Added support for sparse category-dataset
This commit is contained in:
parent
a84731da30
commit
bed280353b
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@ -1,10 +1,11 @@
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from scipy import ndarray,atleast_2d,asarray,intersect1d,zeros
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from scipy import ndarray,atleast_2d,asarray,intersect1d,zeros,empty,sparse
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from scipy import sort as array_sort
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from itertools import izip
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import shelve
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import copy
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import re
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class Dataset:
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"""The Dataset base class.
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@ -42,16 +43,15 @@ class Dataset:
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self._map = {} # internal mapping for dataset: identifier <--> index
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self._name = name
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self._identifiers = identifiers
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self._type = 'n'
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if len(array.shape)==1:
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if not isinstance(array, sparse.spmatrix):
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array = atleast_2d(asarray(array))
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# vectors are column vectors
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if array.shape[0]==1:
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array = array.T
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# vector are column (array)
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if array.shape[0] == 1:
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array = array.T
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self.shape = array.shape
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if identifiers!=None:
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if identifiers != None:
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self._validate_identifiers(identifiers)
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self._set_identifiers(identifiers, self._all_dims)
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else:
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@ -82,14 +82,14 @@ class Dataset:
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dim_names = ['rows','cols']
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ids = []
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for axis,n in enumerate(shape):
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if axis<2:
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for axis, n in enumerate(shape):
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if axis < 2:
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dim_suggestion = dim_names[axis]
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else:
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dim_suggestion = 'dim'
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dim_suggestion = self._suggest_dim_name(dim_suggestion,all_dims)
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identifier_creation = [str(axis) + "_" + i for i in map(str,range(n))]
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ids.append((dim_suggestion,identifier_creation))
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dim_suggestion = self._suggest_dim_name(dim_suggestion, all_dims)
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identifier_creation = [str(axis) + "_" + i for i in map(str, range(n))]
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ids.append((dim_suggestion, identifier_creation))
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all_dims.add(dim_suggestion)
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return ids
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@ -112,18 +112,22 @@ class Dataset:
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new_name = dim_name
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while new_name in all_dims:
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new_name = dim_name + "_" + str(c)
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c+=1
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c += 1
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return new_name
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def asarray(self):
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"""Returns the numeric array (data) of dataset"""
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if isinstance(self._array, sparse.spmatrix):
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return self._array.toarray()
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return self._array
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def add_array(self, array):
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def set_array(self, array):
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"""Adds array as an ArrayType object.
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A one-dim array is transformed to a two-dim array (row-vector)
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"""
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if self.shape!=array.shape:
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if not isinstance(array, type(self._array)):
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raise ValueError("Input array of type: %s does not match existing array type: %s") %(type(array), type(self._array))
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if self.shape != array.shape:
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raise ValueError, "Input array must be of similar dimensions as dataset"
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self._array = atleast_2d(asarray(array))
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@ -138,7 +142,7 @@ class Dataset:
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def get_dim_name(self, axis=None):
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"""Returns dim name for an axis, if no axis is provided it
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returns a list of dims"""
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if type(axis)==int:
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if type(axis) == int:
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return self._dims[axis]
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else:
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return [dim for dim in self._dims]
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@ -149,7 +153,7 @@ class Dataset:
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ds_dims = ds.get_dim_name()
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return [d for d in dims if d in ds_dims]
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def get_identifiers(self, dim, indices=None,sorted=False):
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def get_identifiers(self, dim, indices=None, sorted=False):
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"""Returns identifiers along dim, sorted by position (index)
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is optional.
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@ -163,7 +167,6 @@ class Dataset:
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if indices != None:
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if len(indices) == 0:# if empty list or empty array
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return []
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if indices != None:
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# be sure to match intersection
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#indices = intersect1d(self.get_indices(dim),indices)
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@ -188,7 +191,7 @@ class Dataset:
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"""
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if not isinstance(idents, list) and not isinstance(idents, set):
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raise ValueError("idents needs to be a list/set got: %s" %type(idents))
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if idents==None:
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if idents == None:
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index = array_sort(self._map[dim].values())
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else:
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index = [self._map[dim][key]
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@ -226,7 +229,7 @@ class Dataset:
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As for the moment: only support for 2D-arrays.
