Doctorado-ML/odte
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Become sklearn classifier

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This commit is contained in:
Ricardo Montañana Gómez 2020-06-30 11:14:05 +02:00
parent 580c93d92a
commit 98a28cd271
Signed by: rmontanana
GPG Key ID: 46064262FD9A7ADE
4 changed files with 126 additions and 52 deletions
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2
.coveragerc
View File

@ -10,5 +10,3 @@ exclude_lines =
if __name__ == .__main__.:
ignore_errors = True
omit =
odte/tests/*
odte/__init__.py

2
notebooks/benchmark.ipynb
View File

@ -219,7 +219,7 @@
"outputs": [],
"source": [
"# Oblique Decision Tree Ensemble\n",
"odte = Odte(random_state=random_state, n_estimators=10, max_features=None)"
"odte = Odte(random_state=random_state, n_estimators=10, max_features=\"auto\")"
]
},
{

137
odte/Odte.py
View File

@ -6,13 +6,15 @@ __version__ = "0.1"
Build a forest of oblique trees based on STree
"""
import random
from typing import Union
from itertools import combinations
import numpy as np
from sklearn.utils import check_consistent_length
from sklearn.metrics._classification import _weighted_sum, _check_targets
from sklearn.utils.multiclass import check_classification_targets
from sklearn.base import BaseEstimator, ClassifierMixin
from scipy.stats import mode
from sklearn.base import clone, ClassifierMixin
from sklearn.ensemble import BaseEnsemble
from sklearn.utils.validation import (
check_X_y,
check_array,
@ -23,44 +25,30 @@ from sklearn.utils.validation import (
from stree import Stree
class Odte(BaseEstimator, ClassifierMixin):
class Odte(BaseEnsemble, ClassifierMixin):
def __init__(
self,
base_estimator=None,
random_state: int = None,
C: int = 1,
max_features: Union[str, int, float] = 1.0,
max_samples: Union[int, float] = None,
n_estimators: int = 100,
max_iter: int = 1000,
max_depth: int = None,
min_samples_split: int = 0,
split_criteria: str = "min_distance",
criterion: str = "gini",
tol: float = 1e-4,
gamma="scale",
degree: int = 3,
kernel: str = "linear",
max_features="auto",
max_samples=None,
splitter: str = "random",
):
base_estimator = (
Stree(random_state=random_state)
if base_estimator is None
else base_estimator
)
super().__init__(
base_estimator=base_estimator, n_estimators=n_estimators,
)
self.n_estimators = n_estimators
self.random_state = random_state
self.max_features = max_features
self.max_samples = max_samples # size of bootstrap
self.estimator_params = dict(
C=C,
random_state=random_state,
min_samples_split=min_samples_split,
max_depth=max_depth,
split_criteria=split_criteria,
criterion=criterion,
kernel=kernel,
max_iter=max_iter,
tol=tol,
degree=degree,
gamma=gamma,
splitter=splitter,
max_features=max_features,
)
def _more_tags(self) -> dict:
return {"requires_y": True}
def _initialize_random(self) -> np.random.mtrand.RandomState:
if self.random_state is None:
@ -77,6 +65,12 @@ class Odte(BaseEstimator, ClassifierMixin):
else:
return sample_weight.copy()
def _validate_estimator(self):
"""Check the estimator and set the base_estimator_ attribute."""
super()._validate_estimator(
default=Stree(random_state=self.random_state)
)
def fit(
self, X: np.array, y: np.array, sample_weight: np.array = None
) -> "Odte":
@ -89,9 +83,16 @@ class Odte(BaseEstimator, ClassifierMixin):
# the rest of parameters are checked in estimator
check_classification_targets(y)
X, y = check_X_y(X, y)
sample_weight = _check_sample_weight(sample_weight, X)
sample_weight = _check_sample_weight(
sample_weight, X, dtype=np.float64
)
check_classification_targets(y)
# Initialize computed parameters
# Build the estimator
self.n_features_in_ = X.shape[1]
self.n_features = X.shape[1]
self.max_features_ = self._initialize_max_features()
self._validate_estimator()
self.classes_, y = np.unique(y, return_inverse=True)
self.n_classes_ = self.classes_.shape[0]
self.estimators_ = []
@ -107,15 +108,17 @@ class Odte(BaseEstimator, ClassifierMixin):
boot_samples = self._get_bootstrap_n_samples(n_samples)
for _ in range(self.n_estimators):
# Build clf
clf = Stree().set_params(**self.estimator_params)
clf = clone(self.base_estimator_)
# clf.set_params(**self.estimator_params)
self.estimators_.append(clf)
# bootstrap
indices = random_box.randint(0, n_samples, boot_samples)
# update weights with the chosen samples
weights_update = np.bincount(indices, minlength=n_samples)
features = self.get_subspace(X, y)
current_weights = weights * weights_update
# train the classifier
clf.fit(X[indices, :], y[indices], current_weights[indices])
clf.fit(X[indices, features], y[indices], current_weights[indices])
def _get_bootstrap_n_samples(self, n_samples) -> int:
if self.max_samples is None:
@ -137,15 +140,69 @@ class Odte(BaseEstimator, ClassifierMixin):
{type(self.max_samples)}"
)
def _initialize_max_features(self) -> int:
if isinstance(self.max_features, str):
if self.max_features == "auto":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "sqrt":
max_features = max(1, int(np.sqrt(self.n_features_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_)))
else:
raise ValueError(
"Invalid value for max_features. "
"Allowed string values are 'auto', "
"'sqrt' or 'log2'."
)
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, int):
max_features = self.max_features
else: # float
if self.max_features > 0.0:
max_features = max(
1, int(self.max_features * self.n_features_)
)
else:
raise ValueError(
"Invalid value for max_features."
"Allowed float must be in range (0, 1] "
f"got ({self.max_features})"
)
return max_features
def _get_subspaces_set(
self, dataset: np.array, labels: np.array
) -> np.array:
features = range(dataset.shape[1])
features_sets = list(combinations(features, self.max_features_))
if len(features_sets) > 1:
index = random.randint(0, len(features_sets) - 1)
return features_sets[index]
else:
return features_sets[0]
def get_subspace(self, dataset: np.array, labels: np.array) -> list:
"""Return the best subspace to build a tree
"""
indices = self._get_subspaces_set(dataset, labels)
return dataset[:, indices], indices
def predict(self, X: np.array) -> np.array:
# todo
proba = self.predict_proba(X)
return self.classes_.take((np.argmax(proba, axis=1)), axis=0)
def predict_proba(self, X: np.array) -> np.array:
check_is_fitted(self, ["estimators_"])
# Input validation
X = check_array(X)
result = np.empty((X.shape[0], self.n_estimators))
for index, tree in enumerate(self.estimators_):
result[:, index] = tree.predict(X)
return mode(result, axis=1).mode.ravel()
for tree in self.estimators_:
n_samples = X.shape[0]
result = np.zeros((n_samples, self.n_classes_))
predictions = tree.predict(X)
for i in range(n_samples):
result[i, predictions[i]] += 1
return result
def score(
self, X: np.array, y: np.array, sample_weight: np.array = None

