mirror of
https://github.com/Doctorado-ML/Odte.git
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First commit
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eae2eaf663
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14
.coveragerc
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14
.coveragerc
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[run]
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branch = True
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source = odte
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[report]
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exclude_lines =
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if self.debug:
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pragma: no cover
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raise NotImplementedError
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if __name__ == .__main__.:
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ignore_errors = True
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omit =
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odte/tests/*
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odte/__init__.py
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10
.pre-commit-config.yaml
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10
.pre-commit-config.yaml
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repos:
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- repo: https://github.com/ambv/black
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rev: stable
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hooks:
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- id: black
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language_version: python3.7
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- repo: https://gitlab.com/pycqa/flake8
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rev: 3.7.9
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hooks:
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- id: flake8
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15
.vscode/launch.json
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.vscode/launch.json
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{
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// Use IntelliSense para saber los atributos posibles.
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// Mantenga el puntero para ver las descripciones de los existentes atributos.
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// Para más información, visite: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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{
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"name": "Python: Archivo actual",
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"type": "python",
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"request": "launch",
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"program": "${file}",
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"console": "integratedTerminal"
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}
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]
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}
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.vscode/settings.json
vendored
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.vscode/settings.json
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{
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"python.testing.unittestArgs": [
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],
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"python.testing.pytestEnabled": false,
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"python.testing.nosetestsEnabled": false,
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"python.testing.unittestEnabled": true,
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"python.pythonPath": "/Users/rmontanana/.virtualenvs/general/bin/python",
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"python.linting.flake8Enabled": true,
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"python.linting.enabled": true,
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"editor.rulers": [
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80,
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100
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],
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"python.linting.pylintEnabled": false,
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"restructuredtext.confPath": "${workspaceFolder}/docs/source"
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}
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codecov.yml
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codecov.yml
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overage:
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status:
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project:
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default:
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target: 90%
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comment:
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layout: "reach, diff, flags, files"
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behavior: default
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require_changes: false
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require_base: yes
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require_head: yes
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branches: null
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152
odte/Odte.py
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152
odte/Odte.py
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"""
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__author__ = "Ricardo Montañana Gómez"
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__copyright__ = "Copyright 2020, Ricardo Montañana Gómez"
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__license__ = "MIT"
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__version__ = "0.1"
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Build a forest of oblique trees based on STree
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"""
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import numpy as np
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from sklearn.utils import check_consistent_length
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from sklearn.metrics._classification import _weighted_sum, _check_targets
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from sklearn.utils.multiclass import check_classification_targets
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from sklearn.base import BaseEstimator, ClassifierMixin
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from sklearn.utils.validation import (
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check_X_y,
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check_array,
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check_is_fitted,
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_check_sample_weight,
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)
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from stree import Stree
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class Odte(BaseEstimator, ClassifierMixin):
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def __init__(
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self,
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random_state: int = None,
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C: int = 1,
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n_estimators: int = 100,
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max_iter: int = 1000,
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max_depth: int = None,
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min_samples_split: int = 0,
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bootstrap: bool = True,
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split_criteria: str = "min_distance",
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tol: float = 1e-4,
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gamma="scale",
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degree: int = 3,
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kernel: str = "linear",
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max_features="auto",
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max_samples=None,
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):
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self.n_estimators = n_estimators
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self.bootstrap = bootstrap
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self.random_state = random_state
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self.max_features = max_features
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self.max_samples = max_samples
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self.estimator_params = dict(
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C=C,
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random_state=random_state,
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min_samples_split=min_samples_split,
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max_depth=max_depth,
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split_criteria=split_criteria,
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kernel=kernel,
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max_iter=max_iter,
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tol=tol,
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degree=degree,
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gamma=gamma,
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)
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def _initialize_random(self) -> np.random.mtrand.RandomState:
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if self.random_state is None:
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return np.random.mtrand._rand
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else:
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return np.random.RandomState(self.random_state)
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def _initialize_sample_weight(
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self, sample_weight: np.array, n_samples: int
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) -> np.array:
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if sample_weight is None:
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return np.ones((n_samples,), dtype=np.float64)
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else:
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return sample_weight.copy()
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def fit(
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self, X: np.array, y: np.array, sample_weight: np.array = None
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) -> "Odte":
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# Check parameters are Ok.
