rfam_stats_baseline.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "-Z4e8KnZoD-W"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xBiuvS9QoXA5"
      },
      "outputs": [],
      "source": [
        "#expects a tab delimited csv file where first column is header and second column is the sequence itself\n",
        "# to generate a csvnfile like this from fasta on the command line use this ruby script:\n",
        "#ruby -e 'first_line = true; while line = STDIN.gets; line.chomp!; if line =~ /^>/; puts unless first_line; print line[1..-1]; print \"\\t\"; else; print line; end; first_line = false; end; puts' < sequence.fasta > virus_mrna.fasta.csv\n",
        "def RfamStats (filename):\n",
        "  RF = pd.read_csv(filename, header=None, delimiter = '\\t')\n",
        "  RF['label'] = filename\n",
        "  rf_len = (lambda x:[len(str(i)) for i in x])(RF[1].tolist())\n",
        "  RF['len'] = rf_len\n",
        "  RF = RF[RF['len'] < 3000]\n",
        "  a = {\n",
        "      'fam' : filename,\n",
        "      'num_sequences' :len(RF[1].tolist()),\n",
        "       'max' : max(RF['len']),\n",
        "       'min' :min(RF['len']),\n",
        "       'mean' : np.mean(RF['len']),\n",
        "       'std' : np.std(RF['len']),\n",
        "       'median' : np.median(RF['len']),\n",
        "  }\n",
        "  print('number of sequences')\n",
        "  print(len(RF[1].tolist()))\n",
        "  print('_________')\n",
        "  print('max')\n",
        "  print(max(RF['len']))\n",
        "  print('_________')\n",
        "  print('min')\n",
        "  print(min(RF['len']))\n",
        "  print('_________')\n",
        "  print('mean')\n",
        "  print(np.mean(RF['len']))\n",
        "  print('_________')\n",
        "  print('median')\n",
        "  print(np.median(RF['len']))\n",
        "  print('_________')\n",
        "  print('std')\n",
        "  print(np.std(RF['len']))\n",
        "  print('_________')\n",
        "  print('hist of length distribution')\n",
        "  RF['len'].hist(bins=50)\n",
        "  return a, RF\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VVn5IKTdiLio",
        "outputId": "a7bea500-c35c-49eb-e2c3-3ebaba85a4e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of sequences\n",
            "14465\n",
            "_________\n",
            "max\n",
            "86\n",
            "_________\n",
            "min\n",
            "59\n",
            "_________\n",
            "mean\n",
            "65.97663325267888\n",
            "_________\n",
            "median\n",
            "66.0\n",
            "_________\n",
            "std\n",
            "0.8139002402414047\n",
            "_________\n",
            "hist of length distribution\n",
            "number of sequences\n",
            "11069\n",
            "_________\n",
            "max\n",
            "2994\n",
            "_________\n",
            "min\n",
            "4\n",
            "_________\n",
            "mean\n",
            "1062.877586051134\n",
            "_________\n",
            "median\n",
            "981.0\n",
            "_________\n",
            "std\n",
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            "_________\n",
            "hist of length distribution\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
            "hist of length distribution\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
            "hist of length distribution\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
            "std\n",
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            "_________\n",
            "hist of length distribution\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
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            "_________\n",
            "median\n",
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            "_________\n",
            "std\n",
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            "_________\n",
            "hist of length distribution\n",
            "number of sequences\n",
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            "_________\n",
            "max\n",
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            "median\n",
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            "_________\n",
            "std\n",
            "8.6891205818547\n",
            "_________\n",
            "hist of length distribution\n",
            "number of sequences\n",
            "21390\n",
            "_________\n",
            "max\n",
            "2999\n",
            "_________\n",
            "min\n",
            "41\n",
            "_________\n",
            "mean\n",
