C4I_System_chatbot_persian_nervals.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saba-ramezani/C4I_System2/blob/master/C4I_System_chatbot_persian_nervals.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6Fc7KXb4Rqx",
        "outputId": "6faf642d-c73a-46ab-e98d-02ce30f269d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.22.1-py3-none-any.whl (4.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.9 MB 36.1 MB/s \n",
            "\u001b[?25hCollecting huggingface-hub<1.0,>=0.9.0\n",
            "  Downloading huggingface_hub-0.9.1-py3-none-any.whl (120 kB)\n",
            "\u001b[K     |████████████████████████████████| 120 kB 70.6 MB/s \n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.13,>=0.11.1\n",
            "  Downloading tokenizers-0.12.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (6.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 56.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n",
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            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.12.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.9.0->transformers) (4.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.8.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.9.1 tokenizers-0.12.1 transformers-4.22.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow_addons\n",
            "  Downloading tensorflow_addons-0.18.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1 MB 18.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (2.7.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from tensorflow_addons) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->tensorflow_addons) (3.0.9)\n",
            "Installing collected packages: tensorflow-addons\n",
            "Successfully installed tensorflow-addons-0.18.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install tensorflow_addons"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PROJECT_DATA='/content/drive/MyDrive/C4I_system_chatbot/C4I_System/data'"
      ],
      "metadata": {
        "id": "W9q3HKIu4XJO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(PROJECT_DATA + \"/ner_intent_persion_nervals.json\") as f:\n",
        "  ner_intent = json.load(f)"
      ],
      "metadata": {
        "id": "4e_iqlQZ7WWW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Njwg_sVSb7Zr",
        "outputId": "09d1cc47-1b48-4900-cbd6-afbce5fb2a3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NER_ITEMS = sorted(set([t2 for t1 in ner_intent for t2 in ner_intent[t1]['NERTAGS']]),reverse=True)  \n",
        "print(len(NER_ITEMS), NER_ITEMS)\n",
        "\n",
        "INTENT_ITEMS = sorted(set([t2 for t1 in ner_intent for t2 in ner_intent[t1]['INTENTS']]),reverse=True)  \n",
        "print(len(INTENT_ITEMS),INTENT_ITEMS)"
      ],
      "metadata": {
        "id": "PyF1QKVv72do",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "116e94c9-23d8-4780-d1d9-573c1d1e14c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14 ['O', 'I-staff_name', 'I-area', 'B-staff_name', 'B-sensor_type_2', 'B-sensor_type_1', 'B-sensor_status_1', 'B-sensor_name_2', 'B-sensor_name_1', 'B-rank', 'B-parameter', 'B-barracks_name_2', 'B-barracks_name_1', 'B-area']\n",
            "22 ['is_staff_active', 'is_barracks_insider', 'get_type_of_two_barracks_link', 'get_type_of_barracks', 'get_sub_barracks_of_barracks', 'get_status_of_two_barracks_link', 'get_sensors_status_based_on_location_and_sensor_type', 'get_sensor_type', 'get_sensor_count_based_on_sensor_type', 'get_sensor_count_based_on_sensor_status', 'get_sensor_IP', 'get_parameter_of_sensor_based_on_parameter_type', 'get_enemy_sensors_based_on_barracks_id', 'get_count_of_barracks_staff_based_on_rank', 'get_coordinates_of_sensor', 'get_coordinates_of_barracks', 'get_channel_of_two_barracks_link', 'get_barracks_name_of_a_staff', 'get_all_sensors_that_do_not_interfere_based_on_sensor_type', 'get_all_parameters_of_sensor', 'get_access_level_of_a_staff', 'check_if_two_sensors_interfere']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = \"HooshvareLab/bert-fa-zwnj-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
