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avoid manual pipeline validation

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robcaulk 2023-06-10 11:40:57 +02:00
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67
docs/freqai-feature-engineering.md
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@ -221,39 +221,20 @@ where $W_i$ is the weight of data point $i$ in a total set of $n$ data points. B
# Building the data pipeline
FreqAI uses the the [`DataSieve`](https://github.com/emergentmethods/datasieve) pipeline, which follows the SKlearn pipeline API, but adds, among other features, coherence between the X, y, and sample_weight vector point removals, feature removal, feature name following.
By default, FreqAI builds a dynamic pipeline based on user congfiguration settings. The default settings are robust and designed to work with a variety of methods. These two steps are a `MinMaxScaler(-1,1)` and a `VarianceThreshold` which removes any column that has 0 variance. Users can activate other steps with more configuration parameters. For example if users add `use_SVM_to_remove_outliers: true` to the `freqai` config, then FreqAI will automatically add the [`SVMOutlierExtractor`](#identifying-outliers-using-a-support-vector-machine-svm) to the pipeline. Likewise, users can add `principal_component_analysis: true` to the `freqai` config to activate PCA. The [DissimilarityIndex](#identifying-outliers-with-the-dissimilarity-index-di) is activated with `DI_threshold: 1`. Finally, noise can also be added to the data with `noise_standard_deviation: 0.1`. Finally, users can add [DBSCAN](#identifying-outliers-with-dbscan) outlier removal with `use_DBSCAN_to_remove_outliers: true`.
By default, FreqAI builds the following pipeline inside the `IFreqaiModel` `train()` method:
!!! note "More information available"
Please review the [parameter table](freqai-parameter-table.md) for more information on these parameters.
```py
from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
from datasieve.pipeline import Pipeline
dk.feature_pipeline = Pipeline([
('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))),
('di', ds.DissimilarityIndex(di_threshold=1)),
])
```
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 automatically 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', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))),
('pca', ds.PCA()),
('post-pca-scaler', ds.MinMaxScaler(feature_range=(-1, 1)))
('di', ds.DissimilarityIndex(di_threshold=1)),
])
```
The same concept follows if users activate other config options like `"use_SVM_to_remove_outliers": true` or `"use_DBSCAN_to_remove_outliers": true`. FreqAI will add the appropriate steps to the pipeline for you.
## 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 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`/`define_label_pipeline` functions in their `IFreqaiModel`:
```py
!!! note "More information available"
FreqAI uses the the [`DataSieve`](https://github.com/emergentmethods/datasieve) pipeline, which follows the SKlearn pipeline API, but adds, among other features, coherence between the X, y, and sample_weight vector point removals, feature removal, feature name following.
```python
from datasieve.transforms import SKLearnWrapper, DissimilarityIndex
from datasieve.pipeline import Pipeline
from sklearn.preprocessing import QuantileTransformer, StandardScaler
@ -286,14 +267,42 @@ class MyFreqaiModel(BaseRegressionModel):
"""
User defines their custom label pipeline here (if they wish)
"""
feature_pipeline = Pipeline([
label_pipeline = Pipeline([
('qt', SKLearnWrapper(StandardScaler())),
])
return feature_pipeline
return label_pipeline
```
Here, you are defining the exact pipeline that will be used for your feature set during training and prediction. You can use *most* SKLearn transformation steps by wrapping them in the `SKLearnWrapper` class as shown above.
Here, you are defining the exact pipeline that will be used for your feature set during training and prediction. You can use *most* SKLearn transformation steps by wrapping them in the `SKLearnWrapper` class as shown above. In addition, you can use any of the transformations available in the [`DataSieve` library](https://github.com/emergentmethods/datasieve).
You can easily add your own transformation by creating a class that inherits from the datasieve `BaseTransform` and implementing your `fit()`, `transform()` and `inverse_transform()` methods:
```python
from datasieve.transforms.base_transform import BaseTransform
# import whatever else you need
class MyCoolTransform(BaseTransform):
def __init__(self, **kwargs):
self.param1 = kwargs.get('param1', 1)
def fit(self, X, y=None, sample_weight=None, feature_list=None, **kwargs):
# do something with X, y, sample_weight, or/and feature_list
return X, y, sample_weight, feature_list
def transform(self, X, y=None, sample_weight=None,
feature_list=None, outlier_check=False, **kwargs):
# do something with X, y, sample_weight, or/and feature_list
return X, y, sample_weight, feature_list
def inverse_transform(self, X, y=None, sample_weight=None, feature_list=None, **kwargs):
# do/dont do something with X, y, sample_weight, or/and feature_list
return X, y, sample_weight, feature_list
```
!!! note "Hint"
You can define this custom class in the same file as your `IFreqaiModel`.
## Outlier detection

23
freqtrade/freqai/freqai_interface.py
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@ -509,36 +509,33 @@ class IFreqaiModel(ABC):
def define_data_pipeline(self, threads=-1) -> Pipeline:
ft_params = self.freqai_info["feature_parameters"]
feature_pipeline = Pipeline([
pipe_steps = [
('const', ds.VarianceThreshold(threshold=0)),
('scaler', SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1))))
])
]
if ft_params.get("principal_component_analysis", False):
feature_pipeline.append(('pca', ds.PCA()))
feature_pipeline.append(('post-pca-scaler',
SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
pipe_steps.append(('pca', ds.PCA()))
pipe_steps.append(('post-pca-scaler',
SKLearnWrapper(MinMaxScaler(feature_range=(-1, 1)))))
if ft_params.get("use_SVM_to_remove_outliers", False):
svm_params = ft_params.get(
"svm_params", {"shuffle": False, "nu": 0.01})
feature_pipeline.append(('svm', ds.SVMOutlierExtractor(**svm_params)))
pipe_steps.append(('svm', ds.SVMOutlierExtractor(**svm_params)))
di = ft_params.get("DI_threshold", 0)
if di:
feature_pipeline.append(('di', ds.DissimilarityIndex(di_threshold=di, n_jobs=threads)))
pipe_steps.append(('di', ds.DissimilarityIndex(di_threshold=di, n_jobs=threads)))
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
feature_pipeline.append(('dbscan', ds.DBSCAN(n_jobs=threads)))
pipe_steps.append(('dbscan', ds.DBSCAN(n_jobs=threads)))
sigma = self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0)
if sigma:
feature_pipeline.append(('noise', ds.Noise(sigma=sigma)))
pipe_steps.append(('noise', ds.Noise(sigma=sigma)))
feature_pipeline.fitparams = feature_pipeline._validate_fitparams(
{}, feature_pipeline.steps)
return feature_pipeline
return Pipeline(pipe_steps)
def define_label_pipeline(self, threads=-1) -> Pipeline: