【HuggingFace Transformer库学习笔记】基础组件学习：Evaluate

基础组件学习——Evaluate

Evaluate使用指南

查看支持的评估函数

# include_community：是否添加社区实现的部分
# with_details：是否展示更多细节
evaluate.list_evaluation_modules(include_community=False, with_details=True)

加载评估函数

accuracy = evaluate.load("accuracy")

查看评估函数说明

print(accuracy.description)


Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative

查看示例说明

print(accuracy.inputs_description)

Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {
   'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {
   'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {
   'accuracy': 0.8778625954198473}

accuracy

EvaluationModule(name: "accuracy", module_type: "metric", features: {
   'predictions': Value(dtype='int32', id=None), 'references': Value(dtype='int32', id=None)}, usage: """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = evaluate.load("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
""", stored examples: 0)

评估指标计算——全局计算

accuracy = evaluate.load("accuracy")
results = accuracy.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
results

{
   'accuracy': 0.5}

评估指标计算——迭代计算

# 一个一个传
accuracy = evaluate.load("accuracy")
for ref, pred in zip([0,1,0,1], [1,0,0,1]):
    accuracy.add(references=ref, predictions=pred)
accuracy.compute()

{
   'accuracy': 0.5}

# 一批一批传
accuracy = evaluate.load("accuracy")
for refs, preds in zip([[0,1],[0,1]], [[1,0],[0,1]]):
    accuracy.add_batch(references=refs, predictions=preds)
accuracy.compute()

{
   'accuracy': 0.5}

多个评估指标计算

clf_metrics = evaluate.combine(["accuracy", "f1", "recall", "precision"])
clf_metrics

<evaluate.module.CombinedEvaluations at 0x1e92e72f880>

clf_metrics.compute(predictions=[0, 1, 0], references=[0, 1, 1])

{
   'accuracy': 0.6666666666666666,
 'f1': 0.6666666666666666,
 'recall': 0.5,
 'precision': 1.0}

评估结果对比可视化

from evaluate.visualization import radar_plot   # 目前只支持雷达图

data = [
   {
   "accuracy": 0.99, "precision": 0.8, "f1": 0.95, "latency_in_seconds": 33.6},
   {
   "accuracy": 0.98, "precision": 0.87, "f1": 0.91, "latency_in_seconds": 11.2},
   {
   "accuracy": 0.98, "precision": 0.78, "f1": 0.88, "latency_in_seconds": 87.6}, 
   {
   "accuracy": 0.88, "precision": 0.78, "f1": 0.81, "latency_in_seconds": 101.6}
   ]
model_names = ["Model 1", "Model 2", "Model 3", "Model 4"]

plot = radar_plot(data=data, model_names=model_names)