scikit-learn / scikit-learn

@@ -105,8 +105,6 @@
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        other_node : TreeNode
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            The node to compare with.
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        """
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        if self.split_info is None or other_node.split_info is None:
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            raise ValueError("Cannot compare nodes without split_info")
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        return self.split_info.gain > other_node.split_info.gain
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@@ -212,12 +210,12 @@
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            raise ValueError(
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                "X_binned should be passed as Fortran contiguous "
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                "array for maximum efficiency.")
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        if max_leaf_nodes is not None and max_leaf_nodes < 1:
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        if max_leaf_nodes is not None and max_leaf_nodes <= 1:
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            raise ValueError('max_leaf_nodes={} should not be'
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                             ' smaller than 1'.format(max_leaf_nodes))
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        if max_depth is not None and max_depth < 1:
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                             ' smaller than 2'.format(max_leaf_nodes))
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        if max_depth is not None and max_depth <= 1:
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            raise ValueError('max_depth={} should not be'
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                             ' smaller than 1'.format(max_depth))
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                             ' smaller than 2'.format(max_depth))
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        if min_samples_leaf < 1:
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            raise ValueError('min_samples_leaf={} should '
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                             'not be smaller than 1'.format(min_samples_leaf))
@@ -255,9 +253,6 @@
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        self.root.partition_start = 0
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        self.root.partition_stop = n_samples
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        if self.max_leaf_nodes is not None and self.max_leaf_nodes == 1:
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            self._finalize_leaf(self.root)
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            return
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        if self.root.n_samples < 2 * self.min_samples_leaf:
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            # Do not even bother computing any splitting statistics.
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            self._finalize_leaf(self.root)
@@ -298,9 +293,6 @@
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        right : TreeNode
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            The resulting right child.
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        """
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        if not self.splittable_nodes:
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            raise StopIteration("No more splittable nodes")
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        # Consider the node with the highest loss reduction (a.k.a. gain)
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        node = heappop(self.splittable_nodes)
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@@ -503,12 +503,12 @@
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        The maximum number of iterations of the boosting process, i.e. the
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        maximum number of trees.
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    max_leaf_nodes : int or None, optional (default=31)
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        The maximum number of leaves for each tree. If None, there is no
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        maximum limit.
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        The maximum number of leaves for each tree. Must be strictly greater
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        than 1. If None, there is no maximum limit.
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    max_depth : int or None, optional (default=None)
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        The maximum depth of each tree. The depth of a tree is the number of
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        nodes to go from the root to the deepest leaf. Depth isn't constrained
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        by default.
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        nodes to go from the root to the deepest leaf. Must be strictly greater
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        than 1. Depth isn't constrained by default.
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    min_samples_leaf : int, optional (default=20)
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        The minimum number of samples per leaf.
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    l2_regularization : float, optional (default=0)
@@ -654,12 +654,12 @@
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        maximum number of trees for binary classification. For multiclass
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        classification, `n_classes` trees per iteration are built.
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    max_leaf_nodes : int or None, optional (default=31)
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        The maximum number of leaves for each tree. If None, there is no
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        maximum limit.
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        The maximum number of leaves for each tree. Must be strictly greater
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        than 1. If None, there is no maximum limit.
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    max_depth : int or None, optional (default=None)
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        The maximum depth of each tree. The depth of a tree is the number of
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        nodes to go from the root to the deepest leaf. Depth isn't constrained
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        by default.
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        nodes to go from the root to the deepest leaf. Must be strictly greater
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        than 1. Depth isn't constrained by default.
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    min_samples_leaf : int, optional (default=20)
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        The minimum number of samples per leaf.
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    l2_regularization : float, optional (default=0)
Files Coverage
sklearn 96.76%
setup.py 7.02%
Project Totals (382 files) 96.68%
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comment: false
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coverage:
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  status:
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    project:
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      default:
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        # Commits pushed to master should not make the overall
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        # project coverage decrease by more than 1%:
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        target: auto
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        threshold: 1%
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    patch:
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      default:
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        # Be tolerant on slight code coverage diff on PRs to limit
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        # noisy red coverage status on github PRs.
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        # Note The coverage stats are still uploaded
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        # to codecov so that PR reviewers can see uncovered lines
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        # in the github diff if they install the codecov browser
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        # extension:
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        # https://github.com/codecov/browser-extension
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        target: auto
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        threshold: 1%
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ignore:
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- "sklearn/externals"
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