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int64 0
731k
| package
stringlengths 2
98
⌀ | name
stringlengths 1
76
| docstring
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281k
⌀ | code
stringlengths 4
8.19k
| signature
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42.8k
⌀ | embed_func_code
listlengths 768
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36,342 | statistics | __pos__ | Return a copy of the instance. | def __pos__(x1):
"Return a copy of the instance."
return NormalDist(x1._mu, x1._sigma)
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|
36,344 | statistics | __repr__ | null | def __repr__(self):
return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})'
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]
|
36,346 | statistics | __rsub__ | Subtract a NormalDist from a constant or another NormalDist. | def __rsub__(x1, x2):
"Subtract a NormalDist from a constant or another NormalDist."
return -(x1 - x2)
| (x1, x2) | [
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|
36,347 | statistics | __setstate__ | null | def __setstate__(self, state):
self._mu, self._sigma = state
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|
36,348 | statistics | __sub__ | Subtract a constant or another NormalDist instance.
If *other* is a constant, translate by the constant mu,
leaving sigma unchanged.
If *other* is a NormalDist, subtract the means and add the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
| def __sub__(x1, x2):
"""Subtract a constant or another NormalDist instance.
If *other* is a constant, translate by the constant mu,
leaving sigma unchanged.
If *other* is a NormalDist, subtract the means and add the variances.
Mathematically, this works only if the two distributions are
independent or if they are jointly normally distributed.
"""
if isinstance(x2, NormalDist):
return NormalDist(x1._mu - x2._mu, hypot(x1._sigma, x2._sigma))
return NormalDist(x1._mu - x2, x1._sigma)
| (x1, x2) | [
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0.06158321723341942,
0.010098128579556942,
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0.001927865669131279,
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|
36,349 | statistics | __truediv__ | Divide both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
| def __truediv__(x1, x2):
"""Divide both mu and sigma by a constant.
Used for rescaling, perhaps to change measurement units.
Sigma is scaled with the absolute value of the constant.
"""
return NormalDist(x1._mu / x2, x1._sigma / fabs(x2))
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|
36,350 | statistics | cdf | Cumulative distribution function. P(X <= x) | def cdf(self, x):
"Cumulative distribution function. P(X <= x)"
if not self._sigma:
raise StatisticsError('cdf() not defined when sigma is zero')
return 0.5 * (1.0 + erf((x - self._mu) / (self._sigma * sqrt(2.0))))
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|
36,351 | statistics | inv_cdf | Inverse cumulative distribution function. x : P(X <= x) = p
Finds the value of the random variable such that the probability of
the variable being less than or equal to that value equals the given
probability.
This function is also called the percent point function or quantile
function.
| def inv_cdf(self, p):
"""Inverse cumulative distribution function. x : P(X <= x) = p
Finds the value of the random variable such that the probability of
the variable being less than or equal to that value equals the given
probability.
This function is also called the percent point function or quantile
function.
"""
if p <= 0.0 or p >= 1.0:
raise StatisticsError('p must be in the range 0.0 < p < 1.0')
if self._sigma <= 0.0:
raise StatisticsError('cdf() not defined when sigma at or below zero')
return _normal_dist_inv_cdf(p, self._mu, self._sigma)
| (self, p) | [
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|
36,352 | statistics | overlap | Compute the overlapping coefficient (OVL) between two normal distributions.
Measures the agreement between two normal probability distributions.
Returns a value between 0.0 and 1.0 giving the overlapping area in
the two underlying probability density functions.
>>> N1 = NormalDist(2.4, 1.6)
>>> N2 = NormalDist(3.2, 2.0)
>>> N1.overlap(N2)
0.8035050657330205
| def overlap(self, other):
"""Compute the overlapping coefficient (OVL) between two normal distributions.
Measures the agreement between two normal probability distributions.
Returns a value between 0.0 and 1.0 giving the overlapping area in
the two underlying probability density functions.
>>> N1 = NormalDist(2.4, 1.6)
>>> N2 = NormalDist(3.2, 2.0)
>>> N1.overlap(N2)
0.8035050657330205
"""
# See: "The overlapping coefficient as a measure of agreement between
# probability distributions and point estimation of the overlap of two
# normal densities" -- Henry F. Inman and Edwin L. Bradley Jr
# http://dx.doi.org/10.1080/03610928908830127
if not isinstance(other, NormalDist):
raise TypeError('Expected another NormalDist instance')
X, Y = self, other
if (Y._sigma, Y._mu) < (X._sigma, X._mu): # sort to assure commutativity
X, Y = Y, X
X_var, Y_var = X.variance, Y.variance
if not X_var or not Y_var:
raise StatisticsError('overlap() not defined when sigma is zero')
dv = Y_var - X_var
dm = fabs(Y._mu - X._mu)
if not dv:
return 1.0 - erf(dm / (2.0 * X._sigma * sqrt(2.0)))
a = X._mu * Y_var - Y._mu * X_var
b = X._sigma * Y._sigma * sqrt(dm**2.0 + dv * log(Y_var / X_var))
x1 = (a + b) / dv
x2 = (a - b) / dv
return 1.0 - (fabs(Y.cdf(x1) - X.cdf(x1)) + fabs(Y.cdf(x2) - X.cdf(x2)))
| (self, other) | [
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|
36,353 | statistics | pdf | Probability density function. P(x <= X < x+dx) / dx | def pdf(self, x):
"Probability density function. P(x <= X < x+dx) / dx"
variance = self._sigma ** 2.0
if not variance:
raise StatisticsError('pdf() not defined when sigma is zero')
return exp((x - self._mu)**2.0 / (-2.0*variance)) / sqrt(tau*variance)
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|
36,354 | statistics | quantiles | Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate the normal distribution in to 100 equal sized groups.
| def quantiles(self, n=4):
"""Divide into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate the normal distribution in to 100 equal sized groups.
"""
return [self.inv_cdf(i / n) for i in range(1, n)]
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|
36,355 | statistics | samples | Generate *n* samples for a given mean and standard deviation. | def samples(self, n, *, seed=None):
"Generate *n* samples for a given mean and standard deviation."
gauss = random.gauss if seed is None else random.Random(seed).gauss
mu, sigma = self._mu, self._sigma
return [gauss(mu, sigma) for i in range(n)]
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|
36,356 | statistics | zscore | Compute the Standard Score. (x - mean) / stdev
Describes *x* in terms of the number of standard deviations
above or below the mean of the normal distribution.
| def zscore(self, x):
"""Compute the Standard Score. (x - mean) / stdev
Describes *x* in terms of the number of standard deviations
above or below the mean of the normal distribution.
"""
# https://www.statisticshowto.com/probability-and-statistics/z-score/
if not self._sigma:
raise StatisticsError('zscore() not defined when sigma is zero')
return (x - self._mu) / self._sigma
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|
36,357 | statistics | StatisticsError | null | class StatisticsError(ValueError):
pass
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|
36,358 | statistics | _coerce | Coerce types T and S to a common type, or raise TypeError.
Coercion rules are currently an implementation detail. See the CoerceTest
test class in test_statistics for details.
| def _coerce(T, S):
"""Coerce types T and S to a common type, or raise TypeError.
Coercion rules are currently an implementation detail. See the CoerceTest
test class in test_statistics for details.
"""
# See http://bugs.python.org/issue24068.
assert T is not bool, "initial type T is bool"
# If the types are the same, no need to coerce anything. Put this
# first, so that the usual case (no coercion needed) happens as soon
# as possible.
if T is S: return T
# Mixed int & other coerce to the other type.
if S is int or S is bool: return T
if T is int: return S
# If one is a (strict) subclass of the other, coerce to the subclass.
if issubclass(S, T): return S
if issubclass(T, S): return T
# Ints coerce to the other type.
if issubclass(T, int): return S
if issubclass(S, int): return T
# Mixed fraction & float coerces to float (or float subclass).
if issubclass(T, Fraction) and issubclass(S, float):
return S
if issubclass(T, float) and issubclass(S, Fraction):
return T
# Any other combination is disallowed.
msg = "don't know how to coerce %s and %s"
raise TypeError(msg % (T.__name__, S.__name__))
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|
36,359 | statistics | _convert | Convert value to given numeric type T. | def _convert(value, T):
"""Convert value to given numeric type T."""
if type(value) is T:
# This covers the cases where T is Fraction, or where value is
# a NAN or INF (Decimal or float).
return value
if issubclass(T, int) and value.denominator != 1:
T = float
try:
# FIXME: what do we do if this overflows?
return T(value)
except TypeError:
if issubclass(T, Decimal):
return T(value.numerator) / T(value.denominator)
else:
raise
| (value, T) | [
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|
36,360 | statistics | _exact_ratio | Return Real number x to exact (numerator, denominator) pair.
>>> _exact_ratio(0.25)
(1, 4)
x is expected to be an int, Fraction, Decimal or float.
| def _exact_ratio(x):
"""Return Real number x to exact (numerator, denominator) pair.
>>> _exact_ratio(0.25)
(1, 4)
x is expected to be an int, Fraction, Decimal or float.
"""
try:
return x.as_integer_ratio()
except AttributeError:
pass
except (OverflowError, ValueError):
# float NAN or INF.
assert not _isfinite(x)
return (x, None)
try:
# x may be an Integral ABC.
return (x.numerator, x.denominator)
except AttributeError:
msg = f"can't convert type '{type(x).__name__}' to numerator/denominator"
raise TypeError(msg)
| (x) | [
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]
|
36,361 | statistics | _fail_neg | Iterate over values, failing if any are less than zero. | def _fail_neg(values, errmsg='negative value'):
"""Iterate over values, failing if any are less than zero."""
for x in values:
if x < 0:
raise StatisticsError(errmsg)
yield x
| (values, errmsg='negative value') | [
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]
|
36,362 | statistics | _find_lteq | Locate the leftmost value exactly equal to x | def _find_lteq(a, x):
'Locate the leftmost value exactly equal to x'
i = bisect_left(a, x)
if i != len(a) and a[i] == x:
return i
raise ValueError
| (a, x) | [
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|
36,363 | statistics | _find_rteq | Locate the rightmost value exactly equal to x | def _find_rteq(a, l, x):
'Locate the rightmost value exactly equal to x'
i = bisect_right(a, x, lo=l)
if i != (len(a) + 1) and a[i - 1] == x:
return i - 1
raise ValueError
| (a, l, x) | [
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|
36,364 | statistics | _isfinite | null | def _isfinite(x):
try:
return x.is_finite() # Likely a Decimal.
except AttributeError:
return math.isfinite(x) # Coerces to float first.
| (x) | [
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|
36,365 | statistics | _ss | Return sum of square deviations of sequence data.
If ``c`` is None, the mean is calculated in one pass, and the deviations
from the mean are calculated in a second pass. Otherwise, deviations are
calculated from ``c`` as given. Use the second case with care, as it can
lead to garbage results.
| def _ss(data, c=None):
"""Return sum of square deviations of sequence data.
If ``c`` is None, the mean is calculated in one pass, and the deviations
from the mean are calculated in a second pass. Otherwise, deviations are
calculated from ``c`` as given. Use the second case with care, as it can
lead to garbage results.
"""
if c is not None:
T, total, count = _sum((x-c)**2 for x in data)
return (T, total)
T, total, count = _sum(data)
mean_n, mean_d = (total / count).as_integer_ratio()
partials = Counter()
for n, d in map(_exact_ratio, data):
diff_n = n * mean_d - d * mean_n
diff_d = d * mean_d
partials[diff_d * diff_d] += diff_n * diff_n
if None in partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
total = partials[None]
assert not _isfinite(total)
else:
total = sum(Fraction(n, d) for d, n in partials.items())
return (T, total)
| (data, c=None) | [
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|
36,366 | statistics | _sum | _sum(data) -> (type, sum, count)
Return a high-precision sum of the given numeric data as a fraction,
together with the type to be converted to and the count of items.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 0.25])
(<class 'float'>, Fraction(19, 2), 5)
Some sources of round-off error will be avoided:
# Built-in sum returns zero.
>>> _sum([1e50, 1, -1e50] * 1000)
(<class 'float'>, Fraction(1000, 1), 3000)
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
(<class 'fractions.Fraction'>, Fraction(63, 20), 4)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
(<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
Mixed types are currently treated as an error, except that int is
allowed.
| def _sum(data):
"""_sum(data) -> (type, sum, count)
Return a high-precision sum of the given numeric data as a fraction,
together with the type to be converted to and the count of items.
Examples
--------
>>> _sum([3, 2.25, 4.5, -0.5, 0.25])
(<class 'float'>, Fraction(19, 2), 5)
Some sources of round-off error will be avoided:
# Built-in sum returns zero.
>>> _sum([1e50, 1, -1e50] * 1000)
(<class 'float'>, Fraction(1000, 1), 3000)
Fractions and Decimals are also supported:
>>> from fractions import Fraction as F
>>> _sum([F(2, 3), F(7, 5), F(1, 4), F(5, 6)])
(<class 'fractions.Fraction'>, Fraction(63, 20), 4)
>>> from decimal import Decimal as D
>>> data = [D("0.1375"), D("0.2108"), D("0.3061"), D("0.0419")]
>>> _sum(data)
(<class 'decimal.Decimal'>, Fraction(6963, 10000), 4)
Mixed types are currently treated as an error, except that int is
allowed.
