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kn_​automl_​h2o_​regression_​python

H2O.ai AutoML (wrapped with Python) in KNIME for regression problems

H2O.ai AutoML (wrapped with Python) in KNIME for regression problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)
v 1.25 - https://forum.knime.com/t/h2o-ai-automl-in-knime-for-regression-problems/20924

It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results.

To run this workflow you have to install Python and H2O.ai as well as R and several packages. Please refer to the green box on the right.

The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water)

# Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 = 1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours H2O.ai AutoML (wrapped with Python) in KNIME for regression problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)v 1.25 - https://forum.knime.com/t/h2o-ai-automl-in-knime-for-regression-problems/20924It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). Onemajor parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results.To run this workflow you have to install Python and H2O.ai as well as R and several packages. Please refer to the green box on the right.The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water) Inspect the models so far and see to results. This will also give you a quick idea where you stand and what you would be able to achieve.Along with all parameters to load the respective model. which output is there to be interpretedmodels are stored in the folder /model/H2O_AutoML_Regression_yyyymmdd_hhmmh_.....zip-> as MOJO model format (certain model types cannot be stored and reused - so they are excluded as of now)H2O_AutoML_Regression_yyyymmdd_hhmmh_..... (folder)-> genuine H2O model stored in a folder (can be reused from H2O itself)/model/validate/h2o_list_of_models.csv -> list of all leading model from the runs with their RMSE (among other things) --- individual model results/model/validate/H2O_AutoML_Regression_yyyymmdd_hhmmh.txt-> capture of a print command describing the winning modelmodel_table_H2O_AutoML_Regression_yyyymmdd_hhmmh.table-> a KNIME table with a collection of parameters and information about the modelH2O_AutoML_Regression_yyyymmdd_hhmmh.xlsx-> an Excel file containing important information among these: - leaderboard = the list of all tested models in the run - model_summary = the characteristic of the winning model (depth - variable_importances = !!! check if the variable importance does make sense - model_eval = a check split up into several numeric bins to see if the model does perform across them---- 4 graphics for each model to have visual support when interpreting the results (needs R)(for more details see /script/H2O.ai AutoML in KNIME for regression problems.pdf)model_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh.png-> two lines set next to each other to represent the deviation in a linear formatmodel_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_hexbin.png-> a Hexbin Plot giving you a compact idea about the position of prediction (submission) and truth (solution) withregards to big blocks (are the large block positioned where you would like them)model_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_parallel_plot.png-> a parallel plot to see if there is a trend with regard to certain individual numbersmodel_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_bias.png-> a Bland-Altman plot # Copy input to outputimport numpy as np # linear algebraimport os # accessing directory structureimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)# http://strftime.org'import timevar_timestamp_day = "{}".format(time.strftime("%Y%m%d"))flow_variables['var_timestamp_day'] = var_timestamp_dayprint("var_timestamp_day: ", var_timestamp_day)var_timestamp_time = "{}h".format(time.strftime("%H%M"))flow_variables['var_timestamp_time'] = var_timestamp_timeprint("var_timestamp_time: ", var_timestamp_time)# _edit: if you want to have another model namevar_model_name = "H2O_AutoML_Regression"flow_variables['var_model_name'] = var_model_namevar_model_name_full = var_model_name + "_" + var_timestamp_day + "_" + var_timestamp_timeflow_variables['var_model_name_full'] = var_model_name_fullprint("var_model_name_full: ", var_model_name_full)# df_train = input_table_1.copy()# df_validate = input_table_2.copy()# https://stackoverflow.com/questions/36268749/remove-multiple-items-from-a-python-list-in-just-one-statement# _edit:# manually enter variables you want to remove# this can help if you have a large number of variables you want to keep # and a few you want removes# \ connects the rowsv_remove_variables = {'Date1', \'Location', 'RISK_MM' \ }# grab the columns from the dataframex = input_table_1.columns# name the target variablesy = 'Target'# drop the target variable from the list of all variablesx = x.drop(y)# remove all variables you want to have removes from the listx = [e for e in x if e not in v_remove_variables]# see which variables we have selected in the end# print('x = ', x)flow_variables['var_x_values'] = x# print('y = ', y)flow_variables['var_y_values'] = y# initiate h2o# if it is already running it will cconnect to the running clusterimport h2ofrom h2o.automl import H2OAutoMLh2o.init()# https://forum.knime.com/t/python-script-and-h2o-data-frames-error-under-windows/21099/4?u=mlauber71h2o.no_progress()# import the df data into H2O data systemtrain = h2o.H2OFrame(input_table_1.copy())valid = h2o.H2OFrame(input_table_2.copy())# Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 = 1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/sort_metric.html# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/exclude_algos.html# get the maximum runtime from the KNIME workflowmax_runtime_secs_opts = flow_variables['v_runtime_automl']import timeimport datetime as dtfrom datetime import datevar_now = dt.datetime.now()var_startmodel_day = "{}".format(var_now.strftime("%Y%m%d"))print("var_startmodel_day: ", var_timestamp_day)var_startmodel_time = "{}h".format(var_now.strftime("%H%M"))print("var_startmodel_time: ", var_timestamp_time)v_endtime = var_now + dt.timedelta(seconds=max_runtime_secs_opts)var_endmodel_day = "{}".format(v_endtime.strftime("%Y%m%d"))print("var_endmodel_day: ", var_endmodel_day)var_endmodel_time = "{}h".format(v_endtime.strftime("%H%M"))print("var_endmodel_time: ", var_endmodel_time)# you could exclude algorithms as they might not be suitable eg. for export as MOJO files# or to be used in Big Data environments# exclude_algos =["GBM", "GLM", "DeepLearning", "DRF", "StackedEnsemble", "XGBoost"]# For binomial classification choose between "AUC", "logloss", "mean_per_class_error", "RMSE", "MSE". # For multinomial classification choose between "mean_per_class_error", "logloss", "RMSE", "MSE". # For regression choose between "deviance", "RMSE", "MSE", "MAE", "RMLSE".aml = H2OAutoML(max_runtime_secs = max_runtime_secs_opts, seed =1234, sort_metric ="RMSE", stopping_metric ="RMSE", stopping_tolerance =0.01, stopping_rounds =25, project_name =var_model_name_full , # exclude_algos =["GBM", "GLM", "DeepLearning", "DRF", "StackedEnsemble"] #, # exclude_algos =["DRF", "GLM"] exclude_algos =["DeepLearning", "StackedEnsemble", "XGBoost"] )# x - all our variables we want to use to explain the:# y - Target Variable - in this case "Emp UK percent"aml.train(x = x, y = y, training_frame = train, validation_frame = valid) # View the AutoML Leaderboardlb = aml.leaderboardtb_leaderboard = lb.as_data_frame(use_pandas=True, header=True)# var_selected_model = "GBM_1_AutoML_20191214_123545"var_selected_model = aml.leader.model_id# print("var_selected_model :", var_selected_model)flow_variables['var_selected_model'] = var_selected_model# get the extracted modelextracted_model = h2o.get_model(var_selected_model)# extract important tables from model to store latertb_variable_importances = extracted_model._model_json['output']['variable_importances'].as_data_frame()tb_model_summary = extracted_model._model_json['output']['model_summary'].as_data_frame()# print(tb_variable_importances)# Export the variable importance list# _edit:# var_path_validate = "../model/validate/"v_csv_file_variable_importance = flow_variables['var_path_validate'] + var_model_name_full + "_variable_importance.csv"tb_variable_importances.to_csv(v_csv_file_variable_importance, sep='|', encoding='utf-8')flow_variables['v_csv_file_variable_importance'] = v_csv_file_variable_importance# predict the validation data with the non-MOJO saved model# preds = extracted_model.predict(valid)# save the model as generic H2O modelvar_model_name_path = flow_variables['var_path_model'] + var_model_name_full + "_" + var_selected_model flow_variables['var_model_name_path'] = var_model_name_pathmodel_path = h2o.save_model(model=extracted_model, path=var_model_name_path , force=True)# load the model# saved_model = h2o.load_model(model_path)# save the model as MOJO which you could read back in with KNIMEvar_mojo_file_name = flow_variables['var_path_model'] + var_model_name_full + "_" + var_selected_model + ".zip"flow_variables['var_mojo_file_name'] = var_mojo_file_nameprint("var_mojo_file_name: ", var_mojo_file_name)# reload the saved MOJO modelextracted_model.download_mojo(var_mojo_file_name)saved_mojo_model = h2o.import_mojo(var_mojo_file_name)# the prediction on the validation dataset will be brought back to KNIMEoutput_predict = saved_mojo_model.predict(valid).as_data_frame()# some important tables will be stored