def test_pairwise_mutual_information():
from pandas import DataFrame
from .testdata import adults01
frame = DataFrame(adults01)
mi = pairwise_mutual_information(frame)
from numpy import allclose, alltrue
assert allclose(mi, mi.T)
assert alltrue(mi >= 0.0)
assert alltrue(mi <= 1.0)
def test_plot_figures_output_show():
from pandas import DataFrame
plot_confusion_matrix(DataFrame({
'True': [2, 3],
'False': [5, 0]
}),
otype='show')
plot_confusion_matrix(DataFrame({
'7th-8th': [2, 3, 5, 0],
'Masters': [0, 4, 1, 0],
'11th': [0, 1, 5, 2],
'Bachelors': [2, 0, 0, 6]
}),
otype='show')
bins = np.array([
28., 29.25, 30.5, 31.75, 33., 34.25, 35.5, 36.75, 38., 39.25, 40.5,
41.75, 43., 44.25, 45.5, 46.75, 48., 49.25, 50.5
])
counts = np.array(
[[1, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[1, 0, 1, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['Private', 'Self-emp-not-inc', 'State-gov'])
counts = np.array([[6, 2, 1], [6, 2, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['11th', '9th', 'Bachelors', 'HS-grad', 'Masters'])
counts = np.array([[3, 2, 2, 1, 1], [3, 2, 2, 1, 1]])
plot_histogram(bins, counts, otype='show')
bins = np.array([
5., 5.45, 5.9, 6.35, 6.8, 7.25, 7.7, 8.15, 8.6, 9.05, 9.5, 9.95, 10.4,
10.85, 11.3, 11.75, 12.2, 12.65, 13.1
])
counts = np.array(
[[1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0],
[1, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0]])
plot_histogram(bins, counts, otype='show')
bins = np.array(['Female', 'Male'])
counts = np.array([[5, 4], [5, 4]])
plot_histogram(bins, counts, otype='show')
from .testdata import adults01
from ds4ml.metrics import pairwise_mutual_information
data = pairwise_mutual_information(DataFrame(adults01))
plot_heatmap(data, otype='show')
def mi(self):
""" Return mutual information of pairwise attributes. """
from ds4ml.metrics import pairwise_mutual_information
return pairwise_mutual_information(self)