class OWConfusionMatrix(widget.OWWidget):
name = "Confusion Matrix"
description = "Display confusion matrix constructed from results " \
"of evaluation of classifiers."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [("Evaluation Results", Orange.evaluation.Results, "set_results")]
outputs = [("Selected Data", Orange.data.Table)]
quantities = [
"Number of instances", "Proportion of predicted",
"Proportion of actual"
]
selected_learner = settings.Setting([])
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
def __init__(self):
super().__init__()
self.data = None
self.results = None
self.learners = []
self.headers = []
box = gui.widgetBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(box,
self,
"selected_learner",
"learners",
callback=self._learner_changed)
box = gui.widgetBox(self.controlArea, "Show")
gui.comboBox(box,
self,
"selected_quantity",
items=self.quantities,
callback=self._update)
box = gui.widgetBox(self.controlArea, "Select")
gui.button(box,
self,
"Correct",
callback=self.select_correct,
autoDefault=False)
gui.button(box,
self,
"Misclassified",
callback=self.select_wrong,
autoDefault=False)
gui.button(box,
self,
"None",
callback=self.select_none,
autoDefault=False)
self.outputbox = box = gui.widgetBox(self.controlArea, "Output")
gui.checkBox(box,
self,
"append_predictions",
"Predictions",
callback=self._invalidate)
gui.checkBox(box,
self,
"append_probabilities",
"Probabilities",
callback=self._invalidate)
gui.auto_commit(self.controlArea, self, "autocommit", "Send Data",
"Auto send is on")
grid = QGridLayout()
self.tablemodel = QStandardItemModel(self)
view = self.tableview = QTableView(
editTriggers=QTableView.NoEditTriggers)
view.setModel(self.tablemodel)
view.horizontalHeader().hide()
view.verticalHeader().hide()
view.horizontalHeader().setMinimumSectionSize(60)
view.selectionModel().selectionChanged.connect(self._invalidate)
view.setShowGrid(False)
view.clicked.connect(self.cell_clicked)
grid.addWidget(view, 0, 0)
self.mainArea.layout().addLayout(grid)
def sizeHint(self):
return QSize(750, 490)
def _item(self, i, j):
return self.tablemodel.item(i, j) or QStandardItem()
def _set_item(self, i, j, item):
self.tablemodel.setItem(i, j, item)
def set_results(self, results):
"""Set the input results."""
self.clear()
self.warning([0, 1])
data = None
if results is not None:
if results.data is not None:
data = results.data
if data is not None and not data.domain.has_discrete_class:
data = None
results = None
self.warning(
0, "Confusion Matrix cannot be used for regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is not None:
nmodels, ntests = results.predicted.shape
self.headers = class_values + \
[unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'learner_names' is set in 'Test Learners' widget.
if hasattr(results, "learner_names"):
self.learners = results.learner_names
else:
self.learners = [
"Learner #%i" % (i + 1) for i in range(nmodels)
]
item = self._item(0, 2)
item.setData("Predicted", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignCenter)
item.setFlags(Qt.NoItemFlags)
self._set_item(0, 2, item)
item = self._item(2, 0)
item.setData("Actual", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignHCenter | Qt.AlignBottom)
item.setFlags(Qt.NoItemFlags)
self.tableview.setItemDelegateForColumn(0,
gui.VerticalItemDelegate())
self._set_item(2, 0, item)
self.tableview.setSpan(0, 2, 1, len(class_values))
self.tableview.setSpan(2, 0, len(class_values), 1)
for i in (0, 1):
for j in (0, 1):
item = self._item(i, j)
item.setFlags(Qt.NoItemFlags)
self._set_item(i, j, item)
for p, label in enumerate(self.headers):
for i, j in ((1, p + 2), (p + 2, 1)):
item = self._item(i, j)
item.setData(label, Qt.DisplayRole)
item.setData(QBrush(QColor(208, 208, 208)),
Qt.BackgroundColorRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
self._set_item(i, j, item)
hor_header = self.tableview.horizontalHeader()
if len(' '.join(self.headers)) < 120:
hor_header.setResizeMode(QHeaderView.ResizeToContents)
else:
hor_header.setDefaultSectionSize(60)
self.tablemodel.setRowCount(len(class_values) + 3)
self.tablemodel.setColumnCount(len(class_values) + 3)
self.selected_learner = [0]
self._update()
def clear(self):
self.results = None
self.data = None
self.tablemodel.clear()
self.headers = []
# Clear learners last. This action will invoke `_learner_changed`
# method
self.learners = []
def select_correct(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_wrong(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_none(self):
self.tableview.selectionModel().clear()
def cell_clicked(self, model_index):
i, j = model_index.row(), model_index.column()
if not i or not j:
return
n = self.tablemodel.rowCount()
index = self.tablemodel.index
selection = None
if i == j == 1 or i == j == n - 1:
selection = QItemSelection(index(2, 2), index(n - 1, n - 1))
elif i in (1, n - 1):
selection = QItemSelection(index(2, j), index(n - 1, j))
elif j in (1, n - 1):
selection = QItemSelection(index(i, 2), index(i, n - 1))
if selection is not None:
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def commit(self):
if self.results is not None and self.data is not None \
and self.selected_learner:
indices = self.tableview.selectedIndexes()
indices = {(ind.row() - 2, ind.column() - 2) for ind in indices}
actual = self.results.actual
selected_learner = self.selected_learner[0]
learner_name = self.learners[selected_learner]
predicted = self.results.predicted[selected_learner]
selected = [
i for i, t in enumerate(zip(actual, predicted)) if t in indices
]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values)
metas = metas + (var, )
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[selected_learner, selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [
Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values
]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
row_ids = self.data.ids[row_indices]
M = numpy.hstack((M, ) + tuple(extra))
domain = Orange.data.Domain(self.data.domain.attributes,
self.data.domain.class_vars, metas)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
data.ids = row_ids
data.name = learner_name
else:
data = None
self.send("Selected Data", data)
def _invalidate(self):
self.commit()
def _learner_changed(self):
# The selected learner has changed
indices = self.tableview.selectedIndexes()
self._update()
selection = QItemSelection()
for sel in indices:
selection.select(sel, sel)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
self.commit()
def _update(self):
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
index = self.selected_learner[0]
cmatrix = confusion_matrix(self.results, index)
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
total = rowsum.sum()
if self.selected_quantity == 0:
value = lambda i, j: int(cmatrix[i, j])
elif self.selected_quantity == 1:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / colsum[i])
if colsum[i] else "N/A")
elif self.selected_quantity == 2:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / rowsum[i])
if colsum[i] else "N/A")
else:
assert False
for i, row in enumerate(cmatrix):
for j, _ in enumerate(row):
item = self._item(i + 2, j + 2)
item.setData(value(i, j), Qt.DisplayRole)
item.setToolTip("actual: {}\npredicted: {}".format(
self.headers[i], self.headers[j]))
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
self._set_item(i + 2, j + 2, item)
model = self.tablemodel
font = model.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
def sum_item(value):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
return item
N = len(colsum)
for i in range(N):
model.setItem(N + 2, i + 2, sum_item(int(colsum[i])))
model.setItem(i + 2, N + 2, sum_item(int(rowsum[i])))
model.setItem(N + 2, N + 2, sum_item(int(total)))
class OWKMeans(widget.OWWidget):
name = "k-Means"
description = "k-means clustering algorithm with silhouette-based " \
"quality estimation."
