def dict_key_summary(self):
"""
Returns the sketch summary for all dictionary keys. This is only valid
for sketch object from an SArray of dict type. Dictionary keys are
converted to strings and then do the sketch summary.
Examples
--------
>>> sa = graphlab.SArray([{'I':1, 'love': 2}, {'nature':3, 'beauty':4}])
>>> sa.sketch_summary().dict_key_summary()
+------------------+-------+----------+
| item | value | is exact |
+------------------+-------+----------+
| Length | 4 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 4 | No |
+------------------+-------+----------+
Most frequent items:
+-------+---+------+--------+--------+
| value | I | love | beauty | nature |
+-------+---+------+--------+--------+
| count | 1 | 1 | 1 | 1 |
+-------+---+------+--------+--------+
"""
_mt._get_metric_tracker().track('sketch.dict_key_summary')
with cython_context():
return Sketch(_proxy = self.__proxy__.dict_key_summary())
def remove_column(self, name):
"""
Removes the column with the given name from the SFrame.
Parameters
----------
name : string
The name of the column to remove.
"""
if name not in self.column_names():
raise KeyError('Cannot find column %s' % name)
self.__is_dirty__ = True
try:
with cython_context():
if self._is_vertex_frame():
assert name != '__id', 'Cannot remove \"__id\" column'
graph_proxy = self.__graph__.__proxy__.delete_vertex_field(name)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
assert name != '__src_id', 'Cannot remove \"__src_id\" column'
assert name != '__dst_id', 'Cannot remove \"__dst_id\" column'
graph_proxy = self.__graph__.__proxy__.delete_edge_field(name)
self.__graph__.__proxy__ = graph_proxy
except:
self.__is_dirty__ = False
raise
def frequency_count(self, element):
"""
Returns a sketched estimate of the number of occurrences of a given
element. This estimate is based on the count sketch. The element type
must be of the same type as the input SArray. Throws an exception if
element is of the incorrect type.
Parameters
----------
element : val
An element of the same type as the SArray.
Raises
------
RuntimeError
Throws an exception if element is of the incorrect type.
Returns
-------
out : int
An estimate of the number of occurrences of the element.
"""
_mt._get_metric_tracker().track('sketch.frequency_count')
with cython_context():
return int(self.__proxy__.frequency_count(element))
def frequency_count(self, element):
"""
Returns a sketched estimate of the number of occurrences of a given
element. This estimate is based on the count sketch. The element type
must be of the same type as the input SArray. Throws an exception if
element is of the incorrect type.
Parameters
----------
element : val
An element of the same type as the SArray.
Raises
------
RuntimeError
Throws an exception if element is of the incorrect type.
Returns
-------
out : int
An estimate of the number of occurrences of the element.
"""
_mt._get_metric_tracker().track('sketch.frequency_count')
with cython_context():
return int(self.__proxy__.frequency_count(element))
def quantile(self, quantile_val):
"""
Returns a sketched estimate of the value at a particular quantile
between 0.0 and 1.0. The quantile is guaranteed to be accurate within
1%: meaning that if you ask for the 0.55 quantile, the returned value is
guaranteed to be between the true 0.54 quantile and the true 0.56
quantile. The quantiles are only defined for numeric arrays and this
function will throw an exception if called on a sketch constructed for a
non-numeric column.
Parameters
----------
quantile_val : float
A value between 0.0 and 1.0 inclusive. Values below 0.0 will be
interpreted as 0.0. Values above 1.0 will be interpreted as 1.0.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : float | str
An estimate of the value at a quantile.
"""
_mt._get_metric_tracker().track('sketch.quantile.%g' % quantile_val)
with cython_context():
return self.__proxy__.get_quantile(quantile_val)
def dict_key_summary(self):
"""
Returns the sketch summary for all dictionary keys. This is only valid
for sketch object from an SArray of dict type. Dictionary keys are
converted to strings and then do the sketch summary.
