def do_analysis(dataset, molecules, s_raw, log_file):
#--- calculate guesses ---
msg.info("Calculating guesses ...",2)
p_1e = np.array([
hf.init_guess_by_1e(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_sap = np.array([
hf.init_guess_by_atom(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_minao = np.array([
hf.init_guess_by_minao(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_gwh = np.array([
hf.init_guess_by_wolfsberg_helmholtz(mol.get_pyscf_molecule()) for mol in molecules[1]
])
#---
with open(log_file, "a+") as f:
f.write("\n\n+++++ H_Core +++++\n")
msg.info("Results H_Core: ", 1)
measure_and_display(
p_1e.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ SAP +++++\n")
msg.info("Results SAP: ", 1)
measure_and_display(
p_sap.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ MINAO +++++\n")
msg.info("Results MINAO: ", 1)
measure_and_display(
p_minao.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ GWH +++++\n")
msg.info("Results GWH: ", 1)
measure_and_display(
p_gwh.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
def do_analysis(network_path, dataset, molecules, s_raw, log_file):
#--- fetch network ---
msg.info("Fetching pretained network ", 2)
graph = tf.Graph()
structure, weights, biases = np.load(
network_path,
encoding="latin1"
)[:3]
with graph.as_default():
sess = tf.Session()
network = EluFixedValue(structure, weights, biases)
network.setup()
sess.run(tf.global_variables_initializer())
#---
#--- calculate guesses ---
msg.info("Calculating guesses ...",2)
msg.info("Neural network ", 1)
p_nn = network.run(sess, dataset.testing[0])
msg.info("McWheenys", 1)
p_batch = make_matrix_batch(p_nn, DIM, True)
p_mcw1 = np.array(list(map(lambda x: multi_mc_wheeny(x[0], x[1], n_max=1), zip(p_batch, s_raw))))
p_mcw5 = np.array(list(map(lambda x: multi_mc_wheeny(x[0], x[1], n_max=5), zip (p_batch, s_raw))))
msg.info("Classics", 1)
p_1e = np.array([
hf.init_guess_by_1e(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_sap = np.array([
hf.init_guess_by_atom(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_minao = np.array([
hf.init_guess_by_minao(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_gwh = np.array([
hf.init_guess_by_wolfsberg_helmholtz(mol.get_pyscf_molecule()) for mol in molecules[1]
])
#---
msg.info("Results NN: ", 1)
with open(log_file, "a+") as f:
f.write("\n+++++ Plain NN +++++\n")
measure_and_display(
p_nn, dataset, molecules, True, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ McW 1 +++++\n")
msg.info("Results McWheeny 1: ",1)
measure_and_display(
p_mcw1.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ McW 5 +++++\n")
msg.info("Results McWheeny 5: ", 1)
measure_and_display(
p_mcw5.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ H_Core +++++\n")
msg.info("Results H_Core: ", 1)
measure_and_display(
p_1e.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ SAP +++++\n")
msg.info("Results SAP: ", 1)
measure_and_display(
p_sap.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ MINAO +++++\n")
msg.info("Results MINAO: ", 1)
measure_and_display(
p_minao.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ GWH +++++\n")
msg.info("Results GWH: ", 1)
measure_and_display(
p_gwh.reshape(-1, DIM**2), dataset, molecules, False, log_file, s=s_raw
)
def main(sample_index):
msg.print_level = 2
msg.info("Hi. Measurements for butadien", 2)
#--- fetch dataset and constants ---
msg.info("Fetching sample " + str(sample_index) + " from datase", 2)
dim = DIM
molecules = np.load(
"butadien/data/molecules.npy"
)
S = np.load( "butadien/data/S.npy")
P = np.load( "butadien/data/P.npy")
dataset = make_butadien_dataset(molecules, S, P)[0]
molecule = molecules[sample_index].get_pyscf_molecule()
s = S[sample_index].reshape(dim, dim)
s_normed = dataset.inverse_input_transform(s.reshape(1, -1)).reshape(1,-1)
p = P[sample_index].reshape(dim, dim)
#---
#--- fetch network ---
msg.info("Fetching pretained network ", 2)
graph = tf.Graph()
structure, weights, biases = np.load(
"butadien/data/network.npy",
encoding="latin1"
)
with graph.as_default():
sess = tf.Session()
network = EluFixedValue(structure, weights, biases)
