bert_based_social_mood_index.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SL7CKymq2jEy"
      },
      "source": [
        "# Data collection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKC4Oo1P1Wz9"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import platform\n",
        "# Chose the Operating System\n",
        "#\n",
        "OS = platform.system()                                                               # returns 'Windows', 'Linux', etc\n",
        "\n",
        "if not os.path.exists(\"./smei_bert_dataset.zip\"):\n",
        "    #os.system('gdown --id \"1XzW5NjGD5dwYdhetb8adZMiZzXTYiE-e\" --output \"./smei_bert_dataset.zip\"')  # april, may\n",
        "    #os.system('gdown --id \"1zbpWfRaLQu4ioEu9ixOamrlc3Crr5jZC\" --output \"./smei_bert_dataset.zip\"')  # feb, march\n",
        "    os.system('gdown --id \"1wCMpEdF2TCJHXM4f5G9lm0wFh_-wgmMU\" --output \"./smei_bert_dataset.zip\"')  # jan, feb, march, april, may\n",
        "    \n",
        "    if OS == \"Linux\":\n",
        "        os.system('unzip -o -n \"./smei_bert_dataset.zip\" -d \"./\"')\n",
        "\n",
        "if not os.path.exists(\"./smei_bert_libs.zip\"):\n",
        "    os.system('gdown --id \"1GKrGZFq3kEHd7OfD4hHCwBqas40zPMG-\" --output \"./smei_bert_libs.zip\"')\n",
        "    if OS == \"Linux\":\n",
        "        os.system('unzip -o -n \"./smei_bert_libs.zip\" -d \"./\"')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tvb8Skvu1z09"
      },
      "source": [
        "# Libraries Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5LLKf761u0C",
        "outputId": "45b666e3-7c5d-4b22-af15-f5609661252f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "os.system('pip install transformers==\"3.0.0\"')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsueKYt-Xkzb"
      },
      "source": [
        "# All Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fnWpCZJGXNyJ"
      },
      "outputs": [],
      "source": [
        "import timeit\n",
        "from scipy.stats import spearmanr\n",
        "from smei_bert_libs.preprocessing import SmeiBertPreprocessing\n",
        "from smei_bert_libs.utils import print_computation_time_in_days\n",
        "import csv\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "import pandas as pd\n",
        "from torch import nn  \n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPmBbnFK4RJy"
      },
      "source": [
        "# All Globals"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kKdwoTS4Plq",
        "outputId": "4d398a16-b215-408f-c09b-a97c5316a45d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU Available:  True\n"
          ]
        }
      ],
      "source": [
        "gpu = torch.cuda.is_available()\n",
        "print(\"GPU Available: \", gpu)\n",
        "file_out_txt = './smei_bert_output.csv'\n",
        "limit = None\n",
        "limit_n_days = None\n",
        "limit_n_tweets_to_classify = False\n",
        "n_tweets_to_classify = 100\n",
        "datasetPath = './smei_bert_dataset_2020'\n",
        "fieldSeparator = ';'\n",
        "all_encoded_classes = ['-1','0','1']\n",
        "all_classes = [\"Negative\", \"Neutral\", \"Positive\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EleI65Pj70ZC"
      },
      "source": [
        "# Bert Model Definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "outputId": "79df091a-e172-42f0-ed68-04b7bbcddf9c"
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      "outputs": [
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      ],
      "source": [
        "import glob\n",
        "\n",
        "# Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"neuraly/bert-base-italian-cased-sentiment\")\n",
        "# Load the model, use .cuda() to load it on the GPU\n",
        "if gpu is True: \n",
        "  model = AutoModelForSequenceClassification.from_pretrained(\"neuraly/bert-base-italian-cased-sentiment\").cuda()\n",
        "else: \n",
        "  model = AutoModelForSequenceClassification.from_pretrained(\"neuraly/bert-base-italian-cased-sentiment\")\n",
        "\n",
        "def bert_inference(sentence, model):\n",
        "  input_ids = tokenizer.encode(sentence, add_special_tokens=True)\n",
        "\n",
        "  # Create tensor, use .cuda() to transfer the tensor to GPU\n",
        "  if gpu is True: \n",
        "    tensor = torch.tensor(input_ids).long().cuda()\n",
        "  else:\n",
        "    tensor = torch.tensor(input_ids).long()\n",
        "    \n",
        "  # Fake batch dimension\n",
        "  tensor = tensor.unsqueeze(0)\n",
        "\n",
        "  # Call the model and get the logits\n",
        "  logits, = model(tensor)\n",
        "\n",
        "  # Remove the fake batch dimension\n",
        "  logits = logits.squeeze(0)\n",
        "\n",
        "  # The model was trained with a Log Likelyhood + Softmax combined loss, hence to extract probabilities we need a softmax on top of the logits tensor\n",
        "  proba = nn.functional.softmax(logits, dim=0)\n",
        "\n",
        "  # Unpack the tensor to obtain negative, neutral and positive probabilities\n",
        "  negative, neutral, positive = proba\n",
        "\n",
        "  negative = np.round(float(negative), 3)\n",
        "  neutral = np.round(float(neutral), 3)\n",
        "  positive = np.round(float(positive), 3)\n",
        "\n",
        "  classification = np.argmax([negative, neutral, positive])\n",
        "\n",
        "  return negative, neutral, positive, classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wC5gSHUriEsu"
      },
      "source": [
        "# Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-QV9PqyaJ25",
        "outputId": "90cb3a3c-be12-42b4-f6d5-0b72a42df50f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Loading Smei Bert Data from the files...\n",
            "\n",
            "Smei Bert Data - DataSet Folder : ./smei_bert_dataset_2020\n",
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            "\n",
            "Total lines 11980348: \n",
            "\n",
            "Preprocessing Elapsed Time:  91.72158900099998\n"
          ]
        }
      ],
      "source": [
        "start_time = timeit.default_timer()\n",
        "\n",
        "file_out = open(file_out_txt, 'w')\n",
        "file_out.write(\"Current Day, Number of Positives, Number of Neutrals, Number of Negatives, Total Number, Bert Smei Index 1, Positive Probs Sum, Neutrals Probs Sum, Negatives Probs Sum, Total Probs, Bert Smei Insex 2, Normal Smei Number of Positives, Normal Smei Number of Neutrals, Normal Smei Number of Negatives, Normal Smei Total Number, Normal Smei Index 3\\n\")\n",
        "file_out.close\n",
        "i = 0\n",
        "\n",
        "sbert = SmeiBertPreprocessing(limit)\t\t\t # Test the program on a smaller Dataset - True or Full Dataset - False\t\n",
        "dataset = sbert.loadSmeiBertData(dataPath=datasetPath, fieldSeparator = fieldSeparator, limit = limit, limit_n_days = limit_n_days)\n",
        "\n",
        "end_time = timeit.default_timer()\n",
        "print(\"\\nPreprocessing Elapsed Time: \", end_time - start_time)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "I4J10xOcPoXy",
        "outputId": "764e8351-5b0c-498b-92a5-8a99b8745851"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nsentences_data = list(dataset[d][1][:,8])[0:500]\\n\\nprint(sentences_data)\\n\\nbert_negative, bert_neutral, bert_positive, classification = bert_inference(sentences_data, model)\\nprint(bert_negative)\\nprint(bert_neutral)\\nprint(bert_positive)\\n'"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''\n",
        "sentences_data = list(dataset[d][1][:,8])[0:500]\n",
        "\n",
        "print(sentences_data)\n",
        "\n",
        "bert_negative, bert_neutral, bert_positive, classification = bert_inference(sentences_data, model)\n",
        "print(bert_negative)\n",
        "print(bert_neutral)\n",
        "print(bert_positive)\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_V_hZzt5QFK"
      },
      "source": [
        "# Main Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3J4ptbwiuTG",
        "outputId": "3b8d7cca-314e-4203-bb22-e9706e8f8a8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2020-01-01   -  .....................................................................................................\n",
            "Processing:  ['2020-01-01', 17, 64, 20, 101, -0.0297029702970297, 21.008999999999986, 60.200999999999986, 19.77900000000001, 100.98899999999999, 0.012179544306805451, 27, 50, 24, 101, 0.0297029702970297]\n",
            "2020-01-02   -  .....................................................................................................\n",
            "Processing:  ['2020-01-02', 19, 74, 8, 101, 0.10891089108910891, 17.236999999999995, 74.02899999999998, 9.737000000000009, 101.00299999999999, 0.07425522014197584, 29, 46, 26, 101, 0.0297029702970297]\n",
            "2020-01-03   -  .....................................................................................................\n",
            "Processing:  ['2020-01-03', 10, 75, 16, 101, -0.0594059405940594, 10.833999999999998, 74.90399999999998, 15.256999999999994, 100.99499999999998, -0.04379424723996235, 27, 52, 22, 101, 0.04950495049504951]\n",
            "2020-01-04   -  .....................................................................................................\n",
            "Processing:  ['2020-01-04', 6, 84, 11, 101, -0.04950495049504951, 7.027000000000001, 83.16300000000003, 10.804, 100.99400000000003, -0.03739826128284847, 23, 55, 23, 101, 0.0]\n",
            "2020-01-05   -  .....................................................................................................\n",
            "Processing:  ['2020-01-05', 10, 77, 14, 101, -0.039603960396039604, 9.764, 76.22999999999998, 14.990999999999989, 100.98499999999996, -0.051760162400356406, 37, 41, 23, 101, 0.13861386138613863]\n",
            "2020-01-06   -  .....................................................................................................\n",
