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Compare commits
10 Commits
v1.2.2
...
new_predic
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9e8d03d088
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0a78d5be67
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65923af9b4
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93be8a89a8 | ||
82838fa3e0
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f0b2ce3c7b
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08222f109e | ||
cc931d8547
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4
.github/workflows/main.yml
vendored
4
.github/workflows/main.yml
vendored
@@ -12,8 +12,8 @@ jobs:
|
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runs-on: ${{ matrix.os }}
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strategy:
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matrix:
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os: [macos-latest, ubuntu-latest]
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python: [3.8]
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os: [macos-latest, ubuntu-latest, windows-latest]
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python: [3.8, "3.10"]
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steps:
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- uses: actions/checkout@v2
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|
@@ -11,7 +11,7 @@ authors:
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given-names: "José M."
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orcid: "https://orcid.org/0000-0002-9164-5191"
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title: "STree"
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version: 1.0.2
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version: 1.2.3
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doi: 10.5281/zenodo.5504083
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date-released: 2021-11-02
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url: "https://github.com/Doctorado-ML/STree"
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|
6
Makefile
6
Makefile
@@ -10,6 +10,9 @@ coverage: ## Run tests with coverage
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deps: ## Install dependencies
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pip install -r requirements.txt
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devdeps: ## Install development dependencies
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pip install black pip-audit flake8 mypy coverage
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lint: ## Lint and static-check
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black stree
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flake8 stree
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@@ -32,6 +35,9 @@ build: ## Build package
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doc-clean: ## Update documentation
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make -C docs --makefile=Makefile clean
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audit: ## Audit pip
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pip-audit
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help: ## Show help message
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@IFS=$$'\n' ; \
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help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
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|
4
setup.py
4
setup.py
@@ -1,4 +1,5 @@
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import setuptools
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import os
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|
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def readme():
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@@ -8,7 +9,8 @@ def readme():
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def get_data(field):
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item = ""
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with open("stree/__init__.py") as f:
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file_name = "_version.py" if field == "version" else "__init__.py"
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with open(os.path.join("stree", file_name)) as f:
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for line in f.readlines():
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if line.startswith(f"__{field}__"):
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delim = '"' if '"' in line else "'"
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|
@@ -68,6 +68,7 @@ class Snode:
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self._impurity = impurity
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self._partition_column: int = -1
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self._scaler = scaler
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self._proba = None
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|
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@classmethod
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def copy(cls, node: "Snode") -> "Snode":
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@@ -127,23 +128,44 @@ class Snode:
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def get_up(self) -> "Snode":
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return self._up
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def make_predictor(self):
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def make_predictor(self, num_classes: int) -> None:
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"""Compute the class of the predictor and its belief based on the
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subdataset of the node only if it is a leaf
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"""
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if not self.is_leaf():
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return
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classes, card = np.unique(self._y, return_counts=True)
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if len(classes) > 1:
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self._proba = np.zeros((num_classes,), dtype=np.int64)
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for c, n in zip(classes, card):
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self._proba[c] = n
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try:
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max_card = max(card)
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self._class = classes[card == max_card][0]
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self._belief = max_card / np.sum(card)
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except ValueError:
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self._class = None
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def graph(self):
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"""
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Return a string representing the node in graphviz format
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"""
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output = ""
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count_values = np.unique(self._y, return_counts=True)
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if self.is_leaf():
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output += (
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f'N{id(self)} [shape=box style=filled label="'
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f"class={self._class} impurity={self._impurity:.3f} "
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f'counts={self._proba}"];\n'
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)
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else:
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self._belief = 1
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try:
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self._class = classes[0]
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except IndexError:
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self._class = None
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output += (
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f'N{id(self)} [label="#features={len(self._features)} '
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f"classes={count_values[0]} samples={count_values[1]} "
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f'({sum(count_values[1])})" fontcolor=black];\n'
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)
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output += f"N{id(self)} -> N{id(self.get_up())} [color=black];\n"
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output += f"N{id(self)} -> N{id(self.get_down())} [color=black];\n"
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return output
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def __str__(self) -> str:
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count_values = np.unique(self._y, return_counts=True)
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@@ -367,9 +389,8 @@ class Splitter:
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.get_support(indices=True)
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)
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@staticmethod
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def _fs_mutual(
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dataset: np.array, labels: np.array, max_features: int
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self, dataset: np.array, labels: np.array, max_features: int
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) -> tuple:
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"""Return the best features with mutual information with labels
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@@ -389,7 +410,9 @@ class Splitter:
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indices of the features selected
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"""
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# return best features with mutual info with the label
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feature_list = mutual_info_classif(dataset, labels)
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feature_list = mutual_info_classif(
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dataset, labels, random_state=self._random_state
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)
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return tuple(
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sorted(
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range(len(feature_list)), key=lambda sub: feature_list[sub]
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|
131
stree/Strees.py
131
stree/Strees.py
@@ -17,6 +17,7 @@ from sklearn.utils.validation import (
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_check_sample_weight,
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)
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from .Splitter import Splitter, Snode, Siterator
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from ._version import __version__
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class Stree(BaseEstimator, ClassifierMixin):
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@@ -169,6 +170,11 @@ class Stree(BaseEstimator, ClassifierMixin):
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self.normalize = normalize
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self.multiclass_strategy = multiclass_strategy
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@staticmethod
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def version() -> str:
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"""Return the version of the package."""
