def grid_bin(data, xvar, yvar, xbins, ybins, zvar=None):
x_disc = Discretizer.create_discretized_var(xvar, xbins[1:-1])
y_disc = Discretizer.create_discretized_var(yvar, ybins[1:-1])
x_min, x_max = xbins[0], xbins[-1]
y_min, y_max = ybins[0], ybins[-1]
querydomain = [x_disc, y_disc]
if zvar is not None:
querydomain = querydomain + [zvar]
querydomain = Orange.data.Domain(querydomain)
def interval_filter(var, low, high):
return Orange.data.filter.Values(
[Orange.data.filter.FilterContinuous(
var, max=high, min=low,
oper=Orange.data.filter.FilterContinuous.Between)]
)
def value_filter(var, val):
return Orange.data.filter.Values(
[Orange.data.filter.FilterDiscrete(var, [val])]
)
def filters_join(filters):
return Orange.data.filter.Values(
reduce(list.__iadd__, (f.conditions for f in filters), [])
)
inf_bounds = np.isinf([x_min, x_max, y_min, y_max])
if not all(inf_bounds):
# No need to filter the data
range_filters = [interval_filter(xvar, x_min, x_max),
interval_filter(yvar, y_min, y_max)]
range_filter = filters_join(range_filters)
subset = range_filter(data)
else:
subset = data
if zvar.is_discrete:
filters = [value_filter(zvar, val) for val in zvar.values]
contingencies = [
contingency.get_contingency(
filter_(subset.from_table(querydomain, subset)),
col_variable=y_disc, row_variable=x_disc
)
for filter_ in filters
]
contingencies = np.dstack(contingencies)
else:
contingencies = contingency.get_contingency(
subset.from_table(querydomain, subset),
col_variable=y_disc, row_variable=x_disc
)
contingencies = np.asarray(contingencies)
return Tree(xbins, ybins, contingencies, None)
def burt_table(data, variables):
"""
Construct a 'Burt table' (all values cross-tabulation) for variables.
Return and ordered list of (variable, value) pairs and a
numpy.ndarray contingency
:param Orange.data.Table data: Data table.
:param variables: List of variables (discrete).
:type variables: list of Orange.data.DiscreteVariable
"""
values = [(var, value) for var in variables for value in var.values]
table = np.zeros((len(values), len(values)))
counts = [len(attr.values) for attr in variables]
offsets = np.r_[0, np.cumsum(counts)]
for i in range(len(variables)):
for j in range(i + 1):
var1 = variables[i]
var2 = variables[j]
cm = contingency.get_contingency(data, var2, var1)
start1, end1 = offsets[i], offsets[i] + counts[i]
start2, end2 = offsets[j], offsets[j] + counts[j]
table[start1: end1, start2: end2] += cm
if i != j:
table[start2: end2, start1: end1] += cm.T
return values, table
def compute_score(attr):
if attr is group_var:
return 3
if attr.is_continuous:
# One-way ANOVA
col = data.get_column_view(attr)[0].astype(float)
groups = (col[group_col == i] for i in range(n_groups))
groups = (col[~np.isnan(col)] for col in groups)
groups = [group for group in groups if len(group)]
p = f_oneway(*groups)[1] if len(groups) > 1 else 2
else:
# Chi-square with the given distribution into groups
# (see degrees of freedom in computation of the p-value)
if not attr.values or not group_var.values:
return 2
observed = np.array(
contingency.get_contingency(data, group_var, attr))
observed = observed[observed.sum(axis=1) != 0, :]
observed = observed[:, observed.sum(axis=0) != 0]
if min(observed.shape) < 2:
return 2
expected = \
np.outer(observed.sum(axis=1), observed.sum(axis=0)) / \
np.sum(observed)
p = chisquare(observed.ravel(), f_exp=expected.ravel(),
ddof=n_groups - 1)[1]
if math.isnan(p):
return 2
return p
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
self.is_continuous = attr.is_continuous
if dataset is None or not self.is_continuous and not attr.values or \
self.group_var and not self.group_var.values:
self.stats = self.dist = self.conts = []
return
if self.group_var:
self.dist = []
self.conts = contingency.get_contingency(
dataset, attr, self.group_var)
if self.is_continuous:
self.stats = [BoxData(cont, attr, i, self.group_var)
for i, cont in enumerate(self.conts)]
self.label_txts_all = self.group_var.values
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = []
if self.is_continuous:
self.stats = [BoxData(self.dist, attr, None)]
self.label_txts_all = [""]
self.label_txts = [txts for stat, txts in zip(self.stats,
self.label_txts_all)
if stat.n > 0]
self.stats = [stat for stat in self.stats if stat.n > 0]
def test_mixedtype_metas(self):
import Orange
zoo = Orange.data.Table("zoo")
dom = Orange.data.Domain(zoo.domain.attributes, zoo.domain.class_var,
zoo.domain.metas + zoo.domain.attributes[:2])
t = Orange.data.Table(dom, zoo)
cont = contingency.get_contingency(zoo, 2, t.domain.metas[1])
assert_dist_equal(cont["1"], [38, 5])
assert_dist_equal(cont, [[4, 54], [38, 5]])
zoo[25][t.domain.metas[1]] = float("nan")
zoo[0][2] = float("nan")
cont = contingency.get_contingency(zoo, 2, t.domain.metas[1])
assert_dist_equal(cont["1"], [37, 5])
assert_dist_equal(cont, [[4, 53], [37, 5]])
np.testing.assert_almost_equal(cont.unknowns, [0, 1])
self.assertEqual(cont.unknown_rows, 1)
def _setup(self):
self.plot.clear()
self.plot_prob.clear()
self._legend.clear()
self._legend.hide()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
data = self.data
self._setup_smoothing()
if self.var is None:
return
if self.disc_cont:
data = self.data[:, (self.var, self.cvar) if self.cvar else self.var ]
disc = Orange.preprocess.discretize.EqualWidth(n=self.bins[self.smoothing_index])
data = Orange.preprocess.Discretize(data, method=disc)
self.var = data.domain.variables[0]
self.set_left_axis_name()
self.enable_disable_rel_freq()
if self.cvar:
self.contingencies = \
contingency.get_contingency(data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = \
distribution.get_distribution(data, self.var)
self.display_distribution()
self.plot.autoRange()
def contingency_table(data, columns, rows):
ct = contingency.get_contingency(data, columns, rows)
metavar = StringVariable(rows.name)
metas = [[str(val)] for val in rows.values]
domain = Domain([ContinuousVariable(val, number_of_decimals=0)
for val in columns.values], metas=[metavar])
return Table(domain, ct, metas=metas)
def test_compute_contingency_invalid(self):
rstate = np.random.RandomState(0xFFFF)
X = data.ContinuousVariable("X")
C = data.DiscreteVariable("C", values=["C{}".format(i + 1) for i in range(1024)])
domain = data.Domain([X], [C])
d = data.Table.from_numpy(
domain,
rstate.uniform(size=(20, 1)).round(1),
rstate.randint(0, 1024, size=(20, 1)),
)
c = contingency.get_contingency(d, X, C)
self.assertEqual(c.counts.shape[0], 1024)
d.Y[5] = 1024
with self.assertRaises(IndexError):
contingency.get_contingency(d, X, C)
def fit_storage(self, table):
if not isinstance(table, Storage):
raise TypeError("Data is not a subclass of Orange.data.Storage.")
