{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import os\nimport gc\nimport sys\n\nimport numpy as np\nimport pandas as pd\nimport polars as pl\n\nfrom catboost import CatBoostClassifier, Pool\nCATS = ['event_name', 'name', 'fqid', 'room_fqid', 'text_fqid']\nNUMS = ['page', 'room_coor_x', 'room_coor_y', 'screen_coor_x', 'screen_coor_y',\n        'hover_duration', 'elapsed_time_diff']\nfqid_lists = ['worker', 'archivist', 'gramps', 'wells', 'toentry', 'confrontation', 'crane_ranger', 'groupconvo', 'flag_girl', 'tomap', 'tostacks', 'tobasement', 'archivist_glasses', 'boss', 'journals', 'seescratches', 'groupconvo_flag', 'cs', 'teddy', 'expert', 'businesscards', 'ch3start', 'tunic.historicalsociety', 'tofrontdesk', 'savedteddy', 'plaque', 'glasses', 'tunic.drycleaner', 'reader_flag', 'tunic.library', 'tracks', 'tunic.capitol_2', 'trigger_scarf', 'reader', 'directory', 'tunic.capitol_1', 'journals.pic_0.next', 'unlockdoor', 'tunic', 'what_happened', 'tunic.kohlcenter', 'tunic.humanecology', 'colorbook', 'logbook', 'businesscards.card_0.next', 'journals.hub.topics', 'logbook.page.bingo', 'journals.pic_1.next', 'journals_flag', 'reader.paper0.next', 'tracks.hub.deer', 'reader_flag.paper0.next', 'trigger_coffee', 'wellsbadge', 'journals.pic_2.next', 'tomicrofiche', 'journals_flag.pic_0.bingo', 'plaque.face.date', 'notebook', 'tocloset_dirty', 'businesscards.card_bingo.bingo', 'businesscards.card_1.next', 'tunic.wildlife', 'tunic.hub.slip', 'tocage', 'journals.pic_2.bingo', 'tocollectionflag', 'tocollection', 'chap4_finale_c', 'chap2_finale_c', 'lockeddoor', 'journals_flag.hub.topics', 'tunic.capitol_0', 'reader_flag.paper2.bingo', 'photo', 'tunic.flaghouse', 'reader.paper1.next', 'directory.closeup.archivist', 'intro', 'businesscards.card_bingo.next', 'reader.paper2.bingo', 'retirement_letter', 'remove_cup', 'journals_flag.pic_0.next', 'magnify', 'coffee', 'key', 'togrampa', 'reader_flag.paper1.next', 'janitor', 'tohallway', 'chap1_finale', 'report', 'outtolunch', 'journals_flag.hub.topics_old', 'journals_flag.pic_1.next', 'reader.paper2.next', 'chap1_finale_c', 'reader_flag.paper2.next', 'door_block_talk', 'journals_flag.pic_1.bingo', 'journals_flag.pic_2.next', 'journals_flag.pic_2.bingo', 'block_magnify', 'reader.paper0.prev', 'block', 'reader_flag.paper0.prev', 'block_0', 'door_block_clean', 'reader.paper2.prev', 'reader.paper1.prev', 'doorblock', 'tocloset', 'reader_flag.paper2.prev', 'reader_flag.paper1.prev', 'block_tomap2', 'journals_flag.pic_0_old.next', 'journals_flag.pic_1_old.next', 'block_tocollection', 'block_nelson', 'journals_flag.pic_2_old.next', 'block_tomap1', 'block_badge', 'need_glasses', 'block_badge_2', 'fox', 'block_1']\nDIALOGS = ['that', 'this', 'it', 'you','find','found','Found','notebook','Wells','wells','help','need', 'Oh','Ooh','Jo', 'flag', 'can','and','is','the','to']\n\nname_feature = ['basic', 'undefined', 'close', 'open', 'prev', 'next']\nevent_name_feature = ['cutscene_click', 'person_click', 'navigate_click',\n       'observation_click', 'notification_click', 'object_click',\n       'object_hover', 'map_hover', 'map_click', 'checkpoint',\n       'notebook_click']\ntext_lists = ['tunic.historicalsociety.cage.confrontation', 'tunic.wildlife.center.crane_ranger.crane', 'tunic.historicalsociety.frontdesk.archivist.newspaper', 'tunic.historicalsociety.entry.groupconvo', 'tunic.wildlife.center.wells.nodeer', 'tunic.historicalsociety.frontdesk.archivist.have_glass', 'tunic.drycleaner.frontdesk.worker.hub', 'tunic.historicalsociety.closet_dirty.gramps.news', 'tunic.humanecology.frontdesk.worker.intro', 'tunic.historicalsociety.frontdesk.archivist_glasses.confrontation', 'tunic.historicalsociety.basement.seescratches', 'tunic.historicalsociety.collection.cs', 