diff --git a/convlab/policy/plot_results/README.md b/convlab/policy/plot_results/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..724dda6dfdeac7ca4cc103c445fde531b062df06
--- /dev/null
+++ b/convlab/policy/plot_results/README.md
@@ -0,0 +1,40 @@
+# Plot training curve
+
+Plot training curve directly from tensorboard event files by using for instance
+
+``` python plot_results/plot.py --dir=EXP_DIR --map-file=example_map.json --max-dialogues=MAX_DIALOGUES --out-file=plot_results/```
+
+The structure of the map-file is like this:
+```json
+[
+ {
+ "dir": "dir_1",
+ "legend": "Algorithm1"
+ },
+ {
+ "dir": "dir_2",
+ "legend": "Algorithm2"
+ }
+]
+```
+The value of `legend` will be the depicted in the plot legend.
+
+The file structure of the exp_dir is like this:
+
+ ├── exp_dir
+ └── map["dir_1"]
+ └── experiment_seed0*
+ └── tb_dir
+ └── events.*
+ └── experiment_seed1*
+ └── tb_dir
+ └── events.*
+ └── map["dir_2"]
+ └── experiment_seed0*
+ └── tb_dir
+ └── events.*
+ └── experiment_seed1*
+ └── tb_dir
+ └── events.*
+
+If you want to truncate the figure to a certain number of training dialogues on the x-axis, use the argument `--max-dialogues`.
diff --git a/plot_results/example_map.json b/convlab/policy/plot_results/example_map.json
similarity index 100%
rename from plot_results/example_map.json
rename to convlab/policy/plot_results/example_map.json
diff --git a/plot_results/plot.py b/convlab/policy/plot_results/plot.py
similarity index 100%
rename from plot_results/plot.py
rename to convlab/policy/plot_results/plot.py
diff --git a/convlab/policy/ppo/setsumbt_end_baseline_config.json b/convlab/policy/ppo/setsumbt_end_baseline_config.json
index a2165466e3d46cf85bdc794e5994c6433708c095..ea84dd7670a3a27dcc267e476c894bf939c9ba10 100644
--- a/convlab/policy/ppo/setsumbt_end_baseline_config.json
+++ b/convlab/policy/ppo/setsumbt_end_baseline_config.json
@@ -5,10 +5,10 @@
"use_pretrained_initialisation": false,
"batchsz": 1000,
"seed": 0,
- "epoch": 0,
+ "epoch": 50,
"eval_frequency": 5,
"process_num": 4,
- "num_eval_dialogues": 5,
+ "num_eval_dialogues": 500,
"sys_semantic_to_usr": false
},
"vectorizer_sys": {
diff --git a/data_loaders/utterance_loader.py b/data_loaders/utterance_loader.py
deleted file mode 100644
index 455ddc59cde4a634e81c0382f714df3450929033..0000000000000000000000000000000000000000
--- a/data_loaders/utterance_loader.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import os
-import json
-import pickle
-import torch
-import torch.utils.data as data
-from convlab.policy.vector.dataset import ActDataset
-from abc import ABC, abstractmethod
-from collections import Counter
-import numpy as np
-from tensorboardX import SummaryWriter
-from tqdm import tqdm
-
-from convlab.policy.vector.vector_binary import VectorBinary
-from convlab.dst.setsumbt.multiwoz.Tracker import SetSUMBTTracker
-
-PAD = '<pad>'
-UNK = '<unk>'
-USR = 'YOU:'
-SYS = 'THEM:'
-BOD = '<d>'
-EOD = '</d>'
-BOS = '<s>'
-EOS = '<eos>'
-SEL = '<selection>'
-SEP = "|"
-REQ = "<requestable>"
-INF = "<informable>"
-WILD = "%s"
-SPECIAL_TOKENS = [PAD, UNK, USR, SYS, BOS, BOD, EOS, EOD]
-STOP_TOKENS = [EOS, SEL]
-DECODING_MASKED_TOKENS = [PAD, UNK, USR, SYS, BOD]
-
-
-class DatasetDataloader(ABC):
- def __init__(self):
- self.data = None
-
- @abstractmethod
- def load_data(self, *args, **kwargs):
- """
- load data from file, according to what is need
- :param args:
- :param kwargs:
- :return: data
- """
- pass
-
-
-class MultiWOZDataloader(DatasetDataloader):
- def __init__(self):
- super(MultiWOZDataloader, self).__init__()
-
- def load_data(self,
- data_dir=os.path.abspath(os.path.join(os.path.abspath(__file__), '../../data/multiwoz')),
- data_key='all',
- role='all',
- utterance=False,
- dialog_act=False,
- context=False,
- context_window_size=0,
- context_dialog_act=False,
- belief_state=False,
- last_opponent_utterance=False,
- last_self_utterance=False,
- ontology=False,
- session_id=False,
- span_info=False,
- terminated=False,
- goal=False
- ):
-
-
- assert role in ['sys', 'usr', 'all']
- info_list = ['utterance', 'dialog_act', 'context', 'context_dialog_act', 'belief_state',
- 'last_opponent_utterance', 'last_self_utterance', 'session_id', 'span_info',
- 'terminated', 'goal']
- self.data = {'train': {}, 'val': {}, 'test': {}, 'role': role}
- if data_key == 'all':
- data_key_list = ['train', 'val', 'test']
- else:
- data_key_list = [data_key]
- for data_key in data_key_list:
- #data = read_zipped_json(os.path.join(data_dir, '{}.json.zip'.format(data_key)), '{}.json'.format(data_key))
- with open(os.path.join(data_dir, f'{data_key}.json')) as f:
- data = json.load(f)
-
- print('loaded {}, size {}'.format(data_key, len(data)))
-
- for x in info_list:
- self.data[data_key][x] = []
- self.data[data_key]["dialogs"] = []
-
- for sess_id, sess in data.items():
-
- dialog = {"user": [], "system": [], 'sys_acts': []}
-
- for i, turn in enumerate(sess['log']):
