externals¶
pailab.externals.sklearn_interface¶
Module for pailab to sklearn
This module defines all necessary objects and functions to use sklearn from within pailab.
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class
SKLearnModel
(*args, **kwargs)¶ Class to store all sklearn models in pailab’s MLRepo
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class
SKLearnModelParam
(*args, **kwargs)¶ Interfaces the parameters of the sklearn algorithms
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class
SKLearnPreprocessingParam
(*args, **kwargs)¶ Interfaces the parameters of the sklearn algorithms
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class
SKLearnPreprocessor
(*args, **kwargs)¶ Class to store all sklearn preprocessor
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add_model
(repo, skl_learner, model_name=None, model_param=None, preprocessors=None)¶ Adds a new sklearn model to a pailab MLRepo
Parameters: - repo ([type]) – [description]
- skl_learner ([type]) – [description]
- model_name ([type], optional) – Defaults to None. [description]
- model_param ([type], optional) – Defaults to None. [description]
- preprocessors (list of strings, optional) – List of used preprocessors
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add_preprocessor
(repo, skl_preprocessor, preprocessor_name=None, preprocessor_param=None)¶ Adds a new sklearn preprocessor to a pailab MLRepo
Parameters: - repo ([type]) – [description]
- preprocessor (Class) – The sklearn preprocessor class
- preprocessor_name ([type], optional) – Defaults to None. [description]
- preprocessor_param ([type], optional) – Defaults to None. [description]
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eval_sklearn
(model, data)¶ Function to evaluate an sklearn model
Parameters: - model ([type]) – [description]
- data ([type]) – [description]
Returns: [description]
Return type: [type]
pailab.externals.tensorflow_keras_interface¶
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add_model
(repo, tensorflow_keras_model, model_name, loss, epochs, batch_size, optimizer='ADAM', optimizer_param={}, tensorboard_log_dir=None, validation_split=0.0)¶ Adds a new tensorflow-keras model to a pailab MLRepo
:param : param repo (MLRepo): ml repo :param : param tensorflow_keras_model (keras model): the model created with tensorflows keras (not yet compiled) :param : param model_name (str): name of model used in repo :param : param loss (str): lossfunction :param : param epochs (int): number of epochs used :param : param batch_size (int): batch size