bandit-delayed-feedback

{"output": "total 3008\ndrwxr-xr-x 2 root root    4096 Mar  4 09:09 .\ndrwxr-xr-x 1 root root    4096 Mar  4 09:10 ..\n-rw-rw-r-- 1 root root    1128 Mar  4 09:05 arms_metadata.json\n-rw-rw-r-- 1 root root  135100 Mar  4 09:05 conversions.csv\n-rw-rw-r-- 1 root root 2818804 Mar  4 09:05 impressions.csv\n-rw-rw-r-- 1 root root  108862 Mar  4 09:05 test_contexts.csv", "exit_code": 0, "error": null}

I'll analyze this multi-armed bandit problem with delayed feedback. Let me start by exploring the data files to understand their structure.

→ terminal({"command": "ls -la /app/data/"})

{"content": "     1|impression_id,arm_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n     2|IMP_000000,0,1709340758,9,5,young,mobile,0.7797,0.5969\n     3|IMP_000001,6,1710354874,3,3,young,desktop,0.7081,0.0206\n     4|IMP_000002,9,1710010543,3,6,young,desktop,0.6117,0.0071\n     5|IMP_000003,3,1710416879,20,3,senior,mobile,0.7852,0.1997\n     6|IMP_000004,8,1709492338,3,0,young,desktop,0.1705,0.0651\n     7|IMP_000005,4,1709917161,1,5,young,mobile,0.2309,0.241\n     8|IMP_000006,3,1710022391,7,6,senior,mobile,0.3911,0.1822\n     9|IMP_000007,5,1709490636,3,0,senior,desktop,0.4498,0.3952\n    10|IMP_000008,1,1709706679,15,2,middle,mobile,0.0452,0.3253\n    11|IMP_000009,0,1709752356,4,3,middle,tablet,0.607,0.276\n    12|IMP_000010,2,1709267784,13,4,senior,mobile,0.3949,0.2935\n    13|IMP_000011,6,1709232786,3,4,senior,desktop,0.074,0.3585\n    14|IMP_000012,3,1710297911,11,2,middle,mobile,0.3708,0.6688\n    15|IMP_000013,3,1709243338,6,4,senior,mobile,0.1196,0.7132\n    16|IMP_000014,1,1710116221,9,0,senior,mobile,0.7107,0.1109\n    17|IMP_000015,6,1710317391,17,2,senior,mobile,0.5633,0.6955\n    18|IMP_000016,5,1710192348,6,1,senior,desktop,0.2288,0.077\n    19|IMP_000017,1,1709634315,19,1,middle,desktop,0.2956,0.1055\n    20|IMP_000018,7,1709244739,7,4,middle,tablet,0.3243,0.1221\n    21|IMP_000019,1,1709904240,22,4,young,desktop,0.0005,0.3526\n    22|IMP_000020,7,1709447376,15,6,senior,mobile,0.2441,0.1683\n    23|IMP_000021,5,1709337251,8,5,young,mobile,0.2539,0.2469\n    24|IMP_000022,0,1709810260,20,3,young,tablet,0.2668,0.9766\n    25|IMP_000023,1,1709488336,2,0,young,mobile,0.1449,0.4895\n    26|IMP_000024,4,1710337843,22,2,senior,desktop,0.2376,0.7282\n    27|IMP_000025,1,1710202037,9,1,middle,desktop,0.0903,0.8353\n    28|IMP_000026,1,1710283084,7,2,senior,desktop,0.6776,0.0166\n    29|IMP_000027,0,1709877071,14,4,senior,mobile,0.6909,0.3867\n    30|IMP_000028,9,1710275531,5,2,young,mobile,0.9247,0.8773\n    31|", "total_lines": 50001, "file_size": 2818804, "truncated": true, "hint": "Use offset=31 to continue reading (showing 1-30 of 50001 lines)", "is_binary": false, "is_image": false}

