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Implemented input check

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ricca 2023-10-03 18:28:37 +02:00
parent 2759947a48
commit 37184e21de
1 changed files with 25 additions and 17 deletions
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40
machine_learning/multinomial_naive_bayes_classifier.py
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@ -11,7 +11,7 @@ from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics import accuracy_score from sklearn.metrics import accuracy_score
def group_data_by_target(targets): def group_indices_by_target(targets):
""" """
Associates to each target label the indices of the examples with that label Associates to each target label the indices of the examples with that label
@ -22,21 +22,21 @@ def group_data_by_target(targets):
Returns Returns
---------- ----------
grouped_data : dict of (label : list) grouped_indices : dict of (label : list)
Maps each target label to the list of indices of the examples with that label Maps each target label to the list of indices of the examples with that label
Example Example
---------- ----------
>>> y = np.array([1, 2, 3, 1, 2, 5]) >>> y = np.array([1, 2, 3, 1, 2, 5])
>>> group_data_by_target(y) >>> group_indices_by_target(y)
{1: [0, 3], 2: [1, 4], 3: [2], 5: [5]} {1: [0, 3], 2: [1, 4], 3: [2], 5: [5]}
""" """
grouped_data = {} grouped_indices = {}
for i, y in enumerate(targets): for i, y in enumerate(targets):
if y not in grouped_data: if y not in grouped_indices:
grouped_data[y] = [] grouped_indices[y] = []
grouped_data[y].append(i) grouped_indices[y].append(i)
return grouped_data return grouped_indices
class MultinomialNBClassifier: class MultinomialNBClassifier:
@ -46,6 +46,16 @@ class MultinomialNBClassifier:
self.priors = None self.priors = None
self.alpha = alpha self.alpha = alpha
def _check_X(self, X):
if not sparse.issparse(X):
raise ValueError("Matrix X must be an instance of scipy.sparse.csr_matrix")
def _check_X_y(self, X, y):
self._check_X(X)
if X.shape[0] != len(y):
raise ValueError(
"The expected dimension for array y is (" + str(X.shape[0]) + ",), but got (" + str(len(y)) + ",)")
def fit(self, X, y): def fit(self, X, y):
""" """
Parameters Parameters
@ -56,16 +66,15 @@ class MultinomialNBClassifier:
y : array-like of shape (n_samples,) y : array-like of shape (n_samples,)
Target labels Target labels
""" """
if not sparse.issparse(X): self._check_X_y(X, y)
raise ValueError("Matrix X must be an instance of scipy.sparse.csr_matrix")
n_examples, n_features = X.shape n_examples, n_features = X.shape
grouped_data = group_data_by_target(y) grouped_indices = group_indices_by_target(y)
self.classes = list(grouped_data.keys()) self.classes = list(grouped_indices.keys())
self.priors = np.zeros(shape=len(self.classes)) self.priors = np.zeros(shape=len(self.classes))
self.features_probs = np.zeros(shape=(len(self.classes), n_features)) self.features_probs = np.zeros(shape=(len(self.classes), n_features))
for i, class_i in enumerate(self.classes): for i, class_i in enumerate(self.classes):
data_class_i = X[grouped_data[class_i]] data_class_i = X[grouped_indices[class_i]]
prior_class_i = data_class_i.shape[0] / n_examples prior_class_i = data_class_i.shape[0] / n_examples
self.priors[i] = prior_class_i self.priors[i] = prior_class_i
tot_features_count = data_class_i.sum() # count of all features in class_i tot_features_count = data_class_i.sum() # count of all features in class_i
@ -98,8 +107,7 @@ class MultinomialNBClassifier:
>>> model.predict(X[2:3]) >>> model.predict(X[2:3])
array([3]) array([3])
""" """
if not sparse.issparse(X): self._check_X(X)
raise ValueError("Matrix X must be an instance of scipy.sparse.csr_matrix")
y_pred = [] y_pred = []
log_features_probs = np.log(self.features_probs) log_features_probs = np.log(self.features_probs)
log_priors = np.log(self.priors) log_priors = np.log(self.priors)
@ -126,7 +134,7 @@ def main():
model.fit(X_train, y_train) model.fit(X_train, y_train)
y_pred = model.predict(X_test) y_pred = model.predict(X_test)
print("Accuracy of Naive Bayes text classifier: " + str(accuracy_score(y_test, y_pred))) print("Accuracy of naive bayes text classifier: " + str(accuracy_score(y_test, y_pred)))
if __name__ == "__main__": if __name__ == "__main__":