metagenomic_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "metagenomic_final.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "RRdnF-T7GW3A",
        "SsYlsw4_Gbcc",
        "143wOQzGFQlL"
      ],
      "mount_file_id": "1fL0-eDKgqVA4MlRSci89QiN6Bhk3Uyol",
      "authorship_tag": "ABX9TyOD2FJdrY4jfnzwoqxeyfRE",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/xelanac/metagenomic-project/blob/main/metagenomic_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4AruxVWPFnO"
      },
      "source": [
        "#Import delle librerie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDP2iYK4O-X6"
      },
      "source": [
        "%%capture\n",
        "!pip3 install google-nucleus==0.4.0\n",
        "!pip install -q tensorflow==2.0.0-alpha0\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import os\n",
        "import random\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "#librerie per la lettura dei file fastq\n",
        "from nucleus.io import fastq\n",
        "from nucleus.io import fasta\n",
        "from nucleus.io import sam\n",
        "from nucleus.io import vcf\n",
        "from nucleus.io.genomics_writer import TFRecordWriter\n",
        "from nucleus.protos import reads_pb2\n",
        "from nucleus.util import cigar\n",
        "from nucleus.util import ranges\n",
        "from nucleus.util import utils\n",
        "\n",
        "import torch\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "#matplotlib per il plot dell'accuratezza del modello in base ad ogni epoca\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#librerie per la creazione del vocabolario\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "#librerie per la costruzione della rete neurale RNN\n",
        "from tensorflow.keras.layers import Embedding, LSTM, Dense, Bidirectional\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98V_jRj4PROA"
      },
      "source": [
        "#Generazione di dataset randomici"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDVaV9F9PVRs"
      },
      "source": [
        "bases_list = [\"A\",\"C\",\"G\",\"T\"]\n",
        "seq_length = 20 #lunghezza stringa\n",
        "seq_num = 20 #lunghezza dataset\n",
        "\n",
        "dna_not_sanitized = []\n",
        "dna_sanitized = []"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DETl6UmH-CDh"
      },
      "source": [
        "def random_seq_generation(n_seq, seq_len, dataset):\n",
        "  for index in range(n_seq):\n",
        "    sequence = [random.choice(bases_list) for _ in range(seq_len)]\n",
        "    sequence = \"\".join(sequence)\n",
        "    dataset.append(sequence)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTwW6If7-lQL"
      },
      "source": [
        "random_seq_generation(seq_num, seq_length, dna_not_sanitized)\n",
        "random_seq_generation(seq_num, seq_length, dna_sanitized)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khJvyYzMpeZZ"
      },
      "source": [
        "#Caricamento e analisi di dati reali (in questo caso trascurare la precedente sezione di generazione di dataset randomici)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGydAYSlqKNs"
      },
      "source": [
        "Collegamento al drive e import dei dati reali"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vAhdq0_cqA6n"
      },
      "source": [
        "#accesso al drive\n",
        "\"\"\"\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_GFYpDekiyK"
      },
      "source": [
        "path_not_sanitized = \"/content/drive/MyDrive/fastQ_data/fastQ_01_2021/ERX149279.fastq.gz\"\n",
        "path_sanitized = \"/content/drive/MyDrive/fastQ_data/fastQ_01_2021/ERX149293.fastq.gz\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oKteMsGkDjq"
      },
      "source": [
        "def seq_isolation(dataset, dataset_len, seq_dataset):\n",
        "  index = 0\n",
        "  seq_list = []\n",
        "\n",
        "  for i in dataset:\n",
        "    if index < dataset_len:\n",
        "      seq_list.append(i)\n",
        "      b = str(list(seq_list)[index]).split(\"\\n\")[2]\n",
        "      seq = \"\"\n",
        "      for j in b:\n",
        "        if j == \"A\" or j == \"C\" or j == \"G\" or j == \"T\":\n",
        "          seq += j\n",
        "      seq_dataset.append(seq)\n",
        "      index += 1\n",
        "    else:\n",
        "      break;"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7TOPJEY8kIDk"
      },
      "source": [
        "dataset_length = 50 #se non si usa questo flag l'iterazione sul fastq restituisce: \"Failed to parse FASTQ record\"  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjKPXZltkQ-s"
      },
      "source": [
        "Creazione della lista contenente solo le sequenze dei due fastq"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXc_iQTgkMDc"
      },
      "source": [
        "#lista dna non sanificato -> dna_not_sanitized\n",
        "fastq_dataset_not_sanitized = fastq.FastqReader(path_not_sanitized)\n",
        "\n",
        "dna_not_sanitized = []\n",
        "seq_isolation(fastq_dataset_not_sanitized, dataset_length, dna_not_sanitized)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vayNv_C3k9nl"
      },
      "source": [
        "#lista dna sanificato -> dna_sanitized\n",
        "fastq_dataset_sanitized = fastq.FastqReader(path_sanitized)\n",
        "\n",
        "dna_sanitized = []\n",
        "seq_isolation(fastq_dataset_sanitized, dataset_length, dna_sanitized)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wt9FrIPEsXsf"
      },
      "source": [
        "Check sulla lunghezza delle sequenze"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKNr1LGxsZ4Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0199caed-3924-4011-ceae-510151c1f0ad"
      },
      "source": [
        "#Lunghezza massima e minima del dataset \"not_sanitized\"\n",
        "print(\"Lunghezza massima delle sequenze dna_not_sanitized: \" , len(max(dna_not_sanitized, key=len)))\n",
        "print(\"Lunghezza minima delle sequenze dna_not_sanitized: \" , len(min(dna_not_sanitized, key=len)), \"\\n\")\n",
        "\n",
        "#Lunghezza massima e minima del dataset \"sanitized\"\n",
        "print(\"Lunghezza massima delle sequenze dna_sanitized: \" ,  len(max(dna_sanitized, key=len)))\n",
        "print(\"Lunghezza minima delle sequenze dna_sanitized: \" , len(min(dna_sanitized, key=len)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Lunghezza massima delle sequenze dna_not_sanitized:  76\n",
            "Lunghezza minima delle sequenze dna_not_sanitized:  76 \n",
            "\n",
            "Lunghezza massima delle sequenze dna_sanitized:  76\n",
            "Lunghezza minima delle sequenze dna_sanitized:  76\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5nm26iWyti0e"
      },
      "source": [
        "Check che verifica se la sequenza ha qualche parametro \"K\", il quale indica un'imprecisione in fase di campionamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2jSNcbOtr8H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50b2dfd2-0294-4552-c74b-971ca50591c7"
      },
      "source": [
        "def sequences_check(sequences_list):\n",
        "  i = 0\n",
        "\n",
        "  for single_sequence in sequences_list:\n",
        "    for index in single_sequence:\n",
        "      if index == \"K\":\n",
        "        return True\n",
        "    i+= 1\n",
        "  return False\n",
        "  \n",
        "print(\"Check delle sequenze non sanificate: \\n\", sequences_check(dna_not_sanitized))\n",
        "print(\"Check delle sequenze  sanificate: \\n\", sequences_check(dna_sanitized))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Check delle sequenze non sanificate: \n",
            " False\n",
            "Check delle sequenze  sanificate: \n",
            " False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Fee-J8Hv6Xl"
      },
      "source": [
        "Se la funzione \"sequence_check()\" restituisce \"True\" per una delle due liste, procedere con la correzione; altrimenti procedere con la creazione del vocabolario"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YA9OYhpBwKxS"
      },
      "source": [
        "#con questo metodo sostituisco il parametro \"k\" con una delle basi azotate(in questo caso ho scelto l'Adenina)\n",
        "def sequences_correction(sequences_list): \n",
        "  j = 0\n",
        "  new_dna_sequence_list = []\n",
        "  for single_sequence in sequences_list:\n",
        "    i = 0\n",
        "    new_sequence = \"\"\n",
        "    for index in single_sequence:\n",
        "      if index == \"K\":\n",
        "        new_sequence+=\"A\"\n",
        "      else:\n",
        "        new_sequence+=index\n",
        "      i+=1\n",
        "    new_dna_sequence_list.append(new_sequence)  \n",
        "    j+=1     \n",
        "\n",
        "  sequences_list = new_dna_sequence_list\n",
        "  return sequences_list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXACHaqy05hm"
      },
      "source": [
        "sequences_correction(dna_not_sanitized)\n",
        "sequences_correction(dna_sanitized)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJqAwxPCSsio"
      },
      "source": [
        "#Creo il vocabolario"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqTGuXyUac1t",
