def fit_protein_tica(yaml_file,sparse=False):
mdl_dir = yaml_file["mdl_dir"]
mdl_params = yaml_file["mdl_params"]
current_mdl_params={}
for i in mdl_params.keys():
if i.startswith("tica__"):
current_mdl_params[i.split("tica__")[1]] = mdl_params[i]
if sparse==True:
protein_tica_mdl = SparseTICA(**current_mdl_params)
else:
protein_tica_mdl = tICA(**current_mdl_params)
for protein in yaml_file["protein_list"]:
print("Fitting to protein %s" % protein)
with enter_protein_data_dir(yaml_file, protein):
featurized_traj = sorted(glob.glob("./%s/*.jl" %
yaml_file["feature_dir"]), key=keynat)
for f in featurized_traj:
featurized_path = verboseload(f)
try:
protein_tica_mdl.partial_fit(featurized_path)
except:
pass
print("Done partial fitting to protein %s" % protein)
# dumping the tica_mdl
tica_mdl_path = os.path.join(mdl_dir, "tica_mdl.pkl")
verbosedump(protein_tica_mdl, tica_mdl_path)
return
def landmark_ktica_ticaTraj(tica_dir, clusterer_dir, ktica_dir, clusters_map_file = "", landmarks_dir = "", nystroem_components=1000, n_components=10, lag_time=5, nystroem_data_filename = "", fit_model_filename = "", projected_data_filename = "", landmark_subsample=1, sparse = False, wolf = True, rho = 0.01, shrinkage = None):
if not os.path.exists(ktica_dir): os.makedirs(ktica_dir)
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components = n_components, lag_time = lag_time)
else:
tica_model = tICA(n_components = n_components, lag_time = lag_time, shrinkage = shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho)
else:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho, shrinkage = shrinkage)
if not os.path.exists(nystroem_data_filename):
clusterer = verboseload(clusterer_dir)
tica = verboseload(tica_dir)
features = tica
clusters = clusterer.cluster_centers_
landmarks = clusters
print("here's what goes into the combined class:")
#print(np.shape(features))
print(np.shape(landmarks))
print(type(landmarks))
nys = Nystroem(n_components = np.shape(landmarks)[0], basis = landmarks)#np.shape(landmarks)[0])# basis=landmarks)
nyx = nys.fit_transform(features)
del features
del landmarks
try:
save_dataset(nyx, nystroem_data_filename)
except:
os.system("rm -rf %s" %nystroem_data_filename)
save_dataset(nyx, nystroem_data_filename)
else:
nyx = load_dataset(nystroem_data_filename)
print(np.shape(nyx))
print(dir(nyx))
if not os.path.exists(projected_data_filename):
fit_model = tica_model.fit(nyx)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(nyx)
del(nyx)
try:
save_dataset(transformed_data, projected_data_filename)
except:
os.system("rm -rf %s" %projected_data_filename)
save_dataset(transformed_data, projected_data_filename)
else:
print("Already performed landmark kernel tICA.")
def ktica(features, landmarks, projected_data_filename, nystroem_data_filename, fit_model_filename, sparse = False, shrinkage = 0.05, wolf = True, rho = 0.01):
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components = n_components, lag_time = lag_time)
else:
if wolf:
tica_model = tICA(n_components = n_components, lag_time = lag_time, shrinkage = shrinkage)
else:
tica_model = tICA(n_components = n_components, lag_time = lag_time, gamma = shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho)
else:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho, shrinkage = shrinkage)
if not os.path.exists(nystroem_data_filename):
nys = Nystroem(n_components = np.shape(landmarks)[0], basis = landmarks)#np.shape(landmarks)[0])# basis=landmarks)
nyx = nys.fit_transform(features)
print("Computed Nystroem.")
del features
del landmarks
try:
save_dataset(nyx, nystroem_data_filename)
except:
os.system("rm -rf %s" %nystroem_data_filename)
save_dataset(nyx, nystroem_data_filename)
else:
nyx = load_dataset(nystroem_data_filename)
print("Loaded Nystroem")
if not os.path.exists(projected_data_filename):
fit_model = tica_model.fit(nyx)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(nyx)
del(nyx)
try:
save_dataset(transformed_data, projected_data_filename)
except:
os.system("rm -rf %s" %projected_data_filename)
save_dataset(transformed_data, projected_data_filename)
else:
print("Already performed landmark kernel tICA.")
