def test_nanvar_with_ddof(self):
x = np.random.uniform(0, 10, (20, 100))
np.fill_diagonal(x, np.nan)
for axis in [None, 0, 1]:
np.testing.assert_almost_equal(
np.nanvar(x, axis=axis, ddof=10),
nanvar(csr_matrix(x), axis=axis, ddof=10),
)
def test_nanvar_with_ddof(self):
x = np.random.uniform(0, 10, (20, 100))
np.fill_diagonal(x, np.nan)
for axis in [None, 0, 1]:
np.testing.assert_almost_equal(
np.nanvar(x, axis=axis, ddof=10),
nanvar(csr_matrix(x), axis=axis, ddof=10),
)
def __compute_statistics(self):
# Since data matrices can of mixed sparsity, we need to compute
# attributes separately for each of them.
matrices = [self.__attributes, self.__class_vars, self.__metas]
# Filter out any matrices with size 0
matrices = list(filter(lambda tup: tup[1].size, matrices))
self._variable_types = np.array([type(var) for var in self.variables])
self._variable_names = np.array(
[var.name.lower() for var in self.variables])
self._min = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmin(x, axis=0),
continuous_f=lambda x: ut.nanmin(x, axis=0),
time_f=lambda x: ut.nanmin(x, axis=0),
)
self._dispersion = self.__compute_stat(
matrices,
discrete_f=_categorical_entropy,
continuous_f=lambda x: np.sqrt(ut.nanvar(x, axis=0)) / ut.nanmean(
x, axis=0),
)
self._missing = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.countnans(x, axis=0),
continuous_f=lambda x: ut.countnans(x, axis=0),
string_f=lambda x: (x == StringVariable.Unknown).sum(axis=0),
time_f=lambda x: ut.countnans(x, axis=0),
)
self._max = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmax(x, axis=0),
continuous_f=lambda x: ut.nanmax(x, axis=0),
time_f=lambda x: ut.nanmax(x, axis=0),
)
# Since scipy apparently can't do mode on sparse matrices, cast it to
# dense. This can be very inefficient for large matrices, and should
# be changed
def __mode(x, *args, **kwargs):
if sp.issparse(x):
x = x.todense(order="C")
# return ss.mode(x, *args, **kwargs)[0]
return ut.nanmode(
x, *args,
**kwargs)[0] # Temporary replacement for scipy < 1.2.0
self._center = self.__compute_stat(
matrices,
discrete_f=lambda x: __mode(x, axis=0),
continuous_f=lambda x: ut.nanmean(x, axis=0),
time_f=lambda x: ut.nanmean(x, axis=0),
)
def __compute_statistics(self):
# Since data matrices can of mixed sparsity, we need to compute
# attributes separately for each of them.
matrices = [self.__attributes, self.__class_vars, self.__metas]
# Filter out any matrices with size 0
matrices = list(filter(lambda tup: tup[1].size, matrices))
self._variable_types = np.array([type(var) for var in self.variables])
self._variable_names = np.array([var.name.lower() for var in self.variables])
self._min = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmin(x, axis=0),
continuous_f=lambda x: ut.nanmin(x, axis=0),
time_f=lambda x: ut.nanmin(x, axis=0),
)
self._dispersion = self.__compute_stat(
matrices,
discrete_f=_categorical_entropy,
continuous_f=lambda x: np.sqrt(ut.nanvar(x, axis=0)) / ut.nanmean(x, axis=0),
)
self._missing = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.countnans(x, axis=0),
continuous_f=lambda x: ut.countnans(x, axis=0),
string_f=lambda x: (x == StringVariable.Unknown).sum(axis=0),
time_f=lambda x: ut.countnans(x, axis=0),
)
self._max = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmax(x, axis=0),
continuous_f=lambda x: ut.nanmax(x, axis=0),
time_f=lambda x: ut.nanmax(x, axis=0),
)
# Since scipy apparently can't do mode on sparse matrices, cast it to
# dense. This can be very inefficient for large matrices, and should
# be changed
def __mode(x, *args, **kwargs):
if sp.issparse(x):
x = x.todense(order="C")
# return ss.mode(x, *args, **kwargs)[0]
return ut.nanmode(x, *args, **kwargs)[0] # Temporary replacement for scipy
self._center = self.__compute_stat(
matrices,
discrete_f=lambda x: __mode(x, axis=0),
continuous_f=lambda x: ut.nanmean(x, axis=0),
time_f=lambda x: ut.nanmean(x, axis=0),
)
def get_continuous_stats(self, column):
"""
Return mean, variance and distance betwwen pairs of missing values
for the given columns. The method is called by inherited `fit_rows`
to construct a row-distance model
"""
mean = util.nanmean(column)
var = util.nanvar(column)
if self.normalize:
if var == 0:
return None
dist_missing2_cont = 1
else:
dist_missing2_cont = 2 * var
if np.isnan(dist_missing2_cont):
dist_missing2_cont = 0
return mean, var, dist_missing2_cont
def get_continuous_stats(self, column):
"""
Return mean, variance and distance betwwen pairs of missing values
for the given columns. The method is called by inherited `fit_rows`
to construct a row-distance model
"""
mean = util.nanmean(column)
var = util.nanvar(column)
if self.normalize:
if var == 0:
return None
dist_missing2_cont = 1
else:
dist_missing2_cont = 2 * var
if np.isnan(dist_missing2_cont):
dist_missing2_cont = 0
return mean, var, dist_missing2_cont
def __compute_statistics(self):
# Since data matrices can of mixed sparsity, we need to compute
# attributes separately for each of them.
matrices = [self.__attributes, self.__class_vars, self.__metas]
# Filter out any matrices with size 0
matrices = list(filter(lambda tup: tup[1].size, matrices))
self._variable_types = np.array([type(var) for var in self.variables])
self._variable_names = np.array(
[var.name.lower() for var in self.variables])
self._min = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmin(x, axis=0),
continuous_f=lambda x: ut.nanmin(x, axis=0),
time_f=lambda x: ut.nanmin(x, axis=0),
)
self._dispersion = self.__compute_stat(
matrices,
discrete_f=_categorical_entropy,
continuous_f=lambda x: np.sqrt(ut.nanvar(x, axis=0)) / ut.nanmean(
x, axis=0),
)
self._missing = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.countnans(x, axis=0),
continuous_f=lambda x: ut.countnans(x, axis=0),
string_f=lambda x: (x == StringVariable.Unknown).sum(axis=0),
time_f=lambda x: ut.countnans(x, axis=0),
)
self._max = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmax(x, axis=0),
continuous_f=lambda x: ut.nanmax(x, axis=0),
time_f=lambda x: ut.nanmax(x, axis=0),
)
self._center = self.__compute_stat(
matrices,
discrete_f=lambda x: ss.mode(x)[0],
continuous_f=lambda x: ut.nanmean(x, axis=0),
time_f=lambda x: ut.nanmean(x, axis=0),
)
def __compute_statistics(self):
# Since data matrices can of mixed sparsity, we need to compute
# attributes separately for each of them.
matrices = [self.__attributes, self.__class_vars, self.__metas]
# Filter out any matrices with size 0
matrices = list(filter(lambda tup: tup[1].size, matrices))
self._variable_types = np.array([type(var) for var in self.variables])
self._variable_names = np.array([var.name.lower() for var in self.variables])
self._min = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmin(x, axis=0),
continuous_f=lambda x: ut.nanmin(x, axis=0),
time_f=lambda x: ut.nanmin(x, axis=0),
)
self._dispersion = self.__compute_stat(
matrices,
discrete_f=_categorical_entropy,
continuous_f=lambda x: np.sqrt(ut.nanvar(x, axis=0)) / ut.nanmean(x, axis=0),
)
self._missing = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.countnans(x, axis=0),
continuous_f=lambda x: ut.countnans(x, axis=0),
string_f=lambda x: (x == StringVariable.Unknown).sum(axis=0),
time_f=lambda x: ut.countnans(x, axis=0),
)
self._max = self.__compute_stat(
matrices,
discrete_f=lambda x: ut.nanmax(x, axis=0),
continuous_f=lambda x: ut.nanmax(x, axis=0),
time_f=lambda x: ut.nanmax(x, axis=0),
)
self._center = self.__compute_stat(
matrices,
discrete_f=lambda x: ss.mode(x)[0],
continuous_f=lambda x: ut.nanmean(x, axis=0),
time_f=lambda x: ut.nanmean(x, axis=0),
)
def test_nanvar(self, array):
for X in self.data:
X_sparse = array(X)
np.testing.assert_array_equal(nanvar(X_sparse), np.nanvar(X))
def coefficient_of_variation(x: np.ndarray) -> np.ndarray:
mu = ut.nanmean(x, axis=0)
mask = ~np.isclose(mu, 0, atol=1e-12)
result = np.full_like(mu, fill_value=np.inf)
result[mask] = np.sqrt(ut.nanvar(x, axis=0)[mask]) / mu[mask]
return result
def test_nanvar(self, array):
for X in self.data:
X_sparse = array(X)
np.testing.assert_array_equal(
nanvar(X_sparse),
np.nanvar(X))
def __compute_statistics(self):
# We will compute statistics over all data at once
matrices = [self._data.X, self._data._Y, self._data.metas]
