} ) figure = go.Figure(data=data, layout=layout) return figure ########## ########### INTERPRETATIONS EXPERIMENT ########### from processing.data_management import load_excel df = load_excel(file_name=config.DATA_FILE) interpretations = {0: [(8, 0.1142757655826782), (11, 0.0419749689507324), (17, -0.040193937766442395), (19, 0.021369448257611598), (6, 0.015126651604110641), (13, -0.012431268283180513), (1, -0.010793721082423855), (4, -0.010474517613815413), (9, 0.007865948084430117), (20, -0.00784333216080914)], 1: [(8, 0.16589278177299743), (5, 0.03939243605466441), (20, -0.030085051115543156), (10, -0.028261514208108608),
import dash_core_components as dcc import dash_html_components as html import plotly.graph_objs as go import dash_table #import geopandas as gpd from utils import Header, make_dash_table import pandas as pd import numpy as np import pathlib from dash.dependencies import Input, Output import json import docx df = load_excel(file_name=config.DATA_FILE) conf_mat = load_excel(file_name='confusion_matrix.xlsx') #conf_mat.drop(conf_mat.columns[0], axis=1, inplace=True) classification_report = load_excel(file_name='classification_report.xlsx') y_pred = load_excel(file_name='predicted_probabilities.xlsx') print(np.array(conf_mat)) ########## TEMPORARY MAP DETAILS ###### mapbox_token = 'pk.eyJ1IjoiY2F3aWUiLCJhIjoiY2s1cGVsN3U3MHVrYTNsbnNpd3pubGR3ZSJ9.5sgCAI1IM9pOmmk4dZeD4Q' def create_layout(app): # Page layouts return html.Div( [
import dash_core_components as dcc import dash_html_components as html import plotly.graph_objs as go #import geopandas as gpd from utils import Header, make_dash_table import pandas as pd import numpy as np import pathlib from dash.dependencies import Input,Output import json import docx df = load_excel(file_name=config.DATA_FILE) #model_coefficients = load_excel(file_name='logistic_model_coefficients.xlsx') feature_importances = load_excel(file_name='tree_importances.xlsx') ########## TEMPORARY MAP DETAILS ###### mapbox_token = 'pk.eyJ1IjoiY2F3aWUiLCJhIjoiY2s1cGVsN3U3MHVrYTNsbnNpd3pubGR3ZSJ9.5sgCAI1IM9pOmmk4dZeD4Q' def create_layout(app): # Page layouts return html.Div( [ html.Div([Header(app)]), # page 1 html.Div(
import dash_core_components as dcc import dash_html_components as html import plotly.graph_objs as go import dash_table #import geopandas as gpd from utils import Header, make_dash_table import pandas as pd import numpy as np import pathlib from dash.dependencies import Input, Output import json import docx df = load_excel(file_name=config.DATA_FILE) model_coefficients = load_excel(file_name='logistic_model_coefficients.xlsx') y_pred = load_excel(file_name='predicted_probabilities.xlsx') ########## TEMPORARY MAP DETAILS ###### mapbox_token = 'pk.eyJ1IjoiY2F3aWUiLCJhIjoiY2s1cGVsN3U3MHVrYTNsbnNpd3pubGR3ZSJ9.5sgCAI1IM9pOmmk4dZeD4Q' def create_layout(app): # Page layouts return html.Div( [ html.Div([Header(app)]), # page 1 html.Div( [