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def __setstate__(self, state):
'Necessary for making this object picklable'
if (not isinstance(state, dict)):
raise Exception('invalid pickle state')
if (('_categories' not in state) and ('_levels' in state)):
state['_categories'] = self.dtype.validate_categories(state.pop('_levels'))
if (('_codes' not in state) and ('labels' in state)):
state['_codes'] = coerce_indexer_dtype(state.pop('labels'), state['_categories'])
if ('_ordered' not in state):
if ('ordered' in state):
state['_ordered'] = state.pop('ordered')
else:
state['_ordered'] = False
if ('_dtype' not in state):
state['_dtype'] = CategoricalDtype(state['_categories'], state['_ordered'])
for (k, v) in compat.iteritems(state):
setattr(self, k, v) | -6,085,031,369,710,524,000 | Necessary for making this object picklable | pandas/core/arrays/categorical.py | __setstate__ | Adirio/pandas | python | def __setstate__(self, state):
if (not isinstance(state, dict)):
raise Exception('invalid pickle state')
if (('_categories' not in state) and ('_levels' in state)):
state['_categories'] = self.dtype.validate_categories(state.pop('_levels'))
if (('_codes' not in state) and ('labels' in state)):
state['_codes'] = coerce_indexer_dtype(state.pop('labels'), state['_categories'])
if ('_ordered' not in state):
if ('ordered' in state):
state['_ordered'] = state.pop('ordered')
else:
state['_ordered'] = False
if ('_dtype' not in state):
state['_dtype'] = CategoricalDtype(state['_categories'], state['_ordered'])
for (k, v) in compat.iteritems(state):
setattr(self, k, v) |
def memory_usage(self, deep=False):
'\n Memory usage of my values\n\n Parameters\n ----------\n deep : bool\n Introspect the data deeply, interrogate\n `object` dtypes for system-level memory consumption\n\n Returns\n -------\n bytes used\n\n Notes\n -----\n Memory usage does not include memory consumed by elements that\n are not components of the array if deep=False\n\n See Also\n --------\n numpy.ndarray.nbytes\n '
return (self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)) | -9,068,621,834,873,815,000 | Memory usage of my values
Parameters
----------
deep : bool
Introspect the data deeply, interrogate
`object` dtypes for system-level memory consumption
Returns
-------
bytes used
Notes
-----
Memory usage does not include memory consumed by elements that
are not components of the array if deep=False
See Also
--------
numpy.ndarray.nbytes | pandas/core/arrays/categorical.py | memory_usage | Adirio/pandas | python | def memory_usage(self, deep=False):
'\n Memory usage of my values\n\n Parameters\n ----------\n deep : bool\n Introspect the data deeply, interrogate\n `object` dtypes for system-level memory consumption\n\n Returns\n -------\n bytes used\n\n Notes\n -----\n Memory usage does not include memory consumed by elements that\n are not components of the array if deep=False\n\n See Also\n --------\n numpy.ndarray.nbytes\n '
return (self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)) |
def isna(self):
'\n Detect missing values\n\n Missing values (-1 in .codes) are detected.\n\n Returns\n -------\n a boolean array of whether my values are null\n\n See also\n --------\n isna : top-level isna\n isnull : alias of isna\n Categorical.notna : boolean inverse of Categorical.isna\n\n '
ret = (self._codes == (- 1))
return ret | -5,370,868,005,612,063,000 | Detect missing values
Missing values (-1 in .codes) are detected.
Returns
-------
a boolean array of whether my values are null
See also
--------
isna : top-level isna
isnull : alias of isna
Categorical.notna : boolean inverse of Categorical.isna | pandas/core/arrays/categorical.py | isna | Adirio/pandas | python | def isna(self):
'\n Detect missing values\n\n Missing values (-1 in .codes) are detected.\n\n Returns\n -------\n a boolean array of whether my values are null\n\n See also\n --------\n isna : top-level isna\n isnull : alias of isna\n Categorical.notna : boolean inverse of Categorical.isna\n\n '
ret = (self._codes == (- 1))
return ret |
def notna(self):
'\n Inverse of isna\n\n Both missing values (-1 in .codes) and NA as a category are detected as\n null.\n\n Returns\n -------\n a boolean array of whether my values are not null\n\n See also\n --------\n notna : top-level notna\n notnull : alias of notna\n Categorical.isna : boolean inverse of Categorical.notna\n\n '
return (~ self.isna()) | 6,422,727,367,209,304,000 | Inverse of isna
Both missing values (-1 in .codes) and NA as a category are detected as
null.
Returns
-------
a boolean array of whether my values are not null
See also
--------
notna : top-level notna
notnull : alias of notna
Categorical.isna : boolean inverse of Categorical.notna | pandas/core/arrays/categorical.py | notna | Adirio/pandas | python | def notna(self):
'\n Inverse of isna\n\n Both missing values (-1 in .codes) and NA as a category are detected as\n null.\n\n Returns\n -------\n a boolean array of whether my values are not null\n\n See also\n --------\n notna : top-level notna\n notnull : alias of notna\n Categorical.isna : boolean inverse of Categorical.notna\n\n '
return (~ self.isna()) |
def put(self, *args, **kwargs):
'\n Replace specific elements in the Categorical with given values.\n '
raise NotImplementedError("'put' is not yet implemented for Categorical") | 2,810,362,659,536,142,000 | Replace specific elements in the Categorical with given values. | pandas/core/arrays/categorical.py | put | Adirio/pandas | python | def put(self, *args, **kwargs):
'\n \n '
raise NotImplementedError("'put' is not yet implemented for Categorical") |
def dropna(self):
'\n Return the Categorical without null values.\n\n Missing values (-1 in .codes) are detected.\n\n Returns\n -------\n valid : Categorical\n '
result = self[self.notna()]
return result | 7,246,204,869,647,670,000 | Return the Categorical without null values.
Missing values (-1 in .codes) are detected.
Returns
-------
valid : Categorical | pandas/core/arrays/categorical.py | dropna | Adirio/pandas | python | def dropna(self):
'\n Return the Categorical without null values.\n\n Missing values (-1 in .codes) are detected.\n\n Returns\n -------\n valid : Categorical\n '
result = self[self.notna()]
return result |
def value_counts(self, dropna=True):
"\n Returns a Series containing counts of each category.\n\n Every category will have an entry, even those with a count of 0.\n\n Parameters\n ----------\n dropna : boolean, default True\n Don't include counts of NaN.\n\n Returns\n -------\n counts : Series\n\n See Also\n --------\n Series.value_counts\n\n "
from numpy import bincount
from pandas import Series, CategoricalIndex
(code, cat) = (self._codes, self.categories)
(ncat, mask) = (len(cat), (0 <= code))
(ix, clean) = (np.arange(ncat), mask.all())
if (dropna or clean):
obs = (code if clean else code[mask])
count = bincount(obs, minlength=(ncat or None))
else:
count = bincount(np.where(mask, code, ncat))
ix = np.append(ix, (- 1))
ix = self._constructor(ix, dtype=self.dtype, fastpath=True)
return Series(count, index=CategoricalIndex(ix), dtype='int64') | 4,811,169,102,183,675,000 | Returns a Series containing counts of each category.
Every category will have an entry, even those with a count of 0.
Parameters
----------
dropna : boolean, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
--------
Series.value_counts | pandas/core/arrays/categorical.py | value_counts | Adirio/pandas | python | def value_counts(self, dropna=True):
"\n Returns a Series containing counts of each category.\n\n Every category will have an entry, even those with a count of 0.\n\n Parameters\n ----------\n dropna : boolean, default True\n Don't include counts of NaN.\n\n Returns\n -------\n counts : Series\n\n See Also\n --------\n Series.value_counts\n\n "
from numpy import bincount
from pandas import Series, CategoricalIndex
(code, cat) = (self._codes, self.categories)
(ncat, mask) = (len(cat), (0 <= code))
(ix, clean) = (np.arange(ncat), mask.all())
if (dropna or clean):
obs = (code if clean else code[mask])
count = bincount(obs, minlength=(ncat or None))
else:
count = bincount(np.where(mask, code, ncat))
ix = np.append(ix, (- 1))
ix = self._constructor(ix, dtype=self.dtype, fastpath=True)
return Series(count, index=CategoricalIndex(ix), dtype='int64') |
def get_values(self):
' Return the values.\n\n For internal compatibility with pandas formatting.\n\n Returns\n -------\n values : numpy array\n A numpy array of the same dtype as categorical.categories.dtype or\n Index if datetime / periods\n '
if is_datetimelike(self.categories):
return self.categories.take(self._codes, fill_value=np.nan)
return np.array(self) | 8,280,021,067,267,941,000 | Return the values.
For internal compatibility with pandas formatting.
Returns
-------
values : numpy array
A numpy array of the same dtype as categorical.categories.dtype or
Index if datetime / periods | pandas/core/arrays/categorical.py | get_values | Adirio/pandas | python | def get_values(self):
' Return the values.\n\n For internal compatibility with pandas formatting.\n\n Returns\n -------\n values : numpy array\n A numpy array of the same dtype as categorical.categories.dtype or\n Index if datetime / periods\n '
if is_datetimelike(self.categories):
return self.categories.take(self._codes, fill_value=np.nan)
return np.array(self) |
def check_for_ordered(self, op):
' assert that we are ordered '
if (not self.ordered):
raise TypeError('Categorical is not ordered for operation {op}\nyou can use .as_ordered() to change the Categorical to an ordered one\n'.format(op=op)) | -2,233,153,919,381,657,000 | assert that we are ordered | pandas/core/arrays/categorical.py | check_for_ordered | Adirio/pandas | python | def check_for_ordered(self, op):
' '
if (not self.ordered):
raise TypeError('Categorical is not ordered for operation {op}\nyou can use .as_ordered() to change the Categorical to an ordered one\n'.format(op=op)) |
def argsort(self, *args, **kwargs):
"Return the indices that would sort the Categorical.\n\n Parameters\n ----------\n ascending : bool, default True\n Whether the indices should result in an ascending\n or descending sort.\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n Sorting algorithm.\n *args, **kwargs:\n passed through to :func:`numpy.argsort`.\n\n Returns\n -------\n argsorted : numpy array\n\n See also\n --------\n numpy.ndarray.argsort\n\n Notes\n -----\n While an ordering is applied to the category values, arg-sorting\n in this context refers more to organizing and grouping together\n based on matching category values. Thus, this function can be\n called on an unordered Categorical instance unlike the functions\n 'Categorical.min' and 'Categorical.max'.\n\n Examples\n --------\n >>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()\n array([2, 0, 1, 3])\n\n >>> cat = pd.Categorical(['b', 'b', 'a', 'c'],\n ... categories=['c', 'b', 'a'],\n ... ordered=True)\n >>> cat.argsort()\n array([3, 0, 1, 2])\n "
return super(Categorical, self).argsort(*args, **kwargs) | 4,805,162,541,346,104,000 | Return the indices that would sort the Categorical.
Parameters
----------
ascending : bool, default True
Whether the indices should result in an ascending
or descending sort.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
*args, **kwargs:
passed through to :func:`numpy.argsort`.
Returns
-------
argsorted : numpy array
See also
--------
numpy.ndarray.argsort
Notes
-----
While an ordering is applied to the category values, arg-sorting
in this context refers more to organizing and grouping together
based on matching category values. Thus, this function can be
called on an unordered Categorical instance unlike the functions
'Categorical.min' and 'Categorical.max'.
Examples
--------
>>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()
array([2, 0, 1, 3])
>>> cat = pd.Categorical(['b', 'b', 'a', 'c'],
... categories=['c', 'b', 'a'],
... ordered=True)
>>> cat.argsort()
array([3, 0, 1, 2]) | pandas/core/arrays/categorical.py | argsort | Adirio/pandas | python | def argsort(self, *args, **kwargs):
"Return the indices that would sort the Categorical.\n\n Parameters\n ----------\n ascending : bool, default True\n Whether the indices should result in an ascending\n or descending sort.\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n Sorting algorithm.\n *args, **kwargs:\n passed through to :func:`numpy.argsort`.\n\n Returns\n -------\n argsorted : numpy array\n\n See also\n --------\n numpy.ndarray.argsort\n\n Notes\n -----\n While an ordering is applied to the category values, arg-sorting\n in this context refers more to organizing and grouping together\n based on matching category values. Thus, this function can be\n called on an unordered Categorical instance unlike the functions\n 'Categorical.min' and 'Categorical.max'.\n\n Examples\n --------\n >>> pd.Categorical(['b', 'b', 'a', 'c']).argsort()\n array([2, 0, 1, 3])\n\n >>> cat = pd.Categorical(['b', 'b', 'a', 'c'],\n ... categories=['c', 'b', 'a'],\n ... ordered=True)\n >>> cat.argsort()\n array([3, 0, 1, 2])\n "
return super(Categorical, self).argsort(*args, **kwargs) |
def sort_values(self, inplace=False, ascending=True, na_position='last'):
" Sorts the Categorical by category value returning a new\n Categorical by default.\n\n While an ordering is applied to the category values, sorting in this\n context refers more to organizing and grouping together based on\n matching category values. Thus, this function can be called on an\n unordered Categorical instance unlike the functions 'Categorical.min'\n and 'Categorical.max'.\n\n Parameters\n ----------\n inplace : boolean, default False\n Do operation in place.\n ascending : boolean, default True\n Order ascending. Passing False orders descending. The\n ordering parameter provides the method by which the\n category values are organized.\n na_position : {'first', 'last'} (optional, default='last')\n 'first' puts NaNs at the beginning\n 'last' puts NaNs at the end\n\n Returns\n -------\n y : Categorical or None\n\n See Also\n --------\n Categorical.sort\n Series.sort_values\n\n Examples\n --------\n >>> c = pd.Categorical([1, 2, 2, 1, 5])\n >>> c\n [1, 2, 2, 1, 5]\n Categories (3, int64): [1, 2, 5]\n >>> c.sort_values()\n [1, 1, 2, 2, 5]\n Categories (3, int64): [1, 2, 5]\n >>> c.sort_values(ascending=False)\n [5, 2, 2, 1, 1]\n Categories (3, int64): [1, 2, 5]\n\n Inplace sorting can be done as well:\n\n >>> c.sort_values(inplace=True)\n >>> c\n [1, 1, 2, 2, 5]\n Categories (3, int64): [1, 2, 5]\n >>>\n >>> c = pd.Categorical([1, 2, 2, 1, 5])\n\n 'sort_values' behaviour with NaNs. Note that 'na_position'\n is independent of the 'ascending' parameter:\n\n >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])\n >>> c\n [NaN, 2.0, 2.0, NaN, 5.0]\n Categories (2, int64): [2, 5]\n >>> c.sort_values()\n [2.0, 2.0, 5.0, NaN, NaN]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(ascending=False)\n [5.0, 2.0, 2.0, NaN, NaN]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(na_position='first')\n [NaN, NaN, 2.0, 2.0, 5.0]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(ascending=False, na_position='first')\n [NaN, NaN, 5.0, 2.0, 2.0]\n Categories (2, int64): [2, 5]\n "
inplace = validate_bool_kwarg(inplace, 'inplace')
if (na_position not in ['last', 'first']):
msg = 'invalid na_position: {na_position!r}'
raise ValueError(msg.format(na_position=na_position))
codes = np.sort(self._codes)
if (not ascending):
codes = codes[::(- 1)]
na_mask = (codes == (- 1))
if na_mask.any():
n_nans = len(codes[na_mask])
if (na_position == 'first'):
new_codes = codes.copy()
new_codes[0:n_nans] = (- 1)
new_codes[n_nans:] = codes[(~ na_mask)]
codes = new_codes
elif (na_position == 'last'):
new_codes = codes.copy()
pos = (len(codes) - n_nans)
new_codes[0:pos] = codes[(~ na_mask)]
new_codes[pos:] = (- 1)
codes = new_codes
if inplace:
self._codes = codes
return
else:
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) | -4,179,080,878,714,503,700 | Sorts the Categorical by category value returning a new
Categorical by default.
While an ordering is applied to the category values, sorting in this
context refers more to organizing and grouping together based on
matching category values. Thus, this function can be called on an
unordered Categorical instance unlike the functions 'Categorical.min'
and 'Categorical.max'.
