Arnolfo Acero Part B

numpy_questions.py

@@ -40,8 +40,15 @@ def max_index(X):
     i = 0
     j = 0
 
-    # TODO
+    # Raise ValueError
+    if not isinstance(X, np.ndarray) or X.ndim != 2:
+        raise ValueError("Input must be a 2D numpy array")
 
+    # Find max
+    max_val_index = np.argmax(X)  # returns index of X flattened
+
+    # Find index
+    i, j = np.unravel_index(max_val_index, X.shape)
     return i, j
 
 
@@ -64,4 +71,12 @@ def wallis_product(n_terms):
     """
     # 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
+    else:
+        cumprod = 1
+        for n in range(1, n_terms + 1):
+            prod = (4 * (n ** 2)) / ((4 * (n ** 2)) - 1)
+            cumprod *= prod
+        return cumprod * 2

sklearn_questions.py

@@ -22,53 +22,97 @@
 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.utils.multiclass import check_classification_targets
+from sklearn.utils.validation import check_X_y
+from sklearn.utils.validation import check_array
 
 
-class OneNearestNeighbor(BaseEstimator, ClassifierMixin):
-    "OneNearestNeighbor classifier."
+class OneNearestNeighbor(ClassifierMixin, BaseEstimator):
+    """OneNearestNeighbor classifier."""
 
     def __init__(self):  # noqa: D107
         pass
 
     def fit(self, X, y):
-        """Write docstring.
+        """Fit the model based on X and y.
+
+        Parameters
+        ----------
+        X: array (n_samples, n_features). Training data.
+
+        y: array (n_samples). Target data.
+
+        Returns
+        -------
+        self: object
 
-        And describe parameters
         """
         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 the labels for each x based on Euclidean distance.
+
+        Parameters
+        ----------
+        X: array of test samples
 
-        And describe parameters
+        Returns
+        -------
+        y_pred: array with shape (n_samples, ) of predicted labels
         """
         check_is_fitted(self)
         X = check_array(X)
+
+        # Required by sklearn's check_estimator
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"was fitted with {self.n_features_in_} features."
+            )
+
         y_pred = np.full(
             shape=len(X), fill_value=self.classes_[0],
             dtype=self.classes_.dtype
         )
 
-        # XXX fix
+        # Compute distances
+        for i, x_test in enumerate(X):
+            distances = np.linalg.norm(self.X_ - x_test, axis=1)
+            idx = np.argmin(distances)
+            y_pred[i] = self.y_[idx]
+
         return y_pred
 
     def score(self, X, y):
-        """Write docstring.
+        """Return the accuracy of the classifier.
+
+        Parameters
+        ----------
+        X: array (n_samples, n_features). Training data.
+
+        y: array (n_samples). Target data.
 
-        And describe parameters
+        y_pred: array with shape (n_samples, ) of predicted labels
+
+        Returns
+        -------
+        accuracy: float, fraction of correct predictions.
         """
         X, y = check_X_y(X, y)
-        y_pred = self.predict(X)
 
-        # XXX fix
-        return y_pred.sum()
+        # Same feature check for consistency
+        if X.shape[1] != self.n_features_in_:
+            raise ValueError(
+                f"X has {X.shape[1]} features, but OneNearestNeighbor "
+                f"was fitted with {self.n_features_in_} features."
+            )
+
+        y_pred = self.predict(X)
+        return np.mean(y_pred == y)