Using custom classifiers

The vanilla implementation of autoBOTLib uses the SGDClassifier class, suitable for fast exploration of a wide array of various models. However, should you wish to use your custom, sklearn-syntax compatible classifier, the following snippet is a good start.

import autoBOTLib
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import SGDClassifier

## Load example data frame
dataframe = pd.read_csv("../data/insults/train.tsv", sep="\t")
train_sequences = dataframe['text_a'].values.tolist()
train_targets = dataframe['label'].values

## The syntax for specifying a learner and the hyperparameter space!
## These are the hyperparameters to be explored for each representation.
classifier_hyperparameters = {
        "loss": ["hinge"],
        "penalty": ["elasticnet"],
        "alpha": [0.01, 0.001],
        "l1_ratio": [0, 0.001,1]
}

## This is the classifier compatible with the hyperparameters.
custom_classifier = SGDClassifier()

autoBOTLibObj = autoBOTLib.GAlearner(
        train_sequences,  # input sequences
        train_targets,  # target space
        time_constraint=0.1,  # time in hours
        num_cpu=4,  # number of CPUs to use
        task_name="example test",  # task identifier
        hof_size=3,  # size of the hall of fame
        top_k_importances=25,  # how many top features to output as final ranking
        memory_storage="../memory",
        representation_type="symbolic",
        learner = custom_classifier,
        learner_hyperparameters = classifier_hyperparameters)  # or neurosymbolic or neural

autoBOTLibObj.evolve(
        nind=10,  ## population size
        strategy="evolution",  ## optimization strategy
        crossover_proba=0.6,  ## crossover rate
        mutpb=0.4)  ## mutation rate