bandit-delayed-feedback

{"output": "total 16\ndrwxr-xr-x 1 root root 4096 Mar  4 09:10 .\ndrwxr-xr-x 1 root root 4096 Apr 22 20:35 ..\ndrwxr-xr-x 2 root root 4096 Mar  4 09:09 data\ndrwxr-xr-x 2 root root 4096 Mar  4 09:10 output", "exit_code": 0, "error": null}

I'll start by exploring the environment and understanding the data structure.

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

{"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}

{"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}

Let me examine the data files to understand their structure:

→ read_file({"path": "/app/data/arms_metadata.json"})

{"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|", "total_lines": 50001, "file_size": 2818804, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 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|", "total_lines": 4175, "file_size": 135100, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 4175 lines)", "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|", "total_lines": 2001, "file_size": 108862, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 2001 lines)", "is_binary": false, "is_image": false}

{"output": "Loading data...\nImpressions shape: (50000, 9)\nConversions shape: (4174, 4)\nTest contexts shape: (2000, 8)\n\nDelay distribution analysis:\nMin delay: 0.58 hours\nMax delay: 72.00 hours\nMean delay: 17.42 hours\nMedian delay: 11.86 hours\nStd delay: 16.15 hours\n\nLog-normal parameters:\nmu (mean of log delays): 2.4757\nsigma (std of log delays): 0.8981\nMedian delay (exp(mu)): 11.89 hours\nP90 delay: 37.59 hours\n\nConversion rate: 0.0835\n\nConversion value statistics:\nMean value: $2.24\nMedian value: $1.97\nMin value: $0.55\nMax value: $9.25\n\nArm distribution in impressions:\narm_id\n0    6767\n1    8453\n2    3187\n3    8584\n4    5005\n5    4285\n6    4974\n7    2917\n8    3303\n9    2525\nName: count, dtype: int64", "exit_code": 0, "error": null}

Now let me analyze the data to understand the delay distribution and build a solution:

→ terminal({"command": "cd /app && python3 << 'EOF'\nimport pandas as pd\nimport numpy as np\nimport json\nfrom datetime import datetime\n\n# Load data\nprint(\"Loading data...\")\nimpressions = pd.read_csv('/app/data/impressions.csv')\nconversions = pd.read_csv('/app/data/conversions.csv')\ntest_contexts = pd.read_csv('/app/data/test_contexts.csv')\n\nprint(f\"Impressions shape: {impressions.shape}\")\nprin…)

{"bytes_written": 12181, "dirs_created": true}

Now let me create a comprehensive solution that handles delayed feedback and contextual bandit selection:

→ write_file({"path": "/app/solution.py", "content": "#!/usr/bin/env python3\n\nimport pandas as pd\nimport numpy as np\nimport json\nfrom datetime import datetime, timedelta\nfrom scipy.stats import norm, lognorm\nimport warnings\nwarnings.filterwarnings('ignore')\n\nclass DelayedFeedbackBandit:\n    def __init__(self):\n        self.impressions = None\n        self.conversions = None\n        self.arms_metad…)

