def test_validate_counts_matrix_suppress_cast(self):
# suppress_cast is passed through to _validate_counts_vector
obs = _validate_counts_matrix([[42.2, 42.1, 0], [42.2, 42.1, 1.0]], suppress_cast=True)
npt.assert_array_equal(obs[0], np.array([42.2, 42.1, 0]))
npt.assert_array_equal(obs[1], np.array([42.2, 42.1, 1.0]))
self.assertEqual(obs[0].dtype, float)
self.assertEqual(obs[1].dtype, float)
with self.assertRaises(TypeError):
_validate_counts_matrix([[0.0], [1]], suppress_cast=False)
def test_validate_counts_matrix_suppress_cast(self):
# suppress_cast is passed through to _validate_counts_vector
obs = _validate_counts_matrix([[42.2, 42.1, 0], [42.2, 42.1, 1.0]],
suppress_cast=True)
npt.assert_array_equal(obs[0], np.array([42.2, 42.1, 0]))
npt.assert_array_equal(obs[1], np.array([42.2, 42.1, 1.0]))
self.assertEqual(obs[0].dtype, float)
self.assertEqual(obs[1].dtype, float)
with self.assertRaises(TypeError):
_validate_counts_matrix([[0.0], [1]], suppress_cast=False)
def test_validate_counts_matrix_pandas(self):
obs = _validate_counts_matrix(pd.DataFrame([[0, 1, 1, 0, 2],
[0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]]))
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
npt.assert_array_equal(obs[2], np.array([1, 1, 1, 1, 1]))
def test_validate_counts_matrix(self):
# basic valid input (n=2)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, 1, 3]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
# basic valid input (n=3)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, 1, 3], [1, 1, 1, 1, 1]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
npt.assert_array_equal(obs[2], np.array([1, 1, 1, 1, 1]))
# empty counts vectors
obs = _validate_counts_matrix(np.array([[], []], dtype=int))
npt.assert_array_equal(obs[0], np.array([]))
npt.assert_array_equal(obs[1], np.array([]))
def test_validate_counts_matrix(self):
# basic valid input (n=2)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, 1, 3]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
# basic valid input (n=3)
obs = _validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, 1, 3],
[1, 1, 1, 1, 1]])
npt.assert_array_equal(obs[0], np.array([0, 1, 1, 0, 2]))
npt.assert_array_equal(obs[1], np.array([0, 0, 2, 1, 3]))
npt.assert_array_equal(obs[2], np.array([1, 1, 1, 1, 1]))
# empty counts vectors
obs = _validate_counts_matrix(np.array([[], []], dtype=int))
npt.assert_array_equal(obs[0], np.array([]))
npt.assert_array_equal(obs[1], np.array([]))
def test_validate_counts_matrix_unequal_lengths(self):
# len of vectors not equal
with self.assertRaises(ValueError):
_validate_counts_matrix([[0], [0, 0], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0], [0, 0, 8], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 75], [0, 0, 3], [9, 8, 22, 44]])
def test_validate_counts_matrix_unequal_lengths(self):
# len of vectors not equal
with self.assertRaises(ValueError):
_validate_counts_matrix([[0], [0, 0], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0], [0, 0, 8], [9, 8]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 75], [0, 0, 3], [9, 8, 22, 44]])
def alpha_diversity(metric, counts, ids=None, validate=True, **kwargs):
""" Compute alpha diversity for one or more samples
Parameters
----------
metric : str, callable
The alpha diversity metric to apply to the sample(s). Passing metric as
a string is preferable as this often results in an optimized version of
the metric being used.
counts : 1D or 2D array_like of ints or floats
Vector or matrix containing count/abundance data. If a matrix, each row
should contain counts of OTUs in a given sample.
ids : iterable of strs, optional
Identifiers for each sample in ``counts``. By default, samples will be
assigned integer identifiers in the order that they were provided.
validate: bool, optional
If `False`, validation of the input won't be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you're not certain that your input data are valid. See
:mod:`skbio.diversity` for the description of what validation entails
so you can determine if you can safely disable validation.
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
pd.Series
Values of ``metric`` for all vectors provided in ``counts``. The index
will be ``ids``, if provided.
Raises
------
ValueError, MissingNodeError, DuplicateNodeError
If validation fails. Exact error will depend on what was invalid.
TypeError
If invalid method-specific parameters are provided.
