From 97674276697b34204d5fc9d947afc8e23205e000 Mon Sep 17 00:00:00 2001
From: Ines Montani <ines@ines.io>
Date: Mon, 22 Apr 2019 14:31:11 +0200
Subject: [PATCH] Auto-format

---
 spacy/cli/train.py | 32 ++++++++++++++++++++------------
 1 file changed, 20 insertions(+), 12 deletions(-)

diff --git a/spacy/cli/train.py b/spacy/cli/train.py
index 5cf0f5f6f..ebac339ae 100644
--- a/spacy/cli/train.py
+++ b/spacy/cli/train.py
@@ -226,7 +226,7 @@ def train(
     msg.row(["-" * width for width in row_settings["widths"]], **row_settings)
     try:
         iter_since_best = 0
-        best_score = 0.
+        best_score = 0.0
         for i in range(n_iter):
             train_docs = corpus.train_docs(
                 nlp, noise_level=noise_level, gold_preproc=gold_preproc, max_length=0
@@ -335,8 +335,8 @@ def train(
                         gpu_wps=gpu_wps,
                     )
                     msg.row(progress, **row_settings)
-                # early stopping
                 if early_stopping_iter is not None:
+                # Early stopping
                     current_score = _score_for_model(meta)
                     if current_score < best_score:
                         iter_since_best += 1
@@ -344,8 +344,14 @@ def train(
                         iter_since_best = 0
                         best_score = current_score
                     if iter_since_best >= early_stopping_iter:
-                        msg.text("Early stopping, best iteration is: {}".format(i-iter_since_best))
-                        msg.text("Best score = {}; Final iteration score = {}".format(best_score, current_score))
+                        msg.text(
+                            "Early stopping, best iteration "
+                            "is: {}".format(i - iter_since_best)
+                        )
+                        msg.text(
+                            "Best score = {}; Final iteration "
+                            "score = {}".format(best_score, current_score)
+                        )
                         break
     finally:
         with nlp.use_params(optimizer.averages):
@@ -356,19 +362,21 @@ def train(
             best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
         msg.good("Created best model", best_model_path)
 
+
 def _score_for_model(meta):
     """ Returns mean score between tasks in pipeline that can be used for early stopping. """
     mean_acc = list()
-    pipes = meta['pipeline']
-    acc = meta['accuracy']
-    if 'tagger' in pipes:
-        mean_acc.append(acc['tags_acc'])
-    if 'parser' in pipes:
-        mean_acc.append((acc['uas']+acc['las']) / 2)
-    if 'ner' in pipes:
-        mean_acc.append((acc['ents_p']+acc['ents_r']+acc['ents_f']) / 3)
+    pipes = meta["pipeline"]
+    acc = meta["accuracy"]
+    if "tagger" in pipes:
+        mean_acc.append(acc["tags_acc"])
+    if "parser" in pipes:
+        mean_acc.append((acc["uas"] + acc["las"]) / 2)
+    if "ner" in pipes:
+        mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]) / 3)
     return sum(mean_acc) / len(mean_acc)
 
+
 @contextlib.contextmanager
 def _create_progress_bar(total):
     if int(os.environ.get("LOG_FRIENDLY", 0)):