first commit

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/__init__.py

@@ -0,0 +1,22 @@
+"""
+The :mod:`sklearn.tree` module includes decision tree-based models for
+classification and regression.
+"""
+
+from ._classes import BaseDecisionTree
+from ._classes import DecisionTreeClassifier
+from ._classes import DecisionTreeRegressor
+from ._classes import ExtraTreeClassifier
+from ._classes import ExtraTreeRegressor
+from ._export import export_graphviz, plot_tree, export_text
+
+__all__ = [
+    "BaseDecisionTree",
+    "DecisionTreeClassifier",
+    "DecisionTreeRegressor",
+    "ExtraTreeClassifier",
+    "ExtraTreeRegressor",
+    "export_graphviz",
+    "plot_tree",
+    "export_text",
+]

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/_criterion.pxd

@@ -0,0 +1,79 @@
+# 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>
+#
+# License: BSD 3 clause
+
+# See _criterion.pyx for implementation details.
+
+import numpy as np
+cimport numpy as np
+
+from ._tree cimport DTYPE_t          # Type of X
+from ._tree cimport DOUBLE_t         # Type of y, sample_weight
+from ._tree cimport SIZE_t           # Type for indices and counters
+from ._tree cimport INT32_t          # Signed 32 bit integer
+from ._tree cimport UINT32_t         # Unsigned 32 bit integer
+
+cdef class Criterion:
+    # The criterion computes the impurity of a node and the reduction of
+    # impurity of a split on that node. It also computes the output statistics
+    # such as the mean in regression and class probabilities in classification.
+
+    # Internal structures
+    cdef const DOUBLE_t[:, ::1] y        # Values of y
+    cdef DOUBLE_t* sample_weight         # Sample weights
+
+    cdef SIZE_t* samples                 # Sample indices in X, y
+    cdef SIZE_t start                    # samples[start:pos] are the samples in the left node
+    cdef SIZE_t pos                      # samples[pos:end] are the samples in the right node
+    cdef SIZE_t end
+
+    cdef SIZE_t n_outputs                # Number of outputs
+    cdef SIZE_t n_samples                # Number of samples
+    cdef SIZE_t n_node_samples           # Number of samples in the node (end-start)
+    cdef double weighted_n_samples       # Weighted number of samples (in total)
+    cdef double weighted_n_node_samples  # Weighted number of samples in the node
+    cdef double weighted_n_left          # Weighted number of samples in the left node
+    cdef double weighted_n_right         # Weighted number of samples in the right node
+
+    # The criterion object is maintained such that left and right collected
+    # statistics correspond to samples[start:pos] and samples[pos:end].
+
+    # Methods
+    cdef int init(self, const DOUBLE_t[:, ::1] y, DOUBLE_t* sample_weight,
+                  double weighted_n_samples, SIZE_t* samples, SIZE_t start,
+                  SIZE_t end) nogil except -1
+    cdef int reset(self) nogil except -1
+    cdef int reverse_reset(self) nogil except -1
+    cdef int update(self, SIZE_t new_pos) nogil except -1
+    cdef double node_impurity(self) nogil
+    cdef void children_impurity(self, double* impurity_left,
+                                double* impurity_right) nogil
+    cdef void node_value(self, double* dest) nogil
+    cdef double impurity_improvement(self, double impurity_parent,
+                                     double impurity_left,
+                                     double impurity_right) nogil
+    cdef double proxy_impurity_improvement(self) nogil
+
+cdef class ClassificationCriterion(Criterion):
+    """Abstract criterion for classification."""
+
+    cdef SIZE_t[::1] n_classes
+    cdef SIZE_t max_n_classes
+
+    cdef double[:, ::1] sum_total   # The sum of the weighted count of each label.
+    cdef double[:, ::1] sum_left    # Same as above, but for the left side of the split
+    cdef double[:, ::1] sum_right   # Same as above, but for the right side of the split
+
+cdef class RegressionCriterion(Criterion):
+    """Abstract regression criterion."""
+
+    cdef double sq_sum_total
+
+    cdef double[::1] sum_total   # The sum of w*y.
+    cdef double[::1] sum_left    # Same as above, but for the left side of the split
+    cdef double[::1] sum_right   # Same as above, but for the right side of the split

