Jaime Sendra Berenguer-2020.
MLearner Machine Learning Library Extensions
Author:Jaime Sendra Berenguer<www.linkedin.com/in/jaisenbe>
License: MIT
"""
import numpy as np
import pytest
from mlearner.data import data_uniform, data_normal, data_gamma, create_dataset
n = 1000
config = {
'A': data_uniform(0, 1, n),
'B': data_normal(n),
'C': data_normal(mu=5, sd=2, n=n),
'D': data_gamma(a=5, n=n)
}
config_bad = np.array([1, 2, 3])
def test_config_bad():
with pytest.raises(TypeError):
create_dataset(config_bad, n)
def test_type_n():
with pytest.raises(TypeError):
create_dataset(config, float(n))
def test_minor_n():
def test_float_n_minor():
with pytest.raises(NameError):
data_gamma(a, 0)
def test_result_mean():
data = data_gamma(a, n)
assert (np.mean(data) > np.median(data))
def test_float_n():
with pytest.raises(TypeError):
data_gamma(a, float(n))
def test_type_n():
with pytest.raises(TypeError):
data_gamma(a, str(n))
def test_type_a():
with pytest.raises(TypeError):
data_gamma(str(a), n)
def test_data_len():
data = data_gamma(a, n)
assert (len(data) == n)
"""Jaime Sendra Berenguer-2020.
MLearner Machine Learning Library Extensions
Author: Jaime Sendra Berenguer<www.linkedin.com/in/jaisenbe>
License: MIT
"""
import numpy as np
import pytest
from mlearner.preprocessing import FixSkewness
from mlearner.data import data_gamma, create_dataset
n = 5
config = {
'A': data_gamma(a=2.5, n=n),
'B': data_gamma(a=3.5, n=n),
'C': data_gamma(a=4, n=n),
'D': data_gamma(a=5, n=n),
'E': np.repeat("KO", n)
}
data = create_dataset(config, n)
data_error = [1, 2, 3, 4, 1, 2, 3, 4]
col = ["D"]
col_error = np.array([1, 2])
col_object = ["E"]
def test_error_type_col():
with pytest.raises(TypeError):
FixSkewness(columns=col_error)