network.setup()
sess.run(tf.global_variables_initializer())
#---
#--- calculate guesses ---
msg.info("Calculating guesses ...",2)
msg.info("Neural network ", 1)
p_nn = network.run(sess, s_normed).reshape(dim, dim)
msg.info("McWheenys", 1)
p_mcw1 = multi_mc_wheeny(p_nn, s, n_max=1)
p_mcw5 = multi_mc_wheeny(p_nn, s, n_max=5)
msg.info("Classics", 1)
p_sap = hf.init_guess_by_atom(molecule)
p_minao = hf.init_guess_by_minao(molecule)
p_gwh = hf.init_guess_by_wolfsberg_helmholtz(molecule)
#---
#--- Measureing & print ---
outfile = "butadien/results/cube_" + str(sample_index) + "_"
msg.info("Results Converged ", 1)
cubegen.density(molecule, outfile + "converged.cube", p.astype("float64"))
msg.info("Results NN: ", 1)
cubegen.density(molecule, outfile + "nn.cube", p_nn.astype("float64"))
msg.info("Results McWheeny 1: ",1)
cubegen.density(molecule, outfile + "mcw1.cube", p_mcw1.astype("float64"))
msg.info("Results McWheeny 5: ", 1)
cubegen.density(molecule, outfile + "mcw5.cube", p_mcw5.astype("float64"))
msg.info("Results SAP: ", 1)
cubegen.density(molecule, outfile + "sap.cube", p_sap)
msg.info("Results MINAO: ", 1)
cubegen.density(molecule, outfile + "minao.cube", p_minao)
msg.info("Results GWH: ", 1)
cubegen.density(molecule, outfile + "gwh.cube", p_gwh)
def main(molecule_type):
msg.print_level = 2
msg.info("Hi. Measurements for " + molecule_type, 2)
#--- fetch dataset and constants ---
msg.info("Fetching dataset", 2)
dataset_triu, molecules = fetch_dataset(molecule_type, True)
dataset, _ = fetch_dataset(molecule_type, False)
dim = DIM[molecule_type]
#---
#--- fetch network ---
msg.info("Fetching pretained network ", 2)
graph = tf.Graph()
structure, weights, biases = np.load(
"cc2ai/" + molecule_type + "/network_" + molecule_type + ".npy",
encoding="latin1"
)
#---
with graph.as_default():
sess = tf.Session()
network = EluFixedValue(structure, weights, biases)
network.setup()
sess.run(tf.global_variables_initializer())
#---
#--- calculate guesses ---
msg.info("Calculating guesses ...",2)
s_raw = make_matrix_batch(dataset_triu.inverse_input_transform(dataset_triu.testing[0]), dim, True)
msg.info("Neural network ", 1)
p_nn = network.run(sess, dataset_triu.testing[0])
msg.info("McWheenys", 1)
p_batch = make_matrix_batch(p_nn, dim, True)
p_mcw1 = np.array(list(map(lambda x: multi_mc_wheeny(x[0], x[1], n_max=1), zip(p_batch, s_raw))))
p_mcw5 = np.array(list(map(lambda x: multi_mc_wheeny(x[0], x[1], n_max=5), zip (p_batch, s_raw))))
msg.info("Classics", 1)
p_1e = np.array([
hf.init_guess_by_1e(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_sap = np.array([
hf.init_guess_by_atom(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_minao = np.array([
hf.init_guess_by_minao(mol.get_pyscf_molecule()) for mol in molecules[1]
])
p_gwh = np.array([
hf.init_guess_by_wolfsberg_helmholtz(mol.get_pyscf_molecule()) for mol in molecules[1]
])
#---
#--- Measureing & print ---
log_file = "cc2ai/" + molecule_type + "/pretrained_" + str(date.today()) + ".log"
with open(log_file, "a+") as f:
f.write("##### Analysis of " + str(datetime.now()) + " #####\n")
msg.info("Results NN: ", 1)
with open(log_file, "a+") as f:
f.write("\n+++++ Plain NN +++++\n")
measure_and_display(
p_nn, dataset_triu, molecules, molecule_type, True, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ McW 1 +++++\n")
msg.info("Results McWheeny 1: ",1)
measure_and_display(
p_mcw1.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ McW 5 +++++\n")
msg.info("Results McWheeny 5: ", 1)
measure_and_display(
p_mcw5.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ H_Core +++++\n")
msg.info("Results H_Core: ", 1)
measure_and_display(
p_1e.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ SAP +++++\n")
msg.info("Results SAP: ", 1)
measure_and_display(
p_sap.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ MINAO +++++\n")
msg.info("Results MINAO: ", 1)
measure_and_display(
p_minao.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)
with open(log_file, "a+") as f:
f.write("\n\n+++++ GWH +++++\n")
msg.info("Results GWH: ", 1)
measure_and_display(
p_gwh.reshape(-1, dim**2), dataset, molecules, molecule_type, False, log_file
)