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        {
          "output_type": "stream",
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    },
    {
      "cell_type": "code",
      "source": [
        "def encode_texts(tokenizer, texts):\n",
        "    return tokenizer(texts, padding=True, truncation=True, return_tensors=\"tf\")\n",
        "\n",
        "texts = [d for d in ner_intent]\n",
        "tds = encode_texts(tokenizer, texts)\n",
        "tds.keys()"
      ],
      "metadata": {
        "id": "TPF0M5nB8BcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f76d78ac-cba8-4183-c874-ddf758fc6b32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['input_ids', 'token_type_ids', 'attention_mask'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_texts = tds"
      ],
      "metadata": {
        "id": "UaCZheKQ8IGv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "intent_map = dict() # index -> intent\n",
        "for idx, ui in enumerate(INTENT_ITEMS):\n",
        "    intent_map[ui] = idx\n",
        "intent_map"
      ],
      "metadata": {
        "id": "CjHYuwnD8KUE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da55fcdc-fe5d-47ed-a018-4840c90a5a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'is_staff_active': 0,\n",
              " 'is_barracks_insider': 1,\n",
              " 'get_type_of_two_barracks_link': 2,\n",
              " 'get_type_of_barracks': 3,\n",
              " 'get_sub_barracks_of_barracks': 4,\n",
              " 'get_status_of_two_barracks_link': 5,\n",
              " 'get_sensors_status_based_on_location_and_sensor_type': 6,\n",
              " 'get_sensor_type': 7,\n",
              " 'get_sensor_count_based_on_sensor_type': 8,\n",
              " 'get_sensor_count_based_on_sensor_status': 9,\n",
              " 'get_sensor_IP': 10,\n",
              " 'get_parameter_of_sensor_based_on_parameter_type': 11,\n",
              " 'get_enemy_sensors_based_on_barracks_id': 12,\n",
              " 'get_count_of_barracks_staff_based_on_rank': 13,\n",
              " 'get_coordinates_of_sensor': 14,\n",
              " 'get_coordinates_of_barracks': 15,\n",
              " 'get_channel_of_two_barracks_link': 16,\n",
              " 'get_barracks_name_of_a_staff': 17,\n",
              " 'get_all_sensors_that_do_not_interfere_based_on_sensor_type': 18,\n",
              " 'get_all_parameters_of_sensor': 19,\n",
              " 'get_access_level_of_a_staff': 20,\n",
              " 'check_if_two_sensors_interfere': 21}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "# # map to train_data values\n",
        "def encode_intents(intents, intent_map):\n",
        "    encoded = np.zeros((len(intents),len(intent_map)))\n",
        "    for idx,intnt in enumerate(intents):\n",
        "        encoded[ idx, [intent_map[i] for i in intnt]] = 1\n",
        "    # convert to tf tensor\n",
        "\n",
        "    return tf.convert_to_tensor(encoded, dtype=\"int32\")\n",
        "\n",
        "encoded_intents = encode_intents([ ner_intent[t]['INTENTS'] for t in ner_intent], intent_map)\n",
        "encoded_intents"
      ],
      "metadata": {
        "id": "yLsrDUcy8PSB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79016f56-a5f2-4f91-e0c3-38bdf9cfff24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(16095, 22), dtype=int32, numpy=\n",
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(encoded_intents[0]),ner_intent[next(iter(ner_intent))]"
      ],
      "metadata": {
        "id": "8M5TGRrk8TPZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7948268a-e21d-4fe1-c70b-4f907e49232b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,\n",
              " {'TEXT': ['چند',\n",
              "   'سنسور',\n",
              "   'از',\n",
              "   'نوع',\n",
              "   'رادار',\n",
              "   'مربوط',\n",
              "   'به',\n",
              "   'پادگان',\n",
              "   '1',\n",
              "   'وجود',\n",
              "   'دارد؟'],\n",
              "  'NERTAGS': ['O',\n",
              "   'O',\n",
              "   'O',\n",
              "   'O',\n",
              "   'B-sensor_type_1',\n",
              "   'O',\n",
