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import attr |
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import numpy as np |
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import somoclu |
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from sklearn.cluster import KMeans |
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import logging |
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logger = logging.getLogger(__name__) |
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9
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class SomTrainer: |
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def infer_map(self, nb_cols, nb_rows, dataset, **kwargs): |
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"""Infer a self-organizing map from dataset.\n |
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initialcodebook = None, kerneltype = 0, maptype = 'planar', gridtype = 'rectangular', |
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compactsupport = False, neighborhood = 'gaussian', std_coeff = 0.5, initialization = None |
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""" |
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if not hasattr(dataset, 'feature_vectors'): |
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raise NoFeatureVectorsError("Attempted to train a Som model, but did not find feature vectors in the dataset.") |
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som = somoclu.Somoclu(nb_cols, nb_rows, **kwargs) |
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som.train(data=np.array(dataset.feature_vectors, dtype=np.float32)) |
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return som |
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@attr.s |
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class SomFactory: |
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"""Implementing from the BaseSomFactory allows other class to register/subscribe on (emulated) 'events'. |
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So, when the factory creates a new Som object, other entities can be notified.""" |
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trainer = attr.ib(init=True, default=SomTrainer()) |
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observers = attr.ib(init=False, default=[]) |
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def register(self, observer): |
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if observer not in self.observers: |
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self.observers.append(observer) |
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def unregister(self, observer): |
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if observer in self.observers: |
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self.observers.remove(observer) |
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def unregister_all(self): |
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if self.observers: |
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del self.observers[:] |
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def update_observers(self, *args, **kwargs): |
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for observer in self.observers: |
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observer.update(*args, **kwargs) |
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def create_som(self, nb_cols, nb_rows, dataset, **kwargs): |
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try: |
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map_obj = self.trainer.infer_map(nb_cols, nb_rows, dataset, **kwargs) |
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self.update_observers(nb_rows, nb_cols, map_object=map_obj) |
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return map_obj |
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except NoFeatureVectorsError as e: |
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logger.info(f"{e}. Fire up an 'encode' command.") |
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raise e |
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class NoFeatureVectorsError(Exception): pass |
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@attr.s |
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class SelfOrganizingMapFactory: |
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som_factory = attr.ib(init=True, default=SomFactory()) |
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def create(self, dataset, nb_cols, nb_rows, **kwargs): |
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return SelfOrganizingMap(self.som_factory.create_som(nb_cols, nb_rows, dataset, **kwargs), dataset.name) |
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@attr.s |
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class SelfOrganizingMap: |
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som = attr.ib(init=True) |
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dataset_name = attr.ib(init=True) |
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@property |
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def height(self): |
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return self.som._n_rows |
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@property |
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def width(self): |
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return self.som._n_columns |
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@property |
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def type(self): |
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return self.som._map_type |
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@property |
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def grid_type(self): |
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return self.som._grid_type |
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def __getattr__(self, item): |
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if item in ('n_rows', 'n_columns', 'initialization', 'map_type', 'grid_type'): |
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item = f'_{item}' |
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return getattr(self.som, item) |
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def get_map_id(self): |
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_ = '_'.join(getattr(self, attribute) for attribute in ['dataset_name', 'n_rows', 'n_columns', 'initialization', 'map_type', 'grid_type']) |
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if self.som.clusters: |
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return f'{_}_cl{self.nb_clusters}' |
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return _ |
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@property |
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def nb_clusters(self): |
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return np.max(self.som.clusters) |
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def neurons_coordinates(self): |
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"""""" |
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for i, arr in enumerate(self.som.bmus): # iterate through the array of shape [nb_datapoints, 2]. Each row is the coordinates |
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# of the neuron the datapoint gets attributed to (closest distance) |
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attributed_cluster = self.som.clusters[arr[0], arr[1]] # >= 0 |
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id2members[attributed_cluster].add(dataset[i].id) |
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def datapoint_coordinates(self, index): |
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"""Call this method to get the best-matching unit (bmu) coordinates of the datapoint indexed byt the input pointer.\n |
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Bmu is simply the neuron on the som grid that is closest to the datapoint after being projected to the 2D space.""" |
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return self.som.bmus[index][0], self.som.bmus[index][1] |
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def project(self, datapoint): |
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"""Compute the coordinates of a (potentially unseen) datapoint. It is assumed that the codebook has been computed already.""" |
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pass |
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def cluster(self, nb_clusters, random_state=None): |
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som.cluster(algorithm=KMeans(n_clusters=nb_clusters, random_state=random_state)) |
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@property |
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def visual_umatrix(self): |
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b = '' |
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max_len = len(str(np.max(self.som.clusters))) # i.e. a clustering of 11 clusters with ids 0, 1, .., 10 has a max_len = 2 |
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for j in range(self.som.umatrix.shape[0]): |
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b += ' '.join(' ' * (max_len - len(str(i))) + str(i) for i in self.som.clusters[j, :]) + '\n' |
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return b |
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boromir674 /
so-magic
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