Source code for dask.array.percentile

import warnings
from import Iterator
from functools import wraps
from numbers import Number

import numpy as np
from tlz import merge

from dask.array.core import Array
from dask.array.numpy_compat import _numpy_122
from dask.array.numpy_compat import percentile as np_percentile
from dask.base import tokenize
from dask.highlevelgraph import HighLevelGraph

def _percentile(a, q, method="linear"):
    n = len(a)
    if not len(a):
        return None, n
    if isinstance(q, Iterator):
        q = list(q)
    if == "category":
        result = np_percentile(, q, method=method)
        import pandas as pd

        return pd.Categorical.from_codes(result, a.dtype.categories, a.dtype.ordered), n
    if type(a.dtype).__name__ == "DatetimeTZDtype":
        import pandas as pd

        if isinstance(a, (pd.Series, pd.Index)):
            a = a.values

    if np.issubdtype(a.dtype, np.datetime64):
        values = a
        a2 = values.view("i8")
        result = np_percentile(a2, q, method=method).astype(values.dtype)
        if q[0] == 0:
            result[0] = min(result[0], values.min())
        return result, n
    if not np.issubdtype(a.dtype, np.number):
        method = "nearest"
    return np_percentile(a, q, method=method), n

def _tdigest_chunk(a):

    from crick import TDigest

    t = TDigest()

    return t

def _percentiles_from_tdigest(qs, digests):

    from crick import TDigest

    t = TDigest()

    return np.array(t.quantile(qs / 100.0))

