def _open_sequences(self):
"""Loads the raw sequences, storing them as lists of strings."""
logging.info("loading %s ..." % basename(self._sequencefile))
tic()
# Read entire data file into one big string.
# Manually scanning the string for newlines and tabs is 3x faster than
# using readlines() and then calling split() on each line.
with open(self._sequencefile) as f:
f.readline() # discard header
txt = f.read()
assert txt[-1] == '\n', "Sequence file must end with a newline."
for name in self.sequence_names:
logging.info(" %s" % name)
foldfilter = self._foldfilter
maxrows = self._maxrows
seqloop = range(len(self.sequence_names)-1) # Used in innermost loop
revcomp = self._reverse_complement
# Store each column as its own list of sequences.
# Scan through the txt string until we've hit the end.
sequences = [[] for s in self.sequence_names]
rowidx = []
i,j = 0,txt.find('\n')
for row_index in xrange(len(txt)):
if j == -1 or len(rowidx) >= maxrows:
break
# Check FoldID is wanted (first char of any new line)
if txt[i] in foldfilter:
# Add each sequence in this row to its corresponding list
k = txt.find('\t', i+2) # k = index of first char of first sequence in this row
for s in seqloop:
i, k = k+1, txt.find('\t', k+1)
sequences[s].append(txt[i:k]) # Pull out column 's' sequence
i, k = k+1, txt.find('\t', k+1)
if k == -1 or k > j: # If the next tab is on the next line, then break at the newline
k = j
sequences[-1].append(txt[i:k]) # Pull out the last column's sequence
rowidx.append(row_index) # Also remember the original row index of this example.
# Advance so that txt[i:j] is the next line. The last character of the file must be a '\n'.
i,j = j+1,txt.find('\n',j+1)
txt = None # Release memory for gigantic string immediately, for the stability of debugger
# Convert row indices numpy array for faster indexing when loading features/targets
self.rowidx = np.asarray(rowidx,np.uint32).reshape((-1,1))
self._sequences = sequences
logging.info("... load took %.2fs" % toc())
def _open_targets(self):
if self._targetfile is None:
return
_log_xform_warned = False
logging.info("loading %s ..." % basename(self._targetfile))
tic()
# Read the entire targets file, convert it to numpy array as a string, and slice
# just the rows that we're using.
# It turns out this strategy is MUCH faster than using numpy.loadtxt:
# features = np.loadtxt(self._featurefile, np.float32,
# delimiter='\t', skiprows=1, ndmin=2)
with open(self._targetfile) as f:
f.readline() # discard header
txt = f.read()
ntarget = len(self.target_names)
ntask = len(self._task_ids)
rowidx = self.rowidx
maxrows_to_read = rowidx[-1]+1
if self._targetfile_cols:
# np.fromstring is fast but doesn't support the presence of non-numeric columns
targets = np.asarray([[float(x) for x in line.split('\t')[self._targetfile_cols]]
for line in txt.split('\n',maxrows_to_read)[:-1]])
else:
targets = np.fromstring(txt, np.float32, sep='\t',
count=ntarget*maxrows_to_read).reshape(-1, ntarget)
txt = None
if len(targets) > len(rowidx):
targets = targets[rowidx.ravel(),:]
# Select columns using '_task_ids' no matter what, since the order
# might be different.
usecols = np.asarray([self.target_names.index(name) for name in self._task_ids]) # nparray for faster indexing in
targets = targets[:,usecols]
# Normalize targets by scaling min/max range to [0,1]
if targets.size > 0:
# OPTIONAL: clamp all originally negative values at zero
#targets = np.maximum(0, targets)
# For each individual column, get lo/hi percentile
# and then normalize the non-NaN values in that column
a,b = [],[]
for i in range(ntask):
target_i = targets[:,i]
mask_i = ~np.isnan(target_i)
is_boolean = np.all(np.logical_or(target_i[mask_i] == 0, target_i[mask_i] == 1))
if is_boolean:
# Automatically assume 0/1 classification target
logging.info(" %s \t(classification)" % self._task_ids[i])
ai,bi = 1,0
else:
# Automatically assume regression target
logging.info(" %s \t(regression)" % self._task_ids[i])
if "log" in self._preprocess:
if (not np.all(target_i[mask_i] >= 0)):
if not _log_xform_warned:
_log_xform_warned = True
print "Warning: log transform requires all original targets to be non-negative; biasing the data and proceeding anyway."
