- 1 year ago
How to do Rollling Regression in Pyspark in DataBricks?
Rolling Regression is a useful technique to model relationships between variables over time in a sliding window. In PySpark, we can perform rolling regression using the window
function combined with pandas_udf
.
Here's an example of how to perform rolling regression in PySpark in DataBricks:
Create a PySpark DataFrame:
Let's assume you have a PySpark DataFrame containing the data you want to perform rolling regression on. For this example, we'll use a DataFrame named
df
with columnsdate
,x
, andy
.Define a rolling regression function using
pandas_udf
:We'll define a rolling regression function using
pandas_udf
. This function takes a Pandas DataFrame as input and returns a Pandas DataFrame with the rolling regression results. Here's an example of the rolling regression function:pythonimport pandas as pd
import numpy as np
from pyspark.sql.functions import pandas_udf, PandasUDFType
@pandas_udf("double", PandasUDFType.GROUPED_MAP)
def rolling_regression(pdf):
pdf['intercept'] = 1.0
pdf['coef'], pdf['intercept'] = np.polyfit(pdf['x'], pdf['y'], 1)
return pdf[['coef', 'intercept']]
This function calculates the coefficients of a linear regression model for each sliding window of the DataFrame. The size of the sliding window is determined by the
window
function in the next step.Apply the rolling regression function using the
window
function:We'll use the
window
function to define the size of the sliding window and apply the rolling regression function to each window. Here's an example of how to use thewindow
function to perform rolling regression on the DataFramedf
:sqlfrom pyspark.sql.functions import col, window
result = df.groupBy(window("date", "30 days")).apply(rolling_regression(collect_list(df['x', 'y'])))
In this example, we're grouping the DataFrame by a sliding window of 30 days using the
window
function. We're then applying therolling_regression
function to each window using theapply
function. Thecollect_list
function is used to aggregate thex
andy
values in each window into a Pandas DataFrame, which is then passed to therolling_regression
function.Inspect the results:
The
result
DataFrame contains the coefficients of the rolling regression model for each sliding window. You can inspect the results using theshow
function:scssresult.show()
This will display the coefficients for each sliding window in the DataFrame.
That's it! This is how you can perform rolling regression in PySpark in DataBricks using the window
function and pandas_udf
.