Examples
--------
>>> sa = graphlab.SArray([{'I':1, 'love': 2}, {'nature':3, 'beauty':4}])
>>> sa.sketch_summary().dict_key_summary()
+------------------+-------+----------+
| item | value | is exact |
+------------------+-------+----------+
| Length | 4 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 4 | No |
+------------------+-------+----------+
Most frequent items:
+-------+---+------+--------+--------+
| value | I | love | beauty | nature |
+-------+---+------+--------+--------+
| count | 1 | 1 | 1 | 1 |
+-------+---+------+--------+--------+
"""
_mt._get_metric_tracker().track('sketch.dict_key_summary')
with cython_context():
return Sketch(_proxy=self.__proxy__.dict_key_summary())
def add_column(self, data, name=""):
"""
Adds the specified column to this SFrame. The number of elements in
the data given must match every other column of the SFrame.
Parameters
----------
data : SArray
The 'column' of data.
name : string
The name of the column. If no name is given, a default name is chosen.
"""
# Check type for pandas dataframe or SArray?
if not isinstance(data, SArray):
raise TypeError("Must give column as SArray")
if not isinstance(name, str):
raise TypeError("Invalid column name: must be str")
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.add_vertex_field(
data.__proxy__, name)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.add_edge_field(
data.__proxy__, name)
self.__graph__.__proxy__ = graph_proxy
def quantile(self, quantile_val):
"""
Returns a sketched estimate of the value at a particular quantile
between 0.0 and 1.0. The quantile is guaranteed to be accurate within
1%: meaning that if you ask for the 0.55 quantile, the returned value is
guaranteed to be between the true 0.54 quantile and the true 0.56
quantile. The quantiles are only defined for numeric arrays and this
function will throw an exception if called on a sketch constructed for a
non-numeric column.
Parameters
----------
quantile_val : float
A value between 0.0 and 1.0 inclusive. Values below 0.0 will be
interpreted as 0.0. Values above 1.0 will be interpreted as 1.0.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : float | str
An estimate of the value at a quantile.
"""
_mt._get_metric_tracker().track('sketch.quantile.%g' % quantile_val)
with cython_context():
return self.__proxy__.get_quantile(quantile_val)
def remove_column(self, name):
"""
Removes the column with the given name from the SFrame.
Parameters
----------
name : string
The name of the column to remove.
"""
if name not in self.column_names():
raise KeyError('Cannot find column %s' % name)
self.__is_dirty__ = True
try:
with cython_context():
if self._is_vertex_frame():
assert name != '__id', 'Cannot remove \"__id\" column'
graph_proxy = self.__graph__.__proxy__.delete_vertex_field(
name)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
assert name != '__src_id', 'Cannot remove \"__src_id\" column'
assert name != '__dst_id', 'Cannot remove \"__dst_id\" column'
graph_proxy = self.__graph__.__proxy__.delete_edge_field(
name)
self.__graph__.__proxy__ = graph_proxy
except:
self.__is_dirty__ = False
raise
def add_column(self, data, name=""):
"""
Adds the specified column to this SFrame. The number of elements in
the data given must match every other column of the SFrame.
Parameters
----------
data : SArray
The 'column' of data.
name : string
The name of the column. If no name is given, a default name is chosen.
"""
# Check type for pandas dataframe or SArray?
if not isinstance(data, SArray):
raise TypeError("Must give column as SArray")
if not isinstance(name, str):
raise TypeError("Invalid column name: must be str")
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.add_vertex_field(data.__proxy__, name)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.add_edge_field(data.__proxy__, name)
self.__graph__.__proxy__ = graph_proxy
def sketch_ready(self):
"""
Returns true if the sketch has been executed on all the data.
If the sketch is created with background == False (default), this will
always return True. Otherwise, this will return False until the sketch
is ready.
"""
with cython_context():
return self.__proxy__.sketch_ready()
def num_elements_processed(self):
"""
Returns the number of elements processed so far.
If the sketch is created with background == False (default), this will
always return the length of the input array. Otherwise, this will
return the number of elements processed so far.
"""
with cython_context():
return self.__proxy__.num_elements_processed()
def sketch_ready(self):
"""
Returns true if the sketch has been executed on all the data.
If the sketch is created with background == False (default), this will
always return True. Otherwise, this will return False until the sketch
is ready.
"""
with cython_context():
return self.__proxy__.sketch_ready()
def num_elements_processed(self):
"""
Returns the number of elements processed so far.
If the sketch is created with background == False (default), this will
always return the length of the input array. Otherwise, this will
return the number of elements processed so far.