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"""
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#assert(self._array==ndarray)
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assert(len(self.shape) == 2)
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ds = self.copy()
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ds._array = ds._array.T
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ds._dims.reverse()
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@ -234,12 +237,11 @@ class Dataset:
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return ds
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def _validate_identifiers(self, identifiers):
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for dim_name, ids in identifiers:
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if len(set(ids)) != len(ids):
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raise ValueError("Identifiers not unique in : %s" %dim_name)
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identifier_shape = [len(i[1]) for i in identifiers]
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if len(identifier_shape)!=len(self.shape):
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if len(identifier_shape) != len(self.shape):
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raise ValueError("Identifier list length must equal array dims")
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for ni, na in zip(identifier_shape, self.shape):
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if ni != na:
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@ -252,9 +254,9 @@ class CategoryDataset(Dataset):
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A dataset for representing class information as binary
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matrices (0/1-matrices).
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There is support for using a less memory demanding, and
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fast intersection look-ups by representing the binary matrix as a
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dictionary in each dimension.
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There is support for using a less memory demanding, sparse format. The
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prefered (default) format for a category dataset is the compressed sparse row
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format (csr)
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Always has linked dimension in first dim:
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ex matrix:
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@ -265,33 +267,51 @@ class CategoryDataset(Dataset):
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.
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.
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.
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"""
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def __init__(self, array, identifiers=None, name='C'):
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Dataset.__init__(self, array, identifiers=identifiers, name=name)
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self.has_dictlists = False
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self._type = 'c'
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def as_dict_lists(self):
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"""Returns data as dict of indices along first dim.
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"""Returns data as dict of identifiers along first dim.
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ex: data['gene_id'] = ['map0030','map0010', ...]
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ex: data['gene_1'] = ['map0030','map0010', ...]
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fixme: Deprecated?
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"""
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data={}
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data = {}
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for name, ind in self._map[self.get_dim_name(0)].items():
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data[name] = self.get_identifiers(self.get_dim_name(1),
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list(self._array[ind,:].nonzero()))
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if isinstance(self._array, ndarray):
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indices = self._array[ind,:].nonzero()[0]
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elif isinstance(self._array, sparse.spmatrix):
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if not isinstance(self._array, sparse.csr_matrix):
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array = self._array.tocsr()
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else:
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array = self._array
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indices = array[ind,:].indices
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if len(indices) == 0: # should we allow categories with no members?
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continue
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data[name] = self.get_identifiers(self.get_dim_name(1), indices)
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self._dictlists = data
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self.has_dictlists = True
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return data
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def as_selections(self):
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"""Returns data as a list of Selection objects.
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The list of selections is not ordered (sorted) by any means.
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"""
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ret_list = []
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for cat_name, ind in self._map[self.get_dim_name(1)].items():
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ids = self.get_identifiers(self.get_dim_name(0),
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self._array[:,ind].nonzero()[0])
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if isinstance(self._array, sparse.spmatrix):
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if not isinstance(self._array, sparse.csc_matrix):
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self._array = self._array.tocsc()
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indices = self._array[:,ind].indices
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else:
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indices = self._array[:,ind].nonzero()[0]
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if len(indices) == 0:
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continue
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ids = self.get_identifiers(self.get_dim_name(0), indices)
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selection = Selection(cat_name)
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selection.select(self.get_dim_name(0), ids)
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ret_list.append(selection)
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@ -309,10 +329,10 @@ class GraphDataset(Dataset):
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representing the graph as a NetworkX.Graph, or NetworkX.XGraph structure.
