03_01_big_data_and_surveys/exercise/hfhub_transformer_intro.ipynb

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    "## Using transformer models to extract information from text\n",
    "\n",
    "from https://huggingface.co/course/:\n",
    "\n",
    "What is NLP?\n",
    "\n",
    "NLP is a field of linguistics and machine learning focused on understanding everything related to human language. The aim of NLP tasks is not only to understand single words individually, but to be able to understand the context of those words.\n",
    "\n",
    "The following is a list of common NLP tasks, with some examples of each:\n",
    "\n",
    "* Classifying whole sentences: Getting the sentiment of a review, detecting if an email is spam, determining if a sentence is grammatically correct or whether two sentences are logically related or not\n",
    "    \n",
    "* Classifying each word in a sentence: Identifying the grammatical components of a sentence (noun, verb, adjective), or the named entities (person, location, organization)\n",
    "\n",
    "* Generating text content: Completing a prompt with auto-generated text, filling in the blanks in a text with masked words\n",
    "\n",
    "* Extracting an answer from a text: Given a question and a context, extracting the answer to the question based on the information provided in the context\n",
    "\n",
    "* Generating a new sentence from an input text: Translating a text into another language, summarizing a text\n",
    "\n",
    "NLP isn’t limited to written text though. It also tackles complex challenges in speech recognition and computer vision, such as generating a transcript of an audio sample or a description of an image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hugging Face Model Hub\n",
    "\n",
    "https://huggingface.co/models\n",
    "\n",
    "Examples\n",
    "\n",
    "https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest?text=Covid+cases+are+increasing+fast%21\n",
    "\n",
    "https://huggingface.co/oliverguhr/german-sentiment-bert?text=Das+ist+gar+nicht+mal+so+schlecht"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "import sys\n",
    "\n",
    "# install with pip\n",
    "# !{sys.executable} -m pip install transformers\n",
    "# !{sys.executable} -m pip install pyreadr\n",
    "# !{sys.executable} -m pip install matplotlib\n",
    "# !{sys.executable} -m pip install pandas\n",
    "# !{sys.executable} -m pip install sklearn\n",
    "# !{sys.executable} -m pip install scipy\n",
    "\n",
    "# install with conda\n",
    "# !conda install --yes --prefix {sys.prefix} transformers\n",
    "# !conda install --yes --prefix {sys.prefix} pyreadr\n",
    "# !conda install --yes --prefix {sys.prefix} matplotlib\n",
    "# !conda install --yes --prefix {sys.prefix} pandas\n",
    "# !conda install --yes --prefix {sys.prefix} sklearn\n",
    "# !conda install --yes --prefix {sys.prefix} scipy\n",
    "\n",
    "from transformers import pipeline\n",
    "import pyreadr\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from scipy import stats"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998290538787842}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = pipeline(\"sentiment-analysis\")\n",
    "\n",
    "classifier(\"I've been waiting for this awesome course my whole life!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[{'label': 'positive', 'score': 0.9663448929786682},\n",
       "  {'label': 'negative', 'score': 0.02535451389849186},\n",
       "  {'label': 'neutral', 'score': 0.008300610817968845}]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "german_classifier = pipeline(\"sentiment-analysis\",model=\"oliverguhr/german-sentiment-bert\",return_all_scores=True)\n",
    "\n",
    "german_classifier(\"Extrem happy heute, toller Kurs!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'score': 0.19619838893413544,\n",
       "  'token': 30412,\n",
       "  'token_str': ' mathematical',\n",
       "  'sequence': 'This course will teach you all about mathematical models.'},\n",
       " {'score': 0.040527306497097015,\n",
       "  'token': 38163,\n",
       "  'token_str': ' computational',\n",
       "  'sequence': 'This course will teach you all about computational models.'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unmasker = pipeline(\"fill-mask\")\n",