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/_reingold_tilford.py

@@ -0,0 +1,188 @@
+# Authors: William Mill (bill@billmill.org)
+# License: BSD 3 clause
+
+import numpy as np
+
+
+class DrawTree:
+    def __init__(self, tree, parent=None, depth=0, number=1):
+        self.x = -1.0
+        self.y = depth
+        self.tree = tree
+        self.children = [
+            DrawTree(c, self, depth + 1, i + 1) for i, c in enumerate(tree.children)
+        ]
+        self.parent = parent
+        self.thread = None
+        self.mod = 0
+        self.ancestor = self
+        self.change = self.shift = 0
+        self._lmost_sibling = None
+        # this is the number of the node in its group of siblings 1..n
+        self.number = number
+
+    def left(self):
+        return self.thread or len(self.children) and self.children[0]
+
+    def right(self):
+        return self.thread or len(self.children) and self.children[-1]
+
+    def lbrother(self):
+        n = None
+        if self.parent:
+            for node in self.parent.children:
+                if node == self:
+                    return n
+                else:
+                    n = node
+        return n
+
+    def get_lmost_sibling(self):
+        if not self._lmost_sibling and self.parent and self != self.parent.children[0]:
+            self._lmost_sibling = self.parent.children[0]
+        return self._lmost_sibling
+
+    lmost_sibling = property(get_lmost_sibling)
+
+    def __str__(self):
+        return "%s: x=%s mod=%s" % (self.tree, self.x, self.mod)
+
+    def __repr__(self):
+        return self.__str__()
+
+    def max_extents(self):
+        extents = [c.max_extents() for c in self.children]
+        extents.append((self.x, self.y))
+        return np.max(extents, axis=0)
+
+
+def buchheim(tree):
+    dt = first_walk(DrawTree(tree))
+    min = second_walk(dt)
+    if min < 0:
+        third_walk(dt, -min)
+    return dt
+
+
+def third_walk(tree, n):
+    tree.x += n
+    for c in tree.children:
+        third_walk(c, n)
+
+
+def first_walk(v, distance=1.0):
+    if len(v.children) == 0:
+        if v.lmost_sibling:
+            v.x = v.lbrother().x + distance
+        else:
+            v.x = 0.0
+    else:
+        default_ancestor = v.children[0]
+        for w in v.children:
+            first_walk(w)
+            default_ancestor = apportion(w, default_ancestor, distance)
+        # print("finished v =", v.tree, "children")
+        execute_shifts(v)
+
+        midpoint = (v.children[0].x + v.children[-1].x) / 2
+
+        w = v.lbrother()
+        if w:
+            v.x = w.x + distance
+            v.mod = v.x - midpoint
+        else:
+            v.x = midpoint
+    return v
+
+
+def apportion(v, default_ancestor, distance):
+    w = v.lbrother()
+    if w is not None:
+        # in buchheim notation:
+        # i == inner; o == outer; r == right; l == left; r = +; l = -
+        vir = vor = v
+        vil = w
+        vol = v.lmost_sibling
+        sir = sor = v.mod
+        sil = vil.mod
+        sol = vol.mod
+        while vil.right() and vir.left():
+            vil = vil.right()
+            vir = vir.left()
+            vol = vol.left()
+            vor = vor.right()
+            vor.ancestor = v
+            shift = (vil.x + sil) - (vir.x + sir) + distance
+            if shift > 0:
+                move_subtree(ancestor(vil, v, default_ancestor), v, shift)
+                sir = sir + shift
+                sor = sor + shift
+            sil += vil.mod
+            sir += vir.mod
+            sol += vol.mod
+            sor += vor.mod
+        if vil.right() and not vor.right():
+            vor.thread = vil.right()
+            vor.mod += sil - sor
+        else:
+            if vir.left() and not vol.left():
+                vol.thread = vir.left()
+                vol.mod += sir - sol
+            default_ancestor = v
+    return default_ancestor
+
+
+def move_subtree(wl, wr, shift):
+    subtrees = wr.number - wl.number
+    # print(wl.tree, "is conflicted with", wr.tree, 'moving', subtrees,
+    # 'shift', shift)
+    # print wl, wr, wr.number, wl.number, shift, subtrees, shift/subtrees
+    wr.change -= shift / subtrees
+    wr.shift += shift
+    wl.change += shift / subtrees
+    wr.x += shift
+    wr.mod += shift
+
+
+def execute_shifts(v):
+    shift = change = 0
+    for w in v.children[::-1]:
+        # print("shift:", w, shift, w.change)
+        w.x += shift
+        w.mod += shift
+        change += w.change
+        shift += w.shift + change
+
+
+def ancestor(vil, v, default_ancestor):
+    # the relevant text is at the bottom of page 7 of
+    # "Improving Walker's Algorithm to Run in Linear Time" by Buchheim et al,
+    # (2002)
+    # http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.16.8757&rep=rep1&type=pdf
+    if vil.ancestor in v.parent.children:
+        return vil.ancestor
+    else:
+        return default_ancestor
+
+
+def second_walk(v, m=0, depth=0, min=None):
+    v.x += m
+    v.y = depth
+
+    if min is None or v.x < min:
+        min = v.x
+
+    for w in v.children:
+        min = second_walk(w, m + v.mod, depth + 1, min)
+
+    return min
+
+
+class Tree:
+    def __init__(self, label="", node_id=-1, *children):
+        self.label = label
+        self.node_id = node_id
+        if children:
+            self.children = children
+        else:
+            self.children = []

