[Python-checkins] cpython (3.6): Issue #27181 remove geometric_mean and defer for 3.7.
ned.deily
python-checkins at python.org
Tue Oct 4 14:52:23 EDT 2016
https://hg.python.org/cpython/rev/de0fa478c22e
changeset: 104295:de0fa478c22e
branch: 3.6
parent: 104293:d5eefcfa3458
user: Steven D'Aprano <steve at pearwood.info>
date: Wed Oct 05 03:24:45 2016 +1100
summary:
Issue #27181 remove geometric_mean and defer for 3.7.
files:
Doc/library/statistics.rst | 29 --
Lib/statistics.py | 269 +-----------------------
Lib/test/test_statistics.py | 267 -----------------------
Misc/NEWS | 2 +
4 files changed, 3 insertions(+), 564 deletions(-)
diff --git a/Doc/library/statistics.rst b/Doc/library/statistics.rst
--- a/Doc/library/statistics.rst
+++ b/Doc/library/statistics.rst
@@ -39,7 +39,6 @@
======================= =============================================
:func:`mean` Arithmetic mean ("average") of data.
-:func:`geometric_mean` Geometric mean of data.
:func:`harmonic_mean` Harmonic mean of data.
:func:`median` Median (middle value) of data.
:func:`median_low` Low median of data.
@@ -113,34 +112,6 @@
``mean(data)`` is equivalent to calculating the true population mean μ.
-.. function:: geometric_mean(data)
-
- Return the geometric mean of *data*, a sequence or iterator of
- real-valued numbers.
-
- The geometric mean is the *n*-th root of the product of *n* data points.
- It is a type of average, a measure of the central location of the data.
-
- The geometric mean is appropriate when averaging quantities which
- are multiplied together rather than added, for example growth rates.
- Suppose an investment grows by 10% in the first year, falls by 5% in
- the second, then grows by 12% in the third, what is the average rate
- of growth over the three years?
-
- .. doctest::
-
- >>> geometric_mean([1.10, 0.95, 1.12])
- 1.0538483123382172
-
- giving an average growth of 5.385%. Using the arithmetic mean will
- give approximately 5.667%, which is too high.
-
- :exc:`StatisticsError` is raised if *data* is empty, or any
- element is less than zero.
-
- .. versionadded:: 3.6
-
-
.. function:: harmonic_mean(data)
Return the harmonic mean of *data*, a sequence or iterator of
diff --git a/Lib/statistics.py b/Lib/statistics.py
--- a/Lib/statistics.py
+++ b/Lib/statistics.py
@@ -11,7 +11,6 @@
Function Description
================== =============================================
mean Arithmetic mean (average) of data.
-geometric_mean Geometric mean of data.
harmonic_mean Harmonic mean of data.
median Median (middle value) of data.
median_low Low median of data.
@@ -80,7 +79,7 @@
__all__ = [ 'StatisticsError',
'pstdev', 'pvariance', 'stdev', 'variance',
'median', 'median_low', 'median_high', 'median_grouped',
- 'mean', 'mode', 'geometric_mean', 'harmonic_mean',
+ 'mean', 'mode', 'harmonic_mean',
]
import collections
@@ -287,229 +286,6 @@
yield x
-class _nroot_NS:
- """Hands off! Don't touch!
-
- Everything inside this namespace (class) is an even-more-private
- implementation detail of the private _nth_root function.
- """
- # This class exists only to be used as a namespace, for convenience
- # of being able to keep the related functions together, and to
- # collapse the group in an editor. If this were C# or C++, I would
- # use a Namespace, but the closest Python has is a class.
- #
- # FIXME possibly move this out into a separate module?
- # That feels like overkill, and may encourage people to treat it as
- # a public feature.
- def __init__(self):
- raise TypeError('namespace only, do not instantiate')
-
- def nth_root(x, n):
- """Return the positive nth root of numeric x.
