def _make_nn_regression(n_samples=100, n_features=100, n_informative=10, shuffle=True, random_state=None): generator = check_random_state(random_state) row = np.repeat(np.arange(n_samples), n_informative) col = np.zeros(n_samples * n_informative, dtype=np.int32) data = generator.rand(n_samples * n_informative) n = 0 ind = np.arange(n_features) for i in xrange(n_samples): generator.shuffle(ind) col[n:n+n_informative] = ind[:n_informative] n += n_informative X = sp.coo_matrix((data, (row, col)), shape=(n_samples, n_features)) X = X.tocsr() # Generate a ground truth model with only n_informative features being non # zeros (the other features are not correlated to y and should be ignored # by a sparsifying regularizers such as L1 or elastic net) ground_truth = np.zeros(n_features) v = generator.rand(n_informative) v += np.min(v) ground_truth[:n_informative] = 100 * v y = safe_sparse_dot(X, ground_truth) # Randomly permute samples and features if shuffle: X, y = shuffle_func(X, y, random_state=generator) return X, y, ground_truth
def make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None, flip_y=0.01, class_sep=1.0, hypercube=True, shift=0.0, scale=1.0, shuffle=True, random_state=None): """Generate a random n-class classification problem. Parameters ---------- n_samples : int, optional (default=100) The number of samples. n_features : int, optional (default=20) The total number of features. These comprise `n_informative` informative features, `n_redundant` redundant features, `n_repeated` duplicated features and `n_features-n_informative-n_redundant- n_repeated` useless features drawn at random. n_informative : int, optional (default=2) The number of informative features. Each class is composed of a number of gaussian clusters each located around the vertices of a hypercube in a subspace of dimension `n_informative`. For each cluster, informative features are drawn independently from N(0, 1) and then randomly linearly combined in order to add covariance. The clusters are then placed on the vertices of the hypercube. n_redundant : int, optional (default=2) The number of redundant features. These features are generated as random linear combinations of the informative features. n_repeated : int, optional (default=2) The number of duplicated features, drawn randomly from the informative and the redundant features. n_classes : int, optional (default=2) The number of classes (or labels) of the classification problem. n_clusters_per_class : int, optional (default=2) The number of clusters per class. weights : list of floats or None (default=None) The proportions of samples assigned to each class. If None, then classes are balanced. Note that if `len(weights) == n_classes - 1`, then the last class weight is automatically inferred. flip_y : float, optional (default=0.01) The fraction of samples whose class are randomly exchanged. class_sep : float, optional (default=1.0) The factor multiplying the hypercube dimension. hypercube : boolean, optional (default=True) If True, the clusters are put on the vertices of a hypercube. If False, the clusters are put on the vertices of a random polytope. shift : float or None, optional (default=0.0) Shift all features by the specified value. If None, then features are shifted by a random value drawn in [-class_sep, class_sep]. scale : float or None, optional (default=1.0) Multiply all features by the specified value. If None, then features are scaled by a random value drawn in [1, 100]. Note that scaling happens after shifting. shuffle : boolean, optional (default=True) Shuffle the samples and the features. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Returns ------- X : array of shape [n_samples, n_features] The generated samples. y : array of shape [n_samples] The integer labels for class membership of each sample. Notes ----- The algorithm is adapted from Guyon [1] and was designed to generate the "Madelon" dataset. References ---------- .. [1] I. Guyon, "Design of experiments for the NIPS 2003 variable selection benchmark", 2003. """ generator = check_random_state(random_state) # Count features, clusters and samples if n_informative + n_redundant + n_repeated > n_features: raise ValueError("Number of informative, redundant and repeated " "features must sum to less than the number of total" " features") if 2**n_informative < n_classes * n_clusters_per_class: raise ValueError("n_classes * n_clusters_per_class must" " be smaller or equal 2 ** n_informative") if weights and len(weights) not in [n_classes, n_classes - 1]: raise ValueError("Weights specified but incompatible with number " "of classes.") n_useless = n_features - n_informative - n_redundant - n_repeated n_clusters = n_classes * n_clusters_per_class if weights and len(weights) == (n_classes - 1): weights.append(1.0 - sum(weights)) if weights is None: weights = [1.0 / n_classes] * n_classes weights[-1] = 1.0 - sum(weights[:-1]) n_samples_per_cluster = [] for k in range(n_clusters): n_samples_per_cluster.append( int(n_samples * weights[k % n_classes] / n_clusters_per_class)) for i in range(n_samples - sum(n_samples_per_cluster)): n_samples_per_cluster[i % n_clusters] += 1 # Intialize X and y X = np.zeros((n_samples, n_features)) y = np.zeros(n_samples, dtype=int) # Build the polytope C = np.array(list(product([-class_sep, class_sep], repeat=n_informative))) generator.shuffle(C) if not hypercube: C[:n_clusters] *= generator.rand(n_clusters, 1) C *= generator.rand(1, n_informative) # Loop over all clusters pos = 0 pos_end = 0 for k in range(n_clusters): # Number of samples in cluster k n_samples_k = n_samples_per_cluster[k] # Define the range of samples pos = pos_end pos_end = pos + n_samples_k # Assign labels y[pos:pos_end] = k % n_classes # Draw features at random X[pos:pos_end, :n_informative] = generator.randn( n_samples_k, n_informative) # Multiply by a random matrix to create co-variance of the features A = 2 * generator.rand(n_informative, n_informative) - 1 X[pos:pos_end, :n_informative] = np.dot(X[pos:pos_end, :n_informative], A) # Shift the cluster to a vertice X[pos:pos_end, :n_informative] += np.tile(C[k, :], (n_samples_k, 1)) # Create redundant features if n_redundant > 0: B = 2 * generator.rand(n_informative, n_redundant) - 1 X[:, n_informative:n_informative + n_redundant] = \ np.dot(X[:, :n_informative], B) # Repeat some features if n_repeated > 0: n = n_informative + n_redundant indices = ((n - 1) * generator.rand(n_repeated) + 0.5).astype(np.intp) X[:, n:n + n_repeated] = X[:, indices] # Fill useless features X[:, n_features - n_useless:] = generator.randn(n_samples, n_useless) # Randomly flip labels if flip_y >= 0.0: for i in range(n_samples): if generator.rand() < flip_y: y[i] = generator.randint(n_classes) # Randomly shift and scale constant_shift = shift is not None constant_scale = scale is not None for f in range(n_features): if not constant_shift: shift = (2 * generator.rand() - 1) * class_sep if not constant_scale: scale = 1 + 100 * generator.rand() X[:, f] += shift X[:, f] *= scale # Randomly permute samples and features if shuffle: X, y = shuffle_func(X, y, random_state=generator) indices = np.arange(n_features) generator.shuffle(indices) X[:, :] = X[:, indices] return X, y