if not all(var.is_discrete
for var in table.domain.variables):
raise NotImplementedError("Only discrete variables are supported.")
cont = contingency.get_contingencies(table)
class_freq = np.array(np.diag(
contingency.get_contingency(table, table.domain.class_var)))
nclss = (class_freq != 0).sum()
if not nclss:
raise ValueError("Data has no defined target values")
# Laplacian smoothing considers only classes that appear in the data,
# in part to avoid cases where the probabilities are affected by empty
# (or completely spurious) classes that appear because of Orange's reuse
# of variables. See GH-2943.
# The corresponding elements of class_probs are set to zero only after
# mock non-zero values are used in computation of log_cont_prob to
# prevent division by zero.
class_prob = (class_freq + 1) / (np.sum(class_freq) + nclss)
log_cont_prob = [np.log(
(np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] + nclss)
/ class_prob[:, None])
for c in cont]
class_prob[class_freq == 0] = 0
return NaiveBayesModel(log_cont_prob, class_prob, table.domain)
def _setup(self):
self.plot.clear()
self.plot_prob.clear()
self._legend.clear()
self._legend.hide()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
self.cb_prob.clear()
self.cb_prob.addItem("(None)")
self.cb_prob.addItems(self.cvar.values)
self.cb_prob.addItem("(All)")
self.show_prob = min(max(self.show_prob, 0), len(self.cvar.values) + 1)
data = self.data
self._setup_smoothing()
if self.var is None:
return
if self.disc_cont:
data = self.data[:, (self.var, self.cvar) if self.cvar else self.var]
disc = Orange.preprocess.discretize.EqualWidth(n=self.bins[self.smoothing_index])
data = Orange.preprocess.Discretize(data, method=disc, remove_const=False)
self.var = data.domain[0]
self.set_left_axis_name()
self.enable_disable_rel_freq()
if self.cvar:
self.contingencies = contingency.get_contingency(data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = distribution.get_distribution(data, self.var)
self.display_distribution()
self.plot.autoRange()
def fit_storage(self, table):
if not isinstance(table, Storage):
raise TypeError("Data is not a subclass of Orange.data.Storage.")
if not all(isinstance(var, DiscreteVariable) for var in table.domain.variables):
raise NotImplementedError("Only discrete variables are supported.")
cont = contingency.get_contingencies(table)
class_freq = np.diag(contingency.get_contingency(table, table.domain.class_var))
return NaiveBayesModel(cont, class_freq, table.domain)
def __call__(self, data, attribute):
cont = contingency.get_contingency(data, attribute)
values, I = cont.values, cont.counts.T
cut_ind = np.array(_entropy_discretize_sorted(I, self.force))
if len(cut_ind) > 0:
#"the midpoint between each successive pair of examples" (FI p.1)
points = (values[cut_ind] + values[cut_ind - 1])/2.
return _discretized_var(data, attribute, points)
else:
return None
def __init__(self, data, attr1, attr2):
self.observed = get_contingency(data, attr1, attr2)
self.n = np.sum(self.observed)
self.probs_x = self.observed.sum(axis=0) / self.n
self.probs_y = self.observed.sum(axis=1) / self.n
self.expected = np.outer(self.probs_y, self.probs_x) * self.n
self.residuals = (self.observed - self.expected) / np.sqrt(self.expected)
self.chisqs = self.residuals ** 2
self.chisq = float(np.sum(self.chisqs))
self.p = chi2.sf(self.chisq, (len(self.probs_x) - 1) * (len(self.probs_y) - 1))
def compute_box_data(self):
if self.split_var:
return (
contingency.get_contingency(
self.dataset, self.attribute, self.split_var),
self.split_var.values)
else:
return [
distribution.get_distribution(
self.dataset, self.attribute)], [""]
def __call__(self, data, attribute):
from Orange.statistics import contingency as c
cont = c.get_contingency(data, attribute)
values, I = join_contingency(cont)
cut_ind = numpy.array(entropy_discretize_sorted(I))
if len(cut_ind) > 0:
points = values[cut_ind - 1]
return disc._discretized_var(data, attribute, points)
else:
return None
def draw_distributions(self):
"""Draw distributions with discrete attributes"""
if not (self.show_distributions and self.have_data and self.data_has_discrete_class):
return
class_count = len(self.data_domain.class_var.values)
class_ = self.data_domain.class_var
# we create a hash table of possible class values (happens only if we have a discrete class)
if self.domain_contingencies is None:
self.domain_contingencies = dict(
zip(
[attr for attr in self.data_domain if isinstance(attr, DiscreteVariable)],
get_contingencies(self.raw_data, skipContinuous=True),
)
)
self.domain_contingencies[class_] = get_contingency(self.raw_data, class_, class_)
max_count = max([contingency.max() for contingency in self.domain_contingencies.values()] or [1])
sorted_class_values = get_variable_values_sorted(self.data_domain.class_var)
for axis_idx, attr_idx in enumerate(self.attribute_indices):
attr = self.data_domain[attr_idx]
if isinstance(attr, DiscreteVariable):
continue
contingency = self.domain_contingencies[attr]
attr_len = len(attr.values)
# we create a hash table of variable values and their indices
sorted_variable_values = get_variable_values_sorted(attr)
# create bar curve
for j in range(attr_len):
attribute_value = sorted_variable_values[j]
value_count = contingency[:, attribute_value]
for i in range(class_count):
class_value = sorted_class_values[i]
color = QColor(self.discrete_palette[i])
color.setAlpha(self.alpha_value)
width = float(value_count[class_value] * 0.5) / float(max_count)
y_off = float(1.0 + 2.0 * j) / float(2 * attr_len)
height = 0.7 / float(class_count * attr_len)
y_low_bottom = y_off + float(class_count * height) / 2.0 - i * height
curve = PolygonCurve(
QPen(color),
QBrush(color),
xData=[axis_idx, axis_idx + width, axis_idx + width, axis_idx],
yData=[y_low_bottom, y_low_bottom, y_low_bottom - height, y_low_bottom - height],
tooltip=attr.name,
)
curve.attach(self)
def test_get_contingency(self):
d = self._construct_sparse()
cont = contingency.get_contingency(d, 5)
self.assertIsInstance(cont, contingency.Discrete)
np.testing.assert_almost_equal(cont[0], [1, 0, 0])
np.testing.assert_almost_equal(cont["b"], [0, 1, 1])
np.testing.assert_almost_equal(cont[2], [1, 0, 0])
cont = contingency.get_contingency(d, "c4")
self.assertIsInstance(cont, contingency.Continuous)
np.testing.assert_almost_equal(cont[0], [[], []])
np.testing.assert_almost_equal(cont["b"], [[1], [1]])
np.testing.assert_almost_equal(cont[2], [[2], [1]])
cont = contingency.get_contingency(d, d.domain[13])
self.assertIsInstance(cont, contingency.Continuous)
np.testing.assert_almost_equal(cont[0], [[1.1], [1]])
np.testing.assert_almost_equal(cont["b"], [[1], [1]])
np.testing.assert_almost_equal(cont[2], [[], []])
np.testing.assert_almost_equal(cont[2], [[], []])
def fit_storage(self, table):
if not isinstance(table, Storage):
raise TypeError("Data is not a subclass of Orange.data.Storage.")
