mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 19:27:47 +00:00
Mark Aron Szulyovszky
c611481eb6
* refactor(WalkForward): separate train / test functions (draft) to potentially help with inference later * fix(Training): use the new separate train / test functions * feat(Training): return and pass in scalers that are necessary for inference * fix(Project): runtime errors * fix(WalkForward): use the correct `train_from` value * fix(Tests): for new walk_forward functions() * refactor(WalkForward): rename `walk_forward_test()` to `walk_forward_inference()`
94 lines
2.3 KiB
Python
94 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
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from models.base import Model
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from sklearn.preprocessing import MinMaxScaler
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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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'''
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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[0][0] == 0
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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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X = pd.DataFrame(X)
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y = [row+1 for row in range(no_of_rows)]
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assert y[0] == 1
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assert y[1] == 2
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assert y[2] == 3
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y = pd.Series(y)
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return X, y
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class IncrementingStubModel(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 == y[n]
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'''
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data_scaling = "unscaled"
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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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assert len(X) == self.window_length
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for i in range(len(X)):
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assert X[i][0] + 1 == y[i]
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def predict(self, X):
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return (X[0][0] + 1, np.array([]))
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def clone(self):
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return self
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def get_name(self) -> str:
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return 'test'
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def initialize_network(self, input_dim: int, output_dim: int):
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pass
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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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model = IncrementingStubModel(window_length = window_length)
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scaler = MinMaxScaler()
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models, scalers = walk_forward_train(
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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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predictions, probs = walk_forward_inference(
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model_name='test',
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models=models,
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scalers=scalers,
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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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)
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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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