Merge branch 'develop' of https://github.com/amosproj/amos2024ws01-rt…

…dip-data-quality-checker into develop

src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/duplicate_detection.py

@@ -28,21 +28,27 @@ class DuplicateDetection(WranglerBaseInterface):
     from rtdip_sdk.pipelines.monitoring.spark.data_quality.duplicate_detection import DuplicateDetection
     from pyspark.sql import SparkSession
     from pyspark.sql.dataframe import DataFrame
-    from pyspark.sql.functions import desc
 
-    duplicate_detection_monitor = DuplicateDetection(df)
+    duplicate_detection_monitor = DuplicateDetection(df, primary_key_columns=["TagName", "EventTime"])
 
     result = duplicate_detection_monitor.filter()
     ```
 
     Parameters:
-        df (DataFrame): PySpark DataFrame to be converted
+        df (DataFrame): PySpark DataFrame to be cleansed.
+        primary_key_columns (list): List of column names that serve as primary key for duplicate detection.
     """
 
     df: PySparkDataFrame
+    primary_key_columns: list
 
-    def __init__(self, df: PySparkDataFrame) -> None:
+    def __init__(self, df: PySparkDataFrame, primary_key_columns: list) -> None:
+        if not primary_key_columns or not isinstance(primary_key_columns, list):
+            raise ValueError(
+                "primary_key_columns must be a non-empty list of column names."
+            )
         self.df = df
+        self.primary_key_columns = primary_key_columns
 
     @staticmethod
     def system_type():
@@ -64,10 +70,9 @@ def settings() -> dict:
     def filter(self) -> PySparkDataFrame:
         """
         Returns:
-            DataFrame: A cleansed PySpark DataFrame from all the duplicates.
+            DataFrame: A cleansed PySpark DataFrame from all duplicates based on primary key columns.
         """
-
-        cleansed_df = self.df.dropDuplicates(["TagName", "EventTime"]).orderBy(
+        cleansed_df = self.df.dropDuplicates(self.primary_key_columns).orderBy(
             desc("EventTime")
         )
         return cleansed_df

src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/interval_filtering.py

@@ -14,25 +14,27 @@
 from datetime import timedelta
 
 import pandas as pd
+from pyspark.sql.types import StringType
 from pyspark.sql import functions as F
 from pyspark.sql import SparkSession
 from pyspark.sql import DataFrame
 
+
 from ...._pipeline_utils.models import Libraries, SystemType
 from ...interfaces import WranglerBaseInterface
 
 
 class IntervalFiltering(WranglerBaseInterface):
     """
-    Cleanses a DataFrame by removing rows outside a specified interval window.
-    Example:
-
+    Cleanses a DataFrame by removing rows outside a specified interval window. Supported time stamp columns are DateType and StringType.
 
     Parameters:
         spark (SparkSession): A SparkSession object.
         df (DataFrame): PySpark DataFrame to be converted
         interval (int): The interval length for cleansing.
-         interval_unit (str): 'hours', 'minutes', 'seconds' or 'milliseconds' to specify the unit of the interval.
+        interval_unit (str): 'hours', 'minutes', 'seconds' or 'milliseconds' to specify the unit of the interval.
+        time_stamp_column_name (str): The name of the column containing the time stamps. Default is 'EventTime'.
+        tolerance (int): The tolerance for the interval. Default is None.
     """
 
     """ Default time stamp column name if not set in the constructor """
@@ -57,6 +59,68 @@ def __init__(
         else:
             self.time_stamp_column_name = time_stamp_column_name
 
