{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.13"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":96164,"databundleVersionId":12993472,"sourceType":"competition"},{"sourceId":12462463,"sourceType":"datasetVersion","datasetId":7861436},{"sourceId":12477994,"sourceType":"datasetVersion","datasetId":7870836}],"dockerImageVersionId":31089,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false},"papermill":{"default_parameters":{},"duration":374.271766,"end_time":"2025-07-16T09:01:49.809559","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2025-07-16T08:55:35.537793","version":"2.6.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"id":"0e3be53a","cell_type":"markdown","source":"- 0.90038  v.03 - [XGB + Deep Learning Ensemble](https://www.kaggle.com/code/taylorsamarel/xgb-deep-learning-ensemble) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.83975  v.07 - [REBOOT: Use the lead values](https://www.kaggle.com/code/gromml/reboot-use-the-lead-values/notebook) - [gromml](https://www.kaggle.com/gromml)\n\n \n\n15-july-2025 - LB = [0.93794](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n","metadata":{"papermill":{"duration":0.002933,"end_time":"2025-07-16T08:55:40.394446","exception":false,"start_time":"2025-07-16T08:55:40.391513","status":"completed"},"tags":[]}},{"id":"b16d3f60","cell_type":"markdown","source":"### Archive\n\n- 0.12744  v.10 - [Top Notebook + New Model CSV Ensemble](https://www.kaggle.com/code/migrantworkerdatahub/top-notebook-new-model-csv-ensemble) - [Migrant Worker Data Hub](https://www.kaggle.com/migrantworkerdatahub)\n- 0.12958  v.03 - [DRW - Ensemble of Ensemble of Ensemble](https://www.kaggle.com/code/yingjunmao/drw-ensemble-of-ensemble-of-ensemble) - [YingJunMao](https://www.kaggle.com/yingjunmao)\n- 0.12864  v.26 - [**5 subm.files** from page Ensemble_Envy / averaged_final_submissions -> iBlend](https://www.kaggle.com/code/taylorsamarel/ensemble-envy)\n\n3-july-2025 - LB = [0.12957](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n- 0.57383  v.01 - [All You Need is Random State 700](https://www.kaggle.com/code/shinchen93/all-you-need-is-random-state-700) - [ShinC](https://www.kaggle.com/shinchen93)\n- 0.13628  v.72 - [DRW Add New Feature](https://www.kaggle.com/code/seowoohyeon/drw-add-new-feature/output) - [seowoohyeon](https://www.kaggle.com/seowoohyeon)\n- 0.13439  v.26 - [Small Improvements](https://www.kaggle.com/code/migrantworkerdatahub/small-improvements/notebook?scriptVersionId=249322532) - [Migrant Worker Data Hub](https://www.kaggle.com/migrantworkerdatahub)\n\n7-july-2025 - LB = [0.57389](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n- 0.84629  v.1 - [Improved Unshuffle 700 - Time Series Style](https://www.kaggle.com/code/taylorsamarel/improved-unshuffle-700-time-series-style) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.84585  v.2 - [Add more lags](https://www.kaggle.com/code/dmitriych/add-more-lags) - [Dmitriy Ch](https://www.kaggle.com/dmitriych)\n- 0.84443  v.1 - [Add More Lags - Improvement - Random State 700](https://www.kaggle.com/code/taylorsamarel/add-more-lags-improvement-random-state-700/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n\n\n9-july-2025 - LB = [0.84940](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n- 0.85944  v.2 - [Add more lags](https://www.kaggle.com/code/dmitriych/add-more-lags) - [Dmitriy Ch](https://www.kaggle.com/dmitriych)\n- 0.85472  v.1 - [Add More Lags](https://www.kaggle.com/code/taylorsamarel/add-more-lags/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.84629  v.1 - [Improved Unshuffle 700 - Time Series Style](https://www.kaggle.com/code/taylorsamarel/improved-unshuffle-700-time-series-style) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n\n\n9-july-2025 - LB = [The train data has been modified by the competition copyright holder](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n- 0.82968  v.11 - [DRW - Crypto Market Prediction | Turkish gambit](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-turkish-gambit/) - [riri](https://www.kaggle.com/nina2025)\n- 0.73799  v.13 - [REBOOT THE REBOOT 2.0](https://www.kaggle.com/code/taylorsamarel/reboot-the-reboot-2-0) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.72837  v.01 - [DRW-Ensemble-0.72837](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.70871  v.01 - [Anyone can win on Public LB](https://www.kaggle.com/code/johndoe2011/anyone-can-win-on-public-lb) - [ZULQAR.](https://www.kaggle.com/johndoe2011)\n\n14-july-2025 - LB = [0.82734](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.83975  v.07 - [REBOOT: Use the lead values](https://www.kaggle.com/code/gromml/reboot-use-the-lead-values/notebook) - [gromml](https://www.kaggle.com/gromml)\n\n15-july-2025 - LB = [0.93598](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n- 0.90038  v.03 - [XGB + Deep Learning Ensemble](https://www.kaggle.com/code/taylorsamarel/xgb-deep-learning-ensemble) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n\n\n15-july-2025 - LB = [?](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n- 0.82968  v.11 - [DRW - Crypto Market Prediction | Turkish gambit](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-turkish-gambit/) - [riri](https://www.kaggle.com/nina2025)\n- 0.73799  v.13 - [REBOOT THE REBOOT 2.0](https://www.kaggle.com/code/taylorsamarel/reboot-the-reboot-2-0) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.72837  v.01 - [DRW-Ensemble-0.72837](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.70871  v.01 - [Anyone can win on Public LB](https://www.kaggle.com/code/johndoe2011/anyone-can-win-on-public-lb) - [ZULQAR.](https://www.kaggle.com/johndoe2011)\n\n14-july-2025 - LB = [0.82734](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamarel)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.83975  v.07 - [REBOOT: Use the lead values](https://www.kaggle.com/code/gromml/reboot-use-the-lead-values/notebook) - [gromml](https://www.kaggle.com/gromml)\n\n15-july-2025 - LB = [0.93598](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n- 0.90038  v.03 - [XGB + Deep Learning Ensemble](https://www.kaggle.com/code/taylorsamarel/xgb-deep-learning-ensemble) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n\n\n15-july-2025 - LB = [0.90174](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n \n\n### after added blend.vertical\n\n \n\n- 0.90038  v.03 - [XGB + Deep Learning Ensemble](https://www.kaggle.com/code/taylorsamarel/xgb-deep-learning-ensemble) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.83975  v.07 - [REBOOT: Use the lead values](https://www.kaggle.com/code/gromml/reboot-use-the-lead-values/notebook) - [gromml](https://www.kaggle.com/gromml)\n\n\n17-july-2025 - LB = [?](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n- 