bandit-delayed-feedback

grader verdict: FAIL (exit 0)
reshold
ERROR ../tests/test_outputs.py::TestDelayModel::test_conversion_rate_error_threshold
FAILED ../tests/test_outputs.py::TestOutputFormat::test_predictions_file_exists
FAILED ../tests/test_outputs.py::TestOutputFormat::test_delay_model_file_exists
FAILED ../tests/test_outputs.py::TestOutputFormat::test_arm_statistics_columns
FAILED ../tests/test_outputs.py::TestArmStatistics::test_conversion_rate_rmse_threshold
==================== 4 failed, 2 passed, 14 errors in 1.94s ====================

You are an expert terminal agent. You MUST use the provided tools to complete tasks. Use the terminal tool to run shell commands, read_file to read files, write_file to write files, search_files to search, and patch to edit files. Do NOT write out solutions as text - execute them using the tools. Always start by exploring the environment with terminal commands.

# Multi-Armed Bandit with Delayed Feedback

You are a machine learning engineer at AdTech Corp, an online advertising platform. Your team 
manages a real-time ad selection system that chooses which advertisement to show to each user.
The challenge: **conversions (clicks, purchases) arrive with significant delays** - users may 
convert hours or even days after seeing an ad.

## BUSINESS CONTEXT

The platform serves 10 different ad campaigns (arms). Each impression generates revenue only 
if the user converts. The key challenge is that conversion feedback is **delayed**:
- Some users convert within minutes
- Most conversions happen within 1-24 hours  
- Some conversions take up to 72 hours
- After 72 hours, we assume no conversion will occur

This delay creates a critical problem: **recently shown ads appear to perform worse** because 
their conversions haven't arrived yet. Naive algorithms will under-explore promising new ads 
and over-exploit older ads with complete feedback.

Additionally, ad performance is **non-stationary** - conversion rates vary by:
- Hour of day (higher during business hours)
- Day of week (weekends differ from weekdays)
- User context features

## INPUT DATA

All data files are located in `/app/data/`.

### A. Historical Impressions: `impressions.csv`
Records of past ad impressions with 50,000 rows.

| Column | Type | Description |
|--------|------|-------------|
| `impression_id` | string | Unique identifier (IMP_XXXXXX) |
| `arm_id` | int | Ad campaign ID (0-9) |
| `timestamp` | int | Unix timestamp of impression |
| `hour_of_day` | int | Hour (0-23) |
| `day_of_week` | int | Day (0=Monday, 6=Sunday) |
| `user_segment` | string | User segment ("young", "middle", "senior") |
| `device_type` | string | Device ("mobile", "desktop", "tablet") |
| `context_feature_1` | float | Contextual signal 1 (normalized 0-1) |
| `context_feature_2` | float | Contextual signal 2 (normalized 0-1) |

### B. Conversion Events: `conversions.csv`
Records of conversions linked to impressions.

| Column | Type | Description |
|--------|------|-------------|
| `impression_id` | string | Links to impressions.csv |
| `conversion_timestamp` | int | Unix timestamp when conversion occurred |
| `delay_hours` | float | Hours between impression and conversion |
| `conversion_value` | float | Revenue from conversion ($0.5 - $10.0) |

**Note**: Not all impressions have conversions. An impression without a matching conversion 
record means either: (1) the user didn't convert, or (2) the conversion hasn't arrived yet 
(if the impression is recent).

### C. Arm Metadata: `arms_metadata.json`
Information about each ad campaign.

```json
{
  "arms": [
    {
      "arm_id": 0,
      "name": "Campaign A",
      "category": "electronics",
      "base_bid": 0.5
    },
    ...
  ]
}
```

### D. Test Contexts: `test_contexts.csv`
New contexts where you must select the best arm. Contains 2,000 decision points.

