df_scaled = scaler.fit_transform(X) clf = svm.SVR(kernel='rbf', C=10000.0, gamma=0.01, max_iter=-1).fit(df_scaled, y) @services.publish(ws.workspace_id,ws.authorization_token) @services.dataframe_service(f1=floa
dam_data = pd.DataFrame(new_data, columns=['id', '日期', '开盘', '最高', '最低', '收盘', 'pre_close', 'change', 'pct_chg', 'vol', 'amount', 'amount1', 'index_new','ts_code']).drop_duplicates() dataset = TSDataset.load_from_dataframe( dam_data, # pd.DataFrame time_...
lag_obj=Lag(aggregation="sum",value=5)'''This means taking previous 5 values and perform `sum`. When used inside pipeline, this will be applied to all the columns.If used inside DataFrameMapper, the it will be applied to only those columns which are inside DataFrameMapper.''' ...