icon = "icons/KMeans.svg"
priority = 2100
inputs = [("Data", Table, "set_data")]
outputs = [("Annotated Data", Table, widget.Default),
("Centroids", Table)]
INIT_KMEANS, INIT_RANDOM = range(2)
INIT_METHODS = "Initialize with KMeans++", "Random initialization"
SILHOUETTE, INTERCLUSTER, DISTANCES = range(3)
SCORING_METHODS = [("Silhouette", lambda km: km.silhouette, False),
("Inter-cluster distance",
lambda km: km.inter_cluster, True),
("Distance to centroids",
lambda km: km.inertia, True)]
OUTPUT_CLASS, OUTPUT_ATTRIBUTE, OUTPUT_META = range(3)
OUTPUT_METHODS = ("Class", "Feature", "Meta")
resizing_enabled = False
k = Setting(8)
k_from = Setting(2)
k_to = Setting(8)
optimize_k = Setting(False)
max_iterations = Setting(300)
n_init = Setting(10)
smart_init = Setting(INIT_KMEANS)
scoring = Setting(SILHOUETTE)
append_cluster_ids = Setting(True)
place_cluster_ids = Setting(OUTPUT_CLASS)
output_name = Setting("Cluster")
auto_run = Setting(False)
def __init__(self):
super().__init__()
self.data = None
self.km = None
self.optimization_runs = []
box = gui.widgetBox(self.controlArea, "Number of Clusters")
layout = QGridLayout()
bg = gui.radioButtonsInBox(
box, self, "optimize_k", [], orientation=layout,
callback=self.update)
layout.addWidget(
gui.appendRadioButton(bg, "Fixed", addToLayout=False),
1, 1)
sb = gui.widgetBox(None, margin=0, orientation="horizontal")
self.fixedSpinBox = gui.spin(
sb, self, "k", minv=2, maxv=30,
controlWidth=60, alignment=Qt.AlignRight, callback=self.update_k)
gui.rubber(sb)
layout.addWidget(sb, 1, 2)
layout.addWidget(
gui.appendRadioButton(bg, "Optimized", addToLayout=False), 2, 1)
ftobox = gui.widgetBox(None, orientation="horizontal")
ftobox.layout().setMargin(0)
layout.addWidget(ftobox)
gui.spin(
ftobox, self, "k_from", minv=2, maxv=29,
controlWidth=60, alignment=Qt.AlignRight,
callback=self.update_from)
gui.widgetLabel(ftobox, " To: ")
self.fixedSpinBox = gui.spin(
ftobox, self, "k_to", minv=3, maxv=30,
controlWidth=60, alignment=Qt.AlignRight,
callback=self.update_to)
gui.rubber(ftobox)
layout.addWidget(gui.widgetLabel(None, "Scoring: "),
5, 1, Qt.AlignRight)
layout.addWidget(
gui.comboBox(
None, self, "scoring", label="Scoring",
items=list(zip(*self.SCORING_METHODS))[0],
callback=self.update), 5, 2)
box = gui.widgetBox(self.controlArea, "Initialization")
gui.comboBox(
box, self, "smart_init", items=self.INIT_METHODS,
callback=self.update)
layout = QGridLayout()
box2 = gui.widgetBox(box, orientation=layout)
box2.setSizePolicy(QSizePolicy.Maximum, QSizePolicy.Maximum)
layout.addWidget(gui.widgetLabel(None, "Re-runs: "),
0, 0, Qt.AlignLeft)
sb = gui.widgetBox(None, margin=0, orientation="horizontal")
layout.addWidget(sb, 0, 1)
gui.lineEdit(
sb, self, "n_init", controlWidth=60,
valueType=int, validator=QIntValidator(),
callback=self.update)
layout.addWidget(gui.widgetLabel(None, "Maximal iterations: "),
1, 0, Qt.AlignLeft)
sb = gui.widgetBox(None, margin=0, orientation="horizontal")
layout.addWidget(sb, 1, 1)
gui.lineEdit(sb, self, "max_iterations",
controlWidth=60, valueType=int,
validator=QIntValidator(),
callback=self.update)
box = gui.widgetBox(self.controlArea, "Output")
gui.comboBox(box, self, "place_cluster_ids",
label="Append cluster id as ", orientation="horizontal",
callback=self.send_data, items=self.OUTPUT_METHODS)
gui.lineEdit(box, self, "output_name",
label="Name ", orientation="horizontal",
callback=self.send_data)
gui.auto_commit(self.controlArea, self, "auto_run", "Run",
checkbox_label="Run after any change ",
orientation="horizontal")
gui.rubber(self.controlArea)
self.table_model = QStandardItemModel(self)
self.table_model.setHorizontalHeaderLabels(["k", "Score"])
self.table_model.setColumnCount(2)
self.table_box = gui.widgetBox(
self.mainArea, "Optimization Report", addSpace=0)
table = self.table_view = QTableView(self.table_box)
table.setHorizontalScrollMode(QTableView.ScrollPerPixel)
table.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
table.setSelectionMode(QTableView.SingleSelection)
table.setSelectionBehavior(QTableView.SelectRows)
table.verticalHeader().hide()
table.setItemDelegateForColumn(1, gui.TableBarItem(self))
table.setModel(self.table_model)
table.selectionModel().selectionChanged.connect(
self.table_item_selected)
table.setColumnWidth(0, 40)
table.setColumnWidth(1, 120)
table.horizontalHeader().setStretchLastSection(True)
self.setSizePolicy(QSizePolicy.Preferred, QSizePolicy.Preferred)
self.mainArea.setSizePolicy(QSizePolicy.Maximum,
QSizePolicy.Preferred)
self.table_box.setSizePolicy(QSizePolicy.Fixed,
QSizePolicy.MinimumExpanding)
self.table_view.setSizePolicy(QSizePolicy.Preferred,
QSizePolicy.MinimumExpanding)
self.table_box.layout().addWidget(self.table_view)
self.hide_show_opt_results()
def adjustSize(self):
self.ensurePolished()
s = self.sizeHint()
self.resize(s)
def hide_show_opt_results(self):
[self.mainArea.hide, self.mainArea.show][self.optimize_k]()
QTimer.singleShot(100, self.adjustSize)
def sizeHint(self):
s = self.controlArea.sizeHint()
if self.optimize_k and not self.mainArea.isHidden():
s.setWidth(s.width() + self.mainArea.sizeHint().width() +
4 * self.childrenRect().x())
return s
def update_k(self):
self.optimize_k = False
self.update()
def update_from(self):
self.k_to = max(self.k_from + 1, self.k_to)
self.optimize_k = True
self.update()
def update_to(self):
self.k_from = min(self.k_from, self.k_to - 1)
self.optimize_k = True
self.update()
def set_optimization(self):
self.updateOptimizationGui()
self.update()
def check_data_size(self, n):
if n > len(self.data):
self.error("Not enough unique data instances ({}) for given "
"number of clusters ({}).".format(len(self.data), n))
return False
return True
def run_optimization(self):
# Disabling is needed since this function is not reentrant
# Fast clicking on, say, "To: " causes multiple calls
try:
self.controlArea.setDisabled(True)
self.optimization_runs = []
if self.check_data_size(self.k_to):
self.optimization_runs = []
kmeans = KMeans(
init=['random', 'k-means++'][self.smart_init],
n_init=self.n_init,
max_iter=self.max_iterations)
with self.progressBar(self.k_to - self.k_from + 1) as progress:
for k in range(self.k_from, self.k_to + 1):
progress.advance()
kmeans.params["n_clusters"] = k
self.optimization_runs.append((k, kmeans(self.data)))
finally:
self.controlArea.setDisabled(False)
self.show_results()
self.send_data()
def cluster(self):
if not self.check_data_size(self.k):
return
self.km = KMeans(
n_clusters=self.k,
init=['random', 'k-means++'][self.smart_init],
n_init=self.n_init,
max_iter=self.max_iterations)(self.data)
self.send_data()
def run(self):
self.error()
if not self.data:
return
if self.optimize_k:
self.run_optimization()
else:
self.cluster()
commit = run
def show_results(self):
minimize = self.SCORING_METHODS[self.scoring][2]
k_scores = [(k, self.SCORING_METHODS[self.scoring][1](run)) for
k, run in self.optimization_runs]
scores = list(zip(*k_scores))[1]
if minimize:
best_score, worst_score = min(scores), max(scores)
else:
best_score, worst_score = max(scores), min(scores)
best_run = scores.index(best_score)
score_span = (best_score - worst_score) or 1
max_score = max(scores)
nplaces = min(5, int(abs(math.log(max(max_score, 1e-10)))) + 2)
fmt = "{{:.{}}}".format(nplaces)
model = self.table_model
model.setRowCount(len(k_scores))
for i, (k, score) in enumerate(k_scores):
item = model.item(i, 0)
if item is None:
item = QStandardItem()
item.setData(k, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignCenter)
model.setItem(i, 0, item)
item = model.item(i, 1)
if item is None:
item = QStandardItem()
item.setData(fmt.format(score), Qt.DisplayRole)
bar_ratio = 0.95 * (score - worst_score) / score_span
item.setData(bar_ratio, gui.TableBarItem.BarRole)
model.setItem(i, 1, item)
self.table_view.resizeRowsToContents()
self.table_view.selectRow(best_run)
self.table_view.show()
if minimize:
self.table_box.setTitle("Scoring (smaller is better)")
else:
self.table_box.setTitle("Scoring (bigger is better)")
QTimer.singleShot(0, self.adjustSize)
def update(self):
self.hide_show_opt_results()
self.run()
def selected_row(self):
indices = self.table_view.selectedIndexes()
rows = {ind.row() for ind in indices}
if len(rows) == 1:
return rows.pop()
def table_item_selected(self):
row = self.selected_row()
if row is not None:
self.send_data(row)
def send_data(self, row=None):
if self.optimize_k:
if row is None:
row = self.selected_row()
km = self.optimization_runs[row][1]
else:
km = self.km
if not self.data or not km:
self.send("Annotated Data", None)
self.send("Centroids", None)
return
clust_var = DiscreteVariable(
self.output_name, values=["C%d" % (x + 1) for x in range(km.k)])
clust_ids = km(self.data)
domain = self.data.domain
attributes, classes = domain.attributes, domain.class_vars
meta_attrs = domain.metas
if self.place_cluster_ids == self.OUTPUT_CLASS:
if classes:
meta_attrs += classes
classes = [clust_var]
elif self.place_cluster_ids == self.OUTPUT_ATTRIBUTE:
attributes += (clust_var, )
else:
meta_attrs += (clust_var, )
domain = Domain(attributes, classes, meta_attrs)
new_table = Table.from_table(domain, self.data)
new_table.get_column_view(clust_var)[0][:] = clust_ids.X.ravel()
centroids = Table(Domain(km.pre_domain.attributes), km.centroids)
self.send("Annotated Data", new_table)
self.send("Centroids", centroids)
@check_sql_input
def set_data(self, data):
self.data = data
if data is None:
self.table_model.setRowCount(0)
else:
self.data = data
self.run()
def send_report(self):
self.report_items((
("Number of clusters",
self.optimization_runs[self.selected_row()][1].k
if self.optimize_k else self.k),
("Optimization",
self.optimize_k != 0 and
"{}, {} re-runs limited to {} steps".format(
self.INIT_METHODS[self.smart_init].lower(),
self.n_init, self.max_iterations)),
("Cluster ID in output",
self.append_cluster_ids and
"'{}' (as {})".format(
self.output_name,
self.OUTPUT_METHODS[self.place_cluster_ids].lower()))
))
if self.data:
self.report_data("Data", self.data)
