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未验证 提交 9e0f06d9 编写于 作者: X Xiaoyao Xi 提交者： GitHub
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Merge pull request #65 from wangxiao1021/api 

update evaluation && README
上级 dbd22a7e 33fea5f3
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Showing with 188 addition and 177 deletion
README.md
浏览文件 @ 9e0f06d9
......@@ -30,9 +30,9 @@ You can easily re-produce following competitive results with minor codes, which
<tr>
<th><strong>Dataset</strong>
<br></th>
<th colspan="3"><center><strong>chnsenticorp</strong></center></th>
<th colspan="3"><center><strong>Quora Question Pairs matching</strong><center></th>
<th colspan="3"><strong>MSRA-NER<br>(SIGHAN2006)</strong></th>
<th colspan="2"><center><strong>chnsenticorp</strong></center></th>
<th colspan="2"><center><strong>Quora Question Pairs matching</strong><center></th>
<th colspan="1"><strong>MSRA-NER<br>(SIGHAN2006)</strong></th>
<th colspan="2"><strong>CMRC2018</strong></th>
</tr>
<tr>
......@@ -42,31 +42,19 @@ You can easily re-produce following competitive results with minor codes, which
<br></p>
</td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<center><strong>accuracy</strong></center>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
<br></td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<center><strong>accuracy</strong></center>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
<br></td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
......@@ -79,13 +67,13 @@ You can easily re-produce following competitive results with minor codes, which
<br></td>
</tr>
<tr>
<td colspan="3" width="">
<td colspan="2" width="">
<strong>test</strong>
<br></td>
<td colspan="3" width="">
<td colspan="2" width="">
<strong>test</strong>
<br></td>
<td colspan="3" width="">
<td colspan="1" width="">
<strong>test</strong>
<br></td>
<td colspan="2" width="">
......@@ -94,15 +82,11 @@ You can easily re-produce following competitive results with minor codes, which
</tr>
<tr>
<td><strong>ERNIE Base</strong></td>
<td>95.7</td>
<td>95.0</td>
<td>95.7</td>
<td>85.8</td>
<td>82.4</td>
<td>81.5</td>
<td>94.9</td>
<td>94.5</td>
<td>94.7</td>
<td>95.8</td>
<td>95.8</td>
<td>86.2</td>
<td>82.2</td>
<td>99.2</td>
<td>64.3</td>
<td>85.2</td>
</tr>
......
README_zh.md
浏览文件 @ 9e0f06d9
......@@ -30,9 +30,9 @@ PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易
<tr>
<th><strong>数据集</strong>
<br></th>
<th colspan="3"><center><strong>chnsenticorp</strong></center></th>
<th colspan="3"><center><strong>Quora Question Pairs matching</strong><center></th>
<th colspan="3"><strong>MSRA-NER<br>(SIGHAN2006)</strong></th>
<th colspan="2"><center><strong>chnsenticorp</strong></center></th>
<th colspan="2"><center><strong>Quora Question Pairs matching</strong><center></th>
<th colspan="1"><strong>MSRA-NER<br>(SIGHAN2006)</strong></th>
<th colspan="2"><strong>CMRC2018</strong></th>
</tr>
<tr>
......@@ -42,31 +42,19 @@ PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易
<br></p>
</td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<center><strong>accuracy</strong></center>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
<br></td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<center><strong>accuracy</strong></center>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
<br></td>
<td colspan="1">
<center><strong>precision</strong></center>
<br></td>
<td colspan="1">
<strong>recall</strong>
<br></td>
<td colspan="1">
