pymc.Data#

pymc.Data(name, value, *, dims=None, coords=None, export_index_as_coords=False, mutable=None, **kwargs)[source]#

Data container that registers a data variable with the model.

Depending on the mutable setting (default: True), the variable is registered as a SharedVariable, enabling it to be altered in value and shape, but NOT in dimensionality using pymc.set_data().

To set the value of the data container variable, check out pymc.Model.set_data().

For more information, read the notebook Using shared variables (Data container adaptation).

Parameters

namestr: The name for this variable.
valuearray_like or pandas.Series, pandas.Dataframe: A value to associate with this variable.
dimsstr or tuple of str, optional: Dimension names of the random variables (as opposed to the shapes of these random variables). Use this when value is a pandas Series or DataFrame. The dims will then be the name of the Series / DataFrame’s columns. See ArviZ documentation for more information about dimensions and coordinates: ArviZ Quickstart. If this parameter is not specified, the random variables will not have dimension names.
coordsdict, optional: Coordinate values to set for new dimensions introduced by this Data variable.
export_index_as_coordsbool, default=False: If True, the Data container will try to infer what the coordinates and dimension names should be if there is an index in value.
mutablebool, optional: Switches between creating a SharedVariable (mutable=True) vs. creating a TensorConstant (mutable=False). Consider using pymc.ConstantData or pymc.MutableData as less verbose alternatives to pm.Data(..., mutable=...). If this parameter is not specified, the value it takes will depend on the version of the package. Since v4.1.0 the default value is mutable=False, with previous versions having mutable=True.
**kwargsdict, optional: Extra arguments passed to aesara.shared().

Examples

>>> import pymc as pm
>>> import numpy as np
>>> # We generate 10 datasets
>>> true_mu = [np.random.randn() for _ in range(10)]
>>> observed_data = [mu + np.random.randn(20) for mu in true_mu]

>>> with pm.Model() as model:
...     data = pm.MutableData('data', observed_data[0])
...     mu = pm.Normal('mu', 0, 10)
...     pm.Normal('y', mu=mu, sigma=1, observed=data)

>>> # Generate one trace for each dataset
>>> idatas = []
>>> for data_vals in observed_data:
...     with model:
...         # Switch out the observed dataset
...         model.set_data('data', data_vals)
...         idatas.append(pm.sample())