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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | save_json_dict | Will error if filename is not appropriate, but it's checked elsewhere. | cufflinks/auth.py | def save_json_dict(filename, json_dict):
"""Will error if filename is not appropriate, but it's checked elsewhere.
"""
if isinstance(json_dict, dict):
with open(filename, "w") as f:
f.write(json.dumps(json_dict, indent=4))
else:
raise TypeError("json_dict was not a dictionay. couldn't save.") | def save_json_dict(filename, json_dict):
"""Will error if filename is not appropriate, but it's checked elsewhere.
"""
if isinstance(json_dict, dict):
with open(filename, "w") as f:
f.write(json.dumps(json_dict, indent=4))
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train | QuantFig._get_schema | Returns a dictionary with the schema for a QuantFigure | cufflinks/quant_figure.py | def _get_schema(self):
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d={}
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train | QuantFig._get_sliced | Returns a sliced DataFrame
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slice : tuple(from,to)
from : str
to : str
States the 'from' and 'to' values which
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df : DataFrame
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Parameters
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slice : tuple(from,to)
from : str
to : str
States the 'from' and 'to' values which
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df : DataFrame
If omitted then the QuantFigure.DataFrame is resampled.
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to : str
States the 'from' and 'to' values which
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df : DataFrame
If omitted then the QuantFigure.DataFrame is resampled.
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train | QuantFig._get_resampled | Returns a resampled DataFrame
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rule : str
the offset string or object representing target conversion
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Returns a resampled DataFrame
Parameters
----------
rule : str
the offset string or object representing target conversion
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rule : str
the offset string or object representing target conversion
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train | QuantFig.update | Updates the values for a QuantFigure
The key-values are automatically assigned to the correct
section of the QuantFigure | cufflinks/quant_figure.py | def update(self,**kwargs):
"""
Updates the values for a QuantFigure
The key-values are automatically assigned to the correct
section of the QuantFigure
"""
if 'columns' in kwargs:
self._d=ta._ohlc_dict(self.df,columns=kwargs.pop('columns',None))
schema=self._get_schema()
annotations=kwargs.pop('anno... | def update(self,**kwargs):
"""
Updates the values for a QuantFigure
The key-values are automatically assigned to the correct
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"""
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self._d=ta._ohlc_dict(self.df,columns=kwargs.pop('columns',None))
schema=self._get_schema()
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train | QuantFig.delete | Deletes the values for a QuantFigure
The key-values are automatically deleted from the correct
section of the QuantFigure | cufflinks/quant_figure.py | def delete(self,*args):
"""
Deletes the values for a QuantFigure
The key-values are automatically deleted from the correct
section of the QuantFigure
"""
if args:
args=args[0] if utils.is_list(args[0]) else args
path=utils.dict_path(self.__dict__)
for _ in args:
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... | def delete(self,*args):
"""
Deletes the values for a QuantFigure
The key-values are automatically deleted from the correct
section of the QuantFigure
"""
if args:
args=args[0] if utils.is_list(args[0]) else args
path=utils.dict_path(self.__dict__)
for _ in args:
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train | QuantFig._panel_domains | Returns the panel domains for each axis | cufflinks/quant_figure.py | def _panel_domains(self,n=2,min_panel_size=.15,spacing=0.08,top_margin=1,bottom_margin=0):
"""
Returns the panel domains for each axis
"""
d={}
for _ in range(n+1,1,-1):
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d['yaxis{0}'.format(_)]=dict(domain=(lower,lower+min_panel_size))
... | def _panel_domains(self,n=2,min_panel_size=.15,spacing=0.08,top_margin=1,bottom_margin=0):
"""
Returns the panel domains for each axis
"""
d={}
for _ in range(n+1,1,-1):
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d['yaxis{0}'.format(_)]=dict(domain=(lower,lower+min_panel_size))
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train | QuantFig._get_trendline | Returns a trendline (line), support or resistance
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
on : string
Indicate the data series in which the
trendline should be based.
'close'
'high'
'low'
'open'
kind : string
Def... | cufflinks/quant_figure.py | def _get_trendline(self,date0=None,date1=None,on=None,kind='trend',to_strfmt='%Y-%m-%d',from_strfmt='%d%b%y',**kwargs):
"""
Returns a trendline (line), support or resistance
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
on : string
Indicate the data... | def _get_trendline(self,date0=None,date1=None,on=None,kind='trend',to_strfmt='%Y-%m-%d',from_strfmt='%d%b%y',**kwargs):
"""
Returns a trendline (line), support or resistance
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
on : string
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train | QuantFig.add_trendline | Adds a trendline to the QuantFigure.
Given 2 dates, the trendline is connected on the data points
that correspond to those dates.
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
on : string
Indicate the data series in which the
trendline should be... | cufflinks/quant_figure.py | def add_trendline(self,date0,date1,on='close',text=None,**kwargs):
"""
Adds a trendline to the QuantFigure.
Given 2 dates, the trendline is connected on the data points
that correspond to those dates.
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
on ... | def add_trendline(self,date0,date1,on='close',text=None,**kwargs):
"""
Adds a trendline to the QuantFigure.
Given 2 dates, the trendline is connected on the data points
that correspond to those dates.
Parameters:
date0 : string
Trendline starting date
date1 : string
Trendline end date
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train | QuantFig.add_support | Adds a support line to the QuantFigure
Parameters:
date0 : string
The support line will be drawn at the 'y' level
value that corresponds to this date.
on : string
Indicate the data series in which the
support line should be based.
'close'
'high'
'low'
'open'
mode : str... | cufflinks/quant_figure.py | def add_support(self,date,on='low',mode='starttoend',text=None,**kwargs):
"""
Adds a support line to the QuantFigure
Parameters:
date0 : string
The support line will be drawn at the 'y' level
value that corresponds to this date.
on : string
Indicate the data series in which the
support l... | def add_support(self,date,on='low',mode='starttoend',text=None,**kwargs):
"""
Adds a support line to the QuantFigure
Parameters:
date0 : string
The support line will be drawn at the 'y' level
value that corresponds to this date.
on : string
Indicate the data series in which the
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train | QuantFig.add_annotations | Add an annotation to the QuantFigure.
Parameters:
annotations : dict or list(dict,)
Annotations can be on the form form of
{'date' : 'text'}
and the text will automatically be placed at the
right level on the chart
or
A Plotly fully defined annotation
kwargs :
fontcolor : st... | cufflinks/quant_figure.py | def add_annotations(self,annotations,**kwargs):
"""
Add an annotation to the QuantFigure.
Parameters:
annotations : dict or list(dict,)
Annotations can be on the form form of
{'date' : 'text'}
and the text will automatically be placed at the
right level on the chart
or
A Plotly... | def add_annotations(self,annotations,**kwargs):
"""
Add an annotation to the QuantFigure.
Parameters:
annotations : dict or list(dict,)
Annotations can be on the form form of
{'date' : 'text'}
and the text will automatically be placed at the
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train | QuantFig.add_shapes | Add a shape to the QuantFigure.
kwargs :
hline : int, list or dict
Draws a horizontal line at the
indicated y position(s)
Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : int, list or dict
Draws a vertical line at the
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"""
Add a shape to the QuantFigure.
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hline : int, list or dict
Draws a horizontal line at the
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Extra parameters can be passed in
the form of a dictionary (see shapes)
vline : int, list or dict
Draws a vertical line at the
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"""
Add a shape to the QuantFigure.