<strong>f1-score</strong>
<strong></strong>
......@@ -79,13 +67,13 @@ PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易
<br></td>
</tr>
<tr>
<td colspan="3" width="">
<td colspan="2" width="">
<strong>test</strong>
<br></td>
<td colspan="3" width="">
<td colspan="2" width="">
<strong>test</strong>
<br></td>
<td colspan="3" width="">
<td colspan="1" width="">
<strong>test</strong>
<br></td>
<td colspan="2" width="">
......@@ -94,17 +82,13 @@ PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易
</tr>
<tr>
<td><strong>ERNIE Base</strong></td>
<td>95.7</td>
<td>95.0</td>
<td>95.7</td>
<td>85.8</td>
<td>82.4</td>
<td>81.5</td>
<td>94.9</td>
<td>94.5</td>
<td>94.7</td>
<td>96.3</td>
<td>84.0</td>
<td>95.8</td>
<td>95.8</td>
<td>86.2</td>
<td>82.2</td>
<td>99.2</td>
<td>64.3</td>
<td>85.2</td>
</tr>
</tbody>
......@@ -121,6 +105,16 @@ PaddlePALM (PArallel Learning from Multi-tasks) 是一个灵活，通用且易
</p>
</p>
PaddlePALM是一个设计良好的高级NLP框架。基于PaddlePALM的轻量级代码可以高效实现**监督学习、非监督/自监督学习、多任务学习和迁移学习**。在PaddlePALM架构中有三层，即从下到上依次是component层、trainer层high-level trainer层。
在组件层，PaddlePALM提供了6个 **解耦的**组件来实现NLP任务。每个组件包含丰富的预定义类和一个基本类。预定义类是针对典型的NLP任务的，而基类是帮助用户开发一个新类（基于预定义类或基类）。
训练器层是用选定的构件建立计算图，进行训练和预测。该层描述了训练策略、模型保存和加载、评估和预测过程。一个训练器只能处理一个任务。
高级训练器层用于复杂的学习和推理策略，如多任务学习。您可以添加辅助任务来训练健壮的NLP模型（提高模型的测试集和领域外的性能），或者联合训练多个相关任务来获得每个任务的更高性能。
| 模块 | 描述 |
| - | - |
| **paddlepalm** | 一个开源的NLP预训练和多任务学习框架，建立在paddlepaddle框架上。 |
......@@ -187,6 +181,8 @@ Available pretrain items:
## 使用
#### 快速开始
8个步骤开始一个典型的NLP训练任务。
1. 使用`paddlepalm.reader` 要为数据集加载和输入特征生成创建一个`reader`，然后调用`reader.load_data`方法加载训练数据。
......@@ -205,14 +201,8 @@ Available pretrain items:
- [Tagging](https://github.com/PaddlePaddle/PALM/tree/master/examples/tagging)
- [SQuAD machine Reading Comprehension](https://github.com/PaddlePaddle/PALM/tree/master/examples/mrc).
### 设置saver
在训练时保存 models/checkpoints 和 logs， 调用 `trainer.set_saver` 方法. 更多实现细节见[这里](https://github.com/PaddlePaddle/PALM/tree/master/examples).
### 预测
训练结束后进行预测和评价, 只需创建额外的reader, backbone和head示例（重复上面1~4步骤），注意创建时需设`phase='predict'`。 然后使用trainer的`predict`方法进行预测（不需创建额外的trainer）。更多实现细节请见[这里](https://github.com/PaddlePaddle/PALM/tree/master/examples/predict).
### 多任务学习
#### 多任务学习
多任务学习模式下运行:
......@@ -226,11 +216,31 @@ Available pretrain items:
multi_head_trainer的保存/加载和预测操作与trainer相同。
更多实现`multi_head_trainer`的细节, 请见
更多实现`multi_head_trainer`的细节，请见
- [ATIS: joint training of dialogue intent recognition and slot filling](https://github.com/PaddlePaddle/PALM/tree/master/examples/multi-task)
- [MRQA: learning reading comprehension auxilarized with mask language model]() (初次发版先不用加)
#### 设置saver
在训练时保存 models/checkpoints 和 logs， 调用 `trainer.set_saver` 方法。更多实现细节见[这里](https://github.com/PaddlePaddle/PALM/tree/master/examples)。
#### 评估/预测
训练结束后进行预测和评价, 只需创建额外的reader, backbone和head示例（重复上面1~4步骤），注意创建时需设`phase='predict'`。 然后使用trainer的`predict`方法进行预测（不需创建额外的trainer）。更多实现细节请见[这里](https://github.com/PaddlePaddle/PALM/tree/master/examples/predict)。
#### 使用多GPU
如果您的环境中存在多个GPU，您可以通过环境变量控制这些GPU的数量和索引[CUDA_VISIBLE_DEVICES](https://devblogs.nvidia.com/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/)。例如，如果您的环境中有4个gpu，索引为0、1、2、3，那么您可以运行以下命令来只使用GPU2：
```shell
CUDA_VISIBLE_DEVICES=2 python run.py
```
多GPU的使用需要 `,`作为分隔。例如，使用GPU2和GPU3，运行以下命令：
```shell
CUDA_VISIBLE_DEVICES=2,3 python run.py
```
在多GPU模式下，PaddlePALM会自动将每批数据分配到可用的卡上。例如，如果`batch_size`设置为64，并且有4个GPU可以用于PaddlePALM，那么每个GPU中的batch_size实际上是64/4=16。因此，**当使用多个GPU时，您需要确保设置batch_size可以整除卡片的数量**。
## 许可证书
......