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"""
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def __init__(self, array=None, identifiers=None, shape=None, all_dims=[],**kwds):
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Dataset.__init__(self, array=array, identifiers=identifiers, name='A')
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def __init__(self, array, identifiers=None, name='A'):
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Dataset.__init__(self, array=array, identifiers=identifiers, name=name)
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self._graph = None
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self._type = 'g'
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self._pos = None
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def asnetworkx(self, nx_type='graph'):
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dim = self.get_dim_name()[0]
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@ -334,17 +354,17 @@ class GraphDataset(Dataset):
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import networkx as nx
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except:
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print "Failed in import of NetworkX"
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return
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m, n = A.shape# adjacency matrix must be of type that evals to true/false for neigbours
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if m!=n:
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return None
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m, n = A.shape # adjacency matrix must be of type that evals to true/false for neigbours
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if m != n:
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raise IOError, "Adjacency matrix must be square"
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if A[A[:,0].nonzero()[0][0],0]==1: #unweighted graph
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if A[A[:,0].nonzero()[0][0],0] == 1: #unweighted graph
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G = nx.Graph()
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else:
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G = nx.XGraph()
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if labels==None: # if labels not provided mark vertices with numbers
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if labels == None: # if labels not provided mark vertices with numbers
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labels = [str(i) for i in range(m)]
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for nbrs, head in izip(A, labels):
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@ -371,7 +391,7 @@ class ReverseDict(dict):
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"""
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def __init__(self, *args, **kw):
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dict.__init__(self, *args, **kw)
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self.reverse = dict([[v,k] for k,v in self.items()])
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self.reverse = dict([[v, k] for k, v in self.items()])
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def __setitem__(self, key, value):
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dict.__setitem__(self, key, value)
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@ -380,39 +400,6 @@ class ReverseDict(dict):
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except:
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self.reverse = {value:key}
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def to_file(filepath,dataset,name=None):
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"""Write dataset to file. A file may contain multiple datasets.
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append to file by using option mode='a'
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"""
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if not name:
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name = dataset._name
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data = shelve.open(filepath, flag='c', protocol=2)
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if data: #we have an append
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names = data.keys()
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if name in names:
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print "Data with name: %s overwritten" %dataset._name
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sub_data = {'array':dataset._array,
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'idents':dataset._identifiers,
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'type':dataset._type}
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data[name] = sub_data
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data.close()
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def from_file(filepath):
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"""Read dataset(s) from file """
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data = shelve.open(filepath, flag='r')
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out_data = []
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for name in data.keys():
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sub_data = data[name]
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if sub_data['type']=='c':
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out_data.append(CategoryDataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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elif sub_data['type']=='g':
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out_data.append(GraphDataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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else:
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out_data.append(Dataset(sub_data['array'], identifiers=sub_data['idents'], name=name))
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return out_data
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class Selection(dict):
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"""Handles selected identifiers along each dimension of a dataset"""
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@ -435,32 +422,40 @@ class Selection(dict):
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def select(self, axis, labels):
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self[axis] = labels
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def write_ftsv(fd, ds, decimals=7):
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def write_ftsv(fd, ds, decimals=7, sep='\t', fmt=None):
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"""Writes a dataset in fluents tab separated values (ftsv) form.
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@param fd: An open file descriptor to the output file.
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@param ds: The dataset to be written. The function handles datasets
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of these classes: Dataset, CategoryDataset and GraphDataset
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@param ds: The dataset to be written.
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@param decimals: Number of decimals, only supported for dataset.
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@param fmt: String formating
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The function handles datasets of these classes:
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Dataset, CategoryDataset and GraphDataset
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"""
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opened = False
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if isinstance(fd, str):
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fd = open(fd, 'w')
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opened = True
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printstr = "%s\t"
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# Write header information
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if isinstance(ds, CategoryDataset):
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type = 'category'
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if fmt == None:
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fmt = '%d'
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elif isinstance(ds, GraphDataset):
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type = 'network'
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if fmt == None:
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fmt = '%d'
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elif isinstance(ds, Dataset):
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type = 'dataset'
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printstr = '%%.%df\t' % decimals
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if fmt == None:
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fmt = '%%.%df' % decimals
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else:
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fmt = '%%.%d' %decimals + fmt
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else:
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raise Exception("Unknown object")
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print >> fd, "# type: %s" % type
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raise Exception("Unknown object type")
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fd.write('# type: %s' %type + '\n')
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for dim in ds.get_dim_name():
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print >> fd, "# dimension: %s" % dim,
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@ -469,23 +464,57 @@ def write_ftsv(fd, ds, decimals=7):
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print >> fd
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print >> fd, "# name: %s" % ds.get_name()
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print >> fd
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# Write data
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m = ds.asarray()
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if type == 'category':
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m = m.astype('i')
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y, x = m.shape
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for j in range(y):
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for i in range(x):
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print >> fd, printstr % m[j, i],
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print >> fd
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if isinstance(m, sparse.spmatrix):
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_write_sparse_elements(fd, m, fmt, sep)
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else:
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_write_elements(fd, m, fmt, sep)
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if opened:
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fd.close()
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def read_ftsv(fd):
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def _write_sparse_elements(fd, arr, fmt='%d', sep=None):
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""" Sparse coordinate format."""
