From 53b6633c7249ba525ecc4bf7a3fe46e7d5a39098 Mon Sep 17 00:00:00 2001 From: pyxcode Date: Sat, 15 Nov 2025 19:35:44 +0100 Subject: [PATCH 1/5] Louan BARDOU numpy&sklearn --- numpy_questions.py | 21 ++++++++----- sklearn_questions.py | 71 ++++++++++++++++++++++++++++++-------------- 2 files changed, 62 insertions(+), 30 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 21fcec4b..a806d249 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -37,11 +37,11 @@ def max_index(X): If the input is not a numpy array or if the shape is not 2D. """ - i = 0 - j = 0 - - # TODO - + if not isinstance(X, np.ndarray) or X.ndim != 2: + raise ValueError("X must be a 2D numpy array") + + flat_index = np.argmax(X) + i, j = np.unravel_index(flat_index, X.shape) return i, j @@ -62,6 +62,11 @@ 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 not isinstance(n_terms, int) or n_terms < 0: + raise ValueError("n_terms must be a non-negative integer") + if n_terms == 0: + return 1. + pi = 2. # Commence avec 2, pas 1 + for i in range(1, n_terms + 1): + pi *= (4 * i**2) / (4 * i**2 - 1) + return pi diff --git a/sklearn_questions.py b/sklearn_questions.py index f65038c6..41c4c5d5 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -22,53 +22,80 @@ 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 validate_data from sklearn.utils.validation import check_is_fitted 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 pass def fit(self, X, y): - """Write docstring. - - And describe parameters + """Fit the OneNearestNeighbor classifier. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + y : array-like of shape (n_samples,) + Target values. + + Returns + ------- + self : object + Returns the instance itself. """ - X, y = check_X_y(X, y) + X, y = validate_data(self, X, y) check_classification_targets(y) self.classes_ = np.unique(y) self.n_features_in_ = X.shape[1] - - # XXX fix + self.X_train_ = X + self.y_train_ = y return self def predict(self, X): - """Write docstring. + """Predict the class labels for the provided data. + + Parameters + ---------- + X : Same as before + Test samples. - And describe parameters + Returns + ------- + y_pred : ndarray of shape (n_samples,) + Class labels for each data sample. """ check_is_fitted(self) - X = check_array(X) + X = validate_data(self, X, reset=False) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype ) - - # XXX fix + for i, x in enumerate(X): + distances = np.sqrt(np.sum((self.X_train_ - x)**2, axis=1)) + nearest_idx = np.argmin(distances) + y_pred[i] = self.y_train_[nearest_idx] return y_pred def score(self, X, y): - """Write docstring. - - And describe parameters + """Return the mean accuracy on the given test data and labels. + + Parameters + ---------- + X : Same as before + Test samples. + y : Same as before + True labels for X. + + Returns + ------- + score : float + Mean accuracy of self.predict(X) with respect to y. """ - X, y = check_X_y(X, y) + X, y = validate_data(self, X, y, reset=False) y_pred = self.predict(X) - - # XXX fix - return y_pred.sum() + return np.mean(y_pred == y) From ca94cd9351e8c707d0bec6b4f919d2d2b0c43dab Mon Sep 17 00:00:00 2001 From: pyxcode Date: Sat, 15 Nov 2025 19:48:38 +0100 Subject: [PATCH 2/5] Louan BARDOU Part B --- numpy_questions.py | 2 +- sklearn_questions.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index a806d249..3f0ca251 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -66,7 +66,7 @@ def wallis_product(n_terms): raise ValueError("n_terms must be a non-negative integer") if n_terms == 0: return 1. - pi = 2. # Commence avec 2, pas 1 + pi = 2. for i in range(1, n_terms + 1): pi *= (4 * i**2) / (4 * i**2 - 1) return pi diff --git a/sklearn_questions.py b/sklearn_questions.py index 41c4c5d5..1536a7fd 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -61,7 +61,7 @@ def predict(self, X): Parameters ---------- - X : Same as before + X : array-like of shape (n_samples, n_features) Test samples. Returns @@ -86,9 +86,9 @@ def score(self, X, y): Parameters ---------- - X : Same as before + X : array-like of shape (n_samples, n_features) Test samples. - y : Same as before + y : array-like of shape (n_samples,) True labels for X. Returns From 6efea91126203858f6f97a3de46cbb736b87a1e3 Mon Sep 17 00:00:00 2001 From: pyxcode Date: Sat, 15 Nov 2025 19:53:40 +0100 Subject: [PATCH 3/5] Louan BARDOU Part B --- sklearn_questions.