in an Excel file# -------- START Excel-----------------------------------------------------------from pandas import ExcelWriterfrom pandas import ExcelFilevar_xlsx_summary = flow_variables['var_path_validate'] + var_model_name_full + ".xlsx"flow_variables['var_xlsx_summary'] = var_xlsx_summaryraw_data = {'Model_ID': [var_model_name_full], 'Selected Model Name': [var_selected_model], }df_id = pd.DataFrame(raw_data, columns = ['Model_ID', 'Selected Model Name'])df_id# https://stackoverflow.com/questions/42370977/how-to-save-a-new-sheet-in-an-existing-excel-file-using-pandas/42371251writer = pd.ExcelWriter(var_xlsx_summary, engine = 'xlsxwriter')df_id.to_excel(writer, sheet_name = 'summary')tb_leaderboard.to_excel(writer, sheet_name = 'leaderboard')tb_model_summary.to_excel(writer, sheet_name = 'model_summary')tb_variable_importances.to_excel(writer, sheet_name = 'variable_importances')writer.save()writer.close()# -------- END Excel-----------------------------------------------------------var_txt_summary = flow_variables['var_path_validate'] + var_model_name_full + ".txt"flow_variables['var_txt_summary'] = var_txt_summary# capture the model summary in an TXT file# -------- START summary output to txt -----------------------------------------------------------import syssys.stdout = open(var_txt_summary, 'w')print(extracted_model)sys.stdout.close()# -------- END summary output to txt -----------------------------------------------------------# ------ store Python package versions in KNIME flow variablesflow_variables['var_py_version'] = sys.version_infoflow_variables['var_py_version_pandas'] = pd.__version__flow_variables['var_py_version_h2o'] = h2o.__version__# 1st output is the LeaderBoard to see where the automation stands# and what alternatives were thereoutput_table_1 = tb_leaderboard.copy()# 2nd Output is the new predicion. Make sure the prediction is saved as Double / Float variableoutput_table_2 = pd.concat([valid.as_data_frame(), output_predict], axis=1)output_table_2['predict'] = output_table_2['predict'].astype('float64') Subfolders to check/data/ contains the original data/model/contains the stored models in MOJO and H2O format/model/validate/contains the validations and graphics/script/Jupyter notebook with 'pure' python scrip (if you do not wishto use the KNIME wrapper) kn_automl_h2o_regression_python.ipynba PDF with further informations about the methods usedH2O.ai AutoML in KNIME for regression problems.pdf # make sure you have Python and the necessary Python packages installed, also check aout the pdf in /script/# https://docs.knime.com/latest/python_installation_guide/index.htmlimport numpy as np # linear algebraimport os # accessing directory structureimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)print("pandas (pd) version: ", pd.__version__)print("numpy (np) version", np.__version__)# http://strftime.org'import timeimport datetime as dt# install specific number# conda install -c conda-forge pyarrow=0.15.# conda install -c conda-forge pyarrowimport pyarrow.parquet as pq# pip install -f https://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2oimport h2oprint("numpy (np) version", h2o.__version__)from pandas import ExcelWriterfrom pandas import ExcelFileimport sys House Prices - Advanced Regression TechniquesPredict sales prices and practice feature engineering, RFs, and gradient boostinghttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview/evaluationMetricSubmissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted valueand the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses andcheap houses will affect the result equally.) NUM2.15create initial Test andTraining dataKaggle House Pricestrain.tabletest.tableedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoMLin secondsvar_model_name_fullRMSE ASC^(.*submission|solution).*$solution to doublekeep best modelH2O.ai AutoML=> will start a H2Ocluster via Pythonin the backgroundRead the MOJOmodelScore the test tableyou might also use a third table to validatethat has not been used developing themodelextract parametersfrom Pythonwhich have been usedto calculate the modelh2o_list_of_models.csvappend if CSV already exists to collect allmodel runsno pathsh2o_list_of_models.csvRead VariableimportanceRead the MOJOmodel Model QualityNumeric - Graphics Test Training Table Reader Table Reader Integer Input Numeric Scorer Transpose Joiner ConstantValue Column Column Resorter RowID Sorter Column Rename Column Filter Math Formula Row Filter collect meta data Merge Variables Python Script H2O MOJO Reader String to Path(Variable) H2O MOJO Predictor(Regression) Variable toTable Row CSV Writer Column Filter CSV Reader Table Rowto Variable Column Filter CSV Reader String to Path(Variable) H2O MOJO Reader # Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 = 1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours H2O.ai AutoML (wrapped with Python) in KNIME for regression problems - a powerful auto-machine-learning framework (https://hub.knime.com/mlauber71/spaces/Public/latest/automl/)v 1.25 - https://forum.knime.com/t/h2o-ai-automl-in-knime-for-regression-problems/20924It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). Onemajor parameter to set is the running time the model has to test various models and do some hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results.To run this workflow you have to install Python and H2O.ai as well as R and several packages. Please refer to the green box on the right.The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water) Inspect the models so far and see to results. This will also give you a quick idea where you stand and what you would be able to achieve.Along with all parameters to load the respective model. which output is there to be interpretedmodels are stored in the folder /model/H2O_AutoML_Regression_yyyymmdd_hhmmh_.....zip-> as MOJO model format (certain model types cannot be stored and reused - so they are excluded as of now)H2O_AutoML_Regression_yyyymmdd_hhmmh_..... (folder)-> genuine H2O model stored in a folder (can be reused from H2O itself)/model/validate/h2o_list_of_models.csv -> list of all leading model from the runs with their RMSE (among other things) --- individual model results/model/validate/H2O_AutoML_Regression_yyyymmdd_hhmmh.txt-> capture of a print command describing the winning modelmodel_table_H2O_AutoML_Regression_yyyymmdd_hhmmh.table-> a KNIME table with a collection of parameters and information about the modelH2O_AutoML_Regression_yyyymmdd_hhmmh.xlsx-> an Excel file containing important information among these: - leaderboard = the list of all tested models in the run - model_summary = the characteristic of the winning model (depth - variable_importances = !!! check if the variable importance does make sense - model_eval = a check split up into several numeric bins to see if the model does perform across them---- 4 graphics for each model to have visual support when interpreting the results (needs R)(for more details see /script/H2O.ai AutoML in KNIME for regression problems.pdf)model_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh.png-> two lines set next to each other to represent the deviation in a linear formatmodel_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_hexbin.png-> a Hexbin Plot giving you a compact idea about the position of prediction (submission) and truth (solution) withregards to big blocks (are the large block positioned where you would like them)model_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_parallel_plot.png-> a parallel plot to see if there is a trend with regard to certain individual numbersmodel_graph_H2O_AutoML_Regression_yyyymmdd_hhmmh_bias.png-> a Bland-Altman plot # Copy input to outputimport numpy as np # linear algebraimport os # accessing directory structureimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)# http://strftime.org'import timevar_timestamp_day = "{}".format(time.strftime("%Y%m%d"))flow_variables['var_timestamp_day'] = var_timestamp_dayprint("var_timestamp_day: ", var_timestamp_day)var_timestamp_time = "{}h".format(time.strftime("%H%M"))flow_variables['var_timestamp_time'] = var_timestamp_timeprint("var_timestamp_time: ", var_timestamp_time)# _edit: if you want to have another model namevar_model_name = "H2O_AutoML_Regression"flow_variables['var_model_name'] = var_model_namevar_model_name_full = var_model_name + "_" + var_timestamp_day + "_" + var_timestamp_timeflow_variables['var_model_name_full'] = var_model_name_fullprint("var_model_name_full: ", var_model_name_full)# df_train = input_table_1.copy()# df_validate = input_table_2.copy()# https://stackoverflow.com/questions/36268749/remove-multiple-items-from-a-python-list-in-just-one-statement# _edit:# manually enter variables you want to remove# this can help if you have a large number of variables you want to keep # and a few you want removes# \ connects the rowsv_remove_variables = {'Date1', \'Location', 'RISK_MM' \ }# grab the columns from the dataframex = input_table_1.columns# name the target variablesy = 'Target'# drop the target variable from the list of all variablesx = x.drop(y)# remove all variables you want to have removes from the listx = [e for e in x if e not in v_remove_variables]# see which variables we have selected in the end# print('x = ', x)flow_variables['var_x_values'] = x# print('y = ', y)flow_variables['var_y_values'] = y# initiate h2o# if it is already running it will cconnect to the running clusterimport h2ofrom h2o.automl import