        "outputId": "02ae0e2f-2375-4174-bae6-f14181d60559"
      },
      "source": [
        "tokenizer = Tokenizer(char_level=True)\n",
        "\n",
        "tokenizer.fit_on_texts(dna_not_sanitized)\n",
        "\n",
        "vocab = tokenizer.word_index\n",
        "vocab_length = len(vocab) + 1\n",
        "\n",
        "print(\"Vocabulary: \" ,vocab)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocabulary:  {'c': 1, 'a': 2, 'g': 3, 't': 4}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O5uXy2ogTC5s"
      },
      "source": [
        "input_not_sanitized = []\n",
        "\n",
        "for record in dna_not_sanitized:\n",
        "\ttoken_list = tokenizer.texts_to_sequences([record])[0]\n",
        "\tfor i in range(1, len(token_list)):\n",
        "\t\tn_gram_sequence = token_list[:i+1]\n",
        "\t\tinput_not_sanitized.append(n_gram_sequence)\n",
        "\n",
        "#pad sequences \n",
        "max_sequence_len = max([len(x) for x in input_not_sanitized])\n",
        "input_sequences = np.array(pad_sequences(input_not_sanitized, maxlen=max_sequence_len, padding='pre'))\n",
        "\n",
        "#xs, labels_x = input_sequences[:,:-1],input_sequences[:,-1] #il -1 omette l'ultimo carattere\n",
        "xs = input_sequences[:,:-1] #il -1 omette l'ultimo carattere"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXv9sV36kk6E",
        "outputId": "96fe0f8c-cde2-48b9-983f-45a20e0524bb"
      },
      "source": [
        "print(input_sequences)\n",
        "print(input_sequences[:-1])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 0 0 ... 0 1 1]\n",
            " [0 0 0 ... 1 1 2]\n",
            " [0 0 0 ... 1 2 2]\n",
            " ...\n",
            " [0 0 3 ... 2 1 1]\n",
            " [0 3 4 ... 1 1 3]\n",
            " [3 4 4 ... 1 3 1]]\n",
            "[[0 0 0 ... 0 1 1]\n",
            " [0 0 0 ... 1 1 2]\n",
            " [0 0 0 ... 1 2 2]\n",
            " ...\n",
            " [0 0 0 ... 2 2 1]\n",
            " [0 0 3 ... 2 1 1]\n",
            " [0 3 4 ... 1 1 3]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWSt4TxlUYDG"
      },
      "source": [
        "input_sanitized = []\n",
        "\n",
        "for line in dna_sanitized:\n",
        "\ttoken_list = tokenizer.texts_to_sequences([line])[0]\n",
        "\tfor i in range(1, len(token_list)):\n",
        "\t\tn_gram_sequence = token_list[:i+1]\n",
        "\t\tinput_sanitized.append(n_gram_sequence)\n",
        "\n",
        "#pad sequences\n",
        "max_sequence_len = max([len(x) for x in input_sanitized])\n",
        "input_sequences = np.array(pad_sequences(input_sanitized, maxlen=max_sequence_len, padding='pre'))\n",
        "\n",
        "ys, labels_y = input_sequences[:,:-1],input_sequences[:,-1]\n",
        "\n",
        "ys = tf.keras.utils.to_categorical(labels_y, num_classes=vocab_length)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnTRmRr_Usus"
      },
      "source": [
        "#Costruzione del modello Rnn con layer LSTM"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RRdnF-T7GW3A"
      },
      "source": [
        "##Modello RNN Bidirezionale(LSTM)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cnKFEBGnK7MP"
      },
      "source": [
        "**N.B:** Bidirectional fa una copia del layer RNN passatogli come argomento e, attraverso il campo go_backwards, riesce a capovolgere l'input, quindi ad elaborarlo anche in ordine inverso.\n",
        "Questo apporccio non tiene conto solo delle parole successive da generare, ma anche del contesto che c'è intorno al carattere/parola successiva al fine di ottere una migliore predizione, preservando più informazioni utili nel tempo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyPaav9HU1U-"
      },
      "source": [
        "def bidirectional_rnn(vocab, max_seq_len):\n",
        "  print(\"Bidirectional LSTM MODEL\")\n",
        "  model_bidirectional = Sequential(name=\"metagenomic prediction bidirectional\")\n",
        "  model_bidirectional.add(Embedding(vocab, 110, input_length=max_seq_len, name=\"input_layer\"))\n",
        "  model_bidirectional.add(Bidirectional(LSTM(150), name=\"LSTM\"))\n",
        "  model_bidirectional.add(Dense(vocab, activation='softmax', name=\"output_layer\"))\n",
        "\n",
        "  adam = Adam(lr=0.01)\n",
        "  model_bidirectional.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "  return model_bidirectional"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dop7BAANU21P",
        "outputId": "ec411a15-0f85-447c-94a8-84ffc24a0e80"
      },
      "source": [
        "model_bidirectional = bidirectional_rnn(vocab_length, max_sequence_len-1)\n",