See Also
--------
skbio.diversity
skbio.diversity.alpha
skbio.diversity.get_alpha_diversity_metrics
skbio.diversity.beta_diversity
"""
metric_map = _get_alpha_diversity_metric_map()
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if metric == 'faith_pd':
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
counts_by_node, branch_lengths = _setup_faith_pd(
counts, otu_ids, tree, validate, single_sample=False)
counts = counts_by_node
metric = functools.partial(_faith_pd, branch_lengths=branch_lengths)
elif callable(metric):
metric = functools.partial(metric, **kwargs)
elif metric in metric_map:
metric = functools.partial(metric_map[metric], **kwargs)
else:
raise ValueError('Unknown metric provided: %r.' % metric)
# kwargs is provided here so an error is raised on extra kwargs
results = [metric(c, **kwargs) for c in counts]
return pd.Series(results, index=ids)
def _validate(u_counts, v_counts, otu_ids, tree):
_validate_counts_matrix([u_counts, v_counts], suppress_cast=True)
_validate_otu_ids_and_tree(counts=u_counts, otu_ids=otu_ids, tree=tree)
def test_validate_counts_matrix_negative_counts(self):
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, -1, 3]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 2, -1, 3], [0, 1, 1, 0, 2]])
def beta_diversity(metric, counts, ids=None, validate=True, **kwargs):
"""Compute distances between all pairs of samples
Parameters
----------
metric : str, callable
The pairwise distance function to apply. See the scipy ``pdist`` docs
and the scikit-bio functions linked under *See Also* for available
metrics. Passing metrics as a strings is preferable as this often
results in an optimized version of the metric being used.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs, optional
Identifiers for each sample in ``counts``. By default, samples will be
assigned integer identifiers in the order that they were provided
(where the type of the identifiers will be ``str``).
validate: bool, optional
If `False`, validation of the input won't be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you're not certain that your input data are valid. See
Notes for the description of what validation entails so you can
determine if you can safely disable validation.
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
skbio.DistanceMatrix
Distances between all pairs of samples (i.e., rows). The number of
rows and columns will be equal to the number of rows in ``counts``.
Raises
------
ValueError, MissingNodeError, DuplicateNodeError
If validation fails (see description of validation in Notes). Exact
error will depend on what was invalid.
TypeError
If invalid method-specific parameters are provided.
See Also
--------
skbio.diversity.beta
skbio.diversity.alpha_diversity
scipy.spatial.distance.pdist
Notes
-----
The value that you provide for ``metric`` can be either a string (e.g.,
``"unweighted_unifrac"``) or a function
(e.g., ``skbio.diversity.beta.unweighted_unifrac``). The metric should
generally be passed as a string, as this often uses an optimized version
of the metric. For example, passing ``"unweighted_unifrac"`` (a string)
will be hundreds of times faster than passing the function
``skbio.diversity.beta.unweighted_unifrac``. The latter is faster if
computing only one or a few distances, but in these cases the difference in
runtime is negligible, so it's safer to just err on the side of passing
``metric`` as a string.
Validation of input data confirms the following:
* ``counts`` data can be safely cast to integers
* there are no negative values in ``counts``
* ``counts`` has the correct number of dimensions
* all vectors in ``counts`` are of equal length
* the correct number of ``ids`` is provided (if any are provided)
For phylogenetic diversity metrics, validation additional confirms that:
* ``otu_ids`` does not contain duplicate values
* the length of each ``counts`` vector is equal to ``len(otu_ids)``
* ``tree`` is rooted
* ``tree`` has more than one node
* all nodes in ``tree`` except for the root node have branch lengths
* all tip names in ``tree`` are unique
* all ``otu_ids`` correspond to tip names in ``tree``
"""
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if metric == 'unweighted_unifrac':
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_unweighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, validate=validate)
counts = counts_by_node
elif metric == 'weighted_unifrac':
# get the value for normalized. if it was not provided, it will fall
# back to the default value inside of _weighted_unifrac_pdist_f
normalized = kwargs.pop('normalized',
_normalize_weighted_unifrac_by_default)
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_weighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, normalized=normalized,
validate=validate)
counts = counts_by_node
elif callable(metric):
metric = functools.partial(metric, **kwargs)
# remove all values from kwargs, since they have already been provided
# through the partial
kwargs = {}
else:
# metric is a string that scikit-bio doesn't know about, for
# example one of the SciPy metrics
pass
distances = scipy.spatial.distance.pdist(counts, metric, **kwargs)
return DistanceMatrix(distances, ids)
def test_validate_counts_matrix_negative_counts(self):
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 1, 1, 0, 2], [0, 0, 2, -1, 3]])
with self.assertRaises(ValueError):
_validate_counts_matrix([[0, 0, 2, -1, 3], [0, 1, 1, 0, 2]])
def _validate(u_counts, v_counts, otu_ids, tree):
_validate_counts_matrix([u_counts, v_counts], suppress_cast=True)
_validate_otu_ids_and_tree(counts=u_counts, otu_ids=otu_ids, tree=tree)
def block_beta_diversity(metric,
counts,
ids,
validate=True,
k=64,
reduce_f=None,
map_f=None,
**kwargs):
"""Perform a block-decomposition beta diversity calculation
Parameters
----------
metric : str or callable
The pairwise distance function to apply. If ``metric`` is a string, it
must be resolvable by scikit-bio (e.g., UniFrac methods), or must be
callable.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs
Identifiers for each sample in ``counts``.