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return __version__
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|
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def _more_tags(self) -> dict:
|
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"""Required by sklearn to supply features of the classifier
|
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make mandatory the labels array
|
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@@ -308,7 +314,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
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if np.unique(y).shape[0] == 1:
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# only 1 class => pure dataset
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node.set_title(title + ", <pure>")
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node.make_predictor()
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node.make_predictor(self.n_classes_)
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return node
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# Train the model
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clf = self._build_clf()
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@@ -327,7 +333,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
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if X_U is None or X_D is None:
|
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# didn't part anything
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node.set_title(title + ", <cgaf>")
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node.make_predictor()
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node.make_predictor(self.n_classes_)
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return node
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node.set_up(
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self._train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
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@@ -361,28 +367,66 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
)
|
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)
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@staticmethod
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def _reorder_results(y: np.array, indices: np.array) -> np.array:
|
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"""Reorder an array based on the array of indices passed
|
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def __predict_class(self, X: np.array) -> np.array:
|
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def compute_prediction(xp, indices, node):
|
||||
if xp is None:
|
||||
return
|
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if node.is_leaf():
|
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# set a class for indices
|
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result[indices] = node._proba
|
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return
|
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self.splitter_.partition(xp, node, train=False)
|
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x_u, x_d = self.splitter_.part(xp)
|
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i_u, i_d = self.splitter_.part(indices)
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compute_prediction(x_u, i_u, node.get_up())
|
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compute_prediction(x_d, i_d, node.get_down())
|
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|
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# setup prediction & make it happen
|
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result = np.zeros((X.shape[0], self.n_classes_))
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indices = np.arange(X.shape[0])
|
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compute_prediction(X, indices, self.tree_)
|
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return result
|
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|
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def check_predict(self, X) -> np.array:
|
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check_is_fitted(self, ["tree_"])
|
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# Input validation
|
||||
X = check_array(X)
|
||||
if X.shape[1] != self.n_features_:
|
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raise ValueError(
|
||||
f"Expected {self.n_features_} features but got "
|
||||
f"({X.shape[1]})"
|
||||
)
|
||||
return X
|
||||
|
||||
def predict_proba(self, X: np.array) -> np.array:
|
||||
"""Predict class probabilities of the input samples X.
|
||||
|
||||
The predicted class probability is the fraction of samples of the same
|
||||
class in a leaf.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
y : np.array
|
||||
data untidy
|
||||
indices : np.array
|
||||
indices used to set order
|
||||
X : dataset of samples.