"""
with cython_context():
return self.__proxy__.num_elements_processed()
def size(self):
"""
Returns the size of the input SArray.
Returns
-------
out : int
The number of elements of the input SArray.
"""
with cython_context():
return int(self.__proxy__.size())
def size(self):
"""
Returns the size of the input SArray.
Returns
-------
out : int
The number of elements of the input SArray.
"""
with cython_context():
return int(self.__proxy__.size())
def num_undefined(self):
"""
Returns the the number of undefined elements in the SArray. Return 0
on an empty SArray.
Returns
-------
out : int
The number of missing values in the SArray.
"""
with cython_context():
return int(self.__proxy__.num_undefined())
def cancel(self):
"""
Cancels a background sketch computation immediately if one is ongoing.
Does nothing otherwise.
Examples
--------
>>> s = sa.sketch_summary(array, background=True)
>>> s.cancel()
"""
with cython_context():
self.__proxy__.cancel()
def num_undefined(self):
"""
Returns the the number of undefined elements in the SArray. Return 0
on an empty SArray.
Returns
-------
out : int
The number of missing values in the SArray.
"""
with cython_context():
return int(self.__proxy__.num_undefined())
def _run_toolkit_function(fnname, arguments, args, kwargs):
"""
Dispatches arguments to a toolkit function.
Parameters
----------
fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
The arguments that were passed
kwargs : dictionary
The keyword arguments that were passed
"""
# scan for all the arguments in args
num_args_got = len(args) + len(kwargs)
num_args_required = len(arguments)
if num_args_got != num_args_required:
raise TypeError("Expecting " + str(num_args_required) +
" arguments, got " + str(num_args_got))
## fill the dict first with the regular args
argument_dict = {}
for i in range(len(args)):
argument_dict[arguments[i]] = args[i]
# now fill with the kwargs.
for k in kwargs.keys():
if k in argument_dict:
raise TypeError("Got multiple values for keyword argument '" + k +
"'")
argument_dict[k] = kwargs[k]
argument_dict = _translate_function_arguments(argument_dict)
# unwrap it
with cython_context():
ret = _gl.connect.main.get_unity().run_toolkit(fnname, argument_dict)
# handle errors
if ret[0] != True:
if len(ret[1]) > 0:
raise _ToolkitError(ret[1])
else:
raise _ToolkitError("Toolkit failed with unknown error")
ret = _wrap_function_return(ret[2])
if type(ret) == dict and 'return_value' in ret:
return ret['return_value']
else:
return ret
def cancel(self):
"""
Cancels a background sketch computation immediately if one is ongoing.
Does nothing otherwise.
Examples
--------
>>> s = sa.sketch_summary(array, background=True)
>>> s.cancel()
"""
with cython_context():
self.__proxy__.cancel()
def _run_toolkit_function(fnname, arguments, args, kwargs):
"""
Dispatches arguments to a toolkit function.
Parameters
----------
fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
The arguments that were passed
kwargs : dictionary
The keyword arguments that were passed
"""
# scan for all the arguments in args
num_args_got = len(args) + len(kwargs)
num_args_required = len(arguments)
if num_args_got != num_args_required:
raise TypeError("Expecting " + str(num_args_required) + " arguments, got " + str(num_args_got))
## fill the dict first with the regular args
argument_dict = {}
for i in range(len(args)):
argument_dict[arguments[i]] = args[i]
# now fill with the kwargs.
for k in kwargs.keys():
if k in argument_dict:
raise TypeError("Got multiple values for keyword argument '" + k + "'")
argument_dict[k] = kwargs[k]
argument_dict = _translate_function_arguments(argument_dict)
# unwrap it
with cython_context():
ret = _gl.connect.main.get_unity().run_toolkit(fnname, argument_dict)
# handle errors
if ret[0] != True:
if len(ret[1]) > 0:
raise _ToolkitError(ret[1])
else:
raise _ToolkitError("Toolkit failed with unknown error")
ret = _wrap_function_return(ret[2])
if type(ret) == dict and 'return_value' in ret:
return ret['return_value']
else:
return ret
def num_unique(self):
"""
Returns a sketched estimate of the number of unique values in the
SArray based on the Hyperloglog sketch.