0.90038  v.03 - [XGB + Deep Learning Ensemble](https://www.kaggle.com/code/taylorsamarel/xgb-deep-learning-ensemble) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n\n\n17-july-2025 - LB = [?](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)\n\n\n \n\n- 0.89178  v.13 - [REBOOT: SGD CONVERGENCE ++](https://www.kaggle.com/code/taylorsamarel/reboot-sgd-convergence/notebook) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.88377  v.09 - [REBOOT: Use the lead values - IMPROVED](https://www.kaggle.com/code/taylorsamarel/reboot-use-the-lead-values-improved) - [Taylor S. Amarel](https://www.kaggle.com/taylorsamare)\n- 0.86767  v.01 - [DRW-Ensembling-0.86767](https://www.kaggle.com/code/vishalpainjane/drw-ensemble-0-72837) - [Vishal Painjane](https://www.kaggle.com/vishalpainjane)\n- 0.83975  v.07 - [REBOOT: Use the lead values](https://www.kaggle.com/code/gromml/reboot-use-the-lead-values/notebook) - [gromml](https://www.kaggle.com/gromml)\n\n\n17-july-2025 - LB = [?](https://www.kaggle.com/code/nina2025/drw-crypto-market-prediction-iblend)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"papermill":{"duration":0.002055,"end_time":"2025-07-16T08:55:40.398958","exception":false,"start_time":"2025-07-16T08:55:40.396903","status":"completed"},"tags":[]}},{"id":"e84892be","cell_type":"code","source":"import pandas as pd","metadata":{"execution":{"iopub.status.busy":"2025-07-17T08:18:20.008428Z","iopub.execute_input":"2025-07-17T08:18:20.008736Z","iopub.status.idle":"2025-07-17T08:18:20.013183Z","shell.execute_reply.started":"2025-07-17T08:18:20.008711Z","shell.execute_reply":"2025-07-17T08:18:20.012183Z"},"papermill":{"duration":1.879293,"end_time":"2025-07-16T08:55:42.280345","exception":false,"start_time":"2025-07-16T08:55:40.401052","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"42690245","cell_type":"code","source":"def iBlend(path_to_ds, file_short_names, sls):\n\n def tida(sls):\n \n def read_subm(sls,i):\n tnm = sls[\"subm\"][i][\"name\"]\n FiN = sls[\"path\"] + tnm + \".csv\"\n return pd.read_csv(FiN).rename(columns={'target':tnm, sls[\"target\"]:tnm})\n \n dfs_subm = [read_subm(sls,i) for i in range(len(sls[\"subm\"]))]\n df_subms = pd.merge(dfs_subm[0], dfs_subm[1], on=['ID'])\n \n for i in range(2, len(sls[\"subm\"])): \n df_subms = pd.merge(df_subms, dfs_subm[i], on=['ID'])\n \n cols = [col for col in df_subms.columns if col != \"ID\"]\n short_name_cols = [c.replace(sls[\"prefix\"], '') for c in cols]\n \n weights1,corrects1 = [subm['weight'] for subm in sls[\"subm\"]], [wt for wt in sls[\"subwts\"] ]\n weights2,corrects2 = [subm['weight'] for subm in sls[\"subm2\"]],[wt for wt in sls[\"subwts2\"]]\n weights3,corrects3 = [subm['weight'] for subm in sls[\"subm3\"]],[wt for wt in sls[\"subwts3\"]]\n weights4,corrects4 = [subm['weight'] for subm in sls[\"subm4\"]],[wt for wt in sls[\"subwts4\"]]\n weights5,corrects5 = [subm['weight'] for subm in sls[\"subm5\"]],[wt for wt in sls[\"subwts5\"]]\n \n def alls(x, cs=cols):\n tes = {c: x[c] for c in cs}.items()\n subms_sorted = [\n t[0].replace(sls[\"prefix\"], '')\n for t in sorted(tes,key=lambda k:k[1],reverse=True if sls[\"sort\"]=='desc' else False)]\n return subms_sorted\n \n def correct(x, cs=cols, \n w1=weights1, cw1=corrects1, \n w2=weights2, cw2=corrects2,\n w3=weights3, cw3=corrects3, \n w4=weights4, cw4=corrects4,\n w5=weights5, cw5=corrects5\n ):\n ic = [x['alls'].index(c) for c in short_name_cols]\n\n mxm = x['abs(mx-m)']\n\n if 0.00 < mxm <= 0.26:\n cS = [x[cols[j]] * (w1[j] + cw1[ic[j]]) for j in range(len(cols))]\n elif 0.26 < mxm <= 0.50:\n cS = [x[cols[j]] * (w2[j] + cw2[ic[j]]) for j in range(len(cols))]\n elif 0.50 < mxm <= 0.74:\n cS = [x[cols[j]] * (w3[j] + cw3[ic[j]]) for j in range(len(cols))]\n elif 0.74 < mxm <= 1.00:\n cS = [x[cols[j]] * (w4[j] + cw4[ic[j]]) for j in range(len(cols))]\n else:\n cS = [x[cols[j]] * (w5[j] + cw5[ic[j]]) for j in range(len(cols))]\n return sum(cS)\n\n def amxm(x, cs=cols):\n list_values = x[cs].to_list()\n mxm = abs(max(list_values)-min(list_values))\n return mxm\n\n df_subms['abs(mx-m)'] = df_subms.apply(lambda x: amxm (x), axis=1)\n \n df_subms['alls'] = df_subms.apply(lambda x: alls (x), axis=1)\n df_subms[sls[\"target\"]] = df_subms.apply(lambda x: correct(x), axis=1)\n \n schema_rename = { old_nc:new_shnc for old_nc, new_shnc in zip(cols, short_name_cols) }\n \n df_subms = df_subms.rename(columns=schema_rename)\n df_subms = df_subms.rename(columns={sls[\"target\"]:\"ensemble\"})\n \n df_subms.insert(loc=1, column=' _ ', value=[' '] * sls[\"q_rows\"])\n \n df_subms[' _ '] = df_subms[' _ '].astype(str)\n pd.set_option('display.max_rows',100)\n pd.set_option('display.float_format', '{:.3f}'.format)\n vcols = ['ID'] + [' _ '] + short_name_cols + [' _ '] + ['abs(mx-m)'] + [' _ '] + ['alls'] + [' _ '] + ['ensemble']\n df_subms = df_subms[vcols]\n display(df_subms.head(7))\n pd.set_option('display.float_format', '{:.7f}'.format)\n df_subms = df_subms.rename(columns={\"ensemble\":sls[\"target\"]})\n \n return df_subms\n \n\n sample_subm = pd.read_csv(path_to_ds + file_short_names[1] + \".csv\")\n\n \n def ensemble_tida(sls,submission=sample_subm): \n sls['sort'] = 'desc'\n dfs = tida(sls)\n dfD = dfs[['ID', sls['target']]]\n dfD.to_csv(f'tida_desc.csv', index=False)\n sls['sort'] = 'asc'\n dfs = tida(sls)\n dfA = dfs[['ID', sls['target']]]\n dfA.to_csv(f'tida_asc.csv', index=False)\n target,d,a = sls['target'],sls['desc'],sls['asc']\n submission[target] = dfD[target] * d + a * dfA[target]\n return submission\n\n submission = ensemble_tida(sls)\n \n return submission","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2025-07-17T11:02:41.500528Z","iopub.execute_input":"2025-07-17T11:02:41.500845Z","iopub.status.idle":"2025-07-17T11:02:41.521283Z","shell.execute_reply.started":"2025-07-17T11:02:41.500789Z","shell.execute_reply":"2025-07-17T11:02:41.520278Z"},"papermill":{"duration":0.023404,"end_time":"2025-07-16T08:55:42.306375","exception":false,"start_time":"2025-07-16T08:55:42.282971","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"id":"509adfb0","cell_type":"code","source":"# Archive\n\npath_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.83975','0.86767','0.89178']\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.30,\n 'asc' : 0.70,\n 'subwts': [+1.00, -0.40, -0.60], # LB = 0.93598\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.40, },\n { 'name':file_short_names[1],'weight':0.60, },\n { 'name':file_short_names[2],'weight':1.00, },\n ]\n }\n\n\npath_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.88377','0.89178','0.90038']\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.45,\n 'asc' : 0.55,\n 'subwts': [+0.55, -0.20, -0.35], # LB = 0.89864\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.27, },\n { 'name':file_short_names[1],'weight':0.33, },\n { 'name':file_short_names[2],'weight':0.40, },\n ]\n }\n\n\npath_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.88377','0.89178','0.90038']\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.20,\n 'asc' : 0.80,\n 'subwts': [+0.50, -0.20, -0.30], # LB = 0.90174\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.20, },\n { 'name':file_short_names[1],'weight':0.30, },\n { 'name':file_short_names[2],'weight':0.50, },\n ]\n }\n\n\npath_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.83975','0.86767','0.88377','0.89178','0.90038']\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.30,\n 'asc' : 0.70,\n 'subwts': [+0.40, +0.05, -0.05,-0.15,-0.25], # LB = 0.93794\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.20, },\n { 'name':file_short_names[1],'weight':0.30, },\n { 'name':file_short_names[2],'weight':0.01, },\n { 'name':file_short_names[3],'weight':0.47, },\n { 'name':file_short_names[4],'weight':0.02, },\n ]\n }\n\npath_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.83975','0.86767','0.88377','0.89178','0.90038']\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.30,\n 'asc' : 0.70,\n 'subwts': [+0.20, +0.10, -0.05,-0.10,-0.15], # LB = 0.94828\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.20, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.20, },\n { 'name':file_short_names[3],'weight':0.20, },\n { 'name':file_short_names[4],'weight':0.20, },\n ]\n }\n\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.30,\n 'asc' : 0.70,\n 'subwts': [+0.20, +0.10, -0.05,-0.10,-0.15], # LB = 0.94857 (only Horizont)\n 'subm' : [\n { 'name':file_short_names[0],'weight':0.20, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23, },\n ],\n 'subwts2': [+0.18, +0.09, -0.04,-0.09,-0.14], # LB = 0.94859\n 'subm2' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23, },\n ]\n }","metadata":{"execution":{"iopub.execute_input":"2025-07-16T08:55:42.312371Z","iopub.status.busy":"2025-07-16T08:55:42.312057Z","iopub.status.idle":"2025-07-16T08:55:42.326646Z","shell.execute_reply":"2025-07-16T08:55:42.325848Z"},"papermill":{"duration":0.019492,"end_time":"2025-07-16T08:55:42.328151","exception":false,"start_time":"2025-07-16T08:55:42.308659","status":"completed"},"tags":[],"jupyter":{"source_hidden":true},"_kg_hide-input":true,"_kg_hide-output":true},"outputs":[],"execution_count":null},{"id":"8a202f3c-dde8-44d8-9c18-a122ea3db898","cell_type":"markdown","source":"## 5 solutions","metadata":{}},{"id":"1fc084fa-0991-4fb8-a7af-6e1cec81122c","cell_type":"code","source":"path_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\nfile_short_names = ['0.83975','0.86767','0.88377','0.89178','0.90038']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-17T09:50:24.432779Z","iopub.execute_input":"2025-07-17T09:50:24.433501Z","iopub.status.idle":"2025-07-17T09:50:24.437299Z","shell.execute_reply.started":"2025-07-17T09:50:24.433473Z","shell.execute_reply":"2025-07-17T09:50:24.436335Z"}},"outputs":[],"execution_count":null},{"id":"1cece509-999f-4526-8b2e-5b8d3c208672","cell_type":"code","source":"# option.1 ------- ------- ------- ------- ------- ------- ------- LB = 0.94915\n\n# params = {\n# 'path' : path_to_ds, \n# 'sort' : \"dynamic\",\n# 'target': \"prediction\",\n# 'q_rows': 538_150,\n# 'prefix': \"subm_\",\n# 'desc' : 0.30,\n# 