with.

Examples

>>> import pandas as pd
>>> from cpm.utils.data import convert_to_PTSM
>>> data = pd.DataFrame({
...     "safe": [10, 20, 30],
...     "risky": [0, 50, 100],
...     "prob": [0.2, 0.5, 0.8],
...     "choice": [0, 1, 0],
...     "block": [1, 1, 2],
...     "condition": ["A", "A", "B"]
... })
>>> output = convert_to_PTSM(
...     data=data,
...     safe_magnitudes="safe",
...     risky_magnitudes="risky",
...     risky_probability="prob",
...     response="choice",
...     block="block",
...     condition="condition"
... )
>>> print(output)
    safe_magnitudes  risky_magnitudes  risky_probability  observed  block  condition
0               10                 0                0.2         0       1        A
1               20                50                0.5         1       1        A
2               30               100                0.8         0       2        B
On this page