              "   'O',\n",
              "   'O',\n",
              "   'B-barracks_name_1',\n",
              "   'O',\n",
              "   'O'],\n",
              "  'NERVALS': {'sensor_type_1': 'رادار', 'barracks_name_1': '1'},\n",
              "  'INTENTS': ['get_sensor_count_based_on_sensor_type']})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "slot_map = dict() # slot -> index\n",
        "for idx, us in enumerate(NER_ITEMS):\n",
        "    slot_map[us] = idx\n",
        "slot_map"
      ],
      "metadata": {
        "id": "Ffwt8XZU8cGV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ccc553e-aa82-43b9-c476-2e384cbf57ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'O': 0,\n",
              " 'I-staff_name': 1,\n",
              " 'I-area': 2,\n",
              " 'B-staff_name': 3,\n",
              " 'B-sensor_type_2': 4,\n",
              " 'B-sensor_type_1': 5,\n",
              " 'B-sensor_status_1': 6,\n",
              " 'B-sensor_name_2': 7,\n",
              " 'B-sensor_name_1': 8,\n",
              " 'B-rank': 9,\n",
              " 'B-parameter': 10,\n",
              " 'B-barracks_name_2': 11,\n",
              " 'B-barracks_name_1': 12,\n",
              " 'B-area': 13}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# find the max encoded test length\n",
        "# tokenizer pads all texts to same length anyway so\n",
        "# just get the length of the first one's input_ids\n",
        "#max_len = len(encoded_texts[\"input_ids\"][0])\n",
        "max_len = max([len(x) for x in encoded_texts[\"input_ids\"]])\n",
        "#print(max_len)\n",
        "def encode_slots(all_slots, all_texts, \n",
        "                 toknizer, slot_map, max_len=max_len):\n",
        "    encoded_slots = np.zeros(shape=(len(all_texts), max_len), dtype=np.int32)\n",
        "    \n",
        "    for idx, text in enumerate(all_texts):\n",
        "        enc = [] # for this idx, to be added at the end to encoded_slots\n",
        "        \n",
        "        # slot names for this idx\n",
        "        slot_names = all_slots[idx]\n",
        "        print(text, slot_names)\n",
        "        # raw word tokens\n",
        "        # not using bert for this block because bert uses\n",
        "        # a wordpiece tokenizer which will make \n",
        "        # the slot label to word mapping\n",
        "        # difficult\n",
        "        raw_tokens = text.split()\n",
        "\n",
        "        # words or slot_values associated with a certain\n",
        "        # slot_name are contained in the values of the\n",
        "        # dict slots_names\n",
        "        # now this becomes a two way lookup\n",
        "        # first we check if a word belongs to any\n",
        "        # slot label or not and then we add the value from\n",
        "        # slot map to encoded for that word\n",
        "        for rt,rt_slot_name in zip(raw_tokens,slot_names):\n",
        "            # use bert tokenizer\n",
        "            # to get wordpiece tokens\n",
        "            bert_tokens = tokenizer.tokenize(rt)\n",
        "            \n",
        "            # find the slot name for a token\n",
        "            #rt_slot_name = get_slot_from_word(rt, slot_names)\n",
        "            #rt_slot_name = get_slot_from_word(rt, slot_names) Moved to for and ziped\n",
        "            if rt_slot_name is not None:\n",
        "                # fill with the slot_map value for all ber tokens for rt\n",
        "                enc.append(slot_map[rt_slot_name])\n",
        "                enc.extend([slot_map[rt_slot_name]] * (len(bert_tokens) - 1))\n",
        "\n",
        "            else:\n",
        "                # rt is not associated with any slot name\n",
        "                enc.append(0)\n",
        "                \n",
        "        \n",
        "        # now add to encoded_slots\n",
        "        # ignore the first and the last elements\n",
        "        # in encoded text as they're special chars\n",
        "        encoded_slots[idx, 1:len(enc)+1] = enc\n",
        "    \n",
        "    return encoded_slots"
      ],
