# Path: classification/metadata.py
# PRIMARY_VESSEL_CLASS_COLUMN = 'label'
# VESSEL_CLASS_DETAILED_NAMES = sorted(atomic(schema))
# VESSEL_CATEGORIES = sorted(categories(schema))
# TRAINING_SPLIT = 'Training'
# TEST_SPLIT = 'Test'
#     MAX_WEIGHT = 100.0
# def atomic(obj):
# def categories(obj, include_atomic=True):
# def stable_hash(x):
#     def __init__(self,
#                  metadata_dict,
#                  fishing_ranges_map):
#     def vessel_weight(self, id_):
#     def vessel_label(self, label_name, id_):
#     def ids_for_split(self, split):
#     def weighted_training_list(self,
#                                random_state,
#                                split,
#                                max_replication_factor,
#                                row_filter=lambda row: True,
#                                boundary=1):
#     def fishing_range_only_list(self, random_state, split):
# def read_vessel_time_weighted_metadata_lines(available_ids, lines,
#                                              fishing_range_dict, split):
# def read_vessel_time_weighted_metadata(available_ids,
#                                        metadata_file,
#                                        fishing_range_dict={},
#                                        split=0):
# def read_vessel_multiclass_metadata_lines(available_ids, lines,
#                                           fishing_range_dict):
# def metadata_file_reader(metadata_file):
# def read_vessel_multiclass_metadata(available_ids,
#                                     metadata_file,
#                                     fishing_range_dict={}):
# def find_available_ids(feature_path):
# def parse_date(date):
# def read_fishing_ranges(fishing_range_file):
# def build_multihot_lookup_table():
# def multihot_encode(label):
# class VesselMetadata(object):


import abc
import numpy as np
import six
from classification import metadata

    def __init__(self, num_feature_dimensions, vessel_metadata):
        self.num_feature_dimensions = num_feature_dimensions
        if vessel_metadata:
            self.vessel_metadata = vessel_metadata
            self.fishing_ranges_map = vessel_metadata.fishing_ranges_map
        else:
            self.vessel_metadata = None
            self.fishing_ranges_map = None
        self.training_objectives = None

    def build_training_file_list(self, base_feature_path, split):
        boundary = 1 if (split == metadata.TRAINING_SPLIT) else self.batch_size
        random_state = np.random.RandomState()
        training_ids = self.vessel_metadata.weighted_training_list(
            random_state,
            split,
            self.max_replication_factor,
            boundary=boundary)
        return [
            '%s/%s.tfrecord' % (base_feature_path, six.ensure_text(id_))
            for id_ in training_ids
        ]

    @staticmethod
    def read_metadata(all_available_ids,
                      metadata_file,
                      fishing_ranges,
                      split):
        # Ignore split for the time being

# Path: classification/metadata.py
# PRIMARY_VESSEL_CLASS_COLUMN = 'label'
# VESSEL_CLASS_DETAILED_NAMES = sorted(atomic(schema))
# VESSEL_CATEGORIES = sorted(categories(schema))
# TRAINING_SPLIT = 'Training'
# TEST_SPLIT = 'Test'
#     MAX_WEIGHT = 100.0
# def atomic(obj):
# def categories(obj, include_atomic=True):
# def stable_hash(x):
#     def __init__(self,
#                  metadata_dict,
#                  fishing_ranges_map):
#     def vessel_weight(self, id_):
#     def vessel_label(self, label_name, id_):
#     def ids_for_split(self, split):
#     def weighted_training_list(self,
#                                random_state,
#                                split,
#                                max_replication_factor,
#                                row_filter=lambda row: True,
#                                boundary=1):
#     def fishing_range_only_list(self, random_state, split):
# def read_vessel_time_weighted_metadata_lines(available_ids, lines,
#                                              fishing_range_dict, split):
# def read_vessel_time_weighted_metadata(available_ids,
#                                        metadata_file,
#                                        fishing_range_dict={},
#                                        split=0):
# def read_vessel_multiclass_metadata_lines(available_ids, lines,
#                                           fishing_range_dict):
# def metadata_file_reader(metadata_file):
# def read_vessel_multiclass_metadata(available_ids,
#                                     metadata_file,
#                                     fishing_range_dict={}):
# def find_available_ids(feature_path):
# def parse_date(date):
# def read_fishing_ranges(fishing_range_file):
# def build_multihot_lookup_table():
# def multihot_encode(label):
# class VesselMetadata(object):


# Path: classification/models/model.py
import abc
import numpy as np
import six
from classification import metadata


    def __init__(self, num_feature_dimensions, vessel_metadata):
        self.num_feature_dimensions = num_feature_dimensions
        if vessel_metadata:
            self.vessel_metadata = vessel_metadata
            self.fishing_ranges_map = vessel_metadata.fishing_ranges_map
        else:
            self.vessel_metadata = None
            self.fishing_ranges_map = None
        self.training_objectives = None

    def build_training_file_list(self, base_feature_path, split):
        boundary = 1 if (split == metadata.TRAINING_SPLIT) else self.batch_size
        random_state = np.random.RandomState()
        training_ids = self.vessel_metadata.weighted_training_list(
            random_state,
            split,
            self.max_replication_factor,
            boundary=boundary)
        return [
            '%s/%s.tfrecord' % (base_feature_path, six.ensure_text(id_))
            for id_ in training_ids
        ]

    @staticmethod
    def read_metadata(all_available_ids,
                      metadata_file,
                      fishing_ranges,
                      split):
        # Ignore split for the time being

        return metadata.read_vessel_multiclass_metadata(