-
- This may be more accurate than ** or pow():
-
- >>> math.pow(1000, 1.0/3) #doctest:+SKIP
- 9.999999999999998
-
- >>> _nth_root(1000, 3)
- 10.0
- >>> _nth_root(11**5, 5)
- 11.0
- >>> _nth_root(2, 12)
- 1.0594630943592953
-
- """
- if not isinstance(n, int):
- raise TypeError('degree n must be an int')
- if n < 2:
- raise ValueError('degree n must be 2 or more')
- if isinstance(x, decimal.Decimal):
- return _nroot_NS.decimal_nroot(x, n)
- elif isinstance(x, numbers.Real):
- return _nroot_NS.float_nroot(x, n)
- else:
- raise TypeError('expected a number, got %s') % type(x).__name__
-
- def float_nroot(x, n):
- """Handle nth root of Reals, treated as a float."""
- assert isinstance(n, int) and n > 1
- if x < 0:
- raise ValueError('domain error: root of negative number')
- elif x == 0:
- return math.copysign(0.0, x)
- elif x > 0:
- try:
- isinfinity = math.isinf(x)
- except OverflowError:
- return _nroot_NS.bignum_nroot(x, n)
- else:
- if isinfinity:
- return float('inf')
- else:
- return _nroot_NS.nroot(x, n)
- else:
- assert math.isnan(x)
- return float('nan')
-
- def nroot(x, n):
- """Calculate x**(1/n), then improve the answer."""
- # This uses math.pow() to calculate an initial guess for the root,
- # then uses the iterated nroot algorithm to improve it.
- #
- # By my testing, about 8% of the time the iterated algorithm ends
- # up converging to a result which is less accurate than the initial
- # guess. [FIXME: is this still true?] In that case, we use the
- # guess instead of the "improved" value. This way, we're never
- # less accurate than math.pow().
- r1 = math.pow(x, 1.0/n)
- eps1 = abs(r1**n - x)
- if eps1 == 0.0:
- # r1 is the exact root, so we're done. By my testing, this
- # occurs about 80% of the time for x < 1 and 30% of the
- # time for x > 1.
- return r1
- else:
- try:
- r2 = _nroot_NS.iterated_nroot(x, n, r1)
- except RuntimeError:
- return r1
- else:
- eps2 = abs(r2**n - x)
- if eps1 < eps2:
- return r1
- return r2
-
- def iterated_nroot(a, n, g):
- """Return the nth root of a, starting with guess g.
-
- This is a special case of Newton's Method.
- https://en.wikipedia.org/wiki/Nth_root_algorithm
- """
- np = n - 1
- def iterate(r):
- try:
- return (np*r + a/math.pow(r, np))/n
- except OverflowError:
- # If r is large enough, r**np may overflow. If that
- # happens, r**-np will be small, but not necessarily zero.
- return (np*r + a*math.pow(r, -np))/n
- # With a good guess, such as g = a**(1/n), this will converge in
- # only a few iterations. However a poor guess can take thousands
- # of iterations to converge, if at all. We guard against poor
- # guesses by setting an upper limit to the number of iterations.
- r1 = g
- r2 = iterate(g)
- for i in range(1000):
- if r1 == r2:
- break
- # Use Floyd's cycle-finding algorithm to avoid being trapped
- # in a cycle.
- # https://en.wikipedia.org/wiki/Cycle_detection#Tortoise_and_hare
- r1 = iterate(r1)
- r2 = iterate(iterate(r2))
- else:
- # If the guess is particularly bad, the above may fail to
- # converge in any reasonable time.
- raise RuntimeError('nth-root failed to converge')
- return r2
-
- def decimal_nroot(x, n):
- """Handle nth root of Decimals."""
- assert isinstance(x, decimal.Decimal)
- assert isinstance(n, int)
- if x.is_snan():
- # Signalling NANs always raise.
- raise decimal.InvalidOperation('nth-root of snan')
- if x.is_qnan():
- # Quiet NANs only raise if the context is set to raise,
- # otherwise return a NAN.
