Removing the Keras sub-module from moabb

CONTRIBUTING.md

@@ -78,8 +78,6 @@ _Note 2 (deep learning):_\
 In case you want to install the optional deep learning dependencies (i.e. `pip install .[deeplearning]`),
 
 
-### Tools used
-
 ### Tools used
 
 MOABB uses [`pre-commit`](https://pre-commit.com/). It automatically runs variety of Code Quality

docs/source/pipelines.rst

@@ -0,0 +1,37 @@
+=========
+Pipelines
+=========
+
+.. automodule:: moabb.pipelines
+
+.. currentmodule:: moabb.pipelines
+
+---------
+Pipelines
+---------
+
+.. autosummary::
+    :toctree: generated/
+    :template: class.rst
+
+    features.LogVariance
+    features.FM
+    features.ExtendedSSVEPSignal
+    features.AugmentedDataset
+    features.StandardScaler_Epoch
+    csp.TRCSP
+    classification.SSVEP_CCA
+    classification.SSVEP_TRCA
+    classification.SSVEP_MsetCCA
+
+
+------------
+Base & Utils
+------------
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    utils.create_pipeline_from_config
+    utils.FilterBank

docs/source/whats_new.rst

@@ -28,6 +28,7 @@ Bugs
 
 API changes
 ~~~~~~~~~~~
+- Removing the deep learning module from inside moabb in favour of braindecode integration (:gh:`692` by `Bruno Aristimunha`_ )
 
 
 Version - 1.2.0  (Stable - PyPi)
@@ -66,6 +67,7 @@ API changes
 - Removing the braindecode module from inside moabb (:gh:`666` by `Bruno Aristimunha`_ )
 
 
+
 Version - 1.1.1
 ----------------
 

examples/data_management_and_configuration/noplot_load_model.py

@@ -1,6 +1,6 @@
 """
 ==============================================
-Load Model (Scikit, Keras) with MOABB
+Load Model (Scikit) with MOABB
 ==============================================
 
 This example shows how to use load the pretrained pipeline in MOABB.
@@ -12,12 +12,7 @@
 
 from pickle import load
 
-import keras
-from scikeras.wrappers import KerasClassifier
-from sklearn.pipeline import Pipeline
-
 from moabb import set_log_level
-from moabb.pipelines.features import StandardScaler_Epoch
 from moabb.utils import setup_seed
 
 
@@ -26,9 +21,7 @@
 ###############################################################################
 # In this example, we will use the results computed by the following examples
 #
-# - plot_benchmark_
-# - plot_benchmark_braindecode_
-# - plot_benchmark_DL_
+# - plot_benchmark
 # ---------------------
 
 # Set up reproducibility of Tensorflow and PyTorch
@@ -38,26 +31,7 @@
 # Loading the Scikit-learn pipelines
 
 with open(
-    "./results/Models_WithinSession/Zhou2016/1/0/CSP + SVM/fitted_model_best.pkl",
+    "../how_to_benchmark/results/Models_WithinSession/Zhou2016/1/0/csp+svm/fitted_model_best.pkl",
     "rb",
 ) as pickle_file:
     CSP_SVM_Trained = load(pickle_file)
-
-###############################################################################
-# Loading the Keras model
-# We load the single Keras model, if we want we can set in the exact same pipeline.
-
-model_Keras = keras.models.load_model(
-    "../how_to_benchmark/results/Models_WithinSession/BNCI2014-001/1/1E/Keras_DeepConvNet/kerasdeepconvnet_fitted_model_best.h5"
-)
-# Now we need to instantiate a new SciKeras object since we only saved the Keras model
-Keras_DeepConvNet_Trained = KerasClassifier(model_Keras)
-# Create the pipelines
-
-
-pipes_keras = Pipeline(
-    [
-        ("StandardScaler_Epoch", StandardScaler_Epoch),
-        ("Keras_DeepConvNet_Trained", Keras_DeepConvNet_Trained),
-    ]
-)