Add flat classifier #minor (#128)

Author: mirand863

CONTRIBUTING.md

@@ -29,3 +29,18 @@ pre-commit install
 ```
 
 If black is not executed locally and there are formatting errors, the CI/CD pipeline will fail.
+
+## Building the documentation locally
+
+To build the documentation locally, you need to install another set of dependencies that are specific for the documentation. It is easier to create a separate conda environment and run the following command:
+
+```
+pip install -r docs/requirements.txt
+```
+
+To build the documentation you need to change to run the following commands:
+
+```
+cd docs
+make html
+```

docs/source/api/classifiers.rst

@@ -49,3 +49,12 @@ LocalClassifierPerParentNode
     :show-inheritance:
     :inherited-members:
     :special-members: __init__
+
+Flat Classifier
+===============
+
+FlatClassifier
+^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: FlatClassifier.FlatClassifier
+    :members:
+    :special-members: __init__

hiclass/BinaryPolicy.py

@@ -2,7 +2,7 @@
 
 from abc import ABC
 
-from scipy.sparse import vstack, csr_matrix
+from scipy.sparse import vstack, csr_matrix, csr_array
 import networkx as nx
 import numpy as np
 
@@ -160,7 +160,7 @@ def get_binary_examples(self, node) -> tuple:
             )
             y = np.zeros(len(X))
             y[: len(positive_x)] = 1
-        elif isinstance(self.X, csr_matrix):
+        elif isinstance(self.X, csr_matrix) or isinstance(self.X, csr_array):
             X = vstack([positive_x, negative_x])
             sample_weights = (
                 vstack([positive_weights, negative_weights])

hiclass/FlatClassifier.py

@@ -0,0 +1,99 @@
+"""
+Flat classifier approach, used for comparison purposes.
+
+Implementation by @lpfgarcia
+"""
+
+import numpy as np
+from sklearn.base import BaseEstimator
+from sklearn.linear_model import LogisticRegression
+from sklearn.utils.validation import check_is_fitted
+
+
+class FlatClassifier(BaseEstimator):
+    """
+    A flat classifier utility that accepts as input a hierarchy and flattens it internally.
+
+    Examples
+    --------
+    >>> from hiclass import FlatClassifier
+    >>> y = [['1', '1.1'], ['2', '2.1']]
+    >>> X = [[1, 2], [3, 4]]
+    >>> flat = FlatClassifier()
+    >>> flat.fit(X, y)
+    >>> flat.predict(X)
+    array([['1', '1.1'],
+        ['2', '2.1']])
+    """
+
+    def __init__(
+        self,
+        local_classifier: BaseEstimator = LogisticRegression(),
+    ):
+        """
+        Initialize a flat classifier.
+
+        Parameters
+        ----------
+        local_classifier : BaseEstimator, default=LogisticRegression
+            The scikit-learn model used for the flat classification. Needs to have fit, predict and clone methods.
+        """
+        self.local_classifier = local_classifier
+
+    def fit(self, X, y, sample_weight=None):
+        """
+        Fit a flat classifier.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The training input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csc_matrix``.
+        y : array-like of shape (n_samples, n_levels)
+            The target values, i.e., hierarchical class labels for classification.
+        sample_weight : array-like of shape (n_samples,), default=None
+            Array of weights that are assigned to individual samples.
+            If not provided, then each sample is given unit weight.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        # Convert from hierarchical labels to flat labels
+        self.separator_ = "::HiClass::Separator::"
+        y = [self.separator_.join(i) for i in y]
+
+        # Fit flat classifier
+        self.local_classifier.fit(X, y, sample_weight=sample_weight)
+
+        # Return the classifier
+        return self
+
+    def predict(self, X):
+        """
+        Predict classes for the given data.
+
+        Hierarchical labels are returned.
+
+        Parameters
+        ----------
+        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+            The input samples. Internally, its dtype will be converted
+            to ``dtype=np.float32``. If a sparse matrix is provided, it will be
+            converted into a sparse ``csr_matrix``.
+        Returns
+        -------
+        y : ndarray of shape (n_samples,) or (n_samples, n_outputs)
+            The predicted classes.
+        """
+        # Check if fit has been called
+        check_is_fitted(self)
+
+        # Predict and remove separator
+        predictions = [
+            i.split(self.separator_) for i in self.local_classifier.predict(X)
+        ]
+
+        return np.array(predictions)

hiclass/__init__.py

@@ -5,6 +5,7 @@
 from .LocalClassifierPerLevel import LocalClassifierPerLevel
 from .LocalClassifierPerNode import LocalClassifierPerNode
 from .LocalClassifierPerParentNode import LocalClassifierPerParentNode
+from .FlatClassifier import FlatClassifier
 from .MultiLabelLocalClassifierPerNode import MultiLabelLocalClassifierPerNode
 from .MultiLabelLocalClassifierPerParentNode import (
     MultiLabelLocalClassifierPerParentNode,
@@ -19,6 +20,7 @@
     "LocalClassifierPerNode",
     "LocalClassifierPerParentNode",
     "LocalClassifierPerLevel",
+    "FlatClassifier",
     "Explainer",
     "MultiLabelLocalClassifierPerNode",
     "MultiLabelLocalClassifierPerParentNode",

setup.py

@@ -27,7 +27,7 @@
 KEYWORDS = ["hierarchical classification"]
 DACS_SOFTWARE = "https://gitlab.com/dacs-hpi"
 # What packages are required for this module to be executed?
-REQUIRED = ["networkx", "numpy", "scikit-learn", "scipy<1.13"]
+REQUIRED = ["networkx", "numpy", "scikit-learn<1.5", "scipy<1.13"]
 
 # What packages are optional?
 # 'fancy feature': ['django'],}

tests/test_FlatClassifier.py

@@ -0,0 +1,13 @@
+import numpy as np
+from numpy.testing import assert_array_equal
+
+from hiclass import FlatClassifier
+
+
+def test_fit_predict():
+    flat = FlatClassifier()
+    x = np.array([[1, 2], [3, 4]])
+    y = np.array([["a", "b"], ["b", "c"]])
+    flat.fit(x, y)
+    predictions = flat.predict(x)
+    assert_array_equal(y, predictions)