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/_splitter.pxd

@@ -0,0 +1,93 @@
+# 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>
+#
+# License: BSD 3 clause
+
+# See _splitter.pyx for details.
+
+import numpy as np
+cimport numpy as np
+
+from ._criterion cimport Criterion
+
+from ._tree cimport DTYPE_t          # Type of X
+from ._tree cimport DOUBLE_t         # Type of y, sample_weight
+from ._tree cimport SIZE_t           # Type for indices and counters
+from ._tree cimport INT32_t          # Signed 32 bit integer
+from ._tree cimport UINT32_t         # Unsigned 32 bit integer
+
+cdef struct SplitRecord:
+    # Data to track sample split
+    SIZE_t feature         # Which feature to split on.
+    SIZE_t pos             # Split samples array at the given position,
+                           # i.e. count of samples below threshold for feature.
+                           # pos is >= end if the node is a leaf.
+    double threshold       # Threshold to split at.
+    double improvement     # Impurity improvement given parent node.
+    double impurity_left   # Impurity of the left split.
+    double impurity_right  # Impurity of the right split.
+
+cdef class Splitter:
+    # The splitter searches in the input space for a feature and a threshold
+    # to split the samples samples[start:end].
+    #
+    # The impurity computations are delegated to a criterion object.
+
+    # Internal structures
+    cdef public Criterion criterion      # Impurity criterion
+    cdef public SIZE_t max_features      # Number of features to test
+    cdef public SIZE_t min_samples_leaf  # Min samples in a leaf
+    cdef public double min_weight_leaf   # Minimum weight in a leaf
+
+    cdef object random_state             # Random state
+    cdef UINT32_t rand_r_state           # sklearn_rand_r random number state
+
+    cdef SIZE_t* samples                 # Sample indices in X, y
+    cdef SIZE_t n_samples                # X.shape[0]
+    cdef double weighted_n_samples       # Weighted number of samples
+    cdef SIZE_t* features                # Feature indices in X
+    cdef SIZE_t* constant_features       # Constant features indices
+    cdef SIZE_t n_features               # X.shape[1]
+    cdef DTYPE_t* feature_values         # temp. array holding feature values
+
+    cdef SIZE_t start                    # Start position for the current node
+    cdef SIZE_t end                      # End position for the current node
+
+    cdef const DOUBLE_t[:, ::1] y
+    cdef DOUBLE_t* sample_weight
+
+    # The samples vector `samples` is maintained by the Splitter object such
+    # that the samples contained in a node are contiguous. With this setting,
+    # `node_split` reorganizes the node samples `samples[start:end]` in two
+    # subsets `samples[start:pos]` and `samples[pos:end]`.
+
+    # The 1-d  `features` array of size n_features contains the features
+    # indices and allows fast sampling without replacement of features.
+
+    # The 1-d `constant_features` array of size n_features holds in
+    # `constant_features[:n_constant_features]` the feature ids with
+    # constant values for all the samples that reached a specific node.
+    # The value `n_constant_features` is given by the parent node to its
+    # child nodes.  The content of the range `[n_constant_features:]` is left
+    # undefined, but preallocated for performance reasons
+    # This allows optimization with depth-based tree building.
+
+    # Methods
+    cdef int init(self, object X, const DOUBLE_t[:, ::1] y,
+                  DOUBLE_t* sample_weight) except -1
+
+    cdef int node_reset(self, SIZE_t start, SIZE_t end,
+                        double* weighted_n_node_samples) nogil except -1
+
+    cdef int node_split(self,
+                        double impurity,   # Impurity of the node
+                        SplitRecord* split,
+                        SIZE_t* n_constant_features) nogil except -1
+
+    cdef void node_value(self, double* dest) nogil
+
+    cdef double node_impurity(self) nogil