'asc' : 0.70,\n# 'subwts': [+0.19, +0.10, -0.05,-0.10,-0.14], \n# 'subm' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts2': [+0.17, +0.09, -0.04,-0.09,-0.13], \n# 'subm2' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts3': [+0.15, +0.08, -0.03,-0.08,-0.12], \n# 'subm3' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts4': [+0.14, +0.07, -0.02,-0.07,-0.11], \n# 'subm4' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts5': [+0.11, +0.06, -0.01,-0.06,-0.10], \n# 'subm5' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# }\n\n# ID\tprediction\n# 0\t1\t-0.1542508\n# 1\t2\t 0.2589274\n# 2\t3\t-1.4203646\n# 3\t4\t-0.2054324\n# 4\t5\t 0.1035272\n# ...\t...\t...\n# 538145\t538146\t-0.3137427\n# 538146\t538147\t 0.1481161\n# 538147\t538148\t-0.9100966\n# 538148\t538149\t 0.7786920\n# 538149\t538150\t-0.4061519\n# 538150 rows × 2 columns\n\n\n# option.2 ------- ------- ------- ------- ------- ------- ------- LB = 0.94889\n\n\n# params = {\n# 'path' : path_to_ds, \n# 'sort' : \"dynamic\",\n# 'target': \"prediction\",\n# 'q_rows': 538_150,\n# 'prefix': \"subm_\",\n# 'desc' : 0.30,\n# 'asc' : 0.70,\n# 'subwts': [+0.19, +0.10, -0.05,-0.10,-0.14], \n# 'subm' : [\n# { 'name':file_short_names[0],'weight':0.21, },\n# { 'name':file_short_names[1],'weight':0.22, },\n# { 'name':file_short_names[2],'weight':0.23, },\n# { 'name':file_short_names[3],'weight':0.24, },\n# { 'name':file_short_names[4],'weight':0.25, },\n# ],\n# 'subwts2': [+0.17, +0.09, -0.04,-0.09,-0.13], \n# 'subm2' : [\n# { 'name':file_short_names[0],'weight':0.22, },\n# { 'name':file_short_names[1],'weight':0.23, },\n# { 'name':file_short_names[2],'weight':0.24, },\n# { 'name':file_short_names[3],'weight':0.25, },\n# { 'name':file_short_names[4],'weight':0.26, },\n# ],\n# 'subwts3': [+0.15, +0.08, -0.03,-0.08,-0.12], \n# 'subm3' : [\n# { 'name':file_short_names[0],'weight':0.23, },\n# { 'name':file_short_names[1],'weight':0.24, },\n# { 'name':file_short_names[2],'weight':0.25, },\n# { 'name':file_short_names[3],'weight':0.26, },\n# { 'name':file_short_names[4],'weight':0.27, },\n# ],\n# 'subwts4': [+0.14, +0.07, -0.02,-0.07,-0.11], \n# 'subm4' : [\n# { 'name':file_short_names[0],'weight':0.24, },\n# { 'name':file_short_names[1],'weight':0.25, },\n# { 'name':file_short_names[2],'weight':0.26, },\n# { 'name':file_short_names[3],'weight':0.27, },\n# { 'name':file_short_names[4],'weight':0.28, },\n# ],\n# 'subwts5': [+0.11, +0.06, -0.01,-0.06,-0.10], \n# 'subm5' : [\n# { 'name':file_short_names[0],'weight':0.25, },\n# { 'name':file_short_names[1],'weight':0.26, },\n# { 'name':file_short_names[2],'weight':0.27, },\n# { 'name':file_short_names[3],'weight':0.28, },\n# { 'name':file_short_names[4],'weight':0.29, },\n# ],\n# }\n\n# \tID\tprediction\n# 0\t1\t-0.1673348\n# 1\t2\t 0.2849654\n# 2\t3\t-1.8152673\n# 3\t4\t-0.2242138\n# 4\t5\t 0.1223858\n# ...\t...\t...\n# 538145\t538146\t-0.3409893\n# 538146\t538147\t 0.1720437\n# 538147\t538148\t-1.1151354\n# 538148\t538149\t 1.0286348\n# 538149\t538150\t-0.4889277\n# 538150 rows × 2 columns\n\n\n# # option.3 ------- ------- ------- ------- ------- ------- ------- LB = 0.94717\n\n# params = {\n# 'path' : path_to_ds, \n# 'sort' : \"dynamic\",\n# 'target': \"prediction\",\n# 'q_rows': 538_150,\n# 'prefix': \"subm_\",\n# 'desc' : 0.30,\n# 'asc' : 0.70,\n# 'subwts': [+0.19, +0.10, -0.05,-0.10,-0.14], \n# 