data_check_message_code ==================================================== .. py:module:: evalml.data_checks.data_check_message_code .. autoapi-nested-parse:: Enum for data check message code. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.data_checks.data_check_message_code.DataCheckMessageCode Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: DataCheckMessageCode Enum for data check message code. **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **CLASS_IMBALANCE_BELOW_FOLDS** - Message code for when the number of values for each target is below 2 * number of CV folds. * - **CLASS_IMBALANCE_BELOW_THRESHOLD** - Message code for when balance in classes is less than the threshold. * - **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. * - **COLS_WITH_NULL** - Message code for columns with null values. * - **DATETIME_HAS_MISALIGNED_VALUES** - Message code for when datetime information has values that are not aligned with the inferred frequency. * - **DATETIME_HAS_NAN** - Message code for when input datetime columns contain NaN values. * - **DATETIME_HAS_REDUNDANT_ROW** - Message code for when datetime information has more than one row per datetime. * - **DATETIME_HAS_UNEVEN_INTERVALS** - Message code for when the datetime values have uneven intervals. * - **DATETIME_INFORMATION_NOT_FOUND** - Message code for when datetime information can not be found or is in an unaccepted format. * - **DATETIME_IS_MISSING_VALUES** - Message code for when datetime feature has values missing between the start and end dates. * - **DATETIME_IS_NOT_MONOTONIC** - Message code for when the datetime values are not monotonically increasing. * - **DATETIME_NO_FREQUENCY_INFERRED** - Message code for when no frequency can be inferred in the datetime values through Woodwork's infer_frequency. * - **HAS_ID_COLUMN** - Message code for data that has ID columns. * - **HAS_ID_FIRST_COLUMN** - Message code for data that has an ID column as the first column. * - **HAS_OUTLIERS** - Message code for when outliers are detected. * - **HIGH_VARIANCE** - Message code for when high variance is detected for cross-validation. * - **HIGHLY_NULL_COLS** - Message code for highly null columns. * - **HIGHLY_NULL_ROWS** - Message code for highly null rows. * - **IS_MULTICOLLINEAR** - Message code for when data is potentially multicollinear. * - **MISMATCHED_INDICES** - Message code for when input target and features have mismatched indices. * - **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** - Message code for when input target and features have different lengths. * - **NATURAL_LANGUAGE_HAS_NAN** - Message code for when input natural language columns contain NaN values. * - **NO_VARIANCE** - Message code for when data has no variance (1 unique value). * - **NO_VARIANCE_WITH_NULL** - Message code for when data has one unique value and NaN values. * - **NO_VARIANCE_ZERO_UNIQUE** - Message code for when data has no variance (0 unique value) * - **NOT_UNIQUE_ENOUGH** - Message code for when data does not possess enough 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_HAS_NULL** - Message code for target data that has null values. * - **TARGET_INCOMPATIBLE_OBJECTIVE** - Message code for target data that has incompatible values for the specified objective * - **TARGET_IS_EMPTY_OR_FULLY_NULL** - Message code for target data that is empty or has all null values. * - **TARGET_IS_NONE** - Message code for when target is None. * - **TARGET_LEAKAGE** - Message code for when target leakage is detected. * - **TARGET_LOGNORMAL_DISTRIBUTION** - Message code for target data with a lognormal distribution. * - **TARGET_MULTICLASS_HIGH_UNIQUE_CLASS** - 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_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_NOT_TWO_EXAMPLES_PER_CLASS** - Message code for target data for a multi classification problem that does not have two examples per class. * - **TARGET_UNSUPPORTED_PROBLEM_TYPE** - Message code for target data that is being checked against an unsupported problem type. * - **TARGET_UNSUPPORTED_TYPE** - Message code for target data that is of an unsupported type. * - **TARGET_UNSUPPORTED_TYPE_REGRESSION** - Message code for target data that is incompatible with regression * - **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** - Message code when any training and validation split of the time series target doesn't contain all classes. * - **TOO_SPARSE** - Message code for when multiclass data has values that are too sparsely populated. * - **TOO_UNIQUE** - Message code for when data possesses too many unique values. **Methods** .. autoapisummary:: :nosignatures: evalml.data_checks.data_check_message_code.DataCheckMessageCode.name evalml.data_checks.data_check_message_code.DataCheckMessageCode.value .. py:method:: name(self) The name of the Enum member. .. py:method:: value(self) The value of the Enum member.