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custom_tica.py
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custom_tica.py
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import os
from msmbuilder.decomposition import tICA, SparseTICA
from io_functions import *
import multiprocessing as mp
import glob
from sklearn.preprocessing import StandardScaler
from io_functions import compat_verboseload
def fit_normalizer(features_directory, features=None):
save_file = "%s/normalizer.h5" %features_directory
n = StandardScaler()
if features is None:
for feature_file in get_trajectory_files(features_directory, ".dataset"):
n.partial_fit(load_file(feature_file))
else:
for feature in features:
n.partial_fit(feature)
verbosedump(n, save_file)
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")
#fit_model = load_file(fit_model_filename)
#transformed_data = load_file(projected_data_filename)
#print fit_model.summarize()
#active_pdb = md.load(active_pdb_file)
#top = active_pdb.topology
#atom_indices = atom_indices = [a.index for a in top.atoms if str(a.residue)[0:3] != "POP" and not a.residue.is_water and str(a.residue)[0:2] != "NA" and str(a.residue)[0:2] != "CL"]
#active_pdb = md.load(active_pdb_file, atom_indices=atom_indices)
#read_and_featurize_custom(active_pdb_file, condition = "A-00_custom_features", location = "/scratch/users/enf/b2ar_analysis")
#active_features = [np.transpose(compat_verboseload("/scratch/users/enf/b2ar_analysis/A-00_custom_features.h5"))]
#active_pdb_projected = fit_model.transform(active_features)
#print(active_pdb_projected)
def transform(existing_model, features_directory, tica_dir, normalizer):
model = compat_verboseload(existing_model)
feature_files = get_trajectory_files(features_directory, ext = ".dataset")
features = load_file_list(feature_files)
features = [normalizer.transform(feature) for feature in features]
tica_coords = model.transform(features)
tica_coords = np.concatenate(tica_coords)
if not os.path.exists(tica_dir): os.makedirs(tica_dir)
np.savetxt("%s/refcoords.csv" %tica_dir, tica_coords, delimiter=",")
return
#load features into list
#transform it with model
#save to dataset or just a csv file
#use to make new tica coord plots with inactive and active structures
def transform_features(feature_filename, model, normalizer):
return model.partial_transform(normalizer.transform(load_file(feature_filename)))
def transform_to_h5(existing_model_filename, features_directory,
projected_data_filename, normalizer,
worker_pool=None, parallel=False):
model = compat_verboseload(existing_model_filename)
transform_partial = partial(transform_features, model=model, normalizer=normalizer)
feature_files = get_trajectory_files(features_directory, ext=".dataset")
if worker_pool is not None:
transformed_features = worker_pool.map_sync(transform_partial, feature_files)
elif parallel:
pool = mp.Pool(mp.cpu_count()/4)
transformed_features = pool.map(transform_partial, feature_files)
pool.terminate()
else:
transformed_features = [transform_partial(f) for f in feature_files]
verbosedump(transformed_features, projected_data_filename)
def check_tica_vs_features(tica_coords_dir, feature_dir):
tica_coords = compat_verboseload(tica_coords_dir)
tica_coords = np.concatenate(tica_coords)
print((np.shape(tica_coords)))
feature_files = get_trajectory_files(feature_dir, ext = ".h5")
if len(feature_files) == 0: feature_files = get_trajectory_files(feature_dir, ext = ".dataset")
pool = mp.Pool(mp.cpu_count())
features = pool.map(load_features, feature_files)
pool.terminate()
if np.shape(features[0])[1] != np.shape(features[1])[1]:
for i in range(0, len(features)):
features[i] = np.transpose(features[i])
features = np.concatenate(features)
print((np.shape(features)))
print((np.shape(tica_coords)))
#print out all random forest GINI decreases: