OWCorrelations: Move from prototypes to core

Author: VesnaT

Orange/widgets/data/icons/Correlations.svg

@@ -0,0 +1,293 @@
+"""
+Correlations widget
+"""
+from enum import IntEnum
+from operator import attrgetter
+from itertools import combinations, groupby, chain
+
+import numpy as np
+from scipy.stats import spearmanr, pearsonr
+from sklearn.cluster import KMeans
+
+from AnyQt.QtCore import Qt, QItemSelectionModel, QItemSelection, QSize
+from AnyQt.QtGui import QStandardItem, QColor
+from AnyQt.QtWidgets import QApplication
+
+from Orange.data import Table, Domain, ContinuousVariable, StringVariable
+from Orange.preprocess import SklImpute, Normalize
+from Orange.widgets import gui
+from Orange.widgets.settings import Setting, ContextSetting, \
+    DomainContextHandler
+from Orange.widgets.utils.signals import Input, Output
+from Orange.widgets.visualize.utils import VizRankDialogAttrPair
+from Orange.widgets.widget import OWWidget, AttributeList, Msg
+
+NAN = 2
+SIZE_LIMIT = 1000000
+
+
+class CorrelationType(IntEnum):
+    """
+    Correlation type enumerator. Possible correlations: Pearson, Spearman.
+    """
+    PEARSON, SPEARMAN = 0, 1
+
+    @staticmethod
+    def items():
+        """
+        Texts for correlation types. Can be used in gui controls (eg. combobox).
+        """
+        return ["Pearson correlation", "Spearman correlation"]
+
+
+class KMeansCorrelationHeuristic:
+    """
+    Heuristic to obtain the most promising attribute pairs, when there are to
+    many attributes to calculate correlations for all possible pairs.
+    """
+    n_clusters = 10
+
+    def __init__(self, data):
+        self.n_attributes = len(data.domain.attributes)
+        self.data = data
+        self.states = None
+
+    def get_clusters_of_attributes(self):
+        """
+        Generates groupes of attribute IDs, grouped by cluster. Clusters are
+        obtained by KMeans algorithm.
+
+        :return: generator of attributes grouped by cluster
+        """
+        data = Normalize()(self.data).X.T
+        kmeans = KMeans(n_clusters=self.n_clusters, random_state=0).fit(data)
+        labels_attrs = sorted([(l, i) for i, l in enumerate(kmeans.labels_)])
+        for _, group in groupby(labels_attrs, key=lambda x: x[0]):
+            group = list(group)
+            if len(group) > 1:
+                yield list(pair[1] for pair in group)
+
+    def get_states(self, initial_state):
+        """
+        Generates the most promising states (attribute pairs).
+
+        :param initial_state: initial state; None if this is the first call
+        :return: generator of tuples of states
+        """
+        if self.states is not None:
+            return chain([initial_state], self.states)
+        self.states = chain.from_iterable(combinations(inds, 2) for inds in
+                                          self.get_clusters_of_attributes())
+        return self.states
+
+
+class CorrelationRank(VizRankDialogAttrPair):
+    """
+    Correlations rank widget.
+    """
+    NEGATIVE_COLOR = QColor(70, 190, 250)
+    POSITIVE_COLOR = QColor(170, 242, 43)
+
+    def __init__(self, *args):
+        super().__init__(*args)
+        self.heuristic = None
+        self.use_heuristic = False
+
+    def initialize(self):
+        super().initialize()
+        data = self.master.cont_data
+        self.attrs = data and data.domain.attributes
+        self.model_proxy.setFilterKeyColumn(-1)
+        self.rank_table.horizontalHeader().setStretchLastSection(False)
+        self.heuristic = None
+        self.use_heuristic = False
+        if data:
+            # use heuristic if data is too big
+            n_attrs = len(self.attrs)
+            use_heuristic = n_attrs > KMeansCorrelationHeuristic.n_clusters
+            self.use_heuristic = use_heuristic and \
+                len(data) * n_attrs ** 2 > SIZE_LIMIT
+            if self.use_heuristic:
+                self.heuristic = KMeansCorrelationHeuristic(data)
+
+    def compute_score(self, state):
+        (attr1, attr2), corr_type = state, self.master.correlation_type
+        data = self.master.cont_data.X
+        corr = pearsonr if corr_type == CorrelationType.PEARSON else spearmanr
+        result = corr(data[:, attr1], data[:, attr2])[0]
+        return -abs(result) if not np.isnan(result) else NAN, result
+
+    def row_for_state(self, score, state):
+        attrs = sorted((self.attrs[x] for x in state), key=attrgetter("name"))
+        attrs_item = QStandardItem(
+            "{}, {}".format(attrs[0].name, attrs[1].name))
+        attrs_item.setData(attrs, self._AttrRole)
+        attrs_item.setData(Qt.AlignLeft + Qt.AlignTop, Qt.TextAlignmentRole)
+        correlation_item = QStandardItem("{:+.3f}".format(score[1]))
+        correlation_item.setData(attrs, self._AttrRole)
+        correlation_item.setData(
+            self.NEGATIVE_COLOR if score[1] < 0 else self.POSITIVE_COLOR,
+            gui.TableBarItem.BarColorRole)
+        return [correlation_item, attrs_item]
+
+    def check_preconditions(self):
+        return self.master.cont_data is not None
+
+    def iterate_states(self, initial_state):
+        if self.use_heuristic:
+            return self.heuristic.get_states(initial_state)
+        else:
+            return super().iterate_states(initial_state)
+
+    def state_count(self):
+        if self.use_heuristic:
+            n_clusters = KMeansCorrelationHeuristic.n_clusters