| Column | Type | Description |
|--------|------|-------------|
| `context_id` | string | Unique identifier (CTX_XXXXXX) |
| `timestamp` | int | Decision timestamp |
| `hour_of_day` | int | Hour (0-23) |
| `day_of_week` | int | Day (0=Monday, 6=Sunday) |
| `user_segment` | string | User segment |
| `device_type` | string | Device type |
| `context_feature_1` | float | Contextual signal 1 |
| `context_feature_2` | float | Contextual signal 2 |

## PROBLEM DEFINITION

### Task 1: Delay Distribution Modeling
Model the delay distribution for conversions. This is critical for:
- Estimating "pending" conversions for recent impressions
- Adjusting reward estimates to account for incomplete feedback

### Task 2: Arm Selection Policy
For each context in `test_contexts.csv`, select the best arm (ad campaign) to maximize 
expected conversion value. Your policy should:
- Use historical data to estimate arm performance
- Account for delayed feedback in reward estimation
- Consider contextual features for personalized selection
- Balance exploration vs ex
… (3618 more chars)

<think>Let me start by exploring the environment and understanding the data structure. I need to:

1. First, check what files exist in the data directory
2. Load and understand the data
3. Build a delay distribution model using log-normal distribution
4. Build an arm selection policy
5. Generate predictions forach test context

Let me start by exploring the environment.
</think>

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

{"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\ntotal 8\ndrwxr-xr-x 2 root root 4096 Mar  4 09:10 .\ndrwxr-xr-x 1 root root 4096 Mar  4 09:10 ..", "exit_code": 0, "error": null}

<think>Good, I can see the data files. Let me now explore them more to understand the structure.
</think>

terminal({"command": "head -5 /app/data/impressions.csv && head -5 /app/data/conversions.csv && head -5 /app/data/test_contexts.csv && cat /app/data/arms_metadata.json"})

{"output": "impression_id,arm_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\nIMP_000000,0,1709340758,9,5,young,mobile,0.7797,0.5969\nIMP_000001,6,1710354874,3,3,young,desktop,0.7081,0.0206\nIMP_000002,9,1710010543,3,6,young,desktop,0.6117,0.0071\nIMP_000003,3,1710416879,20,3,senior,mobile,0.7852,0.1997\nimpression_id,conversion_timestamp,delay_hours,conversion_value\nIMP_000010,1709305348,10.43,3.58\nIMP_000013,1709320719,21.49,1.07\nIMP_000022,1709843815,9.32,3.47\nIMP_000069,1710312110,4.57,2.48\ncontext_id,timestamp,hour_of_day,day_of_week,user_segment,device_type,context_feature_1,context_feature_2\nCTX_000000,1710307872,14,2,middle,mobile,0.747,0.0933\nCTX_000001,1710319811,17,2,senior,mobile,0.9059,0.1018\nCTX_000002,1710341834,23,2,senior,mobile,0.0783,0.8936\nCTX_000003,1710405538,17,3,middle,mobile,0.4505,0.7765\n{\n  \"arms\": [\n    {\n      \"arm_id\": 0,\n      \"name\": \"Campaign A\",\n      \"category\": \"electronics\",\n      \"base_bid\": 0.3\n    },\n    {\n      \"arm_id\": 1,\n      \"name\": \"Campaign B\",\n      \"category\": \"fashion\",\n      \"base_bid\": 0.4\n    },\n    {\n      \"arm_id\": 2,\n      \"name\": \"Campaign C\",\n      \"category\": \"home\",\n      \"base_bid\": 0.5\n    },\n    {\n      \"arm_id\": 3,\n      \"name\": \"Campaign D\",\n      \"category\": \"sports\",\n      \"base_bid\": 0.6\n    },\n    {\n      \"arm_id\": 4,\n      \"name\": \"Campaign E\",\n      \"category\": \"beauty\",\n      \"base_bid\": 0.7\n    },\n    {\n      \"arm_id\": 5,\n      \"name\": \"Campaign F\",\n      \"category\": \"food\",\n      \"base_bid\": 0.8\n    },\n    {\n      \"arm_id\": 6,\n      \"name\": \"Campaign G\",\n      \"category\": \"travel\",\n      \"base_bid\": 0.9\n    },\n    {\n      \"arm_id\": 7,\n      \"name\": \"Campaign H\",\n      \"category\": \"finance\",\n      \"base_bid\": 1.0\n    },\n    {\n      \"arm_id\": 8,\n      \"name\": \"Campaign I\",\n      \"category\": \"entertainment\",\n      \"base_bid\": 1.1\n    },\n    {\n      \"arm_id\": 9,\n      \"name\": \"Campaign J\",\n      \"category\": \"health\",\n      \"base_bid\": 1.2\n    }\n  ]\n}", "exit_code": 0, "error": null}