"""
count = 0
partials = {}
partials_get = partials.get
T = int
for typ, values in groupby(data, type):
T = _coerce(T, typ) # or raise TypeError
for n, d in map(_exact_ratio, values):
count += 1
partials[d] = partials_get(d, 0) + n
if None in partials:
# The sum will be a NAN or INF. We can ignore all the finite
# partials, and just look at this special one.
total = partials[None]
assert not _isfinite(total)
else:
# Sum all the partial sums using builtin sum.
total = sum(Fraction(n, d) for d, n in partials.items())
return (T, total, count)
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]
|
36,367 | statistics | correlation | Pearson's correlation coefficient
Return the Pearson's correlation coefficient for two inputs. Pearson's
correlation coefficient *r* takes values between -1 and +1. It measures the
strength and direction of the linear relationship, where +1 means very
strong, positive linear relationship, -1 very strong, negative linear
relationship, and 0 no linear relationship.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> correlation(x, x)
1.0
>>> correlation(x, y)
-1.0
| def correlation(x, y, /):
"""Pearson's correlation coefficient
Return the Pearson's correlation coefficient for two inputs. Pearson's
correlation coefficient *r* takes values between -1 and +1. It measures the
strength and direction of the linear relationship, where +1 means very
strong, positive linear relationship, -1 very strong, negative linear
relationship, and 0 no linear relationship.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> correlation(x, x)
1.0
>>> correlation(x, y)
-1.0
"""
n = len(x)
if len(y) != n:
raise StatisticsError('correlation requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('correlation requires at least two data points')
xbar = fsum(x) / n
ybar = fsum(y) / n
sxy = fsum((xi - xbar) * (yi - ybar) for xi, yi in zip(x, y))
sxx = fsum((xi - xbar) ** 2.0 for xi in x)
syy = fsum((yi - ybar) ** 2.0 for yi in y)
try:
return sxy / sqrt(sxx * syy)
except ZeroDivisionError:
raise StatisticsError('at least one of the inputs is constant')
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|
36,368 | statistics | covariance | Covariance
Return the sample covariance of two inputs *x* and *y*. Covariance
is a measure of the joint variability of two inputs.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> covariance(x, y)
0.75
>>> z = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> covariance(x, z)
-7.5
>>> covariance(z, x)
-7.5
| def covariance(x, y, /):
"""Covariance
Return the sample covariance of two inputs *x* and *y*. Covariance
is a measure of the joint variability of two inputs.
>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
>>> covariance(x, y)
0.75
>>> z = [9, 8, 7, 6, 5, 4, 3, 2, 1]
>>> covariance(x, z)
-7.5
>>> covariance(z, x)
-7.5
"""
n = len(x)
if len(y) != n:
raise StatisticsError('covariance requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('covariance requires at least two data points')
xbar = fsum(x) / n
ybar = fsum(y) / n
sxy = fsum((xi - xbar) * (yi - ybar) for xi, yi in zip(x, y))
return sxy / (n - 1)
| (x, y, /) | [
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|
36,369 | statistics | fmean | Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float.
If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25])
4.25
| def fmean(data):
"""Convert data to floats and compute the arithmetic mean.
This runs faster than the mean() function and it always returns a float.
If the input dataset is empty, it raises a StatisticsError.
>>> fmean([3.5, 4.0, 5.25])
4.25
"""
try:
n = len(data)
except TypeError:
# Handle iterators that do not define __len__().
n = 0
def count(iterable):
nonlocal n
for n, x in enumerate(iterable, start=1):
yield x
total = fsum(count(data))
else:
total = fsum(data)
try:
return total / n
except ZeroDivisionError:
raise StatisticsError('fmean requires at least one data point') from None
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|
36,370 | statistics | geometric_mean | Convert data to floats and compute the geometric mean.
Raises a StatisticsError if the input dataset is empty,
if it contains a zero, or if it contains a negative value.
No special efforts are made to achieve exact results.
(However, this may change in the future.)
>>> round(geometric_mean([54, 24, 36]), 9)
36.0
| def geometric_mean(data):
"""Convert data to floats and compute the geometric mean.
Raises a StatisticsError if the input dataset is empty,
if it contains a zero, or if it contains a negative value.
No special efforts are made to achieve exact results.
(However, this may change in the future.)
>>> round(geometric_mean([54, 24, 36]), 9)
36.0
"""
try:
return exp(fmean(map(log, data)))
except ValueError:
raise StatisticsError('geometric mean requires a non-empty dataset '
'containing positive numbers') from None
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|
36,372 | statistics | harmonic_mean | Return the harmonic mean of data.
The harmonic mean is the reciprocal of the arithmetic mean of the
reciprocals of the data. It can be used for averaging ratios or
rates, for example speeds.
Suppose a car travels 40 km/hr for 5 km and then speeds-up to
60 km/hr for another 5 km. What is the average speed?
>>> harmonic_mean([40, 60])
48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed?
>>> harmonic_mean([40, 60], weights=[5, 30])
56.0
If ``data`` is empty, or any element is less than zero,
``harmonic_mean`` will raise ``StatisticsError``.
| def harmonic_mean(data, weights=None):
"""Return the harmonic mean of data.
The harmonic mean is the reciprocal of the arithmetic mean of the
reciprocals of the data. It can be used for averaging ratios or
rates, for example speeds.
Suppose a car travels 40 km/hr for 5 km and then speeds-up to
60 km/hr for another 5 km. What is the average speed?
>>> harmonic_mean([40, 60])
48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed?
>>> harmonic_mean([40, 60], weights=[5, 30])
56.0
If ``data`` is empty, or any element is less than zero,
``harmonic_mean`` will raise ``StatisticsError``.
"""
if iter(data) is data:
data = list(data)
errmsg = 'harmonic mean does not support negative values'
n = len(data)
if n < 1:
raise StatisticsError('harmonic_mean requires at least one data point')
elif n == 1 and weights is None:
x = data[0]
if isinstance(x, (numbers.Real, Decimal)):
if x < 0:
raise StatisticsError(errmsg)
return x
else:
raise TypeError('unsupported type')
if weights is None:
weights = repeat(1, n)
sum_weights = n
else:
if iter(weights) is weights:
weights = list(weights)
if len(weights) != n:
raise StatisticsError('Number of weights does not match data size')
_, sum_weights, _ = _sum(w for w in _fail_neg(weights, errmsg))
try:
data = _fail_neg(data, errmsg)
T, total, count = _sum(w / x if w else 0 for w, x in zip(weights, data))
except ZeroDivisionError:
return 0
if total <= 0:
raise StatisticsError('Weighted sum must be positive')
return _convert(sum_weights / total, T)
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|
36,373 | operator | itemgetter | itemgetter(item, ...) --> itemgetter object
Return a callable object that fetches the given item(s) from its operand.
After f = itemgetter(2), the call f(r) returns r[2].
After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3]) | class itemgetter:
"""
Return a callable object that fetches the given item(s) from its operand.
After f = itemgetter(2), the call f(r) returns r[2].
After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3])
"""
__slots__ = ('_items', '_call')
def __init__(self, item, *items):
if not items:
self._items = (item,)
def func(obj):
return obj[item]
self._call = func
else:
self._items = items = (item,) + items
def func(obj):
return tuple(obj[i] for i in items)
self._call = func
def __call__(self, obj):
return self._call(obj)
def __repr__(self):
return '%s.%s(%s)' % (self.__class__.__module__,
self.__class__.__name__,
', '.join(map(repr, self._items)))
def __reduce__(self):
return self.__class__, self._items
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|
36,374 | statistics | linear_regression | Slope and intercept for simple linear regression.
Return the slope and intercept of simple linear regression
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between an independent variable
*x* and a dependent variable *y* in terms of linear function:
y = slope * x + intercept + noise
where *slope* and *intercept* are the regression parameters that are
estimated, and noise represents the variability of the data that was
not explained by the linear regression (it is equal to the
difference between predicted and actual values of the dependent
variable).
The parameters are returned as a named tuple.
>>> x = [1, 2, 3, 4, 5]
>>> noise = NormalDist().samples(5, seed=42)
>>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
>>> linear_regression(x, y) #doctest: +ELLIPSIS
LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)
| def linear_regression(x, y, /):
"""Slope and intercept for simple linear regression.
Return the slope and intercept of simple linear regression
parameters estimated using ordinary least squares. Simple linear
regression describes relationship between an independent variable
*x* and a dependent variable *y* in terms of linear function:
y = slope * x + intercept + noise
where *slope* and *intercept* are the regression parameters that are
estimated, and noise represents the variability of the data that was
not explained by the linear regression (it is equal to the
difference between predicted and actual values of the dependent
variable).
The parameters are returned as a named tuple.
>>> x = [1, 2, 3, 4, 5]
>>> noise = NormalDist().samples(5, seed=42)
>>> y = [3 * x[i] + 2 + noise[i] for i in range(5)]
>>> linear_regression(x, y) #doctest: +ELLIPSIS
LinearRegression(slope=3.09078914170..., intercept=1.75684970486...)
"""
n = len(x)
if len(y) != n:
raise StatisticsError('linear regression requires that both inputs have same number of data points')
if n < 2:
raise StatisticsError('linear regression requires at least two data points')
xbar = fsum(x) / n
ybar = fsum(y) / n
sxy = fsum((xi - xbar) * (yi - ybar) for xi, yi in zip(x, y))
sxx = fsum((xi - xbar) ** 2.0 for xi in x)
try:
slope = sxy / sxx # equivalent to: covariance(x, y) / variance(x)
except ZeroDivisionError:
raise StatisticsError('x is constant')
intercept = ybar - slope * xbar
return LinearRegression(slope=slope, intercept=intercept)
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|
36,376 | statistics | mean | Return the sample arithmetic mean of data.
>>> mean([1, 2, 3, 4, 4])
2.8
>>> from fractions import Fraction as F
>>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625')
If ``data`` is empty, StatisticsError will be raised.
| def mean(data):
"""Return the sample arithmetic mean of data.
>>> mean([1, 2, 3, 4, 4])
2.8
>>> from fractions import Fraction as F
>>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
Fraction(13, 21)
>>> from decimal import Decimal as D
>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
Decimal('0.5625')
If ``data`` is empty, StatisticsError will be raised.
"""
if iter(data) is data:
data = list(data)
n = len(data)
if n < 1:
raise StatisticsError('mean requires at least one data point')
T, total, count = _sum(data)
assert count == n
return _convert(total / n, T)
| (data) | [
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|
36,377 | statistics | median | Return the median (middle value) of numeric data.
When the number of data points is odd, return the middle data point.
When the number of data points is even, the median is interpolated by
taking the average of the two middle values:
>>> median([1, 3, 5])
3
>>> median([1, 3, 5, 7])
4.0
| def median(data):
"""Return the median (middle value) of numeric data.
When the number of data points is odd, return the middle data point.
When the number of data points is even, the median is interpolated by
taking the average of the two middle values:
>>> median([1, 3, 5])
3
>>> median([1, 3, 5, 7])
4.0
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
if n % 2 == 1:
return data[n // 2]
else:
i = n // 2
return (data[i - 1] + data[i]) / 2
| (data) | [
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|
36,378 | statistics | median_grouped | Return the 50th percentile (median) of grouped continuous data.
>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
3.7
>>> median_grouped([52, 52, 53, 54])
52.5
This calculates the median as the 50th percentile, and should be
used when your data is continuous and grouped. In the above example,
the values 1, 2, 3, etc. actually represent the midpoint of classes
0.5-1.5, 1.5-2.5, 2.5-3.5, etc. The middle value falls somewhere in
class 3.5-4.5, and interpolation is used to estimate it.
Optional argument ``interval`` represents the class interval, and
defaults to 1. Changing the class interval naturally will change the
interpolated 50th percentile value:
>>> median_grouped([1, 3, 3, 5, 7], interval=1)
3.25
>>> median_grouped([1, 3, 3, 5, 7], interval=2)
3.5
This function does not check whether the data points are at least
``interval`` apart.
| def median_grouped(data, interval=1):
"""Return the 50th percentile (median) of grouped continuous data.
>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
3.7
>>> median_grouped([52, 52, 53, 54])
52.5
This calculates the median as the 50th percentile, and should be
used when your data is continuous and grouped. In the above example,
the values 1, 2, 3, etc. actually represent the midpoint of classes
0.5-1.5, 1.5-2.5, 2.5-3.5, etc. The middle value falls somewhere in
class 3.5-4.5, and interpolation is used to estimate it.
Optional argument ``interval`` represents the class interval, and
defaults to 1. Changing the class interval naturally will change the
interpolated 50th percentile value:
>>> median_grouped([1, 3, 3, 5, 7], interval=1)
3.25
>>> median_grouped([1, 3, 3, 5, 7], interval=2)
3.5
This function does not check whether the data points are at least
``interval`` apart.
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
elif n == 1:
return data[0]
# Find the value at the midpoint. Remember this corresponds to the
# centre of the class interval.
x = data[n // 2]
for obj in (x, interval):
if isinstance(obj, (str, bytes)):
raise TypeError('expected number but got %r' % obj)
try:
L = x - interval / 2 # The lower limit of the median interval.
except TypeError:
# Mixed type. For now we just coerce to float.
L = float(x) - float(interval) / 2
# Uses bisection search to search for x in data with log(n) time complexity
# Find the position of leftmost occurrence of x in data
l1 = _find_lteq(data, x)
# Find the position of rightmost occurrence of x in data[l1...len(data)]
# Assuming always l1 <= l2
l2 = _find_rteq(data, l1, x)
cf = l1
f = l2 - l1 + 1
return L + interval * (n / 2 - cf) / f
| (data, interval=1) | [
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|
36,379 | statistics | median_high | Return the high median of data.
When the number of data points is odd, the middle value is returned.
When it is even, the larger of the two middle values is returned.
>>> median_high([1, 3, 5])
3
>>> median_high([1, 3, 5, 7])
5
| def median_high(data):
"""Return the high median of data.
When the number of data points is odd, the middle value is returned.
When it is even, the larger of the two middle values is returned.
>>> median_high([1, 3, 5])
3
>>> median_high([1, 3, 5, 7])
5
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
return data[n // 2]
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|
36,380 | statistics | median_low | Return the low median of numeric data.
When the number of data points is odd, the middle value is returned.
When it is even, the smaller of the two middle values is returned.
>>> median_low([1, 3, 5])
3
>>> median_low([1, 3, 5, 7])
3
| def median_low(data):
"""Return the low median of numeric data.
When the number of data points is odd, the middle value is returned.
When it is even, the smaller of the two middle values is returned.
>>> median_low([1, 3, 5])
3
>>> median_low([1, 3, 5, 7])
3
"""
data = sorted(data)
n = len(data)
if n == 0:
raise StatisticsError("no median for empty data")
if n % 2 == 1:
return data[n // 2]
else:
return data[n // 2 - 1]
| (data) | [
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|
36,381 | statistics | mode | Return the most common data point from discrete or nominal data.
``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools:
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
This also works with nominal (non-numeric) data:
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red'
If there are multiple modes with same frequency, return the first one
encountered:
>>> mode(['red', 'red', 'green', 'blue', 'blue'])
'red'
If *data* is empty, ``mode``, raises StatisticsError.
| def mode(data):
"""Return the most common data point from discrete or nominal data.
``mode`` assumes discrete data, and returns a single value. This is the
standard treatment of the mode as commonly taught in schools:
>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
3
This also works with nominal (non-numeric) data:
>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
'red'
If there are multiple modes with same frequency, return the first one
encountered:
>>> mode(['red', 'red', 'green', 'blue', 'blue'])
'red'
If *data* is empty, ``mode``, raises StatisticsError.
"""
pairs = Counter(iter(data)).most_common(1)
try:
return pairs[0][0]
except IndexError:
raise StatisticsError('no mode for empty data') from None
| (data) | [
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]
|
36,382 | statistics | multimode | Return a list of the most frequently occurring values.
Will return more than one result if there are multiple modes
or an empty list if *data* is empty.
>>> multimode('aabbbbbbbbcc')
['b']
>>> multimode('aabbbbccddddeeffffgg')
['b', 'd', 'f']
>>> multimode('')
[]
| def multimode(data):
"""Return a list of the most frequently occurring values.
Will return more than one result if there are multiple modes
or an empty list if *data* is empty.
>>> multimode('aabbbbbbbbcc')
['b']
>>> multimode('aabbbbccddddeeffffgg')
['b', 'd', 'f']
>>> multimode('')
[]
"""
counts = Counter(iter(data)).most_common()
maxcount, mode_items = next(groupby(counts, key=itemgetter(1)), (0, []))
return list(map(itemgetter(0), mode_items))
| (data) | [
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|
36,383 | collections | namedtuple | Returns a new subclass of tuple with named fields.
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)
| def namedtuple(typename, field_names, *, rename=False, defaults=None, module=None):
"""Returns a new subclass of tuple with named fields.
>>> Point = namedtuple('Point', ['x', 'y'])
>>> Point.__doc__ # docstring for the new class
'Point(x, y)'
>>> p = Point(11, y=22) # instantiate with positional args or keywords
>>> p[0] + p[1] # indexable like a plain tuple
33
>>> x, y = p # unpack like a regular tuple
>>> x, y
(11, 22)
>>> p.x + p.y # fields also accessible by name
33
>>> d = p._asdict() # convert to a dictionary
>>> d['x']
11
>>> Point(**d) # convert from a dictionary
Point(x=11, y=22)
>>> p._replace(x=100) # _replace() is like str.replace() but targets named fields
Point(x=100, y=22)
"""
# Validate the field names. At the user's option, either generate an error
# message or automatically replace the field name with a valid name.
if isinstance(field_names, str):
field_names = field_names.replace(',', ' ').split()
field_names = list(map(str, field_names))
typename = _sys.intern(str(typename))
if rename:
seen = set()
for index, name in enumerate(field_names):
if (not name.isidentifier()
or _iskeyword(name)
or name.startswith('_')
or name in seen):
field_names[index] = f'_{index}'
seen.add(name)
for name in [typename] + field_names:
if type(name) is not str:
raise TypeError('Type names and field names must be strings')
if not name.isidentifier():
raise ValueError('Type names and field names must be valid '
f'identifiers: {name!r}')
if _iskeyword(name):
raise ValueError('Type names and field names cannot be a '
f'keyword: {name!r}')
seen = set()
for name in field_names:
if name.startswith('_') and not rename:
raise ValueError('Field names cannot start with an underscore: '
f'{name!r}')
if name in seen:
raise ValueError(f'Encountered duplicate field name: {name!r}')
seen.add(name)
field_defaults = {}
if defaults is not None:
defaults = tuple(defaults)
if len(defaults) > len(field_names):
raise TypeError('Got more default values than field names')
field_defaults = dict(reversed(list(zip(reversed(field_names),
reversed(defaults)))))
# Variables used in the methods and docstrings
field_names = tuple(map(_sys.intern, field_names))
num_fields = len(field_names)
arg_list = ', '.join(field_names)
if num_fields == 1:
arg_list += ','
repr_fmt = '(' + ', '.join(f'{name}=%r' for name in field_names) + ')'
tuple_new = tuple.__new__
_dict, _tuple, _len, _map, _zip = dict, tuple, len, map, zip
# Create all the named tuple methods to be added to the class namespace
namespace = {
'_tuple_new': tuple_new,
'__builtins__': {},
'__name__': f'namedtuple_{typename}',
}
code = f'lambda _cls, {arg_list}: _tuple_new(_cls, ({arg_list}))'
__new__ = eval(code, namespace)
__new__.__name__ = '__new__'
__new__.__doc__ = f'Create new instance of {typename}({arg_list})'
if defaults is not None:
__new__.__defaults__ = defaults
@classmethod
def _make(cls, iterable):
result = tuple_new(cls, iterable)
if _len(result) != num_fields:
raise TypeError(f'Expected {num_fields} arguments, got {len(result)}')
return result
_make.__func__.__doc__ = (f'Make a new {typename} object from a sequence '
'or iterable')
def _replace(self, /, **kwds):
result = self._make(_map(kwds.pop, field_names, self))
if kwds:
raise ValueError(f'Got unexpected field names: {list(kwds)!r}')
return result
_replace.__doc__ = (f'Return a new {typename} object replacing specified '
'fields with new values')
def __repr__(self):
'Return a nicely formatted representation string'
return self.__class__.__name__ + repr_fmt % self
def _asdict(self):
'Return a new dict which maps field names to their values.'
return _dict(_zip(self._fields, self))
def __getnewargs__(self):
'Return self as a plain tuple. Used by copy and pickle.'
return _tuple(self)
# Modify function metadata to help with introspection and debugging
for method in (
__new__,
_make.__func__,
_replace,
__repr__,
_asdict,
__getnewargs__,
):
method.__qualname__ = f'{typename}.{method.__name__}'
# Build-up the class namespace dictionary
# and use type() to build the result class
class_namespace = {
'__doc__': f'{typename}({arg_list})',
'__slots__': (),
'_fields': field_names,
'_field_defaults': field_defaults,
'__new__': __new__,
'_make': _make,
'_replace': _replace,
'__repr__': __repr__,
'_asdict': _asdict,
'__getnewargs__': __getnewargs__,
'__match_args__': field_names,
}
for index, name in enumerate(field_names):
doc = _sys.intern(f'Alias for field number {index}')
class_namespace[name] = _tuplegetter(index, doc)
result = type(typename, (tuple,), class_namespace)
# For pickling to work, the __module__ variable needs to be set to the frame
# where the named tuple is created. Bypass this step in environments where
# sys._getframe is not defined (Jython for example) or sys._getframe is not
# defined for arguments greater than 0 (IronPython), or where the user has
# specified a particular module.
if module is None:
try:
module = _sys._getframe(1).f_globals.get('__name__', '__main__')
except (AttributeError, ValueError):
pass
if module is not None:
result.__module__ = module
return result
| (typename, field_names, *, rename=False, defaults=None, module=None) | [
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|
36,385 | statistics | pstdev | Return the square root of the population variance.
See ``pvariance`` for arguments and other details.
>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
0.986893273527251
| def pstdev(data, mu=None):
"""Return the square root of the population variance.
See ``pvariance`` for arguments and other details.
>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
0.986893273527251
"""
# Fixme: Despite the exact sum of squared deviations, some inaccuracy
# remain because there are two rounding steps. The first occurs in
# the _convert() step for pvariance(), the second occurs in math.sqrt().
var = pvariance(data, mu)
try:
return var.sqrt()
except AttributeError:
return math.sqrt(var)
| (data, mu=None) | [
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]
|
36,386 | statistics | pvariance | Return the population variance of ``data``.
data should be a sequence or iterable of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function to calculate the variance from the entire population.
To estimate the variance from a sample, the ``variance`` function is
usually a better choice.
Examples:
>>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
>>> pvariance(data)
1.25
If you have already calculated the mean of the data, you can pass it as
the optional second argument to avoid recalculating it:
>>> mu = mean(data)
>>> pvariance(data, mu)
1.25
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72)
| def pvariance(data, mu=None):
"""Return the population variance of ``data``.
data should be a sequence or iterable of Real-valued numbers, with at least one
value. The optional argument mu, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function to calculate the variance from the entire population.
To estimate the variance from a sample, the ``variance`` function is
usually a better choice.
Examples:
>>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
>>> pvariance(data)
1.25
If you have already calculated the mean of the data, you can pass it as
the optional second argument to avoid recalculating it:
>>> mu = mean(data)
>>> pvariance(data, mu)
1.25
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('24.815')
>>> from fractions import Fraction as F
>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
Fraction(13, 72)
"""
if iter(data) is data:
data = list(data)
n = len(data)
if n < 1:
raise StatisticsError('pvariance requires at least one data point')
T, ss = _ss(data, mu)
return _convert(ss / n, T)
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|
36,387 | statistics | quantiles | Divide *data* into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *data* in to 100 equal sized groups.
The *data* can be any iterable containing sample.
The cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the
maximum value is treated as the 100th percentile.
| def quantiles(data, *, n=4, method='exclusive'):
"""Divide *data* into *n* continuous intervals with equal probability.
Returns a list of (n - 1) cut points separating the intervals.
Set *n* to 4 for quartiles (the default). Set *n* to 10 for deciles.
Set *n* to 100 for percentiles which gives the 99 cuts points that
separate *data* in to 100 equal sized groups.
The *data* can be any iterable containing sample.
The cut points are linearly interpolated between data points.
If *method* is set to *inclusive*, *data* is treated as population
data. The minimum value is treated as the 0th percentile and the
maximum value is treated as the 100th percentile.
"""
if n < 1:
raise StatisticsError('n must be at least 1')
data = sorted(data)
ld = len(data)
if ld < 2:
raise StatisticsError('must have at least two data points')
if method == 'inclusive':
m = ld - 1
result = []
for i in range(1, n):
j, delta = divmod(i * m, n)
interpolated = (data[j] * (n - delta) + data[j + 1] * delta) / n
result.append(interpolated)
return result
if method == 'exclusive':
m = ld + 1
result = []
for i in range(1, n):
j = i * m // n # rescale i to m/n
j = 1 if j < 1 else ld-1 if j > ld-1 else j # clamp to 1 .. ld-1
delta = i*m - j*n # exact integer math
interpolated = (data[j - 1] * (n - delta) + data[j] * delta) / n
result.append(interpolated)
return result
raise ValueError(f'Unknown method: {method!r}')
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|
36,390 | statistics | stdev | Return the square root of the sample variance.
See ``variance`` for arguments and other details.
>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
1.0810874155219827
| def stdev(data, xbar=None):
"""Return the square root of the sample variance.
See ``variance`` for arguments and other details.
>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
1.0810874155219827
"""
# Fixme: Despite the exact sum of squared deviations, some inaccuracy
# remain because there are two rounding steps. The first occurs in
# the _convert() step for variance(), the second occurs in math.sqrt().
var = variance(data, xbar)
try:
return var.sqrt()
except AttributeError:
return math.sqrt(var)
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|
36,391 | statistics | variance | Return the sample variance of data.
data should be an iterable of Real-valued numbers, with at least two
values. The optional argument xbar, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function when your data is a sample from a population. To
calculate the variance from the entire population, see ``pvariance``.
Examples:
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> variance(data)
1.3720238095238095
If you have already calculated the mean of your data, you can pass it as
the optional second argument ``xbar`` to avoid recalculating it:
>>> m = mean(data)
>>> variance(data, m)
1.3720238095238095
This function does not check that ``xbar`` is actually the mean of
``data``. Giving arbitrary values for ``xbar`` may lead to invalid or
impossible results.
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> variance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('31.01875')
>>> from fractions import Fraction as F
>>> variance([F(1, 6), F(1, 2), F(5, 3)])
Fraction(67, 108)
| def variance(data, xbar=None):
"""Return the sample variance of data.
data should be an iterable of Real-valued numbers, with at least two
values. The optional argument xbar, if given, should be the mean of
the data. If it is missing or None, the mean is automatically calculated.
Use this function when your data is a sample from a population. To
calculate the variance from the entire population, see ``pvariance``.
Examples:
>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
>>> variance(data)
1.3720238095238095
If you have already calculated the mean of your data, you can pass it as
the optional second argument ``xbar`` to avoid recalculating it:
>>> m = mean(data)
>>> variance(data, m)
1.3720238095238095
This function does not check that ``xbar`` is actually the mean of
``data``. Giving arbitrary values for ``xbar`` may lead to invalid or
impossible results.
Decimals and Fractions are supported:
>>> from decimal import Decimal as D
>>> variance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
Decimal('31.01875')
>>> from fractions import Fraction as F
>>> variance([F(1, 6), F(1, 2), F(5, 3)])
Fraction(67, 108)
"""
if iter(data) is data:
data = list(data)
n = len(data)
if n < 2:
raise StatisticsError('variance requires at least two data points')
T, ss = _ss(data, xbar)
return _convert(ss / (n - 1), T)
| (data, xbar=None) | [
0.004792107734829187,
0.012972123920917511,
0.03291620686650276,
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0.08166642487049103,
0.015147517435252666,
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|
36,394 | vobject | iCalendar | null | def iCalendar():
return newFromBehavior('vcalendar', '2.0')
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|
36,396 | vobject.base | newFromBehavior |
Given a name, return a behaviored ContentLine or Component.
| def newFromBehavior(name, id=None):
"""
Given a name, return a behaviored ContentLine or Component.
"""
name = name.upper()
behavior = getBehavior(name, id)
if behavior is None:
raise VObjectError("No behavior found named {0!s}".format(name))
if behavior.isComponent:
obj = Component(name)
else:
obj = ContentLine(name, [], '')
obj.behavior = behavior
obj.isNative = False
return obj
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|
36,397 | vobject.base | readComponents |
Generate one Component at a time from a stream.
| def readComponents(streamOrString, validate=False, transform=True,
ignoreUnreadable=False, allowQP=False):
"""
Generate one Component at a time from a stream.
"""
if isinstance(streamOrString, basestring):
stream = six.StringIO(streamOrString)
else:
stream = streamOrString
try:
stack = Stack()
versionLine = None
n = 0
for line, n in getLogicalLines(stream, allowQP):
if ignoreUnreadable:
try:
vline = textLineToContentLine(line, n)
except VObjectError as e:
if e.lineNumber is not None:
msg = "Skipped line {lineNumber}, message: {msg}"
else:
msg = "Skipped a line, message: {msg}"
logger.error(msg.format(**{'lineNumber': e.lineNumber, 'msg': str(e)}))
continue
else:
vline = textLineToContentLine(line, n)
if vline.name == "VERSION":
versionLine = vline
stack.modifyTop(vline)
elif vline.name == "BEGIN":
stack.push(Component(vline.value, group=vline.group))
elif vline.name == "PROFILE":
if not stack.top():
stack.push(Component())
stack.top().setProfile(vline.value)
elif vline.name == "END":
if len(stack) == 0:
err = "Attempted to end the {0} component but it was never opened"
raise ParseError(err.format(vline.value), n)
if vline.value.upper() == stack.topName(): # START matches END
if len(stack) == 1:
component = stack.pop()
if versionLine is not None:
component.setBehaviorFromVersionLine(versionLine)
else:
behavior = getBehavior(component.name)
if behavior:
component.setBehavior(behavior)
if validate:
component.validate(raiseException=True)
if transform:
component.transformChildrenToNative()
yield component # EXIT POINT
else:
stack.modifyTop(stack.pop())
else:
err = "{0} component wasn't closed"
raise ParseError(err.format(stack.topName()), n)
else:
stack.modifyTop(vline) # not a START or END line
if stack.top():
if stack.topName() is None:
logger.warning("Top level component was never named")
elif stack.top().useBegin:
raise ParseError("Component {0!s} was never closed".format(
(stack.topName())), n)
yield stack.pop()
except ParseError as e:
e.input = streamOrString
raise
| (streamOrString, validate=False, transform=True, ignoreUnreadable=False, allowQP=False) | [
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|
36,398 | vobject.base | readOne |
Return the first component from stream.
| def readOne(stream, validate=False, transform=True, ignoreUnreadable=False,
allowQP=False):
"""
Return the first component from stream.
"""
return next(readComponents(stream, validate, transform, ignoreUnreadable,
allowQP))
| (stream, validate=False, transform=True, ignoreUnreadable=False, allowQP=False) | [
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|
36,399 | vobject | vCard | null | def vCard():
return newFromBehavior('vcard', '3.0')
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|
36,401 | torch_pso.optim.AcceleratedPSO | AcceleratedPSO |
ChaoticPSO is a variation on the original Particle Swarm Optimization developed to quickly train SVMs. It simplifies
the original PSO algorithm--most notably eschewing the need for velocity states.
Original Paper:
Yang, X. S., Deb, S., and Fong, S., (2011), Accelerated Particle Swarm Optimization and Support Vector Machine for
Business Optimization and Applications, in: NDT2011, CCIS 136, Springer, pp. 53-66 (2011)
https://arxiv.org/abs/1203.6577
| class AcceleratedPSO(GenericPSO):
"""
ChaoticPSO is a variation on the original Particle Swarm Optimization developed to quickly train SVMs. It simplifies
the original PSO algorithm--most notably eschewing the need for velocity states.
Original Paper:
Yang, X. S., Deb, S., and Fong, S., (2011), Accelerated Particle Swarm Optimization and Support Vector Machine for
Business Optimization and Applications, in: NDT2011, CCIS 136, Springer, pp. 53-66 (2011)
https://arxiv.org/abs/1203.6577
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
alpha: float = 0.3,
beta: float = 0.45,
decay_parameter: float = 0.7,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {'alpha': alpha,
'beta': beta,
'decay_parameter': decay_parameter,
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=AcceleratedParticle, particle_kwargs=particle_kwargs)
| (params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, alpha: float = 0.3, beta: float = 0.45, decay_parameter: float = 0.7, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,402 | torch.optim.optimizer | __getstate__ | null | def __getstate__(self) -> Dict[str, Any]:
return {
'defaults': self.defaults,
'state': self.state,
'param_groups': self.param_groups,
}
| (self) -> Dict[str, Any] | [
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|
36,403 | torch_pso.optim.AcceleratedPSO | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
alpha: float = 0.3,
beta: float = 0.45,
decay_parameter: float = 0.7,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {'alpha': alpha,
'beta': beta,
'decay_parameter': decay_parameter,
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=AcceleratedParticle, particle_kwargs=particle_kwargs)
| (self, params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, alpha: float = 0.3, beta: float = 0.45, decay_parameter: float = 0.7, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,404 | torch.optim.optimizer | __repr__ | null | def __repr__(self) -> str:
format_string = self.__class__.__name__ + ' ('
for i, group in enumerate(self.param_groups):
format_string += '\n'
format_string += f'Parameter Group {i}\n'
for key in sorted(group.keys()):
if key != 'params':
format_string += f' {key}: {group[key]}\n'
format_string += ')'
return format_string
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|
36,405 | torch.optim.optimizer | __setstate__ | null | def __setstate__(self, state: Dict[str, Any]) -> None:
self.__dict__.update(state)
if '_optimizer_step_pre_hooks' not in self.__dict__:
self._optimizer_step_pre_hooks = OrderedDict()
if '_optimizer_step_post_hooks' not in self.__dict__:
self._optimizer_step_post_hooks = OrderedDict()
if '_optimizer_state_dict_pre_hooks' not in self.__dict__:
self._optimizer_state_dict_pre_hooks = OrderedDict()
if '_optimizer_state_dict_post_hooks' not in self.__dict__:
self._optimizer_state_dict_post_hooks = OrderedDict()
if '_optimizer_load_state_dict_pre_hooks' not in self.__dict__:
self._optimizer_load_state_dict_pre_hooks = OrderedDict()
if '_optimizer_load_state_dict_post_hooks' not in self.__dict__:
self._optimizer_load_state_dict_post_hooks = OrderedDict()
self._patch_step_function() # To support multiprocessing pickle/unpickle
self.defaults.setdefault('differentiable', False)
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|
36,406 | torch.optim.optimizer | _cuda_graph_capture_health_check | null | def _cuda_graph_capture_health_check(self) -> None:
# Note [torch.compile x capturable]
# If we are compiling, we try to take the capturable path automatically by
# setting the flag to True during tracing. Due to this, we skip all the checks
# normally required for determining whether we can use CUDA graphs and
# shunt the responsibility to torch.inductor. This saves time during tracing
# since the checks are slow without sacrificing UX since inductor will warn
# later if CUDA graphs cannot be enabled, e.g.,
# https://github.com/pytorch/pytorch/blob/d3ba8901d8640eb16f88b2bfef9df7fa383d4b47/torch/_inductor/compile_fx.py#L390.
# Thus, when compiling, inductor will determine if cudagraphs
# can be enabled based on whether there is input mutation or CPU tensors.
if not is_compiling() and torch.backends.cuda.is_built() and torch.cuda.is_available():
capturing = torch.cuda.is_current_stream_capturing()
if capturing and not all(group['capturable'] for group in self.param_groups):
raise RuntimeError("Attempting CUDA graph capture of step() for an instance of " +
self.__class__.__name__ +
" but param_groups' capturable is False.")
if (
(not getattr(self, "_warned_capturable_if_run_uncaptured", False))
and all(group['capturable'] for group in self.param_groups)
and (not capturing)
):
warnings.warn(
"This instance was constructed with capturable=True or some of all the param_groups came with capturable=True, "
"but step() is running without CUDA graph capture. If you never intend to graph-capture this "
"instance, capturable=True can impair performance, and you should set capturable=False."
)
self._warned_capturable_if_run_uncaptured = True
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|
36,407 | torch.optim.optimizer | _group_tensors_by_device_and_dtype | Groups a list of lists of tensors by device and dtype.
Skips this step if we are compiling since this will occur during inductor lowering. | @staticmethod
def _group_tensors_by_device_and_dtype(
tensorlistlist: TensorListList,
with_indices: bool = False,
) -> Union[
Dict[Tuple[None, None], Tuple[TensorListList, Indices]],
Dict[Tuple[torch.device, torch.dtype], Tuple[TensorListList, Indices]],
]:
"""Groups a list of lists of tensors by device and dtype.
Skips this step if we are compiling since this will occur during inductor lowering."""
if is_compiling():
return {(None, None): (tensorlistlist, list(range(len(tensorlistlist[0]))))}
else:
return _group_tensors_by_device_and_dtype(tensorlistlist, with_indices)
| (tensorlistlist: List[List[Optional[torch.Tensor]]], with_indices: bool = False) -> Union[Dict[Tuple[NoneType, NoneType], Tuple[List[List[Optional[torch.Tensor]]], List[int]]], Dict[Tuple[torch.device, torch.dtype], Tuple[List[List[Optional[torch.Tensor]]], List[int]]]] | [
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|
36,408 | torch.optim.optimizer | _optimizer_step_code | Entry point for `torch.profile.profiler`.
When python tracing is enabled the profiler will hook into this
function at the CPython level to inspect the optimizer's parameters and
param groups. It is called it after `step()` since many optimizers
lazily initialize state.
This is a workaround due to lack of a proper step hook on the optimizer,
and will be removed if it exists.
| def _optimizer_step_code(self) -> None:
"""Entry point for `torch.profile.profiler`.
When python tracing is enabled the profiler will hook into this
function at the CPython level to inspect the optimizer's parameters and
param groups. It is called it after `step()` since many optimizers
lazily initialize state.
This is a workaround due to lack of a proper step hook on the optimizer,
and will be removed if it exists.
"""
pass
| (self) -> NoneType | [
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|
36,409 | torch.optim.optimizer | _patch_step_function | null | def _patch_step_function(self) -> None:
self._zero_grad_profile_name = f"Optimizer.zero_grad#{self.__class__.__name__}.zero_grad"
hooked = getattr(self.__class__.step, "hooked", None)
if not hooked:
self.__class__.step = self.profile_hook_step(self.__class__.step) # type: ignore[assignment]
self.__class__.step.hooked = True # type: ignore[attr-defined]
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|
36,410 | torch.optim.optimizer | _process_value_according_to_param_policy | null | @staticmethod
def _process_value_according_to_param_policy(
param: torch.Tensor,
value: torch.Tensor,
param_id: int,
param_groups: List[Dict[Any, Any]],
key: Hashable = None,
) -> torch.Tensor:
# Floating-point types are a bit special here. They are the only ones
# that are assumed to always match the type of params.
# Make sure state['step'] is not casted https://github.com/pytorch/pytorch/issues/74424
# UNLESS fused or capturable, see note [special device hosting for step]
fused = False
capturable = False
assert param_groups is not None
for pg in param_groups:
if param_id in pg["params"]:
fused = pg["fused"] if "fused" in pg else False
capturable = pg["capturable"] if "capturable" in pg else False
break
if key == "step":
if capturable or fused:
return value.to(dtype=torch.float32, device=param.device)
else:
return value
else:
if param.is_floating_point():
return value.to(dtype=param.dtype, device=param.device)
else:
return value.to(device=param.device)
| (param: torch.Tensor, value: torch.Tensor, param_id: int, param_groups: List[Dict[Any, Any]], key: Optional[Hashable] = None) -> torch.Tensor | [
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|
36,411 | torch.optim.optimizer | add_param_group | Add a param group to the :class:`Optimizer` s `param_groups`.
This can be useful when fine tuning a pre-trained network as frozen layers can be made
trainable and added to the :class:`Optimizer` as training progresses.
Args:
param_group (dict): Specifies what Tensors should be optimized along with group
specific optimization options.
| import math
import functools
import warnings
from collections import OrderedDict, defaultdict
from copy import deepcopy
from itertools import chain
from typing import (
Any,
Callable,
DefaultDict,
Dict,
Hashable,
Iterable,
List,
Optional,
Set,
Tuple,
TypeVar,
Union,
cast,
overload,
)
from typing_extensions import ParamSpec, Self, TypeAlias
import torch
import torch.utils.hooks as hooks
from torch.utils.hooks import RemovableHandle
from torch.utils._foreach_utils import (
Indices,
TensorListList,
_get_foreach_kernels_supported_devices,
_get_fused_kernels_supported_devices,
)
from torch._utils import is_compiling
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
Args: TypeAlias = Tuple[Any, ...]
Kwargs: TypeAlias = Dict[str, Any]
StateDict: TypeAlias = Dict[str, Any]
GlobalOptimizerPreHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]]
GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
__all__ = ['Optimizer', 'register_optimizer_step_pre_hook', 'register_optimizer_step_post_hook']
_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self) -> str:
return "<required parameter>"
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|
36,412 | torch.optim.optimizer | load_state_dict | Loads the optimizer state.
Args:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict`.
| import math
import functools
import warnings
from collections import OrderedDict, defaultdict
from copy import deepcopy
from itertools import chain
from typing import (
Any,
Callable,
DefaultDict,
Dict,
Hashable,
Iterable,
List,
Optional,
Set,
Tuple,
TypeVar,
Union,
cast,
overload,
)
from typing_extensions import ParamSpec, Self, TypeAlias
import torch
import torch.utils.hooks as hooks
from torch.utils.hooks import RemovableHandle
from torch.utils._foreach_utils import (
Indices,
TensorListList,
_get_foreach_kernels_supported_devices,
_get_fused_kernels_supported_devices,
)
from torch._utils import is_compiling
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
Args: TypeAlias = Tuple[Any, ...]
Kwargs: TypeAlias = Dict[str, Any]
StateDict: TypeAlias = Dict[str, Any]
GlobalOptimizerPreHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]]
GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
__all__ = ['Optimizer', 'register_optimizer_step_pre_hook', 'register_optimizer_step_post_hook']
_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self) -> str:
return "<required parameter>"
| (self, state_dict: Dict[str, Any]) -> NoneType | [
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|
36,413 | torch.optim.optimizer | profile_hook_step | null | @staticmethod
def profile_hook_step(func: Callable[_P, R]) -> Callable[_P, R]:
@functools.wraps(func)
def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> R:
self, *_ = args
self = cast(Optimizer, self)
profile_name = f"Optimizer.step#{self.__class__.__name__}.step"
with torch.autograd.profiler.record_function(profile_name):
# call optimizer step pre hooks
for pre_hook in chain(_global_optimizer_pre_hooks.values(), self._optimizer_step_pre_hooks.values()):
result = pre_hook(self, args, kwargs)
if result is not None:
if isinstance(result, tuple) and len(result) == 2:
args, kwargs = result # type: ignore[assignment]
else:
raise RuntimeError(
f"{func} must return None or a tuple of (new_args, new_kwargs), but got {result}."
)
out = func(*args, **kwargs)
self._optimizer_step_code()
# call optimizer step post hooks
for post_hook in chain(self._optimizer_step_post_hooks.values(), _global_optimizer_post_hooks.values()):
post_hook(self, args, kwargs)
return out
return wrapper
| (func: Callable[~_P, ~R]) -> Callable[~_P, ~R] | [
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|
36,414 | torch.optim.optimizer | register_load_state_dict_post_hook | Register a load_state_dict post-hook which will be called after
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` after calling
``load_state_dict`` on ``self``. The registered hook can be used to
perform post-processing after ``load_state_dict`` has loaded the
``state_dict``.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_load_state_dict_post_hook(
self, hook: Callable[["Optimizer"], None], prepend: bool = False
) -> RemovableHandle:
r"""Register a load_state_dict post-hook which will be called after
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` after calling
``load_state_dict`` on ``self``. The registered hook can be used to
perform post-processing after ``load_state_dict`` has loaded the
``state_dict``.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_load_state_dict_post_hooks)
self._optimizer_load_state_dict_post_hooks[handle.id] = hook
if prepend:
self._optimizer_load_state_dict_post_hooks.move_to_end(handle.id, last=False) # type: ignore[attr-defined]
return handle
| (self, hook: Callable[[torch.optim.optimizer.Optimizer], NoneType], prepend: bool = False) -> torch.utils.hooks.RemovableHandle | [
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|
36,415 | torch.optim.optimizer | register_load_state_dict_pre_hook | Register a load_state_dict pre-hook which will be called before
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer, state_dict) -> state_dict or None
The ``optimizer`` argument is the optimizer instance being used and the
``state_dict`` argument is a shallow copy of the ``state_dict`` the user
passed in to ``load_state_dict``. The hook may modify the state_dict inplace
or optionally return a new one. If a state_dict is returned, it will be used
to be loaded into the optimizer.
The hook will be called with argument ``self`` and ``state_dict`` before
calling ``load_state_dict`` on ``self``. The registered hook can be used to
perform pre-processing before the ``load_state_dict`` call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_load_state_dict_pre_hook(
self,
hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a load_state_dict pre-hook which will be called before
:meth:`~torch.optim.Optimizer.load_state_dict` is called. It should have the
following signature::
hook(optimizer, state_dict) -> state_dict or None
The ``optimizer`` argument is the optimizer instance being used and the
``state_dict`` argument is a shallow copy of the ``state_dict`` the user
passed in to ``load_state_dict``. The hook may modify the state_dict inplace
or optionally return a new one. If a state_dict is returned, it will be used
to be loaded into the optimizer.
The hook will be called with argument ``self`` and ``state_dict`` before
calling ``load_state_dict`` on ``self``. The registered hook can be used to
perform pre-processing before the ``load_state_dict`` call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``load_state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_load_state_dict_pre_hooks)
self._optimizer_load_state_dict_pre_hooks[handle.id] = hook
if prepend:
self._optimizer_load_state_dict_pre_hooks.move_to_end(handle.id, last=False)
return handle
| (self, hook: Callable[[torch.optim.optimizer.Optimizer, Dict[str, Any]], Optional[Dict[str, Any]]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle | [
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|
36,416 | torch.optim.optimizer | register_state_dict_post_hook | Register a state dict post-hook which will be called after
:meth:`~torch.optim.Optimizer.state_dict` is called. It should have the
following signature::
hook(optimizer, state_dict) -> state_dict or None
The hook will be called with arguments ``self`` and ``state_dict`` after generating
a ``state_dict`` on ``self``. The hook may modify the state_dict inplace or optionally
return a new one. The registered hook can be used to perform post-processing
on the ``state_dict`` before it is returned.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_state_dict_post_hook(
self,
hook: Callable[["Optimizer", StateDict], Optional[StateDict]],
prepend: bool = False,
) -> RemovableHandle:
r"""Register a state dict post-hook which will be called after
:meth:`~torch.optim.Optimizer.state_dict` is called. It should have the
following signature::
hook(optimizer, state_dict) -> state_dict or None
The hook will be called with arguments ``self`` and ``state_dict`` after generating
a ``state_dict`` on ``self``. The hook may modify the state_dict inplace or optionally
return a new one. The registered hook can be used to perform post-processing
on the ``state_dict`` before it is returned.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided post ``hook`` will be fired before
all the already registered post-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
post-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_state_dict_post_hooks)
self._optimizer_state_dict_post_hooks[handle.id] = hook
if prepend:
self._optimizer_state_dict_post_hooks.move_to_end(handle.id, last=False)
return handle
| (self, hook: Callable[[torch.optim.optimizer.Optimizer, Dict[str, Any]], Optional[Dict[str, Any]]], prepend: bool = False) -> torch.utils.hooks.RemovableHandle | [
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|
36,417 | torch.optim.optimizer | register_state_dict_pre_hook | Register a state dict pre-hook which will be called before
:meth:`~torch.optim.Optimizer.state_dict` is called. It should have the
following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` before calling ``state_dict`` on ``self``.
The registered hook can be used to perform pre-processing before the ``state_dict``
call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_state_dict_pre_hook(
self, hook: Callable[["Optimizer"], None], prepend: bool = False
) -> RemovableHandle:
r"""Register a state dict pre-hook which will be called before
:meth:`~torch.optim.Optimizer.state_dict` is called. It should have the
following signature::
hook(optimizer) -> None
The ``optimizer`` argument is the optimizer instance being used.
The hook will be called with argument ``self`` before calling ``state_dict`` on ``self``.
The registered hook can be used to perform pre-processing before the ``state_dict``
call is made.
Args:
hook (Callable): The user defined hook to be registered.
prepend (bool): If True, the provided pre ``hook`` will be fired before
all the already registered pre-hooks on ``state_dict``. Otherwise,
the provided ``hook`` will be fired after all the already registered
pre-hooks. (default: False)
Returns:
:class:`torch.utils.hooks.RemoveableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_state_dict_pre_hooks)
self._optimizer_state_dict_pre_hooks[handle.id] = hook
if prepend:
self._optimizer_state_dict_pre_hooks.move_to_end(handle.id, last=False)
return handle
| (self, hook: Callable[[torch.optim.optimizer.Optimizer], NoneType], prepend: bool = False) -> torch.utils.hooks.RemovableHandle | [
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|
36,418 | torch.optim.optimizer | register_step_post_hook | Register an optimizer step post hook which will be called after optimizer step.
It should have the following signature::
hook(optimizer, args, kwargs) -> None
The ``optimizer`` argument is the optimizer instance being used.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_step_post_hook(self, hook: OptimizerPostHook) -> RemovableHandle:
r"""Register an optimizer step post hook which will be called after optimizer step.
It should have the following signature::
hook(optimizer, args, kwargs) -> None
The ``optimizer`` argument is the optimizer instance being used.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_step_post_hooks)
self._optimizer_step_post_hooks[handle.id] = hook
return handle
| (self, hook: Callable[[typing_extensions.Self, Tuple[Any, ...], Dict[str, Any]], NoneType]) -> torch.utils.hooks.RemovableHandle | [
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|
36,419 | torch.optim.optimizer | register_step_pre_hook | Register an optimizer step pre hook which will be called before
optimizer step. It should have the following signature::
hook(optimizer, args, kwargs) -> None or modified args and kwargs
The ``optimizer`` argument is the optimizer instance being used. If
args and kwargs are modified by the pre-hook, then the transformed
values are returned as a tuple containing the new_args and new_kwargs.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
| def register_step_pre_hook(self, hook: OptimizerPreHook) -> RemovableHandle:
r"""Register an optimizer step pre hook which will be called before
optimizer step. It should have the following signature::
hook(optimizer, args, kwargs) -> None or modified args and kwargs
The ``optimizer`` argument is the optimizer instance being used. If
args and kwargs are modified by the pre-hook, then the transformed
values are returned as a tuple containing the new_args and new_kwargs.
Args:
hook (Callable): The user defined hook to be registered.
Returns:
:class:`torch.utils.hooks.RemovableHandle`:
a handle that can be used to remove the added hook by calling
``handle.remove()``
"""
handle = hooks.RemovableHandle(self._optimizer_step_pre_hooks)
self._optimizer_step_pre_hooks[handle.id] = hook
return handle
| (self, hook: Callable[[typing_extensions.Self, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Tuple[Any, ...], Dict[str, Any]]]]) -> torch.utils.hooks.RemovableHandle | [
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|
36,420 | torch.optim.optimizer | state_dict | Returns the state of the optimizer as a :class:`dict`.
It contains two entries:
* ``state``: a Dict holding current optimization state. Its content
differs between optimizer classes, but some common characteristics
hold. For example, state is saved per parameter, and the parameter
itself is NOT saved. ``state`` is a Dictionary mapping parameter ids
to a Dict with state corresponding to each parameter.
* ``param_groups``: a List containing all parameter groups where each
parameter group is a Dict. Each parameter group contains metadata
specific to the optimizer, such as learning rate and weight decay,
as well as a List of parameter IDs of the parameters in the group.
NOTE: The parameter IDs may look like indices but they are just IDs
associating state with param_group. When loading from a state_dict,
the optimizer will zip the param_group ``params`` (int IDs) and the
optimizer ``param_groups`` (actual ``nn.Parameter`` s) in order to
match state WITHOUT additional verification.
A returned state dict might look something like:
.. code-block:: text
{
'state': {
0: {'momentum_buffer': tensor(...), ...},
1: {'momentum_buffer': tensor(...), ...},
2: {'momentum_buffer': tensor(...), ...},
3: {'momentum_buffer': tensor(...), ...}
},
'param_groups': [
{
'lr': 0.01,
'weight_decay': 0,
...
'params': [0]
},
{
'lr': 0.001,
'weight_decay': 0.5,
...
'params': [1, 2, 3]
}
]
}
| import math
import functools
import warnings
from collections import OrderedDict, defaultdict
from copy import deepcopy
from itertools import chain
from typing import (
Any,
Callable,
DefaultDict,
Dict,
Hashable,
Iterable,
List,
Optional,
Set,
Tuple,
TypeVar,
Union,
cast,
overload,
)
from typing_extensions import ParamSpec, Self, TypeAlias
import torch
import torch.utils.hooks as hooks
from torch.utils.hooks import RemovableHandle
from torch.utils._foreach_utils import (
Indices,
TensorListList,
_get_foreach_kernels_supported_devices,
_get_fused_kernels_supported_devices,
)
from torch._utils import is_compiling
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
Args: TypeAlias = Tuple[Any, ...]
Kwargs: TypeAlias = Dict[str, Any]
StateDict: TypeAlias = Dict[str, Any]
GlobalOptimizerPreHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]]
GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
__all__ = ['Optimizer', 'register_optimizer_step_pre_hook', 'register_optimizer_step_post_hook']
_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self) -> str:
return "<required parameter>"
| (self) -> Dict[str, Any] | [
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|
36,422 | torch.optim.optimizer | zero_grad | Resets the gradients of all optimized :class:`torch.Tensor` s.
Args:
set_to_none (bool): instead of setting to zero, set the grads to None.
This will in general have lower memory footprint, and can modestly improve performance.
However, it changes certain behaviors. For example:
1. When the user tries to access a gradient and perform manual ops on it,
a None attribute or a Tensor full of 0s will behave differently.
2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s
are guaranteed to be None for params that did not receive a gradient.
3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None
(in one case it does the step with a gradient of 0 and in the other it skips
the step altogether).
| import math
import functools
import warnings
from collections import OrderedDict, defaultdict
from copy import deepcopy
from itertools import chain
from typing import (
Any,
Callable,
DefaultDict,
Dict,
Hashable,
Iterable,
List,
Optional,
Set,
Tuple,
TypeVar,
Union,
cast,
overload,
)
from typing_extensions import ParamSpec, Self, TypeAlias
import torch
import torch.utils.hooks as hooks
from torch.utils.hooks import RemovableHandle
from torch.utils._foreach_utils import (
Indices,
TensorListList,
_get_foreach_kernels_supported_devices,
_get_fused_kernels_supported_devices,
)
from torch._utils import is_compiling
from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
Args: TypeAlias = Tuple[Any, ...]
Kwargs: TypeAlias = Dict[str, Any]
StateDict: TypeAlias = Dict[str, Any]
GlobalOptimizerPreHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], Optional[Tuple[Args, Kwargs]]]
GlobalOptimizerPostHook: TypeAlias = Callable[["Optimizer", Args, Kwargs], None]
__all__ = ['Optimizer', 'register_optimizer_step_pre_hook', 'register_optimizer_step_post_hook']
_global_optimizer_pre_hooks: Dict[int, GlobalOptimizerPreHook] = OrderedDict()
_global_optimizer_post_hooks: Dict[int, GlobalOptimizerPostHook] = OrderedDict()
_foreach_supported_types = [torch.Tensor, torch.nn.parameter.Parameter]
class _RequiredParameter:
"""Singleton class representing a required parameter for an Optimizer."""
def __repr__(self) -> str:
return "<required parameter>"
| (self, set_to_none: bool = True) -> NoneType | [
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|
36,423 | torch_pso.optim.AutotuningPSO | AutotuningPSO |
Autotuning Particle Swarm Optimization is a modification of Particle Swarm Optimization where the coefficients
change over time, as prescribed by Axel Thevenot in his Medium Article entitled ["Particle Swarm Optimization
Visually Explained"](https://towardsdatascience.com/particle-swarm-optimization-visually-explained-46289eeb2e14).
| class AutotuningPSO(ParticleSwarmOptimizer):
"""
Autotuning Particle Swarm Optimization is a modification of Particle Swarm Optimization where the coefficients
change over time, as prescribed by Axel Thevenot in his Medium Article entitled ["Particle Swarm Optimization
Visually Explained"](https://towardsdatascience.com/particle-swarm-optimization-visually-explained-46289eeb2e14).
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_total_iterations: int = 1000,
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.,
):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.num_total_iterations = num_total_iterations
self.current_step = 0
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor], n: Optional[int] = None) -> torch.Tensor:
"""
Performs a single optimization step. This is a standard PSO step, followed by a weight adjustment.
:param n: integer representing which "step number" this step should be treated as in calculating
the weight decays
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
# Calculate the new coefficients for the swarm
n = n if n is not None else self.current_step
w_t = 0.4 * (n - self.num_total_iterations) / self.num_total_iterations ** 2 + 0.4
cognitive_t = -3 * n / self.num_total_iterations + 3.5
social_t = 3 * n / self.num_total_iterations + 0.5
self.inertial_weight = w_t
self.cognitive_coefficient = cognitive_t
self.social_coefficient = social_t
# Update the step number
for particle in self.particles:
particle_loss = particle.step(closure, self.best_known_global_param_groups)
if particle_loss < self.best_known_global_loss_value:
self.best_known_global_param_groups = clone_param_groups(particle.position)
self.best_known_global_loss_value = particle_loss
# Update the particle coefficients
particle.inertial_weight = w_t
particle.cognitive_coefficient = cognitive_t
particle.social_coefficient = social_t
# set the module's parameters to be the best performing ones
for master_group, best_group in zip(self.param_groups, self.best_known_global_param_groups):
clone = clone_param_group(best_group)['params']
for i in range(len(clone)):
master_group['params'][i].data = clone[i].data
return closure() # loss = closure()
| (params: Iterable[torch.nn.parameter.Parameter], num_total_iterations: int = 1000, inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,425 | torch_pso.optim.AutotuningPSO | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_total_iterations: int = 1000,
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.,
):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.num_total_iterations = num_total_iterations
self.current_step = 0
| (self, params: Iterable[torch.nn.parameter.Parameter], num_total_iterations: int = 1000, inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,445 | torch_pso.optim.ChaoticPSO | ChaoticPSO |
ChaoticPSO is a variation on the original Particle Swarm Optimization inspired by techniques in training
Hopfield Networks. It introduces some chaos-like mechanics into the optimization, theoretically improving
convergence speed in some contexts.
Original Paper:
Sun, Yanxia & Qi, Guoyuan & Wang, Zenghui & Van Wyk, Barend & Hamam, Yskandar. (2009). Chaotic particle
swarm optimization. 505-510. 10.1145/1543834.1543902.
https://www.researchgate.net/publication/220741402_Chaotic_particle_swarm_optimization
| class ChaoticPSO(GenericPSO):
"""
ChaoticPSO is a variation on the original Particle Swarm Optimization inspired by techniques in training
Hopfield Networks. It introduces some chaos-like mechanics into the optimization, theoretically improving
convergence speed in some contexts.
Original Paper:
Sun, Yanxia & Qi, Guoyuan & Wang, Zenghui & Van Wyk, Barend & Hamam, Yskandar. (2009). Chaotic particle
swarm optimization. 505-510. 10.1145/1543834.1543902.
https://www.researchgate.net/publication/220741402_Chaotic_particle_swarm_optimization
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
a: float = 0.02,
b: float = 0.01,
c: float = 0.01,
beta: float = .001,
k: float = 15.,
epsilon: float = 1.,
i0: float = 0.2,
z: float = 0.7,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {'a': a,
'b': b,
'c': c,
'beta': beta,
'k': k,
'epsilon': epsilon,
'i0': i0,
'z':z,
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=ChaoticParticle, particle_kwargs=particle_kwargs)
| (params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, a: float = 0.02, b: float = 0.01, c: float = 0.01, beta: float = 0.001, k: float = 15.0, epsilon: float = 1.0, i0: float = 0.2, z: float = 0.7, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,447 | torch_pso.optim.ChaoticPSO | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
a: float = 0.02,
b: float = 0.01,
c: float = 0.01,
beta: float = .001,
k: float = 15.,
epsilon: float = 1.,
i0: float = 0.2,
z: float = 0.7,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {'a': a,
'b': b,
'c': c,
'beta': beta,
'k': k,
'epsilon': epsilon,
'i0': i0,
'z':z,
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=ChaoticParticle, particle_kwargs=particle_kwargs)
| (self, params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, a: float = 0.02, b: float = 0.01, c: float = 0.01, beta: float = 0.001, k: float = 15.0, epsilon: float = 1.0, i0: float = 0.2, z: float = 0.7, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,467 | torch_pso.optim.GenerationalPSO | GenerationalPSO |
Generational PSO is a modification of the naive Particle Swarm Optimization Algorithm, where a certain percentage of
randomly-chosen, low-performing particles are re-initialized after each step.
This is a sample algorithm designed by Andrew Sansom with the sole purpose of demonstrating an example of
alternate PSO algorithms.
| class GenerationalPSO(ParticleSwarmOptimizer):
"""
Generational PSO is a modification of the naive Particle Swarm Optimization Algorithm, where a certain percentage of
randomly-chosen, low-performing particles are re-initialized after each step.
This is a sample algorithm designed by Andrew Sansom with the sole purpose of demonstrating an example of
alternate PSO algorithms.
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.,
generational_turnover_ratio: float = .05,
keep_top_performers: Union[float, int] = .5):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.generational_turnover_ratio = generational_turnover_ratio
if isinstance(keep_top_performers, float):
keep_top_performers = round(num_particles*keep_top_performers)
self.keep_top_performers = keep_top_performers
if round(num_particles*generational_turnover_ratio) > num_particles-keep_top_performers:
raise ValueError(f'The generational turnover ratio is higher than the number of bottom performers. '
f'Turnover: {round(num_particles*generational_turnover_ratio)}, '
f'Bottom Performers: {num_particles-keep_top_performers}')
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor]) -> torch.Tensor:
"""
Performs a single optimization step.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
losses = {}
for i, particle in enumerate(self.particles):
particle_loss = particle.step(closure, self.best_known_global_param_groups)
losses[i] = particle_loss
if particle_loss < self.best_known_global_loss_value:
self.best_known_global_param_groups = clone_param_groups(particle.position)
self.best_known_global_loss_value = particle_loss
# set the module's parameters to be the best performing ones
for master_group, best_group in zip(self.param_groups, self.best_known_global_param_groups):
clone = clone_param_group(best_group)['params']
for i in range(len(clone)):
master_group['params'][i].data = clone[i].data
# Respawn a certain proportion of the worst performing particles, chosen at random
best_performers_indices = list(sorted(losses, key=losses.get, reverse=True))
bottom_performers = best_performers_indices[self.keep_top_performers:]
indices_to_respawn = random.sample(bottom_performers, round(self.generational_turnover_ratio * len(losses)))
for index in indices_to_respawn:
self.particles[index] = Particle(self.param_groups,
self.inertial_weight,
self.cognitive_coefficient,
self.social_coefficient,
max_param_value=self.max_param_value,
min_param_value=self.min_param_value)
return closure() # loss = closure()
| (params: Iterable[torch.nn.parameter.Parameter], inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0, generational_turnover_ratio: float = 0.05, keep_top_performers: Union[float, int] = 0.5) | [
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|
36,469 | torch_pso.optim.GenerationalPSO | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.,
generational_turnover_ratio: float = .05,
keep_top_performers: Union[float, int] = .5):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.generational_turnover_ratio = generational_turnover_ratio
if isinstance(keep_top_performers, float):
keep_top_performers = round(num_particles*keep_top_performers)
self.keep_top_performers = keep_top_performers
if round(num_particles*generational_turnover_ratio) > num_particles-keep_top_performers:
raise ValueError(f'The generational turnover ratio is higher than the number of bottom performers. '
f'Turnover: {round(num_particles*generational_turnover_ratio)}, '
f'Bottom Performers: {num_particles-keep_top_performers}')
| (self, params: Iterable[torch.nn.parameter.Parameter], inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0, generational_turnover_ratio: float = 0.05, keep_top_performers: Union[float, int] = 0.5) | [
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|
36,489 | torch_pso.optim.GenericPSO | GenericPSO | null | class GenericPSO(Optimizer):
def __init__(self, params: Iterable[torch.nn.Parameter], num_particles: int, particle_class: Type[GenericParticle],
particle_args: Optional[List] = None, particle_kwargs: Optional[Dict] = None):
defaults = {}
super().__init__(params, defaults)
if particle_args is None:
particle_args = []
if particle_kwargs is None:
particle_kwargs = {}
self.particles = [particle_class(self.param_groups, *particle_args, **particle_kwargs)
for _ in range(num_particles)]
self.best_known_global_param_groups = clone_param_groups(self.param_groups)
self.best_known_global_loss_value = torch.inf
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor], particle_step_kwargs: Optional[Dict] = None) -> torch.Tensor:
"""
Performs a single optimization step.
:param particle_step_kwargs: Dict of keyword arguments to pass to the particle step function, if needed.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
if particle_step_kwargs is None:
particle_step_kwargs = {}
for particle in self.particles:
particle_loss = particle.step(closure, self.best_known_global_param_groups, **particle_step_kwargs)
if particle_loss < self.best_known_global_loss_value:
self.best_known_global_param_groups = clone_param_groups(particle.position)
self.best_known_global_loss_value = particle_loss
# set the module's parameters to be the best performing ones
for master_group, best_group in zip(self.param_groups, self.best_known_global_param_groups):
clone = clone_param_group(best_group)['params']
for i in range(len(clone)):
master_group['params'][i].data = clone[i].data
return closure() # loss = closure()
subclasses = []
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
cls.subclasses.append(cls)
| (params: Iterable[torch.nn.parameter.Parameter], num_particles: int, particle_class: Type[torch_pso.optim.GenericPSO.GenericParticle], particle_args: Optional[List] = None, particle_kwargs: Optional[Dict] = None) | [
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|
36,491 | torch_pso.optim.GenericPSO | __init__ | null | def __init__(self, params: Iterable[torch.nn.Parameter], num_particles: int, particle_class: Type[GenericParticle],
particle_args: Optional[List] = None, particle_kwargs: Optional[Dict] = None):
defaults = {}
super().__init__(params, defaults)
if particle_args is None:
particle_args = []
if particle_kwargs is None:
particle_kwargs = {}
self.particles = [particle_class(self.param_groups, *particle_args, **particle_kwargs)
for _ in range(num_particles)]
self.best_known_global_param_groups = clone_param_groups(self.param_groups)
self.best_known_global_loss_value = torch.inf
| (self, params: Iterable[torch.nn.parameter.Parameter], num_particles: int, particle_class: Type[torch_pso.optim.GenericPSO.GenericParticle], particle_args: Optional[List] = None, particle_kwargs: Optional[Dict] = None) | [
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|
36,511 | torch_pso.optim.ImprovedSineCosineAlgorithm | ImprovedSineCosineAlgorithm |
The Improved Sine Cosine Algorithm is a variation on the original Sine Cosine Algorithm, specifically attempting to
improve its resilience against local optima and unbalanced exploitation. It also demonstrates better behavior in
high-dimensional optimization problems.
Original Paper:
Long, W., Wu, T., Liang, X., & Xu, S. (2019). Solving high-dimensional global optimization problems using an
improved sine cosine algorithm. Expert systems with applications, 123, 108-126.
https://e-tarjome.com/storage/panel/fileuploads/2019-08-22/1566462251_E11587-e-tarjome.pdf
| class ImprovedSineCosineAlgorithm(GenericPSO):
"""
The Improved Sine Cosine Algorithm is a variation on the original Sine Cosine Algorithm, specifically attempting to
improve its resilience against local optima and unbalanced exploitation. It also demonstrates better behavior in
high-dimensional optimization problems.
Original Paper:
Long, W., Wu, T., Liang, X., & Xu, S. (2019). Solving high-dimensional global optimization problems using an
improved sine cosine algorithm. Expert systems with applications, 123, 108-126.
https://e-tarjome.com/storage/panel/fileuploads/2019-08-22/1566462251_E11587-e-tarjome.pdf
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
a_start: float = 2,
a_end: float = 0,
max_time_steps: int = 1000,
w_end: float = 0,
w_start: float = .1,
k: float = 15.,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=ImprovedSCAParticle, particle_kwargs=particle_kwargs)
self.max_param_value = max_param_value
self.min_param_value = min_param_value
self.magnitude = max_param_value - min_param_value
self.w_end = w_end
self.w_start = w_start
self.a_start = a_start
self.a_end = a_end
self.k = k
self.max_time_steps = max_time_steps
self.current_time_step = 0
def w(self) -> float:
"""
Calculate the inertial weight of the particle at the current time step.
