def re_arrange_learning_results(results_file):
res = np.load(results_file,
allow_pickle=True) # [[y_real, y_pred], [...], ...]
db = DB("y4_python/11k_molecule_database_eV.db")
regression = MyRegression(db)
m_r, c_r = regression.slope, regression.intercept
all_ = db.get_all()
def get_Eblyp(row_idx, dE_pred):
_, i_pm7, *_ = all_[int(row_idx)]
return (m_r * i_pm7 + c_r) - dE_pred
res = res[np.argsort(res[:, 0])]
E_blyp_pred = np.fromiter(
(get_Eblyp(res[idx][0], res[idx][2]) for idx in range(len(res))),
dtype=np.float64)
array_all = np.array(all_)
fig = plt.figure()
ax = fig.add_subplot(121)
ax.hist2d(array_all[:, 1], array_all[:, 2], bins=(200, 100), cmin=1)
ax.set_title("ax")
ax2 = fig.add_subplot(122)
ax2.hist2d(array_all[:, 1], E_blyp_pred, bins=(200, 100), cmin=1)
ax2.set_title("ax2")
rmse = mean_absolute_error(array_all[:, 2], E_blyp_pred)
pearsonr(array_all[:, 2], E_blyp_pred)
print(rmse)
plt.show()
def time_RDF_distance():
db = DB("y4_python/11k_molecule_database_eV.db")
homo_list = db.get_homo_molecular_orbitals()
lumo_list = []
def run():
ctr = 0
for idx in range(0, len(homo_list), 2):
try:
j = homo_list[idx + 1]
except:
break
i = homo_list[idx]
orbital_distance(
i, {},
j, {},
homo_coeff=1.0,
lumo_coeff=0.0,
orbital_distance_kwargs={"radial_distribution_coeff": 1.0})
ctr += 1
#print("counter = ", ctr)
number = 1000
t = timeit(lambda: run(), number=number)
print(t)
def dE_from_row_idx(row_idx):
db = DB("y4_python/11k_molecule_database_eV.db")
regression = MyRegression(db)
all_ = db.get_all()
row1 = all_[row_idx]
i_mol_id, i_pm7, i_blyp, i_smiles, i_fp, i_homo, i_lumo = row1
return regression.distance_from_regress(i_pm7, i_blyp)
def D_RDF_from_mol_ids(mol_id1, mol_id2):
from y4_python.python_modules.orbital_similarity import radial_distribution_difference
db = DB("y4_python/11k_molecule_database_eV.db")
row1 = db.get_row_from_mol_id(mol_id1)
row2 = db.get_row_from_mol_id(mol_id2)
i_mol_id, i_pm7, i_blyp, i_smiles, i_fp, i_homo, i_lumo = row1
j_mol_id, j_pm7, j_blyp, j_smiles, j_fp, j_homo, j_lumo = row2
return radial_distribution_difference(i_homo, j_homo)
def time_euclidean_distance_learning():
"run learning with Euc distance to see the time taken and compare with chemical_distance"
db = DB("y4_python/11k_molecule_database_eV.db")
reg = MyRegression(db)
mol_list = db.get_mol_ids()
pm7_energies = db.get_pm7_energies()
blyp_energies = db.get_blyp_energies()
deviation_list = (list(
map(reg.distance_from_regress, pm7_energies, blyp_energies)))
main_euclidean_distance(k_neighbours=5,
k_folds=10,
metric_params={},
mol_list=mol_list,
deviation_list=deviation_list)
def setUp(self) -> None:
test_db = sqlite3.connect(":memory:")
self.c = test_db.cursor()
self.c.execute("CREATE TABLE data (fingerprints text)")
self.smiles: np.ndarray = DB().get_smiles()
self.smiles_sample = sample(list(self.smiles), 50)
self.pairs = combinations(self.smiles_sample, 2)
def time_structural_distance():
db = DB("y4_python/11k_molecule_database_eV.db")
fingerprint_list = db.get_fingerprints()
def run():
ctr = 0
for idx in range(0, len(fingerprint_list), 2):
try:
j = fingerprint_list[idx + 1]
except:
break
i = fingerprint_list[idx]
structural_distance(i, j)
ctr += 1
print("counter = ", ctr)
number = 1
t = timeit(lambda: run(), number=number)
print(t)
def time_RDF_and_structural():
db = DB("y4_python/11k_molecule_database_eV.db")
all_ = db.get_all()
fp_list = db.get_fingerprints()
homo_list = db.get_homo_molecular_orbitals()
lumo_list = db.get_lumo_molecular_orbitals()
def run():
ctr = 0
for idx in range(0, len(all_), 2):
try:
j = np.array([idx + 1])
except:
break
i = np.array([idx])
chemical_distance(i,
j,
homo_coeff=1.0,
lumo_coeff=0.0,
fingerprint_list=fp_list,
homo_orbital_list=homo_list,
lumo_orbital_list=lumo_list,
**{
"c_struct": 1.0,
"c_orbital": 1.0,
"radial_distribution_coeff": 1.0
})
ctr += 1
#print("counter = ", ctr)
number = 100
t = timeit(lambda: run(), number=number)
print(t)
def time_chemical_distance_learning():
"run learning with different params to see what takes the longest. Use limited set of data so it doesn't take so long on each run."
