mxnet.metric¶
Online evaluation metric module.
Classes
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Computes accuracy classification score. |
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Dummy metric for caffe criterions. |
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Manages multiple evaluation metrics. |
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Computes Cross Entropy loss. |
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Computes a customized evaluation metric. |
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Base class for all evaluation metrics. |
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Computes the F1 score of a binary classification problem. |
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Dummy metric for directly printing loss. |
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Computes Mean Absolute Error (MAE) loss. |
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Computes the Matthews Correlation Coefficient of a binary classification problem. |
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Computes Mean Squared Error (MSE) loss. |
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Computes the negative log-likelihood loss. |
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PCC is a multiclass equivalent for the Matthews correlation coefficient derived from a discrete solution to the Pearson correlation coefficient. |
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Computes Pearson correlation. |
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Computes perplexity. |
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Computes Root Mean Squred Error (RMSE) loss. |
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Computes top k predictions accuracy. |
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Dummy metric for torch criterions. |
Functions
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Helper function for checking shape of label and prediction |
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Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function. |
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Creates a custom evaluation metric that receives its inputs as numpy arrays. |
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class
mxnet.metric.Accuracy(axis=1, name='accuracy', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes accuracy classification score.
The accuracy score is defined as
\[\text{accuracy}(y, \hat{y}) = \frac{1}{n} \sum_{i=0}^{n-1} \text{1}(\hat{y_i} == y_i)\]Methods
update(labels, preds)Updates the internal evaluation result.
- Parameters
axis (int, default=1) – The axis that represents classes
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0, 1, 1])] >>> acc = mx.metric.Accuracy() >>> acc.update(preds = predicts, labels = labels) >>> print acc.get() ('accuracy', 0.6666666666666666)
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update(labels, preds)[source]¶ Updates the internal evaluation result.
- Parameters
labels (list of NDArray) – The labels of the data with class indices as values, one per sample.
preds (list of NDArray) – Prediction values for samples. Each prediction value can either be the class index, or a vector of likelihoods for all classes.
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class
mxnet.metric.Caffe(name='caffe', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.LossDummy metric for caffe criterions.
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class
mxnet.metric.CompositeEvalMetric(metrics=None, name='composite', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricManages multiple evaluation metrics.
- Parameters
metrics (list of EvalMetric) – List of child metrics.
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Methods
add(metric)Adds a child metric.
get()Returns the current evaluation result.
Save configurations of metric.
Returns the current evaluation result.
get_metric(index)Returns a child metric.
reset()Resets the internal evaluation result to initial state.
Resets the local portion of the internal evaluation results to initial state.
update(labels, preds)Updates the internal evaluation result.
update_dict(labels, preds)Update the internal evaluation with named label and pred
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0, 1, 1])] >>> eval_metrics_1 = mx.metric.Accuracy() >>> eval_metrics_2 = mx.metric.F1() >>> eval_metrics = mx.metric.CompositeEvalMetric() >>> for child_metric in [eval_metrics_1, eval_metrics_2]: >>> eval_metrics.add(child_metric) >>> eval_metrics.update(labels = labels, preds = predicts) >>> print eval_metrics.get() (['accuracy', 'f1'], [0.6666666666666666, 0.8])
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get()[source]¶ Returns the current evaluation result.
- Returns
names (list of str) – Name of the metrics.
values (list of float) – Value of the evaluations.
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get_config()[source]¶ Save configurations of metric. Can be recreated from configs with metric.create(
**config)
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get_global()[source]¶ Returns the current evaluation result.
- Returns
names (list of str) – Name of the metrics.
values (list of float) – Value of the evaluations.
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get_metric(index)[source]¶ Returns a child metric.
- Parameters
index (int) – Index of child metric in the list of metrics.
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reset_local()[source]¶ Resets the local portion of the internal evaluation results to initial state.
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class
mxnet.metric.CrossEntropy(eps=1e-12, name='cross-entropy', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes Cross Entropy loss.
