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first approx to grapher

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This commit is contained in:
Ricardo Montañana Gómez
2020-05-20 12:32:17 +02:00
parent 6ebd0f9be3
commit c0ef71f139
10 changed files with 533 additions and 83 deletions
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1
requirements.txt
View File

@@ -1,3 +1,4 @@
numpy==1.18.2
scikit-learn==0.22.2
pandas==1.0.3
matplotlib==3.2.1

76
test2.ipynb
View File

@@ -35,7 +35,7 @@
{
"output_type": "stream",
"name": "stdout",
"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 32.976% 492\nValid: 67.024% 1000\n"
"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.244% 496\nValid: 66.756% 996\n"
}
],
"source": [
@@ -97,7 +97,7 @@
{
"output_type": "stream",
"name": "stdout",
"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9550\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.977346 counts=(array([0, 1]), array([ 7, 302]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.945280 counts=(array([0, 1]), array([691, 40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9569\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.986971 counts=(array([0, 1]), array([ 4, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.944369 counts=(array([0, 1]), array([696, 41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9674\nClassifier's accuracy (test) : 0.9554\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([310]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.953232 counts=(array([0, 1]), array([693, 34]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9693\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([310]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.955494 counts=(array([0, 1]), array([687, 32]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9780\nClassifier's accuracy (test) : 0.9487\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684, 23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n0.7277 secs\n"
"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9559\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.980769 counts=(array([0, 1]), array([ 6, 306]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.945205 counts=(array([0, 1]), array([690, 40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9588\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.990323 counts=(array([0, 1]), array([ 3, 307]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.945205 counts=(array([0, 1]), array([690, 40]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9732\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([6]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([9]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.960839 counts=(array([0, 1]), array([687, 28]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9732\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([7]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.960784 counts=(array([0, 1]), array([686, 28]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9741\nClassifier's accuracy (test) : 0.9531\nroot\nroot - Down\nroot - Down - Down\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679, 27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n\n**************************************************\n0.8116 secs\n"
}
],
"source": [
@@ -132,97 +132,37 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684, 23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n"
"text": "root\nroot - Down\nroot - Down - Down\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679, 27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n"
}
],
"source": [
"#check iterator\n",
"for i in list(clf):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([15]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([15]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967468 counts=(array([0, 1]), array([684, 23]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\n"
"text": "root\nroot - Down\nroot - Down - Down\nroot - Down - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([312]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Down - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.961756 counts=(array([0, 1]), array([679, 27]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\n"
}
],
"source": [
"#check iterator again\n",
"for i in clf:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …",
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0025f832c1734afc944021e5990c2d11"
}
},
"metadata": {}
}
],
"source": [
"%matplotlib widget\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
"import numpy as np\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca(projection='3d')\n",
"\n",
"scale = 8\n",
"# Make data.\n",
"X = np.arange(-scale, scale, 0.25)\n",
"Y = np.arange(-scale, scale, 0.25)\n",
"X, Y = np.meshgrid(X, Y)\n",
"Z = X**2 + Y**2\n",
"\n",
"# Plot the surface.\n",
"surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,\n",
" linewidth=0, antialiased=False)\n",
"\n",
"# Customize the z axis.\n",
"ax.set_zlim(0, 100)\n",
"ax.zaxis.set_major_locator(LinearLocator(10))\n",
"ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))\n",
"\n",
"# rotate the axes and update\n",
"#for angle in range(0, 360):\n",
"# ax.view_init(30, 40)\n",
"\n",
"# Add a color bar which maps values to colors.\n",
"fig.colorbar(surf, shrink=0.5, aspect=5)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

394
test_graphs.ipynb Normal file
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File diff suppressed because one or more lines are too long

2
tests/Snode_test.py
View File

@@ -15,7 +15,7 @@ class Snode_test(unittest.TestCase):
self._clf = Stree(random_state=self._random_state,
use_predictions=True)
self._clf.fit(*self._get_Xy())
super(Snode_test, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
@classmethod
def tearDownClass(cls):

19
tests/Stree_test.py
View File

@@ -16,7 +16,7 @@ class Stree_test(unittest.TestCase):
self._clf = Stree(random_state=self._random_state,
use_predictions=False)
self._clf.fit(*self._get_Xy())
super(Stree_test, self).__init__(*args, **kwargs)
super().__init__(*args, **kwargs)
@classmethod
def tearDownClass(cls):
@@ -217,6 +217,23 @@ class Stree_test(unittest.TestCase):
#
self.assertListEqual(yp_line.tolist(), yp_once.tolist())
def test_iterator(self):
"""Check preorder iterator
"""
expected = [
'root',
'root - Down',
'root - Down - Down, <cgaf> - Leaf class=1 belief=0.975989 counts=(array([0, 1]), array([ 17, 691]))',
'root - Down - Up',
'root - Down - Up - Down, <cgaf> - Leaf class=1 belief=0.750000 counts=(array([0, 1]), array([1, 3]))',
'root - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))',
'root - Up, <cgaf> - Leaf class=0 belief=0.928297 counts=(array([0, 1]), array([725, 56]))',
]
computed = []
for node in self._clf:
computed.append(str(node))
self.assertListEqual(expected, computed)

