{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":38760,"databundleVersionId":4493939,"sourceType":"competition"}],"dockerImageVersionId":30301,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# [OTTO] Easy understanding for beginner）\n\nThis is the competition hosted by OTTO, which is a famous online-shopping in Germany, and to predict e-commerce clicks, cart additions, and orders.   \nMain purpose of this notebook is so that a beginner who join this competition could easily understand how to submit by using what kind of data, and hence please pardon low score.  \nImproved points are summarized as below. I'd be happy if it could help you improve your own model even a little bit better \n\n![image.png](attachment:6e70195a-3b02-4977-9603-8836a258653c.png)\n\n\nCredit to:  \nFor Data Loading for JSON  \nhttps://www.kaggle.com/code/edwardcrookenden/otto-getting-started-eda-baseline  \nhttps://www.kaggle.com/code/columbia2131/otto-read-a-chunk-of-jsonl-to-manageable-df  \nFor Pair items purchased together  \nhttps://www.kaggle.com/code/cdeotte/recommend-items-purchased-together-0-021\n","metadata":{},"attachments":{"6e70195a-3b02-4977-9603-8836a258653c.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# 1. Data Load","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport gc\npd.set_option(\"display.max_columns\", None)\ndata_path = Path('/kaggle/input/otto-recommender-system/')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-03T04:08:07.639800Z","iopub.execute_input":"2023-12-03T04:08:07.640898Z","iopub.status.idle":"2023-12-03T04:08:07.662929Z","shell.execute_reply.started":"2023-12-03T04:08:07.640807Z","shell.execute_reply":"2023-12-03T04:08:07.662138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Training data is huge and has 12,899,778 sessions (216,716,095 actions such as clikcs or cart-added), therefore try to download 100k sessions only. (FYI, it'd be easier to understand session as user)","metadata":{}},{"cell_type":"code","source":"%%time\nsample_size = 100_000\n\nchunks = pd.read_json(data_path / 'train.jsonl' , lines=True, chunksize = sample_size)\n\nfor chunk in chunks :\n    train_df = chunk\n    break\n\ntrain_df.set_index('session', drop=True, inplace=True)\ntrain_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:10:16.439404Z","iopub.execute_input":"2023-12-03T04:10:16.439831Z","iopub.status.idle":"2023-12-03T04:10:26.427393Z","shell.execute_reply.started":"2023-12-03T04:10:16.439798Z","shell.execute_reply":"2023-12-03T04:10:26.426241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. Sample looking\n\nLet's take a look at one of actual user's actions in a session. We'll see session-4 just as an example.","metadata":{}},{"cell_type":"code","source":"train_df.iloc[4,0]","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:10:30.368926Z","iopub.execute_input":"2023-12-03T04:10:30.369846Z","iopub.status.idle":"2023-12-03T04:10:30.379395Z","shell.execute_reply.started":"2023-12-03T04:10:30.369809Z","shell.execute_reply":"2023-12-03T04:10:30.378214Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This user clicked 15 items, ordered 1 item, added 3 items into Cart and the exited the session.  \nLet's check the time length of each action refering to each Unix timestamp. The first puchased aid (= article id) 298827 was clicked at JST2022/8/1 07:00:36 and ordered at 07:01:40. (looks like this article much attracted this user since this was ordered just 1 minute after the first click... (^o^)/   \nSimilarly, another aid 1554752 was clicked at JST 17:56:10 8/26 and just after 10 seconds added into the cart. Seems this was anyway added into the cart without deep consideration. ","metadata":{}},{"cell_type":"markdown","source":"\n\n![image.png](attachment:8ed3c43c-a509-424a-b8fc-6bbdbdc4ee59.png)","metadata":{},"attachments":{"8ed3c43c-a509-424a-b8fc-6bbdbdc4ee59.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Let's have a look at another one.  \n","metadata":{}},{"cell_type":"code","source":"train_df.iloc[1,0]","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:10:39.154002Z","iopub.execute_input":"2023-12-03T04:10:39.154939Z","iopub.status.idle":"2023-12-03T04:10:39.167379Z","shell.execute_reply.started":"2023-12-03T04:10:39.154897Z","shell.execute_reply":"2023-12-03T04:10:39.166182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Create a model\n\nTry to create a model for submission.  \nThe first idea is to recommend \"In-cart items\" after 'orders', \"Clicked items\" after 'carts', though this is very easy idea . Recommendation is up to 20 items, hence insufficient items will be supplemented with \"best-sold items\". Similarly, will recommend \"best-sold items\" after 'Click' as well.  \nThe public score from this easy model is **0.351**.  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"}}},{"cell_type":"markdown","source":"The Dataframe used above was basically same as the original JSON file format. We'll transfer it from section-divided to action-divided so that we could handle it more easily in Pandas.  ","metadata":{}},{"cell_type":"code","source":"del train_df\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:10:42.168862Z","iopub.execute_input":"2023-12-03T04:10:42.169551Z","iopub.status.idle":"2023-12-03T04:10:42.438703Z","shell.execute_reply.started":"2023-12-03T04:10:42.169518Z","shell.execute_reply":"2023-12-03T04:10:42.437596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- pd.read_json() function reads the JSONL file in chunks of 100,000 rows. For each chunk, the code iterates over the session and events lists and creates a dictionary called event_dict. The event_dict dictionary contains the following columns:\n   - session: The ID of the session\n   - aid: The ID of the ad\n   - ts: The timestamp of the event\n   - type: The type of the event\n\n- The code then creates a Pandas DataFrame called train_df from the event_dict dictionary. The break statement ensures that the code only reads the first chunk of the JSONL file.\n\n- Finally, call the reset_index() method on the train_df DataFrame to reset the index and drop the original index. The train_df DataFrame is then printed to the console.\n\n- The %%time magic command prints the execution time of the code block, including the time it takes to read the JSONL file, create the DataFrame, and iterate over the DataFrame. The execution time of the code block will vary depending on the size of the JSONL file and the speed of your computer.","metadata":{}},{"cell_type":"code","source":"%%time\ntrain_df = pd.DataFrame()\nchunks = pd.read_json(data_path / 'train.jsonl', lines=True, chunksize=100_000)\n\nfor chunk in chunks:\n    event_dict = {'session': [], 'aid': [], 'ts': [], 'type': []}\n    \n    for session, events in zip(chunk['session'].tolist(), chunk['events'].tolist()):\n        for event in events:\n            event_dict['session'].append(session)\n            event_dict['aid'].append(event['aid'])\n            event_dict['ts'].append(event['ts'])\n            event_dict['type'].append(event['type'])\n    train_df = pd.DataFrame(event_dict)\n    \n    break\n        \ntrain_df = train_df.reset_index(drop=True)\ntrain_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:11:01.464277Z","iopub.execute_input":"2023-12-03T04:11:01.464881Z","iopub.status.idle":"2023-12-03T04:11:21.461672Z","shell.execute_reply.started":"2023-12-03T04:11:01.464843Z","shell.execute_reply":"2023-12-03T04:11:21.460710Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import matplotlib.pyplot as plt\n# ad_ids = train_df['aid'].unique()\n# plt.bar(ad_ids, train_df['aid'].value_counts())\n# plt.xlabel('Ad ID')\n# plt.ylabel('Number of times aid appeared')\n# plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Would like to know how long time spent for each action, and create a new column \"minutes\" where the diff against the next row is set. The timestamp in this competition is 13 digits, which means milli-seconds.\n\n-  Calculate the difference in minutes between two consecutive rows in a DataFrame, grouped by the session ID. The function takes the following steps:\n\n - It creates a new column called \"minutes\" in the DataFrame.