The cross entropy over a batch of sample size \(N\) is given by
\[-\sum_{n=1}^{N}\sum_{k=1}^{K}t_{nk}\log (y_{nk}),\]Methods
update(labels, preds)Updates the internal evaluation result.
where \(t_{nk}=1\) if and only if sample \(n\) belongs to class \(k\). \(y_{nk}\) denotes the probability of sample \(n\) belonging to class \(k\).
- Parameters
eps (float) – Cross Entropy loss is undefined for predicted value is 0 or 1, so predicted values are added with the small constant.
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0, 1, 1])] >>> ce = mx.metric.CrossEntropy() >>> ce.update(labels, predicts) >>> print ce.get() ('cross-entropy', 0.57159948348999023)
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class
mxnet.metric.CustomMetric(feval, name=None, allow_extra_outputs=False, output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes a customized evaluation metric.
The feval function can return a tuple of (sum_metric, num_inst) or return an int sum_metric.
- Parameters
feval (callable(label, pred)) – Customized evaluation function.
name (str) – The name of the metric. (the default is None).
allow_extra_outputs (bool, optional) – If true, the prediction outputs can have extra outputs. This is useful in RNN, where the states are also produced in outputs for forwarding. (the default is False).
name – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Methods
Save configurations of metric.
update(labels, preds)Updates the internal evaluation result.
Examples
>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))] >>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))] >>> feval = lambda x, y : (x + y).mean() >>> eval_metrics = mx.metric.CustomMetric(feval=feval) >>> eval_metrics.update(labels, predicts) >>> print eval_metrics.get() ('custom(<lambda>)', 6.0)
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class
mxnet.metric.EvalMetric(name, output_names=None, label_names=None, **kwargs)[source]¶ Bases:
objectBase class for all evaluation metrics.
Note
This is a base class that provides common metric interfaces. One should not use this class directly, but instead create new metric classes that extend it.
Methods
get()Gets the current evaluation result.
Save configurations of metric.
Gets the current global evaluation result.
Returns zipped name and value pairs for global results.
Returns zipped name and value pairs.
reset()Resets the internal evaluation result to initial state.
Resets the local portion of the internal evaluation results to initial state.
update(labels, preds)Updates the internal evaluation result.
update_dict(label, pred)Update the internal evaluation with named label and pred
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
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get()[source]¶ Gets the current evaluation result.
- Returns
names (list of str) – Name of the metrics.
values (list of float) – Value of the evaluations.
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get_config()[source]¶ Save configurations of metric. Can be recreated from configs with metric.create(
**config)
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get_global()[source]¶ Gets the current global evaluation result.
- Returns
names (list of str) – Name of the metrics.
values (list of float) – Value of the evaluations.
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get_global_name_value()[source]¶ Returns zipped name and value pairs for global results.
- Returns
A (name, value) tuple list.
- Return type
list of tuples
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get_name_value()[source]¶ Returns zipped name and value pairs.
- Returns
A (name, value) tuple list.
- Return type
list of tuples
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reset_local()[source]¶ Resets the local portion of the internal evaluation results to initial state.
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class
mxnet.metric.F1(name='f1', output_names=None, label_names=None, average='macro')[source]¶ Bases:
mxnet.metric.EvalMetricComputes the F1 score of a binary classification problem.
The F1 score is equivalent to harmonic mean of the precision and recall, where the best value is 1.0 and the worst value is 0.0. The formula for F1 score is:
F1 = 2 * (precision * recall) / (precision + recall)
Methods
reset()Resets the internal evaluation result to initial state.
Resets the internal evaluation result to initial state.
update(labels, preds)Updates the internal evaluation result.
The formula for precision and recall is:
precision = true_positives / (true_positives + false_positives) recall = true_positives / (true_positives + false_negatives)
Note
This F1 score only supports binary classification.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
average (str, default 'macro') –
- Strategy to be used for aggregating across mini-batches.
”macro”: average the F1 scores for each batch. “micro”: compute a single F1 score across all batches.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0., 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0., 1., 1.])] >>> f1 = mx.metric.F1() >>> f1.update(preds = predicts, labels = labels) >>> print f1.get() ('f1', 0.8)
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class
mxnet.metric.Loss(name='loss', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricDummy metric for directly printing loss.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Methods
update(_, preds)Updates the internal evaluation result.