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hline : int, list or dict
Draws a horizontal line at the
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the form of a dictionary (see shapes)
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train | QuantFig._add_study | Adds a study to QuantFigure.studies
Parameters:
study : dict
{'kind':study_kind,
'params':study_parameters,
'display':display_parameters} | cufflinks/quant_figure.py | def _add_study(self,study):
"""
Adds a study to QuantFigure.studies
Parameters:
study : dict
{'kind':study_kind,
'params':study_parameters,
'display':display_parameters}
"""
str='{study} {name}({period})' if study['params'].get('str',None)==None else study['params']['str']
study['params'][... | def _add_study(self,study):
"""
Adds a study to QuantFigure.studies
Parameters:
study : dict
{'kind':study_kind,
'params':study_parameters,
'display':display_parameters}
"""
str='{study} {name}({period})' if study['params'].get('str',None)==None else study['params']['str']
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train | QuantFig.add_volume | Add 'volume' study to QuantFigure.studies
Parameters:
colorchange : bool
If True then each volume bar will have a fill color
depending on if 'base' had a positive or negative
change compared to the previous value
If False then each volume bar will have a fill color
depending on if the volume... | cufflinks/quant_figure.py | def add_volume(self,colorchange=True,column=None,name='',str='{name}',**kwargs):
"""
Add 'volume' study to QuantFigure.studies
Parameters:
colorchange : bool
If True then each volume bar will have a fill color
depending on if 'base' had a positive or negative
change compared to the previous value... | def add_volume(self,colorchange=True,column=None,name='',str='{name}',**kwargs):
"""
Add 'volume' study to QuantFigure.studies
Parameters:
colorchange : bool
If True then each volume bar will have a fill color
depending on if 'base' had a positive or negative
change compared to the previous value... | [
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train | QuantFig.add_macd | Add Moving Average Convergence Divergence (MACD) study to QuantFigure.studies
Parameters:
fast_period : int
MACD Fast Period
slow_period : int
MACD Slow Period
signal_period : int
MACD Signal Period
column :string
Defines the data column name that contains the
data over which the stu... | cufflinks/quant_figure.py | def add_macd(self,fast_period=12,slow_period=26,signal_period=9,column=None,
name='',str=None,**kwargs):
"""
Add Moving Average Convergence Divergence (MACD) study to QuantFigure.studies
Parameters:
fast_period : int
MACD Fast Period
slow_period : int
MACD Slow Period
signal_period : int
... | def add_macd(self,fast_period=12,slow_period=26,signal_period=9,column=None,
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"""
Add Moving Average Convergence Divergence (MACD) study to QuantFigure.studies
Parameters:
fast_period : int
MACD Fast Period
slow_period : int
MACD Slow Period
signal_period : int
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train | QuantFig.add_sma | Add Simple Moving Average (SMA) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
column :string
Defines the data column name that contains the
data over which the study will be applied.
Default: 'close'
name : string
Name given to the study
str :... | cufflinks/quant_figure.py | def add_sma(self,periods=20,column=None,name='',
str=None,**kwargs):
"""
Add Simple Moving Average (SMA) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
column :string
Defines the data column name that contains the
data over which the study will be ap... | def add_sma(self,periods=20,column=None,name='',
str=None,**kwargs):
"""
Add Simple Moving Average (SMA) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
column :string
Defines the data column name that contains the
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train | QuantFig.add_rsi | Add Relative Strength Indicator (RSI) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
rsi_upper : int
bounds [0,100]
Upper (overbought) level
rsi_lower : int
bounds [0,100]
Lower (oversold) level
showbands : boolean
If True, then the rsi_uppe... | cufflinks/quant_figure.py | def add_rsi(self,periods=20,rsi_upper=70,rsi_lower=30,showbands=True,column=None,
name='',str=None,**kwargs):
"""
Add Relative Strength Indicator (RSI) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
rsi_upper : int
bounds [0,100]
Upper (overbough... | def add_rsi(self,periods=20,rsi_upper=70,rsi_lower=30,showbands=True,column=None,
name='',str=None,**kwargs):
"""
Add Relative Strength Indicator (RSI) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
rsi_upper : int
bounds [0,100]
Upper (overbough... | [
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train | QuantFig.add_bollinger_bands | Add Bollinger Bands (BOLL) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
boll_std : int
Number of standard deviations for
the bollinger upper and lower bands
fill : boolean
If True, then the innner area of the
bands will filled
column :string
... | cufflinks/quant_figure.py | def add_bollinger_bands(self,periods=20,boll_std=2,fill=True,column=None,name='',
str='{name}({column},{period})',**kwargs):
"""
Add Bollinger Bands (BOLL) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
boll_std : int
Number of standard deviations for... | def add_bollinger_bands(self,periods=20,boll_std=2,fill=True,column=None,name='',
str='{name}({column},{period})',**kwargs):
"""
Add Bollinger Bands (BOLL) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
boll_std : int
Number of standard deviations for... | [
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train | QuantFig.add_cci | Commodity Channel Indicator study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
cci_upper : int
Upper bands level
default : 100
cci_lower : int
Lower band level
default : -100
showbands : boolean
If True, then the cci_upper and
cci_lower level... | cufflinks/quant_figure.py | def add_cci(self,periods=14,cci_upper=100,cci_lower=-100,
showbands=True,str=None,name='',**kwargs):
"""
Commodity Channel Indicator study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
cci_upper : int
Upper bands level
default : 100
cci_lower : int
... | def add_cci(self,periods=14,cci_upper=100,cci_lower=-100,
showbands=True,str=None,name='',**kwargs):
"""
Commodity Channel Indicator study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
cci_upper : int
Upper bands level
default : 100
cci_lower : int
... | [
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train | QuantFig.add_ptps | Add Parabolic SAR (PTPS) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
af : float
acceleration factor
initial : 'long' or 'short'
Iniital position
default: long
name : string
Name given to the study
str : string
Label factory for studies
... | cufflinks/quant_figure.py | def add_ptps(self,periods=14,af=0.2,initial='long',str=None,name='',**kwargs):
"""
Add Parabolic SAR (PTPS) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
af : float
acceleration factor
initial : 'long' or 'short'
Iniital position
default: long
n... | def add_ptps(self,periods=14,af=0.2,initial='long',str=None,name='',**kwargs):
"""
Add Parabolic SAR (PTPS) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
af : float
acceleration factor
initial : 'long' or 'short'
Iniital position
default: long
n... | [
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train | QuantFig.add_atr | Add Average True Range (ATR) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
name : string
Name given to the study
str : string
Label factory for studies
The following wildcards can be used:
{name} : Name of the column
{study} : Name of the stu... | cufflinks/quant_figure.py | def add_atr(self,periods=14,str=None,name='',**kwargs):
"""
Add Average True Range (ATR) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
name : string
Name given to the study
str : string
Label factory for studies
The following wildcards can be used:... | def add_atr(self,periods=14,str=None,name='',**kwargs):
"""
Add Average True Range (ATR) study to QuantFigure.studies
Parameters:
periods : int or list(int)
Number of periods
name : string
Name given to the study
str : string
Label factory for studies
The following wildcards can be used:... | [
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train | go_offline | connected : bool
If True, the plotly.js library will be loaded
from an online CDN. If False, the plotly.js library will be loaded locally
from the plotly python package | cufflinks/offline.py | def go_offline(connected=None):
"""
connected : bool
If True, the plotly.js library will be loaded
from an online CDN. If False, the plotly.js library will be loaded locally
from the plotly python package
"""
from .auth import get_config_file
if connected is None:
try... | def go_offline(connected=None):
"""
connected : bool
If True, the plotly.js library will be loaded
from an online CDN. If False, the plotly.js library will be loaded locally
from the plotly python package
"""
from .auth import get_config_file
if connected is None:
try... | [
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train | _screen | Filters a DataFrame for columns that contain the given strings.
Parameters:
-----------
include : bool
If False then it will exclude items that match
the given filters.
This is the same as passing a regex ^keyword
kwargs :
Key value pairs that indicate the column and
value to screen for
Examp... | cufflinks/pandastools.py | def _screen(self,include=True,**kwargs):
"""
Filters a DataFrame for columns that contain the given strings.
Parameters:
-----------
include : bool
If False then it will exclude items that match
the given filters.
This is the same as passing a regex ^keyword
kwargs :
Key value pairs that indicate... | def _screen(self,include=True,**kwargs):
"""
Filters a DataFrame for columns that contain the given strings.
Parameters:
-----------
include : bool
If False then it will exclude items that match
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This is the same as passing a regex ^keyword
kwargs :
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train | bestfit | Returns a series with the bestfit values.
Example:
Series.bestfit()
Returns: series
The returned series contains a parameter
called 'formula' which includes the string representation
of the bestfit line. | cufflinks/pandastools.py | def bestfit(self):
"""
Returns a series with the bestfit values.
Example:
Series.bestfit()
Returns: series
The returned series contains a parameter
called 'formula' which includes the string representation
of the bestfit line.
"""
# statsmodel cannot be included on requirements.txt
# see https://g... | def bestfit(self):
"""
Returns a series with the bestfit values.
Example:
Series.bestfit()
Returns: series
The returned series contains a parameter
called 'formula' which includes the string representation
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train | normalize | Returns a normalized series or DataFrame
Example:
Series.normalize()
Returns: series of DataFrame
Parameters:
-----------
asOf : string
Date format
'2015-02-29'
multiplier : int
Factor by which the results will be adjusted | cufflinks/pandastools.py | def normalize(self,asOf=None,multiplier=100):
"""
Returns a normalized series or DataFrame
Example:
Series.normalize()
Returns: series of DataFrame
Parameters:
-----------
asOf : string
Date format
'2015-02-29'
multiplier : int
Factor by which the results will be adjusted
"""
if not asOf:
... | def normalize(self,asOf=None,multiplier=100):
"""
Returns a normalized series or DataFrame
Example:
Series.normalize()
Returns: series of DataFrame
Parameters:
-----------
asOf : string
Date format
'2015-02-29'
multiplier : int
Factor by which the results will be adjusted
"""
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train | merge_dict | d1 <-- d2 | cufflinks/utils.py | def merge_dict(d1,d2):
"""
d1 <-- d2
"""
d=copy.deepcopy(d2)
for k,v in list(d1.items()):
if k not in d:
d[k]=v
else:
if isinstance(v,dict):
d[k]=merge_dict(d1[k],d[k])
return d | def merge_dict(d1,d2):
"""
d1 <-- d2
"""
d=copy.deepcopy(d2)
for k,v in list(d1.items()):
if k not in d:
d[k]=v
else:
if isinstance(v,dict):
d[k]=merge_dict(d1[k],d[k])
return d | [
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train | dict_path | Returns a dictionary with the path in which each of the keys is found
Parameters:
from_d : dict
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to_d : dict
Dictionary to which the results will be appended
Example:
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Returns
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"""
Returns a dictionary with the path in which each of the keys is found
Parameters:
from_d : dict
Dictionary that contains all the keys, values
to_d : dict
Dictionary to which the results will be appended
Example:
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"""
Returns a dictionary with the path in which each of the keys is found
Parameters:
from_d : dict
Dictionary that contains all the keys, values
to_d : dict
Dictionary to which the results will be appended
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train | pp | Formats (prettyprint) a concatenated dictionary | cufflinks/utils.py | def pp(el,preString=''):
"""
Formats (prettyprint) a concatenated dictionary
"""
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if isinstance(el,dict):
keys=list(el.keys())
keys.sort()
for key in keys:
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pp(val,preString+tab)
else:
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"""
Formats (prettyprint) a concatenated dictionary
"""
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keys.sort()
for key in keys:
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pp(val,preString+tab)
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train | inverseDict | Returns a dictionay indexed by values {value_k:key_k}
Parameters:
-----------
d : dictionary | cufflinks/utils.py | def inverseDict(d):
"""
Returns a dictionay indexed by values {value_k:key_k}
Parameters:
-----------
d : dictionary
"""
dt={}
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Parameters:
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d : dictionary
"""
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train | kwargs_from_keyword | Looks for keys of the format keyword_value.
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Parameters:
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from_kwargs : dict
Original dictionary
to_kwargs : dict
Dictionary where the items will be appended
keyword : string
Keyword to look for in the orginal dictionary
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"""
Looks for keys of the format keyword_value.
And return a dictionary with {keyword:value} format
Parameters:
-----------
from_kwargs : dict
Original dictionary
to_kwargs : dict
Dictionary where the items will be appended
key... | def kwargs_from_keyword(from_kwargs,to_kwargs,keyword,clean_origin=True):
"""
Looks for keys of the format keyword_value.
And return a dictionary with {keyword:value} format
Parameters:
-----------
from_kwargs : dict
Original dictionary
to_kwargs : dict
Dictionary where the items will be appended
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train | deep_update | Updates the values (deep form) of a given dictionary
Parameters:
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d : dict
dictionary that contains the values to update
d_update : dict
dictionary to be updated | cufflinks/utils.py | def deep_update(d,d_update):
"""
Updates the values (deep form) of a given dictionary
Parameters:
-----------
d : dict
dictionary that contains the values to update
d_update : dict
dictionary to be updated
"""
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deep_update(d[k... | def deep_update(d,d_update):
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Updates the values (deep form) of a given dictionary
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d : dict
dictionary that contains the values to update
d_update : dict
dictionary to be updated
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train | read_google | Reads a google sheet | cufflinks/utils.py | def read_google(self,url,**kwargs):
"""
Reads a google sheet
"""
if url[-1]!='/':
url+='/'
return self.read_csv(url+'export?gid=0&format=csv',**kwargs) | def read_google(self,url,**kwargs):
"""
Reads a google sheet
"""
if url[-1]!='/':
url+='/'
return self.read_csv(url+'export?gid=0&format=csv',**kwargs) | [
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train | getDateFromToday | Returns a string that represents a date n numbers of days from today.
Parameters:
-----------
delta : int
number of days
strfmt : string
format in which the date will be represented | cufflinks/date_tools.py | def getDateFromToday(delta,strfmt='%Y%m%d'):
""" Returns a string that represents a date n numbers of days from today.
Parameters:
-----------
delta : int
number of days
strfmt : string
format in which the date will be represented
"""
return (dt.date.today() + dt.timedelta(delta)).strftime(strfmt) | def getDateFromToday(delta,strfmt='%Y%m%d'):
""" Returns a string that represents a date n numbers of days from today.
Parameters:
-----------
delta : int
number of days
strfmt : string
format in which the date will be represented
"""
return (dt.date.today() + dt.timedelta(delta)).strftime(strfmt) | [
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train | _ohlc_dict | Returns a dictionary with the actual column names that
correspond to each of the OHLCV values.
df_or_figure : DataFrame or Figure
open : string
Column name to be used for OPEN values
high : string
Column name to be used for HIGH values
low : string
Column name to be used for LOW values
close : string
C... | cufflinks/ta.py | def _ohlc_dict(df_or_figure,open='',high='',low='',close='',volume='',
validate='',**kwargs):
"""
Returns a dictionary with the actual column names that
correspond to each of the OHLCV values.
df_or_figure : DataFrame or Figure
open : string
Column name to be used for OPEN values
high : string
Column... | def _ohlc_dict(df_or_figure,open='',high='',low='',close='',volume='',
validate='',**kwargs):
"""
Returns a dictionary with the actual column names that
correspond to each of the OHLCV values.