    "unmasker(\"This course will teach you all about <mask> models.\", top_k=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'generated_text': 'In this course, we will teach you how to play in one-on-one sessions with real players and other people. One-on-one'},\n",
       " {'generated_text': 'In this course, we will teach you how to get started, build your skills and build your own tools. We’ll put you through more'}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generator = pipeline(\"text-generation\", model=\"distilgpt2\")\n",
    "generator(\n",
    "    \"In this course, we will teach you how to\",\n",
    "    max_length=30,\n",
    "    num_return_sequences=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are three main steps involved when you pass some text to a pipeline:\n",
    "\n",
    "    The text is preprocessed into a format the model can understand.\n",
    "    The preprocessed inputs are passed to the model.\n",
    "    The predictions of the model are post-processed, so you can make sense of them.\n",
    "\n",
    "Some of the currently available pipelines are:\n",
    "\n",
    "    feature-extraction (get the vector representation of a text)\n",
    "    fill-mask\n",
    "    ner (named entity recognition)\n",
    "    question-answering\n",
    "    sentiment-analysis\n",
    "    summarization\n",
    "    text-generation\n",
    "    translation\n",
    "    zero-shot-classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can feed lists of texts to pipelines.\n",
    "In principle, you could loop over pipelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998743534088135},\n",
       " {'label': 'NEGATIVE', 'score': 0.9996669292449951}]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier([\"This restaurant is awesome\", \"This restaurant is awful\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Especially when you are working with GPUs, which you need when you analyse a lot of text (CPUs are much slower for these kinds of operations):\n",
    "\n",
    "You need to set the batch size, how much texts the model feeds at once to the model, so that it still fits RAM of the GPU and other parameters, see\n",
    "\n",
    "https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching\n",
    "\n",
    "So at some point you are better off writing your own loops directly in pytorch, see https://pytorch.org/docs/stable/data.html\n",
    "\n",
    "To load your custom data in huggingface transformers you need to define a class that does that, but Hugging Face datasets has some prebuilt ones, see https://huggingface.co/docs/datasets/loading \n",
    "\n",
    "Also the Hugging Face dataset hub offers a lot of data sets (also huge ones) that can be loaded super-easily, see https://huggingface.co/datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine text analysis results with data from surveys\n",
    "### Replicating findings of a scientific publication\n",
    "\n",
    "\"Validating daily social media macroscopes of emotions\"\n",
    "\n",
    "https://www.nature.com/articles/s41598-022-14579-y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_day</th>\n",
       "      <th>prop_good</th>\n",
       "      <th>sent</th>\n",
       "      <th>sentA</th>\n",
       "      <th>sentB</th>\n",
       "      <th>sent_roll3_weighted</th>\n",
       "      <th>sentA_roll3_weighted</th>\n",
       "      <th>sentB_roll3_weighted</th>\n",
       "      <th>sent_roll3_weighted_shifted1</th>\n",
       "      <th>sentA_roll3_weighted_shifted1</th>\n",
       "      <th>sentB_roll3_weighted_shifted1</th>\n",
       "      <th>sent_roll3_weighted_shifted2</th>\n",
       "      <th>sentA_roll3_weighted_shifted2</th>\n",
       "      <th>sentB_roll3_weighted_shifted2</th>\n",
       "      <th>sentA_pos_roll3_weighted_shifted1</th>\n",
       "      <th>sentA_neg_roll3_weighted_shifted1</th>\n",
       "      <th>sentB_pos_roll3_weighted_shifted1</th>\n",
       "      <th>sentB_neg_roll3_weighted_shifted1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-11-11</td>\n",
       "      <td>0.645394</td>\n",
       "      <td>-0.092979</td>\n",
       "      <td>-0.120723</td>\n",
       "      <td>-0.065234</td>\n",
       "      <td>-0.044990</td>\n",
       "      <td>-0.057526</td>\n",
       "      <td>-0.032453</td>\n",
       "      <td>-0.037658</td>\n",
       "      <td>-0.035755</td>\n",
       "      <td>-0.039561</td>\n",
       "      <td>-0.052630</td>\n",
       "      <td>-0.048885</td>\n",
       "      <td>-0.056375</td>\n",
       "      <td>0.012164</td>\n",
       "      <td>0.047919</td>\n",
       "      <td>-0.031626</td>\n",
       "      <td>0.007935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-11-12</td>\n",
       "      <td>0.625644</td>\n",
       "      <td>-0.010992</td>\n",
       "      <td>0.002503</td>\n",
       "      <td>-0.024487</td>\n",
       "      <td>-0.039622</td>\n",
       "      <td>-0.038742</td>\n",
       "      <td>-0.040501</td>\n",
       "      <td>-0.051353</td>\n",
       "      <td>-0.047523</td>\n",
       "      <td>-0.055183</td>\n",
       "      <td>-0.050103</td>\n",
       "      <td>-0.034181</td>\n",
       "      <td>-0.066024</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.048227</td>\n",
       "      <td>-0.047672</td>\n",
       "      <td>0.007511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-11-13</td>\n",
       "      <td>0.606379</td>\n",
       "      <td>-0.053836</td>\n",
       "      <td>-0.030294</td>\n",
       "      <td>-0.077378</td>\n",
       "      <td>-0.053027</td>\n",
       "      <td>-0.049871</td>\n",
       "      <td>-0.056184</td>\n",
       "      <td>-0.049503</td>\n",
       "      <td>-0.033828</td>\n",
       "      <td>-0.065177</td>\n",
       "      <td>-0.058509</td>\n",
       "      <td>-0.037224</td>\n",
       "      <td>-0.079794</td>\n",
       "      <td>0.004857</td>\n",
       "      <td>0.038685</td>\n",
       "      <td>-0.034898</td>\n",
       "      <td>0.030279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-11-14</td>\n",
       "      <td>0.598761</td>\n",
       "      <td>-0.083013</td>\n",
       "      <td>-0.072666</td>\n",
       "      <td>-0.093359</td>\n",
       "      <td>-0.052114</td>\n",
       "      <td>-0.036636</td>\n",
       "      <td>-0.067592</td>\n",
       "      <td>-0.057744</td>\n",
       "      <td>-0.036130</td>\n",
       "      <td>-0.079358</td>\n",
       "      <td>-0.045848</td>\n",
       "      <td>-0.015309</td>\n",
       "      <td>-0.076386</td>\n",
       "      <td>0.009520</td>\n",
       "      <td>0.045650</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>0.053030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-11-15</td>\n",
       "      <td>0.609519</td>\n",
       "      <td>-0.039853</td>\n",
       "      <td>-0.010408</td>\n",
       "      <td>-0.069298</td>\n",
       "      <td>-0.060300</td>\n",
       "      <td>-0.039814</td>\n",
       "      <td>-0.080785</td>\n",
       "      <td>-0.047440</td>\n",
       "      <td>-0.018248</td>\n",
       "      <td>-0.076633</td>\n",
       "      <td>-0.007445</td>\n",
       "      <td>0.019339</td>\n",
       "      <td>-0.034228</td>\n",
       "      <td>0.021502</td>\n",
       "      <td>0.039750</td>\n",
       "      <td>-0.024410</td>\n",
       "      <td>0.052222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-11-16</td>\n",
       "      <td>0.636596</td>\n",
       "      <td>-0.021604</td>\n",
       "      <td>0.025716</td>\n",
       "      <td>-0.068923</td>\n",
       "      <td>-0.050121</td>\n",
       "      <td>-0.022138</td>\n",
       "      <td>-0.078103</td>\n",
       "      <td>-0.008710</td>\n",
       "      <td>0.017095</td>\n",
       "      <td>-0.034515</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.006517</td>\n",
       "      <td>-0.006087</td>\n",
       "      <td>0.038592</td>\n",
       "      <td>0.021497</td>\n",
       "      <td>-0.008579</td>\n",
       "      <td>0.025936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-11-17</td>\n",
       "      <td>0.640625</td>\n",
       "      <td>0.040925</td>\n",
       "      <td>0.040905</td>\n",
       "      <td>0.040945</td>\n",
       "      <td>-0.007762</td>\n",
       "      <td>0.018308</td>\n",
       "      <td>-0.033832</td>\n",
       "      <td>0.001813</td>\n",
       "      <td>0.006429</td>\n",
       "      <td>-0.002804</td>\n",
       "      <td>0.016625</td>\n",
       "      <td>0.005869</td>\n",
       "      <td>0.027381</td>\n",
       "      <td>0.027815</td>\n",
       "      <td>0.021386</td>\n",
       "      <td>-0.007287</td>\n",
       "      <td>-0.004483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-11-18</td>\n",
       "      <td>0.653232</td>\n",
