Supported Types

This page describes what types are currently supported and what rules are used to check if two objects are equal or not.

The current supported types are:

• jax.numpy.ndarray
• numpy.ndarray
• numpy.ma.MaskedArray
• pandas.DataFrame
• pandas.Series
• polars.DataFrame
• polars.Series
• torch.Tensor
• torch.nn.utils.rnn.PackedSequence
• xarray.DataArray
• xarray.Dataset
• xarray.Variable

coola also provides experimental/partial support for the following types:

• pyarrow.Array (equal_nan, atol and rtol arguments are ignored)
• pyarrow.Table (equal_nan, atol and rtol arguments are ignored)

Equal

object

By default, two objects are equal if:

• they have the same type
• they are equal i.e. actual == expected returns True

Example

>>> from coola import objects_are_equal
>>> objects_are_equal(1, 1)
True
>>> objects_are_equal(1, 2)
False
>>> objects_are_equal(1, 1.0)
False
>>> objects_are_equal(True, True)
True
>>> objects_are_equal("abc", "abcd")
False

collections.abc.Mapping | dict

Two Mappings are equal if:

• they have the same type
• they have the same number of elements i.e. len(mapping1) == len(mapping2) returns True
• they have the same set of keys i.e. set(mapping1.keys()) != set(mapping2.keys()) returns True
• For each key, the values are equal. The value associated to the key k in the first mapping has to be equal to value associated to the key k in the second mapping.

>>> from collections import OrderedDict
>>> from coola import objects_are_equal
>>> objects_are_equal({"int": 1, "str": "abc"}, {"int": 1, "str": "abc"})
True
>>> objects_are_equal({"int": 1, "str": "abc"}, OrderedDict({"int": 1, "str": "abc"}))
False
>>> objects_are_equal({"int": 1, "str": "abc"}, {"int": 1, "str": "abc", "float": 0.2})
False
>>> objects_are_equal({"int": 1, "str": "abc"}, {"int": 1, "float": 0.2})
False
>>> objects_are_equal({"int": 1, "str": "abc"}, {"int": 1, "str": "abcd"})
False

collections.abc.Sequence | list | tuple

Two Sequences are equal if:

• they have the same type
• they have the same number of elements i.e. len(sequence1) == len(sequence2) returns True
• For each position, the elements are equal. The i-th element in the first sequence has to be equal to the i-th element in the second sequence.

Example

>>> from coola import objects_are_equal
>>> objects_are_equal([1, 2, "abc"], [1, 2, "abc"])
True
>>> objects_are_equal([1, 2, "abc"], (1, 2, "abc"))
False
>>> objects_are_equal([1, 2, "abc"], [1, 2, "abc", 4])
False
>>> objects_are_equal([1, 2, "abc"], [1, 2, "abcd"])
False

PyTorch

You need to install coola with PyTorch to check if some PyTorch objects are equal or not. Please check the get started page for more information. coola currently support the following PyTorch objects:

• torch.Tensor
• torch.nn.utils.rnn.PackedSequence

torch.Tensor

Two PyTorch Tensors are equal if:

• they have the same data type i.e. tensor1.dtype == tensor2.dtype returns True
• they have the same device i.e. tensor1.device == tensor2.device returns True
• they have the same shape i.e. tensor1.shape == tensor2.shape returns True
• they have the same values i.e. tensor1.equal(tensor2) returns True

Example

>>> import torch
>>> from coola import objects_are_equal
>>> objects_are_equal(torch.ones(2, 3), torch.ones(2, 3))
True
>>> objects_are_equal(torch.ones(2, 3), torch.ones(2, 3, dtype=torch.long))
False
>>> objects_are_equal(torch.ones(2, 3), torch.zeros(2, 3))
False
>>> objects_are_equal(torch.ones(2, 3), torch.ones(6))
False

torch.nn.utils.rnn.PackedSequence

Two PyTorch PackedSequences are equal if:

• The data attributes are equal
• The batch_sizes attributes are equal
• The sorted_indices attributes are equal
• The unsorted_indices attributes are equal

Example

>>> import torch
>>> from torch.nn.utils.rnn import pack_padded_sequence
>>> from coola import objects_are_equal
>>> objects_are_equal(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
... )
True
>>> objects_are_equal(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).add(1).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
... )  # different values
False
>>> objects_are_equal(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 2], dtype=torch.long),
...         batch_first=True,
...     ),
... )  # different lengths
False

numpy.ndarray

You need to install coola with NumPy to check if some NumPy ndarrays are equal or not. Please check the get started page for more information.