{"output": "Starting Delayed Feedback Bandit Solution\n\nLoading data...\nModeling delay distribution...\nDelay model parameters:\n  mu: 2.4757\n  sigma: 0.8981\n  median_delay: 11.89 hours\n  p90_delay: 37.59 hours\n  conversion_rate: 0.0835\nDelay model saved to /app/output/delay_model.json\nCalculating delay-corrected arm statistics...\n\nArm Statistics:\n arm_id  estimated_conversion_rate  estimated_avg_value  total_impressions  total_conversions\n      0                   0.061670             2.588441               6767                417\n      1                   0.096645             1.878115               8453                817\n      2                   0.043777             4.470504               3187                139\n      3                   0.120303             1.587299               8584               1033\n      4                   0.077951             2.255103               5005                390\n      5                   0.070065             3.151900               4285                300\n      6                   0.088867             2.065204               4974                442\n      7                   0.061475             3.751117               2917                179\n      8                   0.113458             1.785200               3303                375\n      9                   0.032729             4.891463               2525                 82\nArm statistics saved to /app/output/arm_statistics.csv\nBuilding contextual models...\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 301, in <module>\n    bandit.run()\n  File \"/app/solution.py\", line 294, in run\n    self.build_contextual_models()\n  File \"/app/solution.py\", line 197, in build_contextual_models\n    model['converted_stats'][feature] = converted[feature].mean()\n                                        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/series.py\", line 6549, in mean\n    return NDFrame.mean(self, axis, skipna, numeric_only, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/generic.py\", line 12420, in mean\n    return self._stat_function(\n           ^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/generic.py\", line 12377, in _stat_function\n    return self._reduce(\n           ^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/series.py\", line 6457, in _reduce\n    return op(delegate, skipna=skipna, **kwds)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/nanops.py\", line 147, in f\n    result = alt(values, axis=axis, skipna=skipna, **kwds)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/nanops.py\", line 404, in new_func\n    result = func(values, axis=axis, skipna=skipna, mask=mask, **kwargs)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/nanops.py\", line 720, in nanmean\n    the_sum = _ensure_numeric(the_sum)\n              ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/nanops.py\", line 1701, in _ensure_numeric\n    raise TypeError(f\"Could not convert string '{x}' to numeric\")\nTypeError: Could not convert string 'youngmiddlemiddlemiddlemiddleyoungyoungseniormiddleseniormiddleseniormiddleyoungyoungyoungyoungyoungyoungyoungmiddlemiddlesenioryoungyoungyoungseniormiddlemiddlemiddleyoungyoungmiddleyoungseniormiddlesenioryoungmiddlemiddlemiddleseniorsenioryoungmiddleyoungyoungyoungyoungyoungyoungseniormiddleyoungmiddleyoungseniormiddleseniorsenioryoungyoungmiddlesenioryoungmiddlemiddlemiddleyoungmiddleyoungsenioryoungmiddleseniormiddlesenioryoungyoungyoungmiddlemiddleseniormiddleyoungmiddleseniormiddlemiddlemiddleyoungyoungmiddleyoungsenio

{"success": true, "diff": "--- a//app/solution.py\n+++ b//app/solution.py\n@@ -192,10 +192,24 @@\n                 'non_converted_stats': {}\n             }\n             \n-            for feature in categorical_features + numerical_features:\n+            for feature in numerical_features:\n                 if feature in converted.columns:\n-                    model['converted_stats'][feature] = converted[feature].mean()\n-                    model['non_converted_stats'][feature] = non_converted[feature].mean()\n+                    model['converted_stats'][feature] = float(converted[feature].mean())\n+                    model['non_converted_stats'][feature] = float(non_converted[feature].mean())\n+            \n+            # Handle categorical features separately\n+            for feature in categorical_features:\n+                if feature in converted.columns:\n+                    # For categorical features, use mode instead of mean\n+                    if len(converted) > 0:\n+                        model['converted_stats'][feature] = str(converted[feature].mode()[0] if len(converted[feature].mode()) > 0 else 'unknown')\n+                    else:\n+                        model['converted_stats'][feature] = 'unknown'\n+                    \n+                    if len(non_converted) > 0:\n+                        model['non_converted_stats'][feature] = str(non_converted[feature].mode()[0] if len(non_converted[feature].mode()) > 0 else 'unknown')\n+                    else:\n+                        model['non_converted_stats'][feature] = 'unknown'\n             \n             self.arm_models[arm_id] = model\n         \n", "files_modified": ["/app/solution.py"], "lint": {"status": "ok", "output": ""}}

I need to fix the error with categorical features. Let me update the solution:

→ patch({"mode": "replace", "path": "/app/solution.py", "old_string": "            # Simple contextual model: use feature averages for converted vs non-converted\n            converted = arm_data[arm_data['reward'] > 0]\n            non_converted = arm_data[arm_data['reward'] == 0]\n            \n            model = {\n                'conversion_rate': arm_conversion_rate,\n                'avg_value': a…)

{"success": true, "diff": "--- a//app/solution.py\n+++ b//app/solution.py\n@@ -242,7 +242,10 @@\n                     context_val = context[feature]\n                     \n                     # Weighted similarity: closer to converted pattern is better\n-                    similarity_score += (context_val - non_conv_val) / (conv_val - non_conv_val + 1e-6)\n+                    if conv_val != non_conv_val:\n+                        similarity_score += (context_val - non_conv_val) / (conv_val - non_conv_val + 1e-6)\n+                    else:\n+                        similarity_score += 0.5  # Neutral similarity\n             \n             # Normalize similarity\n             similarity_score = max(0, min(1, similarity_score / len(['hour_of_day', 'day_of_week', 'context_feature_1', 'context_feature_2'])))\n", "files_modified": ["/app/solution.py"], "lint": {"status": "ok", "output": ""}}

