def test_knn_predict(self):
"""
Testing the predicted values of the knnRegressor
"""
y_predict = [2, 2, 3, 3]
my_knn = knnRegressor("y~.", data=df1)
my_knn.fit()
self.assertEqual(y_predict, my_knn.predict(df1).tolist())
def test_knn_predict_with_k_value(self):
"""
Testing the predicted values of the knnRegressor with 2 nearest neighbors
"""
y_expected = [1, 1, 4.5, 4.5]
my_knn = knnRegressor("y~.", k=2, data=df1)
my_knn.fit()
y_predicted = my_knn.predict(df1)
self.assertEqual(y_expected, y_predicted.tolist())
def test_knn_predict(self):
"""
Testing the predicted values of the knnClassifier
"""
y_predict = [
0.3333333333333333, 0.3333333333333333, 0.3333333333333333,
0.3333333333333333
]
my_knn = knnRegressor("y~.", data=df_cl)
my_knn.fit()
self.assertEqual(y_predict, my_knn.predict(df1).tolist())
def test_knn_fit(self):
"""
Testing the fit method of the knnClassifier and making sure it is not raising error
"""
my_knn = knnRegressor("y~.", data=df_cl)
my_knn.fit()
def test_knn_init(self):
"""
Making sure if you are able to create instance of the knnRegressor.
"""
my_model = knnRegressor("y~.", df1)
from src.models.BaseModel import BaseModel
if __name__ == "__main__":
model_formula = FormulaParser('dist~speed', ["speed", "dist"])
df = pd.DataFrame({"x": [0, 0, 1, 1], "y": [0, 2, 4, 5]})
print('Data to fit the model: \n', df)
my_lm = lm("y~.", data=df)
my_lm.fit()
my_lm.summary()
#
df1 = pd.DataFrame({"x": [0, 0, 1, 1], "y": [0, 2, 4, 5]})
predict_df = pd.DataFrame({"x": [0, 1]})
my_lm = lm("y~.", data=df1)
my_lm.fit()
my_lm.summary()
y_hat = my_lm.predict(predict_df)
print(y_hat)
my_knn = knnRegressor("y~.", data=df1)
my_knn.fit()
print(my_knn.predict(df1))
df2 = pd.DataFrame({"x": [0, 1, 2, 3], "y": [0, 0, 1, 1]})
my_knn_class = knnClassifier("y~.", data=df2)
my_knn_class.fit()
print(my_knn_class.predict_proba(df2))