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nlu #10

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s444354 merged 8 commits from nlu into master 2022-05-28 15:04:22 +02:00
Conversation 1 Commits 8 Files Changed 16 +3792 -857
16 changed files with 3792 additions and 857 deletions
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2
.gitignore vendored
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@ -214,3 +214,5 @@ fabric.properties
.idea .idea
slot-model slot-model
ConvLab-2

14
README.md
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@ -1,4 +1,4 @@
## Zadanie 7 ## Zadanie 7/8 NLU
- rozwiązanie zadania znajduje się w pliku **lab/08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb**, ostatnia komórka zawiera skrypt ewaluujący model metrykami precision, recall i f1 - rozwiązanie zadania znajduje się w pliku **lab/08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb**, ostatnia komórka zawiera skrypt ewaluujący model metrykami precision, recall i f1
@ -10,4 +10,14 @@
- plik **src/dialogue_system.py** będzie łączył wszystkie moduły systemu dialogowego, narazie wykorzystuje tylko tagger NLU - plik **src/dialogue_system.py** będzie łączył wszystkie moduły systemu dialogowego, narazie wykorzystuje tylko tagger NLU
- aby porozmawiać z systemem należy uruchomić wszystkie komórki pliku **lab/08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb**, w celu anuczenia modelu, po ich wykonaniu należy uruchomić pythonowy skrypt **src/dialogue_system.py** - aby porozmawiać z systemem należy uruchomić wszystkie komórki pliku **lab/08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb**, w celu nauczenia modelu, po ich wykonaniu należy uruchomić pythonowy skrypt **src/dialogue_system.py**
---
## Zadanie 9/10 DST i DP
- implementacja regułowego modułu DST: **src/components DST.py**
- implementacja regułowego DP, tylko happy path: **src/components DP.py**
- wykorzystanie w pliku **src/dialogue_system.py**

105
lab/08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb
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@ -2,9 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {},
"collapsed": false
},
"source": [ "source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n", "<div class=\"alert alert-block alert-info\">\n",
@ -82,7 +80,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -155,7 +153,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -172,7 +170,7 @@
"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>wybieram</td><td>inform</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>batmana </td><td>inform</td><td>B-title</td></tr>\\n</tbody>\\n</table>'" "'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>wybieram</td><td>inform</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>batmana </td><td>inform</td><td>B-title</td></tr>\\n</tbody>\\n</table>'"
] ]
}, },
"execution_count": 14, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -184,7 +182,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -202,7 +200,7 @@
"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>chcę </td><td>inform</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>zarezerwować</td><td>inform</td><td>B-goal</td></tr>\\n<tr><td style=\"text-align: right;\">3</td><td>bilety </td><td>inform</td><td>O </td></tr>\\n</tbody>\\n</table>'" "'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>chcę </td><td>inform</td><td>O </td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>zarezerwować</td><td>inform</td><td>B-goal</td></tr>\\n<tr><td style=\"text-align: right;\">3</td><td>bilety </td><td>inform</td><td>O </td></tr>\\n</tbody>\\n</table>'"
] ]
}, },
"execution_count": 15, "execution_count": 3,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -213,7 +211,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -232,7 +230,7 @@
"'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>chciałbym </td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>anulować </td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">3</td><td>rezerwację</td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">4</td><td>biletu </td><td>inform</td><td>O</td></tr>\\n</tbody>\\n</table>'" "'<table>\\n<tbody>\\n<tr><td style=\"text-align: right;\">1</td><td>chciałbym </td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">2</td><td>anulować </td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">3</td><td>rezerwację</td><td>inform</td><td>O</td></tr>\\n<tr><td style=\"text-align: right;\">4</td><td>biletu </td><td>inform</td><td>O</td></tr>\\n</tbody>\\n</table>'"
] ]
}, },
"execution_count": 16, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -251,7 +249,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -288,20 +286,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 18, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"Corpus: 345 train + 38 dev + 32 test sentences\n", "Corpus: 346 train + 38 dev + 32 test sentences\n",
"Dictionary with 20 tags: <unk>, O, B-interval, I-interval, B-title, B-date, I-date, B-time, B-quantity, B-area, I-area, B-goal, I-goal, I-title, I-time, I-quantity, B-seats, I-seats, <START>, <STOP>\n" "Dictionary with 78 tags: <unk>, O, O/reqmore, B-interval/reqmore, I-interval/reqmore, O/inform, B-title/inform, B-date/inform, I-date/inform, B-time/inform, B-quantity/inform, B-area/inform, I-area/inform, B-goal/inform, O/bye, O/hello, O/reqmore inform, B-goal/reqmore inform, I-goal/reqmore inform, B-date/reqmore inform, B-interval/reqmore inform, O/null, O/help, B-goal/reqmore, I-goal/reqmore, B-title/reqmore, B-title/reqmore inform, I-title/reqmore inform, O/ack, O/reqalts\n"
] ]
} }
], ],
"source": [ "source": [
"def conllu2flair(sentences, label=None):\n", "def conllu2flair(sentences, label1=None, label2=None):\n",
" fsentences = []\n", " fsentences = []\n",
"\n", "\n",
" for sentence in sentences:\n", " for sentence in sentences:\n",
@ -310,16 +308,19 @@
" for token in sentence:\n", " for token in sentence:\n",
" ftoken = Token(token['form'])\n", " ftoken = Token(token['form'])\n",
"\n", "\n",
" if label:\n", " if label1:\n",
" ftoken.add_tag(label, token[label])\n", " if label2:\n",
"\n", " ftoken.add_tag(label1, token[label1] + \"/\" + token[label2])\n",
" else:\n",
" ftoken.add_tag(label1, token[label1])\n",
" \n",
" fsentence.add_token(ftoken)\n", " fsentence.add_token(ftoken)\n",
"\n", "\n",
" fsentences.append(fsentence)\n", " fsentences.append(fsentence)\n",
"\n", "\n",
" return SentenceDataset(fsentences)\n", " return SentenceDataset(fsentences)\n",
"\n", "\n",
"corpus = Corpus(train=conllu2flair(trainset, 'slot'), test=conllu2flair(testset, 'slot'))\n", "corpus = Corpus(train=conllu2flair(trainset, 'slot', \"frame\"), test=conllu2flair(testset, 'slot', \"frame\"))\n",
"print(corpus)\n", "print(corpus)\n",
"tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')\n", "tag_dictionary = corpus.make_tag_dictionary(tag_type='slot')\n",
"print(tag_dictionary)" "print(tag_dictionary)"
@ -334,7 +335,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 19, "execution_count": 7,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -361,7 +362,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -396,7 +397,7 @@
" (locked_dropout): LockedDropout(p=0.5)\n", " (locked_dropout): LockedDropout(p=0.5)\n",
" (embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)\n", " (embedding2nn): Linear(in_features=4446, out_features=4446, bias=True)\n",
" (rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)\n", " (rnn): LSTM(4446, 256, batch_first=True, bidirectional=True)\n",
" (linear): Linear(in_features=512, out_features=20, bias=True)\n", " (linear): Linear(in_features=512, out_features=78, bias=True)\n",
" (beta): 1.0\n", " (beta): 1.0\n",
" (weights): None\n", " (weights): None\n",
" (weight_tensor) None\n", " (weight_tensor) None\n",
@ -417,7 +418,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 21, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -479,14 +480,14 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"2022-05-05 17:34:34,767 loading file slot-model/final-model.pt\n" "2022-05-22 15:25:19,970 loading file slot-model/final-model.pt\n"
] ]
} }
], ],
@ -504,16 +505,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 42, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"[('kiedy', 'O'), ('gracie', 'O'), ('film', 'O'), ('zorro', 'B-title')]" "[('co', 'O/reqmore'), ('gracie', 'O/reqmore'), ('obecnie', 'O/reqmore')]"
] ]
}, },
"execution_count": 42, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -525,7 +526,7 @@
" model.predict(fsentence)\n", " model.predict(fsentence)\n",
" return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]\n", " return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)]\n",
"\n", "\n",
"predict(model, 'kiedy gracie film zorro'.split())" "predict(model, 'co gracie obecnie'.split())"
] ]
}, },
{ {
@ -538,7 +539,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -546,18 +547,18 @@
"text/html": [ "text/html": [
"<table>\n", "<table>\n",
"<tbody>\n", "<tbody>\n",
"<tr><td>kiedy </td><td>O </td></tr>\n", "<tr><td>kiedy </td><td>O/reqmore</td></tr>\n",
"<tr><td>gracie</td><td>O </td></tr>\n", "<tr><td>gracie</td><td>O/reqmore</td></tr>\n",
"<tr><td>film </td><td>O </td></tr>\n", "<tr><td>film </td><td>O/reqmore</td></tr>\n",
"<tr><td>zorro </td><td>B-title</td></tr>\n", "<tr><td>zorro </td><td>O/reqmore</td></tr>\n",
"</tbody>\n", "</tbody>\n",
"</table>" "</table>"
], ],
"text/plain": [ "text/plain": [
"'<table>\\n<tbody>\\n<tr><td>kiedy </td><td>O </td></tr>\\n<tr><td>gracie</td><td>O </td></tr>\\n<tr><td>film </td><td>O </td></tr>\\n<tr><td>zorro </td><td>B-title</td></tr>\\n</tbody>\\n</table>'" "'<table>\\n<tbody>\\n<tr><td>kiedy </td><td>O/reqmore</td></tr>\\n<tr><td>gracie</td><td>O/reqmore</td></tr>\\n<tr><td>film </td><td>O/reqmore</td></tr>\\n<tr><td>zorro </td><td>O/reqmore</td></tr>\\n</tbody>\\n</table>'"
] ]
}, },
"execution_count": 24, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -568,17 +569,19 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 82, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "ename": "NameError",
"output_type": "stream", "evalue": "name 'testset' is not defined",
"text": [ "output_type": "error",
"stats: \n", "traceback": [
"precision: 0.8076923076923077\n", "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"recall: 0.4117647058823529\n", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"f1: 0.5454545454545454\n" "\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb Cell 25'\u001b[0m in \u001b[0;36m<cell line: 40>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=36'>37</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mrecall: \u001b[39m\u001b[39m\"\u001b[39m, recallScore)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=37'>38</a>\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mf1: \u001b[39m\u001b[39m\"\u001b[39m, f1Score)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=39'>40</a>\u001b[0m \u001b[39meval\u001b[39;49m()\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\08-parsing-semantyczny-uczenie(zmodyfikowany).ipynb Cell 25'\u001b[0m in \u001b[0;36meval\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=13'>14</a>\u001b[0m fp \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=14'>15</a>\u001b[0m fn \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=15'>16</a>\u001b[0m sentences \u001b[39m=\u001b[39m [sentence \u001b[39mfor\u001b[39;00m sentence \u001b[39min\u001b[39;00m testset]\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=16'>17</a>\u001b[0m \u001b[39mfor\u001b[39;00m sentence \u001b[39min\u001b[39;00m sentences:\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=17'>18</a>\u001b[0m \u001b[39m# get sentence as terms list\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/08-parsing-semantyczny-uczenie%28zmodyfikowany%29.ipynb#ch0000024?line=18'>19</a>\u001b[0m termsList \u001b[39m=\u001b[39m [w[\u001b[39m\"\u001b[39m\u001b[39mform\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39mfor\u001b[39;00m w \u001b[39min\u001b[39;00m sentence]\n",
"\u001b[1;31mNameError\u001b[0m: name 'testset' is not defined"
] ]
} }
], ],
@ -605,13 +608,13 @@
" # predict tags\n", " # predict tags\n",
" predTags = [tag[1] for tag in predict(model, termsList)]\n", " predTags = [tag[1] for tag in predict(model, termsList)]\n",
" \n", " \n",
" expTags = [token[\"slot\"] for token in sentence]\n", " expTags = [token[\"slot\"] + \"/\" + token[\"frame\"] for token in sentence]\n",
" for i in range(len(predTags)):\n", " for i in range(len(predTags)):\n",
" if (expTags[i] == \"O\" and expTags[i] != predTags[i]):\n", " if (expTags[i][0] == \"O\" and expTags[i] != predTags[i]):\n",
" fp += 1\n", " fp += 1\n",
" elif ((expTags[i] != \"O\") & (predTags[i] == \"O\")):\n", " elif ((expTags[i][0] != \"O\") & (predTags[i][0] == \"O\")):\n",
" fn += 1\n", " fn += 1\n",
" elif ((expTags[i] != \"O\") & (predTags[i] == expTags[i])):\n", " elif ((expTags[i][0] != \"O\") & (predTags[i] == expTags[i])):\n",
" tp += 1\n", " tp += 1\n",
"\n", "\n",
" precisionScore = precision(tp, fp)\n", " precisionScore = precision(tp, fp)\n",
@ -645,7 +648,7 @@
"author": "Marek Kubis", "author": "Marek Kubis",
"email": "mkubis@amu.edu.pl", "email": "mkubis@amu.edu.pl",
"interpreter": { "interpreter": {
"hash": "2be5faf79681da6f2a61fdfdd5405d65d042280f7fba6178067603e3a2925119" "hash": "2f9d6cf1e3d8195079a65c851de355134a77367bcd714b1a5d498c42d3c07114"
}, },
"jupytext": { "jupytext": {
"cell_metadata_filter": "-all", "cell_metadata_filter": "-all",
@ -653,7 +656,7 @@
"notebook_metadata_filter": "-all" "notebook_metadata_filter": "-all"
}, },
"kernelspec": { "kernelspec": {
"display_name": "Python 3.10.4 64-bit", "display_name": "Python 3 (ipykernel)",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@ -668,7 +671,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.3" "version": "3.8.10"
}, },
"subtitle": "8.Parsing semantyczny z wykorzystaniem technik uczenia maszynowego[laboratoria]", "subtitle": "8.Parsing semantyczny z wykorzystaniem technik uczenia maszynowego[laboratoria]",
"title": "Systemy Dialogowe", "title": "Systemy Dialogowe",

