mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-06 07:37:49 +00:00
Mark Aron Szulyovszky
1856fcad22
* feat(Transformations): added Transformations abstraction & handling in walk_forward_train() & inference() * fix(WalkForward): use Dataframes to call Transformation.fit_transform() * feat(WalkForward): restored option for models to recieve unscaled data * fix(Transformations): output DataFrame as expected * fix(Tests): missing new property
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
from __future__ import annotations
|
|
from models.base import Model
|
|
import numpy as np
|
|
from xgboost import XGBClassifier
|
|
from sklearn.base import clone
|
|
|
|
class XGBoostModel(Model):
|
|
|
|
data_transformation = 'transformed'
|
|
only_column = None
|
|
feature_selection = 'on'
|
|
model_type = 'ml'
|
|
predict_window_size = 'single_timestamp'
|
|
|
|
def __init__(self, model: XGBClassifier):
|
|
self.model = model
|
|
|
|
def fit(self, X: np.ndarray, y: np.ndarray) -> None:
|
|
def map_to_xgb(y): return np.array([1 if i == 1 else 0 for i in y])
|
|
self.model.fit(X, map_to_xgb(y))
|
|
|
|
def predict(self, X) -> tuple[float, np.ndarray]:
|
|
pred = self.model.predict(X).item()
|
|
probability = self.model.predict_proba(X).squeeze()
|
|
def map_from_xgb(y): return 1 if y == 1 else -1
|
|
return (map_from_xgb(pred), probability)
|
|
|
|
def clone(self) -> XGBoostModel:
|
|
return XGBoostModel(clone(self.model))
|
|
|
|
def get_name(self) -> str:
|
|
return self.model.__class__.__name__
|
|
|
|
def initialize_network(self, input_dim:int, output_dim:int):
|
|
pass
|
|
|