Completed numpy assignment-Julia Rouzou

numpy_questions.py

@@ -40,17 +40,20 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    if not isinstance(X, np.ndarray):
+        raise ValueError("Input must be a numpy array.")
 
+    if X.ndim != 2:
+        raise ValueError("Input array must be 2-dimensional.")
+
+    flat_index = np.argmax(X)
+    i, j = np.unravel_index(flat_index, X.shape)
     return i, j
 
 
 def wallis_product(n_terms):
     """Implement the Wallis product to compute an approximation of pi.
 
-    See:
-    https://en.wikipedia.org/wiki/Wallis_product
-
     Parameters
     ----------
     n_terms : int
@@ -62,6 +65,14 @@ def wallis_product(n_terms):
     pi : float
         The approximation of order `n_terms` of pi using the Wallis product.
     """
-    # XXX : The n_terms is an int that corresponds to the number of
-    # terms in the product. For example 10000.
-    return 0.
+    if n_terms < 0:
+        raise ValueError("n_terms must be non-negative.")
+
+    product = 1.0
+    for n in range(1, n_terms + 1):
+        product *= (4 * n ** 2) / (4 * n ** 2 - 1)
+
+    if n_terms == 0:
+        return 1.0
+
+    return 2 * product

sklearn_questions.py

@@ -6,18 +6,18 @@
 The nearest neighbor classifier predicts for a point X_i the target y_k of
 the training sample X_k which is the closest to X_i. We measure proximity with
 the Euclidean distance. The model will be evaluated with the accuracy (average
-number of samples corectly classified). You need to implement the `fit`,
-`predict` and `score` methods for this class. The code you write should pass
+number of samples corectly classified). You need to implement the fit,
+predict and score methods for this class. The code you write should pass
 the test we implemented. You can run the tests by calling at the root of the
-repo `pytest test_sklearn_questions.py`.
+repo pytest test_sklearn_questions.py.
 
 We also ask to respect the pep8 convention: https://pep8.org. This will be
-enforced with `flake8`. You can check that there is no flake8 errors by
-calling `flake8` at the root of the repo.
+enforced with flake8. You can check that there is no flake8 errors by
+calling flake8 at the root of the repo.
 
-Finally, you need to write docstring similar to the one in `numpy_questions`
+Finally, you need to write docstring similar to the one in numpy_questions
 for the methods you code and for the class. The docstring will be checked using
-`pydocstyle` that you can also call at the root of the repo.
+pydocstyle that you can also call at the root of the repo.
 """
 import numpy as np
 from sklearn.base import BaseEstimator
@@ -28,47 +28,103 @@
 from sklearn.utils.multiclass import check_classification_targets
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
-    def __init__(self):  # noqa: D107
+    def __init__(self):
+        """Initialize the OneNearestNeighbor classifier."""
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor classifier.
 
-        And describe parameters
+        This method saves the training samples and their labels,
+        used to predict the label of a test sample.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data. Each row represents one sample, and each column
+            represents a feature.
+
+        y : ndarray of shape (n_samples,)
+            Target labels corresponding to the training samples. Must be valid
+            classification targets (e.g., integers or strings).
+
+        Returns
+        -------
+        self : OneNearestNeighbor
+            The fitted OneNearestNeighbor classifier.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
+        self.X_ = X
+        self.y_ = y
+
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for the given samples.
 
-        And describe parameters
+        For each input sample, the label of the closest training sample
+        is returned based on Euclidean distance.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test data, where each row corresponds to a sample and each column
+            corresponds to a feature.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted class labels for each input sample.
         """
         check_is_fitted(self)
         X = check_array(X)
+
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but "
+                f"{self.__class__.__name__} is expecting "
+                f"{self.n_features_in_} features as input"
+            )
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        euc_dist = np.sqrt(((X[None, :, :] - self.X_[:, None, :])**2)
+                           .sum(axis=2))
+        nearest_point = np.argmin(euc_dist, axis=0)
+        y_pred = self.y_[nearest_point]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Compute the accuracy of the classifier on test data.
+
+        This method predicts the labels and compares
+        them to the true labels
+        to calculate the fraction of correctly classified samples.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Test samples, row represents a sample and column a feature.
+
+        y : ndarray of shape (n_samples,)
+            True labels corresponding to the test samples.
+
+        Returns
+        -------
+        accuracy : float
+            Proportion of samples that were correctly classified.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)