Source code for stellargraph.core.indexed_array

# -*- coding: utf-8 -*-
# Copyright 2020 Data61, CSIRO
# 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
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# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import numpy as np

from .utils import zero_sized_array

[docs]class IndexedArray: """ An array where the first dimension is indexed. This is a reduced Pandas DataFrame. It has: - multidimensional data support, where each element ``values[idx, ...]`` can be a vector, matrix or even higher rank object - a requirement that all values have the same type - labels for the elements of the first axis e.g. ``index[0]`` is the label for the ``values[0, ...]`` element. - no labels for other axes - less overhead (but less API) than a Pandas DataFrame Args: values (numpy.ndarray, optional): an array of rank at least 2 of data, where the first axis is indexed. index (sequence, optional): a sequence of labels or IDs, one for each element of the first axis. If not specified, this defaults to sequential integers starting at 0 """ def __init__(self, values=None, index=None): def index_len(): # compute the length of the index, intercepting an error to provide a better message try: return len(index) except: raise TypeError( f"index: expected a sequence (with a '__len__' method), found {type(index).__name__}" ) if values is None: if index is None: index = range(0) # uint8 is essentially maximally promotable values = zero_sized_array((index_len(), 0), dtype=np.uint8) if not isinstance(values, np.ndarray): raise TypeError( f"values: expected a NumPy array for the features, found {type(values).__name__}" ) if len(values.shape) < 2: raise ValueError( f"values: expected an array with shape length >= 2, found shape {values.shape} of length {len(values.shape)}" ) values_len = values.shape[0] if index is None: index = range(values_len) if values_len != index_len(): raise ValueError( f"values: expected the index length {index_len()} to match the first dimension of values, found {values_len} rows" ) self.index = index self.values = values