EHN accept one-vs-all encoding for labels #410
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
10 tasks
|
@massich @mrastgoo @chkoar This is to support one-vs-all encoding targets. It is ready for review or at least some comment regarding the internal changes. |
Codecov Report
@@ Coverage Diff @@
## master #410 +/- ##
==========================================
- Coverage 98.78% 98.77% -0.01%
==========================================
Files 68 68
Lines 3961 4014 +53
==========================================
+ Hits 3913 3965 +52
- Misses 48 49 +1
Continue to review full report at Codecov.
|
massich
reviewed
Mar 19, 2018
| Returns | ||
| ------- | ||
| X_resampled : {ndarray, sparse matrix}, shape \ | ||
| (n_subset, n_samples_new, n_features) |
|
LGTM |
|
Check the doc for the reason of not indenting.
…On 19 March 2018 at 14:40, Joan Massich ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In imblearn/ensemble/base.py
<#410 (comment)>
:
> +
+ def sample(self, X, y):
+ """Resample the dataset.
+
+ Parameters
+ ----------
+ X : {array-like, sparse matrix}, shape (n_samples, n_features)
+ Matrix containing the data which have to be sampled.
+
+ y : array-like, shape (n_samples,)
+ Corresponding label for each sample in X.
+
+ Returns
+ -------
+ X_resampled : {ndarray, sparse matrix}, shape \
+(n_subset, n_samples_new, n_features)
indenting
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<#410 (review)>,
or mute the thread
<https://github.com/notifications/unsubscribe-auth/AHG9PyfVwQgJ_7i16A2_6KAXiQuAKqfjks5tf7VMgaJpZM4SYISL>
.
--
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
|
glemaitre
commented
Mar 19, 2018
imblearn/__init__.py
Outdated
| @@ -13,6 +13,9 @@ | |||
| exceptions | |||
| Module including custom warnings and error clases used across | |||
| imbalanced-learn. | |||
| keras | |||
glemaitre
added a commit
that referenced
this pull request
Aug 22, 2018
This PR attend to provide some utilities for keras: - [x] support for one-vs-all encoded targets (#410) - [x] balanced batch generator TODO: - [x] Add common test to check multiclass == multilabel-indicator (#410) - [x] Manage the specificity of the EasyEnsemble and BalanceCascade (overwrite `sample`) - [x] Add user guide documentation - [x] Add an example for simple use - [x] Add an example for deep training - [x] Add substitution - [x] What's new - [x] Optional depencies
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This PR allows user to provide targets which are one-vs-all encoded.
This is widely used in Keras for the loss function.
This is part of #409