      "metadata": {
        "id": "16ZHfzlE8kTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# all_slots = [td.slots for td in train_data]\n",
        "# all_texts = [td.text for td in train_data]\n",
        "\n",
        "\n",
        "all_slots = [ s['NERTAGS'] for t,s in ner_intent.items() ]\n",
        "all_texts = [ t for t in ner_intent ]"
      ],
      "metadata": {
        "id": "VtjDuTU18nVQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_slots = encode_slots(all_slots, all_texts, tokenizer, slot_map)\n",
        "print(encoded_slots)\n",
        "encoded_slots[2],all_slots[2],all_texts[2]\n",
        "encoded_slots.shape"
      ],
      "metadata": {
        "id": "oG-Sglai8pKN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Classifier Model**"
      ],
      "metadata": {
        "id": "KTCa5AiM8r2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFBertModel\n",
        "from tensorflow.keras.layers import Dropout, Dense, GlobalAveragePooling1D\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.metrics import SparseCategoricalAccuracy\n",
        "from tensorflow.keras.losses import CategoricalCrossentropy\n",
        "from tensorflow.keras.metrics import CategoricalAccuracy\n",
        "\n",
        "class JointIntentAndSlotFillingModel(tf.keras.Model):\n",
        "\n",
        "    def __init__(self, intent_num_labels=None, slot_num_labels=None,\n",
        "                 model_name=model_name, dropout_prob=0.1):\n",
        "        super().__init__(name=\"joint_intent_slot\")\n",
        "        self.bert = TFBertModel.from_pretrained(model_name)\n",
        "        self.dropout = Dropout(dropout_prob)\n",
        "        # self.intent_classifier = Dense(intent_num_labels,\n",
        "        #                                name=\"intent_classifier\")\n",
        "\n",
        "        self.intent_classifier = Dense(intent_num_labels, activation='sigmoid',\n",
        "                                       name=\"intent_classifier\")\n",
        "\n",
        "        self.slot_classifier = Dense(slot_num_labels,\n",
        "                                     name=\"slot_classifier\")\n",
        "\n",
        "    def call(self, inputs, **kwargs):\n",
        "        # two outputs from BERT\n",
        "        trained_bert = self.bert(inputs, **kwargs)\n",
        "        pooled_output = trained_bert.pooler_output\n",
        "        sequence_output = trained_bert.last_hidden_state\n",
        "        \n",
        "        # sequence_output will be used for slot_filling / classification\n",
        "        sequence_output = self.dropout(sequence_output,\n",
        "                                       training=kwargs.get(\"training\", False))\n",
        "        slot_logits = self.slot_classifier(sequence_output)\n",
        "\n",
        "        # pooled_output for intent classification\n",
        "        pooled_output = self.dropout(pooled_output,\n",
        "                                     training=kwargs.get(\"training\", False))\n",
        "        intent_logits = self.intent_classifier(pooled_output)\n",
        "        return slot_logits, intent_logits\n",
        "        #return {'slot': slot_logits,'intent': intent_logits} "
      ],
      "metadata": {
        "id": "CkjkshbB8wQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joint_model = JointIntentAndSlotFillingModel(\n",
        "    intent_num_labels=len(intent_map), slot_num_labels=len(slot_map))"
      ],
      "metadata": {
        "id": "uEf4jJAX8znv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#https://towardsdatascience.com/multi-label-image-classification-in-tensorflow-2-0-7d4cf8a4bc72\n",
        "@tf.function\n",
        "def macro_soft_f1(y, y_hat):\n",
        "    \"\"\"Compute the macro soft F1-score as a cost (average 1 - soft-F1 across all labels).\n",
        "    Use probability values instead of binary predictions.\n",
        "    \n",
        "    Args:\n",
        "        y (int32 Tensor): targets array of shape (BATCH_SIZE, N_LABELS)\n",
        "        y_hat (float32 Tensor): probability matrix from forward propagation of shape (BATCH_SIZE, N_LABELS)\n",