972
lab/09-zarzadzanie-dialogiem-reguly(zmodyfikowany).ipynb Normal file
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@ -0,0 +1,972 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Systemy Dialogowe </h1>\n",
"<h2> 9. <i>Zarządzanie dialogiem z wykorzystaniem reguł</i> [laboratoria]</h2> \n",
"<h3> Marek Kubis (2021)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zarządzanie dialogiem z wykorzystaniem reguł\n",
"============================================\n",
"\n",
"Agent dialogowy wykorzystuje do zarządzanie dialogiem dwa moduły:\n",
"\n",
" - monitor stanu dialogu (dialogue state tracker, DST) — moduł odpowiedzialny za śledzenie stanu dialogu.\n",
"\n",
" - taktykę prowadzenia dialogu (dialogue policy) — moduł, który na podstawie stanu dialogu\n",
" podejmuje decyzję o tym jaką akcję (akt systemu) agent ma podjąć w kolejnej turze.\n",
"\n",
"Oba moduły mogą być realizowane zarówno z wykorzystaniem reguł jak i uczenia maszynowego.\n",
"Mogą one zostać również połączone w pojedynczy moduł zwany wówczas *menedżerem dialogu*.\n",
"\n",
"Przykład\n",
"--------\n",
"\n",
"Zaimplementujemy regułowe moduły monitora stanu dialogu oraz taktyki dialogowej a następnie\n",
"osadzimy je w środowisku *[ConvLab-2](https://github.com/thu-coai/ConvLab-2)*,\n",
"które służy do ewaluacji systemów dialogowych.\n",
"\n",
"**Uwaga:** Niektóre moduły środowiska *ConvLab-2* nie są zgodne z najnowszymi wersjami Pythona,\n",
"dlatego przed uruchomieniem poniższych przykładów należy się upewnić, że mają Państwo interpreter\n",
"Pythona w wersji 3.7. W przypadku nowszych wersji Ubuntu Pythona 3.7 można zainstalować z\n",
"repozytorium `deadsnakes`, wykonując polecenia przedstawione poniżej.\n",
"\n",
"```\n",
"sudo add-apt-repository ppa:deadsnakes/ppa\n",
"sudo apt update\n",
"sudo apt install python3.7 python3.7-dev python3.7-venv\n",
"```\n",
"\n",
"W przypadku innych systemów można skorzystać np. z narzędzia [pyenv](https://github.com/pyenv/pyenv) lub środowiska [conda](https://conda.io).\n",
"\n",
"Ze względu na to, że *ConvLab-2* ma wiele zależności zachęcam również do skorzystania ze środowiska\n",
"wirtualnego `venv`, w którym moduły zależne mogą zostać zainstalowane.\n",
"W tym celu należy wykonać następujące polecenia\n",
"\n",
"```\n",
"python3.7 -m venv convenv # utworzenie nowego środowiska o nazwie convenv\n",
"source convenv/bin/activate # aktywacja środowiska w bieżącej powłoce\n",
"pip install --ignore-installed jupyter # instalacja jupytera w środowisku convenv\n",
"```\n",
"\n",
"Po skonfigurowaniu środowiska można przystąpić do instalacji *ConvLab-2*, korzystając z\n",
"następujących poleceń\n",
"\n",
"```\n",
"mkdir -p l08\n",
"cd l08\n",
"git clone https://github.com/thu-coai/ConvLab-2.git\n",
"cd ConvLab-2\n",
"pip install -e .\n",
"python -m spacy download en_core_web_sm\n",
"cd ../..\n",
"```\n",
"\n",
"Po zakończeniu instalacji należy ponownie uruchomić notatnik w powłoce, w której aktywne jest\n",
"środowisko wirtualne *convenv*.\n",
"\n",
"```\n",
"jupyter notebook 08-zarzadzanie-dialogiem-reguly.ipynb\n",
"```\n",
"\n",
"Działanie zaimplementowanych modułów zilustrujemy, korzystając ze zbioru danych\n",
"[MultiWOZ](https://github.com/budzianowski/multiwoz) (Budzianowski i in., 2018), który zawiera\n",
"wypowiedzi dotyczące m.in. rezerwacji pokoi hotelowych, zamawiania biletów kolejowych oraz\n",
"rezerwacji stolików w restauracji.\n",
"\n",
"### Monitor Stanu Dialogu\n",
"\n",
"Do reprezentowania stanu dialogu użyjemy struktury danych wykorzystywanej w *ConvLab-2*."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'cinema': {'book': {'title': '',\n",
" 'date': '',\n",
" 'time': '',\n",
" 'quantity': '',\n",
" 'seats': '',\n",
" 'area': '',\n",
" 'interval': ''},\n",
" 'semi': {'goal': ''}}},\n",
" 'request_state': {},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# from convlab2.util.multiwoz.state import default_state\n",
"from utils.state import default_state\n",
"default_state()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Metoda `update` naszego monitora stanu dialogu będzie przyjmować akty użytkownika i odpowiednio\n",
"modyfikować stan dialogu.\n",
"W przypadku aktów typu `inform` wartości slotów zostaną zapamiętane w słownikach odpowiadających\n",
"poszczególnym dziedzinom pod kluczem `belief_state`.\n",
"W przypadku aktów typu `request` sloty, o które pyta użytkownik zostaną zapisane pod kluczem\n",
"`request_state`.\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nclass SimpleRuleDST(DST):\\n def __init__(self):\\n DST.__init__(self)\\n self.state = default_state()\\n self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\\n\\n def update(self, user_act=None):\\n for intent, domain, slot, value in user_act:\\n domain = domain.lower()\\n intent = intent.lower()\\n\\n if domain in ['unk', 'general', 'booking']:\\n continue\\n\\n if intent == 'inform':\\n k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n if k is None:\\n continue\\n\\n domain_dic = self.state['belief_state'][domain]\\n\\n if k in domain_dic['semi']:\\n nvalue = normalize_value(self.value_dict, domain, k, value)\\n self.state['belief_state'][domain]['semi'][k] = nvalue\\n elif k in domain_dic['book']:\\n self.state['belief_state'][domain]['book'][k] = value\\n elif k.lower() in domain_dic['book']:\\n self.state['belief_state'][domain]['book'][k.lower()] = value\\n elif intent == 'request':\\n k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\\n\\n if domain not in self.state['request_state']:\\n self.state['request_state'][domain] = {}\\n if k not in self.state['request_state'][domain]:\\n self.state['request_state'][domain][k] = 0\\n\\n return self.state\\n\\n def init_session(self):\\n self.state = default_state()\\n\""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"import os\n",
"from convlab2.dst.dst import DST\n",
"from convlab2.dst.rule.multiwoz.dst_util import normalize_value\n",
"from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA\n",
"\n",
"class SimpleRuleDST(DST):\n",
" def __init__(self):\n",
" DST.__init__(self)\n",
" self.state = default_state()\n",
" self.value_dict = json.load(open('utils/value_dict.json'))\n",
"\n",
" def update(self, user_act=None):\n",
" for intent, domain, slot, value in user_act:\n",
" domain = domain.lower()\n",
" intent = intent.lower()\n",
" value = value.lower()\n",
"\n",
" # if domain in ['unk', 'cinema']:\n",
" # continue\n",
" k = slot\n",
"\n",
" if intent == 'inform':\n",
" # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
" # if k is None:\n",
" # continue\n",
"\n",
" domain_dic = self.state['belief_state'][domain]\n",
"\n",
" if k in domain_dic['semi']:\n",
" # nvalue = normalize_value(self.value_dict, domain, k, value)\n",
" self.state['belief_state'][domain]['semi'][k] = value\n",
" elif k in domain_dic['book']:\n",
" self.state['belief_state'][domain]['book'][k] = value\n",
" elif k.lower() in domain_dic['book']:\n",
" self.state['belief_state'][domain]['book'][k.lower()] = value\n",
" elif intent == 'request':\n",
" # k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
"\n",
" if domain not in self.state['request_state']:\n",
" self.state['request_state'][domain] = {}\n",
" if k not in self.state['request_state'][domain]:\n",
" self.state['request_state'][domain][k] = 0\n",
"\n",
" return self.state\n",
"\n",
" def init_session(self):\n",
" self.state = default_state()\n",
"\n",
"'''\n",
"class SimpleRuleDST(DST):\n",
" def __init__(self):\n",
" DST.__init__(self)\n",
" self.state = default_state()\n",
" self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\n",
"\n",
" def update(self, user_act=None):\n",
" for intent, domain, slot, value in user_act:\n",
" domain = domain.lower()\n",
" intent = intent.lower()\n",
"\n",
" if domain in ['unk', 'general', 'booking']:\n",
" continue\n",
"\n",
" if intent == 'inform':\n",
" k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
"\n",
" if k is None:\n",
" continue\n",
"\n",
" domain_dic = self.state['belief_state'][domain]\n",
"\n",
" if k in domain_dic['semi']:\n",
" nvalue = normalize_value(self.value_dict, domain, k, value)\n",
" self.state['belief_state'][domain]['semi'][k] = nvalue\n",
" elif k in domain_dic['book']:\n",
" self.state['belief_state'][domain]['book'][k] = value\n",
" elif k.lower() in domain_dic['book']:\n",
" self.state['belief_state'][domain]['book'][k.lower()] = value\n",
" elif intent == 'request':\n",
" k = REF_SYS_DA[domain.capitalize()].get(slot, slot)\n",
"\n",
" if domain not in self.state['request_state']:\n",
" self.state['request_state'][domain] = {}\n",
" if k not in self.state['request_state'][domain]:\n",
" self.state['request_state'][domain][k] = 0\n",
"\n",
" return self.state\n",
"\n",
" def init_session(self):\n",
" self.state = default_state()\n",
"'''\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"W definicji metody `update` zakładamy, że akty dialogowe przekazywane do monitora stanu dialogu z\n",
"modułu NLU są czteroelementowymi listami złożonymi z:\n",
"\n",
" - nazwy aktu użytkownika,\n",
" - nazwy dziedziny, której dotyczy wypowiedź,\n",
" - nazwy slotu,\n",
" - wartości slotu.\n",
"\n",
"Zobaczmy na kilku prostych przykładach jak stan dialogu zmienia się pod wpływem przekazanych aktów\n",
"użytkownika."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'cinema': {'book': {'title': '',\n",
" 'date': '',\n",
" 'time': '',\n",
" 'quantity': '',\n",
" 'seats': '',\n",
" 'area': '',\n",
" 'interval': ''},\n",
" 'semi': {'goal': ''}}},\n",
" 'request_state': {},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst = SimpleRuleDST()\n",
"dst.state"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"{'book': {'title': 'batman',\n",
" 'date': '',\n",
" 'time': '15:00',\n",
" 'quantity': '',\n",
" 'seats': '',\n",
" 'area': '',\n",
" 'interval': ''},\n",
" 'semi': {'goal': ''}}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst.update([['Inform', 'Cinema', 'time', '15:00'], ['Inform', 'Cinema', 'title', 'Batman']])\n",
"dst.state['belief_state']['cinema']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"{'book': {'title': 'batman',\n",
" 'date': '',\n",
" 'time': '15:00',\n",
" 'quantity': '',\n",
" 'seats': '',\n",
" 'area': 'na górze na środku',\n",
" 'interval': ''},\n",
" 'semi': {'goal': ''}}"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst.update([['Inform', 'Cinema', 'area', 'na górze na środku']])\n",
"dst.state['belief_state']['cinema']"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"data": {
"text/plain": [
"{'cinema': {'date': 0}}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst.update([['Request', 'Cinema', 'date', '?']])\n",
"dst.state['request_state']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"dst.update([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])\n",
"dst.state['belief_state']['hotel']"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'cinema': {'book': {'title': 'batman',\n",
" 'date': '',\n",
" 'time': '15:00',\n",
" 'quantity': '',\n",
" 'seats': '',\n",
" 'area': 'na górze na środku',\n",
" 'interval': ''},\n",
" 'semi': {'goal': ''}}},\n",
" 'request_state': {'cinema': {'date': 0}},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dst.state"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Taktyka Prowadzenia Dialogu\n",
"\n",
"Prosta taktyka prowadzenia dialogu dla systemu rezerwacji pokoi hotelowych może składać się z następujących reguł:\n",
"\n",
" 1. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Request`, to udziel odpowiedzi na jego\n",
" pytanie.\n",
"\n",
" 2. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform`, to zaproponuj mu hotel\n",
" spełniający zdefiniowane przez niego kryteria.\n",
"\n",
" 3. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform` zawierający szczegóły\n",
" rezerwacji, to zarezerwuj pokój.\n",
"\n",
"Metoda `predict` taktyki `SimpleRulePolicy` realizuje reguły przedstawione powyżej."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"from collections import defaultdict\n",
"import copy\n",
"import json\n",
"from copy import deepcopy\n",
"\n",
"from convlab2.policy.policy import Policy\n",
"from convlab2.util.multiwoz.dbquery import Database\n",
"from convlab2.util.multiwoz.multiwoz_slot_trans import REF_SYS_DA, REF_USR_DA\n",
"\n",
"\n",
"class SimpleRulePolicy(Policy):\n",
" def __init__(self):\n",
" Policy.__init__(self)\n",
" self.db = Database()\n",
"\n",
" def predict(self, state):\n",
" self.results = []\n",
" system_action = defaultdict(list)\n",
" user_action = defaultdict(list)\n",
"\n",
" for intent, domain, slot, value in state['user_action']:\n",
" user_action[(domain, intent)].append((slot, value))\n",
"\n",
" for user_act in user_action:\n",
" self.update_system_action(user_act, user_action, state, system_action)\n",
"\n",
" # Reguła 3\n",
" if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['Stay', 'Day', 'People']):\n",
" if self.results:\n",
" system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
"\n",
" system_acts = [[intent, domain, slot, value] for (domain, intent), slots in system_action.items() for slot, value in slots]\n",
" state['system_action'] = system_acts\n",
" return system_acts\n",
"\n",
" def update_system_action(self, user_act, user_action, state, system_action):\n",
" domain, intent = user_act\n",
" constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()]['semi'].items() if value != '']\n",
" self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
"\n",
" # Reguła 1\n",
" if intent == 'Request':\n",
" if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n",
" else:\n",
" for slot in user_action[user_act]:\n",
" kb_slot_name = REF_SYS_DA[domain].get(slot[0], slot[0])\n",
"\n",
" if kb_slot_name in self.results[0]:\n",
" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(kb_slot_name, 'unknown')])\n",
"\n",
" # Reguła 2\n",
" elif intent == 'Inform':\n",
" if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n",
" else:\n",
" system_action[(domain, 'Inform')].append(['Choice', str(len(self.results))])\n",
" choice = self.results[0]\n",
"\n",
" if domain in [\"Hotel\", \"Attraction\", \"Police\", \"Restaurant\"]:\n",
" system_action[(domain, 'Recommend')].append(['Name', choice['name']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podobnie jak w przypadku aktów użytkownika akty systemowe przekazywane do modułu NLG są czteroelementowymi listami złożonymi z:\n",
"\n",
" - nazwy aktu systemowe,\n",
" - nazwy dziedziny, której dotyczy wypowiedź,\n",
" - nazwy slotu,\n",
" - wartości slotu.\n",
"\n",
"Sprawdźmy jakie akty systemowe zwraca taktyka `SimpleRulePolicy` w odpowiedzi na zmieniający się stan dialogu."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"from convlab2.dialog_agent import PipelineAgent\n",
"dst.init_session()\n",
"policy = SimpleRulePolicy()\n",
"agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response([['Inform', 'Hotel', 'Area', 'north']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response([['Request', 'Hotel', 'Area', '?']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testy End-to-End\n",
"\n",
"Na koniec przeprowadźmy dialog łącząc w potok nasze moduły\n",
"z modułami NLU i NLG dostępnymi dla MultiWOZ w środowisku `ConvLab-2`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"from convlab2.nlu.svm.multiwoz import SVMNLU\n",
"from convlab2.nlg.template.multiwoz import TemplateNLG\n",
"\n",
"nlu = SVMNLU()\n",
"nlg = TemplateNLG(is_user=False)\n",
"agent = PipelineAgent(nlu=nlu, dst=dst, policy=policy, nlg=nlg, name='sys')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response(\"I need a cheap hotel with free parking .\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response(\"Where it is located ?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response(\"I would prefer the hotel be in the north part of town .\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
},
{
"ename": "",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31mRunning cells with 'Python 3.6.15 ('sysdial')' requires ipykernel package.\n",
"Run the following command to install 'ipykernel' into the Python environment. \n",
"Command: 'conda install -n sysdial ipykernel --update-deps --force-reinstall'"
]
}
],
"source": [
"agent.response(\"Yeah , could you book me a room for 2 people for 4 nights starting Tuesday ?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zauważmy, ze nasza prosta taktyka dialogowa zawiera wiele luk, do których należą m.in.:\n",
"\n",
" 1. Niezdolność do udzielenia odpowiedzi na przywitanie, prośbę o pomoc lub restart.\n",
"\n",
" 2. Brak reguł dopytujących użytkownika o szczegóły niezbędne do dokonania rezerwacji takie, jak długość pobytu czy liczba osób.\n",
"\n",
"Bardziej zaawansowane moduły zarządzania dialogiem zbudowane z wykorzystaniem reguł można znaleźć w\n",
"środowisku `ConvLab-2`. Należą do nich m.in. monitor [RuleDST](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/rule/multiwoz/dst.py) oraz taktyka [RuleBasedMultiwozBot](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py).\n",
"\n",
"Zadania\n",
"-------\n",
" 1. Zaimplementować w projekcie monitor stanu dialogu.\n",
"\n",
" 2. Zaimplementować w projekcie taktykę prowadzenia dialogu.\n",
"\n",
"Termin: 24.05.2021, godz. 23:59.\n",
"\n",
"Literatura\n",
"----------\n",
" 1. Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gasic, MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. EMNLP 2018, pp. 5016-5026\n",
" 2. Cathy Pearl, Basic principles for designing voice user interfaces, https://www.oreilly.com/content/basic-principles-for-designing-voice-user-interfaces/ data dostępu: 21 marca 2021\n",
" 3. Cathy Pearl, Designing Voice User Interfaces, Excerpts from Chapter 5: Advanced Voice User Interface Design, https://www.uxmatters.com/mt/archives/2018/01/designing-voice-user-interfaces.php data dostępu: 21 marca 2021"
]
}
],
"metadata": {
"author": "Marek Kubis",
"email": "mkubis@amu.edu.pl",
"interpreter": {
"hash": "91e2b0d1baa6ebb76863bdb1d11380bf032a6a1fc1b919194f041a5133852891"
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"subtitle": "9.Zarządzanie dialogiem z wykorzystaniem reguł[laboratoria]",
"title": "Systemy Dialogowe",
"year": "2021"
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}