if not all(var.is_discrete
for var in table.domain.variables):
raise NotImplementedError("Only discrete variables are supported.")
cont = contingency.get_contingencies(table)
class_freq = np.array(np.diag(
contingency.get_contingency(table, table.domain.class_var)))
class_prob = (class_freq + 1) / (np.sum(class_freq) + len(class_freq))
log_cont_prob = [np.log(
(np.array(c) + 1) / (np.sum(np.array(c), axis=0)[None, :] +
c.shape[0]) / class_prob[:, None])
for c in cont]
return NaiveBayesModel(log_cont_prob, class_prob, table.domain)
def rank(self):
if self.progress:
return
disc = Orange.preprocess.EqualWidth(n=10)
ndomain = Orange.data.Domain(
[
disc(self.data, attr) if type(attr) == Orange.data.variable.ContinuousVariable else attr
for attr in self.data.domain.attributes
],
self.data.domain.class_vars,
)
t = self.data.from_table(ndomain, self.data)
attrs = t.domain.attributes
tables = {}
l = 0
self.progress = gui.ProgressBar(self, len(attrs) * (len(attrs) - 1) / 2)
for i in range(len(attrs)):
for j in range(i):
ct = np.array(contingency.get_contingency(t, attrs[j], attrs[i]))
pindex, _, _ = p_index(ct)
tables[i, j] = ct
item = QStandardItem()
item.setData(float(pindex), Qt.DisplayRole)
self.projectionTableModel.setItem(l, 0, item)
item = QStandardItem()
item.setData(attrs[i].name, Qt.DisplayRole)
self.projectionTableModel.setItem(l, 1, item)
item = QStandardItem()
item.setData(attrs[j].name, Qt.DisplayRole)
self.projectionTableModel.setItem(l, 2, item)
self.progress.advance()
l += 1
self.progress.finish()
self.progress = None
def __init__(self, data, attr1, attr2):
attr1 = data.domain[attr1]
attr2 = data.domain[attr2]
if attr1.is_discrete and not attr1.values or \
attr2.is_discrete and not attr2.values:
self.p = np.nan
return
self.observed = get_contingency(data, attr1, attr2)
self.n = np.sum(self.observed)
self.probs_x = self.observed.sum(axis=0) / self.n
self.probs_y = self.observed.sum(axis=1) / self.n
self.expected = np.outer(self.probs_y, self.probs_x) * self.n
self.residuals = \
(self.observed - self.expected) / np.sqrt(self.expected)
self.residuals = np.nan_to_num(self.residuals)
self.chisqs = self.residuals ** 2
self.chisq = float(np.sum(self.chisqs))
self.p = chi2.sf(
self.chisq, (len(self.probs_x) - 1) * (len(self.probs_y) - 1))
def _setup(self):
self.plot.clear()
self.plot_prob.clear()
self._legend.clear()
self._legend.hide()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
prob = self.controls.show_prob
prob.clear()
prob.addItem("(None)")
prob.addItems(self.cvar.values)
prob.addItem("(All)")
self.show_prob = min(max(self.show_prob, 0),
len(self.cvar.values) + 1)
data = self.data
self._setup_smoothing()
if self.var is None:
return
if self.disc_cont:
domain = Orange.data.Domain(
[self.var, self.cvar] if self.cvar else [self.var])
data = Orange.data.Table(domain, data)
disc = EqualWidth(n=self.bins[self.smoothing_index])
data = Discretize(method=disc, remove_const=False)(data)
self.var = data.domain[0]
self.set_left_axis_name()
self.enable_disable_rel_freq()
self.controls.cumulative_distr.setDisabled(not self.var.is_continuous)
if self.cvar:
self.contingencies = \
contingency.get_contingency(data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = \
distribution.get_distribution(data, self.var)
self.display_distribution()
self.plot.autoRange()
def _setup(self):
self.plot.clear()
varidx = self.variable_idx
self.var = self.cvar = None
if varidx >= 0:
self.var = self.varmodel[varidx]
if self.groupvar_idx > 0:
self.cvar = self.groupvarmodel[self.groupvar_idx]
self.set_left_axis_name()
self.enable_disable_rel_freq()
if self.var is None:
return
if self.cvar:
self.contingencies = \
contingency.get_contingency(self.data, self.var, self.cvar)
self.display_contingency()
else:
self.distributions = \
distribution.get_distribution(self.data, self.var)
self.display_distribution()
def update_XY(self):
self.axis_x_cb.clear()
self.axis_y_cb.clear()
ca_vars = self.selected_vars()
if len(ca_vars) == 0:
return
multi = len(ca_vars) != 2
if multi:
_, ctable = burt_table(self.data, ca_vars)
else:
ctable = contingency.get_contingency(self.data, *ca_vars[::-1])
self.ca = correspondence(ctable, )
rfs = self.ca.row_factors.shape[1]
axes = ["{}".format(i + 1)
for i in range(rfs)]
self.axis_x_cb.addItems(axes)
self.axis_y_cb.addItems(axes)
return rfs
def __init__(self, data, attr1, attr2):
attr1 = data.domain[attr1]
attr2 = data.domain[attr2]
if attr1.is_discrete and not attr1.values or \
attr2.is_discrete and not attr2.values:
self.p = np.nan
return
self.observed = get_contingency(data, attr1, attr2)
self.n = np.sum(self.observed)
# pylint: disable=unexpected-keyword-arg
self.probs_x = self.observed.sum(axis=0) / self.n
self.probs_y = self.observed.sum(axis=1) / self.n
self.expected = np.outer(self.probs_y, self.probs_x) * self.n
with np.errstate(divide="ignore", invalid="ignore"):
self.residuals = \
(self.observed - self.expected) / np.sqrt(self.expected)
self.residuals = np.nan_to_num(self.residuals)
self.chisqs = self.residuals ** 2
self.chisq = float(np.sum(self.chisqs))
self.p = chi2.sf(
self.chisq, (len(self.probs_x) - 1) * (len(self.probs_y) - 1))
def _setup(self):
"""Setup the plot."""
