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feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)

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* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba

* fix(WalkForward): inference mini-batch parallelization

* fix(WalkForward): don't use the parallel version of any of the functions

* feat(CI): download the data required

* fix(Project): 5min_crypto folder added

* fix(Evaluate): make sure we have numerical stability in returns

* feat(Models): use SKLearn models directly to enable composability

* feat(Inference): batched inference now working, added forecasting_horizon

* fix(Inference): works again

* fix(Inference)

* chore(Models): remove unused Ensemble model

* fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then

* Update test.yml
This commit is contained in:
Mark Aron Szulyovszky
2022-02-17 16:36:35 +01:00
committed by GitHub
parent 5c94af8b01
commit 9d47ee942d
52 changed files with 470 additions and 628 deletions
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transformations/rfe.py
+3 -7
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@@ -4,21 +4,17 @@ from typing import Optional
from copy import deepcopy
from sklearn.feature_selection import RFE
import pandas as pd
from models.base import Model
from models.model_map import default_feature_selector_classification
from models.sklearn import SKLearnModel
class RFETransformation(Transformation):
rfe: RFE
n_feature_to_select: int
def __init__(self, n_feature_to_select: int, model: Model, step = 0.1):
def __init__(self, n_feature_to_select: int, model: SKLearnModel, step = 0.1):
self.n_feature_to_keep = n_feature_to_select
self.model = model
if hasattr(self.model, 'model') == False: return
if hasattr(self.model.model, 'feature_importances_') == False and hasattr(self.model.model, 'coef_') == False:
model = default_feature_selector_classification
self.rfe = RFE(model.model, n_features_to_select= n_feature_to_select, step=step)
self.rfe = RFE(model, n_features_to_select= n_feature_to_select, step=step)
def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None) -> None:
if self.rfe is None: return