535
lab/09-zarzadzanie-dialogiem-reguly.ipynb
View File

@ -9,7 +9,7 @@
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n", "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n", "<div class=\"alert alert-block alert-info\">\n",
"<h1> Systemy Dialogowe </h1>\n", "<h1> Systemy Dialogowe </h1>\n",
"<h2> 9. <i>Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142</i> [laboratoria]</h2> \n", "<h2> 9. <i>Zarządzanie dialogiem z wykorzystaniem reguł</i> [laboratoria]</h2> \n",
"<h3> Marek Kubis (2021)</h3>\n", "<h3> Marek Kubis (2021)</h3>\n",
"</div>\n", "</div>\n",
"\n", "\n",
@ -20,30 +20,30 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142\n", "Zarządzanie dialogiem z wykorzystaniem reguł\n",
"============================================\n", "============================================\n",
"\n", "\n",
"Agent dialogowy wykorzystuje do zarz\u0105dzanie dialogiem dwa modu\u0142y:\n", "Agent dialogowy wykorzystuje do zarządzanie dialogiem dwa moduły:\n",
"\n", "\n",
" - monitor stanu dialogu (dialogue state tracker, DST) \u2014 modu\u0142 odpowiedzialny za \u015bledzenie stanu dialogu.\n", " - monitor stanu dialogu (dialogue state tracker, DST) — moduł odpowiedzialny za śledzenie stanu dialogu.\n",
"\n", "\n",
" - taktyk\u0119 prowadzenia dialogu (dialogue policy) \u2014 modu\u0142, kt\u00f3ry na podstawie stanu dialogu\n", " - taktykę prowadzenia dialogu (dialogue policy) — moduł, który na podstawie stanu dialogu\n",
" podejmuje decyzj\u0119 o tym jak\u0105 akcj\u0119 (akt systemu) agent ma podj\u0105\u0107 w kolejnej turze.\n", " podejmuje decyzję o tym jaką akcję (akt systemu) agent ma podjąć w kolejnej turze.\n",
"\n", "\n",
"Oba modu\u0142y mog\u0105 by\u0107 realizowane zar\u00f3wno z wykorzystaniem regu\u0142 jak i uczenia maszynowego.\n", "Oba moduły mogą być realizowane zarówno z wykorzystaniem reguł jak i uczenia maszynowego.\n",
"Mog\u0105 one zosta\u0107 r\u00f3wnie\u017c po\u0142\u0105czone w pojedynczy modu\u0142 zwany w\u00f3wczas *mened\u017cerem dialogu*.\n", "Mogą one zostać również połączone w pojedynczy moduł zwany wówczas *menedżerem dialogu*.\n",
"\n", "\n",
"Przyk\u0142ad\n", "Przykład\n",
"--------\n", "--------\n",
"\n", "\n",
"Zaimplementujemy regu\u0142owe modu\u0142y monitora stanu dialogu oraz taktyki dialogowej a nast\u0119pnie\n", "Zaimplementujemy regułowe moduły monitora stanu dialogu oraz taktyki dialogowej a następnie\n",
"osadzimy je w \u015brodowisku *[ConvLab-2](https://github.com/thu-coai/ConvLab-2)*,\n", "osadzimy je w środowisku *[ConvLab-2](https://github.com/thu-coai/ConvLab-2)*,\n",
"kt\u00f3re s\u0142u\u017cy do ewaluacji system\u00f3w dialogowych.\n", "które służy do ewaluacji systemów dialogowych.\n",
"\n", "\n",
"**Uwaga:** Niekt\u00f3re modu\u0142y \u015brodowiska *ConvLab-2* nie s\u0105 zgodne z najnowszymi wersjami Pythona,\n", "**Uwaga:** Niektóre moduły środowiska *ConvLab-2* nie są zgodne z najnowszymi wersjami Pythona,\n",
"dlatego przed uruchomieniem poni\u017cszych przyk\u0142ad\u00f3w nale\u017cy si\u0119 upewni\u0107, \u017ce maj\u0105 Pa\u0144stwo interpreter\n", "dlatego przed uruchomieniem poniższych przykładów należy się upewnić, że mają Państwo interpreter\n",
"Pythona w wersji 3.7. W przypadku nowszych wersji Ubuntu Pythona 3.7 mo\u017cna zainstalowa\u0107 z\n", "Pythona w wersji 3.7. W przypadku nowszych wersji Ubuntu Pythona 3.7 można zainstalować z\n",
"repozytorium `deadsnakes`, wykonuj\u0105c polecenia przedstawione poni\u017cej.\n", "repozytorium `deadsnakes`, wykonując polecenia przedstawione poniżej.\n",
"\n", "\n",
"```\n", "```\n",
"sudo add-apt-repository ppa:deadsnakes/ppa\n", "sudo add-apt-repository ppa:deadsnakes/ppa\n",
@ -51,20 +51,20 @@
"sudo apt install python3.7 python3.7-dev python3.7-venv\n", "sudo apt install python3.7 python3.7-dev python3.7-venv\n",
"```\n", "```\n",
"\n", "\n",
"W przypadku innych system\u00f3w mo\u017cna skorzysta\u0107 np. z narz\u0119dzia [pyenv](https://github.com/pyenv/pyenv) lub \u015brodowiska [conda](https://conda.io).\n", "W przypadku innych systemów można skorzystać np. z narzędzia [pyenv](https://github.com/pyenv/pyenv) lub środowiska [conda](https://conda.io).\n",
"\n", "\n",
"Ze wzgl\u0119du na to, \u017ce *ConvLab-2* ma wiele zale\u017cno\u015bci zach\u0119cam r\u00f3wnie\u017c do skorzystania ze \u015brodowiska\n", "Ze względu na to, że *ConvLab-2* ma wiele zależności zachęcam również do skorzystania ze środowiska\n",
"wirtualnego `venv`, w kt\u00f3rym modu\u0142y zale\u017cne mog\u0105 zosta\u0107 zainstalowane.\n", "wirtualnego `venv`, w którym moduły zależne mogą zostać zainstalowane.\n",
"W tym celu nale\u017cy wykona\u0107 nast\u0119puj\u0105ce polecenia\n", "W tym celu należy wykonać następujące polecenia\n",
"\n", "\n",
"```\n", "```\n",
"python3.7 -m venv convenv # utworzenie nowego \u015brodowiska o nazwie convenv\n", "python3.7 -m venv convenv # utworzenie nowego środowiska o nazwie convenv\n",
"source convenv/bin/activate # aktywacja \u015brodowiska w bie\u017c\u0105cej pow\u0142oce\n", "source convenv/bin/activate # aktywacja środowiska w bieżącej powłoce\n",
"pip install --ignore-installed jupyter # instalacja jupytera w \u015brodowisku convenv\n", "pip install --ignore-installed jupyter # instalacja jupytera w środowisku convenv\n",
"```\n", "```\n",
"\n", "\n",
"Po skonfigurowaniu \u015brodowiska mo\u017cna przyst\u0105pi\u0107 do instalacji *ConvLab-2*, korzystaj\u0105c z\n", "Po skonfigurowaniu środowiska można przystąpić do instalacji *ConvLab-2*, korzystając z\n",
"nast\u0119puj\u0105cych polece\u0144\n", "następujących poleceń\n",
"\n", "\n",
"```\n", "```\n",
"mkdir -p l08\n", "mkdir -p l08\n",
@ -76,28 +76,68 @@
"cd ../..\n", "cd ../..\n",
"```\n", "```\n",
"\n", "\n",
"Po zako\u0144czeniu instalacji nale\u017cy ponownie uruchomi\u0107 notatnik w pow\u0142oce, w kt\u00f3rej aktywne jest\n", "Po zakończeniu instalacji należy ponownie uruchomić notatnik w powłoce, w której aktywne jest\n",
"\u015brodowisko wirtualne *convenv*.\n", "środowisko wirtualne *convenv*.\n",
"\n", "\n",
"```\n", "```\n",
"jupyter notebook 08-zarzadzanie-dialogiem-reguly.ipynb\n", "jupyter notebook 08-zarzadzanie-dialogiem-reguly.ipynb\n",
"```\n", "```\n",
"\n", "\n",
"Dzia\u0142anie zaimplementowanych modu\u0142\u00f3w zilustrujemy, korzystaj\u0105c ze zbioru danych\n", "Działanie zaimplementowanych modułów zilustrujemy, korzystając ze zbioru danych\n",
"[MultiWOZ](https://github.com/budzianowski/multiwoz) (Budzianowski i in., 2018), kt\u00f3ry zawiera\n", "[MultiWOZ](https://github.com/budzianowski/multiwoz) (Budzianowski i in., 2018), który zawiera\n",
"wypowiedzi dotycz\u0105ce m.in. rezerwacji pokoi hotelowych, zamawiania bilet\u00f3w kolejowych oraz\n", "wypowiedzi dotyczące m.in. rezerwacji pokoi hotelowych, zamawiania biletów kolejowych oraz\n",
"rezerwacji stolik\u00f3w w restauracji.\n", "rezerwacji stolików w restauracji.\n",
"\n", "\n",
"### Monitor Stanu Dialogu\n", "### Monitor Stanu Dialogu\n",
"\n", "\n",
"Do reprezentowania stanu dialogu u\u017cyjemy struktury danych wykorzystywanej w *ConvLab-2*." "Do reprezentowania stanu dialogu użyjemy struktury danych wykorzystywanej w *ConvLab-2*."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
" 'hotel': {'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
" 'semi': {'name': '',\n",
" 'area': '',\n",
" 'parking': '',\n",
" 'pricerange': '',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}},\n",
" 'attraction': {'book': {'booked': []},\n",
" 'semi': {'type': '', 'name': '', 'area': ''}},\n",
" 'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
" 'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
" 'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
" 'taxi': {'book': {'booked': []},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'departure': '',\n",
" 'arriveBy': ''}},\n",
" 'train': {'book': {'booked': [], 'people': ''},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'day': '',\n",
" 'arriveBy': '',\n",
" 'departure': ''}}},\n",
" 'request_state': {},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"from convlab2.util.multiwoz.state import default_state\n", "from convlab2.util.multiwoz.state import default_state\n",
"default_state()" "default_state()"
@ -107,17 +147,17 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Metoda `update` naszego monitora stanu dialogu b\u0119dzie przyjmowa\u0107 akty u\u017cytkownika i odpowiednio\n", "Metoda `update` naszego monitora stanu dialogu będzie przyjmować akty użytkownika i odpowiednio\n",
"modyfikowa\u0107 stan dialogu.\n", "modyfikować stan dialogu.\n",
"W przypadku akt\u00f3w typu `inform` warto\u015bci slot\u00f3w zostan\u0105 zapami\u0119tane w s\u0142ownikach odpowiadaj\u0105cych\n", "W przypadku aktów typu `inform` wartości slotów zostaną zapamiętane w słownikach odpowiadających\n",
"poszczeg\u00f3lnym dziedzinom pod kluczem `belief_state`.\n", "poszczególnym dziedzinom pod kluczem `belief_state`.\n",
"W przypadku akt\u00f3w typu `request` sloty, o kt\u00f3re pyta u\u017cytkownik zostan\u0105 zapisane pod kluczem\n", "W przypadku aktów typu `request` sloty, o które pyta użytkownik zostaną zapisane pod kluczem\n",
"`request_state`.\n" "`request_state`.\n"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -132,7 +172,7 @@
" def __init__(self):\n", " def __init__(self):\n",
" DST.__init__(self)\n", " DST.__init__(self)\n",
" self.state = default_state()\n", " self.state = default_state()\n",
" self.value_dict = json.load(open('l08/ConvLab-2/data/multiwoz/value_dict.json'))\n", " self.value_dict = json.load(open('ConvLab-2/data/multiwoz/value_dict.json'))\n",
"\n", "\n",
" def update(self, user_act=None):\n", " def update(self, user_act=None):\n",
" for intent, domain, slot, value in user_act:\n", " for intent, domain, slot, value in user_act:\n",
@ -175,25 +215,65 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"W definicji metody `update` zak\u0142adamy, \u017ce akty dialogowe przekazywane do monitora stanu dialogu z\n", "W definicji metody `update` zakładamy, że akty dialogowe przekazywane do monitora stanu dialogu z\n",
"modu\u0142u NLU s\u0105 czteroelementowymi listami z\u0142o\u017conymi z:\n", "modułu NLU są czteroelementowymi listami złożonymi z:\n",
"\n", "\n",
" - nazwy aktu u\u017cytkownika,\n", " - nazwy aktu użytkownika,\n",
" - nazwy dziedziny, kt\u00f3rej dotyczy wypowied\u017a,\n", " - nazwy dziedziny, której dotyczy wypowiedź,\n",
" - nazwy slotu,\n", " - nazwy slotu,\n",
" - warto\u015bci slotu.\n", " - wartości slotu.\n",
"\n", "\n",
"Zobaczmy na kilku prostych przyk\u0142adach jak stan dialogu zmienia si\u0119 pod wp\u0142ywem przekazanych akt\u00f3w\n", "Zobaczmy na kilku prostych przykładach jak stan dialogu zmienia się pod wpływem przekazanych aktów\n",
"u\u017cytkownika." "użytkownika."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
" 'hotel': {'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
" 'semi': {'name': '',\n",
" 'area': '',\n",
" 'parking': '',\n",
" 'pricerange': '',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}},\n",
" 'attraction': {'book': {'booked': []},\n",
" 'semi': {'type': '', 'name': '', 'area': ''}},\n",
" 'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
" 'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
" 'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
" 'taxi': {'book': {'booked': []},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'departure': '',\n",
" 'arriveBy': ''}},\n",
" 'train': {'book': {'booked': [], 'people': ''},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'day': '',\n",
" 'arriveBy': '',\n",
" 'departure': ''}}},\n",
" 'request_state': {},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst = SimpleRuleDST()\n", "dst = SimpleRuleDST()\n",
"dst.state" "dst.state"
@ -201,11 +281,29 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
" 'semi': {'name': '',\n",
" 'area': '',\n",
" 'parking': 'yes',\n",
" 'pricerange': 'cheap',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst.update([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n", "dst.update([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])\n",
"dst.state['belief_state']['hotel']" "dst.state['belief_state']['hotel']"
@ -213,11 +311,29 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'book': {'booked': [], 'people': '', 'day': '', 'stay': ''},\n",
" 'semi': {'name': '',\n",
" 'area': 'north',\n",
" 'parking': 'yes',\n",
" 'pricerange': 'cheap',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst.update([['Inform', 'Hotel', 'Area', 'north']])\n", "dst.update([['Inform', 'Hotel', 'Area', 'north']])\n",
"dst.state['belief_state']['hotel']" "dst.state['belief_state']['hotel']"
@ -225,11 +341,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'hotel': {'area': 0}}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst.update([['Request', 'Hotel', 'Area', '?']])\n", "dst.update([['Request', 'Hotel', 'Area', '?']])\n",
"dst.state['request_state']" "dst.state['request_state']"
@ -237,11 +364,29 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'book': {'booked': [], 'people': '2', 'day': 'tuesday', 'stay': '4'},\n",
" 'semi': {'name': '',\n",
" 'area': 'north',\n",