examples/classification/README.md
浏览文件 @ 9e0f06d9
......@@ -75,5 +75,5 @@ The evaluation results are as follows:
```
data num: 1200
precision: 0.956666666667, recall: 0.949013157895, f1: 0.95688225039
accuracy: 0.9575, precision: 0.9634, recall: 0.9523, f1: 0.9578
```
examples/classification/evaluate.py
浏览文件 @ 9e0f06d9
......@@ -8,26 +8,19 @@ def accuracy(preds, labels):
labels = np.array(labels)
return (preds == labels).mean()
def f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
tp = np.sum((labels == '1') & (preds == '1'))
tn = np.sum((labels == '0') & (preds == '0'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
p = tp * 1.0 / (tp + fp)
r = tp * 1.0 / (tp + fn) * 1.0
f1 = (2 * p * r) / (p + r + 1e-8)
return f1
def recall(preds, labels):
def pre_recall_f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
# recall=TP/(TP+FN)
tp = np.sum((labels == '1') & (preds == '1'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
re = tp * 1.0 / (tp + fn)
return re
r = tp * 1.0 / (tp + fn)
# Precision=TP/(TP+FP)
p = tp * 1.0 / (tp + fp)
epsilon = 1e-31
f1 = 2 * p * r / (p+r+epsilon)
return p, r, f1
def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'):
......@@ -58,6 +51,7 @@ def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'
file.close()
assert len(labels) == len(preds), "prediction result doesn't match to labels"
print('data num: {}'.format(len(labels)))
print("precision: {}, recall: {}, f1: {}".format(accuracy(preds, labels), recall(preds, labels), f1(preds, labels)))
p, r, f1 = pre_recall_f1(preds, labels)
print("accuracy: {:.4f}, precision: {:.4f}, recall: {:.4f}, f1: {:.4f}".format(accuracy(preds, labels), p, r, f1))
res_evaluate()
examples/matching/README.md
浏览文件 @ 9e0f06d9
......@@ -81,6 +81,6 @@ python evaluate.py
The evaluation results are as follows:
```
data_num: 4300
precision: 0.857906976744, recall: 0.824249846908, f1: 0.81501664653
data num: 4300
accuracy: 0.8619, precision: 0.8061, recall: 0.8377, f1: 0.8216
```
examples/matching/evaluate.py
浏览文件 @ 9e0f06d9
......@@ -8,26 +8,19 @@ def accuracy(preds, labels):
labels = np.array(labels)
return (preds == labels).mean()
def f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
tp = np.sum((labels == '1') & (preds == '1'))
tn = np.sum((labels == '0') & (preds == '0'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
p = tp * 1.0 / (tp + fp)
r = tp * 1.0 / (tp + fn) * 1.0
f1 = (2 * p * r) / (p + r + 1e-8)
return f1
def recall(preds, labels):
def pre_recall_f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
# recall=TP/(TP+FN)
tp = np.sum((labels == '1') & (preds == '1'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
re = tp * 1.0 / (tp + fn)
return re
r = tp * 1.0 / (tp + fn)
# Precision=TP/(TP+FP)
p = tp * 1.0 / (tp + fp)
epsilon = 1e-31
f1 = 2 * p * r / (p+r+epsilon)
return p, r, f1
def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'):
......@@ -58,6 +51,7 @@ def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'
file.close()
assert len(labels) == len(preds), "prediction result({}) doesn't match to labels({})".format(len(preds),len(labels))
print('data num: {}'.format(len(labels)))
print("precision: {}, recall: {}, f1: {}".format(accuracy(preds, labels), recall(preds, labels), f1(preds, labels)))
p, r, f1 = pre_recall_f1(preds, labels)
