def test_bins(size=500, n_bins=10):
columns = ['var1', 'var2']
df = pandas.DataFrame(random.uniform(size=(size, 2)), columns=columns)
x_limits = numpy.linspace(0, 1, n_bins + 1)[1:-1]
bins = compute_bin_indices(df[columns].values,
bin_limits=[x_limits, x_limits])
assert numpy.all(0 <= bins) and numpy.all(
bins < n_bins * n_bins), "the bins with wrong indices appeared"
def _compute_groups_indices(self, X, y, label):
"""Returns a list, each element is events' indices in some group."""
label_mask = y == label
extended_bin_limits = []
for var in self.uniform_features:
extended_bin_limits.append(numpy.percentile(X[var][label_mask], numpy.linspace(0, 100, 2 * self.n_bins + 1)))
groups_indices = list()
for shift in [0, 1]:
bin_limits = []
for axis_limits in extended_bin_limits:
bin_limits.append(axis_limits[1 + shift:-1:2])
bin_indices = compute_bin_indices(X.ix[:, self.uniform_features].values, bin_limits=bin_limits)
groups_indices += list(bin_to_group_indices(bin_indices, mask=label_mask))
return groups_indices
def test_bins(size=500, n_bins=10):
columns = ['var1', 'var2']
df = pandas.DataFrame(random.uniform(size=(size, 2)), columns=columns)
x_limits = numpy.linspace(0, 1, n_bins + 1)[1:-1]
bins = compute_bin_indices(df[columns].values, bin_limits=[x_limits, x_limits])
assert numpy.all(0 <= bins) and numpy.all(bins < n_bins * n_bins), "the bins with wrong indices appeared"