Returns
-------
out : float
An estimate of the number of unique values in the SArray.
"""
_mt._get_metric_tracker().track('sketch.num_unique')
with cython_context():
return int(self.__proxy__.num_unique())
def num_unique(self):
"""
Returns a sketched estimate of the number of unique values in the
SArray based on the Hyperloglog sketch.
Returns
-------
out : float
An estimate of the number of unique values in the SArray.
"""
_mt._get_metric_tracker().track('sketch.num_unique')
with cython_context():
return int(self.__proxy__.num_unique())
def var(self):
"""
Returns the variance of the values in the sarray. Returns 0 on an empty
array. Throws an exception if called on an SArray with non-numeric type.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : float
The variance of all the values. Returns 0 if the SArray is empty.
"""
with cython_context():
return self.__proxy__.var()
def var(self):
"""
Returns the variance of the values in the sarray. Returns 0 on an empty
array. Throws an exception if called on an SArray with non-numeric type.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : float
The variance of all the values. Returns 0 if the SArray is empty.
"""
with cython_context():
return self.__proxy__.var()
def min(self):
"""
Returns the minimum value in the SArray. Returns *nan* on an empty
array. Throws an exception if called on an SArray with non-numeric type.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : type of SArray
Minimum value of SArray. Returns nan if the sarray is empty.
"""
with cython_context():
return self.__proxy__.min()
def min(self):
"""
Returns the minimum value in the SArray. Returns *nan* on an empty
array. Throws an exception if called on an SArray with non-numeric type.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : type of SArray
Minimum value of SArray. Returns nan if the sarray is empty.
"""
with cython_context():
return self.__proxy__.min()
def element_length_summary(self):
"""
Returns the sketch summary for the element length. This is only valid for
a sketch constructed SArray of type list/array/dict, raises Runtime
exception otherwise.
Examples
--------
>>> sa = graphlab.SArray([[j for j in range(i)] for i in range(1,1000)])
>>> sa.sketch_summary().element_length_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 999 | Yes |
| Min | 1.0 | Yes |
| Max | 999.0 | Yes |
| Mean | 500.0 | Yes |
| Sum | 499500.0 | Yes |
| Variance | 83166.6666667 | Yes |
| Standard Deviation | 288.386314978 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 992 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+---+---+---+---+---+---+---+---+---+----+
| value | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
+-------+---+---+---+---+---+---+---+---+---+----+
| count | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
+-------+---+---+---+---+---+---+---+---+---+----+
Quantiles:
+-----+------+------+-------+-------+-------+-------+-------+-------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+------+------+-------+-------+-------+-------+-------+-------+
| 1.0 | 10.0 | 50.0 | 250.0 | 500.0 | 750.0 | 950.0 | 990.0 | 999.0 |
+-----+------+------+-------+-------+-------+-------+-------+-------+
Returns
-------
out : Sketch
An new sketch object regarding the element length of the current SArray
"""
_mt._get_metric_tracker().track('sketch.element_length_summary')
with cython_context():
return Sketch(_proxy = self.__proxy__.element_length_summary())
def element_length_summary(self):
"""
Returns the sketch summary for the element length. This is only valid for
a sketch constructed SArray of type list/array/dict, raises Runtime
exception otherwise.
Examples
--------
>>> sa = graphlab.SArray([[j for j in range(i)] for i in range(1,1000)])
>>> sa.sketch_summary().element_length_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 999 | Yes |
| Min | 1.0 | Yes |
| Max | 999.0 | Yes |
| Mean | 500.0 | Yes |
| Sum | 499500.0 | Yes |
| Variance | 83166.6666667 | Yes |
| Standard Deviation | 288.386314978 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 992 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+---+---+---+---+---+---+---+---+---+----+
| value | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
+-------+---+---+---+---+---+---+---+---+---+----+
| count | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
+-------+---+---+---+---+---+---+---+---+---+----+
Quantiles:
+-----+------+------+-------+-------+-------+-------+-------+-------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+------+------+-------+-------+-------+-------+-------+-------+
| 1.0 | 10.0 | 50.0 | 250.0 | 500.0 | 750.0 | 950.0 | 990.0 | 999.0 |
+-----+------+------+-------+-------+-------+-------+-------+-------+
Returns
-------
out : Sketch
An new sketch object regarding the element length of the current SArray
"""
_mt._get_metric_tracker().track('sketch.element_length_summary')
with cython_context():
return Sketch(_proxy=self.__proxy__.element_length_summary())
def sum(self):
"""
Returns the sum of all the values in the SArray. Returns 0 on an empty
array. Throws an exception if called on an sarray with non-numeric type.
