fixed some unit tests

Author: davidsebfischer

diffxpy/unit_test/test_continuous.py

@@ -80,7 +80,7 @@ def test_null_distribution_wald(self, n_cells: int = 2000, n_genes: int = 100):
         :param n_cells: Number of cells to simulate (number of observations per test).
         :param n_genes: Number of genes to simulate (number of tests).
         """
-        logging.getLogger("tensorflow").setLevel(logging.INFO)
+        logging.getLogger("tensorflow").setLevel(logging.ERROR)
         logging.getLogger("batchglm").setLevel(logging.WARNING)
         logging.getLogger("diffxpy").setLevel(logging.WARNING)
 

diffxpy/unit_test/test_numeric.py

@@ -1,8 +1,6 @@
 import unittest
 
 import numpy as np
-import pandas as pd
-import scipy.stats as stats
 import logging
 
 from batchglm.api.models.glm_nb import Simulator

diffxpy/unit_test/test_pairwise.py

@@ -4,7 +4,7 @@
 import pandas as pd
 import scipy.stats as stats
 
-from batchglm.api.models.glm_nb import Simulator, Estimator, InputData
+from batchglm.api.models.glm_nb import Simulator
 import diffxpy.api as de
 
 
@@ -247,18 +247,21 @@ def test_ztest_de(self, n_cells: int = 2000, n_genes: int = 500):
         )
         summary = test.summary()
 
+        frac_nonde_sig = np.mean(
+            np.sum(test.qval[~np.eye(test.pval.shape[0]).astype(bool), :num_non_de] < 0.05) /
+            (2 * num_non_de)
+        )
+        frac_de_sig = np.mean(
+            np.sum(test.qval[~np.eye(test.pval.shape[0]).astype(bool), num_non_de:] < 0.05) /
+            (2 * (n_genes - num_non_de))
+        )
         logging.getLogger("diffxpy").info('fraction of non-DE genes with q-value < 0.05: %.1f%%' %
-              float(100 * np.mean(
-                  np.sum(test.qval[~np.eye(test.pval.shape[0]).astype(bool), :num_non_de] < 0.05) /
-                  (2 * num_non_de)
-              )))
+                                          str(np.round(100. * frac_nonde_sig, 3)))
         logging.getLogger("diffxpy").info('fraction of DE genes with q-value < 0.05: %.1f%%' %
-              float(100 * np.mean(
-                  np.sum(test.qval[~np.eye(test.pval.shape[0]).astype(bool), num_non_de:] < 0.05) /
-                  (2 * (n_genes - num_non_de))
-              )))
+                                          str(np.round(100. * frac_de_sig, 3)))
 
-        # TODO asserts
+        assert frac_de_sig > 0.5, "too many DE"
+        assert frac_nonde_sig < 0.5, "too many non-DE"
         return True