Two NumPy ndarrays are equal if:

• they have the same data type i.e. array1.dtype == array2.dtype returns True
• they have the same shape i.e. array1.shape == array2.shape returns True
• they have the same values i.e. numpy.array_equal(array1, array2) returns True

>>> import numpy
>>> from coola import objects_are_equal
>>> objects_are_equal(numpy.ones((2, 3)), numpy.ones((2, 3)))
True
>>> objects_are_equal(numpy.ones((2, 3)), numpy.ones((2, 3), dtype=int))
False
>>> objects_are_equal(numpy.ones((2, 3)), numpy.zeros((2, 3)))
False
>>> objects_are_equal(numpy.ones((2, 3)), numpy.ones((6,)))
False

xarray

You need to install coola with PyTorch to check if some xarray objects are equal or not. Please check the get started page for more information. coola currently support the following xarray objects:

• xarray.DataArray
• xarray.Dataset

xarray.DataArray

Two xarray DataArrays are equal if:

• they have the same data values (attribute data)
• they have the same name (attribute name)
• they have the same dimension names (attribute dims)
• they have the same coordinates (attribute coords)
• they have the same metadata (attribute attrs)

Unlike xarray.DataArray.identical, two DataArrays are not equal if both objects have NaNs in the same positions to follow the standard usage in numpy. You can use objects_are_allclose to compare objects with NaNs.

>>> import numpy as np
>>> import xarray as xr
>>> from coola import objects_are_equal
>>> objects_are_equal(
...     xr.DataArray(np.arange(6), dims=["z"]), xr.DataArray(np.arange(6), dims=["z"])
... )
True
>>> objects_are_equal(
...     xr.DataArray(np.arange(6), dims=["z"]), xr.DataArray(np.zeros(6), dims=["z"])
... )
False

xarray.Dataset

Two xarray Datasets are equal if xarray.Dataset.identical returns True. In contrast to the standard usage in numpy, NaNs are compared like numbers, two Datasets are equal if both objects have NaNs in the same positions.

>>> import numpy as np
>>> import xarray as xr
>>> from coola import objects_are_equal
>>> ds1 = xr.Dataset(
...     {
...         "x": xr.DataArray(
...             np.arange(6),
...             dims=["z"],
...         ),
...         "y": xr.DataArray(
...             np.ones((6, 3)),
...             dims=["z", "t"],
...         ),
...     },
...     coords={"z": np.arange(6) + 1, "t": ["t1", "t2", "t3"]},
... )
>>> ds2 = xr.Dataset(
...     {
...         "x": xr.DataArray(
...             np.arange(6),
...             dims=["z"],
...         ),
...         "y": xr.DataArray(
...             np.ones((6, 3)),
...             dims=["z", "t"],
...         ),
...     },
...     coords={"z": np.arange(6) + 1, "t": ["t1", "t2", "t3"]},
... )
>>> ds3 = xr.Dataset(
...     {
...         "x": xr.DataArray(
...             np.arange(6),
...             dims=["z"],
...         ),
...     },
...     coords={"z": np.arange(6) + 1},
... )
>>> objects_are_equal(ds1, ds2)
True
>>> objects_are_equal(ds1, ds3)
False

Equal within a tolerance (allclose)

object

The concept of equal within a tolerance does not make sense for all objects. In general, the tolerance is not used for objects. The tolerance is only used for numbers (see below). By default, two objects are equal if:

• they have the same type
• they are equal i.e. actual == expected returns True

Example

>>> from coola import objects_are_allclose
>>> objects_are_allclose("abc", "abc")
True
>>> objects_are_allclose("abc", "abcd")
False

Numbers: bool | int | float

Two numbers are equal within a tolerance if:

• they have the same type
• the values are equal with a tolerance

Example

>>> from coola import objects_are_allclose
>>> objects_are_allclose(1, 2)
False
>>> objects_are_allclose(1, 2, atol=1)
True
>>> objects_are_allclose(1, 2, rtol=1)
True
>>> objects_are_allclose(1.0, 2.0)
False
>>> objects_are_allclose(1.0, 2.0, atol=1)
True
>>> objects_are_allclose(1.0, 2.0, rtol=1)
True
>>> objects_are_allclose(1, 2.0, atol=1)
False

Note that booleans are explicitly considered as integers in Python so the tolerance can be used with booleans:

>>> from coola import objects_are_allclose
>>> objects_are_allclose(True, False)
False
>>> objects_are_allclose(True, False, atol=1)
True
>>> objects_are_allclose(True, False, rtol=1)
True

collections.abc.Mapping | dict

Two Mappings are equal within a tolerance if:

• they have the same type
• they have the same number of elements i.e. len(mapping1) == len(mapping2) returns True
• they have the same set of keys i.e. set(mapping1.keys()) != set(mapping2.keys()) returns True
• For each key, the values are equal within a tolerance. The value associated to the key k in the first mapping has to be equal within the tolerance to value associated to the key k in the second mapping.

Example

>>> from collections import OrderedDict
>>> from coola import objects_are_allclose
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 1, "str": "abc"})
True
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 2, "str": "abc"}, atol=2)
True
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 2, "str": "abc"}, rtol=1)
True
>>> objects_are_allclose({"int": 1, "str": "abc"}, OrderedDict({"int": 1, "str": "abc"}))
False
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 1, "str": "abc", "float": 0.2})
False
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 1, "float": 0.2})
False
>>> objects_are_allclose({"int": 1, "str": "abc"}, {"int": 1, "str": "abcd"})
False

collections.abc.Sequence | list | tuple

Two Sequences are equal within a tolerance if:

• they have the same type
• they have the same number of elements i.e. len(sequence1) == len(sequence2) returns True
• For each position, the elements are equal within a tolerance. The i-th element in the first sequence has to be equal within a tolerance to the i-th element in the second sequence.