            "Processing:  ['2020-01-06', 10, 76, 15, 101, -0.04950495049504951, 10.126, 75.675, 15.19599999999999, 100.99699999999999, -0.05019951087656061, 23, 41, 37, 101, -0.13861386138613863]\n",
            "2020-01-07   -  .....................................................................................................\n",
            "Processing:  ['2020-01-07', 6, 71, 24, 101, -0.1782178217821782, 6.937999999999999, 72.36899999999997, 21.694, 101.00099999999998, -0.1460975633904615, 25, 57, 19, 101, 0.0594059405940594]\n",
            "2020-01-08   -  .....................................................................................................\n",
            "Processing:  ['2020-01-08', 18, 64, 19, 101, -0.009900990099009901, 18.389000000000014, 63.76899999999998, 18.839000000000002, 100.99699999999999, -0.00445557788845202, 28, 47, 26, 101, 0.019801980198019802]\n",
            "2020-01-09   -  .....................................................................................................\n",
            "Processing:  ['2020-01-09', 14, 80, 7, 101, 0.06930693069306931, 15.529999999999996, 78.42199999999997, 7.049999999999999, 101.00199999999997, 0.08395873349042593, 48, 37, 16, 101, 0.31683168316831684]\n",
            "2020-01-10   -  .....................................................................................................\n",
            "Processing:  ['2020-01-10', 14, 77, 10, 101, 0.039603960396039604, 14.381000000000002, 73.8, 12.814999999999998, 100.996, 0.015505564576814967, 27, 44, 30, 101, -0.0297029702970297]\n",
            "2020-01-11   -  .....................................................................................................\n",
            "Processing:  ['2020-01-11', 13, 76, 12, 101, 0.009900990099009901, 14.570999999999996, 75.444, 10.986999999999998, 101.002, 0.035484445852557354, 44, 32, 25, 101, 0.18811881188118812]\n",
            "2020-01-12   -  .....................................................................................................\n",
            "Processing:  ['2020-01-12', 4, 79, 18, 101, -0.13861386138613863, 5.132999999999996, 73.85499999999999, 22.018, 101.00599999999999, -0.1671682870324536, 18, 47, 36, 101, -0.1782178217821782]\n",
            "2020-01-13   -  .....................................................................................................\n",
            "Processing:  ['2020-01-13', 19, 69, 13, 101, 0.0594059405940594, 18.567000000000004, 67.724, 14.704999999999997, 100.99600000000001, 0.03823913818369051, 27, 44, 30, 101, -0.0297029702970297]\n",
            "2020-01-14   -  .....................................................................................................\n",
            "Processing:  ['2020-01-14', 11, 82, 8, 101, 0.0297029702970297, 11.823, 80.57000000000002, 8.600000000000003, 100.99300000000004, 0.03191310288831895, 37, 52, 12, 101, 0.24752475247524752]\n",
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            "Processing:  ['2020-01-15', 14, 79, 8, 101, 0.0594059405940594, 15.317, 75.69999999999996, 9.973999999999998, 100.99099999999997, 0.052905704468715065, 32, 43, 26, 101, 0.0594059405940594]\n",
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            "Processing:  ['2020-01-16', 5, 82, 14, 101, -0.0891089108910891, 6.950000000000001, 78.70799999999996, 15.336000000000002, 100.99399999999996, -0.08303463572093396, 43, 31, 27, 101, 0.15841584158415842]\n",
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            "Processing:  ['2020-01-17', 13, 79, 9, 101, 0.039603960396039604, 13.780999999999999, 77.80800000000002, 9.407000000000002, 100.99600000000002, 0.04330864588696578, 32, 48, 21, 101, 0.10891089108910891]\n",
            "2020-01-18   -  .....................................................................................................\n",
            "Processing:  ['2020-01-18', 11, 76, 14, 101, -0.0297029702970297, 11.934000000000001, 75.2, 13.866000000000003, 101.0, -0.01912871287128715, 41, 30, 30, 101, 0.10891089108910891]\n",
            "2020-01-19   -  .....................................................................................................\n",
            "Processing:  ['2020-01-19', 24, 59, 18, 101, 0.0594059405940594, 21.205000000000002, 63.245999999999995, 16.542999999999992, 100.99399999999999, 0.04616115808859943, 35, 46, 20, 101, 0.1485148514851485]\n",
            "2020-01-20   -  .....................................................................................................\n",
            "Processing:  ['2020-01-20', 14, 69, 18, 101, -0.039603960396039604, 13.611, 68.15200000000002, 19.234, 100.99700000000001, -0.05567492103725854, 29, 51, 21, 101, 0.07920792079207921]\n",
            "2020-01-21   -  .....................................................................................................\n",
            "Processing:  ['2020-01-21', 6, 83, 12, 101, -0.0594059405940594, 7.462999999999997, 80.85099999999998, 12.676999999999996, 100.99099999999997, -0.051628362923428824, 34, 46, 21, 101, 0.12871287128712872]\n",
            "2020-01-22   -  .....................................................................................................\n",
            "Processing:  ['2020-01-22', 16, 72, 13, 101, 0.0297029702970297, 15.592000000000002, 70.303, 15.110999999999997, 101.006, 0.004762093340989696, 35, 43, 23, 101, 0.1188118811881188]\n",
            "2020-01-23   -  .....................................................................................................\n",
            "Processing:  ['2020-01-23', 8, 84, 9, 101, -0.009900990099009901, 11.517999999999999, 77.10199999999995, 12.374000000000002, 100.99399999999994, -0.008475751034714972, 27, 53, 21, 101, 0.0594059405940594]\n",
            "2020-01-24   -  .....................................................................................................\n",
            "Processing:  ['2020-01-24', 9, 78, 14, 101, -0.04950495049504951, 9.683999999999996, 77.258, 14.052999999999995, 100.99499999999999, -0.04325956730531215, 35, 49, 17, 101, 0.1782178217821782]\n",
            "2020-01-25   -  .....................................................................................................\n",
            "Processing:  ['2020-01-25', 18, 70, 13, 101, 0.04950495049504951, 15.144999999999992, 71.78399999999999, 14.073999999999996, 101.00299999999999, 0.010603645436274133, 34, 48, 19, 101, 0.1485148514851485]\n",
            "2020-01-26   -  .....................................................................................................\n",
            "Processing:  ['2020-01-26', 12, 68, 21, 101, -0.0891089108910891, 12.958999999999994, 66.30000000000001, 21.741000000000003, 101.0, -0.08695049504950504, 25, 64, 12, 101, 0.12871287128712872]\n",
            "2020-01-27   -  .....................................................................................................\n",
            "Processing:  ['2020-01-27', 8, 73, 20, 101, -0.1188118811881188, 11.759, 70.04800000000002, 19.205999999999996, 101.013, -0.073723184144615, 28, 38, 35, 101, -0.06930693069306931]\n",
            "2020-01-28   -  .....................................................................................................\n",
            "Processing:  ['2020-01-28', 16, 73, 12, 101, 0.039603960396039604, 16.601999999999997, 71.95999999999998, 12.43, 100.99199999999999, 0.04131020278833965, 24, 58, 19, 101, 0.04950495049504951]\n",
            "2020-01-29   -  .....................................................................................................\n",
            "Processing:  ['2020-01-29', 10, 72, 19, 101, -0.0891089108910891, 10.983, 71.74299999999997, 18.267, 100.99299999999997, -0.07212381056112802, 25, 57, 19, 101, 0.0594059405940594]\n",
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            "Processing:  ['2020-01-30', 9, 79, 13, 101, -0.039603960396039604, 9.015999999999998, 77.60399999999996, 14.377, 100.99699999999994, -0.05308078457775979, 24, 44, 33, 101, -0.0891089108910891]\n",
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            "Processing:  ['2020-01-31', 5, 77, 19, 101, -0.13861386138613863, 6.418999999999996, 77.254, 17.351000000000006, 101.024, -0.10821191004117843, 27, 38, 36, 101, -0.0891089108910891]\n",
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            "Processing:  ['2020-02-01', 16, 66, 19, 101, -0.0297029702970297, 16.693999999999996, 64.922, 19.375000000000004, 100.99099999999999, -0.026546920022576352, 32, 47, 22, 101, 0.09900990099009901]\n",
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            "Processing:  ['2020-02-02', 21, 63, 17, 101, 0.039603960396039604, 20.640000000000008, 63.28099999999999, 17.071, 100.99199999999999, 0.03533943282636255, 39, 46, 16, 101, 0.22772277227722773]\n",
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            "Processing:  ['2020-02-03', 14, 64, 23, 101, -0.0891089108910891, 12.933999999999994, 68.43800000000002, 19.619000000000007, 100.99100000000001, -0.0661940172886694, 33, 48, 20, 101, 0.12871287128712872]\n",
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            "Processing:  ['2020-02-04', 9, 76, 16, 101, -0.06930693069306931, 9.299999999999997, 75.80099999999999, 15.895999999999999, 100.99699999999999, -0.06530887056051173, 34, 36, 31, 101, 0.0297029702970297]\n",
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            "2020-02-09   -  .....................................................................................................\n",
            "Processing:  ['2020-02-09', 23, 62, 16, 101, 0.06930693069306931, 22.313999999999993, 61.047999999999966, 17.634999999999998, 100.99699999999996, 0.04632810875570558, 32, 61, 8, 101, 0.2376237623762376]\n",
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            "Processing:  ['2020-02-10', 4, 66, 31, 101, -0.26732673267326734, 5.166999999999998, 68.90300000000003, 26.932999999999993, 101.00300000000003, -0.2154985495480331, 43, 45, 13, 101, 0.297029702970297]\n",
