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 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.
1 maintainer
Metadata
1 maintainer
Release files for extra-keras-metrics 1.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| extra_keras_metrics-1.1.2.tar.gz | 8.3 kB | Without provenance | Details |
Release files / extra_keras_metrics-1.1.2.tar.gz
| Download URL | extra_keras_metrics-1.1.2.tar.gz |
|---|---|
| Size | 8.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
b03ae66f1dde21a0cbe1edf1c72114f95d815570e463a81b8b5014eeb0877eee
|
|
BLAKE2b-256 checksum How to use checksums |
beb543ebcb243910c89cf77a76a9893eb45439915de914a22164e937111f9aa8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.12.1 pkginfo/1.4.2 requests/2.13.0 setuptools/40.6.3 requests-toolbelt/0.9.1 tqdm/4.28.1 CPython/3.7.1
|
Security for extra-keras-metrics 1.1.2
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