target_i[mask_i] -= target_i[mask_i].min()
target_i[mask_i] = np.log(1+target_i[mask_i])
elif "sqrt" in self._preprocess:
if (not np.all(target_i[mask_i] >= 0)):
if not _log_xform_warned:
_log_xform_warned = True
print "Warning: sqrt transform requires all original targets to be non-negative; biasing the data and proceeding anyway."
target_i[mask_i] -= target_i[mask_i].min()
target_i[mask_i] = np.sqrt(target_i[mask_i])
lo_i,hi_i = np.percentile(target_i[mask_i], [0.0, 1.0])
#lo_i,hi_i = np.percentile(target_i[mask_i], [0.05, 99.95])
if lo_i == hi_i:
hi_i += 1 # Avoid divide by zero for degenerate targets
# Convert everything below the "lo" threshold to NaNs
tmp = target_i[mask_i]
tmp[tmp < lo_i] = np.nan
target_i[mask_i] = tmp
mask_i = ~np.isnan(target_i)
# Convert everything above the "hi" threshold to NaNs
tmp = target_i[mask_i]
tmp[tmp > hi_i] = np.nan
target_i[mask_i] = tmp
mask_i = ~np.isnan(target_i)
# Clamp everything to the range [lo,hi]
#target_i[mask_i] = np.maximum(lo_i, target_i[mask_i])
#target_i[mask_i] = np.minimum(hi_i, target_i[mask_i]) # Assume anything above hi_i is a "large" outlier
# Subtract the mean
if self._requirements.get('target',None) == 'logistic':
intercept_i = lo_i
else:
intercept_i = np.mean(target_i[mask_i])
ai = 1./(hi_i-lo_i)
bi = -intercept_i*ai
target_i[mask_i] = ai*target_i[mask_i] + bi
#mask_pos = target_i[mask_i] > 0
#target_i[mask_i][mask_pos] **= 0.5
a.append(ai)
b.append(bi)
if "log" in self._preprocess:
self._targets_preprocess.append(('log',))
self._targets_preprocess.append(('normalize', np.asarray(a).reshape((1,-1)),
np.asarray(b).reshape((1,-1))) )
#targets[self._targets_mask] = np.maximum(0,targets[self._targets_mask])
#targets[self._targets_mask] = np.minimum(1,targets[self._targets_mask])
self._targets = targets
self._targets_mask = ~np.isnan(targets)
logging.info("... load took %.2fs" % toc())
def _open_features(self):
if self._featurefile is None:
return
logging.info("loading %s ..." % basename(self._featurefile))
tic()
# Read the entire features file, convert it to numpy array as a string, and slice
# just the rows that we're using.
# It turns out this strategy is MUCH faster than using numpy.loadtxt:
# features = np.loadtxt(self._featurefile, np.float32,
# delimiter='\t', skiprows=1, ndmin=2)
with open(self._featurefile) as f:
f.readline() # discard header
txt = f.read()
for name in self.feature_names:
logging.info(" %s" % name)
nfeature = len(self.feature_names)
rowidx = self.rowidx
maxrows_to_read = rowidx[-1]+1
if self._featurefile_cols:
# np.fromstring is fast but doesn't support the presence of non-numeric columns
raise NotImplementedError("This code should work but has not been tested.")
features = np.asarray([[float(x) for x in line.split('\t')[self._featurefile_cols]]
for line in txt.split('\n',maxrows_to_read)[:-1]])
else:
features = np.fromstring(txt, np.float32, sep='\t',
count=nfeature*maxrows_to_read).reshape(-1, nfeature)
txt = None
if len(features) > len(rowidx):
features = features[rowidx.ravel(),:]
# Preprocess each feature by normalizing and setting mean to 0
a,b = [],[]
for i in range(nfeature):
col = features[:,i:i+1]
mask = ~np.isnan(col)
lo = np.min(col[mask], axis=0)
hi = np.max(col[mask], axis=0)
if lo == hi:
hi += 1 # Avoid divide by zero for degenerate targets
meani = np.mean(col[mask])
ai = 1./(hi-lo)
bi = -meani*ai
col[mask] = ai*col[mask] + bi
a.append(ai)
b.append(bi)
self._feature_preprocess = [ ('normalize', np.asarray(a).reshape((1,-1)),
np.asarray(b).reshape((1,-1))) ]
nsequence = len(self._sequences[0])
assert len(features) == nsequence, "Number of rows in Features file must match number of rows in Sequences file."