+            n_avg_attrs = len(self.attrs) / n_clusters
+            return n_clusters * n_avg_attrs * (n_avg_attrs - 1) / 2
+        else:
+            n_attrs = len(self.attrs)
+            return n_attrs * (n_attrs - 1) / 2
+
+    @staticmethod
+    def bar_length(score):
+        return abs(score[1])
+
+
+class OWCorrelations(OWWidget):
+    name = "Correlations"
+    description = "Compute all pairwise attribute correlations."
+    icon = "icons/Correlations.svg"
+    priority = 1106
+
+    class Inputs:
+        data = Input("Data", Table)
+
+    class Outputs:
+        data = Output("Data", Table)
+        features = Output("Features", AttributeList)
+        correlations = Output("Correlations", Table)
+
+    want_control_area = False
+
+    settingsHandler = DomainContextHandler()
+    selection = ContextSetting(())
+    correlation_type = Setting(0)
+
+    class Information(OWWidget.Information):
+        not_enough_vars = Msg("Need at least two continuous features.")
+        not_enough_inst = Msg("Need at least two instances.")
+
+    def __init__(self):
+        super().__init__()
+        self.data = None
+        self.cont_data = None
+
+        # GUI
+        box = gui.vBox(self.mainArea)
+        self.correlation_combo = gui.comboBox(
+            box, self, "correlation_type", items=CorrelationType.items(),
+            orientation=Qt.Horizontal, callback=self._correlation_combo_changed)
+
+        self.vizrank, _ = CorrelationRank.add_vizrank(
+            None, self, None, self._vizrank_selection_changed)
+        self.vizrank.progressBar = self.progressBar
+
+        gui.separator(box)
+        box.layout().addWidget(self.vizrank.filter)
+        box.layout().addWidget(self.vizrank.rank_table)
+
+        button_box = gui.hBox(self.mainArea)
+        button_box.layout().addWidget(self.vizrank.button)
+
+    def sizeHint(self):
+        return QSize(350, 400)
+
+    def _correlation_combo_changed(self):
+        self.apply()
+
+    def _vizrank_selection_changed(self, *args):
+        self.selection = args
+        self.commit()
+
+    def _vizrank_select(self):
+        model = self.vizrank.rank_table.model()
+        selection = QItemSelection()
+        names = sorted(x.name for x in self.selection)
+        for i in range(model.rowCount()):
+            if sorted(x.name for x in model.data(model.index(i, 0),
+                                                 CorrelationRank._AttrRole)) \
+                    == names:
+                selection.select(model.index(i, 0), model.index(i, 1))
+                self.vizrank.rank_table.selectionModel().select(
+                    selection, QItemSelectionModel.ClearAndSelect)
+                break
+
+    @Inputs.data
+    def set_data(self, data):
+        self.closeContext()
+        self.clear_messages()
+        self.data = data
+        self.cont_data = None
+        self.selection = ()
+        if data is not None:
+            cont_attrs = [a for a in data.domain.attributes if a.is_continuous]
+            if len(cont_attrs) < 2:
+                self.Information.not_enough_vars()
+            elif len(data) < 2:
+                self.Information.not_enough_inst()
+            else:
+                domain = data.domain
+                cont_dom = Domain(cont_attrs, domain.class_vars, domain.metas)
+                self.cont_data = SklImpute()(Table.from_table(cont_dom, data))
+        self.apply()
+        self.openContext(self.data)
+        self._vizrank_select()
+
+    def apply(self):
+        self.vizrank.initialize()
+        if self.cont_data is not None:
+            # this triggers self.commit() by changing vizrank selection
+            self.vizrank.toggle()
+            header = self.vizrank.rank_table.horizontalHeader()
+            header.setStretchLastSection(True)
+        else:
+            self.commit()
+
+    def commit(self):
+        if self.data is None or self.cont_data is None:
+            self.Outputs.data.send(self.data)
+            self.Outputs.features.send(None)
+            self.Outputs.correlations.send(None)
+            return
+
+        metas = [StringVariable("Feature 1"), StringVariable("Feature 2")]
+        domain = Domain([ContinuousVariable("Correlation")], metas=metas)
+        model = self.vizrank.rank_model
+        x = np.array([[float(model.data(model.index(row, 0)))] for row
+                      in range(model.rowCount())])
+        m = np.array([[attr.name
+                       for attr in model.data(model.index(row, 0),
+                                              CorrelationRank._AttrRole)]
+                      for row in range(model.rowCount())], dtype=object)
+        corr_table = Table(domain, x, metas=m)
+        corr_table.name = "Correlations"
+
+        self.Outputs.data.send(self.data)
+        # data has been imputed; send original attributes
+        self.Outputs.features.send(AttributeList([attr.compute_value.variable
+                                                  for attr in self.selection]))
+        self.Outputs.correlations.send(corr_table)
+
+    def send_report(self):
+        self.report_table(CorrelationType.items()[self.correlation_type],
+                          self.vizrank.rank_table)
+
+
+if __name__ == "__main__":
+    app = QApplication([])
+    ow = OWCorrelations()
+    iris = Table("iris")
+    ow.set_data(iris)
+    ow.show()
+    app.exec_()
+    ow.saveSettings()