def test_cut_small():
nbins = [4, 2, 5, 4, 10, 3, 2, 5]
colnames = [
"bool", "int_pos", "int_neg", "int", "float", "inf_max", "inf_min",
"inf"
]
DT = dt.Frame(
[[True, None, False, False, True, None], [3, None, 4, 1, 5, 4],
[-5, -1, -1, -1, None, 0], [None, -5, -314, 0, 5, 314],
[None, 1.4, 4.1, 1.5, 5.9, 1.4], [math.inf, 1.4, 4.1, 1.5, 5.9, 1.4],
[-math.inf, 1.4, 4.1, 1.5, 5.9, 1.4],
[-math.inf, 1.4, 4.1, math.inf, 5.9, 1.4]],
names=colnames)
DT_ref_right = dt.Frame(
[[3, None, 0, 0, 3, None], [0, None, 1, 0, 1, 1],
[0, 3, 3, 3, None, 4], [None, 1, 0, 1, 2, 3], [None, 0, 5, 0, 9, 0],
[None] * DT.nrows, [None] * DT.nrows, [None] * DT.nrows],
names=colnames,
stypes=[stype.int32] * DT.ncols)
DT_ref_left = dt.Frame(
[[3, None, 0, 0, 3, None], [1, None, 1, 0, 1, 1],
[0, 4, 4, 4, None, 4], [None, 1, 0, 2, 2, 3], [None, 0, 6, 0, 9, 0],
[None] * DT.nrows, [None] * DT.nrows, [None] * DT.nrows],
names=colnames,
stypes=[stype.int32] * DT.ncols)
DT_cut_list = DT[:, cut(DT, nbins=nbins)]
DT_cut_tuple = DT[:, cut(DT, nbins=tuple(nbins))]
DT_cut_list_left = DT[:, cut(DT, nbins=nbins, right_closed=False)]
assert_equals(DT_ref_right, DT_cut_list)
assert_equals(DT_ref_right, DT_cut_tuple)
assert_equals(DT_ref_left, DT_cut_list_left)
def test_cut_one_row():
nbins = [1, 2, 3, 4]
DT = dt.Frame([[True], [404], [3.1415926], [None]])
DT_cut_right = DT[:, cut(DT, nbins=nbins)]
DT_cut_left = DT[:, cut(DT, nbins=nbins, right_closed=False)]
assert DT_cut_right.to_list() == [[0], [0], [1], [None]]
assert DT_cut_left.to_list() == [[0], [1], [1], [None]]
def test_cut_error_inconsistent_bins():
msg = (
"When nbins is a list or a tuple, its length must be the same as "
"the number of columns in the frame/expression, i.e. 2, instead got: 1"
)
DT = dt.Frame([[3, 1, 4], [1, 5, 9]])
with pytest.raises(ValueError, match=msg):
cut(DT, nbins=[10])
def test_cut_trivial_bins():
DT_data = dt.Frame({"data": range(10)})
DT_bins = dt.Frame({"bins": range(-1, 10)})
cut_fexpr = cut(f[:], bins=[DT_bins])
for i in range(5):
DT_cut = DT_data[:, cut_fexpr]
expr_cut = cut(DT_data, bins=[DT_bins])
assert isinstance(expr_cut, FExpr)
assert_equals(DT_data, DT_cut)
def test_cut_small_bins():
DT_bins = [dt.Frame([-1, 0, 1, 2]),
dt.Frame(range(10)),
dt.Frame(range(-10, 0)),
dt.Frame([-1000, 0, 314]),
dt.Frame(range(10)),
dt.Frame([0, 1.4, 2.8, 4.2, 5.6]),
dt.Frame([0, 1.4, 2.8, 4.2, 5.6, 7.0]),
dt.Frame([-5, 0, 15])]
colnames = ["bool", "int_pos", "int_neg", "int",
"float", "inf_max", "inf_min", "inf"]
DT = dt.Frame(
[[True, None, False, False, True, None],
[3, None, 4, 1, 5, 4],
[-5, -1, -1, -1, None, 0],
[None, -5, -314, 0, 5, 314],
[None, 1.4, 4.1, 1.5, 5.9, 1.4],
[math.inf, 1.4, 4.1, 1.5, 5.9, 1.4],
[-math.inf, 1.4, -4.1, 1.5, 5.9, 1.4],
[-math.inf, 1.4, 4.1, math.inf, 5.9, 1.4]],
names = colnames
)
DT_ref_right = dt.Frame(
[[1, None, 0, 0, 1, None],
[2, None, 3, 0, 4, 3],
[4, 8, 8, 8, None, None],
[None, 0, 0, 0, 1, 1],
[None, 1, 4, 1, 5, 1],
[None, 0, 2, 1, None, 0],
[None, 0, None, 1, 4, 0],
[None, 1, 1, None, 1, 1]],
names = colnames,
stypes = [stype.int32] * DT.ncols
)
DT_ref_left = dt.Frame(
[[2, None, 1, 1, 2, None],
[3, None, 4, 1, 5, 4],
[5, None, None, None, None, None],
[None, 0, 0, 1, 1, None],
[None, 1, 4, 1, 5, 1],
[None, 1, 2, 1, None, 1],
[None, 1, None, 1, 4, 1],
[None, 1, 1, None, 1, 1]],
names = colnames,
stypes = [stype.int32] * DT.ncols
)
DT_cut_list = DT[:, cut(DT, bins = DT_bins)]
DT_cut_tuple = DT[:, cut(DT, bins = tuple(DT_bins))]
DT_cut_list_left = DT[:, cut(DT, bins = DT_bins, right_closed = False)]
