}
)
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(
[