<jupyter_start><jupyter_text>Load Wine Dataset<jupyter_code>import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine
X, y = load_wine(return_X_y=True)
y = np.array(y).reshape(len(y), 1)<jupyter_output><empty_output><jupyter_text>Split Data Into Train/Test Sets<jupyter_code>X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.30)<jupyter_output><empty_output><jupyter_text>Train on CellularModel<jupyter_code>from CellularModel import CellularModel
model = CellularModel(fitness_func='accuracy',
                      N=150,
                      mutate_proba=0.0005,
                      min_depth=14,
                      max_depth=14,
                      max_epochs=1000,
                      auto_plot=True)
model.fit(X_train,y_train)<jupyter_output>Depth: 14<jupyter_text>Evaluate Model Score<jupyter_code>y_pred = model.predict(X_test)
from sklearn.metrics import f1_score
f1_score(y_test, y_pred, average='macro')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0933852140077821, 'fraction_numerical': 0.023346303501945526, 'mean_word_length': 4.068965517241379, 'pattern_counts': {'":': 0, '<': 16, '<?xml version=': 0, '>': 16, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '72321', 'license': 'MIT', 'n_tokens_mistral': 389, 'n_tokens_neox': 356, 'n_words': 56, 'path': 'tests-Copy3.ipynb', 'repo_name': 'nightvision04/cellular-learn', 'size': '1028'}