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            "Processing:  ['2020-02-11', 14, 78, 9, 101, 0.04950495049504951, 14.901000000000002, 75.587, 10.502999999999997, 100.991, 0.04354843500906026, 28, 50, 23, 101, 0.04950495049504951]\n",
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            "Processing:  ['2020-02-12', 14, 69, 18, 101, -0.039603960396039604, 13.571000000000002, 69.404, 18.034999999999997, 101.00999999999999, -0.04419364419364415, 29, 45, 27, 101, 0.019801980198019802]\n",
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            "Processing:  ['2020-02-13', 12, 73, 16, 101, -0.039603960396039604, 11.64, 73.08900000000001, 16.273, 101.00200000000001, -0.0458703788043801, 36, 44, 21, 101, 0.1485148514851485]\n",
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            "Processing:  ['2020-02-14', 8, 80, 13, 101, -0.04950495049504951, 11.132000000000001, 76.51899999999999, 13.350999999999999, 101.002, -0.021969861982931008, 38, 42, 21, 101, 0.16831683168316833]\n",
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            "Processing:  ['2020-02-15', 19, 64, 18, 101, 0.009900990099009901, 19.855, 64.23899999999998, 16.905, 100.99899999999998, 0.02920820998227705, 24, 50, 27, 101, -0.0297029702970297]\n",
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            "Processing:  ['2020-02-17', 18, 57, 26, 101, -0.07920792079207921, 18.544000000000004, 57.69399999999998, 24.753999999999998, 100.99199999999999, -0.061490019011406785, 29, 49, 23, 101, 0.0594059405940594]\n",
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            "Processing:  ['2020-02-19', 14, 67, 20, 101, -0.0594059405940594, 14.64199999999999, 67.945, 18.408999999999992, 100.99599999999998, -0.03729850687155929, 27, 43, 31, 101, -0.039603960396039604]\n",
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            "Processing:  ['2020-02-20', 7, 85, 9, 101, -0.019801980198019802, 7.871000000000002, 79.94399999999999, 13.178999999999993, 100.99399999999999, -0.05255757767788177, 20, 44, 37, 101, -0.16831683168316833]\n",
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            "Processing:  ['2020-02-21', 12, 76, 13, 101, -0.009900990099009901, 11.111000000000002, 76.75000000000003, 13.138999999999992, 101.00000000000003, -0.020079207920791972, 29, 53, 19, 101, 0.09900990099009901]\n",
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            "Processing:  ['2020-02-22', 15, 71, 15, 101, 0.0, 13.927999999999994, 69.80399999999999, 17.25899999999999, 100.99099999999997, -0.0329831371112277, 29, 51, 21, 101, 0.07920792079207921]\n",
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            "Processing:  ['2020-02-24', 13, 71, 17, 101, -0.039603960396039604, 14.087999999999997, 68.765, 18.142999999999994, 100.99599999999998, -0.04015010495465164, 23, 58, 20, 101, 0.0297029702970297]\n",
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            "Processing:  ['2020-02-26', 9, 69, 23, 101, -0.13861386138613863, 10.129999999999995, 68.507, 22.357, 100.994, -0.12106659801572374, 36, 47, 18, 101, 0.1782178217821782]\n",
            "2020-02-27   -  .....................................................................................................\n",
            "Processing:  ['2020-02-27', 15, 69, 17, 101, -0.019801980198019802, 15.931999999999993, 67.72500000000001, 17.344999999999995, 101.002, -0.013989821983723115, 26, 46, 29, 101, -0.0297029702970297]\n",
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            "Processing:  ['2020-02-28', 14, 73, 14, 101, 0.0, 14.651000000000005, 71.99199999999998, 14.353999999999996, 100.99699999999999, 0.0029406814063785017, 16, 53, 32, 101, -0.15841584158415842]\n",
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            "Processing:  ['2020-02-29', 16, 59, 26, 101, -0.09900990099009901, 15.205999999999996, 60.06200000000001, 25.732, 101.0, -0.10421782178217825, 28, 50, 23, 101, 0.04950495049504951]\n",
            "2020-03-01   -  .....................................................................................................\n",
            "Processing:  ['2020-03-01', 17, 67, 17, 101, 0.0, 16.582000000000008, 65.81899999999999, 18.600999999999996, 101.002, -0.019989703174194448, 38, 40, 23, 101, 0.1485148514851485]\n",
            "2020-03-02   -  .....................................................................................................\n",
            "Processing:  ['2020-03-02', 17, 70, 14, 101, 0.0297029702970297, 18.260999999999996, 66.72699999999998, 16.016000000000005, 101.00399999999998, 0.022226842501286988, 33, 42, 26, 101, 0.06930693069306931]\n",
            "2020-03-03   -  .....................................................................................................\n",
            "Processing:  ['2020-03-03', 11, 75, 15, 101, -0.039603960396039604, 12.116999999999996, 74.60999999999999, 14.266000000000002, 100.99299999999998, -0.021278702484330662, 23, 60, 18, 101, 0.04950495049504951]\n",
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            "Processing:  ['2020-03-04', 14, 77, 10, 101, 0.039603960396039604, 14.601999999999997, 73.04, 13.356000000000002, 100.99799999999999, 0.012336877957979319, 29, 53, 19, 101, 0.09900990099009901]\n",
            "2020-03-05   -  .....................................................................................................\n",
            "Processing:  ['2020-03-05', 13, 74, 14, 101, -0.009900990099009901, 14.546, 71.52599999999997, 14.922, 100.99399999999997, -0.003722993445155171, 22, 50, 29, 101, -0.06930693069306931]\n",
            "2020-03-06   -  .....................................................................................................\n",
            "Processing:  ['2020-03-06', 11, 70, 20, 101, -0.0891089108910891, 12.688999999999998, 67.68799999999997, 20.610000000000007, 100.98699999999997, -0.07843583827621388, 17, 57, 27, 101, -0.09900990099009901]\n",
            "2020-03-07   -  .....................................................................................................\n",
            "Processing:  ['2020-03-07', 13, 78, 10, 101, 0.0297029702970297, 14.206, 74.27500000000002, 12.529999999999996, 101.01100000000002, 0.016592252328954307, 31, 51, 19, 101, 0.1188118811881188]\n",
            "2020-03-08   -  .....................................................................................................\n",
            "Processing:  ['2020-03-08', 22, 60, 19, 101, 0.0297029702970297, 20.150999999999996, 61.74999999999999, 19.097999999999992, 100.99899999999997, 0.010425845800453517, 24, 46, 31, 101, -0.06930693069306931]\n",
            "2020-03-09   -  .....................................................................................................\n",
            "Processing:  ['2020-03-09', 16, 71, 14, 101, 0.019801980198019802, 17.243000000000002, 68.66400000000002, 15.082999999999998, 100.99000000000001, 0.021388256262996373, 27, 52, 22, 101, 0.04950495049504951]\n",
            "2020-03-10   -  .....................................................................................................\n",
            "Processing:  ['2020-03-10', 12, 71, 18, 101, -0.0594059405940594, 11.026, 70.21800000000003, 19.750999999999998, 100.99500000000003, -0.08639041536709734, 27, 47, 27, 101, 0.0]\n",
            "2020-03-11   -  .....................................................................................................\n",
            "Processing:  ['2020-03-11', 12, 79, 10, 101, 0.019801980198019802, 12.296999999999997, 77.25699999999999, 11.443999999999997, 100.99799999999999, 0.008445711796273191, 29, 62, 10, 101, 0.18811881188118812]\n",
            "2020-03-12   -  .....................................................................................................\n",
            "Processing:  ['2020-03-12', 11, 77, 13, 101, -0.019801980198019802, 12.99, 74.066, 13.932999999999998, 100.98899999999999, -0.009337650635217676, 29, 51, 21, 101, 0.07920792079207921]\n",
            "2020-03-13   -  .....................................................................................................\n",
            "Processing:  ['2020-03-13', 14, 75, 12, 101, 0.019801980198019802, 13.160000000000007, 74.43900000000005, 13.394999999999992, 100.99400000000006, -0.002326870903221826, 19, 51, 31, 101, -0.1188118811881188]\n",
            "2020-03-14   -  .....................................................................................................\n",
            "Processing:  ['2020-03-14', 13, 75, 13, 101, 0.0, 15.068999999999997, 73.40699999999997, 12.523999999999997, 100.99999999999997, 0.025198019801980203, 23, 55, 23, 101, 0.0]\n",
            "2020-03-15   -  .....................................................................................................\n",
            "Processing:  ['2020-03-15', 20, 70, 11, 101, 0.0891089108910891, 19.92400000000001, 69.069, 12.006999999999998, 101.0, 0.07838613861386151, 20, 57, 24, 101, -0.039603960396039604]\n",
            "2020-03-16   -  .....................................................................................................\n",
            "Processing:  ['2020-03-16', 26, 58, 17, 101, 0.0891089108910891, 20.689000000000004, 62.67799999999999, 17.630999999999993, 100.99799999999999, 0.030277827283708696, 30, 52, 19, 101, 0.10891089108910891]\n",
            "2020-03-17   -  .....................................................................................................\n",
            "Processing:  ['2020-03-17', 13, 73, 15, 101, -0.019801980198019802, 13.480999999999996, 70.60999999999999, 16.898999999999994, 100.98999999999998, -0.03384493514209326, 21, 49, 31, 101, -0.09900990099009901]\n",
            "2020-03-18   -  .....................................................................................................\n",
