Doctorado-ML/stree
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This commit is contained in:
Ricardo Montañana Gómez
2020-05-20 14:26:55 +02:00
parent c0ef71f139
commit 6e35628c85
6 changed files with 326 additions and 267 deletions
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7
main.py
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@@ -50,9 +50,8 @@ print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
proba = clf.predict_proba(Xtest) proba = clf.predict_proba(Xtest)
print("Checking that we have correct probabilities, these are probabilities of sample belonging to class 1") print("Checking that we have correct probabilities, these are probabilities of sample belonging to class 1")
res0 = proba[proba[:, 0] == 0] res0 = proba[proba[:, 0] == 0]
res1 = proba[proba[:, 0] == 0] res1 = proba[proba[:, 0] == 1]
print("++++++++++res0++++++++++++") print("++++++++++res0 > .8++++++++++++")
print(res0[res0[:, 1] > .8]) print(res0[res0[:, 1] > .8])
print("**********res1************") print("**********res1 < .4************")
print(res1[res1[:, 1] < .4]) print(res1[res1[:, 1] < .4])
print(clf.predict_proba(Xtest))

42
test2.ipynb
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@@ -5,6 +5,21 @@
"execution_count": 1, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [
"#\n",
"# Google Colab setup\n",
"#\n",
"#!git clone https://github.com/Doctorado-ML/STree.git\n",
"# Set working dir to Stree\n",
"#import os\n",
"#os.chdir(\"STree\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
@@ -17,7 +32,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -29,13 +44,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"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" "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"
} }
], ],
"source": [ "source": [
@@ -84,20 +99,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 5,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"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" "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9559\nClassifier's accuracy (test) : 0.9442\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.986928 counts=(array([0, 1]), array([ 4, 302]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.943089 counts=(array([0, 1]), array([696, 42]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9588\nClassifier's accuracy (test) : 0.9397\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.993443 counts=(array([0, 1]), array([ 2, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.944520 counts=(array([0, 1]), array([698, 41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9703\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 - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.957359 counts=(array([0, 1]), array([696, 31]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9703\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 - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.957182 counts=(array([0, 1]), array([693, 31]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9789\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\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=1 belief=1.000000 counts=(array([1]), array([4]))\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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.968481 counts=(array([0, 1]), array([676, 22]))\n\n**************************************************\n0.6609 secs\n"
} }
], ],
"source": [ "source": [
@@ -115,7 +123,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@@ -132,13 +140,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"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" "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\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=1 belief=1.000000 counts=(array([1]), array([4]))\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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.968481 counts=(array([0, 1]), array([676, 22]))\n"
} }
], ],
"source": [ "source": [
@@ -149,13 +157,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"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" "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\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=1 belief=1.000000 counts=(array([1]), array([4]))\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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.968481 counts=(array([0, 1]), array([676, 22]))\n"
} }
], ],
"source": [ "source": [

512
test_graphs.ipynb
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File diff suppressed because one or more lines are too long

