import os
import pytest
import pandas as pd
import numpy as np

from shclassify.utils import (inverse_logit,
                              choose_from_multinomial_probs,
                              choose_from_binary_probs)

def test_inverse_logit():
    assert inverse_logit(0) == 0.5

def test_choose_from_multinomial_probs():
    n_obs = 3
    classes = ['a', 'b', 'c']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    classes = choose_from_multinomial_probs(df)

    assert type(classes) is pd.DataFrame
    assert classes.shape == (n_obs, 1)
    assert classes.columns == ['class']

    def in_classes(x, classes=classes):
        x in classes

    assert classes['class'].apply(in_classes).all()

def test_choose_from_multinomial_probs_with_bad_input():
    n_obs = 3
    classes = ['a']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    with pytest.raises(ValueError) as e:
        choose_from_multinomial_probs(df)

    assert 'Data frame must have more than 1 column' in str(e.value)

def test_choose_from_binary_probs():
    n_obs = 3
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,1))
    )

    classes = choose_from_binary_probs(df, 'true', 'false')

    assert type(classes) is pd.DataFrame
    assert classes.shape == (n_obs, 1)
    assert classes.applymap(lambda x: x in ['true', 'false']).all()[0]
    assert classes.columns == ['class']

def test_choose_from_binary_probs_with_bad_shape():
    n_obs = 3
    classes = ['a', 'b']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    with pytest.raises(ValueError) as e:
        choose_from_binary_probs(df, 'true', 'false')

    assert 'Data frame must have 1 column' == str(e.value)

def test_choose_from_binary_probs_with_bad_args():
    n_obs = 3
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,1))
    )

    with pytest.raises(ValueError) as e:
        classes = choose_from_binary_probs(df, 'true', 'true')

    assert 'Class names for true and false results must differ' == str(e.value)

    with pytest.raises(ValueError) as e:
        classes = choose_from_binary_probs(df, 'true', 'false', threshold=50)

    assert 'Threshold must be between 0 and 1' == str(e.value)

import os
import pytest
import pandas as pd
import numpy as np

from shclassify.utils import (inverse_logit,
                              choose_from_multinomial_probs,
                              choose_from_binary_probs)

def test_inverse_logit():
    assert inverse_logit(0) == 0.5

def test_choose_from_multinomial_probs():
    n_obs = 3
    classes = ['a', 'b', 'c']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    classes = choose_from_multinomial_probs(df)

    assert type(classes) is pd.DataFrame
    assert classes.shape == (n_obs, 1)
    assert classes.columns == ['class']

    def in_classes(x, classes=classes):
        x in classes

    assert classes['class'].apply(in_classes).all()

def test_choose_from_multinomial_probs_with_bad_input():
    n_obs = 3
    classes = ['a']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    with pytest.raises(ValueError) as e:
        choose_from_multinomial_probs(df)

    assert 'Data frame must have more than 1 column' in str(e.value)

def test_choose_from_binary_probs():
    n_obs = 3
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,1))
    )

    classes = choose_from_binary_probs(df, 'true', 'false')

    assert type(classes) is pd.DataFrame
    assert classes.shape == (n_obs, 1)
    assert classes.applymap(lambda x: x in ['true', 'false']).all()[0]
    assert classes.columns == ['class']

def test_choose_from_binary_probs_with_bad_shape():
    n_obs = 3
    classes = ['a', 'b']
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,len(classes))), columns=classes
    )

    with pytest.raises(ValueError) as e:
        choose_from_binary_probs(df, 'true', 'false')

    assert 'Data frame must have 1 column' == str(e.value)

def test_choose_from_binary_probs_with_bad_args():
    n_obs = 3
    df = pd.DataFrame(
        np.random.uniform(size=(n_obs,1))
    )

    with pytest.raises(ValueError) as e:
        classes = choose_from_binary_probs(df, 'true', 'true')

    assert 'Class names for true and false results must differ' == str(e.value)

    with pytest.raises(ValueError) as e:
        classes = choose_from_binary_probs(df, 'true', 'false', threshold=50)

    assert 'Threshold must be between 0 and 1' == str(e.value)