+    def filter(self) -> DataFrame:
+        """
+        Filters the DataFrame based on the interval
+        """
+
+        if self.time_stamp_column_name not in self.df.columns:
+            raise ValueError(
+                f"Column {self.time_stamp_column_name} not found in the DataFrame."
+            )
+        is_string_time_stamp = isinstance(
+            self.df.schema[self.time_stamp_column_name].dataType, StringType
+        )
+
+        original_schema = self.df.schema
+        self.df = self.convert_column_to_timestamp().orderBy(
+            self.time_stamp_column_name
+        )
+
+        self.df.show()
+
+        tolerance_in_ms = None
+        if self.tolerance is not None:
+            tolerance_in_ms = self.get_time_delta(self.tolerance).total_seconds() * 1000
+        print(tolerance_in_ms)
+
+        time_delta_in_ms = self.get_time_delta(self.interval).total_seconds() * 1000
+
+        rows = self.df.collect()
+        last_time_stamp = rows[0][self.time_stamp_column_name]
+        first_row = rows[0].asDict()
+
+        first_row[self.time_stamp_column_name] = (
+            self.format_date_time_to_string(first_row[self.time_stamp_column_name])
+            if is_string_time_stamp
+            else first_row[self.time_stamp_column_name]
+        )
+
+        cleansed_df = [first_row]
+
+        for i in range(1, len(rows)):
+            current_row = rows[i]
+            current_time_stamp = current_row[self.time_stamp_column_name]
+
+            if self.check_if_outside_of_interval(
+                current_time_stamp, last_time_stamp, time_delta_in_ms, tolerance_in_ms
+            ):
+                current_row_dict = current_row.asDict()
+                current_row_dict[self.time_stamp_column_name] = (
+                    self.format_date_time_to_string(
+                        current_row_dict[self.time_stamp_column_name]
+                    )
+                    if is_string_time_stamp
+                    else current_row_dict[self.time_stamp_column_name]
+                )
+
+                cleansed_df.append(current_row_dict)
+                last_time_stamp = current_time_stamp
+
+        result_df = self.spark.createDataFrame(cleansed_df, schema=original_schema)
+
+        return result_df
+
     @staticmethod
     def system_type():
         """
@@ -82,6 +146,7 @@ def convert_column_to_timestamp(self) -> DataFrame:
         except Exception as e:
             raise ValueError(
                 f"Error converting column {self.time_stamp_column_name} to timestamp: {e}"
+                f"{self.df.schema[self.time_stamp_column_name].dataType} might be unsupported!"
             )
 
     def get_time_delta(self, value: int) -> timedelta:
@@ -121,54 +186,3 @@ def format_date_time_to_string(self, time_stamp: pd.Timestamp) -> str:
             return time_stamp.strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
         except Exception as e:
             raise ValueError(f"Error converting timestamp to string: {e}")
-
-    def filter(self) -> DataFrame:
-        """
-        Filters the DataFrame based on the interval
-        """
-
-        if self.time_stamp_column_name not in self.df.columns:
-            raise ValueError(
-                f"Column {self.time_stamp_column_name} not found in the DataFrame."
-            )
-
-        original_schema = self.df.schema
-        self.df = self.convert_column_to_timestamp().orderBy(
-            self.time_stamp_column_name
-        )
-
-        tolerance_in_ms = None
-        if self.tolerance is not None:
-            tolerance_in_ms = self.get_time_delta(self.tolerance).total_seconds() * 1000
-        print(tolerance_in_ms)
-
-        time_delta_in_ms = self.get_time_delta(self.interval).total_seconds() * 1000
-
-        rows = self.df.collect()
-        last_time_stamp = rows[0][self.time_stamp_column_name]
-        first_row = rows[0].asDict()
-        first_row[self.time_stamp_column_name] = self.format_date_time_to_string(
-            first_row[self.time_stamp_column_name]
-        )
-
-        cleansed_df = [first_row]
-
-        for i in range(1, len(rows)):
-            current_row = rows[i]
-            current_time_stamp = current_row[self.time_stamp_column_name]
-
-            if self.check_if_outside_of_interval(
-                current_time_stamp, last_time_stamp, time_delta_in_ms, tolerance_in_ms
-            ):
-                current_row_dict = current_row.asDict()
-                current_row_dict[self.time_stamp_column_name] = (
-                    self.format_date_time_to_string(
-                        current_row_dict[self.time_stamp_column_name]
-                    )
-                )
-                cleansed_df.append(current_row_dict)
-                last_time_stamp = current_time_stamp
-
-        result_df = self.spark.createDataFrame(cleansed_df, schema=original_schema)
-
-        return result_df

src/sdk/python/rtdip_sdk/pipelines/transformers/spark/machine_learning/one_hot_encoding.py

