# -*- 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
#
# 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.
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