def test_doublewell():
data = build_dataset()
tica = tICA(n_components=1).fit(data)
tic0 = tica.components_[0]
stica = SparseTICA(n_components=1, verbose=False).fit(data)
stic0 = stica.components_[0]
np.testing.assert_array_almost_equal(stic0[1:], np.zeros(9))
np.testing.assert_almost_equal(stic0[0], 0.58, decimal=1)
def test_1():
data = build_dataset()
tica = tICA(n_components=1).fit(data)
tic0 = tica.components_[0]
print('tICA\n', tic0)
stica = SparseTICA(n_components=1, verbose=True).fit(data)
stic0 = stica.components_[0]
print('Sparse tICA\n', stic0)
assert np.allclose(stic0, [1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def fit_protein_tica(yaml_file,sparse=False,ksparse=None):
mdl_dir = yaml_file["mdl_dir"]
mdl_params = yaml_file["mdl_params"]
current_mdl_params={}
for i in mdl_params.keys():
if i.startswith("tica__"):
current_mdl_params[i.split("tica__")[1]] = mdl_params[i]
if sparse==True:
protein_tica_mdl = SparseTICA(**current_mdl_params)
elif type(ksparse)==int:
current_mdl_params["k"] = ksparse
protein_tica_mdl = KSparseTICA(**current_mdl_params)
else:
protein_tica_mdl = tICA(**current_mdl_params)
for protein in yaml_file["protein_list"]:
print("Fitting to protein %s" % protein)
with enter_protein_data_dir(yaml_file, protein):
if os.path.exists("./normalized_features"):
featurized_traj = sorted(glob.glob("./normalized_features/*.jl"), key=keynat)
else:
print('Warning: features have not been scaled')
featurized_traj = sorted(glob.glob("./%s/*.jl" %
yaml_file["feature_dir"]), key=keynat)
for f in featurized_traj:
featurized_path = verboseload(f)
try:
protein_tica_mdl.partial_fit(featurized_path)
except:
print('Error')
print("Done partial fitting to protein %s" % protein)
# dumping the tica_mdl
tica_mdl_path = os.path.join(mdl_dir, "tica_mdl.pkl")
verbosedump(protein_tica_mdl, tica_mdl_path)
return
def fit_and_transform(features_directory,
model_dir,
stride=5,
lag_time=10,
n_components=5,
wolf=True,
shrinkage=None,
rho=0.05,
parallel=True,
sparse=True,
traj_ext=".h5",
normalize=True,
partial_fit=True,
subsample=1,
recompute_tica=False,
features=None):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
projected_data_filename = "%s/phi_psi_chi2_allprot_projected.h5" % model_dir
fit_model_filename = "%s/phi_psi_chi2_allprot_tica_coords.h5" % model_dir
normalizer = "%s/normalizer.h5" % features_directory
n = compat_verboseload(normalizer)
#active_pdb_file = "/scratch/users/enf/b2ar_analysis/renamed_topologies/A-00.pdb"
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components=n_components, lag_time=lag_time)
else:
tica_model = tICA(n_components=n_components,
lag_time=lag_time,
shrinkage=shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho)
else:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho,
shrinkage=shrinkage)
if not os.path.exists(projected_data_filename) or recompute_tica:
print("loading feature files")
feature_files = get_trajectory_files(features_directory,
ext=".dataset")
if partial_fit:
transformed_data = []
for i, feature_file in enumerate(feature_files):
print("fitting tICA model to %s" % feature_file)
if features is None:
featurized_traj = load_file(feature_file)
else:
featurized_traj = features[i]
normalized_featurized_traj = n.transform(featurized_traj)
tica_model.partial_fit(normalized_featurized_traj)
print("Finished computing tICA model. Now transforming.")
