extra-keras-metrics 1.1.2

How do I install this package?

As usual, just download it using pip:

pip install extra_keras_metrics

Tests Coverage

Since some software handling coverages sometime get slightly different results, here’s three of them:

How do I use this package?

Just by importing it you will be able to access all the non-parametric metrics, such as “auprc” and “auroc”:

import extra_keras_metrics

model = my_keras_model()
model.compile(
    optimizer="sgd",
    loss="binary_crossentropy",
    metrics=["auroc", "auprc"]
)

For the parametric metrics, such as “average_precision_at_k”, you will need to import them, such as:

from extra_keras_metrics import average_precision_at_k

model = my_keras_model()
model.compile(
    optimizer="sgd",
    loss="binary_crossentropy",
    metrics=[average_precision_at_k(1), average_precision_at_k(2)]
)

This way in the history of the model you will find both the metrics indexed as “average_precision_at_k_1” and “average_precision_at_k_2” respectively.

Which metrics do I get?

You will get all the metrics from Tensorflow. At the time of writing, the ones available are the following:

The non-parametric ones are:

• auprc

• auroc

• false_negatives

• false_positives

• mean_absolute_error

• mean_squared_error

• precision

• recall

• root_mean_squared_error

• true_negatives

• true_positives

The parametric ones are:

• average_precision_at_k

• false_negatives_at_thresholds

• false_positives_at_thresholds

• mean_cosine_distance

• mean_iou

• mean_per_class_accuracy

• mean_relative_error

• precision_at_k

• precision_at_thresholds

• recall_at_k

• recall_at_thresholds

• sensitivity_at_specificity

• specificity_at_sensitivity

• true_negatives_at_thresholds

• true_positives_at_thresholds

Extras

I’ve created also another couple packages you might enjoy: one, called extra_keras_utils that contains some commonly used code for Keras projects and plot_keras_history which automatically plots a keras training history.