jhfjhfj1/autokeras

# Copyright 2020 The AutoKeras Authors.
#
# 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
# limitations under the License.

from autokeras.engine import block as block_module


class IOHyperModel(block_module.Block):
    """A mixin class connecting the input nodes and heads with the adapters.

    This class is extended by the input nodes and the heads. The AutoModel calls
    the functions to get the corresponding adapters and pass the information
    back to the input nodes and heads.
    """

    def __init__(self, shape=None, **kwargs):
        super().__init__(**kwargs)
        self.shape = shape
        self.data_shape = None
        self.dtype = None
        self.batch_size = None
        self.num_samples = None

    def get_analyser(self):
        """Get the corresponding Analyser.

        # Returns
            An instance of a subclass of autokeras.engine.Analyser.
        """
        raise NotImplementedError

    def get_adapter(self):
        """Get the corresponding Adapter.

        # Returns
            An instance of a subclass of autokeras.engine.Adapter.
        """
        raise NotImplementedError

    def config_from_analyser(self, analyser):
        """Load the learned information on dataset from the Analyser.

        # Arguments
            adapter: An instance of a subclass of autokeras.engine.Adapter.
        """
        self.data_shape = analyser.shape
        self.dtype = analyser.dtype
        self.batch_size = analyser.batch_size
        self.num_samples = analyser.num_samples

    def get_hyper_preprocessors(self):
        """Construct a list of HyperPreprocessors based on learned information.

        # Returns
            A list of HyperPreprocessors for the corresponding data.
        """
        raise NotImplementedError

    def get_config(self):
        config = super().get_config()
        config.update({"shape": self.shape})
        return config