## A simple example showcasing the minimal usecase of autoBOTLib on an insults classification data.

import autoBOTLib
import pandas as pd
from cluster_utils import output_classification_results

def run():
    ## Load example data frame
    dataframe = pd.read_csv("../data/insults/train.tsv", sep="\t").iloc[:200]
    train_sequences = dataframe['text_a']
    train_targets = dataframe['label']
    reptype="symbolic"
    autoBOTLibObj = autoBOTLib.GAlearner(
        train_sequences,
        train_targets,
        representation_type=
        reptype,  ## See the documentation for all possible representation types.
        n_fold_cv=3,
        memory_storage="memory",
        verbose=0,
        sparsity=0.1,
        visualize_progress=True, ## Stores progress as PROGRESS_{generation}.pdf file
        upsample=
        False,  ## Suitable for imbalanced data - randomized upsampling tends to help.
        time_constraint=0.1).evolve(
            strategy="evolution"
        )  ## strategy = "direct-learning" trains a single learner.

    # Store
    autoBOTLib.store_autobot_model(autoBOTLibObj,
                                      f"model.pickle")

    # Load
    autoBOTObj = autoBOTLib.load_autobot_model(
        f"model.pickle")

    # Predict
    dataframe2 = pd.read_csv("../data/insults/test.tsv", sep="\t")
    test_sequences = dataframe2['text_a']
    predictions = autoBOTLibObj.predict(test_sequences)
    prob_predictions = autoBOTLibObj.predict_proba(test_sequences)
    autoBOTLibObj.generate_report(output_folder="./report/",
                                  job_id="REPORTNEW")
    jid = "Some random job ID"
    test_classes = dataframe2['label'].values.tolist()
    output_classification_results(
        predictions,
        test_classes,
        f"./predictions/test_{jid}_autobot{reptype}_report.json",
        model_spec={})


if __name__ == "__main__":
    run()

## A simple example showcasing the minimal usecase of autoBOTLib on an insults classification data.

import autoBOTLib
import pandas as pd
from cluster_utils import output_classification_results

def run():
    ## Load example data frame
    dataframe = pd.read_csv("../data/insults/train.tsv", sep="\t").iloc[:200]
    train_sequences = dataframe['text_a']
    train_targets = dataframe['label']
    reptype="symbolic"
    autoBOTLibObj = autoBOTLib.GAlearner(
        train_sequences,
        train_targets,
        representation_type=
        reptype,  ## See the documentation for all possible representation types.
        n_fold_cv=3,
        memory_storage="memory",
        verbose=0,
        sparsity=0.1,
        visualize_progress=True, ## Stores progress as PROGRESS_{generation}.pdf file
        upsample=
        False,  ## Suitable for imbalanced data - randomized upsampling tends to help.
        time_constraint=0.1).evolve(
            strategy="evolution"
        )  ## strategy = "direct-learning" trains a single learner.

    # Store
    autoBOTLib.store_autobot_model(autoBOTLibObj,
                                      f"model.pickle")

    # Load
    autoBOTObj = autoBOTLib.load_autobot_model(
        f"model.pickle")

    # Predict
    dataframe2 = pd.read_csv("../data/insults/test.tsv", sep="\t")
    test_sequences = dataframe2['text_a']
    predictions = autoBOTLibObj.predict(test_sequences)
    prob_predictions = autoBOTLibObj.predict_proba(test_sequences)
    autoBOTLibObj.generate_report(output_folder="./report/",
                                  job_id="REPORTNEW")
    jid = "Some random job ID"
    test_classes = dataframe2['label'].values.tolist()
    output_classification_results(
        predictions,
        test_classes,
        f"./predictions/test_{jid}_autobot{reptype}_report.json",
        model_spec={})


if __name__ == "__main__":
    run()

## A simple example showcasing the minimal usecase of autoBOTLib on an insults classification data. ## Load example data frame ## See the documentation for all possible representation types. ## Stores progress as PROGRESS_{generation}.pdf file ## Suitable for imbalanced data - randomized upsampling tends to help. ## strategy = "direct-learning" trains a single learner. # Store # Load # Predict