Normalize dataset names (NeuroTechX#455)

* Add depreciation decorator

* Update test_dataset_list

* Ignore aliases in test_dataset_list

* Add is_abreviation function

* Add tests for is_abreviation and is-camel-kebab_case

* Rename is_abbrev

* Fix dashes in is_abbrev

* Fix error message

* Update BNCI datasets names and codes

* Update mamem dataset codes

* Update epfl dataset code

* Update DeamonsP300 dataset code

* Test if codes are unique

* Update Huebner codes

* Relax is_abrev

* Update FakeDataset code

* Update code and name of Passive Head Mounted Display with Music Listening dataset

* Update name of GrosseWentrup2009

* Update code of Nakanishi2015

* Update name of SSVEPExo

* Update add argument suffix to Shin2017 and rename to BaseShin2017

* Update code of Sosulski2019

* Re-rename HeadMountedDisplay because colision with VR-P300

* Rename VirtualReality to Cattan2019_VR

* Add alias for VirtualReality

* Update code of Wand2016

* Update name of braininvaders datasets

* Add compound_dataset_list with test

* Del _init_dataset_list after usage

* Change names of bi-illeteracy datasets

* Fix hardcoded code test for Cattan2019-VR

* Fix benchmark test

* Update whats_new.rst

* Update BI2014b

Fixing the number of subjects in BI2014b.

* Update dataset summaries

* Fix case when note already present

* Fix forgotten change of BNCI2014001

* warn instead of raise when code and class name don't match (compatibility with depreciated names)

* Add test for depreciated datasets init

* Fix asserNoLogs only introduced in python3.10

* Specity test for depreciated warning

* Undo edit to old whats_new.rst lines

* Fix note formatting

* Update SSVEPExo name and code to Kalunga2016

---------

Co-authored-by: Bru <a.bruno@aluno.ufabc.edu.br>
Co-authored-by: Sara Sedlar <sara.sedlar@gmail.com>

docs/source/dataset_summary.rst

@@ -21,14 +21,14 @@ Motor Imagery
    :class: sortable
 
         AlexMI,8,16,3,20,3s,512Hz,1,1,480
-        BNCI2014001,9,22,4,144,4s,250Hz,2,6,62208
-        BNCI2014002,14,15,2,80,5s,512Hz,1,8,17920
-        BNCI2014004,9,3,2,360,4.5s,250Hz,5,1,32400
-        BNCI2015001,12,13,2,200,5s,512Hz,3,1,14400
-        BNCI2015004,9,30,5,80,7s,256Hz,2,1,7200
+        BNCI2014_001,9,22,4,144,4s,250Hz,2,6,62208
+        BNCI2014_002,14,15,2,80,5s,512Hz,1,8,17920
+        BNCI2014_004,9,3,2,360,4.5s,250Hz,5,1,32400
+        BNCI2015_001,12,13,2,200,5s,512Hz,3,1,14400
+        BNCI2015_004,9,30,5,80,7s,256Hz,2,1,7200
         Cho2017,52,64,2,100,3s,512Hz,1,1,9800
-        Lee2019_MI,55,62,2,100,4s,1000Hz,2,1,11000
-        MunichMI,10,128,2,150,7s,500Hz,1,1,3000
+        Lee2019_MI,54,62,2,100,4s,1000Hz,2,1,11000
+        GrosseWentrup2009,10,128,2,150,7s,500Hz,1,1,3000
         Schirrmeister2017,14,128,4,120,4s,500Hz,1,2,13440
         Ofner2017,15,61,7,60,3s,512Hz,1,10,63000
         PhysionetMI,109,64,4,23,3s,160Hz,1,1,69760
@@ -44,19 +44,19 @@ P300/ERP
    :header: Dataset, #Subj, #Chan, #Trials / class, Trials length, Sampling rate, #Sessions
    :class: sortable
 