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/_tree.pxd

@@ -0,0 +1,103 @@
+# 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_node_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_decrease   # Impurity threshold for early stopping
+
+    cpdef build(self, Tree tree, object X, np.ndarray y,
+                np.ndarray sample_weight=*)
+    cdef _check_input(self, object X, np.ndarray y, np.ndarray sample_weight)

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/_utils.pxd

@@ -0,0 +1,111 @@
+# Authors: Gilles Louppe <g.louppe@gmail.com>
+#          Peter Prettenhofer <peter.prettenhofer@gmail.com>
+#          Arnaud Joly <arnaud.v.joly@gmail.com>
+#          Jacob Schreiber <jmschreiber91@gmail.com>
+#          Nelson Liu <nelson@nelsonliu.me>
+#
+# License: BSD 3 clause
+
+# See _utils.pyx for details.
+
+import numpy as np
+cimport numpy as np
+from ._tree cimport Node
+from ..neighbors._quad_tree cimport Cell
+
+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
+
+
+cdef enum:
+    # Max value for our rand_r replacement (near the bottom).
+    # We don't use RAND_MAX because it's different across platforms and
+    # particularly tiny on Windows/MSVC.
+    RAND_R_MAX = 0x7FFFFFFF
+
+
+# safe_realloc(&p, n) resizes the allocation of p to n * sizeof(*p) bytes or
+# raises a MemoryError. It never calls free, since that's __dealloc__'s job.
+#   cdef DTYPE_t *p = NULL
+#   safe_realloc(&p, n)
+# is equivalent to p = malloc(n * sizeof(*p)) with error checking.
+ctypedef fused realloc_ptr:
+    # Add pointer types here as needed.
+    (DTYPE_t*)
+    (SIZE_t*)
+    (unsigned char*)
+    (WeightedPQueueRecord*)
+    (DOUBLE_t*)
+    (DOUBLE_t**)
+    (Node*)
+    (Cell*)
+    (Node**)
+
+cdef realloc_ptr safe_realloc(realloc_ptr* p, size_t nelems) nogil except *
+
+
+cdef np.ndarray sizet_ptr_to_ndarray(SIZE_t* data, SIZE_t size)
+
+
+cdef SIZE_t rand_int(SIZE_t low, SIZE_t high,
+                     UINT32_t* random_state) nogil
+
+
+cdef double rand_uniform(double low, double high,
+                         UINT32_t* random_state) nogil
+
+
+cdef double log(double x) nogil
+
+# =============================================================================
+# WeightedPQueue data structure
+# =============================================================================
+
+# A record stored in the WeightedPQueue
+cdef struct WeightedPQueueRecord:
+    DOUBLE_t data
+    DOUBLE_t weight
+
+cdef class WeightedPQueue:
+    cdef SIZE_t capacity
+    cdef SIZE_t array_ptr
+    cdef WeightedPQueueRecord* array_
+
+    cdef bint is_empty(self) nogil
+    cdef int reset(self) nogil except -1
+    cdef SIZE_t size(self) nogil
+    cdef int push(self, DOUBLE_t data, DOUBLE_t weight) nogil except -1
+    cdef int remove(self, DOUBLE_t data, DOUBLE_t weight) nogil
+    cdef int pop(self, DOUBLE_t* data, DOUBLE_t* weight) nogil
+    cdef int peek(self, DOUBLE_t* data, DOUBLE_t* weight) nogil
+    cdef DOUBLE_t get_weight_from_index(self, SIZE_t index) nogil
+    cdef DOUBLE_t get_value_from_index(self, SIZE_t index) nogil
+
+
+# =============================================================================
+# WeightedMedianCalculator data structure
+# =============================================================================
+
+cdef class WeightedMedianCalculator:
+    cdef SIZE_t initial_capacity
+    cdef WeightedPQueue samples
+    cdef DOUBLE_t total_weight
+    cdef SIZE_t k
+    cdef DOUBLE_t sum_w_0_k            # represents sum(weights[0:k])
+                                       # = w[0] + w[1] + ... + w[k-1]
+
+    cdef SIZE_t size(self) nogil
+    cdef int push(self, DOUBLE_t data, DOUBLE_t weight) nogil except -1
+    cdef int reset(self) nogil except -1
+    cdef int update_median_parameters_post_push(
+        self, DOUBLE_t data, DOUBLE_t weight,
+        DOUBLE_t original_median) nogil
+    cdef int remove(self, DOUBLE_t data, DOUBLE_t weight) nogil
+    cdef int pop(self, DOUBLE_t* data, DOUBLE_t* weight) nogil
+    cdef int update_median_parameters_post_remove(
+        self, DOUBLE_t data, DOUBLE_t weight,
+        DOUBLE_t original_median) nogil
+    cdef DOUBLE_t get_median(self) nogil