" 'parking': 'yes',\n",
" 'pricerange': 'cheap',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst.update([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])\n", "dst.update([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])\n",
"dst.state['belief_state']['hotel']" "dst.state['belief_state']['hotel']"
@ -249,9 +394,52 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'user_action': [],\n",
" 'system_action': [],\n",
" 'belief_state': {'police': {'book': {'booked': []}, 'semi': {}},\n",
" 'hotel': {'book': {'booked': [],\n",
" 'people': '2',\n",
" 'day': 'tuesday',\n",
" 'stay': '4'},\n",
" 'semi': {'name': '',\n",
" 'area': 'north',\n",
" 'parking': 'yes',\n",
" 'pricerange': 'cheap',\n",
" 'stars': '',\n",
" 'internet': '',\n",
" 'type': ''}},\n",
" 'attraction': {'book': {'booked': []},\n",
" 'semi': {'type': '', 'name': '', 'area': ''}},\n",
" 'restaurant': {'book': {'booked': [], 'people': '', 'day': '', 'time': ''},\n",
" 'semi': {'food': '', 'pricerange': '', 'name': '', 'area': ''}},\n",
" 'hospital': {'book': {'booked': []}, 'semi': {'department': ''}},\n",
" 'taxi': {'book': {'booked': []},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'departure': '',\n",
" 'arriveBy': ''}},\n",
" 'train': {'book': {'booked': [], 'people': ''},\n",
" 'semi': {'leaveAt': '',\n",
" 'destination': '',\n",
" 'day': '',\n",
" 'arriveBy': '',\n",
" 'departure': ''}}},\n",
" 'request_state': {'hotel': {'area': 0}},\n",
" 'terminated': False,\n",
" 'history': []}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"dst.state" "dst.state"
] ]
@ -262,23 +450,23 @@
"source": [ "source": [
"### Taktyka Prowadzenia Dialogu\n", "### Taktyka Prowadzenia Dialogu\n",
"\n", "\n",
"Prosta taktyka prowadzenia dialogu dla systemu rezerwacji pokoi hotelowych mo\u017ce sk\u0142ada\u0107 si\u0119 z nast\u0119puj\u0105cych regu\u0142:\n", "Prosta taktyka prowadzenia dialogu dla systemu rezerwacji pokoi hotelowych może składać się z następujących reguł:\n",
"\n", "\n",
" 1. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Request`, to udziel odpowiedzi na jego\n", " 1. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Request`, to udziel odpowiedzi na jego\n",
" pytanie.\n", " pytanie.\n",
"\n", "\n",
" 2. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Inform`, to zaproponuj mu hotel\n", " 2. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform`, to zaproponuj mu hotel\n",
" spe\u0142niaj\u0105cy zdefiniowane przez niego kryteria.\n", " spełniający zdefiniowane przez niego kryteria.\n",
"\n", "\n",
" 3. Je\u017celi u\u017cytkownik przekaza\u0142 w ostatniej turze akt typu `Inform` zawieraj\u0105cy szczeg\u00f3\u0142y\n", " 3. Jeżeli użytkownik przekazał w ostatniej turze akt typu `Inform` zawierający szczegóły\n",
" rezerwacji, to zarezerwuj pok\u00f3j.\n", " rezerwacji, to zarezerwuj pokój.\n",
"\n", "\n",
"Metoda `predict` taktyki `SimpleRulePolicy` realizuje regu\u0142y przedstawione powy\u017cej." "Metoda `predict` taktyki `SimpleRulePolicy` realizuje reguły przedstawione powyżej."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -308,7 +496,7 @@
" for user_act in user_action:\n", " for user_act in user_action:\n",
" self.update_system_action(user_act, user_action, state, system_action)\n", " self.update_system_action(user_act, user_action, state, system_action)\n",
"\n", "\n",
" # Regu\u0142a 3\n", " # Reguła 3\n",
" if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['Stay', 'Day', 'People']):\n", " if any(True for slots in user_action.values() for (slot, _) in slots if slot in ['Stay', 'Day', 'People']):\n",
" if self.results:\n", " if self.results:\n",
" system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n", " system_action = {('Booking', 'Book'): [[\"Ref\", self.results[0].get('Ref', 'N/A')]]}\n",
@ -322,7 +510,7 @@
" constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()]['semi'].items() if value != '']\n", " constraints = [(slot, value) for slot, value in state['belief_state'][domain.lower()]['semi'].items() if value != '']\n",
" self.results = deepcopy(self.db.query(domain.lower(), constraints))\n", " self.results = deepcopy(self.db.query(domain.lower(), constraints))\n",
"\n", "\n",
" # Regu\u0142a 1\n", " # Reguła 1\n",
" if intent == 'Request':\n", " if intent == 'Request':\n",
" if len(self.results) == 0:\n", " if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n", " system_action[(domain, 'NoOffer')] = []\n",
@ -333,7 +521,7 @@
" if kb_slot_name in self.results[0]:\n", " if kb_slot_name in self.results[0]:\n",
" system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(kb_slot_name, 'unknown')])\n", " system_action[(domain, 'Inform')].append([slot[0], self.results[0].get(kb_slot_name, 'unknown')])\n",
"\n", "\n",
" # Regu\u0142a 2\n", " # Reguła 2\n",
" elif intent == 'Inform':\n", " elif intent == 'Inform':\n",
" if len(self.results) == 0:\n", " if len(self.results) == 0:\n",
" system_action[(domain, 'NoOffer')] = []\n", " system_action[(domain, 'NoOffer')] = []\n",
@ -349,19 +537,19 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Podobnie jak w przypadku akt\u00f3w u\u017cytkownika akty systemowe przekazywane do modu\u0142u NLG s\u0105 czteroelementowymi listami z\u0142o\u017conymi z:\n", "Podobnie jak w przypadku aktów użytkownika akty systemowe przekazywane do modułu NLG są czteroelementowymi listami złożonymi z:\n",
"\n", "\n",
" - nazwy aktu systemowe,\n", " - nazwy aktu systemowe,\n",
" - nazwy dziedziny, kt\u00f3rej dotyczy wypowied\u017a,\n", " - nazwy dziedziny, której dotyczy wypowiedź,\n",
" - nazwy slotu,\n", " - nazwy slotu,\n",
" - warto\u015bci slotu.\n", " - wartości slotu.\n",
"\n", "\n",
"Sprawd\u017amy jakie akty systemowe zwraca taktyka `SimpleRulePolicy` w odpowiedzi na zmieniaj\u0105cy si\u0119 stan dialogu." "Sprawdźmy jakie akty systemowe zwraca taktyka `SimpleRulePolicy` w odpowiedzi na zmieniający się stan dialogu."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 11,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
@ -375,42 +563,88 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[['Inform', 'Hotel', 'Choice', '10'],\n",
" ['Recommend', 'Hotel', 'Name', 'alexander bed and breakfast']]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])" "agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[['Inform', 'Hotel', 'Choice', '2'],\n",
" ['Recommend', 'Hotel', 'Name', 'city centre north b and b']]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"agent.response([['Inform', 'Hotel', 'Area', 'north']])" "agent.response([['Inform', 'Hotel', 'Area', 'north']])"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[['Inform', 'Hotel', 'Area', 'north']]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"agent.response([['Request', 'Hotel', 'Area', '?']])" "agent.response([['Request', 'Hotel', 'Area', '?']])"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"[['Book', 'Booking', 'Ref', '00000013']]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])" "agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])"
] ]
@ -421,15 +655,25 @@
"source": [ "source": [
"### Testy End-to-End\n", "### Testy End-to-End\n",
"\n", "\n",
"Na koniec przeprowad\u017amy dialog \u0142\u0105cz\u0105c w potok nasze modu\u0142y\n", "Na koniec przeprowadźmy dialog łącząc w potok nasze moduły\n",
"z modu\u0142ami NLU i NLG dost\u0119pnymi dla MultiWOZ w \u015brodowisku `ConvLab-2`." "z modułami NLU i NLG dostępnymi dla MultiWOZ w środowisku `ConvLab-2`."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[<convlab2.nlu.svm.Features.nbest object at 0x0000028BDD2032C8>]\n",
"loading saved Classifier\n",
"loaded.\n"
]
}
],
"source": [ "source": [
"from convlab2.nlu.svm.multiwoz import SVMNLU\n", "from convlab2.nlu.svm.multiwoz import SVMNLU\n",
"from convlab2.nlg.template.multiwoz import TemplateNLG\n", "from convlab2.nlg.template.multiwoz import TemplateNLG\n",
@ -441,33 +685,90 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"ename": "AttributeError",
"evalue": "'SVC' object has no attribute '_impl'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\3260802613.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"I need a cheap hotel with free parking .\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m 74\u001b[0m ]\n\u001b[0;32m 75\u001b[0m }\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 261\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 499\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 589\u001b[0m \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 591\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 557\u001b[0m raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m 558\u001b[0m \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 560\u001b[0m raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m 561\u001b[0m \" and NuSVC\")\n",
"\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
]
}
],
"source": [ "source": [
"agent.response(\"I need a cheap hotel with free parking .\")" "agent.response(\"I need a cheap hotel with free parking .\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"ename": "AttributeError",
"evalue": "'SVC' object has no attribute '_impl'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\2723043776.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Where it is located ?\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m 74\u001b[0m ]\n\u001b[0;32m 75\u001b[0m }\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 261\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 499\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 589\u001b[0m \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 591\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 557\u001b[0m raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m 558\u001b[0m \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 560\u001b[0m raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m 561\u001b[0m \" and NuSVC\")\n",
"\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
]
}
],
"source": [ "source": [
"agent.response(\"Where it is located ?\")" "agent.response(\"Where it is located ?\")"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 19,
"metadata": { "metadata": {
"lines_to_next_cell": 0 "lines_to_next_cell": 0
}, },
"outputs": [], "outputs": [
{
"ename": "AttributeError",
"evalue": "'SVC' object has no attribute '_impl'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_13500\\871593950.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"I would prefer the hotel be in the north part of town .\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\dialog_agent\\agent.py\u001b[0m in \u001b[0;36mresponse\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[1;31m# get dialog act\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minput_action\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\multiwoz\\nlu.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, utterance, context)\u001b[0m\n\u001b[0;32m 74\u001b[0m ]\n\u001b[0;32m 75\u001b[0m }\n\u001b[1;32m---> 76\u001b[1;33m \u001b[0mslu_hyps\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"decode\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"output\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 77\u001b[0m \u001b[0mact_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mhyp\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mslu_hyps\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode_sent\u001b[1;34m(self, sentinfo, output_fname, config)\u001b[0m\n\u001b[0;32m 320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextractFeatures2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentinfo\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlog_input_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 322\u001b[1;33m \u001b[0mdecode_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 323\u001b[0m \u001b[0mcounter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 324\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 258\u001b[0m \u001b[0mbaseXs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbaseX_pointers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 259\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoSparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbaseXs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdictionary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 260\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifiers\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mthis_tuple\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 261\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\develop\\wmi\\aitech\\sem1\\systemy dialogowe\\lab\\convlab-2\\convlab2\\nlu\\svm\\Classifier.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m 496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 498\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 499\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 589\u001b[0m \"\"\"\n\u001b[1;32m--> 590\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 591\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_predict_proba\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\venv\\lib\\site-packages\\sklearn\\svm\\base.py\u001b[0m in \u001b[0;36m_check_proba\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 557\u001b[0m raise AttributeError(\"predict_proba is not available when \"\n\u001b[0;32m 558\u001b[0m \" probability=False\")\n\u001b[1;32m--> 559\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_impl\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'c_svc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'nu_svc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 560\u001b[0m raise AttributeError(\"predict_proba only implemented for SVC\"\n\u001b[0;32m 561\u001b[0m \" and NuSVC\")\n",