-   BNCI2014008, 8, 8, 3500 NT / 700 T, 1s, 256Hz, 1
-   BNCI2014009, 10, 16, 1440 NT / 288 T, 0.8s, 256Hz, 3
-   BNCI2015003, 10, 8, 1500 NT / 300 T, 0.8s, 256Hz, 1
-   bi2012, 25, 16, 6140 NT / 128 T, 1s, 512Hz, 2
-   bi2013a, 24, 16, 3200 NT / 640 T, 1s, 512Hz, 8 for subjects 1-7 else 1
-   bi2014a, 64, 16, 990 NT / 198 T, 1s, 512Hz, up to 3
-   bi2014b, 37, 32, 200 NT / 40 T, 1s, 512Hz, 3
-   bi2015a, 43, 32, 4131 NT / 825 T, 1s, 512Hz, 3
-   bi2015b, 44, 32, 2160 NT / 480 T, 1s, 512Hz, 2
-   VirtualReality, 21, 16, 600 NT / 120 T, 1s, 512Hz, 2
+   BNCI2014_008, 8, 8, 3500 NT / 700 T, 1s, 256Hz, 1
+   BNCI2014_009, 10, 16, 1440 NT / 288 T, 0.8s, 256Hz, 3
+   BNCI2015_003, 10, 8, 1500 NT / 300 T, 0.8s, 256Hz, 1
+   BI2012, 25, 16, 640 NT / 128 T, 1s, 128Hz, 2
+   BI2013a, 24, 16, 3200 NT / 640 T, 1s, 512Hz, 8 for subjects 1-7 else 1
+   BI2014a, 64, 16, 990 NT / 198 T, 1s, 512Hz, up to 3
+   BI2014b, 38, 32, 200 NT / 40 T, 1s, 512Hz, 3
+   BI2015a, 43, 32, 4131 NT / 825 T, 1s, 512Hz, 3
+   BI2015b, 44, 32, 2160 NT / 480 T, 1s, 512Hz, 1
+   Cattan2019_VR, 21, 16, 600 NT / 120 T, 1s, 512Hz, 2
    Huebner2017, 13, 31, 364 NT / 112 T, 0.9s, 1000Hz, 3
    Huebner2018, 12, 31, 364 NT / 112 T, 0.9s, 1000Hz, 3
-   Sosulski2019, 13, 31, 75 NT / 15 T, , 1000Hz, 3
+   Sosulski2019, 13, 31, 75 NT / 15 T, 1.2s, 1000Hz, 3
    EPFLP300, 8, 32, 2753 NT / 551 T, 1s, 2048Hz, 4
    Lee2019_ERP, 54, 62, 6900 NT / 1380 T, 1s, 1000Hz, 2
 
@@ -70,7 +70,7 @@ SSVEP
    :class: sortable
 
    Lee2019_SSVEP,54,16,4,25,1s,1000Hz,1
-   SSVEPExo,12,8,4,16,2s,256Hz,1
+   Kalunga2016,12,8,4,16,2s,256Hz,1
    MAMEM1,10,256,5,12-15,3s,250Hz,1
    MAMEM2,10,256,5,20-30,3s,250Hz,1
    MAMEM3,10,14,4,20-30,3s,128Hz,1
@@ -89,7 +89,7 @@ is a resting state experiment.
    :header: Dataset, #Subj, #Chan, #Classes, #Blocks / class, Trials length, Sampling rate, #Sessions
    :class: sortable
 
-   HeadMountedDisplay,12,16,2,10,60s,512Hz,1
+   Cattan2019_PHMD,12,16,2,10,60s,512Hz,1
 
 
 Compound Datasets
@@ -103,12 +103,12 @@ select a sample of subject inside a dataset (e.g. subject with high/low performa
    :header: Dataset, #Subj, #Original datasets
    :class: sortable
 
-   bi2014a_il,17,bi2014a
-   bi2014b_il,11,bi2014b
-   bi2015a_il,2,bi2015a
-   bi2015b_il,25,bi2015b
-   VirtualReality_il,4,VirtualReality
-   biIlliteracy,59,bi2014a_il bi2014b_il bi2015a_il bi2015b_il VirtualReality_il
+   BI2014a_Il,17,BI2014a
+   BI2014b_Il,11,BI2014b
+   BI2015a_Il,2,BI2015a
+   BI2015b_Il,25,BI2015b
+   Cattan2019_VR_Il,4,Cattan2019_VR
+   BI_Il,59,BI2014a_Il BI2014b_Il BI2015a_Il BI2015b_Il Cattan2019_VR_Il
 
 
 Submit a new dataset

docs/source/datasets.rst

@@ -15,14 +15,14 @@ Motor Imagery Datasets
     :template: class.rst
 
     AlexMI
-    BNCI2014001
-    BNCI2014002
-    BNCI2014004
-    BNCI2015001
-    BNCI2015004
+    BNCI2014_001
+    BNCI2014_002
+    BNCI2014_004
+    BNCI2015_001
+    BNCI2015_004
     Cho2017
     Lee2019_MI
-    MunichMI
+    GrosseWentrup2009
     Ofner2017
     PhysionetMI
     Schirrmeister2017
@@ -40,16 +40,16 @@ ERP Datasets
     :toctree: generated/
     :template: class.rst
 