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/setup.py

@@ -0,0 +1,50 @@
+import os
+
+import numpy
+from numpy.distutils.misc_util import Configuration
+
+
+def configuration(parent_package="", top_path=None):
+    config = Configuration("tree", parent_package, top_path)
+    libraries = []
+    if os.name == "posix":
+        libraries.append("m")
+    config.add_extension(
+        "_tree",
+        sources=["_tree.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+        language="c++",
+        extra_compile_args=["-O3"],
+    )
+    config.add_extension(
+        "_splitter",
+        sources=["_splitter.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+        extra_compile_args=["-O3"],
+    )
+    config.add_extension(
+        "_criterion",
+        sources=["_criterion.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+        extra_compile_args=["-O3"],
+    )
+    config.add_extension(
+        "_utils",
+        sources=["_utils.pyx"],
+        include_dirs=[numpy.get_include()],
+        libraries=libraries,
+        extra_compile_args=["-O3"],
+    )
+
+    config.add_subpackage("tests")
+
+    return config
+
+
+if __name__ == "__main__":
+    from numpy.distutils.core import setup
+
+    setup(**configuration().todict())

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/tests/test_export.py

@@ -0,0 +1,512 @@
+"""
+Testing for export functions of decision trees (sklearn.tree.export).
+"""
+from re import finditer, search
+from textwrap import dedent
+
+from numpy.random import RandomState
+import pytest
+
+from sklearn.base import is_classifier
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.tree import export_graphviz, plot_tree, export_text
+from io import StringIO
+from sklearn.exceptions import NotFittedError
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+y2 = [[-1, 1], [-1, 1], [-1, 1], [1, 2], [1, 2], [1, 3]]
+w = [1, 1, 1, 0.5, 0.5, 0.5]
+y_degraded = [1, 1, 1, 1, 1, 1]
+
+
+def test_graphviz_toy():
+    # Check correctness of export_graphviz
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    contents1 = export_graphviz(clf, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with feature_names
+    contents1 = export_graphviz(
+        clf, feature_names=["feature0", "feature1"], out_file=None
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test with class_names
+    contents1 = export_graphviz(clf, class_names=["yes", "no"], out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = yes"] ;\n'
+        '1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
+        'class = yes"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
+        'class = no"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test plot_options
+    contents1 = export_graphviz(
+        clf,
+        filled=True,
+        impurity=False,
+        proportion=True,
+        special_characters=True,
+        rounded=True,
+        out_file=None,
+        fontname="sans",
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled, rounded", color="black", '
+        'fontname="sans"] ;\n'
+        'edge [fontname="sans"] ;\n'
+        "0 [label=<X<SUB>0</SUB> &le; 0.0<br/>samples = 100.0%<br/>"
+        'value = [0.5, 0.5]>, fillcolor="#ffffff"] ;\n'
+        "1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, "
+        'fillcolor="#e58139"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        "2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, "
+        'fillcolor="#399de5"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test max_depth
+    contents1 = export_graphviz(clf, max_depth=0, class_names=True, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
+        'value = [3, 3]\\nclass = y[0]"] ;\n'
+        '1 [label="(...)"] ;\n'
+        "0 -> 1 ;\n"
+        '2 [label="(...)"] ;\n'
+        "0 -> 2 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test max_depth with plot_options
+    contents1 = export_graphviz(
+        clf, max_depth=0, filled=True, out_file=None, node_ids=True
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="node #0\\nX[0] <= 0.0\\ngini = 0.5\\n'
+        'samples = 6\\nvalue = [3, 3]", fillcolor="#ffffff"] ;\n'
+        '1 [label="(...)", fillcolor="#C0C0C0"] ;\n'
+        "0 -> 1 ;\n"
+        '2 [label="(...)", fillcolor="#C0C0C0"] ;\n'
+        "0 -> 2 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test multi-output with weighted samples
+    clf = DecisionTreeClassifier(
+        max_depth=2, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf = clf.fit(X, y2, sample_weight=w)
+
+    contents1 = export_graphviz(clf, filled=True, impurity=False, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="X[0] <= 0.0\\nsamples = 6\\n'
+        "value = [[3.0, 1.5, 0.0]\\n"
+        '[3.0, 1.0, 0.5]]", fillcolor="#ffffff"] ;\n'
+        '1 [label="samples = 3\\nvalue = [[3, 0, 0]\\n'
+        '[3, 0, 0]]", fillcolor="#e58139"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=45, "
+        'headlabel="True"] ;\n'
+        '2 [label="X[0] <= 1.5\\nsamples = 3\\n'
+        "value = [[0.0, 1.5, 0.0]\\n"
+        '[0.0, 1.0, 0.5]]", fillcolor="#f1bd97"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="False"] ;\n'
+        '3 [label="samples = 2\\nvalue = [[0, 1, 0]\\n'