            "Processing:  ['2020-03-18', 8, 83, 10, 101, -0.019801980198019802, 8.013999999999998, 81.08899999999998, 11.892999999999997, 100.99599999999998, -0.03840746168165076, 41, 41, 19, 101, 0.21782178217821782]\n",
            "2020-03-19   -  .....................................................................................................\n",
            "Processing:  ['2020-03-19', 19, 63, 19, 101, 0.0, 18.102999999999998, 62.32200000000001, 20.575999999999997, 101.001, -0.024484906090038703, 32, 45, 24, 101, 0.07920792079207921]\n",
            "2020-03-20   -  .....................................................................................................\n",
            "Processing:  ['2020-03-20', 5, 77, 19, 101, -0.13861386138613863, 6.741999999999998, 73.43199999999996, 20.815999999999992, 100.98999999999995, -0.13936033270620854, 37, 46, 18, 101, 0.18811881188118812]\n",
            "2020-03-21   -  .....................................................................................................\n",
            "Processing:  ['2020-03-21', 9, 79, 13, 101, -0.039603960396039604, 10.594999999999999, 74.33399999999997, 16.078000000000003, 101.00699999999998, -0.05428336649935158, 34, 42, 25, 101, 0.0891089108910891]\n",
            "2020-03-22   -  .....................................................................................................\n",
            "Processing:  ['2020-03-22', 4, 79, 18, 101, -0.13861386138613863, 5.535999999999997, 77.09599999999999, 18.365000000000006, 100.997, -0.12702357495767208, 23, 55, 23, 101, 0.0]\n",
            "2020-03-23   -  .....................................................................................................\n",
            "Processing:  ['2020-03-23', 10, 73, 18, 101, -0.07920792079207921, 10.821999999999994, 70.95, 19.222, 100.994, -0.08317325781729615, 42, 40, 19, 101, 0.22772277227722773]\n",
            "2020-03-24   -  .....................................................................................................\n",
            "Processing:  ['2020-03-24', 11, 78, 12, 101, -0.009900990099009901, 11.103, 77.44800000000001, 12.454999999999995, 101.006, -0.013385343444943815, 31, 44, 26, 101, 0.04950495049504951]\n",
            "2020-03-25   -  .....................................................................................................\n",
            "Processing:  ['2020-03-25', 10, 73, 18, 101, -0.07920792079207921, 12.032, 70.19999999999999, 18.769000000000005, 101.00099999999999, -0.06670230987812008, 21, 58, 22, 101, -0.009900990099009901]\n",
            "2020-03-26   -  .....................................................................................................\n",
            "Processing:  ['2020-03-26', 10, 77, 14, 101, -0.039603960396039604, 9.481999999999998, 74.91499999999998, 16.598000000000006, 100.99499999999998, -0.07045893361057488, 25, 48, 28, 101, -0.0297029702970297]\n",
            "2020-03-27   -  .....................................................................................................\n",
            "Processing:  ['2020-03-27', 12, 80, 9, 101, 0.0297029702970297, 12.128, 78.43799999999999, 10.436999999999996, 101.00299999999999, 0.016742076968010895, 36, 46, 19, 101, 0.16831683168316833]\n",
            "2020-03-28   -  .....................................................................................................\n",
            "Processing:  ['2020-03-28', 15, 73, 13, 101, 0.019801980198019802, 16.972, 70.27699999999996, 13.744999999999996, 100.99399999999996, 0.03195239321147798, 26, 49, 26, 101, 0.0]\n",
            "2020-03-29   -  .....................................................................................................\n",
            "Processing:  ['2020-03-29', 10, 76, 15, 101, -0.04950495049504951, 10.449999999999998, 74.90800000000002, 15.641999999999998, 101.00000000000001, -0.0514059405940594, 24, 56, 21, 101, 0.0297029702970297]\n",
            "2020-03-30   -  .....................................................................................................\n",
            "Processing:  ['2020-03-30', 13, 75, 13, 101, 0.0, 14.231000000000003, 74.30199999999999, 12.467, 101.0, 0.017465346534653495, 23, 52, 26, 101, -0.0297029702970297]\n",
            "2020-03-31   -  .....................................................................................................\n",
            "Processing:  ['2020-03-31', 13, 72, 16, 101, -0.0297029702970297, 12.349000000000007, 70.91599999999997, 17.733000000000004, 100.99799999999998, -0.053307986296758335, 19, 44, 38, 101, -0.18811881188118812]\n",
            "2020-04-01   -  .....................................................................................................\n",
            "Processing:  ['2020-04-01', 12, 71, 18, 101, -0.0594059405940594, 12.921000000000003, 70.59599999999998, 17.483999999999998, 101.00099999999998, -0.04517777051712356, 38, 51, 12, 101, 0.25742574257425743]\n",
            "2020-04-02   -  .....................................................................................................\n",
            "Processing:  ['2020-04-02', 14, 74, 13, 101, 0.009900990099009901, 13.967999999999993, 69.37999999999998, 17.651, 100.99899999999997, -0.03646570758126326, 30, 46, 25, 101, 0.04950495049504951]\n",
            "2020-04-03   -  .....................................................................................................\n",
            "Processing:  ['2020-04-03', 3, 90, 8, 101, -0.04950495049504951, 3.888999999999994, 86.567, 10.54400000000001, 101.0, -0.06589108910891105, 27, 49, 25, 101, 0.019801980198019802]\n",
            "2020-04-04   -  .....................................................................................................\n",
            "Processing:  ['2020-04-04', 6, 75, 20, 101, -0.13861386138613863, 8.481999999999996, 74.30099999999999, 18.221999999999998, 101.00499999999998, -0.09643086975892286, 32, 44, 25, 101, 0.06930693069306931]\n",
            "2020-04-05   -  .....................................................................................................\n",
            "Processing:  ['2020-04-05', 15, 72, 14, 101, 0.009900990099009901, 14.59699999999999, 70.94899999999998, 15.453, 100.99899999999998, -0.008475331438925227, 29, 44, 28, 101, 0.009900990099009901]\n",
            "2020-04-06   -  .....................................................................................................\n",
            "Processing:  ['2020-04-06', 10, 71, 20, 101, -0.09900990099009901, 12.349999999999993, 69.32099999999996, 19.34, 101.01099999999995, -0.06920038411658147, 19, 60, 22, 101, -0.0297029702970297]\n",
            "2020-04-07   -  .....................................................................................................\n",
            "Processing:  ['2020-04-07', 7, 68, 26, 101, -0.18811881188118812, 8.558, 68.31899999999997, 24.115999999999996, 100.99299999999998, -0.15405028071252463, 25, 48, 28, 101, -0.0297029702970297]\n",
            "2020-04-08   -  .....................................................................................................\n",
            "Processing:  ['2020-04-08', 7, 78, 16, 101, -0.0891089108910891, 7.314000000000001, 75.52499999999996, 18.156, 100.99499999999998, -0.10735184910144067, 28, 41, 32, 101, -0.039603960396039604]\n",
            "2020-04-09   -  .....................................................................................................\n",
            "Processing:  ['2020-04-09', 10, 79, 12, 101, -0.019801980198019802, 9.587000000000003, 78.315, 13.088999999999993, 100.991, -0.03467635729916518, 23, 56, 22, 101, 0.009900990099009901]\n",
            "2020-04-10   -  .....................................................................................................\n",
            "Processing:  ['2020-04-10', 6, 78, 17, 101, -0.10891089108910891, 8.678999999999998, 74.88299999999998, 17.426999999999992, 100.98899999999998, -0.08662329560645216, 38, 44, 19, 101, 0.18811881188118812]\n",
            "2020-04-11   -  .....................................................................................................\n",
            "Processing:  ['2020-04-11', 13, 64, 24, 101, -0.10891089108910891, 13.864999999999995, 63.23099999999998, 23.894999999999996, 100.99099999999997, -0.09931578061411417, 19, 56, 26, 101, -0.06930693069306931]\n",
            "2020-04-12   -  .....................................................................................................\n",
            "Processing:  ['2020-04-12', 31, 56, 14, 101, 0.16831683168316833, 30.796999999999993, 55.428999999999974, 14.760999999999992, 100.98699999999997, 0.15879271589412505, 33, 46, 22, 101, 0.10891089108910891]\n",
            "2020-04-13   -  .....................................................................................................\n",
            "Processing:  ['2020-04-13', 12, 70, 19, 101, -0.06930693069306931, 12.845999999999993, 68.32199999999997, 19.81700000000001, 100.98499999999997, -0.06903005396841135, 25, 51, 25, 101, 0.0]\n",
            "2020-04-14   -  .....................................................................................................\n",
            "Processing:  ['2020-04-14', 21, 58, 22, 101, -0.009900990099009901, 20.302, 59.00099999999998, 21.695, 100.99799999999999, -0.013792352323808401, 24, 57, 20, 101, 0.039603960396039604]\n",
            "2020-04-15   -  .....................................................................................................\n",
            "Processing:  ['2020-04-15', 12, 74, 15, 101, -0.0297029702970297, 11.100999999999992, 72.56899999999999, 17.328, 100.99799999999999, -0.0616546862314106, 30, 44, 27, 101, 0.0297029702970297]\n",
            "2020-04-16   -  .....................................................................................................\n",
            "Processing:  ['2020-04-16', 17, 75, 9, 101, 0.07920792079207921, 15.916999999999996, 73.63899999999997, 11.446000000000003, 101.00199999999997, 0.04426645016930352, 24, 49, 28, 101, -0.039603960396039604]\n",