self.plot.clear()
varidx = self.variable_idx
var = cvar = None
if varidx >= 0:
var = self.varmodel[varidx]
if self.groupvar_idx >= 0:
cvar = self.groupvarmodel[self.groupvar_idx]
if var is None:
return
if is_discrete(cvar):
cont = contingency.get_contingency(self.data, var, cvar)
self.set_contingency(cont, var, cvar)
else:
dist = distribution.get_distribution(self.data, var)
self.set_distribution(dist, var)
def _update_CA(self):
ca_vars = self.selected_vars()
if len(ca_vars) == 0:
return
multi = len(ca_vars) != 2
if multi:
_, ctable = burt_table(self.data, ca_vars)
else:
ctable = contingency.get_contingency(self.data, *ca_vars[::-1])
self.ca = correspondence(ctable, )
axes = ["{}".format(i + 1)
for i in range(self.ca.row_factors.shape[1])]
self.axis_x_cb.clear()
self.axis_x_cb.addItems(axes)
self.axis_y_cb.clear()
self.axis_y_cb.addItems(axes)
self.component_x, self.component_y = self.component_x, self.component_y
self._setup_plot()
self._update_info()
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
self.is_continuous = attr.is_continuous
if dataset is None or not self.is_continuous and not attr.values or \
self.group_var and not self.group_var.values:
self.stats = self.dist = self.conts = []
return
if self.group_var:
self.dist = []
self.conts = contingency.get_contingency(
dataset, attr, self.group_var)
if self.is_continuous:
stats, label_texts = [], []
for i, cont in enumerate(self.conts):
if np.sum(cont[1]):
stats.append(BoxData(cont, attr, i, self.group_var))
label_texts.append(self.group_var.values[i])
self.stats = stats
self.label_txts_all = label_texts
else:
self.label_txts_all = \
[v for v, c in zip(self.group_var.values, self.conts)
if np.sum(c) > 0]
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = []
if self.is_continuous:
self.stats = [BoxData(self.dist, attr, None)]
self.label_txts_all = [""]
self.label_txts = [txts for stat, txts in zip(self.stats,
self.label_txts_all)
if stat.n > 0]
self.stats = [stat for stat in self.stats if stat.n > 0]
def compute_box_data(self):
attr = self.attribute
if not attr:
return
dataset = self.dataset
self.is_continuous = attr.is_continuous
if dataset is None or not self.is_continuous and not attr.values or \
self.group_var and not self.group_var.values:
self.stats = self.dist = self.conts = []
return
if self.group_var:
self.dist = []
self.conts = contingency.get_contingency(
dataset, attr, self.group_var)
if self.is_continuous:
stats, label_texts = [], []
for i, cont in enumerate(self.conts):
if np.sum(cont[1]):
stats.append(BoxData(cont, attr, i, self.group_var))
label_texts.append(self.group_var.values[i])
self.stats = stats
self.label_txts_all = label_texts
else:
self.label_txts_all = \
[v for v, c in zip(self.group_var.values, self.conts)
if np.sum(c) > 0]
else:
self.dist = distribution.get_distribution(dataset, attr)
self.conts = []
if self.is_continuous:
self.stats = [BoxData(self.dist, attr, None)]
self.label_txts_all = [""]
self.label_txts = [txts for stat, txts in zip(self.stats,
self.label_txts_all)
if stat.n > 0]
self.stats = [stat for stat in self.stats if stat.n > 0]
def updateGraph(self, *args):
for item in self.canvas.items():
self.canvas.removeItem(item)
if self.data is None or len(self.data) == 0 or \
self.attrX is None or self.attrY is None:
return
data = self.data[:, [self.attrX, self.attrY]]
valsX = []
valsY = []
contX = get_contingency(data, self.attrX, self.attrX)
contY = get_contingency(data, self.attrY, self.attrY)
# compute contingency of x and y attributes
for entry in contX:
sum_ = 0
try:
for val in entry:
sum_ += val
except:
pass
valsX.append(sum_)
for entry in contY:
sum_ = 0
try:
for val in entry:
sum_ += val
except:
pass
valsY.append(sum_)
contXY, _ = get_conditional_distribution(
data, [data.domain[self.attrX], data.domain[self.attrY]])
# compute probabilities
probs = {}
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
try:
actualProb = contXY['%s-%s' %
(data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
# for val in contXY['%s-%s' %(i, j)]: actualProb += val
except:
actualProb = 0
probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])] = ((
data.domain[self.attrX].values[i],
valx), (data.domain[self.attrY].values[j],
valy), actualProb, len(data))
#get text width of Y labels
max_ylabel_w = 0
for j in range(len(valsY)):
xl = CanvasText(self.canvas,
"",
0,
0,
html_text=getHtmlCompatibleString(
data.domain[self.attrY].values[j]),
show=False)
max_ylabel_w = max(int(xl.boundingRect().width()), max_ylabel_w)
max_ylabel_w = min(max_ylabel_w, 200) #upper limit for label widths
# get text width of Y attribute name
text = CanvasText(self.canvas,
data.domain[self.attrY].name,
x=0,
y=0,
bold=1,
show=0,
vertical=True)
xOff = int(text.boundingRect().height() + max_ylabel_w)
yOff = 55
sqareSize = min(self.canvasView.width() - xOff - 35,
self.canvasView.height() - yOff - 50)
sqareSize = max(sqareSize, 10)
self.canvasView.setSceneRect(0, 0, self.canvasView.width(),
self.canvasView.height())
# print graph name
name = "<b>P(%s, %s) ≠ P(%s)×P(%s)</b>" % (
self.attrX, self.attrY, self.attrX, self.attrY)
CanvasText(self.canvas,
"",
xOff + sqareSize / 2,
20,
Qt.AlignCenter,
html_text=name)
CanvasText(self.canvas,
"N = " + str(len(data)),
xOff + sqareSize / 2,
38,
Qt.AlignCenter,
bold=0)
######################
# compute chi-square
chisquare = 0.0
for i in range(len(valsX)):
for j in range(len(valsY)):
((xAttr, xVal), (yAttr, yVal), actual,
sum_) = probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
expected = float(xVal * yVal) / float(sum_)
if expected == 0: continue
pearson2 = (actual - expected) * (actual - expected) / expected
chisquare += pearson2
######################
# draw rectangles
currX = xOff
max_xlabel_h = 0
normX, normY = sum(valsX), sum(valsY)
self.areas = []
