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=coefficient_of_variation,
)
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),
default_val=len(matrices[0]) if matrices else 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]
# Temporary replacement for scipy
return ut.nanmode(x, *args, **kwargs)[0]
self._center = self.__compute_stat(
matrices,
discrete_f=None,
continuous_f=lambda x: ut.nanmean(x, axis=0),
time_f=lambda x: ut.nanmean(x, axis=0),
)
self._median = self.__compute_stat(
matrices,
discrete_f=lambda x: __mode(x, axis=0),
continuous_f=lambda x: ut.nanmedian(x, axis=0),
time_f=lambda x: ut.nanmedian(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 test_nanmin_nanmax(self):
for X in self.data:
X_sparse = csr_matrix(X)
for axis in [None, 0, 1]:
np.testing.assert_array_equal(nanmin(X, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(nanmin(X_sparse, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(nanmax(X, axis=axis),
np.nanmax(X, axis=axis))
np.testing.assert_array_equal(nanmax(X_sparse, axis=axis),
np.nanmax(X, axis=axis))
def _get_colors(self):
"""Compute colors for different kinds of histograms."""
if self.target_var and self.target_var.is_discrete:
colors = [[QColor(*color)
for color in self.target_var.colors]] * self.n_bins
elif self.target_var and self.target_var.is_continuous:
palette = ContinuousPaletteGenerator(*self.target_var.colors)
bins = np.arange(self.n_bins)[:, np.newaxis]
edges = self.edges if self.attribute.is_discrete else self.edges[
1:-1]
# Need to digitize on `right` here so the samples will be assigned
# to the correct bin for coloring
bin_indices = ut.digitize(self.x, bins=edges, right=True)
mask = bin_indices == bins
colors = []
for bin_idx in range(self.n_bins):
biny = self.y[mask[bin_idx]]
if np.isfinite(biny).any():
mean = ut.nanmean(biny) / ut.nanmax(self.y)
else:
mean = 0 # bin is empty, color does not matter
colors.append([palette[mean]])
else:
colors = [[QColor('#ccc')]] * self.n_bins
return colors
def update_sel_range(self, y_data):
if y_data is None:
curve1 = curve2 = pg.PlotDataItem(x=self.x_data, y=self.__mean)
else:
curve1 = pg.PlotDataItem(x=self.x_data, y=nanmin(y_data, axis=0))
curve2 = pg.PlotDataItem(x=self.x_data, y=nanmax(y_data, axis=0))
self.sel_range.setCurves(curve1, curve2)
def _get_colors(self):
"""Compute colors for different kinds of histograms."""
if self.target_var and self.target_var.is_discrete:
colors = [[QColor(*color) for color in self.target_var.colors]] * self.n_bins
elif self.target_var and self.target_var.is_continuous:
palette = ContinuousPaletteGenerator(*self.target_var.colors)
bins = np.arange(self.n_bins)[:, np.newaxis]
edges = self.edges if self.attribute.is_discrete else self.edges[1:-1]
# Need to digitize on `right` here so the samples will be assigned
# to the correct bin for coloring
bin_indices = ut.digitize(self.x, bins=edges, right=True)
mask = bin_indices == bins
colors = []
for bin_idx in range(self.n_bins):
biny = self.y[mask[bin_idx]]
if np.isfinite(biny).any():
mean = ut.nanmean(biny) / ut.nanmax(self.y)
else:
mean = 0 # bin is empty, color does not matter
colors.append([palette[mean]])
else:
colors = [[QColor('#ccc')]] * self.n_bins
return colors
def test_nanmin_nanmax(self):
warnings.filterwarnings("ignore", r".*All-NaN slice encountered.*")
for X in self.data:
X_sparse = csr_matrix(X)
for axis in [None, 0, 1]:
np.testing.assert_array_equal(nanmin(X, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(nanmin(X_sparse, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(nanmax(X, axis=axis),
np.nanmax(X, axis=axis))
np.testing.assert_array_equal(nanmax(X_sparse, axis=axis),
np.nanmax(X, axis=axis))
def _get_range_curve(self):
color = QColor(self.color)
color.setAlpha(LinePlotStyle.RANGE_ALPHA)
bottom, top = nanmin(self.y_data, axis=0), nanmax(self.y_data, axis=0)
return pg.FillBetweenItem(pg.PlotDataItem(x=self.x_data, y=bottom),
pg.PlotDataItem(x=self.x_data, y=top),
brush=color)
def update_sel_range(self, y_data):
if y_data is None:
curve1 = curve2 = pg.PlotDataItem(x=self.x_data, y=self.__mean)
else:
curve1 = pg.PlotDataItem(x=self.x_data, y=nanmin(y_data, axis=0))
curve2 = pg.PlotDataItem(x=self.x_data, y=nanmax(y_data, axis=0))
self.sel_range.setCurves(curve1, curve2)
def _get_histogram_edges(self):
"""Get the edges in the histogram based on the attribute type.
In case of a continuous variable, we split the variable range into
n bins. In case of a discrete variable, bins don't make sense, so we
just return the attribute values.
This will return the staring and ending edge, not just the edges in
between (in the case of a continuous variable).