validate : bool, optional
See ``skbio.diversity.beta_diversity`` for details.
reduce_f : function, optional
A method to reduce `PartialDistanceMatrix` objects into a single
`DistanceMatrix`. The expected signature is:
`f(Iterable of DistanceMatrix) -> DistanceMatrix`
Note, this is the reduce within a map/reduce.
map_f: function, optional
A method that accepts a `_block_compute`. The expected signature is:
`f(**kwargs) -> DistanceMatrix`
NOTE: ipyparallel's `map_async` will not work here as we need to be
able to pass around `**kwargs``.
k : int, optional
The blocksize used when computing distances
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
DistanceMatrix
A distance matrix relating all samples represented by counts to each
other.
Note
----
This method is designed to facilitate computing beta diversity in parallel.
In general, if you are processing a few hundred samples or less, then it is
likely the case that `skbio.diversity.beta_diversity` will be faster. The
original need which motivated the development of this method was processing
the Earth Microbiome Project [1]_ dataset which at the time spanned over
25,000 samples and 7.5 million open reference OTUs.
See Also
--------
skbio.diversity.beta_diversity
skbio.diversity.partial_beta_diversity
References
----------
.. [1] http://www.earthmicrobiome.org/
"""
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if reduce_f is None:
reduce_f = _reduce
if map_f is None:
map_f = _map
# The block method uses numeric IDs to take advantage of fancy indexing
# with numpy.
tmp_ids = np.arange(len(counts))
kwargs['ids'] = tmp_ids
kwargs['metric'] = metric
kwargs['counts'] = counts
kwargs['k'] = k
kwargs['validate'] = False # we've already validated if necessary
dm = reduce_f(map_f(_block_compute, _block_kwargs(**kwargs)))
dm.ids = ids
return dm
def block_beta_diversity(metric, counts, ids, validate=True, k=64,
reduce_f=None, map_f=None, **kwargs):
"""Perform a block-decomposition beta diversity calculation
Parameters
----------
metric : str or callable
The pairwise distance function to apply. If ``metric`` is a string, it
must be resolvable by scikit-bio (e.g., UniFrac methods), or must be
callable.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs
Identifiers for each sample in ``counts``.
validate : bool, optional
See ``skbio.diversity.beta_diversity`` for details.
reduce_f : function, optional
A method to reduce `PartialDistanceMatrix` objects into a single
`DistanceMatrix`. The expected signature is:
`f(Iterable of DistanceMatrix) -> DistanceMatrix`
Note, this is the reduce within a map/reduce.
map_f: function, optional
A method that accepts a `_block_compute`. The expected signature is:
`f(**kwargs) -> DistanceMatrix`
NOTE: ipyparallel's `map_async` will not work here as we need to be
able to pass around `**kwargs``.
k : int, optional
The blocksize used when computing distances
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
DistanceMatrix
A distance matrix relating all samples represented by counts to each
other.
Note
----
This method is designed to facilitate computing beta diversity in parallel.
In general, if you are processing a few hundred samples or less, then it is
likely the case that `skbio.diversity.beta_diversity` will be faster. The
original need which motivated the development of this method was processing
the Earth Microbiome Project [1]_ dataset which at the time spanned over
25,000 samples and 7.5 million open reference OTUs.
See Also
--------
skbio.diversity.beta_diversity
skbio.diversity.partial_beta_diversity
References
----------
.. [1] http://www.earthmicrobiome.org/
"""
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if reduce_f is None:
reduce_f = _reduce
if map_f is None:
map_f = _map
# The block method uses numeric IDs to take advantage of fancy indexing
# with numpy.
tmp_ids = np.arange(len(counts))
kwargs['ids'] = tmp_ids
kwargs['metric'] = metric
kwargs['counts'] = counts
kwargs['k'] = k
kwargs['validate'] = False # we've already validated if necessary
dm = reduce_f(map_f(_block_compute, _block_kwargs(**kwargs)))
dm.ids = ids
return dm
def partial_beta_diversity(metric, counts, ids, id_pairs, validate=True,
**kwargs):
"""Compute distances only between specified ID pairs
Parameters
----------
metric : str or callable
The pairwise distance function to apply. If ``metric`` is a string, it
must be resolvable by scikit-bio (e.g., UniFrac methods), or must be
callable.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs
Identifiers for each sample in ``counts``.
id_pairs : iterable of tuple
An iterable of tuples of IDs to compare (e.g., ``[('a', 'b'), ('a',
'c'), ...])``. If specified, the set of IDs described must be a subset
of ``ids``.
validate : bool, optional
See ``skbio.diversity.beta_diversity`` for details.
