Merge pull request #28 from mortonjt/regression_results_part2

Regression results part2

gneiss/_summary.py

@@ -8,6 +8,7 @@
 # The full license is in the file COPYING.txt, distributed with this software.
 # ----------------------------------------------------------------------------
 import pandas as pd
+from skbio.stats.composition import ilr_inv
 
 
 class RegressionResults():
@@ -57,3 +58,73 @@ def __init__(self, stat_results,
         # calculate the overall coefficient of determination (i.e. R2)
         sst = sse + ssr
         self.r2 = 1 - sse / sst
+
+    def _check_projection(self, project):
+        """
+        Parameters
+        ----------
+        project : bool
+           Specifies if a projection into the Aitchison simplex can be
+           performed.
+
+        Raises
+        ------
+        ValueError:
+            Cannot perform projection into Aitchison simplex if `basis`
+            is not specified.
+        ValueError:
+            Cannot perform projection into Aitchison simplex
+            if `feature_names` is not specified.
+        """
+        if self.basis is None and project:
+            raise ValueError("Cannot perform projection into Aitchison simplex"
+                             "if `basis` is not specified.")
+
+        if self.feature_names is None and project:
+            raise ValueError("Cannot perform projection into Aitchison simplex"
+                             "if `feature_names` is not specified.")
+
+    def coefficients(self, project=False):
+        """ Returns coefficients from fit.
+
+        Parameters
+        ----------
+        project : bool, optional
+            Specifies if coefficients should be projected back into
+            the Aitchison simplex.  If false, the coefficients will be
+            represented as balances  (default: False).
+
+        Returns
+        -------
+        pd.DataFrame
+            A table of values where columns are coefficients, and the index
+            is either balances or proportions, depending on the value of
+            `project`.
+
+        Raises
+        ------
+        ValueError:
+            Cannot perform projection into Aitchison simplex if `basis`
+            is not specified.
+        ValueError:
+            Cannot perform projection into Aitchison simplex
+            if `feature_names` is not specified.
+        """
+        self._check_projection(project)
+        coef = pd.DataFrame()
+
+        for r in self.results:
+            c = r.params
+            c.name = r.model.endog_names
+            coef = coef.append(c)
+
+        if project:
+            # `check=False`, due to a problem with error handling
+            # addressed here https://github.com/biocore/scikit-bio/pull/1396
+            # This will need to be fixed here:
+            # https://github.com/biocore/gneiss/issues/34
+            c = ilr_inv(coef.values.T, basis=self.basis, check=False).T
+            return pd.DataFrame(c, index=self.feature_names,
+                                columns=coef.columns)
+        else:
+            return coef

gneiss/tests/test_summary.py

@@ -7,11 +7,13 @@
 #
 # The full license is in the file COPYING.txt, distributed with this software.
 # ----------------------------------------------------------------------------
+import numpy as np
 import pandas as pd
 import pandas.util.testing as pdt
 import statsmodels.formula.api as smf
 import unittest
 from gneiss._summary import RegressionResults
+from skbio.stats.composition import _gram_schmidt_basis, ilr_inv
 
 
 class TestRegressionResults(unittest.TestCase):
@@ -55,5 +57,52 @@ def test_regression_results_pvalues(self):
                                check_exact=False,
                                check_less_precise=True)
 
+    def test_check_projection(self):
+        feature_names = ['Z1', 'Z2', 'Z3']
+        basis = _gram_schmidt_basis(3)
+        res = RegressionResults(self.results, basis=basis,
+                                feature_names=feature_names)
+
+        feature_names = ['Z1', 'Z2', 'Z3']
+        basis = _gram_schmidt_basis(3)
+
+        # Test if feature_names is checked for
+        res = RegressionResults(self.results, basis=basis)
+        with self.assertRaises(ValueError):
+            res._check_projection(True)
+
+        # Test if basis is checked for
+        res = RegressionResults(self.results, feature_names=feature_names)
+        with self.assertRaises(ValueError):
+            res._check_projection(True)
+
+    def test_regression_results_coefficient(self):
+        exp_coef = pd.DataFrame({'Intercept': [1.447368, -0.052632],
+                                 'X': [0.539474, 1.289474]},
+                                index=['Y1', 'Y2'])
+        res = RegressionResults(self.results)
+        pdt.assert_frame_equal(res.coefficients(), exp_coef,
+                               check_exact=False,
+                               check_less_precise=True)
+
+    def test_regression_results_coefficient_projection(self):
+        exp_coef = pd.DataFrame(
+            {'Intercept': ilr_inv(np.array([[1.447368, -0.052632]])),
+             'X': ilr_inv(np.array([[0.539474, 1.289474]]))},
+            index=['Z1', 'Z2', 'Z3'])
+        feature_names = ['Z1', 'Z2', 'Z3']
+        basis = _gram_schmidt_basis(3)
+        res = RegressionResults(self.results, basis=basis,
+                                feature_names=feature_names)
+
+        pdt.assert_frame_equal(res.coefficients(project=True), exp_coef,
+                               check_exact=False,
+                               check_less_precise=True)
+
+    def test_regression_results_coefficient_project_error(self):
+        res = RegressionResults(self.results)
+        with self.assertRaises(ValueError):
+            res.coefficients(project=True)
+
 if __name__ == "__main__":
     unittest.main()