asv_bench/benchmarks/sparse.py - main - githubmirror/pandas

asv_bench/benchmarks/sparse.py
227 строк7 KB
Natalia Mokeeva
DEPR: deprecate strings T, S, L, U, and N in offsets frequencies, resolution abbreviations, _attrname_to_abbrevs (#54061)
29 авг 2023, 18:10
Не верифицирован
29 авг 2023, 18:10b2dcf2e
import numpy as np
import scipy.sparse

import pandas as pd
from pandas import (
    MultiIndex,
    Series,
    date_range,
)
from pandas.arrays import SparseArray


def make_array(size, dense_proportion, fill_value, dtype):
    dense_size = int(size * dense_proportion)
    arr = np.full(size, fill_value, dtype)
    indexer = np.random.choice(np.arange(size), dense_size, replace=False)
    arr[indexer] = np.random.choice(np.arange(100, dtype=dtype), dense_size)
    return arr


class SparseSeriesToFrame:
    def setup(self):
        K = 50
        N = 50001
        rng = date_range("1/1/2000", periods=N, freq="min")
        self.series = {}
        for i in range(1, K):
            data = np.random.randn(N)[:-i]
            idx = rng[:-i]
            data[100:] = np.nan
            self.series[i] = Series(SparseArray(data), index=idx)

    def time_series_to_frame(self):
        pd.DataFrame(self.series)


class SparseArrayConstructor:
    params = ([0.1, 0.01], [0, np.nan], [np.int64, np.float64, object])
    param_names = ["dense_proportion", "fill_value", "dtype"]

    def setup(self, dense_proportion, fill_value, dtype):
        N = 10**6
        self.array = make_array(N, dense_proportion, fill_value, dtype)

    def time_sparse_array(self, dense_proportion, fill_value, dtype):
        SparseArray(self.array, fill_value=fill_value, dtype=dtype)


class SparseDataFrameConstructor:
    def setup(self):
        N = 1000
        self.sparse = scipy.sparse.rand(N, N, 0.005)

    def time_from_scipy(self):
        pd.DataFrame.sparse.from_spmatrix(self.sparse)


class FromCoo:
    def setup(self):
        self.matrix = scipy.sparse.coo_matrix(
            ([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])), shape=(100, 100)
        )

    def time_sparse_series_from_coo(self):
        Series.sparse.from_coo(self.matrix)


class ToCoo:
    params = [True, False]
    param_names = ["sort_labels"]

    def setup(self, sort_labels):
        s = Series([np.nan] * 10000)
        s[0] = 3.0
        s[100] = -1.0
        s[999] = 12.1

        s_mult_lvl = s.set_axis(MultiIndex.from_product([range(10)] * 4))
        self.ss_mult_lvl = s_mult_lvl.astype("Sparse")

        s_two_lvl = s.set_axis(MultiIndex.from_product([range(100)] * 2))
        self.ss_two_lvl = s_two_lvl.astype("Sparse")

    def time_sparse_series_to_coo(self, sort_labels):
        self.ss_mult_lvl.sparse.to_coo(
            row_levels=[0, 1], column_levels=[2, 3], sort_labels=sort_labels
        )

    def time_sparse_series_to_coo_single_level(self, sort_labels):
        self.ss_two_lvl.sparse.to_coo(sort_labels=sort_labels)


class ToCooFrame:
    def setup(self):
        N = 10000
        k = 10
        arr = np.zeros((N, k), dtype=float)
        arr[0, 0] = 3.0
        arr[12, 7] = -1.0
        arr[0, 9] = 11.2
        self.df = pd.DataFrame(arr, dtype=pd.SparseDtype("float", fill_value=0.0))

    def time_to_coo(self):
        self.df.sparse.to_coo()


class Arithmetic:
    params = ([0.1, 0.01], [0, np.nan])
    param_names = ["dense_proportion", "fill_value"]

