Merge remote-tracking branch 'upstream/dev'

diffxpy/api/__init__.py

@@ -5,3 +5,4 @@
 from . import enrich
 from . import stats
 from . import utils
+from .. import pkg_constants

diffxpy/api/stats.py

@@ -3,7 +3,7 @@
 from diffxpy.stats.stats import wald_test
 from diffxpy.stats.stats import wald_test_chisq
 from diffxpy.stats.stats import two_coef_z_test
-from diffxpy.stats.stats import wilcoxon_test
+from diffxpy.stats.stats import mann_whitney_u_test
 from diffxpy.stats.stats import t_test_moments
 from diffxpy.stats.stats import t_test_raw
 

diffxpy/api/test.py

@@ -2,7 +2,7 @@
 from diffxpy.testing.base import lrt
 from diffxpy.testing.base import wald
 from diffxpy.testing.base import t_test
-from diffxpy.testing.base import wilcoxon
+from diffxpy.testing.base import rank_test
 from diffxpy.testing.base import partition
 from diffxpy.testing.base import pairwise
 from diffxpy.testing.base import versus_rest

diffxpy/pkg_constants.py

@@ -1,7 +1,10 @@
-BATCHGLM_OPTIM_GD = True
-BATCHGLM_OPTIM_ADAM = True
+BATCHGLM_OPTIM_GD = False
+BATCHGLM_OPTIM_ADAM = False
 BATCHGLM_OPTIM_ADAGRAD = False
 BATCHGLM_OPTIM_RMSPROP = False
-BATCHGLM_OPTIM_NEWTON = True
-BATCHGLM_OPTIM_IRLS = True
+BATCHGLM_OPTIM_NEWTON = False
+BATCHGLM_OPTIM_NEWTON_TR = True
+BATCHGLM_OPTIM_IRLS = False
+BATCHGLM_OPTIM_IRLS_TR = False
+BATCHGLM_PROVIDE_BATCHED = False
 BATCHGLM_TERMINATION_TYPE = "by_feature"

diffxpy/stats/stats.py

@@ -1,7 +1,9 @@
+from typing import Union
+
 import numpy as np
 import numpy.linalg
 import scipy.stats
-from typing import Union
+import xarray as xr
 
 
 def likelihood_ratio_test(
@@ -37,7 +39,7 @@ def likelihood_ratio_test(
     return pvals
 
 
-def wilcoxon_test(
+def mann_whitney_u_test(
         x0: np.ndarray,
         x1: np.ndarray,
 ):
@@ -68,7 +70,8 @@ def wilcoxon_test(
         scipy.stats.mannwhitneyu(
             x=x0[:, i].flatten(),
             y=x1[:, i].flatten(),
-            alternative='two-sided'
+            use_continuity=True,
+            alternative="two-sided"
         ).pvalue for i in range(x0.shape[1])
     ])
     return pvals
@@ -152,7 +155,7 @@ def t_test_moments(
         out=s_delta
     )
 
-    t_statistic = np.abs((mu0 - mu1) / s_delta)
+    t_statistic = (mu0 - mu1) / s_delta
 
     divisor = (
             (np.square(var0 / n0) / (n0 - 1)) +
@@ -174,7 +177,7 @@ def t_test_moments(
         out=df
     )
 
-    pval = 2 * (1 - scipy.stats.t(df).cdf(t_statistic))
+    pval = 2 * scipy.stats.t.sf(np.abs(t_statistic), df)
     return pval
 
 
@@ -261,6 +264,9 @@ def wald_test_chisq(
             raise ValueError('stats.wald_test(): theta_mle and theta0 have to contain the same number of entries')
 
     theta_diff = theta_mle - theta0
+    # Convert to nd.array to avoid gufunc error.
+    if isinstance(theta_diff, xr.DataArray):
+        theta_diff = theta_diff.values
     wald_statistic = np.array([
         np.matmul(
             np.matmul(