8
tests/Stree_test.py
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@@ -144,15 +144,17 @@ class Stree_test(unittest.TestCase):
"""Check that element 28 has a prediction different that the current label """Check that element 28 has a prediction different that the current label
""" """
# Element 28 has a different prediction than the truth # Element 28 has a different prediction than the truth
decimals = 8
X, y = self._get_Xy() X, y = self._get_Xy()
yp = self._clf.predict_proba(X[28, :].reshape(-1, X.shape[1])) yp = self._clf.predict_proba(X[28, :].reshape(-1, X.shape[1]))
self.assertEqual(0, yp[0:, 0]) self.assertEqual(0, yp[0:, 0])
self.assertEqual(1, y[28]) self.assertEqual(1, y[28])
self.assertEqual(0.29026400766, round(yp[0, 1], 11)) self.assertEqual(round(0.29026400766, decimals), round(yp[0, 1], decimals))
def test_multiple_predict_proba(self): def test_multiple_predict_proba(self):
# First 27 elements the predictions are the same as the truth # First 27 elements the predictions are the same as the truth
num = 27 num = 27
decimals = 8
X, y = self._get_Xy() X, y = self._get_Xy()
yp = self._clf.predict_proba(X[:num, :]) yp = self._clf.predict_proba(X[:num, :])
self.assertListEqual(y[:num].tolist(), yp[:, 0].tolist()) self.assertListEqual(y[:num].tolist(), yp[:, 0].tolist())
@@ -161,7 +163,9 @@ class Stree_test(unittest.TestCase):
0.30756427, 0.8318412, 0.18981198, 0.15564624, 0.25740655, 0.22923355, 0.30756427, 0.8318412, 0.18981198, 0.15564624, 0.25740655, 0.22923355,
0.87365959, 0.49928689, 0.95574351, 0.28761257, 0.28906333, 0.32643692, 0.87365959, 0.49928689, 0.95574351, 0.28761257, 0.28906333, 0.32643692,
0.29788483, 0.01657364, 0.81149083] 0.29788483, 0.01657364, 0.81149083]
self.assertListEqual(expected_proba, np.round(yp[:, 1], decimals=8).tolist()) self.assertListEqual(
np.round(expected_proba, decimals=decimals).tolist(),
np.round(yp[:, 1], decimals=decimals).tolist())
def build_models(self): def build_models(self):
"""Build and train two models, model_clf will use the sklearn classifier to """Build and train two models, model_clf will use the sklearn classifier to

19
trees/Snode_graph.py
View File

@@ -23,17 +23,26 @@ class Snode_graph(Snode):
def set_plot_size(self, size): def set_plot_size(self, size):
self._plot_size = size self._plot_size = size
def _is_pure(self) -> bool:
"""is considered pure a leaf node with one label
"""
if self.is_leaf():
return self._belief == 1.
return False
def plot_hyperplane(self): def plot_hyperplane(self):
# get the splitting hyperplane # get the splitting hyperplane
def hyperplane(x, y): return (-self._interceptor - self._vector[0][0] * x def hyperplane(x, y): return (-self._interceptor - self._vector[0][0] * x
- self._vector[0][1] * y) / self._vector[0][2] - self._vector[0][1] * y) / self._vector[0][2]
fig = plt.figure(figsize=self._plot_size) fig = plt.figure(figsize=self._plot_size)
ax = fig.add_subplot(1, 1, 1, projection='3d') ax = fig.add_subplot(1, 1, 1, projection='3d')
tmpx = np.linspace(self._X[:, 0].min(), self._X[:, 0].max()) if not self._is_pure():
tmpy = np.linspace(self._X[:, 1].min(), self._X[:, 1].max()) # Can't plot hyperplane of leaves with one label because it hasn't classiffier
xx, yy = np.meshgrid(tmpx, tmpy) tmpx = np.linspace(self._X[:, 0].min(), self._X[:, 0].max())
ax.plot_surface(xx, yy, hyperplane(xx, yy), alpha=.5, antialiased=True, tmpy = np.linspace(self._X[:, 1].min(), self._X[:, 1].max())
rstride=1, cstride=1, cmap='seismic') 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) plt.title(self._title)
self.plot_distribution(ax) self.plot_distribution(ax)
return ax return ax

3
trees/Stree_grapher.py
View File

@@ -33,6 +33,9 @@ class Stree_grapher(Stree):
def _copy_tree(self, node: Snode) -> Snode_graph: def _copy_tree(self, node: Snode) -> Snode_graph:
mirror = Snode_graph(node) mirror = Snode_graph(node)
# clone node
mirror._class = node._class
mirror._belief = node._belief
if node.get_down() is not None: if node.get_down() is not None:
mirror.set_down(self._copy_tree(node.get_down())) mirror.set_down(self._copy_tree(node.get_down()))
if node.get_up() is not None: if node.get_up() is not None: