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ignite.contrib.metrics#

Contribution module of metrics

class ignite.contrib.metrics.AveragePrecision(output_transform=<function AveragePrecision.<lambda>>, check_compute_fn=False)[source]#

Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score .

Parameters
  • output_transform (callable, optional) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • check_compute_fn (bool) – Optional default False. If True, average_precision_score is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.

AveragePrecision expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.softmax(y_pred, dim=1)
    return y_pred, y

avg_precision = AveragePrecision(activated_output_transform)
class ignite.contrib.metrics.GpuInfo[source]#

Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations.

Note

In case if gpu utilization reports “N/A” on a given GPU, corresponding metric value is not set.

Examples

# Default GPU measurements
GpuInfo().attach(trainer, name='gpu')  # metric names are 'gpu:X mem(%)', 'gpu:X util(%)'

# Logging with TQDM
ProgressBar(persist=True).attach(trainer, metric_names=['gpu:0 mem(%)', 'gpu:0 util(%)'])
# Progress bar will looks like
# Epoch [2/10]: [12/24]  50%|█████      , gpu:0 mem(%)=79, gpu:0 util(%)=59 [00:17<1:23]

# Logging with Tensorboard
tb_logger.attach(trainer,
                 log_handler=OutputHandler(tag="training", metric_names='all'),
                 event_name=Events.ITERATION_COMPLETED)
attach(engine, name='gpu', event_name=Events.ITERATION_COMPLETED)[source]#

Attaches current metric to provided engine. On the end of engine’s run, engine.state.metrics dictionary will contain computed metric’s value under provided name.

Parameters
  • engine (Engine) – the engine to which the metric must be attached

  • name (str) – the name of the metric to attach

  • usage (str or MetricUsage, optional) – the usage of the metric. Valid string values should be ‘EpochWise.usage_name’ (default) or ‘BatchWise.usage_name’.

Example:

metric = ...
metric.attach(engine, "mymetric")

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine)

Example with usage:

metric = ...
metric.attach(engine, "mymetric", usage=BatchWise.usage_name)

assert "mymetric" in engine.run(data).metrics

assert metric.is_attached(engine, usage=BatchWise.usage_name)
completed(engine, name)[source]#

Helper method to compute metric’s value and put into the engine. It is automatically attached to the engine with attach().

Parameters

engine (Engine) – the engine to which the metric must be attached

compute()[source]#

Computes the metric based on it’s accumulated state.

By default, this is called at the end of each epoch.

Returns

the actual quantity of interest. However, if a Mapping is returned, it will be (shallow) flattened into engine.state.metrics when completed() is called.

Return type

Any

Raises

NotComputableError – raised when the metric cannot be computed.

reset()[source]#

Resets the metric to it’s initial state.

By default, this is called at the start of each epoch.

update(output)[source]#

Updates the metric’s state using the passed batch output.

By default, this is called once for each batch.

Parameters

output – the is the output from the engine’s process function.

class ignite.contrib.metrics.PrecisionRecallCurve(output_transform=<function PrecisionRecallCurve.<lambda>>, check_compute_fn=False)[source]#

Compute precision-recall pairs for different probability thresholds for binary classification task by accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.precision_recall_curve .

Parameters
  • output_transform (callable, optional) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • check_compute_fn (bool) – Optional default False. If True, precision_recall_curve is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.

PrecisionRecallCurve expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.sigmoid(y_pred)
    return y_pred, y

roc_auc = PrecisionRecallCurve(activated_output_transform)
class ignite.contrib.metrics.ROC_AUC(output_transform=<function ROC_AUC.<lambda>>, check_compute_fn=False)[source]#

Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.roc_auc_score .

Parameters
  • output_transform (callable, optional) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • check_compute_fn (bool) – Optional default False. If True, roc_curve is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.

ROC_AUC expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.sigmoid(y_pred)
    return y_pred, y

roc_auc = ROC_AUC(activated_output_transform)
class ignite.contrib.metrics.RocCurve(output_transform=<function RocCurve.<lambda>>, check_compute_fn=False)[source]#

Compute Receiver operating characteristic (ROC) for binary classification task by accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.roc_curve .

Parameters
  • output_transform (callable, optional) – a callable that is used to transform the Engine’s process_function’s output into the form expected by the metric. This can be useful if, for example, you have a multi-output model and you want to compute the metric with respect to one of the outputs.

  • check_compute_fn (bool) –

    Optional default False. If True, sklearn.metrics.roc_curve is run on the first batch of data to ensure there are no issues. User will be warned in case there are any issues computing the function.

