Utils¶

Submodules¶

Package Contents¶

Classes Summary¶

classproperty

Allows function to be accessed as a class level property.

Functions¶

`convert_to_seconds`	Converts a string describing a length of time to its length in seconds.
`deprecate_arg`	Helper to raise warnings when a deprecated arg is used.
`drop_rows_with_nans`	Drop rows that have any NaNs in all dataframes or series.
`get_importable_subclasses`	Get importable subclasses of a base class. Used to list all of our
`get_logger`
`get_random_seed`	Given a numpy.random.RandomState object, generate an int representing a seed value for another random number generator. Or, if given an int, return that int.
`get_random_state`	Generates a numpy.random.RandomState instance using seed.
`import_or_raise`	Attempts to import the requested library by name.
`infer_feature_types`	Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns.
`is_all_numeric`	Checks if the given DataFrame contains only numeric values
`jupyter_check`	Get whether or not the code is being run in a Ipython environment (such as Jupyter Notebook or Jupyter Lab)
`log_subtitle`
`log_title`
`pad_with_nans`	Pad the beginning num_to_pad rows with nans.
`safe_repr`	Convert the given value into a string that can safely be used for repr
`save_plot`	Saves fig to filepath if specified, or to a default location if not.

Attributes Summary¶

SEED_BOUNDS

Contents¶

class evalml.utils.classproperty(func)[source]¶

Allows function to be accessed as a class level property.

Example:

class LogisticRegressionBinaryPipeline(PipelineBase):
    component_graph = ['Simple Imputer', 'Logistic Regression Classifier']

    @classproperty
    def summary(cls):
    summary = ""
    for component in cls.component_graph:
        component = handle_component_class(component)
        summary += component.name + " + "
    return summary

assert LogisticRegressionBinaryPipeline.summary == "Simple Imputer + Logistic Regression Classifier + "
assert LogisticRegressionBinaryPipeline().summary == "Simple Imputer + Logistic Regression Classifier + "

evalml.utils.convert_to_seconds(input_str)[source]¶: Converts a string describing a length of time to its length in seconds.

evalml.utils.deprecate_arg(old_arg, new_arg, old_value, new_value)[source]¶

Helper to raise warnings when a deprecated arg is used.

Parameters

old_arg (str) – Name of old/deprecated argument.
new_arg (str) – Name of new argument.
old_value (Any) – Value the user passed in for the old argument.
new_value (Any) – Value the user passed in for the new argument.

Returns

old_value if not None, else new_value

evalml.utils.drop_rows_with_nans(*pd_data)[source]¶

Drop rows that have any NaNs in all dataframes or series.

Parameters: *pd_data (sequence of pd.Series or pd.DataFrame or None) –
Returns: list of pd.DataFrame or pd.Series or None

evalml.utils.get_importable_subclasses(base_class, used_in_automl=True)[source]¶

Get importable subclasses of a base class. Used to list all of our estimators, transformers, components and pipelines dynamically.

Parameters

base_class (abc.ABCMeta) – Base class to find all of the subclasses for.
args (list) – Args used to instantiate the subclass. [{}] for a pipeline, and [] for all other classes.
used_in_automl – Not all components/pipelines/estimators are used in automl search. If True, only include those subclasses that are used in the search. This would mean excluding classes related to ExtraTrees, ElasticNet, and Baseline estimators.

Returns

List of subclasses.

evalml.utils.get_logger(name)[source]¶

evalml.utils.get_random_seed(random_state, min_bound=SEED_BOUNDS.min_bound, max_bound=SEED_BOUNDS.max_bound)[source]¶

Given a numpy.random.RandomState object, generate an int representing a seed value for another random number generator. Or, if given an int, return that int.

To protect against invalid input to a particular library’s random number generator, if an int value is provided, and it is outside the bounds “[min_bound, max_bound)”, the value will be projected into the range between the min_bound (inclusive) and max_bound (exclusive) using modular arithmetic.

Parameters

random_state (int, numpy.random.RandomState) – random state
min_bound (None, int) – if not default of None, will be min bound when generating seed (inclusive). Must be less than max_bound.
max_bound (None, int) – if not default of None, will be max bound when generating seed (exclusive). Must be greater than min_bound.

Returns

seed for random number generator

Return type

int

evalml.utils.get_random_state(seed)[source]¶

Generates a numpy.random.RandomState instance using seed.

Parameters: seed (None, int, np.random.RandomState object) – seed to use to generate numpy.random.RandomState. Must be between SEED_BOUNDS.min_bound and SEED_BOUNDS.max_bound, inclusive. Otherwise, an exception will be thrown.

evalml.utils.import_or_raise(library, error_msg=None, warning=False)[source]¶

Attempts to import the requested library by name. If the import fails, raises an ImportError or warning.

Parameters

library (str) – the name of the library
error_msg (str) – error message to return if the import fails
warning (bool) – if True, import_or_raise gives a warning instead of ImportError. Defaults to False.

evalml.utils.infer_feature_types(data, feature_types=None)[source]¶

Create a Woodwork structure from the given list, pandas, or numpy input, with specified types for columns.: If a column’s type is not specified, it will be inferred by Woodwork.

Parameters

data (pd.DataFrame, pd.Series) – Input data to convert to a Woodwork data structure.
feature_types (string, ww.logical_type obj, dict, optional) – If data is a 2D structure, feature_types must be a dictionary mapping column names to the type of data represented in the column. If data is a 1D structure, then feature_types must be a Woodwork logical type or a string representing a Woodwork logical type (“Double”, “Integer”, “Boolean”, “Categorical”, “Datetime”, “NaturalLanguage”)

Returns

A Woodwork data structure where the data type of each column was either specified or inferred.

evalml.utils.is_all_numeric(df)[source]¶

Checks if the given DataFrame contains only numeric values

Parameters: df (pd.DataFrame) – The DataFrame to check data types of.
Returns: True if all the columns are numeric and are not missing any values, False otherwise.

evalml.utils.jupyter_check()[source]¶

Get whether or not the code is being run in a Ipython environment (such as Jupyter Notebook or Jupyter Lab)

Parameters: None –
Returns: True if Ipython, False otherwise
Return type: Boolean

evalml.utils.log_subtitle(logger, title, underline='=')[source]¶

evalml.utils.log_title(logger, title)[source]¶

evalml.utils.pad_with_nans(pd_data, num_to_pad)[source]¶

Pad the beginning num_to_pad rows with nans.

Parameters: pd_data (pd.DataFrame or pd.Series) – Data to pad.
Returns: pd.DataFrame or pd.Series

evalml.utils.safe_repr(value)[source]¶

Convert the given value into a string that can safely be used for repr

Parameters: value – the item to convert
Returns: String representation of the value

evalml.utils.save_plot(fig, filepath=None, format='png', interactive=False, return_filepath=False)[source]¶

Saves fig to filepath if specified, or to a default location if not.

Parameters

fig (Figure) – Figure to be saved.
filepath (str or Path, optional) – Location to save file. Default is with filename “test_plot”.
format (str) – Extension for figure to be saved as. Ignored if interactive is True and fig
of type plotly.Figure. Defaults to 'png'. (is) –
interactive (bool, optional) – If True and fig is of type plotly.Figure, saves the fig as interactive
of static (instead) –
format will be set to 'html'. Defaults to False. (and) –
return_filepath (bool, optional) – Whether to return the final filepath the image is saved to. Defaults to False.

Returns

String representing the final filepath the image was saved to if return_filepath is set to True. Defaults to None.

evalml.utils.SEED_BOUNDS¶

tuner_exceptions base_meta