- text = turn['text']
- if i % 2 == 0:
- self.data[data_key]['utterance'].append(text)
- dialog['user'].append(text)
- else:
- self.data[data_key]['last_self_utterance'].append(text)
- dialog['system'].append(text)
- dialog['sys_acts'].append(turn['dialog_act'])
-
- self.data[data_key]['session_id'].append(sess_id)
- self.data[data_key]['terminated'].append(i + 1 >= len(sess['log']))
-
- self.data[data_key]['dialogs'].append(dialog)
-
- if ontology:
- ontology_path = os.path.join(data_dir, 'ontology.json')
- self.data['ontology'] = json.load(open(ontology_path))
-
- return self.data
-
-
-class UtteranceDataLoaderVRNN:
-
- def __init__(self, use_confidence_scores=False, use_entropy=False, use_mutual_info=False, use_action_masking=False,
- tracker_path=None):
-
- root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
- processed_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'processed_utterance_data')
- self.use_confidence_scores = use_confidence_scores
- self.use_entropy = use_entropy
- self.use_mutual_info = use_mutual_info
-
- voc_file = os.path.join(root_dir, 'data/multiwoz/sys_da_voc.txt')
- voc_opp_file = os.path.join(root_dir, 'data/multiwoz/usr_da_voc.txt')
- self.vector = MultiWozVector(voc_file, voc_opp_file, use_entropy=self.use_entropy,
- use_mutual_info = self.use_mutual_info,
- use_confidence_scores=use_confidence_scores)
- self.vector.use_mask = use_action_masking
- print("Action masking activated.")
-
- if os.path.exists(processed_dir):
- print('Load processed data file')
- self._load_data(processed_dir)
- else:
- self.dst = SetSUMBTTracker(model_type="roberta", model_path=tracker_path, get_confidence_scores=use_confidence_scores,
- return_entropy=self.use_entropy, return_mutual_info=self.use_mutual_info)
- print('Start preprocessing the dataset')
- self._build_data(root_dir, processed_dir)
-
- def _build_data(self, root_dir, processed_dir):
- self.data = {}
- data_loader = MultiWOZDataloader()
- for part in ['train', 'val', 'test']:
- self.data[part] = []
- raw_data = data_loader.load_data(data_key=part, role='sys')[part]
- dialogs = raw_data['dialogs']
-
- counter = 0
- for dialog in tqdm(dialogs):
- counter += 1
- self.dst.init_session()
-
- # if counter % 20 == 0:
- # print("Dialogues processed: ", counter)
-
- user_utterances = dialog['user']
- system_utterances = dialog['system']
- system_acts = dialog['sys_acts']
-
- for user_utt, system_utt, act in zip(user_utterances, system_utterances, system_acts):
- # Predict dialog state and user actions
- state = self.dst.update(user_utt)
- state, action_mask = self.vector.state_vectorize(state)
- # Vectorize target action set
- act = [k.split('-', 1)[::-1] + i[0] for k, i in act.items()]
- action = self.vector.action_vectorize(act)
- action_mask[action == 1] = 0.0
- self.data[part].append([state, action_mask, action])
- # Update history and state with system acts
- self.dst.state['history'].append(['usr', user_utt])
- self.dst.state['history'].append(['sys', system_utt])
- self.dst.state['system_action'] = act
-
- os.makedirs(processed_dir)
- for part in ['train', 'val', 'test']:
- with open(os.path.join(processed_dir, '{}.pkl'.format(part)), 'wb') as f:
- pickle.dump(self.data[part], f)
-
- def _load_data(self, processed_dir):
- self.data = {}
- for part in ['train', 'val', 'test']:
- with open(os.path.join(processed_dir, '{}.pkl'.format(part)), 'rb') as f:
- self.data[part] = pickle.load(f)
-
- def create_dataset(self, part, batchsz):
- print('Start creating {} dataset'.format(part))
- s = []
- m = []
- a = []
- for item in self.data[part]:
- s.append(torch.Tensor(item[0]))
- m.append(torch.Tensor(item[1]))
- a.append(torch.Tensor(item[2]))
- s = torch.stack(s)
- m = torch.stack(m)
- a = torch.stack(a)
- dataset = ActDataset(s, m, a)
- dataloader = data.DataLoader(dataset, batchsz, True)
- print('Finish creating {} dataset'.format(part))
- return dataloader
-
-
-if __name__ == '__main__':
- loader = UtteranceDataLoaderVRNN()
- train_data = loader.data['train']
-
- dialog = train_data[0]
- print("State: ", dialog[0])
- print("Acts: ", dialog[1])
diff --git a/plot_results/README.md b/plot_results/README.md
deleted file mode 100644
index b78b5b626a3d01e2bae5532c64ca64c3eede1cc6..0000000000000000000000000000000000000000
--- a/plot_results/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Plot training curve
-
-Plot training curve directly from tensorboard event files.
-
-The file structure of tb_file is like this:
-
- .
- ├── dir
- └── map["dir"]
- └── experiment_*
- └── tb_dir
- └── events.*
-
-The structure of map file is like this:
-```json
-[
- {
- "dir": "Algorithm1_results",
- "legend": "Algorithm1"
- },
- {
- "dir": "Algorithm2_results",
- "legend": "Algorithm2"
- }
-]
-```
-The value of `legend` will be the depicted in the plot legend.
-
-If you want to truncate the figure to a certain number of training dialogues on the x-axis, use the argument `--max-dialogues`.