'tunic.flaghouse.entry.flag_girl.hello', 'tunic.historicalsociety.collection.gramps.found', 'tunic.historicalsociety.basement.ch3start', 'tunic.historicalsociety.entry.groupconvo_flag', 'tunic.library.frontdesk.worker.hello', 'tunic.library.frontdesk.worker.wells', 'tunic.historicalsociety.collection_flag.gramps.flag', 'tunic.historicalsociety.basement.savedteddy', 'tunic.library.frontdesk.worker.nelson', 'tunic.wildlife.center.expert.removed_cup', 'tunic.library.frontdesk.worker.flag', 'tunic.historicalsociety.frontdesk.archivist.hello', 'tunic.historicalsociety.closet.gramps.intro_0_cs_0', 'tunic.historicalsociety.entry.boss.flag', 'tunic.flaghouse.entry.flag_girl.symbol', 'tunic.historicalsociety.closet_dirty.trigger_scarf', 'tunic.drycleaner.frontdesk.worker.done', 'tunic.historicalsociety.closet_dirty.what_happened', 'tunic.wildlife.center.wells.animals', 'tunic.historicalsociety.closet.teddy.intro_0_cs_0', 'tunic.historicalsociety.cage.glasses.afterteddy', 'tunic.historicalsociety.cage.teddy.trapped', 'tunic.historicalsociety.cage.unlockdoor', 'tunic.historicalsociety.stacks.journals.pic_2.bingo', 'tunic.historicalsociety.entry.wells.flag', 'tunic.humanecology.frontdesk.worker.badger', 'tunic.historicalsociety.stacks.journals_flag.pic_0.bingo', 'tunic.historicalsociety.closet.intro', 'tunic.historicalsociety.closet.retirement_letter.hub', 'tunic.historicalsociety.entry.directory.closeup.archivist', 'tunic.historicalsociety.collection.tunic.slip', 'tunic.kohlcenter.halloffame.plaque.face.date', 'tunic.historicalsociety.closet_dirty.trigger_coffee', 'tunic.drycleaner.frontdesk.logbook.page.bingo', 'tunic.library.microfiche.reader.paper2.bingo', 'tunic.kohlcenter.halloffame.togrampa', 'tunic.capitol_2.hall.boss.haveyougotit', 'tunic.wildlife.center.wells.nodeer_recap', 'tunic.historicalsociety.cage.glasses.beforeteddy', 'tunic.historicalsociety.closet_dirty.gramps.helpclean', 'tunic.wildlife.center.expert.recap', 'tunic.historicalsociety.frontdesk.archivist.have_glass_recap', 'tunic.historicalsociety.stacks.journals_flag.pic_1.bingo', 'tunic.historicalsociety.cage.lockeddoor', 'tunic.historicalsociety.stacks.journals_flag.pic_2.bingo', 'tunic.historicalsociety.collection.gramps.lost', 'tunic.historicalsociety.closet.notebook', 'tunic.historicalsociety.frontdesk.magnify', 'tunic.humanecology.frontdesk.businesscards.card_bingo.bingo', 'tunic.wildlife.center.remove_cup', 'tunic.library.frontdesk.wellsbadge.hub', 'tunic.wildlife.center.tracks.hub.deer', 'tunic.historicalsociety.frontdesk.key', 'tunic.library.microfiche.reader_flag.paper2.bingo', 'tunic.flaghouse.entry.colorbook', 'tunic.wildlife.center.coffee', 'tunic.capitol_1.hall.boss.haveyougotit', 'tunic.historicalsociety.basement.janitor', 'tunic.historicalsociety.collection_flag.gramps.recap', 'tunic.wildlife.center.wells.animals2', 'tunic.flaghouse.entry.flag_girl.symbol_recap', 'tunic.historicalsociety.closet_dirty.photo', 'tunic.historicalsociety.stacks.outtolunch', 'tunic.library.frontdesk.worker.wells_recap', 'tunic.historicalsociety.frontdesk.archivist_glasses.confrontation_recap', 'tunic.capitol_0.hall.boss.talktogramps', 'tunic.historicalsociety.closet.photo', 'tunic.historicalsociety.collection.tunic', 'tunic.historicalsociety.closet.teddy.intro_0_cs_5', 'tunic.historicalsociety.closet_dirty.gramps.archivist', 'tunic.historicalsociety.closet_dirty.door_block_talk', 'tunic.historicalsociety.entry.boss.flag_recap', 'tunic.historicalsociety.frontdesk.archivist.need_glass_0', 'tunic.historicalsociety.entry.wells.talktogramps', 'tunic.historicalsociety.frontdesk.block_magnify', 'tunic.historicalsociety.frontdesk.archivist.foundtheodora', 'tunic.historicalsociety.closet_dirty.gramps.nothing', 