'subm' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts2': [+0.17, +0.09, -0.04,-0.09,-0.13], \n# 'subm2' : [\n# { 'name':file_short_names[0],'weight':0.18, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.24, },\n# ],\n# 'subwts3': [+0.15, +0.08, -0.03,-0.08,-0.12], \n# 'subm3' : [\n# { 'name':file_short_names[0],'weight':0.17, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.25, },\n# ],\n# 'subwts4': [+0.14, +0.07, -0.02,-0.07,-0.11], \n# 'subm4' : [\n# { 'name':file_short_names[0],'weight':0.16, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.26, },\n# ],\n# 'subwts5': [+0.11, +0.06, -0.01,-0.06,-0.10], \n# 'subm5' : [\n# { 'name':file_short_names[0],'weight':0.15, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.27, },\n# ],\n# }\n\n\n# ID\tprediction\n# 0\t1\t-0.1542508\n# 1\t2\t 0.2589274\n# 2\t3\t-1.3990823\n# 3\t4\t-0.2054324\n# 4\t5\t 0.1073946\n# ...\t...\t...\n# 538145\t538146\t-0.3137427\n# 538146\t538147\t 0.1482668\n# 538147\t538148\t-0.8857567\n# 538148\t538149\t 0.7211133\n# 538149\t538150\t-0.4089252\n\n\n# option.4 ------- ------- ------- ------- ------- ------- ------- LB = 0.94900\n\n# params = {\n# 'path' : path_to_ds, \n# 'sort' : \"dynamic\",\n# 'target': \"prediction\",\n# 'q_rows': 538_150,\n# 'prefix': \"subm_\",\n# 'desc' : 0.26,\n# 'asc' : 0.74,\n# 'subwts': [+0.19, +0.10, -0.05,-0.10,-0.14], \n# 'subm' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.23, },\n# ],\n# 'subwts2': [+0.17, +0.09, -0.04,-0.09,-0.13], \n# 'subm2' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.225,},\n# ],\n# 'subwts3': [+0.15, +0.08, -0.03,-0.08,-0.12], \n# 'subm3' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.220,},\n# ],\n# 'subwts4': [+0.14, +0.07, -0.02,-0.07,-0.11], \n# 'subm4' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.215,},\n# ],\n# 'subwts5': [+0.11, +0.06, -0.01,-0.06,-0.10], \n# 'subm5' : [\n# { 'name':file_short_names[0],'weight':0.19, },\n# { 'name':file_short_names[1],'weight':0.20, },\n# { 'name':file_short_names[2],'weight':0.21, },\n# { 'name':file_short_names[3],'weight':0.22, },\n# { 'name':file_short_names[4],'weight':0.210,},\n# ],\n# }\n\n\n# ID\tprediction\n# 0\t1\t-0.1575648\n# 1\t2\t 0.2557592\n# 2\t3\t-1.4114128\n# 3\t4\t-0.2071729\n# 4\t5\t 0.0966335\n# ...\t...\t...\n# 538145\t538146\t-0.3189531\n# 538146\t538147\t 0.1430990\n# 538147\t538148\t-0.9122206\n# 538148\t538149\t 0.7518473\n# 538149\t538150\t-0.4125638\n# 538150 rows × 2 columns\n\n\n# option.5 ------- ------- ------- ------- ------- ------- ------- LB = ?\n\nparams = {\n 'path' : path_to_ds, \n 'sort' : \"dynamic\",\n 'target': \"prediction\",\n 'q_rows': 538_150,\n 'prefix': \"subm_\",\n 'desc' : 0.35,\n 'asc' : 0.65,\n 'subwts': [+0.19, +0.10, -0.05,-0.10,-0.14], \n 'subm' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23, },\n ],\n 'subwts2': [+0.14, +0.10, +0.05,-0.10,-0.19], \n 'subm2' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23,},\n ],\n 'subwts3': [+0.25, +0.25, 0, -0.25,-0.25], \n 'subm3' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23,},\n ],\n 'subwts4': [-0.05, -0.10, +0.30,-0.10,-0.05], \n 'subm4' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23,},\n ],\n 'subwts5': [+0.05, +0.10, -0.30, +0.10, +0.05], \n 'subm5' : [\n { 'name':file_short_names[0],'weight':0.19, },\n { 'name':file_short_names[1],'weight':0.20, },\n { 'name':file_short_names[2],'weight':0.21, },\n { 'name':file_short_names[3],'weight':0.22, },\n { 'name':file_short_names[4],'weight':0.23,},\n ],\n}\n\n# \tID\tprediction\n# 0\t1\t-0.1501083\n# 1\t2\t 0.2628877\n# 2\t3\t-1.4322685\n# 3\t4\t-0.2032566\n# 4\t5\t 0.1207345\n# ...\t...\t...\n# 538145\t538146\t-0.3072296\n# 538146\t538147\t 0.1369802\n# 538147\t538148\t-0.7683847\n# 538148\t538149\t 0.9015924\n# 538149\t538150\t-0.3144665\n# 538150 rows × 2 columns\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-07-17T11:02:04.728598Z","iopub.execute_input":"2025-07-17T11:02:04.728931Z","iopub.status.idle":"2025-07-17T11:02:04.744701Z","shell.execute_reply.started":"2025-07-17T11:02:04.728908Z","shell.execute_reply":"2025-07-17T11:02:04.743978Z"}},"outputs":[],"execution_count":null},{"id":"b5bf8bbe-5b3a-4e6a-b1f8-18a2d05883b4","cell_type":"code","source":"# option.1 LB=0.94915 # option.2 LB=0.94889 # option.3 LB=0.94717\n\n# ID\tprediction | ID\t prediction | ID\t prediction\n\n# 0\t1\t-0.1542508 | 0 1\t -0.1673348 | 0\t1\t-0.1542508\n# 1\t2\t 0.2589274 | 1 2\t 0.2849654 | 1\t2\t 0.2589274\n# 2\t3\t-1.4203646 | 2 3\t -1.8152673 | 2\t3\t-1.3990823\n# 3\t4\t-0.2054324 | 3 4\t -0.2242138 | 3\t4\t-0.2054324\n# 4\t5\t 0.1035272 | 4 5\t 0.1223858 | 4\t5\t 0.1073946\n# ...\t...\t...\n# 538145\t538146\t-0.3137427 | 538146\t -0.3409893 | 538145\t538146\t-0.3137427\n# 538146\t538147\t 0.1481161 | 538147\t 0.1720437 | 538146\t538147\t 0.1482668\n# 538147\t538148\t-0.9100966 | 538148\t -1.1151354 | 538147\t538148\t-0.8857567\n# 538148\t538149\t 0.7786920 | 538149\t 1.0286348 | 538148\t538149\t 0.7211133\n# 538149\t538150\t-0.4061519 | 538150\t -0.4889277 | 538149\t538150\t-0.4089252\n# 538150 rows × 2 columns","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"043b4bba-bd77-4d40-ab10-2da073444924","cell_type":"markdown","source":"## 4 solutions ( a )","metadata":{}},{"id":"fc18660b-c9bb-4dc7-a017-693de0b043df","cell_type":"code","source":"# path_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\n# file_short_names = ['0.83975','0.86767','0.89178','0.90038']","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"f7c15b48-26d8-4f4b-abb1-42e94b674144","cell_type":"code","source":"# coming soon..","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"82acaa08-362c-4940-9627-a4884ce87d79","cell_type":"markdown","source":"## 4 solutions ( b )","metadata":{}},{"id":"0c206643-aea8-4958-9856-baf0318c202c","cell_type":"code","source":"# path_to_ds ='/kaggle/input/15-juli-2025-drw/submission '\n\n# file_short_names = ['0.83975','0.86767','0.88377','0.89178']","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"e20f169c-5bf7-4314-9690-30cc6063cb11","cell_type":"code","source":"# coming soon..","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"id":"be76b149","cell_type":"code","source":"df = iBlend ( path_to_ds, file_short_names, params )\n\ndf.to_csv('submission.csv', index=False)\n\ndisplay(df)","metadata":{"execution":{"iopub.status.busy":"2025-07-17T11:02:51.979386Z","iopub.execute_input":"2025-07-17T11:02:51.979654Z","iopub.status.idle":"2025-07-17T11:08:12.982705Z","shell.execute_reply.started":"2025-07-17T11:02:51.979633Z","shell.execute_reply":"2025-07-17T11:08:12.981840Z"},"papermill":{"duration":366.852199,"end_time":"2025-07-16T09:01:49.182681","exception":false,"start_time":"2025-07-16T08:55:42.330482","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null}]}