        "        \n",
        "    Returns:\n",
        "        cost (scalar Tensor): value of the cost function for the batch\n",
        "    \"\"\"\n",
        "    y = tf.cast(y, tf.float32)\n",
        "    y_hat = tf.cast(y_hat, tf.float32)\n",
        "    tp = tf.reduce_sum(y_hat * y, axis=0)\n",
        "    fp = tf.reduce_sum(y_hat * (1 - y), axis=0)\n",
        "    fn = tf.reduce_sum((1 - y_hat) * y, axis=0)\n",
        "    soft_f1 = 2*tp / (2*tp + fn + fp + 1e-16)\n",
        "    cost = 1 - soft_f1 # reduce 1 - soft-f1 in order to increase soft-f1\n",
        "    macro_cost = tf.reduce_mean(cost) # average on all labels\n",
        "    return macro_cost\n",
        "\n",
        "@tf.function\n",
        "def macro_f1(y, y_hat, thresh=0.5):\n",
        "    \"\"\"Compute the macro F1-score on a batch of observations (average F1 across labels)\n",
        "    \n",
        "    Args:\n",
        "        y (int32 Tensor): labels array of shape (BATCH_SIZE, N_LABELS)\n",
        "        y_hat (float32 Tensor): probability matrix from forward propagation of shape (BATCH_SIZE, N_LABELS)\n",
        "        thresh: probability value above which we predict positive\n",
        "        \n",
        "    Returns:\n",
        "        macro_f1 (scalar Tensor): value of macro F1 for the batch\n",
        "    \"\"\"\n",
        "    y_pred = tf.cast(tf.greater(y_hat, thresh), tf.float32)\n",
        "    tp = tf.cast(tf.math.count_nonzero(y_pred * y, axis=0), tf.float32)\n",
        "    fp = tf.cast(tf.math.count_nonzero(y_pred * (1 - y), axis=0), tf.float32)\n",
        "    fn = tf.cast(tf.math.count_nonzero((1 - y_pred) * y, axis=0), tf.float32)\n",
        "    f1 = 2*tp / (2*tp + fn + fp + 1e-16)\n",
        "    macro_f1 = tf.reduce_mean(f1)\n",
        "    return macro_f1"
      ],
      "metadata": {
        "id": "iW4WhXo686ee"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_slots.shape[1], encoded_intents.shape[1]"
      ],
      "metadata": {
        "id": "A2Kpw8K789MJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorcial_slots = tf.keras.utils.to_categorical(encoded_slots, num_classes=len(slot_map))\n",
        "categorcial_slots.shape, categorcial_slots.shape[-1]"
      ],
      "metadata": {
        "id": "fzW_KX-U9Amw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class MacroF1(tf.keras.metrics.Metric):\n",
        "\n",
        "  def __init__(self, name='MacroF1', **kwargs):\n",
        "    super(MacroF1, self).__init__(name=name, **kwargs)\n",
        "    self.true_positives = self.add_weight(name='tp', initializer='zeros')\n",
        "\n",
        "  def update_state(self, y_true, y_pred, sample_weight=None):\n",
        "    thresh = 0.5\n",
        "    y_hat =  tf.cast(tf.reshape(y_pred, [-1,11]),tf.float32)\n",
        "   \n",
        "    y2 = tf.cast(y_true, tf.float32)\n",
        "    y2 = tf.keras.utils.to_categorical(y2, num_classes=11)\n",
        "    y2 = tf.reshape(y2,[-1])\n",
        "    #oh_labels = tf.cast(tf.one_hot(tf.cast(labels, tf.int32), num_classes), dtype=labels.dtype)\n",
        "    #print(y)\n",
        "    #y2 = tf.cast(tf.one_hot(tf.cast(y2, tf.int32), 11), dtype=tf.int32)\n",
        "    y =  tf.cast(y2 , tf.float32)\n",
        "\n",
        "    print(y.shape, y_hat)\n",
        "    print(y.shape, y_hat)\n",
        "    print(y.shape, y_hat)\n",
        "    print(y.shape, y_hat)\n",
        "    print(y.shape, y_hat)\n",
        "    print(y.shape, y_hat)\n",
        "\n",
        "    y_pred = tf.cast(tf.greater(y_hat, thresh), tf.float32)\n",
        "    tp = tf.cast(tf.math.count_nonzero(y_pred * y, axis=0), tf.float32)\n",
        "    fp = tf.cast(tf.math.count_nonzero(y_pred * (1 - y), axis=0), tf.float32)\n",
        "    fn = tf.cast(tf.math.count_nonzero((1 - y_pred) * y, axis=0), tf.float32)\n",
        "    f1 = 2*tp / (2*tp + fn + fp + 1e-16)\n",
        "    macro_f1 = tf.reduce_mean(f1)\n",
        "\n",
        "  def result(self):\n",
        "    return self.true_positives"
      ],
      "metadata": {
        "id": "1lKp2unV9JAW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_addons as tfa\n",