\n - It groups the DataFrame by the session ID.\n - It calculates the difference between the \"ts\" column for each consecutive pair of rows, grouped by the session ID.\n - It multiplies the difference by -1/1000/60, which converts the difference from milliseconds to minutes.\n - The result is a new DataFrame with a column called \"minutes\" that contains the difference in minutes between two consecutive rows, grouped by the session ID.","metadata":{}},{"cell_type":"code","source":"train_df[\"minutes\"] = train_df[[\"session\", \"ts\"]].groupby(\"session\").diff(-1)*(-1/1000/60)\ntrain_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:11:31.059743Z","iopub.execute_input":"2023-12-03T04:11:31.060772Z","iopub.status.idle":"2023-12-03T04:12:06.989948Z","shell.execute_reply.started":"2023-12-03T04:11:31.060734Z","shell.execute_reply":"2023-12-03T04:12:06.988952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Create best-sold item's list.  ","metadata":{}},{"cell_type":"code","source":"temp = train_df.groupby(['type','aid'])['session'].agg('count').reset_index()\ntemp.columns = ['type','aid','count']\norder_num_df = temp.loc[(temp['type'] == 'orders'), ]\norder_num_df = order_num_df.sort_values(['count'],ascending=False).reset_index()\norder_num_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:12:17.923805Z","iopub.execute_input":"2023-12-03T04:12:17.924563Z","iopub.status.idle":"2023-12-03T04:12:19.623985Z","shell.execute_reply.started":"2023-12-03T04:12:17.924530Z","shell.execute_reply":"2023-12-03T04:12:19.623025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- Calculate the top 20 most sold products in a DataFrame.Steps:\n\n  - It converts the \"aid\" column in the DataFrame to a string.\n  - It calculates the sum of the \"aid\" column for the first 20 rows in the DataFrame.\nIt prints the resulting list of product IDs.","metadata":{"execution":{"iopub.status.busy":"2023-08-06T12:36:12.373022Z","iopub.execute_input":"2023-08-06T12:36:12.373519Z","iopub.status.idle":"2023-08-06T12:36:12.384588Z","shell.execute_reply.started":"2023-08-06T12:36:12.373478Z","shell.execute_reply":"2023-08-06T12:36:12.382443Z"}}},{"cell_type":"code","source":"order_num_df.aid = ' ' + order_num_df.aid.astype('str')\nbest_sold_list = order_num_df[:20].aid.sum()\nbest_sold_list","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:12:36.501087Z","iopub.execute_input":"2023-12-03T04:12:36.501992Z","iopub.status.idle":"2023-12-03T04:12:36.585484Z","shell.execute_reply.started":"2023-12-03T04:12:36.501954Z","shell.execute_reply":"2023-12-03T04:12:36.584382Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Download Test-data.","metadata":{}},{"cell_type":"markdown","source":" - Create a DataFrame called test_df. Then, read the test.jsonl file in chunks of 100,000 rows. For each chunk, it creates a dictionary called event_dict with the following keys:\n\n   - session: The session ID of the event\n   - aid: The app ID of the event\n   - ts: The timestamp of the event\n   - type: The type of the event\n   - Iterate over the events in the chunk and appends the corresponding values to the event_dict. Finally, create a DataFrame from the event_dict and add to the test_df.\n   - After all the chunks have been processed, reset the index of the test_df and drops the index column.\n   - The overall purpose is to read the test.jsonl file and create a DataFrame with the following columns:","metadata":{}},{"cell_type":"code","source":"# Original\n# %%time\n# test_df = pd.DataFrame()\n# chunks = pd.read_json(data_path / 'test.jsonl', lines=True, chunksize=100_000)\n\n# for chunk in chunks:\n#     event_dict = {'session': [],'aid': [],'ts': [],'type': []}\n#     for session, events in zip(chunk['session'].tolist(), chunk['events'].tolist()):\n#         for event in events:\n#             event_dict['session'].append(session)\n#             event_dict['aid'].append(event['aid'])\n#             