       "      <td>-0.014795</td>\n",
       "      <td>-0.050543</td>\n",
       "      <td>0.020953</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>-0.003617</td>\n",
       "      <td>0.017233</td>\n",
       "      <td>0.006969</td>\n",
       "      <td>0.027497</td>\n",
       "      <td>0.014280</td>\n",
       "      <td>0.016105</td>\n",
       "      <td>0.012455</td>\n",
       "      <td>0.021603</td>\n",
       "      <td>0.014635</td>\n",
       "      <td>0.022042</td>\n",
       "      <td>-0.005455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-11-19</td>\n",
       "      <td>0.654845</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>0.028484</td>\n",
       "      <td>0.019811</td>\n",
       "      <td>0.016693</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>0.027276</td>\n",
       "      <td>0.014726</td>\n",
       "      <td>0.017053</td>\n",
       "      <td>0.012400</td>\n",
       "      <td>0.054080</td>\n",
       "      <td>0.075261</td>\n",
       "      <td>0.032899</td>\n",
       "      <td>0.014826</td>\n",
       "      <td>-0.002227</td>\n",
       "      <td>0.010831</td>\n",
       "      <td>-0.001569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>0.659050</td>\n",
       "      <td>0.035170</td>\n",
       "      <td>0.074209</td>\n",
       "      <td>-0.003870</td>\n",
       "      <td>0.014535</td>\n",
       "      <td>0.016628</td>\n",
       "      <td>0.012441</td>\n",
       "      <td>0.053925</td>\n",
       "      <td>0.074979</td>\n",
       "      <td>0.032871</td>\n",
       "      <td>0.097554</td>\n",
       "      <td>0.128136</td>\n",
       "      <td>0.066973</td>\n",
       "      <td>0.067948</td>\n",
       "      <td>-0.007031</td>\n",
       "      <td>0.045811</td>\n",
       "      <td>0.012940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-11-21</td>\n",
       "      <td>0.698704</td>\n",
       "      <td>0.103847</td>\n",
       "      <td>0.123958</td>\n",
       "      <td>0.083737</td>\n",
       "      <td>0.051578</td>\n",
       "      <td>0.072702</td>\n",
       "      <td>0.030454</td>\n",
       "      <td>0.094855</td>\n",
       "      <td>0.125335</td>\n",
       "      <td>0.064376</td>\n",
       "      <td>0.112763</td>\n",
       "      <td>0.143410</td>\n",
       "      <td>0.082117</td>\n",
       "      <td>0.117303</td>\n",
       "      <td>-0.008032</td>\n",
       "      <td>0.080720</td>\n",
       "      <td>0.016344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-11-22</td>\n",
       "      <td>0.716379</td>\n",
       "      <td>0.155826</td>\n",
       "      <td>0.188281</td>\n",
       "      <td>0.123372</td>\n",
       "      <td>0.094748</td>\n",
       "      <td>0.125766</td>\n",
       "      <td>0.063729</td>\n",
       "      <td>0.110343</td>\n",
       "      <td>0.140958</td>\n",
       "      <td>0.079728</td>\n",
       "      <td>0.129402</td>\n",
       "      <td>0.168096</td>\n",
       "      <td>0.090708</td>\n",
       "      <td>0.148204</td>\n",
       "      <td>0.007246</td>\n",
       "      <td>0.092164</td>\n",
       "      <td>0.012436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-11-23</td>\n",
       "      <td>0.699046</td>\n",
       "      <td>0.072622</td>\n",
       "      <td>0.113772</td>\n",
       "      <td>0.031472</td>\n",
       "      <td>0.110204</td>\n",
       "      <td>0.141599</td>\n",
       "      <td>0.078809</td>\n",
       "      <td>0.128171</td>\n",
       "      <td>0.167309</td>\n",
       "      <td>0.089033</td>\n",
       "      <td>0.074205</td>\n",
       "      <td>0.104028</td>\n",
       "      <td>0.044382</td>\n",
       "      <td>0.142445</td>\n",
       "      <td>-0.024864</td>\n",
       "      <td>0.083797</td>\n",
       "      <td>-0.005235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-11-24</td>\n",
       "      <td>0.648037</td>\n",
       "      <td>0.154924</td>\n",
       "      <td>0.198516</td>\n",
       "      <td>0.111332</td>\n",
       "      <td>0.128232</td>\n",
       "      <td>0.167531</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.073008</td>\n",
       "      <td>0.102294</td>\n",
       "      <td>0.043722</td>\n",
       "      <td>0.037394</td>\n",
       "      <td>0.051135</td>\n",
       "      <td>0.023654</td>\n",
       "      <td>0.089752</td>\n",