Returns
-------
np.ndarray
"""
if self.attribute.is_discrete:
return np.array(
[self.attribute.to_val(v) for v in self.attribute.values])
else:
edges = np.linspace(ut.nanmin(self.x), ut.nanmax(self.x),
self.n_bins)
edge_diff = edges[1] - edges[0]
edges = np.hstack((edges, [edges[-1] + edge_diff]))
# If the variable takes on a single value, we still need to spit
# out some reasonable bin edges
if np.all(edges == edges[0]):
edges = np.array([edges[0] - 1, edges[0], edges[0] + 1])
return edges
def _get_colors(self):
"""Compute colors for different kinds of histograms."""
target = self.target_var
if target and target.is_discrete:
colors = [list(target.palette)[:len(target.values)]] * self.n_bins
elif self.target_var and self.target_var.is_continuous:
palette = self.target_var.palette
bins = np.arange(self.n_bins)[:, np.newaxis]
edges = self.edges if self.attribute.is_discrete else self.edges[
1:-1]
bin_indices = ut.digitize(self.x, bins=edges)
mask = bin_indices == bins
colors = []
for bin_idx in range(self.n_bins):
biny = self.y[mask[bin_idx]]
if np.isfinite(biny).any():
mean = ut.nanmean(biny) / ut.nanmax(self.y)
else:
mean = 0 # bin is empty, color does not matter
colors.append([palette.value_to_qcolor(mean)])
else:
colors = [[QColor('#ccc')]] * self.n_bins
return colors
def _get_histogram_edges(self):
"""Get the edges in the histogram based on the attribute type.
In case of a continuous variable, we split the variable range into
n bins. In case of a discrete variable, bins don't make sense, so we
just return the attribute values.
This will return the staring and ending edge, not just the edges in
between (in the case of a continuous variable).
Returns
-------
np.ndarray
"""
if self.attribute.is_discrete:
return np.array([self.attribute.to_val(v) for v in self.attribute.values])
else:
edges = np.linspace(ut.nanmin(self.x), ut.nanmax(self.x), self.n_bins)
edge_diff = edges[1] - edges[0]
edges = np.hstack((edges, [edges[-1] + edge_diff]))
# If the variable takes on a single value, we still need to spit
# out some reasonable bin edges
if np.all(edges == edges[0]):
edges = np.array([edges[0] - 1, edges[0], edges[0] + 1])
return edges
def _get_range_curve(self):
color = QColor(self.color)
color.setAlpha(LinePlotStyle.RANGE_ALPHA)
bottom, top = nanmin(self.y_data, axis=0), nanmax(self.y_data, axis=0)
return pg.FillBetweenItem(
pg.PlotDataItem(x=self.x_data, y=bottom),
pg.PlotDataItem(x=self.x_data, y=top), brush=color
)
def _get_range_curve(self):
color = QColor(self.color)
color.setAlpha(self.graph.range_settings[Updater.ALPHA_LABEL])
bottom, top = nanmin(self.y_data, axis=0), nanmax(self.y_data, axis=0)
return pg.FillBetweenItem(
pg.PlotDataItem(x=self.x_data, y=bottom),
pg.PlotDataItem(x=self.x_data, y=top), brush=color
)
def test_nanmin_nanmax(self):
for X in self.data:
X_sparse = csr_matrix(X)
for axis in [None, 0, 1]:
np.testing.assert_array_equal(
nanmin(X, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(
nanmin(X_sparse, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(
nanmax(X, axis=axis),
np.nanmax(X, axis=axis))
np.testing.assert_array_equal(
nanmax(X_sparse, axis=axis),
np.nanmax(X, axis=axis))
def test_nanmin_nanmax(self):
warnings.filterwarnings("ignore", r".*All-NaN slice encountered.*")
for X in self.data:
X_sparse = csr_matrix(X)
for axis in [None, 0, 1]:
np.testing.assert_array_equal(
nanmin(X, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(
nanmin(X_sparse, axis=axis),
np.nanmin(X, axis=axis))
np.testing.assert_array_equal(
nanmax(X, axis=axis),
np.nanmax(X, axis=axis))
np.testing.assert_array_equal(
nanmax(X_sparse, axis=axis),
np.nanmax(X, axis=axis))
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 __call__(self, data: Table, attribute):
values, _ = data.get_column_view(attribute)
points = []
if values.size:
mn, mx = ut.nanmin(values), ut.nanmax(values)
if not np.isnan(mn):
minf = int(1 + np.floor(mn / self.width))
maxf = int(1 + np.floor(mx / self.width))
if maxf - minf - 1 >= 100:
raise TooManyIntervals
points = [i * self.width for i in range(minf, maxf)]
return Discretizer.create_discretized_var(data.domain[attribute],
points,
ndigits=self.digits)
def __call__(self, data, attribute, fixed=None):
if fixed:
min, max = fixed[attribute.name]
points = self._split_eq_width(min, max)
else:
if type(data) == SqlTable:
stats = BasicStats(data, attribute)