@@ -0,0 +1,129 @@
+# Copyright 2022 RTDIP
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pyspark.sql import DataFrame as PySparkDataFrame
+from pyspark.sql import functions as F
+from ...interfaces import TransformerInterface
+from ...._pipeline_utils.models import Libraries, SystemType
+
+
+class OneHotEncoding(TransformerInterface):
+    """
+    Performs One-Hot Encoding on a specified column of a PySpark DataFrame.
+
+    Example
+    --------
+    ```python
+    from src.sdk.python.rtdip_sdk.pipelines.data_wranglers.spark.data_quality.one_hot_encoding import OneHotEncoding
+    from pyspark.sql import SparkSession
+
+
+    spark = ... # SparkSession
+    df = ... # Get a PySpark DataFrame
+
+    one_hot_encoder = OneHotEncoding(df, "column_name", ["list_of_distinct_values"])
+    result_df = one_hot_encoder.encode()
+    result_df.show()
+    ```
+
+    Parameters:
+        df (DataFrame): The PySpark DataFrame to apply encoding on.
+        column (str): The name of the column to apply the encoding to.
+        values (list, optional): A list of distinct values to encode. If not provided,
+                                 the distinct values from the data will be used.
+    """
+
+    df: PySparkDataFrame
+    column: str
+    values: list
+
+    def __init__(self, df: PySparkDataFrame, column: str, values: list = None) -> None:
+        self.df = df
+        self.column = column
+        self.values = values
+
+    @staticmethod
+    def system_type():
+        """
+        Attributes:
+            SystemType (Environment): Requires PYSPARK
+        """
+        return SystemType.PYSPARK
+
+    @staticmethod
+    def libraries():
+        libraries = Libraries()
+        return libraries
+
+    @staticmethod
+    def settings() -> dict:
+        return {}
+
+    def pre_transform_validation(self):
+        """
+        Validate the input data before transformation.
+        - Check if the specified column exists in the DataFrame.
+        - If no values are provided, check if the distinct values can be computed.
+        - Ensure the DataFrame is not empty.
+        """
+        if self.df is None or self.df.count() == 0:
+            raise ValueError("The DataFrame is empty.")
+
+        if self.column not in self.df.columns:
+            raise ValueError(f"Column '{self.column}' does not exist in the DataFrame.")
+
+        if not self.values:
+            distinct_values = [
+                row[self.column]
+                for row in self.df.select(self.column).distinct().collect()
+            ]
+            if not distinct_values:
+                raise ValueError(f"No distinct values found in column '{self.column}'.")
+            self.values = distinct_values
+
+    def post_transform_validation(self):
+        """
+        Validate the result after transformation.
+        - Ensure that new columns have been added based on the distinct values.
+        - Verify the transformed DataFrame contains the expected number of columns.
+        """
+        expected_columns = [
+            f"{self.column}_{value if value is not None else 'None'}"
+            for value in self.values
+        ]
+        missing_columns = [
+            col for col in expected_columns if col not in self.df.columns
+        ]
+
+        if missing_columns:
+            raise ValueError(
+                f"Missing columns in the transformed DataFrame: {missing_columns}"
+            )
+
+        if self.df.count() == 0:
+            raise ValueError("The transformed DataFrame is empty.")
+
+    def transform(self) -> PySparkDataFrame:
+        if not self.values:
+            self.values = [
+                row[self.column]
+                for row in self.df.select(self.column).distinct().collect()
+            ]
+
+        for value in self.values:
+            self.df = self.df.withColumn(
+                f"{self.column}_{value if value is not None else 'None'}",
+                F.when(F.col(self.column) == value, 1).otherwise(0),
+            )
+        return self.df