apoor.train_test_split() - Code Metrics - a-poor/apoor - Measure and Improve Code Quality continuously with Scrutinizer

apoor.train_test_split() A
last analyzed 2021-01-18 17:36 UTC

↳ Parent: apoor

Complexity

Conditions

Size

Total Lines	89
Code Lines	29

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	29
dl	0
loc	89
rs	9.184
c	0
b	0
f	0
cc	3
nop	4

How to fix Long Method

"""A small personal package created to store code and data I often reuse. 


I'll continue to update it with useful functions that I find myself reusing. The `apoor.data` module has some common datasets and functions for reading them in as pandas DataFrames.

"""

# Version string
__version__ = "1.3.2"



import itertools as it
import numpy as np
from . import data

from typing import Any, List, Callable, Tuple, Iterable



def fdir(o: Any) -> List[str]:

    """Filtered dir(). Same as builtin dir()
    function without private attributes.

    :param o: Object being inspected
    :return: "Public attributes" of o
    """
    return [a for a in dir(o) if a[0] != "_"]

def set_seed(n:int):

    """Sets numpy's random seed.

    :param n: The value used to set numpy's random seed.
    :type n: int
    """
    np.random.seed(n)


def make_scale(dmin:float,dmax:float,rmin:float,rmax:float,clamp:bool=False) -> Callable[[float],float]:

    """Scale function factory.
    

    Creates a scale function to map a number from a domain to a range.

    :param dmin: Domain's start value
    :param dmax: Domain's end value
    :param rmin: Range's start value
    :param rmax: Range's end value
    :param clamp: If the result is outside the range, return 

        clamped value (default: False)
    :return: A scale function taking one numeric argument and 

        returns the value mapped from the domain to the range 

        (and clamped if `clamp` flag is set).

    Examples:
        >>> s = make_scale(0,1,0,10)
        >>> s(0.1)
        1.0

        >>> s = make_scale(0,10,10,0)
        >>> s(1.0)
        9.0

        >>> s = make_scale(0,1,0,1,clamp=True)
        >>> s(100)
        1.0
    """
    drange = dmax - dmin
    rrange = rmax - rmin
    scale_factor = rrange / drange
    def scale(n):

        n_ = (n - dmin) * scale_factor + rmin

        if clamp: return min(max(n_,rmin),rmax)

        else: return n_ 

    return scale
    


def train_test_split(*arrays, test_pct: float = 0.15, val_set: bool = False, val_pct: float = 0.15) -> Tuple[np.ndarray]:

    """Splits arrays into train & test sets.

    Splits arrays into train, test, and (optionally) validation sets using the supplied percentages.

    :param *arrays: An arbitrary number of sequences to be split
        into train, test, and (optionally) validation sets. Must 

        have at least one array.
    :param test_pct: Float in the range ``[0,1]``. Percent of total 

        ``n`` values to include in test set.
        

        The train set will have `1.0 - test_pct` pct of
        values (or `1.0 - test_pct - val_pct` pct of values
        if `val_set == True`).

    :param val_set: Whether or not to return a validation set,
        in addition to a test set.

    :param val_pct: `float` in the range ``[0,1]``. Percent 

        of total n values to include in test set.
        

        Ignored if ``val_set == False``.
        

        The train set will have ``1.0 - test_pct - val_pct`` 

        pct of values.

    :returns: splits tuple of numpy arrays. Input arrays 

        split into train, test, val sets.
        

        If ``val_set == False``, ``len(splits) == 2 * len(arrays)``,
        or if ``val_set == True``, ``len(splits) == 3 * len(arrays)``.

    Example:
        >>> x = np.arange(10)
        >>> train_test_split(x)
        (array([3, 9, 4, 2, 1, 0, 7, 5, 8]), array([6]))

        >>> x = np.arange(10)
        >>> y = x[::-1]
        >>> x_train, x_test, y_train, y_test = train_test_split(x,y)
        >>> x_train, x_test, y_train, y_test
        (array([1, 3, 5, 8, 4, 7, 6, 9]),
         array([0, 2]),
         array([8, 6, 4, 1, 5, 2, 3, 0]),
         array([9, 7]))

        >>> train_test_split(x,test_pct=0.3,val_set=True,val_pct=0.2)
        (array([0, 9, 5, 7, 6, 2, 8]), 

         array([1, 3, 4]), 

         array([3, 4]))

    """
    # Perform input checks
    assert arrays, "No arrays supplied"
    lens = [len(a) for a in arrays]
    assert len(set(lens)) == 1, "arrays have varying lengths"
    assert lens[0] > 0, "supplied arrays have `len == 0`"
    if val_set:
        assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`"
        assert 0.0 <= val_pct <= 1.0, "`val_pct` must be in the range `0.0 <= val_pct <= 1.0`"
        assert test_pct + val_pct <= 1.0, "Can't have `test_pc + val_pct >= 1.0`"
    else:
        assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`"
        assert test_pct <= 1.0, "Can't have `test_pc >= 1.0`"
    # Calculate lengths
    n = lens[0]

    n_test = int(n * test_pct)
    # Shuffle the indexes
    indexes = np.arange(n)
    np.random.shuffle(indexes)
    # Split the data
    if val_set:
        n_val = int(n * val_pct)
        n_train = n - n_test - n_val
        splits = (
            (
                a[indexes[:n_train]], 

                a[indexes[n_train:n_train+n_test]], 

                a[indexes[-n_val:]]
            )
            for a in map(np.asarray,arrays)

        )
    else:
        n_train = n - n_test
        splits = (
            (a[indexes[:n_train]], a[indexes[n_train:]])
            for a in map(np.asarray,arrays)

        )
    return tuple(it.chain(*splits))


def to_onehot(y: np.ndarray, num_classes: int = None, dtype="float32") -> np.ndarray:

    """Expands a 1D categorical vector to
    a 2D, onehot-encoded categorical matrix.

    :param y: 1D categorical vector
    :param num_classes: Number of categories in (and width of) 

        the output matrix. 


        If ``num_classes`` is ``None``, setsto ``max(y) + 1``.
    :param dtype: Data type of output matrix
    :returns: 2D one-hot encoded category matrix
    

    Examples:
        >>> data = np.array([0,2,1,3])
        >>> apoor.to_onehot(data)
        array([[1., 0., 0., 0.],
               [0., 0., 1., 0.],
               [0., 1., 0., 0.],
               [0., 0., 0., 1.]])

    """
    if num_classes is None:
        num_classes = np.max(y) + 1
    return np.identity(num_classes,dtype=dtype)[y]



def ibuff(itr: Iterable, bsize: int = 1) -> Iterable[List]:
    """Creates an iterable that yields elements
    from ``itr`` grouped into lists of size ``bsize``.

    If ``itr`` can't evenly be grouped into lists of size
    ``bsize``, the final list will have the remaining
    elements.

    :param itr: The interable to be buffered.
    :param bsize: Positive integer, representing the number of
        values from ``itr`` to be yielded together.

        The final list yielded may not be of size ``bsize`` if
        ``len(itr)`` doesn't evenly divide into groups of ``bsize``.
    :yields: Buffered elements from ``itr``, grouped into lists
        of size up to ``bsize``.
    :raises TypeError: If ``bsize`` isn't an integer.
    :raises ValueError: If ``bsize`` isn't positive.
    

