def merge_cache_scores(dset_id='bcrp',
expids=None,
feats='ecfps1',
model='logreg1',
lso=True,
calib=None):
"""
Returns a 5-tuple (scores, expids, folds, molids, y) where:
- scores is a num_mols x num_valid_expids matrix with the scores of each molecule in each (valid) experiment
- expids is a num_valid_expids array of valid expids
- folds is a num_mols x num_valid_expids matrix of fold assignment for each molecule in each (valid) experiment
- molids is a num_mols array with the pertinent mol_ids
- y is a num_mols array with the labels of the molecules
"""
cache_file = op.join(MANYSOURCES_DATA_ROOT, 'results', 'scores_df.h5')
if expids is None:
expids = range(4096)
group_id = '/dset=%s/feats=%s/model=%s/lso=%r/calib=%r' % (dset_id, feats, model, lso, calib)
dset_feats_id = '/dset=%s/feats=%s' % (dset_id, feats)
with h5py.File(cache_file, 'a') as h5:
if group_id not in h5:
group = h5.require_group(group_id)
dset_feats_group = h5[dset_feats_id]
scoress = []
foldss = []
correct_expids = []
for expid in expids:
print dset_id, expid, model, feats
res = ManysourcesResult(expid=expid, dset=dset_id, feats=feats, model=model)
cv = res.lsocv() if lso else res.crscv()
try:
scores, ys, folds = cv.merge_scores(calibration=calib)
if 'y' not in dset_feats_group:
dset_feats_group['y'] = np.array(ys, dtype=np.int32)
if 'molids' not in dset_feats_group:
dset_feats_group['molids'] = res.molids()
scoress.append(scores)
foldss.append(folds)
correct_expids.append(expid)
except:
pass
finally:
res._close_h5() # TODO: make result a context manager...
group['scores'] = np.array(scoress).T
group['folds'] = np.array(foldss).T
group['expids'] = np.array(correct_expids, dtype=np.int32)
with h5py.File(cache_file, 'r') as h5:
group1 = h5[group_id]
group2 = h5[dset_feats_id]
try:
return \
group1['scores'][:], \
group1['expids'][:], \
group1['folds'][:], \
group2['molids'][:], \
group2['y'][:]
except:
return None
def sources_coocurrences_df(dset='bcrp',
expids=None,
feats='ecfps1',
model='logreg3',
lso=True):
cache_file = op.join(MANYSOURCES_DATA_ROOT, 'results', 'sources_coocurrences_df.h5')
if expids is None:
expids = range(4096)
group_id = '/dset=%s/feats=%s/model=%s/lso=%r' % (dset, feats, model, lso)
sources_dataset_id = '/dset=%s/sources' % dset
with h5py.File(cache_file, 'a') as h5:
if group_id not in h5:
coocurrences = []
valid_expids = []
fold_ids = []
dset_name = dset
dset = None
for expid in expids:
print dset_name, expid, model, feats, lso
res = ManysourcesResult(expid=expid, dset=dset_name, feats=feats, model=model)
if dset is None:
dset = res.ms_dset()
if sources_dataset_id not in h5:
sources_as_in_the_matrix = dset.mols().i2sources_order()
h5[sources_dataset_id] = sources_as_in_the_matrix
if lso is False:
print '\tWARNING: source coocurrences do not make much sense without LSO splitting'
cv = res.lsocv() if lso else res.crscv()
if cv is None:
continue
for fold_num, fold in enumerate(cv.folds()):
try:
coocurrences.append(fold.sources_coocurrences(dset=dset))
valid_expids.append(expid)
fold_ids.append(fold_num)
except:
pass
res._close_h5()
group = h5.require_group(group_id)
group['coocurrences'] = np.array(coocurrences)
group['expids'] = valid_expids
group['folds'] = fold_ids
with h5py.File(cache_file, 'r') as h5:
sources = h5[sources_dataset_id][:]
coocurrences = h5[group_id]['coocurrences'][:].astype(np.bool)
expids = h5[group_id]['expids'][:]
folds = h5[group_id]['folds'][:]
return coocurrences, sources, expids, folds
def molecules_coocurrences_df(dset='bcrp',
expids=None,
feats='ecfps1',
model='logreg3',
lso=True):
cache_file = op.join(MANYSOURCES_DATA_ROOT, 'results', 'molecules_coocurrences_df.h5')
if expids is None:
expids = range(4096)
group_id = '/dset=%s/feats=%s/model=%s/lso=%r' % (dset, feats, model, lso)
molecules_dataset_id = '/dset=%s/molecules' % dset
with h5py.File(cache_file, 'a') as h5:
if group_id not in h5:
