def Run(self): try: ((html)) = ((Net.Conn().Httplib("pgp.mit.edu","GET","/pks/lookup?search="+str(self.UrlCheck(self.Keyword))+"&op=index","pgp.mit.edu",""))) if html: ((self.Results)) += ((html)) except Exception,err: pass
def Run(self): try: ((html)) = ((Net.Conn().Httplib( "www.bing.com", "GET", "/search?q=%40" + str(self.UrlCheck(self.Keyword)), "www.bing.com", "SRCHHPGUSR=ADLT=DEMOTE&NRSLT=50"))) if html: ((self.Results)) += ((html)) except Exception, err: pass
def Run(self): try: ((url)) = (( "http://www.google.com/search?num=500&start=50&hl=en&meta=&q=%40\"" + self.UrlCheck(self.Keyword) + "\"")) ((html)) = ((Net.Conn().Requests(url))) if html: ((self.Results)) += ((html)) except Exception, err: pass
def Run(self): try: ((html)) = ((Net.Conn().Httplib( "search.yahoo.com", "GET", "/search?p=\"%40" + self.UrlCheck(self.Keyword) + "\"&b=500&pz=10", "search.yahoo.com", ""))) if html: ((self.Results)) += ((html)) except Exception, err: pass
def worker_func(env_name, worker_id, params_queue, rewards_queue, device, noise_std, nhid): env = gym.make(env_name) net = Net(env.observation_space.shape[0], env.action_space.shape[0], nhid).to(device) net.eval() while True: params = params_queue.get() if params is None: break net.load_state_dict(params) for _ in range(ITERS_PER_UPDATE): seed = np.random.randint(low=0, high=65535) np.random.seed(seed) noise, neg_noise = sample_noise(net, device=device) pos_reward, pos_steps = eval_with_noise(env, net, noise, noise_std, device=device) neg_reward, neg_steps = eval_with_noise(env, net, neg_noise, noise_std, device=device) rewards_queue.put( RewardsItem(seed=seed, pos_reward=pos_reward, neg_reward=neg_reward, steps=pos_steps + neg_steps))
def Run(self): try: ((url)) = (( "http://searchdns.netcraft.com/?restriction=site+contains&host=%s&lookup=wait..&position=limited" % (self.target))) ((html)) = ((Net.Conn().Urllib2(url, None, self.headers))) if html: ((reg)) = ((re.findall('url=\S+"', html, re.I))) print "" ((Printer.MyPrinter().nprint("Searching \"" + (self.target) + "\" Websites Correlation..."))) if reg: ((Printer.MyPrinter().nprint("Found %s sites " % (len(reg))))) print "" for x in range(len(reg)): ((host)) = ((reg[x].split('"')[0])) print((" - %s" % (host.split("url=")[1]))) print "" else: ((Printer.MyPrinter().iprint("Not found sites"))) except Exception, err: pass
def main(): df = pd.read_csv(config["train"]["data_path"]) y = np.array(df[config["train"]["label_column"]]) df = preprocess(df) label_nbr = len(df[config["train"]["label_column"]].unique()) label_names = config["train"]["label"] y = np.array(df[config["train"]["label_column"]]) df = df.drop([config["train"]["label_column"]] + config['train']['to_drop'], axis=1) X = np.array(df) print(X.shape, y.shape) try: device = torch.device(config["train"]["device"]) except: device = torch.device("cpu") classifier = Net(input_dim=df.shape[1], hidden_dim=config["train"]["hidden_dim"]).to(device) criterion = torch.nn.functional.mse_loss X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42) X_train, y_train = torch.tensor(X_train).float(), torch.tensor( y_train).float() X_test, y_test = torch.tensor(X_test).float(), torch.tensor(y_test).float() # create dataloader with specified batch_size ds_train = torch.utils.data.TensorDataset(X_train, y_train) dataloader_train = torch.utils.data.DataLoader( ds_train, batch_size=config["train"]["batch_size"], shuffle=True) ds_test = torch.utils.data.TensorDataset(X_test, y_test) dataloader_test = torch.utils.data.DataLoader( ds_test, batch_size=config["train"]["batch_size"], shuffle=True) trainer = Trainer(classifier, device=device, criterion=criterion) trainer.train(dataloader_train, dataloader_test, config["train"]["epochs"], config["train"]["log_every"], task="regression") # eval step metrics = {} metrics["mse"] = trainer.metric mlflow.log_params(metrics) mlflow.pytorch.log_model( pytorch_model=classifier, artifact_path="model", registered_model_name=config["mlflow"]["model_name"]) api_request_model = get_request_features(df) with open("request_model.json", "w") as rmodel: json.dump(api_request_model, rmodel, indent=4) # checking if there are any productions models, # so we can put at least one in production model_name = config['mlflow']['model_name'] try: mlflow.pytorch.load_model(f"models:/{model_name}/Production") except: client = MlflowClient() version = client.search_model_versions( f"name='{model_name}'")[0].version client.transition_model_version_stage(name=model_name, version=version, stage="Production")
action='store_true', help="Enable CUDA mode") parser.add_argument("--lr", type=float, default=LEARNING_RATE) parser.add_argument("--noise-std", type=float, default=NOISE_STD) parser.add_argument("--iters", type=int, default=MAX_ITERS) args = parser.parse_args() device = "cuda" if args.cuda else "cpu" writer = SummaryWriter(comment="%s-es_lr=%.3e_sigma=%.3e" % (args.env, args.lr, args.noise_std)) env = gym.make(args.env) net = Net(env.observation_space.shape[0], env.action_space.shape[0], args.hid) print(net) params_queues = [mp.Queue(maxsize=1) for _ in range(PROCESSES_COUNT)] rewards_queue = mp.Queue(maxsize=ITERS_PER_UPDATE) workers = [] for idx, params_queue in enumerate(params_queues): p_args = (args.env, idx, params_queue, rewards_queue, device, args.noise_std, args.hid) proc = mp.Process(target=worker_func, args=p_args) proc.start() workers.append(proc) print("All started!")