Parameters
----------
inplace : boolean, default False
Do operation in place.
ascending : boolean, default True
Order ascending. Passing False orders descending. The
ordering parameter provides the method by which the
category values are organized.
na_position : {'first', 'last'} (optional, default='last')
'first' puts NaNs at the beginning
'last' puts NaNs at the end
Returns
-------
y : Categorical or None
See Also
--------
Categorical.sort
Series.sort_values
Examples
--------
>>> c = pd.Categorical([1, 2, 2, 1, 5])
>>> c
[1, 2, 2, 1, 5]
Categories (3, int64): [1, 2, 5]
>>> c.sort_values()
[1, 1, 2, 2, 5]
Categories (3, int64): [1, 2, 5]
>>> c.sort_values(ascending=False)
[5, 2, 2, 1, 1]
Categories (3, int64): [1, 2, 5]
Inplace sorting can be done as well:
>>> c.sort_values(inplace=True)
>>> c
[1, 1, 2, 2, 5]
Categories (3, int64): [1, 2, 5]
>>>
>>> c = pd.Categorical([1, 2, 2, 1, 5])
'sort_values' behaviour with NaNs. Note that 'na_position'
is independent of the 'ascending' parameter:
>>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])
>>> c
[NaN, 2.0, 2.0, NaN, 5.0]
Categories (2, int64): [2, 5]
>>> c.sort_values()
[2.0, 2.0, 5.0, NaN, NaN]
Categories (2, int64): [2, 5]
>>> c.sort_values(ascending=False)
[5.0, 2.0, 2.0, NaN, NaN]
Categories (2, int64): [2, 5]
>>> c.sort_values(na_position='first')
[NaN, NaN, 2.0, 2.0, 5.0]
Categories (2, int64): [2, 5]
>>> c.sort_values(ascending=False, na_position='first')
[NaN, NaN, 5.0, 2.0, 2.0]
Categories (2, int64): [2, 5] | pandas/core/arrays/categorical.py | sort_values | Adirio/pandas | python | def sort_values(self, inplace=False, ascending=True, na_position='last'):
" Sorts the Categorical by category value returning a new\n Categorical by default.\n\n While an ordering is applied to the category values, sorting in this\n context refers more to organizing and grouping together based on\n matching category values. Thus, this function can be called on an\n unordered Categorical instance unlike the functions 'Categorical.min'\n and 'Categorical.max'.\n\n Parameters\n ----------\n inplace : boolean, default False\n Do operation in place.\n ascending : boolean, default True\n Order ascending. Passing False orders descending. The\n ordering parameter provides the method by which the\n category values are organized.\n na_position : {'first', 'last'} (optional, default='last')\n 'first' puts NaNs at the beginning\n 'last' puts NaNs at the end\n\n Returns\n -------\n y : Categorical or None\n\n See Also\n --------\n Categorical.sort\n Series.sort_values\n\n Examples\n --------\n >>> c = pd.Categorical([1, 2, 2, 1, 5])\n >>> c\n [1, 2, 2, 1, 5]\n Categories (3, int64): [1, 2, 5]\n >>> c.sort_values()\n [1, 1, 2, 2, 5]\n Categories (3, int64): [1, 2, 5]\n >>> c.sort_values(ascending=False)\n [5, 2, 2, 1, 1]\n Categories (3, int64): [1, 2, 5]\n\n Inplace sorting can be done as well:\n\n >>> c.sort_values(inplace=True)\n >>> c\n [1, 1, 2, 2, 5]\n Categories (3, int64): [1, 2, 5]\n >>>\n >>> c = pd.Categorical([1, 2, 2, 1, 5])\n\n 'sort_values' behaviour with NaNs. Note that 'na_position'\n is independent of the 'ascending' parameter:\n\n >>> c = pd.Categorical([np.nan, 2, 2, np.nan, 5])\n >>> c\n [NaN, 2.0, 2.0, NaN, 5.0]\n Categories (2, int64): [2, 5]\n >>> c.sort_values()\n [2.0, 2.0, 5.0, NaN, NaN]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(ascending=False)\n [5.0, 2.0, 2.0, NaN, NaN]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(na_position='first')\n [NaN, NaN, 2.0, 2.0, 5.0]\n Categories (2, int64): [2, 5]\n >>> c.sort_values(ascending=False, na_position='first')\n [NaN, NaN, 5.0, 2.0, 2.0]\n Categories (2, int64): [2, 5]\n "
inplace = validate_bool_kwarg(inplace, 'inplace')
if (na_position not in ['last', 'first']):
msg = 'invalid na_position: {na_position!r}'
raise ValueError(msg.format(na_position=na_position))
codes = np.sort(self._codes)
if (not ascending):
codes = codes[::(- 1)]
na_mask = (codes == (- 1))
if na_mask.any():
n_nans = len(codes[na_mask])
if (na_position == 'first'):
new_codes = codes.copy()
new_codes[0:n_nans] = (- 1)
new_codes[n_nans:] = codes[(~ na_mask)]
codes = new_codes
elif (na_position == 'last'):
new_codes = codes.copy()
pos = (len(codes) - n_nans)
new_codes[0:pos] = codes[(~ na_mask)]
new_codes[pos:] = (- 1)
codes = new_codes
if inplace:
self._codes = codes
return
else:
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) |
def _values_for_rank(self):
'\n For correctly ranking ordered categorical data. See GH#15420\n\n Ordered categorical data should be ranked on the basis of\n codes with -1 translated to NaN.\n\n Returns\n -------\n numpy array\n\n '
from pandas import Series
if self.ordered:
values = self.codes
mask = (values == (- 1))
if mask.any():
values = values.astype('float64')
values[mask] = np.nan
elif self.categories.is_numeric():
values = np.array(self)
else:
values = np.array(self.rename_categories(Series(self.categories).rank().values))
return values | 8,110,166,736,292,636,000 | For correctly ranking ordered categorical data. See GH#15420
Ordered categorical data should be ranked on the basis of
codes with -1 translated to NaN.
Returns
-------
numpy array | pandas/core/arrays/categorical.py | _values_for_rank | Adirio/pandas | python | def _values_for_rank(self):
'\n For correctly ranking ordered categorical data. See GH#15420\n\n Ordered categorical data should be ranked on the basis of\n codes with -1 translated to NaN.\n\n Returns\n -------\n numpy array\n\n '
from pandas import Series
if self.ordered:
values = self.codes
mask = (values == (- 1))
if mask.any():
values = values.astype('float64')
values[mask] = np.nan
elif self.categories.is_numeric():
values = np.array(self)
else:
values = np.array(self.rename_categories(Series(self.categories).rank().values))
return values |
def ravel(self, order='C'):
' Return a flattened (numpy) array.\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n raveled : numpy array\n '
return np.array(self) | 2,510,775,633,478,743,600 | Return a flattened (numpy) array.
For internal compatibility with numpy arrays.
Returns
-------
raveled : numpy array | pandas/core/arrays/categorical.py | ravel | Adirio/pandas | python | def ravel(self, order='C'):
' Return a flattened (numpy) array.\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n raveled : numpy array\n '
return np.array(self) |
def view(self):
'Return a view of myself.\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n view : Categorical\n Returns `self`!\n '
return self | 7,402,810,746,540,229,000 | Return a view of myself.
For internal compatibility with numpy arrays.
Returns
-------
view : Categorical
Returns `self`! | pandas/core/arrays/categorical.py | view | Adirio/pandas | python | def view(self):
'Return a view of myself.\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n view : Categorical\n Returns `self`!\n '
return self |
def to_dense(self):
"Return my 'dense' representation\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n dense : array\n "
return np.asarray(self) | -296,838,486,681,540,900 | Return my 'dense' representation
For internal compatibility with numpy arrays.
Returns
-------
dense : array | pandas/core/arrays/categorical.py | to_dense | Adirio/pandas | python | def to_dense(self):
"Return my 'dense' representation\n\n For internal compatibility with numpy arrays.\n\n Returns\n -------\n dense : array\n "
return np.asarray(self) |
@deprecate_kwarg(old_arg_name='fill_value', new_arg_name='value')
def fillna(self, value=None, method=None, limit=None):
" Fill NA/NaN values using the specified method.\n\n Parameters\n ----------\n value : scalar, dict, Series\n If a scalar value is passed it is used to fill all missing values.\n Alternatively, a Series or dict can be used to fill in different\n values for each index. The value should not be a list. The\n value(s) passed should either be in the categories or should be\n NaN.\n method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n Method to use for filling holes in reindexed Series\n pad / ffill: propagate last valid observation forward to next valid\n backfill / bfill: use NEXT valid observation to fill gap\n limit : int, default None\n (Not implemented yet for Categorical!)\n If method is specified, this is the maximum number of consecutive\n NaN values to forward/backward fill. In other words, if there is\n a gap with more than this number of consecutive NaNs, it will only\n be partially filled. If method is not specified, this is the\n maximum number of entries along the entire axis where NaNs will be\n filled.\n\n Returns\n -------\n filled : Categorical with NA/NaN filled\n "
(value, method) = validate_fillna_kwargs(value, method, validate_scalar_dict_value=False)
if (value is None):
value = np.nan
if (limit is not None):
raise NotImplementedError('specifying a limit for fillna has not been implemented yet')
codes = self._codes
if (method is not None):
values = self.to_dense().reshape((- 1), len(self))
values = interpolate_2d(values, method, 0, None, value).astype(self.categories.dtype)[0]
codes = _get_codes_for_values(values, self.categories)
elif isinstance(value, ABCSeries):
if (not value[(~ value.isin(self.categories))].isna().all()):
raise ValueError('fill value must be in categories')
values_codes = _get_codes_for_values(value, self.categories)
indexer = np.where((values_codes != (- 1)))
codes[indexer] = values_codes[(values_codes != (- 1))]
elif is_hashable(value):
if ((not isna(value)) and (value not in self.categories)):
raise ValueError('fill value must be in categories')
mask = (codes == (- 1))
if mask.any():
codes = codes.copy()
if isna(value):
codes[mask] = (- 1)
else:
codes[mask] = self.categories.get_loc(value)
else:
raise TypeError('"value" parameter must be a scalar, dict or Series, but you passed a "{0}"'.format(type(value).__name__))
return self._constructor(codes, dtype=self.dtype, fastpath=True) | -1,140,899,440,471,338,100 | Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, dict, Series
If a scalar value is passed it is used to fill all missing values.
Alternatively, a Series or dict can be used to fill in different
values for each index. The value should not be a list. The
value(s) passed should either be in the categories or should be
NaN.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use NEXT valid observation to fill gap
limit : int, default None
(Not implemented yet for Categorical!)
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled.
Returns
-------
filled : Categorical with NA/NaN filled | pandas/core/arrays/categorical.py | fillna | Adirio/pandas | python | @deprecate_kwarg(old_arg_name='fill_value', new_arg_name='value')
def fillna(self, value=None, method=None, limit=None):
" Fill NA/NaN values using the specified method.\n\n Parameters\n ----------\n value : scalar, dict, Series\n If a scalar value is passed it is used to fill all missing values.\n Alternatively, a Series or dict can be used to fill in different\n values for each index. The value should not be a list. The\n value(s) passed should either be in the categories or should be\n NaN.\n method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n Method to use for filling holes in reindexed Series\n pad / ffill: propagate last valid observation forward to next valid\n backfill / bfill: use NEXT valid observation to fill gap\n limit : int, default None\n (Not implemented yet for Categorical!)\n If method is specified, this is the maximum number of consecutive\n NaN values to forward/backward fill. In other words, if there is\n a gap with more than this number of consecutive NaNs, it will only\n be partially filled. If method is not specified, this is the\n maximum number of entries along the entire axis where NaNs will be\n filled.\n\n Returns\n -------\n filled : Categorical with NA/NaN filled\n "
(value, method) = validate_fillna_kwargs(value, method, validate_scalar_dict_value=False)
if (value is None):
value = np.nan
if (limit is not None):
raise NotImplementedError('specifying a limit for fillna has not been implemented yet')
codes = self._codes
if (method is not None):
values = self.to_dense().reshape((- 1), len(self))
values = interpolate_2d(values, method, 0, None, value).astype(self.categories.dtype)[0]
codes = _get_codes_for_values(values, self.categories)
elif isinstance(value, ABCSeries):
if (not value[(~ value.isin(self.categories))].isna().all()):
raise ValueError('fill value must be in categories')
values_codes = _get_codes_for_values(value, self.categories)
indexer = np.where((values_codes != (- 1)))
codes[indexer] = values_codes[(values_codes != (- 1))]
elif is_hashable(value):
if ((not isna(value)) and (value not in self.categories)):
raise ValueError('fill value must be in categories')
mask = (codes == (- 1))
if mask.any():
codes = codes.copy()
if isna(value):
codes[mask] = (- 1)
else:
codes[mask] = self.categories.get_loc(value)
else:
raise TypeError('"value" parameter must be a scalar, dict or Series, but you passed a "{0}"'.format(type(value).__name__))
return self._constructor(codes, dtype=self.dtype, fastpath=True) |
def take_nd(self, indexer, allow_fill=None, fill_value=None):
'\n Take elements from the Categorical.\n\n Parameters\n ----------\n indexer : sequence of integers\n allow_fill : bool, default None.\n How to handle negative values in `indexer`.\n\n * False: negative values in `indices` indicate positional indices\n from the right. This is similar to\n :func:`numpy.take`.\n\n * True: negative values in `indices` indicate missing values\n (the default). These values are set to `fill_value`. Any other\n other negative values raise a ``ValueError``.\n\n .. versionchanged:: 0.23.0\n\n Deprecated the default value of `allow_fill`. The deprecated\n default is ``True``. In the future, this will change to\n ``False``.\n\n Returns\n -------\n Categorical\n This Categorical will have the same categories and ordered as\n `self`.\n '
indexer = np.asarray(indexer, dtype=np.intp)
if (allow_fill is None):
if (indexer < 0).any():
warn(_take_msg, FutureWarning, stacklevel=2)
allow_fill = True
if isna(fill_value):
fill_value = (- 1)
codes = take(self._codes, indexer, allow_fill=allow_fill, fill_value=fill_value)
result = self._constructor(codes, dtype=self.dtype, fastpath=True)
return result | -7,154,992,052,181,375,000 | Take elements from the Categorical.
Parameters
----------
indexer : sequence of integers
allow_fill : bool, default None.
How to handle negative values in `indexer`.
* False: negative values in `indices` indicate positional indices
from the right. This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate missing values
(the default). These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
.. versionchanged:: 0.23.0
Deprecated the default value of `allow_fill`. The deprecated
default is ``True``. In the future, this will change to
``False``.
Returns
-------
Categorical
This Categorical will have the same categories and ordered as
`self`. | pandas/core/arrays/categorical.py | take_nd | Adirio/pandas | python | def take_nd(self, indexer, allow_fill=None, fill_value=None):
'\n Take elements from the Categorical.\n\n Parameters\n ----------\n indexer : sequence of integers\n allow_fill : bool, default None.\n How to handle negative values in `indexer`.\n\n * False: negative values in `indices` indicate positional indices\n from the right. This is similar to\n :func:`numpy.take`.\n\n * True: negative values in `indices` indicate missing values\n (the default). These values are set to `fill_value`. Any other\n other negative values raise a ``ValueError``.\n\n .. versionchanged:: 0.23.0\n\n Deprecated the default value of `allow_fill`. The deprecated\n default is ``True``. In the future, this will change to\n ``False``.\n\n Returns\n -------\n Categorical\n This Categorical will have the same categories and ordered as\n `self`.\n '
indexer = np.asarray(indexer, dtype=np.intp)
if (allow_fill is None):
if (indexer < 0).any():
warn(_take_msg, FutureWarning, stacklevel=2)
allow_fill = True
if isna(fill_value):
fill_value = (- 1)
codes = take(self._codes, indexer, allow_fill=allow_fill, fill_value=fill_value)
result = self._constructor(codes, dtype=self.dtype, fastpath=True)
return result |
def _slice(self, slicer):
' Return a slice of myself.\n\n For internal compatibility with numpy arrays.\n '
if (isinstance(slicer, tuple) and (len(slicer) == 2)):
if (not com.is_null_slice(slicer[0])):
raise AssertionError('invalid slicing for a 1-ndim categorical')
slicer = slicer[1]
codes = self._codes[slicer]
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) | -1,092,373,934,511,431,700 | Return a slice of myself.
For internal compatibility with numpy arrays. | pandas/core/arrays/categorical.py | _slice | Adirio/pandas | python | def _slice(self, slicer):
' Return a slice of myself.\n\n For internal compatibility with numpy arrays.\n '
if (isinstance(slicer, tuple) and (len(slicer) == 2)):
if (not com.is_null_slice(slicer[0])):
raise AssertionError('invalid slicing for a 1-ndim categorical')
slicer = slicer[1]
codes = self._codes[slicer]
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) |
def __len__(self):
'The length of this Categorical.'
return len(self._codes) | 8,220,366,926,502,292,000 | The length of this Categorical. | pandas/core/arrays/categorical.py | __len__ | Adirio/pandas | python | def __len__(self):
return len(self._codes) |
def __iter__(self):
'Returns an Iterator over the values of this Categorical.'
return iter(self.get_values().tolist()) | -8,865,596,715,025,023,000 | Returns an Iterator over the values of this Categorical. | pandas/core/arrays/categorical.py | __iter__ | Adirio/pandas | python | def __iter__(self):
return iter(self.get_values().tolist()) |
def __contains__(self, key):
'Returns True if `key` is in this Categorical.'
if isna(key):
return self.isna().any()
return contains(self, key, container=self._codes) | 8,849,127,086,134,680,000 | Returns True if `key` is in this Categorical. | pandas/core/arrays/categorical.py | __contains__ | Adirio/pandas | python | def __contains__(self, key):
if isna(key):
return self.isna().any()
return contains(self, key, container=self._codes) |
def _tidy_repr(self, max_vals=10, footer=True):
' a short repr displaying only max_vals and an optional (but default\n footer)\n '
num = (max_vals // 2)
head = self[:num]._get_repr(length=False, footer=False)
tail = self[(- (max_vals - num)):]._get_repr(length=False, footer=False)
result = u('{head}, ..., {tail}').format(head=head[:(- 1)], tail=tail[1:])
if footer:
result = u('{result}\n{footer}').format(result=result, footer=self._repr_footer())
return compat.text_type(result) | -7,886,453,035,888,673,000 | a short repr displaying only max_vals and an optional (but default
footer) | pandas/core/arrays/categorical.py | _tidy_repr | Adirio/pandas | python | def _tidy_repr(self, max_vals=10, footer=True):
' a short repr displaying only max_vals and an optional (but default\n footer)\n '
num = (max_vals // 2)
head = self[:num]._get_repr(length=False, footer=False)
tail = self[(- (max_vals - num)):]._get_repr(length=False, footer=False)
result = u('{head}, ..., {tail}').format(head=head[:(- 1)], tail=tail[1:])
if footer:
result = u('{result}\n{footer}').format(result=result, footer=self._repr_footer())
return compat.text_type(result) |
def _repr_categories(self):
' return the base repr for the categories '
max_categories = (10 if (get_option('display.max_categories') == 0) else get_option('display.max_categories'))
from pandas.io.formats import format as fmt
if (len(self.categories) > max_categories):
num = (max_categories // 2)
head = fmt.format_array(self.categories[:num], None)
tail = fmt.format_array(self.categories[(- num):], None)
category_strs = ((head + ['...']) + tail)
else:
category_strs = fmt.format_array(self.categories, None)
category_strs = [x.strip() for x in category_strs]
return category_strs | 2,356,138,557,134,862,300 | return the base repr for the categories | pandas/core/arrays/categorical.py | _repr_categories | Adirio/pandas | python | def _repr_categories(self):
' '
max_categories = (10 if (get_option('display.max_categories') == 0) else get_option('display.max_categories'))
from pandas.io.formats import format as fmt
if (len(self.categories) > max_categories):
num = (max_categories // 2)
head = fmt.format_array(self.categories[:num], None)
tail = fmt.format_array(self.categories[(- num):], None)
category_strs = ((head + ['...']) + tail)
else:
category_strs = fmt.format_array(self.categories, None)
category_strs = [x.strip() for x in category_strs]
return category_strs |
def _repr_categories_info(self):
' Returns a string representation of the footer.'