9
trees/Siterator.py
View File

@@ -4,14 +4,13 @@ __copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
__license__ = "MIT"
__version__ = "0.9"
Inorder iterator for the binary tree of Snodes
Uses LinearSVC
'''
from trees.Snode import Snode
class Siterator:
"""Inorder iterator
"""Stree preorder iterator
"""
def __init__(self, tree: Snode):
@@ -22,13 +21,13 @@ class Siterator:
return self
def _push(self, node: Snode):
while (node is not None):
self._stack.insert(0, node)
node = node.get_down()
if node is not None:
self._stack.append(node)
def __next__(self) -> Snode:
if len(self._stack) == 0:
raise StopIteration()
node = self._stack.pop()
self._push(node.get_up())
self._push(node.get_down())
return node

8
trees/Snode.py
View File

@@ -11,7 +11,6 @@ import os
import numpy as np
from sklearn.svm import LinearSVC
class Snode:
def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
self._clf = clf
@@ -26,6 +25,10 @@ class Snode:
self._up = None
self._class = None
@classmethod
def copy(cls, node: 'Snode') -> 'Snode':
return cls(node._clf, node._X, node._y, node._title)
def set_down(self, son):
self._down = son
@@ -45,9 +48,6 @@ class Snode:
"""Compute the class of the predictor and its belief based on the subdataset of the node
only if it is a leaf
"""
# Clean memory
#self._X = None
#self._y = None
if not self.is_leaf():
return
classes, card = np.unique(self._y, return_counts=True)

49
trees/Snode_graph.py Normal file
View File

@@ -0,0 +1,49 @@
'''
__author__ = "Ricardo Montañana Gómez"
__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
__license__ = "MIT"
__version__ = "0.9"
Plot 3D views of nodes in Stree
'''
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from trees.Snode import Snode
from trees.Stree import Stree
class Snode_graph(Snode):
def __init__(self, node: Stree):
self._plot_size = (8, 8)
n = Snode.copy(node)
super().__init__(n._clf, n._X, n._y, n._title)
def set_plot_size(self, size):
self._plot_size = size
def plot_hyperplane(self):
# get the splitting hyperplane
def hyperplane(x, y): return (-self._interceptor - self._vector[0][0] * x
- self._vector[0][1] * y) / self._vector[0][2]
fig = plt.figure(figsize=self._plot_size)
ax = fig.add_subplot(1, 1, 1, projection='3d')
tmpx = np.linspace(self._X[:, 0].min(), self._X[:, 0].max())
tmpy = np.linspace(self._X[:, 1].min(), self._X[:, 1].max())
xx, yy = np.meshgrid(tmpx, tmpy)
ax.plot_surface(xx, yy, hyperplane(xx, yy), alpha=.5, antialiased=True,
rstride=1, cstride=1, cmap='seismic')
plt.title(self._title)
self.plot_distribution(ax)
return ax
def plot_distribution(self, ax: Axes3D = None):
if ax is None:
fig = plt.figure(figsize=self._plot_size)
ax = fig.add_subplot(1, 1, 1, projection='3d')
ax.scatter(self._X[:, 0], self._X[:, 1], self._X[:, 2], c=self._y)
ax.set_xlabel('X0')
ax.set_ylabel('X1')
ax.set_zlabel('X2')
plt.show()

3
trees/Stree.py
View File

@@ -13,7 +13,6 @@ import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import LinearSVC
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from trees.Snode import Snode
from trees.Siterator import Siterator
@@ -22,7 +21,7 @@ class Stree(BaseEstimator, ClassifierMixin):
"""
"""
def __init__(self, C=1.0, max_iter: int = 1000, random_state: int = 0, use_predictions: bool = False):
def __init__(self, C: float=1.0, max_iter: int = 1000, random_state: int = 0, use_predictions: bool = False):
self._max_iter = max_iter
self._C = C
self._random_state = random_state

51
trees/Stree_grapher.py Normal file
View File

@@ -0,0 +1,51 @@
'''
__author__ = "Ricardo Montañana Gómez"
__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
__license__ = "MIT"
__version__ = "0.9"
Plot 3D views of nodes in Stree
'''
import os
import numpy as np
from sklearn.decomposition import PCA
import trees
import matplotlib.pyplot as plt
from trees.Snode import Snode
from trees.Snode_graph import Snode_graph
from trees.Stree import Stree
from trees.Siterator import Siterator
class Stree_grapher(Stree):
def __init__(self, params: dict):
self._plot_size = (8, 8)
self._tree_gr = None
# make Snode store X's
os.environ['TESTING'] = '1'
super().__init__(**params)
def __del__(self):
try:
os.environ.pop('TESTING')
except:
pass
plt.close('all')
def _copy_tree(self, node: Snode) -> Snode_graph:
mirror = Snode_graph(node)
if node.get_down() is not None:
mirror.set_down(self._copy_tree(node.get_down()))
if node.get_up() is not None:
mirror.set_up(self._copy_tree(node.get_up()))
return mirror
def fit(self, X: np.array, y: np.array) -> Stree:
if X.shape[1] != 3:
pca = PCA(n_components=3)
X = pca.fit_transform(X)
res = super().fit(X, y)
self._tree_gr = self._copy_tree(self._tree)
return res
def __iter__(self):
return Siterator(self._tree_gr)