Merge pull request #19 from choosewhatulike/master

add metrics

fastNLP/core/metrics.py

@@ -5,4 +5,179 @@ 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
+import warnings
+
+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"
+    "unknown"
+    '''
+    # 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
+    '''
+    y_true, y_pred = _conver_numpy(y_true), _conver_numpy(y_pred)
+    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'):
+    y_type, y_true, y_pred = _check_data(y_true, y_pred)
+    if average == 'binary':
+        if y_type != 'binary':
+            raise ValueError("data type is {} but  use average type {}".format(y_type, average))
+        else:
+            pos = (y_true == pos_label)
+            tp = np.logical_and((y_true == y_pred), pos).sum()
+            pos_sum = pos.sum()
+            return tp / pos_sum if pos_sum > 0 else 0
+    elif average == None:
+        y_labels = set(list(np.unique(y_true)))
+        if labels is None:
+            labels = list(y_labels)
+        else:
+            for i in labels:
+                if i not in y_labels:
+                    warnings.warn('label {} is not contained in data'.format(i), UserWarning)
+
+        if y_type in ['binary', 'multiclass']:
+            y_pred_right = y_true == y_pred
+            pos_list = [y_true == i for i in labels]
+            pos_sum_list = [pos_i.sum() for pos_i in pos_list]
+            return np.array([np.logical_and(y_pred_right, pos_i).sum() / sum_i if sum_i > 0 else 0 \
+                    for pos_i, sum_i in zip(pos_list, pos_sum_list)])
+        elif y_type == 'multilabel':
+            y_pred_right = y_true == y_pred
+            pos = (y_true == pos_label)
+            tp = np.logical_and(y_pred_right, pos).sum(0)
+            pos_sum = pos.sum(0)
+            return np.array([tp[i] / pos_sum[i] if pos_sum[i] > 0 else 0 for i in labels])
+        else:
+            raise ValueError('not support targets type {}'.format(y_type))
+    raise ValueError('not support for average type {}'.format(average))
+
+def precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
+    y_type, y_true, y_pred = _check_data(y_true, y_pred)
+    if average == 'binary':
+        if y_type != 'binary':
+            raise ValueError("data type is {} but  use average type {}".format(y_type, average))
+        else:
+            pos = (y_true == pos_label)
+            tp = np.logical_and((y_true == y_pred), pos).sum()
+            pos_pred = (y_pred == pos_label).sum()
+            return tp / pos_pred if pos_pred > 0 else 0
+    elif average == None:
+        y_labels = set(list(np.unique(y_true)))
+        if labels is None:
+            labels = list(y_labels)
+        else:
+            for i in labels:
+                if i not in y_labels:
+                    warnings.warn('label {} is not contained in data'.format(i), UserWarning)
+
+        if y_type in ['binary', 'multiclass']:
+            y_pred_right = y_true == y_pred
+            pos_list = [y_true == i for i in labels]
+            pos_sum_list = [(y_pred == i).sum() for i in labels]
+            return np.array([np.logical_and(y_pred_right, pos_i).sum() / sum_i if sum_i > 0 else 0 \
+                    for pos_i, sum_i in zip(pos_list, pos_sum_list)])
+        elif y_type == 'multilabel':
+            y_pred_right = y_true == y_pred
+            pos = (y_true == pos_label)
+            tp = np.logical_and(y_pred_right, pos).sum(0)
+            pos_sum = (y_pred == pos_label).sum(0)
+            return np.array([tp[i] / pos_sum[i] if pos_sum[i] > 0 else 0 for i in labels])
+        else:
+            raise ValueError('not support targets type {}'.format(y_type))
+    raise ValueError('not support for average type {}'.format(average))
+
+def f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary'):
+    precision = precision_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average)
+    recall = recall_score(y_true, y_pred, labels=labels, pos_label=pos_label, average=average)
+    if isinstance(precision, np.ndarray):
+        res = 2 * precision * recall / (precision + recall)
+        res[(precision + recall) <= 0] = 0
+        return res
+    return 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0
+
+
+def classification_report(y_true, y_pred, labels=None, target_names=None, digits=2):
+    raise NotImplementedError
+
+if __name__ == '__main__':
+    y = np.array([1,0,1,0,1,1])
+    print(_label_types(y))

fastNLP/core/optimizer.py

@@ -0,0 +1,5 @@
+'''
+use optimizer from Pytorch
+'''
+
+from torch.optim import *

fastNLP/core/optimizor.py

@@ -1,50 +0,0 @@
-from torch import optim
-
-
-def get_torch_optimizer(params, alg_name='sgd', **args):
-    """
-    construct PyTorch optimizer by algorithm's name
-    optimizer's arguments can be specified, for different optimizer's arguments, please see PyTorch doc
-
-    usage:
-        optimizer = get_torch_optimizer(model.parameters(), 'SGD', lr=0.01)
-
-    """
-
-    name = alg_name.lower()
-    if name == 'adadelta':
-        return optim.Adadelta(params, **args)
-    elif name == 'adagrad':
-        return optim.Adagrad(params, **args)
-    elif name == 'adam':
-        return optim.Adam(params, **args)
-    elif name == 'adamax':
-        return optim.Adamax(params, **args)
-    elif name == 'asgd':
-        return optim.ASGD(params, **args)
-    elif name == 'lbfgs':
-        return optim.LBFGS(params, **args)
-    elif name == 'rmsprop':
-        return optim.RMSprop(params, **args)
-    elif name == 'rprop':
-        return optim.Rprop(params, **args)
-    elif name == 'sgd':
-        # SGD's parameter lr is required
-        if 'lr' not in args:
-            args['lr'] = 0.01
-        return optim.SGD(params, **args)
-    elif name == 'sparseadam':
-        return optim.SparseAdam(params, **args)
-    else:
-        raise TypeError('no such optimizer named {}'.format(alg_name))
-
-
-if __name__ == '__main__':
-    from torch.nn.modules import Linear
-
-    net = Linear(2, 5)
-
-    test1 = get_torch_optimizer(net.parameters(), 'adam', lr=1e-2, weight_decay=1e-3)
-    print(test1)
-    test2 = get_torch_optimizer(net.parameters(), 'SGD')
-    print(test2)

test/test_metrics.py

@@ -0,0 +1,93 @@
+import sys, os
+sys.path = [os.path.join(os.path.dirname(__file__), '..')] + sys.path
+
+from fastNLP.action import metrics
+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)
+    
+    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)
+        # 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)
+
+    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)
+    
+    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)
+
+    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)
+        # 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)
+
+if __name__ == '__main__':
+    unittest.main()