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str', str(self.categories.dtype))
levheader = 'Categories ({length}, {dtype}): '.format(length=len(self.categories), dtype=dtype)
(width, height) = get_terminal_size()
max_width = (get_option('display.width') or width)
if console.in_ipython_frontend():
max_width = 0
levstring = ''
start = True
cur_col_len = len(levheader)
(sep_len, sep) = ((3, ' < ') if self.ordered else (2, ', '))
linesep = (sep.rstrip() + '\n')
for val in category_strs:
if ((max_width != 0) and (((cur_col_len + sep_len) + len(val)) > max_width)):
levstring += (linesep + (' ' * (len(levheader) + 1)))
cur_col_len = (len(levheader) + 1)
elif (not start):
levstring += sep
cur_col_len += len(val)
levstring += val
start = False
return (((levheader + '[') + levstring.replace(' < ... < ', ' ... ')) + ']') | -1,056,543,306,580,598,300 | Returns a string representation of the footer. | pandas/core/arrays/categorical.py | _repr_categories_info | Adirio/pandas | python | def _repr_categories_info(self):
' '
category_strs = self._repr_categories()
dtype = getattr(self.categories, 'dtype_str', str(self.categories.dtype))
levheader = 'Categories ({length}, {dtype}): '.format(length=len(self.categories), dtype=dtype)
(width, height) = get_terminal_size()
max_width = (get_option('display.width') or width)
if console.in_ipython_frontend():
max_width = 0
levstring =
start = True
cur_col_len = len(levheader)
(sep_len, sep) = ((3, ' < ') if self.ordered else (2, ', '))
linesep = (sep.rstrip() + '\n')
for val in category_strs:
if ((max_width != 0) and (((cur_col_len + sep_len) + len(val)) > max_width)):
levstring += (linesep + (' ' * (len(levheader) + 1)))
cur_col_len = (len(levheader) + 1)
elif (not start):
levstring += sep
cur_col_len += len(val)
levstring += val
start = False
return (((levheader + '[') + levstring.replace(' < ... < ', ' ... ')) + ']') |
def __unicode__(self):
' Unicode representation. '
_maxlen = 10
if (len(self._codes) > _maxlen):
result = self._tidy_repr(_maxlen)
elif (len(self._codes) > 0):
result = self._get_repr(length=(len(self) > _maxlen))
else:
msg = self._get_repr(length=False, footer=True).replace('\n', ', ')
result = '[], {repr_msg}'.format(repr_msg=msg)
return result | -2,285,762,338,194,706,700 | Unicode representation. | pandas/core/arrays/categorical.py | __unicode__ | Adirio/pandas | python | def __unicode__(self):
' '
_maxlen = 10
if (len(self._codes) > _maxlen):
result = self._tidy_repr(_maxlen)
elif (len(self._codes) > 0):
result = self._get_repr(length=(len(self) > _maxlen))
else:
msg = self._get_repr(length=False, footer=True).replace('\n', ', ')
result = '[], {repr_msg}'.format(repr_msg=msg)
return result |
def _maybe_coerce_indexer(self, indexer):
' return an indexer coerced to the codes dtype '
if (isinstance(indexer, np.ndarray) and (indexer.dtype.kind == 'i')):
indexer = indexer.astype(self._codes.dtype)
return indexer | -8,060,042,278,688,720,000 | return an indexer coerced to the codes dtype | pandas/core/arrays/categorical.py | _maybe_coerce_indexer | Adirio/pandas | python | def _maybe_coerce_indexer(self, indexer):
' '
if (isinstance(indexer, np.ndarray) and (indexer.dtype.kind == 'i')):
indexer = indexer.astype(self._codes.dtype)
return indexer |
def __getitem__(self, key):
' Return an item. '
if isinstance(key, (int, np.integer)):
i = self._codes[key]
if (i == (- 1)):
return np.nan
else:
return self.categories[i]
else:
return self._constructor(values=self._codes[key], dtype=self.dtype, fastpath=True) | 7,442,828,030,867,244,000 | Return an item. | pandas/core/arrays/categorical.py | __getitem__ | Adirio/pandas | python | def __getitem__(self, key):
' '
if isinstance(key, (int, np.integer)):
i = self._codes[key]
if (i == (- 1)):
return np.nan
else:
return self.categories[i]
else:
return self._constructor(values=self._codes[key], dtype=self.dtype, fastpath=True) |
def __setitem__(self, key, value):
' Item assignment.\n\n\n Raises\n ------\n ValueError\n If (one or more) Value is not in categories or if a assigned\n `Categorical` does not have the same categories\n '
if isinstance(value, Categorical):
if (not value.categories.equals(self.categories)):
raise ValueError('Cannot set a Categorical with another, without identical categories')
rvalue = (value if is_list_like(value) else [value])
from pandas import Index
to_add = Index(rvalue).difference(self.categories)
if (len(to_add) and (not isna(to_add).all())):
raise ValueError('Cannot setitem on a Categorical with a new category, set the categories first')
if isinstance(key, (int, np.integer)):
pass
elif isinstance(key, tuple):
if (len(key) == 2):
if (not com.is_null_slice(key[0])):
raise AssertionError('invalid slicing for a 1-ndim categorical')
key = key[1]
elif (len(key) == 1):
key = key[0]
else:
raise AssertionError('invalid slicing for a 1-ndim categorical')
elif isinstance(key, slice):
pass
else:
key = np.asarray(key)
lindexer = self.categories.get_indexer(rvalue)
lindexer = self._maybe_coerce_indexer(lindexer)
self._codes[key] = lindexer | 4,775,883,318,009,241,000 | Item assignment.
Raises
------
ValueError
If (one or more) Value is not in categories or if a assigned
`Categorical` does not have the same categories | pandas/core/arrays/categorical.py | __setitem__ | Adirio/pandas | python | def __setitem__(self, key, value):
' Item assignment.\n\n\n Raises\n ------\n ValueError\n If (one or more) Value is not in categories or if a assigned\n `Categorical` does not have the same categories\n '
if isinstance(value, Categorical):
if (not value.categories.equals(self.categories)):
raise ValueError('Cannot set a Categorical with another, without identical categories')
rvalue = (value if is_list_like(value) else [value])
from pandas import Index
to_add = Index(rvalue).difference(self.categories)
if (len(to_add) and (not isna(to_add).all())):
raise ValueError('Cannot setitem on a Categorical with a new category, set the categories first')
if isinstance(key, (int, np.integer)):
pass
elif isinstance(key, tuple):
if (len(key) == 2):
if (not com.is_null_slice(key[0])):
raise AssertionError('invalid slicing for a 1-ndim categorical')
key = key[1]
elif (len(key) == 1):
key = key[0]
else:
raise AssertionError('invalid slicing for a 1-ndim categorical')
elif isinstance(key, slice):
pass
else:
key = np.asarray(key)
lindexer = self.categories.get_indexer(rvalue)
lindexer = self._maybe_coerce_indexer(lindexer)
self._codes[key] = lindexer |
def _reverse_indexer(self):
"\n Compute the inverse of a categorical, returning\n a dict of categories -> indexers.\n\n *This is an internal function*\n\n Returns\n -------\n dict of categories -> indexers\n\n Example\n -------\n In [1]: c = pd.Categorical(list('aabca'))\n\n In [2]: c\n Out[2]:\n [a, a, b, c, a]\n Categories (3, object): [a, b, c]\n\n In [3]: c.categories\n Out[3]: Index([u'a', u'b', u'c'], dtype='object')\n\n In [4]: c.codes\n Out[4]: array([0, 0, 1, 2, 0], dtype=int8)\n\n In [5]: c._reverse_indexer()\n Out[5]: {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}\n\n "
categories = self.categories
(r, counts) = libalgos.groupsort_indexer(self.codes.astype('int64'), categories.size)
counts = counts.cumsum()
result = [r[counts[indexer]:counts[(indexer + 1)]] for indexer in range((len(counts) - 1))]
result = dict(zip(categories, result))
return result | -6,168,884,411,218,855,000 | Compute the inverse of a categorical, returning
a dict of categories -> indexers.
*This is an internal function*
Returns
-------
dict of categories -> indexers
Example
-------
In [1]: c = pd.Categorical(list('aabca'))
In [2]: c
Out[2]:
[a, a, b, c, a]
Categories (3, object): [a, b, c]
In [3]: c.categories
Out[3]: Index([u'a', u'b', u'c'], dtype='object')
In [4]: c.codes
Out[4]: array([0, 0, 1, 2, 0], dtype=int8)
In [5]: c._reverse_indexer()
Out[5]: {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])} | pandas/core/arrays/categorical.py | _reverse_indexer | Adirio/pandas | python | def _reverse_indexer(self):
"\n Compute the inverse of a categorical, returning\n a dict of categories -> indexers.\n\n *This is an internal function*\n\n Returns\n -------\n dict of categories -> indexers\n\n Example\n -------\n In [1]: c = pd.Categorical(list('aabca'))\n\n In [2]: c\n Out[2]:\n [a, a, b, c, a]\n Categories (3, object): [a, b, c]\n\n In [3]: c.categories\n Out[3]: Index([u'a', u'b', u'c'], dtype='object')\n\n In [4]: c.codes\n Out[4]: array([0, 0, 1, 2, 0], dtype=int8)\n\n In [5]: c._reverse_indexer()\n Out[5]: {'a': array([0, 1, 4]), 'b': array([2]), 'c': array([3])}\n\n "
categories = self.categories
(r, counts) = libalgos.groupsort_indexer(self.codes.astype('int64'), categories.size)
counts = counts.cumsum()
result = [r[counts[indexer]:counts[(indexer + 1)]] for indexer in range((len(counts) - 1))]
result = dict(zip(categories, result))
return result |
def min(self, numeric_only=None, **kwargs):
' The minimum value of the object.\n\n Only ordered `Categoricals` have a minimum!\n\n Raises\n ------\n TypeError\n If the `Categorical` is not `ordered`.\n\n Returns\n -------\n min : the minimum of this `Categorical`\n '
self.check_for_ordered('min')
if numeric_only:
good = (self._codes != (- 1))
pointer = self._codes[good].min(**kwargs)
else:
pointer = self._codes.min(**kwargs)
if (pointer == (- 1)):
return np.nan
else:
return self.categories[pointer] | 3,181,977,709,918,372,000 | The minimum value of the object.
Only ordered `Categoricals` have a minimum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
min : the minimum of this `Categorical` | pandas/core/arrays/categorical.py | min | Adirio/pandas | python | def min(self, numeric_only=None, **kwargs):
' The minimum value of the object.\n\n Only ordered `Categoricals` have a minimum!\n\n Raises\n ------\n TypeError\n If the `Categorical` is not `ordered`.\n\n Returns\n -------\n min : the minimum of this `Categorical`\n '
self.check_for_ordered('min')
if numeric_only:
good = (self._codes != (- 1))
pointer = self._codes[good].min(**kwargs)
else:
pointer = self._codes.min(**kwargs)
if (pointer == (- 1)):
return np.nan
else:
return self.categories[pointer] |
def max(self, numeric_only=None, **kwargs):
' The maximum value of the object.\n\n Only ordered `Categoricals` have a maximum!\n\n Raises\n ------\n TypeError\n If the `Categorical` is not `ordered`.\n\n Returns\n -------\n max : the maximum of this `Categorical`\n '
self.check_for_ordered('max')
if numeric_only:
good = (self._codes != (- 1))
pointer = self._codes[good].max(**kwargs)
else:
pointer = self._codes.max(**kwargs)
if (pointer == (- 1)):
return np.nan
else:
return self.categories[pointer] | 2,410,826,779,364,321,000 | The maximum value of the object.
Only ordered `Categoricals` have a maximum!
Raises
------
TypeError
If the `Categorical` is not `ordered`.
Returns
-------
max : the maximum of this `Categorical` | pandas/core/arrays/categorical.py | max | Adirio/pandas | python | def max(self, numeric_only=None, **kwargs):
' The maximum value of the object.\n\n Only ordered `Categoricals` have a maximum!\n\n Raises\n ------\n TypeError\n If the `Categorical` is not `ordered`.\n\n Returns\n -------\n max : the maximum of this `Categorical`\n '
self.check_for_ordered('max')
if numeric_only:
good = (self._codes != (- 1))
pointer = self._codes[good].max(**kwargs)
else:
pointer = self._codes.max(**kwargs)
if (pointer == (- 1)):
return np.nan
else:
return self.categories[pointer] |
def mode(self, dropna=True):
"\n Returns the mode(s) of the Categorical.\n\n Always returns `Categorical` even if only one value.\n\n Parameters\n ----------\n dropna : boolean, default True\n Don't consider counts of NaN/NaT.\n\n .. versionadded:: 0.24.0\n\n Returns\n -------\n modes : `Categorical` (sorted)\n "
import pandas._libs.hashtable as htable
codes = self._codes
if dropna:
good = (self._codes != (- 1))
codes = self._codes[good]
codes = sorted(htable.mode_int64(ensure_int64(codes), dropna))
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) | 2,182,157,549,901,241,900 | Returns the mode(s) of the Categorical.
Always returns `Categorical` even if only one value.
Parameters
----------
dropna : boolean, default True
Don't consider counts of NaN/NaT.
.. versionadded:: 0.24.0
Returns
-------
modes : `Categorical` (sorted) | pandas/core/arrays/categorical.py | mode | Adirio/pandas | python | def mode(self, dropna=True):
"\n Returns the mode(s) of the Categorical.\n\n Always returns `Categorical` even if only one value.\n\n Parameters\n ----------\n dropna : boolean, default True\n Don't consider counts of NaN/NaT.\n\n .. versionadded:: 0.24.0\n\n Returns\n -------\n modes : `Categorical` (sorted)\n "
import pandas._libs.hashtable as htable
codes = self._codes
if dropna:
good = (self._codes != (- 1))
codes = self._codes[good]
codes = sorted(htable.mode_int64(ensure_int64(codes), dropna))
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) |
def unique(self):
"\n Return the ``Categorical`` which ``categories`` and ``codes`` are\n unique. Unused categories are NOT returned.\n\n - unordered category: values and categories are sorted by appearance\n order.\n - ordered category: values are sorted by appearance order, categories\n keeps existing order.\n\n Returns\n -------\n unique values : ``Categorical``\n\n Examples\n --------\n An unordered Categorical will return categories in the\n order of appearance.\n\n >>> pd.Categorical(list('baabc'))\n [b, a, c]\n Categories (3, object): [b, a, c]\n\n >>> pd.Categorical(list('baabc'), categories=list('abc'))\n [b, a, c]\n Categories (3, object): [b, a, c]\n\n An ordered Categorical preserves the category ordering.\n\n >>> pd.Categorical(list('baabc'),\n ... categories=list('abc'),\n ... ordered=True)\n [b, a, c]\n Categories (3, object): [a < b < c]\n\n See Also\n --------\n unique\n CategoricalIndex.unique\n Series.unique\n\n "
unique_codes = unique1d(self.codes)
cat = self.copy()
cat._codes = unique_codes
take_codes = unique_codes[(unique_codes != (- 1))]
if self.ordered:
take_codes = np.sort(take_codes)
return cat.set_categories(cat.categories.take(take_codes)) | 1,525,540,672,474,470,100 | Return the ``Categorical`` which ``categories`` and ``codes`` are
unique. Unused categories are NOT returned.
- unordered category: values and categories are sorted by appearance
order.
- ordered category: values are sorted by appearance order, categories
keeps existing order.
Returns
-------
unique values : ``Categorical``
Examples
--------
An unordered Categorical will return categories in the
order of appearance.
>>> pd.Categorical(list('baabc'))
[b, a, c]
Categories (3, object): [b, a, c]
>>> pd.Categorical(list('baabc'), categories=list('abc'))
[b, a, c]
Categories (3, object): [b, a, c]
An ordered Categorical preserves the category ordering.
>>> pd.Categorical(list('baabc'),
... categories=list('abc'),
... ordered=True)
[b, a, c]
Categories (3, object): [a < b < c]
See Also
--------
unique
CategoricalIndex.unique
Series.unique | pandas/core/arrays/categorical.py | unique | Adirio/pandas | python | def unique(self):
"\n Return the ``Categorical`` which ``categories`` and ``codes`` are\n unique. Unused categories are NOT returned.\n\n - unordered category: values and categories are sorted by appearance\n order.\n - ordered category: values are sorted by appearance order, categories\n keeps existing order.\n\n Returns\n -------\n unique values : ``Categorical``\n\n Examples\n --------\n An unordered Categorical will return categories in the\n order of appearance.\n\n >>> pd.Categorical(list('baabc'))\n [b, a, c]\n Categories (3, object): [b, a, c]\n\n >>> pd.Categorical(list('baabc'), categories=list('abc'))\n [b, a, c]\n Categories (3, object): [b, a, c]\n\n An ordered Categorical preserves the category ordering.\n\n >>> pd.Categorical(list('baabc'),\n ... categories=list('abc'),\n ... ordered=True)\n [b, a, c]\n Categories (3, object): [a < b < c]\n\n See Also\n --------\n unique\n CategoricalIndex.unique\n Series.unique\n\n "
unique_codes = unique1d(self.codes)
cat = self.copy()
cat._codes = unique_codes
take_codes = unique_codes[(unique_codes != (- 1))]
if self.ordered:
take_codes = np.sort(take_codes)
return cat.set_categories(cat.categories.take(take_codes)) |
def equals(self, other):
'\n Returns True if categorical arrays are equal.\n\n Parameters\n ----------\n other : `Categorical`\n\n Returns\n -------\n are_equal : boolean\n '
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
other_codes = other._codes
else:
other_codes = _recode_for_categories(other.codes, other.categories, self.categories)
return np.array_equal(self._codes, other_codes)
return False | 2,929,335,446,468,426,000 | Returns True if categorical arrays are equal.
