def select_target(value):
if value:
df = read_df(value, 'first')
return [
dcc.Dropdown(id='target_dropdown',
options=[{
'label': i,
'value': i
} for i in df.columns])
]
def select_target_value(target_value, df_value):
if (target_value is not None) and (df_value is not None):
df = read_df(df_value, 'all')
return [[
dcc.Dropdown(
placeholder="Select Target value (0/1, Default/Performing)",
options=[{
'label': i,
'value': i
} for i in df[target_value].unique()])
],
[
dcc.Dropdown(placeholder="Optional: Drop Columns",
options=[{
'label': i,
'value': i
} for i in df.columns],
multi=True)
]]
def intermediate_results(n_clicks, drop_columns, target_dropdown, target_value,
df_value):
if n_clicks > 0:
df = read_df(df_value, 'all')
target = df[[target_dropdown]]
gb = target.columns[0]
if drop_columns:
drop = [gb] + drop_columns
df.drop(drop, axis=1, inplace=True)
else:
df.drop(gb, axis=1, inplace=True)
target[target[gb] != target_value] = 0
target[target[gb] == target_value] = 1
new = ScorecardBuilder(df, target)
model = new.create_scorecard()
return json.dumps(datasets)