       "      <td>-0.012542</td>\n",
       "      <td>0.023842</td>\n",
       "      <td>-0.019880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-11-25</td>\n",
       "      <td>0.640931</td>\n",
       "      <td>-0.001511</td>\n",
       "      <td>0.004328</td>\n",
       "      <td>-0.007351</td>\n",
       "      <td>0.074606</td>\n",
       "      <td>0.104220</td>\n",
       "      <td>0.044992</td>\n",
       "      <td>0.037118</td>\n",
       "      <td>0.050827</td>\n",
       "      <td>0.023409</td>\n",
       "      <td>-0.010055</td>\n",
       "      <td>-0.007164</td>\n",
       "      <td>-0.012945</td>\n",
       "      <td>0.049612</td>\n",
       "      <td>-0.001215</td>\n",
       "      <td>0.008928</td>\n",
       "      <td>-0.014481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-11-26</td>\n",
       "      <td>0.633657</td>\n",
       "      <td>-0.029808</td>\n",
       "      <td>-0.034947</td>\n",
       "      <td>-0.024669</td>\n",
       "      <td>0.040039</td>\n",
       "      <td>0.054509</td>\n",
       "      <td>0.025570</td>\n",
       "      <td>-0.010017</td>\n",
       "      <td>-0.007126</td>\n",
       "      <td>-0.012908</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>0.009192</td>\n",
       "      <td>-0.006896</td>\n",
       "      <td>0.006119</td>\n",
       "      <td>0.013245</td>\n",
       "      <td>-0.026783</td>\n",
       "      <td>-0.013875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-11-27</td>\n",
       "      <td>0.646296</td>\n",
       "      <td>0.001528</td>\n",
       "      <td>0.009591</td>\n",
       "      <td>-0.006534</td>\n",
       "      <td>-0.010116</td>\n",
       "      <td>-0.007278</td>\n",
       "      <td>-0.012954</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>0.008051</td>\n",
       "      <td>-0.007352</td>\n",
       "      <td>0.036611</td>\n",
       "      <td>0.047108</td>\n",
       "      <td>0.026113</td>\n",
       "      <td>0.010813</td>\n",
       "      <td>0.002762</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>0.005571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-11-28</td>\n",
       "      <td>0.657143</td>\n",
       "      <td>0.030785</td>\n",
       "      <td>0.051594</td>\n",
       "      <td>0.009976</td>\n",
       "      <td>-0.000321</td>\n",
       "      <td>0.007095</td>\n",
       "      <td>-0.007736</td>\n",
       "      <td>0.035791</td>\n",
       "      <td>0.046292</td>\n",
       "      <td>0.025290</td>\n",
       "      <td>0.048276</td>\n",
       "      <td>0.063106</td>\n",
       "      <td>0.033447</td>\n",
       "      <td>0.049461</td>\n",
       "      <td>0.003169</td>\n",
       "      <td>0.038753</td>\n",
       "      <td>0.013463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-11-29</td>\n",
       "      <td>0.689163</td>\n",
       "      <td>0.079576</td>\n",
       "      <td>0.081802</td>\n",
       "      <td>0.077350</td>\n",
       "      <td>0.036539</td>\n",
       "      <td>0.046846</td>\n",
       "      <td>0.026231</td>\n",
       "      <td>0.047875</td>\n",
       "      <td>0.062886</td>\n",
       "      <td>0.032864</td>\n",
       "      <td>0.043105</td>\n",
       "      <td>0.044315</td>\n",
       "      <td>0.041894</td>\n",
       "      <td>0.050086</td>\n",
       "      <td>-0.012800</td>\n",
       "      <td>0.049621</td>\n",
       "      <td>0.016757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-11-30</td>\n",
       "      <td>0.679912</td>\n",
       "      <td>0.034464</td>\n",
       "      <td>0.056049</td>\n",
       "      <td>0.012880</td>\n",
       "      <td>0.048015</td>\n",
       "      <td>0.063017</td>\n",
       "      <td>0.033012</td>\n",
       "      <td>0.041682</td>\n",
       "      <td>0.042460</td>\n",
       "      <td>0.040904</td>\n",
       "      <td>0.003318</td>\n",
       "      <td>0.016098</td>\n",
       "      <td>-0.009462</td>\n",
       "      <td>0.041085</td>\n",
       "      <td>-0.001375</td>\n",
       "      <td>0.033053</td>\n",
       "      <td>-0.007852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date_day  prop_good      sent     sentA     sentB  sent_roll3_weighted  \\\n",
       "4  2020-11-11   0.645394 -0.092979 -0.120723 -0.065234            -0.044990   \n",
       "5  2020-11-12   0.625644 -0.010992  0.002503 -0.024487            -0.039622   \n",
