[BIG] pytorch-transformers => transformers

transformers/data/metrics/__init__.py

@@ -0,0 +1,83 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import csv
+import sys
+import logging
+
+logger = logging.getLogger(__name__)
+
+try:
+    from scipy.stats import pearsonr, spearmanr
+    from sklearn.metrics import matthews_corrcoef, f1_score
+    _has_sklearn = True
+except (AttributeError, ImportError) as e:
+    logger.warning("To use data.metrics please install scikit-learn. See https://scikit-learn.org/stable/index.html")
+    _has_sklearn = False
+
+def is_sklearn_available():
+    return _has_sklearn
+
+if _has_sklearn:
+
+    def simple_accuracy(preds, labels):
+        return (preds == labels).mean()
+
+
+    def acc_and_f1(preds, labels):
+        acc = simple_accuracy(preds, labels)
+        f1 = f1_score(y_true=labels, y_pred=preds)
+        return {
+            "acc": acc,
+            "f1": f1,
+            "acc_and_f1": (acc + f1) / 2,
+        }
+
+
+    def pearson_and_spearman(preds, labels):
+        pearson_corr = pearsonr(preds, labels)[0]
+        spearman_corr = spearmanr(preds, labels)[0]
+        return {
+            "pearson": pearson_corr,
+            "spearmanr": spearman_corr,
+            "corr": (pearson_corr + spearman_corr) / 2,
+        }
+
+
+    def glue_compute_metrics(task_name, preds, labels):
+        assert len(preds) == len(labels)
+        if task_name == "cola":
+            return {"mcc": matthews_corrcoef(labels, preds)}
+        elif task_name == "sst-2":
+            return {"acc": simple_accuracy(preds, labels)}
+        elif task_name == "mrpc":
+            return acc_and_f1(preds, labels)
+        elif task_name == "sts-b":
+            return pearson_and_spearman(preds, labels)
+        elif task_name == "qqp":
+            return acc_and_f1(preds, labels)
+        elif task_name == "mnli":
+            return {"acc": simple_accuracy(preds, labels)}
+        elif task_name == "mnli-mm":
+            return {"acc": simple_accuracy(preds, labels)}
+        elif task_name == "qnli":
+            return {"acc": simple_accuracy(preds, labels)}
+        elif task_name == "rte":
+            return {"acc": simple_accuracy(preds, labels)}
+        elif task_name == "wnli":
+            return {"acc": simple_accuracy(preds, labels)}
+        else:
+            raise KeyError(task_name)