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
skbio.DistanceMatrix
Distances between pairs of samples indicated by id_pairs. Pairwise
distances not defined by id_pairs will be 0.0. Use this resulting
DistanceMatrix with caution as 0.0 is a valid distance.
Raises
------
ValueError
If ``ids`` are not specified.
If ``id_pairs`` are not a subset of ``ids``.
If ``metric`` is not a callable or is unresolvable string by
scikit-bio.
If duplicates are observed in ``id_pairs``.
See Also
--------
skbio.diversity.beta_diversity
skbio.diversity.get_beta_diversity_metrics
"""
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
id_pairs = list(id_pairs)
all_ids_in_pairs = set(itertools.chain.from_iterable(id_pairs))
if not all_ids_in_pairs.issubset(ids):
raise ValueError("`id_pairs` are not a subset of `ids`")
hashes = {i for i in id_pairs}.union({i[::-1] for i in id_pairs})
if len(hashes) != len(id_pairs) * 2:
raise ValueError("A duplicate or a self-self pair was observed.")
if metric == 'unweighted_unifrac':
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_unweighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, validate=validate)
counts = counts_by_node
elif metric == 'weighted_unifrac':
# get the value for normalized. if it was not provided, it will fall
# back to the default value inside of _weighted_unifrac_pdist_f
normalized = kwargs.pop('normalized',
_normalize_weighted_unifrac_by_default)
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_weighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, normalized=normalized,
validate=validate)
counts = counts_by_node
elif callable(metric):
metric = functools.partial(metric, **kwargs)
# remove all values from kwargs, since they have already been provided
# through the partial
kwargs = {}
else:
raise ValueError("partial_beta_diversity is only compatible with "
"optimized unifrac methods and callable functions.")
dm = np.zeros((len(ids), len(ids)), dtype=float)
id_index = {id_: idx for idx, id_ in enumerate(ids)}
id_pairs_indexed = ((id_index[u], id_index[v]) for u, v in id_pairs)
for u, v in id_pairs_indexed:
dm[u, v] = metric(counts[u], counts[v], **kwargs)
return DistanceMatrix(dm + dm.T, ids)
def beta_diversity(metric, counts, ids=None, validate=True, pairwise_func=None,
**kwargs):
"""Compute distances between all pairs of samples
Parameters
----------
metric : str, callable
The pairwise distance function to apply. See the scipy ``pdist`` docs
and the scikit-bio functions linked under *See Also* for available
metrics. Passing metrics as a strings is preferable as this often
results in an optimized version of the metric being used.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs, optional
Identifiers for each sample in ``counts``. By default, samples will be
assigned integer identifiers in the order that they were provided
(where the type of the identifiers will be ``str``).
validate : bool, optional
If `False`, validation of the input won't be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you're not certain that your input data are valid. See
:mod:`skbio.diversity` for the description of what validation entails
so you can determine if you can safely disable validation.
pairwise_func : callable, optional
The function to use for computing pairwise distances. This function
must take ``counts`` and ``metric`` and return a square, hollow, 2-D
``numpy.ndarray`` of dissimilarities (floats). Examples of functions
that can be provided are ``scipy.spatial.distance.pdist`` and
``sklearn.metrics.pairwise_distances``. By default,
``sklearn.metrics.pairwise_distances`` will be used.
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
skbio.DistanceMatrix
Distances between all pairs of samples (i.e., rows). The number of
rows and columns will be equal to the number of rows in ``counts``.
Raises
------
ValueError, MissingNodeError, DuplicateNodeError
If validation fails. Exact error will depend on what was invalid.
TypeError
If invalid method-specific parameters are provided.