for i, feature_file in enumerate(feature_files):
print("Transforming %s" % feature_file)
if features is None:
featurized_traj = load_file(feature_file)
else:
featurized_traj = features[i]
normalized_featurized_traj = n.transform(featurized_traj)
transformed_data.append(
tica_model.partial_transform(n.transform(featurized_traj)))
fit_model = tica_model
else:
if features is None:
if not parallel:
features = []
for feature_file in feature_files:
#if "A-00" not in feature_file and "A-01" not in feature_file: continue
#print("Loading feature files one at a time")
print("loading %s" % feature_file)
#if sparse:
# features.append(load_features(feature_file)[0:1000,0:10])
#else:
features.append(
load_file(feature_file)[::subsample, :])
else:
pool = mp.Pool(mp.cpu_count())
features = pool.map(load_file, feature_files)
pool.terminate()
transpose = False
for i in range(0, len(features)):
if np.shape(features[0])[1] != np.shape(features[i])[1]:
transpose = True
break
if transpose:
for i in range(0, len(features)):
features[i] = np.transpose(features[i])
print(np.shape(features[0]))
#print np.shape(features[1])
print((features[0][0][0:10]))
#print(features[1][0][0:10])
print((np.shape(features)))
if normalize:
features = [n.transform(f) for f in features]
print("fitting data to tICA model")
fit_model = tica_model.fit(features)
if subsample == 1:
transformed_data = fit_model.transform(features)
else:
transformed_data = [
fit_model.transform(n.transform(load_file(f)))
for f in feature_files
]
print("transformed data with tICA model")
print((fit_model.summarize()))
#print(dir(fit_model))
#save_dataset(fit_model, fit_model_filename)
verbosedump(fit_model, fit_model_filename)
print("saved tICA model")
verbosedump(transformed_data, projected_data_filename)
print("saved data projected onto tICA coords")
else:
print("already computed tICA model")
def landmark_ktica_ticaTraj(tica_dir,
clusterer_dir,
ktica_dir,
clusters_map_file="",
landmarks_dir="",
nystroem_components=1000,
n_components=10,
lag_time=5,
nystroem_data_filename="",
fit_model_filename="",
projected_data_filename="",
landmark_subsample=1,
sparse=False,
wolf=True,
rho=0.01,
shrinkage=None):
if not os.path.exists(ktica_dir): os.makedirs(ktica_dir)
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components=n_components, lag_time=lag_time)
else:
tica_model = tICA(n_components=n_components,
lag_time=lag_time,
shrinkage=shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho)
else:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho,
shrinkage=shrinkage)
if not os.path.exists(nystroem_data_filename):
clusterer = verboseload(clusterer_dir)
tica = verboseload(tica_dir)
features = tica
clusters = clusterer.cluster_centers_
landmarks = clusters
print("here's what goes into the combined class:")
#print(np.shape(features))
print((np.shape(landmarks)))
print((type(landmarks)))
nys = Nystroem(
n_components=np.shape(landmarks)[0],
basis=landmarks) #np.shape(landmarks)[0])# basis=landmarks)
nyx = nys.fit_transform(features)
del features
del landmarks
try:
save_dataset(nyx, nystroem_data_filename)
except:
os.system("rm -rf %s" % nystroem_data_filename)
save_dataset(nyx, nystroem_data_filename)
else:
nyx = load_dataset(nystroem_data_filename)
print((np.shape(nyx)))
print((dir(nyx)))
if not os.path.exists(projected_data_filename):
fit_model = tica_model.fit(nyx)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(nyx)
del (nyx)
try:
save_dataset(transformed_data, projected_data_filename)
except:
os.system("rm -rf %s" % projected_data_filename)
save_dataset(transformed_data, projected_data_filename)
else:
print("Already performed landmark kernel tICA.")
