fix bug in test_model and hyper (#277)

* fix bug in test_model and hyper

* fix pipeline

* add hyper example

* unify example

hyper_example.json

@@ -0,0 +1,6 @@
+{
+    "learning_rate": {
+        "type": "choice",
+        "list": [0.01, 0.005, 0.001]
+    }
+}

hyper_example.txt

@@ -0,0 +1 @@
+learning_rate choice [0.01, 0.005, 0.001]

hyper_tune.py

@@ -1,5 +1,5 @@
 """
-训练并评估单一模型的脚本
+模型调参脚本 (based on the ray[tune])
 """
 
 import argparse
@@ -20,7 +20,7 @@
     parser.add_argument('--config_file', type=str,
                         default=None, help='the file name of config file')
     parser.add_argument('--space_file', type=str,
-                        default=None, help='the file which specifies the parameter search space')
+                        default='hyper_example', help='the file which specifies the parameter search space')
     parser.add_argument('--scheduler', type=str,
                         default='FIFO', help='the trial sheduler which will be used in ray.tune.run')
     parser.add_argument('--search_alg', type=str,

libcity/pipeline/pipeline.py

@@ -136,8 +136,15 @@ def hyper_parameter(task=None, model_name=None, dataset_name=None, config_file=N
     # load config
     experiment_config = ConfigParser(task, model_name, dataset_name, config_file=config_file,
                                      other_args=other_args)
+    # exp_id
+    exp_id = experiment_config.get('exp_id', None)
+    if exp_id is None:
+        exp_id = int(random.SystemRandom().random() * 100000)
+        experiment_config['exp_id'] = exp_id
     # logger
     logger = get_logger(experiment_config)
+    logger.info('Begin ray-tune, task={}, model_name={}, dataset_name={}, exp_id={}'.
+                format(str(task), str(model_name), str(dataset_name), str(exp_id)))
     logger.info(experiment_config.config)
     # check space_file
     if space_file is None:
@@ -167,8 +174,11 @@ def train(config, checkpoint_dir=None, experiment_config=None,
                 experiment_config[key] = config[key]
         experiment_config['hyper_tune'] = True
         logger = get_logger(experiment_config)
-        logger.info('Begin pipeline, task={}, model_name={}, dataset_name={}'
-                    .format(str(task), str(model_name), str(dataset_name)))
+        # exp_id
+        exp_id = int(random.SystemRandom().random() * 100000)
+        experiment_config['exp_id'] = exp_id
+        logger.info('Begin pipeline, task={}, model_name={}, dataset_name={}, exp_id={}'.
+                    format(str(task), str(model_name), str(dataset_name), str(exp_id)))
         logger.info('running parameters: ' + str(config))
         # load model
         model = get_model(experiment_config, data_feature)
@@ -215,9 +225,9 @@ def train(config, checkpoint_dir=None, experiment_config=None,
     # save best
     best_path = os.path.join(best_trial.checkpoint.value, "checkpoint")
     model_state, optimizer_state = torch.load(best_path)
-    model_cache_file = './libcity/cache/model_cache/{}_{}.m'.format(
-        model_name, dataset_name)
-    ensure_dir('./libcity/cache/model_cache')
+    model_cache_file = './libcity/cache/{}/model_cache/{}_{}.m'.format(
+        exp_id, model_name, dataset_name)
+    ensure_dir('./libcity/cache/{}/model_cache'.format(exp_id))
     torch.save((model_state, optimizer_state), model_cache_file)
 