<think>Good, now I have a better understanding of the data. Let me write a Python script to:
1. Load all the data
2. Model the delay distribution using log-normal
3. Build an arm selection policy
4. Generate predictions

Let me write a comprehensive Python script to do all this.
</think>

write_file({"path": "/app/solution.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nfrom collections import defaultdict\n\n# Load all data\nprint(\"Loading data...\")\ndf_impressions = pd.read_csv('/app/data/impressions.csv')\ndf_conversions = pd.read_csv('/app/data/conversions.csv')\ndf_test_contexts = pd.read_csv('/app/data/test_contexts.csv')\nwith open('/app/data/arms_metadata.json'…)

{"bytes_written": 9460, "dirs_created": true}

<think>I wrote a Python script but it has some issues. Let me fix it and run it properly. The script has some bugs:

1. The context_rewards dictionary is not initialized properly
2. The loop for context key is wrong
3. Need to properly initialize the dictionary

Let me rewrite the script more carefully.
</think>

write_file({"path": "/app/solution.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nfrom collections import defaultdict\n\nprint(\"Loading data...\")\ndf_impressions = pd.read_csv('/app/data/impressions.csv')\ndf_conversions = pd.read_csv('/app/data/conversions.csv')\ndf_test_contexts = pd.read_csv('/app/data/test_contexts.csv')\nwith open('/app/data/arms_metadata.json', 'r') as f:\n   …)

{"bytes_written": 5491, "dirs_created": true}

<think>I need to run this script. Let me execute it.
</think>

terminal({"command": "cd /app && python solution.py", "timeout": 120})

{"output": "Loading data...\nImpressions: 50000 rows\nConversions: 4174 rows\nTest contexts: 2000 rows\n\n=== Task 1: Delay Distribution Modeling ===\n\nLog-normal parameters (MLE):\n  mu: 2.475730\n  sigma: 0.897982\n  median_delay: 11.8904\n\n=== Task 2: Arm Selection Policy ===\n\nBuilding arm statistics...\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/generic.py\", line 576, in _get_axis_number\n    return cls._AXIS_TO_AXIS_NUMBER[axis]\n           ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^\nKeyError: 0.001\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 59, in <module>\n    prob_delay = norm.cdf((np.log(np.max(hours_since, 0.001)) - mu) / sigma)\n                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 3199, in max\n    return _wrapreduction(a, np.maximum, 'max', axis, None, out,\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/numpy/_core/fromnumeric.py\", line 84, in _wrapreduction\n    return reduction(axis=axis, out=out, **passkwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/series.py\", line 6517, in max\n    return NDFrame.max(self, axis, skipna, numeric_only, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/generic.py\", line 12404, in max\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 6439, in _reduce\n    self._get_axis_number(axis)\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/generic.py\", line 578, in _get_axis_number\n    raise ValueError(f\"No axis named {axis} for object type {cls.__name__}\")\nValueError: No axis named 0.001 for object type Series", "exit_code": 1, "error": null}

<think>There's an issue with using np.max on a pandas Series. Let me fix the script.
</think>

write_file({"path": "/app/solution.py", "content": "import pandas as pd\nimport numpy as np\nimport json\n\nprint(\"Loading data...\")\ndf_impressions = pd.read_csv('/app/data/impressions.csv')\ndf_conversions = pd.read_csv('/app/data/conversions.csv')\ndf_test_contexts = pd.read_csv('/app/data/test_contexts.csv')\n\nprint(f\"Impressions: {len(df_impressions)} rows\")\nprint(f\"Conversions: {len(df_conversio…)

