def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--data', action='store', help='', metavar='FILE')
parser.add_argument('--scratch',
action='store',
help='',
metavar="DIR",
default="_tmp_")
parser.add_argument('-j',
'--procs',
action='store',
help='',
type=int,
metavar='int',
default=0)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
header, data = read_csv(args.data, read_header=True)
data = clean_data(data)
misc.save_obj(args.scratch + "molecule_data", data)
misc.save_json(args.scratch + "molecule_data", data)
return
def voc2index(self):
""" creates a vocabulary of the words in the table"""
total_cells = self.short_sentences_table.shape[
0] * self.short_sentences_table.shape[1]
counter = 0
word_dict = defaultdict(int)
for row in range(self.short_sentences_table.shape[0]):
for col in range(self.short_sentences_table.shape[1]):
counter += 1
if counter % 100 == 0:
print(f'done {counter * 100 / total_cells} %')
if col == 1 or col == 4 or col == 5: # already numerical values
continue
if col == 0: # name has two words
sentence = self.short_sentences_table[row, col]
split_words = sentence.split()
for word in split_words:
word_dict[word] += 1
else: # name has one word
sentence = self.short_sentences_table[row, col]
word = str(sentence)
word_dict[word] += 1
index_dict_word = Vocab(Counter(word_dict))
save_obj(index_dict_word.stoi, self.dict_name)
return index_dict_word.stoi
def create_indices_matrix(self, is_dict_existing):
""" :param is_dict_existing: if a dictionary already exists (from previous calls), skip creating one"""
if is_dict_existing:
word_dict = load_obj(self.dict_name)
else: # create a new vocabulary
word_dict = self.voc2index()
word_2_num_sentence = lambda t: [
word_dict[word] for word in t.split()
] # replace every word in the cell with the matching vocab number
word_2_num_one_word = lambda t: [
word_dict[t]
] # refer to the cell content as one string and replace this string with the matching vocab number
''' for each column of the table, replace (if needed) the words / sentence with the matching index from the vocabulary'''
names_indices = np.array(
[word_2_num_sentence(t) for t in self.short_sentences_table[:, 0]])
item_conditions = np.expand_dims(
self.short_sentences_table[:, 1].astype('float'), axis=1)
category_names_indices = np.array(
[word_2_num_one_word(t) for t in self.short_sentences_table[:, 2]])
brand_names_indices = np.array(
[word_2_num_one_word(t) for t in self.short_sentences_table[:, 3]])
price = np.expand_dims(self.short_sentences_table[:,
4].astype('float'),
axis=1)
is_shipping = np.expand_dims(
self.short_sentences_table[:, 5].astype('float'), axis=1)
indices_matrix = np.concatenate(
(names_indices, item_conditions, category_names_indices,
brand_names_indices, price, is_shipping),
axis=1)
save_obj(indices_matrix, self.final_matrix_name)
return indices_matrix
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="DIR",
default="_tmp_")
parser.add_argument('--json', action='store', help='', metavar="FILE")
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
metavar="int",
default=0,
type=int)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
data = misc.load_json(args.json)
keys = data.keys()
keys = list(keys)
canonical_data = {}
for key in keys:
molobj, status = cheminfo.smiles_to_molobj(key)
if molobj is None:
print("error none mol:", key)
continue
smiles = cheminfo.molobj_to_smiles(molobj, remove_hs=True)
if "." in smiles:
print("error multi mol:", smiles)
continue
atoms = cheminfo.molobj_to_atoms(molobj)
if not is_mol_allowed(atoms):
print("error heavy mol:", smiles)
continue
canonical_data[smiles] = data[key]
misc.save_json(args.scratch + "molecule_data", canonical_data)
misc.save_obj(args.scratch + "molecule_data", canonical_data)
return
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch', action='store', help='', metavar="DIR", default="_tmp_")
parser.add_argument('--sdf', action='store', help='', metavar="FILE", nargs="+")
parser.add_argument('-j', '--procs', action='store', help='pararallize', metavar="int", default=0, type=int)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
if args.procs == -1:
args.procs = os.cpu_count()
print("starting", args.procs, "procs")
# fsdf = gzip.open(args.scratch + "structures.sdf.gz", 'w')
# fprop = open(args.scratch + "properties.csv", 'w')
mol_val_dict = {}
for sdf in args.sdf:
print("reading", sdf)
molobjs, values = parse_ochem(sdf, debug=True, procs=args.procs)