Will overflow without warning.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : type of SArray
Sum of all values in SArray. Returns 0 if the SArray is empty.
"""
with cython_context():
return self.__proxy__.sum()
def sum(self):
"""
Returns the sum of all the values in the SArray. Returns 0 on an empty
array. Throws an exception if called on an sarray with non-numeric type.
Will overflow without warning.
Raises
------
RuntimeError
If the sarray is a non-numeric type.
Returns
-------
out : type of SArray
Sum of all values in SArray. Returns 0 if the SArray is empty.
"""
with cython_context():
return self.__proxy__.sum()
def dict_value_summary(self):
"""
Returns the sketch summary for all dictionary values. This is only valid
for sketch object from an SArray of dict type.
Type of value summary is inferred from first set of values.
Examples
--------
>>> sa = graphlab.SArray([{'I':1, 'love': 2}, {'nature':3, 'beauty':4}])
>>> sa.sketch_summary().dict_value_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 4 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 10.0 | Yes |
| Variance | 1.25 | Yes |
| Standard Deviation | 1.11803398875 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 4 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+-----+-----+-----+-----+
| value | 1.0 | 2.0 | 3.0 | 4.0 |
+-------+-----+-----+-----+-----+
| count | 1 | 1 | 1 | 1 |
+-------+-----+-----+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
"""
_mt._get_metric_tracker().track('sketch.dict_value_summary')
with cython_context():
return Sketch(_proxy=self.__proxy__.dict_value_summary())
def dict_value_summary(self):
"""
Returns the sketch summary for all dictionary values. This is only valid
for sketch object from an SArray of dict type.
Type of value summary is inferred from first set of values.
Examples
--------
>>> sa = graphlab.SArray([{'I':1, 'love': 2}, {'nature':3, 'beauty':4}])
>>> sa.sketch_summary().dict_value_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 4 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 10.0 | Yes |
| Variance | 1.25 | Yes |
| Standard Deviation | 1.11803398875 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 4 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+-----+-----+-----+-----+
| value | 1.0 | 2.0 | 3.0 | 4.0 |
+-------+-----+-----+-----+-----+
| count | 1 | 1 | 1 | 1 |
+-------+-----+-----+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
"""
_mt._get_metric_tracker().track('sketch.dict_value_summary')
with cython_context():
return Sketch(_proxy = self.__proxy__.dict_value_summary())
def swap_columns(self, column_1, column_2):
"""
Swaps the columns with the given names.
Parameters
----------
column_1 : string
Name of column to swap
column_2 : string
Name of other column to swap
"""
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.swap_vertex_fields(column_1, column_2)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.swap_edge_fields(column_1, column_2)
self.__graph__.__proxy__ = graph_proxy
def element_summary(self):
"""
Returns the sketch summary for all element values. This is only valid for
sketch object created from SArray of list or vector(array) type.
For SArray of list type, all list values are treated as string for
sketch summary.
For SArray of vector type, the sketch summary is on FLOAT type.
Examples
--------
>>> sa = graphlab.SArray([[1,2,3], [4,5]])
>>> sa.sketch_summary().element_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 5 | Yes |
| Min | 1.0 | Yes |
| Max | 5.0 | Yes |
| Mean | 3.0 | Yes |
| Sum | 15.0 | Yes |
| Variance | 2.0 | Yes |
| Standard Deviation | 1.41421356237 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 5 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+-----+-----+-----+-----+-----+
| value | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 |
+-------+-----+-----+-----+-----+-----+
| count | 1 | 1 | 1 | 1 | 1 |
+-------+-----+-----+-----+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 | 5.0 | 5.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
"""
_mt._get_metric_tracker().track('sketch.element_summary')
with cython_context():
return Sketch(_proxy = self.__proxy__.element_summary())
def element_summary(self):
"""
Returns the sketch summary for all element values. This is only valid for
sketch object created from SArray of list or vector(array) type.
