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Solution for skew/drift detection in distribution of numerical feature #113

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Solution for skew/drift detection in distribution of numerical feature #113

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12 changes: 12 additions & 0 deletions tensorflow_data_validation/api/validation_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
import logging
import apache_beam as beam
import pyarrow as pa
import pandas as pd
import tensorflow as tf
from tensorflow_data_validation import constants
from tensorflow_data_validation import types
Expand Down Expand Up @@ -52,6 +53,17 @@
anomalies_pb2.AnomalyInfo.NO_DATA_IN_SPAN,
])

def preprocess_numerical_to_categorical_by_own_quantiles(
dataframe: pd.DataFrame,
):
# TODO: refactor implementation from private project
return dataframe

def preprocess_numerical_to_categorical_by_training_quantiles(
dataframe: pd.DataFrame,
):
# TODO: refactor implementation from private project
return dataframe

def infer_schema(
statistics: statistics_pb2.DatasetFeatureStatisticsList,
Expand Down