            "2020-04-17   -  .....................................................................................................\n",
            "Processing:  ['2020-04-17', 17, 76, 8, 101, 0.0891089108910891, 18.139, 73.11099999999999, 9.748, 100.99799999999999, 0.08308085308619974, 36, 42, 23, 101, 0.12871287128712872]\n",
            "2020-04-18   -  .....................................................................................................\n",
            "Processing:  ['2020-04-18', 20, 71, 10, 101, 0.09900990099009901, 21.427000000000007, 67.12199999999999, 12.448999999999998, 100.99799999999999, 0.08889284936335383, 17, 53, 31, 101, -0.13861386138613863]\n",
            "2020-04-19   -  .....................................................................................................\n",
            "Processing:  ['2020-04-19', 8, 77, 16, 101, -0.07920792079207921, 13.064000000000002, 73.06, 14.857999999999999, 100.98200000000001, -0.017765542373888384, 32, 42, 27, 101, 0.04950495049504951]\n",
            "2020-04-20   -  .....................................................................................................\n",
            "Processing:  ['2020-04-20', 7, 77, 17, 101, -0.09900990099009901, 9.203999999999995, 74.37100000000004, 17.417, 100.99200000000003, -0.08132327313054503, 19, 44, 38, 101, -0.18811881188118812]\n",
            "2020-04-21   -  .....................................................................................................\n",
            "Processing:  ['2020-04-21', 10, 81, 10, 101, 0.0, 9.813999999999998, 80.74000000000001, 10.451, 101.005, -0.006306618484233476, 27, 52, 22, 101, 0.04950495049504951]\n",
            "2020-04-22   -  .....................................................................................................\n",
            "Processing:  ['2020-04-22', 10, 78, 13, 101, -0.0297029702970297, 11.251000000000003, 76.52199999999999, 13.225000000000003, 100.998, -0.019544941483989782, 27, 53, 21, 101, 0.0594059405940594]\n",
            "2020-04-23   -  .....................................................................................................\n",
            "Processing:  ['2020-04-23', 15, 77, 9, 101, 0.0594059405940594, 15.514999999999988, 73.61199999999997, 11.863000000000001, 100.98999999999995, 0.0361619962372511, 32, 47, 22, 101, 0.09900990099009901]\n",
            "2020-04-24   -  .....................................................................................................\n",
            "Processing:  ['2020-04-24', 14, 71, 16, 101, -0.019801980198019802, 15.003999999999996, 68.97200000000001, 17.011, 100.987, -0.019873845148385468, 42, 45, 14, 101, 0.27722772277227725]\n",
            "2020-04-25   -  .....................................................................................................\n",
            "Processing:  ['2020-04-25', 11, 71, 19, 101, -0.07920792079207921, 12.822000000000006, 69.88299999999998, 18.297, 101.00199999999998, -0.05420684738916057, 24, 44, 33, 101, -0.0891089108910891]\n",
            "2020-04-26   -  .....................................................................................................\n",
            "Processing:  ['2020-04-26', 9, 75, 17, 101, -0.07920792079207921, 10.109, 74.25199999999998, 16.636999999999997, 100.99799999999998, -0.06463494326620328, 26, 45, 30, 101, -0.039603960396039604]\n",
            "2020-04-27   -  .....................................................................................................\n",
            "Processing:  ['2020-04-27', 6, 76, 19, 101, -0.12871287128712872, 8.503000000000005, 71.71699999999998, 20.778000000000006, 100.99799999999999, -0.12153706013980477, 18, 47, 36, 101, -0.1782178217821782]\n",
            "2020-04-28   -  .....................................................................................................\n",
            "Processing:  ['2020-04-28', 13, 69, 19, 101, -0.0594059405940594, 13.204999999999998, 66.98, 20.813000000000006, 100.998, -0.07532822432127376, 26, 45, 30, 101, -0.039603960396039604]\n",
            "2020-04-29   -  .....................................................................................................\n",
            "Processing:  ['2020-04-29', 17, 65, 19, 101, -0.019801980198019802, 16.87499999999999, 66.01099999999997, 18.106, 100.99199999999996, -0.012189084283903803, 27, 46, 28, 101, -0.009900990099009901]\n",
            "2020-04-30   -  .....................................................................................................\n",
            "Processing:  ['2020-04-30', 7, 73, 21, 101, -0.13861386138613863, 9.554999999999998, 70.73299999999998, 20.70299999999999, 100.99099999999996, -0.1103860740065946, 22, 46, 33, 101, -0.10891089108910891]\n",
            "2020-05-01   -  .....................................................................................................\n",
            "Processing:  ['2020-05-01', 19, 75, 7, 101, 0.1188118811881188, 20.006999999999994, 73.81599999999995, 7.167000000000002, 100.98999999999994, 0.12714130111892266, 36, 49, 16, 101, 0.19801980198019803]\n",
            "2020-05-02   -  .....................................................................................................\n",
            "Processing:  ['2020-05-02', 5, 74, 22, 101, -0.16831683168316833, 6.537999999999997, 71.79199999999999, 22.672, 101.00199999999998, -0.15973941110077036, 32, 40, 29, 101, 0.0297029702970297]\n",
            "2020-05-03   -  .....................................................................................................\n",
            "Processing:  ['2020-05-03', 24, 67, 10, 101, 0.13861386138613863, 23.159000000000006, 67.46699999999998, 10.381999999999994, 101.00799999999998, 0.12649493109456691, 32, 44, 25, 101, 0.06930693069306931]\n",
            "2020-05-04   -  .....................................................................................................\n",
            "Processing:  ['2020-05-04', 13, 76, 12, 101, 0.009900990099009901, 13.68599999999999, 75.89700000000003, 11.419999999999993, 101.00300000000001, 0.02243497717889564, 28, 54, 19, 101, 0.0891089108910891]\n",
            "2020-05-05   -  .....................................................................................................\n",
            "Processing:  ['2020-05-05', 6, 80, 15, 101, -0.0891089108910891, 8.168, 78.37599999999999, 14.460999999999991, 101.00499999999997, -0.06230384634424033, 32, 52, 17, 101, 0.1485148514851485]\n",
            "2020-05-06   -  .....................................................................................................\n",
            "Processing:  ['2020-05-06', 18, 59, 24, 101, -0.0594059405940594, 17.297, 60.33700000000003, 23.347000000000016, 100.98100000000005, -0.05991226072231422, 23, 68, 10, 101, 0.12871287128712872]\n",
            "2020-05-07   -  .....................................................................................................\n",
            "Processing:  ['2020-05-07', 13, 72, 16, 101, -0.0297029702970297, 12.456000000000003, 71.55, 16.999000000000002, 101.005, -0.04497797138755506, 21, 59, 21, 101, 0.0]\n",
            "2020-05-08   -  .....................................................................................................\n",
            "Processing:  ['2020-05-08', 6, 78, 17, 101, -0.10891089108910891, 7.375999999999998, 77.24, 16.379, 100.99499999999999, -0.08914302688251897, 30, 52, 19, 101, 0.10891089108910891]\n",
            "2020-05-09   -  .....................................................................................................\n",
            "Processing:  ['2020-05-09', 8, 78, 15, 101, -0.06930693069306931, 11.417, 74.774, 14.814999999999994, 101.006, -0.03364156584757336, 29, 56, 16, 101, 0.12871287128712872]\n",
            "2020-05-10   -  .....................................................................................................\n",
            "Processing:  ['2020-05-10', 12, 75, 14, 101, -0.019801980198019802, 13.614999999999998, 70.437, 16.938999999999997, 100.99099999999999, -0.032913824004119165, 18, 61, 22, 101, -0.039603960396039604]\n",
            "2020-05-11   -  .....................................................................................................\n",
            "Processing:  ['2020-05-11', 7, 82, 12, 101, -0.04950495049504951, 7.787000000000002, 80.23299999999998, 12.971999999999996, 100.99199999999998, -0.05134070025348538, 16, 63, 22, 101, -0.0594059405940594]\n",
            "2020-05-12   -  .....................................................................................................\n",
            "Processing:  ['2020-05-12', 8, 79, 14, 101, -0.0594059405940594, 8.603999999999992, 78.60899999999995, 13.782999999999998, 100.99599999999994, -0.051279258584498486, 16, 71, 14, 101, 0.019801980198019802]\n",
            "2020-05-13   -  .....................................................................................................\n",
            "Processing:  ['2020-05-13', 12, 81, 8, 101, 0.039603960396039604, 10.528999999999996, 80.346, 10.119000000000003, 100.994, 0.004059647107748907, 33, 23, 45, 101, -0.1188118811881188]\n",
            "2020-05-14   -  .....................................................................................................\n",
            "Processing:  ['2020-05-14', 11, 78, 12, 101, -0.009900990099009901, 11.079999999999995, 76.03899999999997, 13.879999999999999, 100.99899999999997, -0.027723046762839286, 38, 37, 26, 101, 0.1188118811881188]\n",
            "2020-05-15   -  .....................................................................................................\n",
            "Processing:  ['2020-05-15', 11, 70, 20, 101, -0.0891089108910891, 11.772999999999993, 67.68099999999998, 21.544000000000008, 100.99799999999999, -0.09674448998990094, 34, 41, 26, 101, 0.07920792079207921]\n",