        "model_bidirectional.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Bidirectional LSTM MODEL\n",
            "Model: \"metagenomic prediction bidirectional\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (Embedding)      (None, 19, 110)           550       \n",
            "_________________________________________________________________\n",
            "LSTM (Bidirectional)         (None, 300)               313200    \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 5)                 1505      \n",
            "=================================================================\n",
            "Total params: 315,255\n",
            "Trainable params: 315,255\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsYlsw4_Gbcc"
      },
      "source": [
        "##Training del modello bidirezionale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nLpqqVH6U4mG",
        "outputId": "d68bbfae-b4ed-4daf-cd84-2304d777dcf9"
      },
      "source": [
        "history_bidirectional = model_bidirectional.fit(xs, ys, epochs=50, verbose=1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "380/380 [==============================] - 1s 4ms/sample - loss: 1.4921 - accuracy: 0.2395\n",
            "Epoch 2/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.4117 - accuracy: 0.2579\n",
            "Epoch 3/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.3988 - accuracy: 0.2842\n",
            "Epoch 4/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.4088 - accuracy: 0.2579\n",
            "Epoch 5/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 1.3851 - accuracy: 0.3158\n",
            "Epoch 6/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.3659 - accuracy: 0.3237\n",
            "Epoch 7/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.3498 - accuracy: 0.3553\n",
            "Epoch 8/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.3179 - accuracy: 0.3763\n",
            "Epoch 9/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.3023 - accuracy: 0.3921\n",
            "Epoch 10/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.2703 - accuracy: 0.4211\n",
            "Epoch 11/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.1874 - accuracy: 0.4474\n",
            "Epoch 12/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.1200 - accuracy: 0.4789\n",
            "Epoch 13/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.9875 - accuracy: 0.5763\n",
            "Epoch 14/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.8597 - accuracy: 0.6342\n",
            "Epoch 15/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.6739 - accuracy: 0.7447\n",
            "Epoch 16/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.5439 - accuracy: 0.8053\n",
            "Epoch 17/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.3623 - accuracy: 0.8763\n",
            "Epoch 18/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.2409 - accuracy: 0.9237\n",
            "Epoch 19/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.2006 - accuracy: 0.9289\n",
            "Epoch 20/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1732 - accuracy: 0.9368\n",
            "Epoch 21/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1377 - accuracy: 0.9395\n",
            "Epoch 22/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1394 - accuracy: 0.9368\n",
            "Epoch 23/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1257 - accuracy: 0.9395\n",
            "Epoch 24/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1235 - accuracy: 0.9474\n",
            "Epoch 25/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1149 - accuracy: 0.9395\n",
            "Epoch 26/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1115 - accuracy: 0.9342\n",
            "Epoch 27/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1176 - accuracy: 0.9395\n",
            "Epoch 28/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1249 - accuracy: 0.9316\n",
            "Epoch 29/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1187 - accuracy: 0.9316\n",
            "Epoch 30/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1088 - accuracy: 0.9368\n",
            "Epoch 31/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1096 - accuracy: 0.9447\n",
            "Epoch 32/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1157 - accuracy: 0.9368\n",
            "Epoch 33/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1067 - accuracy: 0.9526\n",
            "Epoch 34/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1166 - accuracy: 0.9368\n",