       "6  2020-11-13   0.606379 -0.053836 -0.030294 -0.077378            -0.053027   \n",
       "7  2020-11-14   0.598761 -0.083013 -0.072666 -0.093359            -0.052114   \n",
       "8  2020-11-15   0.609519 -0.039853 -0.010408 -0.069298            -0.060300   \n",
       "9  2020-11-16   0.636596 -0.021604  0.025716 -0.068923            -0.050121   \n",
       "10 2020-11-17   0.640625  0.040925  0.040905  0.040945            -0.007762   \n",
       "11 2020-11-18   0.653232 -0.014795 -0.050543  0.020953             0.001049   \n",
       "12 2020-11-19   0.654845  0.024147  0.028484  0.019811             0.016693   \n",
       "13 2020-11-20   0.659050  0.035170  0.074209 -0.003870             0.014535   \n",
       "14 2020-11-21   0.698704  0.103847  0.123958  0.083737             0.051578   \n",
       "15 2020-11-22   0.716379  0.155826  0.188281  0.123372             0.094748   \n",
       "16 2020-11-23   0.699046  0.072622  0.113772  0.031472             0.110204   \n",
       "17 2020-11-24   0.648037  0.154924  0.198516  0.111332             0.128232   \n",
       "18 2020-11-25   0.640931 -0.001511  0.004328 -0.007351             0.074606   \n",
       "19 2020-11-26   0.633657 -0.029808 -0.034947 -0.024669             0.040039   \n",
       "20 2020-11-27   0.646296  0.001528  0.009591 -0.006534            -0.010116   \n",
       "21 2020-11-28   0.657143  0.030785  0.051594  0.009976            -0.000321   \n",
       "22 2020-11-29   0.689163  0.079576  0.081802  0.077350             0.036539   \n",
       "23 2020-11-30   0.679912  0.034464  0.056049  0.012880             0.048015   \n",
       "\n",
       "    sentA_roll3_weighted  sentB_roll3_weighted  sent_roll3_weighted_shifted1  \\\n",
       "4              -0.057526             -0.032453                     -0.037658   \n",
       "5              -0.038742             -0.040501                     -0.051353   \n",
       "6              -0.049871             -0.056184                     -0.049503   \n",
       "7              -0.036636             -0.067592                     -0.057744   \n",
       "8              -0.039814             -0.080785                     -0.047440   \n",
       "9              -0.022138             -0.078103                     -0.008710   \n",
       "10              0.018308             -0.033832                      0.001813   \n",
       "11              0.005716             -0.003617                      0.017233   \n",
       "12              0.006110              0.027276                      0.014726   \n",
       "13              0.016628              0.012441                      0.053925   \n",
       "14              0.072702              0.030454                      0.094855   \n",
       "15              0.125766              0.063729                      0.110343   \n",
       "16              0.141599              0.078809                      0.128171   \n",
       "17              0.167531              0.088933                      0.073008   \n",
       "18              0.104220              0.044992                      0.037118   \n",
       "19              0.054509              0.025570                     -0.010017   \n",
       "20             -0.007278             -0.012954                      0.000349   \n",
       "21              0.007095             -0.007736                      0.035791   \n",
       "22              0.046846              0.026231                      0.047875   \n",
       "23              0.063017              0.033012                      0.041682   \n",
       "\n",
       "    sentA_roll3_weighted_shifted1  sentB_roll3_weighted_shifted1  \\\n",
       "4                       -0.035755                      -0.039561   \n",
       "5                       -0.047523                      -0.055183   \n",
       "6                       -0.033828                      -0.065177   \n",
       "7                       -0.036130                      -0.079358   \n",
       "8                       -0.018248                      -0.076633   \n",
       "9                        0.017095                      -0.034515   \n",
       "10                       0.006429                      -0.002804   \n",
       "11                       0.006969                       0.027497   \n",
       "12                       0.017053                       0.012400   \n",