'tunic.historicalsociety.closet_dirty.door_block_clean', 'tunic.capitol_1.hall.boss.writeitup', 'tunic.library.frontdesk.worker.nelson_recap', 'tunic.library.frontdesk.worker.hello_short', 'tunic.historicalsociety.stacks.block', 'tunic.historicalsociety.frontdesk.archivist.need_glass_1', 'tunic.historicalsociety.entry.boss.talktogramps', 'tunic.historicalsociety.frontdesk.archivist.newspaper_recap', 'tunic.historicalsociety.entry.wells.flag_recap', 'tunic.drycleaner.frontdesk.worker.done2', 'tunic.library.frontdesk.worker.flag_recap', 'tunic.humanecology.frontdesk.block_0', 'tunic.library.frontdesk.worker.preflag', 'tunic.historicalsociety.basement.gramps.seeyalater', 'tunic.flaghouse.entry.flag_girl.hello_recap', 'tunic.historicalsociety.closet.doorblock', 'tunic.drycleaner.frontdesk.worker.takealook', 'tunic.historicalsociety.basement.gramps.whatdo', 'tunic.library.frontdesk.worker.droppedbadge', 'tunic.historicalsociety.entry.block_tomap2', 'tunic.library.frontdesk.block_nelson', 'tunic.library.microfiche.block_0', 'tunic.historicalsociety.entry.block_tocollection', 'tunic.historicalsociety.entry.block_tomap1', 'tunic.historicalsociety.collection.gramps.look_0', 'tunic.library.frontdesk.block_badge', 'tunic.historicalsociety.cage.need_glasses', 'tunic.library.frontdesk.block_badge_2', 'tunic.kohlcenter.halloffame.block_0', 'tunic.capitol_0.hall.chap1_finale_c', 'tunic.capitol_1.hall.chap2_finale_c', 'tunic.capitol_2.hall.chap4_finale_c', 'tunic.wildlife.center.fox.concern', 'tunic.drycleaner.frontdesk.block_0', 'tunic.historicalsociety.entry.gramps.hub', 'tunic.humanecology.frontdesk.block_1', 'tunic.drycleaner.frontdesk.block_1']\nroom_lists = ['tunic.historicalsociety.entry', 'tunic.wildlife.center', 'tunic.historicalsociety.cage', 'tunic.library.frontdesk', 'tunic.historicalsociety.frontdesk', 'tunic.historicalsociety.stacks', 'tunic.historicalsociety.closet_dirty', 'tunic.humanecology.frontdesk', 'tunic.historicalsociety.basement', 'tunic.kohlcenter.halloffame', 'tunic.library.microfiche', 'tunic.drycleaner.frontdesk', 'tunic.historicalsociety.collection', 'tunic.historicalsociety.closet', 'tunic.flaghouse.entry', 'tunic.historicalsociety.collection_flag', 'tunic.capitol_1.hall', 'tunic.capitol_0.hall', 'tunic.capitol_2.hall']\n\nLEVELS = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]\nlevel_groups = [\"0-4\", \"5-12\", \"13-22\"]","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-22T15:27:18.476706Z","iopub.execute_input":"2023-03-22T15:27:18.477201Z","iopub.status.idle":"2023-03-22T15:27:19.796782Z","shell.execute_reply.started":"2023-03-22T15:27:18.477107Z","shell.execute_reply":"2023-03-22T15:27:19.795491Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"columns = [\n    pl.col(\"page\").cast(pl.Float32),\n    (\n        (pl.col(\"elapsed_time\") - pl.col(\"elapsed_time\").shift(1))\n        .fill_null(0)\n        .clip(0, 1e9)\n        .over([\"session_id\", \"level\"])\n        .alias(\"elapsed_time_diff\")\n    ),\n    (\n        (pl.col(\"screen_coor_x\") - pl.col(\"screen_coor_x\").shift(1))\n        .abs()\n        .over([\"session_id\", \"level\"])\n    ),\n    (\n        (pl.col(\"screen_coor_y\") - pl.col(\"screen_coor_y\").shift(1))\n        .abs()\n        .over([\"session_id\", \"level\"])\n    ),\n    pl.col(\"fqid\").fill_null(\"fqid_None\"),\n    pl.col(\"text_fqid\").fill_null(\"text_fqid_None\")\n\n]\n\nmodels_list = [[CatBoostClassifier().load_model(\n    f\"/kaggle/input/catboost-predict/fold{fold}_q{q}.cbm\"\n) for fold in range(5)] for q in range(1, 19)]","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:19.798898Z","iopub.execute_input":"2023-03-22T15:27:19.799592Z","iopub.status.idle":"2023-03-22T15:27:21.122827Z","shell.execute_reply.started":"2023-03-22T15:27:19.799554Z","shell.execute_reply":"2023-03-22T15:27:21.121820Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def