for molobj, value in zip(molobjs, values):
smiles = cheminfo.molobj_to_smiles(molobj, remove_hs=True)
if "smiles" not in mol_val_dict:
mol_val_dict[smiles] = []
else:
print("duplicate", smiles)
mol_val_dict[smiles].append(value)
# sdfstr = cheminfo.molobj_to_sdfstr(molobj)
# sdfstr += "$$$$\n"
#
# propstr = "{:} {:}\n".format(value, 0.0)
# fprop.write(propstr)
# fsdf.close()
# fprop.close()
keys = mol_val_dict.keys()
print("TOTAL ITEMS", len(keys))
misc.save_json(args.scratch + "molecule_data", mol_val_dict)
misc.save_obj(args.scratch + "molecule_data", mol_val_dict)
return
def create_short_sentences_database(self):
""" outout: function creates short sentences from the relevant df columns and than concatenates all columns
back to matrix """
names = np.expand_dims(self.get_list_of_two_most_important_word_in_sentence(self.shorten_table[:, 0].astype('str'), self.num_best_words), axis=1)
item_conditions = np.expand_dims(self.shorten_table[:, 1], axis=1)
category_names = np.expand_dims(self.shorten_table[:, 2].astype('str'), axis=1)
brand_names = np.expand_dims(self.shorten_table[:, 3].astype('str'), axis=1)
prices = np.expand_dims(self.shorten_table[:, 4],axis=1)
is_shipping = np.expand_dims(self.shorten_table[:, 5], axis=1)
#item_descriptions = np.expand_dims(self.get_list_of_two_most_important_word_in_sentence(sel.foriginal_table[:, 6].astype('str'), self.num_best_words), axis=1) # decided not to use it
short_sentence_table = np.concatenate((names,item_conditions, category_names,brand_names, prices, is_shipping), axis=1)
save_obj(short_sentence_table, 'short_sentences_table')
return short_sentence_table
def clean_data(df, scratch):
smiles = df.iloc[1]
data = {}
atom_types = []
for index, row in df.iterrows():
smi = row.smiles
value = row.mpC + 273.15
molobj, status = cheminfo.smiles_to_molobj(smi)
if molobj is None:
print("error:", smi)
continue
smi = cheminfo.molobj_to_smiles(molobj, remove_hs=True)
# Atoms
atoms = cheminfo.molobj_to_atoms(molobj)
atom_types += list(atoms)
if smi not in data:
data[smi] = []
data[smi].append(value)
atom_types, counts = np.unique(atom_types, return_counts=True)
for atom, count in zip(atom_types, counts):
print(atom, count)
misc.save_obj(scratch + "molecule_data", data)
misc.save_json(scratch + "molecule_data", data)
return
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="DIR",
default="_tmp_")
parser.add_argument('--sdf', action='store', help='',
metavar="FILE") #, nargs="+", default=[])
parser.add_argument('--properties',
action='store',
help='',
metavar="FILE") #, nargs="+", default=[])
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
metavar="int",
default=0,
type=int)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
fsdf = gzip.open(args.scratch + "structures.sdf.gz", 'w')
fprop = open(args.scratch + "properties.csv", 'w')
molecules = cheminfo.read_sdffile(args.sdf)
properties = open(args.properties, 'r')
moledict = {}
for molobj, line in zip(molecules, properties):
status = molobjfilter(molobj)
if not status:
continue
status = valuefilter(line)
if not status:
continue
smiles = cheminfo.molobj_to_smiles(molobj, remove_hs=True)
print(smiles)
sdfstr = cheminfo.molobj_to_sdfstr(molobj)
sdfstr += "$$$$\n"
fsdf.write(sdfstr.encode())
fprop.write(line)
values = [float(x) for x in line.split()[1:]]
moledict[smiles] = values
fsdf.close()
fprop.close()
properties.close()
misc.save_json(args.scratch + "molecules", moledict)
misc.save_obj(args.scratch + "molecules", moledict)
return
def RunAll(self, total_episodes=10**3, bob=1):
dict={}
method = "ep-greedy"
fav_keys=[]
for ep in [0.01,0.3,1]:
exper = training.Experiment(searching_method = method, layers=self.layers, ep=ep,resolution=self.resolution, bound_displacements=self.bound_displacements, states_wasted=total_episodes,ep_method="normal", min_ep=0.01, guessing_rule=self.guessing_rule, efficient_time=self.efficient_time)
exper.train(bob)
with open(str(exper.layers)+"L"+str(exper.number_phases)+"PH"+str(exper.resolution)+"R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_"+str(c)] = {}
dict["run_"+str(c)]["label"] = str(ep) +"-greedy "
dict["run_"+str(c)]["info"] = [exper.number_phases, exper.amplitude, exper.layers, exper.resolution, exper.searching_method, exper.guessing_rule, exper.method_guess, exper.number_bobs, exper.bound_displacements, exper.efficient_time,exper.ts_method]
