ACCURACY

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Take two dataframes with the true and predicted labels from a classification task, and indicates whether the prediction was correct or not. These dataframes should both be single columns. Params: true_label : optional str true label users can select from original data predicted_label : optional str resulting predicted label users can select Returns: out : DataFrame The input predictions dataframe, with an extra boolean column "prediction_correct".

Python Code

from typing import Optional

from flojoy import DataFrame, flojoy


@flojoy
def ACCURACY(
    true_data: DataFrame,
    predicted_data: DataFrame,
    true_label: Optional[str] = None,
    predicted_label: Optional[str] = None,
) -> DataFrame:
    """Take two dataframes with the true and predicted labels from a classification task, and indicates whether the prediction was correct or not.

    These dataframes should both be single columns.

    Parameters
    ----------
    true_label : optional str
        true label users can select from original data
    predicted_label : optional str
        resulting predicted label users can select

    Returns
    -------
    DataFrame
        The input predictions dataframe, with an extra boolean column "prediction_correct".
    """

    true_df = true_data.m
    predicted_df = predicted_data.m

    # if users prov
    if true_label:
        true_label = true_df[true_label]
    else:
        true_label = true_df.iloc[:, 0]

    if predicted_label:
        predicted_label = predicted_df[predicted_label]
    else:
        predicted_label = predicted_df.iloc[:, 0]

    predicted_df["prediction_correct"] = true_label == predicted_label

    return DataFrame(df=predicted_df)

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Example App

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In this example, the iris dataset is split into two parts, one for training and the other for testing. The labels from the test data are stripped using an EXTRACT_COLUMNS node, taking only the features of the data.

The true labels are also extracted with another EXTRACT_COLUMNS to be passed to the the ACCURACY node, along with the SUPPORT_VECTOR_MACHINE predictions.

In the output, we see that the SUPPORT_VECTOR_MACHINE has made correct predictions for all of the test data.