for i in range(len(valsX)):
if valsX[i] == 0: continue
currY = yOff
width = int(float(sqareSize * valsX[i]) / float(normX))
for j in range(len(valsY) - 1, -1,
-1): # this way we sort y values correctly
((xAttr, xVal), (yAttr, yVal), actual,
sum_) = probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
if valsY[j] == 0: continue
height = int(float(sqareSize * valsY[j]) / float(normY))
# create rectangle
selected = len(self.areas) in self.selection
rect = CanvasRectangle(self.canvas,
currX + 2,
currY + 2,
width - 4,
height - 4,
z=-10,
onclick=self.select_area)
rect.value_pair = i, j
self.areas.append(rect)
self.addRectIndependencePearson(
rect,
currX + 2,
currY + 2,
width - 4,
height - 4,
(xAttr, xVal),
(yAttr, yVal),
actual,
sum_,
width=1 + 3 * selected, # Ugly! This is needed since
# resize redraws the graph! When this is handled by resizing
# just the viewer, update_selection will take care of this
)
expected = float(xVal * yVal) / float(sum_)
pearson = (actual - expected) / sqrt(expected)
tooltipText = """<b>X Attribute: %s</b><br>Value: <b>%s</b><br>Number of instances (p(x)): <b>%d (%.2f%%)</b><hr>
<b>Y Attribute: %s</b><br>Value: <b>%s</b><br>Number of instances (p(y)): <b>%d (%.2f%%)</b><hr>
<b>Number Of Instances (Probabilities):</b><br>Expected (p(x)p(y)): <b>%.1f (%.2f%%)</b><br>Actual (p(x,y)): <b>%d (%.2f%%)</b>
<hr><b>Statistics:</b><br>Chi-square: <b>%.2f</b><br>Standardized Pearson residual: <b>%.2f</b>""" % (
self.attrX, getHtmlCompatibleString(xAttr), xVal,
100.0 * float(xVal) / float(sum_), self.attrY,
getHtmlCompatibleString(yAttr), yVal,
100.0 * float(yVal) / float(sum_), expected,
100.0 * float(xVal * yVal) / float(sum_ * sum_), actual,
100.0 * float(actual) / float(sum_), chisquare, pearson)
rect.setToolTip(tooltipText)
currY += height
if currX == xOff:
CanvasText(self.canvas,
"",
xOff,
currY - height / 2,
Qt.AlignRight | Qt.AlignVCenter,
html_text=getHtmlCompatibleString(
data.domain[self.attrY].values[j]))
xl = CanvasText(self.canvas,
"",
currX + width / 2,
yOff + sqareSize,
Qt.AlignHCenter | Qt.AlignTop,
html_text=getHtmlCompatibleString(
data.domain[self.attrX].values[i]))
max_xlabel_h = max(int(xl.boundingRect().height()), max_xlabel_h)
currX += width
# show attribute names
CanvasText(self.canvas,
self.attrY,
0,
yOff + sqareSize / 2,
Qt.AlignLeft | Qt.AlignVCenter,
bold=1,
vertical=True)
CanvasText(self.canvas,
self.attrX,
xOff + sqareSize / 2,
yOff + sqareSize + max_xlabel_h,
Qt.AlignHCenter | Qt.AlignTop,
bold=1)
def updateGraph(self, *args):
for item in self.canvas.items():
self.canvas.removeItem(item) # remove all canvas items
if not self.data: return
if not self.attrX or not self.attrY: return
data = self.getConditionalData()
if not data or len(data) == 0: return
valsX = []
valsY = []
# contX = orange.ContingencyAttrAttr(self.attrX, self.attrX, data) # distribution of X attribute
# contY = orange.ContingencyAttrAttr(self.attrY, self.attrY, data) # distribution of Y attribute
contX = get_contingency(data, self.attrX, self.attrX)
contY = get_contingency(data, self.attrY, self.attrY)
# compute contingency of x and y attributes
for entry in contX:
sum_ = 0
try:
for val in entry: sum_ += val
except: pass
valsX.append(sum_)
for entry in contY:
sum_ = 0
try:
for val in entry: sum_ += val
except: pass
valsY.append(sum_)
# create cartesian product of selected attributes and compute contingency
# (cart, profit) = FeatureByCartesianProduct(data, [data.domain[self.attrX], data.domain[self.attrY]])
# tempData = data.select(list(data.domain) + [cart])
# contXY = orange.ContingencyAttrAttr(cart, cart, tempData) # distribution of X attribute
# contXY = get_contingency(tempData, cart, cart)
contXY = self.getConditionalDistributions(data, [data.domain[self.attrX], data.domain[self.attrY]])
# compute probabilities
probs = {}
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
actualProb = 0
try:
actualProb = contXY['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
# for val in contXY['%s-%s' %(i, j)]: actualProb += val
except:
actualProb = 0
probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])] = ((data.domain[self.attrX].values[i], valx), (data.domain[self.attrY].values[j], valy), actualProb, len(data))
# get text width of Y attribute name
text = OWCanvasText(self.canvas, data.domain[self.attrY].name, x = 0, y = 0, bold = 1, show = 0, vertical=True)
xOff = int(text.boundingRect().height() + 40)
yOff = 50
sqareSize = min(self.canvasView.width() - xOff - 35, self.canvasView.height() - yOff - 30)
if sqareSize < 0: return # canvas is too small to draw rectangles
self.canvasView.setSceneRect(0, 0, self.canvasView.width(), self.canvasView.height())
# print graph name
if self.attrCondition == "(None)":
name = "<b>P(%s, %s) ≠ P(%s)×P(%s)</b>" %(self.attrX, self.attrY, self.attrX, self.attrY)
else:
name = "<b>P(%s, %s | %s = %s) ≠ P(%s | %s = %s)×P(%s | %s = %s)</b>" %(self.attrX, self.attrY, self.attrCondition, getHtmlCompatibleString(self.attrConditionValue), self.attrX, self.attrCondition, getHtmlCompatibleString(self.attrConditionValue), self.attrY, self.attrCondition, getHtmlCompatibleString(self.attrConditionValue))
OWCanvasText(self.canvas, "" , xOff+ sqareSize/2, 20, Qt.AlignCenter, htmlText = name)
OWCanvasText(self.canvas, "N = " + str(len(data)), xOff+ sqareSize/2, 38, Qt.AlignCenter, bold = 0)
######################
# compute chi-square
chisquare = 0.0
for i in range(len(valsX)):
for j in range(len(valsY)):
((xAttr, xVal), (yAttr, yVal), actual, sum_) = probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
expected = float(xVal*yVal)/float(sum_)
if expected == 0: continue
pearson2 = (actual - expected)*(actual - expected) / expected
chisquare += pearson2
######################
# draw rectangles
currX = xOff
max_ylabel_w = 0