df_or_figure : DataFrame or Figure
open : string
Column name to be used for OPEN values
high : string
Column... | [
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train | correl | how : string
value
pct_chg
diff | cufflinks/ta.py | def correl(df,periods=21,columns=None,include=True,str=None,detail=False,how='value',**correl_kwargs):
"""
how : string
value
pct_chg
diff
"""
def _correl(df,periods=21,columns=None,include=True,str=None,detail=False,**correl_kwargs):
study='CORREL'
df,_df,columns=validate(df,columns)
_df['CORREL']... | def correl(df,periods=21,columns=None,include=True,str=None,detail=False,how='value',**correl_kwargs):
"""
how : string
value
pct_chg
diff
"""
def _correl(df,periods=21,columns=None,include=True,str=None,detail=False,**correl_kwargs):
study='CORREL'
df,_df,columns=validate(df,columns)
_df['CORREL']... | [
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train | scattergeo | Returns | cufflinks/datagen.py | def scattergeo():
"""
Returns
"""
path=os.path.join(os.path.dirname(__file__), '../data/scattergeo.csv')
df=pd.read_csv(path)
del df['Unnamed: 0']
df['text'] = df['airport'] + ' ' + df['city'] + ', ' + df['state'] + ' ' + 'Arrivals: ' + df['cnt'].astype(str)
df=df.rename(columns={'cnt':'z','long':'lon'})
retu... | def scattergeo():
"""
Returns
"""
path=os.path.join(os.path.dirname(__file__), '../data/scattergeo.csv')
df=pd.read_csv(path)
del df['Unnamed: 0']
df['text'] = df['airport'] + ' ' + df['city'] + ', ' + df['state'] + ' ' + 'Arrivals: ' + df['cnt'].astype(str)
df=df.rename(columns={'cnt':'z','long':'lon'})
retu... | [
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train | choropleth | Returns | cufflinks/datagen.py | def choropleth():
"""
Returns
"""
path=os.path.join(os.path.dirname(__file__), '../data/choropleth.csv')
df=pd.read_csv(path)
del df['Unnamed: 0']
df['z']=[np.random.randint(0,100) for _ in range(len(df))]
return df | def choropleth():
"""
Returns
"""
path=os.path.join(os.path.dirname(__file__), '../data/choropleth.csv')
df=pd.read_csv(path)
del df['Unnamed: 0']
df['z']=[np.random.randint(0,100) for _ in range(len(df))]
return df | [
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train | pie | Returns a DataFrame with the required format for
a pie plot
Parameters:
-----------
n_labels : int
Number of labels
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names | cufflinks/datagen.py | def pie(n_labels=5,mode=None):
"""
Returns a DataFrame with the required format for
a pie plot
Parameters:
-----------
n_labels : int
Number of labels
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names
"""
return pd.DataFrame({'values':np.random.ra... | def pie(n_labels=5,mode=None):
"""
Returns a DataFrame with the required format for
a pie plot
Parameters:
-----------
n_labels : int
Number of labels
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names
"""
return pd.DataFrame({'values':np.random.ra... | [
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train | scatter | Returns a DataFrame with the required format for
a scatter plot
Parameters:
-----------
n_categories : int
Number of categories
n : int
Number of points for each category
prefix : string
Name for each category
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for r... | cufflinks/datagen.py | def scatter(n_categories=5,n=10,prefix='category',mode=None):
"""
Returns a DataFrame with the required format for
a scatter plot
Parameters:
-----------
n_categories : int
Number of categories
n : int
Number of points for each category
prefix : string
Name for each category
mode : string
Fo... | def scatter(n_categories=5,n=10,prefix='category',mode=None):
"""
Returns a DataFrame with the required format for
a scatter plot
Parameters:
-----------
n_categories : int
Number of categories
n : int
Number of points for each category
prefix : string
Name for each category
mode : string
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train | heatmap | Returns a DataFrame with the required format for
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Parameters:
-----------
n_x : int
Number of x categories
n_y : int
Number of y categories | cufflinks/datagen.py | def heatmap(n_x=5,n_y=10):
"""
Returns a DataFrame with the required format for
a heatmap plot
Parameters:
-----------
n_x : int
Number of x categories
n_y : int
Number of y categories
"""
x=['x_'+str(_) for _ in range(n_x)]
y=['y_'+str(_) for _ in range(n_y)]
return pd.DataFrame(surface(n_x-1,n_y... | def heatmap(n_x=5,n_y=10):
"""
Returns a DataFrame with the required format for
a heatmap plot
Parameters:
-----------
n_x : int
Number of x categories
n_y : int
Number of y categories
"""
x=['x_'+str(_) for _ in range(n_x)]
y=['y_'+str(_) for _ in range(n_y)]
return pd.DataFrame(surface(n_x-1,n_y... | [
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train | lines | Returns a DataFrame with the required format for
a scatter (lines) plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
columns : [str]
List of column names
dateIndex : bool
If True it will return a datetime index
if False it will return a enu... | cufflinks/datagen.py | def lines(n_traces=5,n=100,columns=None,dateIndex=True,mode=None):
"""
Returns a DataFrame with the required format for
a scatter (lines) plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
columns : [str]
List of column names
dateIndex : bool
... | def lines(n_traces=5,n=100,columns=None,dateIndex=True,mode=None):
"""
Returns a DataFrame with the required format for
a scatter (lines) plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
columns : [str]
List of column names
dateIndex : bool
... | [
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train | bars | Returns a DataFrame with the required format for
a bar plot
Parameters:
-----------
n : int
Number of points for each trace
n_categories : int
Number of categories for each point
prefix : string
Name for each category
columns : [str]
List of column names
mode : string
Format for each item
... | cufflinks/datagen.py | def bars(n=3,n_categories=3,prefix='category',columns=None,mode='abc'):
"""
Returns a DataFrame with the required format for
a bar plot
Parameters:
-----------
n : int
Number of points for each trace
n_categories : int
Number of categories for each point
prefix : string
Name for each category
co... | def bars(n=3,n_categories=3,prefix='category',columns=None,mode='abc'):
"""
Returns a DataFrame with the required format for
a bar plot
Parameters:
-----------
n : int
Number of points for each trace
n_categories : int
Number of categories for each point
prefix : string
Name for each category
co... | [
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train | ohlc | Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close']]
Parameters:
-----------
n : int
Number of ohlc points | cufflinks/datagen.py | def ohlc(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close']]
Parameters:
-----------
n : int
Number of ohlc points
"""
index=pd.date_range('1/1/15',periods=n*288,freq='5min',tz='utc')
data=np.random.randn(n*288)
data[0]=np.array(... | def ohlc(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close']]
Parameters:
-----------
n : int
Number of ohlc points
"""
index=pd.date_range('1/1/15',periods=n*288,freq='5min',tz='utc')
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data[0]=np.array(... | [
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train | ohlcv | Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close','volume']
Parameters:
-----------
n : int
Number of ohlc points | cufflinks/datagen.py | def ohlcv(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close','volume']
Parameters:
-----------
n : int
Number of ohlc points
"""
df=ohlc()
df['volume']=[np.random.randint(1000,10000) for _ in range(len(df))]
return df | def ohlcv(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close','volume']
Parameters:
-----------
n : int
Number of ohlc points
"""
df=ohlc()
df['volume']=[np.random.randint(1000,10000) for _ in range(len(df))]
return df | [
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train | box | Returns a DataFrame with the required format for
a box plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names | cufflinks/datagen.py | def box(n_traces=5,n=100,mode=None):
"""
Returns a DataFrame with the required format for
a box plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock name... | def box(n_traces=5,n=100,mode=None):
"""
Returns a DataFrame with the required format for
a box plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock name... | [
