mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 11:17:47 +00:00
Mark Aron Szulyovszky
95573eb9dd
* feat(Data): add option to predict 3 classes * feat(Evaluation): added ability to evaluate 3 class predictions * chore(Config): set sensible config for regression models * feat(Data): added option to use balanced or imbalanced three-class data * feat(Evaluate): correctly track "no_of_samples" now that we have three classes * chore(Sweep): remove probably not useful scaler values from sweep
97 lines
2.4 KiB
Python
97 lines
2.4 KiB
Python
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import numpy as np
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import pandas as pd
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from training.walk_forward import walk_forward_train_test
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from models.base import Model
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from utils.evaluate import evaluate_predictions
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no_of_rows = 100
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def __generate_even_odd_test_data(no_of_rows) -> tuple[pd.DataFrame, pd.Series]:
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''' Test data, where X[n][any_column] == 1 if n is even, else 0
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'''
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no_columns = 6
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X = [[-1 if row % 2 == 0 else 1] * no_columns for row in range(no_of_rows)]
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assert X[0][0] == -1
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assert X[1][0] == 1
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assert X[2][0] == -1
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assert X[3][0] == 1
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X = pd.DataFrame(X)
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y = [-1 if (row+1) % 2 == 0 else 1 for row in range(no_of_rows)]
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assert y[0] == 1
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assert y[1] == -1
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assert y[2] == 1
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assert y[3] == -1
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y = pd.Series(y)
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return X, y
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class EvenOddStubModel(Model):
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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 if n is even,
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'''
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data_scaling = "unscaled"
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only_column = None
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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, prev_model):
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assert len(X) == self.window_length
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for i in range(len(X)):
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assert y[i] == -1 if X[i][0] == 1 else 1
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def predict(self, X):
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return np.array([-1 if X[0][0] == 1 else 1])
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def clone(self):
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return self
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def test_evaluation():
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X, y = __generate_even_odd_test_data(no_of_rows)
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window_length = 10
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model = EvenOddStubModel(window_length = window_length)
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scaler = None
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models, predictions = walk_forward_train_test(
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model_name='test',
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model=model,
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X=X,
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y=y,
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target_returns=y,
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expanding_window=False,
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window_size=window_length,
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retrain_every=10,
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scaler=scaler
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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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fake_target_returns = y * 0.1
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processed_predictions_to_match_returns = predictions * 0.1
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result = evaluate_predictions(
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model_name='test',
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target_returns=fake_target_returns,
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y_pred=processed_predictions_to_match_returns,
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y_true=y,
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method='classification',
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no_of_classes='two'
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)
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assert result['accuracy'] == 100.0
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