ignite.contrib.metrics#
Contribution module of metrics
- class ignite.contrib.metrics.AveragePrecision(activation=None, output_transform=<function AveragePrecision.<lambda>>)[source]#
Computes Average Precision accumulating predictions and the ground-truth during an epoch and applying sklearn.metrics.average_precision_score
- Parameters
activation (Callable, optional) – optional function to apply on prediction tensors, e.g. activation=torch.sigmoid to transform logits.
output_transform (callable, optional) – a callable that is used to transform the
ignite.engine.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.
- class ignite.contrib.metrics.ROC_AUC(activation=None, output_transform=<function ROC_AUC.<lambda>>)[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
activation (Callable, optional) – optional function to apply on prediction tensors, e.g. activation=torch.sigmoid to transform logits.
output_transform (callable, optional) – a callable that is used to transform the
ignite.engine.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.
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”.
- class ignite.contrib.metrics.regression.FractionalBias(output_transform=<function Metric.<lambda>>)[source]#
Calculates the Fractional Bias:
$\text{FB} = \frac{1}{n}\sum_{j=1}^n\frac{2 * (A_j - P_j)}{A_j + P_j}$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in Botchkarev 2018.
update must receive output of the form (y_pred, y).
y and y_pred must be of same shape.
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed
- class ignite.contrib.metrics.regression.ManhattanDistance(output_transform=<function Metric.<lambda>>)[source]#
Calculates the Manhattan Distance:
$\text{MD} = \sum_{j=1}^n (A_j - P_j)$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in Botchkarev 2018.
update must receive output of the form (y_pred, y).
y and y_pred must be of same shape.
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed
- class ignite.contrib.metrics.regression.MaximumAbsoluteError(output_transform=<function Metric.<lambda>>)[source]#
Calculates the Maximum Absolute Error:
$\text{MaxAE} = \max_{j=1,n} \left( \lvert A_j-P_j \rvert \right)$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in Botchkarev 2018.
update must receive output of the form (y_pred, y).
y and y_pred must be of same shape.
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed
- class ignite.contrib.metrics.regression.MeanAbsoluteRelativeError(output_transform=<function Metric.<lambda>>)[source]#
Calculate Mean Absolute Relative Error:
$\text{MARE} = \frac{1}{n}\sum_{j=1}^n\frac{\left|A_j-P_j\right|}{\left|A_j\right|}$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in the reference Botchkarev 2018.
update must receive output of the form (y_pred, y)
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed
- class ignite.contrib.metrics.regression.MeanError(output_transform=<function Metric.<lambda>>)[source]#
Calculates the Mean Error:
$\text{ME} = \frac{1}{n}\sum_{j=1}^n (A_j - P_j)$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in the reference Botchkarev 2018.
update must receive output of the form (y_pred, y).
y and y_pred must be of same shape.
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed
- class ignite.contrib.metrics.regression.MeanNormalizedBias(output_transform=<function Metric.<lambda>>)[source]#
Calculates the Mean Normalized Bias:
$\text{MNB} = \frac{1}{n}\sum_{j=1}^n\frac{A_j - P_j}{A_j}$,
where $A_j$ is the ground truth and $P_j$ is the predicted value.
More details can be found in the reference Botchkarev 2018.
update must receive output of the form (y_pred, y).
y and y_pred must be of same shape.
- compute()[source]#
Computes the metric based on it’s accumulated state.
This is called at the end of each epoch.
- Returns
the actual quantity of interest
- Return type
Any
- Raises
NotComputableError – raised when the metric cannot be computed