{"content": "     1|impression_id,conversion_timestamp,delay_hours,conversion_value\n     2|IMP_000010,1709305348,10.43,3.58\n     3|IMP_000013,1709320719,21.49,1.07\n     4|IMP_000022,1709843815,9.32,3.47\n     5|IMP_000069,1710312110,4.57,2.48\n     6|IMP_000071,1710446648,25.86,1.37\n     7|IMP_000090,1709973192,28.94,1.46\n     8|IMP_000097,1710100917,68.48,1.67\n     9|IMP_000109,1709851031,16.83,1.67\n    10|IMP_000119,1709561787,17.17,3.28\n    11|IMP_000124,1709437238,6.31,1.1\n    12|IMP_000126,1709398071,12.93,2.35\n    13|IMP_000127,1709550349,3.65,3.02\n    14|IMP_000130,1709845772,67.73,1.5\n    15|IMP_000131,1709832027,11.88,0.96\n    16|IMP_000138,1709742702,27.46,2.38\n    17|IMP_000152,1710203669,9.83,1.31\n    18|IMP_000188,1710048427,4.77,1.79\n    19|IMP_000201,1709914355,4.67,2.85\n    20|IMP_000202,1710336058,72.0,1.63\n    21|IMP_000213,1710348149,29.0,3.11\n    22|IMP_000218,1709681051,25.35,1.82\n    23|IMP_000220,1710010244,12.4,1.14\n    24|IMP_000224,1710015389,41.47,1.45\n    25|IMP_000231,1710325201,8.36,1.11\n    26|IMP_000246,1709806072,12.57,2.36\n    27|IMP_000255,1710275599,11.5,2.59\n    28|IMP_000258,1709250382,3.75,1.87\n    29|IMP_000270,1709913684,72.0,1.63\n    30|IMP_000271,1710186715,11.24,2.48\n    31|", "total_lines": 4175, "file_size": 135100, "truncated": true, "hint": "Use offset=31 to continue reading (showing 1-30 of 4175 lines)", "is_binary": false, "is_image": false}