Example

>>> from coola import objects_are_allclose
>>> objects_are_allclose([1, 2, "abc"], [1, 2, "abc"])
True
>>> objects_are_allclose([1, 2, "abc"], [1, 3, "abc"], atol=2)
True
>>> objects_are_allclose([1, 2, "abc"], [1, 3, "abc"], rtol=1)
True
>>> objects_are_allclose([1, 2, "abc"], (1, 2, "abc"))
False
>>> objects_are_allclose([1, 2, "abc"], [1, 2, "abc", 4])
False
>>> objects_are_allclose([1, 2, "abc"], [1, 2, "abcd"])
False

PyTorch

You need to install coola with PyTorch to check if some PyTorch objects are equal within a tolerance or not. Please check the get started page for more information. coola currently support the following PyTorch objects:

• torch.Tensor
• torch.nn.utils.rnn.PackedSequence

torch.Tensor

Two PyTorch Tensors are equal if:

• they have the same data type i.e. tensor1.dtype == tensor2.dtype returns True
• they have the same device i.e. tensor1.device == tensor2.device returns True
• they have the same shape i.e. tensor1.shape == tensor2.shape returns True
• the values are equal within a tolerance i.e. tensor1.allclose(tensor2) returns True

Example

>>> import torch
>>> from coola import objects_are_allclose
>>> objects_are_allclose(torch.ones(2, 3), torch.ones(2, 3))
True
>>> objects_are_allclose(torch.ones(2, 3), torch.ones(2, 3) + 1, atol=2)
True
>>> objects_are_allclose(torch.ones(2, 3), torch.ones(2, 3) + 1, rtol=1)
True
>>> objects_are_allclose(torch.ones(2, 3), torch.ones(2, 3, dtype=torch.long))
False
>>> objects_are_allclose(torch.ones(2, 3), torch.zeros(2, 3))
False
>>> objects_are_allclose(torch.ones(2, 3), torch.ones(6))
False

torch.nn.utils.rnn.PackedSequence

Two PyTorch PackedSequences are equal within a tolerance if:

• The data attributes are equal within a tolerance
• The batch_sizes attributes are equal
• The sorted_indices attributes are equal
• The unsorted_indices attributes are equal

Example

>>> import torch
>>> from torch.nn.utils.rnn import pack_padded_sequence
>>> from coola import objects_are_allclose
>>> objects_are_allclose(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
... )
True
>>> objects_are_allclose(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float() + 1,
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     atol=2,
... )
True
>>> objects_are_allclose(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).add(1).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
... )  # different values
False
>>> objects_are_allclose(
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 3], dtype=torch.long),
...         batch_first=True,
...     ),
...     pack_padded_sequence(
...         input=torch.arange(10).view(2, 5).float(),
...         lengths=torch.tensor([5, 2], dtype=torch.long),
...         batch_first=True,
...     ),
... )  # different lengths
False

numpy.ndarray

You need to install coola with NumPy to check if some NumPy ndarrays are equal or not. Please check the get started page for more information.

Two NumPy ndarrays are equal within a tolerance if:

• they have the same data type i.e. array1.dtype == array2.dtype returns True
• they have the same shape i.e. array1.shape == array2.shape returns True
• the values are equal within a tolerance i.e. numpy.allclose(array1, array2) returns True

>>> import numpy
>>> from coola import objects_are_allclose
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.ones((2, 3)))
True
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.ones((2, 3)) + 1, atol=2)
True
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.ones((2, 3)) + 1, rtol=1)
True
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.ones((2, 3), dtype=int))
False
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.zeros((2, 3)))
False
>>> objects_are_allclose(numpy.ones((2, 3)), numpy.ones((6,)))
False

xarray

You need to install coola with PyTorch to check if some xarray objects are equal or not. Please check the get started page for more information. coola currently support the following xarray objects:

• xarray.DataArray
• xarray.Dataset

xarray.DataArray

Two xarray DataArrays are equal within a tolerance if:

• they have the same data values within the tolerance (attribute data)
• they have the same name (attribute name)
• they have the same dimension names (attribute dims)
• they have the same coordinates (attribute coords)
• they have the same metadata (attribute attrs)

Unlike xarray.DataArray.identical, two DataArrays are not equal if both objects have NaNs in the same positions to follow the standard usage in numpy. You can use objects_are_allclose to compare objects with NaNs.

>>> import numpy as np
>>> import xarray as xr
>>> from coola import objects_are_allclose
>>> objects_are_allclose(
...     xr.DataArray(np.arange(6), dims=["z"]), xr.DataArray(np.arange(6), dims=["z"])
... )
True
>>> objects_are_allclose(
...     xr.DataArray(np.arange(6), dims=["z"]), xr.DataArray(np.zeros(6), dims=["z"])
... )
False