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selector

data_selector(data_set)

Selects the river data set to be used.

Parameters:

Name Type Description Default
data_set str

Name of the data set to be used. Can be one of the following: - “Bananas” - “CreditCard” - “Elec2” - “Higgs” - “HTTP” - “Phishing” - “AirlinePassengers” - “Bikes” - “ChickWeights” - “Taxis” - “TrumpApproval” - “WaterFlow” - “WebTraffic”

 required

Returns:

Name Type Description
dataset object

Data set to use. This is a dataset object from the river library.
n_samples int

Number of samples in the data set.

Examples:

>>> from spotpython.data.selector import data_selector
    dataset, n_samples = data_selector("Phishing")
Notes
  • The Higgs data set is very large and may take a long time to load.
Source code in spotriver/data/selector.py 
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def data_selector(
    data_set,
) -> tuple:
    """
    Selects the river data set to be used.

    Args:
        data_set (str):
            Name of the data set to be used. Can be one of the following:
            - "Bananas"
            - "CreditCard"
            - "Elec2"
            - "Higgs"
            - "HTTP"
            - "Phishing"
            - "AirlinePassengers"
            - "Bikes"
            - "ChickWeights"
            - "Taxis"
            - "TrumpApproval"
            - "WaterFlow"
            - "WebTraffic"

    Returns:
        dataset (object):
            Data set to use. This is a dataset object from the river library.
        n_samples (int):
            Number of samples in the data set.

    Examples:
        >>> from spotpython.data.selector import data_selector
            dataset, n_samples = data_selector("Phishing")

    Notes:
        - The Higgs data set is very large and may take a long time to load.

    """
    # TODO: Check and update the number of samples for each data set.
    dataset = None
    if data_set == "Bananas":
        dataset = datasets.Bananas()
        n_samples = 5300
    elif data_set == "CreditCard":
        dataset = datasets.CreditCard()
        n_samples = 284_807
    elif data_set == "Elec2":
        dataset = datasets.Elec2()
        n_samples = 45_312
    elif data_set == "Higgs":
        dataset = datasets.Higgs()
        n_samples = 11_000_000
    elif data_set == "HTTP":
        dataset = datasets.HTTP()
        n_samples = 567_498
    elif data_set == "Phishing":
        dataset = datasets.Phishing()
        n_samples = 1250
    elif data_set == "AirlinePassengers":
        dataset = datasets.AirlinePassengers()
        n_samples = 144
    elif data_set == "Bikes":
        dataset = datasets.Bikes()
        n_samples = 182470
    elif data_set == "ChickWeights":
        dataset = datasets.ChickWeights()
        n_samples = 578
    elif data_set == "Taxis":
        dataset = datasets.Taxis()
        n_samples = 1458644
    elif data_set == "TrumpApproval":
        dataset = datasets.TrumpApproval()
        n_samples = 1_000
    elif data_set == "WaterFlow":
        dataset = datasets.WaterFlow()
        n_samples = 1_000
    else:
        raise ValueError(f"Data set '{data_set}' not found.")
    return dataset, n_samples

get_river_dataset_from_name(data_set_name, n_total=None, river_datasets=None)

Converts a data set name to a pandas DataFrame.

Parameters:

Name Type Description Default
data_set_name str

The name of the data set.

 required
n_total int

The number of samples to be used from the data set. If n_total is None or inf, the full data set is used. Defaults to None.

 None
river_datasets list

A list of the available river data sets.

 None

Returns:

Name Type Description
dataset DataFrame

The data set as a pandas DataFrame.
n_samples int

The number of samples in the data set.
Source code in spotriver/data/selector.py 
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def get_river_dataset_from_name(
    data_set_name,
    n_total=None,
    river_datasets=None,
) -> tuple:
    """Converts a data set name to a pandas DataFrame.

    Args:
        data_set_name (str):
            The name of the data set.
        n_total (int):
            The number of samples to be used from the data set.
            If n_total is None or inf, the full data set is used.
            Defaults to None.
        river_datasets (list):
            A list of the available river data sets.

    Returns:
        dataset (pd.DataFrame):
            The data set as a pandas DataFrame.
        n_samples (int):
            The number of samples in the data set.
    """
    if data_set_name in river_datasets:
        dataset, n_samples = data_selector(
            data_set=data_set_name,
        )
        dataset = convert_to_df(dataset, target_column="y", n_total=n_total)
        return dataset, n_samples
    else:
        print(f"Data set '{data_set_name}' not found in river datasets.")
        return None, None

get_train_test_from_data_set(df, n_total, test_size, target_column='y')

Converts a data set to a data frame with target column and splits it into training and test sets.

Parameters:

Name Type Description Default
df DataFrame

data set to be used.

 required
n_total int

total number of samples to be used in the data set.

 required
test_size float

percentage of the data set to be used as test set.

 required
target_column str

name of the target column. Defaults to “y”.

 'y'

Returns:

Name Type Description
train tuple

training data set.
test tuple

test data set.
n_samples tuple

total number of samples (train and test) in the data set.
Source code in spotriver/data/selector.py 
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def get_train_test_from_data_set(df, n_total, test_size, target_column="y") -> tuple:
    """Converts a data set to a data frame with target column
        and splits it into training and test sets.

    Args:
        df (DataFrame):
            data set to be used.
        n_total (int):
            total number of samples to be used in the data set.
        test_size (float):
            percentage of the data set to be used as test set.
        target_column (str, optional):
            name of the target column. Defaults to "y".

    Returns:
        train:
            training data set.
        test:
            test data set.
        n_samples:
            total number of samples (train and test) in the data set.

    """
    n_features = len(df.columns) - 1
    df.columns = [f"x{i}" for i in range(1, n_features + 1)] + ["y"]
    df["y"] = df["y"].astype(int)
    # update n_samples to the actual number of samples in the data set,
    # because n_total might be smaller than n_samples which results in a smaller data set:
    test_size = float(test_size)
    n_samples = len(df)
    n_train = int((1.0 - test_size) * n_samples)
    train = df[:n_train]
    test = df[n_train:]
    return train, test, n_samples