- ctx = decimal.getcontext()
- if ctx.traps[decimal.InvalidOperation]:
- raise decimal.InvalidOperation('nth-root of nan')
- else:
- # Preserve the input NAN.
- return x
- if x < 0:
- raise ValueError('domain error: root of negative number')
- if x.is_infinite():
- return x
- # FIXME this hasn't had the extensive testing of the float
- # version _iterated_nroot so there's possibly some buggy
- # corner cases buried in here. Can it overflow? Fail to
- # converge or get trapped in a cycle? Converge to a less
- # accurate root?
- np = n - 1
- def iterate(r):
- return (np*r + x/r**np)/n
- r0 = x**(decimal.Decimal(1)/n)
- assert isinstance(r0, decimal.Decimal)
- r1 = iterate(r0)
- while True:
- if r1 == r0:
- return r1
- r0, r1 = r1, iterate(r1)
-
- def bignum_nroot(x, n):
- """Return the nth root of a positive huge number."""
- assert x > 0
- # I state without proof that ⁿ√x ≈ ⁿ√2·ⁿ√(x//2)
- # and that for sufficiently big x the error is acceptable.
- # We now halve x until it is small enough to get the root.
- m = 0
- while True:
- x //= 2
- m += 1
- try:
- y = float(x)
- except OverflowError:
- continue
- break
- a = _nroot_NS.nroot(y, n)
- # At this point, we want the nth-root of 2**m, or 2**(m/n).
- # We can write that as 2**(q + r/n) = 2**q * ⁿ√2**r where q = m//n.
- q, r = divmod(m, n)
- b = 2**q * _nroot_NS.nroot(2**r, n)
- return a * b
-
-
-# This is the (private) function for calculating nth roots:
-_nth_root = _nroot_NS.nth_root
-assert type(_nth_root) is type(lambda: None)
-
-
-def _product(values):
- """Return product of values as (exponent, mantissa)."""
- errmsg = 'mixed Decimal and float is not supported'
- prod = 1
- for x in values:
- if isinstance(x, float):
- break
- prod *= x
- else:
- return (0, prod)
- if isinstance(prod, Decimal):
- raise TypeError(errmsg)
- # Since floats can overflow easily, we calculate the product as a
- # sort of poor-man's BigFloat. Given that:
- #
- # x = 2**p * m # p == power or exponent (scale), m = mantissa
- #
- # we can calculate the product of two (or more) x values as:
- #
- # x1*x2 = 2**p1*m1 * 2**p2*m2 = 2**(p1+p2)*(m1*m2)
- #
- mant, scale = 1, 0 #math.frexp(prod) # FIXME
- for y in chain([x], values):
- if isinstance(y, Decimal):
- raise TypeError(errmsg)
- m1, e1 = math.frexp(y)
- m2, e2 = math.frexp(mant)
- scale += (e1 + e2)
- mant = m1*m2
- return (scale, mant)
-
-
# === Measures of central tendency (averages) ===
def mean(data):
@@ -538,49 +314,6 @@
return _convert(total/n, T)
-def geometric_mean(data):
- """Return the geometric mean of data.
-
- The geometric mean is appropriate when averaging quantities which
- are multiplied together rather than added, for example growth rates.
- Suppose an investment grows by 10% in the first year, falls by 5% in
- the second, then grows by 12% in the third, what is the average rate
- of growth over the three years?
-
- >>> geometric_mean([1.10, 0.95, 1.12])
- 1.0538483123382172
-
- giving an average growth of 5.385%. Using the arithmetic mean will
- give approximately 5.667%, which is too high.
-
- ``StatisticsError`` will be raised if ``data`` is empty, or any
- element is less than zero.
- """
- if iter(data) is data:
- data = list(data)
- errmsg = 'geometric mean does not support negative values'
- n = len(data)
- if n < 1:
- raise StatisticsError('geometric_mean requires at least one data point')
- elif n == 1:
- x = data[0]
- if isinstance(g, (numbers.Real, Decimal)):
- if x < 0:
- raise StatisticsError(errmsg)
- return x
- else:
- raise TypeError('unsupported type')
- else:
- scale, prod = _product(_fail_neg(data, errmsg))
- r = _nth_root(prod, n)
- if scale:
- p, q = divmod(scale, n)
- s = 2**p * _nth_root(2**q, n)
- else:
- s = 1
- return s*r
-
-
def harmonic_mean(data):
"""Return the harmonic mean of data.
diff --git a/Lib/test/test_statistics.py b/Lib/test/test_statistics.py
--- a/Lib/test/test_statistics.py
+++ b/Lib/test/test_statistics.py
@@ -1010,273 +1010,6 @@
self.assertEqual(errmsg, msg)
-class Test_Product(NumericTestCase):
- """Test the private _product function."""
-
- def test_ints(self):
- data = [1, 2, 5, 7, 9]
- self.assertEqual(statistics._product(data), (0, 630))
- self.assertEqual(statistics._product(data*100), (0, 630**100))
-
- def test_floats(self):
- data = [1.0, 2.0, 4.0, 8.0]
- self.assertEqual(statistics._product(data), (8, 0.25))
-
- def test_overflow(self):
- # Test with floats that overflow.
- data = [1e300]*5
- self.assertEqual(statistics._product(data), (5980, 0.6928287951283193))
-
- def test_fractions(self):
- F = Fraction
- data = [F(14, 23), F(69, 1), F(665, 529), F(299, 105), F(1683, 39)]
- exp, mant = statistics._product(data)
- self.assertEqual(exp, 0)
- self.assertEqual(mant, F(2*3*7*11*17*19, 23))
- self.assertTrue(isinstance(mant, F))
- # Mixed Fraction and int.
- data = [3, 25, F(2, 15)]
- exp, mant = statistics._product(data)
- self.assertEqual(exp, 0)
- self.assertEqual(mant, F(10))
- self.assertTrue(isinstance(mant, F))
-
- def test_decimal(self):
- D = Decimal
- data = [D('24.5'), D('17.6'), D('0.025'), D('1.3')]
- expected = D('14.014000')
- self.assertEqual(statistics._product(data), (0, expected))
-
- def test_mixed_decimal_float(self):
- # Test that mixed Decimal and float raises.
- self.assertRaises(TypeError, statistics._product, [1.0, Decimal(1)])
- self.assertRaises(TypeError, statistics._product, [Decimal(1), 1.0])
-
-
- at unittest.skipIf(True, "FIXME: tests known to fail, see issue #27181")
-class Test_Nth_Root(NumericTestCase):
- """Test the functionality of the private _nth_root function."""
-
- def setUp(self):
- self.nroot = statistics._nth_root
-
- # --- Special values (infinities, NANs, zeroes) ---
-
- def test_float_NAN(self):
- # Test that the root of a float NAN is a float NAN.
- NAN = float('nan')
- for n in range(2, 9):
- with self.subTest(n=n):
- result = self.nroot(NAN, n)
- self.assertTrue(math.isnan(result))
-
- def test_decimal_QNAN(self):
- # Test the behaviour when taking the root of a Decimal quiet NAN.
- NAN = decimal.Decimal('nan')
- with decimal.localcontext() as ctx:
- ctx.traps[decimal.InvalidOperation] = 1
- self.assertRaises(decimal.InvalidOperation, self.nroot, NAN, 5)
- ctx.traps[decimal.InvalidOperation] = 0
- self.assertTrue(self.nroot(NAN, 5).is_qnan())
-
- def test_decimal_SNAN(self):
- # Test that taking the root of a Decimal sNAN always raises.
