{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"library(reticulate) # Calling python from R\n\n# Import the module\nmlb <- import_from_path('competition','../input/mlb-player-digital-engagement-forecasting/mlb/')\nenv <- mlb$make_env()\niter <- env$iter_test()\n\nwhile (TRUE) {\n  # Step\n  df <- iter_next(iter)\n  if (is.null(df)) break # Reached end of the test set\n    \n  # Unpack list from the iteraror\n  test_df = py_to_r(df[[1]]) # test dataframe\n  pred_df = py_to_r(df[[2]]) # prediction dataframe \n  \n  print(length(unique(pred_df$date))==1)\n    \n  # TODO: Make your predictions here\n    \n  # Save prediction\n  env$predict(pred_df)\n}","metadata":{"_uuid":"051d70d956493feee0c6d64651c6a088724dca2a","_execution_state":"idle","execution":{"iopub.status.busy":"2021-06-29T14:12:49.851758Z","iopub.execute_input":"2021-06-29T14:12:49.854548Z","iopub.status.idle":"2021-06-29T14:12:56.738523Z"},"trusted":true},"execution_count":null,"outputs":[]}]}