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new dd1f21b Support single array input for metric (#9930)
dd1f21b is described below
commit dd1f21b4369371f4d20fc8a88c1d10834f8cf53b
Author: Tong He <hetong...@gmail.com>
AuthorDate: Tue Mar 13 11:47:26 2018 -0700
Support single array input for metric (#9930)
* fix #9865
* add unittest
* fix format
* fix format
* fix superfluous loop in metric
* fix lint
---
python/mxnet/metric.py | 59 +++++++++++++++++++++++++++---------
tests/python/unittest/test_metric.py | 21 +++++++++++++
2 files changed, 66 insertions(+), 14 deletions(-)
diff --git a/python/mxnet/metric.py b/python/mxnet/metric.py
index ddffc01..ff4cce9 100644
--- a/python/mxnet/metric.py
+++ b/python/mxnet/metric.py
@@ -30,8 +30,25 @@ from . import ndarray
from . import registry
-def check_label_shapes(labels, preds, shape=0):
- if shape == 0:
+def check_label_shapes(labels, preds, wrap=False, shape=False):
+ """Helper function for checking shape of label and prediction
+
+ Parameters
+ ----------
+ labels : list of `NDArray`
+ The labels of the data.
+
+ preds : list of `NDArray`
+ Predicted values.
+
+ wrap : boolean
+ If True, wrap labels/preds in a list if they are single NDArray
+
+ shape : boolean
+ If True, check the shape of labels and preds;
+ Otherwise only check their length.
+ """
+ if not shape:
label_shape, pred_shape = len(labels), len(preds)
else:
label_shape, pred_shape = labels.shape, preds.shape
@@ -40,6 +57,13 @@ def check_label_shapes(labels, preds, shape=0):
raise ValueError("Shape of labels {} does not match shape of "
"predictions {}".format(label_shape, pred_shape))
+ if wrap:
+ if isinstance(labels, ndarray.ndarray.NDArray):
+ labels = [labels]
+ if isinstance(preds, ndarray.ndarray.NDArray):
+ preds = [preds]
+
+ return labels, preds
class EvalMetric(object):
"""Base class for all evaluation metrics.
@@ -386,7 +410,7 @@ class Accuracy(EvalMetric):
Prediction values for samples. Each prediction value can either be
the class index,
or a vector of likelihoods for all classes.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred_label in zip(labels, preds):
if pred_label.shape != label.shape:
@@ -394,7 +418,7 @@ class Accuracy(EvalMetric):
pred_label = pred_label.asnumpy().astype('int32')
label = label.asnumpy().astype('int32')
- check_label_shapes(label, pred_label)
+ labels, preds = check_label_shapes(label, pred_label)
self.sum_metric += (pred_label.flat == label.flat).sum()
self.num_inst += len(pred_label.flat)
@@ -456,7 +480,7 @@ class TopKAccuracy(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred_label in zip(labels, preds):
assert(len(pred_label.shape) <= 2), 'Predictions should be no more
than 2 dims'
@@ -614,7 +638,7 @@ class F1(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
self.metrics.update_binary_stats(label, pred)
@@ -785,7 +809,7 @@ class MAE(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
label = label.asnumpy()
@@ -793,6 +817,8 @@ class MAE(EvalMetric):
if len(label.shape) == 1:
label = label.reshape(label.shape[0], 1)
+ if len(pred.shape) == 1:
+ pred = pred.reshape(pred.shape[0], 1)
self.sum_metric += numpy.abs(label - pred).mean()
self.num_inst += 1 # numpy.prod(label.shape)
@@ -843,7 +869,7 @@ class MSE(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
label = label.asnumpy()
@@ -851,6 +877,8 @@ class MSE(EvalMetric):
if len(label.shape) == 1:
label = label.reshape(label.shape[0], 1)
+ if len(pred.shape) == 1:
+ pred = pred.reshape(pred.shape[0], 1)
self.sum_metric += ((label - pred)**2.0).mean()
self.num_inst += 1 # numpy.prod(label.shape)
@@ -901,7 +929,7 @@ class RMSE(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
label = label.asnumpy()
@@ -909,6 +937,8 @@ class RMSE(EvalMetric):
if len(label.shape) == 1:
label = label.reshape(label.shape[0], 1)
+ if len(pred.shape) == 1:
+ pred = pred.reshape(pred.shape[0], 1)
self.sum_metric += numpy.sqrt(((label - pred)**2.0).mean())
self.num_inst += 1
@@ -969,7 +999,7 @@ class CrossEntropy(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
label = label.asnumpy()
@@ -1037,7 +1067,7 @@ class NegativeLogLikelihood(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for label, pred in zip(labels, preds):
label = label.asnumpy()
@@ -1095,9 +1125,10 @@ class PearsonCorrelation(EvalMetric):
preds : list of `NDArray`
Predicted values.
"""
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
+
for label, pred in zip(labels, preds):
- check_label_shapes(label, pred, 1)
+ check_label_shapes(label, pred, False, True)
label = label.asnumpy()
pred = pred.asnumpy()
self.sum_metric += numpy.corrcoef(pred.ravel(), label.ravel())[0,
1]
@@ -1209,7 +1240,7 @@ class CustomMetric(EvalMetric):
Predicted values.
"""
if not self._allow_extra_outputs:
- check_label_shapes(labels, preds)
+ labels, preds = check_label_shapes(labels, preds, True)
for pred, label in zip(preds, labels):
label = label.asnumpy()
diff --git a/tests/python/unittest/test_metric.py
b/tests/python/unittest/test_metric.py
index fee8b66..bcb0e2d 100644
--- a/tests/python/unittest/test_metric.py
+++ b/tests/python/unittest/test_metric.py
@@ -120,6 +120,27 @@ def test_pearsonr():
_, pearsonr = metric.get()
assert pearsonr == pearsonr_expected
+def test_single_array_input():
+ pred = mx.nd.array([[1,2,3,4]])
+ label = pred + 0.1
+
+ mse = mx.metric.create('mse')
+ mse.update(label, pred)
+ _, mse_res = mse.get()
+ np.testing.assert_almost_equal(mse_res, 0.01)
+
+ mae = mx.metric.create('mae')
+ mae.update(label, pred)
+ mae.get()
+ _, mae_res = mae.get()
+ np.testing.assert_almost_equal(mae_res, 0.1)
+
+ rmse = mx.metric.create('rmse')
+ rmse.update(label, pred)
+ rmse.get()
+ _, rmse_res = rmse.get()
+ np.testing.assert_almost_equal(rmse_res, 0.1)
+
if __name__ == '__main__':
import nose
nose.runmodule()
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