data_conversion

compare_two_tree_models(model1, model2, headers=['Parameter', 'Default', 'Spot'])

Compares two tree models and returns a table of the differences. Args: model1 (Pipeline): A river model pipeline. model2 (Pipeline): A river model pipeline. Returns: (str): A table of the differences between the two models.

Source code in spotRiver/utils/data_conversion.py

def compare_two_tree_models(model1, model2, headers=["Parameter", "Default", "Spot"]):
    """Compares two tree models and returns a table of the differences.
    Args:
        model1 (Pipeline): A river model pipeline.
        model2 (Pipeline): A river model pipeline.
    Returns:
        (str): A table of the differences between the two models.
    """
    keys = model1[1].summary.keys()
    values1 = model1[1].summary.values()
    values2 = model2[1].summary.values()
    tbl = []
    for key, value1, value2 in zip(keys, values1, values2):
        tbl.append([key, value1, value2])
    return tabulate(tbl, headers=headers, numalign="right", tablefmt="github")

convert_to_df(dataset, target_column='y', n_total=None)

Converts a river dataset into a pandas DataFrame.

Parameters:

Name	Type	Description	Default
dataset	Dataset	The river dataset to be converted.	required
target_column	str	The name of the target column in the resulting DataFrame. Defaults to “y”.	'y'
n_total	int	The number of samples to be converted Defaults to None, i.e, the full dataset is converted.	None

Returns:

Type	Description
DataFrame	A pandas DataFrame representation of the given dataset.

Examples:

>>> dataset = datasets.TrumpApproval()
    target_column = "Approval"
    df = convert_to_df(dataset, target_column)
    df.rename(columns={
        'date': 'ordinal_date',
        'Gallup': 'gallup',
        'Ipsos': 'ipsos',
        'Morning Consult': 'morning_consult',
        'Rasmussen': 'rasmussen',
        'YouGov': 'you_gov'},
        inplace=True)
    # Split the data into train and test sets
    train = df[:500]
    test = df[500:]

Source code in spotRiver/utils/data_conversion.py

def convert_to_df(dataset: datasets.base.Dataset, target_column: str = "y", n_total: int = None) -> pd.DataFrame:
    """Converts a river dataset into a pandas DataFrame.

    Args:
        dataset (datasets.base.Dataset):
            The river dataset to be converted.
        target_column (str):
            The name of the target column in the resulting DataFrame.
            Defaults to "y".
        n_total (int, optional):
            The number of samples to be converted
            Defaults to None, i.e, the full dataset is converted.

    Returns:
        (pd.DataFrame): A pandas DataFrame representation of the given dataset.

    Examples:
        >>> dataset = datasets.TrumpApproval()
            target_column = "Approval"
            df = convert_to_df(dataset, target_column)
            df.rename(columns={
                'date': 'ordinal_date',
                'Gallup': 'gallup',
                'Ipsos': 'ipsos',
                'Morning Consult': 'morning_consult',
                'Rasmussen': 'rasmussen',
                'YouGov': 'you_gov'},
                inplace=True)
            # Split the data into train and test sets
            train = df[:500]
            test = df[500:]
    """
    data_dict = {key: [] for key in list(dataset.take(1))[0][0].keys()}
    data_dict[target_column] = []
    if n_total is None:
        for x in dataset:
            for key, value in x[0].items():
                data_dict[key].append(value)
            data_dict[target_column].append(x[1])
    else:
        for x in dataset.take(n_total):
            for key, value in x[0].items():
                data_dict[key].append(value)
            data_dict[target_column].append(x[1])
    df = pd.DataFrame(data_dict)
    return df

rename_df_to_xy(df, target_column='y')

Renames the columns of a DataFrame to x1, x2, …, xn, y.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame to be renamed.	required
target_column	str	The name of the target column. Defaults to “y”.	'y'

Returns:

Type	Description
DataFrame	The renamed DataFrame.

Examples:

>>> from spotRiver.utils.data_conversion import rename_df_to_xy
    df = pd.DataFrame({
    "feature1": [1, 2, 3],
    "feature2": [4, 5, 6],
    "target": [7, 8, 9]
})
>>> df = rename_df_to_xy(df, "target")
>>> print(df)
   x1  x2  y
0   1   4  7
1   2   5  8
2   3   6  9

Source code in spotRiver/utils/data_conversion.py

def rename_df_to_xy(df, target_column="y"):
    """Renames the columns of a DataFrame to x1, x2, ..., xn, y.

    Args:
        df (pd.DataFrame):
            The DataFrame to be renamed.
        target_column (str, optional):
            The name of the target column. Defaults to "y".

    Returns:
        (pd.DataFrame): The renamed DataFrame.

    Examples:
        >>> from spotRiver.utils.data_conversion import rename_df_to_xy
            df = pd.DataFrame({
            "feature1": [1, 2, 3],
            "feature2": [4, 5, 6],
            "target": [7, 8, 9]
        })
        >>> df = rename_df_to_xy(df, "target")
        >>> print(df)
           x1  x2  y
        0   1   4  7
        1   2   5  8
        2   3   6  9
    """
    n_features = len(df.columns) - 1
    df.columns = [f"x{i}" for i in range(1, n_features + 1)] + [target_column]
    return df