def test_VonMisesFeaturizer_describe_features():
feat = VonMisesFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
bin_index = int(df.iloc[f_index].otherinfo.strip('bin-'))
dihedral_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
feature_value = [
vm.pdf(i, loc=feat.loc, kappa=feat.kappa)[bin_index]
for i in dihedral_value
]
assert (features[0][:, f_index] == feature_value).all()
def test_von_mises_featurizer_2():
trajectories = MinimalFsPeptide().get_cached().trajectories
# test to make sure results are being put in the right order
feat = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
_, all_phi = compute_phi(trajectories[0])
X_all = feat.transform(trajectories)
all_res = []
for frame in all_phi:
for dihedral_value in frame:
all_res.extend(vm.pdf(dihedral_value,
loc=feat.loc, kappa=feat.kappa))
print(len(all_res))
# this checks 10 random dihedrals to make sure that they appear in the right columns
# for the vonmises bins
n_phi = all_phi.shape[1]
for k in range(5):
# pick a random phi dihedral
rndint = np.random.choice(range(n_phi))
# figure out where we expect it to be in X_all
indices_to_expect = []
for i in range(10):
indices_to_expect += [n_phi * i + rndint]
# we know the results in all_res are dihedral1(bin1-bin10) dihedral2(bin1 to bin10)
# we are checking if X is alldihedrals(bin1) then all dihedrals(bin2)
expected_res = all_res[rndint * 10:10 + rndint * 10]
assert (np.array(
[X_all[0][0, i] for i in indices_to_expect]) == expected_res).all()
def test_von_mises_featurizer_2():
trajectories = MinimalFsPeptide().get_cached().trajectories
# test to make sure results are being put in the right order
feat = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
_, all_phi = compute_phi(trajectories[0])
X_all = feat.transform(trajectories)
all_res = []
for frame in all_phi:
for dihedral_value in frame:
all_res.extend(
vm.pdf(dihedral_value, loc=feat.loc, kappa=feat.kappa))
print(len(all_res))
# this checks 10 random dihedrals to make sure that they appear in the right columns
# for the vonmises bins
n_phi = all_phi.shape[1]
for k in range(5):
# pick a random phi dihedral
rndint = np.random.choice(range(n_phi))
# figure out where we expect it to be in X_all
indices_to_expect = []
for i in range(10):
indices_to_expect += [n_phi * i + rndint]
# we know the results in all_res are dihedral1(bin1-bin10) dihedral2(bin1 to bin10)
# we are checking if X is alldihedrals(bin1) then all dihedrals(bin2)
expected_res = all_res[rndint * 10:10 + rndint * 10]
assert (np.array([X_all[0][0, i]
for i in indices_to_expect]) == expected_res).all()
def test_von_mises_featurizer():
dataset = fetch_alanine_dipeptide()
trajectories = dataset["trajectories"]
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames, 36), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
X_all = featurizer.transform(trajectories)
assert X_all[0].shape == (n_frames, 20), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
def test_that_all_featurizers_run():
# TODO: include all featurizers, perhaps with generator tests
trajectories = AlanineDipeptide().get_cached().trajectories
trj0 = trajectories[0][0]
atom_indices, pair_indices = get_atompair_indices(trj0)
featurizer = AtomPairsFeaturizer(pair_indices)
X_all = featurizer.transform(trajectories)
featurizer = SuperposeFeaturizer(np.arange(15), trj0)
X_all = featurizer.transform(trajectories)
featurizer = DihedralFeaturizer(["phi", "psi"])
X_all = featurizer.transform(trajectories)
featurizer = VonMisesFeaturizer(["phi", "psi"])
X_all = featurizer.transform(trajectories)
# Below doesn't work on ALA dipeptide
# featurizer = msmbuilder.featurizer.ContactFeaturizer()
# X_all = featurizer.transform(trajectories)
featurizer = RMSDFeaturizer(trj0)
X_all = featurizer.transform(trajectories)
def test_VonMisesFeaturizer_describe_features():
feat = VonMisesFeaturizer()
rnd_traj = np.random.randint(len(trajectories))
features = feat.transform([trajectories[rnd_traj]])
df = pd.DataFrame(feat.describe_features(trajectories[rnd_traj]))
for f in range(25):
f_index = np.random.choice(len(df))
atom_inds = df.iloc[f_index].atominds
bin_index = int(df.iloc[f_index].otherinfo.strip('bin-'))
dihedral_value = md.compute_dihedrals(trajectories[rnd_traj],
[atom_inds])
feature_value = [vm.pdf(i, loc=feat.loc, kappa=feat.kappa)[bin_index]
for i in dihedral_value]
assert (features[0][:, f_index] == feature_value).all()
def test_von_mises_featurizer():
trajectories = AlanineDipeptide().get_cached().trajectories
featurizer = VonMisesFeaturizer(["phi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames,
18), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames,
36), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
X_all = featurizer.transform(trajectories)
assert X_all[0].shape == (n_frames,
20), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
def test_von_mises_featurizer():
dataset = fetch_alanine_dipeptide()
trajectories = dataset["trajectories"]
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames,
36), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
X_all = featurizer.transform(trajectories)
assert X_all[0].shape == (n_frames,
20), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
def test_von_mises_featurizer():
trajectories = AlanineDipeptide().get_cached().trajectories
featurizer = VonMisesFeaturizer(["phi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames, 18), (
"unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames, 36), (
"unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
X_all = featurizer.transform(trajectories)
assert X_all[0].shape == (n_frames, 20), (
"unexpected shape returned: (%s, %s)" %
X_all[0].shape)
def test_von_mises_featurizer():
dataset = fetch_alanine_dipeptide()
trajectories = dataset["trajectories"]
featurizer = VonMisesFeaturizer(["phi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames,
18), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=18)
X_all = featurizer.transform(trajectories)
n_frames = trajectories[0].n_frames
assert X_all[0].shape == (n_frames,
36), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
featurizer = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
X_all = featurizer.transform(trajectories)
assert X_all[0].shape == (n_frames,
20), ("unexpected shape returned: (%s, %s)" %
X_all[0].shape)
dataset = fetch_fs_peptide()
trajectories = dataset["trajectories"][0]
#test to make sure results are being put in the right order
feat = VonMisesFeaturizer(["phi", "psi"], n_bins=10)
_, all_phi = compute_phi(trajectories[0])
X_all = feat.transform([trajectories])
all_res = []
for frame in all_phi:
for dihedral_value in frame:
all_res.extend(
vm.pdf(dihedral_value, loc=feat.loc, kappa=feat.kappa))
print(len(all_res))
#this checks 10 random dihedrals to make sure that they appear in the right columns
#for the vonmises bins
n_phi = all_phi.shape[1]
for k in range(5):
#pick a random phi dihedral
rndint = np.random.choice(range(n_phi))
#figure out where we expect it to be in X_all
indices_to_expect = []
for i in range(10):
indices_to_expect += [n_phi * i + rndint]
#we know the results in all_res are dihedral1(bin1-bin10) dihedral2(bin1 to bin10)
# we are checking if X is alldihedrals(bin1) then all dihedrals(bin2)
expected_res = all_res[rndint * 10:10 + rndint * 10]
assert (np.array([X_all[0][0, i]
for i in indices_to_expect]) == expected_res).all()