Parameters
----------
other : `Categorical`
Returns
-------
are_equal : boolean | pandas/core/arrays/categorical.py | equals | Adirio/pandas | python | def equals(self, other):
'\n Returns True if categorical arrays are equal.\n\n Parameters\n ----------\n other : `Categorical`\n\n Returns\n -------\n are_equal : boolean\n '
if self.is_dtype_equal(other):
if self.categories.equals(other.categories):
other_codes = other._codes
else:
other_codes = _recode_for_categories(other.codes, other.categories, self.categories)
return np.array_equal(self._codes, other_codes)
return False |
def is_dtype_equal(self, other):
'\n Returns True if categoricals are the same dtype\n same categories, and same ordered\n\n Parameters\n ----------\n other : Categorical\n\n Returns\n -------\n are_equal : boolean\n '
try:
return (hash(self.dtype) == hash(other.dtype))
except (AttributeError, TypeError):
return False | -2,454,401,285,141,445,600 | Returns True if categoricals are the same dtype
same categories, and same ordered
Parameters
----------
other : Categorical
Returns
-------
are_equal : boolean | pandas/core/arrays/categorical.py | is_dtype_equal | Adirio/pandas | python | def is_dtype_equal(self, other):
'\n Returns True if categoricals are the same dtype\n same categories, and same ordered\n\n Parameters\n ----------\n other : Categorical\n\n Returns\n -------\n are_equal : boolean\n '
try:
return (hash(self.dtype) == hash(other.dtype))
except (AttributeError, TypeError):
return False |
def describe(self):
' Describes this Categorical\n\n Returns\n -------\n description: `DataFrame`\n A dataframe with frequency and counts by category.\n '
counts = self.value_counts(dropna=False)
freqs = (counts / float(counts.sum()))
from pandas.core.reshape.concat import concat
result = concat([counts, freqs], axis=1)
result.columns = ['counts', 'freqs']
result.index.name = 'categories'
return result | 4,132,264,890,513,181,000 | Describes this Categorical
Returns
-------
description: `DataFrame`
A dataframe with frequency and counts by category. | pandas/core/arrays/categorical.py | describe | Adirio/pandas | python | def describe(self):
' Describes this Categorical\n\n Returns\n -------\n description: `DataFrame`\n A dataframe with frequency and counts by category.\n '
counts = self.value_counts(dropna=False)
freqs = (counts / float(counts.sum()))
from pandas.core.reshape.concat import concat
result = concat([counts, freqs], axis=1)
result.columns = ['counts', 'freqs']
result.index.name = 'categories'
return result |
def repeat(self, repeats, *args, **kwargs):
'\n Repeat elements of a Categorical.\n\n See also\n --------\n numpy.ndarray.repeat\n\n '
nv.validate_repeat(args, kwargs)
codes = self._codes.repeat(repeats)
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) | 1,980,116,856,874,389,800 | Repeat elements of a Categorical.
See also
--------
numpy.ndarray.repeat | pandas/core/arrays/categorical.py | repeat | Adirio/pandas | python | def repeat(self, repeats, *args, **kwargs):
'\n Repeat elements of a Categorical.\n\n See also\n --------\n numpy.ndarray.repeat\n\n '
nv.validate_repeat(args, kwargs)
codes = self._codes.repeat(repeats)
return self._constructor(values=codes, dtype=self.dtype, fastpath=True) |
def isin(self, values):
"\n Check whether `values` are contained in Categorical.\n\n Return a boolean NumPy Array showing whether each element in\n the Categorical matches an element in the passed sequence of\n `values` exactly.\n\n Parameters\n ----------\n values : set or list-like\n The sequence of values to test. Passing in a single string will\n raise a ``TypeError``. Instead, turn a single string into a\n list of one element.\n\n Returns\n -------\n isin : numpy.ndarray (bool dtype)\n\n Raises\n ------\n TypeError\n * If `values` is not a set or list-like\n\n See Also\n --------\n pandas.Series.isin : equivalent method on Series\n\n Examples\n --------\n\n >>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',\n ... 'hippo'])\n >>> s.isin(['cow', 'lama'])\n array([ True, True, True, False, True, False])\n\n Passing a single string as ``s.isin('lama')`` will raise an error. Use\n a list of one element instead:\n\n >>> s.isin(['lama'])\n array([ True, False, True, False, True, False])\n "
from pandas.core.series import _sanitize_array
if (not is_list_like(values)):
raise TypeError('only list-like objects are allowed to be passed to isin(), you passed a [{values_type}]'.format(values_type=type(values).__name__))
values = _sanitize_array(values, None, None)
null_mask = np.asarray(isna(values))
code_values = self.categories.get_indexer(values)
code_values = code_values[(null_mask | (code_values >= 0))]
return algorithms.isin(self.codes, code_values) | 7,898,877,785,712,441,000 | Check whether `values` are contained in Categorical.
Return a boolean NumPy Array showing whether each element in
the Categorical matches an element in the passed sequence of
`values` exactly.
Parameters
----------
values : set or list-like
The sequence of values to test. Passing in a single string will
raise a ``TypeError``. Instead, turn a single string into a
list of one element.
Returns
-------
isin : numpy.ndarray (bool dtype)
Raises
------
TypeError
* If `values` is not a set or list-like
See Also
--------
pandas.Series.isin : equivalent method on Series
Examples
--------
>>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',
... 'hippo'])
>>> s.isin(['cow', 'lama'])
array([ True, True, True, False, True, False])
Passing a single string as ``s.isin('lama')`` will raise an error. Use
a list of one element instead:
>>> s.isin(['lama'])
array([ True, False, True, False, True, False]) | pandas/core/arrays/categorical.py | isin | Adirio/pandas | python | def isin(self, values):
"\n Check whether `values` are contained in Categorical.\n\n Return a boolean NumPy Array showing whether each element in\n the Categorical matches an element in the passed sequence of\n `values` exactly.\n\n Parameters\n ----------\n values : set or list-like\n The sequence of values to test. Passing in a single string will\n raise a ``TypeError``. Instead, turn a single string into a\n list of one element.\n\n Returns\n -------\n isin : numpy.ndarray (bool dtype)\n\n Raises\n ------\n TypeError\n * If `values` is not a set or list-like\n\n See Also\n --------\n pandas.Series.isin : equivalent method on Series\n\n Examples\n --------\n\n >>> s = pd.Categorical(['lama', 'cow', 'lama', 'beetle', 'lama',\n ... 'hippo'])\n >>> s.isin(['cow', 'lama'])\n array([ True, True, True, False, True, False])\n\n Passing a single string as ``s.isin('lama')`` will raise an error. Use\n a list of one element instead:\n\n >>> s.isin(['lama'])\n array([ True, False, True, False, True, False])\n "
from pandas.core.series import _sanitize_array
if (not is_list_like(values)):
raise TypeError('only list-like objects are allowed to be passed to isin(), you passed a [{values_type}]'.format(values_type=type(values).__name__))
values = _sanitize_array(values, None, None)
null_mask = np.asarray(isna(values))
code_values = self.categories.get_indexer(values)
code_values = code_values[(null_mask | (code_values >= 0))]
return algorithms.isin(self.codes, code_values) |
def submit(self, request, transaction, mobile: str=None, valid_card_number: str=None, callback: str=None):
"Submits a transaction to Pay.ir.\n\n When called, the method submits the necessary information about the transaction to Pay.ir and returns a\n HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an\n error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.\n\n :param request: The WSGIRequest object passed to the view.\n :param transaction: A transaction object (or a similar class) that's already been saved to the database.\n :param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.\n :param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.\n :param callback: (Optional) Overrides the default callback of the gateway.\n "
payload = self._prepare_submission_payload(request, transaction, mobile, valid_card_number, (callback or self.default_callback))
response = requests.post(self.submission_url, data=payload)
data = response.json()
if response:
transaction.token = data['token']
transaction.save()
return redirect(f'https://pay.ir/pg/{transaction.token}')
raise exceptions.GatewayError(error_code=data['errorCode'], error_message=data['errorMessage']) | -8,579,580,414,340,304,000 | Submits a transaction to Pay.ir.
When called, the method submits the necessary information about the transaction to Pay.ir and returns a
HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an
error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.
:param request: The WSGIRequest object passed to the view.
:param transaction: A transaction object (or a similar class) that's already been saved to the database.
:param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.
:param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.
:param callback: (Optional) Overrides the default callback of the gateway. | payir/models.py | submit | farahmand-m/django-payir | python | def submit(self, request, transaction, mobile: str=None, valid_card_number: str=None, callback: str=None):
"Submits a transaction to Pay.ir.\n\n When called, the method submits the necessary information about the transaction to Pay.ir and returns a\n HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an\n error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.\n\n :param request: The WSGIRequest object passed to the view.\n :param transaction: A transaction object (or a similar class) that's already been saved to the database.\n :param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.\n :param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.\n :param callback: (Optional) Overrides the default callback of the gateway.\n "
payload = self._prepare_submission_payload(request, transaction, mobile, valid_card_number, (callback or self.default_callback))
response = requests.post(self.submission_url, data=payload)
data = response.json()
if response:
transaction.token = data['token']
transaction.save()
return redirect(f'https://pay.ir/pg/{transaction.token}')
raise exceptions.GatewayError(error_code=data['errorCode'], error_message=data['errorMessage']) |
def create_and_submit(self, request, account, amount: int, mobile: str=None, valid_card_number: str=None, callback: str=None):
"Creates a transaction object and submits the transaction to Pay.ir.\n\n When called, the method submits the necessary information about the transaction to Pay.ir and returns a\n HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an\n error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.\n\n :param request: The WSGIRequest object passed to the view.\n :param account: Payer's account object. The account will be assigned to the transaction through a ForeignKey.\n :param amount: The amount of the transaction in IRR. The amount has to be more than 1000.\n :param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.\n :param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.\n :param callback: (Optional) Overrides the default callback of the gateway.\n "
transaction = Transaction(account=account, amount=amount)
transaction.save()
return self.submit(request, transaction, mobile, valid_card_number, callback) | -2,672,328,821,681,613,000 | Creates a transaction object and submits the transaction to Pay.ir.
When called, the method submits the necessary information about the transaction to Pay.ir and returns a
HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an
error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.
:param request: The WSGIRequest object passed to the view.
:param account: Payer's account object. The account will be assigned to the transaction through a ForeignKey.
:param amount: The amount of the transaction in IRR. The amount has to be more than 1000.
:param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.
:param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.
:param callback: (Optional) Overrides the default callback of the gateway. | payir/models.py | create_and_submit | farahmand-m/django-payir | python | def create_and_submit(self, request, account, amount: int, mobile: str=None, valid_card_number: str=None, callback: str=None):
"Creates a transaction object and submits the transaction to Pay.ir.\n\n When called, the method submits the necessary information about the transaction to Pay.ir and returns a\n HttpResponseRedirect object that can redirect the user to the gateway, if nothing goes wrong. In case of an\n error, a GatewayError is raised, containing the error_code and error_message reported by Pay.ir.\n\n :param request: The WSGIRequest object passed to the view.\n :param account: Payer's account object. The account will be assigned to the transaction through a ForeignKey.\n :param amount: The amount of the transaction in IRR. The amount has to be more than 1000.\n :param mobile: (Optional) Phone number of the payer. If provided, payer's saved card numbers will be listed for them in the gateway.\n :param valid_card_number: (Optional) Specifies a single card number as the only one that can complete the transaction.\n :param callback: (Optional) Overrides the default callback of the gateway.\n "
transaction = Transaction(account=account, amount=amount)
transaction.save()
return self.submit(request, transaction, mobile, valid_card_number, callback) |
def verify(self, transaction):
"Verifies the transaction with Pay.ir.\n\n When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to\n the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.\n The boolean value would be True if the `verified` flag of the transaction was switched to True. If the\n `verified` attribute of transaction object and the returned boolean value do not match, the user might be trying\n to confirm a payment for a second time.\n\n :param transaction: The transaction object corresponding to the specified token in request.GET.\n "
payload = {'api': self.api_key, 'token': transaction.token}
response = requests.post(self.verification_url, data=payload)
data = response.json()
if response:
if (not transaction.verified):
transaction.gateway = self
transaction.verified = True
transaction.verified_at = timezone.now()
transaction.save()
return (transaction, True)
else:
return (transaction, False)
raise exceptions.GatewayError(error_code=data['errorCode'], error_message=data['errorMessage']) | -1,236,591,773,615,743,000 | Verifies the transaction with Pay.ir.
When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to
the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.
The boolean value would be True if the `verified` flag of the transaction was switched to True. If the
`verified` attribute of transaction object and the returned boolean value do not match, the user might be trying
to confirm a payment for a second time.
:param transaction: The transaction object corresponding to the specified token in request.GET. | payir/models.py | verify | farahmand-m/django-payir | python | def verify(self, transaction):
"Verifies the transaction with Pay.ir.\n\n When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to\n the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.\n The boolean value would be True if the `verified` flag of the transaction was switched to True. If the\n `verified` attribute of transaction object and the returned boolean value do not match, the user might be trying\n to confirm a payment for a second time.\n\n :param transaction: The transaction object corresponding to the specified token in request.GET.\n "
payload = {'api': self.api_key, 'token': transaction.token}
response = requests.post(self.verification_url, data=payload)
data = response.json()
if response:
if (not transaction.verified):
transaction.gateway = self
transaction.verified = True
transaction.verified_at = timezone.now()
transaction.save()
return (transaction, True)
else:
return (transaction, False)
raise exceptions.GatewayError(error_code=data['errorCode'], error_message=data['errorMessage']) |
def find_and_verify(self, token: str):
"Finds a transaction with a matching token value and verifies it with Pay.ir.\n\n When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to\n the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.\n The boolean value would be True if the `verified` flag of the transaction was switched to True. If the\n `verified` attribute of transaction object and the returned boolean value do not match, the user might be trying\n to confirm a payment for a second time.\n\n :param token: The token of the transaction, which can be found in request.GET. The method will look for a\n transaction object with the same token and return it as the first argument.\n "
transaction = Transaction.objects.get(token=token)
return self.verify(transaction) | 7,657,918,661,398,567,000 | Finds a transaction with a matching token value and verifies it with Pay.ir.
When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to
the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.
The boolean value would be True if the `verified` flag of the transaction was switched to True. If the
`verified` attribute of transaction object and the returned boolean value do not match, the user might be trying
to confirm a payment for a second time.
:param token: The token of the transaction, which can be found in request.GET. The method will look for a
transaction object with the same token and return it as the first argument. | payir/models.py | find_and_verify | farahmand-m/django-payir | python | def find_and_verify(self, token: str):
"Finds a transaction with a matching token value and verifies it with Pay.ir.\n\n When a transaction returns with status '1', it must be verified with Pay.ir. Otherwise, it will be returned to\n the payer's bank account in 30 minutes. The method returns the updated transaction object and a boolean value.\n The boolean value would be True if the `verified` flag of the transaction was switched to True. If the\n `verified` attribute of transaction object and the returned boolean value do not match, the user might be trying\n to confirm a payment for a second time.\n\n :param token: The token of the transaction, which can be found in request.GET. The method will look for a\n transaction object with the same token and return it as the first argument.\n "
transaction = Transaction.objects.get(token=token)
return self.verify(transaction) |
async def test_hls_stream(hass, hass_client, stream_worker_sync):
'\n Test hls stream.\n\n Purposefully not mocking anything here to test full\n integration with the stream component.\n '
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
stream.add_provider('hls')
url = request_stream(hass, source)
http_client = (await hass_client())
parsed_url = urlparse(url)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
playlist = (await playlist_response.text())
playlist_url = '/'.join(parsed_url.path.split('/')[:(- 1)])
init_url = (playlist_url + '/init.mp4')
init_response = (await http_client.get(init_url))
assert (init_response.status == 200)
playlist = (await playlist_response.text())
playlist_url = '/'.join(parsed_url.path.split('/')[:(- 1)])
segment_url = ((playlist_url + '/') + playlist.splitlines()[(- 1)])
segment_response = (await http_client.get(segment_url))
assert (segment_response.status == 200)
stream_worker_sync.resume()
stream.stop()
fail_response = (await http_client.get(parsed_url.path))
assert (fail_response.status == HTTP_NOT_FOUND) | 8,384,524,558,263,135,000 | Test hls stream.
Purposefully not mocking anything here to test full
integration with the stream component. | tests/components/stream/test_hls.py | test_hls_stream | BoresXP/core | python | async def test_hls_stream(hass, hass_client, stream_worker_sync):
'\n Test hls stream.\n\n Purposefully not mocking anything here to test full\n integration with the stream component.\n '
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
stream.add_provider('hls')
url = request_stream(hass, source)
http_client = (await hass_client())
parsed_url = urlparse(url)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
playlist = (await playlist_response.text())
playlist_url = '/'.join(parsed_url.path.split('/')[:(- 1)])
init_url = (playlist_url + '/init.mp4')
init_response = (await http_client.get(init_url))
assert (init_response.status == 200)
playlist = (await playlist_response.text())
playlist_url = '/'.join(parsed_url.path.split('/')[:(- 1)])
segment_url = ((playlist_url + '/') + playlist.splitlines()[(- 1)])
segment_response = (await http_client.get(segment_url))
assert (segment_response.status == 200)
stream_worker_sync.resume()
stream.stop()
fail_response = (await http_client.get(parsed_url.path))
assert (fail_response.status == HTTP_NOT_FOUND) |
async def test_stream_timeout(hass, hass_client, stream_worker_sync):
'Test hls stream timeout.'
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
stream.add_provider('hls')
url = request_stream(hass, source)
http_client = (await hass_client())
parsed_url = urlparse(url)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
future = (dt_util.utcnow() + timedelta(minutes=1))
async_fire_time_changed(hass, future)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
stream_worker_sync.resume()
future = (dt_util.utcnow() + timedelta(minutes=5))
async_fire_time_changed(hass, future)
fail_response = (await http_client.get(parsed_url.path))
assert (fail_response.status == HTTP_NOT_FOUND) | 1,161,223,324,562,560,800 | Test hls stream timeout. | tests/components/stream/test_hls.py | test_stream_timeout | BoresXP/core | python | async def test_stream_timeout(hass, hass_client, stream_worker_sync):
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
stream.add_provider('hls')
url = request_stream(hass, source)
http_client = (await hass_client())
parsed_url = urlparse(url)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
future = (dt_util.utcnow() + timedelta(minutes=1))
async_fire_time_changed(hass, future)
playlist_response = (await http_client.get(parsed_url.path))
assert (playlist_response.status == 200)
stream_worker_sync.resume()
future = (dt_util.utcnow() + timedelta(minutes=5))
async_fire_time_changed(hass, future)
fail_response = (await http_client.get(parsed_url.path))
assert (fail_response.status == HTTP_NOT_FOUND) |
async def test_stream_ended(hass, stream_worker_sync):
'Test hls stream packets ended.'
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
track = stream.add_provider('hls')
request_stream(hass, source)
while True:
segment = (await track.recv())
if (segment is None):
break
segments = segment.sequence
if (segments > 1):
stream_worker_sync.resume()
assert (segments > 1)
assert (not track.get_segment())
stream.stop() | -5,946,003,191,077,505,000 | Test hls stream packets ended. | tests/components/stream/test_hls.py | test_stream_ended | BoresXP/core | python | async def test_stream_ended(hass, stream_worker_sync):
(await async_setup_component(hass, 'stream', {'stream': {}}))
stream_worker_sync.pause()
source = generate_h264_video()
stream = preload_stream(hass, source)
track = stream.add_provider('hls')
request_stream(hass, source)
while True:
segment = (await track.recv())
if (segment is None):
break
segments = segment.sequence
if (segments > 1):
stream_worker_sync.resume()
assert (segments > 1)
assert (not track.get_segment())
stream.stop() |
async def test_stream_keepalive(hass):
'Test hls stream retries the stream when keepalive=True.'
