Major code refactor to unify quasi experiment classes

CONTRIBUTING.md

@@ -128,12 +128,12 @@ We recommend that your contribution complies with the following guidelines befor
     make doctest
     ```
 
-- Doctest can also be run directly via pytest, which can be helpful to run only specific tests during development. The following commands run all doctests, only doctests in the pymc_models module, and only the doctests for the `ModelBuilder` class in pymc_models:
+- Doctest can also be run directly via pytest, which can be helpful to run only specific tests during development. The following commands run all doctests, only doctests in the pymc_models module, and only the doctests for the `PyMCModel` class in pymc_models:
 
     ```bash
     pytest --doctest-modules causalpy/
     pytest --doctest-modules causalpy/pymc_models.py
-    pytest --doctest-modules causalpy/pmyc_models.py::causalpy.pymc_models.ModelBuilder
+    pytest --doctest-modules causalpy/pmyc_models.py::causalpy.pymc_models.PyMCModel
     ```
 
 - To indicate a work in progress please mark the PR as `draft`. Drafts may be useful to (1) indicate you are working on something to avoid duplicated work, (2) request broad review of functionality or API, or (3) seek collaborators.

README.md

@@ -44,7 +44,7 @@ df = (
     )
 
 # Run the analysis
-result = cp.pymc_experiments.RegressionDiscontinuity(
+result = cp.RegressionDiscontinuity(
     df,
     formula="all ~ 1 + age + treated",
     running_variable_name="age",

causalpy/__init__.py

@@ -13,18 +13,38 @@
 #   limitations under the License.
 import arviz as az
 
-from causalpy import pymc_experiments, pymc_models, skl_experiments, skl_models
+import causalpy.pymc_experiments as pymc_experiments  # to be depricated
+import causalpy.pymc_models as pymc_models
+import causalpy.skl_experiments as skl_experiments  # to be depricated
+import causalpy.skl_models as skl_models
+from causalpy.skl_models import create_causalpy_compatible_class
 from causalpy.version import __version__
 
 from .data import load_data
+from .experiments.diff_in_diff import DifferenceInDifferences
+from .experiments.instrumental_variable import InstrumentalVariable
+from .experiments.inverse_propensity_weighting import InversePropensityWeighting
+from .experiments.prepostfit import InterruptedTimeSeries, SyntheticControl
+from .experiments.prepostnegd import PrePostNEGD
+from .experiments.regression_discontinuity import RegressionDiscontinuity
+from .experiments.regression_kink import RegressionKink
 
 az.style.use("arviz-darkgrid")
 
 __all__ = [
-    "pymc_experiments",
+    "__version__",
+    "DifferenceInDifferences",
+    "create_causalpy_compatible_class",
+    "InstrumentalVariable",
+    "InterruptedTimeSeries",
+    "InversePropensityWeighting",
+    "load_data",
+    "PrePostNEGD",
+    "pymc_experiments",  # to be depricated
     "pymc_models",
-    "skl_experiments",
+    "RegressionDiscontinuity",
+    "RegressionKink",
+    "skl_experiments",  # to be depricated
     "skl_models",
-    "load_data",
-    "__version__",
+    "SyntheticControl",
 ]

causalpy/experiments/__init__.py

@@ -0,0 +1,13 @@
+#   Copyright 2024 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.

causalpy/experiments/base.py

@@ -0,0 +1,80 @@
+#   Copyright 2024 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+"""
+Base class for quasi experimental designs.
+"""
+
+from abc import abstractmethod
+
+from sklearn.base import RegressorMixin
+
+from causalpy.pymc_models import PyMCModel
+from causalpy.skl_models import create_causalpy_compatible_class
+
+
+class BaseExperiment:
+    """Base class for quasi experimental designs."""
+
+    supports_bayes: bool
+    supports_ols: bool
+
+    def __init__(self, model=None):
+        # Ensure we've made any provided Scikit Learn model (as identified as being type
+        # RegressorMixin) compatible with CausalPy by appending our custom methods.
+        if isinstance(model, RegressorMixin):
+            model = create_causalpy_compatible_class(model)
+
+        if model is not None:
+            self.model = model
+
+        if isinstance(self.model, PyMCModel) and not self.supports_bayes:
+            raise ValueError("Bayesian models not supported.")
+
+        if isinstance(self.model, RegressorMixin) and not self.supports_ols:
+            raise ValueError("OLS models not supported.")
+
+        if self.model is None:
+            raise ValueError("model not set or passed.")
+
+    @property
+    def idata(self):
+        """Return the InferenceData object of the model. Only relevant for PyMC models."""
+        return self.model.idata
+
+    def print_coefficients(self, round_to=None):
+        """Ask the model to print its coefficients."""
+        self.model.print_coefficients(self.labels, round_to)
+
+    def plot(self, *args, **kwargs) -> tuple:
+        """Plot the model.
+
+        Internally, this function dispatches to either `bayesian_plot` or `ols_plot`
+        depending on the model type.
+        """
+        if isinstance(self.model, PyMCModel):
+            return self.bayesian_plot(*args, **kwargs)
+        elif isinstance(self.model, RegressorMixin):
+            return self.ols_plot(*args, **kwargs)
+        else:
+            raise ValueError("Unsupported model type")
+
+    @abstractmethod
+    def bayesian_plot(self, *args, **kwargs):
+        """Abstract method for plotting the model."""
+        raise NotImplementedError("bayesian_plot method not yet implemented")
+
+    @abstractmethod
+    def ols_plot(self, *args, **kwargs):
+        """Abstract method for plotting the model."""
+        raise NotImplementedError("ols_plot method not yet implemented")