dict["run_"+str(c)]["info_ep"] = [exper.ep_method, exper.ep, exper.min_ep, exper.time_tau]
dict["run_"+str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_"+str(c))
plot_dict = filter_keys(dict,fav_keys)
save_obj(plot_dict, "ep-greedy-Dolinar", exper.layers, exper.number_phases, exper.resolution, bob)
ploting(plot_dict, mode="minimax")
if bob>1:
ploting(plot_dict, mode="stds")
fav_keys=[]
for tau in [200]:
for min_ep in [0.01]:
for method_guess in ["undefined"]:
exper = training.Experiment(searching_method = "ep-greedy", layers=self.layers, min_ep = min_ep, time_tau = tau, ep=ep,resolution=self.resolution, bound_displacements=self.bound_displacements, states_wasted=total_episodes,ep_method="exp-decay",guessing_rule=self.guessing_rule, efficient_time=self.efficient_time, method_guess = method_guess)
exper.train(bob)
with open(str(exper.layers)+"L"+str(exper.number_phases)+"PH"+str(exper.resolution)+"R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_"+str(c)] = {}
dict["run_"+str(c)]["label"] = "max("+ str(min_ep) +", e^-t/"+str(tau) +")-greedy "
dict["run_"+str(c)]["info"] = [exper.number_phases, exper.amplitude, exper.layers, exper.resolution, exper.searching_method, exper.guessing_rule, exper.method_guess, exper.number_bobs, exper.bound_displacements, exper.efficient_time,exper.ts_method]
dict["run_"+str(c)]["info_ep"] = [exper.ep_method, exper.ep, exper.min_ep, exper.time_tau]
dict["run_"+str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_"+str(c))
plot_dict = filter_keys(dict,fav_keys)
save_obj(plot_dict, "exp-ep-greedy-Dolinar", exper.layers, exper.number_phases, exper.resolution, bob)
ploting(plot_dict, mode="minimax")
if bob>1:
ploting(plot_dict, mode="stds")
fav_keys=[]
method = "ucb"
for ucbm in ["ucb1", "ucb2", "ucb3"]:
exper = training.Experiment(searching_method = method, layers=self.layers, ucb_method=ucbm , resolution=self.resolution, bound_displacements=self.bound_displacements, states_wasted=total_episodes, guessing_rule=self.guessing_rule, efficient_time=self.efficient_time)
exper.train(bob)
with open(str(exper.layers)+"L"+str(exper.number_phases)+"PH"+str(exper.resolution)+"R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_"+str(c)] = {}
dict["run_"+str(c)]["label"] = ucbm
dict["run_"+str(c)]["info"] = [exper.number_phases, exper.amplitude, exper.layers, exper.resolution, exper.searching_method, exper.guessing_rule, exper.method_guess, exper.number_bobs,exper.bound_displacements, exper.efficient_time,exper.ts_method]
dict["run_"+str(c)]["info_ep"] = [exper.ep_method, exper.ep, exper.min_ep, exper.time_tau]
dict["run_"+str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_"+str(c))
plot_dict = filter_keys(dict,fav_keys)
save_obj(plot_dict, "ucbs-Dolinar", exper.layers, exper.number_phases, exper.resolution, bob, total_episodes)
ploting(plot_dict, mode="minimax")
if bob>1:
ploting(plot_dict, mode="stds")
fav_keys=[]
method = "thompson-sampling"
# for soft in [0.75, 1.25,1]:
for soft in [1]:
for mode_ts in ["None"]: #This is if you want to relate the q-table with the TS-update, but it doesnt' give any enhancement (for what i see).
exper = training.Experiment(searching_method = method, layers=self.layers,resolution=self.resolution, bound_displacements=self.bound_displacements, states_wasted=total_episodes, guessing_rule=self.guessing_rule, soft_ts=soft, efficient_time=self.efficient_time, ts_method=mode_ts)
exper.train(bob)
with open(str(exper.layers)+"L"+str(exper.number_phases)+"PH"+str(exper.resolution)+"R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_"+str(c)] = {}
dict["run_"+str(c)]["label"] = str(soft)+"-TS"
dict["run_"+str(c)]["info"] = [exper.number_phases, exper.amplitude, exper.layers, exper.resolution, exper.searching_method, exper.guessing_rule, exper.method_guess, exper.number_bobs,exper.bound_displacements, exper.efficient_time, exper.ts_method]
dict["run_"+str(c)]["info_ep"] = [exper.ep_method, exper.ep, exper.min_ep, exper.time_tau]
dict["run_"+str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_"+str(c))
plot_dict = filter_keys(dict,fav_keys)
save_obj(plot_dict, "TS", exper.layers, exper.number_phases, exper.resolution, bob)
ploting(plot_dict, mode="minimax")
if bob>1:
ploting(plot_dict, mode="stds")
save_obj(dict, "all_methods", exper.layers, exper.number_phases, exper.resolution, bob)
return
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="DIR",
default="_tmp_")
parser.add_argument('--sdf',
