Source code for sparknlp.annotator.param.classifier_encoder

#  Copyright 2017-2022 John Snow Labs
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
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from sparknlp.common import *
from sparknlp.internal import ParamsGettersSetters


[docs]class ClassifierEncoder(ParamsGettersSetters):

    maxEpochs = Param(Params._dummy(), "maxEpochs", "Maximum number of epochs to train", TypeConverters.toInt)

    lr = Param(Params._dummy(), "lr", "Learning Rate", TypeConverters.toFloat)

    batchSize = Param(Params._dummy(), "batchSize", "Batch size", TypeConverters.toInt)

    labelColumn = Param(Params._dummy(),
                        "labelColumn",
                        "Column with label per each token",
                        typeConverter=TypeConverters.toString)

    randomSeed = Param(Params._dummy(), "randomSeed", "Random seed", TypeConverters.toInt)

    configProtoBytes = Param(Params._dummy(), "configProtoBytes",
                             "ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
                             TypeConverters.toListInt)

[docs]    def setMaxEpochs(self, epochs):
        """Sets maximum number of epochs to train, by default 30

        Parameters
        ----------
        epochs : int
            Maximum number of epochs to train
        """
        return self._set(maxEpochs=epochs)

[docs]    def setLr(self, v):
        """Sets Learning Rate, by default 0.005

        Parameters
        ----------
        v : float
            Learning Rate
        """
        self._set(lr=v)
        return self

[docs]    def setBatchSize(self, v):
        """Sets batch size, by default 64.

        Parameters
        ----------
        v : int
            Batch size
        """
        self._set(batchSize=v)
        return self

[docs]    def setLabelColumn(self, value):
        """Sets name of column for data labels

        Parameters
        ----------
        value : str
            Column for data labels
        """
        return self._set(labelColumn=value)

[docs]    def setRandomSeed(self, seed):
        """Sets random seed for shuffling

        Parameters
        ----------
        seed : int
            Random seed for shuffling
        """
        return self._set(randomSeed=seed)

[docs]    def setConfigProtoBytes(self, b):
        """Sets configProto from tensorflow, serialized into byte array.

        Parameters
        ----------
        b : List[int]
            ConfigProto from tensorflow, serialized into byte array
        """
        return self._set(configProtoBytes=b)