text stringlengths 81 112k |
|---|
The type of this inline shape as a member of
``docx.enum.shape.WD_INLINE_SHAPE``, e.g. ``LINKED_PICTURE``.
Read-only.
def type(self):
"""
The type of this inline shape as a member of
``docx.enum.shape.WD_INLINE_SHAPE``, e.g. ``LINKED_PICTURE``.
Read-only.
"""
... |
Extracts the data from a Plotly Figure
Parameters
----------
figure : plotly_figure
Figure from which data will be
extracted
Returns a DataFrame or list of DataFrame
def to_df(figure):
"""
Extracts the data from a Plotly Figure
Parameters
----... |
Converts from hex|rgb to rgba
Parameters:
-----------
color : string
Color representation on hex or rgb
alpha : float
Value from 0 to 1.0 that represents the
alpha value.
Example:
to_rgba('#E1E5ED',0.6)
... |
Converts from hex to rgb
Parameters:
-----------
color : string
Color representation on hex or rgb
Example:
hex_to_rgb('#E1E5ED')
hex_to_rgb('#f03')
def hex_to_rgb(color):
"""
Converts from hex to rgb
Parameters:
-----------
... |
Returns an hex color
Parameters:
-----------
color : string
Color representation in rgba|rgb|hex
Example:
normalize('#f03')
def normalize(color):
"""
Returns an hex color
Parameters:
-----------
color : string
Co... |
Converts from rgb to hex
Parameters:
-----------
color : string
Color representation on hex or rgb
Example:
rgb_to_hex('rgb(23,25,24)')
def rgb_to_hex(color):
"""
Converts from rgb to hex
Parameters:
-----------
color : string
... |
Converts from rgba to rgb
Parameters:
-----------
color : string
Color representation in rgba
bg : string
Color representation in rgb
Example:
rgba_to_rgb('rgb(23,25,24,.4)''
def rgba_to_rgb(color, bg='rgb(255,255,255)'):
"""... |
Converts from hex to hsv
Parameters:
-----------
color : string
Color representation on color
Example:
hex_to_hsv('#ff9933')
def hex_to_hsv(color):
"""
Converts from hex to hsv
Parameters:
-----------
color : string
... |
Generates a scale of colours from a base colour
Parameters:
-----------
color : string
Color representation in hex
N : int
number of colours to generate
Example:
color_range('#ff9933',20)
def color_range(color, N=20):
"""
... |
Generates a colour table
Parameters:
-----------
color : string | list | dict
Color representation in rgba|rgb|hex
If a list of colors is passed then these
are displayed in a table
N : int
number of colou... |
Returns a generator with a list of colors
and gradients of those colors
Parameters:
-----------
colors : list(colors)
List of colors to use
Example:
colorgen()
colorgen(['blue','red','pink'])
colorgen(['#f03','rgb(23,25,25)'])
def co... |
Displays a color scale (HTML)
Parameters:
-----------
scale : str
Color scale name
If no scale name is provided then all scales are returned
(max number for each scale)
If scale='all' then all scale combinations... |
Returns a color scale
Parameters:
-----------
scale : str
Color scale name
If the color name is preceded by a minus (-)
then the scale is inversed
n : int
Number of colors
If n < numbe... |
Returns a color scale to be used for a plotly figure
Parameters:
-----------
scale : str or list
Color scale name
If the color name is preceded by a minus (-)
then the scale is inversed.
Also accepts a list of colors (... |
Generates a plotly Data object
Parameters
----------
colors : list or dict
{key:color} to specify the color for each column
[colors] to use the colors in the defined order
colorscale : str
Color scale name
Only valid if 'colors' is null
See cufflinks.colors.scales() for available scales
kind :... |
Returns a plotly chart either as inline chart, image of Figure object
Parameters:
-----------
kind : string
Kind of chart
scatter
bar
box
spread
ratio
heatmap
surface
histogram
bubble
bubble3d
scatter3d
scattergeo
ohlc
candle
pie
choroplet
data... |
Returns a dict with an item per key
Parameters:
-----------
items : string, list or dict
Items (ie line styles)
keys: list
List of keys
items_names : string
Name of items
def get_items_as_list(items,keys,items_names='styles'):
"""
Returns a dict with an item per key
Parameters:
-----------
it... |
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 : s... |
Plots a figure in IPython, creates an HTML or generates an Image
figure : figure
Plotly figure to be charted
validate : bool
If True then all values are validated before
it is charted
sharing : string
Sets the sharing level permission
public - anyone can see this chart
private - only you can see this... |
Generates a Technical Study Chart
Parameters:
-----------
study : string
Technical Study to be charted
sma - 'Simple Moving Average'
rsi - 'R Strength Indicator'
periods : int
Number of periods
column : string
Name of the column on which the
study will be done
include : bool
... |
Plots a figure in IPython
figure : figure
Plotly figure to be charted
validate : bool
If True then all values are validated before
it is charted
sharing : string
Sets the sharing level permission
public - anyone can see this chart
private - only you can see this chart
secret - only people with the... |
Ensure that filesystem is setup/filled out in a valid way
def ensure_local_files():
"""
Ensure that filesystem is setup/filled out in a valid way
"""
if _file_permissions:
if not os.path.isdir(AUTH_DIR):
os.mkdir(AUTH_DIR)
for fn in [CONFIG_FILE]:
contents = load_json_dict(fn)
for key, val in list(_FI... |
Set the keyword-value pairs in `~/.config`.