    Examples:
        >>> for b in apoor.ibuff(range(10),3):
        ...     print(b)
        [0, 1, 2]
        [3, 4, 5]
        [6, 7, 8]
        [9]
    """
    # Perform checks
    if not isinstance(bsize,int):

        raise TypeError("bsize needs to be a positive integer.")
    if bsize < 1:
        raise ValueError("bsize needs to be a positive integer.")
    # Initialize the buffer
    buff = []
    for v in itr:

        if len(buff) < bsize: # If buff not full, append
            buff.append(v)
        else:                 # Otherwise yield and reinit
            yield buff
            buff = [v]
    # Check if there's anything left in the buffer
    if len(buff) > 0:
        yield buff






1			"""A small personal package created to store code and data I often reuse.
			0 ignored issues – show Coding Style introduced 2020-08-20 19:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
2
3			I'll continue to update it with useful functions that I find myself reusing. The `apoor.data` module has some common datasets and functions for reading them in as pandas DataFrames.
			0 ignored issues – show Coding Style introduced 2020-08-20 19:50 UTC by Report Bug Copy Issue Report This line is too long as per the coding-style (181/100). This check looks for lines that are too long. You can specify the maximum line length. Loading history...
4			"""
5
6			# Version string
7			__version__ = "1.3.2"
8
9
10
11			import itertools as it
12			import numpy as np
13			from . import data
14
15			from typing import Any, List, Callable, Tuple, Iterable
			0 ignored issues – show introduced 2021-01-18 17:27 UTC by Report Bug Copy Issue Report standard import "from typing import Any, List, Callable, Tuple, Iterable" should be placed before "import numpy as np" Loading history...
16
17
18			def fdir(o: Any) -> List[str]:
			0 ignored issues – show Coding Style Naming introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Argument name "o" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
19			"""Filtered dir(). Same as builtin dir()
20			function without private attributes.
21
22			:param o: Object being inspected
23			:return: "Public attributes" of o
24			"""
25			return [a for a in dir(o) if a[0] != "_"]
26
27			def set_seed(n:int):
			0 ignored issues – show Coding Style introduced 2020-09-24 13:21 UTC by Report Bug Copy Issue Report Exactly one space required after : Loading history... Coding Style Naming introduced 2020-09-24 13:21 UTC by Report Bug Copy Issue Report Argument name "n" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
28			"""Sets numpy's random seed.
29
30			:param n: The value used to set numpy's random seed.
31			:type n: int
32			"""
33			np.random.seed(n)
34
35
36			def make_scale(dmin:float,dmax:float,rmin:float,rmax:float,clamp:bool=False) -> Callable[[float],float]:
			0 ignored issues – show Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report This line is too long as per the coding-style (104/100). This check looks for lines that are too long. You can specify the maximum line length. Loading history... Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Exactly one space required after : Loading history... Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Exactly one space required after comma Loading history... Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Exactly one space required around keyword argument assignment Loading history...
37			"""Scale function factory.
38
			0 ignored issues – show Coding Style introduced 2020-08-20 19:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
39			Creates a scale function to map a number from a domain to a range.
40
41			:param dmin: Domain's start value
42			:param dmax: Domain's end value
43			:param rmin: Range's start value
44			:param rmax: Range's end value
45			:param clamp: If the result is outside the range, return
			0 ignored issues – show Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
46			clamped value (default: False)
47			:return: A scale function taking one numeric argument and
			0 ignored issues – show Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
48			returns the value mapped from the domain to the range
			0 ignored issues – show Coding Style introduced 2020-10-27 21:07 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
49			(and clamped if `clamp` flag is set).
50
51			Examples:
52			>>> s = make_scale(0,1,0,10)
53			>>> s(0.1)
54			1.0
55
56			>>> s = make_scale(0,10,10,0)
57			>>> s(1.0)
58			9.0
59
60			>>> s = make_scale(0,1,0,1,clamp=True)
61			>>> s(100)
62			1.0
63			"""
64			drange = dmax - dmin
