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Make Stree a sklearn estimator

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Added check_estimator in notebook test2
Added a Stree test with check_estimator
This commit is contained in:
Ricardo Montañana Gómez
2020-05-25 19:51:39 +02:00
parent 5956cd0cd2
commit e95bd9697a
5 changed files with 109 additions and 48 deletions
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2
setup.py
View File

@@ -1,6 +1,6 @@
import setuptools
__version__ = "0.9rc2"
__version__ = "0.9rc3"
__author__ = "Ricardo Montañana Gómez"
def readme():

84
stree/Strees.py
View File

@@ -15,6 +15,7 @@ from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import LinearSVC
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
class Snode:
def __init__(self, clf: LinearSVC, X: np.ndarray, y: np.ndarray, title: str):
self._clf = clf
@@ -22,7 +23,7 @@ class Snode:
self._interceptor = 0. if clf is None else clf.intercept_
self._title = title
self._belief = 0. # belief of the prediction in a leaf node based on samples
# Only store dataset in Testing
# Only store dataset in Testing
self._X = X if os.environ.get('TESTING', 'NS') != 'NS' else None
self._y = y
self._down = None
@@ -97,24 +98,27 @@ class Siterator:
self._push(node.get_down())
return node
class Stree(BaseEstimator, ClassifierMixin):
"""
"""
__folder = 'data/'
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
self._tree = None
self.__folder = 'data/'
self.__use_predictions = use_predictions
self.__trained = False
self.__proba = False
self.max_iter = max_iter
self.C = C
self.random_state = random_state
self.use_predictions = use_predictions
def get_params(self, deep=True):
"""Get dict with hyperparameters and its values to accomplish sklearn rules
"""
return {"C": self._C, "random_state": self._random_state, 'max_iter': self._max_iter}
return {
'C': self.C,
'random_state': self.random_state,
'max_iter': self.max_iter,
'use_predictions': self.use_predictions
}
def set_params(self, **parameters):
"""Set hyperparmeters as specified by sklearn, needed in Gridsearchs
@@ -123,12 +127,16 @@ class Stree(BaseEstimator, ClassifierMixin):
setattr(self, parameter, value)
return self
# Added binary_only tag as required by sklearn check_estimator
def _more_tags(self):
return {'binary_only': True}
def _linear_function(self, data: np.array, node: Snode) -> np.array:
coef = node._vector[0, :].reshape(-1, data.shape[1])
return data.dot(coef.T) + node._interceptor[0]
def _split_data(self, node: Snode, data: np.ndarray, indices: np.ndarray) -> list:
if self.__use_predictions:
if self.use_predictions:
yp = node._clf.predict(data)
down = (yp == 1).reshape(-1, 1)
res = np.expand_dims(node._clf.decision_function(data), 1)
@@ -147,11 +155,16 @@ class Stree(BaseEstimator, ClassifierMixin):
return [data_up, indices_up, data_down, indices_down, res_up, res_down]
def fit(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> 'Stree':
X, y = check_X_y(X, y.ravel())
from sklearn.utils.multiclass import check_classification_targets
if type(y).__name__ == 'np.ndarray':
y = y.ravel()
X, y = check_X_y(X, y)
self.classes_ = np.unique(y)
self.n_iter_ = self.max_iter
check_classification_targets(y)
self.n_features_in_ = X.shape[1]
self._tree = self.train(X, y.ravel(), title)
self.tree_ = self.train(X, y.ravel(), title)
self._build_predictor()
self.__trained = True
return self
def _build_predictor(self):
@@ -165,15 +178,15 @@ class Stree(BaseEstimator, ClassifierMixin):
