$ODCLBRD${"onedataVersion":{"clientVersion":"1.188.0","serverVersion":"46.46.2-SNAPSHOT","bundleVersion":"3.28.1"},"reports":[],"workflow":{"id":"c2f386e1-1406-4958-b5c8-252f699238b5","versionNumber":21,"name":"Step 1. House Price Prediction: Create Linear Regression Models","tags":[],"description":"","notes":null,"createdAt":"2020-11-09T09:55:50.671Z","updatedAt":"2020-11-16T08:55:42.326Z","projects":[{"id":"279c4642-8683-453e-b675-38baeabb1f57","projectType":"USE_CASES","owner":"d40abed4-8cea-4ba3-9716-dd23903fbd9a","name":"Matthi's TestProject","description":null,"notes":null,"tags":[],"createdAt":"2020-02-26T09:16:11.985Z","updatedAt":"2020-11-12T10:30:06.313Z","secured":false,"ownerInformation":null}],"nodes":{"690a88be-2073-403a-88bd-13d8334011a8":{"id":"690a88be-2073-403a-88bd-13d8334011a8","processor":"00000000-0000-0000-0001-000000000004","config":{"dataLocation":{"reference":{"type":"referenceById","id":null}},"parseMode":["FAIL_FAST"],"tablename":""},"result":null,"options":{"configEditExpertModeEnabled":false,"x":250,"y":38,"name":"Data Table Load: homeprices_train","color":"#78909c"},"out":{"data":{"65107bd9-1bdf-49c0-9370-b811dd025425":"dataframe"}}},"65107bd9-1bdf-49c0-9370-b811dd025425":{"id":"65107bd9-1bdf-49c0-9370-b811dd025425","processor":"00000000-0000-0000-0065-000000000002","config":{"multipleModelLoad":{"triggerState":false,"values":{}},"multipleModelGroupSave":{"triggerState":true,"values":{"0":{"modelGroupSave":{"reference":{"type":"referenceByName","name":"docu_example_predict_homeprices_group"}}}}},"generateEmptyDatasetOutput":false,"multipleModelGroupLoad":{"triggerState":false,"values":{"0":{"modelGroupLoad":{"reference":{"type":"referenceById","id":"3dbeed00-13bf-4d98-a4cb-edee7290bb9e"}}}}},"multipleModelSave":{"triggerState":false,"values":{}},"manuallyAdded":[],"tablename":"","inputName":"input","script":"from sklearn.linear_model import LinearRegression\nimport pandas as pd\n\n# load train dataset\ndf = od_input['input'].get_as_pandas()\n\ncountries = df.country.unique()  # Select distinct countries\nnum_countries = len(countries)   # Count distinct countries\nmodels=[[]]*num_countries        # a matrix to write models, number of models = num of countries\n\nfor idx in range(num_countries): # Go through all countries to create a model\n    X_train = df[df['country'] == countries[idx]]  # Select rows for rows only for the current country\n    model_name = countries[idx] + '_house_price_model'   # Create model name \n    models[idx]=LinearRegression()    # Train model\n    models[idx].fit(X_train[['area']], X_train['price'])\n    od_output.add_model(model_name, models[idx], \"docu_example_predict_homeprices_group\")\n\n# publish your output: initial dataset\nod_output.add_data(\"output\", df)\n","timeout":1000},"result":null,"options":{"configEditExpertModeEnabled":false,"x":225,"y":151,"name":"Python Script Single Input: Create model groups","color":"#d5672d"},"out":{"output":{"59022176-6477-4fcc-8ebc-42fb9bdcaa75":"data"}}},"59022176-6477-4fcc-8ebc-42fb9bdcaa75":{"id":"59022176-6477-4fcc-8ebc-42fb9bdcaa75","processor":"00000000-0000-0000-0014-000000000001","config":{"take":400,"sortBy":[],"groupBy":[],"tablename":"","order":["ASC"]},"result":null,"options":{"configEditExpertModeEnabled":false,"x":225,"y":263,"name":"Result Table","color":"#0d47a1"},"out":{}}},"nodeNotes":{},"owner":"d40abed4-8cea-4ba3-9716-dd23903fbd9a","resourceType":"WORKFLOW","groups":[],"auxiliaries":[],"variables":[],"recentExecution":null,"jobNamingRule":"#@sys_job_version@","ownerInformation":{"type":"user","ownerId":"d40abed4-8cea-4ba3-9716-dd23903fbd9a","firstName":"Matthias","lastName":"Rauch"},"permissions":["READ","WRITE","CREATE","DELETE","EXECUTE","SHARE"],"sinks":[]}}