"\u001b[1;31mAttributeError\u001b[0m: 'SVC' object has no attribute '_impl'"
]
}
],
"source": [ "source": [
"agent.response(\"I would prefer the hotel be in the north part of town .\")" "agent.response(\"I would prefer the hotel be in the north part of town .\")"
] ]
@ -485,41 +786,61 @@
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"Zauwa\u017cmy, ze nasza prosta taktyka dialogowa zawiera wiele luk, do kt\u00f3rych nale\u017c\u0105 m.in.:\n", "Zauważmy, ze nasza prosta taktyka dialogowa zawiera wiele luk, do których należą m.in.:\n",
"\n", "\n",
" 1. Niezdolno\u015b\u0107 do udzielenia odpowiedzi na przywitanie, pro\u015bb\u0119 o pomoc lub restart.\n", " 1. Niezdolność do udzielenia odpowiedzi na przywitanie, prośbę o pomoc lub restart.\n",
"\n", "\n",
" 2. Brak regu\u0142 dopytuj\u0105cych u\u017cytkownika o szczeg\u00f3\u0142y niezb\u0119dne do dokonania rezerwacji takie, jak d\u0142ugo\u015b\u0107 pobytu czy liczba os\u00f3b.\n", " 2. Brak reguł dopytujących użytkownika o szczegóły niezbędne do dokonania rezerwacji takie, jak długość pobytu czy liczba osób.\n",
"\n", "\n",
"Bardziej zaawansowane modu\u0142y zarz\u0105dzania dialogiem zbudowane z wykorzystaniem regu\u0142 mo\u017cna znale\u017a\u0107 w\n", "Bardziej zaawansowane moduły zarządzania dialogiem zbudowane z wykorzystaniem reguł można znaleźć w\n",
"\u015brodowisku `ConvLab-2`. Nale\u017c\u0105 do nich m.in. monitor [RuleDST](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/rule/multiwoz/dst.py) oraz taktyka [RuleBasedMultiwozBot](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py).\n", "środowisku `ConvLab-2`. Należą do nich m.in. monitor [RuleDST](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/dst/rule/multiwoz/dst.py) oraz taktyka [RuleBasedMultiwozBot](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/rule/multiwoz/rule_based_multiwoz_bot.py).\n",
"\n", "\n",
"Zadania\n", "Zadania\n",
"-------\n", "-------\n",
" 1. Zaimplementowa\u0107 w projekcie monitor stanu dialogu.\n", " 1. Zaimplementować w projekcie monitor stanu dialogu.\n",
"\n", "\n",
" 2. Zaimplementowa\u0107 w projekcie taktyk\u0119 prowadzenia dialogu.\n", " 2. Zaimplementować w projekcie taktykę prowadzenia dialogu.\n",
"\n", "\n",
"Termin: 24.05.2021, godz. 23:59.\n", "Termin: 24.05.2021, godz. 23:59.\n",
"\n", "\n",
"Literatura\n", "Literatura\n",
"----------\n", "----------\n",
" 1. Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, I\u00f1igo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gasic, MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. EMNLP 2018, pp. 5016-5026\n", " 1. Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, Milica Gasic, MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling. EMNLP 2018, pp. 5016-5026\n",
" 2. Cathy Pearl, Basic principles for designing voice user interfaces, https://www.oreilly.com/content/basic-principles-for-designing-voice-user-interfaces/ data dost\u0119pu: 21 marca 2021\n", " 2. Cathy Pearl, Basic principles for designing voice user interfaces, https://www.oreilly.com/content/basic-principles-for-designing-voice-user-interfaces/ data dostępu: 21 marca 2021\n",
" 3. Cathy Pearl, Designing Voice User Interfaces, Excerpts from Chapter 5: Advanced Voice User Interface Design, https://www.uxmatters.com/mt/archives/2018/01/designing-voice-user-interfaces.php data dost\u0119pu: 21 marca 2021" " 3. Cathy Pearl, Designing Voice User Interfaces, Excerpts from Chapter 5: Advanced Voice User Interface Design, https://www.uxmatters.com/mt/archives/2018/01/designing-voice-user-interfaces.php data dostępu: 21 marca 2021"
] ]
} }
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"author": "Marek Kubis",
"email": "mkubis@amu.edu.pl",
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"subtitle": "9.Zarz\u0105dzanie dialogiem z wykorzystaniem regu\u0142[laboratoria]", "language_info": {
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"title": "Systemy Dialogowe", "title": "Systemy Dialogowe",
"year": "2021" "year": "2021"
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},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Systemy Dialogowe </h1>\n",
"<h2> 10. <i>Zarządzanie dialogiem z wykorzystaniem technik uczenia maszynowego</i> [laboratoria]</h2> \n",
"<h3> Marek Kubis (2021)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zarządzanie dialogiem z wykorzystaniem technik uczenia maszynowego\n",
"==================================================================\n",
"\n",
"Uczenie przez wzmacnianie\n",
"-------------------------\n",
"\n",
"Zamiast ręcznie implementować zbiór reguł odpowiedzialnych za wyznaczenie akcji, którą powinien podjąć agent będąc w danym stanie, odpowiednią taktykę prowadzenia dialogu można zbudować, wykorzystując techniki uczenia maszynowego.\n",
"\n",
"Obok metod uczenia nadzorowanego, które wykorzystaliśmy do zbudowania modelu NLU, do konstruowania taktyk\n",
"prowadzenia dialogu wykorzystuje się również *uczenie przez wzmacnianie* (ang. *reinforcement learning*).\n",
"\n",
"W tym ujęciu szukać będziemy funkcji $Q*: S \\times A \\to R$, która dla stanu dialogu $s \\in S$ oraz aktu\n",
"dialogowego $a \\in A$ zwraca nagrodę (ang. *reward*) $r \\in R$, tj. wartość rzeczywistą pozwalającą ocenić na ile\n",
"podjęcie akcji $a$ w stanie $s$ jest korzystne.\n",
"\n",
"Założymy również, że poszukiwana funkcja powinna maksymalizować *zwrot* (ang. *return*), tj.\n",
"skumulowaną nagrodę w toku prowadzonego dialogu, czyli dla tury $t_0$ cel uczenia powinien mieć postać:\n",
"\n",
"$$ \\sum_{t=t_0}^{\\infty}{\\gamma^{t-1}r_t} $$\n",
"\n",
"gdzie:\n",
"\n",
" - $t$: tura agenta,\n",
"\n",
" - $r_t$: nagroda w turze $t$,\n",
"\n",
" - $\\gamma \\in [0, 1]$: współczynnik dyskontowy (w przypadku agentów dialogowych bliżej $1$ niż $0$, por. np. Rieser i Lemon (2011))."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agent dialogowy w procesie uczenia przez wzmacnianie wchodzi w interakcję ze *środowiskiem*, które\n",
"dla akcji podejmowanej przez taktykę prowadzenia dialogu zwraca kolejny stan oraz nagrodę powiązaną z\n",
"wykonaniem tej akcji w bieżącym stanie.\n",
"\n",
"Sposób w jaki informacje pochodzące ze środowiska są wykorzystywane do znalezienia funkcji $Q*$\n",
"zależy od wybranej metody uczenia.\n",
"W przykładzie przestawionym poniżej skorzystamy z algorytmu $DQN$ (Mnih i in., 2013) co oznacza, że:\n",
"\n",
" 1. będziemy aproksymować funkcję $Q*$ siecią neuronową,\n",
"\n",
" 2. wagi sieci będziemy wyznaczać korzystając z metody spadku gradientu.\n",
"\n",
"Przykład\n",
"--------\n",
"\n",
"Taktykę prowadzenia dialogu osadzimy w środowisku *ConvLab-2*."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'convlab2'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Develop\\wmi\\AITECH\\sem1\\Systemy dialogowe\\lab\\10-zarzadzanie-dialogiem-uczenie.ipynb Cell 4'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/10-zarzadzanie-dialogiem-uczenie.ipynb#ch0000003?line=0'>1</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mconvlab2\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdialog_agent\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39magent\u001b[39;00m \u001b[39mimport\u001b[39;00m PipelineAgent\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/10-zarzadzanie-dialogiem-uczenie.ipynb#ch0000003?line=1'>2</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mconvlab2\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdialog_agent\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39menv\u001b[39;00m \u001b[39mimport\u001b[39;00m Environment\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Develop/wmi/AITECH/sem1/Systemy%20dialogowe/lab/10-zarzadzanie-dialogiem-uczenie.ipynb#ch0000003?line=2'>3</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mconvlab2\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdst\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mrule\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mmultiwoz\u001b[39;00m \u001b[39mimport\u001b[39;00m RuleDST\n",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'convlab2'"
]
}
],
"source": [
"from convlab2.dialog_agent.agent import PipelineAgent\n",
"from convlab2.dialog_agent.env import Environment\n",
"from convlab2.dst.rule.multiwoz import RuleDST\n",
"from convlab2.policy.rule.multiwoz import RulePolicy\n",
"from convlab2.policy.dqn import DQN\n",
"from convlab2.policy.rlmodule import Memory\n",
"from convlab2.evaluator.multiwoz_eval import MultiWozEvaluator\n",
"import logging\n",
"\n",
"logging.disable(logging.DEBUG)\n",
"\n",
"# determinizacja obliczeń\n",
"import random\n",
"import torch\n",
"import numpy as np\n",
"\n",
"np.random.seed(123)\n",
"random.seed(123)\n",
"torch.manual_seed(123)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Środowisko, z którym agent będzie wchodził w interakcje zawierać będzie\n",
"symulator użytkownika wykorzystujący taktykę prowadzenia dialogu zbudowaną z wykorzystaniem reguł."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"usr_policy = RulePolicy(character='usr')\n",
"usr_simulator = PipelineAgent(None, None, usr_policy, None, 'user') # type: ignore\n",
"\n",
"dst = RuleDST()\n",
"evaluator = MultiWozEvaluator()\n",
"env = Environment(None, usr_simulator, None, dst, evaluator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zobaczmy jak w *ConvLab-2* zdefiniowana jest nagroda w klasie `Environment`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%script false --no-raise-error\n",
"#\n",
"# plik convlab2/dialog_agent/env.py\n",
"#\n",
"class Environment():\n",
"\n",
" # (...)\n",
"\n",
" def step(self, action):\n",
"\n",
" # (...)\n",
"\n",
" if self.evaluator:\n",
" if self.evaluator.task_success():\n",
" reward = 40\n",
" elif self.evaluator.cur_domain and self.evaluator.domain_success(self.evaluator.cur_domain):\n",
" reward = 5\n",
" else:\n",
" reward = -1\n",
" else:\n",
" reward = self.usr.get_reward()\n",
" terminated = self.usr.is_terminated()\n",
"\n",
" return state, reward, terminated\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jak można zauważyć powyżej akcja, która prowadzi do pomyślnego zakończenia zadania uzyskuje nagrodę $40$,\n",
"akcja która prowadzi do prawidłowego rozpoznania dziedziny uzyskuje nagrodę $5$,\n",
"natomiast każda inna akcja uzyskuje \"karę\" $-1$. Taka definicja zwrotu premiuje krótkie dialogi\n",
"prowadzące do pomyślnego wykonania zadania.\n",
"\n",
"Sieć neuronowa, którą wykorzystamy do aproksymacji funkcji $Q*$ ma następującą architekturę"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%script false --no-raise-error\n",
"#\n",
"# plik convlab2/policy/rlmodule.py\n",
"# klasa EpsilonGreedyPolicy wykorzystywana w DQN\n",
"#\n",
"class EpsilonGreedyPolicy(nn.Module):\n",
" def __init__(self, s_dim, h_dim, a_dim, epsilon_spec={'start': 0.1, 'end': 0.0, 'end_epoch': 200}):\n",
" super(EpsilonGreedyPolicy, self).__init__()\n",
"\n",
" self.net = nn.Sequential(nn.Linear(s_dim, h_dim),\n",
" nn.ReLU(),\n",
" nn.Linear(h_dim, h_dim),\n",
" nn.ReLU(),\n",
" nn.Linear(h_dim, a_dim))\n",
" # (...)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"policy = DQN(is_train=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Każdy krok procedury uczenia składa się z dwóch etapów:\n",
"\n",
" 1. Wygenerowania przy użyciu taktyki (metoda `policy.predict`) oraz środowiska (metoda `env.step`) *trajektorii*, tj. sekwencji przejść pomiędzy stanami złożonych z krotek postaci:\n",
" - stanu źródłowego,\n",
" - podjętej akcji (aktu systemowego),\n",
" - nagrody,\n",
" - stanu docelowego,\n",
" - znacznika końca dialogu."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# por. ConvLab-2/convlab2/policy/dqn/train.py\n",
"def sample(env, policy, batch_size, warm_up):\n",
" buff = Memory()\n",
" sampled_num = 0\n",
" max_trajectory_len = 50\n",
"\n",
" while sampled_num < batch_size:\n",
" # rozpoczęcie nowego dialogu\n",
" s = env.reset()\n",
"\n",
" for t in range(max_trajectory_len):\n",
" try:\n",
" # podjęcie akcji przez agenta dialogowego\n",
" a = policy.predict(s, warm_up=warm_up)\n",
"\n",
" # odpowiedź środowiska na podjętą akcje\n",
" next_s, r, done = env.step(a)\n",
"\n",
" # dodanie krotki do zbioru danych\n",
" buff.push(torch.Tensor(policy.vector.state_vectorize(s)).numpy(), # stan źródłowy\n",
" policy.vector.action_vectorize(a), # akcja\n",
" r, # nagroda\n",
" torch.Tensor(policy.vector.state_vectorize(next_s)).numpy(), # stan docelowy\n",
" 0 if done else 1) # znacznik końca\n",
"\n",
" s = next_s\n",
"\n",
" if done:\n",
" break\n",
" except:\n",