For SArray of list type, all list values are treated as string for
sketch summary.
For SArray of vector type, the sketch summary is on FLOAT type.
Examples
--------
>>> sa = graphlab.SArray([[1,2,3], [4,5]])
>>> sa.sketch_summary().element_summary()
+--------------------+---------------+----------+
| item | value | is exact |
+--------------------+---------------+----------+
| Length | 5 | Yes |
| Min | 1.0 | Yes |
| Max | 5.0 | Yes |
| Mean | 3.0 | Yes |
| Sum | 15.0 | Yes |
| Variance | 2.0 | Yes |
| Standard Deviation | 1.41421356237 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 5 | No |
+--------------------+---------------+----------+
Most frequent items:
+-------+-----+-----+-----+-----+-----+
| value | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 |
+-------+-----+-----+-----+-----+-----+
| count | 1 | 1 | 1 | 1 | 1 |
+-------+-----+-----+-----+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 5.0 | 5.0 | 5.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
"""
_mt._get_metric_tracker().track('sketch.element_summary')
with cython_context():
return Sketch(_proxy=self.__proxy__.element_summary())
def rename(self, names):
"""
Rename the columns using the 'names' dict. This changes the names of
the columns given as the keys and replaces them with the names given as
the values.
Parameters
----------
names : dict[string, string]
Dictionary of [old_name, new_name]
"""
if (type(names) is not dict):
raise TypeError('names must be a dictionary: oldname -> newname')
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.rename_vertex_fields(names.keys(), names.values())
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.rename_edge_fields(names.keys(), names.values())
self.__graph__.__proxy__ = graph_proxy
def swap_columns(self, column_1, column_2):
"""
Swaps the columns with the given names.
Parameters
----------
column_1 : string
Name of column to swap
column_2 : string
Name of other column to swap
"""
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.swap_vertex_fields(
column_1, column_2)
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.swap_edge_fields(
column_1, column_2)
self.__graph__.__proxy__ = graph_proxy
def frequent_items(self):
"""
Returns a sketched estimate of the most frequent elements in the SArray
based on the SpaceSaving sketch. It is only guaranteed that all
elements which appear in more than 0.01% rows of the array will
appear in the set of returned elements. However, other elements may
also appear in the result. The item counts are estimated using
the CountSketch.
Missing values are not taken into account when copmuting frequent items.
If this function returns no elements, it means that all elements appear
with less than 0.01% occurrence.
Returns
-------
out : dict
A dictionary mapping items and their estimated occurrence frequencies.
"""
_mt._get_metric_tracker().track('sketch.frequent_items')
with cython_context():
return self.__proxy__.frequent_items()
def frequent_items(self):
"""
Returns a sketched estimate of the most frequent elements in the SArray
based on the SpaceSaving sketch. It is only guaranteed that all
elements which appear in more than 0.01% rows of the array will
appear in the set of returned elements. However, other elements may
also appear in the result. The item counts are estimated using
the CountSketch.
Missing values are not taken into account when copmuting frequent items.
If this function returns no elements, it means that all elements appear
with less than 0.01% occurrence.
Returns
-------
out : dict
A dictionary mapping items and their estimated occurrence frequencies.
"""
_mt._get_metric_tracker().track('sketch.frequent_items')
with cython_context():
return self.__proxy__.frequent_items()
def rename(self, names):
"""
Rename the columns using the 'names' dict. This changes the names of
the columns given as the keys and replaces them with the names given as
the values.
Parameters
----------
names : dict[string, string]
Dictionary of [old_name, new_name]
"""
if (type(names) is not dict):
raise TypeError('names must be a dictionary: oldname -> newname')
self.__is_dirty__ = True
with cython_context():
if self._is_vertex_frame():
graph_proxy = self.__graph__.__proxy__.rename_vertex_fields(
names.keys(), names.values())
self.__graph__.__proxy__ = graph_proxy
elif self._is_edge_frame():
graph_proxy = self.__graph__.__proxy__.rename_edge_fields(
names.keys(), names.values())
self.__graph__.__proxy__ = graph_proxy
def element_sub_sketch(self, keys=None):
"""
Returns the sketch summary for the given set of keys. This is only
applicable for sketch summary created from SArray of sarray or dict type.