            "2020-05-16   -  .....................................................................................................\n",
            "Processing:  ['2020-05-16', 11, 83, 7, 101, 0.039603960396039604, 11.910999999999996, 75.57500000000003, 13.501999999999994, 100.98800000000003, -0.015754347051134758, 49, 33, 19, 101, 0.297029702970297]\n",
            "2020-05-17   -  .....................................................................................................\n",
            "Processing:  ['2020-05-17', 11, 77, 13, 101, -0.019801980198019802, 11.644999999999994, 75.84099999999997, 13.527, 101.01299999999996, -0.018631265282686445, 35, 42, 24, 101, 0.10891089108910891]\n",
            "2020-05-18   -  .....................................................................................................\n",
            "Processing:  ['2020-05-18', 8, 84, 9, 101, -0.009900990099009901, 8.988999999999999, 83.03299999999999, 8.98, 101.002, 8.910714639312653e-05, 23, 50, 28, 101, -0.04950495049504951]\n",
            "2020-05-19   -  .....................................................................................................\n",
            "Processing:  ['2020-05-19', 4, 83, 14, 101, -0.09900990099009901, 5.538999999999994, 80.97200000000001, 14.493999999999998, 101.005, -0.08865897727835259, 25, 40, 36, 101, -0.10891089108910891]\n",
            "2020-05-20   -  .....................................................................................................\n",
            "Processing:  ['2020-05-20', 9, 79, 13, 101, -0.039603960396039604, 8.876999999999999, 79.00099999999999, 13.116, 100.99399999999999, -0.04197279046279978, 27, 60, 14, 101, 0.12871287128712872]\n",
            "2020-05-21   -  .....................................................................................................\n",
            "Processing:  ['2020-05-21', 5, 88, 8, 101, -0.0297029702970297, 6.340999999999994, 84.95699999999998, 9.701000000000002, 100.99899999999998, -0.03326765611540717, 33, 51, 17, 101, 0.15841584158415842]\n",
            "2020-05-22   -  .....................................................................................................\n",
            "Processing:  ['2020-05-22', 13, 83, 5, 101, 0.07920792079207921, 14.999999999999995, 79.77599999999997, 6.212000000000005, 100.98799999999997, 0.08702024002851816, 40, 35, 26, 101, 0.13861386138613863]\n",
            "2020-05-23   -  .....................................................................................................\n",
            "Processing:  ['2020-05-23', 5, 79, 17, 101, -0.1188118811881188, 7.0790000000000015, 75.90199999999999, 18.020000000000007, 101.001, -0.10832566014197885, 16, 51, 34, 101, -0.1782178217821782]\n",
            "2020-05-24   -  .....................................................................................................\n",
            "Processing:  ['2020-05-24', 6, 84, 11, 101, -0.04950495049504951, 6.980999999999999, 81.41599999999998, 12.611999999999997, 101.00899999999997, -0.05574750764783335, 27, 51, 23, 101, 0.039603960396039604]\n",
            "2020-05-25   -  .....................................................................................................\n",
            "Processing:  ['2020-05-25', 10, 76, 15, 101, -0.04950495049504951, 11.396999999999995, 74.89400000000002, 14.702999999999998, 100.99400000000001, -0.032734617898092976, 36, 43, 22, 101, 0.13861386138613863]\n",
            "2020-05-26   -  .....................................................................................................\n",
            "Processing:  ['2020-05-26', 10, 77, 14, 101, -0.039603960396039604, 9.936000000000003, 76.47500000000002, 14.58799999999999, 100.99900000000002, -0.04605986197883133, 33, 45, 23, 101, 0.09900990099009901]\n",
            "2020-05-27   -  .....................................................................................................\n",
            "Processing:  ['2020-05-27', 14, 70, 17, 101, -0.0297029702970297, 14.426999999999994, 70.82500000000002, 15.744999999999992, 100.997, -0.013049892571066446, 31, 59, 11, 101, 0.19801980198019803]\n",
            "2020-05-28   -  .....................................................................................................\n",
            "Processing:  ['2020-05-28', 11, 83, 7, 101, 0.039603960396039604, 13.052999999999995, 79.72799999999998, 8.224000000000002, 101.00499999999998, 0.04780951438047616, 38, 34, 29, 101, 0.0891089108910891]\n",
            "2020-05-29   -  .....................................................................................................\n",
            "Processing:  ['2020-05-29', 2, 85, 14, 101, -0.1188118811881188, 2.3059999999999996, 82.10400000000011, 16.587000000000007, 100.99700000000011, -0.14140023961107748, 11, 43, 47, 101, -0.3564356435643564]\n",
            "2020-05-30   -  .....................................................................................................\n",
            "Processing:  ['2020-05-30', 6, 83, 12, 101, -0.0594059405940594, 6.133999999999997, 76.79400000000001, 18.10399999999999, 101.032, -0.11847731411829908, 29, 32, 40, 101, -0.10891089108910891]\n",
            "2020-05-31   -  .....................................................................................................\n",
            "Processing:  ['2020-05-31', 4, 75, 22, 101, -0.1782178217821782, 7.111999999999998, 71.68599999999999, 22.196000000000005, 100.994, -0.149355407251916, 28, 49, 24, 101, 0.039603960396039604]\n",
            "\n",
            "Index Computation Elapsed Time:  311.6622670109998\n"
          ]
        }
      ],
      "source": [
        "start_time = timeit.default_timer()\n",
        "\n",
        "data_set_smei_bert = []\n",
        "smei_indexes_list = []\n",
        "for d in range(len(dataset)):\n",
        "  bert_sentiment_data = []\n",
        "  smei_sentiment_data = []\n",
        "  bert_negative_data = [] \n",
        "  bert_neutral_data = []\n",
        "  bert_positive_data = []\n",
        "  sentences_data = list(dataset[d][1][:,8])\n",
        "  smei_sentiments = list(dataset[d][1][:,9])\n",
        "  current_day = dataset[d][0]\n",
        "\n",
        "  i = 0\n",
        "  print(current_day, \"  -  \", end = \"\")\n",
        "  for s,smei_sent in zip(sentences_data, smei_sentiments):\n",
        "    try:\n",
        "      bert_negative, bert_neutral, bert_positive, classification = bert_inference(s, model)\n",
        "      print('.', end = \"\")\n",
        "      bert_sentiment = all_encoded_classes[classification]\n",
        "      bert_classification = all_classes[classification]\n",
        "      data_set_smei_bert.append([current_day, bert_sentiment, bert_classification, bert_negative, bert_neutral, bert_positive])\n",
        "      bert_sentiment_data.append(int(bert_sentiment))\n",
        "      smei_sentiment_data.append(int(smei_sent))\n",
        "      bert_negative_data.append(bert_negative)\n",
        "      bert_neutral_data.append(bert_neutral)\n",
        "      bert_positive_data.append(bert_positive)\n",
        "    except ValueError: \n",
        "      print(\"\\nError on this Tweet: \", s)\n",
        "\n",
        "    if limit_n_tweets_to_classify is True: \n",
        "      i += 1\n",
        "      if i > n_tweets_to_classify: \n",
        "        break\n",
        "\n",
        "  n_positives = bert_sentiment_data.count(1)\n",
        "  n_neutrals = bert_sentiment_data.count(0)\n",
        "  n_negatives = bert_sentiment_data.count(-1)\n",
        "  total_cases = n_positives + n_neutrals + n_negatives\n",
        "  smei_index_1 = (n_positives - n_negatives) / total_cases \n",
        "\n",
        "  prob_sum_positives = sum(bert_positive_data)\n",
        "  prob_sum_neutrals = sum(bert_neutral_data)\n",
        "  prob_sum_negatives = sum(bert_negative_data)\n",
        "  total_probs = prob_sum_positives + prob_sum_neutrals + prob_sum_negatives\n",
        "  smei_index_2 = (prob_sum_positives - prob_sum_negatives) / total_probs \n",
        "\n",
        "  n_positives_smei = smei_sentiment_data.count(1)\n",
        "  n_neutrals_smei = smei_sentiment_data.count(0)\n",
        "  n_negatives_smei = smei_sentiment_data.count(-1)\n",
        "  total_cases_smei = n_positives_smei + n_neutrals_smei + n_negatives_smei\n",
        "  smei_index_3 = (n_positives_smei - n_negatives_smei) / total_cases_smei \n",
        "\n",
        "  bert_sentiment_data_count = [current_day, n_positives, n_neutrals, n_negatives, total_cases, smei_index_1, prob_sum_positives, prob_sum_neutrals, prob_sum_negatives, total_probs, smei_index_2, n_positives_smei, n_neutrals_smei, n_negatives_smei, total_cases_smei, smei_index_3]\n",
        "  smei_indexes_list.append([current_day, smei_index_1, smei_index_2, smei_index_3])\n",
        "  print(\"\\nProcessing: \",bert_sentiment_data_count)\n",
        "\n",
        "  file_out = open(file_out_txt, 'a')\n",
        "  file_out.write(str(current_day) + ',' + str(n_positives) + ',' + str(n_neutrals) + ',' + str(n_negatives) + ',' + str(total_cases) + ',' + str(smei_index_1) + ',' + str(prob_sum_positives) + ',' + str(prob_sum_neutrals) + ',' + str(prob_sum_negatives) + ',' + str(total_probs) + ',' + str(smei_index_2) + ',' + str(n_positives_smei) + ',' + str(n_neutrals_smei) + ',' + str(n_negatives_smei) + ',' + str(total_cases_smei) + ',' + str(smei_index_3) + '\\n')\n",
        "  file_out.close()\n",
        "\n",
        "end_time = timeit.default_timer()\n",
        "\n",
        "print(\"\\nIndex Computation Elapsed Time: \", end_time - start_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7qggvj5Ak03"
      },
      "source": [
        "# Output Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwWVPdbIAb30",
        "outputId": "a5bbd87a-d43e-456b-e8a9-a728469a88ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "153 lines read\n"
          ]
        }
