mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
90d9cdd0e9
* udpate plot * log and reduce token * trace tag * add simple_background parameter to get_scenario_all_desc * update trace * update first version code * chat model map * add annotation for stack index * add annotation * reformatted by black * several update on kaggle scenarios * update some new change * fix CI * fix CI * fix a bug * fix bugs in graph RAG --------- Co-authored-by: Tim <illking@foxmail.com>
25 lines
868 B
Plaintext
25 lines
868 B
Plaintext
import os
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import pickle
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import numpy as np
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import pandas as pd
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import torch
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from model import fit, predict
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train_X = pd.DataFrame(np.random.randn(8, 30), columns=[f"{i}" for i in range(30)])
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train_y = pd.Series(np.random.randint(0, 2, 8))
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valid_X = pd.DataFrame(np.random.randn(8, 30), columns=[f"{i}" for i in range(30)])
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valid_y = pd.Series(np.random.randint(0, 2, 8))
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model = fit(train_X, train_y, valid_X, valid_y)
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execution_model_output = predict(model, valid_X)
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if isinstance(execution_model_output, torch.Tensor):
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execution_model_output = execution_model_output.cpu().detach().numpy()
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execution_feedback_str = f"Execution successful, output numpy ndarray shape: {execution_model_output.shape}"
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pickle.dump(execution_model_output, open("execution_model_output.pkl", "wb"))
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pickle.dump(execution_feedback_str, open("execution_feedback_str.pkl", "wb"))
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