self._features = features
logging.info("... load took %.2fs" % toc())
def _open_targets(self):
if self._targetfile is None:
return
_log_xform_warned = False
logging.info("loading %s ..." % basename(self._targetfile))
tic()
# Read the entire targets file, convert it to numpy array as a string, and slice
# just the rows that we're using.
# It turns out this strategy is MUCH faster than using numpy.loadtxt:
# features = np.loadtxt(self._featurefile, np.float32,
# delimiter='\t', skiprows=1, ndmin=2)
with open(self._targetfile) as f:
f.readline() # discard header
txt = f.read()
ntarget = len(self.target_names)
ntask = len(self._task_ids)
rowidx = self.rowidx
maxrows_to_read = rowidx[-1] + 1
if self._targetfile_cols:
# np.fromstring is fast but doesn't support the presence of non-numeric columns
targets = np.asarray(
[[float(x) for x in line.split('\t')[self._targetfile_cols]]
for line in txt.split('\n', maxrows_to_read)[:-1]])
else:
targets = np.fromstring(txt,
np.float32,
sep='\t',
count=ntarget * maxrows_to_read).reshape(
-1, ntarget)
txt = None
if len(targets) > len(rowidx):
targets = targets[rowidx.ravel(), :]
# Select columns using '_task_ids' no matter what, since the order
# might be different.
usecols = np.asarray([
self.target_names.index(name) for name in self._task_ids
]) # nparray for faster indexing in
targets = targets[:, usecols]
# Normalize targets by scaling min/max range to [0,1]
if targets.size > 0:
# OPTIONAL: clamp all originally negative values at zero
#targets = np.maximum(0, targets)
# For each individual column, get lo/hi percentile
# and then normalize the non-NaN values in that column
a, b = [], []
for i in range(ntask):
target_i = targets[:, i]
mask_i = ~np.isnan(target_i)
is_boolean = np.all(
np.logical_or(target_i[mask_i] == 0,
target_i[mask_i] == 1))
if is_boolean:
# Automatically assume 0/1 classification target
logging.info(" %s \t(classification)" %
self._task_ids[i])
ai, bi = 1, 0
else:
# Automatically assume regression target
logging.info(" %s \t(regression)" % self._task_ids[i])
if "log" in self._preprocess:
if (not np.all(target_i[mask_i] >= 0)):
if not _log_xform_warned:
_log_xform_warned = True
print "Warning: log transform requires all original targets to be non-negative; biasing the data and proceeding anyway."
target_i[mask_i] -= target_i[mask_i].min()
target_i[mask_i] = np.log(1 + target_i[mask_i])
elif "sqrt" in self._preprocess:
if (not np.all(target_i[mask_i] >= 0)):
if not _log_xform_warned:
_log_xform_warned = True
print "Warning: sqrt transform requires all original targets to be non-negative; biasing the data and proceeding anyway."
target_i[mask_i] -= target_i[mask_i].min()
target_i[mask_i] = np.sqrt(target_i[mask_i])
lo_i, hi_i = np.percentile(target_i[mask_i], [0.0, 1.0])
#lo_i,hi_i = np.percentile(target_i[mask_i], [0.05, 99.95])
if lo_i == hi_i:
hi_i += 1 # Avoid divide by zero for degenerate targets
# Convert everything below the "lo" threshold to NaNs
tmp = target_i[mask_i]
tmp[tmp < lo_i] = np.nan
target_i[mask_i] = tmp
mask_i = ~np.isnan(target_i)
# Convert everything above the "hi" threshold to NaNs
tmp = target_i[mask_i]
tmp[tmp > hi_i] = np.nan
target_i[mask_i] = tmp
mask_i = ~np.isnan(target_i)
# Clamp everything to the range [lo,hi]
#target_i[mask_i] = np.maximum(lo_i, target_i[mask_i])
#target_i[mask_i] = np.minimum(hi_i, target_i[mask_i]) # Assume anything above hi_i is a "large" outlier
# Subtract the mean
if self._requirements.get('target', None) == 'logistic':
intercept_i = lo_i
else:
intercept_i = np.mean(target_i[mask_i])
ai = 1. / (hi_i - lo_i)
bi = -intercept_i * ai
target_i[mask_i] = ai * target_i[mask_i] + bi
#mask_pos = target_i[mask_i] > 0
#target_i[mask_i][mask_pos] **= 0.5
a.append(ai)
b.append(bi)
if "log" in self._preprocess:
self._targets_preprocess.append(('log', ))
self._targets_preprocess.append(
('normalize', np.asarray(a).reshape(
(1, -1)), np.asarray(b).reshape((1, -1))))
#targets[self._targets_mask] = np.maximum(0,targets[self._targets_mask])
#targets[self._targets_mask] = np.minimum(1,targets[self._targets_mask])
self._targets = targets
self._targets_mask = ~np.isnan(targets)
logging.info("... load took %.2fs" % toc())
def _open_features(self):
if self._featurefile is None:
return
logging.info("loading %s ..." % basename(self._featurefile))
tic()
