LIU Guangyue - Assignment Submission

numpy_questions.py

@@ -1,67 +1,48 @@
-"""Assignment - using numpy and making a PR.
+"""Numpy related utility functions."""
 
-The goals of this assignment are:
-    * Use numpy in practice with two easy exercises.
-    * Use automated tools to validate the code (`pytest` and `flake8`)
-    * Submit a Pull-Request on github to practice `git`.
-
-The two functions below are skeleton functions. The docstrings explain what
-are the inputs, the outputs and the expected error. Fill the function to
-complete the assignment. The code should be able to pass the test that we
-wrote. To run the tests, use `pytest test_numpy_questions.py` at the root of
-the repo. It should say that 2 tests ran with success.
-
-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.
-"""
 import numpy as np
 
 
 def max_index(X):
-    """Return the index of the maximum in a numpy array.
+    """Return the indices of the maximum value in a 2D numpy array.
 
     Parameters
     ----------
-    X : ndarray of shape (n_samples, n_features)
-        The input array.
+    X : np.ndarray
+        Input 2D array
 
     Returns
     -------
-    (i, j) : tuple(int)
-        The row and columnd index of the maximum.
-
-    Raises
-    ------
-    ValueError
-        If the input is not a numpy array or
-        if the shape is not 2D.
+    tuple
+        Tuple of (row_index, column_index) of the maximum element.
     """
-    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 must be a 2D array")
 
+    max_idx = np.argmax(X)
+    i, j = np.unravel_index(max_idx, 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
+    """Compute approximation of pi using Wallis product formula.
 
     Parameters
     ----------
     n_terms : int
-        Number of steps in the Wallis product. Note that `n_terms=0` will
-        consider the product to be `1`.
+        Number of terms to include in the product.
 
     Returns
     -------
-    pi : float
-        The approximation of order `n_terms` of pi using the Wallis product.
+    float
+        Approximation of pi.
     """
-    # 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:
+        return 1.0
+
+    product = 1.0
+    for k in range(1, n_terms + 1):
+        product *= (4 * k**2) / (4 * k**2 - 1)
+    return 2 * product

sklearn_questions.py

@@ -1,74 +1,86 @@
-"""Assignment - making a sklearn estimator.
-
-The goal of this assignment is to implement by yourself a scikit-learn
-estimator for the OneNearestNeighbor and check that it is working properly.
-
-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
-the test we implemented. You can run the tests by calling at the root of the
-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.
-
-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.
-"""
+"""Custom sklearn estimator: One Nearest Neighbor classifier."""
+
 import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.base import ClassifierMixin
-from sklearn.utils.validation import check_X_y
-from sklearn.utils.validation import check_array
-from sklearn.utils.validation import check_is_fitted
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
 from sklearn.utils.multiclass import check_classification_targets
 
 
 class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+    """One Nearest Neighbor classifier.
+
+    Assigns to each sample the label of the closest training point
+    using Euclidean distance.
+    """
 
-    def __init__(self):  # noqa: D107
+    def __init__(self):
+        """Initialize the classifier (no parameters)."""
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the OneNearestNeighbor classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Training data.
 
-        And describe parameters
+        y : ndarray of shape (n_samples,)
+            Target labels.
+
+        Returns
+        -------
+        self : object
+            Fitted classifier.
         """
         X, y = check_X_y(X, y)
         check_classification_targets(y)
+
+        self.X_train_ = X
+        self.y_train_ = y
         self.classes_ = np.unique(y)
         self.n_features_in_ = X.shape[1]
 
-        # XXX fix
         return self
 
     def predict(self, X):
-        """Write docstring.
+        """Predict class labels for samples in X.
 
-        And describe parameters
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input samples.
+
+        Returns
+        -------
+        y_pred : ndarray of shape (n_samples,)
+            Predicted labels.
         """
         check_is_fitted(self)
         X = check_array(X)
-        y_pred = np.full(
-            shape=len(X), fill_value=self.classes_[0],
-            dtype=self.classes_.dtype
-        )
 
-        # XXX fix
-        return y_pred
+        distances = np.linalg.norm(
+            X[:, None, :] - self.X_train_[None, :, :],
+            axis=2
+        )
+        nearest_idx = np.argmin(distances, axis=1)
+        return self.y_train_[nearest_idx]
 
     def score(self, X, y):
-        """Write docstring.
-
-        And describe parameters
+        """Return accuracy score of the classifier.
+
+        Parameters
+        ----------
+        X : ndarray of shape (n_samples, n_features)
+            Input samples.
+        y : ndarray of shape (n_samples,)
+            True labels.
+
+        Returns
+        -------
+        float
+            Accuracy score.
         """
         X, y = check_X_y(X, y)
         y_pred = self.predict(X)
-
-        # XXX fix
-        return y_pred.sum()
+        return np.mean(y_pred == y)

ssh-keygen -t ed25519 -C "你的GitHub邮箱"

@@ -0,0 +1,7 @@
+-----BEGIN OPENSSH PRIVATE KEY-----
+b3BlbnNzaC1rZXktdjEAAAAABG5vbmUAAAAEbm9uZQAAAAAAAAABAAAAMwAAAAtzc2gtZW
+QyNTUxOQAAACC+nIN2ZmV7i3tLTK82iLZ55hTImb3J2vo2gxHCqFY1PgAAAJg6M29MOjNv
+TAAAAAtzc2gtZWQyNTUxOQAAACC+nIN2ZmV7i3tLTK82iLZ55hTImb3J2vo2gxHCqFY1Pg
+AAAEAvo6TJa/cpJpuuaNQAx+6V9yzlXpTyZVqILJlLmTsZh76cg3ZmZXuLe0tMrzaItnnm
+FMiZvcna+jaDEcKoVjU+AAAAEuS9oOeahEdpdEh1YumCrueusQECAw==
+-----END OPENSSH PRIVATE KEY-----

ssh-keygen -t ed25519 -C "你的GitHub邮箱".pub

@@ -0,0 +1 @@
+ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIL6cg3ZmZXuLe0tMrzaItnnmFMiZvcna+jaDEcKoVjU+ 你的GitHub邮箱