(await async_setup_component(hass, 'stream', {'stream': {}}))
source = 'test_stream_keepalive_source'
stream = preload_stream(hass, source)
track = stream.add_provider('hls')
track.num_segments = 2
cur_time = 0
def time_side_effect():
nonlocal cur_time
if (cur_time >= 80):
stream.keepalive = False
cur_time += 40
return cur_time
with patch('av.open') as av_open, patch('homeassistant.components.stream.worker.time') as mock_time, patch('homeassistant.components.stream.worker.STREAM_RESTART_INCREMENT', 0):
av_open.side_effect = av.error.InvalidDataError((- 2), 'error')
mock_time.time.side_effect = time_side_effect
request_stream(hass, source, keepalive=True)
stream._thread.join()
stream._thread = None
assert (av_open.call_count == 2)
stream.stop() | 5,628,805,682,028,231,000 | Test hls stream retries the stream when keepalive=True. | tests/components/stream/test_hls.py | test_stream_keepalive | BoresXP/core | python | async def test_stream_keepalive(hass):
(await async_setup_component(hass, 'stream', {'stream': {}}))
source = 'test_stream_keepalive_source'
stream = preload_stream(hass, source)
track = stream.add_provider('hls')
track.num_segments = 2
cur_time = 0
def time_side_effect():
nonlocal cur_time
if (cur_time >= 80):
stream.keepalive = False
cur_time += 40
return cur_time
with patch('av.open') as av_open, patch('homeassistant.components.stream.worker.time') as mock_time, patch('homeassistant.components.stream.worker.STREAM_RESTART_INCREMENT', 0):
av_open.side_effect = av.error.InvalidDataError((- 2), 'error')
mock_time.time.side_effect = time_side_effect
request_stream(hass, source, keepalive=True)
stream._thread.join()
stream._thread = None
assert (av_open.call_count == 2)
stream.stop() |
def split(self, batch_size, data):
'TODO: Annotation\n params:\n batch_size: int\n data: [B, x]\n '
if self.memory_net.use_rnn:
data = tf.reshape(data, [batch_size, (- 1), tf.shape(data)[(- 1)]])
(d, d_) = (data[:, :(- 1)], data[:, 1:])
(d, d_) = (tf.reshape(d, [(- 1), tf.shape(d)[(- 1)]]), tf.reshape(d_, [(- 1), tf.shape(d_)[(- 1)]]))
return (d, d_)
else:
return tf.split(data, num_or_size_splits=2, axis=0) | -1,039,740,822,517,881,500 | TODO: Annotation
params:
batch_size: int
data: [B, x] | rls/utils/build_networks.py | split | kiminh/RLs | python | def split(self, batch_size, data):
'TODO: Annotation\n params:\n batch_size: int\n data: [B, x]\n '
if self.memory_net.use_rnn:
data = tf.reshape(data, [batch_size, (- 1), tf.shape(data)[(- 1)]])
(d, d_) = (data[:, :(- 1)], data[:, 1:])
(d, d_) = (tf.reshape(d, [(- 1), tf.shape(d)[(- 1)]]), tf.reshape(d_, [(- 1), tf.shape(d_)[(- 1)]]))
return (d, d_)
else:
return tf.split(data, num_or_size_splits=2, axis=0) |
def __call__(self, s, visual_s, cell_state, *, need_split=False):
'\n params:\n s: [B*T, x]\n visual_s: [B*T, y]\n cell_state: Tuple([B, z],)\n return:\n feat: [B, a]\n cell_state: Tuple([B, z],)\n '
batch_size = tf.shape(s)[0]
if self.memory_net.use_rnn:
s = tf.reshape(s, [(- 1), tf.shape(s)[(- 1)]])
if self.visual_net.use_visual:
visual_s = tf.reshape(visual_s, [(- 1), *tf.shape(visual_s)[2:]])
feat = self.get_encoder_feature(s, visual_s)
if self.memory_net.use_rnn:
feat = tf.reshape(feat, (batch_size, (- 1), tf.shape(feat)[(- 1)]))
(feat, cell_state) = self.memory_net(feat, *cell_state)
feat = tf.reshape(feat, ((- 1), tf.shape(feat)[(- 1)]))
if need_split:
feat = self.split(batch_size, feat)
return (feat, cell_state) | 170,773,381,567,324,300 | params:
s: [B*T, x]
visual_s: [B*T, y]
cell_state: Tuple([B, z],)
return:
feat: [B, a]
cell_state: Tuple([B, z],) | rls/utils/build_networks.py | __call__ | kiminh/RLs | python | def __call__(self, s, visual_s, cell_state, *, need_split=False):
'\n params:\n s: [B*T, x]\n visual_s: [B*T, y]\n cell_state: Tuple([B, z],)\n return:\n feat: [B, a]\n cell_state: Tuple([B, z],)\n '
batch_size = tf.shape(s)[0]
if self.memory_net.use_rnn:
s = tf.reshape(s, [(- 1), tf.shape(s)[(- 1)]])
if self.visual_net.use_visual:
visual_s = tf.reshape(visual_s, [(- 1), *tf.shape(visual_s)[2:]])
feat = self.get_encoder_feature(s, visual_s)
if self.memory_net.use_rnn:
feat = tf.reshape(feat, (batch_size, (- 1), tf.shape(feat)[(- 1)]))
(feat, cell_state) = self.memory_net(feat, *cell_state)
feat = tf.reshape(feat, ((- 1), tf.shape(feat)[(- 1)]))
if need_split:
feat = self.split(batch_size, feat)
return (feat, cell_state) |
def get_vis_feature(self, visual_s):
'\n params:\n visual_s: [B, N, H, W, C]\n return:\n feat: [B, x]\n '
viss = [visual_s[:, i] for i in range(visual_s.shape[1])]
return self.visual_net(*viss) | -8,670,938,418,787,934,000 | params:
visual_s: [B, N, H, W, C]
return:
feat: [B, x] | rls/utils/build_networks.py | get_vis_feature | kiminh/RLs | python | def get_vis_feature(self, visual_s):
'\n params:\n visual_s: [B, N, H, W, C]\n return:\n feat: [B, x]\n '
viss = [visual_s[:, i] for i in range(visual_s.shape[1])]
return self.visual_net(*viss) |
def get_vec_feature(self, s):
'\n params:\n s: [B, x]\n return:\n feat: [B, y]\n '
return self.vector_net(s) | 1,844,289,576,694,748,700 | params:
s: [B, x]
return:
feat: [B, y] | rls/utils/build_networks.py | get_vec_feature | kiminh/RLs | python | def get_vec_feature(self, s):
'\n params:\n s: [B, x]\n return:\n feat: [B, y]\n '
return self.vector_net(s) |
def get_encoder_feature(self, s, visual_s):
'\n params:\n s: [B, x]\n visual_s: [B, y]\n return:\n feat: [B, z]\n '
if (self.vector_net.use_vector and self.visual_net.use_visual):
feat = self.get_vec_feature(s)
vis_feat = self.get_vis_feature(visual_s)
feat = tf.concat([feat, vis_feat], axis=(- 1))
elif self.visual_net.use_visual:
vis_feat = self.get_vis_feature(visual_s)
feat = vis_feat
else:
feat = self.get_vec_feature(s)
encoder_feature = self.encoder_net(feat)
return encoder_feature | 4,595,706,411,565,255,000 | params:
s: [B, x]
visual_s: [B, y]
return:
feat: [B, z] | rls/utils/build_networks.py | get_encoder_feature | kiminh/RLs | python | def get_encoder_feature(self, s, visual_s):
'\n params:\n s: [B, x]\n visual_s: [B, y]\n return:\n feat: [B, z]\n '
if (self.vector_net.use_vector and self.visual_net.use_visual):
feat = self.get_vec_feature(s)
vis_feat = self.get_vis_feature(visual_s)
feat = tf.concat([feat, vis_feat], axis=(- 1))
elif self.visual_net.use_visual:
vis_feat = self.get_vis_feature(visual_s)
feat = vis_feat
else:
feat = self.get_vec_feature(s)
encoder_feature = self.encoder_net(feat)
return encoder_feature |
@property
def _policy_models(self):
'重载'
models = super()._policy_models
models.update({((self.name + '/') + 'policy_net'): self.policy_net})
return models | -3,166,260,519,035,728,000 | 重载 | rls/utils/build_networks.py | _policy_models | kiminh/RLs | python | @property
def _policy_models(self):
models = super()._policy_models
models.update({((self.name + '/') + 'policy_net'): self.policy_net})
return models |
def update_bid(self, bid_basket_item_id, amount):
'\n Update amount of bid. Delete bid if amount is 0.\n '
try:
amount = Decimal(amount)
except Exception as e:
amount = Decimal('0')
bid_basket_item = self.bids.get(pk=bid_basket_item_id)
if (not bid_basket_item.is_locked()):
if (amount == 0):
bid_basket_item.delete()
else:
bid_basket_item.amount = amount
bid_basket_item.save()
self.save()
return bid_basket_item | -4,093,918,117,560,578,000 | Update amount of bid. Delete bid if amount is 0. | auction/models/bases.py | update_bid | JohnRomanski/django-auction | python | def update_bid(self, bid_basket_item_id, amount):
'\n \n '
try:
amount = Decimal(amount)
except Exception as e:
amount = Decimal('0')
bid_basket_item = self.bids.get(pk=bid_basket_item_id)
if (not bid_basket_item.is_locked()):
if (amount == 0):
bid_basket_item.delete()
else:
bid_basket_item.amount = amount
bid_basket_item.save()
self.save()
return bid_basket_item |
def delete_bid(self, bid_basket_item_id):
'\n Delete a single item from bid basket.\n '
bid_basket_item = self.bids.get(pk=bid_basket_item_id)
if (not bid_basket_item.is_locked()):
bid_basket_item.delete()
return bid_basket_item | 8,172,840,115,005,275,000 | Delete a single item from bid basket. | auction/models/bases.py | delete_bid | JohnRomanski/django-auction | python | def delete_bid(self, bid_basket_item_id):
'\n \n '
bid_basket_item = self.bids.get(pk=bid_basket_item_id)
if (not bid_basket_item.is_locked()):
bid_basket_item.delete()
return bid_basket_item |
def empty(self):
'\n Remove all bids from bid basket.\n '
if self.pk:
bids = self.bids.all()
for bid in bids:
if (not bid.is_locked()):
bid.delete() | -8,989,054,926,361,665,000 | Remove all bids from bid basket. | auction/models/bases.py | empty | JohnRomanski/django-auction | python | def empty(self):
'\n \n '
if self.pk:
bids = self.bids.all()
for bid in bids:
if (not bid.is_locked()):
bid.delete() |
@property
def bids(self):
'\n Used as accessor for abstract related (BaseBidItem.bid_items).\n\n If you override BaseBidItem and use a label other than "auction"\n you will also need to set AUCTION_BIDBASKET_BIDS_RELATED_NAME.\n Example: foo_biditem_related\n (where your label is "foo" and your model is "BidItem")\n '
bids = getattr(settings, 'AUCTION_BIDBASKET_BIDS_RELATED_NAME', 'auction_biditem_related')
return getattr(self, bids) | -5,169,305,097,375,760,000 | Used as accessor for abstract related (BaseBidItem.bid_items).
If you override BaseBidItem and use a label other than "auction"
you will also need to set AUCTION_BIDBASKET_BIDS_RELATED_NAME.
Example: foo_biditem_related
(where your label is "foo" and your model is "BidItem") | auction/models/bases.py | bids | JohnRomanski/django-auction | python | @property
def bids(self):
'\n Used as accessor for abstract related (BaseBidItem.bid_items).\n\n If you override BaseBidItem and use a label other than "auction"\n you will also need to set AUCTION_BIDBASKET_BIDS_RELATED_NAME.\n Example: foo_biditem_related\n (where your label is "foo" and your model is "BidItem")\n '
bids = getattr(settings, 'AUCTION_BIDBASKET_BIDS_RELATED_NAME', 'auction_biditem_related')
return getattr(self, bids) |
@property
def total_bids(self):
'\n Returns total bids in basket.\n '
return len(self.bids.all()) | -476,392,268,769,202,370 | Returns total bids in basket. | auction/models/bases.py | total_bids | JohnRomanski/django-auction | python | @property
def total_bids(self):
'\n \n '
return len(self.bids.all()) |
@property
def is_locked(self):
'\n This property is meant to be overwritten with your own logic. Bid baskets\n check this method to find out if a bid can be manipulated.\n '
import auction.utils.generic
now = auction.utils.generic.get_current_time()
return (self.content_object.end_date <= now) | 4,951,673,990,888,619,000 | This property is meant to be overwritten with your own logic. Bid baskets
check this method to find out if a bid can be manipulated. | auction/models/bases.py | is_locked | JohnRomanski/django-auction | python | @property
def is_locked(self):
'\n This property is meant to be overwritten with your own logic. Bid baskets\n check this method to find out if a bid can be manipulated.\n '
import auction.utils.generic
now = auction.utils.generic.get_current_time()
return (self.content_object.end_date <= now) |
def __init__(self, session, object_factory, request_validator):
'Initialize a new Clients\n object with the provided RestSession.\n\n Args:\n session(RestSession): The RESTful session object to be used for\n API calls to the DNA Center service.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n\n '
check_type(session, RestSession)
super(Clients, self).__init__()
self._session = session
self._object_factory = object_factory
self._request_validator = request_validator | 9,048,934,399,632,874,000 | Initialize a new Clients
object with the provided RestSession.
Args:
session(RestSession): The RESTful session object to be used for
API calls to the DNA Center service.
Raises:
TypeError: If the parameter types are incorrect. | dnacentersdk/api/v1_3_1/clients.py | __init__ | cisco-en-programmability/dnacentersdk | python | def __init__(self, session, object_factory, request_validator):
'Initialize a new Clients\n object with the provided RestSession.\n\n Args:\n session(RestSession): The RESTful session object to be used for\n API calls to the DNA Center service.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n\n '
check_type(session, RestSession)
super(Clients, self).__init__()
self._session = session
self._object_factory = object_factory
self._request_validator = request_validator |
def get_client_enrichment_details(self, headers=None, **request_parameters):
"Enriches a given network End User context (a network user-id or\n end user's device Mac Address) with details about the\n user, the devices that the user is connected to and the\n assurance issues that the user is impacted by.\n\n Args:\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n list: JSON response. A list of MyDict objects.\n Access the object's properties by using the dot notation\n or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
if (headers is not None):
if ('entity_type' in headers):
check_type(headers.get('entity_type'), basestring, may_be_none=False)
if ('entity_value' in headers):
check_type(headers.get('entity_value'), basestring, may_be_none=False)
if ('issueCategory' in headers):
check_type(headers.get('issueCategory'), basestring)
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {}
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-enrichment-details'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_b199685d4d089a67_v1_3_1', json_data) | -7,564,726,252,209,738,000 | Enriches a given network End User context (a network user-id or
end user's device Mac Address) with details about the
user, the devices that the user is connected to and the
assurance issues that the user is impacted by.
Args:
headers(dict): Dictionary of HTTP Headers to send with the Request
.
**request_parameters: Additional request parameters (provides
support for parameters that may be added in the future).
Returns:
list: JSON response. A list of MyDict objects.
Access the object's properties by using the dot notation
or the bracket notation.
Raises:
TypeError: If the parameter types are incorrect.
MalformedRequest: If the request body created is invalid.
ApiError: If the DNA Center cloud returns an error. | dnacentersdk/api/v1_3_1/clients.py | get_client_enrichment_details | cisco-en-programmability/dnacentersdk | python | def get_client_enrichment_details(self, headers=None, **request_parameters):
"Enriches a given network End User context (a network user-id or\n end user's device Mac Address) with details about the\n user, the devices that the user is connected to and the\n assurance issues that the user is impacted by.\n\n Args:\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n list: JSON response. A list of MyDict objects.\n Access the object's properties by using the dot notation\n or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
if (headers is not None):
if ('entity_type' in headers):
check_type(headers.get('entity_type'), basestring, may_be_none=False)
if ('entity_value' in headers):
check_type(headers.get('entity_value'), basestring, may_be_none=False)
if ('issueCategory' in headers):
check_type(headers.get('issueCategory'), basestring)
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {}
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-enrichment-details'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_b199685d4d089a67_v1_3_1', json_data) |
def get_overall_client_health(self, timestamp=None, headers=None, **request_parameters):
"Returns Overall Client Health information by Client type (Wired\n and Wireless) for any given point of time.\n\n Args:\n timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n MyDict: JSON response. Access the object's properties by using\n the dot notation or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
check_type(timestamp, (basestring, int))
if (headers is not None):
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {'timestamp': timestamp}
if (_params['timestamp'] is None):
_params['timestamp'] = ''
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-health'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_149aa93b4ddb80dd_v1_3_1', json_data) | -784,818,564,822,489,000 | Returns Overall Client Health information by Client type (Wired
and Wireless) for any given point of time.
Args:
timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.
headers(dict): Dictionary of HTTP Headers to send with the Request
.
**request_parameters: Additional request parameters (provides
support for parameters that may be added in the future).
Returns:
MyDict: JSON response. Access the object's properties by using
the dot notation or the bracket notation.
Raises:
TypeError: If the parameter types are incorrect.
MalformedRequest: If the request body created is invalid.
ApiError: If the DNA Center cloud returns an error. | dnacentersdk/api/v1_3_1/clients.py | get_overall_client_health | cisco-en-programmability/dnacentersdk | python | def get_overall_client_health(self, timestamp=None, headers=None, **request_parameters):
"Returns Overall Client Health information by Client type (Wired\n and Wireless) for any given point of time.\n\n Args:\n timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n MyDict: JSON response. Access the object's properties by using\n the dot notation or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
check_type(timestamp, (basestring, int))
if (headers is not None):
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {'timestamp': timestamp}
if (_params['timestamp'] is None):
_params['timestamp'] =
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-health'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_149aa93b4ddb80dd_v1_3_1', json_data) |
def get_client_detail(self, mac_address, timestamp=None, headers=None, **request_parameters):
"Returns detailed Client information retrieved by Mac Address for\n any given point of time. .\n\n Args:\n timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.\n mac_address(basestring): MAC Address of the client.\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n MyDict: JSON response. Access the object's properties by using\n the dot notation or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
check_type(timestamp, (basestring, int))
check_type(mac_address, basestring, may_be_none=False)
if (headers is not None):
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {'timestamp': timestamp, 'macAddress': mac_address}
if (_params['timestamp'] is None):
_params['timestamp'] = ''
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-detail'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_e2adba7943bab3e9_v1_3_1', json_data) | 5,349,272,156,075,314,000 | Returns detailed Client information retrieved by Mac Address for
any given point of time. .
