From 9701ab2897757e8c09cf05e046beb33381d36cf6 Mon Sep 17 00:00:00 2001
From: choosewhatulike <1901722105@qq.com>
Date: Wed, 25 Jul 2018 00:14:16 +0800
Subject: [PATCH] add accuracy_score; fix optimizer

---
 fastNLP/action/metrics.py                     | 102 ++++++++++++++++++
 fastNLP/action/{optimizor.py => optimizer.py} |   0
 test/test_metrics.py                          |  27 +++++
 3 files changed, 129 insertions(+)
 rename fastNLP/action/{optimizor.py => optimizer.py} (100%)
 create mode 100644 test/test_metrics.py

diff --git a/fastNLP/action/metrics.py b/fastNLP/action/metrics.py
index 7c8a6bec..4ac463d5 100644
--- a/fastNLP/action/metrics.py
+++ b/fastNLP/action/metrics.py
@@ -5,4 +5,106 @@ To do:
  建议是每种metric写成一个函数 （由Tester的evaluate函数调用）
  参数表里只需考虑基本的参数即可，可以没有像它那么多的参数配置
   
+    support numpy array and torch tensor
 """
+import numpy as np
+import torch
+import sklearn.metrics as M
+
+
+def _conver_numpy(x):
+    '''
+    converte input data to numpy array
+    '''
+    if isinstance(x, np.ndarray): 
+        return x
+    elif isinstance(x, torch.Tensor): 
+        return x.numpy()
+    elif isinstance(x, list): 
+        return np.array(x)
+    raise TypeError('cannot accept obejct: {}'.format(x))
+
+def _check_same_len(*arrays, axis=0):
+    '''
+    check if input array list has same length for one dimension
+    '''
+    lens = set([x.shape[axis] for x in arrays if x is not None])
+    return len(lens) == 1
+        
+
+def _label_types(y):
+    '''
+    determine the type
+    "binary"
+    "multiclass"
+    "multiclass-multioutput"
+    "multilabel"
+    '''
+    # never squeeze the first dimension
+    y = np.squeeze(y, list(range(1, len(y.shape))))
+    shape = y.shape
+    if len(shape) < 1: 
+        raise ValueError('cannot accept data: {}'.format(y))
+    if len(shape) == 1:
+        return 'multiclass' if np.unique(y).shape[0] > 2 else 'binary', y
+    if len(shape) == 2:
+        return 'multiclass-multioutput' if np.unique(y).shape[0] > 2 else 'multilabel', y
+    return 'unknown', y
+        
+
+def _check_data(y_true, y_pred):
+    '''
+    check if y_true and y_pred is same type of data e.g both binary or multiclass
+    '''
+    if not _check_same_len(y_true, y_pred):
+        raise ValueError('cannot accept data with different shape {0}, {1}'.format(y_true, y_pred))
+    type_true, y_true = _label_types(y_true)
+    type_pred, y_pred = _label_types(y_pred)
+
+    type_set = set(['binary', 'multiclass'])
+    if type_true in type_set and type_pred in type_set:
+        return type_true if type_true == type_pred else 'multiclass', y_true, y_pred
+
+    type_set = set(['multiclass-multioutput', 'multilabel'])
+    if type_true in type_set and type_pred in type_set:
+        return type_true if type_true == type_pred else 'multiclass-multioutput', y_true, y_pred
+    
+    raise ValueError('cannot accept data mixed of {0} and {1} target'.format(type_true, type_pred))
+    
+
+def _weight_sum(y, normalize=True, sample_weight=None):
+    if normalize:
+        return np.average(y, weights=sample_weight)
+    if sample_weight is None:
+        return y.sum()
+    else:
+        return np.dot(y, sample_weight)
+
+
+def accuracy_score(y_true, y_pred, normalize=True, sample_weight=None):
+    y_type, y_true, y_pred = _check_data(y_true, y_pred)
+    if y_type == 'multiclass-multioutput':
+        raise ValueError('cannot accept data type {0}'.format(y_type))
+    if y_type == 'multilabel':
+        equel = (y_true == y_pred).sum(1)
+        count = equel == y_true.shape[1]
+    else:
+        count = y_true == y_pred
+    return _weight_sum(count, normalize=normalize, sample_weight=sample_weight)
+
+
+def recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None):
+    raise NotImplementedError
+
+def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None):
+    raise NotImplementedError
+
+def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None):
+    raise NotImplementedError
+
+def classification_report(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2):
+    raise NotImplementedError
+
+if __name__ == '__main__':
+    y = np.array([1,0,1,0,1,1])
+    print(_label_types(y))
\ No newline at end of file
diff --git a/fastNLP/action/optimizor.py b/fastNLP/action/optimizer.py
similarity index 100%
rename from fastNLP/action/optimizor.py
rename to fastNLP/action/optimizer.py
diff --git a/test/test_metrics.py b/test/test_metrics.py
new file mode 100644
index 00000000..20e50940
--- /dev/null
+++ b/test/test_metrics.py
@@ -0,0 +1,27 @@
+import sys, os
+sys.path = [os.path.abspath('..')] + sys.path
+
+from fastNLP.action.metrics import accuracy_score
+from sklearn import metrics as M
+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
+    def test_accuracy_score(self):
+        for shape, high_bound in [((1000,), 2), ((1000,), 10), ((1000, 10), 2)]:
+            # test for binary, multiclass, multilabel
+            y_true, y_pred = generate_fake_label(0, high_bound, shape)
+            for normalize in [True, False]:
+                for sample_weight in [None, random.rand(shape[0])]:
+                    test = accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
+                    ans = M.accuracy_score(y_true, y_pred, normalize=normalize, sample_weight=sample_weight)
+                    self.assertAlmostEqual(test, ans, delta=self.delta)
+
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file