print("accuracy: {:.4f}, precision: {:.4f}, recall: {:.4f}, f1: {:.4f}".format(accuracy(preds, labels), p, r, f1))
res_evaluate()
examples/mrc/README.md
浏览文件 @ 9e0f06d9
......@@ -94,5 +94,5 @@ The evaluation results are as follows:
```
data_num: 3219
em_sroce: 64.3367505436, f1: 85.1781896843
em_sroce: 0.6434, f1: 0.8518
```
examples/mrc/evaluate.py
浏览文件 @ 9e0f06d9
......@@ -121,8 +121,8 @@ def evaluate(ground_truth_file, prediction_file):
f1 += calc_f1_score(answers, prediction)
em += calc_em_score(answers, prediction)
f1_score = 100.0 * f1 / total_count
em_score = 100.0 * em / total_count
f1_score = f1 / total_count
em_score = em / total_count
return f1_score, em_score, total_count, skip_count
......@@ -164,4 +164,4 @@ def eval_file(dataset_file, prediction_file):
if __name__ == '__main__':
EM, F1, AVG, TOTAL = eval_file("data/dev.json", "outputs/predict/predictions.json")
print('data_num: {}'.format(TOTAL))
print('em_sroce: {}, f1: {}'.format(EM,F1))
print('em_sroce: {:.4f}, f1: {:.4f}'.format(EM,F1))
examples/multi-task/README.md
浏览文件 @ 9e0f06d9
......@@ -118,11 +118,12 @@ The evaluation results are as follows:
`atis_slot`:
```
precision: 0.894397728514, recall: 0.894104803493, f1: 0.894251242016
data num: 891
f1: 0.8934
```
`atis_intent`:
```
data num: 893
precision: 0.708846584546, recall: 1.0, f1: 0.999999995
accuracy: 0.7088, precision: 1.0000, recall: 1.0000, f1: 1.0000
```
examples/multi-task/evaluate-intent.py
浏览文件 @ 9e0f06d9
......@@ -7,27 +7,20 @@ def accuracy(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
return (preds == labels).mean()
def f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
tp = np.sum((labels == '1') & (preds == '1'))
tn = np.sum((labels == '0') & (preds == '0'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
p = tp * 1.0 / (tp + fp) * 1.0
r = tp * 1.0 / (tp + fn) * 1.0
f1 = (2 * p * r) / (p + r + 1e-8)
return f1
def recall(preds, labels):
def pre_recall_f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
# recall=TP/(TP+FN)
tp = np.sum((labels == '1') & (preds == '1'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
re = tp * 1.0 / (tp + fn)
return re
r = tp * 1.0 / (tp + fn)
# Precision=TP/(TP+FP)
p = tp * 1.0 / (tp + fp)
epsilon = 1e-31
f1 = 2 * p * r / (p+r+epsilon)
return p, r, f1
def res_evaluate(res_dir="./outputs/predict-intent/predictions.json", eval_phase='test'):
......@@ -59,6 +52,7 @@ def res_evaluate(res_dir="./outputs/predict-intent/predictions.json", eval_phase
file.close()
assert len(labels) == len(preds), "prediction result doesn't match to labels"
print('data num: {}'.format(len(labels)))
print("precision: {}, recall: {}, f1: {}".format(accuracy(preds, labels), recall(preds, labels), f1(preds, labels)))
p, r, f1 = pre_recall_f1(preds, labels)
print("accuracy: {:.4f}, precision: {:.4f}, recall: {:.4f}, f1: {:.4f}".format(accuracy(preds, labels), p, r, f1))
res_evaluate()
examples/multi-task/evaluate-slot.py
浏览文件 @ 9e0f06d9
......@@ -11,22 +11,31 @@ def load_label_map(map_dir="./data/atis/atis_slot/label_map.json"):
return json.load(open(map_dir, "r"))
def cal_chunk(total_res, total_label):
assert len(total_label) == len(total_res), "prediction result doesn't match to labels, {}, {}".format(len(total_res),len(total_label))
num_labels = 0
num_corr = 0
num_infers = 0