action='store',
help='',
metavar="FILE",
nargs="+",
default=[])
parser.add_argument('--dict',
action='store',
help='',
metavar="FILE",
nargs="+",
default=[])
parser.add_argument('--name',
action='store',
help='',
metavar="STR",
nargs="+")
parser.add_argument('--filename', action='store', help='', metavar="STR")
parser.add_argument('--filter', action='store_true', help='')
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
metavar="int",
default=0,
type=int)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
print()
databases_set = []
databases_dict = []
for sdf in args.sdf:
molobjs = cheminfo.read_sdffile(sdf)
molobjs = list(molobjs)
smiles = [
cheminfo.molobj_to_smiles(molobj, remove_hs=True)
for molobj in molobjs
]
smiles = set(smiles)
databases_set.append(smiles)
print(sdf, len(smiles))
for filename in args.dict:
data = misc.load_obj(filename)
smiles = data.keys()
smiles = set(smiles)
databases_set.append(smiles)
databases_dict.append(data)
print(filename, len(smiles))
if args.scratch is not None:
# Merge databases
everything = {}
for data in databases_dict:
keys = data.keys()
for key in keys:
if key not in everything:
everything[key] = []
everything[key] += data[key]
if args.filter:
everything = filter_dict(everything)
keys = everything.keys()
print("n items", len(keys))
# Save
misc.save_json(args.scratch + "molecule_data", everything)
misc.save_obj(args.scratch + "molecule_data", everything)
if args.name is not None:
n_db = len(databases_set)
if n_db == 2:
venn2(databases_set, set_labels=args.name)
elif n_db == 3:
venn3(databases_set, set_labels=args.name)
plt.savefig(args.scratch + "venndiagram")
return
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="dir",
default="_tmp_")
parser.add_argument('--randomseed',
action='store',
help='random seed',
metavar="int",
default=1)
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
type=int,
metavar="int",
default=0)
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
# Not that random
np.random.seed(args.randomseed)
# Get properties
properties = misc.load_npy(args.scratch + "properties")
molobjs = cheminfo.read_sdffile(args.scratch + "structures.sdf.gz")
# Get features
filename = "repr.ols"
if os.path.exists(args.scratch + filename + ".pkl"):
features = misc.load_obj(args.scratch + filename)
else:
features = extract_features(properties, molobjs, procs=args.procs)
features = pd.DataFrame(features)
features = features.fillna(0)
misc.save_obj(args.scratch + filename, features)
n_items = len(features)
X = np.arange(n_items)
assert len(properties) == n_items
# Train
n_splits = 5
n_train = misc.load_npy(args.scratch + "n_train")
fold_five = sklearn.model_selection.KFold(n_splits=n_splits,
random_state=45,
shuffle=True)
scores = []
for i, (idxs_train, idxs_test) in enumerate(fold_five.split(X)):
# un-ordered idxs_train
np.random.seed(45 + i)
np.random.shuffle(idxs_train)
learning_curve = []
for n in n_train:
idxs = idxs_train[:n]
# signed difference
sign_diff = fit_model(features, idxs, idxs_test)
# rmse
diff = sign_diff**2
rmse_test = np.sqrt(diff.mean())
# save
learning_curve.append(rmse_test)
scores.append(learning_curve)
scores = np.array(scores)
scores = scores.T
mean_score = np.mean(scores, axis=1)
print(mean_score)
misc.save_npy(args.scratch + "score.ols", scores)
return
def EnhancedQL(self, total_episodes=10**3, bob=1, plots=False):
dict = {}
method = "ep-greedy"
fav_keys = []
for ep in [0.01, 0.3, 1]:
exper = training.Experiment(
searching_method=method,
layers=self.layers,
ep=ep,
resolution=self.resolution,
bound_displacements=self.bound_displacements,
states_wasted=total_episodes,
ep_method="normal",
min_ep=0.01,
guessing_rule=self.guessing_rule,
efficient_time=self.efficient_time)
exper.train(bob)
with open(
str(exper.layers) + "L" + str(exper.number_phases) + "PH" +
str(exper.resolution) + "R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_" + str(c)] = {}
dict["run_" + str(c)]["label"] = str(ep) + "-greedy "
dict["run_" + str(c)]["info"] = [
exper.number_phases, exper.amplitude, exper.layers,
exper.resolution, exper.searching_method, exper.guessing_rule,
exper.method_guess, exper.number_bobs,
exper.bound_displacements, exper.efficient_time,
exper.ts_method
]
dict["run_" + str(c)]["info_ep"] = [
exper.ep_method, exper.ep, exper.min_ep, exper.time_tau
]
dict["run_" + str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_" + str(c))
plot_dict = filter_keys(dict, fav_keys)