coocurrences = []
valid_expids = []
fold_ids = []
dset_name = dset
dset = None
for expid in expids:
print dset_name, expid, model, feats, lso
res = ManysourcesResult(expid=expid, dset=dset_name, feats=feats, model=model)
if dset is None:
dset = res.ms_dset()
if molecules_dataset_id not in h5:
molecules_as_in_the_matrix = res.molids()
h5[molecules_dataset_id] = molecules_as_in_the_matrix
cv = res.lsocv() if lso else res.crscv()
if cv is None:
continue
for fold_num, fold in enumerate(cv.folds()):
try:
c = np.zeros(len(res.molids()), dtype=np.int)
c[fold.test_indices()] = 1
coocurrences.append(c)
valid_expids.append(expid)
fold_ids.append(fold_num)
except:
pass
res._close_h5()
group = h5.require_group(group_id)
group['coocurrences'] = np.array(coocurrences)
group['expids'] = valid_expids
group['folds'] = fold_ids
with h5py.File(cache_file, 'r') as h5:
molids = h5[molecules_dataset_id][:]
coocurrences = h5[group_id]['coocurrences'][:].astype(np.bool)
expids = h5[group_id]['expids'][:]
folds = h5[group_id]['folds'][:]
return coocurrences, molids, expids, folds
def logreg_weights(dset='bcrp',
expids=None,
feats='ecfps1',
model='logreg3',
lso=True,
eps=1E-6):
"""
Parameters
----------
dset: string, default 'bcrp'
The dataset id
expids: int list, default None
The experiment ids; if None, use from 0 to 4096
feats: string, default 'ecfps1'
The id of the feature set
model: string, default 'logreg3'
The id of the model used
lso: boolean, default True
Whether the experiment corresponds to a LSO or CRS partitioning scheme
eps: float, default 0.000001
Little number to be considered 0
Returns
-------
A four-tuple (matrix, intercepts, expids, folds)
matrix: csr sparse matrix (num_folds x num_features)
intercepts: numpy array (num_folds)
expids: num_folds experiment ids
folds: num_folds array of fold_ids within each experiment
Each row of the matrix corresponds to the tuple (expid, fold).
:rtype: (scipy.sparse.csr_matrix, np.array, np.array, np.array)
"""
cache_file = op.join(MANYSOURCES_DATA_ROOT, 'results', 'logreg_weights_df.h5')
if expids is None:
expids = range(4096)
group_id = '/dset=%s/feats=%s/model=%s/lso=%r' % (dset, feats, model, lso)
with h5py.File(cache_file, 'a') as h5:
if group_id not in h5:
is_sparse = eps is not None
if not is_sparse:
raise NotImplementedError()
else:
row = 0
rows = array('I')
cols = array('I')
vals = array('d')
intercepts = array('d')
correct_expids = array('I')
correct_folds = array('I')
num_feats = None # real nasty
for expid in expids:
print dset, expid, model, feats, lso
res = ManysourcesResult(expid=expid, dset=dset, feats=feats, model=model)
cv = res.lsocv() if lso else res.crscv()
if cv is None:
continue
for fold_num, fold in enumerate(cv.folds()):
try:
coef, intercept = fold.model_data()
coef = coef[0] # Lame
if num_feats is None:
num_feats = len(coef)
else:
assert num_feats == len(coef), 'astiness is all around me, and so the feelin is gross'
intercept = intercept[()] # Lame
non_zero = np.where(np.abs(coef) > eps)[0]
density = float(len(non_zero)) / len(coef)
if density > 0.35:
print '\tWARNING: sparsity %.2f' % density
cols.extend(non_zero)
rows.extend([row] * len(non_zero))
vals.extend(coef[non_zero])
correct_expids.append(expid)
correct_folds.append(fold_num)
intercepts.append(intercept[0])
row += 1
except:
pass
res._close_h5()
group = h5.require_group(group_id)
matrix = coo_matrix((vals, (rows, cols))).tocsr()
group['indices'] = matrix.indices
group['indptr'] = matrix.indptr
group['data'] = matrix.data
group['shape'] = (matrix.shape[0], num_feats) # nasty nasty
group['expids'] = correct_expids
group['folds'] = correct_folds
group['intercepts'] = intercepts
with h5py.File(cache_file, 'r') as h5:
group = h5[group_id]
matrix = csr_matrix((group['data'][:], group['indices'][:], group['indptr'][:]),
shape=group['shape'][:])
return matrix, group['intercepts'][:], group['expids'][:], group['folds'][:]