Args:
timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.
mac_address(basestring): MAC Address of the client.
headers(dict): Dictionary of HTTP Headers to send with the Request
.
**request_parameters: Additional request parameters (provides
support for parameters that may be added in the future).
Returns:
MyDict: JSON response. Access the object's properties by using
the dot notation or the bracket notation.
Raises:
TypeError: If the parameter types are incorrect.
MalformedRequest: If the request body created is invalid.
ApiError: If the DNA Center cloud returns an error. | dnacentersdk/api/v1_3_1/clients.py | get_client_detail | cisco-en-programmability/dnacentersdk | python | def get_client_detail(self, mac_address, timestamp=None, headers=None, **request_parameters):
"Returns detailed Client information retrieved by Mac Address for\n any given point of time. .\n\n Args:\n timestamp(basestring, int): Epoch time(in milliseconds) when the Client health data is required.\n mac_address(basestring): MAC Address of the client.\n headers(dict): Dictionary of HTTP Headers to send with the Request\n .\n **request_parameters: Additional request parameters (provides\n support for parameters that may be added in the future).\n\n Returns:\n MyDict: JSON response. Access the object's properties by using\n the dot notation or the bracket notation.\n\n Raises:\n TypeError: If the parameter types are incorrect.\n MalformedRequest: If the request body created is invalid.\n ApiError: If the DNA Center cloud returns an error.\n "
check_type(headers, dict)
check_type(timestamp, (basestring, int))
check_type(mac_address, basestring, may_be_none=False)
if (headers is not None):
if ('X-Auth-Token' in headers):
check_type(headers.get('X-Auth-Token'), basestring, may_be_none=False)
_params = {'timestamp': timestamp, 'macAddress': mac_address}
if (_params['timestamp'] is None):
_params['timestamp'] =
_params.update(request_parameters)
_params = dict_from_items_with_values(_params)
path_params = {}
with_custom_headers = False
_headers = (self._session.headers or {})
if headers:
_headers.update(dict_of_str(headers))
with_custom_headers = True
e_url = '/dna/intent/api/v1/client-detail'
endpoint_full_url = apply_path_params(e_url, path_params)
if with_custom_headers:
json_data = self._session.get(endpoint_full_url, params=_params, headers=_headers)
else:
json_data = self._session.get(endpoint_full_url, params=_params)
return self._object_factory('bpm_e2adba7943bab3e9_v1_3_1', json_data) |
@property
def download_url(self):
'\n the URL at which the file can be downloaded. This is only valid for a short time, so should not be cached.\n '
file_url = ('/files/%s' % self.id)
rsp = self.session.get(file_url, allow_redirects=False)
return rsp.headers['location'] | -3,402,634,989,467,524,000 | the URL at which the file can be downloaded. This is only valid for a short time, so should not be cached. | yandeley/models/files.py | download_url | shuichiro-makigaki/yandeley-python-sdk | python | @property
def download_url(self):
'\n \n '
file_url = ('/files/%s' % self.id)
rsp = self.session.get(file_url, allow_redirects=False)
return rsp.headers['location'] |
def document(self, view=None):
'\n :param view: document view to return.\n :return: a :class:`UserDocument <yandeley.models.documents.UserDocument>` or\n :class:`CatalogDocument <yandeley.models.catalog.CatalogDocument>`, depending on which the document is\n attached to.\n '
if ('document_id' in self.json):
return self.session.documents.get_lazy(self.json['document_id'], view=view)
elif ('catalog_id' in self.json):
return self.session.catalog.get_lazy(self.json['catalog_id'], view=view)
else:
return None | -7,163,505,978,791,217,000 | :param view: document view to return.
:return: a :class:`UserDocument <yandeley.models.documents.UserDocument>` or
:class:`CatalogDocument <yandeley.models.catalog.CatalogDocument>`, depending on which the document is
attached to. | yandeley/models/files.py | document | shuichiro-makigaki/yandeley-python-sdk | python | def document(self, view=None):
'\n :param view: document view to return.\n :return: a :class:`UserDocument <yandeley.models.documents.UserDocument>` or\n :class:`CatalogDocument <yandeley.models.catalog.CatalogDocument>`, depending on which the document is\n attached to.\n '
if ('document_id' in self.json):
return self.session.documents.get_lazy(self.json['document_id'], view=view)
elif ('catalog_id' in self.json):
return self.session.catalog.get_lazy(self.json['catalog_id'], view=view)
else:
return None |
def download(self, directory):
'\n Downloads the file.\n\n :param directory: the directory to download the file to. This must exist.\n :return: the path to the downloaded file.\n '
rsp = self.session.get(('/files/%s' % self.id), stream=True)
filename = self.filename_regex.search(rsp.headers['content-disposition']).group(1)
path = os.path.join(directory, filename)
with open(path, 'wb') as f:
for block in rsp.iter_content(1024):
if (not block):
break
f.write(block)
return path | -7,057,783,901,090,330,000 | Downloads the file.
:param directory: the directory to download the file to. This must exist.
:return: the path to the downloaded file. | yandeley/models/files.py | download | shuichiro-makigaki/yandeley-python-sdk | python | def download(self, directory):
'\n Downloads the file.\n\n :param directory: the directory to download the file to. This must exist.\n :return: the path to the downloaded file.\n '
rsp = self.session.get(('/files/%s' % self.id), stream=True)
filename = self.filename_regex.search(rsp.headers['content-disposition']).group(1)
path = os.path.join(directory, filename)
with open(path, 'wb') as f:
for block in rsp.iter_content(1024):
if (not block):
break
f.write(block)
return path |
def delete(self):
'\n Deletes the file.\n '
self.session.delete(('/files/%s' % self.id)) | -7,918,245,386,690,900,000 | Deletes the file. | yandeley/models/files.py | delete | shuichiro-makigaki/yandeley-python-sdk | python | def delete(self):
'\n \n '
self.session.delete(('/files/%s' % self.id)) |
def add_sticky_note(self, text, x_position, y_position, page_number):
'\n Adds a sticky note to this file.\n\n :param text: the text of the sticky_note.\n :param x_position: the x position on the file of the sticky_note.\n :param y_position: the y position on the file of the stick_note.\n :param page_number: the page_number on the file of the sticky_note.\n :return: a :class:`Annotation <yandeley.models.annotations.Annotation>`.\n '
position = {'x': x_position, 'y': y_position}
bounding_box = {'top_left': position, 'bottom_right': position, 'page': page_number}
annotation = {'document_id': self.document().id, 'text': text, 'filehash': self.filehash, 'positions': [bounding_box]}
rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type})
return Annotation(self.session, rsp.json()) | -6,243,088,608,459,309,000 | Adds a sticky note to this file.
:param text: the text of the sticky_note.
:param x_position: the x position on the file of the sticky_note.
:param y_position: the y position on the file of the stick_note.
:param page_number: the page_number on the file of the sticky_note.
:return: a :class:`Annotation <yandeley.models.annotations.Annotation>`. | yandeley/models/files.py | add_sticky_note | shuichiro-makigaki/yandeley-python-sdk | python | def add_sticky_note(self, text, x_position, y_position, page_number):
'\n Adds a sticky note to this file.\n\n :param text: the text of the sticky_note.\n :param x_position: the x position on the file of the sticky_note.\n :param y_position: the y position on the file of the stick_note.\n :param page_number: the page_number on the file of the sticky_note.\n :return: a :class:`Annotation <yandeley.models.annotations.Annotation>`.\n '
position = {'x': x_position, 'y': y_position}
bounding_box = {'top_left': position, 'bottom_right': position, 'page': page_number}
annotation = {'document_id': self.document().id, 'text': text, 'filehash': self.filehash, 'positions': [bounding_box]}
rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type})
return Annotation(self.session, rsp.json()) |
def add_highlight(self, bounding_boxes, color):
'\n Adds a highlight to this file.\n\n :param bounding_boxes: the area the highlight covers on the file.\n :param color: the color of the highlight.\n :return: a :class:`Annotation <yandeley.models.annotations.Annotation>`.\n '
annotation = {'document_id': self.document().id, 'filehash': self.filehash, 'positions': [box.json for box in bounding_boxes], 'color': color.json}
rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type})
return Annotation(self.session, rsp.json()) | -813,811,400,724,956,800 | Adds a highlight to this file.
:param bounding_boxes: the area the highlight covers on the file.
:param color: the color of the highlight.
:return: a :class:`Annotation <yandeley.models.annotations.Annotation>`. | yandeley/models/files.py | add_highlight | shuichiro-makigaki/yandeley-python-sdk | python | def add_highlight(self, bounding_boxes, color):
'\n Adds a highlight to this file.\n\n :param bounding_boxes: the area the highlight covers on the file.\n :param color: the color of the highlight.\n :return: a :class:`Annotation <yandeley.models.annotations.Annotation>`.\n '
annotation = {'document_id': self.document().id, 'filehash': self.filehash, 'positions': [box.json for box in bounding_boxes], 'color': color.json}
rsp = self.session.post('/annotations/', data=json.dumps(annotation), headers={'Accept': Annotation.content_type, 'Content-Type': Annotation.content_type})
return Annotation(self.session, rsp.json()) |
def _middle(values):
'Lower bound of median, without using numpy (heavy reqs)'
n = len(values)
is_odd = (n % 2)
middle_idx = (int(((n + is_odd) / 2)) - 1)
return sorted(values)[middle_idx] | 958,713,930,854,413,800 | Lower bound of median, without using numpy (heavy reqs) | nuts_finder/nuts_finder.py | _middle | nestauk/nuts_finder | python | def _middle(values):
n = len(values)
is_odd = (n % 2)
middle_idx = (int(((n + is_odd) / 2)) - 1)
return sorted(values)[middle_idx] |
def _setattr(obj, value, value_name, regex, selector):
'Either apply setattr on `obj` with value `value`, if `value` is not None, otherwise\n select a `value` from the available range of allowed values, selected by a custom `selector`\n function.\n\n Args:\n obj: An object on which to run setattr\n value: A value which if not None will be set as an attribute of object\n value_name (str): The name of the new attribute\n regex (str): regex string by which to find allowed values on the NUTS website.\n selector (function): Function which takes an iterable and selects a value.\n '
allowed_values = _get_available(regex)
if (value is None):
value = selector(allowed_values)
if (value not in allowed_values):
raise ValueError(f"'{value_name}' must be one of {allowed_values}")
setattr(obj, value_name, value) | 6,017,101,556,157,815,000 | Either apply setattr on `obj` with value `value`, if `value` is not None, otherwise
select a `value` from the available range of allowed values, selected by a custom `selector`
function.
Args:
obj: An object on which to run setattr
value: A value which if not None will be set as an attribute of object
value_name (str): The name of the new attribute
regex (str): regex string by which to find allowed values on the NUTS website.
selector (function): Function which takes an iterable and selects a value. | nuts_finder/nuts_finder.py | _setattr | nestauk/nuts_finder | python | def _setattr(obj, value, value_name, regex, selector):
'Either apply setattr on `obj` with value `value`, if `value` is not None, otherwise\n select a `value` from the available range of allowed values, selected by a custom `selector`\n function.\n\n Args:\n obj: An object on which to run setattr\n value: A value which if not None will be set as an attribute of object\n value_name (str): The name of the new attribute\n regex (str): regex string by which to find allowed values on the NUTS website.\n selector (function): Function which takes an iterable and selects a value.\n '
allowed_values = _get_available(regex)
if (value is None):
value = selector(allowed_values)
if (value not in allowed_values):
raise ValueError(f"'{value_name}' must be one of {allowed_values}")
setattr(obj, value_name, value) |
@lru_cache()
def _get_available(regex):
'Use the provided regex to find allowed values on the NUTS website.'
r = requests.get(TOP_URL, verify=True)
values = set((int(yr) for yr in re.findall(regex, r.text)))
return values | -3,870,046,505,436,721,000 | Use the provided regex to find allowed values on the NUTS website. | nuts_finder/nuts_finder.py | _get_available | nestauk/nuts_finder | python | @lru_cache()
def _get_available(regex):
r = requests.get(TOP_URL, verify=True)
values = set((int(yr) for yr in re.findall(regex, r.text)))
return values |
def __init__(self, year=None, scale=None):
'\n Args:\n year (int): If provided, NUTS regions for this year will be used (if available)\n scale (int): If provided, NUTS regions at this resolution will be used (if available)\n '
self.years = list(_get_available(YEAR_REGEX))
self.year_selector = max
_setattr(self, year, 'year', YEAR_REGEX, self.year_selector)
_setattr(self, scale, 'scale', SCALE_REGEX, _middle)
self.shapes = self._get_shapes() | 3,857,591,972,723,282,000 | Args:
year (int): If provided, NUTS regions for this year will be used (if available)
scale (int): If provided, NUTS regions at this resolution will be used (if available) | nuts_finder/nuts_finder.py | __init__ | nestauk/nuts_finder | python | def __init__(self, year=None, scale=None):
'\n Args:\n year (int): If provided, NUTS regions for this year will be used (if available)\n scale (int): If provided, NUTS regions at this resolution will be used (if available)\n '
self.years = list(_get_available(YEAR_REGEX))
self.year_selector = max
_setattr(self, year, 'year', YEAR_REGEX, self.year_selector)
_setattr(self, scale, 'scale', SCALE_REGEX, _middle)
self.shapes = self._get_shapes() |
def _get_shapes(self):
'Load the shape files for the given year and scale'
scale = str(self.scale).zfill(2)
filename = NESTED_FILE.format(year=self.year, scale=scale)
url = ZIP_URL.format(year=self.year, scale=scale)
r = requests.get(url, verify=True)
r.raise_for_status()
try:
with ZipFile(BytesIO(r.content)) as zipfile:
with zipfile.open(filename) as f:
shapes = geojson.load(f)
except KeyError:
logging.warning(f'No match for this year ({self.year}) and scale ({self.scale})')
self.years.remove(self.year)
self.year = self.year_selector(self.years)
logging.warning(f'Retrying with year ({self.year})')
return self._get_shapes()
return shapes | 1,341,815,081,636,938,800 | Load the shape files for the given year and scale | nuts_finder/nuts_finder.py | _get_shapes | nestauk/nuts_finder | python | def _get_shapes(self):
scale = str(self.scale).zfill(2)
filename = NESTED_FILE.format(year=self.year, scale=scale)
url = ZIP_URL.format(year=self.year, scale=scale)
r = requests.get(url, verify=True)
r.raise_for_status()
try:
with ZipFile(BytesIO(r.content)) as zipfile:
with zipfile.open(filename) as f:
shapes = geojson.load(f)
except KeyError:
logging.warning(f'No match for this year ({self.year}) and scale ({self.scale})')
self.years.remove(self.year)
self.year = self.year_selector(self.years)
logging.warning(f'Retrying with year ({self.year})')
return self._get_shapes()
return shapes |
def find(self, lat, lon):
'Find every NUTS region for this lat, lon'
p = geometry.Point(lon, lat)
nuts = []
for region in self.shapes['features']:
s = geometry.shape(region['geometry'])
if s.contains(p):
nuts.append(region['properties'])
return sorted(nuts, key=(lambda row: row['LEVL_CODE'])) | 4,858,413,133,958,075,000 | Find every NUTS region for this lat, lon | nuts_finder/nuts_finder.py | find | nestauk/nuts_finder | python | def find(self, lat, lon):
p = geometry.Point(lon, lat)
nuts = []
for region in self.shapes['features']:
s = geometry.shape(region['geometry'])
if s.contains(p):
nuts.append(region['properties'])
return sorted(nuts, key=(lambda row: row['LEVL_CODE'])) |
def _pad_tensors_to_same_length(x, y):
'Pad x and y so that the results have the same length (second dimension).'
with tf.name_scope('pad_to_same_length'):
x_length = tf.shape(x)[1]
y_length = tf.shape(y)[1]
max_length = tf.maximum(x_length, y_length)
x = tf.pad(x, [[0, 0], [0, (max_length - x_length)], [0, 0]])
y = tf.pad(y, [[0, 0], [0, (max_length - y_length)]])
return (x, y) | 6,004,746,840,324,201,000 | Pad x and y so that the results have the same length (second dimension). | official/nlp/transformer/utils/metrics.py | _pad_tensors_to_same_length | 1110sillabo/models | python | def _pad_tensors_to_same_length(x, y):
with tf.name_scope('pad_to_same_length'):
x_length = tf.shape(x)[1]
y_length = tf.shape(y)[1]
max_length = tf.maximum(x_length, y_length)
x = tf.pad(x, [[0, 0], [0, (max_length - x_length)], [0, 0]])
y = tf.pad(y, [[0, 0], [0, (max_length - y_length)]])
return (x, y) |
def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
'Calculate cross entropy loss while ignoring padding.\n\n Args:\n logits: Tensor of size [batch_size, length_logits, vocab_size]\n labels: Tensor of size [batch_size, length_labels]\n smoothing: Label smoothing constant, used to determine the on and off values\n vocab_size: int size of the vocabulary\n Returns:\n Returns the cross entropy loss and weight tensors: float32 tensors with\n shape [batch_size, max(length_logits, length_labels)]\n '
with tf.name_scope('loss', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
with tf.name_scope('smoothing_cross_entropy', values=[logits, labels]):
confidence = (1.0 - smoothing)
low_confidence = ((1.0 - confidence) / tf.to_float((vocab_size - 1)))
soft_targets = tf.one_hot(tf.cast(labels, tf.int32), depth=vocab_size, on_value=confidence, off_value=low_confidence)
xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=soft_targets)
normalizing_constant = (- ((confidence * tf.log(confidence)) + ((tf.to_float((vocab_size - 1)) * low_confidence) * tf.log((low_confidence + 1e-20)))))
xentropy -= normalizing_constant
weights = tf.to_float(tf.not_equal(labels, 0))
return ((xentropy * weights), weights) | 3,564,442,464,147,787,300 | Calculate cross entropy loss while ignoring padding.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch_size, length_labels]
smoothing: Label smoothing constant, used to determine the on and off values
vocab_size: int size of the vocabulary
Returns:
Returns the cross entropy loss and weight tensors: float32 tensors with
shape [batch_size, max(length_logits, length_labels)] | official/nlp/transformer/utils/metrics.py | padded_cross_entropy_loss | 1110sillabo/models | python | def padded_cross_entropy_loss(logits, labels, smoothing, vocab_size):
'Calculate cross entropy loss while ignoring padding.\n\n Args:\n logits: Tensor of size [batch_size, length_logits, vocab_size]\n labels: Tensor of size [batch_size, length_labels]\n smoothing: Label smoothing constant, used to determine the on and off values\n vocab_size: int size of the vocabulary\n Returns:\n Returns the cross entropy loss and weight tensors: float32 tensors with\n shape [batch_size, max(length_logits, length_labels)]\n '
with tf.name_scope('loss', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
with tf.name_scope('smoothing_cross_entropy', values=[logits, labels]):
confidence = (1.0 - smoothing)
low_confidence = ((1.0 - confidence) / tf.to_float((vocab_size - 1)))
soft_targets = tf.one_hot(tf.cast(labels, tf.int32), depth=vocab_size, on_value=confidence, off_value=low_confidence)
xentropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=soft_targets)
normalizing_constant = (- ((confidence * tf.log(confidence)) + ((tf.to_float((vocab_size - 1)) * low_confidence) * tf.log((low_confidence + 1e-20)))))
xentropy -= normalizing_constant
weights = tf.to_float(tf.not_equal(labels, 0))
return ((xentropy * weights), weights) |
def _convert_to_eval_metric(metric_fn):
"Wrap a metric fn that returns scores and weights as an eval metric fn.\n\n The input metric_fn returns values for the current batch. The wrapper\n aggregates the return values collected over all of the batches evaluated.\n\n Args:\n metric_fn: function that returns scores and weights for the current batch's\n logits and predicted labels.\n\n Returns:\n function that aggregates the scores and weights from metric_fn.\n "
def problem_metric_fn(*args):
"Returns an aggregation of the metric_fn's returned values."