"""
return self.w_end + (self.w_start - self.w_end) * (
self.max_time_steps - self.current_time_step) / self.max_time_steps
def r1(self):
"""Calculate the r1 value for the current timestep"""
return (self.a_start - self.a_end) * math.exp(
-self.current_time_step ** 2 / (self.k * self.max_time_steps) ** 2) + self.a_end
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor]) -> torch.Tensor:
"""
Performs a single optimization step.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
r3 = 2 * torch.rand((1,))
r2 = 2 * torch.pi * torch.rand((1,))
use_sine = torch.rand((1,)).item() < .5
r1 = self.r1()
w = self.w()
self.current_time_step += 1
return super().step(closure, particle_step_kwargs={'r1': r1,
'r2': r2,
'r3': r3,
'use_sine': use_sine,
'w': w
})
| (params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, a_start: float = 2, a_end: float = 0, max_time_steps: int = 1000, w_end: float = 0, w_start: float = 0.1, k: float = 15.0, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,513 | torch_pso.optim.ImprovedSineCosineAlgorithm | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
a_start: float = 2,
a_end: float = 0,
max_time_steps: int = 1000,
w_end: float = 0,
w_start: float = .1,
k: float = 15.,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=ImprovedSCAParticle, particle_kwargs=particle_kwargs)
self.max_param_value = max_param_value
self.min_param_value = min_param_value
self.magnitude = max_param_value - min_param_value
self.w_end = w_end
self.w_start = w_start
self.a_start = a_start
self.a_end = a_end
self.k = k
self.max_time_steps = max_time_steps
self.current_time_step = 0
| (self, params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, a_start: float = 2, a_end: float = 0, max_time_steps: int = 1000, w_end: float = 0, w_start: float = 0.1, k: float = 15.0, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,524 | torch_pso.optim.ImprovedSineCosineAlgorithm | r1 | Calculate the r1 value for the current timestep | def r1(self):
"""Calculate the r1 value for the current timestep"""
return (self.a_start - self.a_end) * math.exp(
-self.current_time_step ** 2 / (self.k * self.max_time_steps) ** 2) + self.a_end
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|
36,532 | torch_pso.optim.ImprovedSineCosineAlgorithm | step |
Performs a single optimization step.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
| def r1(self):
"""Calculate the r1 value for the current timestep"""
return (self.a_start - self.a_end) * math.exp(
-self.current_time_step ** 2 / (self.k * self.max_time_steps) ** 2) + self.a_end
| (self, closure: Callable[[], torch.Tensor]) -> torch.Tensor | [
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|
36,533 | torch_pso.optim.ImprovedSineCosineAlgorithm | w |
Calculate the inertial weight of the particle at the current time step.
| def w(self) -> float:
"""
Calculate the inertial weight of the particle at the current time step.
"""
return self.w_end + (self.w_start - self.w_end) * (
self.max_time_steps - self.current_time_step) / self.max_time_steps
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|
36,535 | torch_pso.optim.ParticleSwarmOptimizer | ParticleSwarmOptimizer |
Algorithm from Wikipedia: https://en.wikipedia.org/wiki/Particle_swarm_optimization
Let S be the number of particles in the swarm, each having a position xi ∈ ℝn in the search-space
and a velocity vi ∈ ℝn. Let pi be the best known position of particle i and let g be the best known
position of the entire swarm.
The values blo and bup represent the lower and upper boundaries of the search-space respectively.
The w parameter is the inertia weight. The parameters φp and φg are often called cognitive coefficient and
social coefficient.
The termination criterion can be the number of iterations performed, or a solution where the adequate
objective function value is found. The parameters w, φp, and φg are selected by the practitioner and control
the behaviour and efficacy of the PSO method.
for each particle i = 1, ..., S do
Initialize the particle's position with a uniformly distributed random vector: xi ~ U(blo, bup)
Initialize the particle's best known position to its initial position: pi ← xi
if f(pi) < f(g) then
update the swarm's best known position: g ← pi
Initialize the particle's velocity: vi ~ U(-|bup-blo|, |bup-blo|)
while a termination criterion is not met do:
for each particle i = 1, ..., S do
for each dimension d = 1, ..., n do
Pick random numbers: rp, rg ~ U(0,1)
Update the particle's velocity: vi,d ← w vi,d + φp rp (pi,d-xi,d) + φg rg (gd-xi,d)
Update the particle's position: xi ← xi + vi
if f(xi) < f(pi) then
Update the particle's best known position: pi ← xi
if f(pi) < f(g) then
Update the swarm's best known position: g ← pi
:param params:iterable of parameters to optimize or dicts defining parameter groups
:param inertial_weight: float representing inertial weight of the particles
:param cognitive_coefficient: float representing cognitive coefficient of the particles
:param social_coefficient: float representing social coefficient of the particles
:param num_particles: int representing the number of particles in the swarm
:param max_param_value: Maximum value of the parameters in the search space
:param min_param_value: Minimum value of the parameters in the search space
| class ParticleSwarmOptimizer(GenericPSO):
r"""
Algorithm from Wikipedia: https://en.wikipedia.org/wiki/Particle_swarm_optimization
Let S be the number of particles in the swarm, each having a position xi ∈ ℝn in the search-space
and a velocity vi ∈ ℝn. Let pi be the best known position of particle i and let g be the best known
position of the entire swarm.
The values blo and bup represent the lower and upper boundaries of the search-space respectively.
The w parameter is the inertia weight. The parameters φp and φg are often called cognitive coefficient and
social coefficient.
The termination criterion can be the number of iterations performed, or a solution where the adequate
objective function value is found. The parameters w, φp, and φg are selected by the practitioner and control
the behaviour and efficacy of the PSO method.
for each particle i = 1, ..., S do
Initialize the particle's position with a uniformly distributed random vector: xi ~ U(blo, bup)
Initialize the particle's best known position to its initial position: pi ← xi
if f(pi) < f(g) then
update the swarm's best known position: g ← pi
Initialize the particle's velocity: vi ~ U(-|bup-blo|, |bup-blo|)
while a termination criterion is not met do:
for each particle i = 1, ..., S do
for each dimension d = 1, ..., n do
Pick random numbers: rp, rg ~ U(0,1)
Update the particle's velocity: vi,d ← w vi,d + φp rp (pi,d-xi,d) + φg rg (gd-xi,d)
Update the particle's position: xi ← xi + vi
if f(xi) < f(pi) then
Update the particle's best known position: pi ← xi
if f(pi) < f(g) then
Update the swarm's best known position: g ← pi
:param params:iterable of parameters to optimize or dicts defining parameter groups
:param inertial_weight: float representing inertial weight of the particles
:param cognitive_coefficient: float representing cognitive coefficient of the particles
:param social_coefficient: float representing social coefficient of the particles
:param num_particles: int representing the number of particles in the swarm
:param max_param_value: Maximum value of the parameters in the search space
:param min_param_value: Minimum value of the parameters in the search space
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.):
self.num_particles = num_particles
self.inertial_weight = inertial_weight
self.cognitive_coefficient = cognitive_coefficient
self.social_coefficient = social_coefficient
self.max_param_value = max_param_value
self.min_param_value = min_param_value
kwargs = {'inertial_weight': inertial_weight,
'cognitive_coefficient': cognitive_coefficient,
'social_coefficient': social_coefficient,
'max_param_value': max_param_value,
'min_param_value': min_param_value}
super().__init__(params, num_particles, Particle, particle_kwargs=kwargs)
# defaults = {}
# super().__init__(params, defaults)
# # print('self.param_groups', self.param_groups)
# self.particles = [Particle(self.param_groups,
# self.inertial_weight,
# self.cognitive_coefficient,
# self.social_coefficient,
# max_param_value=self.max_param_value,
# min_param_value=self.min_param_value)
# for _ in range(self.num_particles)]
#
# self.best_known_global_param_groups = clone_param_groups(self.param_groups)
# self.best_known_global_loss_value = torch.inf
# @torch.no_grad()
# def step(self, closure: Callable[[], torch.Tensor]) -> torch.Tensor:
# """
# Performs a single optimization step.
#
# :param closure: A callable that reevaluates the model and returns the loss.
# :return: the final loss after the step (as calculated by the closure)
# """
# for particle in self.particles:
# particle_loss = particle.step(closure, self.best_known_global_param_groups)
# if particle_loss < self.best_known_global_loss_value:
# self.best_known_global_param_groups = clone_param_groups(particle.position)
# self.best_known_global_loss_value = particle_loss
#
# # set the module's parameters to be the best performing ones
# for master_group, best_group in zip(self.param_groups, self.best_known_global_param_groups):
# clone = clone_param_group(best_group)['params']
# for i in range(len(clone)):
# master_group['params'][i].data = clone[i].data
#
# return closure() # loss = closure()
| (params: Iterable[torch.nn.parameter.Parameter], inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,537 | torch_pso.optim.ParticleSwarmOptimizer | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.):
self.num_particles = num_particles
self.inertial_weight = inertial_weight
self.cognitive_coefficient = cognitive_coefficient
self.social_coefficient = social_coefficient
self.max_param_value = max_param_value
self.min_param_value = min_param_value
kwargs = {'inertial_weight': inertial_weight,
'cognitive_coefficient': cognitive_coefficient,
'social_coefficient': social_coefficient,
'max_param_value': max_param_value,
'min_param_value': min_param_value}
super().__init__(params, num_particles, Particle, particle_kwargs=kwargs)
# defaults = {}
# super().__init__(params, defaults)
# # print('self.param_groups', self.param_groups)
# self.particles = [Particle(self.param_groups,
# self.inertial_weight,
# self.cognitive_coefficient,
# self.social_coefficient,
# max_param_value=self.max_param_value,
# min_param_value=self.min_param_value)
# for _ in range(self.num_particles)]
#
# self.best_known_global_param_groups = clone_param_groups(self.param_groups)
# self.best_known_global_loss_value = torch.inf
| (self, params: Iterable[torch.nn.parameter.Parameter], inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,557 | torch_pso.optim.RingTopologyPSO | RingTopologyPSO |
Ring Topology PSO is a modification of the naive Particle Swarm Optimization Algorithm, where instead of feeding
each particle the global optimum, each particle only receives the minimum of itself and two other particles. These
two particles are the same through the entire iteration.
This is called a "ring" because the network graph showing these connections has a single cycle.
| class RingTopologyPSO(ParticleSwarmOptimizer):
"""
Ring Topology PSO is a modification of the naive Particle Swarm Optimization Algorithm, where instead of feeding
each particle the global optimum, each particle only receives the minimum of itself and two other particles. These
two particles are the same through the entire iteration.
This is called a "ring" because the network graph showing these connections has a single cycle.
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_neighbors: int = 2,
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.losses = {i: (particle.position, torch.inf) for i, particle in enumerate(self.particles)}
self.num_neighbors = num_neighbors
def _find_minimum_of_neighbors(self, particle_index: int) -> List[Dict]:
neighbors = [(particle_index + i) % len(self.particles) for i in range(self.num_neighbors)]
best = sorted([self.losses[n] for n in neighbors],
key=lambda x: x[1],
reverse=True)[0]
return clone_param_groups(best[0])
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor]) -> torch.Tensor:
"""
Performs a single optimization step.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
losses = {}
for i, particle in enumerate(self.particles):
particle_loss = particle.step(closure, self._find_minimum_of_neighbors(i))
losses[i] = (particle.position, particle_loss)
if particle_loss < self.best_known_global_loss_value:
self.best_known_global_param_groups = clone_param_groups(particle.position)
self.best_known_global_loss_value = particle_loss
# set the module's parameters to be the best performing ones
for master_group, best_group in zip(self.param_groups, self.best_known_global_param_groups):
clone = clone_param_group(best_group)['params']
for i in range(len(clone)):
master_group['params'][i].data = clone[i].data
self.losses = losses
return closure() # loss = closure()
| (params: Iterable[torch.nn.parameter.Parameter], num_neighbors: int = 2, inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,559 | torch_pso.optim.RingTopologyPSO | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_neighbors: int = 2,
inertial_weight: float = .9,
cognitive_coefficient: float = 1.,
social_coefficient: float = 1.,
num_particles: int = 100,
max_param_value: float = 10.,
min_param_value: float = -10.):
super().__init__(params, inertial_weight, cognitive_coefficient, social_coefficient, num_particles,
max_param_value, min_param_value)
self.losses = {i: (particle.position, torch.inf) for i, particle in enumerate(self.particles)}
self.num_neighbors = num_neighbors
| (self, params: Iterable[torch.nn.parameter.Parameter], num_neighbors: int = 2, inertial_weight: float = 0.9, cognitive_coefficient: float = 1.0, social_coefficient: float = 1.0, num_particles: int = 100, max_param_value: float = 10.0, min_param_value: float = -10.0) | [
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|
36,563 | torch_pso.optim.RingTopologyPSO | _find_minimum_of_neighbors | null | def _find_minimum_of_neighbors(self, particle_index: int) -> List[Dict]:
neighbors = [(particle_index + i) % len(self.particles) for i in range(self.num_neighbors)]
best = sorted([self.losses[n] for n in neighbors],
key=lambda x: x[1],
reverse=True)[0]
return clone_param_groups(best[0])
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|
36,580 | torch_pso.optim.SineCosineAlgorithm | SineCosineAlgorithm |
The Sine Cosine Algorithm is an algorithm, conceived by Seyedali Mirjalili, similar to PSO, although the author
does not use particle terminology in the paper. Each particle moves towards a destination (the best candidate
solution) in a step size determined by several random parameters. The "sine" and "cosine" in the name come from the
use of sine and cosine functions to determine whether the particle's step will explore or exploit the currently
best solution.
Original Paper:
Seyedali Mirjalili , SCA: A Sine Cosine Algorithm for Solving Optimization Problems, Knowledge-Based Systems (2016),
doi: 10.1016/j.knosys.2015.12.022
https://dl.programstore.ir/files/Uploades/Lib/PDF/SCA.pdf
| class SineCosineAlgorithm(GenericPSO):
"""
The Sine Cosine Algorithm is an algorithm, conceived by Seyedali Mirjalili, similar to PSO, although the author
does not use particle terminology in the paper. Each particle moves towards a destination (the best candidate
solution) in a step size determined by several random parameters. The "sine" and "cosine" in the name come from the
use of sine and cosine functions to determine whether the particle's step will explore or exploit the currently
best solution.
Original Paper:
Seyedali Mirjalili , SCA: A Sine Cosine Algorithm for Solving Optimization Problems, Knowledge-Based Systems (2016),
doi: 10.1016/j.knosys.2015.12.022
https://dl.programstore.ir/files/Uploades/Lib/PDF/SCA.pdf
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
max_movement_radius: float = 2,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=SCAParticle, particle_kwargs=particle_kwargs)
self.max_param_value = max_param_value
self.min_param_value = min_param_value
self.max_movement_radius = max_movement_radius
self.magnitude = max_param_value-min_param_value
self.initial_movement_radius = max_movement_radius
@torch.no_grad()
def step(self, closure: Callable[[], torch.Tensor]) -> torch.Tensor:
"""
Performs a single optimization step.