# Since data matrices can of mixed sparsity, we need to compute
# attributes separately for each of them.
matrices = zip([
self._domain.attributes, self._domain.class_vars, self._domain.metas
], matrices)
# Filter out any matrices with size 0, filter the zipped matrices to
# eliminate variables in a single swoop
matrices = list(filter(lambda tup: tup[1].size, matrices))
def _apply_to_types(attrs_x_pair, discrete_f=None, continuous_f=None,
time_f=None, string_f=None, default_val=np.nan):
"""Apply functions to variable types e.g. discrete_f to discrete
variables. Default value is returned if there is no function
defined for specific variable types."""
attrs, x = attrs_x_pair
result = np.full(len(attrs), default_val)
disc_var_idx, cont_var_idx, time_var_idx, str_var_idx = self._attr_indices(attrs)
if discrete_f and x[:, disc_var_idx].size:
result[disc_var_idx] = discrete_f(x[:, disc_var_idx].astype(np.float64))
if continuous_f and x[:, cont_var_idx].size:
result[cont_var_idx] = continuous_f(x[:, cont_var_idx].astype(np.float64))
if time_f and x[:, time_var_idx].size:
result[time_var_idx] = time_f(x[:, time_var_idx].astype(np.float64))
if string_f and x[:, str_var_idx].size:
result[str_var_idx] = string_f(x[:, str_var_idx].astype(np.object))
return result
self._variable_types = [type(var) for var in self._attributes]
self._variable_names = [var.name.lower() for var in self._attributes]
# Compute the center
_center = partial(
_apply_to_types,
discrete_f=lambda x: ss.mode(x)[0],
continuous_f=lambda x: ut.nanmean(x, axis=0),
)
self._center = np.hstack(map(_center, matrices))
# Compute the dispersion
def _entropy(x):
p = [ut.bincount(row)[0] for row in x.T]
p = [pk / np.sum(pk) for pk in p]
return np.fromiter((ss.entropy(pk) for pk in p), dtype=np.float64)
_dispersion = partial(
_apply_to_types,
discrete_f=lambda x: _entropy(x),
continuous_f=lambda x: ut.nanvar(x, axis=0),
)
self._dispersion = np.hstack(map(_dispersion, matrices))
# Compute minimum values
_max = partial(
_apply_to_types,
discrete_f=lambda x: ut.nanmax(x, axis=0),
continuous_f=lambda x: ut.nanmax(x, axis=0),
)
self._max = np.hstack(map(_max, matrices))
# Compute maximum values
_min = partial(
_apply_to_types,
discrete_f=lambda x: ut.nanmin(x, axis=0),
continuous_f=lambda x: ut.nanmin(x, axis=0),
)
self._min = np.hstack(map(_min, matrices))
# Compute # of missing values
_missing = partial(
_apply_to_types,
discrete_f=lambda x: ut.countnans(x, axis=0),
continuous_f=lambda x: ut.countnans(x, axis=0),
string_f=lambda x: (x == StringVariable.Unknown).sum(axis=0),
time_f=lambda x: ut.countnans(x, axis=0),
)
self._missing = np.hstack(map(_missing, matrices))
def test_nanvar(self, array):
for X in self.data:
X_sparse = array(X)
np.testing.assert_almost_equal(
nanvar(X_sparse), np.nanvar(X),
decimal=14) # np.nanvar and bn.nanvar differ slightly