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class
mxnet.metric.MAE(name='mae', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes Mean Absolute Error (MAE) loss.
The mean absolute error is given by
\[\frac{\sum_i^n |y_i - \hat{y}_i|}{n}\]Methods
update(labels, preds)Updates the internal evaluation result.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))] >>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))] >>> mean_absolute_error = mx.metric.MAE() >>> mean_absolute_error.update(labels = labels, preds = predicts) >>> print mean_absolute_error.get() ('mae', 0.5)
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class
mxnet.metric.MCC(name='mcc', output_names=None, label_names=None, average='macro')[source]¶ Bases:
mxnet.metric.EvalMetricComputes the Matthews Correlation Coefficient of a binary classification problem.
While slower to compute than F1 the MCC can give insight that F1 or Accuracy cannot. For instance, if the network always predicts the same result then the MCC will immeadiately show this. The MCC is also symetric with respect to positive and negative categorization, however, there needs to be both positive and negative examples in the labels or it will always return 0. MCC of 0 is uncorrelated, 1 is completely correlated, and -1 is negatively correlated.
\[\text{MCC} = \frac{ TP \times TN - FP \times FN } {\sqrt{ (TP + FP) ( TP + FN ) ( TN + FP ) ( TN + FN ) } }\]Methods
reset()Resets the internal evaluation result to initial state.
Resets the internal evaluation result to initial state.
update(labels, preds)Updates the internal evaluation result.
where 0 terms in the denominator are replaced by 1.
Note
This version of MCC only supports binary classification. See PCC.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
average (str, default 'macro') –
- Strategy to be used for aggregating across mini-batches.
”macro”: average the MCC for each batch. “micro”: compute a single MCC across all batches.
Examples
>>> # In this example the network almost always predicts positive >>> false_positives = 1000 >>> false_negatives = 1 >>> true_positives = 10000 >>> true_negatives = 1 >>> predicts = [mx.nd.array( [[.3, .7]]*false_positives + [[.7, .3]]*true_negatives + [[.7, .3]]*false_negatives + [[.3, .7]]*true_positives )] >>> labels = [mx.nd.array( [0.]*(false_positives + true_negatives) + [1.]*(false_negatives + true_positives) )] >>> f1 = mx.metric.F1() >>> f1.update(preds = predicts, labels = labels) >>> mcc = mx.metric.MCC() >>> mcc.update(preds = predicts, labels = labels) >>> print f1.get() ('f1', 0.95233560306652054) >>> print mcc.get() ('mcc', 0.01917751877733392)
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class
mxnet.metric.MSE(name='mse', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes Mean Squared Error (MSE) loss.
The mean squared error is given by
\[\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}\]Methods
update(labels, preds)Updates the internal evaluation result.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))] >>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))] >>> mean_squared_error = mx.metric.MSE() >>> mean_squared_error.update(labels = labels, preds = predicts) >>> print mean_squared_error.get() ('mse', 0.375)
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class
mxnet.metric.NegativeLogLikelihood(eps=1e-12, name='nll-loss', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes the negative log-likelihood loss.
The negative log-likelihoodd loss over a batch of sample size \(N\) is given by
\[-\sum_{n=1}^{N}\sum_{k=1}^{K}t_{nk}\log (y_{nk}),\]Methods
update(labels, preds)Updates the internal evaluation result.
where \(K\) is the number of classes, \(y_{nk}\) is the prediceted probability for \(k\)-th class for \(n\)-th sample. \(t_{nk}=1\) if and only if sample \(n\) belongs to class \(k\).
- Parameters
eps (float) – Negative log-likelihood loss is undefined for predicted value is 0, so predicted values are added with the small constant.
