def plot_net_parameters(self,
sort_index=True,
plotly_online=False,
mode='lines+markers+text',
overlay_hlines=None,
asFigure=False,
**kwargs):
"""Plot the current values of the parameters of the network."""
import cufflinks
import plotly
df = self.net_parameters_to_dataframe()
# stringify index (otherwise error is thrown by plotly)
df.index = df.index.map(str)
# optionally sort the index, grouping together self-interactions
# if sort_index:
# def sorter(elem):
# return len(elem[0][0])
# sorted_data = sorted(list(df.iloc[:, 0].to_dict().items()),
# key=sorter)
# x, y = tuple(zip(*sorted_data))
# df = pd.DataFrame({'x': x, 'y': y}).set_index('x')
# df.index = df.index.map(str)
# decide online/offline
if plotly_online:
cufflinks.go_online()
else:
cufflinks.go_offline()
# draw overlapping horizontal lines for reference if asked
if overlay_hlines is None:
overlay_hlines = np.arange(-np.pi, np.pi, np.pi / 2)
# return df.iplot(kind='scatter', mode=mode, size=6,
# title='Values of parameters',
# asFigure=asFigure, **kwargs)
from .plotly_utils import hline
fig = df.iplot(kind='scatter',
mode=mode,
size=6,
title='Values of parameters',
text=df.index.tolist(),
asFigure=True,
**kwargs)
fig.layout.shapes = hline(0,
len(self.free_parameters),
overlay_hlines,
dash='dash')
fig.data[0].textposition = 'top'
fig.data[0].textfont = dict(color='white', size=13)
if asFigure:
return fig
else:
return plotly.offline.iplot(fig)
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
from plotly.subplots import make_subplots
cf.go_online()
import plotly.express as px
import plotly.graph_objects as go
from dash.dependencies import Input, Output
import seaborn as sns
from app import Indian_data as Inddata
import chart_studio.plotly as py
from world import World_data as wd
from makerp import makecnt as rp
# init_notebook_mode(connected=True)
obj_ofind = Inddata()
external_stylesheets = [
'https://codepen.io/chriddyp/pen/bWLwgP.css', 'assets/style.css',
'https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css'
]
confirmed_df = pd.read_csv(
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
)
deaths_df = pd.read_csv(
'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
)
recoveries_df = pd.read_csv(
Zero One Hinge Loss - Not displayed currently
"""
import ClassiferHelperAPI as CH
import ClassifierCapsuleClass as CLF
import RegressionCapsuleClass as RGR
import importlib
import numpy as np
import pandas as pd
importlib.reload(CH)
from ast import literal_eval
import plotly.plotly as py
import plotly.graph_objs as go
import htmltag as HT
import cufflinks as cf # this is necessary to link pandas to plotly
cf.go_online()
minSplit = 0.2
maxSplit = 0.6
methods = ['dummy','bayesian','logistic','svm','dtree','random_forests','ada_boost']
kwargsDict = {'dummy' : {'strategy' : 'most_frequent'},
'bayesian' : {'fit_prior' : True},
'logistic' : {'penalty' : 'l2'},
'svm' : {'kernel' : 'rbf','probability' : True},
'dtree' : {'criterion' : 'entropy'},
'random_forests' : {'n_estimators' : 10 },
'ada_boost' : {'n_estimators' : 50 }}
def eval_clf_perfs_bag_of_words():
minSplit = 0.2