import numpy as np
from sklearn import datasets, linear_model
from homework2_rent import score_rent
# Load the diabetes dataset
testData = datasets.load_diabetes()
# Load the diabetes dataset
# testData = datasets.load_diabetes()
# Use only one feature
data_X = testData.data[:, np.newaxis, 2]
# Split the data into training/testing sets
X_train = data_X[:-20]
X_test = data_X[-20:]
y_train = testData.target[:-20]
y_test = testData.target[-20:]
print("Mean squared error is: %.2f" % score_rent(X_test,y_test,X_train,y_train))
def test_rent(): R2 = score_rent() assert R2> 0.5
def test_rent():
assert score_rent() > 0.25
def test_rent():
assert (h.score_rent() < 0.3)
import homework2_rent as p if __name__ == "__main__": assert(p.score_rent()>0.5)
def test_rent():
if (h.score_rent() < 0.2):
return 1
else:
return 0
def test_rent():
"""Run test to check accuracy
"""
accuracy = score_rent()
assert accuracy >= 0.42
def test_rent():
p = score_rent()
assert p >= 0.59
def test_rent():
r2_value, _, _, _ = score_rent()
assert r2_value > 0.5