ENH: Q2 summary interval allow float

.travis.yml

@@ -5,6 +5,10 @@ language: python
 env:
   - PYVERSION=3.5
 before_install:
+  - sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
+  - sudo apt-get update
+  - sudo apt-get -y install gcc-4.9
+  - sudo apt-get -y install --only-upgrade libstdc++6
   - export MPLBACKEND='Agg'
   - wget -q https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
   - export MINICONDA_PREFIX="$HOME/miniconda"

songbird/multinomial.py

@@ -142,7 +142,7 @@ def fit(self, epochs=10, summary_interval=100, checkpoint_interval=3600):
         ----------
         epochs : int
            Number of epochs to train
-        summary_interval : int
+        summary_interval : float
            Number of seconds until a summary is recorded
         checkpoint_interval : int
            Number of seconds until a checkpoint is recorded

songbird/q2/plugin_setup.py

@@ -49,7 +49,7 @@
         'clipnorm': Float,
         'min_sample_count': Int,
         'min_feature_count': Int,
-        'summary_interval': Int,
+        'summary_interval': Float,
         'random_seed': Int,
     },
     outputs=[

songbird/q2/tests/test_method.py

@@ -8,6 +8,8 @@
 from skbio import OrdinationResults
 from skbio.stats.composition import clr, clr_inv
 import numpy.testing as npt
+import pandas as pd
+from songbird.q2.plugin_setup import plugin as songbird_plugin
 
 
 class TestMultinomial(unittest.TestCase):
@@ -73,6 +75,51 @@ def test_fit_consistency(self):
         npt.assert_array_equal(res_biplot1.samples, res_biplot2.samples)
         npt.assert_array_equal(res_biplot1.features, res_biplot2.features)
 
+    def test_fit_float_summary_interval(self):
+        tf.set_random_seed(0)
+        md = self.md
+
+        multregression = songbird_plugin.actions['multinomial']
+
+        md.name = 'sampleid'
+        md = qiime2.Metadata(md)
+
+        exp_beta = clr(clr_inv(np.hstack((np.zeros((2, 1)), self.beta.T))))
+
+        q2_table = qiime2.Artifact.import_data('FeatureTable[Frequency]',
+                                               self.table)
+
+        q2_res_beta, q2_res_stats, q2_res_biplot = multregression(
+            table=q2_table, metadata=md,
+            min_sample_count=0, min_feature_count=0,
+            formula="X", epochs=1000,
+            summary_interval=0.5,
+        )
+
+        try:
+            res_biplot = q2_res_biplot.view(OrdinationResults)
+        except Exception:
+            raise AssertionError('res_biplot unable to be coerced to '
+                                 'OrdinationResults')
+        try:
+            res_beta = q2_res_beta.view(pd.DataFrame)
+        except Exception:
+            raise AssertionError('res_beta unable to be coerced to '
+                                 'pd.DataFrame')
+        try:
+            res_stats = q2_res_stats.view(qiime2.Metadata)
+        except Exception:
+            raise AssertionError('res_stats unable to be coerced to '
+                                 'qiime2.Metadata')
+
+        u = res_biplot.samples.values
+        v = res_biplot.features.values.T
+        npt.assert_allclose(u @ v, res_beta.values,
+                            atol=0.5, rtol=0.5)
+
+        npt.assert_allclose(exp_beta, res_beta.T, atol=0.6, rtol=0.6)
+        self.assertGreater(len(res_stats.to_dataframe().index), 1)
+
 
 if __name__ == "__main__":
     unittest.main()