{"bytes_written": 5435, "dirs_created": true}

<think>Let me run the script again.
</think>

terminal({"command": "cd /app && python solution.py", "timeout": 120})

{"output": "Loading data...\nImpressions: 50000 rows\nConversions: 4174 rows\nTest contexts: 2000 rows\n\n=== Task 1: Delay Distribution Modeling ===\n\nLog-normal parameters (MLE):\n  mu: 2.475730\n  sigma: 0.897982\n  median_delay: 11.8904\n\n=== Task 2: Arm Selection Policy ===\n\nBuilding arm statistics...\n  Arm 0: rate=0.9473, value=2.5884, impressions=6767, conversions=417\n  Arm 1: rate=0.9492, value=1.8781, impressions=8453, conversions=817\n  Arm 2: rate=0.9450, value=4.4705, impressions=3187, conversions=139\n  Arm 3: rate=0.9466, value=1.5873, impressions=8584, conversions=1033\n  Arm 4: rate=0.9421, value=2.2551, impressions=5005, conversions=390\n  Arm 5: rate=0.9511, value=3.1519, impressions=4285, conversions=300\n  Arm 6: rate=0.9511, value=2.0652, impressions=4974, conversions=442\n  Arm 7: rate=0.9500, value=3.7511, impressions=2917, conversions=179\n  Arm 8: rate=0.9403, value=1.7852, impressions=3303, conversions=375\n  Arm 9: rate=0.9472, value=4.8915, impressions=2525, conversions=82\nSaved arm_statistics.csv\n\n=== Task 3: Generate predictions ===\nBuilding contextual model...\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 87, in <module>\n    context_rewards = defaultdict(dict)\n                      ^^^^^^^^^^^\nNameError: name 'defaultdict' is not defined", "exit_code": 1, "error": null}

<think>I need to import defaultdict from collections. Let me fix this.
</think>

write_file({"path": "/app/solution.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nfrom collections import defaultdict\n\nprint(\"Loading data...\")\ndf_impressions = pd.read_csv('/app/data/impressions.csv')\ndf_conversions = pd.read_csv('/app/data/conversions.csv')\ndf_test_contexts = pd.read_csv('/app/data/test_contexts.csv')\n\nprint(f\"Impressions: {len(df_impressions)} rows\")\npr…)

{"bytes_written": 5471, "dirs_created": true}

<think>Let me run the script again.
</think>

terminal({"command": "cd /app && python solution.py", "timeout": 120})

{"output": "Loading data...\nImpressions: 50000 rows\nConversions: 4174 rows\nTest contexts: 2000 rows\n\n=== Task 1: Delay Distribution Modeling ===\n\nLog-normal parameters (MLE):\n  mu: 2.475730\n  sigma: 0.897982\n  median_delay: 11.8904\n\n=== Task 2: Arm Selection Policy ===\n\nBuilding arm statistics...\n  Arm 0: rate=0.9473, value=2.5884, impressions=6767, conversions=417\n  Arm 1: rate=0.9492, value=1.8781, impressions=8453, conversions=817\n  Arm 2: rate=0.9450, value=4.4705, impressions=3187, conversions=139\n  Arm 3: rate=0.9466, value=1.5873, impressions=8584, conversions=1033\n  Arm 4: rate=0.9421, value=2.2551, impressions=5005, conversions=390\n  Arm 5: rate=0.9511, value=3.1519, impressions=4285, conversions=300\n  Arm 6: rate=0.9511, value=2.0652, impressions=4974, conversions=442\n  Arm 7: rate=0.9500, value=3.7511, impressions=2917, conversions=179\n  Arm 8: rate=0.9403, value=1.7852, impressions=3303, conversions=375\n  Arm 9: rate=0.9472, value=4.8915, impressions=2525, conversions=82\nSaved arm_statistics.csv\n\n=== Task 3: Generate predictions ===\nBuilding contextual model...\nGenerating predictions...\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 110, in <module>\n    row['hour_of_day'] + '_' +\n    ~~~~~~~~~~~~~~~~~~~^~~~~\nTypeError: unsupported operand type(s) for +: 'int' and 'str'", "exit_code": 1, "error": null}