run_tree(node.get_down())
run_tree(node.get_up())
run_tree(self._tree)
run_tree(self.tree_)
def train(self, X: np.ndarray, y: np.ndarray, title: str = 'root') -> Snode:
if np.unique(y).shape[0] == 1:
# only 1 class => pure dataset
return Snode(None, X, y, title + ', <pure>')
# Train the model
clf = LinearSVC(max_iter=self._max_iter, C=self._C,
random_state=self._random_state)
clf = LinearSVC(max_iter=self.max_iter, C=self.C,
random_state=self.random_state)
clf.fit(X, y)
tree = Snode(clf, X, y, title)
X_U, y_u, X_D, y_d, _, _ = self._split_data(tree, X, y)
@@ -184,8 +197,13 @@ class Stree(BaseEstimator, ClassifierMixin):
tree.set_down(self.train(X_D, y_d, title + ' - Down'))
return tree
def _reorder_results(self, y: np.array, indices: np.array) -> np.array:
y_ordered = np.zeros(y.shape, dtype=int if y.ndim == 1 else float)
def _reorder_results(self, y: np.array, indices: np.array, proba=False) -> np.array:
if proba:
# if predict_proba return np.array of floats
y_ordered = np.zeros(y.shape, dtype=float)
else:
# return array of same type given in y
y_ordered = y.copy()
indices = indices.astype(int)
for i, index in enumerate(indices):
y_ordered[index] = y[i]
@@ -205,17 +223,15 @@ class Stree(BaseEstimator, ClassifierMixin):
return np.append(k, m), np.append(l, n)
# sklearn check
check_is_fitted(self)
check_is_fitted(self, ['tree_'])
# Input validation
X = check_array(X)
# setup prediction & make it happen
indices = np.arange(X.shape[0])
return self._reorder_results(*predict_class(X, indices, self._tree))
return self._reorder_results(*predict_class(X, indices, self.tree_)).ravel()
def predict_proba(self, X: np.array) -> np.array:
"""Computes an approximation of the probability of samples belonging to class 1
(nothing more, nothing less)
"""Computes an approximation of the probability of samples belonging to class 0 and 1
:param X: dataset
:type X: np.array
"""
@@ -247,29 +263,31 @@ class Stree(BaseEstimator, ClassifierMixin):
return np.append(k, m), np.append(l, n)
# sklearn check
check_is_fitted(self)
check_is_fitted(self, ['tree_'])
# Input validation
X = check_array(X)
# setup prediction & make it happen
indices = np.arange(X.shape[0])
result, indices = predict_class(X, indices, [], self._tree)
empty_dist = np.empty((X.shape[0], 1), dtype=float)
result, indices = predict_class(X, indices, empty_dist, self.tree_)
result = result.reshape(X.shape[0], 2)
# Turn distances to hyperplane into probabilities based on fitting distances
# of samples to its hyperplane that classified them, to the sigmoid function
result[:, 1] = 1 / (1 + np.exp(-result[:, 1]))
return self._reorder_results(result, indices)
result[:, 1] = 1 / (1 + np.exp(-result[:, 1])) # Probability of being 1
result[:, 0] = 1 - result[:, 1] # Probability of being 0
return self._reorder_results(result, indices, proba=True)
def score(self, X: np.array, y: np.array) -> float:
"""Return accuracy
"""
if not self.__trained:
self.fit(X, y)
# sklearn check
check_is_fitted(self)
yp = self.predict(X).reshape(y.shape)
right = (yp == y).astype(int)
return np.sum(right) / len(y)
def __iter__(self):
return Siterator(self._tree)
return Siterator(self.tree_)
def __str__(self) -> str:
output = ''
@@ -305,7 +323,5 @@ class Stree(BaseEstimator, ClassifierMixin):
if not os.path.isdir(self.__folder):
os.mkdir(self.__folder)
with open(self.get_catalog_name(), 'w', encoding='utf-8') as catalog:
self._save_datasets(self._tree, catalog, 1)
self._save_datasets(self.tree_, catalog, 1)