            "Epoch 35/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1008 - accuracy: 0.9395\n",
            "Epoch 36/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1065 - accuracy: 0.9395\n",
            "Epoch 37/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1062 - accuracy: 0.9368\n",
            "Epoch 38/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1037 - accuracy: 0.9368\n",
            "Epoch 39/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1064 - accuracy: 0.9447\n",
            "Epoch 40/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1073 - accuracy: 0.9395\n",
            "Epoch 41/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1027 - accuracy: 0.9368\n",
            "Epoch 42/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1014 - accuracy: 0.9474\n",
            "Epoch 43/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1059 - accuracy: 0.9368\n",
            "Epoch 44/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1064 - accuracy: 0.9395\n",
            "Epoch 45/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1107 - accuracy: 0.9395\n",
            "Epoch 46/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1053 - accuracy: 0.9368\n",
            "Epoch 47/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.1045 - accuracy: 0.9368\n",
            "Epoch 48/50\n",
            "380/380 [==============================] - 1s 3ms/sample - loss: 0.0990 - accuracy: 0.9368\n",
            "Epoch 49/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1025 - accuracy: 0.9342\n",
            "Epoch 50/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 0.1005 - accuracy: 0.9421\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0Q2oHY_E7oH"
      },
      "source": [
        "##Modello RNN Unidirezionale(LSTM)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVgPhqxqFDKD"
      },
      "source": [
        "def unidirectional_rnn(vocab, max_seq_len):\n",
        "  print(\"Unidirectional LSTM MODEL\")\n",
        "  model_unidirectional = Sequential(name=\"metagenomic prediction unidirectional\")\n",
        "  model_unidirectional.add(Embedding(vocab, 110, input_length=max_seq_len, name=\"input_layer\"))\n",
        "  model_unidirectional.add(LSTM(150, go_backwards=False))\n",
        "  model_unidirectional.add(Dense(vocab, activation='softmax', name=\"output_layer\"))\n",
        "\n",
        "  adam = Adam(lr=0.01)\n",
        "  model_unidirectional.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])\n",
        "\n",
        "  return model_unidirectional"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o6rwZa3-FHej",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38aa766e-9934-4f90-9aab-e5d37006e54b"
      },
      "source": [
        "model_unidirectional = unidirectional_rnn(vocab_length, max_sequence_len-1)\n",
        "model_unidirectional.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Unidirectional LSTM MODEL\n",
            "Model: \"metagenomic prediction unidirectional\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_layer (Embedding)      (None, 19, 110)           550       \n",
            "_________________________________________________________________\n",
            "unified_lstm_3 (UnifiedLSTM) (None, 150)               156600    \n",
            "_________________________________________________________________\n",
            "output_layer (Dense)         (None, 5)                 755       \n",
            "=================================================================\n",
            "Total params: 157,905\n",
            "Trainable params: 157,905\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6IJzIqbFoxN"
      },
      "source": [
        "##Training del modello unidirezionale"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11HoxjlsFL5V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "405bd3a0-1046-4258-9432-8933c3044078"
      },
      "source": [
        "history_unidirectional = model_unidirectional.fit(xs, ys, epochs=50, verbose=1)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "380/380 [==============================] - 1s 2ms/sample - loss: 1.5715 - accuracy: 0.2474\n",
            "Epoch 2/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.4132 - accuracy: 0.2395\n",
            "Epoch 3/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3968 - accuracy: 0.2395\n",
            "Epoch 4/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3846 - accuracy: 0.2842\n",
            "Epoch 5/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3826 - accuracy: 0.3026\n",