def build_net(env, seeds, nhid, noise_std): torch.manual_seed(seeds[0]) net = Net(env.observation_space.shape[0], env.action_space.shape[0], nhid) for seed in seeds[1:]: net = mutate_net(net, seed, noise_std, copy_net=False) return net
def main(): df = pd.read_csv(config["train"]["data_path"]) y = np.array(df[config["train"]["labels_column"]]) df = preprocess(df) label_nbr = len(df[config["train"]["labels_column"]].unique()) label_names = config["train"]["labels"] y = np.array(df[config["train"]["labels_column"]]) df = df.drop([config["train"]["labels_column"]] + config['train']['to_drop'], axis=1) X = np.array(df) print(X.shape, y.shape) try: device = torch.device(config["train"]["device"]) except: device = torch.device("cpu") classifier = Net(input_dim=df.shape[1], output_dim=label_nbr, hidden_dim=config["train"]["hidden_dim"]).to(device) criterion = torch.nn.CrossEntropyLoss() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=42) X_train, y_train = torch.tensor(X_train).float(), torch.tensor( y_train).float() X_test, y_test = torch.tensor(X_test).float(), torch.tensor(y_test).float() # create dataloader with specified batch_size ds_train = torch.utils.data.TensorDataset(X_train, y_train) dataloader_train = torch.utils.data.DataLoader( ds_train, batch_size=config["train"]["batch_size"], shuffle=True) ds_test = torch.utils.data.TensorDataset(X_test, y_test) dataloader_test = torch.utils.data.DataLoader( ds_test, batch_size=config["train"]["batch_size"], shuffle=True) trainer = Trainer(classifier, device=device, criterion=criterion) trainer.train(dataloader_train, dataloader_test, config["train"]["epochs"], config["train"]["log_every"]) # eval step y_true, y_pred, scores = get_preds_labels_scores(dataloader_test, classifier, device) metrics = eval_model_per_class(y_true, y_pred, label_names) metrics["accuracy"] = trainer.metric / 100 mlflow.log_params(metrics) mlflow.pytorch.log_model( pytorch_model=classifier, artifact_path="model", registered_model_name=config["mlflow"]["model_name"]) conf_matrix_fname = save_confusion_matrix(y_true, y_pred, label_names) mlflow.log_artifact(conf_matrix_fname) os.remove(conf_matrix_fname) roc_curve_fname = save_roc_curve(y_true, scores, label_names) mlflow.log_artifact(roc_curve_fname) os.remove(roc_curve_fname) pr_curve_fname = save_pr_curve(y_true, scores, label_names) mlflow.log_artifact(pr_curve_fname) os.remove(pr_curve_fname) eval_fnames = eval_classification_model_predictions_per_feature( config["train"]["data_path"], classifier, config['train']['labels_column'], config['train']['labels'], config['train']['to_drop'], use_torch=True, device=device, preprocess=preprocess) for eval_fname in eval_fnames: mlflow.log_artifact(eval_fname) os.remove(eval_fname) api_request_model = get_request_features(df) with open("request_model.json", "w") as rmodel: json.dump(api_request_model, rmodel, indent=4) # checking if there are any productions models, # so we can put at least one in production model_name = config['mlflow']['model_name'] try: mlflow.pytorch.load_model(f"models:/{model_name}/Production") except: client = MlflowClient() version = client.search_model_versions( f"name='{model_name}'")[0].version client.transition_model_version_stage(name=model_name, version=version, stage="Production")