def _transform_cell_feats((cache_dir, images, normalization_name, output_filename, key, header)):
try:
import numpy as np
from cpa.util.cache import Cache, normalizations
cache = Cache(cache_dir)
normalization = normalizations[normalization_name]
normalizeddata, normalized_colnames = cache.load(images,
normalization=normalization)
if len(normalizeddata) == 0:
return np.empty(len(normalized_colnames)) * np.nan
normalizeddata = normalizeddata[
~np.isnan(np.sum(normalizeddata,1)),:]
if len(normalizeddata) == 0:
return np.empty(len(normalized_colnames)) * np.nan
# save the features to csv
import csv
filename = output_filename + "-" + "-".join(key) + ".csv"
f = open(filename, 'w')
w = csv.writer(f)
w.writerow(header)
for vector in normalizeddata:
w.writerow(tuple(key) + tuple(vector))
f.close()
except: # catch *all* exceptions
from traceback import print_exc
import sys
print_exc(None, sys.stderr)
return None
def profile_gmm(cache_dir, group_name, ncomponents=50, filter=None,
ipython_profile=None):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name, reverse=True, filter=filter)
keys = group.keys()
subsamples = subsample(cache_dir, [group[g] for g in keys], ipython_profile)
subsampled = np.vstack(subsamples)
meanvector = np.mean(subsampled, 0)
mean_centered = subsampled - meanvector
#perform PCA
U, s, V = linalg.svd(mean_centered, full_matrices=False)
percvar_expl = s ** 2 / np.sum(s ** 2)
scores = np.dot(U, np.diag(s))
loadings = np.transpose(V)
# Find the number of PCs required to explain x% of variance
cutoffpercentage = 80
percvar_cum = np.cumsum(percvar_expl)
npc = np.nonzero(percvar_cum > float(cutoffpercentage) / 100)[0][0]
if npc < 20:
npc = 20
# GMM
gmm = GMM(ncomponents, cvtype='full')
gmm.fit(scores[:, :npc], n_iter=100000, thresh=1e-7)
parameters = [(cache_dir, group[g], gmm, meanvector, loadings[:, :npc])
for g in keys]
variables = ['Component %d' % i for i in range(ncomponents)]
return Profiles.compute(keys, variables, _compute_mixture_probabilities,
parameters, ipython_profile, group_name=group_name)
def profile_svmnormalvector(cache_dir,
group_name,
control_filter,
filter=None,
rfe=False,
ipython_profile=None,
job=None):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name,
reverse=True,
filter=filter)
variables = RobustLinearNormalization(cache).colnames
control_images_by_plate = images_by_plate(control_filter)
plate_by_image = dict(
(row[:-2], row[-2]) for row in cpa.db.GetPlatesAndWellsPerImage())
def control_images(treated_images):
return [
r for image in treated_images
for r in control_images_by_plate[plate_by_image[image]]
]
keys = group.keys()
parameters = [(cache_dir, group[k], control_images(group[k]), rfe)
for k in keys]
if job:
i = job - 1
memoized(_compute_svmnormalvector(parameters[i]))
else:
return Profiles.compute(keys,
variables,
memoized(_compute_svmnormalvector),
parameters,
ipython_profile,
group_name=group_name)
def profile_factoranalysis_mean(cache_dir, group_name, nfactors=5, filter=None,
ipython_profile=None, save_model=None):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name, reverse=True, filter=filter)
keys = group.keys()
subsamples = subsample(cache_dir, [group[g] for g in keys], ipython_profile)
mean = np.mean(subsamples, axis=0)
subsampled_data = subsamples - mean
stdev = np.std(subsampled_data, axis=0)
subsampled_data = subsampled_data / stdev
nfactors = min(nfactors, subsampled_data.shape[1])
variables = ['Factor %d' % (i + 1) for i in range(nfactors)]
fa_node = nodes.FANode(input_dim=None, output_dim=nfactors, dtype=None, max_cycles=30)
print 'Training'
fa_node.train(subsampled_data)
fa_node.stop_training()
if save_model:
cpa.util.pickle(save_model, fa_node)
parameters = [(cache_dir, group[g], fa_node, mean, stdev)
for g in keys]
return Profiles.compute(keys, variables, _compute_group_projection_and_mean,
parameters, ipython_profile, group_name=group_name)
def profile_ksstatistic(cache_dir,
group_name,
control_filter,
filter=None,
ipython_profile=None):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name,
reverse=True,
filter=filter)
variables = RobustLinearNormalization(cache).colnames
control_images_by_plate = images_by_plate(control_filter)
plate_by_image = dict(
(row[:-2], row[-2]) for row in cpa.db.GetPlatesAndWellsPerImage())
def control_images(treated_images):
return [
r for image in treated_images
for r in control_images_by_plate[plate_by_image[image]]
]
keys = group.keys()
parameters = [(cache_dir, group[k], control_images(group[k]))
for k in keys]
return Profiles.compute(keys,
variables,
_compute_ksstatistic,
parameters,
ipython_profile,
group_name=group_name)
def _compute_mixture_probabilities((cache_dir, images, gmm, meanvector, loadings)):
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
mean_centered = normalizeddata - meanvector
projected = np.dot(mean_centered, loadings)
mixture_probabilities = gmm.predict_proba(projected)
return mixture_probabilities.mean(0)
def _compute_ksstatistic((cache_dir, images, control_images)):
#try:
import numpy as np
import sys
from cpa.util.cache import Cache, RobustLinearNormalization
from cpa.util.ks_2samp import ks_2samp
cache = Cache(cache_dir)
normalizeddata, variables = cache.load(images, normalization=RobustLinearNormalization)
control_data, control_colnames = cache.load(control_images, normalization=RobustLinearNormalization)
assert len(control_data) >= len(normalizeddata)
assert variables == control_colnames