            "Epoch 6/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3787 - accuracy: 0.3079\n",
            "Epoch 7/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3940 - accuracy: 0.2289\n",
            "Epoch 8/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3951 - accuracy: 0.3026\n",
            "Epoch 9/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3838 - accuracy: 0.2974\n",
            "Epoch 10/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3669 - accuracy: 0.2974\n",
            "Epoch 11/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3650 - accuracy: 0.3132\n",
            "Epoch 12/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3598 - accuracy: 0.3500\n",
            "Epoch 13/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3498 - accuracy: 0.3342\n",
            "Epoch 14/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.3382 - accuracy: 0.3579\n",
            "Epoch 15/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3341 - accuracy: 0.3553\n",
            "Epoch 16/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.3027 - accuracy: 0.3658\n",
            "Epoch 17/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.2849 - accuracy: 0.3921\n",
            "Epoch 18/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.2444 - accuracy: 0.4184\n",
            "Epoch 19/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.1943 - accuracy: 0.4684\n",
            "Epoch 20/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 1.1270 - accuracy: 0.5079\n",
            "Epoch 21/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 1.0206 - accuracy: 0.5421\n",
            "Epoch 22/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.8842 - accuracy: 0.6368\n",
            "Epoch 23/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.7209 - accuracy: 0.7184\n",
            "Epoch 24/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.5628 - accuracy: 0.7947\n",
            "Epoch 25/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.4113 - accuracy: 0.8553\n",
            "Epoch 26/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.2813 - accuracy: 0.9105\n",
            "Epoch 27/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.2026 - accuracy: 0.9342\n",
            "Epoch 28/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1729 - accuracy: 0.9263\n",
            "Epoch 29/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1484 - accuracy: 0.9342\n",
            "Epoch 30/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.1366 - accuracy: 0.9395\n",
            "Epoch 31/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1273 - accuracy: 0.9395\n",
            "Epoch 32/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1164 - accuracy: 0.9447\n",
            "Epoch 33/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1190 - accuracy: 0.9447\n",
            "Epoch 34/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1135 - accuracy: 0.9368\n",
            "Epoch 35/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1095 - accuracy: 0.9421\n",
            "Epoch 36/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1059 - accuracy: 0.9395\n",
            "Epoch 37/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.1105 - accuracy: 0.9368\n",
            "Epoch 38/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1054 - accuracy: 0.9421\n",
            "Epoch 39/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1038 - accuracy: 0.9474\n",
            "Epoch 40/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.1073 - accuracy: 0.9421\n",
            "Epoch 41/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1032 - accuracy: 0.9474\n",
            "Epoch 42/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.1042 - accuracy: 0.9447\n",
            "Epoch 43/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1052 - accuracy: 0.9395\n",
            "Epoch 44/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1054 - accuracy: 0.9421\n",
            "Epoch 45/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1039 - accuracy: 0.9447\n",
            "Epoch 46/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1030 - accuracy: 0.9395\n",
            "Epoch 47/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1085 - accuracy: 0.9342\n",
            "Epoch 48/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.1024 - accuracy: 0.9368\n",
            "Epoch 49/50\n",
            "380/380 [==============================] - 1s 1ms/sample - loss: 0.1053 - accuracy: 0.9289\n",
            "Epoch 50/50\n",
