LinearRegression#

class causalpy.pymc_models.LinearRegression[source]#

Custom PyMC model for linear regression.

Defines the PyMC model

\[\begin{split}\beta &\sim \mathrm{Normal}(0, 50) \\ \sigma &\sim \mathrm{HalfNormal}(1) \\ \mu &= X \cdot \beta \\ y &\sim \mathrm{Normal}(\mu, \sigma) \\\end{split}\]

Example

>>> import causalpy as cp
>>> import numpy as np
>>> import xarray as xr
>>> from causalpy.pymc_models import LinearRegression
>>> rd = cp.load_data("rd")
>>> rd["treated"] = rd["treated"].astype(int)
>>> coeffs = ["x", "treated"]
>>> X = xr.DataArray(
...     rd[coeffs].values,
...     dims=["obs_ind", "coeffs"],
...     coords={"obs_ind": rd.index, "coeffs": coeffs},
... )
>>> y = xr.DataArray(
...     rd["y"].values[:, None],
...     dims=["obs_ind", "treated_units"],
...     coords={"obs_ind": rd.index, "treated_units": ["unit_0"]},
... )
>>> lr = LinearRegression(sample_kwargs={"progressbar": False})
>>> coords={"coeffs": coeffs, "obs_ind": np.arange(rd.shape[0]), "treated_units": ["unit_0"]}
>>> lr.fit(X, y, coords=coords)
Inference data...

Methods

`LinearRegression.__init__`([sample_kwargs])
`LinearRegression.add_coord`(name[, values, ...])	Register a dimension coordinate with the model.
`LinearRegression.add_coords`(coords, *[, lengths])	Vectorized version of `Model.add_coord`.
`LinearRegression.add_named_variable`(var[, dims])	Add a random graph variable to the named variables of the model.
`LinearRegression.build_model`(X, y, coords)	Defines the PyMC model
`LinearRegression.calculate_cumulative_impact`(impact)
`LinearRegression.calculate_impact`(y_true, y_pred)
`LinearRegression.check_start_vals`(start, ...)	Check that the logp is defined and finite at the starting point.
`LinearRegression.compile_d2logp`([vars, ...])	Compiled log probability density hessian function.
`LinearRegression.compile_dlogp`([vars, jacobian])	Compiled log probability density gradient function.
`LinearRegression.compile_fn`(outs, *[, ...])	Compiles a PyTensor function.
`LinearRegression.compile_logp`([vars, ...])	Compiled log probability density function.
`LinearRegression.copy`()	Clone the model.
`LinearRegression.create_value_var`(rv_var, *, ...)	Create a `TensorVariable` that will be used as the random variable's "value" in log-likelihood graphs.
`LinearRegression.d2logp`([vars, jacobian, ...])	Hessian of the models log-probability w.r.t.
`LinearRegression.debug`([point, fn, verbose])	Debug model function at point.
`LinearRegression.dlogp`([vars, jacobian])	Gradient of the models log-probability w.r.t.
`LinearRegression.eval_rv_shapes`()	Evaluate shapes of untransformed AND transformed free variables.
`LinearRegression.fit`(X, y[, coords])	Draw samples from posterior, prior predictive, and posterior predictive distributions, placing them in the model's idata attribute.
`LinearRegression.get_context`([...])
`LinearRegression.initial_point`([random_seed])	Compute the initial point of the model.
`LinearRegression.logp`([vars, jacobian, sum])	Elemwise log-probability of the model.
`LinearRegression.logp_dlogp_function`([...])	Compile a PyTensor function that computes logp and gradient.
`LinearRegression.make_obs_var`(rv_var, data, ...)	Create a TensorVariable for an observed random variable.
`LinearRegression.name_for`(name)	Check if name has prefix and adds if needed.
`LinearRegression.name_of`(name)	Check if name has prefix and deletes if needed.
`LinearRegression.point_logps`([point, round_vals])	Compute the log probability of point for all random variables in the model.
`LinearRegression.predict`(X)	Predict data given input data X
`LinearRegression.print_coefficients`(labels)
`LinearRegression.profile`(outs, *[, n, ...])	Compile and profile a PyTensor function which returns `outs` and takes values of model vars as a dict as an argument.
`LinearRegression.register_data_var`(data[, dims])	Register a data variable with the model.
`LinearRegression.register_rv`(rv_var, name, *)	Register an (un)observed random variable with the model.
`LinearRegression.replace_rvs_by_values`(...)	Clone and replace random variables in graphs with their value variables.
`LinearRegression.score`(X, y)	Score the Bayesian \(R^2\) given inputs `X` and outputs `y`.
`LinearRegression.set_data`(name, values[, coords])	Change the values of a data variable in the model.
`LinearRegression.set_dim`(name, new_length[, ...])	Update a mutable dimension.
`LinearRegression.set_initval`(rv_var, initval)	Set an initial value (strategy) for a random variable.
`LinearRegression.shape_from_dims`(dims)
`LinearRegression.to_graphviz`(*[, var_names, ...])	Produce a graphviz Digraph from a PyMC model.

Attributes

`basic_RVs`	List of random variables the model is defined in terms of.
`continuous_value_vars`	All the continuous value variables in the model.
`coords`	Coordinate values for model dimensions.
`datalogp`	PyTensor scalar of log-probability of the observed variables and potential terms.
`dim_lengths`	The symbolic lengths of dimensions in the model.
`discrete_value_vars`	All the discrete value variables in the model.
`isroot`
`observedlogp`	PyTensor scalar of log-probability of the observed variables.
`parent`
`potentiallogp`	PyTensor scalar of log-probability of the Potential terms.
`prefix`
`root`
`unobserved_RVs`	List of all random variables, including deterministic ones.
`unobserved_value_vars`	List of all random variables (including untransformed projections), as well as deterministics used as inputs and outputs of the model's log-likelihood graph.
`value_vars`	List of unobserved random variables used as inputs to the model's log-likelihood (which excludes deterministics).
`varlogp`	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic).
`varlogp_nojac`	PyTensor scalar of log-probability of the unobserved random variables (excluding deterministic) without jacobian term.

__init__(sample_kwargs=None)#

Parameters:: sample_kwargs (Optional[Dict[str, Any]]) – A dictionary of kwargs that get unpacked and passed to the pymc.sample() function. Defaults to an empty dictionary.

classmethod __new__(*args, **kwargs)#