      ],
      "source": [
        "index_data = pd.read_csv(file_out_txt, sep=',', header=None, usecols = [0, 5, 10, 15]).values             # select only the interested columns\n",
        "print ('%i lines read' % len(index_data))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "toKN_GKChIsQ"
      },
      "source": [
        "# Index 1 Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "po5B8xACBLc7",
        "outputId": "f189bcda-b437-4ca9-f5db-523312b04681"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "index_1 = index_data [1:,1].astype('float')\n",
        "\n",
        "x = dates\n",
        "y = index_1\n",
        "\n",
        "plt.figure()\n",
        "p = sns.lineplot(x, y, markers=True, color='blue', linewidth=2.5)\n",
        "p.set(title='Index 1')\n",
        "p.set_xlabel(\"Dates\", fontsize = 20)\n",
        "p.set_ylabel(\"Index 1 Value\", fontsize = 20)\n",
        "\n",
        "for ind, label in enumerate(p.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "x = list(range(0, len(index_1)))\n",
        "y = index_1\n",
        "\n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='black', ls = ':')\n",
        "\n",
        "fig = p.get_figure()\n",
        "fig.savefig(\"./bert_smei_index_1.jpg\") \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "fU7zxUchkq3H",
        "outputId": "15c8e2c1-01bf-4635-ffd1-1b87ed0118b5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "\n",
        "x = dates\n",
        "y = index_1\n",
        "\n",
        "plot_ = sns.barplot(x, y)\n",
        "plot_.set(title='Index 1')\n",
        "plot_.set_xlabel(\"Dates\", fontsize = 20)\n",
        "plot_.set_ylabel(\"Index 1 Value\", fontsize = 20)\n",
        "\n",
        "for ind, label in enumerate(plot_.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "fig = plot_.get_figure()\n",
        "fig.savefig(\"./bert_smei_index_1_bar_plots.jpg\") \n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4_DSgeOhWrx"
      },
      "source": [
        "# Index 2 Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "Pxi0nDithdIt",
        "outputId": "4f569cd5-c2d8-4c36-87a1-1ca980a8e285"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "index_2 = index_data [1:,2].astype('float')\n",
        "\n",
        "x = dates\n",
        "y = index_2\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "p = sns.lineplot(x, y, markers=True, color='green', linewidth=2.5)\n",
        "p.set(title='Index 2')\n",
        "p.set_xlabel(\"Dates\", fontsize = 20)\n",
        "p.set_ylabel(\"Index 2 Value\", fontsize = 20)\n",
        "\n",
        "for ind, label in enumerate(p.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "x = list(range(0, len(index_2)))\n",
        "y = index_2\n",
        "\n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='black', ls = ':')\n",
        "\n",
        "fig = p.get_figure()\n",
        "fig.savefig(\"./bert_smei_index_2.jpg\") \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "myfKhZ9bqwd2",
        "outputId": "986e07f3-6cd9-4606-845e-bf7f8e622137"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "\n",
        "x = dates\n",
        "y = index_2\n",
        "\n",
        "plot_ = sns.barplot(x, y)\n",
        "plot_.set(title='Index 2')\n",
        "plot_.set_xlabel(\"Dates\", fontsize = 20)\n",
        "plot_.set_ylabel(\"Index 2 Value\", fontsize = 20)\n",
        "\n",
        "for ind, label in enumerate(plot_.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "fig = plot_.get_figure()\n",
        "fig.savefig(\"./bert_smei_index_2_bar_plots.jpg\") \n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VFa_XmxhaWB"
      },
      "source": [
        "# Index 3 Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "Ws7Ipp6OhdtX",
        "outputId": "28b21561-2c3f-4cea-e6d8-5af1135e2ca7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "index_3 = index_data [1:,3].astype('float')\n",
        "\n",
        "x = dates\n",
        "y = index_3\n",
        "\n",
        "plt.figure()\n",
        "p = sns.lineplot(x, y, markers=True, color='red', linewidth=2.5)\n",
        "p.set(title='Index 3')\n",
        "p.set_xlabel(\"Dates\", fontsize = 20)\n",
        "p.set_ylabel(\"Index 3 Value\", fontsize = 20)\n",
        "\n",
        "x = list(range(0, len(index_3)))\n",
        "y = index_2\n",
        "\n",
        "for ind, label in enumerate(p.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='black', ls = ':')\n",
        "\n",
        "fig = p.get_figure()\n",
        "fig.savefig(\"./normal_smei_index_3.jpg\") "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "NclPiTnRsjeM",
        "outputId": "0c13274e-4040-4e1b-dc36-06c256b8de20"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dates = index_data [1:,0]\n",
        "\n",
        "x = dates\n",
        "y = index_3\n",
        "\n",
        "plot_ = sns.barplot(x, y)\n",
        "plot_.set(title='Index 3')\n",
        "plot_.set_xlabel(\"Dates\", fontsize = 20)\n",
        "plot_.set_ylabel(\"Index 3 Value\", fontsize = 20)\n",
        "\n",
        "for ind, label in enumerate(plot_.get_xticklabels()):\n",
        "    if ind % 35 == 0:  # every 14th label is kept\n",
        "        label.set_visible(True)\n",
        "    else:\n",
        "        label.set_visible(False)\n",
        "\n",
        "fig = plot_.get_figure()\n",
        "fig.savefig(\"./normal_smei_index_3_bar_plots.jpg\") \n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBmBHWvjjA-S"
      },
      "source": [
        "# Index 1 - Index 2 Correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "Al2a5TL8jF3B",
        "outputId": "04ccc302-224d-4f11-8b54-18a606176c3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index 1 - Index 2 Correlation Coefficient:  [[1.         0.97955296]\n",
            " [0.97955296 1.        ]]\n",
            "Spearmans correlation: 0.970\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "corr_c = np.corrcoef(index_1, index_2)\n",
        "print(\"Index 1 - Index 2 Correlation Coefficient: \", corr_c)\n",
        "  \n",
        "#ploting the heatmap for correlation\n",
        "ax = sns.heatmap(corr_c, annot=True)\n",
        "plt.savefig('index_1_index_2_correlation_matrix.jpg')\n",
        "\n",
        "corr, _ = spearmanr(index_1, index_2)\n",
        "print('Spearmans correlation: %.3f' % corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zeUpsoOlkVvI",
        "outputId": "2cad33e5-28c0-402f-8b3a-071cec15db9d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# adds the title\n",
        "plt.figure()\n",
        "plt.title('Index 1 - Index 2 Regression')\n",
        "\n",
        "x = index_1\n",
        "y = index_2\n",
        "\n",
        "# plot the data\n",
        "plt.scatter(x, y)\n",
        "  \n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='red')\n",
        "  \n",
        "# Labelling axes\n",
        "plt.xlabel('Index 1')\n",
        "plt.ylabel('Index 2')\n",
        "\n",
        "plt.savefig('index_1_index_2_regression_curve.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJf8PkiviNdv"
      },
      "source": [
        "# Index 1 - Index 3 Correlation "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "IQ8IZ0iLiqBm",
        "outputId": "6548a2bb-7d83-4912-b6e1-6a42a29a2f65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index 1 - Index 3 Correlation Coefficient:  [[1.         0.18594728]\n",
            " [0.18594728 1.        ]]\n",
            "Spearmans correlation: 0.206\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "corr_c = np.corrcoef(index_1, index_3)\n",
        "print(\"Index 1 - Index 3 Correlation Coefficient: \", corr_c)\n",
        "\n",
        "#ploting the heatmap for correlation\n",
        "ax = sns.heatmap(corr_c, annot=True)\n",
        "plt.savefig('index_1_index_3_correlation_matrix.jpg')\n",
        "\n",
        "corr, _ = spearmanr(index_1, index_3)\n",
        "print('Spearmans correlation: %.3f' % corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qBSm7jcykbFz",
        "outputId": "5d4fbbef-5dd9-4ccd-8b09-e5cfb1af3b09"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "# adds the title\n",
        "plt.title('Index 1 - Index 3 Regression')\n",
        "\n",
        "x = index_1\n",
        "y = index_3\n",
        "\n",
        "# plot the data\n",
        "plt.scatter(x, y)\n",
        "  \n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='red')\n",
        "  \n",
        "# Labelling axes\n",
        "plt.xlabel('Index 1')\n",
        "plt.ylabel('Index 3')\n",
        "\n",
        "plt.savefig('index_1_index_3_regression_curve.jpg')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BvS72cqjXLr"
      },
      "source": [
        "# Index 2 - Index 3 Correlation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "DcAIut7wjdef",
        "outputId": "58ec9a80-b093-47df-9f96-dafd08e70a80"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index 2 - Index 3 Correlation Coefficient:  [[1.         0.17448586]\n",
            " [0.17448586 1.        ]]\n",
            "Spearmans correlation: 0.196\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure()\n",
        "corr_c = np.corrcoef(index_2, index_3)\n",
        "print(\"Index 2 - Index 3 Correlation Coefficient: \", corr_c)\n",
        "  \n",
        "#ploting the heatmap for correlation\n",
        "ax = sns.heatmap(corr_c, annot=True)\n",
        "plt.savefig('index_2_index_3_correlation_matrix.jpg')\n",
        "\n",
        "corr, _ = spearmanr(index_2, index_3)\n",
        "print('Spearmans correlation: %.3f' % corr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ifaYm5Hnkv1_",
