.. |algo| replace:: name_me ======================================== Taking control : Use your own algorithms ======================================== One of the main goals of this platform is to be able to add a classifier to it without modifying the main code. Simple task : Adding a monoview classifier ------------------------------------------ Make it work <<<<<<<<<<<< Let's say we want to add a monoview classifier called "|algo|" to the platform in order to compare it to the other available ones. Let's suppose that we have a python module ``algo_module.py`` in which |algo| is defined in the class :python:`Algo` following ``scikit-learn``'s `guidelines `_ . To add "algo" to the platform, let's create a file called ``algo.py`` in ``summit/multiview_platform/monoview_classifiers/`` In this file let's define the class :python:`AlgoClassifier`, inheriting from :python:`Algo` and :base_source:`BaseMonoviewClassifier ` that contains the required methods for |platf|. Moreover, one has to add a variable called :python:`classifier_class_name` that contains the class name (here ``'AlgoClassifier'``) .. code-block:: python import Algo from ..monoview.monoview_utils import BaseMonoviewClassifier classifier_class_name = "AlgoClassifier" class AlgoClassifier(Algo, BaseMonoviewClassifier): To be able to use the randomized hyper-parameter optimization, we need to provide some information in the :python:`__init__()` method. Indeed, all the algorithms included in the platform must provide two hyper-parameter-related attributes : - :python:`self.param_names` that contain the name of the hyper-parameters that have to be optimized (they must correspond to the name of the attributes of the class :python:`Algo`) - :python:`self.distribs` that contain the distributions for each of these hyper-parameters. For example, let's suppose that |algo| need three hyper-parameters and a random state parameter allowing reproducibility : - :python:`trade_off` that is a float between 0 and 1, - :python:`norm_type` that is a string in :python:`["l1", "l2"]`, - :python:`max_depth` that is an integer between 0 and 100. Then, the :python:`__init__()` method of the :python:`AlgoClassifier` class wil be : .. code-block:: python import Algo from ..monoview.monoview_utils import BaseMonoviewClassifier, CustomUniform, CustomRandint classifier_class_name = "AlgoClassifier" class AlgoClassifier(Algo, BaseMonoviewClassifier): def __init__(self, random_sate=42, trade_off=0.5, norm_type='l1', max_depth=50) super(AlgoClassifier, self).__init__(random_sate=random_sate, trade_off=trade_off, norm_type=norm_type, max_depth=max_depth) self.param_names = ["trade_off", "norm_type", "max_depth"] self.distribs = [CustomUniform(), ["l1", "l2"], CustomRandint()] In this method, we added the needed attributes. See REF TO DOC OF DISTRIBS for the documentation on the used distributions. If "algo" is implemented in a sklearn fashion, it is now usable in the platform. Interpretation <<<<<<<<<<<<<< It is possible to provide some information about the decision process of the algorithm in the :python:`get_interpretation` method. It inputs four arguments : * :python:`directory`, a string containing the directory where figures should be sotred * :python:`base_file_name`, a string containing the file name prefix that should be used to store figures * :python:`y_test`, an array containing the labels of the test set * :python:`multiclass` a boolean that is True if the target is multiclass This method must return a string that will be appended to the summary file. An example of method can be : .. code-block:: python def get_interpretation(self, directory, base_file_name, y_test, multiclass=False): interpret_string = "Algo is a very relevant algorithm that used all the features to classify" # Save a figure in os.path.join(directory, base_file_name+figure_name.png") return interpretString More complex task : Adding a multiview classifier ------------------------------------------------- This part is a bit more complex as to the best of our knowledge, there is no consensus regarding a multiview input for a classifier. The first step of the integration of a multiview classifier is very similar to the monoview one let us suppose one wants to add "new mv algo", that is implemented in the class `NewMVAlgo`. To do so, create a "new_mv_algo.py" file in ``summit/multiview_platform/multiview_classifiers/``. In this file let's define the class :python:`NewMVAlgoClassifier`, inheriting from :python:`NewMVAlgo` and :base_source:`BaseMultiviewClassifier ` that contains the required methods for the platform. Moreover, one has to add a variable called :python:`classifier_class_name` that contains the class name (here ``'NewMVAlgoClassifier'``) .. code-block:: python from new_mv_algo_module import NewMVAlgo from ..multiview.multiview_utils import BaseMultiviewClassifier from ..utils.hyper_parameter_search import CustomRandint classifier_class_name = "NewMVAlgoClassifier" class NewMVAlgoClassifier(BaseMultiviewClassifier, NewMVAlgo): def __init__(self, param_1=50, random_state=None, param_2="edge"): BaseMultiviewClassifier.__init__(self, random_state) NewMVAlgo.__init__(self, param_1=param_1, random_state=random_state, param_2=param_2) self.param_names = ["param_1", "random_state", "param_2"] self.distribs = [CustomRandint(5,200), [random_state], ["val_1", "val_2"]] In |platf| the input of the :python:`fit()` method is `X`, a dataset object that provide access to each view with a method : :python:`dataset_var.get_v(view_index, sample_indices)`. So in order to add a mutliview classifier to |platf|, one will probably have to add a data-transformation step before using the class's :python:`fit()` method. Moreover, to get restrain the samples and descriptors used in the method, |platf| provides two supplementary arguments : - ``train_indices`` is an array of samples indices that compose the training set, - ``view_indices`` is an array of view indices to restrain the number of views on which the algorithm will train. These two arguments are useful to reduce memory usage. Indeed, `X`, the dataset object is just a wrapper for an HDF5 file object, so the data will only be loaded once the `get_v` method is called, so the train and test set are not loaded at the same time. .. code-block:: python def fit(self, X, y, train_indices=None, view_indices=None): # This function is used to initialize the sample and view indices, in case they are None, it transforms them in the correct values train_indices, view_indices = get_samples_views_indices(X, train_indices, view_indices) needed_input = transform_data_if_needed(X, train_indices, view_indices) return NewMVAlgo.fit(self, needed_input, y[train_indices]) def predict(self, X, sample_indices=None, view_indices=None): sample_indices, view_indices = get_samples_views_indices(X, sample_indices, view_indices) needed_input = transform_data_if_needed(X, sample_indices, view_indices) return NewMVAlgo.predict(self, needed_input) Similarly to monoview algorithms, it is possible to add an interpretation method. Manipulate the dataset object ----------------------------- The input of the fit and predict method is a :base_source:`Dataset object `. The useful methods of this object are :base_source:`get_v ` <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< The :base_source:`get_v ` method is **the** way to access the view data in the dataset object. As explained earlier, |platf| communicates the **full** dataset object and two arrays through the :python:`fit()` and :python:`predict()` methods to avoid loading the views if it is not mandatory. Example : build a list of all the views arrays >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> Let us suppose that the mutliview algorithm that one wants to add to |platf| takes as input a list :python:`list_X` of all the views. Then an example of :python:`self.transform_data_if_needed(X, sample_indices, view_indices)` could be : .. code-block:: python def transform_data_if_needed(self, X, sample_indices, view_indices): views_list = [] # Browse the asked views indices for view_index in view_indices: # Get the data from the dataset object, for the asked samples view_data = X.get_v(view_index, sample_indices=sample_indices) # Store it in the list views_list.append(view_data) return views_list