def chi2_contingency_matrix(X_train, y_train):
X = X_train.copy()
X.data = np.ones_like(X.data)
X = check_array(X, accept_sparse='csr')
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative.")
Y = LabelBinarizer().fit_transform(y_train)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = safe_sparse_dot(Y.T, X) # n_classes * n_features
# feature_count = check_array(X.sum(axis=0))
# class_prob = check_array(Y.mean(axis=0))
feature_count = X.sum(axis=0).reshape(1, -1)
class_prob = Y.mean(axis=0).reshape(1, -1)
expected = np.dot(class_prob.T, feature_count)
observed = np.asarray(observed, dtype=np.float64)
k = len(observed)
# Reuse observed for chi-squared statistics
contingency_matrix = observed
contingency_matrix -= expected
contingency_matrix **= 2
expected[expected == 0.0] = 1.0
contingency_matrix /= expected
# weights = contingency_matrix.max(axis=0)
return contingency_matrix
def chi2_contingency_matrix(X_train, y_train):
X = X_train.copy()
X.data = np.ones_like(X.data)
X = check_array(X, accept_sparse='csr')
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative.")
Y = LabelBinarizer().fit_transform(y_train)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = safe_sparse_dot(Y.T, X) # n_classes * n_features
# feature_count = check_array(X.sum(axis=0))
# class_prob = check_array(Y.mean(axis=0))
feature_count = X.sum(axis=0).reshape(1, -1)
class_prob = Y.mean(axis=0).reshape(1, -1)
expected = np.dot(class_prob.T, feature_count)
observed = np.asarray(observed, dtype=np.float64)
k = len(observed)
# Reuse observed for chi-squared statistics
contingency_matrix = observed
contingency_matrix -= expected
contingency_matrix **= 2
expected[expected == 0.0] = 1.0
contingency_matrix /= expected
# weights = contingency_matrix.max(axis=0)
return contingency_matrix
def _chi2(X, y, alpha, reduce):
Y = LabelBinarizer().fit_transform(y)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = Y.T * X
feature_count = np.asarray(X.sum(axis=0))
class_prob = np.asarray(Y.mean(axis=0)).reshape(-1, 1)
expected = np.dot(class_prob, feature_count)
observed += alpha
expected += alpha
return _chisquare(observed, expected, reduce)
def chi2(X, y):
X = check_array(X, accept_sparse='csr')
if np.any((X.data if issparse(X) else X) < 0):
raise ValueError("Input X must be non-negative.")
Y = LabelBinarizer().fit_transform(y)
if Y.shape[1] == 1:
Y = np.append(1 - Y, Y, axis=1)
observed = safe_sparse_dot(Y.T, X) # n_classes * n_features
feature_count = X.sum(axis=0).reshape(1, -1)
class_prob = Y.mean(axis=0).reshape(1, -1)
expected = np.dot(class_prob.T, feature_count)
return _chisquare(observed, expected)
