def get_variables_response_layout():
variable_response_p = \
dbc.FormGroup(
[
dbc.Label("P value for variable response relation", html_for="var-per-page"),
dbc.Input(id="p-var-res-relation", min=0.0, max=0.1, step=.00001, value=0.05),
], style={'margin-left': '5%'}
)
use_correction = \
dbc.FormGroup(
[
dbc.Label("Use p correction", html_for="var-per-page"),
dcc.Dropdown(id="use-correction", options=[
{'label': 'Yes', 'value': 'True'},
{'label': 'No', 'value': 'False'},
],
value='True'),
], style={'margin-left': '5%'}
)
cl = dbc.Col([use_correction,
variable_response_p,
dsc.spinner_button('Show variables/response relationship', 'variables-response-spinner',
id='variables-response-button', n_clicks=0, color="primary", style={'margin': '5% 5% 10%'})
], width=3)
cr = dbc.Col([html.Div(children=[], id='variables-response-results-container')], width=9)
return dbc.Row(children=[cl, cr])
def get_all_data_model():
cl = dbc.Col([dsc.spinner_button('Show model', 'all-data-model-spinner', id='all-data-model-button',
n_clicks=0, color="primary", style={'margin': '5% 5% 10%'})
], width=1)
cr = dbc.Col([html.Div(children=[], id='all-data-model-container')], width=11)
return dbc.Row(children=[cl, cr])
def get_variables_distribution_layout():
number_of_graphs = \
dbc.FormGroup(
[
dbc.Label("Max number of variables per page", html_for="var-per-page"),
dbc.Input(id="n-var-per-page", min=3, max=15, step=1, value=5),
], style={'margin-left': '5%'}
)
cl = dbc.Col([number_of_graphs,
dsc.spinner_button('Show variables distribution', 'variables-distribution-spinner',
id='variables-distribution-button', n_clicks=0, color="primary",
style={'margin': '5% 5% 10%'})
], width=3)
cr = dbc.Col([html.Div(children=[], id='variables-distributions-results-container')], width=9)
return dbc.Row(children=[cl, cr])
def get_variables_relationship_layout():
setting_threshold = dbc.FormGroup(
[
dbc.Label("Variables network threshold", html_for="network-threshold"),
dbc.Input(id="network-threshold-input", min=0.01, max=1.0, step=.01, value=0.83),
], style={'margin-left': '5%'}
)
setting_correlated = dbc.FormGroup(
[
dbc.Label("Correlated threshold", html_for="correlated-threshold"),
dbc.Input(id="correlated-threshold-text", min=0.01, max=1.0, step=.01, value=0.9),
], style={'margin-left': '5%'}
)
nodes_size = dbc.FormGroup(
[
dbc.Label("Nodes size", html_for="nodes-size"),
dbc.Input(id="nodes-sizes-text", min=100, max=5000, step=10, value=3000),
], style={'margin-left': '5%'}
)
setting_p_val = dbc.FormGroup(
[
dbc.Label("P-value for correlations", html_for="p-val-threshold"),
dbc.Input(id="network-pval-input", min=0.001, max=0.1, step=.001, value=0.05),
], style={'margin-left': '5%'}
)
cl = dbc.Col([setting_p_val,
setting_threshold,
setting_correlated,
nodes_size,
dsc.spinner_button('Show variables network', 'variables-network-spinner',
id='variables-network-button', n_clicks=0, color="primary",
style={'margin': '5% 5% 10%'})
], width=3)
cr = dbc.Col([html.Div(children=[], id='variables-network-results-container')], width=9)
return dbc.Row(children=[cl, cr])
def get_variables_importance():
# against_shuffle = {'scaler': None, 'train_perc': 0.9, 'runs': 200,
# 'selection_type': 'keep_order', 'importance_stability_threshold': 0.9,
# 'bootstrap_ci': 0.95, 'model': None, 'layout': 'spring', 'nodes_size': 4000}
c1 = dsc.NamedInput('Number of runs', id='number-runs-insights', min=1, max=1000000, step=1, value=200,
style={'margin': '5%'})
c3 = dsc.NamedInput('Bagging %', id='bagging-perc-insights', min=.01, max=1.0, step=.01, value=.5, style={'margin': '5%'})
models = available_settings.available_models.keys()
options_list = [{'value': r, 'label': r} for r in models]
c4 = dsc.NamedDropdown("Model", id='select-model-insights', options=options_list, style={'width': '100%', 'margin': '5%'})
scalers = available_settings.available_models_preprocessing.keys()
options_list = [{'value': r, 'label': r} for r in scalers]
c5 = dsc.NamedDropdown("Scaler", id='select-scaler-insights', style={'width': '100%', 'margin': '5%'}, options=options_list)
selections = available_settings.available_random_selections
options_list = [{'value': r, 'label': r} for r in selections]
c6 = dsc.NamedDropdown("Random slection", id='select-random-insights', style={'width': '100%', 'margin': '5%'},
options=options_list)
c7 = dsc.NamedInput('Train %', id='train-perc-insights', min=0.1, max=1.0, step=.01, value=0.9, style={'margin': '5%'})
div = html.Div(children=[c1, c3, c4, c5, c6, c7], style={'margin': '1%'})
cl = dbc.Col([div,
dsc.spinner_button('Run insight', 'insights-spinner',
id='insights-button', n_clicks=0, color="primary",
style={'margin': '5% 5% 10%'}),
# dbc.Button('Run comparison', id='compare-against-shuffle-button',
# n_clicks=0, color="primary", style={'margin': '5% 5% 10%'})
], width=3)
cr = dbc.Col([html.Div(children=[], id='insights-container')], width=9)
return dbc.Row(children=[cl, cr])
def row_1():
c1 = dbc.Col([
dcc.Upload(id="upload-data",
children=html.Div(
["Drag and drop or click to select a file."]),
multiple=False,
style={
"width": "90%",
"height": "60px",
"lineHeight": "60px",
"borderWidth": "1px",
"borderStyle": "dashed",
"borderRadius": "2px",
"textAlign": "center",
"margin": "5%"
}),
html.Div(id='output-data-upload', style={"margin": "5%"})
],
width=4)
c2 = dbc.Col([
dsc.spinner_button('Load data',
'load-data-spinner',
id='load-data-button',
n_clicks=0,
color="primary",
block=True,
size='lg'),
],
width={
"size": 2,
"offset": 5
})
r = dbc.Row(children=[c1, c2], align='center', style={'margin-top': '2%'})
return r