{"output": "Starting Delayed Feedback Bandit Solution\n\nLoading data...\nModeling delay distribution...\nDelay model parameters:\n  mu: 2.4757\n  sigma: 0.8981\n  median_delay: 11.89 hours\n  p90_delay: 37.59 hours\n  conversion_rate: 0.0835\nDelay model saved to /app/output/delay_model.json\nCalculating delay-corrected arm statistics...\n\nArm Statistics:\n arm_id  estimated_conversion_rate  estimated_avg_value  total_impressions  total_conversions\n      0                   0.061670             2.588441               6767                417\n      1                   0.096645             1.878115               8453                817\n      2                   0.043777             4.470504               3187                139\n      3                   0.120303             1.587299               8584               1033\n      4                   0.077951             2.255103               5005                390\n      5                   0.070065             3.151900               4285                300\n      6                   0.088867             2.065204               4974                442\n      7                   0.061475             3.751117               2917                179\n      8                   0.113458             1.785200               3303                375\n      9                   0.032729             4.891463               2525                 82\nArm statistics saved to /app/output/arm_statistics.csv\nBuilding contextual models...\nContextual models built for all arms\nSelecting arms for test contexts...\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 318, in <module>\n    bandit.run()\n  File \"/app/solution.py\", line 312, in run\n    self.select_arms()\n  File \"/app/solution.py\", line 283, in select_arms\n    predictions = self.predict_arm_performance(context)\n                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution.py\", line 246, in predict_arm_performance\n    similarity_score += (context_val - non_conv_val) / (conv_val - non_conv_val + 1e-6)\n                         ~~~~~~~~~~~~^~~~~~~~~~~~~~\nTypeError: unsupported operand type(s) for -: 'int' and 'str'", "exit_code": 1, "error": null}

{"success": true, "diff": "--- a//app/solution.py\n+++ b//app/solution.py\n@@ -235,6 +235,7 @@\n             # Calculate similarity to converted/non-converted patterns\n             similarity_score = 0.0\n             \n+            # Only use numerical features for similarity calculation\n             for feature in ['hour_of_day', 'day_of_week', 'context_feature_1', 'context_feature_2']:\n                 if feature in model['converted_stats']:\n                     conv_val = model['converted_stats'][feature]\n@@ -242,10 +243,11 @@\n                     context_val = context[feature]\n                     \n                     # Weighted similarity: closer to converted pattern is better\n-                    if conv_val != non_conv_val:\n-                        similarity_score += (context_val - non_conv_val) / (conv_val - non_conv_val + 1e-6)\n-                    else:\n-                        similarity_score += 0.5  # Neutral similarity\n+                    if isinstance(conv_val, (int, float)) and isinstance(non_conv_val, (int, float)):\n+                        if conv_val != non_conv_val:\n+                            similarity_score += (context_val - non_conv_val) / (conv_val - non_conv_val + 1e-6)\n+                        else:\n+                            similarity_score += 0.5  # Neutral similarity\n             \n             # Normalize similarity\n             similarity_score = max(0, min(1, similarity_score / len(['hour_of_day', 'day_of_week', 'context_feature_1', 'context_feature_2'])))\n", "files_modified": ["/app/solution.py"], "lint": {"status": "ok", "output": ""}}

I need to handle the categorical features properly in the prediction function:

→ patch({"mode": "replace", "path": "/app/solution.py", "old_string": "            # Simple similarity-based prediction\n            # Calculate similarity to converted/non-converted patterns\n            similarity_score = 0.0\n            \n            for feature in ['hour_of_day', 'day_of_week', 'context_feature_1', 'context_feature_2']:\n                if feature in model['converted_stats']:\n      …)

