def test_linear_regressor_predict_not_fitted():
"""Test of `LinearRegressor` class with prediction without fit."""
lr = LinearRegressor(learning_rate=0.1, iterations=1, standardize=False)
X = np.array([1])
with pytest.raises(NotFitted):
lr.predict(X)
history=True),
standardize=True)
lr.fit(X, y)
# Plot the results
figure, (a0, a1) = plt.subplots(1,
2,
num="Linear Regression",
figsize=(16, 9),
gridspec_kw={"width_ratios": [3, 1]})
figure.suptitle("Linear Regression on Boston house prices dataset",
fontsize=16)
a0.plot(X, y, "b.")
a0.plot(
range(3, 10),
[lr.predict(np.array([[x]])).flat[0] for x in range(3, 10)],
"r-",
label="Linear regression",
)
a0.set(
xlabel="Average number of rooms per dwelling",
ylabel="Median value of owner-occupied homes in $1000's",
)
a0.legend(loc="upper left")
a1.plot(range(len(lr.history)), lr.history)
a1.set(xlabel="Number of iterations", ylabel="Training error objective")
plt.show()