35
odte/tests/Odte_tests.py
View File

@ -62,9 +62,13 @@ class Odte_test(unittest.TestCase):
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)
X, y = [[1, 2], [5, 6], [9, 10], [16, 17]], [0, 1, 1, 2]
expected = [0, 1, 1, 0]
tclf = Odte(
random_state=self._random_state, n_estimators=10, kernel="rbf"
expected = [1, 1, 1, 1]
tclf = Odte(random_state=self._random_state, n_estimators=10,)
tclf.set_params(
**dict(
base_estimator__kernel="rbf",
base_estimator__random_state=self._random_state,
)
)
computed = tclf.fit(X, y).predict(X)
self.assertListEqual(expected, computed.tolist())
@ -77,32 +81,47 @@ class Odte_test(unittest.TestCase):
tclf = Odte(
random_state=self._random_state,
max_features=None,
kernel="linear",
max_samples=0.1,
)
tclf.set_params(**dict(base_estimator__kernel="linear",))
computed = tclf.fit(X, y).predict(X)
self.assertListEqual(expected[:27].tolist(), computed[:27].tolist())
def test_score(self):
X, y = load_dataset(self._random_state)
expected = 0.9526666666666667
expected = 0.948
tclf = Odte(
random_state=self._random_state, max_features=None, n_estimators=10
random_state=self._random_state,
max_features=None,
n_estimators=10,
)
computed = tclf.fit(X, y).score(X, y)
self.assertAlmostEqual(expected, computed)
def test_score_splitter_max_features(self):
X, y = load_dataset(self._random_state, n_features=12, n_samples=150)
results = [1.0, 0.94, 0.9933333333333333, 0.9933333333333333]
results = [
0.9866666666666667,
0.9866666666666667,
0.9866666666666667,
0.9866666666666667,
]
for max_features in ["auto", None]:
for splitter in ["best", "random"]:
tclf = Odte(
random_state=self._random_state,
splitter=splitter,
max_features=max_features,
n_estimators=10,
)
tclf.set_params(**dict(base_estimator__splitter=splitter,))
expected = results.pop(0)
computed = tclf.fit(X, y).score(X, y)
self.assertAlmostEqual(expected, computed)
@staticmethod
def test_is_a_sklearn_classifier():
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)
from sklearn.utils.estimator_checks import check_estimator
check_estimator(Odte())