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            "_________\n",
            "median\n",
            "711.0\n",
            "_________\n",
            "std\n",
            "660.042949278589\n",
            "_________\n",
            "hist of length distribution\n",
            "number of sequences\n",
            "10269\n",
            "_________\n",
            "max\n",
            "29\n",
            "_________\n",
            "min\n",
            "25\n",
            "_________\n",
            "mean\n",
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            "_________\n",
            "median\n",
            "28.0\n",
            "_________\n",
            "std\n",
            "0.04413000773047282\n",
            "_________\n",
            "hist of length distribution\n",
            "number of sequences\n",
            "47589\n",
            "_________\n",
            "max\n",
            "432\n",
            "_________\n",
            "min\n",
            "49\n",
            "_________\n",
            "mean\n",
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            "_________\n",
            "median\n",
            "106.0\n",
            "_________\n",
            "std\n",
            "8.066432703371325\n",
            "_________\n",
            "hist of length distribution\n"
          ]
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              "      <td>RF00026.fa.csv</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>476299 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1231296d-4cd2-45b7-810f-0de3b2c5eb68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1231296d-4cd2-45b7-810f-0de3b2c5eb68 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1231296d-4cd2-45b7-810f-0de3b2c5eb68');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                       0  \\\n",
              "5112   CM000672.2/85673226-85673291 Homo sapiens chro...   \n",
              "4006   CM000571.1/45077396-45077461 Pongo abelii chro...   \n",
              "12781  CM000314.3/122223259-122223323 Pan troglodytes...   \n",
              "819    ALWS01073947.1/14042-14108 Pteropus alecto con...   \n",
              "12518  CM000296.1/51867683-51867618 Macaca mulatta ch...   \n",
              "...                                                  ...   \n",
              "19726  CM000671.2/87998335-87998229 Homo sapiens chro...   \n",
              "23349  ALAR01026327.1/14565-14461 Tupaia chinensis co...   \n",
              "36912  GL010028.1/15448774-15448879 Loxodonta african...   \n",
              "4891   CM000872.1/65838925-65838823 Callithrix jacchu...   \n",
              "16931  CM000566.1/45928748-45928642 Pongo abelii chro...   \n",
              "\n",
              "                                                       1           label  len  \n",
              "5112   TCTTACAGATGAGCAAAGAAAGTGGCTTCTTGAGATAGAATATACT...  RF00994.fa.csv   66  \n",
              "4006   TCTTATGGAAGAACAAAGAAAGTGGTTTCTTGAGATGGAATCTACT...  RF00994.fa.csv   66  \n",
              "12781  TCCTACGGATGAGCAAAGAAGTGGCTTCTTGAGATGGAATCTACTC...  RF00994.fa.csv   65  \n",
              "819    TCTTAAGGATGAGCAAAGAAAGTGGTTTTTGAGATGGAATCTTCTC...  RF00994.fa.csv   67  \n",
              "12518  TCTTATGGATAAGCAAAGAAAGTGGTTTGTTGAGATATAATCTACT...  RF00994.fa.csv   66  \n",
              "...                                                  ...             ...  ...  \n",
              "19726  GTGCTTGCTTCTGCAGCACATATACTAAAACTGGAACGATACAGAG...  RF00026.fa.csv  107  \n",
              "23349  GTGCTTGCTTCAGCAGCACATATACTAAAATTGGAAAGATACAGAG...  RF00026.fa.csv  105  \n",
              "36912  AGTGCTTGCACCGGCACCACATATAAAAATTGGAACCATACAGACA...  RF00026.fa.csv  106  \n",
              "4891   TTGCTCTGACAGCACATATCCTAAAATTGGATACATAGAGAGAAGA...  RF00026.fa.csv  103  \n",
              "16931  GTGCTCACTTTGGCAGCATATACGCTAAAGCTGGAATGATACAGAG...  RF00026.fa.csv  107  \n",
              "\n",
              "[476299 rows x 4 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df_list = []\n",
        "df_RF_list = []\n",
        "for file in os.listdir('./'):\n",
        "  if file.endswith(\".csv\"):\n",
        "    a, RF = RfamStats(file)\n",
        "    df_list.append(a)\n",
        "    df_RF_list.append(RF.sample(n = min(RF[1].shape[0], 20000), replace = False))\n",
        "pd.DataFrame(df_list)\n",
        "big_RF = pd.concat(df_RF_list)\n",
        "big_RF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RQq6YovHEqd7"
      },
      "outputs": [],
      "source": [
        "big_RF.reindex\n",
        "big_RF = big_RF.reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QhNG4jNhb3qG"
      },
      "outputs": [],
      "source": [
        "big_RF_512 = big_RF[big_RF.len <= 512]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sk3JyAT8goEY"
      },
      "outputs": [],
      "source": [
        "big_RF_512.to_csv('RFAM_512_viral_subset_no_replacement.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "qeb4B5BNc9tp",
        "outputId": "209c43a0-ff96-4dfc-f1fc-1e959d3da3ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6033d88f90>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "big_RF_512['label'].value_counts().plot(kind='bar')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "XlHBHBUj8Q8F",
        "outputId": "4d672eee-8fd8-4402-9dad-9c9e13664aaf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.histplot(data=big_RF_512, x=\"len\", hue=\"label\", bins = 50)\n",
        "sns.move_legend(ax, \"upper left\", bbox_to_anchor=(1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "NVTo1jhceVhX",
        "outputId": "d1d626d0-73ca-48ae-e7a4-2dc1f6ed04d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of sequences\n",
            "418305\n",
            "_________\n",
            "max\n",
            "512\n",
            "_________\n",
            "min\n",
            "4\n",
            "_________\n",
            "mean\n",
            "132.48350127299457\n",
            "_________\n",
            "median\n",
            "112.0\n",
            "_________\n",
            "std\n",
            "83.67019346166627\n",
            "_________\n",
            "hist of length distribution\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f603465a410>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "RF = big_RF_512\n",
        "print('number of sequences')\n",
        "print(len(RF[1].tolist()))\n",
        "print('_________')\n",
        "print('max')\n",
        "print(max(RF['len']))\n",
        "print('_________')\n",
        "print('min')\n",
        "print(min(RF['len']))\n",
        "print('_________')\n",
        "print('mean')\n",
        "print(np.mean(RF['len']))\n",
        "print('_________')\n",
        "print('median')\n",
        "print(np.median(RF['len']))\n",
        "print('_________')\n",
        "print('std')\n",
        "print(np.std(RF['len']))\n",
        "print('_________')\n",
        "print('hist of length distribution')\n",
        "RF['len'].hist(bins=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AGRxwDFsKI18"
      },
      "outputs": [],
      "source": [
        "big_RF.to_csv('RF_20k_dataset_noreplacement.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pL35ZVmTKrT5",
        "outputId": "c3714ce9-fe2c-4f66-9755-6699e35459fe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "RF_more_100 = big_RF[big_RF['len'] >= 100]\n",
        "len(list(set(RF_more_100['label'].tolist())))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QvRtJuGJMWzU"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "def select(x):\n",
        "  inte = random.randint(0, len(x) - 300)\n",
        "  return x[inte:inte+300]\n",
        "\n",
        "def curate_dataset(RF):\n",
        "  df_list = []\n",
        "  for i in set(RF.label.tolist()):\n",
        "    subset = RF[RF.label == i]\n",
        "    subset.loc[subset.len > 300, 1] = subset.loc[subset.len > 300, 1].apply(select)\n",
        "    df_list.append(subset)\n",
        "    \n",
        "  return pd.concat(df_list)\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYzie7sIMR69",
        "outputId": "fcd1c3f5-cfba-4d19-d615-047c41afca4e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_column(ilocs[0], value, pi)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2999\n",
            "300\n"
          ]
        }
      ],
      "source": [
        "RF_curated = curate_dataset(RF_more_100)\n",
        "print(max(RF_curated.len.tolist()))\n",
        "RF_curated.len = (lambda x:[len(str(i)) for i in x])(RF_curated[1].tolist())\n",
        "print(max(RF_curated.len.tolist()))\n",
        "RF_curated.to_csv('rna_RF_curated_len.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzC9NfH2xBlQ"
      },
      "source": [
        "# LSTM MODEL"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "f-TvMfazEoNH",
        "outputId": "ae443892-06f0-4bdb-d3f4-1837dabb716b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0  index                                                  0  \\\n",
              "0                0   5112  CM000672.2/85673226-85673291 Homo sapiens chro...   \n",
              "1                1   4006  CM000571.1/45077396-45077461 Pongo abelii chro...   \n",
              "2                2  12781  CM000314.3/122223259-122223323 Pan troglodytes...   \n",
              "3                3    819  ALWS01073947.1/14042-14108 Pteropus alecto con...   \n",
