<jupyter_start><jupyter_text>Now, make the training data for the GBDTs<jupyter_code>FEATURES = ['max_[W]', 'max_[Tr]', 'max_[Ta]', 'max_[R]', '[W]', '[Tr]', '[Ta]', '[R]', 'length']
df_full_factorial_feat = pd.read_csv(os.path.join(DATADIR, 'new_features_full_random.csv'))[FEATURES].values
a2 = pd.read_csv(os.path.join(DATADIR, 'b1-b21_random_virial_large_new.csv'))['A2_normalized'].values
deltaGMax = pd.read_csv(os.path.join(DATADIR, 'b1-b21_random_virial_large_new.csv'))['A2_normalized'].values  # pylint:disable=unused-variable
gibbs = pd.read_csv(os.path.join(DATADIR, 'b1-b21_random_deltaG.csv'))['deltaGmin'].values * (-1)
gibbs_max = pd.read_csv(os.path.join(DATADIR, 'b1-b21_random_virial_large_new.csv'))['deltaGmax'].values
force_max = pd.read_csv(os.path.join(DATADIR, 'b1-b21_random_virial_large_fit2.csv'))['F_repel_max'].values  # pylint:disable=unused-variable
rg = pd.read_csv(os.path.join(DATADIR, 'rg_results.csv'))['Rg'].values
y = np.hstack([rg.reshape(-1, 1), gibbs.reshape(-1, 1), gibbs_max.reshape(-1, 1)])
assert len(df_full_factorial_feat) == len(a2) == len(gibbs) == len(y)

feat_scaler = StandardScaler()
X_new = feat_scaler.fit_transform(df_full_factorial_feat)

X_new.shape
predictions = np.hstack([
    means0.flatten().reshape(-1,1), 
    means1.flatten().reshape(-1,1),
    means2.flatten().reshape(-1,1)
])
predictions
np.save('../work/X_train_GBDT.npy', X_new)
np.save('../work/y_train_GBDT.npy', predictions)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1955345060893099, 'fraction_numerical': 0.02435723951285521, 'mean_word_length': 7.308988764044944, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '26235', 'license': 'Apache-2.0', 'n_tokens_mistral': 649, 'n_tokens_neox': 635, 'n_words': 68, 'path': 'notebooks/test_gpr_predictions.ipynb', 'repo_name': 'byooooo/dispersant_screening_PAL', 'size': '1478'}