feature_engineer(x, grp, use_extra, feature_suffix):\n    aggs = [\n        pl.col(\"index\").count().alias(f\"session_number_{feature_suffix}\"),\n\n        *[pl.col('index').filter(pl.col('text').str.contains(c)).count().alias(f'word_{c}') for c in DIALOGS],\n        *[pl.col(\"elapsed_time_diff\").filter((pl.col('text').str.contains(c))).mean().alias(f'word_mean_{c}') for c in\n          DIALOGS],\n        *[pl.col(\"elapsed_time_diff\").filter((pl.col('text').str.contains(c))).std().alias(f'word_std_{c}') for c in\n          DIALOGS],\n        *[pl.col(\"elapsed_time_diff\").filter((pl.col('text').str.contains(c))).max().alias(f'word_max_{c}') for c in\n          DIALOGS],\n        *[pl.col(\"elapsed_time_diff\").filter((pl.col('text').str.contains(c))).sum().alias(f'word_sum_{c}') for c in\n          DIALOGS],\n        *[pl.col(\"elapsed_time_diff\").filter((pl.col('text').str.contains(c))).median().alias(f'word_median_{c}') for c\n          in DIALOGS],\n\n        *[pl.col(c).drop_nulls().n_unique().alias(f\"{c}_unique_{feature_suffix}\") for c in CATS],\n\n        *[pl.col(c).mean().alias(f\"{c}_mean_{feature_suffix}\") for c in NUMS],\n        *[pl.col(c).std().alias(f\"{c}_std_{feature_suffix}\") for c in NUMS],\n        *[pl.col(c).min().alias(f\"{c}_min_{feature_suffix}\") for c in NUMS],\n        *[pl.col(c).max().alias(f\"{c}_max_{feature_suffix}\") for c in NUMS],\n        *[pl.col(c).median().alias(f\"{c}_median_{feature_suffix}\") for c in NUMS],\n\n        *[pl.col(\"fqid\").filter(pl.col(\"fqid\") == c).count().alias(f\"{c}_fqid_counts{feature_suffix}\")\n          for c in fqid_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"fqid\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for\n          c in fqid_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"fqid\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\") for\n          c in fqid_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"fqid\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for\n          c in fqid_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"fqid\") == c).median().alias(f\"{c}_ET_median_{feature_suffix}\") for\n          c in fqid_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"fqid\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for\n          c in fqid_lists],\n\n        *[pl.col(\"text_fqid\").filter(pl.col(\"text_fqid\") == c).count().alias(f\"{c}_text_fqid_counts{feature_suffix}\")\n          for\n          c in text_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"text_fqid\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for\n          c in text_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"text_fqid\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\") for\n          c in text_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"text_fqid\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for\n          c in text_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"text_fqid\") == c).median().alias(f\"{c}_ET_median_{feature_suffix}\")\n          for\n          c in text_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"text_fqid\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for\n          c in text_lists],\n\n        *[pl.col(\"room_fqid\").filter(pl.col(\"room_fqid\") == c).count().alias(f\"{c}_room_fqid_counts{feature_suffix}\")\n          for c in room_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"room_fqid\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for\n          c in