        "tf.config.run_functions_eagerly(True)\n",
        "class MY_F1Score(tfa.metrics.FBetaScore):\n",
        "    def __init__(\n",
        "        self,\n",
        "        num_classes: 11,\n",
        "        average = None,\n",
        "        threshold = None,\n",
        "        name = \"MY_F1Score\",\n",
        "        dtype = None,\n",
        "    ):\n",
        "        super().__init__(num_classes, average, 1.0, threshold, name=name, dtype=dtype)\n",
        "\n",
        "\n",
        "    def get_config(self):\n",
        "        base_config = super().get_config()\n",
        "        del base_config[\"beta\"]\n",
        "        return base_config\n",
        "\n",
        "    def update_state(self, y_true, y_pred, sample_weight=None):   \n",
        "        #print(y_true,y_pred) \n",
        "        #y_true_ = tf.keras.utils.to_categorical(y_true, num_classes=11)\n",
        "        y_true_ = tf.constant(tf.reshape(y_true,[-1,y_true.shape[-1]])   )\n",
        "        y_pred_ = tf.constant(tf.reshape(y_pred,[-1,y_pred.shape[-1]])   )\n",
        "        #print(y_true_,y_pred_) \n",
        " \n",
        "        super().update_state(y_true_, y_pred_, sample_weight=None)"
      ],
      "metadata": {
        "id": "3VO6e0p09M3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "opt = Adam(learning_rate=3e-5, epsilon=1e-08)\n",
        "\n",
        "# two outputs, one for slots, another for intents\n",
        "# we have to fine tune for both\n",
        "# losses = [SparseCategoricalCrossentropy(from_logits=True),\n",
        "#           'binary_crossentropy'\n",
        "#           #macro_soft_f1\n",
        "#           ]\n",
        "\n",
        "losses = [CategoricalCrossentropy(from_logits=True),\n",
        "          'binary_crossentropy'\n",
        "          #macro_soft_f1\n",
        "          ]\n",
        "                    \n",
        "# losses = {'slot': CategoricalCrossentropy(from_logits=True),'intent': 'binary_crossentropy'}\n",
        "\n",
        "#from tensorflow_addons import metrics\n",
        "#metric1 = tfa.metrics.F1Score(num_classes=encoded_slots.shape[1], average = 'macro', threshold=0.7, name='m1')\n",
        "metric2 = tfa.metrics.F1Score(num_classes=encoded_intents.shape[1], average = 'macro', threshold=0.7, name='m2')\n",
        "\n",
        "\n",
        "#metrics = ['accuracy']\n",
        "#metrics = [ metric1, metric2]\n",
        "# metric1 = MY_F1Score(num_classes=11, average = 'micro', threshold=0.7, name='m1')\n",
        "# metrics = {'slot':metric1, \"'intent'\":metric2} \n",
        "metrics = [ MY_F1Score(num_classes=11, average = 'micro', threshold=0.7, name='m1')]\n",
        "\n",
        "# compile model\n",
        "joint_model.compile(optimizer=opt, loss=losses, loss_weights = [1,1], metrics=metrics)"
      ],
      "metadata": {
        "id": "NVIcOnru9QBF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = {\"input_ids\": encoded_texts[\"input_ids\"], \"token_type_ids\": encoded_texts[\"token_type_ids\"],  \"attention_mask\": encoded_texts[\"attention_mask\"]}\n",
        "\n",
        "#history = joint_model.fit(x, (encoded_slots, encoded_intents), epochs=1, batch_size=32, shuffle=True)\n",
        "history = joint_model.fit(x, (categorcial_slots, encoded_intents), epochs=3, batch_size=32, shuffle=True)\n",
        "#history = joint_model.fit(x, {'slot': categorcial_slots,'intent': encoded_intents}, epochs=1, batch_size=32, shuffle=True)"
      ],
      "metadata": {
        "id": "eExG259D9W63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = joint_model.predict(x)\n",
        "res"
      ],
      "metadata": {
        "id": "UsKgwCGf9g28"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# test = MY_F1Score(num_classes=11, average = 'macro', threshold=0.7, name='m1')\n",
        "\n",
        "# test.update_state(encoded_slots,res[0])\n",
        "# test.result()"
      ],
      "metadata": {
        "id": "bb7oXNmt9iko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "joint_model.save_weights(PROJECT_DATA + \"/checkpoints/my_checkpoint\")\n",