save_obj(plot_dict, "ep-greedy", exper.layers, exper.number_phases,
exper.resolution, bob)
fav_keys = []
for tau in [200]:
for min_ep in [0.01]:
exper = training.Experiment(
searching_method="ep-greedy",
layers=self.layers,
min_ep=min_ep,
time_tau=tau,
resolution=self.resolution,
bound_displacements=self.bound_displacements,
states_wasted=total_episodes,
ep_method="exp-decay",
guessing_rule=self.guessing_rule,
efficient_time=self.efficient_time)
exper.train(bob)
with open(
str(exper.layers) + "L" + str(exper.number_phases) +
"PH" + str(exper.resolution) + "R/number_rune.txt",
"r") as f:
c = f.readlines()[0]
f.close()
dict["run_" + str(c)] = {}
dict["run_" + str(c)]["label"] = "max(" + str(
min_ep) + ", e^-t/" + str(tau) + ")-greedy "
dict["run_" + str(c)]["info"] = [
exper.number_phases, exper.amplitude, exper.layers,
exper.resolution, exper.searching_method,
exper.guessing_rule, exper.method_guess, exper.number_bobs,
exper.bound_displacements, exper.efficient_time,
exper.ts_method
]
dict["run_" + str(c)]["info_ep"] = [
exper.ep_method, exper.ep, exper.min_ep, exper.time_tau
]
dict["run_" + str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_" + str(c))
plot_dict = filter_keys(dict, fav_keys)
save_obj(plot_dict, "exp-ep-greedy", exper.layers, exper.number_phases,
exper.resolution, bob)
fav_keys = []
method = "ucb"
for ucbm in ["ucb1", "ucb2", "ucb3"]:
exper = training.Experiment(
searching_method=method,
layers=self.layers,
ucb_method=ucbm,
resolution=self.resolution,
bound_displacements=self.bound_displacements,
states_wasted=total_episodes,
guessing_rule=self.guessing_rule,
efficient_time=self.efficient_time)
exper.train(bob)
with open(
str(exper.layers) + "L" + str(exper.number_phases) + "PH" +
str(exper.resolution) + "R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_" + str(c)] = {}
dict["run_" + str(c)]["label"] = ucbm
dict["run_" + str(c)]["info"] = [
exper.number_phases, exper.amplitude, exper.layers,
exper.resolution, exper.searching_method, exper.guessing_rule,
exper.method_guess, exper.number_bobs,
exper.bound_displacements, exper.efficient_time,
exper.ts_method
]
dict["run_" + str(c)]["info_ep"] = [
exper.ep_method, exper.ep, exper.min_ep, exper.time_tau
]
dict["run_" + str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_" + str(c))
plot_dict = filter_keys(dict, fav_keys)
save_obj(plot_dict, "ucbs", exper.layers, exper.number_phases,
exper.resolution, bob, total_episodes)
fav_keys = []
method = "thompson-sampling"
for soft in [1]:
for mode_ts in [
"None"
]: #This is if you want to relate the q-table with the TS-update, but it doesnt' give any enhancement (for what i see).
exper = training.Experiment(
searching_method=method,
layers=self.layers,
resolution=self.resolution,
bound_displacements=self.bound_displacements,
states_wasted=total_episodes,
guessing_rule=self.guessing_rule,
soft_ts=soft,
efficient_time=self.efficient_time,
ts_method=mode_ts)
exper.train(bob)
with open(
str(exper.layers) + "L" + str(exper.number_phases) +
"PH" + str(exper.resolution) + "R/number_rune.txt",
"r") as f:
c = f.readlines()[0]
f.close()
dict["run_" + str(c)] = {}
dict["run_" + str(c)]["label"] = str(soft) + "-TS"
dict["run_" + str(c)]["info"] = [
exper.number_phases, exper.amplitude, exper.layers,
exper.resolution, exper.searching_method,
exper.guessing_rule, exper.method_guess, exper.number_bobs,
exper.bound_displacements, exper.efficient_time,
exper.ts_method
]
dict["run_" + str(c)]["info_ep"] = [
exper.ep_method, exper.ep, exper.min_ep, exper.time_tau
]
dict["run_" + str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_" + str(c))
plot_dict = filter_keys(dict, fav_keys)
save_obj(plot_dict, "TS", exper.layers, exper.number_phases,
exper.resolution, bob)
method = "ep-greedy"
ep = "TS+0.01exp"
fav_keys = []
exper = training.Experiment(
searching_method="ep-greedy",
layers=self.layers,
min_ep=0.01,
time_tau=200,
ep=0.01,
resolution=self.resolution,
bound_displacements=self.bound_displacements,
states_wasted=total_episodes,
ep_method="exp-decay",
guessing_rule="None",
efficient_time=self.efficient_time,
method_guess="thompson-sampling")
exper.train(bob)
with open(
str(exper.layers) + "L" + str(exper.number_phases) + "PH" +
str(exper.resolution) + "R/number_rune.txt", "r") as f:
c = f.readlines()[0]
f.close()
dict["run_" + str(c)] = {}
dict["run_" + str(c)]["label"] = str(ep) + "-greedy "