assert_equals(DT_ref_right, DT_cut_list)
assert_equals(DT_ref_right, DT_cut_tuple)
assert_equals(DT_ref_left, DT_cut_list_left)
def test_cut_one_row_bins():
DT_bins = [dt.Frame([0, 1]),
dt.Frame(range(1000)),
dt.Frame([-100, 3.1415926, 100]),
dt.Frame(range(5))]
DT = dt.Frame([[True], [404], [3.1415926], [None]])
DT_cut_right = DT[:, cut(DT, bins = DT_bins)]
DT_cut_left = DT[:, cut(DT, bins = DT_bins, right_closed = False)]
assert DT_cut_right.to_list() == [[0], [403], [0], [None]]
assert DT_cut_left.to_list() == [[None], [404], [1], [None]]
def test_cut_pandas_issue_35126(pandas):
nbins = 42
data = [-97, 0, 97]
DT = dt.Frame(data)
DT_cut_right = DT[:, cut(DT, nbins=nbins)]
DT_cut_left = DT[:, cut(DT, nbins=nbins, right_closed=False)]
assert DT_cut_right.to_list() == [[0, 20, 41]]
assert DT_cut_left.to_list() == [[0, 21, 41]]
# Testing that Pandas results are inconsistent
PD = pandas.cut(data, nbins, labels=False)
assert list(PD) == [0, 21, 41]
def test_cut_vs_pandas_random_bins(pandas, seed):
random.seed(seed)
max_bins = 20
max_elements = 20
max_value = 100
n_elements = random.randint(1, max_elements)
right_closed = bool(random.getrandbits(1))
DT_bins = []
bins = [[] for _ in range(3)]
for i in range(3):
nbins = random.randint(2, max_bins)
bins[i] = random.sample(range(-max_value, max_value), nbins)
bins[i].sort()
DT_bins.append(dt.Frame(bins[i]))
data = [[] for _ in range(3)]
for _ in range(n_elements):
data[0].append(random.randint(0, 1))
data[1].append(random.randint(-max_value, max_value))
data[2].append(random.random() * 2 * max_value - max_value)
DT = dt.Frame(data, stypes = [stype.bool8, stype.int32, stype.float64])
DT_cut = DT[:, cut(DT, bins = DT_bins, right_closed = right_closed)]
PD_cut = [pandas.cut(data[i], bins[i], labels=False, right=right_closed) for i in range(3)]
PD_l = [list(PD_cut[i]) for i in range(3)]
# Replace `nan`s with `None` for pandas
for i in range(3):
PD_l[i] = [None if math.isnan(PD_l[i][j]) else PD_l[i][j] for j in range(n_elements)]
assert PD_l == DT_cut.to_list()
def test_cut_vs_pandas_random(pandas, seed):
random.seed(seed)
max_size = 20
max_value = 100
n = random.randint(1, max_size)
nbins = [random.randint(1, max_size) for _ in range(3)]
right_closed = bool(random.getrandbits(1))
data = [[] for _ in range(3)]
for _ in range(n):
data[0].append(random.randint(0, 1))
data[1].append(random.randint(-max_value, max_value))
data[2].append(random.random() * 2 * max_value - max_value)
DT = dt.Frame(data, stypes=[stype.bool8, stype.int32, stype.float64])
DT_cut = DT[:, cut(DT, nbins=nbins, right_closed=right_closed)]
PD_cut = [
pandas.cut(data[i], nbins[i], labels=False, right=right_closed)
for i in range(3)
]
assert [list(PD_cut[i]) for i in range(3)] == DT_cut.to_list()
def test_cut_error_inconsistent_nbins():
msg = (
"When nbins has more than one element, its length must be the same as "
"the number of columns in the frame/expression, i.e. 2, instead got: 3"
)
DT = dt.Frame([[3, 1, 4], [1, 5, 9]])
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, nbins=[10, 11, 12])]
def test_cut_error_noniterable_bins():
msg = "bins parameter must be a list or a tuple, instead got <class 'float'>"
DT = dt.Frame(range(10))
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT, bins=1.5)]
def test_cut_expr():
DT = dt.Frame([range(0, 30, 3), range(0, 20, 2)])
DT_cut = DT[:, cut(cut(f[0] - f[1]))]
assert_equals(dt.Frame(range(10)), DT_cut)
def test_cut_trivial():
DT = dt.Frame({"trivial": range(10)})
DT_cut = DT[:, cut(f[:])]
expr_cut = cut(DT)