#downsampled = control_data[np.random.randint(0, len(control_data), len(normalizeddata)), :]
m = len(variables)
profile = np.empty(m)
for j in range(m):
profile[j] = ks_2samp(control_data[:, j], normalizeddata[:, j],
signed=True)[0]
return profile
def _compute_ksstatistic((cache_dir, images, control_images)):
import numpy as np
import sys
from cpa.util.cache import Cache, RobustLinearNormalization
from cpa.util.ks_2samp import ks_2samp
cache = Cache(cache_dir)
normalizeddata, variables = cache.load(images, normalization=RobustLinearNormalization)
control_data, control_colnames = cache.load(control_images, normalization=RobustLinearNormalization)
print normalizeddata.shape, control_data.shape
assert len(control_data) >= len(normalizeddata)
assert variables == control_colnames
#downsampled = control_data[np.random.randint(0, len(control_data), len(normalizeddata)), :]
m = len(variables)
profile = np.empty(m)
for j in range(m):
profile[j] = ks_2samp(control_data[:, j], normalizeddata[:, j],
signed=True)[0]
return profile
def _compute_svmnormalvector((cache_dir, images, control_images, rfe)):
#try:
import numpy as np
import sys
from cpa.util.cache import Cache, RobustLinearNormalization
from sklearn.svm import LinearSVC
from cpa.util.profile_svmnormalvector import _compute_rfe
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
control_data, control_colnames = cache.load(control_images, normalization=RobustLinearNormalization)
assert len(control_data) >= len(normalizeddata)
downsampled = control_data[np.random.randint(0, len(control_data), len(normalizeddata)), :]
x = np.vstack((normalizeddata, downsampled))
y = np.array([1] * len(normalizeddata) + [0] * len(downsampled))
clf = LinearSVC(C=1.0)
m = clf.fit(x, y)
normal_vector = m.coef_[0]
if rfe:
normal_vector[~_compute_rfe(x, y)] = 0
return normal_vector
def profile_mean(cache_dir, group_name, filter=None, ipython_profile=None,
normalization=RobustLinearNormalization):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name, reverse=True,
filter=filter)
variables = normalization(cache).colnames
keys = group.keys()
parameters = [(cache_dir, group[g], normalization.__name__)
for g in keys]
return Profiles.compute(keys, variables, _compute_group_mean, parameters,
ipython_profile, group_name=group_name)
def _compute_group_mean((cache_dir, images, normalization_name)):
try:
import numpy as np
from cpa.util.cache import Cache, normalizations
cache = Cache(cache_dir)
normalization = normalizations[normalization_name]
normalizeddata, normalized_colnames = cache.load(images,
normalization=normalization)
if len(normalizeddata) == 0:
return np.empty(len(normalized_colnames)) * np.nan
normalizeddata = normalizeddata[
~np.isnan(np.sum(normalizeddata,1)),:]
if len(normalizeddata) == 0:
return np.empty(len(normalized_colnames)) * np.nan
return np.mean(normalizeddata, axis = 0)
except: # catch *all* exceptions
from traceback import print_exc
import sys
print_exc(None, sys.stderr)
return None
def _compute_group_subsample((cache_dir, images)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
np.random.shuffle(normalizeddata)
normalizeddata_sample = [x for i, x in enumerate(normalizeddata) if i % 100 == 0]
return normalizeddata_sample
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
def _compute_svmnormalvector((cache_dir, images, control_images, rfe)):
#try:
import numpy as np
import sys
from cpa.util.cache import Cache, RobustLinearNormalization
from sklearn.svm import LinearSVC
from cpa.util.profile_svmnormalvector import _compute_rfe
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(
images, normalization=RobustLinearNormalization)
control_data, control_colnames = cache.load(
control_images, normalization=RobustLinearNormalization)
assert len(control_data) >= len(normalizeddata)
downsampled = control_data[
np.random.randint(0, len(control_data), len(normalizeddata)), :]
x = np.vstack((normalizeddata, downsampled))
y = np.array([1] * len(normalizeddata) + [0] * len(downsampled))
clf = LinearSVC(C=1.0)
m = clf.fit(x, y)
normal_vector = m.coef_[0]
if rfe:
normal_vector[~_compute_rfe(x, y)] = 0
return normal_vector
def _compute_group_projection_and_mean((cache_dir, images, fa_node, mean, stdev)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
normalizeddata = (normalizeddata - mean) / stdev
normalizeddata_projected = fa_node.execute(normalizeddata)
normalizeddata_projected_mean = np.mean(normalizeddata_projected, axis = 0)
return normalizeddata_projected_mean
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
def profile_ksstatistic(cache_dir, group_name, control_filter, plate_group,
filter=None, parallel=Uniprocessing()):
cache = Cache(cache_dir)
group, colnames_group = cpa.db.group_map(group_name, reverse=True,
filter=filter)
variables = RobustLinearNormalization(cache).colnames
control_images_by_plate = images_by_plate(control_filter, plate_group)
plate_by_image = dict((row[:-2], tuple(row[-2:-1]))
for row in cpa.db.GetPlatesAndWellsPerImage())
def control_images(treated_images):
if plate_group is None:
return control_images_by_plate[None]
else:
return list(set(r for image in treated_images
for r in control_images_by_plate[plate_by_image[image]]))
keys = group.keys()
parameters = [(cache_dir, group[k], control_images(group[k]))
for k in keys]
return Profiles.compute(keys, variables, _compute_ksstatistic,
parameters, parallel=parallel,
group_name=group_name)