room_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"room_fqid\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\") for\n          c in room_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"room_fqid\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for\n          c in room_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"room_fqid\") == c).median().alias(f\"{c}_ET_median_{feature_suffix}\")\n          for\n          c in room_lists],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"room_fqid\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for\n          c in room_lists],\n\n        *[pl.col(\"event_name\").filter(pl.col(\"event_name\") == c).count().alias(f\"{c}_event_name_counts{feature_suffix}\")\n          for c in event_name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"event_name\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for\n          c in event_name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"event_name\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\")\n          for\n          c in event_name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"event_name\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for\n          c in event_name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"event_name\") == c).median().alias(\n            f\"{c}_ET_median_{feature_suffix}\") for\n          c in event_name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"event_name\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for\n          c in event_name_feature],\n\n        *[pl.col(\"name\").filter(pl.col(\"name\") == c).count().alias(f\"{c}_name_counts{feature_suffix}\") for c in\n          name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"name\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for c in\n          name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"name\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\") for c in\n          name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"name\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for c in\n          name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"name\") == c).median().alias(f\"{c}_ET_median_{feature_suffix}\") for\n          c in\n          name_feature],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"name\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for c in\n          name_feature],\n\n        *[pl.col(\"level\").filter(pl.col(\"level\") == c).count().alias(f\"{c}_LEVEL_count{feature_suffix}\") for c in\n          LEVELS],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for c in\n          LEVELS],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\") for c\n          in\n          LEVELS],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for c in\n          LEVELS],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level\") == c).median().alias(f\"{c}_ET_median_{feature_suffix}\") for\n          c in\n          LEVELS],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for c in\n          LEVELS],\n\n        *[pl.col(\"level_group\").filter(pl.col(\"level_group\") == c).count().alias(\n            f\"{c}_LEVEL_group_count{feature_suffix}\") for c in\n          level_groups],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level_group\") == c).std().alias(f\"{c}_ET_std_{feature_suffix}\") for\n          c in\n          level_groups],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level_group\") == c).mean().alias(f\"{c}_ET_mean_{feature_suffix}\")\n          for c in\n          level_groups],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level_group\") == c).sum().alias(f\"{c}_ET_sum_{feature_suffix}\") for\n          c in\n          level_groups],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level_group\") == c).median().alias(\n            f\"{c}_ET_median_{feature_suffix}\") for c in\n          level_groups],\n        *[pl.col(\"elapsed_time_diff\").filter(pl.col(\"level_group\") == c).max().alias(f\"{c}_ET_max_{feature_suffix}\") for\n          c in\n          level_groups],\n\n    ]\n\n    df = x.groupby(['session_id'], maintain_order=True).agg(aggs).sort(\"session_id\")\n\n    if use_extra:\n        if grp == '5-12':\n            aggs = [\n                pl.col(\"elapsed_time\").filter((pl.col(\"text\") == \"Here's the log book.\")\n                                              | (pl.col(\"fqid\") == 'logbook.page.bingo'))\n                    .apply(lambda s: s.max() - s.min()).alias(\"logbook_bingo_duration\"),\n                pl.col(\"index\").filter(\n                    (pl.col(\"text\") == \"Here's the log book.\") | (pl.col(\"fqid\") == 'logbook.page.bingo')).apply(\n                    lambda s: s.max() - s.min()).alias(\"logbook_bingo_indexCount\"),\n                pl.col(\"elapsed_time\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'reader')) | (\n                            pl.col(\"fqid\") == \"reader.paper2.bingo\")).apply(lambda s: s.max() - s.min()).alias(\n                    \"reader_bingo_duration\"),\n                pl.col(\"index\").filter(((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'reader')) | (\n                        pl.col(\"fqid\") == \"reader.paper2.bingo\")).apply(lambda s: s.max() - s.min()).alias(\n                    \"reader_bingo_indexCount\"),\n                pl.col(\"elapsed_time\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'journals')) | (\n                            pl.col(\"fqid\") == \"journals.pic_2.bingo\")).apply(lambda s: s.max() - s.min()).alias(\n                    \"journals_bingo_duration\"),\n                pl.col(\"index\").filter(((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'journals')) | (\n                        pl.col(\"fqid\") == \"journals.pic_2.bingo\")).apply(lambda s: s.max() - s.min()).alias(\n                    \"journals_bingo_indexCount\"),\n            ]\n            tmp = x.groupby([\"session_id\"], maintain_order=True).agg(aggs).sort(\"session_id\")\n            df = df.join(tmp, on=\"session_id\", how='left')\n\n        if grp == '13-22':\n            aggs = [\n                pl.col(\"elapsed_time\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'reader_flag')) | (\n                            pl.col(\"fqid\") == \"tunic.library.microfiche.reader_flag.paper2.bingo\")).apply(\n                    lambda s: s.max() - s.min() if s.len() > 0 else 0).alias(\"reader_flag_duration\"),\n                pl.col(\"index\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'reader_flag')) | (\n                            pl.col(\"fqid\") == \"tunic.library.microfiche.reader_flag.paper2.bingo\")).apply(\n                    lambda s: s.max() - s.min() if s.len() > 0 else 0).alias(\"reader_flag_indexCount\"),\n                pl.col(\"elapsed_time\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'journals_flag')) | (\n                            pl.col(\"fqid\") == \"journals_flag.pic_0.bingo\")).apply(\n                    lambda s: s.max() - s.min() if s.len() > 0 else 0).alias(\"journalsFlag_bingo_duration\"),\n                pl.col(\"index\").filter(\n                    ((pl.col(\"event_name\") == 'navigate_click') & (pl.col(\"fqid\") == 'journals_flag')) | (\n                            pl.col(\"fqid\") == \"journals_flag.pic_0.bingo\")).apply(\n                    lambda s: s.max() - s.min() if s.len() > 0 else 0).alias(\"journalsFlag_bingo_indexCount\")\n            ]\n            tmp = x.groupby([\"session_id\"], maintain_order=True).agg(aggs).sort(\"session_id\")\n            