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