def main(fragment_file, lead_file):
fragment_mols = read_file(fragment_file)
lead_mols = read_file(lead_file)
fragment_mols += lead_mols
logging.info("Read %s molecules for fragmentation library",
len(fragment_mols))
logging.info("Read %s lead moleculs", len(lead_mols))
fragments, used_mols = get_fragments(fragment_mols)
logging.info("Num fragments: %s", len(fragments))
logging.info("Total molecules used: %s", len(used_mols))
assert len(fragments)
assert len(used_mols)
encodings, decodings = get_encodings(fragments)
save_decodings(decodings)
logging.info("Saved decodings")
lead_mols = np.asarray(
fragment_mols[-len(lead_mols):])[used_mols[-len(lead_mols):]]
X = encode_list(lead_mols, encodings)
logging.info("Building models")
actor, critic = build_models(X.shape[1:])
X = clean_good(X, decodings)
logging.info("Training")
history = train(X, actor, critic, decodings)
logging.info("Saving")
np.save("History/history.npy", history)
def main(fragment_file, lead_file):
fragment_mols = read_file(fragment_file)
lead_mols = read_file(lead_file)
fragment_mols += lead_mols
logging.info("Read %s molecules for fragmentation library",
len(fragment_mols))
logging.info("Read %s lead molecules", len(lead_mols))
fragments, used_mols = get_fragments(fragment_mols)
logging.info("Num fragments: %s", len(fragments))
logging.info("Total molecules used: %s", len(used_mols))
assert len(fragments)
assert len(used_mols)
# =============================================================================
# encodings, decodings = get_encodings(fragments)
# save_encodings(encodings)
# save_decodings(decodings)
# logging.info("Saved encodings and decodings")
# =============================================================================
lead_mols = np.asarray(
fragment_mols[-len(lead_mols):])[used_mols[-len(lead_mols):]]
decodings = read_decodings()
encodings = read_encodings()
logging.info("Loaded encodings and decodings")
X = encode_list(lead_mols, encodings)
#print(X.shape)
if X.shape[0] == 0:
return -1
logging.info("Building models")
actor, critic = build_models(X.shape[1:])
# X = clean_good(X, decodings)
# logging.info("Remaining molecules after clean good: %s",X.shape[0])
if X.shape[0] == 0:
return -1
logging.info("Training")
history = train(X, actor, critic, decodings)
logging.info("Saving")
np.save("History/history.npy", history)
actor.save('./saved_models/generation')
critic.save('./saved_models/critic')
def main(fragment_file, lead_file):
fragment_mols = read_file(fragment_file)
lead_mols = read_file(lead_file)
fragment_mols += lead_mols
fragments, used_mols = get_fragments(fragment_mols)
encodings, decodings = get_encodings(fragments)
save_decodings(decodings)
lead_mols = np.asarray(fragment_mols[-len(lead_mols):])[used_mols[-len(lead_mols):]]
X = encode_list(lead_mols, encodings)
actor, critic = build_models(X.shape[1:])
X = clean_good(X, decodings)
history = train(X, actor, critic, decodings)
np.save("History/history.npy", history)
app.secret_key = config["flask"]["secret_key"]
app.config['SQLALCHEMY_DATABASE_URI'] = config["app"]["database_uri"]
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.config['DEBUG'] = os.environ.get('FLASK_DEBUG', False)
if type(app.config['DEBUG']) == str:
app.config['DEBUG'] = app.config['DEBUG'].lower() == "true"
if app.config["DEBUG"]:
app.debug = True
app.logger.addHandler(logging.StreamHandler(sys.stdout))
app.logger.setLevel(logging.DEBUG)
logging.getLogger('sqlalchemy.engine').setLevel(logging.WARN)
log = app.logger
app.config['TEMPLATES_AUTO_RELOAD'] = True
db = SQLAlchemy(app)
migrate = Migrate(app, db)
models = build_models(db)
for m in models:
globals()[m.__name__] = m
goodreads = OAuth1Service(
consumer_key=config["goodreads"]["key"],
consumer_secret=config["goodreads"]["secret"],
name='goodreads',
request_token_url='https://www.goodreads.com/oauth/request_token',
authorize_url='https://www.goodreads.com/oauth/authorize',
access_token_url='https://www.goodreads.com/oauth/access_token',
base_url='https://www.goodreads.com/')
@app.route("/")
def index():
"beeminder": {
"client_id": os.environ["BEEMINDER_CLIENT_ID"],
"client_secret": os.environ["BEEMINDER_CLIENT_SECRET"]
},
"flask": {
"secret_key": os.environ["FLASK_ENCRYPTION_KEY"]
}
}
else:
config = yaml.safe_load(open('config.yaml', 'r'))
app.secret_key = config["flask"]["secret_key"]
app.config['SQLALCHEMY_DATABASE_URI'] = config["app"]["database_uri"]
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
db = SQLAlchemy(app)
models = build_models(db)
User = models["User"]
@app.route("/")
def index():
if "todoist_id" in session:
config["beeminder_url"] = urlparse.urljoin(
request.url_root, url_for('beeminder_oauth'))
existing = User.query.filter_by(
todoist_id=session["todoist_id"]).first()
if existing is None:
del session["todoist_id"]
return redirect(url_for("index"))
return render_template('index.html', data=existing, **config)
else:
with open(os.path.join(ddir, fname), 'w') as f:
json.dump(data, f, indent=4)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
params = fetch_args(parser)
if params['vis']:
params['split'] = 'val_easy'
dataset = PlotDataset(params, 'val_easy')
extra_params = get_extra_params(dataset)
models = build_models(params, extra_params)
visualize_model(models, dataset, params, extra_params,
int(params['vis_idx']))
exit() # Abrupt exit
if params['split'] == 'train':
train_dataset = PlotDataset(params, 'train')
val_dataset = PlotDataset(params, 'val_easy')
extra_params = get_extra_params(train_dataset)
save_json(params, params['checkpoint_path'], 'params.json')
save_json(extra_params, params['checkpoint_path'], 'extra_params.json')
models = build_models(params, extra_params)