dict["run_" + str(c)]["info"] = [
exper.number_phases, exper.amplitude, exper.layers,
exper.resolution, exper.searching_method, exper.guessing_rule,
exper.method_guess, exper.number_bobs, exper.bound_displacements,
exper.efficient_time, exper.ts_method
]
dict["run_" + str(c)]["info_ep"] = [
exper.ep_method, exper.ep, exper.min_ep, exper.time_tau
]
dict["run_" + str(c)]["info_ucb"] = [exper.ucb_method]
fav_keys.append("run_" + str(c))
plot_dict = filter_keys(dict, fav_keys)
save_obj(plot_dict, "ep-TS", exper.layers, exper.number_phases,
exper.resolution, bob)
###################################################################################################3###
save_obj(dict, "all_methods", exper.layers, exper.number_phases,
exper.resolution, bob)
if plots == True:
mode_log = "on"
# os.system("python3 trad_ql.py")
# os.system("python3 enhanced_ql.py")
# os.system("python3 trad_ql.py")
matplotlib.rc('font', serif='cm10')
matplotlib.rc('text', usetex=True)
plt.rcParams.update({'font.size': 45})
color1 = "purple"
color2 = (225 / 255, 15 / 255, 245 / 255)
color3 = (150 / 255, 22 / 255, 9 / 255)
color_2l = [46 / 255, 30 / 255, 251 / 255]
colorexp = (13 / 255, 95 / 255, 14 / 255)
colorucb1 = (19 / 255, 115 / 255, 16 / 255)
colorucb2 = (170 / 255, 150 / 255, 223 / 255)
colorucb3 = (74 / 255, 90 / 255, 93 / 255)
colors = {
"run_1": "orange",
"run_2": color2,
"run_3": "brown",
"run_4": colorexp,
"run_5": colorucb1,
"run_6": colorucb2,
"run_7": colorucb3,
"run_8": "yellow",
"run_9": "purple"
}
labels = {
"run_1": r'$\epsilon = 0.01$' + "-greedy",
"run_2": r'$\epsilon = 0.3$' + "-greedy",
"run_3": r'$\epsilon = 1$' + "-greedy",
"run_4": "max(0.01, " + r'$e^{-t/\tau}$' + ")-greedy",
"run_5": "UCB-1",
"run_6": "UCB-2",
"run_7": "UCB-3",
"run_8": "TS",
"run_9": "max(0.01, " + r'$e^{-t/\tau}$' + ")-greedy + TS"
} #
####### Q-LEARNING PLOT ######
####### Q-LEARNING PLOT ######
####### Q-LEARNING PLOT ######
####### Q-LEARNING PLOT ######
interesting = ["run_1", "run_2", "run_3", "run_4"]
dict_plot = {}
print(dict.keys())
for i in interesting:
dict_plot[i] = dict[i]
for run in interesting:
dict_plot[run]["label"] = labels[run]
plt.figure(figsize=(30, 22), dpi=150)
ax1 = plt.subplot2grid((2, 1), (0, 0))
ax2 = plt.subplot2grid((2, 1), (1, 0))
axinticks = []
name = str(dict.keys())
once = True
neg = "Not"
print("ploting Q learning")
for run in dict_plot.keys():
number_phases, amplitude, layers, resolution, searching_method, guessing_rule, method_guess, number_bobs, bound_displacements, efficient_time, ts_method = dict[
run]["info"]
exp = Experiment(number_phases=number_phases,
amplitude=amplitude,
layers=layers,
resolution=resolution,
bound_displacements=bound_displacements)
exp.load_data(run)
run_color = colors[run]
if mode_log == "on":
times = np.log10(exp.results[0])
else:
times = exp.results[0]
if once == True:
ax1.plot(times,
exp.optimal_value * np.ones(len(exp.results[0])),
'--',
linewidth=9,
alpha=0.8,
label=r'$P_*^{(2)}$',
color=color_2l)
ax1.plot([times[0], times[-1]], [exp.homodyne_limit] * 2,
'--',
linewidth=9,
color="black",
label="Homodyne limit")
ax2.plot([times[0], times[-1]], [exp.homodyne_limit] * 2,
'--',
linewidth=9,
color="black",
label="Homodyne limit")
ax2.plot(times,
exp.optimal_value * np.ones(len(times)),
'--',
linewidth=9,
alpha=0.6,
color=color_2l)
axins = zoomed_inset_axes(ax2, zoom=2.7, loc="lower right")
loc1 = -int(len(exp.results[0]) * 0.7)
loc2 = -1
once = False
ax1.plot(times,
exp.results[1] / exp.results[0],
linewidth=9,
alpha=0.9,
label=dict[run]["label"],
color=run_color)
ax2.plot(times,
exp.results[2],
linewidth=3,
alpha=.5,
label=dict[run]["label"],
color=run_color)
axins.plot(np.log10(exp.results[0][loc1:loc2]),
np.log10(exp.results[2][loc1:loc2]),
'-',
linewidth=9,
alpha=.8,
color=colors[run],
label=dict[run]["label"])
axins.plot(np.log10(
[exp.results[0][loc1], exp.results[0][loc2 - 1]]),
np.log10([1 - exp.opt_2l] * 2),
'-.',
alpha=.8,
linewidth=9,
color=color_2l,
label="Optimal 2L")
axinticks.append(np.log10(exp.results[2][loc1]))
ax1.fill_between(
times, (exp.results[1] - exp.stds[0] / 2) / exp.results[0],
(exp.results[1] + exp.stds[0] / 2) / exp.results[0],
alpha=.4,
color=run_color)
ax2.fill_between(times,
np.log10(exp.results[2] - exp.stds[1] / 2),
np.log10(exp.results[2] + exp.stds[1] / 2),
alpha=0.4,
color=run_color)
ax1.legend()
mark_inset(ax2,
axins,
loc1=1,
loc2=2,
fc="green",
ec="0.3",
alpha=0.5)
axinticks.append(exp.opt_2l)