sharing : string
Sets the sharing level permission
public - anyone can see this chart
private - only you can see this chart
secret - only people with the link can see the chart
theme : string
Sets the default theme
See cufflinks.getThemes() for availab... |
Checks if file exists. Returns {} if something fails.
def load_json_dict(filename, *args):
"""Checks if file exists. Returns {} if something fails."""
data = {}
if os.path.exists(filename):
with open(filename, "r") as f:
try:
data = json.load(f)
if not isinstance(data, dict):
data = {}
except:
... |
Will error if filename is not appropriate, but it's checked elsewhere.
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 Ty... |
Returns a dictionary with the schema for a QuantFigure
def _get_schema(self):
"""
Returns a dictionary with the schema for a QuantFigure
"""
d={}
layout_kwargs=dict((_,'') for _ in get_layout_kwargs())
for _ in ('data','layout','theme','panels'):
d[_]={}
for __ in eval('__QUANT_FIGURE_{0}'.format(_.... |
Returns a sliced DataFrame
Parameters
----------
slice : tuple(from,to)
from : str
to : str
States the 'from' and 'to' values which
will get rendered as df.ix[from:to]
df : DataFrame
If omitted then the QuantFigure.DataFrame is resampled.
def _get_sliced(self,slice,df=None):
"""
R... |
Returns a resampled DataFrame
Parameters
----------
rule : str
the offset string or object representing target conversion
for all aliases available see http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
how : str or dict
states the form in which the resampling will be done... |
Updates the values for a QuantFigure
The key-values are automatically assigned to the correct
section of the QuantFigure
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:
... |
Deletes the values for a QuantFigure
The key-values are automatically deleted from the correct
section of the QuantFigure
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] ... |
Returns the panel domains for each axis
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):
lower=round(bottom_margin+(min_panel_size+spacing)*(n+1-_),2)
d['yaxis{0}'.format(_)]=dic... |
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... |
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... |
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... |
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... |
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
indicated x position(s)
Extra pa... |
Adds a study to QuantFigure.studies
Parameters:
study : dict
{'kind':study_kind,
'params':study_parameters,
'display':display_parameters}
def _add_study(self,study):
"""
Adds a study to QuantFigure.studies
Parameters:
study : dict
{'kind':study_kind,
'params':study_parameters,
... |
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... |
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... |
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 :... |
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... |
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
... |
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... |
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
... |
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... |
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
def go_offline(connected=None):
"""
connected : bool
If True, the plotly.js library will be loaded
fro... |
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... |
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.
def bestfit(self):
"""
Returns a series with the bestfit values.
Example:
Series.bestfit()
... |
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
def normalize(self,asOf=None,multiplier=100):
"""
Returns a normalized... |
d1 <-- d2
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 |
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:
dict_path({'level1':{'level2':{'level3':'value'}}})
Returns
{'level1': [],
'level... |
Formats (prettyprint) a concatenated dictionary
def pp(el,preString=''):
"""
Formats (prettyprint) a concatenated dictionary
"""
tab=' '*4
if isinstance(el,dict):
keys=list(el.keys())
keys.sort()
for key in keys:
val=el[key]
if isinstance(val,dict) or isinstance(val,list):
print('%s%s :' % (preSt... |
Returns a dictionay indexed by values {value_k:key_k}
Parameters:
-----------
d : dictionary
def inverseDict(d):
"""
Returns a dictionay indexed by values {value_k:key_k}
Parameters:
-----------
d : dictionary
"""
dt={}
for k,v in list(d.items()):
if type(v) in (list,tuple):
for i in v:
dt[i]=k
... |
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
keyword : string
Keyword to look for in the orginal dictionary
clean_origin : ... |
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
def deep_update(d,d_update):
"""
Updates the values (deep form) of a given dictionary
Parameters:
-----------
d : dict
dict... |
Reads a google sheet
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) |
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
def getDateFromToday(delta,strfmt='%Y%m%d'):
""" Returns a string that represents a date n numbers of days from today.