for res, label in zip(total_res, total_label):
assert len(res) == len(label), "prediction result doesn't match to labels, {}, {}".format(len(res),len(label))
num_labels += sum([0 if i == 6 else 1 for i in label])
num_corr += sum([1 if label[i] == res[i] and label[i] != 6 else 0 for i in range(len(label))])
num_infers += sum([0 if i == 6 else 1 for i in res])
def cal_chunk(pred_label, refer_label):
tp = dict()
fn = dict()
fp = dict()
for i in range(len(refer_label)):
if refer_label[i] == pred_label[i]:
if refer_label[i] not in tp:
tp[refer_label[i]] = 0
tp[refer_label[i]] += 1
else:
if pred_label[i] not in fp:
fp[pred_label[i]] = 0
fp[pred_label[i]] += 1
if refer_label[i] not in fn:
fn[refer_label[i]] = 0
fn[refer_label[i]] += 1
precision = num_corr * 1.0 / num_infers if num_infers > 0 else 0.0
recall = num_corr * 1.0 / num_labels if num_labels > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
tp_total = sum(tp.values())
fn_total = sum(fn.values())
fp_total = sum(fp.values())
p_total = float(tp_total) / (tp_total + fp_total)
r_total = float(tp_total) / (tp_total + fn_total)
f_micro = 2 * p_total * r_total / (p_total + r_total)
return precision, recall, f1
return f_micro
def res_evaluate(res_dir="./outputs/predict-slot/predictions.json", data_dir="./data/atis/atis_slot/test.tsv"):
......@@ -72,7 +81,18 @@ def res_evaluate(res_dir="./outputs/predict-slot/predictions.json", data_dir="./
cnt += 1
precision, recall, f1 = cal_chunk(total_res, total_label)
print("precision: {}, recall: {}, f1: {}".format(precision, recall, f1))
total_res_equal = []
total_label_equal = []
assert len(total_label) == len(total_res), "prediction result doesn't match to labels"
for i in range(len(total_label)):
num = len(total_label[i])
total_label_equal.extend(total_label[i])
total_res[i] = total_res[i][:num]
total_res_equal.extend(total_res[i])
f1 = cal_chunk(total_res_equal, total_label_equal)
print('data num: {}'.format(len(total_label)))
print("f1: {:.4f}".format(f1))
res_evaluate()
examples/predict/README.md
浏览文件 @ 9e0f06d9
......@@ -44,5 +44,5 @@ The evaluation results are as follows:
```
data num: 1200
precision: 0.494166666667, recall: 0.0444078947368, f1: 0.0816944009455
accuracy: 0.4758, precision: 0.4730, recall: 0.3026, f1: 0.3691
```
examples/predict/evaluate.py
浏览文件 @ 9e0f06d9
......@@ -8,26 +8,19 @@ def accuracy(preds, labels):
labels = np.array(labels)
return (preds == labels).mean()
def f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
tp = np.sum((labels == '1') & (preds == '1'))
tn = np.sum((labels == '0') & (preds == '0'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
p = tp * 1.0 / (tp + fp)
r = tp * 1.0 / (tp + fn) * 1.0
f1 = (2 * p * r) / (p + r + 1e-8)
return f1
def recall(preds, labels):
def pre_recall_f1(preds, labels):
preds = np.array(preds)
labels = np.array(labels)
# recall=TP/(TP+FN)
tp = np.sum((labels == '1') & (preds == '1'))
fp = np.sum((labels == '0') & (preds == '1'))
fn = np.sum((labels == '1') & (preds == '0'))
re = tp * 1.0 / (tp + fn)
return re
r = tp * 1.0 / (tp + fn)
# Precision=TP/(TP+FP)
p = tp * 1.0 / (tp + fp)
epsilon = 1e-31
f1 = 2 * p * r / (p+r+epsilon)
return p, r, f1
def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'):
......@@ -58,6 +51,7 @@ def res_evaluate(res_dir="./outputs/predict/predictions.json", eval_phase='test'
