Merge branch 'master' into more_code_comments

.github/ISSUE_TEMPLATE/bug-report-bug--.md

@@ -0,0 +1,41 @@
+---
+name: Bug report Bug报告
+about: Create a report to help us improve  报告bug帮助我们改进
+
+---
+
+**Describe the bug** 
+A clear and concise description of what the bug is.
+清晰而简要地描述bug
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+重现这个bug的步骤
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+清晰而简要地描述你期望的结果
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+如果可以，请提供截图
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+相关环境
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.
+备注

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,35 @@
+---
+name: Bug report
+about: Create a report to help us improve
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/custom-issue-template-------.md

@@ -0,0 +1,7 @@
+---
+name: Custom issue template 普通事项模板
+about: Describe this issue template's purpose here. 在这里描述事项
+
+---
+
+

.github/ISSUE_TEMPLATE/custom.md

@@ -0,0 +1,7 @@
+---
+name: Custom issue template
+about: Describe this issue template's purpose here.
+
+---
+
+

.github/ISSUE_TEMPLATE/feature-request------.md

@@ -0,0 +1,17 @@
+---
+name: Feature request  功能提议
+about: Suggest an idea for this project  提议新功能
+
+---
+
+**Is your feature request related to a problem? Please describe.**  问题是什么
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like** 解决方案是什么
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered** 其他解决方案
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context** 备注
+Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,17 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

fastNLP/core/README.md

@@ -1,8 +1 @@
-SpaCy "Doc"
-https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/tokens/doc.pyx#L80
 
-SpaCy "Vocab"
-https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/vocab.pyx#L25
-
-SpaCy "Token"
-https://github.com/explosion/spaCy/blob/75d2a05c2938f412f0fae44748374e4de19cc2be/spacy/tokens/token.pyx#L27

fastNLP/core/metrics.py

@@ -34,7 +34,7 @@ def _label_types(y):
     "unknown"
     """
     # never squeeze the first dimension
-    y = np.squeeze(y, list(range(1, len(y.shape))))
+    y = y.squeeze() if y.shape[0] > 1 else y.resize(1, -1)
     shape = y.shape
     if len(shape) < 1:
         raise ValueError('cannot accept data: {}'.format(y))
@@ -103,7 +103,7 @@ def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
             labels = list(y_labels)
         else:
             for i in labels:
-                if i not in y_labels:
+                if (i not in y_labels and y_type != 'multilabel') or (y_type == 'multilabel' and i >= y_true.shape[1]):
                     warnings.warn('label {} is not contained in data'.format(i), UserWarning)
 
         if y_type in ['binary', 'multiclass']:
@@ -139,7 +139,7 @@ def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
             labels = list(y_labels)
         else:
             for i in labels:
-                if i not in y_labels:
+                if (i not in y_labels and y_type != 'multilabel') or (y_type == 'multilabel' and i >= y_true.shape[1]):
                     warnings.warn('label {} is not contained in data'.format(i), UserWarning)
 
         if y_type in ['binary', 'multiclass']:

test/test_metrics.py

@@ -1,98 +1,77 @@
 import sys, os
-
 sys.path = [os.path.join(os.path.dirname(__file__), '..')] + sys.path
 
 from fastNLP.core import metrics
-from sklearn import metrics as skmetrics
+# from sklearn import metrics as skmetrics
 import unittest
 import numpy as np
 from numpy import random
 
-
 def generate_fake_label(low, high, size):
     return random.randint(low, high, size), random.randint(low, high, size)
 
-
 class TestMetrics(unittest.TestCase):
     delta = 1e-5
     # test for binary, multiclass, multilabel
     data_types = [((1000,), 2), ((1000,), 10), ((1000, 10), 2)]
     fake_data = [generate_fake_label(0, high, shape) for shape, high in data_types]
-
     def test_accuracy_score(self):
         for y_true, y_pred in self.fake_data:
             for normalize in [True, False]:
                 for sample_weight in [None, random.rand(y_true.shape[0])]:
-                    ans = skmetrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
                     test = metrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
-                    self.assertAlmostEqual(test, ans, delta=self.delta)
-
+                    # ans = skmetrics.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
+                    # self.assertAlmostEqual(test, ans, delta=self.delta)
+    
     def test_recall_score(self):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.recall_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.recall_score(y_true, y_pred, labels=labels, average=None)
-            ans = list(ans)
             if not isinstance(test, list):
                 test = list(test)
-            for a, b in zip(test, ans):
-                # print('{}, {}'.format(a, b))
-                self.assertAlmostEqual(a, b, delta=self.delta)
+            # ans = skmetrics.recall_score(y_true, y_pred,labels=labels, average=None)
+            # ans = list(ans)
+            # for a, b in zip(test, ans):
+            #     # print('{}, {}'.format(a, b))
+            #     self.assertAlmostEqual(a, b, delta=self.delta)
         # test binary
         y_true, y_pred = generate_fake_label(0, 2, 1000)
-        ans = skmetrics.recall_score(y_true, y_pred)
         test = metrics.recall_score(y_true, y_pred)
-        self.assertAlmostEqual(ans, test, delta=self.delta)
+        # ans = skmetrics.recall_score(y_true, y_pred)
+        # self.assertAlmostEqual(ans, test, delta=self.delta)
 
     def test_precision_score(self):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.precision_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.precision_score(y_true, y_pred, labels=labels, average=None)
-            ans, test = list(ans), list(test)
-            for a, b in zip(test, ans):
-                # print('{}, {}'.format(a, b))
-                self.assertAlmostEqual(a, b, delta=self.delta)
+            # ans = skmetrics.precision_score(y_true, y_pred,labels=labels, average=None)
+            # ans, test = list(ans), list(test)
+            # for a, b in zip(test, ans):
+            #     # print('{}, {}'.format(a, b))
+            #     self.assertAlmostEqual(a, b, delta=self.delta)
         # test binary
         y_true, y_pred = generate_fake_label(0, 2, 1000)
-        ans = skmetrics.precision_score(y_true, y_pred)
         test = metrics.precision_score(y_true, y_pred)
-        self.assertAlmostEqual(ans, test, delta=self.delta)
-
-    def test_precision_score(self):
-        for y_true, y_pred in self.fake_data:
-            # print(y_true.shape)
-            labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.precision_score(y_true, y_pred, labels=labels, average=None)
-            test = metrics.precision_score(y_true, y_pred, labels=labels, average=None)
-            ans, test = list(ans), list(test)
-            for a, b in zip(test, ans):
-                # print('{}, {}'.format(a, b))
-                self.assertAlmostEqual(a, b, delta=self.delta)
-        # test binary
-        y_true, y_pred = generate_fake_label(0, 2, 1000)
-        ans = skmetrics.precision_score(y_true, y_pred)
-        test = metrics.precision_score(y_true, y_pred)
-        self.assertAlmostEqual(ans, test, delta=self.delta)
+        # ans = skmetrics.precision_score(y_true, y_pred)
+        # self.assertAlmostEqual(ans, test, delta=self.delta)
 
     def test_f1_score(self):
         for y_true, y_pred in self.fake_data:
             # print(y_true.shape)
             labels = list(range(y_true.shape[1])) if len(y_true.shape) >= 2 else None
-            ans = skmetrics.f1_score(y_true, y_pred, labels=labels, average=None)
             test = metrics.f1_score(y_true, y_pred, labels=labels, average=None)
-            ans, test = list(ans), list(test)
-            for a, b in zip(test, ans):
-                # print('{}, {}'.format(a, b))
-                self.assertAlmostEqual(a, b, delta=self.delta)
+            # ans = skmetrics.f1_score(y_true, y_pred,labels=labels, average=None)
+            # ans, test = list(ans), list(test)
+            # for a, b in zip(test, ans):
+            #     # print('{}, {}'.format(a, b))
+            #     self.assertAlmostEqual(a, b, delta=self.delta)
         # test binary
         y_true, y_pred = generate_fake_label(0, 2, 1000)
-        ans = skmetrics.f1_score(y_true, y_pred)
         test = metrics.f1_score(y_true, y_pred)
-        self.assertAlmostEqual(ans, test, delta=self.delta)
-
+        # ans = skmetrics.f1_score(y_true, y_pred)
+        # self.assertAlmostEqual(ans, test, delta=self.delta)
 
 if __name__ == '__main__':
     unittest.main()