normX, normY = sum(valsX), sum(valsY)
for i in range(len(valsX)):
if valsX[i] == 0: continue
currY = yOff
width = int(float(sqareSize * valsX[i])/float(normX))
#for j in range(len(valsY)):
for j in range(len(valsY)-1, -1, -1): # this way we sort y values correctly
((xAttr, xVal), (yAttr, yVal), actual, sum_) = probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
if valsY[j] == 0: continue
height = int(float(sqareSize * valsY[j])/float(normY))
# create rectangle
rect = OWCanvasRectangle(self.canvas, currX+2, currY+2, width-4, height-4, z = -10)
self.addRectIndependencePearson(rect, currX+2, currY+2, width-4, height-4, (xAttr, xVal), (yAttr, yVal), actual, sum_)
expected = float(xVal*yVal)/float(sum_)
pearson = (actual - expected) / sqrt(expected)
tooltipText = """<b>X Attribute: %s</b><br>Value: <b>%s</b><br>Number of examples (p(x)): <b>%d (%.2f%%)</b><hr>
<b>Y Attribute: %s</b><br>Value: <b>%s</b><br>Number of examples (p(y)): <b>%d (%.2f%%)</b><hr>
<b>Number Of Examples (Probabilities):</b><br>Expected (p(x)p(y)): <b>%.1f (%.2f%%)</b><br>Actual (p(x,y)): <b>%d (%.2f%%)</b>
<hr><b>Statistics:</b><br>Chi-square: <b>%.2f</b><br>Standardized Pearson residual: <b>%.2f</b>""" %(self.attrX, getHtmlCompatibleString(xAttr), xVal, 100.0*float(xVal)/float(sum_), self.attrY, getHtmlCompatibleString(yAttr), yVal, 100.0*float(yVal)/float(sum_), expected, 100.0*float(xVal*yVal)/float(sum_*sum_), actual, 100.0*float(actual)/float(sum_), chisquare, pearson )
rect.setToolTip(tooltipText)
currY += height
if currX == xOff:
xl = OWCanvasText(self.canvas, "", xOff - 10, currY - height/2, Qt.AlignRight | Qt.AlignVCenter, htmlText = getHtmlCompatibleString(data.domain[self.attrY].values[j]))
max_ylabel_w = max(int(xl.boundingRect().width()), max_ylabel_w)
OWCanvasText(self.canvas, "", currX + width/2, yOff + sqareSize + 5, Qt.AlignCenter, htmlText = getHtmlCompatibleString(data.domain[self.attrX].values[i]))
currX += width
# show attribute names
OWCanvasText(self.canvas, self.attrY, max(xOff-20-max_ylabel_w, 20), yOff + sqareSize/2, Qt.AlignRight | Qt.AlignVCenter, bold = 1, vertical=True)
OWCanvasText(self.canvas, self.attrX, xOff + sqareSize/2, yOff + sqareSize + 15, Qt.AlignCenter, bold = 1)
def updateGraph(self, *args):
for item in self.canvas.items():
self.canvas.removeItem(item)
if self.data is None or len(self.data) == 0 or \
self.attrX is None or self.attrY is None:
return
data = self.data[:, [self.attrX, self.attrY]]
valsX = []
valsY = []
contX = get_contingency(data, self.attrX, self.attrX)
contY = get_contingency(data, self.attrY, self.attrY)
# compute contingency of x and y attributes
for entry in contX:
sum_ = 0
try:
for val in entry: sum_ += val
except: pass
valsX.append(sum_)
for entry in contY:
sum_ = 0
try:
for val in entry: sum_ += val
except: pass
valsY.append(sum_)
contXY, _ = get_conditional_distribution(
data, [data.domain[self.attrX], data.domain[self.attrY]])
# compute probabilities
probs = {}
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
try:
actualProb = contXY['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
# for val in contXY['%s-%s' %(i, j)]: actualProb += val
except:
actualProb = 0
probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])] = ((data.domain[self.attrX].values[i], valx), (data.domain[self.attrY].values[j], valy), actualProb, len(data))
#get text width of Y labels
max_ylabel_w = 0
for j in range(len(valsY)):
xl = CanvasText(self.canvas, "", 0, 0, html_text= getHtmlCompatibleString(data.domain[self.attrY].values[j]), show=False)
max_ylabel_w = max(int(xl.boundingRect().width()), max_ylabel_w)
max_ylabel_w = min(max_ylabel_w, 200) #upper limit for label widths
# get text width of Y attribute name
text = CanvasText(self.canvas, data.domain[self.attrY].name, x = 0, y = 0, bold = 1, show = 0, vertical=True)
xOff = int(text.boundingRect().height() + max_ylabel_w)
yOff = 55
sqareSize = min(self.canvasView.width() - xOff - 35, self.canvasView.height() - yOff - 50)
sqareSize = max(sqareSize, 10)
self.canvasView.setSceneRect(0, 0, self.canvasView.width(), self.canvasView.height())
# print graph name
name = "<b>P(%s, %s) ≠ P(%s)×P(%s)</b>" %(self.attrX, self.attrY, self.attrX, self.attrY)
CanvasText(self.canvas, "", xOff + sqareSize / 2, 20, Qt.AlignCenter, html_text= name)
CanvasText(self.canvas, "N = " + str(len(data)), xOff + sqareSize / 2, 38, Qt.AlignCenter, bold = 0)
######################
# compute chi-square
chisquare = 0.0
for i in range(len(valsX)):
for j in range(len(valsY)):
((xAttr, xVal), (yAttr, yVal), actual, sum_) = probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
expected = float(xVal*yVal)/float(sum_)
if expected == 0: continue
pearson2 = (actual - expected)*(actual - expected) / expected
chisquare += pearson2
######################
# draw rectangles
currX = xOff
max_xlabel_h = 0
normX, normY = sum(valsX), sum(valsY)
self.areas = []
for i in range(len(valsX)):
if valsX[i] == 0: continue
currY = yOff
width = int(float(sqareSize * valsX[i])/float(normX))
for j in range(len(valsY)-1, -1, -1): # this way we sort y values correctly
((xAttr, xVal), (yAttr, yVal), actual, sum_) = probs['%s-%s' %(data.domain[self.attrX].values[i], data.domain[self.attrY].values[j])]
if valsY[j] == 0: continue
height = int(float(sqareSize * valsY[j])/float(normY))
# create rectangle
selected = len(self.areas) in self.selection
rect = CanvasRectangle(
self.canvas, currX+2, currY+2, width-4, height-4, z = -10,
onclick=self.select_area)
rect.value_pair = i, j
self.areas.append(rect)
self.addRectIndependencePearson(rect, currX+2, currY+2, width-4, height-4, (xAttr, xVal), (yAttr, yVal), actual, sum_,
width=1 + 3 * selected, # Ugly! This is needed since
# resize redraws the graph! When this is handled by resizing
# just the viewer, update_selection will take care of this
)