+        '[0, 1, 0]]", fillcolor="#e58139"] ;\n'
+        "2 -> 3 ;\n"
+        '4 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n'
+        '[0.0, 0.0, 0.5]]", fillcolor="#e58139"] ;\n'
+        "2 -> 4 ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test regression output with plot_options
+    clf = DecisionTreeRegressor(
+        max_depth=3, min_samples_split=2, criterion="squared_error", random_state=2
+    )
+    clf.fit(X, y)
+
+    contents1 = export_graphviz(
+        clf,
+        filled=True,
+        leaves_parallel=True,
+        out_file=None,
+        rotate=True,
+        rounded=True,
+        fontname="sans",
+    )
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled, rounded", color="black", '
+        'fontname="sans"] ;\n'
+        "graph [ranksep=equally, splines=polyline] ;\n"
+        'edge [fontname="sans"] ;\n'
+        "rankdir=LR ;\n"
+        '0 [label="X[0] <= 0.0\\nsquared_error = 1.0\\nsamples = 6\\n'
+        'value = 0.0", fillcolor="#f2c09c"] ;\n'
+        '1 [label="squared_error = 0.0\\nsamples = 3\\'
+        'nvalue = -1.0", '
+        'fillcolor="#ffffff"] ;\n'
+        "0 -> 1 [labeldistance=2.5, labelangle=-45, "
+        'headlabel="True"] ;\n'
+        '2 [label="squared_error = 0.0\\nsamples = 3\\nvalue = 1.0", '
+        'fillcolor="#e58139"] ;\n'
+        "0 -> 2 [labeldistance=2.5, labelangle=45, "
+        'headlabel="False"] ;\n'
+        "{rank=same ; 0} ;\n"
+        "{rank=same ; 1; 2} ;\n"
+        "}"
+    )
+
+    assert contents1 == contents2
+
+    # Test classifier with degraded learning set
+    clf = DecisionTreeClassifier(max_depth=3)
+    clf.fit(X, y_degraded)
+
+    contents1 = export_graphviz(clf, filled=True, out_file=None)
+    contents2 = (
+        "digraph Tree {\n"
+        'node [shape=box, style="filled", color="black", '
+        'fontname="helvetica"] ;\n'
+        'edge [fontname="helvetica"] ;\n'
+        '0 [label="gini = 0.0\\nsamples = 6\\nvalue = 6.0", '
+        'fillcolor="#ffffff"] ;\n'
+        "}"
+    )
+
+
+def test_graphviz_errors():
+    # Check for errors of export_graphviz
+    clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
+
+    # Check not-fitted decision tree error
+    out = StringIO()
+    with pytest.raises(NotFittedError):
+        export_graphviz(clf, out)
+
+    clf.fit(X, y)
+
+    # Check if it errors when length of feature_names
+    # mismatches with number of features
+    message = "Length of feature_names, 1 does not match number of features, 2"
+    with pytest.raises(ValueError, match=message):
+        export_graphviz(clf, None, feature_names=["a"])
+
+    message = "Length of feature_names, 3 does not match number of features, 2"
+    with pytest.raises(ValueError, match=message):
+        export_graphviz(clf, None, feature_names=["a", "b", "c"])
+
+    # Check error when argument is not an estimator
+    message = "is not an estimator instance"
+    with pytest.raises(TypeError, match=message):
+        export_graphviz(clf.fit(X, y).tree_)
+
+    # Check class_names error
+    out = StringIO()
+    with pytest.raises(IndexError):
+        export_graphviz(clf, out, class_names=[])
+
+    # Check precision error
+    out = StringIO()
+    with pytest.raises(ValueError, match="should be greater or equal"):
+        export_graphviz(clf, out, precision=-1)
+    with pytest.raises(ValueError, match="should be an integer"):
+        export_graphviz(clf, out, precision="1")
+
+
+def test_friedman_mse_in_graphviz():
+    clf = DecisionTreeRegressor(criterion="friedman_mse", random_state=0)
+    clf.fit(X, y)
+    dot_data = StringIO()
+    export_graphviz(clf, out_file=dot_data)
+
+    clf = GradientBoostingClassifier(n_estimators=2, random_state=0)
+    clf.fit(X, y)
+    for estimator in clf.estimators_:
+        export_graphviz(estimator[0], out_file=dot_data)
+
+    for finding in finditer(r"\[.*?samples.*?\]", dot_data.getvalue()):
+        assert "friedman_mse" in finding.group()
+
+
+def test_precision():
+
+    rng_reg = RandomState(2)
+    rng_clf = RandomState(8)
+    for X, y, clf in zip(
+        (rng_reg.random_sample((5, 2)), rng_clf.random_sample((1000, 4))),
+        (rng_reg.random_sample((5,)), rng_clf.randint(2, size=(1000,))),
+        (
+            DecisionTreeRegressor(
+                criterion="friedman_mse", random_state=0, max_depth=1
+            ),
+            DecisionTreeClassifier(max_depth=1, random_state=0),
+        ),
+    ):
+
+        clf.fit(X, y)
+        for precision in (4, 3):
+            dot_data = export_graphviz(
+                clf, out_file=None, precision=precision, proportion=True
+            )
+
+            # With the current random state, the impurity and the threshold
+            # will have the number of precision set in the export_graphviz
+            # function. We will check the number of precision with a strict
+            # equality. The value reported will have only 2 precision and
+            # therefore, only a less equal comparison will be done.
+
+            # check value
+            for finding in finditer(r"value = \d+\.\d+", dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) <= precision + 1
+            # check impurity
+            if is_classifier(clf):