yticks = np.arange(np.round(min(exp.results[2]), 3),
1 - exp.opt_2l, .1)
ax1.set_yticks(yticks)
ax2.set_yticks(yticks)
axins.set_yticks(axinticks)
axins.set_yticklabels([str(np.round(i, 3)) for i in axinticks])
plt.setp(axins.get_yticklabels(), size=27)
plt.setp(axins.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax2.set_xticks([0, 1, 2, 3, 4, 5, np.log10(5 * 10**5)])
ax2.set_xticklabels([
r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$',
r'$10^{4}$', r'$10^{5}$', r'$5 \; 10^{5}$'
])
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=0.1)
ax2.tick_params(axis='x', which='both', top='off')
ax1.legend(loc="lower right", prop={"size": 35})
ax2.set_xlabel("t", size=54)
ax1.set_ylabel(r'\textbf{R}$_t$', size=54)
ax2.set_ylabel(r'\textbf{P}$_t$', size=54)
inf = dict[run]["info"]
layers, phases, resolution = inf[2], inf[0], inf[3]
plt.savefig(
str(layers) + "L" + str(phases) + "PH" + str(resolution) +
"R/figures/Qlearning.png")
####### ENH-Q-LEARINIG PLOT ######
####### ENH-Q-LEARINIG PLOT ######
####### ENH-Q-LEARINIG PLOT ######
####### ENH-Q-LEARINIG PLOT ######
interesting = ["run_2", "run_9", "run_8", "run_5"]
dict_plot = {}
print(dict.keys())
for i in interesting:
dict_plot[i] = dict[i]
for run in interesting:
dict_plot[run]["label"] = labels[run]
plt.figure(figsize=(30, 22), dpi=150)
ax1 = plt.subplot2grid((2, 1), (0, 0))
ax2 = plt.subplot2grid((2, 1), (1, 0))
axinticks = []
name = str(dict.keys())
once = True
neg = "Not"
for run in dict_plot.keys():
print(run)
number_phases, amplitude, layers, resolution, searching_method, guessing_rule, method_guess, number_bobs, bound_displacements, efficient_time, ts_method = dict[
run]["info"]
exp = Experiment(number_phases=number_phases,
amplitude=amplitude,
layers=layers,
resolution=resolution,
bound_displacements=bound_displacements)
exp.load_data(run)
run_color = colors[run]
if mode_log == "on":
times = np.log10(exp.results[0])
else:
times = exp.results[0]
if once == True:
ax1.plot(times,
exp.optimal_value * np.ones(len(exp.results[0])),
'--',
linewidth=9,
alpha=0.8,
label=r'$P_*^{(2)}$',
color=color_2l)
ax1.plot([times[0], times[-1]], [exp.homodyne_limit] * 2,
'--',
linewidth=9,
color="black",
label="Homodyne limit")
ax2.plot([times[0], times[-1]], [exp.homodyne_limit] * 2,
'--',
linewidth=9,
color="black",
label="Homodyne limit")
ax2.plot(times,
exp.optimal_value * np.ones(len(times)),
'--',
linewidth=9,
alpha=0.6,
color=color_2l)
axins = zoomed_inset_axes(ax2, zoom=2.7, loc="lower right")
loc1 = -int(len(exp.results[0]) * 0.35)
loc2 = -1
once = False
ax1.plot(times,
exp.results[1] / exp.results[0],
linewidth=9,
alpha=0.9,
label=dict[run]["label"],
color=run_color)
ax2.plot(times,
exp.results[2],
linewidth=3,
alpha=.5,
label=dict[run]["label"],
color=run_color)
axins.plot(np.log10(exp.results[0][loc1:loc2]),
np.log10(exp.results[2][loc1:loc2]),
'-',
linewidth=9,
alpha=.8,
color=colors[run],
label=dict[run]["label"])
axins.plot(np.log10(
[exp.results[0][loc1], exp.results[0][loc2 - 1]]),
np.log10([1 - exp.opt_2l] * 2),
'-.',
alpha=.8,
linewidth=9,
color=color_2l,
label="Optimal 2L")
axinticks.append(np.log10(exp.results[2][loc1]))
ax1.fill_between(
times, (exp.results[1] - exp.stds[0] / 2) / exp.results[0],
(exp.results[1] + exp.stds[0] / 2) / exp.results[0],
alpha=.4,
color=run_color)
ax2.fill_between(times,
np.log10(exp.results[2] - exp.stds[1] / 2),
np.log10(exp.results[2] + exp.stds[1] / 2),
alpha=0.4,
color=run_color)
ax1.legend()
mark_inset(ax2,
axins,
loc1=1,
loc2=2,
fc="green",
ec="0.3",
alpha=0.5)
axinticks.append(exp.opt_2l)
yticks = np.arange(np.round(min(exp.results[2]), 3),
1 - exp.opt_2l, .1)
ax1.set_yticks(yticks)
ax2.set_yticks(yticks)
axins.set_yticks(axinticks)
axins.set_yticklabels([str(np.round(i, 3)) for i in axinticks])
plt.setp(axins.get_yticklabels(), size=27)
plt.setp(axins.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax2.set_xticks([0, 1, 2, 3, 4, 5, np.log10(5 * 10**5)])
ax2.set_xticklabels([
r'$10^{0}$', r'$10^{1}$', r'$10^{2}$', r'$10^{3}$',
r'$10^{4}$', r'$10^{5}$', r'$5 \; 10^{5}$'
])
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=0.1)
ax2.tick_params(axis='x', which='both', top='off')
ax1.legend(loc="lower right", prop={"size": 35})
ax2.set_xlabel("t", size=54)
ax1.set_ylabel(r'\textbf{R}$_t$', size=54)
ax2.set_ylabel(r'\textbf{P}$_t$', size=54)
inf = dict[run]["info"]
layers, phases, resolution = inf[2], inf[0], inf[3]
