adding sampling functionality to linearregression model

CHANGELOG.md

@@ -3,6 +3,10 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
+## [1.1.4] - 2018-07-21
+### Added
+- sample method for LinearRegression model
+
 ## [1.1.3] - 2018-05-25
 ### Fixed
 - HLR fit method sets shared vars if no minibatch_size given

docs/api/modules.rst

@@ -1,7 +1,7 @@
-api
+pymc3_models
 ============
 
 .. toctree::
    :maxdepth: 4
 
-   pymc3_models.models
+   pymc3_models

docs/api/pymc3_models.models.rst

@@ -1,6 +1,9 @@
-models
+pymc3\_models\.models package
 =============================
 
+Submodules
+----------
+
 pymc3\_models\.models\.HierarchicalLogisticRegression module
 ------------------------------------------------------------
 

docs/api/pymc3_models.rst

@@ -0,0 +1,29 @@
+pymc3\_models package
+=====================
+
+Subpackages
+-----------
+
+.. toctree::
+
+    pymc3_models.models
+
+Submodules
+----------
+
+pymc3\_models\.exc module
+-------------------------
+
+.. automodule:: pymc3_models.exc
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: pymc3_models
+    :members:
+    :undoc-members:
+    :show-inheritance:

pymc3_models/models/LinearRegression.py

@@ -93,17 +93,17 @@ def fit(self, X, y, inference_type='advi', minibatch_size=None, inference_args=N
 
         return self
 
-    def predict(self, X, return_std=False):
+
+    def sample(self, X, samples=2000):
         """
-        Predicts values of new data with a trained Linear Regression model
+        samples the conditional posterior estimates
 
         Parameters
         ----------
         X : numpy array, shape [n_samples, n_features]
 
-        return_std : Boolean flag of whether to return standard deviations with mean values. Defaults to False.
+        samples : number of draws to make for each point
         """
-
         if self.trace is None:
             raise PyMC3ModelsError('Run fit on the model before predict.')
 
@@ -116,6 +116,23 @@ def predict(self, X, return_std=False):
 
         ppc = pm.sample_ppc(self.trace, model=self.cached_model, samples=2000)
 
+        return ppc
+
+    def predict(self, X, return_std=False, samples=2000):
+        """
+        Predicts values of new data with a trained Linear Regression model
+
+        Parameters
+        ----------
+        X : numpy array, shape [n_samples, n_features]
+
+        return_std : Boolean flag of whether to return standard deviations with mean values. Defaults to False.
+
+        samples: numberof draws to make for each input
+        """
+
+        ppc = self.sample(X, samples)
+
         if return_std:
             return ppc['y'].mean(axis=0), ppc['y'].std(axis=0)
         else: