Additional metrics integrated with the keras NN library, taken directly from `Tensorflow <https://www.tensorflow.org/api_docs/python/tf/metrics/>`_
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_metrics
Since some software handling coverages sometimes 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 (tested against sklearn’s average_precision_score).
- AUROC (tested against sklearn’s roc_auc_score).
- false_negatives (tested against false negatives from sklearn’s confusion_matrix).
- false_positives (tested against false positives from sklearn’s confusion_matrix).
- mean_absolute_error (tested against sklearn’s mean_absolute_error)
- mean_squared_error (tested against sklearn’s mean_squared_error)
- precision (tested against sklearn’s precision_score)
- recall (tested against sklearn’s recall_score)
- root_mean_squared_error (tested against squared root of sklean’s mean_squared_error)
- true_negatives (tested against true negatives from sklearn’s confusion_matrix)
- true_positives (tested against true positives from sklearn’s confusion_matrix)
The parametric ones are (only execution is tested, no baseline in sklearn was available):
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