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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
0395715fa1
* fix(Config): set remove_overlapping_events=True * fix(Config): only use level_1 features * feat(Config): added get_minimal_config
88 lines
2.3 KiB
Python
88 lines
2.3 KiB
Python
import numpy as np
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import pandas as pd
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from training.walk_forward import walk_forward_train, walk_forward_inference_batched
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from models.base import Model
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from sklearn.base import BaseEstimator, ClassifierMixin
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no_of_rows = 100
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def __generate_incremental_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
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"""Test data, where X[n][any_column] == y[n]+1"""
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no_columns = 6
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X = [[row] * no_columns for row in range(no_of_rows)]
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assert X[1][0] == 1
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assert X[2][0] == 2
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assert X[3][0] == 3
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assert X[4][0] == 4
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X = pd.DataFrame(X)
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y = [row + 1 for row in range(no_of_rows)]
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assert y[1] == 2
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assert y[2] == 3
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assert y[3] == 4
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y = pd.Series(y)
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return X, y
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class IncrementingStubModel(Model, BaseEstimator, ClassifierMixin):
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"""
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A deteministic model that can predict the future with 100% accuracy
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It verifies that the X[n][any_column]+1 == y[n]
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"""
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data_transformation = "original"
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only_column = None
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predict_window_size = "single_timestamp"
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def __init__(self, window_length) -> None:
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super().__init__()
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self.window_length = window_length
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def fit(self, X, y):
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for i in range(len(X)):
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assert X[i][-1] + 1 == y[i]
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def predict(self, X):
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return np.array([row[0] + 1 for row in X])
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def predict_proba(self, X):
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return np.array([[row[0] + 1, 0] for row in X])
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def test_walk_forward_train_test():
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X, y = __generate_incremental_test_data(no_of_rows)
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window_length = 10
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retrain_every = 10
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model = IncrementingStubModel(window_length=window_length)
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model_over_time = walk_forward_train(
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model=model,
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X=X,
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y=y,
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forward_returns=y,
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window_size=window_length,
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retrain_every=retrain_every,
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from_index=None,
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transformations_over_time=[],
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)
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predictions, _ = walk_forward_inference_batched(
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model_name="test",
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model_over_time=model_over_time,
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transformations_over_time=[],
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X=X,
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expanding_window=False,
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window_size=window_length,
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retrain_every=retrain_every,
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class_labels=[0, 1],
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from_index=None,
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)
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# verify if predictions are the same as y
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for i in range(window_length + 2, no_of_rows):
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assert predictions[i] == y[i]
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