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.array
|
||||
array y ordered
|
||||
proba : array of shape (n_samples, n_classes)
|
||||
The class probabilities of the input samples.
|
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|
||||
Raises
|
||||
------
|
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ValueError
|
||||
if dataset with inconsistent number of features
|
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NotFittedError
|
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if model is not fitted
|
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"""
|
||||
# return array of same type given in y
|
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y_ordered = y.copy()
|
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indices = indices.astype(int)
|
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for i, index in enumerate(indices):
|
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y_ordered[index] = y[i]
|
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return y_ordered
|
||||
|
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X = self.check_predict(X)
|
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# return # of samples of each class in leaf node
|
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values = self.__predict_class(X)
|
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normalizer = values.sum(axis=1)[:, np.newaxis]
|
||||
normalizer[normalizer == 0.0] = 1.0
|
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return values / normalizer
|
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|
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def predict(self, X: np.array) -> np.array:
|
||||
"""Predict labels for each sample in dataset passed
|
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@@ -404,40 +448,8 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
NotFittedError
|
||||
if model is not fitted
|
||||
"""
|
||||
|
||||
def predict_class(
|
||||
xp: np.array, indices: np.array, node: Snode
|
||||
) -> np.array:
|
||||
if xp is None:
|
||||
return [], []
|
||||
if node.is_leaf():
|
||||
# set a class for every sample in dataset
|
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prediction = np.full((xp.shape[0], 1), node._class)
|
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return prediction, indices
|
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self.splitter_.partition(xp, node, train=False)
|
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x_u, x_d = self.splitter_.part(xp)
|
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i_u, i_d = self.splitter_.part(indices)
|
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prx_u, prin_u = predict_class(x_u, i_u, node.get_up())
|
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prx_d, prin_d = predict_class(x_d, i_d, node.get_down())
|
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return np.append(prx_u, prx_d), np.append(prin_u, prin_d)
|
||||
|
||||
# sklearn check
|
||||
check_is_fitted(self, ["tree_"])
|
||||
# Input validation
|
||||
X = check_array(X)
|
||||
if X.shape[1] != self.n_features_:
|
||||
raise ValueError(
|
||||
f"Expected {self.n_features_} features but got "
|
||||
f"({X.shape[1]})"
|
||||
)
|
||||
# setup prediction & make it happen
|
||||
indices = np.arange(X.shape[0])
|
||||
result = (
|
||||
self._reorder_results(*predict_class(X, indices, self.tree_))
|
||||
.astype(int)
|
||||
.ravel()
|
||||
)
|
||||
return self.classes_[result]
|
||||
X = self.check_predict(X)
|
||||
return self.classes_[np.argmax(self.__predict_class(X), axis=1)]
|
||||
|
||||
def nodes_leaves(self) -> tuple:
|
||||
"""Compute the number of nodes and leaves in the built tree
|
||||
@@ -470,6 +482,23 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
tree = None
|
||||
return Siterator(tree)
|
||||
|
||||
def graph(self, title="") -> str:
|
||||
"""Graphviz code representing the tree
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
graphviz code
|
||||
"""
|
||||
output = (
|
||||
"digraph STree {\nlabel=<STree "
|
||||
f"{title}>\nfontsize=30\nfontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
for node in self:
|
||||
output += node.graph()
|
||||
output += "}\n"
|
||||
return output
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""String representation of the tree
|
||||
|
||||
|
@@ -1,7 +1,5 @@
|
||||
from .Strees import Stree, Siterator
|
||||
|
||||
__version__ = "1.2.2"
|
||||
|
||||
__author__ = "Ricardo Montañana Gómez"
|
||||
__copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
|
||||
__license__ = "MIT License"
|
||||
|
1
stree/_version.py
Normal file
1
stree/_version.py
Normal file
@@ -0,0 +1 @@
|
||||
__version__ = "1.2.4"
|
@@ -67,10 +67,28 @@ class Snode_test(unittest.TestCase):
|
||||
|
||||
def test_make_predictor_on_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.make_predictor()
|
||||
test.make_predictor(2)
|
||||
self.assertEqual(1, test._class)
|
||||
self.assertEqual(0.75, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertListEqual([1, 3], test._proba.tolist())