+                pattern = r"gini = \d+\.\d+"
+            else:
+                pattern = r"friedman_mse = \d+\.\d+"
+
+            # check impurity
+            for finding in finditer(pattern, dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
+            # check threshold
+            for finding in finditer(r"<= \d+\.\d+", dot_data):
+                assert len(search(r"\.\d+", finding.group()).group()) == precision + 1
+
+
+def test_export_text_errors():
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+
+    err_msg = "max_depth bust be >= 0, given -1"
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, max_depth=-1)
+    err_msg = "feature_names must contain 2 elements, got 1"
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, feature_names=["a"])
+    err_msg = "decimals must be >= 0, given -1"
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, decimals=-1)
+    err_msg = "spacing must be > 0, given 0"
+    with pytest.raises(ValueError, match=err_msg):
+        export_text(clf, spacing=0)
+
+
+def test_export_text():
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: -1
+    |--- feature_1 >  0.00
+    |   |--- class: 1
+    """
+    ).lstrip()
+
+    assert export_text(clf) == expected_report
+    # testing that leaves at level 1 are not truncated
+    assert export_text(clf, max_depth=0) == expected_report
+    # testing that the rest of the tree is truncated
+    assert export_text(clf, max_depth=10) == expected_report
+
+    expected_report = dedent(
+        """
+    |--- b <= 0.00
+    |   |--- class: -1
+    |--- b >  0.00
+    |   |--- class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, feature_names=["a", "b"]) == expected_report
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- weights: [3.00, 0.00] class: -1
+    |--- feature_1 >  0.00
+    |   |--- weights: [0.00, 3.00] class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, show_weights=True) == expected_report
+
+    expected_report = dedent(
+        """
+    |- feature_1 <= 0.00
+    | |- class: -1
+    |- feature_1 >  0.00
+    | |- class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, spacing=1) == expected_report
+
+    X_l = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1], [-1, 1]]
+    y_l = [-1, -1, -1, 1, 1, 1, 2]
+    clf = DecisionTreeClassifier(max_depth=4, random_state=0)
+    clf.fit(X_l, y_l)
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: -1
+    |--- feature_1 >  0.00
+    |   |--- truncated branch of depth 2
+    """
+    ).lstrip()
+    assert export_text(clf, max_depth=0) == expected_report
+
+    X_mo = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+    y_mo = [[-1, -1], [-1, -1], [-1, -1], [1, 1], [1, 1], [1, 1]]
+
+    reg = DecisionTreeRegressor(max_depth=2, random_state=0)
+    reg.fit(X_mo, y_mo)
+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.0
+    |   |--- value: [-1.0, -1.0]
+    |--- feature_1 >  0.0
+    |   |--- value: [1.0, 1.0]
+    """
+    ).lstrip()
+    assert export_text(reg, decimals=1) == expected_report
+    assert export_text(reg, decimals=1, show_weights=True) == expected_report
+
+    X_single = [[-2], [-1], [-1], [1], [1], [2]]
+    reg = DecisionTreeRegressor(max_depth=2, random_state=0)
+    reg.fit(X_single, y_mo)
+
+    expected_report = dedent(
+        """
+    |--- first <= 0.0
+    |   |--- value: [-1.0, -1.0]
+    |--- first >  0.0
+    |   |--- value: [1.0, 1.0]
+    """
+    ).lstrip()
+    assert export_text(reg, decimals=1, feature_names=["first"]) == expected_report
+    assert (
+        export_text(reg, decimals=1, show_weights=True, feature_names=["first"])
+        == expected_report
+    )
+
+
+def test_plot_tree_entropy(pyplot):
+    # mostly smoke tests
+    # Check correctness of export_graphviz for criterion = entropy
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="entropy", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    feature_names = ["first feat", "sepal_width"]
+    nodes = plot_tree(clf, feature_names=feature_names)
+    assert len(nodes) == 3
+    assert (
+        nodes[0].get_text()
+        == "first feat <= 0.0\nentropy = 1.0\nsamples = 6\nvalue = [3, 3]"
+    )
+    assert nodes[1].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [3, 0]"
+    assert nodes[2].get_text() == "entropy = 0.0\nsamples = 3\nvalue = [0, 3]"
+
+
+def test_plot_tree_gini(pyplot):
+    # mostly smoke tests
+    # Check correctness of export_graphviz for criterion = gini
+    clf = DecisionTreeClassifier(
+        max_depth=3, min_samples_split=2, criterion="gini", random_state=2
+    )
+    clf.fit(X, y)
+
+    # Test export code
+    feature_names = ["first feat", "sepal_width"]
+    nodes = plot_tree(clf, feature_names=feature_names)
+    assert len(nodes) == 3
+    assert (
+        nodes[0].get_text()
+        == "first feat <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]"
+    )
+    assert nodes[1].get_text() == "gini = 0.0\nsamples = 3\nvalue = [3, 0]"
+    assert nodes[2].get_text() == "gini = 0.0\nsamples = 3\nvalue = [0, 3]"
+
+
+def test_not_fitted_tree(pyplot):
+
+    # Testing if not fitted tree throws the correct error
+    clf = DecisionTreeRegressor()
+    with pytest.raises(NotFittedError):
+        plot_tree(clf)