db = DB("y4_python/11k_molecule_database_eV.db")
reg = MyRegression(db)
cutoff = 1000
mol_list = db.get_mol_ids()[:cutoff]
pm7_energies = db.get_pm7_energies()[:cutoff]
blyp_energies = db.get_blyp_energies()[:cutoff]
deviation_list = (list(
map(reg.distance_from_regress, pm7_energies, blyp_energies)))
fingerprint_list = db.get_fingerprints()[:cutoff]
homo_list = db.get_homo_molecular_orbitals()[:cutoff]
lumo_list = db.get_lumo_molecular_orbitals()[:cutoff]
params = MetricParams(homo_coeff=1.0,
lumo_coeff=0.0,
fingerprint_list=fingerprint_list,
homo_orbital_list=homo_list,
lumo_orbital_list=lumo_list,
c_struct=0.0,
c_orbital=1.0,
inertia_coefficient=0.0,
IPR_coefficient=0.0,
N_coefficient=0.0,
O_coefficient=0.0,
S_coefficient=0.0,
P_coefficient=0.0,
radial_distribution_coeff=1.0)
results = main_chemical_distance(k_neighbours=5,
k_folds=10,
metric_params=params,
mol_list=mol_list,
deviation_list=deviation_list,
save=False)
print(results)
def time_chemical_distance_metric():
""
db = DB("y4_python/11k_molecule_database_eV.db")
all_ = db.get_all_cursor()
fingerprint_list = db.get_fingerprints()
homo_list = db.get_homo_molecular_orbitals()
lumo_list = db.get_lumo_molecular_orbitals()
idx = 0
rows = []
i = np.array([0])
j = np.array([1])
def run_euc():
return euc(i, j)
def run(it):
for a, b in it:
i = np.array([a])
j = np.array([b])
return chemical_distance(i,
j,
homo_coeff=1.0,
lumo_coeff=0.0,
fingerprint_list=fingerprint_list,
homo_orbital_list=homo_list,
lumo_orbital_list=lumo_list,
c_orbital=1.0,
c_struct=1.0,
inertia_coefficient=0.0,
IPR_coefficient=0.0,
N_coefficient=0.0,
O_coefficient=0.0,
S_coefficient=0.0,
P_coefficient=0.0,
radial_distribution_coeff=1.0)
number = 100_000
it_range = 10
it = combinations(range(it_range), 2)
print(
f"len(it) = {it_range} * ({it_range-1}) / 2 = {it_range*(it_range-1) / 2}"
)
print(timeit(lambda: run(it), number=number) / number)
print(timeit(lambda: run_euc(), number=number) / number)
def time_euc():
db = DB("y4_python/11k_molecule_database_eV.db")
all_ = db.get_all()
fp_list = db.get_fingerprints()
homo_list = db.get_homo_molecular_orbitals()
lumo_list = db.get_lumo_molecular_orbitals()
i = np.array([1000, 1000, 1000])
j = np.array([2000, 2000, 2000])
def run():
ctr = 0
for idx in range(0, len(all_), 2):
euc(i, j)
ctr += 1
#print("counter = ", ctr)
number = 100
t = timeit(lambda: run(), number=number)
print(t)
mol_list,
deviation_list,
save=False)
return rmse
if __name__ == "__main__":
c_struct_lim = (0.0, 10.0)
c_orbital_lim = (0.0, 10.0)
bounds = [c_struct_lim] + [
c_orbital_lim
] # this will be whatever hyperparameters you want to optimize. The length of this sequence is what indicates the number of hyperparameters mini_args = (X, y, condition,fixed_hyperparams) # this will be a tuple with the rest of arguments needed for your function NCPU = 20 # this will be the number of CPUs you want
num_CPU = 20
db = DB("y4_python/11k_molecule_database_eV.db")
my_regression = MyRegression(db)
mol_list = db.get_mol_ids()
pm7_energies = db.get_pm7_energies()
blyp_energies = db.get_blyp_energies()
deviation_list = (list(
map(my_regression.distance_from_regress, pm7_energies, blyp_energies)))
fingerprint_list = db.get_fingerprints()
homo_molecular_orbital_list = db.get_homo_molecular_orbitals()
lumo_molecular_orbital_list = db.get_lumo_molecular_orbitals()
fixed_hyperparams = MetricParams(
homo_coeff=1.0,
lumo_coeff=0.0,
fingerprint_list=fingerprint_list,
homo_orbital_list=homo_molecular_orbital_list,
def Delta_Ei_from_mol_id(mol_id):
db = DB("y4_python/11k_molecule_database_eV.db")
regression = MyRegression(db)
row1 = db.get_row_from_mol_id(mol_id)
i_mol_id, i_pm7, i_blyp, i_smiles, i_fp, i_homo, i_lumo = row1
return regression.distance_from_regress(i_pm7, i_blyp)
def plot_testing_results(relative_file_path, x_max=None):
db = DB("y4_python/11k_molecule_database_eV.db")
reg = MyRegression(db)
plot_testing_metric_results(relative_file_path, reg, x_max=x_max)