Removed condition from trainer on duplicated callbacks. Tab2Vec optionally returns pandas dataframes. Added unit tests for new functionalities and all notebooks follow now black coding style. All this relates to PR #55

code cleaned + added workaround for ¨ImportError: libGL.so.1: cannot open shared object file: No such file or directory ``` apt-get update apt-get install ffmpeg libsm6 libxext6  -y ```¨

* added RayTuneReporter callback
* adjusted history so it reports metrics per class in case of multiple values per metric. eg. multiple classes

* added notebook and 1 report for metrics

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Fixed a series of breaking changes related to the metrics returning arrays. Adapted docs to the new functionalities

Refined documentation. Added some test for saving model. Added an example notebook. Ready to test installations and publish v1 to pypi

Added test for new trainer methods. Back to documentation style more in line with some popular packages like pytorch

added get and setstate methods in the EarlyStopping and ModelCheckpoint callbacks. Added the possibility of using GRUs in the deeptext component and also predict using the hidden state or the output. Fixed a small bug in the text processor. Improved the save method in the Trainer

ModelCheckpoint saves best epoch as well. Added dropout option for tabnet. Adjusted RAdam for new signatures. Adjusted the training so it can take ReduceLROnPlateau. Also so that it automatically restores the best weights after training

Modified the callbacks to accomodate the neccessity of the ReduceLROnPlateau scheduler. Replace weight by pos_weight in BCEWithLogitsLoss. Added on_eval_begin method to reset metrics

Added notebook on how to use custom components. Version will be still beta because I prefer to wait for V1 until I have tried it with more datasets

Finished documentation. Adjusted Notebooks. Renamed WarmUp class to what it really is, FineTune. Alias the corresponding parameters so that the user can use both, finetune and alias params. Modified a few defaults

Added Test. Ignored a number of typing related errors after updated to latest version of numpy (need to look into this. Ready to move on with notebooks

redo all docs to match pytorch theme. Adjust all documentation to the new code structure. Remove partial imports from...everywhere. Updated examples

decoupled the model from the training process via the introduciton of a Trainer class. All seems easier now. Need to adjust all documentation. Also added a few losses and an R2 metric, as well as the possibiliy of customising virually every single component of the model

Adjusted documentation and examples. Refactored the LRHistory callback and fix bug in the History callback. Added multiple tests and increased code coverage to 94 per cent

Changed loss value printed on the screen for regression. From RMSE to MSE. Adjusted documentation. Also changed unit test for callbacks to the new Linear-Embedding implementation, which implied incresing delta for early stopping

finished docs apart from examples. Changed name of DeepPreprocessor to DensePreprocessor for consistency. Added raise error if model components do not have an 'output_dim' attribute

Added support for sparse matrices. Added the possibility of using a head for the deep branch. Started with the examples