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if self.n_estimators < 10:
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raise ValueError(
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f"n_estimators must be greater than 9... got (n_estimators=\
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{self.n_estimators:f})"
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)
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# the rest of parameters are checked in estimator
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check_classification_targets(y)
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X, y = check_X_y(X, y)
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sample_weight = _check_sample_weight(sample_weight, X)
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check_classification_targets(y)
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# Initialize computed parameters
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self.classes_, y = np.unique(y, return_inverse=True)
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self.n_classes_ = self.classes_.shape[0]
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self.estimators_ = []
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self._train(X, y, sample_weight)
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def _train(
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self, X: np.array, y: np.array, sample_weight: np.array
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) -> "Odte":
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random_box = self._initialize_random()
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n_samples = X.shape[0]
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weights = self._initialize_sample_weight(sample_weight, n_samples)
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boot_samples = self._get_bootstrap_n_samples(n_samples)
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for _ in range(self.n_estimators):
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# Build clf
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clf = Stree().set_params(**self.estimator_params)
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self.estimators_.append(clf)
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# bootstrap
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indices = random_box.randint(0, n_samples, boot_samples)
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# update weights with the chosen samples
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weights_update = np.bincount(indices, minlength=n_samples)
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current_weights = weights * weights_update
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# train the classifier
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clf.fit(X[indices, :], y[indices, :], current_weights[indices, :])
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def _get_bootstrap_n_samples(self, n_samples) -> int:
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if self.max_samples is None:
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return n_samples
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if type(self.max_samples) == int:
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if not (1 <= self.max_samples <= n_samples):
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message = f"max_samples should be in the range 1 to \
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{n_samples} but got {self.max_samples}"
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raise ValueError(message)
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return self.max_samples
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if type(self.max_samples) == float:
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if not (0 < self.max_samples < 1):
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message = f"max_samples should be in the range (0, 1)\
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but got {self.max_samples}"
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raise ValueError(message)
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return int(round(self.max_samples * n_samples))
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raise ValueError(
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f"Expected values int, float but got \
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{type(self.max_samples)}"
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)
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def predict(self, X: np.array):
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# todo
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check_is_fitted(self, ["estimators_"])
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# Input validation
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X = check_array(X)
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def score(
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self, X: np.array, y: np.array, sample_weight: np.array
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) -> float:
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# todo
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check_is_fitted(self, ["estimators_"])
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check_classification_targets(y)
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X, y = check_X_y(X, y)
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y_pred = self.predict(X).reshape(y.shape)
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# Compute accuracy for each possible representation
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y_type, y_true, y_pred = _check_targets(y, y_pred)
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check_consistent_length(y_true, y_pred, sample_weight)
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score = y_true == y_pred
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return _weighted_sum(score, sample_weight, normalize=True)
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3
odte/__init__.py
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3
odte/__init__.py
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from .Odte import Odte
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__all__ = ["Odte"]
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49
odte/tests/Odte_tests.py
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49
odte/tests/Odte_tests.py
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import unittest
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import numpy as np
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from odte import Odte
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from .utils import load_dataset
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class Odte_test(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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self._random_state = 1
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super().__init__(*args, **kwargs)
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def test_max_samples_bogus(self):
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values = [0, 3000, 1.1, 0.0, "hi!"]
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for max_samples in values:
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with self.assertRaises(ValueError):
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tclf = Odte(max_samples=max_samples)
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tclf.fit(*load_dataset(self._random_state))
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def test_get_bootstrap_nsamples(self):
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expected_values = [(1, 1), (1500, 1500), (0.1, 150)]
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for value, expected in expected_values:
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tclf = Odte(max_samples=value)
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computed = tclf._get_bootstrap_n_samples(1500)
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self.assertEqual(expected, computed)
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def test_initialize_sample_weight(self):
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m = 5
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ones = np.ones(m,)
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weights = np.random.rand(m,)
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expected_values = [(None, ones), (weights, weights)]
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for value, expected in expected_values:
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tclf = Odte()
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computed = tclf._initialize_sample_weight(value, m)
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self.assertListEqual(expected.tolist(), computed.tolist())
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def test_initialize_random(self):
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expected = [37, 235, 908]
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tclf = Odte(random_state=self._random_state)
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box = tclf._initialize_random()
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computed = box.randint(0, 1000, 3)
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self.assertListEqual(expected, computed.tolist())
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# test None
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tclf = Odte()
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box = tclf._initialize_random()
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computed = box.randint(101, 1000, 3)
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for value in computed.tolist():
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self.assertGreaterEqual(value, 101)
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self.assertLessEqual(value, 1000)
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3
odte/tests/__init__.py
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3
odte/tests/__init__.py
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from .Odte_tests import Odte_test
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__all__ = ["Odte_test"]
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17
odte/tests/utils.py
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17
odte/tests/utils.py
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from sklearn.datasets import make_classification
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def load_dataset(random_state=0, n_classes=2):
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X, y = make_classification(
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n_samples=1500,
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n_features=3,
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n_informative=3,
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n_redundant=0,
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n_repeated=0,
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n_classes=n_classes,
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n_clusters_per_class=2,
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class_sep=1.5,
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flip_y=0,
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random_state=random_state,
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)
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return X, y
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16
pyproject.toml
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16
pyproject.toml
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[tool.black]
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line-length = 79
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include = '\.pyi?$'
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exclude = '''
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/(
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\.git
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| \.hg
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| \.mypy_cache
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| \.tox
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| \.venv
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| _build
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| buck-out
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| build
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| dist
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)/
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'''
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5
requirements.txt
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5
requirements.txt
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numpy
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scikit-learn
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pandas
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ipympl
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stree
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