<think>There's an issue with string concatenation. Let me fix the script to properly handle the context key construction.
</think>

write_file({"path": "/app/solution.py", "content": "import pandas as pd\nimport numpy as np\nimport json\nfrom collections import defaultdict\n\nprint(\"Loading data...\")\ndf_impressions = pd.read_csv('/app/data/impressions.csv')\ndf_conversions = pd.read_csv('/app/data/conversions.csv')\ndf_test_contexts = pd.read_csv('/app/data/test_contexts.csv')\n\nprint(f\"Impressions: {len(df_impressions)} rows\")\npr…)

{"bytes_written": 5487, "dirs_created": true}

<think>Let me run the script again.
</think>

terminal({"command": "cd /app && python solution.py", "timeout": 120})

{"output": "Loading data...\nImpressions: 50000 rows\nConversions: 4174 rows\nTest contexts: 2000 rows\n\n=== Task 1: Delay Distribution Modeling ===\n\nLog-normal parameters (MLE):\n  mu: 2.475730\n  sigma: 0.897982\n  median_delay: 11.8904\n\n=== Task 2: Arm Selection Policy ===\n\nBuilding arm statistics...\n  Arm 0: rate=0.9473, value=2.5884, impressions=6767, conversions=417\n  Arm 1: rate=0.9492, value=1.8781, impressions=8453, conversions=817\n  Arm 2: rate=0.9450, value=4.4705, impressions=3187, conversions=139\n  Arm 3: rate=0.9466, value=1.5873, impressions=8584, conversions=1033\n  Arm 4: rate=0.9421, value=2.2551, impressions=5005, conversions=390\n  Arm 5: rate=0.9511, value=3.1519, impressions=4285, conversions=300\n  Arm 6: rate=0.9511, value=2.0652, impressions=4974, conversions=442\n  Arm 7: rate=0.9500, value=3.7511, impressions=2917, conversions=179\n  Arm 8: rate=0.9403, value=1.7852, impressions=3303, conversions=375\n  Arm 9: rate=0.9472, value=4.8915, impressions=2525, conversions=82\nSaved arm_statistics.csv\n\n=== Task 3: Generate predictions ===\nBuilding contextual model...\nTraceback (most recent call last):\n  File \"/app/solution.py\", line 97, in <module>\n    grouped = arm_df.groupby(context_key)['reward'].agg(func='mean')\n              ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/frame.py\", line 9183, in groupby\n    return DataFrameGroupBy(\n           ^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/groupby/groupby.py\", line 1329, in __init__\n    grouper, exclusions, obj = get_grouper(\n                               ^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/site-packages/pandas/core/groupby/grouper.py\", line 1043, in get_grouper\n    raise KeyError(gpr)\nKeyError: '0         9\\n9         4\\n22       20\\n27       14\\n43        1\\n         ..\\n49952     8\\n49962    15\\n49977     2\\n49989     6\\n49990     6\\nName: hour_of_day, Length: 6767, dtype: int64_0        5\\n9        3\\n22       3\\n27       4\\n43       0\\n        ..\\n49952    3\\n49962    1\\n49977    4\\n49989    3\\n49990    6\\nName: day_of_week, Length: 6767, dtype: int64_0         young\\n9        middle\\n22        young\\n27       senior\\n43       middle\\n          ...  \\n49952     young\\n49962    senior\\n49977     young\\n49989     young\\n49990    senior\\nName: user_segment, Length: 6767, dtype: object_0         mobile\\n9         tablet\\n22        tablet\\n27        mobile\\n43       desktop\\n          ...   \\n49952    desktop\\n49962     mobile\\n49977     mobile\\n49989     mobile\\n49990     mobile\\nName: device_type, Length: 6767, dtype: object'", "exit_code": 1, "error": null}