(scores, weights) = metric_fn(*args)
return tf.metrics.mean(scores, weights)
return problem_metric_fn | -2,359,971,840,431,848,000 | Wrap a metric fn that returns scores and weights as an eval metric fn.
The input metric_fn returns values for the current batch. The wrapper
aggregates the return values collected over all of the batches evaluated.
Args:
metric_fn: function that returns scores and weights for the current batch's
logits and predicted labels.
Returns:
function that aggregates the scores and weights from metric_fn. | official/nlp/transformer/utils/metrics.py | _convert_to_eval_metric | 1110sillabo/models | python | def _convert_to_eval_metric(metric_fn):
"Wrap a metric fn that returns scores and weights as an eval metric fn.\n\n The input metric_fn returns values for the current batch. The wrapper\n aggregates the return values collected over all of the batches evaluated.\n\n Args:\n metric_fn: function that returns scores and weights for the current batch's\n logits and predicted labels.\n\n Returns:\n function that aggregates the scores and weights from metric_fn.\n "
def problem_metric_fn(*args):
"Returns an aggregation of the metric_fn's returned values."
(scores, weights) = metric_fn(*args)
return tf.metrics.mean(scores, weights)
return problem_metric_fn |
def get_eval_metrics(logits, labels, params):
'Return dictionary of model evaluation metrics.'
metrics = {'accuracy': _convert_to_eval_metric(padded_accuracy)(logits, labels), 'accuracy_top5': _convert_to_eval_metric(padded_accuracy_top5)(logits, labels), 'accuracy_per_sequence': _convert_to_eval_metric(padded_sequence_accuracy)(logits, labels), 'neg_log_perplexity': _convert_to_eval_metric(padded_neg_log_perplexity)(logits, labels, params['vocab_size'])}
if (not params['use_tpu']):
metrics.update({'approx_bleu_score': _convert_to_eval_metric(bleu_score)(logits, labels), 'rouge_2_fscore': _convert_to_eval_metric(rouge_2_fscore)(logits, labels), 'rouge_L_fscore': _convert_to_eval_metric(rouge_l_fscore)(logits, labels)})
metrics = {('metrics/%s' % k): v for (k, v) in six.iteritems(metrics)}
return metrics | 3,380,199,622,328,414,000 | Return dictionary of model evaluation metrics. | official/nlp/transformer/utils/metrics.py | get_eval_metrics | 1110sillabo/models | python | def get_eval_metrics(logits, labels, params):
metrics = {'accuracy': _convert_to_eval_metric(padded_accuracy)(logits, labels), 'accuracy_top5': _convert_to_eval_metric(padded_accuracy_top5)(logits, labels), 'accuracy_per_sequence': _convert_to_eval_metric(padded_sequence_accuracy)(logits, labels), 'neg_log_perplexity': _convert_to_eval_metric(padded_neg_log_perplexity)(logits, labels, params['vocab_size'])}
if (not params['use_tpu']):
metrics.update({'approx_bleu_score': _convert_to_eval_metric(bleu_score)(logits, labels), 'rouge_2_fscore': _convert_to_eval_metric(rouge_2_fscore)(logits, labels), 'rouge_L_fscore': _convert_to_eval_metric(rouge_l_fscore)(logits, labels)})
metrics = {('metrics/%s' % k): v for (k, v) in six.iteritems(metrics)}
return metrics |
def padded_accuracy(logits, labels):
'Percentage of times that predictions matches labels on non-0s.'
with tf.variable_scope('padded_accuracy', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
padded_labels = tf.to_int32(labels)
return (tf.to_float(tf.equal(outputs, padded_labels)), weights) | 8,946,189,065,367,476,000 | Percentage of times that predictions matches labels on non-0s. | official/nlp/transformer/utils/metrics.py | padded_accuracy | 1110sillabo/models | python | def padded_accuracy(logits, labels):
with tf.variable_scope('padded_accuracy', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
padded_labels = tf.to_int32(labels)
return (tf.to_float(tf.equal(outputs, padded_labels)), weights) |
def padded_accuracy_topk(logits, labels, k):
'Percentage of times that top-k predictions matches labels on non-0s.'
with tf.variable_scope('padded_accuracy_topk', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
effective_k = tf.minimum(k, tf.shape(logits)[(- 1)])
(_, outputs) = tf.nn.top_k(logits, k=effective_k)
outputs = tf.to_int32(outputs)
padded_labels = tf.to_int32(labels)
padded_labels = tf.expand_dims(padded_labels, axis=(- 1))
padded_labels += tf.zeros_like(outputs)
same = tf.to_float(tf.equal(outputs, padded_labels))
same_topk = tf.reduce_sum(same, axis=(- 1))
return (same_topk, weights) | 4,883,132,312,032,429,000 | Percentage of times that top-k predictions matches labels on non-0s. | official/nlp/transformer/utils/metrics.py | padded_accuracy_topk | 1110sillabo/models | python | def padded_accuracy_topk(logits, labels, k):
with tf.variable_scope('padded_accuracy_topk', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
effective_k = tf.minimum(k, tf.shape(logits)[(- 1)])
(_, outputs) = tf.nn.top_k(logits, k=effective_k)
outputs = tf.to_int32(outputs)
padded_labels = tf.to_int32(labels)
padded_labels = tf.expand_dims(padded_labels, axis=(- 1))
padded_labels += tf.zeros_like(outputs)
same = tf.to_float(tf.equal(outputs, padded_labels))
same_topk = tf.reduce_sum(same, axis=(- 1))
return (same_topk, weights) |
def padded_sequence_accuracy(logits, labels):
'Percentage of times that predictions matches labels everywhere (non-0).'
with tf.variable_scope('padded_sequence_accuracy', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
padded_labels = tf.to_int32(labels)
not_correct = (tf.to_float(tf.not_equal(outputs, padded_labels)) * weights)
axis = list(range(1, len(outputs.get_shape())))
correct_seq = (1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis)))
return (correct_seq, tf.constant(1.0)) | -1,587,949,503,459,970,300 | Percentage of times that predictions matches labels everywhere (non-0). | official/nlp/transformer/utils/metrics.py | padded_sequence_accuracy | 1110sillabo/models | python | def padded_sequence_accuracy(logits, labels):
with tf.variable_scope('padded_sequence_accuracy', values=[logits, labels]):
(logits, labels) = _pad_tensors_to_same_length(logits, labels)
weights = tf.to_float(tf.not_equal(labels, 0))
outputs = tf.to_int32(tf.argmax(logits, axis=(- 1)))
padded_labels = tf.to_int32(labels)
not_correct = (tf.to_float(tf.not_equal(outputs, padded_labels)) * weights)
axis = list(range(1, len(outputs.get_shape())))
correct_seq = (1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis)))
return (correct_seq, tf.constant(1.0)) |
def padded_neg_log_perplexity(logits, labels, vocab_size):
'Average log-perplexity excluding padding 0s. No smoothing.'
(num, den) = padded_cross_entropy_loss(logits, labels, 0, vocab_size)
return ((- num), den) | 4,644,916,350,470,578,000 | Average log-perplexity excluding padding 0s. No smoothing. | official/nlp/transformer/utils/metrics.py | padded_neg_log_perplexity | 1110sillabo/models | python | def padded_neg_log_perplexity(logits, labels, vocab_size):
(num, den) = padded_cross_entropy_loss(logits, labels, 0, vocab_size)
return ((- num), den) |
def bleu_score(logits, labels):
'Approximate BLEU score computation between labels and predictions.\n\n An approximate BLEU scoring method since we do not glue word pieces or\n decode the ids and tokenize the output. By default, we use ngram order of 4\n and use brevity penalty. Also, this does not have beam search.\n\n Args:\n logits: Tensor of size [batch_size, length_logits, vocab_size]\n labels: Tensor of size [batch-size, length_labels]\n\n Returns:\n bleu: int, approx bleu score\n '
predictions = tf.to_int32(tf.argmax(logits, axis=(- 1)))
bleu = tf.py_func(compute_bleu, (labels, predictions), tf.float32)
return (bleu, tf.constant(1.0)) | 292,391,744,416,105,400 | Approximate BLEU score computation between labels and predictions.
An approximate BLEU scoring method since we do not glue word pieces or
decode the ids and tokenize the output. By default, we use ngram order of 4
and use brevity penalty. Also, this does not have beam search.
Args:
logits: Tensor of size [batch_size, length_logits, vocab_size]
labels: Tensor of size [batch-size, length_labels]
Returns:
bleu: int, approx bleu score | official/nlp/transformer/utils/metrics.py | bleu_score | 1110sillabo/models | python | def bleu_score(logits, labels):
'Approximate BLEU score computation between labels and predictions.\n\n An approximate BLEU scoring method since we do not glue word pieces or\n decode the ids and tokenize the output. By default, we use ngram order of 4\n and use brevity penalty. Also, this does not have beam search.\n\n Args:\n logits: Tensor of size [batch_size, length_logits, vocab_size]\n labels: Tensor of size [batch-size, length_labels]\n\n Returns:\n bleu: int, approx bleu score\n '
predictions = tf.to_int32(tf.argmax(logits, axis=(- 1)))
bleu = tf.py_func(compute_bleu, (labels, predictions), tf.float32)
return (bleu, tf.constant(1.0)) |
def _get_ngrams_with_counter(segment, max_order):
'Extracts all n-grams up to a given maximum order from an input segment.\n\n Args:\n segment: text segment from which n-grams will be extracted.\n max_order: maximum length in tokens of the n-grams returned by this\n methods.\n\n Returns:\n The Counter containing all n-grams upto max_order in segment\n with a count of how many times each n-gram occurred.\n '
ngram_counts = collections.Counter()
for order in xrange(1, (max_order + 1)):
for i in xrange(0, ((len(segment) - order) + 1)):
ngram = tuple(segment[i:(i + order)])
ngram_counts[ngram] += 1
return ngram_counts | 1,716,925,804,233,848,000 | Extracts all n-grams up to a given maximum order from an input segment.
Args:
segment: text segment from which n-grams will be extracted.
max_order: maximum length in tokens of the n-grams returned by this
methods.
Returns:
The Counter containing all n-grams upto max_order in segment
with a count of how many times each n-gram occurred. | official/nlp/transformer/utils/metrics.py | _get_ngrams_with_counter | 1110sillabo/models | python | def _get_ngrams_with_counter(segment, max_order):
'Extracts all n-grams up to a given maximum order from an input segment.\n\n Args:\n segment: text segment from which n-grams will be extracted.\n max_order: maximum length in tokens of the n-grams returned by this\n methods.\n\n Returns:\n The Counter containing all n-grams upto max_order in segment\n with a count of how many times each n-gram occurred.\n '
ngram_counts = collections.Counter()
for order in xrange(1, (max_order + 1)):
for i in xrange(0, ((len(segment) - order) + 1)):
ngram = tuple(segment[i:(i + order)])
ngram_counts[ngram] += 1
return ngram_counts |
def compute_bleu(reference_corpus, translation_corpus, max_order=4, use_bp=True):
'Computes BLEU score of translated segments against one or more references.\n\n Args:\n reference_corpus: list of references for each translation. Each\n reference should be tokenized into a list of tokens.\n translation_corpus: list of translations to score. Each translation\n should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n use_bp: boolean, whether to apply brevity penalty.\n\n Returns:\n BLEU score.\n '
reference_length = 0
translation_length = 0
bp = 1.0
geo_mean = 0
matches_by_order = ([0] * max_order)
possible_matches_by_order = ([0] * max_order)
precisions = []
for (references, translations) in zip(reference_corpus, translation_corpus):
reference_length += len(references)
translation_length += len(translations)
ref_ngram_counts = _get_ngrams_with_counter(references, max_order)
translation_ngram_counts = _get_ngrams_with_counter(translations, max_order)
overlap = dict(((ngram, min(count, translation_ngram_counts[ngram])) for (ngram, count) in ref_ngram_counts.items()))
for ngram in overlap:
matches_by_order[(len(ngram) - 1)] += overlap[ngram]
for ngram in translation_ngram_counts:
possible_matches_by_order[(len(ngram) - 1)] += translation_ngram_counts[ngram]
precisions = ([0] * max_order)
smooth = 1.0
for i in xrange(0, max_order):
if (possible_matches_by_order[i] > 0):
precisions[i] = (float(matches_by_order[i]) / possible_matches_by_order[i])
if (matches_by_order[i] > 0):
precisions[i] = (float(matches_by_order[i]) / possible_matches_by_order[i])
else:
smooth *= 2
precisions[i] = (1.0 / (smooth * possible_matches_by_order[i]))
else:
precisions[i] = 0.0
if (max(precisions) > 0):
p_log_sum = sum((math.log(p) for p in precisions if p))
geo_mean = math.exp((p_log_sum / max_order))
if use_bp:
ratio = (translation_length / reference_length)
bp = (math.exp((1 - (1.0 / ratio))) if (ratio < 1.0) else 1.0)
bleu = (geo_mean * bp)
return np.float32(bleu) | 8,272,636,851,736,137,000 | Computes BLEU score of translated segments against one or more references.
Args:
reference_corpus: list of references for each translation. Each
reference should be tokenized into a list of tokens.
translation_corpus: list of translations to score. Each translation
should be tokenized into a list of tokens.
max_order: Maximum n-gram order to use when computing BLEU score.
use_bp: boolean, whether to apply brevity penalty.
Returns:
BLEU score. | official/nlp/transformer/utils/metrics.py | compute_bleu | 1110sillabo/models | python | def compute_bleu(reference_corpus, translation_corpus, max_order=4, use_bp=True):
'Computes BLEU score of translated segments against one or more references.\n\n Args:\n reference_corpus: list of references for each translation. Each\n reference should be tokenized into a list of tokens.\n translation_corpus: list of translations to score. Each translation\n should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n use_bp: boolean, whether to apply brevity penalty.\n\n Returns:\n BLEU score.\n '
reference_length = 0
translation_length = 0
bp = 1.0
geo_mean = 0
matches_by_order = ([0] * max_order)
possible_matches_by_order = ([0] * max_order)
precisions = []
for (references, translations) in zip(reference_corpus, translation_corpus):
reference_length += len(references)
translation_length += len(translations)
ref_ngram_counts = _get_ngrams_with_counter(references, max_order)
translation_ngram_counts = _get_ngrams_with_counter(translations, max_order)
overlap = dict(((ngram, min(count, translation_ngram_counts[ngram])) for (ngram, count) in ref_ngram_counts.items()))
for ngram in overlap:
matches_by_order[(len(ngram) - 1)] += overlap[ngram]
for ngram in translation_ngram_counts:
possible_matches_by_order[(len(ngram) - 1)] += translation_ngram_counts[ngram]
precisions = ([0] * max_order)
smooth = 1.0
for i in xrange(0, max_order):
if (possible_matches_by_order[i] > 0):
precisions[i] = (float(matches_by_order[i]) / possible_matches_by_order[i])
if (matches_by_order[i] > 0):
precisions[i] = (float(matches_by_order[i]) / possible_matches_by_order[i])
else:
smooth *= 2
precisions[i] = (1.0 / (smooth * possible_matches_by_order[i]))
else:
precisions[i] = 0.0
if (max(precisions) > 0):
p_log_sum = sum((math.log(p) for p in precisions if p))
geo_mean = math.exp((p_log_sum / max_order))
if use_bp:
ratio = (translation_length / reference_length)
bp = (math.exp((1 - (1.0 / ratio))) if (ratio < 1.0) else 1.0)
bleu = (geo_mean * bp)
return np.float32(bleu) |
def rouge_2_fscore(logits, labels):
'ROUGE-2 F1 score computation between labels and predictions.\n\n This is an approximate ROUGE scoring method since we do not glue word pieces\n or decode the ids and tokenize the output.\n\n Args:\n logits: tensor, model predictions\n labels: tensor, gold output.\n\n Returns:\n rouge2_fscore: approx rouge-2 f1 score.\n '
predictions = tf.to_int32(tf.argmax(logits, axis=(- 1)))
rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), tf.float32)
return (rouge_2_f_score, tf.constant(1.0)) | 8,274,710,431,071,724,000 | ROUGE-2 F1 score computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
logits: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge2_fscore: approx rouge-2 f1 score. | official/nlp/transformer/utils/metrics.py | rouge_2_fscore | 1110sillabo/models | python | def rouge_2_fscore(logits, labels):
'ROUGE-2 F1 score computation between labels and predictions.\n\n This is an approximate ROUGE scoring method since we do not glue word pieces\n or decode the ids and tokenize the output.\n\n Args:\n logits: tensor, model predictions\n labels: tensor, gold output.\n\n Returns:\n rouge2_fscore: approx rouge-2 f1 score.\n '
predictions = tf.to_int32(tf.argmax(logits, axis=(- 1)))
rouge_2_f_score = tf.py_func(rouge_n, (predictions, labels), tf.float32)
return (rouge_2_f_score, tf.constant(1.0)) |
def _get_ngrams(n, text):
'Calculates n-grams.\n\n Args:\n n: which n-grams to calculate\n text: An array of tokens\n\n Returns:\n A set of n-grams\n '
ngram_set = set()
text_length = len(text)
max_index_ngram_start = (text_length - n)
for i in range((max_index_ngram_start + 1)):
ngram_set.add(tuple(text[i:(i + n)]))
return ngram_set | 2,263,363,960,569,367,600 | Calculates n-grams.