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train | histogram | Returns a DataFrame with the required format for
a histogram plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names | cufflinks/datagen.py | def histogram(n_traces=1,n=500,dispersion=2,mode=None):
"""
Returns a DataFrame with the required format for
a histogram plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stoc... | def histogram(n_traces=1,n=500,dispersion=2,mode=None):
"""
Returns a DataFrame with the required format for
a histogram plot
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stoc... | [
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train | distplot | Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet columns
'stocks' for random stock names | cufflinks/datagen.py | def distplot(n_traces=1,n=500,dispersion=3,mode=None):
"""
Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet colu... | def distplot(n_traces=1,n=500,dispersion=3,mode=None):
"""
Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n_traces : int
Number of traces
n : int
Number of points for each trace
mode : string
Format for each item
'abc' for alphabet colu... | [
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train | violin | Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n : int
Number of points
categories : bool or int
If True, then a column with categories is added
n_categories : int
Number of categories | cufflinks/datagen.py | def violin(n=500,dispersion=3,categories=True,n_categories=5):
"""
Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n : int
Number of points
categories : bool or int
If True, then a column with categories is added
n_categories : int
Number of... | def violin(n=500,dispersion=3,categories=True,n_categories=5):
"""
Returns a DataFrame with the required format for
a distribution plot (distplot)
Parameters:
-----------
n : int
Number of points
categories : bool or int
If True, then a column with categories is added
n_categories : int
Number of... | [
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train | surface | Returns a DataFrame with the required format for
a surface plot
Parameters:
-----------
n_x : int
Number of points along the X axis
n_y : int
Number of points along the Y axis | cufflinks/datagen.py | def surface(n_x=20,n_y=20):
"""
Returns a DataFrame with the required format for
a surface plot
Parameters:
-----------
n_x : int
Number of points along the X axis
n_y : int
Number of points along the Y axis
"""
x=[float(np.random.randint(0,100))]
for i in range(n_x):
x.append(x[:1][0]+np.random.... | def surface(n_x=20,n_y=20):
"""
Returns a DataFrame with the required format for
a surface plot
Parameters:
-----------
n_x : int
Number of points along the X axis
n_y : int
Number of points along the Y axis
"""
x=[float(np.random.randint(0,100))]
for i in range(n_x):
x.append(x[:1][0]+np.random.... | [
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train | sinwave | Returns a DataFrame with the required format for
a surface (sine wave) plot
Parameters:
-----------
n : int
Ranges for X and Y axis (-n,n)
n_y : int
Size of increment along the axis | cufflinks/datagen.py | def sinwave(n=4,inc=.25):
"""
Returns a DataFrame with the required format for
a surface (sine wave) plot
Parameters:
-----------
n : int
Ranges for X and Y axis (-n,n)
n_y : int
Size of increment along the axis
"""
x=np.arange(-n,n,inc)
y=np.arange(-n,n,inc)
X,Y=np.meshgrid(x,y)
R = np.sqrt(X**2... | def sinwave(n=4,inc=.25):
"""
Returns a DataFrame with the required format for
a surface (sine wave) plot
Parameters:
-----------
n : int
Ranges for X and Y axis (-n,n)
n_y : int
Size of increment along the axis
"""
x=np.arange(-n,n,inc)
y=np.arange(-n,n,inc)
X,Y=np.meshgrid(x,y)
R = np.sqrt(X**2... | [
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train | getTheme | Returns a theme definition.
To see the colors translated (hex) use
cufflinks.getLayout(theme) instead. | cufflinks/tools.py | def getTheme(theme=None):
"""
Returns a theme definition.
To see the colors translated (hex) use
cufflinks.getLayout(theme) instead.
"""
if not theme:
theme = auth.get_config_file()['theme']
if theme in THEMES:
return updateColors(copy.deepcopy(THEMES[theme]))
else:
raise Exception("Invalid Theme: {0}".... | def getTheme(theme=None):
"""
Returns a theme definition.
To see the colors translated (hex) use
cufflinks.getLayout(theme) instead.
"""
if not theme:
theme = auth.get_config_file()['theme']
if theme in THEMES:
return updateColors(copy.deepcopy(THEMES[theme]))
else:
raise Exception("Invalid Theme: {0}".... | [
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train | getLayout | Generates a plotly Layout
Parameters:
-----------
theme : string
Layout Theme
solar
pearl
white
title : string
Chart Title
xTitle : string
X Axis Title
yTitle : string
Y Axis Title
zTitle : string
Z Axis Title
Applicable only for 3d charts
barmode : string
Mode when displ... | cufflinks/tools.py | def getLayout(kind=None,theme=None,title='',xTitle='',yTitle='',zTitle='',barmode='',bargap=None,bargroupgap=None,
margin=None, dimensions=None, width=None, height=None,
annotations=None,is3d=False,**kwargs):
"""
Generates a plotly Layout
Parameters:
-----------
theme : string
Layout Theme
solar... | def getLayout(kind=None,theme=None,title='',xTitle='',yTitle='',zTitle='',barmode='',bargap=None,bargroupgap=None,
margin=None, dimensions=None, width=None, height=None,
annotations=None,is3d=False,**kwargs):
"""
Generates a plotly Layout
Parameters:
-----------
theme : string
Layout Theme
solar... | [
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] | santosjorge/cufflinks | python | https://github.com/santosjorge/cufflinks/blob/ca1cbf93998dc793d0b1f8ac30fe1f2bd105f63a/cufflinks/tools.py#L92-L455 | [
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train | get_annotations | Generates an annotations object
Parameters:
-----------
df : DataFrame
Original DataFrame of values
annotations : dict or list
Dictionary of annotations
{x_point : text}
or
List of Plotly annotations | cufflinks/tools.py | def get_annotations(df,annotations,kind='lines',theme=None,**kwargs):
"""
Generates an annotations object
Parameters:
-----------
df : DataFrame
Original DataFrame of values
annotations : dict or list
Dictionary of annotations
{x_point : text}
or
List of Plotly annotations
"""
for key in ... | def get_annotations(df,annotations,kind='lines',theme=None,**kwargs):
"""
Generates an annotations object
Parameters:
-----------
df : DataFrame
Original DataFrame of values
annotations : dict or list
Dictionary of annotations
{x_point : text}
or
List of Plotly annotations
"""
for key in ... | [
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train | strip_figures | Strips a figure into multiple figures with a trace on each of them
Parameters:
-----------
figure : Figure
Plotly Figure | cufflinks/tools.py | def strip_figures(figure):
"""
Strips a figure into multiple figures with a trace on each of them
Parameters:
-----------
figure : Figure
Plotly Figure
"""
fig=[]
for trace in figure['data']:
fig.append(dict(data=[trace],layout=figure['layout']))
return fig | def strip_figures(figure):
"""
Strips a figure into multiple figures with a trace on each of them
Parameters:
-----------
figure : Figure
Plotly Figure
"""
fig=[]
for trace in figure['data']:
fig.append(dict(data=[trace],layout=figure['layout']))
return fig | [
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train | get_base_layout | Generates a layout with the union of all properties of multiple
figures' layouts
Parameters:
-----------
fig : list(Figures)
List of Plotly Figures | cufflinks/tools.py | def get_base_layout(figs):
"""
Generates a layout with the union of all properties of multiple
figures' layouts
Parameters:
-----------
fig : list(Figures)
List of Plotly Figures
"""
layout={}
for fig in figs:
if not isinstance(fig,dict):
fig=fig.to_dict()
for k,v in list(fig['layout'].items()):
... | def get_base_layout(figs):
"""
Generates a layout with the union of all properties of multiple
figures' layouts
Parameters:
-----------
fig : list(Figures)
List of Plotly Figures
"""
layout={}
for fig in figs:
if not isinstance(fig,dict):
fig=fig.to_dict()
for k,v in list(fig['layout'].items()):
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train | figures | Generates multiple Plotly figures for a given DataFrame
Parameters:
-----------
df : DataFrame
Pandas DataFrame
specs : list(dict)
List of dictionaries with the properties
of each figure.
