feyn.tools

Common helper functions that makes it easier to get started using the SDK.

function get_model_parameters

def get_model_parameters(
    model: feyn._model.Model,
    feature_name: str
) -> pandas.core.frame.DataFrame

Given a model and the name of one of its features (input or output nodes),
get a pandas.DataFrame with the feature's associated parameters. If the feature
is categorical, the function returns the weight associated with each categorical
value. If the feature is numerical, the function returns the scale, weight and
bias.

Arguments:
    model {feyn.Model} -- feyn Model of interest.
    feature_name {str} -- Name of the input or output feature of interest.

Returns:
    pd.DataFrame -- DataFrame with the feature's parameters.

function split

def split(
    data: Iterable,
    ratio: List[float] = [1, 1],
    random_state: int = None
) -> List[Iterable]

Split datasets into random subsets

This function is used to split a dataset into random subsets - typically training and test data.

The input dataset should be either a pandas DataFrames or a dictionary of numpy arrays. The ratio parameter controls how the data is split, and how many subsets it is split into.

Example: Split data in the ratio 2:1 into train and test data
>>> train, test = feyn.tools.split(data, [2,1])

Example: Split data in to train, test and validation data. 80% training data and 10% validation and holdout data each
>>> train, validation, holdout = feyn.tools.split(data, [.8, .1, .1])

Arguments:
    data -- The data to split (DataFrame or dict of numpy arrays).
    ratio -- the size ratio of the resulting subsets
    random_state -- the random state of the split (integer)

Returns:
    list of subsets -- Subsets of the dataset (same type as the input dataset).

function sympify_model

def sympify_model(
    m,
    signif=6,
    symbolic_lr=False,
    include_weights=True
)