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update docs, improve the interaction with define_data_pipeline

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robcaulk
2023-06-07 18:26:49 +02:00
parent dc577d2a1a
commit 135aaa2be2
7 changed files with 114 additions and 108 deletions
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docs/freqai-feature-engineering.md
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@@ -226,8 +226,10 @@ FreqAI uses the the [`DataSieve`](https://github.com/emergentmethods/datasieve)
This means that users can use/customize any SKLearn modules and easily add them to their FreqAI data pipeline. By default, FreqAI builds the following pipeline:
```py
from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
from datasieve.pipeline import Pipeline
dk.feature_pipeline = Pipeline([
('scaler', ds.DataSieveMinMaxScaler(feature_range=(-1, 1))),
('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))),
('di', ds.DissimilarityIndex(di_threshold=1)),
])
```
@@ -235,10 +237,12 @@ dk.feature_pipeline = Pipeline([
But users will find that they can add PCA and other steps just by changing their configuration settings, for example, if you add `"principal_component_analysis": true` to the `feature_parameters` dict in the `freqai` config, then FreqAI will add the PCA step for you resulting in the following pipeline:
```py
from datasieve.transforms import SKLearnWrapper, DissimilarityIndex, PCA
from datasieve.pipeline import Pipeline
dk.feature_pipeline = Pipeline([
('scaler', ds.DataSieveMinMaxScaler(feature_range=(-1, 1))),
('pca', ds.DataSievePCA()),
('post-pca-scaler', ds.DataSieveMinMaxScaler(feature_range=(-1, 1)))
('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))),
('pca', ds.PCA()),
('post-pca-scaler', ds.MinMaxScaler(feature_range=(-1, 1)))
('di', ds.DissimilarityIndex(di_threshold=1)),
])
```
@@ -247,16 +251,19 @@ The same concept follows if users activate other config options like `"use_SVM_t
## Customizing the pipeline
Users are encouraged to customize the data pipeline to their needs by building their own data pipeline. This can be done by overriding `define_data_pipeline` in their `IFreqaiModel`. For example:
Users are encouraged to customize the data pipeline to their needs by building their own data pipeline. This can be done by simply setting `dk.feature_pipeline` to their desired `Pipeline` object inside their `IFreqaiModel` `train()` function, or if they prefer not to touch the `train()` function, they can override `define_data_pipeline` in their `IFreqaiModel`:
```py
from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
from datasieve.pipeline import Pipeline
from sklearn.preprocessing import QuantileTransformer
def define_data_pipeline(self, dk: FreqaiDataKitchen) -> None:
"""
User defines their custom eature pipeline here (if they wish)
"""
from sklearn.preprocessing import QuantileTransformer
dk.feature_pipeline = Pipeline([
('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal')))
('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal'))),
('di', ds.DissimilarityIndex(di_threshold=1)
])
return