Args:
n: which n-grams to calculate
text: An array of tokens
Returns:
A set of n-grams | official/nlp/transformer/utils/metrics.py | _get_ngrams | 1110sillabo/models | python | def _get_ngrams(n, text):
'Calculates n-grams.\n\n Args:\n n: which n-grams to calculate\n text: An array of tokens\n\n Returns:\n A set of n-grams\n '
ngram_set = set()
text_length = len(text)
max_index_ngram_start = (text_length - n)
for i in range((max_index_ngram_start + 1)):
ngram_set.add(tuple(text[i:(i + n)]))
return ngram_set |
def rouge_n(eval_sentences, ref_sentences, n=2):
'Computes ROUGE-N f1 score of two text collections of sentences.\n\n Source: https://www.microsoft.com/en-us/research/publication/\n rouge-a-package-for-automatic-evaluation-of-summaries/\n\n Args:\n eval_sentences: Predicted sentences.\n ref_sentences: Sentences from the reference set\n n: Size of ngram. Defaults to 2.\n\n Returns:\n f1 score for ROUGE-N\n '
f1_scores = []
for (eval_sentence, ref_sentence) in zip(eval_sentences, ref_sentences):
eval_ngrams = _get_ngrams(n, eval_sentence)
ref_ngrams = _get_ngrams(n, ref_sentence)
ref_count = len(ref_ngrams)
eval_count = len(eval_ngrams)
overlapping_ngrams = eval_ngrams.intersection(ref_ngrams)
overlapping_count = len(overlapping_ngrams)
if (eval_count == 0):
precision = 0.0
else:
precision = (float(overlapping_count) / eval_count)
if (ref_count == 0):
recall = 0.0
else:
recall = (float(overlapping_count) / ref_count)
f1_scores.append((2.0 * ((precision * recall) / ((precision + recall) + 1e-08))))
return np.mean(f1_scores, dtype=np.float32) | -306,006,793,984,337,500 | Computes ROUGE-N f1 score of two text collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Args:
eval_sentences: Predicted sentences.
ref_sentences: Sentences from the reference set
n: Size of ngram. Defaults to 2.
Returns:
f1 score for ROUGE-N | official/nlp/transformer/utils/metrics.py | rouge_n | 1110sillabo/models | python | def rouge_n(eval_sentences, ref_sentences, n=2):
'Computes ROUGE-N f1 score of two text collections of sentences.\n\n Source: https://www.microsoft.com/en-us/research/publication/\n rouge-a-package-for-automatic-evaluation-of-summaries/\n\n Args:\n eval_sentences: Predicted sentences.\n ref_sentences: Sentences from the reference set\n n: Size of ngram. Defaults to 2.\n\n Returns:\n f1 score for ROUGE-N\n '
f1_scores = []
for (eval_sentence, ref_sentence) in zip(eval_sentences, ref_sentences):
eval_ngrams = _get_ngrams(n, eval_sentence)
ref_ngrams = _get_ngrams(n, ref_sentence)
ref_count = len(ref_ngrams)
eval_count = len(eval_ngrams)
overlapping_ngrams = eval_ngrams.intersection(ref_ngrams)
overlapping_count = len(overlapping_ngrams)
if (eval_count == 0):
precision = 0.0
else:
precision = (float(overlapping_count) / eval_count)
if (ref_count == 0):
recall = 0.0
else:
recall = (float(overlapping_count) / ref_count)
f1_scores.append((2.0 * ((precision * recall) / ((precision + recall) + 1e-08))))
return np.mean(f1_scores, dtype=np.float32) |
def rouge_l_fscore(predictions, labels):
'ROUGE scores computation between labels and predictions.\n\n This is an approximate ROUGE scoring method since we do not glue word pieces\n or decode the ids and tokenize the output.\n\n Args:\n predictions: tensor, model predictions\n labels: tensor, gold output.\n\n Returns:\n rouge_l_fscore: approx rouge-l f1 score.\n '
outputs = tf.to_int32(tf.argmax(predictions, axis=(- 1)))
rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels), tf.float32)
return (rouge_l_f_score, tf.constant(1.0)) | 3,165,181,742,827,321,000 | ROUGE scores computation between labels and predictions.
This is an approximate ROUGE scoring method since we do not glue word pieces
or decode the ids and tokenize the output.
Args:
predictions: tensor, model predictions
labels: tensor, gold output.
Returns:
rouge_l_fscore: approx rouge-l f1 score. | official/nlp/transformer/utils/metrics.py | rouge_l_fscore | 1110sillabo/models | python | def rouge_l_fscore(predictions, labels):
'ROUGE scores computation between labels and predictions.\n\n This is an approximate ROUGE scoring method since we do not glue word pieces\n or decode the ids and tokenize the output.\n\n Args:\n predictions: tensor, model predictions\n labels: tensor, gold output.\n\n Returns:\n rouge_l_fscore: approx rouge-l f1 score.\n '
outputs = tf.to_int32(tf.argmax(predictions, axis=(- 1)))
rouge_l_f_score = tf.py_func(rouge_l_sentence_level, (outputs, labels), tf.float32)
return (rouge_l_f_score, tf.constant(1.0)) |
def rouge_l_sentence_level(eval_sentences, ref_sentences):
'Computes ROUGE-L (sentence level) of two collections of sentences.\n\n Source: https://www.microsoft.com/en-us/research/publication/\n rouge-a-package-for-automatic-evaluation-of-summaries/\n\n Calculated according to:\n R_lcs = LCS(X,Y)/m\n P_lcs = LCS(X,Y)/n\n F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)\n\n where:\n X = reference summary\n Y = Candidate summary\n m = length of reference summary\n n = length of candidate summary\n\n Args:\n eval_sentences: The sentences that have been picked by the summarizer\n ref_sentences: The sentences from the reference set\n\n Returns:\n A float: F_lcs\n '
f1_scores = []
for (eval_sentence, ref_sentence) in zip(eval_sentences, ref_sentences):
m = float(len(ref_sentence))
n = float(len(eval_sentence))
lcs = _len_lcs(eval_sentence, ref_sentence)
f1_scores.append(_f_lcs(lcs, m, n))
return np.mean(f1_scores, dtype=np.float32) | -6,966,317,381,581,726,000 | Computes ROUGE-L (sentence level) of two collections of sentences.
Source: https://www.microsoft.com/en-us/research/publication/
rouge-a-package-for-automatic-evaluation-of-summaries/
Calculated according to:
R_lcs = LCS(X,Y)/m
P_lcs = LCS(X,Y)/n
F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
where:
X = reference summary
Y = Candidate summary
m = length of reference summary
n = length of candidate summary
Args:
eval_sentences: The sentences that have been picked by the summarizer
ref_sentences: The sentences from the reference set
Returns:
A float: F_lcs | official/nlp/transformer/utils/metrics.py | rouge_l_sentence_level | 1110sillabo/models | python | def rouge_l_sentence_level(eval_sentences, ref_sentences):
'Computes ROUGE-L (sentence level) of two collections of sentences.\n\n Source: https://www.microsoft.com/en-us/research/publication/\n rouge-a-package-for-automatic-evaluation-of-summaries/\n\n Calculated according to:\n R_lcs = LCS(X,Y)/m\n P_lcs = LCS(X,Y)/n\n F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)\n\n where:\n X = reference summary\n Y = Candidate summary\n m = length of reference summary\n n = length of candidate summary\n\n Args:\n eval_sentences: The sentences that have been picked by the summarizer\n ref_sentences: The sentences from the reference set\n\n Returns:\n A float: F_lcs\n '
f1_scores = []
for (eval_sentence, ref_sentence) in zip(eval_sentences, ref_sentences):
m = float(len(ref_sentence))
n = float(len(eval_sentence))
lcs = _len_lcs(eval_sentence, ref_sentence)
f1_scores.append(_f_lcs(lcs, m, n))
return np.mean(f1_scores, dtype=np.float32) |
def _len_lcs(x, y):
'Returns the length of the Longest Common Subsequence between two seqs.\n\n Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n Args:\n x: sequence of words\n y: sequence of words\n\n Returns\n integer: Length of LCS between x and y\n '
table = _lcs(x, y)
(n, m) = (len(x), len(y))
return table[(n, m)] | 8,835,899,292,213,967,000 | Returns the length of the Longest Common Subsequence between two seqs.
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: sequence of words
y: sequence of words
Returns
integer: Length of LCS between x and y | official/nlp/transformer/utils/metrics.py | _len_lcs | 1110sillabo/models | python | def _len_lcs(x, y):
'Returns the length of the Longest Common Subsequence between two seqs.\n\n Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n Args:\n x: sequence of words\n y: sequence of words\n\n Returns\n integer: Length of LCS between x and y\n '
table = _lcs(x, y)
(n, m) = (len(x), len(y))
return table[(n, m)] |
def _lcs(x, y):
'Computes the length of the LCS between two seqs.\n\n The implementation below uses a DP programming algorithm and runs\n in O(nm) time where n = len(x) and m = len(y).\n Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n Args:\n x: collection of words\n y: collection of words\n\n Returns:\n Table of dictionary of coord and len lcs\n '
(n, m) = (len(x), len(y))
table = dict()
for i in range((n + 1)):
for j in range((m + 1)):
if ((i == 0) or (j == 0)):
table[(i, j)] = 0
elif (x[(i - 1)] == y[(j - 1)]):
table[(i, j)] = (table[((i - 1), (j - 1))] + 1)
else:
table[(i, j)] = max(table[((i - 1), j)], table[(i, (j - 1))])
return table | 4,657,099,889,117,814,000 | Computes the length of the LCS between two seqs.
The implementation below uses a DP programming algorithm and runs
in O(nm) time where n = len(x) and m = len(y).
Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence
Args:
x: collection of words
y: collection of words
Returns:
Table of dictionary of coord and len lcs | official/nlp/transformer/utils/metrics.py | _lcs | 1110sillabo/models | python | def _lcs(x, y):
'Computes the length of the LCS between two seqs.\n\n The implementation below uses a DP programming algorithm and runs\n in O(nm) time where n = len(x) and m = len(y).\n Source: http://www.algorithmist.com/index.php/Longest_Common_Subsequence\n\n Args:\n x: collection of words\n y: collection of words\n\n Returns:\n Table of dictionary of coord and len lcs\n '
(n, m) = (len(x), len(y))
table = dict()
for i in range((n + 1)):
for j in range((m + 1)):
if ((i == 0) or (j == 0)):
table[(i, j)] = 0
elif (x[(i - 1)] == y[(j - 1)]):
table[(i, j)] = (table[((i - 1), (j - 1))] + 1)
else:
table[(i, j)] = max(table[((i - 1), j)], table[(i, (j - 1))])
return table |
def _f_lcs(llcs, m, n):
'Computes the LCS-based F-measure score.\n\n Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n rouge-working-note-v1.3.1.pdf\n\n Args:\n llcs: Length of LCS\n m: number of words in reference summary\n n: number of words in candidate summary\n\n Returns:\n Float. LCS-based F-measure score\n '
r_lcs = (llcs / m)
p_lcs = (llcs / n)
beta = (p_lcs / (r_lcs + 1e-12))
num = (((1 + (beta ** 2)) * r_lcs) * p_lcs)
denom = (r_lcs + ((beta ** 2) * p_lcs))
f_lcs = (num / (denom + 1e-12))
return f_lcs | 1,444,177,794,281,056,500 | Computes the LCS-based F-measure score.
Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/
rouge-working-note-v1.3.1.pdf
Args:
llcs: Length of LCS
m: number of words in reference summary
n: number of words in candidate summary
Returns:
Float. LCS-based F-measure score | official/nlp/transformer/utils/metrics.py | _f_lcs | 1110sillabo/models | python | def _f_lcs(llcs, m, n):
'Computes the LCS-based F-measure score.\n\n Source: http://research.microsoft.com/en-us/um/people/cyl/download/papers/\n rouge-working-note-v1.3.1.pdf\n\n Args:\n llcs: Length of LCS\n m: number of words in reference summary\n n: number of words in candidate summary\n\n Returns:\n Float. LCS-based F-measure score\n '
r_lcs = (llcs / m)
p_lcs = (llcs / n)
beta = (p_lcs / (r_lcs + 1e-12))
num = (((1 + (beta ** 2)) * r_lcs) * p_lcs)
denom = (r_lcs + ((beta ** 2) * p_lcs))
f_lcs = (num / (denom + 1e-12))
return f_lcs |
def problem_metric_fn(*args):
"Returns an aggregation of the metric_fn's returned values."
(scores, weights) = metric_fn(*args)
return tf.metrics.mean(scores, weights) | 1,779,688,788,499,172,900 | Returns an aggregation of the metric_fn's returned values. | official/nlp/transformer/utils/metrics.py | problem_metric_fn | 1110sillabo/models | python | def problem_metric_fn(*args):
(scores, weights) = metric_fn(*args)
return tf.metrics.mean(scores, weights) |
def __init__(self, app=None):
'Initialize the Flask-CLI.'
if (app is not None):
self.init_app(app) | 3,250,348,104,500,289,500 | Initialize the Flask-CLI. | virtual/lib/python3.6/site-packages/flask_cli/ext.py | __init__ | muneneee/blog | python | def __init__(self, app=None):
if (app is not None):
self.init_app(app) |
def init_app(self, app):
'Initialize a Flask application.'
if (not hasattr(app, 'extensions')):
app.extensions = {}
if ('flask-cli' in app.extensions):
raise RuntimeError('Flask-CLI application already initialized')
app.extensions['flask-cli'] = self
self.setup_pre10(app) | -4,036,279,792,543,169,000 | Initialize a Flask application. | virtual/lib/python3.6/site-packages/flask_cli/ext.py | init_app | muneneee/blog | python | def init_app(self, app):
if (not hasattr(app, 'extensions')):
app.extensions = {}
if ('flask-cli' in app.extensions):
raise RuntimeError('Flask-CLI application already initialized')
app.extensions['flask-cli'] = self
self.setup_pre10(app) |
def setup_pre10(self, app):
'Setup Flask pre-1.0 application object.'
if hasattr(app, 'cli'):
return
from flask_cli.app import make_shell_context, shell_context_processor
app.cli = AppGroup(app.name)
app.shell_context_processors = []
app.make_shell_context = types.MethodType(make_shell_context, app)
app.shell_context_processor = types.MethodType(shell_context_processor, app) | 8,057,332,060,420,983,000 | Setup Flask pre-1.0 application object. | virtual/lib/python3.6/site-packages/flask_cli/ext.py | setup_pre10 | muneneee/blog | python | def setup_pre10(self, app):
if hasattr(app, 'cli'):
return
from flask_cli.app import make_shell_context, shell_context_processor
app.cli = AppGroup(app.name)
app.shell_context_processors = []
app.make_shell_context = types.MethodType(make_shell_context, app)
app.shell_context_processor = types.MethodType(shell_context_processor, app) |
def test_qnode_intergration():
'Test a simple use of qnode with a JAX interface and non-JAX device'
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='jax')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RZ(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
weights = jnp.array([0.1, 0.2])
val = circuit(weights)
assert ('DeviceArray' in val.__repr__()) | 7,754,218,695,551,231,000 | Test a simple use of qnode with a JAX interface and non-JAX device | tests/tape/interfaces/test_qnode_jax.py | test_qnode_intergration | PritishSehzpaul/pennylane | python | def test_qnode_intergration():
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='jax')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RZ(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
weights = jnp.array([0.1, 0.2])
val = circuit(weights)
assert ('DeviceArray' in val.__repr__()) |
def test_to_jax():
'Test the to_jax method'
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='autograd')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RZ(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
circuit.to_jax()
weights = jnp.array([0.1, 0.2])
val = circuit(weights)
assert ('DeviceArray' in val.__repr__()) | 5,437,203,877,633,381,000 | Test the to_jax method | tests/tape/interfaces/test_qnode_jax.py | test_to_jax | PritishSehzpaul/pennylane | python | def test_to_jax():
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='autograd')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RZ(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
circuit.to_jax()
weights = jnp.array([0.1, 0.2])
val = circuit(weights)
assert ('DeviceArray' in val.__repr__()) |
def test_simple_jacobian():
'Test the use of jax.jaxrev'
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='jax', diff_method='parameter-shift')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RY(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
weights = jnp.array([0.1, 0.2])
grads = jax.jacrev(circuit)(weights)
assert ('DeviceArray' in grads.__repr__())
assert (grads.shape == (2,))
np.testing.assert_allclose(grads, np.array([(- 0.09784342), (- 0.19767685)])) | -2,640,744,455,169,629,000 | Test the use of jax.jaxrev | tests/tape/interfaces/test_qnode_jax.py | test_simple_jacobian | PritishSehzpaul/pennylane | python | def test_simple_jacobian():
dev = qml.device('default.mixed', wires=2)
@qml.qnode(dev, interface='jax', diff_method='parameter-shift')
def circuit(weights):
qml.RX(weights[0], wires=0)
qml.RY(weights[1], wires=1)
return qml.expval((qml.PauliZ(0) @ qml.PauliZ(1)))
weights = jnp.array([0.1, 0.2])
grads = jax.jacrev(circuit)(weights)
assert ('DeviceArray' in grads.__repr__())
assert (grads.shape == (2,))
np.testing.assert_allclose(grads, np.array([(- 0.09784342), (- 0.19767685)])) |
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