Additional metrics integrated with the keras NN library, taken directly from `Tensorflow <https://www.tensorflow.org/api_docs/python/tf/metrics/>`_
Project description
Additional metrics integrated with the keras NN library, taken directly from Tensorflow
How do I install this package?
As usual, just download it using pip:
pip install extra_keras_metricsTests 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 following metrics taken directly from Tensorflow. At the time of writing, the ones available are the following:
The non-parametric ones are (tested against their conterpart from sklearn):
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 (only execution is tested, no baseline in sklearn was available):
average_precision_at_k
precision_at_k
recall_at_k
sensitivity_at_specificity
specificity_at_sensitivity
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.
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