if self.optimize_k:
self.report_table(
"Scoring by {}".format(self.SCORING_METHODS[self.scoring][0]
),
self.table_view)
class OWConfusionMatrix(widget.OWWidget):
name = "Confusion Matrix"
description = "Shows a confusion matrix."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [{"name": "Evaluation Results",
"type": Orange.evaluation.testing.Results,
"handler": "set_results"}]
outputs = [{"name": "Selected Data",
"type": Orange.data.Table}]
quantities = ["Number of instances",
"Observed and expected instances",
"Proportion of predicted",
"Proportion of true"]
selected_learner = settings.Setting([])
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
def __init__(self, parent=None):
super().__init__(parent)
self.results = None
self.learners = []
self._invalidated = False
box = gui.widgetBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(
box, self, "selected_learner", "learners",
callback=self._learner_changed
)
box = gui.widgetBox(self.controlArea, "Show")
combo = gui.comboBox(box, self, "selected_quantity",
items=self.quantities,
callback=self._update)
box = gui.widgetBox(self.controlArea, "Selection")
gui.button(box, self, "Correct",
callback=self.select_correct, autoDefault=False)
gui.button(box, self, "Misclassified",
callback=self.select_wrong, autoDefault=False)
gui.button(box, self, "None",
callback=self.select_none, autoDefault=False)
self.outputbox = box = gui.widgetBox(self.controlArea, "Output")
gui.checkBox(box, self, "append_predictions",
"Append class predictions", callback=self._invalidate)
gui.checkBox(box, self, "append_probabilities",
"Append predicted class probabilities",
callback=self._invalidate)
b = gui.button(box, self, "Commit", callback=self.commit, default=True)
cb = gui.checkBox(box, self, "autocommit", "Commit automatically")
gui.setStopper(self, b, cb, "_invalidated", callback=self.commit)
grid = QGridLayout()
grid.setContentsMargins(0, 0, 0, 0)
grid.addWidget(QLabel("Predicted"), 0, 1, Qt.AlignCenter)
grid.addWidget(VerticalLabel("Correct Class"), 1, 0, Qt.AlignCenter)
self.tablemodel = QStandardItemModel()
self.tableview = QTableView(
editTriggers=QTableView.NoEditTriggers,
)
self.tableview.setModel(self.tablemodel)
self.tableview.selectionModel().selectionChanged.connect(
self._invalidate
)
grid.addWidget(self.tableview, 1, 1)
self.mainArea.layout().addLayout(grid)
def set_results(self, results):
"""Set the input results."""
self.clear()
self.warning([0, 1])
data = None
if results is not None:
if results.data is not None:
data = results.data
if data is not None and \
not isinstance(data.domain.class_var,
Orange.data.DiscreteVariable):
data = None
results = None
self.warning(
0, "Confusion Matrix cannot be used for regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is not None:
nmodels, ntests = results.predicted.shape
headers = class_values + [unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'fitter_names' is set in 'Test Learners' widget.
if hasattr(results, "fitter_names"):
self.learners = results.fitter_names
else:
self.learners = ["L %i" % (i + 1) for i in range(nmodels)]
self.tablemodel.setVerticalHeaderLabels(headers)
self.tablemodel.setHorizontalHeaderLabels(headers)
self.tablemodel.setRowCount(len(class_values) + 1)
self.tablemodel.setColumnCount(len(class_values) + 1)
self.selected_learner = [0]
self._update()
def clear(self):
self.learners = []
self.results = None
self.data = None
self.tablemodel.clear()
def select_correct(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
def select_wrong(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
def select_none(self):
self.tableview.selectionModel().clear()
def commit(self):
if self.results and self.data:
indices = self.tableview.selectedIndexes()
indices = {(ind.row(), ind.column()) for ind in indices}
actual = self.results.actual
selected_learner = self.selected_learner[0]
learner_name = self.learners[selected_learner]
predicted = self.results.predicted[selected_learner]
selected = [i for i, t in enumerate(zip(actual, predicted))
if t in indices]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values
)
metas = metas + (var,)
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[selected_learner, selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
M = numpy.hstack((M,) + tuple(extra))
domain = Orange.data.Domain(
self.data.domain.attributes,
self.data.domain.class_vars,
metas
)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
else:
data = None
self.send("Selected Data", data)
self._invalidated = False
def _invalidate(self):
if self.autocommit:
self.commit()
else:
self._invalidated = True
def _learner_changed(self):
# The selected learner has changed
self._update()
def _update(self):
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
index = self.selected_learner[0]
cmatrix = confusion_matrix(self.results, index)
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
total = rowsum.sum()
if self.selected_quantity == 0:
value = lambda i, j: int(cmatrix[i, j])
elif self.selected_quantity == 1:
priors = numpy.outer(rowsum, colsum) / total
value = lambda i, j: \
"{} / {:5.3f}".format(cmatrix[i, j], priors[i, j])
elif self.selected_quantity == 2:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / colsum[i])
if colsum[i] else "N/A")
elif self.selected_quantity == 3:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / rowsum[i])
if colsum[i] else "N/A")
else:
assert False
model = self.tablemodel
for i, row in enumerate(cmatrix):
for j, _ in enumerate(row):
item = model.item(i, j)
if item is None:
item = QStandardItem()
item.setData(value(i, j), Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
model.setItem(i, j, item)
font = model.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
def sum_item(value):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
return item
N = len(colsum)
for i in range(N):
model.setItem(N, i, sum_item(int(colsum[i])))
model.setItem(i, N, sum_item(int(rowsum[i])))
model.setItem(N, N, sum_item(int(total)))
class OWConfusionMatrix(widget.OWWidget):
"""Confusion matrix widget"""
name = "Confusion Matrix"
description = "Display a confusion matrix constructed from " \
"the results of classifier evaluations."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [("Evaluation Results", Orange.evaluation.Results, "set_results")]
outputs = [("Selected Data", Orange.data.Table)]
quantities = [
"Number of instances", "Proportion of predicted",
"Proportion of actual"
]
settingsHandler = settings.ClassValuesContextHandler()
selected_learner = settings.Setting([0], schema_only=True)
selection = settings.ContextSetting(set())
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
UserAdviceMessages = [
widget.Message(
"Clicking on cells or in headers outputs the corresponding "
"data instances", "click_cell")
]
def __init__(self):
super().__init__()
self.data = None
self.results = None
self.learners = []
self.headers = []
box = gui.vBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(box,
self,
"selected_learner",
"learners",
callback=self._learner_changed)
box = gui.vBox(self.controlArea, "Show")
gui.comboBox(box,
self,
"selected_quantity",
items=self.quantities,
callback=self._update)
box = gui.vBox(self.controlArea, "Select")
gui.button(box,
self,
"Select Correct",
callback=self.select_correct,
autoDefault=False)
gui.button(box,
self,
"Select Misclassified",
callback=self.select_wrong,
autoDefault=False)
gui.button(box,
self,
"Clear Selection",
callback=self.select_none,
autoDefault=False)
self.outputbox = box = gui.vBox(self.controlArea, "Output")
gui.checkBox(box,
self,
"append_predictions",
"Predictions",
callback=self._invalidate)
gui.checkBox(box,
self,
"append_probabilities",
"Probabilities",
callback=self._invalidate)
gui.auto_commit(self.controlArea, self, "autocommit", "Send Selected",
"Send Automatically")
grid = QGridLayout()
self.tablemodel = QStandardItemModel(self)
view = self.tableview = QTableView(
editTriggers=QTableView.NoEditTriggers)
view.setModel(self.tablemodel)
view.horizontalHeader().hide()
view.verticalHeader().hide()
view.horizontalHeader().setMinimumSectionSize(60)
view.selectionModel().selectionChanged.connect(self._invalidate)
view.setShowGrid(False)
view.setItemDelegate(BorderedItemDelegate(Qt.white))
view.clicked.connect(self.cell_clicked)
grid.addWidget(view, 0, 0)
self.mainArea.layout().addLayout(grid)
def sizeHint(self):
"""Initial size"""
return QSize(750, 490)
def _item(self, i, j):
return self.tablemodel.item(i, j) or QStandardItem()
def _set_item(self, i, j, item):
self.tablemodel.setItem(i, j, item)
def _init_table(self, nclasses):
item = self._item(0, 2)
item.setData("Predicted", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignCenter)
item.setFlags(Qt.NoItemFlags)
self._set_item(0, 2, item)
item = self._item(2, 0)
item.setData("Actual", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignHCenter | Qt.AlignBottom)
item.setFlags(Qt.NoItemFlags)
self.tableview.setItemDelegateForColumn(0, gui.VerticalItemDelegate())
self._set_item(2, 0, item)
self.tableview.setSpan(0, 2, 1, nclasses)
self.tableview.setSpan(2, 0, nclasses, 1)
font = self.tablemodel.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
for i in (0, 1):
for j in (0, 1):
item = self._item(i, j)
item.setFlags(Qt.NoItemFlags)
self._set_item(i, j, item)
for p, label in enumerate(self.headers):
for i, j in ((1, p + 2), (p + 2, 1)):
item = self._item(i, j)
item.setData(label, Qt.DisplayRole)
item.setFont(bold_font)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
if p < len(self.headers) - 1:
item.setData("br"[j == 1], BorderRole)
item.setData(QColor(192, 192, 192), BorderColorRole)
self._set_item(i, j, item)
hor_header = self.tableview.horizontalHeader()
if len(' '.join(self.headers)) < 120:
hor_header.setResizeMode(QHeaderView.ResizeToContents)
else:
hor_header.setDefaultSectionSize(60)
self.tablemodel.setRowCount(nclasses + 3)
self.tablemodel.setColumnCount(nclasses + 3)
def set_results(self, results):
"""Set the input results."""