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fd.write('# sp_format: True\n\n')
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fmt = '%d %d ' + fmt + '\n'
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csr = arr.tocsr()
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for ii in xrange(csr.size):
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ir, ic = csr.rowcol(ii)
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data = csr.getdata(ii)
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fd.write(fmt % (ir, ic, data))
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def _write_elements(fd, arr, fmt='%f', sep='\t'):
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"""Standard value separated format."""
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fmt = fmt + sep
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fd.write('\n')
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y, x = arr.shape
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for j in range(y):
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for i in range(x):
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fd.write(fmt %arr[j, i])
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fd.write('\n')
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def _read_elements(fd, arr, sep=None):
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line = fd.readline()
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i = 0
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while line:
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values = line.split(sep)
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for j, val in enumerate(values):
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arr[i,j] = float(val)
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i += 1
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line = fd.readline()
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return arr
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def _read_sparse_elements(fd, arr, sep=None):
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line = fd.readline()
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while line:
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i, j, val = line.split()
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arr[int(i),int(j)] = float(val)
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line = fd.readline()
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return arr.tocsr()
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def read_ftsv(fd, sep=None):
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"""Read a dataset in fluents tab separated values (ftsv) form and return it.
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@param fd: An open file descriptor.
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@ -502,7 +531,8 @@ def read_ftsv(fd):
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identifiers = {}
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type = 'dataset'
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name = 'Unnamed dataset'
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graphtype = 'graph'
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sp_format = False
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# graphtype = 'graph'
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# Read header lines from file.
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line = fd.readline()
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@ -525,9 +555,19 @@ def read_ftsv(fd):
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elif key == 'name':
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name = val
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elif key == 'graphtype':
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graphtype = val
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# storage format
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# if sp_format is True then use coordinate triplets
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elif key == 'sp_format':
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if val in ['False', 'false', '0', 'F', 'f',]:
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sp_format = False
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elif val in ['True', 'true', '1', 'T', 't']:
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sp_format = True
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else:
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raise ValueError("sp_format: %s not valid " %sp_format)
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# elif key == 'graphtype':
