Uploaded Test files

venv/Lib/site-packages/sklearn/tree/_tree.pxd

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+# Authors: Gilles Louppe <g.louppe@gmail.com>
+#          Peter Prettenhofer <peter.prettenhofer@gmail.com>
+#          Brian Holt <bdholt1@gmail.com>
+#          Joel Nothman <joel.nothman@gmail.com>
+#          Arnaud Joly <arnaud.v.joly@gmail.com>
+#          Jacob Schreiber <jmschreiber91@gmail.com>
+#          Nelson Liu <nelson@nelsonliu.me>
+#
+# License: BSD 3 clause
+
+# See _tree.pyx for details.
+
+import numpy as np
+cimport numpy as np
+
+ctypedef np.npy_float32 DTYPE_t          # Type of X
+ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
+ctypedef np.npy_intp SIZE_t              # Type for indices and counters
+ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
+ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
+
+from ._splitter cimport Splitter
+from ._splitter cimport SplitRecord
+
+cdef struct Node:
+    # Base storage structure for the nodes in a Tree object
+
+    SIZE_t left_child                    # id of the left child of the node
+    SIZE_t right_child                   # id of the right child of the node
+    SIZE_t feature                       # Feature used for splitting the node
+    DOUBLE_t threshold                   # Threshold value at the node
+    DOUBLE_t impurity                    # Impurity of the node (i.e., the value of the criterion)
+    SIZE_t n_node_samples                # Number of samples at the node
+    DOUBLE_t weighted_n_node_samples     # Weighted number of samples at the node
+
+
+cdef class Tree:
+    # The Tree object is a binary tree structure constructed by the
+    # TreeBuilder. The tree structure is used for predictions and
+    # feature importances.
+
+    # Input/Output layout
+    cdef public SIZE_t n_features        # Number of features in X
+    cdef SIZE_t* n_classes               # Number of classes in y[:, k]
+    cdef public SIZE_t n_outputs         # Number of outputs in y
+    cdef public SIZE_t max_n_classes     # max(n_classes)
+
+    # Inner structures: values are stored separately from node structure,
+    # since size is determined at runtime.
+    cdef public SIZE_t max_depth         # Max depth of the tree
+    cdef public SIZE_t node_count        # Counter for node IDs
+    cdef public SIZE_t capacity          # Capacity of tree, in terms of nodes
+    cdef Node* nodes                     # Array of nodes
+    cdef double* value                   # (capacity, n_outputs, max_n_classes) array of values
+    cdef SIZE_t value_stride             # = n_outputs * max_n_classes
+
+    # Methods
+    cdef SIZE_t _add_node(self, SIZE_t parent, bint is_left, bint is_leaf,
+                          SIZE_t feature, double threshold, double impurity,
+                          SIZE_t n_node_samples,
+                          double weighted_n_samples) nogil except -1
+    cdef int _resize(self, SIZE_t capacity) nogil except -1
+    cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1
+
+    cdef np.ndarray _get_value_ndarray(self)
+    cdef np.ndarray _get_node_ndarray(self)
+
+    cpdef np.ndarray predict(self, object X)
+
+    cpdef np.ndarray apply(self, object X)
+    cdef np.ndarray _apply_dense(self, object X)
+    cdef np.ndarray _apply_sparse_csr(self, object X)
+
+    cpdef object decision_path(self, object X)
+    cdef object _decision_path_dense(self, object X)
+    cdef object _decision_path_sparse_csr(self, object X)
+
+    cpdef compute_feature_importances(self, normalize=*)
+
+
+# =============================================================================
+# Tree builder
+# =============================================================================
+
+cdef class TreeBuilder:
+    # The TreeBuilder recursively builds a Tree object from training samples,
+    # using a Splitter object for splitting internal nodes and assigning
+    # values to leaves.
+    #
+    # This class controls the various stopping criteria and the node splitting
+    # evaluation order, e.g. depth-first or best-first.
+
+    cdef Splitter splitter              # Splitting algorithm
+
+    cdef SIZE_t min_samples_split       # Minimum number of samples in an internal node
+    cdef SIZE_t min_samples_leaf        # Minimum number of samples in a leaf
+    cdef double min_weight_leaf         # Minimum weight in a leaf
+    cdef SIZE_t max_depth               # Maximal tree depth
+    cdef double min_impurity_split
+    cdef double min_impurity_decrease   # Impurity threshold for early stopping
+
+    cpdef build(self, Tree tree, object X, np.ndarray y,
+                np.ndarray sample_weight=*,
+                np.ndarray X_idx_sorted=*)
+    cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)