|
||||
|
||||
def test_make_predictor_on_not_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
||||
test.make_predictor(2)
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(0, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertEqual(-1, test.get_up()._partition_column)
|
||||
self.assertIsNone(test._proba)
|
||||
|
||||
def test_make_predictor_on_leaf_bogus_data(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
|
||||
test.make_predictor(2)
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertListEqual([0, 0], test._proba.tolist())
|
||||
|
||||
def test_set_title(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
@@ -97,21 +115,6 @@ class Snode_test(unittest.TestCase):
|
||||
test.set_features([1, 2])
|
||||
self.assertListEqual([1, 2], test.get_features())
|
||||
|
||||
def test_make_predictor_on_not_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
||||
test.make_predictor()
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(0, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertEqual(-1, test.get_up()._partition_column)
|
||||
|
||||
def test_make_predictor_on_leaf_bogus_data(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
|
||||
test.make_predictor()
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
|
||||
def test_copy_node(self):
|
||||
px = [1, 2, 3, 4]
|
||||
py = [1]
|
||||
|
@@ -10,6 +10,7 @@ from sklearn.svm import LinearSVC
|
||||
from stree import Stree
|
||||
from stree.Splitter import Snode
|
||||
from .utils import load_dataset
|
||||
from .._version import __version__
|
||||
|
||||
|
||||
class Stree_test(unittest.TestCase):
|
||||
@@ -114,6 +115,38 @@ class Stree_test(unittest.TestCase):
|
||||
yp = clf.fit(X, y).predict(X[:num, :])
|
||||
self.assertListEqual(y[:num].tolist(), yp.tolist())
|
||||
|
||||
def test_multiple_predict_proba(self):
|
||||
expected = {
|
||||
"liblinear": {
|
||||
0: [0.02401129943502825, 0.9759887005649718],
|
||||
17: [0.9282970550576184, 0.07170294494238157],
|
||||
},
|
||||
"linear": {
|
||||
0: [0.029329608938547486, 0.9706703910614525],
|
||||
17: [0.9298469387755102, 0.07015306122448979],
|
||||
},
|
||||
"rbf": {
|
||||
0: [0.023448275862068966, 0.976551724137931],
|
||||
17: [0.9458064516129032, 0.05419354838709677],
|
||||
},
|
||||
"poly": {
|
||||
0: [0.01601164483260553, 0.9839883551673945],
|
||||
17: [0.9089790897908979, 0.0910209102091021],
|
||||
},
|
||||
}
|
||||
indices = [0, 17]
|
||||
X, y = load_dataset(self._random_state)
|
||||
for kernel in ["liblinear", "linear", "rbf", "poly"]:
|
||||
clf = Stree(
|
||||
kernel=kernel,
|
||||
multiclass_strategy="ovr" if kernel == "liblinear" else "ovo",
|
||||
random_state=self._random_state,
|
||||
)
|
||||
yp = clf.fit(X, y).predict_proba(X)
|
||||
for index in indices:
|
||||
for exp, comp in zip(expected[kernel][index], yp[index]):
|
||||
self.assertAlmostEqual(exp, comp)
|
||||
|
||||
def test_single_vs_multiple_prediction(self):
|
||||
"""Check if predicting sample by sample gives the same result as
|
||||
predicting all samples at once
|
||||
@@ -357,6 +390,7 @@ class Stree_test(unittest.TestCase):
|
||||
|
||||
# Tests of score
|
||||
def test_score_binary(self):
|
||||
"""Check score for binary classification."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
accuracies = [
|
||||
0.9506666666666667,
|
||||
@@ -379,6 +413,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertAlmostEqual(accuracy_expected, accuracy_score)
|
||||
|
||||
def test_score_max_features(self):
|
||||
"""Check score using max_features."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
clf = Stree(
|
||||
kernel="liblinear",
|
||||
@@ -390,6 +425,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertAlmostEqual(0.9453333333333334, clf.score(X, y))
|
||||
|
||||
def test_bogus_splitter_parameter(self):
|
||||
"""Check that bogus splitter parameter raises exception."""
|
||||
clf = Stree(splitter="duck")
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(*load_dataset())
|
||||
@@ -445,6 +481,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertListEqual([47], resdn[1].tolist())
|
||||
|
||||
def test_score_multiclass_rbf(self):
|
||||
"""Test score for multiclass classification with rbf kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -462,6 +499,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_poly(self):
|
||||
"""Test score for multiclass classification with poly kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -483,6 +521,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_liblinear(self):
|
||||
"""Test score for multiclass classification with liblinear kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -508,6 +547,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_sigmoid(self):
|
||||
"""Test score for multiclass classification with sigmoid kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -528,6 +568,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(0.9662921348314607, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_linear(self):
|
||||
"""Test score for multiclass classification with linear kernel."""