All properties avaialbe can be seen with
help(cufflinks.pd.DataFrame.iplot)
asList : boolean
If True, the... | cufflinks/tools.py | def figures(df,specs,asList=False):
"""
Generates multiple Plotly figures for a given DataFrame
Parameters:
-----------
df : DataFrame
Pandas DataFrame
specs : list(dict)
List of dictionaries with the properties
of each figure.
All properties avaialbe can be seen with
help(cufflinks.pd.DataFrame... | def figures(df,specs,asList=False):
"""
Generates multiple Plotly figures for a given DataFrame
Parameters:
-----------
df : DataFrame
Pandas DataFrame
specs : list(dict)
List of dictionaries with the properties
of each figure.
All properties avaialbe can be seen with
help(cufflinks.pd.DataFrame... | [
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train | merge_figures | Generates a single Figure from a list of figures
Parameters:
-----------
figures : list(Figures)
List of figures to be merged. | cufflinks/tools.py | def merge_figures(figures):
"""
Generates a single Figure from a list of figures
Parameters:
-----------
figures : list(Figures)
List of figures to be merged.
"""
figure={}
data=[]
for fig in figures:
for trace in fig['data']:
data.append(trace)
layout=get_base_layout(figures)
figure['data']=data
... | def merge_figures(figures):
"""
Generates a single Figure from a list of figures
Parameters:
-----------
figures : list(Figures)
List of figures to be merged.
"""
figure={}
data=[]
for fig in figures:
for trace in fig['data']:
data.append(trace)
layout=get_base_layout(figures)
figure['data']=data
... | [
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train | subplots | Generates a subplot view for a set of figures
This is a wrapper for plotly.tools.make_subplots
Parameters:
-----------
figures : [Figures]
List of Plotly Figures
shape : (rows,cols)
Tuple indicating the size of rows and columns
If omitted then the layout is automatically set
shared_xaxes : bool
As... | cufflinks/tools.py | def subplots(figures,shape=None,
shared_xaxes=False, shared_yaxes=False,
start_cell='top-left', theme=None,base_layout=None,
**kwargs):
"""
Generates a subplot view for a set of figures
This is a wrapper for plotly.tools.make_subplots
Parameters:
-----------
figures : [Figures]
List of Plot... | def subplots(figures,shape=None,
shared_xaxes=False, shared_yaxes=False,
start_cell='top-left', theme=None,base_layout=None,
**kwargs):
"""
Generates a subplot view for a set of figures
This is a wrapper for plotly.tools.make_subplots
Parameters:
-----------
figures : [Figures]
List of Plot... | [
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train | get_subplots | Generates a subplot view for a set of figures
Parameters:
-----------
rows : int
Number of rows
cols : int
Number of cols
shared_xaxes : bool
Assign shared x axes.
If True, subplots in the same grid column have one common
shared x-axis at the bottom of the gird.
shared_yaxes : bool
Assign s... | cufflinks/tools.py | def get_subplots(rows=1,cols=1,
shared_xaxes=False, shared_yaxes=False,
start_cell='top-left', theme=None,base_layout=None,
**kwargs):
"""
Generates a subplot view for a set of figures
Parameters:
-----------
rows : int
Number of rows
cols : int
Number of cols
shared_xaxes : bool
... | def get_subplots(rows=1,cols=1,
shared_xaxes=False, shared_yaxes=False,
start_cell='top-left', theme=None,base_layout=None,
**kwargs):
"""
Generates a subplot view for a set of figures
Parameters:
-----------
rows : int
Number of rows
cols : int
Number of cols
shared_xaxes : bool
... | [
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train | scatter_matrix | Displays a matrix with scatter plot for each pair of
Series in the DataFrame.
The diagonal shows a histogram for each of the Series
Parameters:
-----------
df : DataFrame
Pandas DataFrame
theme : string
Theme to be used (if not the default)
bins : int
Number of bins to use for histogram
color : st... | cufflinks/tools.py | def scatter_matrix(df,theme=None,bins=10,color='grey',size=2):
"""
Displays a matrix with scatter plot for each pair of
Series in the DataFrame.
The diagonal shows a histogram for each of the Series
Parameters:
-----------
df : DataFrame
Pandas DataFrame
theme : string
Theme to be used (if not the defa... | def scatter_matrix(df,theme=None,bins=10,color='grey',size=2):
"""
Displays a matrix with scatter plot for each pair of
Series in the DataFrame.
The diagonal shows a histogram for each of the Series
Parameters:
-----------
df : DataFrame
Pandas DataFrame
theme : string
Theme to be used (if not the defa... | [
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train | _set_axis | Sets the axis in which each trace should appear
If the axis doesn't exist then a new axis is created
Parameters:
-----------
traces : list(str)
List of trace names
on : string
The axis in which the traces should be placed.
If this is not indicated then a new axis will be
created
side : string
S... | cufflinks/tools.py | def _set_axis(self,traces,on=None,side='right',title=''):
"""
Sets the axis in which each trace should appear
If the axis doesn't exist then a new axis is created
Parameters:
-----------
traces : list(str)
List of trace names
on : string
The axis in which the traces should be placed.
If this is not i... | def _set_axis(self,traces,on=None,side='right',title=''):
"""
Sets the axis in which each trace should appear
If the axis doesn't exist then a new axis is created
Parameters:
-----------
traces : list(str)
List of trace names
on : string
The axis in which the traces should be placed.
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train | get_shape | Returns a plotly shape
Parameters:
-----------
kind : string
Shape kind
line
rect
circle
x : float
x values for the shape.
This assumes x0=x1
x0 : float
x0 value for the shape
x1 : float
x1 value for the shape
y : float
y values for the shape.