RocCurve expects y to be comprised of 0’s and 1’s. y_pred must either be probability estimates or confidence values. To apply an activation to y_pred, use output_transform as shown below:

def activated_output_transform(output):
    y_pred, y = output
    y_pred = torch.sigmoid(y_pred)
    return y_pred, y

roc_auc = RocCurve(activated_output_transform)

Regression metrics#

Module ignite.contrib.metrics.regression provides implementations of metrics useful for regression tasks. Definitions of metrics are based on Botchkarev 2018, page 30 “Appendix 2. Metrics mathematical definitions”.

Complete list of metrics:

class ignite.contrib.metrics.regression.CanberraMetric(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Canberra Metric.

CM=j=1nAjPjAj+Pj\text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{A_j + P_j}

where, AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.FractionalAbsoluteError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Fractional Absolute Error.

FAE=1nj=1n2AjPjAj+Pj\text{FAE} = \frac{1}{n}\sum_{j=1}^n\frac{2 |A_j - P_j|}{|A_j| + |P_j|}

where, AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.FractionalBias(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Fractional Bias:

FB=1nj=1n2(AjPj)Aj+Pj\text{FB} = \frac{1}{n}\sum_{j=1}^n\frac{2 (A_j - P_j)}{A_j + P_j},

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.GeometricMeanAbsoluteError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Geometric Mean Absolute Error.

GMAE=exp(1nj=1nln(AjPj))\text{GMAE} = \exp(\frac{1}{n}\sum_{j=1}^n\ln(|A_j - P_j|))

where, AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.GeometricMeanRelativeAbsoluteError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Geometric Mean Relative Absolute Error:

GMRAE=exp(1nj=1nlnAjPjAjAˉ)\text{GMRAE} = \exp(\frac{1}{n}\sum_{j=1}^n \ln\frac{|A_j - P_j|}{|A_j - \bar{A}|})

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.ManhattanDistance(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Manhattan Distance:

MD=j=1n(AjPj)\text{MD} = \sum_{j=1}^n (A_j - P_j),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.MaximumAbsoluteError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Maximum Absolute Error:

MaxAE=maxj=1,n(AjPj)\text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.MeanAbsoluteRelativeError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculate Mean Absolute Relative Error:

MARE=1nj=1nAjPjAj\text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|A_j\right|},

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in the reference Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.MeanError(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Mean Error:

ME=1nj=1n(AjPj)\text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in the reference Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.MeanNormalizedBias(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Mean Normalized Bias:

MNB=1nj=1nAjPjAj\text{MNB} = \frac{1}{n}\sum_{j=1}^n\frac{A_j - P_j}{A_j},

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in the reference Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.MedianAbsoluteError(output_transform=<function MedianAbsoluteError.<lambda>>)[source]#

Calculates the Median Absolute Error:

MdAE=MDj=1,n(AjPj)\text{MdAE} = \text{MD}_{j=1,n} \left( |A_j - P_j| \right),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1) and of type float32.

Warning

Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM.

class ignite.contrib.metrics.regression.MedianAbsolutePercentageError(output_transform=<function MedianAbsolutePercentageError.<lambda>>)[source]#

Calculates the Median Absolute Percentage Error:

MdAPE=100MDj=1,n(AjPjAj)\text{MdAPE} = 100 \cdot \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j|} \right),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1) and of type float32.

Warning

Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM.

class ignite.contrib.metrics.regression.MedianRelativeAbsoluteError(output_transform=<function MedianRelativeAbsoluteError.<lambda>>)[source]#

Calculates the Median Relative Absolute Error:

MdRAE=MDj=1,n(AjPjAjAˉ)\text{MdRAE} = \text{MD}_{j=1,n} \left( \frac{|A_j - P_j|}{|A_j - \bar{A}|} \right),

where AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1) and of type float32.

Warning

Current implementation stores all input data (output and target) in as tensors before computing a metric. This can potentially lead to a memory error if the input data is larger than available RAM.

class ignite.contrib.metrics.regression.R2Score(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the R-Squared, the coefficient of determination:

R2=1j=1n(AjPj)2j=1n(AjAˉ)2R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2},

where AjA_j is the ground truth, PjP_j is the predicted value and Aˉ\bar{A} is the mean of the ground truth.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1) and of type float32.

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –

class ignite.contrib.metrics.regression.WaveHedgesDistance(output_transform=<function Metric.<lambda>>, device=None)[source]#

Calculates the Wave Hedges Distance.

WHD=j=1nAjPjmax(Aj,Pj)\text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)}, where, AjA_j is the ground truth and PjP_j is the predicted value.

More details can be found in Botchkarev 2018.

  • update must receive output of the form (y_pred, y) or {'y_pred': y_pred, 'y': y}.

  • y and y_pred must be of same shape (N, ) or (N, 1).

Parameters
  • output_transform (Callable) –

  • device (Optional[Union[str, torch.device]]) –