              "4                4  12518  CM000296.1/51867683-51867618 Macaca mulatta ch...   \n",
              "...            ...    ...                                                ...   \n",
              "418300      476294  19726  CM000671.2/87998335-87998229 Homo sapiens chro...   \n",
              "418301      476295  23349  ALAR01026327.1/14565-14461 Tupaia chinensis co...   \n",
              "418302      476296  36912  GL010028.1/15448774-15448879 Loxodonta african...   \n",
              "418303      476297   4891  CM000872.1/65838925-65838823 Callithrix jacchu...   \n",
              "418304      476298  16931  CM000566.1/45928748-45928642 Pongo abelii chro...   \n",
              "\n",
              "                                                        1           label  len  \n",
              "0       TCTTACAGATGAGCAAAGAAAGTGGCTTCTTGAGATAGAATATACT...  RF00994.fa.csv   66  \n",
              "1       TCTTATGGAAGAACAAAGAAAGTGGTTTCTTGAGATGGAATCTACT...  RF00994.fa.csv   66  \n",
              "2       TCCTACGGATGAGCAAAGAAGTGGCTTCTTGAGATGGAATCTACTC...  RF00994.fa.csv   65  \n",
              "3       TCTTAAGGATGAGCAAAGAAAGTGGTTTTTGAGATGGAATCTTCTC...  RF00994.fa.csv   67  \n",
              "4       TCTTATGGATAAGCAAAGAAAGTGGTTTGTTGAGATATAATCTACT...  RF00994.fa.csv   66  \n",
              "...                                                   ...             ...  ...  \n",
              "418300  GTGCTTGCTTCTGCAGCACATATACTAAAACTGGAACGATACAGAG...  RF00026.fa.csv  107  \n",
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              "418304  GTGCTCACTTTGGCAGCATATACGCTAAAGCTGGAATGATACAGAG...  RF00026.fa.csv  107  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "big_RF_512 = pd.read_csv('RFAM_512_viral_subset_no_replacement.csv')\n",
        "big_RF_512"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e5z6f8QVe6Ah",
        "outputId": "a08c1eb3-0729-4fef-dbea-39f92f924c67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "416249\n",
            "416249\n",
            "2056\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "  q = 0\n",
        "  keep = []\n",
        "  sequences = []\n",
        "  for j in list(set(big_RF_512['label'].tolist())):\n",
        "    rf_subset = big_RF_512[big_RF_512.label == j]\n",
        "    for i in rf_subset['1'].tolist():\n",
        "      l = i.replace(\"T\", \"U\" )\n",
        "      l = i.replace(\"N\", \"\" )\n",
        "      l = i.replace(\"t\", \"u\" )\n",
        "      l = i.replace(\"n\", \"\" )\n",
        "      x = re.findall(\"[^AUCTGauctg]\", l)\n",
        "      if x:\n",
        "        if len(x) / len(l) > 0.01:\n",
        "          q +=1\n",
        "          continue\n",
        "      sequences.append(l)\n",
        "      keep.append(i)\n",
        "\n",
        "print(len(sequences))\n",
        "print(len(keep))\n",
        "print(q)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7VeOCWfvfaeG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0244323-439a-48df-fa97-e403a0eec9e3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
        }
      ],
      "source": [
        "big_RF_512 = big_RF_512[big_RF_512['1'].isin(keep)]\n",
        "big_RF_512['corrected_sequences'] = sequences"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NzF3CrhVw9Lh"
      },
      "outputs": [],
      "source": [
        "#get kmers of each sequence \n",
        "\n",
        "def getKmers(sequence, size):\n",
        "    return [sequence[x:x+size].upper() for x in range(len(sequence) - size + 1)]\n",
        "\n",
        "kmer = 6\n",
        "full_list = []\n",
        "for j in list(set(big_RF_512['label'].tolist())):\n",
        "  rf_subset = big_RF_512[big_RF_512.label == j]\n",
        "  intr_texts = [' '.join(getKmers(i, kmer)) for i in rf_subset['corrected_sequences'].tolist()]\n",
        "  full_list.append(intr_texts)\n",
        "#depl_texts = [' '.join(getKmers(i, kmer)) for i in depl_seqs]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4AMDRCPAerjs",
        "outputId": "55adec80-a89f-4e5c-ad8e-84b89d083c1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2656"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(intr_texts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "BuDM0Rss9Yj6"
      },
      "outputs": [],
      "source": [
        "#concat and organize data\n",
        "merge_texts = full_list[0]\n",
        "labels = list(np.full(len(full_list[0]), 0))\n",
        "for j in range(len(full_list) - 1):\n",
        "  merge_texts += full_list[j+1]\n",
        "  labels += list(np.full(len(full_list[j+1]), j+1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "YttCeU09yMNu"
      },
