def func(m=5000, n=10, k=9, convert_to_float32=False):
np.random.seed(1234)
X = np.random.rand(m, n)
if convert_to_float32:
X = X.astype(np.float32)
gpu_id = 0
total_gpu, total_mem, gpu_type = gpu.get_gpu_info()
if(total_gpu > 0):
gpu_id = 1 #Use second gpu
print("\n")
print("SVD on gpu id -> " + str(gpu_id))
print("SVD on " + str(X.shape[0]) + " by " + str(X.shape[1]) + " matrix")
print("Original X Matrix")
print(X)
print("\n")
print("h2o4gpu tsvd run")
start_time = time.time()
h2o4gpu_tsvd = TruncatedSVDH2O(n_components=k, gpu_id=gpu_id)
h2o4gpu_tsvd.fit(X)
end_time = time.time() - start_time
print("Total time for h2o4gpu tsvd is " + str(end_time))
print("h2o4gpu tsvd Singular Values")
print(h2o4gpu_tsvd.singular_values_)
print("h2o4gpu tsvd Components (V^T)")
print(h2o4gpu_tsvd.components_)
print("h2o4gpu tsvd Explained Variance")
print(h2o4gpu_tsvd.explained_variance_)
print("h2o4gpu tsvd Explained Variance Ratio")
print(h2o4gpu_tsvd.explained_variance_ratio_)
def func(m=5000000,
n=10,
k=9,
change_gpu_id=False,
use_wrappper=False,
convert_to_float32=False):
np.random.seed(1234)
X = np.random.rand(m, n)
if convert_to_float32:
X = X.astype(np.float32)
gpu_id = 0
if change_gpu_id:
total_gpu, total_mem, gpu_type = gpu.get_gpu_info()
if (total_gpu > 0):
gpu_id = 1 #Use second gpu
print("\n")
print(X)
print(np.mean(X, axis=0))
if use_wrappper:
h2o4gpu_pca = PCA(n_components=k, gpu_id=gpu_id)
else:
h2o4gpu_pca = PCAH2O(n_components=k, gpu_id=gpu_id)
scikit_pca = PCASklearn(n_components=k, svd_solver="arpack")
scikit_pca.fit(X)
h2o4gpu_pca.fit(X)
print("Mean")
print(h2o4gpu_pca.mean_)
print(scikit_pca.mean_)
if convert_to_float32:
assert np.allclose(h2o4gpu_pca.mean_, scikit_pca.mean_, 1e-4)
else:
assert np.allclose(h2o4gpu_pca.mean_, scikit_pca.mean_)
print("Noise Variance")
print(h2o4gpu_pca.noise_variance_)
print(scikit_pca.noise_variance_)
assert np.allclose(h2o4gpu_pca.noise_variance_,
h2o4gpu_pca.noise_variance_)
print("Explained variance")
print(h2o4gpu_pca.explained_variance_)
print(scikit_pca.explained_variance_)
assert np.allclose(h2o4gpu_pca.explained_variance_,
scikit_pca.explained_variance_)
print("Explained variance ratio")
print(h2o4gpu_pca.explained_variance_ratio_)
print(scikit_pca.explained_variance_ratio_)
assert np.allclose(h2o4gpu_pca.explained_variance_ratio_,
scikit_pca.explained_variance_ratio_, .1)
print("Singular values")
print(h2o4gpu_pca.singular_values_)
print(scikit_pca.singular_values_)
assert np.allclose(h2o4gpu_pca.singular_values_,
scikit_pca.singular_values_)
print("Components")
print(h2o4gpu_pca.components_)
print(scikit_pca.components_)
assert np.allclose(h2o4gpu_pca.components_, scikit_pca.components_, .1)
print("Num components")
print(h2o4gpu_pca.n_components)
print(scikit_pca.n_components)
assert h2o4gpu_pca.n_components_ == scikit_pca.n_components_
def func(m=5000000,
n=10,
k=9,
change_gpu_id=False,
use_wrappper=False,
convert_to_float32=False,
whiten=False):
np.random.seed(1234)
X = np.random.rand(m, n)
if convert_to_float32:
X = X.astype(np.float32)
gpu_id = 0
if change_gpu_id:
total_gpu, total_mem, gpu_type = gpu.get_gpu_info()
if (total_gpu > 1): #More than one gpu?
gpu_id = 1 #Use second gpu
print("\n")
print(X)
print(np.mean(X, axis=0))
if use_wrappper:
h2o4gpu_pca = PCA(n_components=k, gpu_id=gpu_id, whiten=whiten)
else:
h2o4gpu_pca = PCAH2O(n_components=k, gpu_id=gpu_id, whiten=whiten)
scikit_pca = PCASklearn(n_components=k, svd_solver="arpack", whiten=whiten)
print("Fitting scikit PCA")
scikit_pca.fit(X)
print("Fitting h2o4gpu PCA")
h2o4gpu_pca.fit(X)
print("Column Means")
print(h2o4gpu_pca.mean_)
print(scikit_pca.mean_)
if convert_to_float32:
assert np.allclose(h2o4gpu_pca.mean_, scikit_pca.mean_, 1e-4)
else:
assert np.allclose(h2o4gpu_pca.mean_, scikit_pca.mean_)
print("Noise Variance")
print(h2o4gpu_pca.noise_variance_)
print(scikit_pca.noise_variance_)
assert np.allclose(h2o4gpu_pca.noise_variance_,
h2o4gpu_pca.noise_variance_)
print("Explained variance")
print(h2o4gpu_pca.explained_variance_)
print(scikit_pca.explained_variance_)
assert np.allclose(h2o4gpu_pca.explained_variance_,
scikit_pca.explained_variance_)
print("Explained variance ratio")
print(h2o4gpu_pca.explained_variance_ratio_)
print(scikit_pca.explained_variance_ratio_)
assert np.allclose(h2o4gpu_pca.explained_variance_ratio_,
scikit_pca.explained_variance_ratio_, .1)
print("Singular values")
print(h2o4gpu_pca.singular_values_)
print(scikit_pca.singular_values_)
assert np.allclose(h2o4gpu_pca.singular_values_,
scikit_pca.singular_values_)
print("Components")
if whiten:
#Need to manually calculate as Scikit does not store whiten components. See link below:
#https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/decomposition/pca.py#L567
scikit_pca.components_ = (scikit_pca.components_ * sqrt(
X.shape[0])) / scikit_pca.singular_values_[:, np.newaxis]
print(h2o4gpu_pca.components_)
print(scikit_pca.components_)
assert np.allclose(h2o4gpu_pca.components_, scikit_pca.components_, .1)
print("Num components")
print(h2o4gpu_pca.n_components)
print(scikit_pca.n_components)
assert h2o4gpu_pca.n_components_ == scikit_pca.n_components_