2
stree/Strees_grapher.py
View File

@@ -143,7 +143,7 @@ class Stree_grapher(Stree):
self._pca = PCA(n_components=3)
X = self._pca.fit_transform(X)
res = super().fit(X, y)
self._tree_gr = self._copy_tree(self._tree)
self._tree_gr = self._copy_tree(self.tree_)
self._fitted = True
return res

23
stree/tests/Strees_test.py
View File

@@ -71,7 +71,7 @@ class Stree_test(unittest.TestCase):
def test_build_tree(self):
"""Check if the tree is built the same way as predictions of models
"""
self._check_tree(self._clf._tree)
self._check_tree(self._clf.tree_)
def _get_file_data(self, file_name: str) -> tuple:
"""Return X, y from data, y is the last column in array
@@ -145,12 +145,14 @@ class Stree_test(unittest.TestCase):
"""
# Element 28 has a different prediction than the truth
decimals = 5
prob = 0.29026400766
X, y = self._get_Xy()
yp = self._clf.predict_proba(X[28, :].reshape(-1, X.shape[1]))
self.assertEqual(0, yp[0:, 0])
self.assertEqual(np.round(1 - prob, decimals), np.round(yp[0:, 0], decimals))
self.assertEqual(1, y[28])
self.assertAlmostEqual(
round(0.29026400766, decimals),
round(prob, decimals),
round(yp[0, 1], decimals),
decimals
)
@@ -161,7 +163,7 @@ class Stree_test(unittest.TestCase):
decimals = 5
X, y = self._get_Xy()
yp = self._clf.predict_proba(X[:num, :])
self.assertListEqual(y[:num].tolist(), yp[:, 0].tolist())
self.assertListEqual(y[:num].tolist(), np.argmax(yp[:num], axis=1).tolist())
expected_proba = [0.88395641, 0.36746962, 0.84158767, 0.34106833, 0.14269291, 0.85193236,
0.29876058, 0.7282164, 0.85958616, 0.89517877, 0.99745224, 0.18860349,
0.30756427, 0.8318412, 0.18981198, 0.15564624, 0.25740655, 0.22923355,
@@ -243,6 +245,14 @@ class Stree_test(unittest.TestCase):
computed.append(str(node))
self.assertListEqual(expected, computed)
def test_is_a_sklearn_classifier(self):
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings('ignore', category=ConvergenceWarning)
warnings.filterwarnings('ignore', category=RuntimeWarning)
from sklearn.utils.estimator_checks import check_estimator
check_estimator(Stree())
class Snode_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
@@ -291,7 +301,7 @@ class Snode_test(unittest.TestCase):
class_computed = classes[card == max_card]
self.assertEqual(class_computed, node._class)
check_leave(self._clf._tree)
check_leave(self._clf.tree_)
def test_nodes_coefs(self):
"""Check if the nodes of the tree have the right attributes filled
@@ -309,5 +319,4 @@ class Snode_test(unittest.TestCase):
run_tree(node.get_down())
run_tree(node.get_up())
run_tree(self._clf._tree)
run_tree(self._clf.tree_)