{"content": "     1|{\n     2|  \"arms\": [\n     3|    {\n     4|      \"arm_id\": 0,\n     5|      \"name\": \"Campaign A\",\n     6|      \"category\": \"electronics\",\n     7|      \"base_bid\": 0.3\n     8|    },\n     9|    {\n    10|      \"arm_id\": 1,\n    11|      \"name\": \"Campaign B\",\n    12|      \"category\": \"fashion\",\n    13|      \"base_bid\": 0.4\n    14|    },\n    15|    {\n    16|      \"arm_id\": 2,\n    17|      \"name\": \"Campaign C\",\n    18|      \"category\": \"home\",\n    19|      \"base_bid\": 0.5\n    20|    },\n    21|    {\n    22|      \"arm_id\": 3,\n    23|      \"name\": \"Campaign D\",\n    24|      \"category\": \"sports\",\n    25|      \"base_bid\": 0.6\n    26|    },\n    27|    {\n    28|      \"arm_id\": 4,\n    29|      \"name\": \"Campaign E\",\n    30|      \"category\": \"beauty\",\n    31|      \"base_bid\": 0.7\n    32|    },\n    33|    {\n    34|      \"arm_id\": 5,\n    35|      \"name\": \"Campaign F\",\n    36|      \"category\": \"food\",\n    37|      \"base_bid\": 0.8\n    38|    },\n    39|    {\n    40|      \"arm_id\": 6,\n    41|      \"name\": \"Campaign G\",\n    42|      \"category\": \"travel\",\n    43|      \"base_bid\": 0.9\n    44|    },\n    45|    {\n    46|      \"arm_id\": 7,\n    47|      \"name\": \"Campaign H\",\n    48|      \"category\": \"finance\",\n    49|      \"base_bid\": 1.0\n    50|    },\n    51|    {\n    52|      \"arm_id\": 8,\n    53|      \"name\": \"Campaign I\",\n    54|      \"category\": \"entertainment\",\n    55|      \"base_bid\": 1.1\n    56|    },\n    57|    {\n    58|      \"arm_id\": 9,\n    59|      \"name\": \"Campaign J\",\n    60|      \"category\": \"health\",\n    61|      \"base_bid\": 1.2\n    62|    }\n    63|  ]\n    64|}", "total_lines": 63, "file_size": 1128, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|context_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\n     2|CTX_000000,1710307872,14,2,middle,mobile,0.747,0.0933\n     3|CTX_000001,1710319811,17,2,senior,mobile,0.9059,0.1018\n     4|CTX_000002,1710341834,23,2,senior,mobile,0.0783,0.8936\n     5|CTX_000003,1710405538,17,3,middle,mobile,0.4505,0.7765\n     6|CTX_000004,1710411058,19,3,middle,mobile,0.1365,0.0416\n     7|CTX_000005,1710408367,18,3,middle,desktop,0.9182,0.4332\n     8|CTX_000006,1710363663,6,3,young,tablet,0.8606,0.5293\n     9|CTX_000007,1710307909,14,2,young,mobile,0.3778,0.9319\n    10|CTX_000008,1710410830,19,3,senior,desktop,0.0584,0.8807\n    11|CTX_000009,1710264974,2,2,senior,mobile,0.154,0.8882\n    12|CTX_000010,1710422428,22,3,middle,tablet,0.7284,0.6934\n    13|CTX_000011,1710261336,1,2,middle,tablet,0.0929,0.1021\n    14|CTX_000012,1710355197,3,3,young,desktop,0.5009,0.7427\n    15|CTX_000013,1710261873,1,2,young,desktop,0.5248,0.7311\n    16|CTX_000014,1710312989,15,2,senior,desktop,0.7438,0.8447\n    17|CTX_000015,1710275640,5,2,young,mobile,0.8382,0.1219\n    18|CTX_000016,1710276425,5,2,middle,desktop,0.9619,0.0435\n    19|CTX_000017,1710382484,11,3,middle,desktop,0.8974,0.1923\n    20|CTX_000018,1710350540,2,3,young,mobile,0.3106,0.0453\n    21|", "total_lines": 2001, "file_size": 108862, "truncated": true, "hint": "Use offset=21 to continue reading (showing 1-20 of 2001 lines)", "is_binary": false, "is_image": false}

{"output": "", "exit_code": 0, "error": null}

Now let me create the output directory and write a Python script to solve this problem:

→ terminal({"command": "mkdir -p /app/output"})

{"bytes_written": 17115, "dirs_created": true}

{"output": "Loading data...\nLoaded 50000 impressions, 4174 conversions\n\nEstimating delay distribution...\nDelay model: mu=2.4757, sigma=0.8980\nMedian delay: 11.89 hours\nP90 delay: 37.58 hours\nConversion rate: 0.0835\n\nSaved delay model to /app/output/delay_model.json\n\nCalculating arm statistics with delay adjustment...\nSaved arm statistics to /app/output/arm_statistics.csv\n\nBuilding contextual model...\nTraceback (most recent call last):\n  File \"/app/solve_bandit.py\", line 450, in <module>\n    main()\n  File \"/app/solve_bandit.py\", line 403, in main\n    model, X_mean, X_std, merged = build_contextual_model(impressions, conversions)\n                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solve_bandit.py\", line 168, in build_contextual_model\n    X = merged[feature_cols].copy()\n        ~~~~~~^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/frame.py\", line 4108, in __getitem__\n    indexer = self.columns._get_indexer_strict(key, \"columns\")[1]\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6200, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6252, in _raise_if_missing\n    raise KeyError(f\"{not_found} not in index\")\nKeyError: \"['segment_middle', 'segment_senior', 'segment_young', 'device_desktop', 'device_mobile', 'device_tablet'] not in index\"", "exit_code": 1, "error": null}