    def setup(self, dense_proportion, fill_value):
        N = 10**6
        arr1 = make_array(N, dense_proportion, fill_value, np.int64)
        self.array1 = SparseArray(arr1, fill_value=fill_value)
        arr2 = make_array(N, dense_proportion, fill_value, np.int64)
        self.array2 = SparseArray(arr2, fill_value=fill_value)

    def time_make_union(self, dense_proportion, fill_value):
        self.array1.sp_index.make_union(self.array2.sp_index)

    def time_intersect(self, dense_proportion, fill_value):
        self.array1.sp_index.intersect(self.array2.sp_index)

    def time_add(self, dense_proportion, fill_value):
        self.array1 + self.array2

    def time_divide(self, dense_proportion, fill_value):
        self.array1 / self.array2


class ArithmeticBlock:
    params = [np.nan, 0]
    param_names = ["fill_value"]

    def setup(self, fill_value):
        N = 10**6
        self.arr1 = self.make_block_array(
            length=N, num_blocks=1000, block_size=10, fill_value=fill_value
        )
        self.arr2 = self.make_block_array(
            length=N, num_blocks=1000, block_size=10, fill_value=fill_value
        )

    def make_block_array(self, length, num_blocks, block_size, fill_value):
        arr = np.full(length, fill_value)
        indices = np.random.choice(
            np.arange(0, length, block_size), num_blocks, replace=False
        )
        for ind in indices:
            arr[ind : ind + block_size] = np.random.randint(0, 100, block_size)
        return SparseArray(arr, fill_value=fill_value)

    def time_make_union(self, fill_value):
        self.arr1.sp_index.make_union(self.arr2.sp_index)

    def time_intersect(self, fill_value):
        self.arr2.sp_index.intersect(self.arr2.sp_index)

    def time_addition(self, fill_value):
        self.arr1 + self.arr2

    def time_division(self, fill_value):
        self.arr1 / self.arr2


class MinMax:
    params = (["min", "max"], [0.0, np.nan])
    param_names = ["func", "fill_value"]

    def setup(self, func, fill_value):
        N = 1_000_000
        arr = make_array(N, 1e-5, fill_value, np.float64)
        self.sp_arr = SparseArray(arr, fill_value=fill_value)

    def time_min_max(self, func, fill_value):
        getattr(self.sp_arr, func)()


class Take:
    params = ([np.array([0]), np.arange(100_000), np.full(100_000, -1)], [True, False])
    param_names = ["indices", "allow_fill"]

    def setup(self, indices, allow_fill):
        N = 1_000_000
        fill_value = 0.0
        arr = make_array(N, 1e-5, fill_value, np.float64)
        self.sp_arr = SparseArray(arr, fill_value=fill_value)

    def time_take(self, indices, allow_fill):
        self.sp_arr.take(indices, allow_fill=allow_fill)


class GetItem:
    def setup(self):
        N = 1_000_000
        d = 1e-5
        arr = make_array(N, d, np.nan, np.float64)
        self.sp_arr = SparseArray(arr)

    def time_integer_indexing(self):
        self.sp_arr[78]

    def time_slice(self):
        self.sp_arr[1:]


class GetItemMask:
    params = [True, False, np.nan]
    param_names = ["fill_value"]

    def setup(self, fill_value):
        N = 1_000_000
        d = 1e-5
        arr = make_array(N, d, np.nan, np.float64)
        self.sp_arr = SparseArray(arr)
        b_arr = np.full(shape=N, fill_value=fill_value, dtype=np.bool_)
        fv_inds = np.unique(
            np.random.randint(low=0, high=N - 1, size=int(N * d), dtype=np.int32)
        )
        b_arr[fv_inds] = True if pd.isna(fill_value) else not fill_value
        self.sp_b_arr = SparseArray(b_arr, dtype=np.bool_, fill_value=fill_value)

    def time_mask(self, fill_value):
        self.sp_arr[self.sp_b_arr]


from .pandas_vb_common import setup  # noqa: F401 isort:skip