Core Module Documentation¶
- class neuralprophet.metrics.BatchMetric(name=None, specific_column=None)¶
Calculates a metric from batch model predictions.
- abstract new(specific_column=None)¶
- Parameters
specific_column (int) – calculate metric only over target at pos
- Returns
copy of metric instance, reset
- update(predicted, target, **kwargs)¶
Updates the metric’s state using the passed batch output.
By default, this is called once for each batch. :param predicted: the output from the model’s forward function. :param target: actual values :param kwargs: passed on to function that computes the metric.
- class neuralprophet.metrics.LossMetric(loss_fn, specific_column=None)¶
Calculates the average loss according to the passed loss_fn.
- Parameters
loss_fn (callable) – a callable taking a prediction tensor, a target tensor, optionally other arguments, and returns the average loss over all observations in the batch.
- new(specific_column=None)¶
- Parameters
specific_column (int) – calculate metric only over target at pos
- Returns
copy of metric instance, reset
- class neuralprophet.metrics.MAE(specific_column=None, shift_scale=None)¶
Calculates the mean absolute error.
- new(specific_column=None, shift_scale=None)¶
- Parameters
specific_column (int) – calculate metric only over target at pos
shift_scale (tuple, float) – data shift and scale parameters
- Returns
copy of metric instance, reset
- set_shift_scale(shift_scale)¶
Adds data denormalization params
- Parameters
shift_scale (tuple, float) – data shift and scale parameters
- class neuralprophet.metrics.MSE(specific_column=None, shift_scale=None)¶
Calculates the mean squared error.
- new(specific_column=None, shift_scale=None)¶
- Parameters
specific_column (int) – calculate metric only over target at pos
shift_scale (tuple, float) – data shift and scale parameters
- Returns
copy of metric instance, reset
- set_shift_scale(shift_scale)¶
Adds data denormalization params.
- Parameters
shift_scale (tuple, float) – data shift and scale parameters
- class neuralprophet.metrics.Metric(name=None)¶
Base class for all Metrics.
- compute(save=False)¶
calculates the current value of the metric
- Parameters
save (bool) – whether to add the current value to stored_values
- Returns
current value of the metric
- print_stored()¶
Nice-prints stored values
- reset(hard=False)¶
Resets the metric to it’s initial state.
By default, this is called at the start of each epoch.
- set_shift_scale(shift_scale)¶
placeholder for subclasses to implement if applicable
- abstract update(predicted, target)¶
Updates the metric’s state using the passed batch output.
By default, this is called once for each batch. :param predicted: the output from the model’s forward function. :param target: actual values
- class neuralprophet.metrics.MetricsCollection(metrics, value_metrics=None)¶
Collection of Metrics that performs action over all
- add_specific_target(target_pos)¶
Duplicates BatchMetrics with their version for a specific target.
- Parameters
target_pos (int, list) – index of target to compute metrics over
- compute(save=False)¶
calculates the current value of the metric
- Parameters
save (bool) – whether to add the current value to stored_values
- Returns
dict of current values of all metrics
- get_stored(loc=None)¶
Creates an OrderedDict from stored metric values
- Parameters
loc (int) – if only stored value at this location to be retrieved
- Returns
OrderedDict
- get_stored_as_df(loc=None)¶
Creates an Dataframe from stored metric values
- Parameters
loc (int) – if only stored value at this location to be retrieved
- Returns
pd.Dataframe
- print(loc=None)¶
Nice-prints stored values
- reset(hard=False)¶
reset all
- set_shift_scale(shift_scale)¶
Adds data denormalization params to applicable metrics
- Parameters
shift_scale (tuple, float) – data shift and scale parameters
- update(predicted, target, values=None)¶
update all metrics.
- Parameters
predicted – the output from the model’s forward function.
target – actual values
values (dict) – dict with matching names to defined ValueMetrics Note: if the correct name is not supplied, the metric is not updated.
- update_batch(predicted, target)¶
update BatchMetrics
- update_values(values, num)¶
update ValueMetrics.
- Parameters
values (dict) – dict with matching names (to defined ValueMetrics), containing average values over batch/update step
num (int) – number of samples in batch/update step
- class neuralprophet.metrics.RMSE(specific_column=None, shift_scale=None)¶
Calculates the root mean squared error.
- new(specific_column=None, shift_scale=None)¶
- Parameters
specific_column (int) – calculate metric only over target at pos
shift_scale (tuple, float) – data shift and scale parameters
- Returns
copy of metric instance, reset
- set_shift_scale(shift_scale)¶
Adds data denormalization params.
- Parameters
shift_scale (tuple, float) – data shift and scale parameters