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index 1536a7fd..535e4cba 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -22,7 +22,8 @@ import numpy as np from sklearn.base import BaseEstimator from sklearn.base import ClassifierMixin -from sklearn.utils.validation import validate_data +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 @@ -48,7 +49,7 @@ def fit(self, X, y): self : object Returns the instance itself. """ - X, y = validate_data(self, X, y) + X, y = check_X_y(X, y) check_classification_targets(y) self.classes_ = np.unique(y) self.n_features_in_ = X.shape[1] @@ -70,7 +71,13 @@ def predict(self, X): Class labels for each data sample. """ check_is_fitted(self) - X = validate_data(self, X, reset=False) + X = check_array(X) + # Vérifier que le nombre de features correspond + if X.shape[1] != self.n_features_in_: + raise ValueError( + f"X has {X.shape[1]} features, but OneNearestNeighbor " + f"is expecting {self.n_features_in_} features." + ) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], dtype=self.classes_.dtype @@ -96,6 +103,6 @@ def score(self, X, y): score : float Mean accuracy of self.predict(X) with respect to y. """ - X, y = validate_data(self, X, y, reset=False) + X, y = check_X_y(X, y) y_pred = self.predict(X) return np.mean(y_pred == y) From d86cc58ae929f2aed59bf0a3cc2b18dc520f6230 Mon Sep 17 00:00:00 2001 From: pyxcode Date: Sat, 15 Nov 2025 19:57:00 +0100 Subject: [PATCH 4/5] Louan BARDOU Part B --- numpy_questions.py | 2 +- sklearn_questions.py | 7 +++---- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/numpy_questions.py b/numpy_questions.py index 3f0ca251..c31f4b9e 100644 --- a/numpy_questions.py +++ b/numpy_questions.py @@ -39,7 +39,7 @@ def max_index(X): """ if not isinstance(X, np.ndarray) or X.ndim != 2: raise ValueError("X must be a 2D numpy array") - + flat_index = np.argmax(X) i, j = np.unravel_index(flat_index, X.shape) return i, j diff --git a/sklearn_questions.py b/sklearn_questions.py index 535e4cba..dd56016d 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -71,12 +71,11 @@ def predict(self, X): Class labels for each data sample. """ check_is_fitted(self) - X = check_array(X) - # Vérifier que le nombre de features correspond + X = check_array(X, ensure_2d=True) if X.shape[1] != self.n_features_in_: raise ValueError( - f"X has {X.shape[1]} features, but OneNearestNeighbor " - f"is expecting {self.n_features_in_} features." + "X has {} features, but {} is expecting {} features as input" + .format(X.shape[1], self.__class__.__name__, self.n_features_in_) ) y_pred = np.full( shape=len(X), fill_value=self.classes_[0], From 50167a1f3acdb578769e5ad5093ebcf5792c9170 Mon Sep 17 00:00:00 2001 From: pyxcode Date: Sat, 15 Nov 2025 19:58:46 +0100 Subject: [PATCH 5/5] Louan BARDOU Part B --- sklearn_questions.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/sklearn_questions.py b/sklearn_questions.py index dd56016d..8893cf1c 100644 --- a/sklearn_questions.py +++ b/sklearn_questions.py @@ -75,7 +75,8 @@ def predict(self, X): if X.shape[1] != self.n_features_in_: raise ValueError( "X has {} features, but {} is expecting {} features as input" - .format(X.shape[1], self.__class__.__name__, self.n_features_in_) + .format( + X.shape[1], self.__class__.__name__, self.n_features_in_) ) y_pred = np.full( shape=len(X), fill_value=self.classes_[0],