For dict SArray, the keys are the keys in dict value.
For array Sarray, the keys are indexes into the array value.
The keys must be passed into original sketch_summary() call in order to
be able to be retrieved later
Parameters
-----------
keys : list of str | str | list of int | int
The list of dictionary keys or array index to get sub sketch from.
if not given, then retrieve all sub sketches that are available
Returns
-------
A dictionary that maps from the key(index) to the actual sketch summary
for that key(index)
Examples
--------
>>> sa = graphlab.SArray([{'a':1, 'b':2}, {'a':4, 'd':1}])
>>> s = sa.sketch_summary(sub_sketch_keys=['a','b'])
>>> s.element_sub_sketch(['a'])
{'a':
+--------------------+-------+----------+
| item | value | is exact |
+--------------------+-------+----------+
| Length | 2 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 5.0 | Yes |
| Variance | 2.25 | Yes |
| Standard Deviation | 1.5 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 2 | No |
+--------------------+-------+----------+
Most frequent items:
+-------+-----+-----+
| value | 1.0 | 4.0 |
+-------+-----+-----+
| count | 1 | 1 |
+-------+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+}
"""
single_val = False
if keys == None:
keys = []
else:
if not hasattr(keys, "__iter__"):
single_val = True
keys = [keys]
value_types = set([type(i) for i in keys])
if (len(value_types) > 1):
raise ValueError("All keys should have the same type.")
_mt._get_metric_tracker().track('sketch.element_sub_sketch')
with cython_context():
ret_sketches = self.__proxy__.element_sub_sketch(keys)
ret = {}
# check return key matches input key
for key in keys:
if key not in ret_sketches:
raise KeyError(
"Cannot retrieve element sub sketch for key '" +
str(key) +
"'. Element sub sketch can only be retrieved when the sketch_summary object was created using the 'sub_sketch_keys' option."
)
for key in ret_sketches:
ret[key] = Sketch(_proxy=ret_sketches[key])
if single_val:
return ret[keys[0]]
else:
return ret
def element_sub_sketch(self, keys = None):
"""
Returns the sketch summary for the given set of keys. This is only
applicable for sketch summary created from SArray of sarray or dict type.
For dict SArray, the keys are the keys in dict value.
For array Sarray, the keys are indexes into the array value.
The keys must be passed into original sketch_summary() call in order to
be able to be retrieved later
Parameters
-----------
keys : list of str | str | list of int | int
The list of dictionary keys or array index to get sub sketch from.
if not given, then retrieve all sub sketches that are available
Returns
-------
A dictionary that maps from the key(index) to the actual sketch summary
for that key(index)
Examples
--------
>>> sa = graphlab.SArray([{'a':1, 'b':2}, {'a':4, 'd':1}])
>>> s = sa.sketch_summary(sub_sketch_keys=['a','b'])
>>> s.element_sub_sketch(['a'])
{'a':
+--------------------+-------+----------+
| item | value | is exact |
+--------------------+-------+----------+
| Length | 2 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 5.0 | Yes |
| Variance | 2.25 | Yes |
| Standard Deviation | 1.5 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 2 | No |
+--------------------+-------+----------+
Most frequent items:
+-------+-----+-----+
| value | 1.0 | 4.0 |
+-------+-----+-----+
| count | 1 | 1 |
+-------+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+}
"""
single_val = False
if keys == None:
keys = []
else:
if not hasattr(keys, "__iter__"):
single_val = True
keys = [keys]
value_types = set([type(i) for i in keys])
if (len(value_types) > 1):
raise ValueError("All keys should have the same type.")
_mt._get_metric_tracker().track('sketch.element_sub_sketch')
with cython_context():
ret_sketches = self.__proxy__.element_sub_sketch(keys)
ret = {}
# check return key matches input key
for key in keys:
if key not in ret_sketches:
raise KeyError("Cannot retrieve element sub sketch for key '" + str(key) + "'. Element sub sketch can only be retrieved when the sketch_summary object was created using the 'sub_sketch_keys' option.")
for key in ret_sketches:
ret[key] = Sketch(_proxy = ret_sketches[key])
if single_val:
return ret[keys[0]]
else:
return ret
