Restore compatibility with modern Python, pandas, and TensorFlow#43
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victalejo wants to merge 3 commits into
Open
Restore compatibility with modern Python, pandas, and TensorFlow#43victalejo wants to merge 3 commits into
victalejo wants to merge 3 commits into
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Pinned versions were ~5 years old and the project no longer installs or
runs on a fresh environment:
- requirements.txt: switch git:// (deprecated by GitHub since 2022) to
https://, relax pins to numpy>=1.23, pandas>=2.0, tensorflow>=2.13,
scikit-learn>=1.3 so installs succeed on Python 3.10+
- iq.py: replace DataFrame.append() (removed in pandas 2.0) with
pd.concat() in get_all_candles and fast_data
- training.py: drop("future", 1) -> drop("future", axis=1) for pandas
2.x; Adam(lr=, decay=) -> Adam(learning_rate=) (lr kwarg removed,
decay no longer a constructor arg in Keras 3); val_acc -> val_accuracy
so the ModelCheckpoint actually saves the best epoch
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ModelCheckpoint in Keras 3 (bundled with TF 2.16+) rejects custom
extensions like .model and requires .keras (whole model) or .weights.h5
(weights only). The previous suffix would have aborted training as soon
as val_accuracy improved.
- training.py:220: "models/{}.model" -> "models/{}.keras"
- testing.py:125, 139: train_data() + '.model' -> + '.keras'
Validated end-to-end with TF 2.16.2: ModelCheckpoint saves on
val_accuracy improvement, load_model() reloads the file, and the
restored model produces valid softmax predictions (339 554 params
preserved).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
fix: restore compatibility with modern Python/pandas/TensorFlow
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This pull request updates data handling and model training code to improve compatibility with recent versions of pandas and TensorFlow/Keras, and standardizes model file naming conventions. The main changes focus on replacing deprecated methods, updating optimizer parameters, and ensuring consistency in model file extensions.
Pandas and Data Handling Updates:
DataFrame.append()method withpd.concat()in both theget_data_neededandfast_datafunctions iniq.py, ensuring compatibility with newer pandas versions. [1] [2]df.drop()call inpreprocess_dfintraining.pyto use theaxis=1keyword argument instead of a positional argument, aligning with current pandas best practices.TensorFlow/Keras and Model Handling Updates:
train_data(intraining.py) to use thelearning_rateparameter instead of the deprecatedlr, and removed the unuseddecayparameter.ModelCheckpointcallback intrain_datato save models with a.kerasextension and monitorval_accuracyinstead of the deprecatedval_acc.testing.pyby replacing.modelwith.keraswhen loading models, ensuring consistency with the new saving convention. [1] [2]