prev_sel_learner = self.selected_learner.copy()
self.clear()
self.warning()
self.closeContext()
data = None
if results is not None and results.data is not None:
data = results.data
if data is not None and not data.domain.has_discrete_class:
self.warning("Confusion Matrix cannot show regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is None:
self.report_button.setDisabled(True)
else:
self.report_button.setDisabled(False)
nmodels = results.predicted.shape[0]
self.headers = class_values + \
[unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'learner_names' is set in 'Test Learners' widget.
if hasattr(results, "learner_names"):
self.learners = results.learner_names
else:
self.learners = [
"Learner #{}".format(i + 1) for i in range(nmodels)
]
self._init_table(len(class_values))
self.openContext(data.domain.class_var)
if not prev_sel_learner or prev_sel_learner[0] >= len(
self.learners):
self.selected_learner[:] = [0]
else:
self.selected_learner[:] = prev_sel_learner
self._update()
self._set_selection()
self.unconditional_commit()
def clear(self):
"""Reset the widget, clear controls"""
self.results = None
self.data = None
self.tablemodel.clear()
self.headers = []
# Clear learners last. This action will invoke `_learner_changed`
self.learners = []
def select_correct(self):
"""Select the diagonal elements of the matrix"""
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_wrong(self):
"""Select the off-diagonal elements of the matrix"""
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_none(self):
"""Reset selection"""
self.tableview.selectionModel().clear()
def cell_clicked(self, model_index):
"""Handle cell click event"""
i, j = model_index.row(), model_index.column()
if not i or not j:
return
n = self.tablemodel.rowCount()
index = self.tablemodel.index
selection = None
if i == j == 1 or i == j == n - 1:
selection = QItemSelection(index(2, 2), index(n - 1, n - 1))
elif i in (1, n - 1):
selection = QItemSelection(index(2, j), index(n - 1, j))
elif j in (1, n - 1):
selection = QItemSelection(index(i, 2), index(i, n - 1))
if selection is not None:
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def commit(self):
"""Output data instances corresponding to selected cells"""
if self.results is not None and self.data is not None \
and self.selected_learner:
indices = self.tableview.selectedIndexes()
indices = {(ind.row() - 2, ind.column() - 2) for ind in indices}
actual = self.results.actual
learner_name = self.learners[self.selected_learner[0]]
predicted = self.results.predicted[self.selected_learner[0]]
selected = [
i for i, t in enumerate(zip(actual, predicted)) if t in indices
]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values)
metas = metas + (var, )
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[self.selected_learner[0],
selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [
Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values
]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
row_ids = self.data.ids[row_indices]
M = numpy.hstack((M, ) + tuple(extra))
domain = Orange.data.Domain(self.data.domain.attributes,
self.data.domain.class_vars, metas)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
data.ids = row_ids
data.name = learner_name
else:
data = None
self.send("Selected Data", data)
def _invalidate(self):
indices = self.tableview.selectedIndexes()
self.selection = {(ind.row() - 2, ind.column() - 2) for ind in indices}
self.commit()
def _set_selection(self):
selection = QItemSelection()
index = self.tableview.model().index
for row, col in self.selection:
sel = index(row + 2, col + 2)
selection.select(sel, sel)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def _learner_changed(self):
self._update()
self._set_selection()
self.commit()
def _update(self):
def _isinvalid(x):
return isnan(x) or isinf(x)
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
cmatrix = confusion_matrix(self.results, self.selected_learner[0])
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
n = len(cmatrix)
diag = numpy.diag_indices(n)
colors = cmatrix.astype(numpy.double)
colors[diag] = 0
if self.selected_quantity == 0:
normalized = cmatrix.astype(numpy.int)
formatstr = "{}"
div = numpy.array([colors.max()])
else:
if self.selected_quantity == 1:
normalized = 100 * cmatrix / colsum
div = colors.max(axis=0)
else:
normalized = 100 * cmatrix / rowsum[:, numpy.newaxis]
div = colors.max(axis=1)[:, numpy.newaxis]
formatstr = "{:2.1f} %"
div[div == 0] = 1
colors /= div
colors[diag] = normalized[diag] / normalized[diag].max()
for i in range(n):
for j in range(n):
val = normalized[i, j]
col_val = colors[i, j]
item = self._item(i + 2, j + 2)
item.setData(
"NA" if _isinvalid(val) else formatstr.format(val),
Qt.DisplayRole)
bkcolor = QColor.fromHsl(
[0, 240][i == j], 160,
255 if _isinvalid(col_val) else int(255 -
30 * col_val))
item.setData(QBrush(bkcolor), Qt.BackgroundRole)
item.setData("trbl", BorderRole)
item.setToolTip("actual: {}\npredicted: {}".format(
self.headers[i], self.headers[j]))
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
self._set_item(i + 2, j + 2, item)
bold_font = self.tablemodel.invisibleRootItem().font()
bold_font.setBold(True)
def _sum_item(value, border=""):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
item.setData(border, BorderRole)
item.setData(QColor(192, 192, 192), BorderColorRole)
return item
for i in range(n):
self._set_item(n + 2, i + 2, _sum_item(int(colsum[i]), "t"))
self._set_item(i + 2, n + 2, _sum_item(int(rowsum[i]), "l"))
self._set_item(n + 2, n + 2, _sum_item(int(rowsum.sum())))
def send_report(self):
"""Send report"""
if self.results is not None and self.selected_learner:
self.report_table(
"Confusion matrix for {} (showing {})".format(
self.learners[self.selected_learner[0]],
self.quantities[self.selected_quantity].lower()),
self.tableview)
class OWConfusionMatrix(widget.OWWidget):
"""Confusion matrix widget"""
name = "Confusion Matrix"
description = "Display a confusion matrix constructed from " \
"the results of classifier evaluations."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [("Evaluation Results", Orange.evaluation.Results, "set_results")]
outputs = [("Selected Data", Orange.data.Table)]
quantities = ["Number of instances",
"Proportion of predicted",
"Proportion of actual"]
settingsHandler = settings.ClassValuesContextHandler()
selected_learner = settings.Setting([0], schema_only=True)
selection = settings.ContextSetting(set())
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
UserAdviceMessages = [
widget.Message(
"Clicking on cells or in headers outputs the corresponding "
"data instances",
"click_cell")]
def __init__(self):
super().__init__()
self.data = None
self.results = None
self.learners = []
self.headers = []
box = gui.vBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(
box, self, "selected_learner", "learners",
callback=self._learner_changed
)
box = gui.vBox(self.controlArea, "Show")
gui.comboBox(box, self, "selected_quantity", items=self.quantities,
callback=self._update)
box = gui.vBox(self.controlArea, "Select")
gui.button(box, self, "Select Correct",
callback=self.select_correct, autoDefault=False)
gui.button(box, self, "Select Misclassified",
callback=self.select_wrong, autoDefault=False)
gui.button(box, self, "Clear Selection",
callback=self.select_none, autoDefault=False)
self.outputbox = box = gui.vBox(self.controlArea, "Output")
gui.checkBox(box, self, "append_predictions",
"Predictions", callback=self._invalidate)
gui.checkBox(box, self, "append_probabilities",
"Probabilities",
callback=self._invalidate)
gui.auto_commit(self.controlArea, self, "autocommit",
"Send Selected", "Send Automatically")
grid = QGridLayout()
self.tablemodel = QStandardItemModel(self)
view = self.tableview = QTableView(
editTriggers=QTableView.NoEditTriggers)
view.setModel(self.tablemodel)
view.horizontalHeader().hide()
view.verticalHeader().hide()
view.horizontalHeader().setMinimumSectionSize(60)
view.selectionModel().selectionChanged.connect(self._invalidate)
view.setShowGrid(False)
view.setItemDelegate(BorderedItemDelegate(Qt.white))
view.clicked.connect(self.cell_clicked)
grid.addWidget(view, 0, 0)
self.mainArea.layout().addLayout(grid)
def sizeHint(self):
"""Initial size"""
return QSize(750, 490)
def _item(self, i, j):
return self.tablemodel.item(i, j) or QStandardItem()
def _set_item(self, i, j, item):
self.tablemodel.setItem(i, j, item)
def _init_table(self, nclasses):
item = self._item(0, 2)
item.setData("Predicted", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignCenter)
item.setFlags(Qt.NoItemFlags)
self._set_item(0, 2, item)
item = self._item(2, 0)
item.setData("Actual", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignHCenter | Qt.AlignBottom)
item.setFlags(Qt.NoItemFlags)
self.tableview.setItemDelegateForColumn(0, gui.VerticalItemDelegate())
self._set_item(2, 0, item)
self.tableview.setSpan(0, 2, 1, nclasses)
self.tableview.setSpan(2, 0, nclasses, 1)
font = self.tablemodel.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
for i in (0, 1):
for j in (0, 1):
item = self._item(i, j)
item.setFlags(Qt.NoItemFlags)
self._set_item(i, j, item)
for p, label in enumerate(self.headers):
for i, j in ((1, p + 2), (p + 2, 1)):
item = self._item(i, j)
item.setData(label, Qt.DisplayRole)
item.setFont(bold_font)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
if p < len(self.headers) - 1:
item.setData("br"[j == 1], BorderRole)
item.setData(QColor(192, 192, 192), BorderColorRole)
self._set_item(i, j, item)
hor_header = self.tableview.horizontalHeader()
if len(' '.join(self.headers)) < 120:
hor_header.setResizeMode(QHeaderView.ResizeToContents)
else:
hor_header.setDefaultSectionSize(60)
self.tablemodel.setRowCount(nclasses + 3)
self.tablemodel.setColumnCount(nclasses + 3)
def set_results(self, results):
"""Set the input results."""
prev_sel_learner = self.selected_learner.copy()
self.clear()
self.warning()
self.closeContext()
data = None
if results is not None and results.data is not None:
data = results.data
if data is not None and not data.domain.has_discrete_class:
self.warning("Confusion Matrix cannot show regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is None:
self.report_button.setDisabled(True)
else:
self.report_button.setDisabled(False)
nmodels = results.predicted.shape[0]
self.headers = class_values + \
[unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'learner_names' is set in 'Test Learners' widget.
if hasattr(results, "learner_names"):
self.learners = results.learner_names
else:
self.learners = ["Learner #{}".format(i + 1)
for i in range(nmodels)]
self._init_table(len(class_values))
self.openContext(data.domain.class_var)
if not prev_sel_learner or prev_sel_learner[0] >= len(self.learners):
self.selected_learner[:] = [0]
else:
self.selected_learner[:] = prev_sel_learner
self._update()
self._set_selection()
self.unconditional_commit()
def clear(self):
"""Reset the widget, clear controls"""
self.results = None
self.data = None
self.tablemodel.clear()
self.headers = []
# Clear learners last. This action will invoke `_learner_changed`
self.learners = []
def select_correct(self):
"""Select the diagonal elements of the matrix"""
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_wrong(self):
"""Select the off-diagonal elements of the matrix"""
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_none(self):
"""Reset selection"""
self.tableview.selectionModel().clear()
def cell_clicked(self, model_index):
"""Handle cell click event"""
i, j = model_index.row(), model_index.column()
if not i or not j:
return
n = self.tablemodel.rowCount()
index = self.tablemodel.index
selection = None
if i == j == 1 or i == j == n - 1:
selection = QItemSelection(index(2, 2), index(n - 1, n - 1))
elif i in (1, n - 1):
selection = QItemSelection(index(2, j), index(n - 1, j))
elif j in (1, n - 1):
selection = QItemSelection(index(i, 2), index(i, n - 1))
if selection is not None:
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def commit(self):
"""Output data instances corresponding to selected cells"""
if self.results is not None and self.data is not None \
and self.selected_learner:
indices = self.tableview.selectedIndexes()
indices = {(ind.row() - 2, ind.column() - 2) for ind in indices}
actual = self.results.actual
learner_name = self.learners[self.selected_learner[0]]
predicted = self.results.predicted[self.selected_learner[0]]
selected = [i for i, t in enumerate(zip(actual, predicted))
if t in indices]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values
)
metas = metas + (var,)
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[self.selected_learner[0],
selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
row_ids = self.data.ids[row_indices]
M = numpy.hstack((M,) + tuple(extra))
domain = Orange.data.Domain(
self.data.domain.attributes,
self.data.domain.class_vars,
metas
)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
data.ids = row_ids
data.name = learner_name
else:
data = None
self.send("Selected Data", data)
def _invalidate(self):
indices = self.tableview.selectedIndexes()
self.selection = {(ind.row() - 2, ind.column() - 2) for ind in indices}
self.commit()
def _set_selection(self):
selection = QItemSelection()
index = self.tableview.model().index
for row, col in self.selection:
sel = index(row + 2, col + 2)
selection.select(sel, sel)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def _learner_changed(self):
self._update()
self._set_selection()
self.commit()
def _update(self):
def _isinvalid(x):
return isnan(x) or isinf(x)
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
cmatrix = confusion_matrix(self.results, self.selected_learner[0])
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
n = len(cmatrix)
diag = numpy.diag_indices(n)
colors = cmatrix.astype(numpy.double)
colors[diag] = 0
if self.selected_quantity == 0:
normalized = cmatrix.astype(numpy.int)
formatstr = "{}"
div = numpy.array([colors.max()])
else:
if self.selected_quantity == 1:
normalized = 100 * cmatrix / colsum
div = colors.max(axis=0)
else:
normalized = 100 * cmatrix / rowsum[:, numpy.newaxis]
div = colors.max(axis=1)[:, numpy.newaxis]
formatstr = "{:2.1f} %"
div[div == 0] = 1
colors /= div
colors[diag] = normalized[diag] / normalized[diag].max()
for i in range(n):
for j in range(n):
val = normalized[i, j]
col_val = colors[i, j]
item = self._item(i + 2, j + 2)
item.setData(
"NA" if _isinvalid(val) else formatstr.format(val),
Qt.DisplayRole)
bkcolor = QColor.fromHsl(
[0, 240][i == j], 160,
255 if _isinvalid(col_val) else int(255 - 30 * col_val))
item.setData(QBrush(bkcolor), Qt.BackgroundRole)
item.setData("trbl", BorderRole)
item.setToolTip("actual: {}\npredicted: {}".format(
self.headers[i], self.headers[j]))
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
self._set_item(i + 2, j + 2, item)
bold_font = self.tablemodel.invisibleRootItem().font()
bold_font.setBold(True)
def _sum_item(value, border=""):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
item.setData(border, BorderRole)
item.setData(QColor(192, 192, 192), BorderColorRole)
return item
for i in range(n):
self._set_item(n + 2, i + 2, _sum_item(int(colsum[i]), "t"))
self._set_item(i + 2, n + 2, _sum_item(int(rowsum[i]), "l"))
self._set_item(n + 2, n + 2, _sum_item(int(rowsum.sum())))
def send_report(self):
"""Send report"""
if self.results is not None and self.selected_learner:
self.report_table(
"Confusion matrix for {} (showing {})".