event_dict['ts'].append(event['ts'])\n#             event_dict['type'].append(event['type'])\n#     chunk_session = pd.DataFrame(event_dict)\n#     test_df = pd.concat([test_df, chunk_session])\n            \n# test_df = test_df.reset_index(drop=True)\n# test_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:12:44.868599Z","iopub.execute_input":"2023-12-03T04:12:44.869537Z","iopub.status.idle":"2023-12-03T04:13:43.352156Z","shell.execute_reply.started":"2023-12-03T04:12:44.869502Z","shell.execute_reply":"2023-12-03T04:13:43.351085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Edit Version\nimport pandas as pd\n\nevent_list = []\nchunks = pd.read_json(data_path / 'test.jsonl', lines=True, chunksize=100_000)\n\nfor chunk in chunks:\n    events = [(session, event['aid'], event['ts'], event['type']) for session, events in zip(chunk['session'].tolist(), chunk['events'].tolist()) for event in events]\n    event_list.extend(events)\n\ntest_df = pd.DataFrame(event_list, columns=['session', 'aid', 'ts', 'type'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:46:50.500414Z","iopub.execute_input":"2023-12-03T04:46:50.500804Z","iopub.status.idle":"2023-12-03T04:47:24.684615Z","shell.execute_reply.started":"2023-12-03T04:46:50.500770Z","shell.execute_reply":"2023-12-03T04:47:24.683701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"- try/except blocks to handle errors that may occur when reading the JSONL file or creating the Pandas DataFrame. For example, the pd.read_json() function may raise an error if the JSONL file is not found or if the format of the JSONL file is invalid. The try/except block will catch these errors and print a message to the console.\n- Descriptive variable names. For example, the variable chunks could be renamed to json_chunks to make it clear that it contains the JSONL chunks.\n- More modular design. The code could be broken down into smaller functions that each perform a specific task. This would make the code easier to read and understand, and it would also make it easier to test and debug.\n- Efficient algorithm. The current code iterates over the session and events lists twice for each chunk. This can be improved by using a more efficient algorithm, such as a hash table.","metadata":{}},{"cell_type":"code","source":"# import pandas as pd\n\n# def read_json_file(data_path):\n#     try:\n#         json_chunks = pd.read_json(data_path, lines=True, chunksize=100_000)\n#     except FileNotFoundError:\n#         print(\"The JSONL file could not be found.\")\n#     except Exception as e:\n#         print(f\"An error occurred: {e}\")\n#     return json_chunks\n\n# def create_dataframe(json_chunks):\n#     event_dict = {'session': [], 'aid': [], 'ts': [], 'type': []}\n\n#     for chunk in json_chunks:\n#         for session, events in zip(chunk['session'].tolist(), chunk['events'].tolist()):\n#             for event in events:\n#                 event_dict['session'].append(session)\n#                 event_dict['aid'].append(event['aid'])\n#                 event_dict['ts'].append(event['ts'])\n#                 event_dict['type'].append(event['type'])\n#     return pd.DataFrame(event_dict)\n\n# def main():\n#     data_path = 'data/test.jsonl'\n#     json_chunks = read_json_file(data_path)\n#     test_df = create_dataframe(json_chunks)\n#     test_df = test_df.reset_index(drop=True)\n#     print(test_df)\n\n# if __name__ == '__main__':\n#     main()","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:46:14.374549Z","iopub.execute_input":"2023-12-03T04:46:14.375360Z","iopub.status.idle":"2023-12-03T04:46:14.380819Z","shell.execute_reply.started":"2023-12-03T04:46:14.375316Z","shell.execute_reply":"2023-12-03T04:46:14.379713Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Similarly to Train-data, add a new column \"minutes\".  \nSorting by descending \"minutes\".It's better to recommend the items watched longer time .\n\n\n- ' ' + test_df.aid.astype('str'): This adds a space (' ') to each element in the 'aid' column, effectively concatenating a space with each string representation of the original values.