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#53: Fixed supporting datetype columns
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Signed-off-by: Dominik Hoffmann <[email protected]>
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@dh1542
dh1542 committed Nov 25, 2024
1 parent 4e5471c commit 636ae42
Showing 1 changed file with 66 additions and 53 deletions.
119 changes: 66 additions & 53 deletions src/sdk/python/rtdip_sdk/pipelines/data_wranglers/spark/data_quality/interval_filtering.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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 """
Expand All @@ -57,6 +59,64 @@ 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():
"""
Expand All @@ -74,6 +134,7 @@ def libraries():
def settings() -> dict:
return {}


def convert_column_to_timestamp(self) -> DataFrame:
try:
return self.df.withColumn(
Expand All @@ -82,6 +143,7 @@ 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:
Expand Down Expand Up @@ -122,53 +184,4 @@ def format_date_time_to_string(self, time_stamp: pd.Timestamp) -> str:
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

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