format(self.learners[self.selected_learner[0]],
self.quantities[self.selected_quantity].lower()),
self.tableview)
def main(icon_spec):
app = QApplication(sys.argv)
main_window = QMainWindow()
def sigint_handler(*args):
main_window.close()
signal.signal(signal.SIGINT, sigint_handler)
# the timer enables triggering the sigint_handler
signal_timer = QTimer()
signal_timer.start(100)
signal_timer.timeout.connect(lambda: None)
tool_bar = QToolBar()
main_window.addToolBar(Qt.TopToolBarArea, tool_bar)
table_view = QTableView()
table_view.setSelectionBehavior(QAbstractItemView.SelectRows)
table_view.setSelectionMode(QAbstractItemView.SingleSelection)
table_view.setSortingEnabled(True)
main_window.setCentralWidget(table_view)
proxy_model = QSortFilterProxyModel()
proxy_model.setFilterCaseSensitivity(Qt.CaseInsensitive)
proxy_model.setFilterKeyColumn(1)
table_view.setModel(proxy_model)
proxy_model.layoutChanged.connect(table_view.resizeRowsToContents)
item_model = QStandardItemModel()
proxy_model.setSourceModel(item_model)
# get all icons and their available sizes
QIcon.setThemeName("gnome")
icons = []
all_sizes = set([])
for context, icon_names in icon_spec:
for icon_name in icon_names:
icon = QIcon.fromTheme(icon_name)
sizes = []
for size in icon.availableSizes():
size = (size.width(), size.height())
sizes.append(size)
all_sizes.add(size)
sizes.sort()
icons.append({
'context': context,
'icon_name': icon_name,
'icon': icon,
'sizes': sizes,
})
all_sizes = list(all_sizes)
all_sizes.sort()
# input field for filter
def filter_changed(value):
proxy_model.setFilterRegExp(value)
table_view.resizeRowsToContents()
filter_line_edit = QLineEdit()
filter_line_edit.setMaximumWidth(200)
filter_line_edit.setPlaceholderText('Filter name')
filter_line_edit.setToolTip('Filter name optionally using regular expressions (' + QKeySequence(QKeySequence.Find).toString() + ')')
filter_line_edit.textChanged.connect(filter_changed)
tool_bar.addWidget(filter_line_edit)
# actions to toggle visibility of available sizes/columns
def action_toggled(index):
column = 2 + index
table_view.setColumnHidden(column, not table_view.isColumnHidden(column))
table_view.resizeColumnsToContents()
table_view.resizeRowsToContents()
signal_mapper = QSignalMapper()
for i, size in enumerate(all_sizes):
action = QAction('%dx%d' % size, tool_bar)
action.setCheckable(True)
action.setChecked(True)
tool_bar.addAction(action)
action.toggled.connect(signal_mapper.map)
signal_mapper.setMapping(action, i)
# set tool tip and handle key sequence
tool_tip = 'Toggle visibility of column'
if i < 10:
digit = ('%d' % (i + 1))[-1]
tool_tip += ' (%s)' % QKeySequence('Ctrl+%s' % digit).toString()
action.setToolTip(tool_tip)
signal_mapper.mapped.connect(action_toggled)
# label columns
header_labels = ['context', 'name']
for width, height in all_sizes:
header_labels.append('%dx%d' % (width, height))
item_model.setColumnCount(len(header_labels))
item_model.setHorizontalHeaderLabels(header_labels)
# fill rows
item_model.setRowCount(len(icons))
for row, icon_data in enumerate(icons):
# context
item = QStandardItem(icon_data['context'])
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, 0, item)
# icon name
item = QStandardItem(icon_data['icon_name'])
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, 1, item)
for index_in_all_sizes, size in enumerate(all_sizes):
column = 2 + index_in_all_sizes
if size in icon_data['sizes']:
# icon as pixmap to keep specific size
item = QStandardItem('')
pixmap = icon_data['icon'].pixmap(size[0], size[1])
item.setData(pixmap, Qt.DecorationRole)
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, column, item)
else:
# single space to be sortable against icons
item = QStandardItem(' ')
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, column, item)
table_view.resizeColumnsToContents()
# manually set row heights because resizeRowsToContents is not working properly
for row, icon_data in enumerate(icons):
if len(icon_data['sizes']) > 0:
max_size = icon_data['sizes'][-1]
table_view.setRowHeight(row, max_size[1])
# enable focus find (ctrl+f) and toggle columns (ctrl+NUM)
def main_window_keyPressEvent(self, event, old_keyPressEvent=QMainWindow.keyPressEvent):
if event.matches(QKeySequence.Find):
filter_line_edit.setFocus()
return
if event.modifiers() == Qt.ControlModifier and event.key() >= Qt.Key_0 and event.key() <= Qt.Key_9:
index = event.key() - Qt.Key_1
if event.key() == Qt.Key_0:
index += 10
action = signal_mapper.mapping(index)
if action:
action.toggle()
return
old_keyPressEvent(self, event)
main_window.keyPressEvent = new.instancemethod(main_window_keyPressEvent, table_view, None)
# enable copy (ctrl+c) name of icon to clipboard
def table_view_keyPressEvent(self, event, old_keyPressEvent=QTableView.keyPressEvent):
if event.matches(QKeySequence.Copy):
selection_model = self.selectionModel()
if selection_model.hasSelection():
index = selection_model.selectedRows()[0]
source_index = self.model().mapToSource(index)
item = self.model().sourceModel().item(source_index.row(), 1)
icon_name = item.data(Qt.EditRole)
app.clipboard().setText(icon_name.toString())
return
old_keyPressEvent(self, event)
table_view.keyPressEvent = new.instancemethod(table_view_keyPressEvent, table_view, None)
print 'Icon Theme: ', QIcon.themeName()
print 'Theme Search Paths:'
for item in QIcon.themeSearchPaths():
print item
main_window.showMaximized()
return app.exec_()
def main(icon_spec):
app = QApplication(sys.argv)
main_window = QMainWindow()
def sigint_handler(*args):
main_window.close()
signal.signal(signal.SIGINT, sigint_handler)
# the timer enables triggering the sigint_handler
signal_timer = QTimer()
signal_timer.start(100)
signal_timer.timeout.connect(lambda: None)
tool_bar = QToolBar()
main_window.addToolBar(Qt.TopToolBarArea, tool_bar)
table_view = QTableView()
table_view.setSelectionBehavior(QAbstractItemView.SelectRows)
table_view.setSelectionMode(QAbstractItemView.SingleSelection)
table_view.setSortingEnabled(True)
main_window.setCentralWidget(table_view)
proxy_model = QSortFilterProxyModel()
proxy_model.setFilterCaseSensitivity(Qt.CaseInsensitive)
proxy_model.setFilterKeyColumn(1)
table_view.setModel(proxy_model)
proxy_model.layoutChanged.connect(table_view.resizeRowsToContents)
item_model = QStandardItemModel()
proxy_model.setSourceModel(item_model)
# get all icons and their available sizes
QIcon.setThemeName("gnome")
icons = []
all_sizes = set([])
for context, icon_names in icon_spec:
for icon_name in icon_names:
icon = QIcon.fromTheme(icon_name)
sizes = []
for size in icon.availableSizes():
size = (size.width(), size.height())
sizes.append(size)
all_sizes.add(size)
sizes.sort()
icons.append({
'context': context,
'icon_name': icon_name,
'icon': icon,
'sizes': sizes,
})
all_sizes = list(all_sizes)
all_sizes.sort()
# input field for filter
def filter_changed(value):
proxy_model.setFilterRegExp(value)
table_view.resizeRowsToContents()
filter_line_edit = QLineEdit()
filter_line_edit.setMaximumWidth(200)
filter_line_edit.setPlaceholderText('Filter name')
filter_line_edit.setToolTip(
'Filter name optionally using regular expressions (' +
QKeySequence(QKeySequence.Find).toString() + ')')
filter_line_edit.textChanged.connect(filter_changed)
tool_bar.addWidget(filter_line_edit)
# actions to toggle visibility of available sizes/columns
def action_toggled(index):
column = 2 + index
table_view.setColumnHidden(column,
not table_view.isColumnHidden(column))
table_view.resizeColumnsToContents()
table_view.resizeRowsToContents()
signal_mapper = QSignalMapper()
for i, size in enumerate(all_sizes):
action = QAction('%dx%d' % size, tool_bar)
action.setCheckable(True)
action.setChecked(True)
tool_bar.addAction(action)
action.toggled.connect(signal_mapper.map)
signal_mapper.setMapping(action, i)
# set tool tip and handle key sequence
tool_tip = 'Toggle visibility of column'
if i < 10:
digit = ('%d' % (i + 1))[-1]
tool_tip += ' (%s)' % QKeySequence('Ctrl+%s' % digit).toString()
action.setToolTip(tool_tip)
signal_mapper.mapped.connect(action_toggled)
# label columns
header_labels = ['context', 'name']
for width, height in all_sizes:
header_labels.append('%dx%d' % (width, height))
item_model.setColumnCount(len(header_labels))
item_model.setHorizontalHeaderLabels(header_labels)
# fill rows
item_model.setRowCount(len(icons))
for row, icon_data in enumerate(icons):
# context
item = QStandardItem(icon_data['context'])
