Merge branch 'dev' of https://github.com/theislab/batchglm into dev

theislab · Feb 21, 2020 · 9a7d92b · 9a7d92b
2 parents e699985 + 5707d0a
commit 9a7d92b
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Showing 4 changed files with 9 additions and 29 deletions.
diff --git a/batchglm/models/base_glm/input.py b/batchglm/models/base_glm/input.py
@@ -197,4 +197,4 @@ def fetch_design_scale(self, idx):
         return self.design_scale[idx, :]
 
     def fetch_size_factors(self, idx):
-        return self.size_factors[idx]
+        return self.size_factors[idx, :]
diff --git a/batchglm/train/tf1/glm_beta/estimator.py b/batchglm/train/tf1/glm_beta/estimator.py
@@ -169,8 +169,6 @@ def init_par(
         $$
         """
 
-        size_factors_init = input_data.size_factors
-
         if init_model is None:
             groupwise_means = None
             init_a_str = None
@@ -185,7 +183,7 @@ def init_par(
                         x=input_data.x,
                         design_loc=input_data.design_loc,
                         constraints_loc=input_data.constraints_loc,
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         link_fn=lambda mean: np.log(
                             1/(1/self.np_clip_param(mean, "mean")-1)
                         )
@@ -221,7 +219,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale[:, [0]],
                         constraints=input_data.constraints_scale[[0], :][:, [0]],
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=None,
                         link_fn=lambda samplesize: np.log(self.np_clip_param(samplesize, "samplesize"))
                     )
@@ -248,7 +246,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale,
                         constraints=input_data.constraints_scale,
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=groupwise_means,
                         link_fn=lambda samplesize: np.log(self.np_clip_param(samplesize, "samplesize"))
                     )
@@ -291,4 +289,3 @@ def init_par(
                 logging.getLogger("batchglm").debug("Using initialization based on input model for dispersion")
 
         return init_a, init_b
-
diff --git a/batchglm/train/tf1/glm_nb/estimator.py b/batchglm/train/tf1/glm_nb/estimator.py
@@ -176,14 +176,6 @@ def init_par(
         $$
         """
 
-        size_factors_init = input_data.size_factors
-        if size_factors_init is not None:
-            size_factors_init = np.expand_dims(size_factors_init, axis=1)
-            size_factors_init = np.broadcast_to(
-                array=size_factors_init,
-                shape=[input_data.num_observations, input_data.num_features]
-            )
-
         if init_model is None:
             groupwise_means = None
             init_a_str = None
@@ -198,7 +190,7 @@ def init_par(
                         x=input_data.x,
                         design_loc=input_data.design_loc,
                         constraints_loc=input_data.constraints_loc,
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         link_fn=lambda mu: np.log(self.np_clip_param(mu, "mu"))
                     )
 
@@ -239,7 +231,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale[:, [0]],
                         constraints=input_data.constraints_scale[[0], :][:, [0]],
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=None,
                         link_fn=lambda r: np.log(self.np_clip_param(r, "r"))
                     )
@@ -267,7 +259,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale,
                         constraints=input_data.constraints_scale,
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=groupwise_means,
                         link_fn=lambda r: np.log(self.np_clip_param(r, "r"))
                     )

diff --git a/batchglm/train/tf1/glm_norm/estimator.py b/batchglm/train/tf1/glm_norm/estimator.py
@@ -172,14 +172,6 @@ def init_par(
         $$
         """
 
-        size_factors_init = input_data.size_factors
-        if size_factors_init is not None:
-            size_factors_init = np.expand_dims(size_factors_init, axis=1)
-            size_factors_init = np.broadcast_to(
-                array=size_factors_init,
-                shape=[input_data.num_observations, input_data.num_features]
-            )
-
         sf_given = False
         if input_data.size_factors is not None:
             if np.any(np.abs(input_data.size_factors - 1.) > 1e-8):
@@ -268,7 +260,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale,
                         constraints=input_data.constraints_scale,
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=groupwise_means,
                         link_fn=lambda sd: np.log(self.np_clip_param(sd, "sd"))
                     )
@@ -282,7 +274,7 @@ def init_par(
                         x=input_data.x,
                         design_scale=input_data.design_scale[:, [0]],
                         constraints=input_data.constraints_scale[[0], :][:, [0]],
-                        size_factors=size_factors_init,
+                        size_factors=input_data.size_factors,
                         groupwise_means=None,
                         link_fn=lambda sd: np.log(self.np_clip_param(sd, "sd"))
                     )
@@ -331,4 +323,3 @@ def init_par(
                 logger.debug("Using initialization based on input model for dispersion")
 
         return init_a, init_b
-