def __init__(self, data, index=None, pca=False, min_std=10e-9):
self.data = data
self._pca = None
self._zs = ZScore(data, replace_inf=True)
data = self._zs.normalize(data)
# to remove columns that contaim nan's and have zero variance
mask_nan = np.invert(np.isnan(data.sum(axis=0)))
self._mask = np.ones(mask_nan.shape).astype(bool)
if index is not None:
self._mask[:] = False
self._mask[index] = True
self._mask *= mask_nan
data = self.filter(data)
if pca:
# data1 = data self.normalize(data)
self._pca = PCA(data, minfrac=0.05)
self.traindata = self._pca.project(data)
else:
self.traindata = data #self.normalize(data)
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
distsq = [np.power((x - mu), 2).sum() for x in data_zs]
return np.sqrt(distsq)
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
distsq = [np.power((x - mu), 2).sum() for x in data_zs]
return np.sqrt(distsq)
def __init__(self, data, index=None, pca=False, min_std=10e-9):
self.data = data
self._pca = None
self._zs = ZScore(data, replace_inf=True)
data = self._zs.normalize(data)
# to remove columns that contaim nan's and have zero variance
mask_nan = np.invert(np.isnan(data.sum(axis=0)))
self._mask = np.ones(mask_nan.shape).astype(bool)
if index is not None:
self._mask[:] = False
self._mask[index] = True
self._mask *= mask_nan
data = self.filter(data)
if pca:
# data1 = data self.normalize(data)
self._pca = PCA(data, minfrac=0.05)
self.traindata = self._pca.project(data)
else:
self.traindata = data #self.normalize(data)
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
if self.treedata.shape[1] < 2:
raise SortingError(("CosineSimilarity needs at least 2 "
"features for sorting"))
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
denom = np.sqrt((mu**2).sum()) * np.sqrt((data_zs**2).sum(axis=1))
s_cos = np.sum(mu * data_zs, axis=1) / denom
return -1.0 * s_cos
def __call__(self):
# z-scoring
if self.treedata is None:
raise SortingError("No examples for similarity measure available!")
if self.treedata.shape[1] < 2:
raise SortingError(("CosineSimilarity needs at least 2 "
"features for sorting"))
zs = ZScore(self.data, replace_inf=True)
data_zs = zs.normalize(self.data)
# z-scored mean value of pca procjected treedata
mu = zs.normalize(self.treedata)
data_zs, mu = filter_nans(data_zs, mu)
mu = mu.mean(axis=0)
denom = np.sqrt((mu**2).sum())*np.sqrt((data_zs**2).sum(axis=1))
s_cos = np.sum(mu*data_zs, axis=1)/denom
return -1.0*s_cos
class PreProcessor(object):
"""PreProcessor is used to normalize the data and remove columns that
contain NaN's and have zero variance. If index is None all features are
taken into account otherwise only those specified with index. Index can
be an integer or a list of integers.
"""
def __init__(self, data, index=None, pca=False, min_std=10e-9):
self.data = data
self._pca = None
self._zs = ZScore(data, replace_inf=True)
data = self._zs.normalize(data)
# to remove columns that contaim nan's and have zero variance
mask_nan = np.invert(np.isnan(data.sum(axis=0)))
self._mask = np.ones(mask_nan.shape).astype(bool)
if index is not None:
self._mask[:] = False
self._mask[index] = True
self._mask *= mask_nan
data = self.filter(data)
if pca:
# data1 = data self.normalize(data)
self._pca = PCA(data, minfrac=0.05)
self.traindata = self._pca.project(data)
else:
self.traindata = data #self.normalize(data)
@property
def std(self):
return self.data.std(axis=0)
@property
def mean(self):
return self.data.mean(axis=0)
@property
def mask(self):
return self._mask
@property
def nfeatures(self):
return self.traindata.shape[1]
@property
def nsamples(self):
return self.data.shape[0]
def normalize(self, data):
return self._zs.normalize(data)
def filter(self, data):
return data[:, self._mask]
def __call__(self, data):
data1 = self.filter(self.normalize(data))
if self._pca is None:
return data1
else:
return self._pca.project(data1)
class PreProcessor(object):
"""PreProcessor is used to normalize the data and remove columns that
contain NaN's and have zero variance. If index is None all features are
taken into account otherwise only those specified with index. Index can
be an integer or a list of integers.
"""
def __init__(self, data, index=None, pca=False, min_std=10e-9):
self.data = data
self._pca = None
self._zs = ZScore(data, replace_inf=True)
data = self._zs.normalize(data)
# to remove columns that contaim nan's and have zero variance
mask_nan = np.invert(np.isnan(data.sum(axis=0)))
self._mask = np.ones(mask_nan.shape).astype(bool)
if index is not None:
self._mask[:] = False
self._mask[index] = True
self._mask *= mask_nan
data = self.filter(data)
if pca:
# data1 = data self.normalize(data)
self._pca = PCA(data, minfrac=0.05)
self.traindata = self._pca.project(data)
else:
self.traindata = data #self.normalize(data)
@property
def std(self):
return self.data.std(axis=0)
@property
def mean(self):
return self.data.mean(axis=0)
@property
def mask(self):
return self._mask
@property
def nfeatures(self):
return self.traindata.shape[1]
@property
def nsamples(self):
return self.data.shape[0]
def normalize(self, data):
return self._zs.normalize(data)
def filter(self, data):
return data[:, self._mask]
def __call__(self, data):
data1 = self.filter(self.normalize(data))
if self._pca is None:
return data1
else:
return self._pca.project(data1)