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, 0, item)
# icon name
item = QStandardItem(icon_data['icon_name'])
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, 1, item)
for index_in_all_sizes, size in enumerate(all_sizes):
column = 2 + index_in_all_sizes
if size in icon_data['sizes']:
# icon as pixmap to keep specific size
item = QStandardItem('')
pixmap = icon_data['icon'].pixmap(size[0], size[1])
item.setData(pixmap, Qt.DecorationRole)
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, column, item)
else:
# single space to be sortable against icons
item = QStandardItem(' ')
item.setFlags(item.flags() ^ Qt.ItemIsEditable)
item_model.setItem(row, column, item)
table_view.resizeColumnsToContents()
# manually set row heights because resizeRowsToContents is not working properly
for row, icon_data in enumerate(icons):
if len(icon_data['sizes']) > 0:
max_size = icon_data['sizes'][-1]
table_view.setRowHeight(row, max_size[1])
# enable focus find (ctrl+f) and toggle columns (ctrl+NUM)
def main_window_keyPressEvent(self,
event,
old_keyPressEvent=QMainWindow.keyPressEvent):
if event.matches(QKeySequence.Find):
filter_line_edit.setFocus()
return
if event.modifiers() == Qt.ControlModifier and event.key(
) >= Qt.Key_0 and event.key() <= Qt.Key_9:
index = event.key() - Qt.Key_1
if event.key() == Qt.Key_0:
index += 10
action = signal_mapper.mapping(index)
if action:
action.toggle()
return
old_keyPressEvent(self, event)
main_window.keyPressEvent = new.instancemethod(main_window_keyPressEvent,
table_view, None)
# enable copy (ctrl+c) name of icon to clipboard
def table_view_keyPressEvent(self,
event,
old_keyPressEvent=QTableView.keyPressEvent):
if event.matches(QKeySequence.Copy):
selection_model = self.selectionModel()
if selection_model.hasSelection():
index = selection_model.selectedRows()[0]
source_index = self.model().mapToSource(index)
item = self.model().sourceModel().item(source_index.row(), 1)
icon_name = item.data(Qt.EditRole)
app.clipboard().setText(icon_name.toString())
return
old_keyPressEvent(self, event)
table_view.keyPressEvent = new.instancemethod(table_view_keyPressEvent,
table_view, None)
print 'Icon Theme: ', QIcon.themeName()
print 'Theme Search Paths:'
for item in QIcon.themeSearchPaths():
print item
main_window.showMaximized()
return app.exec_()
class OWConfusionMatrix(widget.OWWidget):
name = "Confusion Matrix"
description = "Shows a confusion matrix."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [{
"name": "Evaluation Results",
"type": Orange.evaluation.Results,
"handler": "set_results"
}]
outputs = [{"name": "Selected Data", "type": Orange.data.Table}]
quantities = [
"Number of instances", "Proportion of predicted",
"Proportion of actual"
]
selected_learner = settings.Setting([])
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
def __init__(self, parent=None):
super().__init__(parent)
self.data = None
self.results = None
self.learners = []
box = gui.widgetBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(box,
self,
"selected_learner",
"learners",
callback=self._learner_changed)
box = gui.widgetBox(self.controlArea, "Show")
gui.comboBox(box,
self,
"selected_quantity",
items=self.quantities,
callback=self._update)
box = gui.widgetBox(self.controlArea, "Select")
gui.button(box,
self,
"Correct",
callback=self.select_correct,
autoDefault=False)
gui.button(box,
self,
"Misclassified",
callback=self.select_wrong,
autoDefault=False)
gui.button(box,
self,
"None",
callback=self.select_none,
autoDefault=False)
self.outputbox = box = gui.widgetBox(self.controlArea, "Output")
gui.checkBox(box,
self,
"append_predictions",
"Predictions",
callback=self._invalidate)
gui.checkBox(box,
self,
"append_probabilities",
"Probabilities",
callback=self._invalidate)
gui.auto_commit(self.controlArea, self, "autocommit", "Send Data",
"Auto send is on")
grid = QGridLayout()
grid.setContentsMargins(0, 0, 0, 0)
grid.addWidget(QLabel("Predicted"), 0, 1, Qt.AlignCenter)
grid.addWidget(VerticalLabel("Actual Class"), 1, 0, Qt.AlignCenter)
self.tablemodel = QStandardItemModel()
self.tableview = QTableView(editTriggers=QTableView.NoEditTriggers)
self.tableview.setModel(self.tablemodel)
self.tableview.selectionModel().selectionChanged.connect(
self._invalidate)
grid.addWidget(self.tableview, 1, 1)
self.mainArea.layout().addLayout(grid)
def set_results(self, results):
"""Set the input results."""
self.clear()
self.warning([0, 1])
data = None
if results is not None:
if results.data is not None:
data = results.data
if data is not None and \
not isinstance(data.domain.class_var,
Orange.data.DiscreteVariable):
data = None
results = None
self.warning(
0, "Confusion Matrix cannot be used for regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is not None:
nmodels, ntests = results.predicted.shape
headers = class_values + [unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'learner_names' is set in 'Test Learners' widget.
if hasattr(results, "learner_names"):
self.learners = results.learner_names
else:
self.learners = ["L %i" % (i + 1) for i in range(nmodels)]
self.tablemodel.setVerticalHeaderLabels(headers)
self.tablemodel.setHorizontalHeaderLabels(headers)
self.tablemodel.setRowCount(len(class_values) + 1)
self.tablemodel.setColumnCount(len(class_values) + 1)
self.selected_learner = [0]
self._update()
def clear(self):
self.results = None
self.data = None
self.tablemodel.clear()
# Clear learners last. This action will invoke `_learner_changed`
# method
self.learners = []
def select_correct(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_wrong(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect)
def select_none(self):
self.tableview.selectionModel().clear()
def commit(self):
if self.results is not None and self.data is not None \
and self.selected_learner:
indices = self.tableview.selectedIndexes()
indices = {(ind.row(), ind.column()) for ind in indices}
actual = self.results.actual
selected_learner = self.selected_learner[0]
learner_name = self.learners[selected_learner]
predicted = self.results.predicted[selected_learner]
selected = [
i for i, t in enumerate(zip(actual, predicted)) if t in indices
]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values)
metas = metas + (var, )
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[selected_learner, selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [
Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values
]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
row_ids = self.data.ids[row_indices]
M = numpy.hstack((M, ) + tuple(extra))
domain = Orange.data.Domain(self.data.domain.attributes,
self.data.domain.class_vars, metas)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
data.ids = row_ids
data.name = learner_name
else:
data = None
self.send("Selected Data", data)
def _invalidate(self):
self.commit()
def _learner_changed(self):
# The selected learner has changed
self._update()
self._invalidate()
def _update(self):
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
index = self.selected_learner[0]
cmatrix = confusion_matrix(self.results, index)
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
total = rowsum.sum()
if self.selected_quantity == 0:
value = lambda i, j: int(cmatrix[i, j])
elif self.selected_quantity == 1:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / colsum[i])
if colsum[i] else "N/A")
elif self.selected_quantity == 2:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / rowsum[i])
if colsum[i] else "N/A")
else:
assert False
model = self.tablemodel
for i, row in enumerate(cmatrix):
for j, _ in enumerate(row):
item = model.item(i, j)
if item is None:
item = QStandardItem()
item.setData(value(i, j), Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
model.setItem(i, j, item)
font = model.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
def sum_item(value):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
return item
N = len(colsum)
for i in range(N):
model.setItem(N, i, sum_item(int(colsum[i])))
model.setItem(i, N, sum_item(int(rowsum[i])))
model.setItem(N, N, sum_item(int(total)))
def _create_model(self, rows, columns):
model = QStandardItemModel()
model.setColumnCount(columns)
model.setRowCount(rows)
return model
class OWConfusionMatrix(widget.OWWidget):
name = "Confusion Matrix"
description = "Display confusion matrix constructed from results " \
"of evaluation of classifiers."
