Lightstar/venv/Lib/site-packages/transformers/data/metrics/__init__.py

# 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 warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
    from scipy.stats import pearsonr, spearmanr
    from sklearn.metrics import f1_score, matthews_corrcoef


DEPRECATION_WARNING = (
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)


def simple_accuracy(preds, labels):
    warnings.warn(DEPRECATION_WARNING, FutureWarning)
    requires_backends(simple_accuracy, "sklearn")
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    warnings.warn(DEPRECATION_WARNING, FutureWarning)
    requires_backends(acc_and_f1, "sklearn")
    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):
    warnings.warn(DEPRECATION_WARNING, FutureWarning)
    requires_backends(pearson_and_spearman, "sklearn")
    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):
    warnings.warn(DEPRECATION_WARNING, FutureWarning)
    requires_backends(glue_compute_metrics, "sklearn")
    assert len(preds) == len(labels), f"Predictions and labels have mismatched lengths {len(preds)} and {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 {"mnli/acc": simple_accuracy(preds, labels)}
    elif task_name == "mnli-mm":
        return {"mnli-mm/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)}
    elif task_name == "hans":
        return {"acc": simple_accuracy(preds, labels)}
    else:
        raise KeyError(task_name)


def xnli_compute_metrics(task_name, preds, labels):
    warnings.warn(DEPRECATION_WARNING, FutureWarning)
    requires_backends(xnli_compute_metrics, "sklearn")
    if len(preds) != len(labels):
        raise ValueError(f"Predictions and labels have mismatched lengths {len(preds)} and {len(labels)}")
    if task_name == "xnli":
        return {"acc": simple_accuracy(preds, labels)}
    else:
        raise KeyError(task_name)