import numpy as np
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.pipeline import FeatureUnion
from skits.feature_extraction import AutoregressiveTransformer, SeasonalTransformer
from skits.pipeline import ForecasterPipeline, ClassifierPipeline
from skits.preprocessing import (
ReversibleImputer,
DifferenceTransformer,
HorizonTransformer,
)
SEED = 666 # \m/
class TestPipelines:
steps = [
("pre_differencer", DifferenceTransformer(period=1)),
("pre_imputer_1", ReversibleImputer()),
(
"features",
FeatureUnion(
[
("ar_transformer", AutoregressiveTransformer(num_lags=3)),
("seasonal_transformer", SeasonalTransformer(seasonal_period=4)),
]
),
),
("post_lag_imputer_2", ReversibleImputer()),
]
dt = DifferenceTransformer(period=1)
ri1 = ReversibleImputer()
fe = FeatureUnion(
[
("ar_transformer", AutoregressiveTransformer(num_lags=3)),
("seasonal_transformer", SeasonalTransformer(seasonal_period=4)),
]
)
ri2 = ReversibleImputer()
def test_predict(self):
# Let's just see if it works
# TODO: Make this a real test
np.random.seed(SEED)
l = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 0.1, size=100)
# Ignore the DifferenceTransformer. It's actually bad.
steps = list(self.steps[1:])
steps.append(("regressor", LinearRegression(fit_intercept=False)))
pipeline = ForecasterPipeline(steps)
pipeline.fit(y[:, np.newaxis], y)
y_pred = pipeline.predict(y[:, np.newaxis], to_scale=True, refit=True)
assert np.mean((y_pred - y.squeeze()) ** 2) < 0.05
def test_forecast(self):
# Let's just see if it works
# TODO: Make this a real test
l = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 0.1, size=100)
steps = list(self.steps)
steps.append(("regressor", LinearRegression(fit_intercept=False)))
pipeline = ForecasterPipeline(steps)
pipeline.fit(y[:, np.newaxis], y)
pipeline.forecast(y[:, np.newaxis], 20)
def test_classifier(self):
# Let's just see if it works
# TODO: Make this a real test
np.random.seed(SEED)
l = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 0.1, size=100)
steps = list(self.steps)
steps.append(
("classifier", LogisticRegression(solver="lbfgs", fit_intercept=False))
)
pipeline = ClassifierPipeline(steps)
y_true = y > 0
pipeline.fit(y[:, np.newaxis], y_true)
y_pred = pipeline.predict(y[:, np.newaxis])
assert (y_pred == y_true).mean() > 0.75
def test_multiouput_prediction(self):
# TODO: Make this a real test
steps = [
("pre_horizon", HorizonTransformer(horizon=4)),
("pre_imputer", ReversibleImputer(y_only=True)),
(
"features",
FeatureUnion(
[("ar_transformer", AutoregressiveTransformer(num_lags=3))]
),
),
("post_lag_imputer", ReversibleImputer()),
("regressor", LinearRegression()),
]
pipeline = ForecasterPipeline(steps)
l = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 0.1, size=100)
pipeline.fit(y[:, np.newaxis], y)
pipeline.predict(y[:, np.newaxis], to_scale=True, refit=True)
def test_multiouput_forecast(self):
# TODO: Make this a real test
steps = [
("pre_horizon", HorizonTransformer(horizon=4)),
("pre_imputer", ReversibleImputer(y_only=True)),
(
"features",
FeatureUnion(
[("ar_transformer", AutoregressiveTransformer(num_lags=3))]
),
),
("post_lag_imputer", ReversibleImputer()),
("regressor", LinearRegression()),
]
pipeline = ForecasterPipeline(steps)
l = np.linspace(0, 1, 100)
y = np.sin(2 * np.pi * 5 * l) + np.random.normal(0, 0.1, size=100)
pipeline.fit(y[:, np.newaxis], y)
pipeline.forecast(y[:, np.newaxis], 20)
