From c066f014e35ba89684e116e5dc4358957ca336b9 Mon Sep 17 00:00:00 2001 From: robcaulk Date: Wed, 7 Jun 2023 18:36:07 +0200 Subject: [PATCH] fix docs --- docs/freqai-feature-engineering.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/freqai-feature-engineering.md b/docs/freqai-feature-engineering.md index 364b920a1..c91e78afc 100644 --- a/docs/freqai-feature-engineering.md +++ b/docs/freqai-feature-engineering.md @@ -257,16 +257,16 @@ Users are encouraged to customize the data pipeline to their needs by building t from datasieve.transforms import SKLearnWrapper, DissimilarityIndex from datasieve.pipeline import Pipeline from sklearn.preprocessing import QuantileTransformer - def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None: + def define_data_pipeline(self) -> Pipeline: """ User defines their custom eature pipeline here (if they wish) """ - dk.feature_pipeline = Pipeline([ + feature_pipeline = Pipeline([ ('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal'))), ('di', ds.DissimilarityIndex(di_threshold=1) ]) - return + return feature_pipeline ``` Here, you are defining the exact pipeline that will be used for your feature set during training and prediction. Here you can use *most* SKLearn transformation steps by wrapping them in the `SKLearnWrapper` class.