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# graphtype = val
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else:
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break
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@ -537,22 +577,20 @@ def read_ftsv(fd):
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dims = [(x, identifiers[x]) for x in dimensions]
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dim_lengths = [len(identifiers[x]) for x in dimensions]
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# Create matrix
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# Create matrix and assign element reader
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if type == 'category':
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matrix = zeros(dim_lengths, dtype=bool)
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if sp_format:
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matrix = sparse.lil_matrix(dim_lengths)
|
||||
read_elements = _read_sparse_elements
|
||||
else:
|
||||
matrix = empty(dim_lengths, dtype='i')
|
||||
read_elements = _read_elements
|
||||
elif type == 'network':
|
||||
matrix = zeros(dim_lengths)
|
||||
matrix = empty(dim_lengths)
|
||||
else:
|
||||
matrix = zeros(dim_lengths)
|
||||
|
||||
line = fd.readline()
|
||||
y = 0
|
||||
while line:
|
||||
values = line.split()
|
||||
for x, v in enumerate(values):
|
||||
matrix[y,x] = float(v)
|
||||
y += 1
|
||||
line = fd.readline()
|
||||
matrix = empty(dim_lengths)
|
||||
|
||||
matrix = read_elements(fd, matrix, sep)
|
||||
|
||||
# Create dataset of specified type
|
||||
if type == 'category':
|
||||
|
|
|
|||
|
|
@ -392,7 +392,7 @@ class NavigatorMenu(gtk.Menu):
|
|||
ds = self.dataset.copy()
|
||||
ds._name = self.dataset._name + ".rsc"
|
||||
axis = 1
|
||||
ds._array = ds._array/scipy.expand_dims(ds._array.std(axis), axis)
|
||||
ds._array = ds.asarray()/scipy.expand_dims(ds.asarray().std(axis), axis)
|
||||
icon = fluents.icon_factory.get(ds)
|
||||
project.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
|
||||
|
||||
|
|
@ -401,21 +401,21 @@ class NavigatorMenu(gtk.Menu):
|
|||
ds = self.dataset.copy()
|
||||
ds._name = self.dataset._name + ".csc"
|
||||
axis = 0
|
||||
ds._array = ds._array/scipy.expand_dims(ds._array.std(axis), axis)
|
||||
ds._array = ds.asarray()/scipy.expand_dims(ds.asarray().std(axis), axis)
|
||||
icon = fluents.icon_factory.get(ds)
|
||||
project.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
|
||||
|
||||
def on_log(self, item, navigator):
|
||||
project = main.project
|
||||
try:
|
||||
if not scipy.all(self.dataset._array>0):
|
||||
if not scipy.all(self.dataset.asarray()>0):
|
||||
raise ValueError
|
||||
except:
|
||||
logger.log('warning', 'Datasets needs to be strictly positive for a log transform')
|
||||
return
|
||||
|
||||
ds = self.dataset.copy()
|
||||
ds._array = scipy.log(ds._array)
|
||||
ds._array = scipy.log(ds.asarray())
|
||||
icon = fluents.icon_factory.get(ds)
|
||||
ds._name = ds._name + ".log"
|
||||
project.data_tree_insert(self.tree_iter, ds.get_name(), ds, None, "black", icon)
|
||||
|
|
|
|||
|
|
@ -305,8 +305,8 @@ class ScatterMarkerPlot(Plot):
|
|||
self.ms = s
|
||||
x_index = dataset_1[sel_dim][id_1]
|
||||
y_index = dataset_2[sel_dim][id_2]
|
||||
self.xaxis_data = dataset_1._array[:, x_index]
|
||||
self.yaxis_data = dataset_2._array[:, y_index]
|
||||
self.xaxis_data = dataset_1.asarray()[:, x_index]
|
||||
self.yaxis_data = dataset_2.asarray()[:, y_index]
|
||||
|
||||
# init draw
|
||||
self._selection_line = None
|
||||
|
|
@ -390,8 +390,8 @@ class ScatterPlot(Plot):
|
|||
y_index = dataset_2[sel_dim_2][id_2]
|
||||
else:
|
||||
y_index = dataset_2[sel_dim][id_2]
|
||||
self.xaxis_data = dataset_1._array[:, x_index]
|
||||
self.yaxis_data = dataset_2._array[:, y_index]
|
||||
self.xaxis_data = dataset_1.asarray()[:, x_index]
|
||||
self.yaxis_data = dataset_2.asarray()[:, y_index]
|
||||
|
||||
# init draw
|
||||
self.init_draw()
|
||||
|
|
@ -436,7 +436,7 @@ class ScatterPlot(Plot):
|
|||
|
||||
def set_absicca(self, sb):
|
||||
self._absi = sb.get_value_as_int() - 1
|
||||
xy = self.dataset_1._array[:,[self._absi, self._ordi]]
|
||||
xy = self.dataset_1.asarray()[:,[self._absi, self._ordi]]
|
||||
self.xaxis_data = xy[:,0]
|
||||
self.yaxis_data = xy[:,1]
|
||||
self.sc._offsets = xy
|
||||
|
|
@ -446,7 +446,7 @@ class ScatterPlot(Plot):
|
|||
|
||||
def set_ordinate(self, sb):
|
||||
self._ordi = sb.get_value_as_int() - 1
|
||||
xy = self.dataset_1._array[:,[self._absi, self._ordi]]
|
||||
xy = self.dataset_1.asarray()[:,[self._absi, self._ordi]]
|
||||
self.xaxis_data = xy[:,0]
|
||||
self.yaxis_data = xy[:,1]
|
||||
self.sc._offsets = xy
|
||||
|
|
|
|||
Reference in New Issue