H2OAutoMLh2o.init()# https://forum.knime.com/t/python-script-and-h2o-data-frames-error-under-windows/21099/4?u=mlauber71h2o.no_progress()# import the df data into H2O data systemtrain = h2o.H2OFrame(input_table_1.copy())valid = h2o.H2OFrame(input_table_2.copy())# Run AutoML for 60 seconds or# 300 = 5 min, 600 = 10 min, 900 = 15 min, 1800 = 30 min, 3600 = 1 hour, # 7200 = 2 hours# 14400 = 4 hours# 16200 = 4.5 hours# 18000 = 5 Stunden# 21600 = 6 hours# 25200 = 7 hours# 28800 = 8 hours# 36000 = 10 hours# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/sort_metric.html# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/exclude_algos.html# get the maximum runtime from the KNIME workflowmax_runtime_secs_opts = flow_variables['v_runtime_automl']import timeimport datetime as dtfrom datetime import datevar_now = dt.datetime.now()var_startmodel_day = "{}".format(var_now.strftime("%Y%m%d"))print("var_startmodel_day: ", var_timestamp_day)var_startmodel_time = "{}h".format(var_now.strftime("%H%M"))print("var_startmodel_time: ", var_timestamp_time)v_endtime = var_now + dt.timedelta(seconds=max_runtime_secs_opts)var_endmodel_day = "{}".format(v_endtime.strftime("%Y%m%d"))print("var_endmodel_day: ", var_endmodel_day)var_endmodel_time = "{}h".format(v_endtime.strftime("%H%M"))print("var_endmodel_time: ", var_endmodel_time)# you could exclude algorithms as they might not be suitable eg. for export as MOJO files# or to be used in Big Data environments# exclude_algos =["GBM", "GLM", "DeepLearning", "DRF", "StackedEnsemble", "XGBoost"]# For binomial classification choose between "AUC", "logloss", "mean_per_class_error", "RMSE", "MSE". # For multinomial classification choose between "mean_per_class_error", "logloss", "RMSE", "MSE". # For regression choose between "deviance", "RMSE", "MSE", "MAE", "RMLSE".aml = H2OAutoML(max_runtime_secs = max_runtime_secs_opts, seed =1234, sort_metric ="RMSE", stopping_metric ="RMSE", stopping_tolerance =0.01, stopping_rounds =25, project_name =var_model_name_full , # exclude_algos =["GBM", "GLM", "DeepLearning", "DRF", "StackedEnsemble"] #, # exclude_algos =["DRF", "GLM"] exclude_algos =["DeepLearning", "StackedEnsemble", "XGBoost"] )# x - all our variables we want to use to explain the:# y - Target Variable - in this case "Emp UK percent"aml.train(x = x, y = y, training_frame = train, validation_frame = valid) # View the AutoML Leaderboardlb = aml.leaderboardtb_leaderboard = lb.as_data_frame(use_pandas=True, header=True)# var_selected_model = "GBM_1_AutoML_20191214_123545"var_selected_model = aml.leader.model_id# print("var_selected_model :", var_selected_model)flow_variables['var_selected_model'] = var_selected_model# get the extracted modelextracted_model = h2o.get_model(var_selected_model)# extract important tables from model to store latertb_variable_importances = extracted_model._model_json['output']['variable_importances'].as_data_frame()tb_model_summary = extracted_model._model_json['output']['model_summary'].as_data_frame()# print(tb_variable_importances)# Export the variable importance list# _edit:# var_path_validate = "../model/validate/"v_csv_file_variable_importance = flow_variables['var_path_validate'] + var_model_name_full + "_variable_importance.csv"tb_variable_importances.to_csv(v_csv_file_variable_importance, sep='|', encoding='utf-8')flow_variables['v_csv_file_variable_importance'] = v_csv_file_variable_importance# predict the validation data with the non-MOJO saved model# preds = extracted_model.predict(valid)# save the model as generic H2O modelvar_model_name_path = flow_variables['var_path_model'] + var_model_name_full + "_" + var_selected_model flow_variables['var_model_name_path'] = var_model_name_pathmodel_path = h2o.save_model(model=extracted_model, path=var_model_name_path , force=True)# load the model# saved_model = h2o.load_model(model_path)# save the model as MOJO which you could read back in with KNIMEvar_mojo_file_name = flow_variables['var_path_model'] + var_model_name_full + "_" + var_selected_model + ".zip"flow_variables['var_mojo_file_name'] = var_mojo_file_nameprint("var_mojo_file_name: ", var_mojo_file_name)# reload the saved MOJO modelextracted_model.download_mojo(var_mojo_file_name)saved_mojo_model = h2o.import_mojo(var_mojo_file_name)# the prediction on the validation dataset will be brought back to KNIMEoutput_predict = saved_mojo_model.predict(valid).as_data_frame()# some important tables will be stored in an Excel file# -------- START Excel-----------------------------------------------------------from pandas import ExcelWriterfrom pandas import ExcelFilevar_xlsx_summary = flow_variables['var_path_validate'] + var_model_name_full + ".xlsx"flow_variables['var_xlsx_summary'] = var_xlsx_summaryraw_data = {'Model_ID': [var_model_name_full], 