This assumes y0=y1
y0 : floa... | cufflinks/tools.py | def get_shape(kind='line',x=None,y=None,x0=None,y0=None,x1=None,y1=None,span=0,color='red',dash='solid',width=1,
fillcolor=None,fill=False,opacity=1,xref='x',yref='y'):
"""
Returns a plotly shape
Parameters:
-----------
kind : string
Shape kind
line
rect
circle
x : float
x values for the ... | def get_shape(kind='line',x=None,y=None,x0=None,y0=None,x1=None,y1=None,span=0,color='red',dash='solid',width=1,
fillcolor=None,fill=False,opacity=1,xref='x',yref='y'):
"""
Returns a plotly shape
Parameters:
-----------
kind : string
Shape kind
line
rect
circle
x : float
x values for the ... | [
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train | get_range_selector | Returns a range selector
Reference: https://plot.ly/python/reference/#layout-xaxis-rangeselector
Parameters:
-----------
steps : string or list(string)
Steps for the range
Examples:
['1y','2 months','5 weeks','ytd','2mtd']
bgocolor : string or tuple(color,alpha)
Background color
Examples:
... | cufflinks/tools.py | def get_range_selector(steps=['1m','1y'],bgcolor='rgba(150, 200, 250, 0.4)',x=0,y=0.9,
visible=True,**kwargs):
"""
Returns a range selector
Reference: https://plot.ly/python/reference/#layout-xaxis-rangeselector
Parameters:
-----------
steps : string or list(string)
Steps for the range
Examples:
... | def get_range_selector(steps=['1m','1y'],bgcolor='rgba(150, 200, 250, 0.4)',x=0,y=0.9,
visible=True,**kwargs):
"""
Returns a range selector
Reference: https://plot.ly/python/reference/#layout-xaxis-rangeselector
Parameters:
-----------
steps : string or list(string)
Steps for the range
Examples:
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train | view_label_matrix | Display an [n, m] matrix of labels | metal/contrib/visualization/analysis.py | def view_label_matrix(L, colorbar=True):
"""Display an [n, m] matrix of labels"""
L = L.todense() if sparse.issparse(L) else L
plt.imshow(L, aspect="auto")
plt.title("Label Matrix")
if colorbar:
labels = sorted(np.unique(np.asarray(L).reshape(-1, 1).squeeze()))
boundaries = np.array(... | def view_label_matrix(L, colorbar=True):
"""Display an [n, m] matrix of labels"""
L = L.todense() if sparse.issparse(L) else L
plt.imshow(L, aspect="auto")
plt.title("Label Matrix")
if colorbar:
labels = sorted(np.unique(np.asarray(L).reshape(-1, 1).squeeze()))
boundaries = np.array(... | [
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train | view_overlaps | Display an [m, m] matrix of overlaps | metal/contrib/visualization/analysis.py | def view_overlaps(L, self_overlaps=False, normalize=True, colorbar=True):
"""Display an [m, m] matrix of overlaps"""
L = L.todense() if sparse.issparse(L) else L
G = _get_overlaps_matrix(L, normalize=normalize)
if not self_overlaps:
np.fill_diagonal(G, 0) # Zero out self-overlaps
plt.imshow... | def view_overlaps(L, self_overlaps=False, normalize=True, colorbar=True):
"""Display an [m, m] matrix of overlaps"""
L = L.todense() if sparse.issparse(L) else L
G = _get_overlaps_matrix(L, normalize=normalize)
if not self_overlaps:
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train | view_conflicts | Display an [m, m] matrix of conflicts | metal/contrib/visualization/analysis.py | def view_conflicts(L, normalize=True, colorbar=True):
"""Display an [m, m] matrix of conflicts"""
L = L.todense() if sparse.issparse(L) else L
C = _get_conflicts_matrix(L, normalize=normalize)
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plt.title("Conflicts")
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plt.colorbar()
plt.show() | def view_conflicts(L, normalize=True, colorbar=True):
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C = _get_conflicts_matrix(L, normalize=normalize)
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train | plot_probabilities_histogram | Plot a histogram from a numpy array of probabilities
Args:
Y_p: An [n] or [n, 1] np.ndarray of probabilities (floats in [0,1]) | metal/contrib/visualization/analysis.py | def plot_probabilities_histogram(Y_p, title=None):
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train | plot_predictions_histogram | Plot a histogram comparing int predictions vs true labels by class
Args:
Y_ph: An [n] or [n, 1] np.ndarray of predicted int labels
Y: An [n] or [n, 1] np.ndarray of gold labels | metal/contrib/visualization/analysis.py | def plot_predictions_histogram(Y_ph, Y, title=None):
"""Plot a histogram comparing int predictions vs true labels by class
Args:
Y_ph: An [n] or [n, 1] np.ndarray of predicted int labels
Y: An [n] or [n, 1] np.ndarray of gold labels
"""
labels = list(set(Y).union(set(Y_ph)))
edges =... | def plot_predictions_histogram(Y_ph, Y, title=None):
"""Plot a histogram comparing int predictions vs true labels by class
Args:
Y_ph: An [n] or [n, 1] np.ndarray of predicted int labels
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train | get_clique_tree | Given a set of int nodes i and edges (i,j), returns an nx.Graph object G
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train | Logger.check | Returns True if the logging frequency has been met. | metal/logging/logger.py | def check(self, batch_size):
"""Returns True if the logging frequency has been met."""
self.increment(batch_size)
return self.unit_count >= self.config["log_train_every"] | def check(self, batch_size):
"""Returns True if the logging frequency has been met."""
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train | Logger.increment | Update the total and relative unit counts | metal/logging/logger.py | def increment(self, batch_size):
"""Update the total and relative unit counts"""
self.example_count += batch_size
self.example_total += batch_size
if self.log_unit == "seconds":
self.unit_count = int(self.timer.elapsed())
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train | Logger.calculate_metrics | Add standard and custom metrics to metrics_dict | metal/logging/logger.py | def calculate_metrics(self, model, train_loader, valid_loader, metrics_dict):
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train | Logger.log | Print calculated metrics and optionally write to file (json/tb) | metal/logging/logger.py | def log(self, metrics_dict):
"""Print calculated metrics and optionally write to file (json/tb)"""
if self.writer:
self.write_to_file(metrics_dict)
if self.verbose:
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train | Logger.print_to_screen | Print all metrics in metrics_dict to screen | metal/logging/logger.py | def print_to_screen(self, metrics_dict):
"""Print all metrics in metrics_dict to screen"""
score_strings = defaultdict(list)
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train | LSTMModule._reduce_output | Reduces the output of an LSTM step
Args:
outputs: (torch.FloatTensor) the hidden state outputs from the
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outputs: (torch.FloatTensor) the hidden state outputs from the
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train | LSTMModule.forward | Applies one step of an lstm (plus reduction) to the input X, which
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train | choose_other_label | Given a cardinality k and true label y, return random value in
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"""Given a cardinality k and true label y, return random value in
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Y: np.array of true labels
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bag_size: (list) the min and max length of bags of words
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X: (Tensor) a tensor of indices representing tokens
... | synthetic/generate.py | def gaussian_bags_of_words(Y, vocab=vocab1k, sigma=1, bag_size=[25, 50], **kwargs):
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Y: np.array of true labels
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train | SingleTaskTreeDepsGenerator._generate_edges | Generate a random tree-structured dependency graph based on a
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"""Generate a random tree-structured dependency graph based on a
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train | SingleTaskTreeDepsGenerator.P_conditional | Compute the conditional probability
P_\theta(li | lj, y)
=
Z^{-1} exp(
theta_{i|y} \indpm{ \lambda_i = Y }
+ \theta_{i,j} \indpm{ \lambda_i = \lambda_j }
)
In other words, compute the conditional probability that LF i outputs
... | synthetic/generate.py | def P_conditional(self, i, li, j, lj, y):
"""Compute the conditional probability
P_\theta(li | lj, y)
=
Z^{-1} exp(
theta_{i|y} \indpm{ \lambda_i = Y }
+ \theta_{i,j} \indpm{ \lambda_i = \lambda_j }
)
In other words, compute... | def P_conditional(self, i, li, j, lj, y):
"""Compute the conditional probability
P_\theta(li | lj, y)
=
Z^{-1} exp(
theta_{i|y} \indpm{ \lambda_i = Y }
+ \theta_{i,j} \indpm{ \lambda_i = \lambda_j }
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train | SingleTaskTreeDepsGenerator._generate_label_matrix | Generate an [n,m] label matrix with entries in {0,...,k} | synthetic/generate.py | def _generate_label_matrix(self):
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self.L = np.zeros((self.n, self.m))
self.Y = np.zeros(self.n, dtype=np.int64)
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self.Y = np.zeros(self.n, dtype=np.int64)
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train | SingleTaskTreeDepsGenerator._get_conditional_probs | Compute the true clique conditional probabilities P(\lC | Y) by
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Note that this generates an attribute, self.c_probs, that has the same
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TODO: Can compute thes... | synthetic/generate.py | def _get_conditional_probs(self):
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counting given L, Y; we'll use this as ground truth to compare to.
Note that this generates an attribute, self.c_probs, that has the same
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train | Featurizer.fit_transform | Execute fit and transform in sequence. | metal/contrib/featurizers/featurizer.py | def fit_transform(self, input, **fit_kwargs):
"""Execute fit and transform in sequence."""
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sents: A list of lists of tokens (representing sentences)
Vocab kwargs include:
max_size
min_freq
specials
unk_init | metal/contrib/featurizers/embedding_featurizer.py | def fit(self, sents, **kwargs):
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Args:
sents: A list of lists of tokens (representing sentences)
Vocab kwargs include:
max_size
min_freq
specials
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Args:
sents: A list of lists of tokens (representing sentences)
Vocab kwargs include:
max_size
min_freq
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Args:
sents: A list of lists of tokens (representing sentences)
NOTE: These sentences should already be marked using the
mark_entities() helper.
