def test_incremental_pca_sparse(matrix_class):
# Incremental PCA on sparse arrays.
X = iris.data
pca = PCA(n_components=2)
pca.fit_transform(X)
X_sparse = matrix_class(X)
batch_size = X_sparse.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
X_transformed = ipca.fit_transform(X_sparse)
assert X_transformed.shape == (X_sparse.shape[0], 2)
np.testing.assert_allclose(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(),
rtol=1e-3)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X_sparse)
cov = ipca.get_covariance()
precision = ipca.get_precision()
np.testing.assert_allclose(np.dot(cov, precision),
np.eye(X_sparse.shape[1]),
atol=1e-13)
with pytest.raises(TypeError,
match="IncrementalPCA.partial_fit does not support "
"sparse input. Either convert data to dense "
"or use IncrementalPCA.fit to do so in batches."):
ipca.partial_fit(X_sparse)
def test_incremental_pca():
# Incremental PCA on dense arrays.
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
assert X_transformed.shape == (X.shape[0], 2)
np.testing.assert_allclose(
ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(),
rtol=1e-3,
)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
np.testing.assert_allclose(
np.dot(cov, precision), np.eye(X.shape[1]), atol=1e-13
)
class IPCA(object):
def __init__(self,
n_components=None,
whiten=False,
copy=True,
batch_size=None):
"""
:param n_components: default为None ，int 或None， 想要保留的分量数，None 时，
min(n_samples, n_features)
:param whiten: bool型，可选项， 默认为False, 当true（默认情况下为false）时，components_ 向量除以
n_samples*components_以确保具有单位组件级方差的不相关输出。
:param copy: 默认为True, False时，x 将被覆盖，将节约能存，但存在不安全
:param batch_size: default None， 批量样本数， 只在fit 中使用，设为None,系统自动设成5*n_features,
以保持经度与内存开销的平衡
"""
self.model = IncrementalPCA(n_components=n_components,
whiten=whiten,
copy=copy,
batch_size=batch_size)
def fit(self, x, y=None):
self.model.fit(X=x, y=y)
def transform(self, x):
return self.model.transform(X=x)
def fit_transform(self, x, y=None):
return self.model.fit_transform(X=x, y=y)
def get_params(self, deep=True): # 获取评估器的参数
return self.model.get_params(deep=deep)
def set_params(self, **params): # 设置评估器的参数
self.model.set_params(**params)
def inverse_transform(self, x): # 与 fit_tansform 刚好相反的两个操作
return self.model.inverse_transform(X=x)
def get_precision(self): # 根据生成模型计算精度矩阵
return self.model.get_precision()
def get_covariance(self): # 根据生成模型获取协方差
return self.model.get_covariance()
def partial_fit(self, x, y=None, check_input=True): # 增量训练
self.model.partial_fit(X=x, y=y, check_input=check_input)
def get_attributes(self):
component = self.model.components_
explained_variance = self.model.explained_variance_
explained_variance_ratio = self.model.explained_variance_ratio_
singular_values = self.model.singular_values_
means = self.model.mean_ # 每个特征的均值
var = self.model.var_ # 每个特征的方差
noise_variance = self.model.noise_variance_ # 评估的噪声协方差
n_component = self.model.n_components_
n_samples_seen = self.model.n_samples_seen_
return component, explained_variance, explained_variance_ratio, singular_values, means, var, noise_variance, \
n_component, n_samples_seen
def test_incremental_pca():
"""Incremental PCA on dense arrays."""
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2))
assert_almost_equal(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(), 1)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
assert_array_almost_equal(np.dot(cov, precision), np.eye(X.shape[1]))
def test_incremental_pca():
"""Incremental PCA on dense arrays."""
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2))
assert_almost_equal(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(), 1)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]))
# In[ ]:
# In[16]:
# IncrementalPCA
ipca = IncrementalPCA(n_components=3)
print(ipca)
ipca.fit(feature)
x = ipca.transform(feature)
print(ipca.explained_variance_ratio_)
# In[17]:
# ipca.get_covariance()
ipca.get_precision()
ipca.get_params(deep=True)
# ## Gaussian random projection
# In[27]:
from sklearn import random_projection
transform = random_projection.GaussianRandomProjection(n_components=5)
feature_new = transform.fit_transform(feature)
classifier_f = open('n_GaussianRandomProjection.pickle', 'wb')
pickle.dump(feature_new, classifier_f)
classifier_f.close()
print(feature_new)