assert isinstance(expr_cut, FExpr)
assert_equals(DT, DT_cut)
def test_cut_empty_frame():
DT = dt.Frame()
expr_cut = cut(DT)
assert isinstance(expr_cut, FExpr)
assert_equals(DT[:, f[:]], DT)
def test_cut_error_wrong_right():
msg = "Expected a boolean, instead got <class 'int'>"
DT = dt.Frame(range(10))
with pytest.raises(TypeError, match=msg):
cut(DT, right_closed=1492)
def test_cut_error_negative_nbins_list():
msg = r"All elements in nbins must be positive, got nbins\[0\]: 0"
DT = dt.Frame([[3, 1, 4], [1, 5, 9]])
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, nbins=[0, -1])]
def test_cut_error_float_nbins():
msg = "Expected an integer, instead got <class 'float'>"
DT = dt.Frame(range(10))
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT, nbins=1.5)]
def test_cut_error_wrong_column_type():
DT = dt.Frame([[1, 0], ["1", "0"]])
msg = r"cut\(\) can only be applied to numeric columns, instead column 1 " \
"has an stype: str32"
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT)]
def test_cut_empty_frame():
DT_cut = cut(dt.Frame())
assert_equals(DT_cut, dt.Frame())
def test_cut_error_inconsistent_bins():
msg = ("Number of elements in bins must be equal to the number of columns "
"in the frame/expression, i.e. 2, instead got: 1")
DT = dt.Frame([[3, 1, 4], [1, 5, 9]])
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, bins=[dt.Frame([1, 2])])]
def test_cut_error_string_bins():
msg = "bins parameter must be a list or a tuple, instead got <class 'str'>"
DT = dt.Frame(range(10))
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT, bins="bin1")]
def test_cut_error_one_bin_edge():
msg = "To bin data at least two edges are required, instead for the frame 0 got: 1"
DT = dt.Frame(range(10))
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, bins=[dt.Frame([1])])]
def test_cut_error_noargs():
msg = r"Function datatable\.cut\(\) requires exactly 1 positional " \
r"argument, but none were given"
with pytest.raises(TypeError, match=msg):
cut()
def test_cut_error_none_bin_edge():
msg = "Bin edges must be numeric values only, instead for the frame 0 got None at row 2"
DT = dt.Frame(range(10))
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, bins=[dt.Frame([1, 2, None, 3])])]
def test_cut_error_wrong_column_type_zero_rows():
DT = dt.Frame(str=[] / dt.str32)
msg = r"cut\(\) can only be applied to numeric columns, instead column 0 " \
"has an stype: str32"
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT)]
def test_cut_error_bin_edges_not_increasing():
msg = "Bin edges must be strictly increasing, instead for the frame 0 at rows 2 and 3 the values are 4 and 3.99"
DT = dt.Frame(range(10))
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, bins=[dt.Frame([1, 2, 4.0, 3.99])])]
def test_cut_error_negative_nbins():
msg = "Number of bins must be positive, instead got: -10"
DT = dt.Frame(range(10))
with pytest.raises(ValueError, match=msg):
DT[:, cut(DT, nbins=-10)]
def test_cut_error_groupby():
msg = r"cut\(\) cannot be used in a groupby context"
DT = dt.Frame(range(10))
with pytest.raises(NotImplementedError, match=msg):
DT[:, cut(f[0]), f[0]]
def test_cut_error_wrong_right():
msg = r"Argument right_closed in function datatable\.cut\(\) should " \
r"be a boolean, instead got <class 'int'>"
DT = dt.Frame(range(10))
with pytest.raises(TypeError, match=msg):
DT[:, cut(DT, right_closed=1492)]
def test_cut_error_zero_bins():
msg = "Number of bins must be positive, instead got: 0"
DT = dt.Frame(range(10))
with pytest.raises(ValueError, match=msg):
cut(DT, nbins=0)