Returns:
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train | rargmax | Argmax with random tie-breaking
Args:
x: a 1-dim numpy array
Returns:
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"""Argmax with random tie-breaking
Args:
x: a 1-dim numpy array
Returns:
the argmax index
"""
idxs = np.where(abs(x - np.max(x, axis=0)) < eps)[0]
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Args:
x: a 1-dim numpy array
Returns:
the argmax index
"""
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train | pred_to_prob | Converts a 1D tensor of predicted labels into a 2D tensor of probabilistic labels
Args:
Y_h: an [n], or [n,1] tensor of predicted (int) labels in {1,...,k}
k: the largest possible label in Y_h
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... | metal/utils.py | def pred_to_prob(Y_h, k):
"""Converts a 1D tensor of predicted labels into a 2D tensor of probabilistic labels
Args:
Y_h: an [n], or [n,1] tensor of predicted (int) labels in {1,...,k}
k: the largest possible label in Y_h
Returns:
Y_s: a torch.FloatTensor of shape [n, k] where Y_s[i... | def pred_to_prob(Y_h, k):
"""Converts a 1D tensor of predicted labels into a 2D tensor of probabilistic labels
Args:
Y_h: an [n], or [n,1] tensor of predicted (int) labels in {1,...,k}
k: the largest possible label in Y_h
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train | arraylike_to_numpy | Convert a 1d array-like (e.g,. list, tensor, etc.) to an np.ndarray | metal/utils.py | def arraylike_to_numpy(array_like):
"""Convert a 1d array-like (e.g,. list, tensor, etc.) to an np.ndarray"""
orig_type = type(array_like)
# Convert to np.ndarray
if isinstance(array_like, np.ndarray):
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array_like = np.array(array_like)
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orig_type = type(array_like)
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train | convert_labels | Convert a matrix from one label type to another
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source: The convention the labels are currently expressed in
dest: The convention to convert the labels to
Conventions:
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... | metal/utils.py | def convert_labels(Y, source, dest):
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Args:
Y: A np.ndarray or torch.Tensor of labels (ints)
source: The convention the labels are currently expressed in
dest: The convention to convert the labels to
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Y: A np.ndarray or torch.Tensor of labels (ints)
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train | label_matrix_to_one_hot | Converts a 2D [n,m] label matrix into an [n,m,k] one hot 3D tensor
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L: a [n,m] label matrix with categorical labels (0 = abstain)
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Note that in the returned 3D matrix, abstain votes continue to be
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train | recursive_merge_dicts | Merge dictionary y into a copy of x, overwriting elements of x when there
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misses: what to do if a key in y is not in x
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train | split_data | Splits inputs into multiple splits of defined sizes
Args:
inputs: correlated tuples/lists/arrays/matrices/tensors to split
splits: list containing split sizes (fractions or counts);
shuffle: if True, shuffle the data before splitting
stratify_by: (None or an input) if not None, use ... | metal/utils.py | def split_data(
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splits=[0.5, 0.5],
shuffle=True,
stratify_by=None,
index_only=False,
seed=None,
):
"""Splits inputs into multiple splits of defined sizes
Args:
inputs: correlated tuples/lists/arrays/matrices/tensors to split
splits: list containing split sizes (... | def split_data(
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stratify_by=None,
index_only=False,
seed=None,
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train | place_on_gpu | Utility to place data on GPU, where data could be a torch.Tensor, a tuple
or list of Tensors, or a tuple or list of tuple or lists of Tensors | metal/utils.py | def place_on_gpu(data):
"""Utility to place data on GPU, where data could be a torch.Tensor, a tuple
or list of Tensors, or a tuple or list of tuple or lists of Tensors"""
data_type = type(data)
if data_type in (list, tuple):
data = [place_on_gpu(data[i]) for i in range(len(data))]
data ... | def place_on_gpu(data):
"""Utility to place data on GPU, where data could be a torch.Tensor, a tuple
or list of Tensors, or a tuple or list of tuple or lists of Tensors"""
data_type = type(data)
if data_type in (list, tuple):
data = [place_on_gpu(data[i]) for i in range(len(data))]
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train | MTEndModel._build | TBD | metal/multitask/mt_end_model.py | def _build(self, input_modules, middle_modules, head_modules):
"""
TBD
"""
self.input_layer = self._build_input_layer(input_modules)
self.middle_layers = self._build_middle_layers(middle_modules)
self.heads = self._build_task_heads(head_modules)
# Construct loss ... | def _build(self, input_modules, middle_modules, head_modules):
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TBD
"""
self.input_layer = self._build_input_layer(input_modules)
self.middle_layers = self._build_middle_layers(middle_modules)
self.heads = self._build_task_heads(head_modules)
# Construct loss ... | [
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train | MTEndModel._build_task_heads | Creates and attaches task_heads to the appropriate network layers | metal/multitask/mt_end_model.py | def _build_task_heads(self, head_modules):
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num_layers = len(self.config["layer_out_dims"])
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train | MTEndModel.forward | Returns a list of outputs for tasks 0,...t-1
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x: a [batch_size, ...] batch from X | metal/multitask/mt_end_model.py | def forward(self, x):
"""Returns a list of outputs for tasks 0,...t-1
Args:
x: a [batch_size, ...] batch from X
"""
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train | MTEndModel._preprocess_Y | Convert Y to t-length list of probabilistic labels if necessary | metal/multitask/mt_end_model.py | def _preprocess_Y(self, Y, k=None):
"""Convert Y to t-length list of probabilistic labels if necessary"""
# If not a list, convert to a singleton list
if not isinstance(Y, list):
if self.t != 1:
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... | def _preprocess_Y(self, Y, k=None):
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train | MTEndModel._get_loss_fn | Returns the loss function to use in the train_model routine | metal/multitask/mt_end_model.py | def _get_loss_fn(self):
"""Returns the loss function to use in the train_model routine"""
criteria = self.criteria.to(self.config["device"])
loss_fn = lambda X, Y: sum(
criteria(Y_tp, Y_t) for Y_tp, Y_t in zip(self.forward(X), Y)
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return loss_fn | def _get_loss_fn(self):
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criteria = self.criteria.to(self.config["device"])
loss_fn = lambda X, Y: sum(
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train | MTLabelModel._create_L_ind | Convert T label matrices with labels in 0...K_t to a one-hot format
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a _label vector_ emitted by LF j for data point i.
Args:
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in... | metal/multitask/mt_label_model.py | def _create_L_ind(self, L):
"""Convert T label matrices with labels in 0...K_t to a one-hot format
Here we can view e.g. the $(i,j)$ entries of the $T$ label matrices as
a _label vector_ emitted by LF j for data point i.
Args:
L: a T-length list of [n,m] scipy.sparse label ... | def _create_L_ind(self, L):
"""Convert T label matrices with labels in 0...K_t to a one-hot format
Here we can view e.g. the $(i,j)$ entries of the $T$ label matrices as
a _label vector_ emitted by LF j for data point i.
Args:
L: a T-length list of [n,m] scipy.sparse label ... | [
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train | MTLabelModel.predict_proba | Returns the task marginals estimated by the model: a t-length list of
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estimated P((Y_i)_s | \lambda_j(x_i))
Args:
L: A t-length list of [n,m] scipy.sparse label matrices with values
in {0,1,...,k... | metal/multitask/mt_label_model.py | def predict_proba(self, L):
"""Returns the task marginals estimated by the model: a t-length list of
[n,k_t] matrices where the (i,j) entry of the sth matrix represents the
estimated P((Y_i)_s | \lambda_j(x_i))
Args:
L: A t-length list of [n,m] scipy.sparse label matrices wi... | def predict_proba(self, L):
"""Returns the task marginals estimated by the model: a t-length list of
[n,k_t] matrices where the (i,j) entry of the sth matrix represents the
estimated P((Y_i)_s | \lambda_j(x_i))
Args:
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