        "a = np.random.random((10))\n",
        "print(a,np.nonzero(a>0.5)[0])"
      ],
      "metadata": {
        "id": "WdNsSEvl9lP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def map2cat(t):\n",
        "    return t.replace(\"B-\", \"\").replace(\"I-\", \"\") \n",
        "\n",
        "def nlu(text, tokenizer, model, intent_names, slot_names):\n",
        "    inputs = tf.constant(tokenizer.encode(text))[None, :]  # batch_size = 1\n",
        "    outputs = model(inputs)\n",
        "    #print(\"output:\",outputs)\n",
        "    slot_logits, intent_logits = outputs\n",
        "\n",
        "    slot_ids = slot_logits.numpy().argmax(axis=-1)[0, :]\n",
        "    #print(\"slot_ids:\",slot_ids)\n",
        "    #intent_id = intent_logits.numpy().argmax(axis=-1)[0]\n",
        "    intent_ids = np.nonzero(intent_logits.numpy()>0.8)[1]\n",
        "    #print(intent_logits,np.nonzero(intent_logits.numpy()>0.5))\n",
        "    #print(\"intent_ids:\",intent_ids)\n",
        "    #info = {\"intent\": intent_names[intent_id], \"slots\": {}}\n",
        "    info = {\"intent\": [intent_names[intent_id] for intent_id in intent_ids], \"slots\": {}}\n",
        "    #print(\"info:\", info)\n",
        "    out_dict = {}\n",
        "    # get all slot names and add to out_dict as keys\n",
        "    predicted_slots = set([slot_names[s] for s in slot_ids if s != 0])\n",
        "    #print(\"predicted_slots:\",predicted_slots)\n",
        "    #merge slots\n",
        "    predicted_slots = set([ map2cat(t) for t in predicted_slots])\n",
        "    #print(\"predicted_slots:\",predicted_slots)\n",
        "    #print(predicted_slots)\n",
        "    for ps in predicted_slots:\n",
        "      out_dict[ps] = []\n",
        "\n",
        "    # check if the text starts with a small letter\n",
        "    #if text[0].islower():\n",
        "    #  tokens = tokenizer.tokenize(text, add_special_tokens=True)\n",
        "    #else:\n",
        "    #add speial tokens for persian \n",
        "    tokens = tokenizer.tokenize(text, add_special_tokens=True)\n",
        "    #print(\"tokens:\", tokens)\n",
        "    for token, slot_id in zip(tokens, slot_ids):\n",
        "        # add all to out_dict\n",
        "        #slot_name = slot_names[slot_id]\n",
        "        slot_name = map2cat(slot_names[slot_id])\n",
        "\n",
        "        if slot_name == \"O\":\n",
        "            continue\n",
        "\n",
        "\n",
        "        # collect tokens\n",
        "        collected_tokens = [token]\n",
        "        idx = tokens.index(token)\n",
        "\n",
        "        \n",
        "        # see if it starts with ##\n",
        "        # then it belongs to the previous token\n",
        "        if token.startswith(\"##\"):\n",
        "          # check if the token already exists or not\n",
        "          if tokens[idx - 1] not in out_dict[slot_name]:\n",
        "           collected_tokens.insert(0, tokens[idx - 1])\n",
        "\n",
        "\n",
        "        # add collected tokens to slots\n",
        "        out_dict[slot_name].extend(collected_tokens)\n",
        "\n",
        "    #print(\"out dict:\", out_dict)\n",
        "    # process out_dict\n",
        "    for slot_name in out_dict:\n",
        "        tokens = out_dict[slot_name]\n",
        "        slot_value = tokenizer.convert_tokens_to_string(tokens)\n",
        "\n",
        "        info[\"slots\"][slot_name] = slot_value.strip()\n",
        "\n",
        "    return info\n",
        "\n",
        "t_itrator=iter(ner_intent)\n",
        "for i in range(3):\n",
        "    tkey = next(t_itrator)\n",
        "    print(tkey, nlu(tkey, tokenizer, joint_model, INTENT_ITEMS, NER_ITEMS), ner_intent[tkey]['INTENTS'], ner_intent[tkey]['NERVALS'])\n",
        "    print()"
      ],
      "metadata": {
        "id": "8s8phDsu9pGO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nlu(\"انتنهای شمال شرق کشور چه وضعیتی دارند؟\", tokenizer, joint_model, \n",
        "    INTENT_ITEMS, NER_ITEMS)"
      ],
      "metadata": {
        "id": "FOUWl4tL9sr4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}