:param closure: A callable that reevaluates the model and returns the loss.
:return: the final loss after the step (as calculated by the closure)
"""
r3 = 2 * torch.rand((1,))
r2 = 2*torch.pi*torch.rand((1,))
use_sine = torch.rand((1,)).item() < .5
r1 = self.max_movement_radius*torch.rand((1,))
# self.max_movement_radius *= .99
return super().step(closure, particle_step_kwargs={'r1': r1, 'r2': r2, 'r3': r3, 'use_sine': use_sine})
| (params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, max_movement_radius: float = 2, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,582 | torch_pso.optim.SineCosineAlgorithm | __init__ | null | def __init__(self,
params: Iterable[torch.nn.Parameter],
num_particles: int = 100,
max_movement_radius: float = 2,
max_param_value: float = -10,
min_param_value: float = 10):
particle_kwargs = {
'max_param_value': max_param_value,
'min_param_value': min_param_value,
}
super().__init__(params, num_particles, particle_class=SCAParticle, particle_kwargs=particle_kwargs)
self.max_param_value = max_param_value
self.min_param_value = min_param_value
self.max_movement_radius = max_movement_radius
self.magnitude = max_param_value-min_param_value
self.initial_movement_radius = max_movement_radius
| (self, params: Iterable[torch.nn.parameter.Parameter], num_particles: int = 100, max_movement_radius: float = 2, max_param_value: float = -10, min_param_value: float = 10) | [
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|
36,603 | rfc3987 | <lambda> | null | _bmp = lambda s: _re.sub(r'\\U[0-9A-F]{8}-\\U[0-9A-F]{8}', '', s)
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|
36,604 | rfc3987 | _i2u | null | def _i2u(dic):
for (name, iname) in [('authority', 'iauthority'), ('path', 'ipath'),
('query', 'iquery'), ('fragment', 'ifragment')]:
if dic.get(name) is None:
dic[name] = dic.get(iname)
return dic
| (dic) | [
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|
36,605 | rfc3987 | _interpret_unicode_escapes | null | def _interpret_unicode_escapes(string):
return string.encode('ascii').decode('raw-unicode-escape')
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|
36,607 | rfc3987 | _remove_dot_segments | null | def _remove_dot_segments(path):
path = _dot_segments.sub('', path)
c = 1
while c:
path, c = _2dots_segments.subn('/', path, 1)
return path
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|
36,610 | rfc3987 | compose | Returns an URI composed_ from named parts.
.. _composed: http://tools.ietf.org/html/rfc3986#section-5.3
| def compose(scheme=None, authority=None, path=None, query=None, fragment=None,
iauthority=None, ipath=None, iquery=None, ifragment=None, **kw):
"""Returns an URI composed_ from named parts.
.. _composed: http://tools.ietf.org/html/rfc3986#section-5.3
"""
_i2u(locals())
res = ''
if scheme is not None:
res += scheme + ':'
if authority is not None:
res += '//' + authority
res += path or ''
if query is not None:
res += '?' + query
if fragment is not None:
res += '#' + fragment
return res
| (scheme=None, authority=None, path=None, query=None, fragment=None, iauthority=None, ipath=None, iquery=None, ifragment=None, **kw) | [
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|
36,611 | rfc3987 | format_patterns | Returns a dict of patterns (regular expressions) keyed by
`rule names for URIs`_ and `rule names for IRIs`_.
See also the module level dicts of patterns, and `get_compiled_pattern`.
To wrap a rule in a named capture group, pass it as keyword argument:
rule_name='group_name'. By default, the formatted patterns contain no
named groups.
Patterns are `str` instances (be it in python 2.x or 3.x) containing ASCII
characters only.
Caveats:
- with re_, named capture groups cannot occur on multiple branches of an
alternation
- with re_ before python 3.3, ``\u`` and ``\U`` escapes must be
preprocessed (see `issue3665 <http://bugs.python.org/issue3665>`_)
- on narrow builds, character ranges beyond BMP are not supported
.. _rule names for URIs: http://tools.ietf.org/html/rfc3986#appendix-A
.. _rule names for IRIs: http://tools.ietf.org/html/rfc3987#section-2.2
| def format_patterns(**names):
r"""Returns a dict of patterns (regular expressions) keyed by
`rule names for URIs`_ and `rule names for IRIs`_.
See also the module level dicts of patterns, and `get_compiled_pattern`.
To wrap a rule in a named capture group, pass it as keyword argument:
rule_name='group_name'. By default, the formatted patterns contain no
named groups.
Patterns are `str` instances (be it in python 2.x or 3.x) containing ASCII
characters only.
Caveats:
- with re_, named capture groups cannot occur on multiple branches of an
alternation
- with re_ before python 3.3, ``\u`` and ``\U`` escapes must be
preprocessed (see `issue3665 <http://bugs.python.org/issue3665>`_)
- on narrow builds, character ranges beyond BMP are not supported
.. _rule names for URIs: http://tools.ietf.org/html/rfc3986#appendix-A
.. _rule names for IRIs: http://tools.ietf.org/html/rfc3987#section-2.2
"""
formatted = {}
for name, pat in _common_rules[::-1] + _uri_rules[::-1] + _iri_rules[::-1]:
if name in names:
n = names[name]
if callable(n):
pat = n(pat)
else:
pat = '(?P<%s>%s)' % (n, pat)
formatted[name] = pat.format(**formatted)
return formatted
| (**names) | [
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|
36,612 | rfc3987 | get_compiled_pattern | Returns a compiled pattern object for a rule name or template string.
Usage for validation::
>>> uri = get_compiled_pattern('^%(URI)s$')
>>> assert uri.match('http://tools.ietf.org/html/rfc3986#appendix-A')
>>> assert not get_compiled_pattern('^%(relative_ref)s$').match('#f#g')
>>> from unicodedata import lookup
>>> smp = 'urn:' + lookup('OLD ITALIC LETTER A') # U+00010300
>>> assert not uri.match(smp)
>>> m = get_compiled_pattern('^%(IRI)s$').match(smp)
On narrow builds, non-BMP characters are (incorrectly) excluded::
>>> assert NARROW_BUILD == (not m)
For parsing, some subcomponents are captured in named groups (*only if*
regex_ is available, otherwise see `parse`)::
>>> match = uri.match('http://tools.ietf.org/html/rfc3986#appendix-A')
>>> d = match.groupdict()
>>> if REGEX:
... assert all([ d['scheme'] == 'http',
... d['authority'] == 'tools.ietf.org',
... d['path'] == '/html/rfc3986',
... d['query'] == None,
... d['fragment'] == 'appendix-A' ])
>>> for r in patterns.keys():
... assert get_compiled_pattern(r)
| def get_compiled_pattern(rule, flags=0):
"""Returns a compiled pattern object for a rule name or template string.
Usage for validation::
>>> uri = get_compiled_pattern('^%(URI)s$')
>>> assert uri.match('http://tools.ietf.org/html/rfc3986#appendix-A')
>>> assert not get_compiled_pattern('^%(relative_ref)s$').match('#f#g')
>>> from unicodedata import lookup
>>> smp = 'urn:' + lookup('OLD ITALIC LETTER A') # U+00010300
>>> assert not uri.match(smp)
>>> m = get_compiled_pattern('^%(IRI)s$').match(smp)
On narrow builds, non-BMP characters are (incorrectly) excluded::
>>> assert NARROW_BUILD == (not m)
For parsing, some subcomponents are captured in named groups (*only if*
regex_ is available, otherwise see `parse`)::
>>> match = uri.match('http://tools.ietf.org/html/rfc3986#appendix-A')
>>> d = match.groupdict()
>>> if REGEX:
... assert all([ d['scheme'] == 'http',
... d['authority'] == 'tools.ietf.org',
... d['path'] == '/html/rfc3986',
... d['query'] == None,
... d['fragment'] == 'appendix-A' ])
>>> for r in patterns.keys():
... assert get_compiled_pattern(r)
"""
cache, key = get_compiled_pattern.cache, (rule, flags)
if key not in cache:
if NARROW_BUILD:
pats = bmp_patterns if REGEX else bmp_upatterns_no_names
else:
pats = patterns if REGEX else upatterns_no_names
p = pats.get(rule) or rule % pats
cache[key] = _re.compile(p, flags)
return cache[key]
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|
36,613 | rfc3987 | match | Convenience function for checking if `string` matches a specific rule.
Returns a match object or None::
>>> assert match('%C7X', 'pct_encoded') is None
>>> assert match('%C7', 'pct_encoded')
>>> assert match('%c7', 'pct_encoded')
| def match(string, rule='IRI_reference'):
"""Convenience function for checking if `string` matches a specific rule.
Returns a match object or None::
>>> assert match('%C7X', 'pct_encoded') is None
>>> assert match('%C7', 'pct_encoded')
>>> assert match('%c7', 'pct_encoded')
"""
return get_compiled_pattern('^%%(%s)s$' % rule).match(string)
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]
|
36,614 | rfc3987 | normalize | Syntax-Based Normalization | def normalize(uri):
"Syntax-Based Normalization"
# TODO:
raise NotImplementedError
| (uri) | [
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|
36,615 | rfc3987 | parse | Parses `string` according to `rule` into a dict of subcomponents.
If `rule` is None, parse an IRI_reference `without validation
<http://tools.ietf.org/html/rfc3986#appendix-B>`_.
If regex_ is available, any rule is supported; with re_, `rule` must be
'IRI_reference' or some special case thereof ('IRI', 'absolute_IRI',
'irelative_ref', 'irelative_part', 'URI_reference', 'URI', 'absolute_URI',
'relative_ref', 'relative_part'). ::
>>> d = parse('http://tools.ietf.org/html/rfc3986#appendix-A',
... rule='URI')
>>> assert all([ d['scheme'] == 'http',
... d['authority'] == 'tools.ietf.org',
... d['path'] == '/html/rfc3986',
... d['query'] == None,
... d['fragment'] == 'appendix-A' ])
| def parse(string, rule='IRI_reference'):
"""Parses `string` according to `rule` into a dict of subcomponents.
If `rule` is None, parse an IRI_reference `without validation
<http://tools.ietf.org/html/rfc3986#appendix-B>`_.
If regex_ is available, any rule is supported; with re_, `rule` must be
'IRI_reference' or some special case thereof ('IRI', 'absolute_IRI',
'irelative_ref', 'irelative_part', 'URI_reference', 'URI', 'absolute_URI',
'relative_ref', 'relative_part'). ::
>>> d = parse('http://tools.ietf.org/html/rfc3986#appendix-A',
... rule='URI')
>>> assert all([ d['scheme'] == 'http',
... d['authority'] == 'tools.ietf.org',
... d['path'] == '/html/rfc3986',
... d['query'] == None,
... d['fragment'] == 'appendix-A' ])
"""
if not REGEX and rule and rule not in REFERENCE_RULES:
raise ValueError(rule)
if rule:
m = match(string, rule)
if not m:
raise ValueError('%r is not a valid %r.' % (string, rule))
if REGEX:
return _i2u(m.groupdict())
return _i2u(_iri_non_validating_re.match(string).groupdict())
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|
36,616 | rfc3987 | resolve | Resolves_ an `URI reference` relative to a `base` URI.
`Test cases <http://tools.ietf.org/html/rfc3986#section-5.4>`_::
>>> base = resolve.test_cases_base
>>> for relative, resolved in resolve.test_cases.items():
... assert resolve(base, relative) == resolved
If `return_parts` is True, returns a dict of named parts instead of
a string.
Examples::
>>> assert resolve('urn:rootless', '../../name') == 'urn:name'
>>> assert resolve('urn:root/less', '../../name') == 'urn:/name'
>>> assert resolve('http://a/b', 'http:g') == 'http:g'
>>> assert resolve('http://a/b', 'http:g', strict=False) == 'http://a/g'
.. _Resolves: http://tools.ietf.org/html/rfc3986#section-5.2
| def resolve(base, uriref, strict=True, return_parts=False):
"""Resolves_ an `URI reference` relative to a `base` URI.
`Test cases <http://tools.ietf.org/html/rfc3986#section-5.4>`_::
>>> base = resolve.test_cases_base
>>> for relative, resolved in resolve.test_cases.items():
... assert resolve(base, relative) == resolved
If `return_parts` is True, returns a dict of named parts instead of
a string.
Examples::
>>> assert resolve('urn:rootless', '../../name') == 'urn:name'
>>> assert resolve('urn:root/less', '../../name') == 'urn:/name'
>>> assert resolve('http://a/b', 'http:g') == 'http:g'
>>> assert resolve('http://a/b', 'http:g', strict=False) == 'http://a/g'
.. _Resolves: http://tools.ietf.org/html/rfc3986#section-5.2
"""
#base = normalize(base)
if isinstance(base, basestring):
B = parse(base, 'IRI')
else:
B = _i2u(dict(base))
if not B.get('scheme'):
raise ValueError('Expected an IRI (with scheme), not %r.' % base)
if isinstance(uriref, basestring):
R = parse(uriref, 'IRI_reference')
else:
R = _i2u(dict(uriref))
# _last_segment = get_compiled_pattern(r'(?<=^|/)%(segment)s$')
if R['scheme'] and (strict or R['scheme'] != B['scheme']):
T = R
else:
T = {}
T['scheme'] = B['scheme']
if R['authority'] is not None:
T['authority'] = R['authority']
T['path'] = R['path']
T['query'] = R['query']
else:
T['authority'] = B['authority']
if R['path']:
if R['path'][:1] == "/":
T['path'] = R['path']
elif B['authority'] is not None and not B['path']:
T['path'] = '/%s' % R['path']
else:
T['path'] = ''.join(B['path'].rpartition('/')[:2]) + R['path']
# _last_segment.sub(R['path'], B['path'])
T['query'] = R['query']
else:
T['path'] = B['path']
if R['query'] is not None:
T['query'] = R['query']
else:
T['query'] = B['query']
T['fragment'] = R['fragment']
T['path'] = _remove_dot_segments(T['path'])
if return_parts:
return T
else:
return compose(**T)
| (base, uriref, strict=True, return_parts=False) | [
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|
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