[docs]def percentile(a, q, method="linear", internal_method="default", **kwargs): """Approximate percentile of 1-D array Parameters ---------- a : Array q : array_like of float Percentile or sequence of percentiles to compute, which must be between 0 and 100 inclusive. method : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}, optional The interpolation method to use when the desired percentile lies between two data points ``i < j``. Only valid for ``method='dask'``. - 'linear': ``i + (j - i) * fraction``, where ``fraction`` is the fractional part of the index surrounded by ``i`` and ``j``. - 'lower': ``i``. - 'higher': ``j``. - 'nearest': ``i`` or ``j``, whichever is nearest. - 'midpoint': ``(i + j) / 2``. .. versionchanged:: 2022.1.0 This argument was previously called "interpolation" internal_method : {'default', 'dask', 'tdigest'}, optional What internal method to use. By default will use dask's internal custom algorithm (``'dask'``). If set to ``'tdigest'`` will use tdigest for floats and ints and fallback to the ``'dask'`` otherwise. .. versionchanged:: 2022.1.0 This argument was previously called “method”. interpolation : str, optional Deprecated name for the method keyword argument. .. deprecated:: 2022.1.0 See Also -------- numpy.percentile : Numpy's equivalent Percentile function """ from dask.array.dispatch import percentile_lookup as _percentile from dask.array.utils import array_safe, meta_from_array allowed_internal_methods = ["default", "dask", "tdigest"] if method in allowed_internal_methods: warnings.warn( "In Dask 2022.1.0, the `method=` argument was renamed to `internal_method=`", FutureWarning, ) internal_method = method if "interpolation" in kwargs: if _numpy_122: warnings.warn( "In Dask 2022.1.0, the `interpolation=` argument to percentile was renamed to " "`method= ` ", FutureWarning, ) method = kwargs.pop("interpolation") if kwargs: raise TypeError( f"percentile() got an unexpected keyword argument {kwargs.keys()}" ) if not a.ndim == 1: raise NotImplementedError("Percentiles only implemented for 1-d arrays") if isinstance(q, Number): q = [q] q = array_safe(q, like=meta_from_array(a)) token = tokenize(a, q, method) dtype = a.dtype if np.issubdtype(dtype, np.integer): dtype = (array_safe([], dtype=dtype, like=meta_from_array(a)) / 0.5).dtype meta = meta_from_array(a, dtype=dtype) if internal_method not in allowed_internal_methods: raise ValueError( f"`internal_method=` must be one of {allowed_internal_methods}" ) # Allow using t-digest if method is allowed and dtype is of floating or integer type if ( internal_method == "tdigest" and method == "linear" and (np.issubdtype(dtype, np.floating) or np.issubdtype(dtype, np.integer)) ): from dask.utils import import_required import_required( "crick", "crick is a required dependency for using the t-digest method." ) name = "percentile_tdigest_chunk-" + token dsk = { (name, i): (_tdigest_chunk, key) for i, key in enumerate(a.__dask_keys__()) } name2 = "percentile_tdigest-" + token dsk2 = {(name2, 0): (_percentiles_from_tdigest, q, sorted(dsk))} # Otherwise use the custom percentile algorithm else: # Add 0 and 100 during calculation for more robust behavior (hopefully) calc_q = np.pad(q, 1, mode="constant") calc_q[-1] = 100 name = "percentile_chunk-" + token dsk = { (name, i): (_percentile, key, calc_q, method) for i, key in enumerate(a.__dask_keys__()) } name2 = "percentile-" + token dsk2 = { (name2, 0): ( merge_percentiles, q, [calc_q] * len(a.chunks[0]), sorted(dsk), method, ) } dsk = merge(dsk, dsk2) graph = HighLevelGraph.from_collections(name2, dsk, dependencies=[a]) return Array(graph, name2, chunks=((len(q),),), meta=meta)
def merge_percentiles(finalq, qs, vals, method="lower", Ns=None, raise_on_nan=True): """Combine several percentile calculations of different data. Parameters ---------- finalq : numpy.array Percentiles to compute (must use same scale as ``qs``). qs : sequence of :class:`numpy.array`s Percentiles calculated on different sets of data. vals : sequence of :class:`numpy.array`s Resulting values associated with percentiles ``qs``. Ns : sequence of integers The number of data elements associated with each data set. method : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} Specify the interpolation method to use to calculate final percentiles. For more information, see :func:`numpy.percentile`. Examples -------- >>> finalq = [10, 20, 30, 40, 50, 60, 70, 80] >>> qs = [[20, 40, 60, 80], [20, 40, 60, 80]] >>> vals = [np.array([1, 2, 3, 4]), np.array([10, 11, 12, 13])] >>> Ns = [100, 100] # Both original arrays had 100 elements >>> merge_percentiles(finalq, qs, vals, Ns=Ns) array([ 1, 2, 3, 4, 10, 11, 12, 13]) """ from dask.array.utils import array_safe if isinstance(finalq, Iterator): finalq = list(finalq) finalq = array_safe(finalq, like=finalq) qs = list(map(list, qs)) vals = list(vals) if Ns is None: vals, Ns = zip(*vals) Ns = list(Ns) L = list(zip(*[(q, val, N) for q, val, N in zip(qs, vals, Ns) if N])) if not L: if raise_on_nan: raise ValueError("No non-trivial arrays found") return np.full(len(qs[0]) - 2, np.nan) qs, vals, Ns = L # TODO: Perform this check above in percentile once dtype checking is easy # Here we silently change meaning if vals[0] == "category": result = merge_percentiles( finalq, qs, [ for v in vals], method, Ns, raise_on_nan ) import pandas as pd return pd.Categorical.from_codes(result, vals[0].categories, vals[0].ordered) if not np.issubdtype(vals[0].dtype, np.number): method = "nearest" if len(vals) != len(qs) or len(Ns) != len(qs): raise ValueError("qs, vals, and Ns parameters must be the same length") # transform qs and Ns into number of observations between percentiles counts = [] for q, N in zip(qs, Ns): count = np.empty_like(finalq, shape=len(q)) count[1:] = np.diff(array_safe(q, like=q[0])) count[0] = q[0] count *= N counts.append(count) # Sort by calculated percentile values, then number of observations. combined_vals = np.concatenate(vals) combined_counts = array_safe(np.concatenate(counts), like=combined_vals) sort_order = np.argsort(combined_vals) combined_vals = np.take(combined_vals, sort_order) combined_counts = np.take(combined_counts, sort_order) # percentile-like, but scaled by total number of observations combined_q = np.cumsum(combined_counts) # rescale finalq percentiles to match combined_q finalq = array_safe(finalq, like=combined_vals) desired_q = finalq * sum(Ns) # the behavior of different interpolation methods should be # investigated further. if method == "linear": rv = np.interp(desired_q, combined_q, combined_vals) else: left = np.searchsorted(combined_q, desired_q, side="left") right = np.searchsorted(combined_q, desired_q, side="right") - 1 np.minimum(left, len(combined_vals) - 1, left) # don't exceed max index lower = np.minimum(left, right) upper = np.maximum(left, right) if method == "lower": rv = combined_vals[lower] elif method == "higher": rv = combined_vals[upper] elif method == "midpoint": rv = 0.5 * (combined_vals[lower] + combined_vals[upper]) elif method == "nearest": lower_residual = np.abs(combined_q[lower] - desired_q) upper_residual = np.abs(combined_q[upper] - desired_q) mask = lower_residual > upper_residual index = lower # alias; we no longer need lower index[mask] = upper[mask] rv = combined_vals[index] else: raise ValueError( "interpolation method can only be 'linear', 'lower', " "'higher', 'midpoint', or 'nearest'" ) return rv