diff --git a/convlab/policy/README.md b/convlab/policy/README.md
index 6cdac456c14b50618ba8a8edc5ee947a6627d43a..233ea0eece10d0789791e3a97c5d1aa8311f232a 100755
--- a/convlab/policy/README.md
+++ b/convlab/policy/README.md
@@ -43,7 +43,7 @@ The necessary step before starting a training is to set up the environment and p
```
{
"model": {
- "load_path": "", # specify a loading path to load a pre-trained model
+ "load_path": "", # specify a loading path to load a pre-trained model, omit the ending .pol.mdl
"use_pretrained_initialisation": false, # will download a provided ConvLab-3 model
"pretrained_load_path": "",
"seed": 0, # the seed for the experiment
diff --git a/convlab/policy/mle/README.md b/convlab/policy/mle/README.md
index db62faaef6855ba92fc449e6d19556269d9aa971..c13140497508d79ec6faedfc588fa4f6af7043f7 100644
--- a/convlab/policy/mle/README.md
+++ b/convlab/policy/mle/README.md
@@ -14,6 +14,9 @@ The dataset name can be "multiwoz21" or "sgd" for instance. The first time you r
Other hyperparameters such as learning rate or number of epochs can be set in the config.json file.
+We provide a model trained on multiwoz21 on hugging-face: https://huggingface.co/ConvLab/mle-policy-multiwoz21
+
+
## Evaluation
Evaluation on the validation data set takes place during training.
\ No newline at end of file
diff --git a/convlab/policy/vector/vector_nodes.py b/convlab/policy/vector/vector_nodes.py
index c2f6258f48dfc7b27f2bce6c17b7c6e1f92e7705..2e073669effc518cee4efd1f03d25bbd501b65af 100644
--- a/convlab/policy/vector/vector_nodes.py
+++ b/convlab/policy/vector/vector_nodes.py
@@ -1,6 +1,8 @@
# -*- coding: utf-8 -*-
import sys
import numpy as np
+import logging
+
from convlab.util.multiwoz.lexicalize import delexicalize_da, flat_da
from .vector_base import VectorBase
@@ -8,9 +10,11 @@ from .vector_base import VectorBase
class VectorNodes(VectorBase):
def __init__(self, dataset_name='multiwoz21', character='sys', use_masking=False, manually_add_entity_names=True,
- seed=0):
+ seed=0, filter_state=True):
super().__init__(dataset_name, character, use_masking, manually_add_entity_names, seed)
+ self.filter_state = filter_state
+ logging.info(f"We filter state by active domains: {self.filter_state}")
def get_state_dim(self):
self.belief_state_dim = 0
@@ -56,9 +60,16 @@ class VectorNodes(VectorBase):
self.get_user_act_feature(state)
self.get_sys_act_feature(state)
domain_active_dict = self.get_user_goal_feature(state, domain_active_dict)
- number_entities_dict = self.get_db_features()
self.get_general_features(state, domain_active_dict)
+ if self.db is not None:
+ number_entities_dict = self.get_db_features()
+ else:
+ number_entities_dict = None
+
+ if self.filter_state:
+ self.kg_info = self.filter_inactive_domains(domain_active_dict)
+
if self.use_mask:
mask = self.get_mask(domain_active_dict, number_entities_dict)
for i in range(self.da_dim):
@@ -89,13 +100,15 @@ class VectorNodes(VectorBase):
feature_type = 'user goal'
for domain in self.belief_domains:
- for slot, value in state['belief_state'][domain].items():
- description = f"user goal-{domain}-{slot}".lower()
- value = 1.0 if (value and value != "not mentioned") else 0.0
- self.add_graph_node(domain, feature_type, description, value)
-
- if [slot for slot, value in state['belief_state'][domain].items() if value]:
- domain_active_dict[domain] = True
+ # the if case is needed because SGD only saves the dialogue state info for active domains
+ if domain in state['belief_state']:
+ for slot, value in state['belief_state'][domain].items():
+ description = f"user goal-{domain}-{slot}".lower()
+ value = 1.0 if (value and value != "not mentioned") else 0.0
+ self.add_graph_node(domain, feature_type, description, value)
+
+ if [slot for slot, value in state['belief_state'][domain].items() if value]:
+ domain_active_dict[domain] = True
return domain_active_dict
def get_sys_act_feature(self, state):
@@ -128,11 +141,12 @@ class VectorNodes(VectorBase):
def get_general_features(self, state, domain_active_dict):
feature_type = 'general'
- for i, domain in enumerate(self.db_domains):
- if domain in state['booked']:
- description = f"general-{domain}-booked".lower()
- value = 1.0 if state['booked'][domain] else 0.0
- self.add_graph_node(domain, feature_type, description, value)
+ if 'booked' in state:
+ for i, domain in enumerate(self.db_domains):
+ if domain in state['booked']:
+ description = f"general-{domain}-booked".lower()
+ value = 1.0 if state['booked'][domain] else 0.0
+ self.add_graph_node(domain, feature_type, description, value)
for domain in self.domains:
if domain == 'general':
@@ -140,3 +154,17 @@ class VectorNodes(VectorBase):
value = 1.0 if domain_active_dict[domain] else 0
description = f"general-{domain}".lower()
self.add_graph_node(domain, feature_type, description, value)
+
+ def filter_inactive_domains(self, domain_active_dict):
+
+ kg_filtered = []
+ for node in self.kg_info:
+ domain = node['domain']
+ if domain in domain_active_dict:
+ if domain_active_dict[domain]:
+ kg_filtered.append(node)
+ else:
+ kg_filtered.append(node)
+
+ return kg_filtered
+
diff --git a/convlab/policy/vtrace_DPT/README.md b/convlab/policy/vtrace_DPT/README.md
index 6dcee257bd655eb1e873fb5945de0f226d469210..002a8a050cc8bf573761a1b5ba2276d844a6db7d 100644
--- a/convlab/policy/vtrace_DPT/README.md
+++ b/convlab/policy/vtrace_DPT/README.md
@@ -20,7 +20,11 @@ You can specify the dataset that you would like to use, e.g. "multiwoz21" or "sg
You can specify hyperparamters such as epoch, supervised_lr and data_percentage (how much of the data you want to use) in the config.json file.
+We provide several supervised trained models on hugging-face to reproduce the results:
+- pre-trained on SGD: https://huggingface.co/ConvLab/ddpt-policy-sgd
+- pre-trained on 1% multiwoz21: https://huggingface.co/ConvLab/ddpt-policy-0.01multiwoz21
+- pre-trained on SGD and afterwards on 1% multiwoz21: https://huggingface.co/ConvLab/ddpt-policy-sgd_0.01multiwoz21
## RL training