icon = "icons/ConfusionMatrix.svg"
priority = 1001
inputs = [("Evaluation Results", Orange.evaluation.Results, "set_results")]
outputs = [("Selected Data", Orange.data.Table)]
quantities = ["Number of instances",
"Proportion of predicted",
"Proportion of actual"]
selected_learner = settings.Setting([])
selected_quantity = settings.Setting(0)
append_predictions = settings.Setting(True)
append_probabilities = settings.Setting(False)
autocommit = settings.Setting(True)
UserAdviceMessages = [
widget.Message(
"Clicking on cells or in headers outputs the corresponding "
"data instances",
"click_cell")]
def __init__(self):
super().__init__()
self.data = None
self.results = None
self.learners = []
self.headers = []
box = gui.widgetBox(self.controlArea, "Learners")
self.learners_box = gui.listBox(
box, self, "selected_learner", "learners",
callback=self._learner_changed
)
box = gui.widgetBox(self.controlArea, "Show")
gui.comboBox(box, self, "selected_quantity", items=self.quantities,
callback=self._update)
box = gui.widgetBox(self.controlArea, "Select")
gui.button(box, self, "Correct",
callback=self.select_correct, autoDefault=False)
gui.button(box, self, "Misclassified",
callback=self.select_wrong, autoDefault=False)
gui.button(box, self, "None",
callback=self.select_none, autoDefault=False)
self.outputbox = box = gui.widgetBox(self.controlArea, "Output")
gui.checkBox(box, self, "append_predictions",
"Predictions", callback=self._invalidate)
gui.checkBox(box, self, "append_probabilities",
"Probabilities",
callback=self._invalidate)
gui.auto_commit(self.controlArea, self, "autocommit",
"Send Data", "Auto send is on")
grid = QGridLayout()
self.tablemodel = QStandardItemModel(self)
view = self.tableview = QTableView(
editTriggers=QTableView.NoEditTriggers)
view.setModel(self.tablemodel)
view.horizontalHeader().hide()
view.verticalHeader().hide()
view.horizontalHeader().setMinimumSectionSize(60)
view.selectionModel().selectionChanged.connect(self._invalidate)
view.setShowGrid(False)
view.clicked.connect(self.cell_clicked)
grid.addWidget(view, 0, 0)
self.mainArea.layout().addLayout(grid)
def sizeHint(self):
return QSize(750, 490)
def _item(self, i, j):
return self.tablemodel.item(i, j) or QStandardItem()
def _set_item(self, i, j, item):
self.tablemodel.setItem(i, j, item)
def set_results(self, results):
"""Set the input results."""
self.clear()
self.warning([0, 1])
data = None
if results is not None:
if results.data is not None:
data = results.data
if data is not None and not data.domain.has_discrete_class:
data = None
results = None
self.warning(
0, "Confusion Matrix cannot be used for regression results.")
self.results = results
self.data = data
if data is not None:
class_values = data.domain.class_var.values
elif results is not None:
raise NotImplementedError
if results is not None:
nmodels, ntests = results.predicted.shape
self.headers = class_values + \
[unicodedata.lookup("N-ARY SUMMATION")]
# NOTE: The 'learner_names' is set in 'Test Learners' widget.
if hasattr(results, "learner_names"):
self.learners = results.learner_names
else:
self.learners = ["Learner #%i" % (i + 1)
for i in range(nmodels)]
item = self._item(0, 2)
item.setData("Predicted", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignCenter)
item.setFlags(Qt.NoItemFlags)
self._set_item(0, 2, item)
item = self._item(2, 0)
item.setData("Actual", Qt.DisplayRole)
item.setTextAlignment(Qt.AlignHCenter | Qt.AlignBottom)
item.setFlags(Qt.NoItemFlags)
self.tableview.setItemDelegateForColumn(
0, gui.VerticalItemDelegate())
self._set_item(2, 0, item)
self.tableview.setSpan(0, 2, 1, len(class_values))
self.tableview.setSpan(2, 0, len(class_values), 1)
for i in (0, 1):
for j in (0, 1):
item = self._item(i, j)
item.setFlags(Qt.NoItemFlags)
self._set_item(i, j, item)
for p, label in enumerate(self.headers):
for i, j in ((1, p + 2), (p + 2, 1)):
item = self._item(i, j)
item.setData(label, Qt.DisplayRole)
item.setData(QBrush(QColor(208, 208, 208)),
Qt.BackgroundColorRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
self._set_item(i, j, item)
hor_header = self.tableview.horizontalHeader()
if len(' '.join(self.headers)) < 120:
hor_header.setResizeMode(QHeaderView.ResizeToContents)
else:
hor_header.setDefaultSectionSize(60)
self.tablemodel.setRowCount(len(class_values) + 3)
self.tablemodel.setColumnCount(len(class_values) + 3)
self.selected_learner = [0]
self._update()
def clear(self):
self.results = None
self.data = None
self.tablemodel.clear()
self.headers = []
# Clear learners last. This action will invoke `_learner_changed`
# method
self.learners = []
def select_correct(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
index = self.tablemodel.index(i, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
def select_wrong(self):
selection = QItemSelection()
n = self.tablemodel.rowCount()
for i in range(2, n):
for j in range(i + 1, n):
index = self.tablemodel.index(i, j)
selection.select(index, index)
index = self.tablemodel.index(j, i)
selection.select(index, index)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
def select_none(self):
self.tableview.selectionModel().clear()
def cell_clicked(self, model_index):
i, j = model_index.row(), model_index.column()
if not i or not j:
return
n = self.tablemodel.rowCount()
index = self.tablemodel.index
selection = None
if i == j == 1 or i == j == n - 1:
selection = QItemSelection(index(2, 2), index(n - 1, n - 1))
elif i in (1, n - 1):
selection = QItemSelection(index(2, j), index(n - 1, j))
elif j in (1, n - 1):
selection = QItemSelection(index(i, 2), index(i, n - 1))
if selection is not None:
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
def commit(self):
if self.results is not None and self.data is not None \
and self.selected_learner:
indices = self.tableview.selectedIndexes()
indices = {(ind.row() - 2, ind.column() - 2) for ind in indices}
actual = self.results.actual
selected_learner = self.selected_learner[0]
learner_name = self.learners[selected_learner]
predicted = self.results.predicted[selected_learner]
selected = [i for i, t in enumerate(zip(actual, predicted))
if t in indices]
row_indices = self.results.row_indices[selected]
extra = []
class_var = self.data.domain.class_var
metas = self.data.domain.metas
if self.append_predictions:
predicted = numpy.array(predicted[selected], dtype=object)
extra.append(predicted.reshape(-1, 1))
var = Orange.data.DiscreteVariable(
"{}({})".format(class_var.name, learner_name),
class_var.values
)
metas = metas + (var,)
if self.append_probabilities and \
self.results.probabilities is not None:
probs = self.results.probabilities[selected_learner, selected]
extra.append(numpy.array(probs, dtype=object))
pvars = [Orange.data.ContinuousVariable("p({})".format(value))
for value in class_var.values]
metas = metas + tuple(pvars)
X = self.data.X[row_indices]
Y = self.data.Y[row_indices]
M = self.data.metas[row_indices]
row_ids = self.data.ids[row_indices]
M = numpy.hstack((M,) + tuple(extra))
domain = Orange.data.Domain(
self.data.domain.attributes,
self.data.domain.class_vars,
metas
)
data = Orange.data.Table.from_numpy(domain, X, Y, M)
data.ids = row_ids
data.name = learner_name
else:
data = None
self.send("Selected Data", data)
def _invalidate(self):
self.commit()
def _learner_changed(self):
# The selected learner has changed
indices = self.tableview.selectedIndexes()
self._update()
selection = QItemSelection()
for sel in indices:
selection.select(sel, sel)
self.tableview.selectionModel().select(
selection, QItemSelectionModel.ClearAndSelect
)
self.commit()
def _update(self):
# Update the displayed confusion matrix
if self.results is not None and self.selected_learner:
index = self.selected_learner[0]
cmatrix = confusion_matrix(self.results, index)
colsum = cmatrix.sum(axis=0)
rowsum = cmatrix.sum(axis=1)
total = rowsum.sum()
if self.selected_quantity == 0:
value = lambda i, j: int(cmatrix[i, j])
elif self.selected_quantity == 1:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / colsum[i])
if colsum[i] else "N/A")
elif self.selected_quantity == 2:
value = lambda i, j: \
("{:2.1f} %".format(100 * cmatrix[i, j] / rowsum[i])
if colsum[i] else "N/A")
else:
assert False
for i, row in enumerate(cmatrix):
for j, _ in enumerate(row):
item = self._item(i + 2, j + 2)
item.setData(value(i, j), Qt.DisplayRole)
item.setToolTip("actual: {}\npredicted: {}".format(
self.headers[i], self.headers[j]))
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled | Qt.ItemIsSelectable)
self._set_item(i + 2, j + 2, item)
model = self.tablemodel
font = model.invisibleRootItem().font()
bold_font = QFont(font)
bold_font.setBold(True)
def sum_item(value):
item = QStandardItem()
item.setData(value, Qt.DisplayRole)
item.setTextAlignment(Qt.AlignRight | Qt.AlignVCenter)
item.setFlags(Qt.ItemIsEnabled)
item.setFont(bold_font)
return item
N = len(colsum)
for i in range(N):
model.setItem(N + 2, i + 2, sum_item(int(colsum[i])))
model.setItem(i + 2, N + 2, sum_item(int(rowsum[i])))
model.setItem(N + 2, N + 2, sum_item(int(total)))