def ktica(features,
landmarks,
projected_data_filename,
nystroem_data_filename,
fit_model_filename,
sparse=False,
shrinkage=0.05,
wolf=True,
rho=0.01,
n_components=25,
lag_time=5,
refcoords_csv=None):
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components=n_components, lag_time=lag_time)
else:
if wolf:
tica_model = tICA(n_components=n_components,
lag_time=lag_time,
shrinkage=shrinkage)
else:
tica_model = tICA(n_components=n_components,
lag_time=lag_time,
gamma=shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho)
else:
tica_model = SparseTICA(n_components=n_components,
lag_time=lag_time,
rho=rho,
shrinkage=shrinkage)
if not os.path.exists(nystroem_data_filename):
nys = Nystroem(
n_components=np.shape(landmarks)[0],
basis=landmarks) #np.shape(landmarks)[0])# basis=landmarks)
nyx = nys.fit_transform(features)
print("Computed Nystroem.")
del features
del landmarks
try:
save_dataset(nyx, nystroem_data_filename)
except:
os.system("rm -rf %s" % nystroem_data_filename)
save_dataset(nyx, nystroem_data_filename)
else:
nyx = load_dataset(nystroem_data_filename)
print("Loaded Nystroem")
if not os.path.exists(fit_model_filename):
print("Fitting Kernel tICA model")
fit_model = tica_model.fit(nyx)
verbosedump(fit_model, fit_model_filename)
transformed_data = fit_model.transform(nyx)
del (nyx)
try:
save_dataset(transformed_data, projected_data_filename)
except:
os.system("rm -rf %s" % projected_data_filename)
save_dataset(transformed_data, projected_data_filename)
else:
fit_model = verboseload(fit_model_filename)
transformed_data = fit_model.transform(nyx)
os.system("rm -rf %s" % projected_data_filename)
save_dataset(transformed_data, projected_data_filename)
if refcoords_csv is not None:
np.savetxt(refcoords_csv, transformed_data, delimiter=",")
return
def fit_and_transform(features_directory, model_dir, stride=5, lag_time=10, n_components = 5, wolf = True, shrinkage = None, rho = 0.05, parallel=True, sparse = True, traj_ext = ".h5"):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
projected_data_filename = "%s/phi_psi_chi2_allprot_projected.h5" %model_dir
fit_model_filename = "%s/phi_psi_chi2_allprot_tica_coords.h5" %model_dir
#active_pdb_file = "/scratch/users/enf/b2ar_analysis/renamed_topologies/A-00.pdb"
if not sparse:
if shrinkage is None:
tica_model = tICA(n_components = n_components, lag_time = lag_time)
else:
tica_model = tICA(n_components = n_components, lag_time = lag_time, shrinkage = shrinkage)
else:
if shrinkage is None:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho)
else:
tica_model = SparseTICA(n_components = n_components, lag_time = lag_time, rho = rho, shrinkage = shrinkage)
if not os.path.exists(projected_data_filename):
print("loading feature files")
feature_files = get_trajectory_files(features_directory, ext = traj_ext)
if len(feature_files) == 0: feature_files = get_trajectory_files(features_directory, ext = ".dataset")
if not parallel:
features = []
for feature_file in feature_files:
#if "A-00" not in feature_file and "A-01" not in feature_file: continue
#print("Loading feature files one at a time")
print "loading %s" %feature_file
#if sparse:
# features.append(load_features(feature_file)[0:1000,0:10])
#else:
features.append(load_features(feature_file))
else:
pool = mp.Pool(mp.cpu_count())
features = pool.map(load_features, feature_files)
pool.terminate()
transpose = False
for i in range(0, len(features)):
if np.shape(features[0])[1] != np.shape(features[i])[1]:
transpose = True
break
if transpose:
for i in range(0, len(features)):
features[i] = np.transpose(features[i])
print np.shape(features[0])
#print np.shape(features[1])
print(features[0][0][0:10])
#print(features[1][0][0:10])
print(np.shape(features))
print("fitting data to tICA model")
fit_model = tica_model.fit(features)
print(fit_model.summarize())
#print(dir(fit_model))
#save_dataset(fit_model, fit_model_filename)
transformed_data = fit_model.transform(features)
print("transformed data with tICA model")
verbosedump(fit_model, fit_model_filename)
print("saved tICA model")
verbosedump(transformed_data, projected_data_filename)
print("saved data projected onto tICA coords")
else:
print("already computed tICA model")