" break\n",
"\n",
" sampled_num += t\n",
"\n",
" return buff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2. Wykorzystania wygenerowanych krotek do aktualizacji taktyki.\n",
"\n",
"Funkcja `train` realizująca pojedynczy krok uczenia przez wzmacnianie ma następującą postać"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def train(env, policy, batch_size, epoch, warm_up):\n",
" print(f'epoch: {epoch}')\n",
" buff = sample(env, policy, batch_size, warm_up)\n",
" policy.update_memory(buff)\n",
" policy.update(epoch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Metoda `update` klasy `DQN` wykorzystywana do aktualizacji wag ma następującą postać"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%script false --no-raise-error\n",
"#\n",
"# plik convlab2/policy/dqn/dqn.py\n",
"# klasa DQN\n",
"#\n",
"class DQN(Policy):\n",
" # (...)\n",
" def update(self, epoch):\n",
" total_loss = 0.\n",
" for i in range(self.training_iter):\n",
" round_loss = 0.\n",
" # 1. batch a sample from memory\n",
" batch = self.memory.get_batch(batch_size=self.batch_size)\n",
"\n",
" for _ in range(self.training_batch_iter):\n",
" # 2. calculate the Q loss\n",
" loss = self.calc_q_loss(batch)\n",
"\n",
" # 3. make a optimization step\n",
" self.net_optim.zero_grad()\n",
" loss.backward()\n",
" self.net_optim.step()\n",
"\n",
" round_loss += loss.item()\n",
"\n",
" # (...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Przebieg procesu uczenia zilustrujemy wykonując 100 iteracji. W każdej iteracji ograniczymy się do 100 przykładów."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"epoch = 100\n",
"batch_size = 100\n",
"\n",
"train(env, policy, batch_size, 0, warm_up=True)\n",
"\n",
"for i in range(1, epoch):\n",
" train(env, policy, batch_size, i, warm_up=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sprawdźmy jakie akty systemowe zwraca taktyka `DQN` w odpowiedzi na zmieniający się stan dialogu."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"from convlab2.dialog_agent import PipelineAgent\n",
"dst.init_session()\n",
"agent = PipelineAgent(nlu=None, dst=dst, policy=policy, nlg=None, name='sys')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"agent.response([['Inform', 'Hotel', 'Price', 'cheap'], ['Inform', 'Hotel', 'Parking', 'yes']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"agent.response([['Inform', 'Hotel', 'Area', 'north']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"agent.response([['Request', 'Hotel', 'Area', '?']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"agent.response([['Inform', 'Hotel', 'Day', 'tuesday'], ['Inform', 'Hotel', 'People', '2'], ['Inform', 'Hotel', 'Stay', '4']])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jakość wyuczonego modelu możemy ocenić mierząc tzw. wskaźnik sukcesu (ang. *task success rate*),\n",
"tj. stosunek liczby dialogów zakończonych powodzeniem do liczby wszystkich dialogów."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from convlab2.dialog_agent.session import BiSession\n",
"\n",
"sess = BiSession(agent, usr_simulator, None, evaluator)\n",
"dialog_num = 100\n",
"task_success_num = 0\n",
"max_turn_num = 50\n",
"\n",
"# por. ConvLab-2/convlab2/policy/evaluate.py\n",
"for dialog in range(dialog_num):\n",
" random.seed(dialog)\n",
" np.random.seed(dialog)\n",
" torch.manual_seed(dialog)\n",
" sess.init_session()\n",
" sys_act = []\n",
" task_success = 0\n",
"\n",
" for _ in range(max_turn_num):\n",
" sys_act, _, finished, _ = sess.next_turn(sys_act)\n",
"\n",
" if finished is True:\n",
" task_success = sess.evaluator.task_success()\n",
" break\n",
"\n",
" print(f'dialog: {dialog:02} success: {task_success}')\n",
" task_success_num += task_success\n",
"\n",
"print('')\n",
"print(f'task success rate: {task_success_num/dialog_num:.2f}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Uwaga**: Chcąc uzyskać taktykę o skuteczności porównywalnej z wynikami przedstawionymi na stronie\n",
"[ConvLab-2](https://github.com/thu-coai/ConvLab-2/blob/master/README.md) trzeba odpowiednio\n",
"zwiększyć zarówno liczbę iteracji jak i liczbę przykładów generowanych w każdym przyroście.\n",
"W celu przyśpieszenia procesu uczenia warto zrównoleglić obliczenia, jak pokazano w\n",
"skrypcie [train.py](https://github.com/thu-coai/ConvLab-2/blob/master/convlab2/policy/dqn/train.py).\n",
"\n",
"Literatura\n",
"----------\n",
" 1. Rieser, V., Lemon, O., (2011). Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation. (Theory and Applications of Natural Language Processing). Springer. https://doi.org/10.1007/978-3-642-24942-6\n",
"\n",
" 2. Richard S. Sutton and Andrew G. Barto, (2018). Reinforcement Learning: An Introduction, Second Edition, MIT Press, Cambridge, MA http://incompleteideas.net/book/RLbook2020.pdf\n",
"\n",
" 3. Volodymyr Mnih and Koray Kavukcuoglu and David Silver and Alex Graves and Ioannis Antonoglou and Daan Wierstra and Martin Riedmiller, (2013). Playing Atari with Deep Reinforcement Learning, NIPS Deep Learning Workshop, https://arxiv.org/pdf/1312.5602.pdf"
]
}
],
"metadata": {
"author": "Marek Kubis",
"email": "mkubis@amu.edu.pl",
"interpreter": {
"hash": "2f9d6cf1e3d8195079a65c851de355134a77367bcd714b1a5d498c42d3c07114"
},
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
},
"kernelspec": {
"display_name": "Python 3.8.3 64-bit",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "10.Zarządzanie dialogiem z wykorzystaniem technik uczenia maszynowego[laboratoria]",
"title": "Systemy Dialogowe",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Systemy Dialogowe </h1>\n",
"<h2> 11. <i>Generowanie odpowiedzi</i> [laboratoria]</h2> \n",
"<h3> Marek Kubis (2021)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generowanie odpowiedzi\n",
"======================\n",
"\n",
"W systemie dialogowym taktyka prowadzenia dialogu odpowiada za wyznaczanie aktów systemowych, czyli wskazanie tego **co ma zostać przez system wypowiedziane** i/lub wykonane.\n",
"Zadaniem modułu generowania odpowiedzi jest zamiana aktów dialogowych na wypowiedzi w języku\n",
"naturalnym, czyli wskazanie tego **w jaki sposób** ma zostać wypowiedziane to co ma zostać\n",
"wypowiedziane.\n",
"\n",
"Generowanie odpowiedzi przy użyciu szablonów\n",
"--------------------------------------------\n",
"Podstawowe narzędzie wykorzystywane w modułach generowania odpowiedzi stanowią szablony tekstowe\n",
"interpolujące zmienne. W Pythonie mechanizm ten jest dostępny za pośrednictwem\n",
"[f-stringów](https://docs.python.org/3/reference/lexical_analysis.html#f-strings), metody\n",
"[format](https://docs.python.org/3/library/string.html#formatstrings) oraz zewnętrznych bibliotek takich, jak [Jinja2](https://jinja.palletsprojects.com/).\n",
"\n",
"O ile podejście wykorzystujące wbudowane mechanizmy języka Python sprawdza się w prostych\n",
"przypadkach..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"def nlg(system_act):\n",
" domain, intent, slot, value = system_act\n",
"\n",
" if intent == 'Inform' and slot == 'Phone':\n",
" return f'Numer telefonu to {value}'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nlg(['Hotel', 'Inform', 'Phone', '1234567890'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lines_to_next_cell": 0
},
"source": [
"... to trzeba mieć świadomość, że w toku prac nad agentem dialogowym może być konieczne\n",
"uwzględnienie m.in.:\n",
"\n",
" 1. szablonów zależnych od wartości slotów"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"def nlg(system_act):\n",
" domain, intent, slot, value = system_act\n",
"\n",
" if domain == 'Restaurant' and intent == 'Inform' and slot == 'Count':\n",
" if value == 0:\n",
" return f'Nie znalazłem restauracji spełniających podane kryteria.'\n",
" elif value == 1:\n",
" return f'Znalazłem jedną restaurację spełniającą podane kryteria.'\n",
" elif value <= 4:\n",
" return f'Znalazłem {value} restauracje spełniające podane kryteria.'\n",
" elif value <= 9:\n",
" return f'Znalazłem {value} restauracji spełniających podane kryteria.'\n",
" else:\n",
" return f'Znalazłem wiele restauracji spełniających podane kryteria.'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"nlg(['Restaurant', 'Inform', 'Count', 0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"nlg(['Restaurant', 'Inform', 'Count', 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"nlg(['Restaurant', 'Inform', 'Count', 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"nlg(['Restaurant', 'Inform', 'Count', 6])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nlg(['Restaurant', 'Inform', 'Count', 100])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lines_to_next_cell": 0
},
"source": [
" 2. wielu wariantów tej samej wypowiedzi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"import random\n",
"\n",
"def nlg(system_act):\n",
" domain, intent, slot, value = system_act\n",
"\n",
" if intent == 'Affirm':\n",
" r = random.randint(1, 3)\n",
"\n",
" if r == 1:\n",
" return 'Tak'\n",
" elif r == 2:\n",
" return 'Zgadza się'\n",
" else:\n",
" return 'Potwierdzam'\n",
"\n",
"nlg(['Hotel', 'Affirm', '', ''])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lines_to_next_cell": 0
},
"source": [
" 3. wielojęzycznego interfejsu użytkownika"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"lines_to_next_cell": 0
},
"outputs": [],
"source": [
"def nlg_en(system_act):\n",
" domain, intent, slot, value = system_act\n",
"\n",
" if domain == 'Hotel' and intent == 'Request' and slot == 'CreditCardNo':\n",
" return 'What is your credit card number?'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nlg_en(['Hotel', 'Request', 'CreditCardNo', '?'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"lines_to_next_cell": 0
},
"source": [
"Generowanie odpowiedzi z wykorzystaniem uczenia maszynowego\n",
"-----------------------------------------------------------\n",
"Obok mechanizmu szablonów do generowania odpowiedzi można również\n",
"stosować techniki uczenia maszynowego.\n",
"Zagadnienie to stanowiło\n",
"przedmiot konkursu [E2E NLG Challenge](http://www.macs.hw.ac.uk/InteractionLab/E2E/) (Novikova i in., 2017).\n",
"Przyjrzyjmy się danym, jakie udostępnili organizatorzy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!mkdir -p l10\n",
"!curl -L -C - https://github.com/tuetschek/e2e-dataset/releases/download/v1.0.0/e2e-dataset.zip -o l10/e2e-dataset.zip\n",
"!unzip l10/e2e-dataset.zip -d l10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"trainset = pd.read_csv('l10/e2e-dataset/trainset.csv')\n",
"trainset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zadanie\n",
"-------\n",
"Zaimplementować moduł generowania odpowiedzi obejmujący akty systemowe występujące w zgromadzonym korpusie.\n",
"\n",
"Termin: 1.06.2022, godz. 23:59.\n",
"\n",
"Literatura\n",
"----------\n",
" 1. Jekaterina Novikova, Ondřej Dušek, Verena Rieser, The E2E Dataset: New Challenges For End-to-End Generation, Proceedings of the SIGDIAL 2017 Conference, pages 201-206, Saarbrücken, Germany https://arxiv.org/pdf/1706.09254.pdf"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.read_csv('../data/dialog-17-04-03.tsv', sep='\\t', header=None)\n",
"df.columns = ['user','text','data']\n",
"df= df[df.user=='system']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"df.drop(axis=1, labels=['user'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user</th>\n",
" <th>text</th>\n",
" <th>data</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>system</td>\n",
" <td>Witamy w internetowym systemie rezerwacji Nach...</td>\n",
" <td>welcomemsg()</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>system</td>\n",
" <td>System Nachos obsługuje następujące kina: Mult...</td>\n",
" <td>select(location)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" user text data\n",
"1 system Witamy w internetowym systemie rezerwacji Nach... welcomemsg()\n",
"3 system System Nachos obsługuje następujące kina: Mult... select(location)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def nlg(system_act):\n",
" intent, slot, value = system_act\n",
"\n",
" if intent=='welcomemsg':\n",
" return 'Witamy w internetowym systemie rezerwacji Nachos, w czym mogę pomóc?'\n",
" elif intent=='inform':\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" \n",
" if domain == 'Restaurant' and intent == 'Inform' and slot == 'Count':\n",
" if value == 0:\n",
" return f'Nie znalazłem restauracji spełniających podane kryteria.'\n",
" elif value == 1:\n",
" return f'Znalazłem jedną restaurację spełniającą podane kryteria.'\n",
" elif value <= 4:\n",
" return f'Znalazłem {value} restauracje spełniające podane kryteria.'\n",
" elif value <= 9:\n",
" return f'Znalazłem {value} restauracji spełniających podane kryteria.'\n",
" else:\n",
" return f'Znalazłem wiele restauracji spełniających podane kryteria.'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"inform(quantity=2) AND inform(time=12:00)\n",
"['inform','quantity','2']"
]
}
],
"metadata": {
"author": "Marek Kubis",
"email": "mkubis@amu.edu.pl",
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
},
"subtitle": "11.Generowanie odpowiedzi[laboratoria]",
"title": "Systemy Dialogowe",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}