        "outputId": "eff1fcae-df3f-4a2c-97ac-403d12837b87"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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/3AIZQ69kki0L30yoOGMdOHslXQ20NzVPGY4/DiBjcHO1szgfVHHGOnD2UmCfaF0buatkGY4/hXZhSToT+ADQB1wZEctGrP9d4P3AbwPnRsS1mXXnAW9LX14WEZ9sdfl6qWleFr6qvRhj5TTrtXQ10FxdGx2AL8Pxp7AAIqkP+DBwOrARWC1pZUTcntnsXuC1wN+O2HcGcDEwCARwc7rvQ60so/vcrR0a6ecuqk98vANnLwX2Zuva6CzCMhx/imyBHA+si4i7ASRdAywGngogEXFPuu7JEfsuAq6PiK3p+uuBM4GrW1nAXskk2406ffCxopGz0qKnkPZSkMhDoy2KMhx/ihwD6Qc2ZF5vTJe1dF9JF0haI2nNli1bGiqg+9zLqQyDjxWN9HOXoU/camt0AL4Mx5+un8YbEcuB5QCDg4PR6P4+6yqfMl1w2MhZaRn6xK22ZloUnX78KbIFMgTMybyenS7Le1/rcmU60DZyVtpL02W7URlaFI1qqAUiaUZl3KEFVgNHSppPcvA/F3hVnfuuAv5R0vT09RnARS0ql5VcGQYfKxo5Ky1Dn7iNrdNbFI2q2QKRdKKkOyTdJumEdKB6taQNkp4/0Q+OiN3Am0iCwR3A5yLiNkmXSHppWobnStoIvAL4qKTb0n23ApeSBKHVwCUtDGxWcmW6LqGRs9JuPIO1clNE9WEBST8EzgcOAL4CLImIb0v6HeBfIuLE9hWzNQYHB2PNmjVFF8PaoCyzsMzKQNLNETE4cvlYXViTIuLWdOctEfFtgIj4kaTO6wuwXJT1QNxtXQVmDYuAzZvh3nvhvvtg8eKWf8RYASTbvTVyfGFyy0tiHafo6w7yNlZ68jIGTesxu3bBxo2wfn0SJO69d8/zys9du/Zsv2MH7L9/S4swVgB5u6T9IuLRiFhRWSjpCOA/WloK60hlmg7bqFrBcc36rXzh5qGuDZpWEhGwbdvYweH++5Ptsg47DObOhWOPTVocAwPJY+5c2GeflhezZgCJiJU1lv8CeHfLS2Idp0zTYRtVKzhe/YMNPDHin7JbgqZ1kN27k26lsQLEjh3D99lnnz3B4Kyzkp/ZADF7di5BYixdfyGhNa9M02Eb7XaqFQRHBo/xtjeraseO0QEhGyyGhuCJ4ScwHHRQEgie+Uw4/fQ9gaHyc+ZMkIqpTw0OIFZTWa47aGasplZw7JOqBpFODJrWOg2dgDz5JPzqV6ODQjZQPDQir+veeycthIEBeOELhweHyqPF4xPt0FQAkTQ5Ih5rdWGss5QlTXczYzW1guPLjusfNgZSWd5pQdNaZ+QJyAMPbOcjV36NmQv258TJj45uQWzYAI+NOPxNnbonELzgBaMDxOGHQ19flU8vt3EDiKSbgNdmMuMeD3wMeE6uJbOOUIbpsM2M1YwVHAfnzuj4oGlNioAHHxwWEHZe+23et/k+Zj2yhf7tW5j56Lbh+0gwa1YSEJ77XHj5y4ePPQwMJAGkB9XTArkc+JqkD5JkvD0LeF2upTJrQLNjNbWCYxmCptXw2GPJ+EKt7qV774VHHx22y5K992HowJncd+BM7njGfIamHsLQgYew6cCZXPOP50J/P0yaVFCFOtu4ASQiVkl6I3A98ACwMCLuz71kZnUqy1iNtcD27dUHpSvP77tv9NTWQw5JWgrHHDN89lL687SP/Zih7b8Z9VH906bAvHntqVdJ1dOF9XbgD4DfJbm17E2S/iYirsu7cGb1KMtYjY3jiSdg06axB6cffnj4PpMnw5w5SUAYOXNpYCBZN2XsluiFZz7LJyBNqqcL6yDg+IjYCXxP0teAKwEHEOsY7nbKT8uuzP/1r6t3KVVeb9yYXB+RNX16EhDmz4dTThk+a2nuXDj0UNhrYnel8AlI82omUxy2UZL7aiAiSn3rMydTNGvMyBlKkJydj8oCnM27VK0FsX59Mnid1de3Z3A6GxQqjzlz4GlPa1NNbSzNJFOs7PgS4L0k+a/mSzqWJH36S1tfTDPrJJUp0pN3P85hjzxA/8Ob6X94Cw987xqY21c77xLAAQfsCQ7Pfe6osQdmzUquj7DSquev907geOAmgIi4RdLTcyyTmbVTRHLhW5Uxhw99+yfMemQLM3c8xF6M6K04/PAkGCxcCEuWjJ7aOm1ax105ba1VTwB5PCK2a/gX4cmcymNmrdZs3qW5c9m9/wF8Y+ZcNj3t4Kemt26cegh9s/u56W1nFlMf6xj1BJDbJL0K6JN0JPBXwHfzLZZZZyhFavdHHqk99lAr79LBByethKOOSmYvjexeSvMuDa0d4l3VxkB+b0GbK2mdqJ4A8pfAW4FdwNUkt6C9NM9CmXWCt624lf/8/r1PddwUktq9kndprMR8tfIuzZ2b5F0aGRwGBmC//er6eM9QsrHUNQurW3gWltVrxdoh/vqzt4zs9QeSC8y+s/RFrfmg3/ymdrdSrbxLBx44Oihkfx52WFfmXbLiNDwLS9JXoOr/DwCehWXd7D2r7qz55a87tXsl79JYYw+bNw/fZ6+9ktlJlZlLlbxL2dZDj+Zdss4zVhfWe9Of5wCHAZ9OX78S+FWehTIr2lhB4qkcW48/nowvjNW9NCLvEvvtt+cah2OP3XPNQyU4zJ7tvEtWGmPdkfCbAJLeN6Lp8hVJLekHknQm8AGgD7gyIpaNWL8Pye1zjwMeBP4wIu6RNA+4A6hc2Pj9iHhjK8pkBkmQePhXD9C/fTOzHt7CrIe3MPvh5PmJkx+FD20aP+/S2WeP7l6aMcNTW61r1DOIvr+kp0fE3QCS5gMTvvOJpD7gw8DpwEZgtaSVEXF7ZrPzgYci4hmSzgWuAP4wXfeLiDh2ouWwHlUt71Lm5033rGfSjkeG7bKrb292zDycg44+Eo49ZnjroXJL0XHyLpl1k3oCyF+TJFC8GxAwF/jTFnz28cC6TGC6BlgMZAPIYpILGQGuBT4k+fTN6pDNu1QtSFTLuzRjRhIMjjiCSaeeyk/7pnLN5r24rW8qu+cMcP45J7DkuDm5F70UU4fNqC+d+9fS6z+elS76WUTsGmufOvUDGzKvNwIn1NomInZL2k6S3BGStCprgYeBt0XEt6p9iKQLgAsABgYGWlBsK1w271I2MGTHHqrlXervT1oKJ544+naiAwOj8i49G7isfbUCmrs9r1lR6k1EcxwwL93+OZKIiP/IrVTj20SS3PFBSccBKyQdExEPj9wwIpYDyyGZxtvmclozdu1KWgi17vuwYUMy/TXj0clT2Pi0mTx40GEcdspZzD/u6NzyLuXZQmjm9rxmRaknmeKngCOAW4DKNztIBrcnYgjI9gfMTpdV22ajpL2BqcCDkVy8sgsgIm6W9AvgmYAv8uh02bxLtWYv3V/lfmWHH54EgmzepblzufHRfVm6Zjub99r3qcHpKZP6uPzMBbkccPNuITRze95O5y657lXPKdkgcHS0/orD1cCR6aD8EHAu8KoR26wEzgO+B7wcuDEiQtJMYGtEPJEmdjwSuLvF5bNm7N6955aitVJrjMy7tO++e7qRzj579MVxs2cnuZmqePuyG9ncN3zgOs8z9rxbCM3enrdTuUuuu9UTQH5Kch3IplZ+cDqm8SaS1Ch9wFURcZukS4A1EbES+DjwKUnrgK0kQQaSuyNeIulxksSOb4yIra0sn9VQybtU6+K4oaEk/UZWnXmXmtHuM/a8P6/bbs/rLrnuVk8AORi4XdIPSbuNoDVXokfEV4Gvjlj2jszz3wCvqLLfF4AvTPTzbYQnn0y6j8ZKrVEt71LllqKnnjr6uoc5c+rOu9SMdp+x5/153ZZ7qhu75GyPeu8HYt1g587qU1uzg9OPPz58n6lT9wSEk04anVajDXmXxupDb/cZezs+r5tuz9ttXXI2XD3TeL/ZjoLYBEXAAw/UbjmsXw9btgzfJ5t36YQT4BWvGN16KDjv0nh96O0+Y++2FkLeuq1LzoarmY1X0iNUT6YoICLiwDwLlodSZ+N97LFkamut8Yd7701aGFmVvEsjWw2V5/39HZ936cRlN1Y9g21pRlzLlWdhlV/D2Xgjwnezb5cI2L597KytmzaNzrt06KFJIFiwAH7v90YHiS7Iu+Q+9PLrpi45G853tG+HJ55IEu+NNXvpkeF5l5g8eU8wOOOM0S2JOXOS6a9dzn3oZp3LAaQVKnmXagWHjRtH31I0k3eJU08d3Xo45JBkjKLHuQ/drHM5gNRj82a4557aA9TV8i7Nnp0Eg5NPHj32MDAABxxQSFXKxoPWZp3LAaQeJ50Ed9215/XTnrYnGDzveaOT8rUw75J1Xx+6B5WtW/goV493vztpVVRaEVOnln5w2orh1B57OJCWnwNIPZYsKboE1iXyTO1RpgOyA2l75P2dcACxpuT1xSzTQbAZeU1LLtsB2Tmy8teO74Sn+VjDKl/MoW07CfZ8MVesHZmNvzPet5PUmn480WnJYx2QO5Gv78lfO74TDiDWsLy+mGU7CDbjwkVHMWXS8NxhrZiWXLYDcl6B1PZox3fCAcQa1uov5oq1QzVTlkzkfTvRkoX9XH7OAvqnTUEkKVkuP2fiN78q2wE5r0Bqe7TjO+ExEGtYK68OH9lPW+vzukke05LLdsGlr+/JXzu+Ew4g1rBWfjGrdVtldfJBsJYiJgKU8YDcbdf3dJp2fCdqZuPtRqXOxtthWnWQnL/0uqopnyHp3inyINhMHau1qKZM6mtJN5VZURrOxms2lladPdbqDpu+36RC07U3OwXS01Otl3gQ3Qp14aKjmNQ3+qr+Hb/ZXej03WZnhJVtNpTZRDiAWKGWLOxn/8mjG8KPPxmFTt9tNhCUbTZUNZVZcfOXXseJy27squtwrLUcQKxw23c+XnV5kWftzQaCsk9P7YWLOa11Cg0gks6UdKekdZKWVlm/j6TPput/IGleZt1F6fI7JS1qZ7mttTrxrL3ZQJDXdR7t0gsXc1rrFDaILqkP+DBwOrARWC1pZUTcntnsfOChiHiGpHOBK4A/lHQ0cC5wDDAL+LqkZ0ZE7fmgJdDteaBq6cRrGCYyBbLM01M9hmONKHIW1vHAuoi4G0DSNcBiIBtAFgPvTJ9fC3xIktLl10TELuCXktal7/e9NpW95cqWDK+VOvUahjIHgmb5FsLWiCIDSD+wIfN6I3BCrW0iYrek7cBB6fLvj9i36n+6pAuACwAGBgZaUvA89Pr0z148WHeiTmwNWufq+kH0iFgeEYMRMThz5syii1OTuw6sE5R9DMfaq8gWyBAwJ/N6drqs2jYbJe0NTAUerHPfUnHXgXUKtwatXkW2QFYDR0qaL2kyyaD4yhHbrATOS5+/HLgxktwrK4Fz01la84EjgR+2qdy5KPv0TzPrPYW1QNIxjTcBq4A+4KqIuE3SJcCaiFgJfBz4VDpIvpUkyJBu9zmSAffdwF+UfQZWpw4km5nV4mSKZmY2plrJFLt+EN3MzPLhAGJmZk1xOnezHPRqVgHrLQ4gZi3Wy1kFrLe4C8usxZyQ0HqFA4hZizmrgPUKBxCzFuvE9PRmeXAAMWsxZxWwXuFBdLMWc1YB6xUOIGY5cEJC6wUOINYWvi7CrPs4gFjufF2EWXfyILrlztdFmHUnBxDLna+LMOtODiCWO18XYdadHEAsd74uwqw7eRDdcufrIsbnWWpWRg4g1ha+LqI2z1KzsnIXllnBPEvNysoBxKxgnqVmZeUuLOs4vTYeMGvaFIaqBAvPUrNOV0gLRNIMSddLuiv9Ob3Gduel29wl6bzM8psk3SnplvRxSPtKb3mqjAcMbdtJsGc8YMXaoaKLlhvPUmu/FWuHOHHZjcxfeh0nLruxq79feSqqC2spcENEHAnckL4eRtIM4GLgBOB44OIRgebVEXFs+tjcjkJb/npxPGDJwn4uP2cB/dOmIKB/2hQuP2dBV7e6itSLJyl5KaoLazFwSvr8k8BNwFtGbLMIuD4itgJIuh44E7i6PUW0iWqmK6pXxwM8S619xjpJ8d+gMUUFkEMjYlP6/H7g0Crb9AMbMq83pssq/l3SE8AXgMsiInIpqTWl2ampHg/obe0Y/+rVk5Q85NaFJenrkn5a5bE4u1164G/04P/qiFgAnJw+XjNGOS6QtEbSmi1btoSzYaEAAAmqSURBVDRcD2tOs11RHg/oXe3qWnJqndbJLYBExGkR8ewqjy8Dv5J0OED6s9oYxhAwJ/N6drqMiKj8fAT4DMkYSa1yLI+IwYgYnDlzZmsqZ+Nq9izP4wG9q13jXz5JaZ2iurBWAucBy9KfX66yzSrgHzMD52cAF0naG5gWEQ9ImgT8PvD1NpTZGjCRriiPB/SmdnUtObVO6xQVQJYBn5N0PrAe+AMASYPAGyPiDRGxVdKlwOp0n0vSZfsDq9Lg0UcSPD7W/irYWC5cdNSwMRDwWZ5VVxn3qNWPnUfXkk9SWqOQABIRDwIvrrJ8DfCGzOurgKtGbPNr4Li8y2gT47M8q8fIyRYj+aSjs/lKdMuNz/JsPNXGPSr6fdLR8RxAzKwwtcY3BHxn6YvaWxhrmANIC/Ra7iazVvF1P+XmbLwT5LQIZs3zlNpycwCZoF7M3WTWKr7up9zchTVBTotgNjGebFFeboFMkNMimFmvcgCZIPfhmlmvchfWBPmCOTPrVQ4gLeA+XDPrRe7CMjOzpjiAmJlZUxxAzMysKR4Dsa7hlDJm7eUAYl2h2Xuwm1nz3IVlXcEpZczazwHEuoJTypi1nwOIdQWnlDFrPwcQ6wpOKWPWfh5Et67glDIJz0SzdnIAsa5R5pQyrTjweyaatZu7sMwK1qq7WnommrVbIQFE0gxJ10u6K/05vcZ2X5O0TdJ/jVg+X9IPJK2T9FlJk9tTcrPWa9WB3zPRrN2KaoEsBW6IiCOBG9LX1bwHeE2V5VcA/xwRzwAeAs7PpZRmbdCqA79nolm7FRVAFgOfTJ9/ElhSbaOIuAF4JLtMkoAXAdeOt79ZGbTqwO+ZaNZuRQWQQyNiU/r8fuDQBvY9CNgWEbvT1xuBmiOEki6QtEbSmi1btjRXWrMcterAv2RhP5efs4D+aVMQ0D9tCpefs8AD6Jab3GZhSfo6cFiVVW/NvoiIkBR5lSMilgPLAQYHB3P7HLNmtXIKcplnoln55BZAIuK0Wusk/UrS4RGxSdLhwOYG3vpBYJqkvdNWyGygsekqZh3GB34ro6K6sFYC56XPzwO+XO+OERHAN4CXN7O/mZm1RlEBZBlwuqS7gNPS10galHRlZSNJ3wI+D7xY0kZJi9JVbwHeLGkdyZjIx9taejMzK+ZK9Ih4EHhxleVrgDdkXp9cY/+7geNzK6CZmY3LV6KbmVlTHEDMzKwpSsake4OkLcD6ossxhoOBB4ouRAu5Pp3N9elsnVSfuRExc+TCngognU7SmogYLLocreL6dDbXp7OVoT7uwjIzs6Y4gJiZWVMcQDrL8qIL0GKuT2dzfTpbx9fHYyBmZtYUt0DMzKwpDiBmZtYUB5A2a+B2vuel29wl6bzM8ldKulXST9Jb/h7cvtJXLedE6zNZ0nJJP5f0M0kva1/pq5ZzQvXJrF8p6af5l3hsE6mPpP0kXZf+XW6TtKy9pR9WvjMl3ZnexnrUHUwl7ZPe3npdervreZl1F6XL78zk0ytUs/WRdLqkm9NjwM2SXtTusg8TEX608QG8G1iaPl8KXFFlmxnA3enP6enz6SS5yzYDB2fe651lrU+67l3AZenzvSp1K2t90vXnAJ8Bflry79t+wKnpNpOBbwFnFVCHPuAXwNPTcvwYOHrENn8OfCR9fi7w2fT50en2+wDz0/fpK/hvMpH6LARmpc+fDQwVWRe3QNqvntv5LgKuj4itEfEQcD1wJqD0sX96a98DgfvyL/KYJlIfgNcDlwNExJMRUfSVtxOqj6QDgDcDl7WhrPVouj4R8WhEfAMgIh4DfkRy/512Ox5YFxF3p+W4hqReWdl6XkuSwVvp8msiYldE/BJYR/GJWJuuT0SsjYjK//xtwBRJ+7Sl1FU4gLRfPbfz7Qc2ZF5vBPoj4nHgz4BbSQLH0RSfyr7p+kialr6+VNKPJH1eUiO3N85D0/VJn18KvA94NLcSNmai9QEg/Vu9BLghj0KOY9zyZbeJ5EZz20lu9VDPvu02kfpkvQz4UUTsyqmc4yoknXu3y+t2vpImkQSQhSTdDP8CXETOZ7s53p54b5Iz2u9GxJslvRl4L/Capgtbhxz/PscCR0TEX2f74POW9+2jJe0NXA18MJJbKVjBJB0DXAGcUWQ5HEByEBO/ne8QcErm9WzgJuDY9P1/kb7X50j6tXOVY30eJDlT/2K6/PPA+a0o81hyrM/zgUFJ95D8bx0i6aaIOIUc5VifiuXAXRHx/hYUtxlDwJzM62q3sa5sszENeFNJvl/17NtuE6kPkmYDXwL+uHIsKIq7sNqvntv5rgLOkDQ9nTVzRrpsCDhaUiUr5unAHTmXdzxN1yeSkcCvsOfg9WLg9nyLO66J1OffImJWRMwDTgJ+nnfwqMNEvm9Iuozk4PX/2lDWWlYDR0qaL2kyyaDyyhHbZOv5cuDG9Pu1Ejg3ndU0HzgS+GGbyl1L0/VJuxKvI5kY8Z22lbiWIkfwe/FB0o95A3AX8HVgRrp8ELgys93rSQb81gGvyyx/I0nQ+AnJwfegktdnLvC/aX1uAAbKXJ/M+nl0xiysputDcmYc6fftlvTxhoLqcTbwc5LZS29Nl10CvDR9vi9JC3YdSYB4embft6b73UkBs8haWR/gbcCvM3+PW4BDiqqHU5mYmVlT3IVlZmZNcQAxM7OmOICYmVlTHEDMzKwpDiBmZtYUBxCzCZK0o8HtT5H0Xy343Fcrycp8q6TvSnrORN/TrBG+Et2svH4JvDAiHpJ0FskV4ycUXCbrIW6BmLVI2rK4SdK16T00/jPNCFu5/8PPJP2IJN17ZZ/9JV0l6YeS1kpanC7/gKR3pM8XSfpfScP+XyPiu5FkzwX4PsVkyrUe5haIWWstBI4hyZb8HeBESWuAjwEvIrmy+LOZ7d9Kkqbi9Wmaih+myREvAlZL+hbwQeDsiHhyjM89H/jvltfGbAxugZi11g8jYmN6sL+FJKXJs4BfRsRdkaR++HRm+zOApZJuIUlguC9JOpdHgT8huTfHh2KMpHmSTiUJIG/JoT5mNbkFYtZa2XszPMH4/2MCXhYRd1ZZt4AkA+usmjtLvw1cSZLj6cEGy2o2IW6BmOXvZ8A8SUekr1+ZWbcK+MvMWMnC9Odc4G9IusTOkjRqcFzSAEkq/NdExM9zLL9ZVQ4gZjmLiN8AFwDXpYPo2XtyXApMAn4i6TaSuzOK5E6TfxvJ7UvPB66UtO+It34HSbbdf5V0SzrWYtY2zsZrZmZNcQvEzMya4gBiZmZNcQAxM7OmOICYmVlTHEDMzKwpDiBmZtYUBxAzM2vK/wccTq6lOWhaEAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# adds the title\n",
        "plt.figure()\n",
        "plt.title('Index 2 - Index 3 Regression')\n",
        "\n",
        "x = index_2\n",
        "y = index_3\n",
        "\n",
        "# plot the data\n",
        "plt.scatter(x, y)\n",
        "  \n",
        "# fits the best fitting line to the data\n",
        "plt.plot(np.unique(x), \n",
        "         np.poly1d(np.polyfit(x, y, 1))\n",
        "         (np.unique(x)), color='red')\n",
        "  \n",
        "# Labelling axes\n",
        "plt.xlabel('Index 2')\n",
        "plt.ylabel('Index 3')\n",
        "\n",
        "plt.savefig('index_2_index_3_regression_curve.jpg')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "bert_based_social_mood_index.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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