46
test2.ipynb
View File

@@ -48,7 +48,7 @@
{
"output_type": "stream",
"name": "stdout",
"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.110% 494\nValid: 66.890% 998\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": [
@@ -94,12 +94,16 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"metadata": {
"tags": [
"outputPrepend"
]
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9521\nClassifier's accuracy (test) : 0.9598\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.980519 counts=(array([0, 1]), array([ 6, 302]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.940217 counts=(array([0, 1]), array([692, 44]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9521\nClassifier's accuracy (test) : 0.9643\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.986842 counts=(array([0, 1]), array([ 4, 300]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.937754 counts=(array([0, 1]), array([693, 46]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9636\nClassifier's accuracy (test) : 0.9688\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([308]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([8]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.947802 counts=(array([0, 1]), array([690, 38]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9665\nClassifier's accuracy (test) : 0.9621\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([308]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([11]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - 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([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.951456 counts=(array([0, 1]), array([686, 35]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9741\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\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([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675, 27]))\n\n**************************************************\n0.7816 secs\n"
"text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9579\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.987013 counts=(array([0, 1]), array([ 4, 304]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.945652 counts=(array([0, 1]), array([696, 40]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9579\nClassifier's accuracy (test) : 0.9509\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.990196 counts=(array([0, 1]), array([ 3, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.944444 counts=(array([0, 1]), array([697, 41]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9693\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([311]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([6]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief=0.955923 counts=(array([0, 1]), array([694, 32]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9713\nClassifier's accuracy (test) : 0.9576\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([314]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([6]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.958564 counts=(array([0, 1]), array([694, 30]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9780\nClassifier's accuracy (test) : 0.9420\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682, 23]))\n\n**************************************************\n0.4537 secs\n"
}
],
"source": [
@@ -140,7 +144,7 @@
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\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([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675, 27]))\n"
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682, 23]))\n"
}
],
"source": [
@@ -157,7 +161,7 @@
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([306]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([10]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Down - Up, <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([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\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([7]))\nroot - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.961538 counts=(array([0, 1]), array([675, 27]))\n"
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([301]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([13]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([17]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([3]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.967376 counts=(array([0, 1]), array([682, 23]))\n"
}
],
"source": [
@@ -165,6 +169,38 @@
"for i in clf:\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Check if the classifier is a sklearn estimator\n",
"from sklearn.utils.estimator_checks import check_estimator\n",
"check_estimator(Stree())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x12d18e0e0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x12d185200>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x12d1850e0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12d17eb00>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12d17ec20>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12d17ed40>, 'Stree')\n7 functools.partial(<function check_sample_weights_invariance at 0x12d17ee60>, 'Stree')\n8 functools.partial(<function check_estimators_fit_returns_self at 0x12d189200>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x12d189200>, 'Stree', readonly_memmap=True)\n10 functools.partial(<function check_complex_data at 0x12d181050>, 'Stree')\n11 functools.partial(<function check_dtype_object at 0x12d17ef80>, 'Stree')\n12 functools.partial(<function check_estimators_empty_data_messages at 0x12d185320>, 'Stree')\n13 functools.partial(<function check_pipeline_consistency at 0x12d181f80>, 'Stree')\n14 functools.partial(<function check_estimators_nan_inf at 0x12d185440>, 'Stree')\n15 functools.partial(<function check_estimators_overwrite_params at 0x12d189f80>, 'Stree')\n16 functools.partial(<function check_estimator_sparse_data at 0x12d17e9e0>, 'Stree')\n17 functools.partial(<function check_estimators_pickle at 0x12d185680>, 'Stree')\n18 functools.partial(<function check_classifier_data_not_an_array at 0x12d18e320>, 'Stree')\n19 functools.partial(<function check_classifiers_one_label at 0x12d185d40>, 'Stree')\n20 functools.partial(<function check_classifiers_classes at 0x12d1897a0>, 'Stree')\n21 functools.partial(<function check_estimators_partial_fit_n_features at 0x12d1857a0>, 'Stree')\n22 functools.partial(<function check_classifiers_train at 0x12d185e60>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x12d185e60>, 'Stree', readonly_memmap=True)\n24 functools.partial(<function check_classifiers_regression_target at 0x12d18ed40>, 'Stree')\n25 functools.partial(<function check_supervised_y_no_nan at 0x12d17cb00>, 'Stree')\n26 functools.partial(<function check_supervised_y_2d at 0x12d189440>, 'Stree')\n27 functools.partial(<function check_estimators_unfitted at 0x12d189320>, 'Stree')\n28 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12d18e8c0>, 'Stree')\n29 functools.partial(<function check_decision_proba_consistency at 0x12d18ee60>, 'Stree')\n30 functools.partial(<function check_fit2d_predict1d at 0x12d181560>, 'Stree')\n31 functools.partial(<function check_methods_subset_invariance at 0x12d181710>, 'Stree')\n32 functools.partial(<function check_fit2d_1sample at 0x12d181830>, 'Stree')\n33 functools.partial(<function check_fit2d_1feature at 0x12d181950>, 'Stree')\n34 functools.partial(<function check_fit1d at 0x12d181a70>, 'Stree')\n35 functools.partial(<function check_get_params_invariance at 0x12d18eb00>, 'Stree')\n36 functools.partial(<function check_set_params at 0x12d18ec20>, 'Stree')\n37 functools.partial(<function check_dict_unchanged at 0x12d181170>, 'Stree')\n38 functools.partial(<function check_dont_overwrite_parameters at 0x12d181440>, 'Stree')\n39 functools.partial(<function check_fit_idempotent at 0x12d192050>, 'Stree')\n"
}
],
"source": [
"# Make checks one by one\n",
"c = 0\n",
"checks = check_estimator(Stree(), generate_only=True)\n",
"for check in checks:\n",
" c += 1\n",
" print(c, check[1])\n",
" check[1](check[0])"
]
}
],
"metadata": {