\n- take the 'aid' column from a DataFrame test_df, converting its values to strings, adding a space to each string, and then assigning the result to a column named 'aid' in another DataFrame test_action_df. ","metadata":{}},{"cell_type":"code","source":"# # Original\n# %%time\n# test_df[\"minutes\"] = test_df[[\"session\", \"ts\"]].groupby(\"session\").diff(-1)*(-1/1000/60)\n# test_df = test_df.sort_values(['minutes'],ascending=False)\n\n# test_action_df = test_df.copy()\n# test_action_df.aid = ' ' + test_df.aid.astype('str')\n# test_action_df = test_action_df.groupby(['session','type'])['aid'].sum().reset_index()\n# test_action_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:13:51.798749Z","iopub.execute_input":"2023-12-03T04:13:51.799175Z","iopub.status.idle":"2023-12-03T04:22:41.546833Z","shell.execute_reply.started":"2023-12-03T04:13:51.799123Z","shell.execute_reply":"2023-12-03T04:22:41.545913Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Edit Version\nimport pandas as pd\n# Calculate minutes\ntest_df[\"minutes\"] = test_df.groupby(\"session\")[\"ts\"].diff(-1) * (-1 / 1000 / 60)\n\n# Sort by minutes\ntest_df = test_df.sort_values(by='minutes', ascending=False)\n\n# Create test_action_df\ntest_action_df = (\n    test_df.assign(aid=test_df['aid'].astype(str).radd(' '))  # Prefix ' ' to aid\n            .groupby(['session', 'type'])['aid'].sum()\n            .reset_index()\n)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:48:42.874175Z","iopub.execute_input":"2023-12-03T04:48:42.874558Z","iopub.status.idle":"2023-12-03T04:53:51.183365Z","shell.execute_reply.started":"2023-12-03T04:48:42.874518Z","shell.execute_reply":"2023-12-03T04:53:51.182528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"next_orders_df = pd.DataFrame(test_action_df.loc[(test_action_df[\"type\"] == 'carts'), ])\nnext_orders_df['type'] = 'orders'\nnext_orders_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:54:41.536865Z","iopub.execute_input":"2023-12-03T04:54:41.537229Z","iopub.status.idle":"2023-12-03T04:54:42.081862Z","shell.execute_reply.started":"2023-12-03T04:54:41.537197Z","shell.execute_reply":"2023-12-03T04:54:42.080919Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"next_carts_df = pd.DataFrame(test_action_df.loc[(test_action_df[\"type\"] == 'clicks'), ])\nnext_carts_df['type'] = 'carts'\nnext_carts_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:54:45.983697Z","iopub.execute_input":"2023-12-03T04:54:45.984685Z","iopub.status.idle":"2023-12-03T04:54:46.400660Z","shell.execute_reply.started":"2023-12-03T04:54:45.984631Z","shell.execute_reply":"2023-12-03T04:54:46.399693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For Clicks, changed the recommendation items from 'best-sold item' to 'clicked item', which improved the public score from 0.352 to **0.383**. It seems better to recommend items which user once clicked(=show his/her interest) rather than best-sold items which may has nothing to do with him/her.  \n","metadata":{}},{"cell_type":"code","source":"next_clicks_df = pd.DataFrame(test_action_df.loc[(test_action_df[\"type\"] == 'clicks'), ]).copy()","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:54:49.853847Z","iopub.execute_input":"2023-12-03T04:54:49.854220Z","iopub.status.idle":"2023-12-03T04:54:50.386716Z","shell.execute_reply.started":"2023-12-03T04:54:49.854187Z","shell.execute_reply":"2023-12-03T04:54:50.385872Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Updated 2022/12/10**\n\nAs shown in the later \"4. Add pair-sold items\", by recommending 'pair-sold' items adding to cart-in items, public score was slightly improved from **0.383→0.384**.  \nHowever, by recommending clicked-items instead of 'pair-sold items' adding to cart-in items, the score was imroved to **0.410**. Looks like it'd be better to again recommend the items which users once showed his/her interest to.  ","metadata":{}},{"cell_type":"code","source":"next_orders_df = pd.merge(next_orders_df, next_clicks_df[['session', 'aid']], on ='session', how = 'left')\nnext_orders_df[\"aid\"] = next_orders_df[\"aid_x\"] + next_orders_df[\"aid_y\"]\nnext_orders_df = next_orders_df.drop(['aid_x', 'aid_y'], axis =1)\nnext_orders_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:54:52.428579Z","iopub.execute_input":"2023-12-03T04:54:52.428961Z","iopub.status.idle":"2023-12-03T04:54:53.300590Z","shell.execute_reply.started":"2023-12-03T04:54:52.428929Z","shell.execute_reply":"2023-12-03T04:54:53.299622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"recommend_df = pd.concat([next_orders_df, next_carts_df, next_clicks_df], axis =0)\nrecommend_df[\"session_type\"] = recommend_df[\"session\"].astype('str') + \"_\" + recommend_df[\"type\"] \nrecommend_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:55:10.057074Z","iopub.execute_input":"2023-12-03T04:55:10.057815Z","iopub.status.idle":"2023-12-03T04:55:14.810582Z","shell.execute_reply.started":"2023-12-03T04:55:10.057776Z","shell.execute_reply":"2023-12-03T04:55:14.809688Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub = pd.read_csv('/kaggle/input/otto-recommender-system/sample_submission.csv')\nsample_sub","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:55:17.714000Z","iopub.execute_input":"2023-12-03T04:55:17.714730Z","iopub.status.idle":"2023-12-03T04:55:21.818761Z","shell.execute_reply.started":"2023-12-03T04:55:17.714691Z","shell.execute_reply":"2023-12-03T04:55:21.817239Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub = pd.merge(sample_sub, recommend_df[[\"session_type\",\"aid\"]], on = \"session_type\", how =\"left\")\nsample_sub['next'] = sample_sub['aid'] + best_sold_list\nsample_sub['next'].fillna(best_sold_list, inplace = True)\nsample_sub['next'] = sample_sub['next'].str.strip()\nsample_sub = sample_sub.drop([\"labels\", \"aid\"], axis = 1)\nsample_sub.columns = (\"session_type\", \"labels\")\nsample_sub","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:55:30.613940Z","iopub.execute_input":"2023-12-03T04:55:30.614310Z","iopub.status.idle":"2023-12-03T04:55:46.667198Z","shell.execute_reply.started":"2023-12-03T04:55:30.614278Z","shell.execute_reply":"2023-12-03T04:55:46.666250Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_sub.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:55:50.613940Z","iopub.execute_input":"2023-12-03T04:55:50.614832Z","iopub.status.idle":"2023-12-03T04:56:30.411376Z","shell.execute_reply.started":"2023-12-03T04:55:50.614792Z","shell.execute_reply":"2023-12-03T04:56:30.410499Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Add 'pair-sold' items\n\nIf you submit the csv here, the score will show 0.383.  \nTrying to do something for more improvement, try to add pair-sold items list (= other items ordered at the same time when a certain item was ordered).  \nThe image is, when A was ordered 'B,C,D' were also ordered at the same time, 'B,C,D' could be regarded as pair-sold items for A.  \nThis contributed the slight score improvement from 0.383 to **0.384**.\n\nAs mentioned above, public score is better by recommending clicked-items(**0.410**) rather than pair-sold items, but this pair-sold items logic is purposely remained just for your reference.     ","metadata":{}},{"cell_type":"code","source":"train_order_df = train_df.loc[(train_df[\"type\"] == 'orders'),].copy()\naid_counts = train_df.aid.value_counts()\naid_counts","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:56:45.394169Z","iopub.execute_input":"2023-12-03T04:56:45.394577Z","iopub.status.idle":"2023-12-03T04:56:46.531023Z","shell.execute_reply.started":"2023-12-03T04:56:45.394541Z","shell.execute_reply":"2023-12-03T04:56:46.530087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Original\npairs = {}\nfor i in aid_counts.index.values[:10000]:\n    \n    custs = train_order_df.loc[train_order_df.aid==i.item(),'session'].unique()\n    aid_orders = train_order_df.loc[(train_order_df.session.isin(custs))&(train_order_df.aid!=i.item()),'aid'].value_counts()   \n    try :\n        pairs[i.item()] = [aid_orders.index[0], aid_orders.index[1], aid_orders.index[2]]\n    except :\n        continue","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:56:48.923972Z","iopub.execute_input":"2023-12-03T04:56:48.924838Z","iopub.status.idle":"2023-12-03T04:57:08.333129Z","shell.execute_reply.started":"2023-12-03T04:56:48.924802Z","shell.execute_reply":"2023-12-03T04:57:08.332069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Edit\n# import pandas as pd\n\n\n\n# pairs = {}\n\n# # Select the top 10000 values from the index\n# for i in aid_counts.index.values[:10000]:\n#     # Use .loc to filter rows based on the condition\n#     custs = train_order_df.loc[train_order_df['aid'] == i.item(), 'session'].unique()\n    \n#     # Filter orders based on conditions using boolean indexing\n#     aid_orders = train_order_df.loc[\n#         (train_order_df['session'].isin(custs)) & (train_order_df['aid'] != i.item()), 'aid'\n#     ].value_counts()\n    \n#     try:\n#         # Use .values to access the values as a list\n#         pairs[i.item()] = aid_orders.index[:3].values.tolist()\n#     except IndexError:\n#         # Handle the case when there are not enough values in aid_orders.index\n#         continue\n\n# # resulting pairs\n# # print(pairs)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:38:23.736761Z","iopub.execute_input":"2023-12-03T04:38:23.737120Z","iopub.status.idle":"2023-12-03T04:38:42.821870Z","shell.execute_reply.started":"2023-12-03T04:38:23.737090Z","shell.execute_reply":"2023-12-03T04:38:42.821054Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pairs_df = pd.DataFrame(pairs)\npairs_df = pairs_df.T\npairs_df['aid'] = pairs_df.index\npairs_df['3pairs'] =' '+pairs_df[0].astype('str')+' '+pairs_df[1].astype('str')+' '+pairs_df[2].astype('str')\npairs_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:57:16.595432Z","iopub.execute_input":"2023-12-03T04:57:16.596219Z","iopub.status.idle":"2023-12-03T04:57:16.856029Z","shell.execute_reply.started":"2023-12-03T04:57:16.596179Z","shell.execute_reply":"2023-12-03T04:57:16.855087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_orders_df = pd.DataFrame(test_df.loc[(test_df[\"type\"] == 'carts'), ]).copy()\ntest_orders_df = pd.merge(test_orders_df, pairs_df[['aid','3pairs']], on = 'aid', how = 'left')\ntest_orders_df['type'] = 'orders'\ntest_orders_df['aid'] = ' ' + test_orders_df['aid'].astype('str')\ntest_orders_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:57:19.628888Z","iopub.execute_input":"2023-12-03T04:57:19.629292Z","iopub.status.idle":"2023-12-03T04:57:22.093629Z","shell.execute_reply.started":"2023-12-03T04:57:19.629259Z","shell.execute_reply":"2023-12-03T04:57:22.092612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Original \n# next_orders_df = test_orders_df.groupby(['session','type'])[['aid','3pairs']].sum().reset_index()\n# next_orders_df = next_orders_df.replace(0, ' ')\n# next_orders_df['aid'] = next_orders_df['aid'] + next_orders_df['3pairs']\n# next_orders_df = next_orders_df.drop(['3pairs'], axis = 1)\n\n# recommend_df = pd.concat([next_orders_df, next_carts_df, next_clicks_df], axis =0)\n# recommend_df[\"session_type\"] = recommend_df[\"session\"].astype('str') + \"_\" + recommend_df[\"type\"] \n# recommend_df","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:42:05.658908Z","iopub.execute_input":"2023-12-03T04:42:05.659679Z","iopub.status.idle":"2023-12-03T04:42:10.427222Z","shell.execute_reply.started":"2023-12-03T04:42:05.659635Z","shell.execute_reply":"2023-12-03T04:42:10.426326Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Edit Version\nimport pandas as pd\n\n# Grouping and summing based on session and type\nnext_orders_df = (\n    test_orders_df.groupby(['session', 'type'])[['aid', '3pairs']]\n    .sum()\n    .reset_index()\n)\n\n# Replace 0 with empty string\nnext_orders_df = next_orders_df.replace(0, '')\n\n# Combining 'aid' and '3pairs' columns\nnext_orders_df['aid'] = next_orders_df['aid'] + next_orders_df['3pairs']\n\n# Dropping