            "380/380 [==============================] - 0s 1ms/sample - loss: 0.0998 - accuracy: 0.9342\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kjVVsF1rGo-T"
      },
      "source": [
        "##Plot curva di Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZidmnc-U79N"
      },
      "source": [
        "def plot_graphs(history, string, name_model):\n",
        "  title = \"Visualizzazione della curva di Training del modello \" + name_model\n",
        "  plt.suptitle(title)\n",
        "  plt.plot(history.history[string])\n",
        "  plt.xlabel(\"Epoche\")\n",
        "  plt.ylabel(ylabel=\"Accuratezza\")\n",
        "  plt.show()"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "tt-T68mJU98S",
        "outputId": "19a29fe7-3b4d-409b-9e45-161fd6de1c00"
      },
      "source": [
        "plot_graphs(history_bidirectional, 'accuracy', \"bidirezionale\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nc16N7CXG6dM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "77423730-5771-45c0-d55f-3e5c53d3ed70"
      },
      "source": [
        "plot_graphs(history_unidirectional, 'accuracy', \"unidirezionale\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfiDYGL-gxHo"
      },
      "source": [
        "def make_prediction(next_word_len, tokenizer, model, text_to_predict, j):\n",
        "\tfor _ in range(next_word_len):\n",
        "\t\ttoken_list = tokenizer.texts_to_sequences([text_to_predict])[0]\n",
        "\t\ttoken_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')\n",
        "\t\tpredicted = model.predict_classes(token_list, verbose=0)\n",
        "\t\toutput_word = \"\"\n",
        "\t\tfor word, index in tokenizer.word_index.items():\n",
        "\t\t\tif index == predicted:\n",
        "\t\t\t\toutput_word = word\n",
        "\t\t\t\tbreak\n",
        "\t\ttext_to_predict += \"\" + output_word \n",
        "\t\tj += \"\" + output_word \n",
        "\t\tnew_sequence = \"\".join(j) #isolo la nuova sequenza generata\n",
        "\treturn new_sequence"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0ienvjiiw8_"
      },
      "source": [
        "def make_upper(text, index):\n",
        "  s = \"\"\n",
        "  for i in text:\n",
        "    if i == \"a\":\n",
        "      s = \"A\"\n",
        "    if i == \"c\":\n",
        "      s = \"C\"\n",
        "    if i == \"g\":\n",
        "      s = \"G\"\n",
        "    if i == \"t\":\n",
        "      s = \"T\"\n",
        "    index += \"\" + s\n",
        "    text = \"\".join(index)\n",
        "  return text"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "143wOQzGFQlL"
      },
      "source": [
        "## Predizione partendo da metà sequenza non sanificata appartenente al dataset di partenza utilizzato per il training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RvJrUsGH3nL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f7121ed-2e4d-4f77-c632-1b78b75a7091"
      },
      "source": [
        "#predizione con metà sequenza esistente\n",
        "existing_dna_seq = dna_not_sanitized[1]\n",
        "half_seq_length = int(seq_length / 2)\n",
        "\n",
        "\n",
        "text_to_predict = existing_dna_seq[0:10]\n",
        "next_words = half_seq_length\n",
        "new_sequence = []\n",
        "increase_string = \"\"\n",
        "\n",
        "new_sequence = make_prediction(next_words, tokenizer, model_unidirectional, text_to_predict, increase_string)\n",
        "\n",
        "print(make_upper(new_sequence, \"\")) #concatenazione tra sequenza passata in input e la nuova generata"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CCCCCCCCTC\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95KCZvvAHxdo",
        "outputId": "59fb1f26-914d-4fe9-fcfc-12718786a8bd"
      },
      "source": [
        "print(\"Dna not sanitized in pos 0:\" , dna_not_sanitized[1])\n",
        "print(\"Dna sanitized in pos 0:    \" ,dna_sanitized[1])\n",
        "print(vocab)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dna not sanitized in pos 0: CTCGTGTAACTGCCACTTCA\n",
            "Dna sanitized in pos 0:     ATGTTGAGTGCGCATGACAC\n",
            "{'c': 1, 'a': 2, 'g': 3, 't': 4}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybE9QpyqGCHM"
      },
      "source": [
        "##Predizione partendo da metà sequenza randomica"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ONs5spxg1rp",
        "outputId": "cb8f0d28-ce85-4c86-a29e-a687adc87615"
      },
      "source": [
        "#genero metà sequenza di dna non sanificato\n",
        "half_seq_length = int(seq_length / 2)\n",
        "half_sequence = [random.choice(bases_list) for _ in range(half_seq_length)]\n",
        "half_sequence = \"\".join(half_sequence)\n",
        "\n",