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0, 1, 1])] >>> nll_loss = mx.metric.NegativeLogLikelihood() >>> nll_loss.update(labels, predicts) >>> print nll_loss.get() ('nll-loss', 0.57159948348999023)
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class
mxnet.metric.PCC(name='pcc', output_names=None, label_names=None, has_global_stats=True)[source]¶ Bases:
mxnet.metric.EvalMetricPCC is a multiclass equivalent for the Matthews correlation coefficient derived from a discrete solution to the Pearson correlation coefficient.
\[\text{PCC} = \frac {\sum _{k}\sum _{l}\sum _{m}C_{kk}C_{lm}-C_{kl}C_{mk}} {{\sqrt {\sum _{k}(\sum _{l}C_{kl})(\sum _{k'|k'\neq k}\sum _{l'}C_{k'l'})}} {\sqrt {\sum _{k}(\sum _{l}C_{lk})(\sum _{k'|k'\neq k}\sum _{l'}C_{l'k'})}}}\]Methods
reset()Resets the internal evaluation result to initial state.
Resets the local portion of the internal evaluation results to initial state.
update(labels, preds)Updates the internal evaluation result.
defined in terms of a K x K confusion matrix C.
When there are more than two labels the PCC will no longer range between -1 and +1. Instead the minimum value will be between -1 and 0 depending on the true distribution. The maximum value is always +1.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> # In this example the network almost always predicts positive >>> false_positives = 1000 >>> false_negatives = 1 >>> true_positives = 10000 >>> true_negatives = 1 >>> predicts = [mx.nd.array( [[.3, .7]]*false_positives + [[.7, .3]]*true_negatives + [[.7, .3]]*false_negatives + [[.3, .7]]*true_positives )] >>> labels = [mx.nd.array( [0]*(false_positives + true_negatives) + [1]*(false_negatives + true_positives) )] >>> f1 = mx.metric.F1() >>> f1.update(preds = predicts, labels = labels) >>> pcc = mx.metric.PCC() >>> pcc.update(preds = predicts, labels = labels) >>> print f1.get() ('f1', 0.95233560306652054) >>> print pcc.get() ('pcc', 0.01917751877733392)
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class
mxnet.metric.PearsonCorrelation(name='pearsonr', output_names=None, label_names=None, average='macro')[source]¶ Bases:
mxnet.metric.EvalMetricComputes Pearson correlation.
The pearson correlation is given by
\[\frac{cov(y, \hat{y})}{\sigma{y}\sigma{\hat{y}}}\]Methods
get()Gets the current evaluation result.
reset()Resets the internal evaluation result to initial state.
update(labels, preds)Updates the internal evaluation result.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
average (str, default 'macro') –
- Strategy to be used for aggregating across mini-batches.
”macro”: average the pearsonr scores for each batch. “micro”: compute a single pearsonr score across all batches.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([[1, 0], [0, 1], [0, 1]])] >>> pr = mx.metric.PearsonCorrelation() >>> pr.update(labels, predicts) >>> print pr.get() ('pearsonr', 0.42163704544016178)
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class
mxnet.metric.Perplexity(ignore_label, axis=-1, name='perplexity', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes perplexity.
Perplexity is a measurement of how well a probability distribution or model predicts a sample. A low perplexity indicates the model is good at predicting the sample.
The perplexity of a model q is defined as
\[b^{\big(-\frac{1}{N} \sum_{i=1}^N \log_b q(x_i) \big)} = \exp \big(-\frac{1}{N} \sum_{i=1}^N \log q(x_i)\big)\]Methods
get()Returns the current evaluation result.
Returns the current global evaluation result.
update(labels, preds)Updates the internal evaluation result.
where we let b = e.
\(q(x_i)\) is the predicted value of its ground truth label on sample \(x_i\).
For example, we have three samples \(x_1, x_2, x_3\) and their labels are \([0, 1, 1]\). Suppose our model predicts \(q(x_1) = p(y_1 = 0 | x_1) = 0.3\) and \(q(x_2) = 1.0\), \(q(x_3) = 0.6\). The perplexity of model q is \(exp\big(-(\log 0.3 + \log 1.0 + \log 0.6) / 3\big) = 1.77109762852\).
- Parameters
ignore_label (int or None) – Index of invalid label to ignore when counting. By default, sets to -1. If set to None, it will include all entries.
axis (int (default -1)) – The axis from prediction that was used to compute softmax. By default use the last axis.
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array([[0.3, 0.7], [0, 1.], [0.4, 0.6]])] >>> labels = [mx.nd.array([0, 1, 1])] >>> perp = mx.metric.Perplexity(ignore_label=None) >>> perp.update(labels, predicts) >>> print perp.get() ('Perplexity', 1.7710976285155853)
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get()[source]¶ Returns the current evaluation result.