25
lab/utils/state.py Normal file
View File

@ -0,0 +1,25 @@
def default_state():
state = dict(user_action=[],
system_action=[],
belief_state={},
request_state={},
terminated=False,
history=[])
state['belief_state'] = {
"cinema": {
"book": {
"title": "",
"date": "",
"time": "",
"quantity": "",
"seats": "",
"area": "",
"interval": "",
},
"semi": {
"goal": ""
}
},
}
return state

507
lab/utils/value_dict.json Normal file
View File

@ -0,0 +1,507 @@
{
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"trzecią",
"piątą",
"szóstą",
"siódmą",
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"dziewiatą",
"dziesiątą",
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"szesnastą",
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"osiemnastą",
"dziewiętnastą",
"dwudziestą pierwszą",
"dwudziestą drugą",
"dwudziestą trzecią",
"dwudziestą czwartą",
"o północy"
],
"date": [
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"w najbliższy wtorek",
"w najbliższą środę",
"w najbliższy czwartek",
"w najbliższy piątek",
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"na najbliższy poniedziałek",
"na najbliższy wtorek",
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"najbliższy czwartek",
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"jutro",
"w dniu jutrzejszym",
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"pojutrze",
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"za trzy dni",
"za tydzień",
"dzisiaj",
"dziś",
"na dzień 21/09",
"na 21/09",
"dzień 21/09",
"dnia 21/09",
"21/09",
"12/09",
"5/09",
"21/12",
"12/12",
"5/12",
"21/3",
"12/3",
"5/3"
],
"quantities" : [
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10",
"11",
"12",
"13",
"14",
"15",
"16",
"17",
"18",
"19",
"20",
"jeden",
"dwa",
"trzy",
"cztery",
"pięć",
"sześć",
"siedem",
"osiem",
"dziewięć",
"dziesięć",
"jedenaście",
"dwanaście",
"trzynaście"
],
"seats" : [
"a1",
"a1 a2",
"a1 a2 a3",
"a1 a2 a3 a4",
"a1 a2 a3 a4 a5",
"h1",
"h1 h2",
"h1 h2 h3",
"h1 h2 h3 h4",
"h1 h2 h3 h4 h5",
"a12",
"a11 a12",
"a11 a12 a13",
"a11 a12 a13 a14",
"a11 a12 a13 a14 a15"
],
"areas" : [
"u góry po prawej",
"u góry po lewej",
"u góry na środku",
"na środku po prawej",
"na środku po lewej",
"na dole po prawej",
"na dole po lewej",
"na dole na środku",
"po lewej",
"po prawej",
"na środku",
"lewo",
"prawo",
"środek",
"blisko od ekranu",
"daleko od ekranu",
"blisko ekranu",
"daleko ekranu"
],
"interval" : [
"rano",
"wieczorem",
"w południe",
"po południu",
"popołudniu",
"w nocy"
]
}
}

