Source code for evalml.data_checks.data_check_message_code

"""Enum for data check message code."""
from enum import Enum


[docs]class DataCheckMessageCode(Enum): """Enum for data check message code.""" COLS_WITH_NULL = "cols_with_null" """Message code for columns with null values.""" HIGHLY_NULL_COLS = "highly_null_cols" """Message code for highly null columns.""" HIGHLY_NULL_ROWS = "highly_null_rows" """Message code for highly null rows.""" HAS_ID_COLUMN = "has_id_column" """Message code for data that has ID columns.""" TARGET_INCOMPATIBLE_OBJECTIVE = "target_incompatible_objective" """Message code for target data that has incompatible values for the specified objective""" TARGET_IS_NONE = "target_is_none" """Message code for when target is None.""" TARGET_IS_EMPTY_OR_FULLY_NULL = "target_is_empty_or_fully_null" """Message code for target data that is empty or has all null values.""" TARGET_HAS_NULL = "target_has_null" """Message code for target data that has null values.""" TARGET_UNSUPPORTED_TYPE = "target_unsupported_type" """Message code for target data that is of an unsupported type.""" TARGET_UNSUPPORTED_PROBLEM_TYPE = "target_unsupported_problem_type" """Message code for target data that is being checked against an unsupported problem type.""" TARGET_BINARY_NOT_TWO_UNIQUE_VALUES = "target_binary_not_two_unique_values" """Message code for target data for a binary classification problem that does not have two unique values.""" TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS = ( "target_multiclass_not_two_examples_per_class" ) """Message code for target data for a multi classification problem that does not have two examples per class.""" TARGET_MULTICLASS_NOT_ENOUGH_CLASSES = "target_multiclass_not_enough_classes" """Message code for target data for a multi classification problem that does not have more than two unique classes.""" TARGET_MULTICLASS_HIGH_UNIQUE_CLASS = "target_multiclass_high_unique_class_warning" """Message code for target data for a multi classification problem that has an abnormally large number of unique classes relative to the number of target values.""" TARGET_LOGNORMAL_DISTRIBUTION = "target_lognormal_distribution" """Message code for target data with a lognormal distribution.""" HIGH_VARIANCE = "high_variance" """Message code for when high variance is detected for cross-validation.""" TARGET_LEAKAGE = "target_leakage" """Message code for when target leakage is detected.""" HAS_OUTLIERS = "has_outliers" """Message code for when outliers are detected.""" CLASS_IMBALANCE_BELOW_THRESHOLD = "class_imbalance_below_threshold" """Message code for when balance in classes is less than the threshold.""" CLASS_IMBALANCE_SEVERE = "class_imbalance_severe" """Message code for when balance in classes is less than the threshold and minimum class is less than minimum number of accepted samples.""" CLASS_IMBALANCE_BELOW_FOLDS = "class_imbalance_below_folds" """Message code for when the number of values for each target is below 2 * number of CV folds.""" NO_VARIANCE = "no_variance" """Message code for when data has no variance (1 unique value).""" NO_VARIANCE_ZERO_UNIQUE = "no_variance_zero_unique" """Message code for when data has no variance (0 unique value)""" NO_VARIANCE_WITH_NULL = "no_variance_with_null" """Message code for when data has one unique value and NaN values.""" IS_MULTICOLLINEAR = "is_multicollinear" """Message code for when data is potentially multicollinear.""" NOT_UNIQUE_ENOUGH = "not_unique_enough" """Message code for when data does not possess enough unique values.""" TOO_UNIQUE = "too_unique" """Message code for when data possesses too many unique values.""" TOO_SPARSE = "too sparse" """Message code for when multiclass data has values that are too sparsely populated.""" MISMATCHED_INDICES = "mismatched_indices" """Message code for when input target and features have mismatched indices.""" MISMATCHED_INDICES_ORDER = "mismatched_indices_order" """Message code for when input target and features have mismatched indices order. The two inputs have the same index values, but shuffled.""" MISMATCHED_LENGTHS = "mismatched_lengths" """Message code for when input target and features have different lengths.""" DATETIME_HAS_NAN = "datetime_has_nan" """Message code for when input datetime columns contain NaN values.""" NATURAL_LANGUAGE_HAS_NAN = "natural_language_has_nan" """Message code for when input natural language columns contain NaN values.""" DATETIME_INFORMATION_NOT_FOUND = "datetime_information_not_found" """Message code for when datetime information can not be found or is in an unaccepted format.""" DATETIME_NO_FREQUENCY_INFERRED = "datetime_no_frequency_inferred" """Message code for when no frequency can be inferred in the datetime values through Woodwork's infer_frequency.""" DATETIME_HAS_UNEVEN_INTERVALS = "datetime_has_uneven_intervals" """Message code for when the datetime values have uneven intervals.""" DATETIME_HAS_REDUNDANT_ROW = "datetime_has_redundant_row" """Message code for when datetime information has more than one row per datetime.""" DATETIME_IS_MISSING_VALUES = "datetime_is_missing_values" """Message code for when datetime feature has values missing between the start and end dates.""" DATETIME_HAS_MISALIGNED_VALUES = "datetime_has_misaligned_values" """Message code for when datetime information has values that are not aligned with the inferred frequency.""" DATETIME_IS_NOT_MONOTONIC = "datetime_is_not_monotonic" """Message code for when the datetime values are not monotonically increasing.""" TIMESERIES_PARAMETERS_NOT_COMPATIBLE_WITH_SPLIT = ( "timeseries_parameters_not_compatible_with_split" ) """Message code when the time series parameters are too large for the smallest data split.""" TIMESERIES_TARGET_NOT_COMPATIBLE_WITH_SPLIT = ( "timeseries_target_not_compatible_with_split" ) """Message code when any training and validation split of the time series target doesn't contain all classes."""