Data Labeler Column Profile¶
Contains class for for profiling data labeler col.
- class dataprofiler.profilers.data_labeler_column_profile.DataLabelerColumn(name: Optional[str], options: Optional[dataprofiler.profilers.profiler_options.DataLabelerOptions] = None)¶
Bases:
dataprofiler.profilers.base_column_profilers.BaseColumnProfiler
Sublass of BaseColumnProfiler for profiling data labeler col.
Initialize Data Label profiling for structured datasets.
- Parameters
name (String) – name of column being profiled
options (DataLabelerOptions) – Options for the data labeler column
- type = 'data_labeler'¶
- thread_safe: bool¶
- static assert_equal_conditions(data_labeler: dataprofiler.profilers.data_labeler_column_profile.DataLabelerColumn, data_labeler2: dataprofiler.profilers.data_labeler_column_profile.DataLabelerColumn) None ¶
Ensure data labelers have the same values. Raise error otherwise.
- Parameters
data_labeler (DataLabelerColumn) – first data_labeler
data_labeler2 (DataLabelerColumn) – second data_labeler
- Returns
None
- property reverse_label_mapping: Dict¶
Return reverse label mapping.
- property possible_data_labels: List[str]¶
Return possible data labels.
- property rank_distribution: Dict[str, int]¶
Return rank distribution.
- property sum_predictions: numpy.ndarray¶
Sum predictions.
- property data_label: Optional[str]¶
Return data labels which best fit data it has seen based on DataLabeler used.
Data labels must be within the minimum probability differential of the top predicted value. If nothing is more than minimum top label value, it says it could not determine the data label.
- property avg_predictions: Optional[Dict[str, float]]¶
Average all sample predictions for each data label.
- property label_representation: Optional[Dict[str, float]]¶
Represent label found within the dataset based on ranked voting.
When top_k=1, this is simply the distribution of data labels found within the dataset.
- property profile: Dict¶
Return the profile of the column.
- report(remove_disabled_flag: bool = False) Dict ¶
Return report.
Private abstract method.
- Parameters
remove_disabled_flag (boolean) – flag to determine if disabled options should be excluded in the report.
- col_type = None¶
- diff(other_profile: dataprofiler.profilers.data_labeler_column_profile.DataLabelerColumn, options: Optional[Dict] = None) Dict ¶
Generate differences between the orders of two DataLabeler columns.
- Returns
Dict containing the differences between orders in their
appropriate output formats :rtype: dict
- name: Optional[str]¶
- sample_size: int¶
- metadata: Dict¶
- times: Dict¶
- update(df_series: pandas.core.series.Series) dataprofiler.profilers.data_labeler_column_profile.DataLabelerColumn ¶
Update the column profile.
- Parameters
df_series (pandas.core.series.Series) – df series
- Returns
updated DataLabelerColumn
- Return type