dashboard/flask-server/venv/Lib/site-packages/sklearn/tree/tests/test_reingold_tilford.py

@@ -0,0 +1,48 @@
+import numpy as np
+import pytest
+from sklearn.tree._reingold_tilford import buchheim, Tree
+
+simple_tree = Tree("", 0, Tree("", 1), Tree("", 2))
+
+bigger_tree = Tree(
+    "",
+    0,
+    Tree(
+        "",
+        1,
+        Tree("", 3),
+        Tree("", 4, Tree("", 7), Tree("", 8)),
+    ),
+    Tree("", 2, Tree("", 5), Tree("", 6)),
+)
+
+
+@pytest.mark.parametrize("tree, n_nodes", [(simple_tree, 3), (bigger_tree, 9)])
+def test_buchheim(tree, n_nodes):
+    def walk_tree(draw_tree):
+        res = [(draw_tree.x, draw_tree.y)]
+        for child in draw_tree.children:
+            # parents higher than children:
+            assert child.y == draw_tree.y + 1
+            res.extend(walk_tree(child))
+        if len(draw_tree.children):
+            # these trees are always binary
+            # parents are centered above children
+            assert (
+                draw_tree.x == (draw_tree.children[0].x + draw_tree.children[1].x) / 2
+            )
+        return res
+
+    layout = buchheim(tree)
+    coordinates = walk_tree(layout)
+    assert len(coordinates) == n_nodes
+    # test that x values are unique per depth / level
+    # we could also do it quicker using defaultdicts..
+    depth = 0
+    while True:
+        x_at_this_depth = [node[0] for node in coordinates if node[1] == depth]
+        if not x_at_this_depth:
+            # reached all leafs
+            break
+        assert len(np.unique(x_at_this_depth)) == len(x_at_this_depth)
+        depth += 1