expected = float(xVal*yVal)/float(sum_)
pearson = (actual - expected) / sqrt(expected)
tooltipText = """<b>X Attribute: %s</b><br>Value: <b>%s</b><br>Number of instances (p(x)): <b>%d (%.2f%%)</b><hr>
<b>Y Attribute: %s</b><br>Value: <b>%s</b><br>Number of instances (p(y)): <b>%d (%.2f%%)</b><hr>
<b>Number Of Instances (Probabilities):</b><br>Expected (p(x)p(y)): <b>%.1f (%.2f%%)</b><br>Actual (p(x,y)): <b>%d (%.2f%%)</b>
<hr><b>Statistics:</b><br>Chi-square: <b>%.2f</b><br>Standardized Pearson residual: <b>%.2f</b>""" %(self.attrX, getHtmlCompatibleString(xAttr), xVal, 100.0*float(xVal)/float(sum_), self.attrY, getHtmlCompatibleString(yAttr), yVal, 100.0*float(yVal)/float(sum_), expected, 100.0*float(xVal*yVal)/float(sum_*sum_), actual, 100.0*float(actual)/float(sum_), chisquare, pearson )
rect.setToolTip(tooltipText)
currY += height
if currX == xOff:
CanvasText(self.canvas, "", xOff, currY - height / 2, Qt.AlignRight | Qt.AlignVCenter, html_text= getHtmlCompatibleString(data.domain[self.attrY].values[j]))
xl = CanvasText(self.canvas, "", currX + width / 2, yOff + sqareSize, Qt.AlignHCenter | Qt.AlignTop, html_text= getHtmlCompatibleString(data.domain[self.attrX].values[i]))
max_xlabel_h = max(int(xl.boundingRect().height()), max_xlabel_h)
currX += width
# show attribute names
CanvasText(self.canvas, self.attrY, 0, yOff + sqareSize / 2, Qt.AlignLeft | Qt.AlignVCenter, bold = 1, vertical=True)
CanvasText(self.canvas, self.attrX, xOff + sqareSize / 2, yOff + sqareSize + max_xlabel_h, Qt.AlignHCenter | Qt.AlignTop, bold = 1)
def fit_storage(self, table):
cont = contingency.get_contingencies(table)
class_freq = np.diag(
contingency.get_contingency(table, table.domain.class_var))
return BayesStorageClassifier(cont, class_freq, table.domain)
print(cattrs)
print(dattrs)
t0 = time.time()
for i in range(len(cattrs)):
for j in range(i):
print(p_index(cdata, cattrs[i], cattrs[j]))
t1 = time.time() - t0
print('t1', t1)
t0 = time.time()
for i in range(len(dattrs)):
for j in range(i):
ct = np.array(
contingency.get_contingency(ddata, dattrs[j], dattrs[i]))
print(p_index_ct(ct))
t2 = time.time() - t0
print('t2', t2)
Ns.append(N)
times1.append(t1)
times2.append(t2)
fig, ax = plt.subplots()
ax.plot(Ns, times1, '-', Ns, times2, '-')
ax.set_xlabel('Number of examples')
ax.set_ylabel('Time in seconds')
ax.set_title('Rank Scatterplots - {}'.format(dataset))
plt.xlim(min(Ns), max(Ns))
def updateGraph(self, *args):
for item in self.canvas.items():
self.canvas.removeItem(item) # remove all canvas items
if not self.data: return
if not self.attrX or not self.attrY: return
data = self.getConditionalData()
if not data or len(data) == 0: return
valsX = []
valsY = []
# contX = orange.ContingencyAttrAttr(self.attrX, self.attrX, data) # distribution of X attribute
# contY = orange.ContingencyAttrAttr(self.attrY, self.attrY, data) # distribution of Y attribute
contX = get_contingency(data, self.attrX, self.attrX)
contY = get_contingency(data, self.attrY, self.attrY)
# compute contingency of x and y attributes
for entry in contX:
sum_ = 0
try:
for val in entry:
sum_ += val
except:
pass
valsX.append(sum_)
for entry in contY:
sum_ = 0
try:
for val in entry:
sum_ += val
except:
pass
valsY.append(sum_)
# create cartesian product of selected attributes and compute contingency
# (cart, profit) = FeatureByCartesianProduct(data, [data.domain[self.attrX], data.domain[self.attrY]])
# tempData = data.select(list(data.domain) + [cart])
# contXY = orange.ContingencyAttrAttr(cart, cart, tempData) # distribution of X attribute
# contXY = get_contingency(tempData, cart, cart)
contXY = self.getConditionalDistributions(
data, [data.domain[self.attrX], data.domain[self.attrY]])
# compute probabilities
probs = {}
for i in range(len(valsX)):
valx = valsX[i]
for j in range(len(valsY)):
valy = valsY[j]
actualProb = 0
try:
actualProb = contXY['%s-%s' %
(data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
# for val in contXY['%s-%s' %(i, j)]: actualProb += val
except:
actualProb = 0
probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])] = ((
data.domain[self.attrX].values[i],
valx), (data.domain[self.attrY].values[j],
valy), actualProb, len(data))
# get text width of Y attribute name
text = OWCanvasText(self.canvas,
data.domain[self.attrY].name,
x=0,
y=0,
bold=1,
show=0,
vertical=True)
xOff = int(text.boundingRect().height() + 40)
yOff = 50
sqareSize = min(self.canvasView.width() - xOff - 35,
self.canvasView.height() - yOff - 30)
if sqareSize < 0: return # canvas is too small to draw rectangles
self.canvasView.setSceneRect(0, 0, self.canvasView.width(),
self.canvasView.height())
# print graph name
if self.attrCondition == "(None)":
name = "<b>P(%s, %s) ≠ P(%s)×P(%s)</b>" % (
self.attrX, self.attrY, self.attrX, self.attrY)
else:
name = "<b>P(%s, %s | %s = %s) ≠ P(%s | %s = %s)×P(%s | %s = %s)</b>" % (
self.attrX, self.attrY, self.attrCondition,
getHtmlCompatibleString(
self.attrConditionValue), self.attrX, self.attrCondition,
getHtmlCompatibleString(
self.attrConditionValue), self.attrY, self.attrCondition,
getHtmlCompatibleString(self.attrConditionValue))
OWCanvasText(self.canvas,
"",
xOff + sqareSize / 2,
20,
Qt.AlignCenter,
htmlText=name)
OWCanvasText(self.canvas,
"N = " + str(len(data)),
xOff + sqareSize / 2,
38,
Qt.AlignCenter,
bold=0)
######################
# compute chi-square
chisquare = 0.0
for i in range(len(valsX)):
for j in range(len(valsY)):
((xAttr, xVal), (yAttr, yVal), actual,
sum_) = probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
expected = float(xVal * yVal) / float(sum_)
if expected == 0: continue
pearson2 = (actual - expected) * (actual - expected) / expected
chisquare += pearson2
######################
# draw rectangles
currX = xOff