      "outputs": [],
      "source": [
        "#one hot encoding with padding \n",
        "import numpy as np\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras_preprocessing.sequence import pad_sequences\n",
        "#merge_texts = intr_texts + depl_texts\n",
        "#labels = list(np.ones(len(intr_texts))) + list(np.zeros(len(depl_texts)))\n",
        "\n",
        "tokenizer = Tokenizer()\n",
        "tokenizer.fit_on_texts(merge_texts)\n",
        "encoded_docs = tokenizer.texts_to_sequences(merge_texts)\n",
        "max_length = max([len(s.split()) for s in merge_texts])\n",
        "X = pad_sequences(encoded_docs, maxlen = max_length, padding = 'post')\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,labels,\n",
        "                                                 test_size=0.20,random_state=42)\n",
        "vocab_size = len(tokenizer.word_index) + 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOjNuRYdebxb",
        "outputId": "02a22ae6-23cf-4838-cf73-43ea295fbcd3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(332999, 507)\n",
            "11728\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(vocab_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MRAetSlOyRfy",
        "outputId": "9e5736ca-616a-42ee-d379-26614e832c0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, None, 100)         1172800   \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 200)              160800    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               20100     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 30)                3030      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,356,730\n",
            "Trainable params: 1,356,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# LSTM MODEL\n",
        "from keras.models import Sequential\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import SGD, Adam, Adadelta, RMSprop\n",
        "from keras.layers import Embedding, LSTM, Dense, Bidirectional\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 100))\n",
        "model.add(Bidirectional(LSTM(100,dropout=0.2, recurrent_dropout=0.2)))\n",
        "model.add(Dense(100, activation = 'relu'))\n",
        "model.add(Dense(30, activation = 'softmax'))\n",
        "\n",
        "epochs = 3\n",
        "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "checkpoint=ModelCheckpoint(\"LSTM.weights.best.hdf5\", monitor='val_acc',verbose = 1,\n",
        "                           save_best_only = True, mode = 'max')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8_8Q5YFvCHqw"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "type(X_train)\n",
        "X_train = np.array(X_train)\n",
        "y_train = np.array(y_train)\n",
        "y_train = to_categorical(y_train, 30)\n",
        "y_test = to_categorical(y_test, 30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WzEIqWeC80b",
        "outputId": "f6ac8458-f574-44fa-d07d-0767d9133a13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(332999, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "y_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjkaF_JWyWNb",
        "outputId": "a97c95bb-502e-416d-fd40-d279bef5dda6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            " 278/4163 [=>............................] - ETA: 3:30:48 - loss: 1.8196 - accuracy: 0.4743"
          ]
        }
      ],
      "source": [
        "#training and testing\n",
        "import matplotlib.pyplot as plt\n",
        "epochs = 5\n",
        "batch_size = 64\n",
        "history = model.fit(X_train, y_train, \n",
        "                    epochs = epochs, verbose = 1, validation_split = 0.2, \n",
        "                    batch_size = batch_size, shuffle = True, \n",
        "                    callbacks = [EarlyStopping(monitor='val_loss', patience=3, min_delta=0.0001)])\n",
        "\n",
        "\n",
        "plt.figure(figsize=(20,15))\n",
        "plt.plot(history.history['loss']); plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss', fontsize = 20)\n",
        "plt.ylabel('Loss', fontsize = 20); plt.xlabel('Epoch', fontsize = 20)\n",
        "plt.legend(['Train', 'Validation'], fontsize = 20)\n",
        "\n",
        "plt.figure(figsize=(20,15))\n",
        "plt.plot(history.history['acc']); plt.plot(history.history['val_acc'])\n",
        "plt.title('Model Accuracy', fontsize = 20)\n",
        "plt.ylabel('Accuracy', fontsize = 20); plt.xlabel('Epoch', fontsize = 20)\n",
        "plt.legend(['Train', 'Validation'], fontsize = 20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ii1AKD_2yg0O"
      },
      "outputs": [],
      "source": [
        "#"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}