file.close()
assert len(labels) == len(preds), "prediction result doesn't match to labels"
print('data num: {}'.format(len(labels)))
print("precision: {}, recall: {}, f1: {}".format(accuracy(preds, labels), recall(preds, labels), f1(preds, labels)))
p, r, f1 = pre_recall_f1(preds, labels)
print("accuracy: {:.4f}, precision: {:.4f}, recall: {:.4f}, f1: {:.4f}".format(accuracy(preds, labels), p, r, f1))
res_evaluate()
examples/tagging/README.md
浏览文件 @ 9e0f06d9
......@@ -74,5 +74,6 @@ python evaluate.py
The evaluation results are as follows:
```
precision: 0.948718989809, recall: 0.944806113784, f1: 0.946758508914
data num: 4636
f1: 0.9918
```
examples/tagging/evaluate.py
浏览文件 @ 9e0f06d9
......@@ -11,22 +11,31 @@ def load_label_map(map_dir="./data/label_map.json"):
return json.load(open(map_dir, "r"))
def cal_chunk(total_res, total_label):
assert len(total_label) == len(total_res), 'prediction result doesn\'t match to labels'
num_labels = 0
num_corr = 0
num_infers = 0
for res, label in zip(total_res, total_label):
assert len(res) == len(label), "prediction result doesn\'t match to labels"
num_labels += sum([0 if i == 6 else 1 for i in label])
num_corr += sum([1 if label[i] == res[i] and label[i] != 6 else 0 for i in range(len(label))])
num_infers += sum([0 if i == 6 else 1 for i in res])
def cal_chunk(pred_label, refer_label):
tp = dict()
fn = dict()
fp = dict()
for i in range(len(refer_label)):
if refer_label[i] == pred_label[i]:
if refer_label[i] not in tp:
tp[refer_label[i]] = 0
tp[refer_label[i]] += 1
else:
if pred_label[i] not in fp:
fp[pred_label[i]] = 0
fp[pred_label[i]] += 1
if refer_label[i] not in fn:
fn[refer_label[i]] = 0
fn[refer_label[i]] += 1
precision = num_corr * 1.0 / num_infers if num_infers > 0 else 0.0
recall = num_corr * 1.0 / num_labels if num_labels > 0 else 0.0
f1 = 2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
tp_total = sum(tp.values())
fn_total = sum(fn.values())
fp_total = sum(fp.values())
p_total = float(tp_total) / (tp_total + fp_total)
r_total = float(tp_total) / (tp_total + fn_total)
f_micro = 2 * p_total * r_total / (p_total + r_total)
return precision, recall, f1
return f_micro
def res_evaluate(res_dir="./outputs/predict/predictions.json", data_dir="./data/test.tsv"):
......@@ -48,7 +57,7 @@ def res_evaluate(res_dir="./outputs/predict/predictions.json", data_dir="./data/
labels = line[1].split("\x02")
total_label.append(labels)
total_label = [[label_map[j] for j in i] for i in total_label]
total_res = []
with open(res_dir, "r") as file:
cnt = 0
......@@ -72,7 +81,17 @@ def res_evaluate(res_dir="./outputs/predict/predictions.json", data_dir="./data/
cnt += 1
precision, recall, f1 = cal_chunk(total_res, total_label)
print("precision: {}, recall: {}, f1: {}".format(precision, recall, f1))
total_res_equal = []
total_label_equal = []
assert len(total_label) == len(total_res), "prediction result doesn't match to labels"
for i in range(len(total_label)):
num = len(total_label[i])
total_label_equal.extend(total_label[i])
total_res[i] = total_res[i][:num]
total_res_equal.extend(total_res[i])
f1 = cal_chunk(total_res_equal, total_label_equal)
print('data num: {}'.format(len(total_label)))
print("f1: {:.4f}".format(f1))
res_evaluate()
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