# Read the entire features file, convert it to numpy array as a string, and slice
# just the rows that we're using.
# It turns out this strategy is MUCH faster than using numpy.loadtxt:
# features = np.loadtxt(self._featurefile, np.float32,
# delimiter='\t', skiprows=1, ndmin=2)
with open(self._featurefile) as f:
f.readline() # discard header
txt = f.read()
for name in self.feature_names:
logging.info(" %s" % name)
nfeature = len(self.feature_names)
rowidx = self.rowidx
maxrows_to_read = rowidx[-1] + 1
if self._featurefile_cols:
# np.fromstring is fast but doesn't support the presence of non-numeric columns
raise NotImplementedError(
"This code should work but has not been tested.")
features = np.asarray(
[[float(x) for x in line.split('\t')[self._featurefile_cols]]
for line in txt.split('\n', maxrows_to_read)[:-1]])
else:
features = np.fromstring(txt,
np.float32,
sep='\t',
count=nfeature * maxrows_to_read).reshape(
-1, nfeature)
txt = None
if len(features) > len(rowidx):
features = features[rowidx.ravel(), :]
# Preprocess each feature by normalizing and setting mean to 0
a, b = [], []
for i in range(nfeature):
col = features[:, i:i + 1]
mask = ~np.isnan(col)
lo = np.min(col[mask], axis=0)
hi = np.max(col[mask], axis=0)
if lo == hi:
hi += 1 # Avoid divide by zero for degenerate targets
meani = np.mean(col[mask])
ai = 1. / (hi - lo)
bi = -meani * ai
col[mask] = ai * col[mask] + bi
a.append(ai)
b.append(bi)
self._feature_preprocess = [('normalize', np.asarray(a).reshape(
(1, -1)), np.asarray(b).reshape((1, -1)))]
nsequence = len(self._sequences[0])
assert len(
features
) == nsequence, "Number of rows in Features file must match number of rows in Sequences file."
self._features = features
logging.info("... load took %.2fs" % toc())
def _open_sequences(self):
"""Loads the raw sequences, storing them as lists of strings."""
logging.info("loading %s ..." % basename(self._sequencefile))
tic()
# Read entire data file into one big string.
# Manually scanning the string for newlines and tabs is 3x faster than
# using readlines() and then calling split() on each line.
with open(self._sequencefile) as f:
f.readline() # discard header
txt = f.read()
assert txt[-1] == '\n', "Sequence file must end with a newline."
for name in self.sequence_names:
logging.info(" %s" % name)
foldfilter = self._foldfilter
maxrows = self._maxrows
seqloop = range(len(self.sequence_names) - 1) # Used in innermost loop
revcomp = self._reverse_complement
# Store each column as its own list of sequences.
# Scan through the txt string until we've hit the end.
sequences = [[] for s in self.sequence_names]
rowidx = []
i, j = 0, txt.find('\n')
for row_index in xrange(len(txt)):
if j == -1 or len(rowidx) >= maxrows:
break
# Check FoldID is wanted (first char of any new line)
if txt[i] in foldfilter:
# Add each sequence in this row to its corresponding list
k = txt.find(
'\t', i +
2) # k = index of first char of first sequence in this row
for s in seqloop:
i, k = k + 1, txt.find('\t', k + 1)
sequences[s].append(
txt[i:k]) # Pull out column 's' sequence
i, k = k + 1, txt.find('\t', k + 1)
if k == -1 or k > j: # If the next tab is on the next line, then break at the newline
k = j
sequences[-1].append(
txt[i:k]) # Pull out the last column's sequence
rowidx.append(
row_index
) # Also remember the original row index of this example.
# Advance so that txt[i:j] is the next line. The last character of the file must be a '\n'.
i, j = j + 1, txt.find('\n', j + 1)
txt = None # Release memory for gigantic string immediately, for the stability of debugger
# Convert row indices numpy array for faster indexing when loading features/targets
self.rowidx = np.asarray(rowidx, np.uint32).reshape((-1, 1))
self._sequences = sequences
logging.info("... load took %.2fs" % toc())
