gen_utils#

General utility methods.

Module Contents#

Classes Summary#

classproperty

Allows function to be accessed as a class level property.

Functions#

`are_datasets_separated_by_gap_time_index`	Determine if the train and test datasets are separated by gap number of units using the time_index.
`are_ts_parameters_valid_for_split`	Validates the time series parameters in problem_configuration are compatible with split sizes.
`contains_all_ts_parameters`	Validates that the problem configuration contains all required keys.
`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_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.
`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).
`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.
`validate_holdout_datasets`	Validate the holdout datasets match our expectations.

Attributes Summary#

`logger`
`SEED_BOUNDS`

Contents#

evalml.utils.gen_utils.are_datasets_separated_by_gap_time_index(train, test, pipeline_params)[source]#

Determine if the train and test datasets are separated by gap number of units using the time_index.

This will be true when users are predicting on unseen data but not during cross validation since the target is known.

Parameters

train (pd.DataFrame) – Training data.
test (pd.DataFrame) – Data of shape [n_samples, n_features].
pipeline_params (dict) – Dictionary of time series parameters.

Returns

True if the difference in time units is equal to gap + 1.

Return type

bool

evalml.utils.gen_utils.are_ts_parameters_valid_for_split(gap, max_delay, forecast_horizon, n_obs, n_splits)[source]#

Validates the time series parameters in problem_configuration are compatible with split sizes.

Parameters

gap (int) – gap value.
max_delay (int) – max_delay value.
forecast_horizon (int) – forecast_horizon value.
n_obs (int) – Number of observations in the dataset.
n_splits (int) – Number of cross validation splits.

Returns

TsParameterValidationResult - named tuple with four fields: is_valid (bool): True if parameters are valid. msg (str): Contains error message to display. Empty if is_valid. smallest_split_size (int): Smallest split size given n_obs and n_splits. max_window_size (int): Max window size given gap, max_delay, forecast_horizon.

class evalml.utils.gen_utils.classproperty(func)[source]#

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.gen_utils.contains_all_ts_parameters(problem_configuration)[source]#

Validates that the problem configuration contains all required keys.

Parameters

problem_configuration (dict) – Problem configuration.

Returns

True if the configuration contains all parameters. If False, msg is a non-empty: string with error message.

Return type

bool, str

evalml.utils.gen_utils.convert_to_seconds(input_str)[source]#

Converts a string describing a length of time to its length in seconds.

Parameters: input_str (str) – The string to be parsed and converted to seconds.
Returns: Returns the library if importing succeeded.
Raises: AssertionError – If an invalid unit is used.

Examples

>>> assert convert_to_seconds("10 hr") == 36000.0
>>> assert convert_to_seconds("30 minutes") == 1800.0
>>> assert convert_to_seconds("2.5 min") == 150.0

evalml.utils.gen_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.gen_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.gen_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.
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.gen_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

Raises

ValueError – If boundaries are not valid.

evalml.utils.gen_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.
Raises: ValueError – If the input seed is not within the acceptable range.
Returns: A numpy.random.RandomState instance.

evalml.utils.gen_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) – 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.gen_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.gen_utils.jupyter_check()[source]#

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.gen_utils.logger#

evalml.utils.gen_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.
num_to_pad (int) – Number of nans to pad.

Returns

pd.DataFrame or pd.Series

evalml.utils.gen_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.gen_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 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.gen_utils.SEED_BOUNDS#

evalml.utils.gen_utils.validate_holdout_datasets(X, X_train, pipeline_params)[source]#

Validate the holdout datasets match our expectations.

This function is run before calling predict in a time series pipeline. It verifies that X (the holdout set) is gap units away from the training set and is less than or equal to the forecast_horizon.

Parameters

X (pd.DataFrame) – Data of shape [n_samples, n_features].
X_train (pd.DataFrame) – Training data.
pipeline_params (dict) – Dictionary of time series parameters with gap, forecast_horizon, and time_index being required.

Returns

TSHoldoutValidationResult - named tuple with three fields: is_valid (bool): True if holdout data is valid. error_messages (list): List of error messages to display. Empty if is_valid. error_codes (list): List of error codes to display. Empty if is_valid.