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https://github.com/NicolasBohn/NexQuant.git
synced 2026-08-01 17:37:43 +00:00
update all (#530)
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@@ -58,12 +58,22 @@ class DataLoaderCoSTEEREvaluator(CoSTEEREvaluator):
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implementation.inject_files(**{fname: test_code})
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stdout = implementation.execute(env=de, entry=f"python {fname}")
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if "main.py" in implementation.file_dict:
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workflow_stdout = implementation.execute(env=de, entry="python main.py")
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else:
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workflow_stdout = None
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system_prompt = T(".prompts:data_loader_eval.system").r(
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task_desc=target_task.get_task_information(),
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test_code=test_code,
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code=implementation.file_dict["load_data.py"],
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workflow_stdout=workflow_stdout,
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workflow_code=implementation.all_codes,
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)
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user_prompt = T(".prompts:data_loader_eval.user").r(
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stdout=stdout,
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workflow_stdout=workflow_stdout,
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)
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user_prompt = T(".prompts:data_loader_eval.user").r(stdout=stdout)
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resp = APIBackend().build_messages_and_create_chat_completion(user_prompt, system_prompt, json_mode=True)
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return DataLoaderEvalFeedback(**json.loads(resp))
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+33
-6
@@ -8,18 +8,45 @@ Please make sure the stdout is rich enough to support informative feedback
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"""
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import pickle
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import pandas as pd
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from load_data import load_data
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X, y, X_test, test_ids = load_data()
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def get_length(data):
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return len(data) if isinstance(data, list) else data.shape[0]
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assert get_length(X_test) == get_length(test_ids), (
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f"Mismatch in length of test images and test IDs: X_test ({get_length(X_test)}) and test_ids ({get_length(test_ids)})"
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)
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assert get_length(X) == get_length(y), (
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f"Mismatch in length of training images and labels: X ({get_length(X)}) and y ({get_length(y)})"
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)
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def get_width(data):
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return 1 if isinstance(data, list) else data.shape[1:]
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def get_column_list(data):
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return data.columns.tolist() if isinstance(data, pd.DataFrame) else None
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assert get_length(X_test) == get_length(
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test_ids
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), f"Mismatch in length of test images and test IDs: X_test ({get_length(X_test)}) and test_ids ({get_length(test_ids)})"
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assert get_length(X) == get_length(
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y
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), f"Mismatch in length of training images and labels: X ({get_length(X)}) and y ({get_length(y)})"
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assert get_length(X) != 0, f"Training data is empty."
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assert get_length(y) != 0, f"Training labels are empty."
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assert get_length(X_test) != 0, f"Test data is empty."
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assert get_width(X) == get_width(
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X_test
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), "Mismatch in width of training and test data. Width means the number of features."
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if isinstance(X, pd.DataFrame) and isinstance(X_test, pd.DataFrame):
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assert get_column_list(X) == get_column_list(X_test), "Mismatch in column names of training and test data."
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assert get_width(X) == get_width(
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X_test
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), "Mismatch in width of training and test data. Width means the number of features."
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print("Data loader test passed successfully. Length of test images matches length of test IDs.")
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@@ -373,6 +373,13 @@ data_loader_eval:
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{{test_code}}
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```
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{% if workflow_stdout is not none %}
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Your feature engineering code is also part of the whole workflow, the user also tested the whole workflow and provided you the stdout.
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The whole workflow code is:
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{{workflow_code}}
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Please consider both stdout and approve the code when both the feature engineering test and the whole workflow test pass.
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{% endif %}
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You'll be given the stdout of your testing scripts.
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Please respond with your feedback in the following JSON format and order
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```json
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@@ -384,6 +391,9 @@ data_loader_eval:
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}
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```
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user: |-
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```
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{{stdout}}
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```
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Data loader test stdout:
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{{stdout}}
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{% if workflow_stdout is not none %}
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Whole workflow test stdout:
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{{workflow_stdout}}
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{% endif %}
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