See Also
--------
skbio.diversity
skbio.diversity.beta
skbio.diversity.get_beta_diversity_metrics
skbio.diversity.alpha_diversity
scipy.spatial.distance.pdist
sklearn.metrics.pairwise_distances
"""
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if 0 in counts.shape:
# if the input counts are empty, return an empty DistanceMatrix.
# this check is not necessary for scipy.spatial.distance.pdist but
# it is necessary for sklearn.metrics.pairwise_distances where the
# latter raises an exception over empty data.
return DistanceMatrix(np.zeros((len(ids), len(ids))), ids)
if metric == 'unweighted_unifrac':
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_unweighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, validate=validate)
counts = counts_by_node
elif metric == 'weighted_unifrac':
# get the value for normalized. if it was not provided, it will fall
# back to the default value inside of _weighted_unifrac_pdist_f
normalized = kwargs.pop('normalized',
_normalize_weighted_unifrac_by_default)
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
metric, counts_by_node = _setup_multiple_weighted_unifrac(
counts, otu_ids=otu_ids, tree=tree, normalized=normalized,
validate=validate)
counts = counts_by_node
elif callable(metric):
metric = functools.partial(metric, **kwargs)
# remove all values from kwargs, since they have already been provided
# through the partial
kwargs = {}
else:
# metric is a string that scikit-bio doesn't know about, for
# example one of the SciPy metrics
pass
if pairwise_func is None:
pairwise_func = sklearn.metrics.pairwise_distances
distances = pairwise_func(counts, metric=metric, **kwargs)
return DistanceMatrix(distances, ids)
def alpha_diversity(metric, counts, ids=None, validate=True, **kwargs):
""" Compute alpha diversity for one or more samples
Parameters
----------
metric : str, callable
The alpha diversity metric to apply to the sample(s). Passing metric as
a string is preferable as this often results in an optimized version of
the metric being used.
counts : 1D or 2D array_like of ints or floats
Vector or matrix containing count/abundance data. If a matrix, each row
should contain counts of OTUs in a given sample.
ids : iterable of strs, optional
Identifiers for each sample in ``counts``. By default, samples will be
assigned integer identifiers in the order that they were provided.
validate: bool, optional
If `False`, validation of the input won't be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you're not certain that your input data are valid. See
Notes for the description of what validation entails so you can
determine if you can safely disable validation.
kwargs : kwargs, optional
Metric-specific parameters.
Returns
-------
pd.Series
Values of ``metric`` for all vectors provided in ``counts``. The index
will be ``ids``, if provided.
Raises
------
ValueError, MissingNodeError, DuplicateNodeError
If validation fails (see description of validation in Notes). Exact
error will depend on what was invalid.
TypeError
If invalid method-specific parameters are provided.
See Also
--------
skbio.diversity.alpha
skbio.diversity.beta_diversity
Notes
-----
The value that you provide for ``metric`` can be either a string (e.g.,
``"faith_pd"``) or a function (e.g., ``skbio.diversity.alpha.faith_pd``).
The metric should generally be passed as a string, as this often uses an
optimized version of the metric. For example, passing ``"faith_pd"`` (a
string) will be tens of times faster than passing the function
``skbio.diversity.alpha.faith_pd``. The latter may be faster if computing
alpha diversity for only one or a few samples, but in these cases the
difference in runtime is negligible, so it's safer to just err on the side
of passing ``metric`` as a string.
Validation of input data confirms the following:
* ``counts`` data can be safely cast to integers
* there are no negative values in ``counts``
* ``counts`` has the correct number of dimensions
* if ``counts`` is 2-D, all vectors are of equal length
* the correct number of ``ids`` is provided (if any are provided)
For phylogenetic diversity metrics, validation additional confirms that:
* ``otu_ids`` does not contain duplicate values
* the length of each ``counts`` vector is equal to ``len(otu_ids)``
* ``tree`` is rooted
* ``tree`` has more than one node
* all nodes in ``tree`` except for the root node have branch lengths
* all tip names in ``tree`` are unique
* all ``otu_ids`` correspond to tip names in ``tree``
"""
metric_map = _get_alpha_diversity_metric_map()
if validate:
counts = _validate_counts_matrix(counts, ids=ids)
if metric == 'faith_pd':
otu_ids, tree, kwargs = _get_phylogenetic_kwargs(counts, **kwargs)
counts_by_node, branch_lengths = _setup_faith_pd(
counts, otu_ids, tree, validate, single_sample=False)
counts = counts_by_node
metric = functools.partial(_faith_pd, branch_lengths=branch_lengths)
elif callable(metric):
metric = functools.partial(metric, **kwargs)
elif metric in metric_map:
metric = functools.partial(metric_map[metric], **kwargs)
else:
raise ValueError('Unknown metric provided: %r.' % metric)
results = [metric(c) for c in counts]
return pd.Series(results, index=ids)