df = df.join(tmp, on=\"session_id\", how='left')\n\n    return df.to_pandas()","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.124860Z","iopub.execute_input":"2023-03-22T15:27:21.125657Z","iopub.status.idle":"2023-03-22T15:27:21.187100Z","shell.execute_reply.started":"2023-03-22T15:27:21.125618Z","shell.execute_reply":"2023-03-22T15:27:21.185972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def time_feature(train):\n    train[\"year\"] = train[\"session_id\"].apply(lambda x: int(str(x)[:2])).astype(np.uint8)\n    train[\"month\"] = train[\"session_id\"].apply(lambda x: int(str(x)[2:4])+1).astype(np.uint8)\n    train[\"day\"] = train[\"session_id\"].apply(lambda x: int(str(x)[4:6])).astype(np.uint8)\n    train[\"hour\"] = train[\"session_id\"].apply(lambda x: int(str(x)[6:8])).astype(np.uint8)\n    train[\"minute\"] = train[\"session_id\"].apply(lambda x: int(str(x)[8:10])).astype(np.uint8)\n    train[\"second\"] = train[\"session_id\"].apply(lambda x: int(str(x)[10:12])).astype(np.uint8)\n    return train","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.189712Z","iopub.execute_input":"2023-03-22T15:27:21.190464Z","iopub.status.idle":"2023-03-22T15:27:21.198900Z","shell.execute_reply.started":"2023-03-22T15:27:21.190421Z","shell.execute_reply":"2023-03-22T15:27:21.198006Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.200291Z","iopub.execute_input":"2023-03-22T15:27:21.200887Z","iopub.status.idle":"2023-03-22T15:27:21.233019Z","shell.execute_reply.started":"2023-03-22T15:27:21.200850Z","shell.execute_reply":"2023-03-22T15:27:21.232116Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pickle\nf_read = open('/kaggle/input/catboost-predict/importance_dict.pkl', 'rb')\nimportance_dict = pickle.load(f_read)\nf_read.close()","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.234266Z","iopub.execute_input":"2023-03-22T15:27:21.234764Z","iopub.status.idle":"2023-03-22T15:27:21.243926Z","shell.execute_reply.started":"2023-03-22T15:27:21.234733Z","shell.execute_reply":"2023-03-22T15:27:21.242817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"limits = {'0-4':(1,4), '5-12':(4,14), '13-22':(14,19)}\nfor (test, sample_submission) in iter_test:\n    grp = test.level_group.values[0]\n    session_id = test.session_id.values[0]\n\n    \n    df = (pl.from_pandas(test)\n          .drop([\"fullscreen\", \"hq\", \"music\"])\n          .with_columns(columns))\n    df = feature_engineer(df, grp, use_extra=True, feature_suffix='')\n    df = time_feature(df)\n    \n    fold = 0\n    preds = []\n    a,b = limits[grp]\n    for q in range(a, b):\n        print(q)\n        FEATURES = importance_dict[str(q)]\n        model = models_list[q-1][fold]\n        \n        pred = model.predict_proba(df[FEATURES].astype(np.float32))[0,1]\n        preds.append(int(pred > 0.63))\n    try:\n        sample_submission[\"correct\"] = preds\n    except Exception as e:\n        print(e)\n    env.predict(sample_submission)","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.245531Z","iopub.execute_input":"2023-03-22T15:27:21.246306Z","iopub.status.idle":"2023-03-22T15:27:21.825663Z","shell.execute_reply.started":"2023-03-22T15:27:21.246273Z","shell.execute_reply":"2023-03-22T15:27:21.824693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = pd.read_csv('submission.csv')\nprint(sub.shape, sub.correct.mean())\nsub.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-22T15:27:21.838597Z","iopub.execute_input":"2023-03-22T15:27:21.839231Z","iopub.status.idle":"2023-03-22T15:27:21.862188Z","shell.execute_reply.started":"2023-03-22T15:27:21.839197Z","shell.execute_reply":"2023-03-22T15:27:21.861179Z"},"trusted":true},"execution_count":null,"outputs":[]}]}