Utils

Utility methods.

Submodules

• base_meta
• cli_utils
• gen_utils
• logger
• update_checker
• woodwork_utils

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 estimators, transformers, components and pipelines dynamically.
get_logger	Get the logger with the associated name.
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. If the import fails, raises an ImportError or warning.
infer_feature_types	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.
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 with a subtitle.
log_title	Log with a 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)

Allows function to be accessed as a class level property.

Example: .. code-block:

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)

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)

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)

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)

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.

• 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)

Get the logger with the associated name.

Parameters

name (str) – Name of the logger to get.

Returns

The logger object with the associated name.

evalml.utils.get_random_seed(random_state, min_bound=SEED_BOUNDS.min_bound, max_bound=SEED_BOUNDS.max_bound)

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

Raises

ValueError – If boundaries are not valid.

evalml.utils.get_random_state(seed)

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.

Raises

ValueError – If the input seed is not within the acceptable range.

Returns

A numpy.random.RandomState instance.

evalml.utils.import_or_raise(library, error_msg=None, warning=False)

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) – Rrror message to return if the import fails.

• warning (bool) – If True, import_or_raise gives a warning instead of ImportError. Defaults to False.

Returns

Returns the library if importing succeeded.

Raises

• ImportError – If attempting to import the library fails because the library is not installed.

• Exception – If importing the library fails.

evalml.utils.infer_feature_types(data, feature_types=None)

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.

Raises

ValueError – If there is a mismatch between the dataframe and the woodwork schema.

evalml.utils.is_all_numeric(df)

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()

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

Returns

True if Ipython, False otherwise.

Return type

boolean

evalml.utils.log_subtitle(logger, title, underline='=')

Log with a subtitle.

evalml.utils.log_title(logger, title)

Log with a title.

evalml.utils.pad_with_nans(pd_data, num_to_pad)

Pad the beginning num_to_pad rows with nans.

Parameters

• pd_data (pd.DataFrame or pd.Series) – Data to pad.

• num_to_pad (int) – Number of nans to pad.

Returns

pd.DataFrame or pd.Series

evalml.utils.safe_repr(value)

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)

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 is of type plotly.Figure. Defaults to ‘png’.

• interactive (bool, optional) – If True and fig is of type plotly.Figure, saves the fig as interactive instead of static, and format will be set to ‘html’. Defaults to False.

• 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