From 20b0ec5dcf5b97a3c406ec6bd7aa3f32223c63fa Mon Sep 17 00:00:00 2001
From: svlandeg <sofie.vanlandeghem@gmail.com>
Date: Wed, 23 Sep 2020 10:37:12 +0200
Subject: [PATCH] avoid logging performance of frozen components

---
 spacy/cli/train.py        | 6 ++++--
 spacy/training/loggers.py | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index bf3749c9e..811a3ba86 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -152,7 +152,8 @@ def train(
         exclude=frozen_components,
     )
     msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
-    print_row, finalize_logger = train_logger(nlp)
+    with nlp.select_pipes(disable=[*frozen_components]):
+        print_row, finalize_logger = train_logger(nlp)
 
     try:
         progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
@@ -163,7 +164,8 @@ def train(
                 progress.close()
                 print_row(info)
                 if is_best_checkpoint and output_path is not None:
-                    update_meta(T_cfg, nlp, info)
+                    with nlp.select_pipes(disable=[*frozen_components]):
+                        update_meta(T_cfg, nlp, info)
                     with nlp.use_params(optimizer.averages):
                         nlp.to_disk(output_path / "model-best")
                 progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
diff --git a/spacy/training/loggers.py b/spacy/training/loggers.py
index 92b598033..dddf20169 100644
--- a/spacy/training/loggers.py
+++ b/spacy/training/loggers.py
@@ -11,9 +11,11 @@ def console_logger():
     def setup_printer(
         nlp: "Language",
     ) -> Tuple[Callable[[Dict[str, Any]], None], Callable]:
+        # we assume here that only components are enabled that should be trained & logged
+        logged_pipes = nlp.pipe_names
         score_cols = list(nlp.config["training"]["score_weights"])
         score_widths = [max(len(col), 6) for col in score_cols]
-        loss_cols = [f"Loss {pipe}" for pipe in nlp.pipe_names]
+        loss_cols = [f"Loss {pipe}" for pipe in logged_pipes]
         loss_widths = [max(len(col), 8) for col in loss_cols]
         table_header = ["E", "#"] + loss_cols + score_cols + ["Score"]
         table_header = [col.upper() for col in table_header]
@@ -26,7 +28,7 @@ def console_logger():
             try:
                 losses = [
                     "{0:.2f}".format(float(info["losses"][pipe_name]))
-                    for pipe_name in nlp.pipe_names
+                    for pipe_name in logged_pipes
                 ]
             except KeyError as e:
                 raise KeyError(