the '3pairs' column\nnext_orders_df = next_orders_df.drop(['3pairs'], axis=1)\n\n# Concatenating dataframes vertically\nrecommend_df = pd.concat([next_orders_df, next_carts_df, next_clicks_df], axis=0)\n\n# Creating a new column 'session_type'\nrecommend_df[\"session_type\"] = recommend_df[\"session\"].astype(str) + \"_\" + recommend_df[\"type\"]\n\n# Displaying resulting dataframe\nprint(recommend_df)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:59:42.513807Z","iopub.execute_input":"2023-12-03T04:59:42.514652Z","iopub.status.idle":"2023-12-03T04:59:47.527127Z","shell.execute_reply.started":"2023-12-03T04:59:42.514609Z","shell.execute_reply":"2023-12-03T04:59:47.526174Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. Submission","metadata":{}},{"cell_type":"code","source":"sample_sub = pd.read_csv('/kaggle/input/otto-recommender-system/sample_submission.csv')\nsample_sub = pd.merge(sample_sub, recommend_df[[\"session_type\",\"aid\"]], on = \"session_type\", how =\"left\")\nsample_sub['next'] = sample_sub['aid'] + best_sold_list\nsample_sub['next'].fillna(best_sold_list, inplace = True)\nsample_sub['next'] = sample_sub['next'].str.strip()\nsample_sub = sample_sub.drop([\"labels\", \"aid\"], axis = 1)\nsample_sub.columns = (\"session_type\", \"labels\")\nsample_sub.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:59:53.173761Z","iopub.execute_input":"2023-12-03T04:59:53.174506Z","iopub.status.idle":"2023-12-03T05:00:52.170482Z","shell.execute_reply.started":"2023-12-03T04:59:53.174473Z","shell.execute_reply":"2023-12-03T05:00:52.169282Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# import pandas as pd\n\n# def read_csv(file_path):\n#     try:\n#         df = pd.read_csv(file_path)\n#         return df\n#     except FileNotFoundError:\n#         print(f\"Error: File not found at {file_path}\")\n#         return None\n\n# def merge_data(sample_sub, recommend_df, merge_column):\n#     merged_df = pd.merge(sample_sub, recommend_df[[merge_column, \"aid\"]], on=merge_column, how=\"left\")\n#     return merged_df\n\n# def process_data(sample_sub, best_sold_list):\n#     sample_sub['next'] = sample_sub['aid'] + best_sold_list\n#     sample_sub['next'].fillna(best_sold_list, inplace=True)\n#     sample_sub['next'] = sample_sub['next'].str.strip()\n#     sample_sub = sample_sub.drop([\"labels\", \"aid\"], axis=1)\n#     sample_sub.columns = [\"session_type\", \"labels\"]\n#     return sample_sub\n\n# def save_submission_csv(df, output_path):\n#     df.to_csv(output_path, index=False)\n\n# def main():\n#     sample_sub_path = '/kaggle/input/otto-recommender-system/sample_submission.csv'\n#     recommend_df_path = 'your_recommendation_data.csv'\n#     best_sold_list = ['item1', 'item2']  # Replace with your actual best_sold_list\n\n#     # Step 1: Read CSV files\n#     sample_sub = read_csv(sample_sub_path)\n#     recommend_df = read_csv(recommend_df_path)\n\n#     if sample_sub is not None and recommend_df is not None:\n#         # Step 2: Merge data\n#         merged_data = merge_data(sample_sub, recommend_df, \"session_type\")\n\n#         # Step 3: Process data\n#         processed_data = process_data(merged_data, best_sold_list)\n\n#         # Step 4: Save submission CSV\n#         save_submission_csv(processed_data, 'submission.csv')\n\n# if __name__ == \"__main__\":\n#     main()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-03T04:45:26.605009Z","iopub.execute_input":"2023-12-03T04:45:26.605405Z","iopub.status.idle":"2023-12-03T04:45:26.612065Z","shell.execute_reply.started":"2023-12-03T04:45:26.605372Z","shell.execute_reply":"2023-12-03T04:45:26.610937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#sample_sub","metadata":{"execution":{"iopub.status.busy":"2022-12-04T02:29:03.84472Z","iopub.execute_input":"2022-12-04T02:29:03.845275Z","iopub.status.idle":"2022-12-04T02:29:03.862227Z","shell.execute_reply.started":"2022-12-04T02:29:03.845242Z","shell.execute_reply":"2022-12-04T02:29:03.860771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}