{"output": "Starting Delayed Feedback Bandit Solution\n\nLoading data...\nModeling delay distribution...\nDelay model parameters:\n  mu: 2.4757\n  sigma: 0.8981\n  median_delay: 11.89 hours\n  p90_delay: 37.59 hours\n  conversion_rate: 0.0835\nDelay model saved to /app/output/delay_model.json\nCalculating delay-corrected arm statistics...\n\nArm Statistics:\n arm_id  estimated_conversion_rate  estimated_avg_value  total_impressions  total_conversions\n      0                   0.061670             2.588441               6767                417\n      1                   0.096645             1.878115               8453                817\n      2                   0.043777             4.470504               3187                139\n      3                   0.120303             1.587299               8584               1033\n      4                   0.077951             2.255103               5005                390\n      5                   0.070065             3.151900               4285                300\n      6                   0.088867             2.065204               4974                442\n      7                   0.061475             3.751117               2917                179\n      8                   0.113458             1.785200               3303                375\n      9                   0.032729             4.891463               2525                 82\nArm statistics saved to /app/output/arm_statistics.csv\nBuilding contextual models...\nContextual models built for all arms\nSelecting arms for test contexts...\nPredictions saved to /app/output/predictions.csv\nSelected arms distribution: {2: 927, 7: 933, 9: 140}\n\nSolution completed successfully!", "exit_code": 0, "error": null}

{"content": "     1|{\n     2|  \"distribution_type\": \"lognormal\",\n     3|  \"parameters\": {\n     4|    \"mu\": 2.4757300431035563,\n     5|    \"sigma\": 0.8980893607320689\n     6|  },\n     7|  \"median_delay_hours\": 11.89038443846117,\n     8|  \"p90_delay_hours\": 37.589372637220386,\n     9|  \"conversion_rate_estimate\": 0.08348\n    10|}", "total_lines": 9, "file_size": 246, "truncated": false, "is_binary": false, "is_image": false}

Great! The solution ran successfully. Let me verify the output files:

→ read_file({"path": "/app/output/delay_model.json"})

{"content": "     1|arm_id,estimated_conversion_rate,estimated_avg_value,total_impressions,total_conversions\n     2|0,0.061670109176748304,2.5884412470023985,6767,417\n     3|1,0.09664461247637052,1.8781150550795596,8453,817\n     4|2,0.043777360850531584,4.470503597122302,3187,139\n     5|3,0.12030250145433391,1.58729912875121,8584,1033\n     6|4,0.0779505582137161,2.255102564102564,5005,390\n     7|5,0.07006517690875233,3.1519,4285,300\n     8|6,0.08886659979939819,2.0652036199095023,4974,442\n     9|7,0.06147540983606557,3.7511173184357545,2917,179\n    10|8,0.11345805672902837,1.7852000000000001,3303,375\n    11|9,0.032728706624605676,4.8914634146341465,2525,82\n    12|", "total_lines": 11, "file_size": 575, "truncated": false, "is_binary": false, "is_image": false}

{"content": "     1|context_id,selected_arm,expected_reward,confidence_lower,confidence_upper\n     2|CTX_000000,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\n     3|CTX_000001,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\n     4|CTX_000002,2,1.1331541311036513,0.7932078917725558,1.4731003704347467\n     5|CTX_000003,9,1.0101210963535376,0.7070847674474763,1.313157425259599\n     6|CTX_000004,2,1.1331541311036513,0.7932078917725558,1.4731003704347467\n     7|CTX_000005,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\n     8|CTX_000006,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\n     9|CTX_000007,9,1.0101210963535376,0.7070847674474763,1.313157425259599\n    10|CTX_000008,2,1.1331541311036513,0.7932078917725558,1.4731003704347467\n    11|", "total_lines": 2001, "file_size": 139928, "truncated": true, "hint": "Use offset=11 to continue reading (showing 1-10 of 2001 lines)", "is_binary": false, "is_image": false}

{"output": "CTX_001995,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\nCTX_001996,2,1.1331541311036513,0.7932078917725558,1.4731003704347467\nCTX_001997,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\nCTX_001998,7,1.1224868153934842,0.7857407707754389,1.4592328600115294\nCTX_001999,2,1.1331541311036513,0.7932078917725558,1.4731003704347467", "exit_code": 0, "error": null}

Let me check the end of the predictions file to make sure all 2000 contexts are covered:

→ terminal({"command": "cd /app && tail -5 /app/output/predictions.csv"})