- Returns
Representing name of the metric and evaluation result.
- Return type
Tuple of (str, float)
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class
mxnet.metric.RMSE(name='rmse', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes Root Mean Squred Error (RMSE) loss.
The root mean squared error is given by
\[\sqrt{\frac{\sum_i^n (y_i - \hat{y}_i)^2}{n}}\]Methods
update(labels, preds)Updates the internal evaluation result.
- Parameters
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Examples
>>> predicts = [mx.nd.array(np.array([3, -0.5, 2, 7]).reshape(4,1))] >>> labels = [mx.nd.array(np.array([2.5, 0.0, 2, 8]).reshape(4,1))] >>> root_mean_squared_error = mx.metric.RMSE() >>> root_mean_squared_error.update(labels = labels, preds = predicts) >>> print root_mean_squared_error.get() ('rmse', 0.612372457981)
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class
mxnet.metric.TopKAccuracy(top_k=1, name='top_k_accuracy', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.EvalMetricComputes top k predictions accuracy.
TopKAccuracy differs from Accuracy in that it considers the prediction to be
Trueas long as the ground truth label is in the top K predicated labels.If top_k =
1, then TopKAccuracy is identical to Accuracy.- Parameters
top_k (int) – Whether targets are in top k predictions.
name (str) – Name of this metric instance for display.
output_names (list of str, or None) – Name of predictions that should be used when updating with update_dict. By default include all predictions.
label_names (list of str, or None) – Name of labels that should be used when updating with update_dict. By default include all labels.
Methods
update(labels, preds)Updates the internal evaluation result.
Examples
>>> np.random.seed(999) >>> top_k = 3 >>> labels = [mx.nd.array([2, 6, 9, 2, 3, 4, 7, 8, 9, 6])] >>> predicts = [mx.nd.array(np.random.rand(10, 10))] >>> acc = mx.metric.TopKAccuracy(top_k=top_k) >>> acc.update(labels, predicts) >>> print acc.get() ('top_k_accuracy', 0.3)
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class
mxnet.metric.Torch(name='torch', output_names=None, label_names=None)[source]¶ Bases:
mxnet.metric.LossDummy metric for torch criterions.
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mxnet.metric.check_label_shapes(labels, preds, wrap=False, shape=False)[source]¶ 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.
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mxnet.metric.create(metric, *args, **kwargs)[source]¶ Creates evaluation metric from metric names or instances of EvalMetric or a custom metric function.
- Parameters
metric (str or callable) –
Specifies the metric to create. This argument must be one of the below:
Name of a metric.
An instance of EvalMetric.
A list, each element of which is a metric or a metric name.
An evaluation function that computes custom metric for a given batch of labels and predictions.
*args (list) – Additional arguments to metric constructor. Only used when metric is str.
**kwargs (dict) – Additional arguments to metric constructor. Only used when metric is str
Examples
>>> def custom_metric(label, pred): ... return np.mean(np.abs(label - pred)) ... >>> metric1 = mx.metric.create('acc') >>> metric2 = mx.metric.create(custom_metric) >>> metric3 = mx.metric.create([metric1, metric2, 'rmse'])
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mxnet.metric.np(numpy_feval, name=None, allow_extra_outputs=False)[source]¶ Creates a custom evaluation metric that receives its inputs as numpy arrays.
- Parameters
numpy_feval (callable(label, pred)) – Custom evaluation function that receives labels and predictions for a minibatch as numpy arrays and returns the corresponding custom metric as a floating point number.
name (str, optional) – Name of the custom metric.
allow_extra_outputs (bool, optional) – Whether prediction output is allowed to have extra outputs. This is useful in cases like RNN where states are also part of output which can then be fed back to the RNN in the next step. By default, extra outputs are not allowed.
- Returns
Custom metric corresponding to the provided labels and predictions.
- Return type
float
Example
>>> def custom_metric(label, pred): ... return np.mean(np.abs(label-pred)) ... >>> metric = mx.metric.np(custom_metric)