65			rrange = rmax - rmin
66			scale_factor = rrange / drange
67			def scale(n):
			0 ignored issues – show Coding Style Naming introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Argument name "n" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
68			n_ = (n - dmin) * scale_factor + rmin
			0 ignored issues – show Coding Style Naming introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Variable name "n_" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
69			if clamp: return min(max(n_,rmin),rmax)
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70			else: return n_
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71			return scale
72
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73
74			def train_test_split(*arrays, test_pct: float = 0.15, val_set: bool = False, val_pct: float = 0.15) -> Tuple[np.ndarray]:
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report This line is too long as per the coding-style (121/100). This check looks for lines that are too long. You can specify the maximum line length. Loading history...
75			"""Splits arrays into train & test sets.
76
77			Splits arrays into train, test, and (optionally) validation sets using the supplied percentages.
78
79			:param *arrays: An arbitrary number of sequences to be split
80			into train, test, and (optionally) validation sets. Must
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
81			have at least one array.
82			:param test_pct: Float in the range ``[0,1]``. Percent of total
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
83			``n`` values to include in test set.
84
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
85			The train set will have `1.0 - test_pct` pct of
86			values (or `1.0 - test_pct - val_pct` pct of values
87			if `val_set == True`).
88
89			:param val_set: Whether or not to return a validation set,
90			in addition to a test set.
91
92			:param val_pct: `float` in the range ``[0,1]``. Percent
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
93			of total n values to include in test set.
94
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
95			Ignored if ``val_set == False``.
96
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
97			The train set will have ``1.0 - test_pct - val_pct``
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
98			pct of values.
99
100			:returns: splits tuple of numpy arrays. Input arrays
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
101			split into train, test, val sets.
102
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
103			If ``val_set == False``, ``len(splits) == 2 * len(arrays)``,
104			or if ``val_set == True``, ``len(splits) == 3 * len(arrays)``.
105
106			Example:
107			>>> x = np.arange(10)
108			>>> train_test_split(x)
109			(array([3, 9, 4, 2, 1, 0, 7, 5, 8]), array([6]))
110
111			>>> x = np.arange(10)
112			>>> y = x[::-1]
113			>>> x_train, x_test, y_train, y_test = train_test_split(x,y)
114			>>> x_train, x_test, y_train, y_test
115			(array([1, 3, 5, 8, 4, 7, 6, 9]),
116			array([0, 2]),
117			array([8, 6, 4, 1, 5, 2, 3, 0]),
118			array([9, 7]))
119
120			>>> train_test_split(x,test_pct=0.3,val_set=True,val_pct=0.2)
121			(array([0, 9, 5, 7, 6, 2, 8]),
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122			array([1, 3, 4]),
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123			array([3, 4]))
124
125			"""
126			# Perform input checks
127			assert arrays, "No arrays supplied"
128			lens = [len(a) for a in arrays]
129			assert len(set(lens)) == 1, "arrays have varying lengths"
130			assert lens[0] > 0, "supplied arrays have `len == 0`"
131			if val_set:
132			assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`"
133			assert 0.0 <= val_pct <= 1.0, "`val_pct` must be in the range `0.0 <= val_pct <= 1.0`"
134			assert test_pct + val_pct <= 1.0, "Can't have `test_pc + val_pct >= 1.0`"
135			else:
136			assert 0.0 <= test_pct <= 1.0, "`test_pct` must be in the range `0.0 <= test_pct <= 1.0`"
137			assert test_pct <= 1.0, "Can't have `test_pc >= 1.0`"
138			# Calculate lengths
139			n = lens[0]
			0 ignored issues – show Coding Style Naming introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Variable name "n" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
140			n_test = int(n * test_pct)
141			# Shuffle the indexes
142			indexes = np.arange(n)
143			np.random.shuffle(indexes)
144			# Split the data
145			if val_set:
146			n_val = int(n * val_pct)
147			n_train = n - n_test - n_val
148			splits = (
149			(
150			a[indexes[:n_train]],
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151			a[indexes[n_train:n_train+n_test]],