- sNAN = decimal.Decimal('snan')
- with decimal.localcontext() as ctx:
- ctx.traps[decimal.InvalidOperation] = 1
- self.assertRaises(decimal.InvalidOperation, self.nroot, sNAN, 5)
- ctx.traps[decimal.InvalidOperation] = 0
- self.assertRaises(decimal.InvalidOperation, self.nroot, sNAN, 5)
-
- def test_inf(self):
- # Test that the root of infinity is infinity.
- for INF in (float('inf'), decimal.Decimal('inf')):
- for n in range(2, 9):
- with self.subTest(n=n, inf=INF):
- self.assertEqual(self.nroot(INF, n), INF)
-
- # FIXME: need to check Decimal zeroes too.
- def test_zero(self):
- # Test that the root of +0.0 is +0.0.
- for n in range(2, 11):
- with self.subTest(n=n):
- result = self.nroot(+0.0, n)
- self.assertEqual(result, 0.0)
- self.assertEqual(sign(result), +1)
-
- # FIXME: need to check Decimal zeroes too.
- def test_neg_zero(self):
- # Test that the root of -0.0 is -0.0.
- for n in range(2, 11):
- with self.subTest(n=n):
- result = self.nroot(-0.0, n)
- self.assertEqual(result, 0.0)
- self.assertEqual(sign(result), -1)
-
- # --- Test return types ---
-
- def check_result_type(self, x, n, outtype):
- self.assertIsInstance(self.nroot(x, n), outtype)
- class MySubclass(type(x)):
- pass
- self.assertIsInstance(self.nroot(MySubclass(x), n), outtype)
-
- def testDecimal(self):
- # Test that Decimal arguments return Decimal results.
- self.check_result_type(decimal.Decimal('33.3'), 3, decimal.Decimal)
-
- def testFloat(self):
- # Test that other arguments return float results.
- for x in (0.2, Fraction(11, 7), 91):
- self.check_result_type(x, 6, float)
-
- # --- Test bad input ---
-
- def testBadOrderTypes(self):
- # Test that nroot raises correctly when n has the wrong type.
- for n in (5.0, 2j, None, 'x', b'x', [], {}, set(), sign):
- with self.subTest(n=n):
- self.assertRaises(TypeError, self.nroot, 2.5, n)
-
- def testBadOrderValues(self):
- # Test that nroot raises correctly when n has a wrong value.
- for n in (1, 0, -1, -2, -87):
- with self.subTest(n=n):
- self.assertRaises(ValueError, self.nroot, 2.5, n)
-
- def testBadTypes(self):
- # Test that nroot raises correctly when x has the wrong type.
- for x in (None, 'x', b'x', [], {}, set(), sign):
- with self.subTest(x=x):
- self.assertRaises(TypeError, self.nroot, x, 3)
-
- def testNegativeError(self):
- # Test negative x raises correctly.
- x = random.uniform(-20.0, -0.1)
- assert x < 0
- for n in range(3, 7):
- with self.subTest(x=x, n=n):
- self.assertRaises(ValueError, self.nroot, x, n)
- # And Decimal.
- self.assertRaises(ValueError, self.nroot, Decimal(-27), 3)
-
- # --- Test that nroot is never worse than calling math.pow() ---
-
- def check_error_is_no_worse(self, x, n):
- y = math.pow(x, n)
- with self.subTest(x=x, n=n, y=y):
- err1 = abs(self.nroot(y, n) - x)
- err2 = abs(math.pow(y, 1.0/n) - x)
- self.assertLessEqual(err1, err2)
-
- def testCompareWithPowSmall(self):
- # Compare nroot with pow for small values of x.
- for i in range(200):
- x = random.uniform(1e-9, 1.0-1e-9)
- n = random.choice(range(2, 16))
- self.check_error_is_no_worse(x, n)
-
- def testCompareWithPowMedium(self):
- # Compare nroot with pow for medium-sized values of x.
- for i in range(200):
- x = random.uniform(1.0, 100.0)
- n = random.choice(range(2, 16))
- self.check_error_is_no_worse(x, n)
-
- def testCompareWithPowLarge(self):
- # Compare nroot with pow for largish values of x.
- for i in range(200):
- x = random.uniform(100.0, 10000.0)
- n = random.choice(range(2, 16))
- self.check_error_is_no_worse(x, n)
-
- def testCompareWithPowHuge(self):
- # Compare nroot with pow for huge values of x.
- for i in range(200):
- x = random.uniform(1e20, 1e50)
- # We restrict the order here to avoid an Overflow error.
- n = random.choice(range(2, 7))
- self.check_error_is_no_worse(x, n)
-
- # --- Test for numerically correct answers ---
-
- def testExactPowers(self):
- # Test that small integer powers are calculated exactly.
- for i in range(1, 51):
- for n in range(2, 16):
- if (i, n) == (35, 13):
- # See testExpectedFailure35p13
- continue
- with self.subTest(i=i, n=n):
- x = i**n
- self.assertEqual(self.nroot(x, n), i)
-
- def testExpectedFailure35p13(self):
- # Test the expected failure 35**13 is almost exact.
- x = 35**13
- err = abs(self.nroot(x, 13) - 35)
- self.assertLessEqual(err, 0.000000001)
-
- def testOne(self):
- # Test that the root of 1.0 is 1.0.
- for n in range(2, 11):
- with self.subTest(n=n):
- self.assertEqual(self.nroot(1.0, n), 1.0)
-
- def testFraction(self):
- # Test Fraction results.
- x = Fraction(89, 75)
- self.assertEqual(self.nroot(x**12, 12), float(x))
-
- def testInt(self):
- # Test int results.
- x = 276
- self.assertEqual(self.nroot(x**24, 24), x)
-
- def testBigInt(self):
- # Test that ints too big to convert to floats work.
- bignum = 10**20 # That's not that big...
- self.assertEqual(self.nroot(bignum**280, 280), bignum)
- # Can we make it bigger?
- hugenum = bignum**50
- # Make sure that it is too big to convert to a float.
- try:
- y = float(hugenum)
- except OverflowError:
- pass
- else:
- raise AssertionError('hugenum is not big enough')
- self.assertEqual(self.nroot(hugenum, 50), float(bignum))
-
- def testDecimal(self):
- # Test Decimal results.
- for s in '3.759 64.027 5234.338'.split():
- x = decimal.Decimal(s)
- with self.subTest(x=x):
- a = self.nroot(x**5, 5)
- self.assertEqual(a, x)
- a = self.nroot(x**17, 17)
- self.assertEqual(a, x)
-
- def testFloat(self):
- # Test float results.
- for x in (3.04e-16, 18.25, 461.3, 1.9e17):
- with self.subTest(x=x):
- self.assertEqual(self.nroot(x**3, 3), x)
- self.assertEqual(self.nroot(x**8, 8), x)
- self.assertEqual(self.nroot(x**11, 11), x)
-
-
-class Test_NthRoot_NS(unittest.TestCase):
- """Test internals of the nth_root function, hidden in _nroot_NS."""
-
- def test_class_cannot_be_instantiated(self):
- # Test that _nroot_NS cannot be instantiated.
- # It should be a namespace, like in C++ or C#, but Python
- # lacks that feature and so we have to make do with a class.
- self.assertRaises(TypeError, statistics._nroot_NS)
-
-
# === Tests for public functions ===
class UnivariateCommonMixin:
diff --git a/Misc/NEWS b/Misc/NEWS
--- a/Misc/NEWS
+++ b/Misc/NEWS
@@ -50,6 +50,8 @@
Library
-------
+- Issue #27181 remove statistics.geometric_mean and defer until 3.7.
+
- Issue #28229: lzma module now supports pathlib.
- Issue #28321: Fixed writing non-BMP characters with binary format in plistlib.
--
Repository URL: https://hg.python.org/cpython
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