        "print(half_sequence)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GCACCTAACT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cZx5OkTB-5_R",
        "outputId": "b0d76205-e7d9-43e5-aff7-ef62e6e62ce0"
      },
      "source": [
        "text_to_predict = half_sequence\n",
        "next_words = half_seq_length\n",
        "new_sequence = []\n",
        "increase_string = \"\"\n",
        "\n",
        "new_sequence = make_prediction(next_words, tokenizer, model_bidirectional, text_to_predict, increase_string)\n",
        "#new_sequence , text_to_predict = make_prediction(next_words, tokenizer, model, text_to_predict, increase_string)\n",
        "\n",
        "#print(text_to_predict) #concatenazione tra sequenza passata in input e la nuova generata\n",
        "print(make_upper(new_sequence,\"\"))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CTCGCACAGG\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ievTU1NIGin9"
      },
      "source": [
        "##Predizione di una sequenza costituita da 440 caratteri."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "esbyPwmy2ac9"
      },
      "source": [
        " Come si procede? Si scompone la sequenza lunga 440 in 44 sottosequenze, ognuna lunga 10 caratteri (questo perchè il modello risulta accurato per la predizione di stringhe lunghe 10 caratteri); poi per ognuna si genera la sequenza sanificata; infine si costruisce la sequenza finale tenendo conto dell'ordine di partenza relativo alle sottosequenze.\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scHVDYYgaVdv"
      },
      "source": [
        "rnd_max_len = 440\n",
        "sub_seq_len = 10\n",
        "n_sub_seq = 44\n",
        "not_sanitized_max = []\n",
        "sub_seq_data = []\n",
        "\n",
        "random_seq_generation(1, rnd_max_len, not_sanitized_max)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zECPPK0d-vLo"
      },
      "source": [
        "sequence = not_sanitized_max[0]"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nEqFPcbb_BOG"
      },
      "source": [
        "#grazie a questa funzione si riesce a dividere l'intera sequenza di lunghezza 440 in 44 sottosequenze, ognuna di 10 caratteri; si crea, quindi, una lista di sottosequenze\n",
        "def sub_seq_data_generator(entire_sequence, sub_len, n_sub_seq):\n",
        "  for i in range(n_sub_seq):\n",
        "    sub_seq = entire_sequence[i*sub_len:(i+1)*sub_len]\n",
        "    sub_seq_data.append(sub_seq)\n",
        "\n",
        "  return sub_seq_data"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c4_b6VhjCFHd"
      },
      "source": [
        "sub_seq_data = sub_seq_data_generator(sequence, sub_seq_len, n_sub_seq)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks5g6QXOHkWx"
      },
      "source": [
        "sanitized_sub_seq_data = []\n",
        "increase_sequence = \"\"\n",
        "\n",
        "for i in range(n_sub_seq):\n",
        "  sanitized_sub_seq_data.append(make_prediction(next_words, tokenizer, model_bidirectional, sub_seq_data[i], increase_sequence))"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MgITChfEO9Jm"
      },
      "source": [
        "sanitized_max = \"\"\n",
        "\n",
        "for i in range(n_sub_seq):\n",
        "  sanitized_max += sanitized_sub_seq_data[i]"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nqaslNI_1ww7",
        "outputId": "e627bff8-ff5a-4e9d-baaa-036199c37612"
      },
      "source": [
        "print(\"Sequenza sanificata    : \" , make_upper(sanitized_max, \"\"))\n",
        "print(\"Sequenza non sanificata: \" , sequence)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sequenza sanificata    :  GATTTAGCGCCATCACCCCCCGCTCACCTTGCACACCCTCTCATCATCACCACACACATGTCTTTCGAGGATATCGCCCAGACGCGTCAGCAGACTCCTCAACCCCCCCGCTCTCAGCGCTGCGATCTACTAACGCACCCCTCATTCGCAAACGCATTACCATCACCCCCCACCCCCCCTTCTCTACGGACCCCTCATCAACTCTCTCTAATGCGCACCCGATATCGTCATCATTCGCACCGCATTCATAGTGCGATCGCGACCCCTGTCCCCCCCCCCTTATCCTCAGGGACGCTTAGGACATTCACGCACGTAATCACCGCACCCCCCCCCCCTGTATTTAGCGCAGGGAAATCAGCCAGGACCCCCCCGCACCCCCCTACCCCCCCCCTTTGACGATACGTCACTTTTCATCAGCATTTAGCCAATCAGCGACGTTC\n",
            "Sequenza non sanificata:  GCGTACTTGCCCGGTAGGATTCGCCTCATTCAGGTGGTCACGTGCGCCGGTTTCACTGCGACCAGGCTCCTGCAGTAGATTAACATTGTCTCATTTAAACTTTGGGAGGCCCCTCGGTCTCTGAGATCTGGGAGGCGGATATAAGCCGCTCTCGCCGCTTCCCGGGATGTTTATTTAATAGCCCTCCCTGCTAAAGACAATATTGCGTGCAGTAAATTGTCCGAATACACGGCAGGGTCAGCGTATTCTATATAACTTGTTTTCTGTGCCGAACTTTGATAGGCAACCTGGGTGTCTCGACGTAGAATCCCTTCCGATAAGAGTTATATACGCACCAAACGGACACAAGCCTATGCTAAGGAGGGGATGGAGATGTGGTTCCTCATTATTACATTTAACAACCTCTCAGATCCCGCAAAACTAGTACCCTCAAGAAAGTT\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}