 

run_hyper.py

@@ -1,9 +1,9 @@
 """
-单一模型调参脚本
+模型调参脚本 (based on the hyperopt)
 """
 
 import argparse
-
+import random
 from libcity.pipeline import objective_function
 from libcity.executor import HyperTuning
 from libcity.utils import str2bool, get_logger, set_random_seed, add_general_args
@@ -26,7 +26,7 @@
                         help='whether re-train model if the model is \
                              trained before')
     parser.add_argument('--params_file', type=str,
-                        default=None, help='the file which specify the \
+                        default='hyper_example.txt', help='the file which specify the \
                              hyper-parameters and ranges to be adjusted')
     parser.add_argument('--hyper_algo', type=str,
                         default='grid_search', help='hyper-parameters search algorithm')
@@ -43,11 +43,18 @@
     other_args = {key: val for key, val in dict_args.items() if key not in [
         'task', 'model', 'dataset', 'config_file', 'saved_model', 'train',
         'params_file', 'hyper_algo'] and val is not None}
-
-    logger = get_logger({'model': args.model, 'dataset': args.dataset})
+    # exp_id
+    exp_id = dict_args.get('exp_id', None)
+    if exp_id is None:
+        # Make a new experiment ID
+        exp_id = int(random.SystemRandom().random() * 100000)
+        other_args['exp_id'] = exp_id
+    # logger
+    logger = get_logger({'model': args.model, 'dataset': args.dataset, 'exp_id': exp_id})
     # seed
     seed = dict_args.get('seed', 0)
     set_random_seed(seed)
+    other_args['seed'] = seed
     hp = HyperTuning(objective_function, params_file=args.params_file, algo=args.hyper_algo,
                      max_evals=args.max_evals, task=args.task, model_name=args.model,
                      dataset_name=args.dataset, config_file=args.config_file,

test_model.py

@@ -1,14 +1,26 @@
 from libcity.config import ConfigParser
 from libcity.data import get_dataset
-from libcity.utils import get_model, get_executor
+from libcity.utils import get_model, get_executor, get_logger, set_random_seed
+import random
+
+"""
+取一个batch的数据进行初步测试
+Take the data of a batch for preliminary testing
+"""
 
 # 加载配置文件
-config = ConfigParser(task='traj_loc_pred', model='TemplateTLP',
-                      dataset='foursquare_tky', config_file=None,
-                      other_args={'batch_size': 2})
-# 如果是交通流量\速度预测任务，请使用下面的加载配置文件语句
-# config = ConfigParser(task='traffic_state_pred', model='TemplateTSP',
-#       dataset='METR_LA', config_file=None, other_args={'batch_size': 2})
+config = ConfigParser(task='traffic_state_pred', model='RNN',
+                      dataset='METR_LA', other_args={'batch_size': 2})
+exp_id = config.get('exp_id', None)
+if exp_id is None:
+    exp_id = int(random.SystemRandom().random() * 100000)
+    config['exp_id'] = exp_id
+# logger
+logger = get_logger(config)
+logger.info(config.config)
+# seed
+seed = config.get('seed', 0)
+set_random_seed(seed)
 # 加载数据模块
 dataset = get_dataset(config)
 # 数据预处理，划分数据集
@@ -18,10 +30,11 @@
 batch = train_data.__iter__().__next__()
 # 加载模型
 model = get_model(config, data_feature)
-self = model.to(config['device'])
+model = model.to(config['device'])
+# 加载执行器
+executor = get_executor(config, model, data_feature)
 # 模型预测
 batch.to_tensor(config['device'])
 res = model.predict(batch)
-# 请自行确认 res 的 shape 是否符合赛道的约束
-# 如果要加载执行器的话
-executor = get_executor(config, model)
+logger.info('Result shape is {}'.format(res.shape))
+logger.info('Success test the model!')

unit_test.py

@@ -5,10 +5,10 @@
 
 #############################################
 # The parameter to control the unit testing #
-tested_trajectory_model = 'TemplateTLP'
-tested_trajectory_dataset = 'foursquare_tky'
+tested_trajectory_model = 'RNN'
+tested_trajectory_dataset = 'foursquare_nyc'
 tested_trajectory_encoder = 'StandardTrajectoryEncoder'
-tested_traffic_state_model = 'DCRNN'
+tested_traffic_state_model = 'RNN'
 tested_traffic_state_dataset = 'METR_LA'
 #############################################