-    bi2012
-    bi2013a
-    bi2014a
-    bi2014b
-    bi2015a
-    bi2015b
-    VirtualReality
-    BNCI2014008
-    BNCI2014009
-    BNCI2015003
+    BI2012
+    BI2013a
+    BI2014a
+    BI2014b
+    BI2015a
+    BI2015b
+    Cattan2019_VR
+    BNCI2014_008
+    BNCI2014_009
+    BNCI2015_003
     DemonsP300
     EPFLP300
     Huebner2017
@@ -66,7 +66,7 @@ SSVEP Datasets
     :toctree: generated/
     :template: class.rst
 
-    SSVEPExo
+    Kalunga2016
     Nakanishi2015
     Wang2016
     MAMEM1
@@ -83,7 +83,7 @@ Resting State Datasets
     :toctree: generated/
     :template: class.rst
 
-    HeadMountedDisplay
+    Cattan2019_PHMD
 
 
 ------------
@@ -131,9 +131,9 @@ ERP Datasets
     :toctree: generated/
     :template: class.rst
 
-    bi2014a_il
-    bi2014b_il
-    bi2015a_il
-    bi2015b_il
-    VirtualReality_il
-    biIlliteracy
+    BI2014a_Il
+    BI2014b_Il
+    BI2015a_Il
+    BI2015b_Il
+    Cattan2019_VR_Il
+    BI_Il

docs/source/whats_new.rst

@@ -40,6 +40,10 @@ Enhancements
 - Allow :func:`moabb.datasets.utils.dataset_search` to search across paradigms ``paradigm=None`` (PR :gh:`408` by `Pierre Guetschel`_)
 - Improving the review processing with more pre-commit bots (:gh:`435` by `Bruno Aristimunha`_)
 - Update all dataset codes to remove white spaces and underscores (:gh:`448` by `Pierre Guetschel`_)
+- Add :func:`moabb.utils.depreciated_alias` decorator (:gh:`455` by `Pierre Guetschel`_)
+- Rename many dataset class names to standardize and deprecate old names (:gh:`455` by `Pierre Guetschel`_)
+- Change many dataset codes to match the class names (:gh:`455` by `Pierre Guetschel`_)
+- Add :obj:`moabb.datasets.compound_dataset.utils.compound_dataset_list`  (:gh:`455` by `Pierre Guetschel`_)
 
