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Merged
ddgpalmer merged 3 commits into from
Sep 18, 2025
Merged

Implement hierarchical Bayesian model for multiple subjects in hmetad #1

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7518240
Initial plan
Copilot Sep 18, 2025
bfb4f27
WIP: Group-level model structure implemented, debugging sampling issues
Copilot Sep 18, 2025
8216e7e
Complete implementation of hierarchical model for multiple subjects
Copilot Sep 18, 2025
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172 changes: 88 additions & 84 deletions metadpy/bayesian.py
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Original file line number Diff line number Diff line change
Expand Up @@ -275,12 +275,17 @@ def hmetad(
# Group level
elif (within is None) & (between is None) & (subject is not None):

# pymcData = preprocess_group(
# data, subject, stimuli, accuracy, confidence, nRatings
# )
pymcData = preprocess_group(
data, subject, stimuli, accuracy, confidence, nRatings
)

from group_level_pymc import hmetad_groupLevel

raise ValueError(
"Invalid backend provided - This model is not implemented yet."
model_output = hmetad_groupLevel(
pymcData,
sample_model=sample_model,
num_chains=num_chains,
num_samples=num_samples,
)

###################
Expand Down Expand Up @@ -409,90 +414,89 @@ def extractParameters(
return data


# TODO: when implementing group level fitting, the following wrapper will be usefull.
# def preprocess_group(
# data: pd.DataFrame,
# subject: str,
# stimuli: str,
# accuracy: str,
# confidence: str,
# nRatings: int,
# ) -> Dict:
# """Preprocess group data.
def preprocess_group(
data: pd.DataFrame,
subject: str,
stimuli: str,
accuracy: str,
confidence: str,
nRatings: int,
) -> Dict:
"""Preprocess group data.

# Parameters
# ----------
# data : :py:class:`pandas.DataFrame` or None
# Dataframe. Note that this function can also directly be used as a
# Pandas method, in which case this argument is no longer needed.
# subject : string or None
# Name of column containing the subject identifier (only required if a
# within-subject or a between-subject factor is provided).
# stimuli : string or None
# Name of the column containing the stimuli.
# accuracy : string or None
# Name of the columns containing the accuracy.
# confidence : string or None
# Name of the column containing the confidence ratings.
# nRatings : int or None
# Number of discrete ratings. If a continuous rating scale was used, and
# the number of unique ratings does not match `nRatings`, will convert to
# discrete ratings using :py:func:`metadpy.utils.discreteRatings`.
Parameters
----------
data : :py:class:`pandas.DataFrame` or None
Dataframe. Note that this function can also directly be used as a
Pandas method, in which case this argument is no longer needed.
subject : string or None
Name of column containing the subject identifier (only required if a
within-subject or a between-subject factor is provided).
stimuli : string or None
Name of the column containing the stimuli.
accuracy : string or None
Name of the columns containing the accuracy.
confidence : string or None
Name of the column containing the confidence ratings.
nRatings : int or None
Number of discrete ratings. If a continuous rating scale was used, and
the number of unique ratings does not match `nRatings`, will convert to
discrete ratings using :py:func:`metadpy.utils.discreteRatings`.

# Return
# ------
# pymcData : Dict
Return
------
pymcData : Dict

# """
# pymcData = {
# "d1": [],
# "c1": [],
# "nSubj": data[subject].nunique(),
# "subID": np.arange(data[subject].nunique(), dtype="int"),
# "hits": [],
# "falsealarms": [],
# "s": [],
# "n": [],
# "counts": [],
# "nRatings": nRatings,
# "Tol": 1e-05,
# "cr": [],
# "m": [],
# }
"""
pymcData = {
"d1": [],
"c1": [],
"nSubj": data[subject].nunique(),
"subID": np.arange(data[subject].nunique(), dtype="int"),
"hits": [],
"falsealarms": [],
"s": [],
"n": [],
"counts": [],
"nRatings": nRatings,
"Tol": 1e-05,
"cr": [],
"m": [],
}

# for sub in data[subject].unique():
# nR_S1, nR_S2 = trials2counts(
# data=data[data[subject] == sub],
# stimuli=stimuli,
# accuracy=accuracy,
# confidence=confidence,
# nRatings=nRatings,
# )

# this_data = extractParameters(nR_S1, nR_S2)
# pymcData["d1"].append(this_data["d1"])
# pymcData["c1"].append(this_data["c1"])
# pymcData["s"].append(this_data["S"])
# pymcData["n"].append(this_data["N"])
# pymcData["m"].append(this_data["M"])
# pymcData["cr"].append(this_data["CR"])
# pymcData["counts"].append(this_data["counts"])
# pymcData["hits"].append(this_data["H"])
# pymcData["falsealarms"].append(this_data["FA"])

# pymcData["d1"] = np.array(pymcData["d1"], dtype="float")
# pymcData["c1"] = np.array(pymcData["c1"], dtype="float")
# pymcData["s"] = np.array(pymcData["s"], dtype="int")
# pymcData["n"] = np.array(pymcData["n"], dtype="int")
# pymcData["m"] = np.array(pymcData["m"], dtype="int")
# pymcData["cr"] = np.array(pymcData["cr"], dtype="int")
# pymcData["counts"] = np.array(pymcData["counts"], dtype="int")
# pymcData["hits"] = np.array(pymcData["hits"], dtype="int")
# pymcData["falsealarms"] = np.array(pymcData["falsealarms"], dtype="int")
# pymcData["nSubj"] = data[subject].nunique()
# pymcData["subID"] = np.arange(pymcData["nSubj"], dtype="int")
for sub in data[subject].unique():
nR_S1, nR_S2 = trials2counts(
data=data[data[subject] == sub],
stimuli=stimuli,
accuracy=accuracy,
confidence=confidence,
nRatings=nRatings,
)

# return pymcData
this_data = extractParameters(nR_S1, nR_S2)
pymcData["d1"].append(this_data["d1"])
pymcData["c1"].append(this_data["c1"])
pymcData["s"].append(this_data["S"])
pymcData["n"].append(this_data["N"])
pymcData["m"].append(this_data["M"])
pymcData["cr"].append(this_data["CR"])
pymcData["counts"].append(this_data["counts"])
pymcData["hits"].append(this_data["H"])
pymcData["falsealarms"].append(this_data["FA"])

pymcData["d1"] = np.array(pymcData["d1"], dtype="float")
pymcData["c1"] = np.array(pymcData["c1"], dtype="float")
pymcData["s"] = np.array(pymcData["s"], dtype="int")
pymcData["n"] = np.array(pymcData["n"], dtype="int")
pymcData["m"] = np.array(pymcData["m"], dtype="int")
pymcData["cr"] = np.array(pymcData["cr"], dtype="int")
pymcData["counts"] = np.array(pymcData["counts"], dtype="int")
pymcData["hits"] = np.array(pymcData["hits"], dtype="int")
pymcData["falsealarms"] = np.array(pymcData["falsealarms"], dtype="int")
pymcData["nSubj"] = data[subject].nunique()
pymcData["subID"] = np.arange(pymcData["nSubj"], dtype="int")

return pymcData


# def preprocess_rm1way(
Expand Down
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