mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 07:57:44 +00:00
Xu Yang
68d47e1f1f
* Init todo * Evaluation & dataset * Generate new data * dataset generation * add the result * Analysis * Factor update * Updates * Reformat analysis.py * CI fix * Revised Preprocessing & Supported Random Forest * Revised to support three models with feature * Further revised prompts * Slight Revision * docs: update contributors (#230) * Revised to support three models with feature * Further revised prompts * Slight Revision * feat: kaggle model and feature (#238) * update first version code * make hypothesis_gen and experiment_builder fit for both feature and model * feat: continue kaggle feature and model coder (#239) * use qlib docker to run qlib models * feature coder ready * model coder ready * fix CI * finish the first round of runner (#240) * Optimized the factor scenario and added the front-end. * fix a small bug * fix a typo * update the kaggle scenario * delete model_template folder * use experiment to run data preprocess script * add source data to scenarios * minor fix * minor bug fix * train.py debug * fixed a bug in train.py and added some TODOs * For Debugging * fix two small bugs in based_exp * fix some bugs * update preprocess * fix a bug in preprocess * fix a bug in train.py * reformat * Follow-up * fix a bug in train.py * fix a bug in workspace * fix a bug in feature duplication * fix a bug in feedback * fix a bug in preprocessed data * fix a bug om feature engineering * fix a ci error * Debugged & Connected * Fixed error on feedback & added other fixes * fix CI errors * fix a CI bug * fix: fix_dotenv_error (#257) * fix_dotenv_error * format with isort * Update rdagent/app/cli.py --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * chore(main): release 0.2.1 (#249) Release-As: 0.2.1 * init a scenario for kaggle feature engineering * delete error codes * Delete rdagent/app/kaggle_feature/conf.py --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: Taozhi Wang <taozhi.mark.wang@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: cyncyw <47289405+taozhiwang@users.noreply.github.com> Co-authored-by: Xisen-Wang <xisen_application@163.com> Co-authored-by: Haotian Chen <113661982+Hytn@users.noreply.github.com> Co-authored-by: WinstonLiye <1957922024@qq.com> Co-authored-by: WinstonLiyt <104308117+WinstonLiyt@users.noreply.github.com> Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com>
21 lines
746 B
Plaintext
21 lines
746 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, select
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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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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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