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152			a[indexes[-n_val:]]
153			)
154			for a in map(np.asarray,arrays)
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Exactly one space required after comma Loading history...
155			)
156			else:
157			n_train = n - n_test
158			splits = (
159			(a[indexes[:n_train]], a[indexes[n_train:]])
160			for a in map(np.asarray,arrays)
			0 ignored issues – show Coding Style introduced 2020-08-20 18:55 UTC by Report Bug Copy Issue Report Exactly one space required after comma Loading history...
161			)
162			return tuple(it.chain(*splits))
163
164
165			def to_onehot(y: np.ndarray, num_classes: int = None, dtype="float32") -> np.ndarray:
			0 ignored issues – show Coding Style Naming introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Argument name "y" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
166			"""Expands a 1D categorical vector to
167			a 2D, onehot-encoded categorical matrix.
168
169			:param y: 1D categorical vector
170			:param num_classes: Number of categories in (and width of)
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
171			the output matrix.
			0 ignored issues – show Coding Style introduced 2021-01-17 04:40 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
172
173			If ``num_classes`` is ``None``, setsto ``max(y) + 1``.
174			:param dtype: Data type of output matrix
175			:returns: 2D one-hot encoded category matrix
176
			0 ignored issues – show Coding Style introduced 2021-01-17 03:50 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
177			Examples:
178			>>> data = np.array([0,2,1,3])
179			>>> apoor.to_onehot(data)
180			array([[1., 0., 0., 0.],
181			[0., 0., 1., 0.],
182			[0., 1., 0., 0.],
183			[0., 0., 0., 1.]])
184
185			"""
186			if num_classes is None:
187			num_classes = np.max(y) + 1
188			return np.identity(num_classes,dtype=dtype)[y]
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189
190
191			def ibuff(itr: Iterable, bsize: int = 1) -> Iterable[List]:
192			"""Creates an iterable that yields elements
193			from ``itr`` grouped into lists of size ``bsize``.
194
195			If ``itr`` can't evenly be grouped into lists of size
196			``bsize``, the final list will have the remaining
197			elements.
198
199			:param itr: The interable to be buffered.
200			:param bsize: Positive integer, representing the number of
201			values from ``itr`` to be yielded together.
202
203			The final list yielded may not be of size ``bsize`` if
204			``len(itr)`` doesn't evenly divide into groups of ``bsize``.
205			:yields: Buffered elements from ``itr``, grouped into lists
206			of size up to ``bsize``.
207			:raises TypeError: If ``bsize`` isn't an integer.
208			:raises ValueError: If ``bsize`` isn't positive.
209
			0 ignored issues – show Coding Style introduced 2021-01-18 17:23 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
210			Examples:
211			>>> for b in apoor.ibuff(range(10),3):
212			... print(b)
213			[0, 1, 2]
214			[3, 4, 5]
215			[6, 7, 8]
216			[9]
217			"""
218			# Perform checks
219			if not isinstance(bsize,int):
			0 ignored issues – show Coding Style introduced 2021-01-18 17:23 UTC by Report Bug Copy Issue Report Exactly one space required after comma Loading history...
220			raise TypeError("bsize needs to be a positive integer.")
221			if bsize < 1:
222			raise ValueError("bsize needs to be a positive integer.")
223			# Initialize the buffer
224			buff = []
225			for v in itr:
			0 ignored issues – show Coding Style Naming introduced 2021-01-18 17:23 UTC by Report Bug Copy Issue Report Variable name "v" doesn't conform to snake_case naming style ('([^\\W\\dA-Z][^\\WA-Z]2,\|_[^\\WA-Z]*\|__[^\\WA-Z\\d_][^\\WA-Z]+__)$' pattern) This check looks for invalid names for a range of different identifiers. You can set regular expressions to which the identifiers must conform if the defaults do not match your requirements. If your project includes a Pylint configuration file, the settings contained in that file take precedence. To find out more about Pylint, please refer to their site. Loading history...
226			if len(buff) < bsize: # If buff not full, append
227			buff.append(v)
228			else: # Otherwise yield and reinit
229			yield buff
230			buff = [v]
231			# Check if there's anything left in the buffer
232			if len(buff) > 0:
233			yield buff
234
235
236
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237

a-poor / apoor

apoor.train_test_split() A last analyzed 2021-01-18 17:36 UTC

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apoor.train_test_split() A
last analyzed 2021-01-18 17:36 UTC