 Bugs
 ~~~~

examples/advanced_examples/plot_filterbank_csp_vs_csp.py

@@ -18,7 +18,7 @@
 from sklearn.pipeline import make_pipeline
 
 import moabb
-from moabb.datasets import BNCI2014001
+from moabb.datasets import BNCI2014_001
 from moabb.evaluations import CrossSessionEvaluation
 from moabb.paradigms import FilterBankLeftRightImagery, LeftRightImagery
 from moabb.pipelines.utils import FilterBank
@@ -61,7 +61,7 @@
 # from 8 to 35 Hz.
 
 # Because this is being auto-generated we only use 2 subjects
-dataset = BNCI2014001()
+dataset = BNCI2014_001()
 dataset.subject_list = dataset.subject_list[:2]
 datasets = [dataset]
 overwrite = False  # set to True if we want to overwrite cached results

examples/advanced_examples/plot_grid_search_withinsession.py

@@ -17,7 +17,7 @@
 from sklearn.linear_model import LogisticRegression
 from sklearn.pipeline import Pipeline
 
-from moabb.datasets import BNCI2014001
+from moabb.datasets import BNCI2014_001
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import MotorImagery
 
@@ -30,7 +30,7 @@
 # Select the Subject
 subjects = [1]
 # Load the dataset
-dataset = BNCI2014001()
+dataset = BNCI2014_001()
 
 events = ["right_hand", "left_hand"]
 
@@ -144,7 +144,7 @@
     os.path.join(
         path,
         "GridSearch_WithinSession",
-        "001-2014",
+        "BNCI2014-001",
         "1",
         "session_E",
         "GridSearchEN",
@@ -164,7 +164,7 @@
     os.path.join(
         path,
         "GridSearch_WithinSession",
-        "001-2014",
+        "BNCI2014-001",
         "1",
         "session_T",
         "GridSearchEN",

examples/advanced_examples/plot_mne_and_scikit_estimators.py

@@ -37,7 +37,7 @@
     find_significant_differences,
 )
 from moabb.analysis.plotting import paired_plot, summary_plot
-from moabb.datasets import BNCI2014009
+from moabb.datasets import BNCI2014_009
 from moabb.evaluations import CrossSessionEvaluation
 from moabb.paradigms import P300
 
@@ -52,7 +52,7 @@
 #
 # Load 2 subjects of BNCI 2014-009 dataset, with 3 session each
 
-dataset = BNCI2014009()
+dataset = BNCI2014_009()
 dataset.subject_list = dataset.subject_list[:3]
 datasets = [dataset]
 paradigm = P300()

examples/advanced_examples/plot_select_electrodes_resample.py

@@ -20,7 +20,7 @@
 from sklearn.pipeline import make_pipeline
 
 import moabb.analysis.plotting as moabb_plt
-from moabb.datasets import BNCI2014001, Zhou2016
+from moabb.datasets import BNCI2014_001, Zhou2016
 from moabb.datasets.utils import find_intersecting_channels
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import LeftRightImagery
@@ -33,7 +33,7 @@
 # Load 2 subjects of BNCI 2014-004 and Zhou2016 datasets, with 2 sessions each
 
 subj = [1, 2]
-datasets = [Zhou2016(), BNCI2014001()]
+datasets = [Zhou2016(), BNCI2014_001()]
 for d in datasets:
     d.subject_list = subj
 

examples/advanced_examples/plot_statistical_analysis.py

@@ -27,7 +27,7 @@
     compute_dataset_statistics,
     find_significant_differences,
 )
-from moabb.datasets import BNCI2014001
+from moabb.datasets import BNCI2014_001
 from moabb.evaluations import CrossSessionEvaluation
 from moabb.paradigms import LeftRightImagery
 
@@ -70,7 +70,7 @@
 # Evaluation
 # ----------
 #
-# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014001).
+# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
 # The evaluation will return a DataFrame containing a single AUC score for
 # each subject / session of the dataset, and for each pipeline.
 #
@@ -79,7 +79,7 @@
 # be overwritten if necessary.
 
 paradigm = LeftRightImagery()
-dataset = BNCI2014001()
+dataset = BNCI2014_001()
 dataset.subject_list = dataset.subject_list[:4]
 datasets = [dataset]
 overwrite = True  # set to False if we want to use cached results

examples/example_codecarbon.py

@@ -19,7 +19,7 @@
 ###############################################################################
 from moabb import benchmark, set_log_level
 from moabb.analysis.plotting import codecarbon_plot
-from moabb.datasets import BNCI2014001, Zhou2016
+from moabb.datasets import BNCI2014_001, Zhou2016
 from moabb.paradigms import LeftRightImagery
 
 
@@ -69,7 +69,7 @@
 # folder.
 
 dataset = Zhou2016()
-dataset2 = BNCI2014001()
+dataset2 = BNCI2014_001()
 dataset.subject_list = dataset.subject_list[:1]
 dataset2.subject_list = dataset2.subject_list[:1]
 datasets = [dataset, dataset2]

examples/external/plot_learning_curve_p300_external.py

@@ -33,7 +33,7 @@
 from tdlda import Vectorizer as JumpingMeansVectorizer
 
 import moabb
-from moabb.datasets import BNCI2014009
+from moabb.datasets import BNCI2014_009
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import P300
 
@@ -99,7 +99,7 @@
 # and dataset size.
 
 paradigm = P300(resample=processing_sampling_rate)
-dataset = BNCI2014009()
+dataset = BNCI2014_009()
 # Remove the slicing of the subject list to evaluate multiple subjects
 dataset.subject_list = dataset.subject_list[0:1]
 datasets = [dataset]

examples/learning_curve/plot_learning_curve_motor_imagery.py

@@ -30,7 +30,7 @@
 from sklearn.pipeline import make_pipeline
 
 import moabb
-from moabb.datasets import BNCI2014001
+from moabb.datasets import BNCI2014_001
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import LeftRightImagery
 
@@ -63,7 +63,7 @@
 # Evaluation
 # ----------
 #
-# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014001).
+# We define the paradigm (LeftRightImagery) and the dataset (BNCI2014_001).
 # The evaluation will return a DataFrame containing a single AUC score for
 # each subject / session of the dataset, and for each pipeline.
 #
@@ -72,7 +72,7 @@
 # be overwritten if necessary.
 
 paradigm = LeftRightImagery()
-dataset = BNCI2014001()
+dataset = BNCI2014_001()
 dataset.subject_list = dataset.subject_list[:1]
 datasets = [dataset]
 overwrite = True  # set to True if we want to overwrite cached results

examples/learning_curve/plot_learning_curve_p300.py

@@ -32,7 +32,7 @@
 from sklearn.pipeline import make_pipeline
 
 import moabb
-from moabb.datasets import BNCI2014009
+from moabb.datasets import BNCI2014_009
 from moabb.evaluations import WithinSessionEvaluation
 from moabb.paradigms import P300
 
@@ -77,7 +77,7 @@
 # and dataset size.
 
 paradigm = P300(resample=processing_sampling_rate)
-dataset = BNCI2014009()
+dataset = BNCI2014_009()
 # Remove the slicing of the subject list to evaluate multiple subjects
 dataset.subject_list = dataset.subject_list[1:2]
 datasets = [dataset]