36
src/components/DP.py
View File

@ -1,11 +1,31 @@
# Martyna from urllib import request
class DP: class DP:
def __init__(self, slots): def __init__(self):
self.slots = slots
def getAct(slots):
# iterate over slots
# find empty
# returns system act
pass pass
def getAction(self, lastUserAct, emptySlots, systemSlots):
systemAct = None
slotVal = None
if ((lastUserAct == "hello") | (lastUserAct == "inform") | (lastUserAct == None)):
# there are no empty slots
if not emptySlots:
systemAct = "inform"
slotVal = systemSlots
# there are empty slots
else:
for slot in systemSlots:
if slot in emptySlots:
systemAct = "request"
slotVal = slot
break
return ["Cinema", systemAct, slotVal, ""]
elif (lastUserAct == "request"):
# todo policy for user request
return ["Cinema", "", "", ""]
else:
systemAct = "repeat"
return ["Cinema", systemAct, "", ""]

99
src/components/DST.py
View File

@ -1,22 +1,87 @@
# Adrian
class DST: class DST:
def __init__(self, slots):
self.slots = slots
def getDialogueState(self, userActs): def __init__(self):
# iterate over speech acts self.init_session()
if userActs['act'] == 'book': # self.value_dict = json.load(open('utils/value_dict.json'))
if userActs['slots']:
for (k, v) in userActs['slots']:
print(k)
if k in self.slots:
self.slots[k] = v
# returns all slots def update(self, user_act=None):
return self.slots intentVal = None
for intent, domain, slot, value in user_act:
domain = domain.lower()
intent = intent.lower()
value = value.lower()
if intentVal is None : intentVal = intent
#dst = DST() k = slot
# userActs = {'inform': {'name': 'edyta', 'age': 18}, 'reqmore': {'date', 'time'}}
#ivona_acts = {'act': 'book', 'slots': [('name', 'ewa'), ('hour', 'dziesiąta'), ('size', 'trzech')]} if intent == 'inform':
#print(dst.getDialogueState(ivona_acts))
domain_dic = self.state['belief_state'][domain]
if k in domain_dic['semi']:
self.state['belief_state'][domain]['semi'][k] = value
elif k in domain_dic['book']:
self.state['belief_state'][domain]['book'][k] = value
elif k.lower() in domain_dic['book']:
self.state['belief_state'][domain]['book'][k.lower()] = value
elif intent == 'reqmore':
if domain not in self.state['request_state']:
self.state['request_state'][domain] = {}
if k not in self.state['request_state'][domain]:
self.state['request_state'][domain][k] = 0
self.state['user_action'].append(intentVal)
return self.state
def addSystemAct(self, act):
self.state["system_action"].append(act)
def getLastUserAct(self):
try:
return self.state["user_action"][-1]
except:
return None
def getEmptySlots(self):
result = []
for key in self.state['belief_state']["cinema"]["book"].keys():
if self.state['belief_state']["cinema"]["book"][key] == "":
result.append(key)
return result
def getSystemSlots(self):
result = []
for key in self.state['belief_state']["cinema"]["book"].keys():
if key != "interval":
result.append(key)
return result
def init_session(self):
self.state = self.default_state()
def default_state(self):
state = dict(user_action=[],
system_action=[],
belief_state={},
request_state={},
terminated=False,
history=[])
state['belief_state'] = {
"cinema": {
"book": {
"title": "",
"date": "",
"time": "",
"quantity": "",
"seats": "",
"area": "",
"interval": "",
},
"semi": {
"goal": ""
}
},
}
return state

32
src/components/NLU.py
View File

@ -13,7 +13,7 @@ class NLU:
csentence = [{'form': word} for word in sentence] csentence = [{'form': word} for word in sentence]
fsentence = self.conllu2flair([csentence])[0] fsentence = self.conllu2flair([csentence])[0]
self.nluModel.predict(fsentence) self.nluModel.predict(fsentence)
return [(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)] return self.toDSTInput([(token, ftoken.get_tag('slot').value) for token, ftoken in zip(sentence, fsentence)])
def conllu2flair(self, sentences, label=None): def conllu2flair(self, sentences, label=None):
fsentences = [] fsentences = []
@ -41,3 +41,33 @@ class NLU:
for char in punctuation: for char in punctuation:
messageLower = messageLower.replace(char,'') messageLower = messageLower.replace(char,'')
return messageLower.split() return messageLower.split()
def toDSTInput(self, taggedSentence):
result = []
intent = None
slotValue = None
slot = None
for tuple in taggedSentence:
value, tagInt = tuple
if intent is None: intent = tagInt.split("/")[1]
if tagInt.split("/")[0] == "O":
if slotValue is not None:
result.append([intent, 'Cinema', slot, slotValue])
slot = None
slotValue = None
elif tagInt.split("/")[0].split("-")[0] == "B":
slot = tagInt.split("/")[0].split("-")[1]
slotValue = value
elif tagInt.split("/")[0].split("-")[0] == "I":
try:
slotValue += " " + value
except:
slot = tagInt.split("/")[0].split("-")[1]
slotValue = value
if slotValue is not None:
result.append([intent, 'Cinema', slot, slotValue])
return result

21
src/dialogue_system.py
View File

@ -1,17 +1,7 @@
from components.NLU import NLU from components.NLU import NLU
from components.NLG import NLG from components.NLG import NLG
from components.DST import DST from components.DST import DST
from components.DP import DP
slots = [
("title", None),
("date", None),
("time", None),
("quantity", None),
("seats", None),
("goal", None),
("area", None),
("interval", None),
]
def chatbot(): def chatbot():
@ -19,6 +9,8 @@ def chatbot():
# NLU # NLU
nlu = NLU() nlu = NLU()
dst = DST()
dp = DP()
# hello message # hello message
print("wpisz /exit aby zakończyć") print("wpisz /exit aby zakończyć")
@ -32,5 +24,10 @@ def chatbot():
isActive = False isActive = False
else: else:
nluPred = nlu.predict(sentence=userMessage) nluPred = nlu.predict(sentence=userMessage)
print(nluPred) # print(nluPred)
dst.update(nluPred)
# print(dst.state)
dpAct = dp.getAction(dst.getLastUserAct(), dst.getEmptySlots(), dst.getSystemSlots())
print(dpAct)
# todo update DST system act
chatbot() chatbot()

28
tasks/zad8/pl/intentClassification.py Normal file
View File

@ -0,0 +1,28 @@
import pandas as pd
def flatten(t):
return [item for sublist in t for item in sublist]
def getData():
Xdata = []
Ydata = []
pathOut = './tasks/zad8/pl/'
pathIn = "./data/clean/"
i = 0
j = 0
nr = 0
for i in range(16,20):
for j in range(20):
for nr in range(1,5):
fileName = pathIn + "dialog-" + str(i).zfill(2) + "-" + str(j).zfill(2) + "-" + str(nr).zfill(2) + ".tsv"
try:
df = pd.read_csv(fileName, sep='\t', header=None, encoding="utf-8")
Xdata.append(df[1].tolist())
Ydata.append(df[2].tolist())
except:
pass
return flatten(Xdata), flatten(Ydata)
x,y = getData()
print(y)

10
tasks/zad8/pl/test.conllu
View File

@ -19,9 +19,9 @@
# text: o której godzinie w piątek mogę zobaczyć na noże # text: o której godzinie w piątek mogę zobaczyć na noże
# intent: reqmore inform # intent: reqmore inform
# slots: októrejgodzinie:goal,wpiątek:date,nanoże:title # slots: októrejgodzinie:goal,wpiątek:date,nanoże:title
1 o reqmore inform B-goal 1 o reqmore inform B-time
2 której reqmore inform I-goal 2 której reqmore inform I-time
3 godzinie reqmore inform I-goal 3 godzinie reqmore inform I-time
4 w reqmore inform NoLabel 4 w reqmore inform NoLabel
5 piątek reqmore inform B-date 5 piątek reqmore inform B-date
6 mogę reqmore inform NoLabel 6 mogę reqmore inform NoLabel
@ -237,11 +237,11 @@
# slots: 1:quantity # slots: 1:quantity
1 1 inform B-quantity 1 1 inform B-quantity
# text: jakie sš dostępne ulgi # text: jakie są dostępne ulgi
# intent: nan # intent: nan
# slots: # slots:
1 jakie NoLabel 1 jakie NoLabel
2 sš NoLabel 2 są NoLabel
3 dostępne NoLabel 3 dostępne NoLabel
4 ulgi NoLabel 4 ulgi NoLabel

51
tasks/zad8/pl/train.conllu
View File

@ -561,12 +561,12 @@
2 potrafisz help NoLabel 2 potrafisz help NoLabel
# text: jakie są najbliższe seanse # text: jakie są najbliższe seanse
# intent: inform # intent: reqmore
# slots: # slots:
1 jakie inform B-goal 1 jakie reqmore NoLabel
2 są inform I-goal 2 są reqmore NoLabel
3 najbliższe inform I-goal 3 najbliższe reqmore B-interval
4 seanse inform I-goal 4 seanse reqmore NoLabel
# text: a jakie są dostępne # text: a jakie są dostępne
# intent: reqmore # intent: reqmore
@ -729,7 +729,7 @@
5 grają reqmore NoLabel 5 grają reqmore NoLabel
6 jakieś reqmore NoLabel 6 jakieś reqmore NoLabel
7 stare reqmore NoLabel 7 stare reqmore NoLabel
8 filmy reqmore NoLabel 8 filmy reqmore B-title
# text: to dziękuję # text: to dziękuję
# intent: thankyou # intent: thankyou
@ -808,14 +808,14 @@
3 film inform NoLabel 3 film inform NoLabel
# text: jakie filmy są teraz w kinach # text: jakie filmy są teraz w kinach
# intent: request # intent: reqmore
# slots: jakiefilmysą:goal # slots: jakiefilmysą:goal
1 jakie request B-goal 1 jakie reqmore B-goal
2 filmy request I-goal 2 filmy reqmore I-goal
3 są request I-goal 3 są reqmore I-goal
4 teraz request NoLabel 4 teraz reqmore B-interval
5 w request NoLabel 5 w reqmore NoLabel
6 kinach request NoLabel 6 kinach reqmore NoLabel
# text: poproszę 3x bilet na ambulans # text: poproszę 3x bilet na ambulans
# intent: inform # intent: inform
@ -1824,13 +1824,13 @@
# slots: # slots:
1 witam hello NoLabel 1 witam hello NoLabel
# text: jakie sš najbliższe seanse # text: jakie są najbliższe seanse
# intent: reqmore # intent: reqmore
# slots: najbliższeseanse:goal # slots: najbliższeseanse:goal
1 jakie reqmore NoLabel 1 jakie reqmore NoLabel
2 sš reqmore NoLabel 2 są reqmore NoLabel
3 najbliższe reqmore B-goal 3 najbliższe reqmore B-interval
4 seanse reqmore I-goal 4 seanse reqmore B-goal
# text: w jakim to kinie # text: w jakim to kinie
# intent: reqmore # intent: reqmore
@ -2525,7 +2525,7 @@
1 niech ack NoLabel 1 niech ack NoLabel
2 będzie ack NoLabel 2 będzie ack NoLabel
# text: jakieś potwierdzenie rezerwacji dostanę czy mogę po prostu wejść i usišść # text: jakieś potwierdzenie rezerwacji dostanę czy mogę po prostu wejść i usiąść
# intent: request # intent: request
# slots: # slots:
1 jakieś request NoLabel 1 jakieś request NoLabel
@ -2538,7 +2538,7 @@
8 prostu request NoLabel 8 prostu request NoLabel
9 wejść request NoLabel 9 wejść request NoLabel
10 i request NoLabel 10 i request NoLabel
11 usišść request NoLabel 11 usiąść request NoLabel
# text: wybieram batmana o 16:20 # text: wybieram batmana o 16:20
# intent: inform # intent: inform
@ -2853,14 +2853,15 @@
2 filmy inform B-goal 2 filmy inform B-goal
3 są inform NoLabel 3 są inform NoLabel
4 wieczorem inform B-interval 4 wieczorem inform B-interval
# text: co gracie za trzy dni # text: co gracie za trzy dni
# intent: inform # intent: inform
# slots: zatrzydni:date # slots: zatrzydni:date
1 co inform NoLabel 1 co reqmore NoLabel
2 gracie inform NoLabel 2 gracie reqmore NoLabel
3 za inform B-date 3 za reqmore B-date
4 trzy inform I-date 4 trzy reqmore I-date
5 dni inform I-date 5 dni reqmore I-date
# text: co gracie za dwa dni # text: co gracie za dwa dni
# intent: inform # intent: inform
@ -2940,7 +2941,7 @@
4 moje inform NoLabel 4 moje inform NoLabel
5 miejsca inform NoLabel 5 miejsca inform NoLabel
6 to inform NoLabel 6 to inform NoLabel
7 h1h3h56 inform NoLabel 7 h1h3h56 inform B-seats
# text: wymień seansy jutro # text: wymień seansy jutro
# intent: inform # intent: inform