|
||||
warnings.filterwarnings("ignore", category=ConvergenceWarning)
|
||||
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
||||
X, y = load_dataset(
|
||||
@@ -555,11 +596,13 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_zero_all_sample_weights(self):
|
||||
"""Test exception raises when all sample weights are zero."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
with self.assertRaises(ValueError):
|
||||
Stree().fit(X, y, np.zeros(len(y)))
|
||||
|
||||
def test_mask_samples_weighted_zero(self):
|
||||
"""Check that the weighted zero samples are masked."""
|
||||
X = np.array(
|
||||
[
|
||||
[1, 1],
|
||||
@@ -587,6 +630,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(model2.score(X, y, w), 1)
|
||||
|
||||
def test_depth(self):
|
||||
"""Check depth of the tree."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -602,6 +646,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(4, clf.depth_)
|
||||
|
||||
def test_nodes_leaves(self):
|
||||
"""Check number of nodes and leaves."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -621,6 +666,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(6, leaves)
|
||||
|
||||
def test_nodes_leaves_artificial(self):
|
||||
"""Check leaves of artificial dataset."""
|
||||
n1 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test1")
|
||||
n2 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test2")
|
||||
n3 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test3")
|
||||
@@ -639,12 +685,14 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(2, leaves)
|
||||
|
||||
def test_bogus_multiclass_strategy(self):
|
||||
"""Check invalid multiclass strategy."""
|
||||
clf = Stree(multiclass_strategy="other")
|
||||
X, y = load_wine(return_X_y=True)
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(X, y)
|
||||
|
||||
def test_multiclass_strategy(self):
|
||||
"""Check multiclass strategy."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf_o = Stree(multiclass_strategy="ovo")
|
||||
clf_r = Stree(multiclass_strategy="ovr")
|
||||
@@ -654,6 +702,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(0.9269662921348315, score_r)
|
||||
|
||||
def test_incompatible_hyperparameters(self):
|
||||
"""Check incompatible hyperparameters."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(kernel="liblinear", multiclass_strategy="ovo")
|
||||
with self.assertRaises(ValueError):
|
||||
@@ -661,3 +710,50 @@ class Stree_test(unittest.TestCase):
|
||||
clf = Stree(multiclass_strategy="ovo", split_criteria="max_samples")
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(X, y)
|
||||
|
||||
def test_version(self):
|
||||
"""Check STree version."""
|
||||
clf = Stree()
|
||||
self.assertEqual(__version__, clf.version())
|
||||
|
||||
def test_graph(self):
|
||||
"""Check graphviz representation of the tree."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(random_state=self._random_state)
|
||||
|
||||
expected_head = (
|
||||
"digraph STree {\nlabel=<STree >\nfontsize=30\n"
|
||||
"fontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
expected_tail = (
|
||||
' [shape=box style=filled label="class=1 impurity=0.000 '
|
||||
'counts=[0 1 0]"];\n}\n'
|
||||
)
|
||||
self.assertEqual(clf.graph(), expected_head + "}\n")
|
||||
clf.fit(X, y)
|
||||
computed = clf.graph()
|
||||
computed_head = computed[: len(expected_head)]
|
||||
num = -len(expected_tail)
|
||||
computed_tail = computed[num:]
|
||||
self.assertEqual(computed_head, expected_head)
|
||||
self.assertEqual(computed_tail, expected_tail)
|
||||
|
||||
def test_graph_title(self):
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(random_state=self._random_state)
|
||||
expected_head = (
|
||||
"digraph STree {\nlabel=<STree Sample title>\nfontsize=30\n"
|
||||
"fontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
expected_tail = (
|
||||
' [shape=box style=filled label="class=1 impurity=0.000 '
|
||||
'counts=[0 1 0]"];\n}\n'
|
||||
)
|
||||
self.assertEqual(clf.graph("Sample title"), expected_head + "}\n")
|
||||
clf.fit(X, y)
|
||||
computed = clf.graph("Sample title")
|
||||
computed_head = computed[: len(expected_head)]
|
||||
num = -len(expected_tail)
|
||||
computed_tail = computed[num:]
|
||||
self.assertEqual(computed_head, expected_head)
|
||||
self.assertEqual(computed_tail, expected_tail)
|
||||
|
Reference in New Issue
Block a user