points = self._split_eq_width(stats.min, stats.max)
else:
values = data[:, attribute]
values = values.X if values.X.size else values.Y
min, max = ut.nanmin(values), ut.nanmax(values)
points = self._split_eq_width(min, max)
return Discretizer.create_discretized_var(
data.domain[attribute], points)
def __call__(self, data: Table, attribute, fixed=None):
if fixed:
mn, mx = fixed[attribute.name]
points = self._split_eq_width(mn, mx)
else:
if type(data) == SqlTable:
stats = BasicStats(data, attribute)
points = self._split_eq_width(stats.min, stats.max)
else:
values, _ = data.get_column_view(attribute)
if values.size:
mn, mx = ut.nanmin(values), ut.nanmax(values)
points = self._split_eq_width(mn, mx)
else:
points = []
return Discretizer.create_discretized_var(data.domain[attribute],
points)
def calculate_log_reg_coefficients(self):
self.log_reg_coeffs = []
self.log_reg_cont_data_extremes = []
self.b0 = None
if self.classifier is None or self.domain is None:
return
if not isinstance(self.classifier, LogisticRegressionClassifier):
return
self.domain = self.reconstruct_domain(self.classifier.original_domain,
self.domain)
self.data = Table.from_table(self.domain,
self.classifier.original_data)
attrs, ranges, start = self.domain.attributes, [], 0
for attr in attrs:
stop = start + len(attr.values) if attr.is_discrete else start + 1
ranges.append(slice(start, stop))
start = stop
self.b0 = self.classifier.intercept
coeffs = self.classifier.coefficients
if len(self.domain.class_var.values) == 2:
self.b0 = np.hstack((self.b0 * (-1), self.b0))
coeffs = np.vstack((coeffs * (-1), coeffs))
self.log_reg_coeffs = [coeffs[:, ranges[i]] for i in range(len(attrs))]
self.log_reg_coeffs_orig = self.log_reg_coeffs.copy()
min_values = nanmin(self.data.X, axis=0)
max_values = nanmax(self.data.X, axis=0)
for i, min_t, max_t in zip(range(len(self.log_reg_coeffs)), min_values,
max_values):
if self.log_reg_coeffs[i].shape[1] == 1:
coef = self.log_reg_coeffs[i]
self.log_reg_coeffs[i] = np.hstack(
(coef * min_t, coef * max_t))
self.log_reg_cont_data_extremes.append(
[sorted([min_t, max_t], reverse=(c < 0)) for c in coef])
else:
self.log_reg_cont_data_extremes.append([None])
def calculate_log_reg_coefficients(self):
self.log_reg_coeffs = []
self.log_reg_cont_data_extremes = []
self.b0 = None
if self.classifier is None or self.domain is None:
return
if not isinstance(self.classifier, LogisticRegressionClassifier):
return
self.domain = self.reconstruct_domain(self.classifier.original_domain,
self.domain)
self.data = self.classifier.original_data.transform(self.domain)
attrs, ranges, start = self.domain.attributes, [], 0
for attr in attrs:
stop = start + len(attr.values) if attr.is_discrete else start + 1
ranges.append(slice(start, stop))
start = stop
self.b0 = self.classifier.intercept
coeffs = self.classifier.coefficients
if len(self.domain.class_var.values) == 2:
self.b0 = np.hstack((self.b0 * (-1), self.b0))
coeffs = np.vstack((coeffs * (-1), coeffs))
self.log_reg_coeffs = [coeffs[:, ranges[i]] for i in range(len(attrs))]
self.log_reg_coeffs_orig = self.log_reg_coeffs.copy()
min_values = nanmin(self.data.X, axis=0)
max_values = nanmax(self.data.X, axis=0)
for i, min_t, max_t in zip(range(len(self.log_reg_coeffs)),
min_values, max_values):
if self.log_reg_coeffs[i].shape[1] == 1:
coef = self.log_reg_coeffs[i]
self.log_reg_coeffs[i] = np.hstack((coef * min_t, coef * max_t))
self.log_reg_cont_data_extremes.append(
[sorted([min_t, max_t], reverse=(c < 0)) for c in coef])
else:
self.log_reg_cont_data_extremes.append([None])
def __call__(self, data: Table, attribute):
fmt = [
"%Y", "%y %b", "%y %b %d", "%y %b %d %H:%M", "%y %b %d %H:%M",
"%H:%M:%S"
][self.unit]
values, _ = data.get_column_view(attribute)
times = []
if values.size:
mn, mx = ut.nanmin(values), ut.nanmax(values)
if not np.isnan(mn):
mn = utc_from_timestamp(mn).timetuple()
mx = utc_from_timestamp(mx).timetuple()
times = _time_range(mn, mx, self.unit, self.width, 0, 100)
if times is None:
raise TooManyIntervals
times = [time.struct_time(t + (0, 0, 0)) for t in times][1:-1]
points = np.array([calendar.timegm(t) for t in times])
values = [time.strftime(fmt, t) for t in times]
values = _simplified_time_intervals(values)
var = data.domain[attribute]
return DiscreteVariable(name=var.name,
values=values,
compute_value=Discretizer(var, points),
sparse=var.sparse)
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))