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)