def run_variables_network_visualization(n, network_threshold, p_val,
correlated_threshold, nodes_sizes):
# available_companies_list = glob.glob("/data/*.csv")
if n:
sh_ai = variables_storage['sherlock']
settings = {
'network_connections_threshold_variables_relations':
float(network_threshold),
'p_value_correlations':
float(p_val),
'correlated_threshold':
float(correlated_threshold),
'node_size':
int(nodes_sizes),
'show_report':
False,
'fig_size': (12, 10),
'max_nodes_to_draw_graph':
90
}
fig = sh_ai.visualize_variables_network(settings)
img = None
if fig is not None:
out_url = callbackshelpers.fig_to_uri(fig)
img = html.Img(src=out_url)
return img, 'Done'
# new_tags = input_tags.split(';')
return [], None
def insight(n, n_runs, bagging_perc, model, scaler, random_selector,
train_perc):
if n:
scaler_object = available_settings.available_models_preprocessing[
scaler]
model_object = available_settings.available_models[model]
settings = {
'scaler': scaler_object,
'train_perc': train_perc,
'runs': n_runs,
'selection_type': random_selector,
'model': model_object,
'bootstrap_aggregate_percentage': bagging_perc,
'fig_size': (12, 10)
}
sh_ai = variables_storage['sherlock']
figs = sh_ai.visualize_insights(settings)
imgs = []
for fig in figs:
out_url = callbackshelpers.fig_to_uri(fig, bbox_inches='tight')
imgs.append(html.Img(src=out_url))
# out_url = fig_to_uri(fig, bbox_inches='tight')
# img = html.Img(src=out_url)
return imgs, [f'Done']
# new_tags = input_tags.split(';')
return None, [f'Not compared']
def run_model_potential(n):
# available_companies_list = glob.glob("/data/*.csv")
if n:
sh_ai = variables_storage['sherlock']
settings = None
fig = sh_ai.visualize_all_data_model(settings)
out_url = callbackshelpers.fig_to_uri(fig, bbox_inches='tight')
img = html.Img(src=out_url)
return img, 'Done'
# new_tags = input_tags.split(';')
return [], None
def run_variables_visualization(n, variables_per_page):
# available_companies_list = glob.glob("/data/*.csv")
if n:
print('graphs to show')
settings = {'num_var_per_graph': int(variables_per_page)}
sh_ai = variables_storage['sherlock']
figs = sh_ai.visualize_variables_distributions(settings)
imgs = []
for fig in figs:
out_url = callbackshelpers.fig_to_uri(fig, bbox_inches='tight')
imgs.append(html.Img(src=out_url))
return imgs, 'Done'
return [], None
def run_variables_response_visualization(n, use_correction, p_value):
# available_companies_list = glob.glob("/data/*.csv")
if n:
sh_ai = variables_storage['sherlock']
settings = {
'p_variables_response_relation': float(p_value),
'use_correction': use_correction == 'True'
}
figs = sh_ai.visualize_variables_response_relationship(settings)
imgs = []
for fig in figs:
out_url = callbackshelpers.fig_to_uri(fig, bbox_inches='tight')
imgs.append(html.Img(src=out_url))
return imgs, 'Done'
# new_tags = input_tags.split(';')
return [], None