max_ylabel_w = 0
normX, normY = sum(valsX), sum(valsY)
for i in range(len(valsX)):
if valsX[i] == 0: continue
currY = yOff
width = int(float(sqareSize * valsX[i]) / float(normX))
#for j in range(len(valsY)):
for j in range(len(valsY) - 1, -1,
-1): # this way we sort y values correctly
((xAttr, xVal), (yAttr, yVal), actual,
sum_) = probs['%s-%s' % (data.domain[self.attrX].values[i],
data.domain[self.attrY].values[j])]
if valsY[j] == 0: continue
height = int(float(sqareSize * valsY[j]) / float(normY))
# create rectangle
rect = OWCanvasRectangle(self.canvas,
currX + 2,
currY + 2,
width - 4,
height - 4,
z=-10)
self.addRectIndependencePearson(rect, currX + 2, currY + 2,
width - 4, height - 4,
(xAttr, xVal), (yAttr, yVal),
actual, sum_)
expected = float(xVal * yVal) / float(sum_)
pearson = (actual - expected) / sqrt(expected)
tooltipText = """<b>X Attribute: %s</b><br>Value: <b>%s</b><br>Number of examples (p(x)): <b>%d (%.2f%%)</b><hr>
<b>Y Attribute: %s</b><br>Value: <b>%s</b><br>Number of examples (p(y)): <b>%d (%.2f%%)</b><hr>
<b>Number Of Examples (Probabilities):</b><br>Expected (p(x)p(y)): <b>%.1f (%.2f%%)</b><br>Actual (p(x,y)): <b>%d (%.2f%%)</b>
<hr><b>Statistics:</b><br>Chi-square: <b>%.2f</b><br>Standardized Pearson residual: <b>%.2f</b>""" % (
self.attrX, getHtmlCompatibleString(xAttr), xVal,
100.0 * float(xVal) / float(sum_), self.attrY,
getHtmlCompatibleString(yAttr), yVal,
100.0 * float(yVal) / float(sum_), expected,
100.0 * float(xVal * yVal) / float(sum_ * sum_), actual,
100.0 * float(actual) / float(sum_), chisquare, pearson)
rect.setToolTip(tooltipText)
currY += height
if currX == xOff:
xl = OWCanvasText(self.canvas,
"",
xOff - 10,
currY - height / 2,
Qt.AlignRight | Qt.AlignVCenter,
htmlText=getHtmlCompatibleString(
data.domain[self.attrY].values[j]))
max_ylabel_w = max(int(xl.boundingRect().width()),
max_ylabel_w)
OWCanvasText(self.canvas,
"",
currX + width / 2,
yOff + sqareSize + 5,
Qt.AlignCenter,
htmlText=getHtmlCompatibleString(
data.domain[self.attrX].values[i]))
currX += width
# show attribute names
OWCanvasText(self.canvas,
self.attrY,
max(xOff - 20 - max_ylabel_w, 20),
yOff + sqareSize / 2,
Qt.AlignRight | Qt.AlignVCenter,
bold=1,
vertical=True)
OWCanvasText(self.canvas,
self.attrX,
xOff + sqareSize / 2,
yOff + sqareSize + 15,
Qt.AlignCenter,
bold=1)
def draw_distributions(self):
"""Draw distributions with discrete attributes"""
if not (self.show_distributions and self.data is not None
and self.domain.has_discrete_class):
return
class_count = len(self.domain.class_var.values)
class_ = self.domain.class_var
# we create a hash table of possible class values (happens only if we have a discrete class)
if self.domain_contingencies is None:
self.domain_contingencies = dict(
zip([attr for attr in self.domain if attr.is_discrete],
get_contingencies(self.data, skipContinuous=True)))
self.domain_contingencies[class_] = get_contingency(
self.data, class_, class_)
max_count = max([
contingency.max()
for contingency in self.domain_contingencies.values()
] or [1])
sorted_class_values = get_variable_values_sorted(self.domain.class_var)
for axis_idx, attr_idx in enumerate(self.attribute_indices):
attr = self.domain[attr_idx]
if attr.is_discrete:
continue
contingency = self.domain_contingencies[attr]
attr_len = len(attr.values)
# we create a hash table of variable values and their indices
sorted_variable_values = get_variable_values_sorted(attr)
# create bar curve
for j in range(attr_len):
attribute_value = sorted_variable_values[j]
value_count = contingency[:, attribute_value]
for i in range(class_count):
class_value = sorted_class_values[i]
color = QColor(*self.colors[i])
color.setAlpha(self.alpha_value)
width = float(
value_count[class_value] * 0.5) / float(max_count)
y_off = float(1.0 + 2.0 * j) / float(2 * attr_len)
height = 0.7 / float(class_count * attr_len)
y_low_bottom = y_off + float(
class_count * height) / 2.0 - i * height
curve = PolygonCurve(QPen(color),
QBrush(color),
xData=[
axis_idx, axis_idx + width,
axis_idx + width, axis_idx
],
yData=[
y_low_bottom, y_low_bottom,
y_low_bottom - height,
y_low_bottom - height
],
tooltip=attr.name)
curve.attach(self)
def grid_bin(data, xvar, yvar, xbins, ybins, zvar=None):
x_disc = Discretizer.create_discretized_var(data, xvar, xbins[1:-1])
y_disc = Discretizer.create_discretized_var(data, yvar, ybins[1:-1])
x_min, x_max = xbins[0], xbins[-1]
y_min, y_max = ybins[0], ybins[-1]
querydomain = [x_disc, y_disc]
if zvar is not None:
querydomain = querydomain + [zvar]
querydomain = Orange.data.Domain(querydomain)
def interval_filter(var, low, high):
return Orange.data.filter.Values([
Orange.data.filter.FilterContinuous(
var,
max=high,
min=low,
oper=Orange.data.filter.FilterContinuous.Between)
])
def value_filter(var, val):
return Orange.data.filter.Values(
[Orange.data.filter.FilterDiscrete(var, [val])])
def filters_join(filters):
return Orange.data.filter.Values(
reduce(list.__iadd__, (f.conditions for f in filters), []))
inf_bounds = np.isinf([x_min, x_max, y_min, y_max])
if not all(inf_bounds):
# No need to filter the data
range_filters = [
interval_filter(xvar, x_min, x_max),
interval_filter(yvar, y_min, y_max)
]
range_filter = filters_join(range_filters)
subset = range_filter(data)
else:
subset = data
if is_discrete(zvar):
filters = [value_filter(zvar, val) for val in zvar.values]
contingencies = [
contingency.get_contingency(filter_(
subset.from_table(querydomain, subset)),
col_variable=y_disc,
row_variable=x_disc)
for filter_ in filters
]
contingencies = np.dstack(contingencies)
else:
contingencies = contingency.get_contingency(subset.from_table(
querydomain, subset),
col_variable=y_disc,
row_variable=x_disc)
contingencies = np.asarray(contingencies)
return Tree(xbins, ybins, contingencies, None)