plt.savefig(
str(layers) + "L" + str(phases) + "PH" + str(resolution) +
"R/figures/ENH-QL.png")
return
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--scratch',
action='store',
help='',
metavar="dir",
default="_tmp_")
parser.add_argument('--conformers', action='store_true', help='')
parser.add_argument('--sdf', action='store', help='', metavar="file")
parser.add_argument('-j',
'--procs',
action='store',
help='pararallize',
metavar="int",
default=0,
type=int)
parser.add_argument('-r',
'--representations',
action='store',
help='',
metavar="STR",
nargs="+")
args = parser.parse_args()
if args.scratch[-1] != "/":
args.scratch += "/"
if args.procs == -1:
args.procs = int(os.cpu_count())
print("set procs", args.procs)
representation_names_coordbased = [
"cm", "fchl18", "fchl19", "slatm", "bob"
]
representation_names_molbased = ["morgan", "rdkitfp"]
if args.representations is None:
# representation_names = ["cm", "fchl18", "fchl19", "slatm", "bob"]
# representation_names = ["fchl18"]
# representation_names = ["bob"]
representation_names = ["slatm", "bob", "cm", "rdkitfp", "morgan"]
else:
representation_names = args.representations
molobjs = cheminfo.read_sdffile(args.sdf)
molobjs = [mol for mol in molobjs]
xyzs = molobjs_to_xyzs(molobjs)
mol_atoms, mol_coords = xyzs
misc.save_obj(args.scratch + "atoms", mol_atoms)
# Print unique atoms
unique_atoms = []
for atoms in mol_atoms:
unique_atoms += list(np.unique(atoms))
unique_atoms = np.array(unique_atoms)
unique_atoms = unique_atoms.flatten()
unique_atoms = np.unique(unique_atoms)
# Calculate max_size
max_atoms = [len(atoms) for atoms in mol_atoms]
max_atoms = max(max_atoms)
n_items = len(mol_coords)
print("total mols:", n_items)
print("atom types:", unique_atoms)
print("max atoms: ", max_atoms)
print()
print("representations:", representation_names)
print()
misc.save_txt(args.scratch + "n_items", n_items)
# Gas phase
for name in representation_names:
if name not in representation_names_coordbased: continue
representations = xyzs_to_representations(mol_atoms,
mol_coords,
name=name,
scr=args.scratch,
max_atoms=max_atoms,
procs=args.procs)
if isinstance(representations, (np.ndarray, np.generic)):
misc.save_npy(args.scratch + "repr." + name, representations)
else:
misc.save_obj(args.scratch + "repr." + name, representations)
representations = None
del representations
for name in representation_names:
if name not in representation_names_molbased: continue
representations = molobjs_to_representations(molobjs,
name=name,
procs=args.procs)
if isinstance(representations, (np.ndarray, np.generic)):
misc.save_npy(args.scratch + "repr." + name, representations)
else:
misc.save_obj(args.scratch + "repr." + name, representations)
representations = None
del representations
quit()
# Ensemble
# if args.conformers:
# generate_conformer_representation(scr=args.scratch, procs=args.procs)
return
def get_representations_slatm(atoms,
structures,
scr="_tmp_/",
mbtypes=None,
debug=True,
procs=0,
**kwargs):
"""
atoms -- list of molecule atoms
"""
# from qml.representations import get_slatm_mbtypes # Assume 'qm7' is a
# list of Compound() objects. mbtypes =
# get_slatm_mbtypes([mol.nuclear_charges for compound in qm7]) # Assume the
# QM7 dataset is loaded into a list of Compound() for compound in qm7: #
# Generate the desired representation for each compound
# compound.generate_slatm(mbtypes, local=True, rcut=2.7)
if mbtypes is None:
filename_mbtypes = scr + "slatm.mbtypes"
try:
mbtypes = misc.load_obj(filename_mbtypes)
except FileNotFoundError:
print("Generate slatm mbtypes")
mbtypes = qml.representations.get_slatm_mbtypes(atoms)
misc.save_obj(filename_mbtypes, mbtypes)
if debug:
print("Generate slatm representations")
replist = []
# Set OMP
if procs > 1:
os.environ["OMP_NUM_THREADS"] = "1"
workargs = zip(structures, atoms)
workargs = list(workargs)
pool = Pool(processes=procs)
funcname = partial(procs_representation_slatm, mbtypes=mbtypes)
replist = pool.map(funcname, workargs)
else:
for i, (coord, atom) in enumerate(zip(structures, atoms)):
rep = qml.representations.generate_slatm(coord, atom, mbtypes)
replist.append(rep)
# replist = [qml.representations.generate_slatm(coordinate, atom, mbtypes) for coordinate, atom in zip(structures, atoms)]
replist = np.array(replist)
# for i, rep in enumerate(replist):
# m = rep.mean()
# if np.isnan(m):
# print(i, rep.mean())
# print(replist.mean())
return replist