'Selected Model Name': [var_selected_model], }df_id = pd.DataFrame(raw_data, columns = ['Model_ID', 'Selected Model Name'])df_id# https://stackoverflow.com/questions/42370977/how-to-save-a-new-sheet-in-an-existing-excel-file-using-pandas/42371251writer = pd.ExcelWriter(var_xlsx_summary, engine = 'xlsxwriter')df_id.to_excel(writer, sheet_name = 'summary')tb_leaderboard.to_excel(writer, sheet_name = 'leaderboard')tb_model_summary.to_excel(writer, sheet_name = 'model_summary')tb_variable_importances.to_excel(writer, sheet_name = 'variable_importances')writer.save()writer.close()# -------- END Excel-----------------------------------------------------------var_txt_summary = flow_variables['var_path_validate'] + var_model_name_full + ".txt"flow_variables['var_txt_summary'] = var_txt_summary# capture the model summary in an TXT file# -------- START summary output to txt -----------------------------------------------------------import syssys.stdout = open(var_txt_summary, 'w')print(extracted_model)sys.stdout.close()# -------- END summary output to txt -----------------------------------------------------------# ------ store Python package versions in KNIME flow variablesflow_variables['var_py_version'] = sys.version_infoflow_variables['var_py_version_pandas'] = pd.__version__flow_variables['var_py_version_h2o'] = h2o.__version__# 1st output is the LeaderBoard to see where the automation stands# and what alternatives were thereoutput_table_1 = tb_leaderboard.copy()# 2nd Output is the new predicion. Make sure the prediction is saved as Double / Float variableoutput_table_2 = pd.concat([valid.as_data_frame(), output_predict], axis=1)output_table_2['predict'] = output_table_2['predict'].astype('float64') Subfolders to check/data/ contains the original data/model/contains the stored models in MOJO and H2O format/model/validate/contains the validations and graphics/script/Jupyter notebook with 'pure' python scrip (if you do not wishto use the KNIME wrapper)kn_automl_h2o_regression_python.ipynba PDF with further informations about the methods usedH2O.ai AutoML in KNIME for regression problems.pdf # make sure you have Python and the necessary Python packages installed, also check aout the pdf in /script/# https://docs.knime.com/latest/python_installation_guide/index.htmlimport numpy as np # linear algebraimport os # accessing directory structureimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)print("pandas (pd) version: ", pd.__version__)print("numpy (np) version", np.__version__)# http://strftime.org'import timeimport datetime as dt# install specific number# conda install -c conda-forge pyarrow=0.15.# conda install -c conda-forge pyarrowimport pyarrow.parquet as pq# pip install -f https://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2oimport h2oprint("numpy (np) version", h2o.__version__)from pandas import ExcelWriterfrom pandas import ExcelFileimport sys House Prices - Advanced Regression TechniquesPredict sales prices and practice feature engineering, RFs, and gradient boostinghttps://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview/evaluationMetricSubmissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted valueand the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses andcheap houses will affect the result equally.) NUM2.15create initial Test andTraining dataKaggle House Pricestrain.tabletest.tableedit: v_runtime_automlset the maximum runtime ofH2O.ai AutoMLin secondsvar_model_name_fullRMSE ASC^(.*submission|solution).*$solution to doublekeep best modelH2O.ai AutoML=> will start a H2Ocluster via Pythonin the backgroundRead the MOJOmodelScore the test tableyou might also use a third table to validatethat has not been used developing themodelextract parametersfrom Pythonwhich have been usedto calculate the modelh2o_list_of_models.csvappend if CSV already exists to collect allmodel runsno pathsh2o_list_of_models.csvRead VariableimportanceRead the MOJOmodelModel QualityNumeric - Graphics Test Training Table Reader Table Reader Integer Input Numeric Scorer Transpose Joiner ConstantValue Column Column Resorter RowID Sorter Column Rename Column Filter Math Formula Row Filter collect meta data Merge Variables Python Script H2O MOJO Reader String to Path(Variable) H2O MOJO Predictor(Regression) Variable toTable Row CSV Writer Column Filter CSV Reader Table Rowto Variable Column Filter CSV Reader String to Path(Variable) H2O MOJO Reader

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Created by: m_lauber
Created at: 2020-01-23
On NodePit since: 2022-01-15
Last update: 2024-11-21
Created with KNIME version: v4.3.1
Tags: h2oautomlknimexgboosth2o.aipythonanacondaautomachinelearningregression

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