Par... |
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... |
how : string
value
pct_chg
diff
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,col... |
Returns
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... |
Returns
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 |
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
def pie(n_labels=5,mode=None):
"""
Returns a DataFrame with the required format for... |
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... |
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
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... |
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... |
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
... |
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
def ohlc(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close']]
Parame... |
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
def ohlcv(n=100):
"""
Returns a DataFrame with the required format for
a candlestick or ohlc plot
df[['open','high','low','close','... |
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
def box(n_traces=5,n=100,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
'stocks' for random stock names
def histogram(n_traces=1,n=500,dis... |
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
def distplot(n_trace... |
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
def violin(n=500,dispersion=3,categories=True,n_categori... |
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
def surface(n_x=20,n_y=20):
"""
Returns a DataFrame with the required format for
a surface plot
Parameters:
-----------
... |
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
def sinwave(n=4,inc=.25):
"""
Returns a DataFrame with the required format for
a surface (sine wave) plot
Parameters... |
Returns a theme definition.
To see the colors translated (hex) use
cufflinks.getLayout(theme) instead.
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... |
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... |
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
def get_annotations(df,annotations,kind='lines',theme=None,**kwargs):
"""
Generates an annotati... |
Strips a figure into multiple figures with a trace on each of them
Parameters:
-----------
figure : Figure
Plotly Figure
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 f... |
Generates a layout with the union of all properties of multiple
figures' layouts
Parameters:
-----------
fig : list(Figures)
List of Plotly Figures
def get_base_layout(figs):
"""
Generates a layout with the union of all properties of multiple
figures' layouts
Parameters:
-----------
fig : list(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... |
Generates a single Figure from a list of figures
Parameters:
-----------
figures : list(Figures)
List of figures to be merged.
def merge_figures(figures):
"""
Generates a single Figure from a list of figures
Parameters:
-----------
figures : list(Figures)
List of figures to be merged.
"""
figure={}... |
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... |
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... |
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... |
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... |
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... |
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:
... |
Display an [n, m] matrix of labels
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).squeez... |
Display an [m, m] matrix of overlaps
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) #... |
Display an [m, m] matrix of conflicts
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)
plt.imshow(C, aspect="auto")
plt.title("Conflicts")
if colorbar:... |
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])
def plot_probabilities_histogram(Y_p, title=None):
"""Plot a histogram from a numpy array of probabilities
Args:
Y_p: An [n] or [n, 1] np.ndarray of probabilities... |
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
def plot_predictions_histogram(Y_ph, Y, title=None):
"""Plot a histogram comparing int predictions vs true labels by ... |
Given a set of int nodes i and edges (i,j), returns an nx.Graph object G
which is a clique tree, where:
- G.node[i]['members'] contains the set of original nodes in the ith
maximal clique
- G[i][j]['members'] contains the set of original nodes in the seperator
set between max... |
Returns True if the logging frequency has been met.
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"] |
Update the total and relative unit counts
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())
... |
Add standard and custom metrics to metrics_dict
def calculate_metrics(self, model, train_loader, valid_loader, metrics_dict):
"""Add standard and custom metrics to metrics_dict"""
# Check whether or not it's time for validation as well
self.log_count += 1
log_valid = (
valid... |
Print calculated metrics and optionally write to file (json/tb)
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:
self.print_to_screen(metrics_dict)
... |
Print all metrics in metrics_dict to screen
def print_to_screen(self, metrics_dict):
"""Print all metrics in metrics_dict to screen"""
score_strings = defaultdict(list)
for split_metric, value in metrics_dict.items():
split, metric = split_metric.split("/", 1)
if isinst... |
Reduces the output of an LSTM step
Args:
outputs: (torch.FloatTensor) the hidden state outputs from the
lstm, with shape [batch_size, max_seq_length, hidden_size]
def _reduce_output(self, outputs, seq_lengths):
"""Reduces the output of an LSTM step
Args:
... |
Applies one step of an lstm (plus reduction) to the input X, which
is handled by self.encoder
def forward(self, X):
"""Applies one step of an lstm (plus reduction) to the input X, which
is handled by self.encoder"""
# Identify the first non-zero integer from the right (i.e., the length
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.