       "13                       0.074979                       0.032871   \n",
       "14                       0.125335                       0.064376   \n",
       "15                       0.140958                       0.079728   \n",
       "16                       0.167309                       0.089033   \n",
       "17                       0.102294                       0.043722   \n",
       "18                       0.050827                       0.023409   \n",
       "19                      -0.007126                      -0.012908   \n",
       "20                       0.008051                      -0.007352   \n",
       "21                       0.046292                       0.025290   \n",
       "22                       0.062886                       0.032864   \n",
       "23                       0.042460                       0.040904   \n",
       "\n",
       "    sent_roll3_weighted_shifted2  sentA_roll3_weighted_shifted2  \\\n",
       "4                      -0.052630                      -0.048885   \n",
       "5                      -0.050103                      -0.034181   \n",
       "6                      -0.058509                      -0.037224   \n",
       "7                      -0.045848                      -0.015309   \n",
       "8                      -0.007445                       0.019339   \n",
       "9                       0.000215                       0.006517   \n",
       "10                      0.016625                       0.005869   \n",
       "11                      0.014280                       0.016105   \n",
       "12                      0.054080                       0.075261   \n",
       "13                      0.097554                       0.128136   \n",
       "14                      0.112763                       0.143410   \n",
       "15                      0.129402                       0.168096   \n",
       "16                      0.074205                       0.104028   \n",
       "17                      0.037394                       0.051135   \n",
       "18                     -0.010055                      -0.007164   \n",
       "19                      0.001148                       0.009192   \n",
       "20                      0.036611                       0.047108   \n",
       "21                      0.048276                       0.063106   \n",
       "22                      0.043105                       0.044315   \n",
       "23                      0.003318                       0.016098   \n",
       "\n",
       "    sentB_roll3_weighted_shifted2  sentA_pos_roll3_weighted_shifted1  \\\n",
       "4                       -0.056375                           0.012164   \n",
       "5                       -0.066024                           0.000704   \n",
       "6                       -0.079794                           0.004857   \n",
       "7                       -0.076386                           0.009520   \n",
       "8                       -0.034228                           0.021502   \n",
       "9                       -0.006087                           0.038592   \n",
       "10                       0.027381                           0.027815   \n",
       "11                       0.012455                           0.021603   \n",
       "12                       0.032899                           0.014826   \n",
       "13                       0.066973                           0.067948   \n",
       "14                       0.082117                           0.117303   \n",
       "15                       0.090708                           0.148204   \n",
       "16                       0.044382                           0.142445   \n",
       "17                       0.023654                           0.089752   \n",
       "18                      -0.012945                           0.049612   \n",
       "19                      -0.006896                           0.006119   \n",
       "20                       0.026113                           0.010813   \n",
       "21                       0.033447                           0.049461   \n",
       "22                       0.041894                           0.050086   \n",
       "23                      -0.009462                           0.041085   \n",
       "\n",
       "    sentA_neg_roll3_weighted_shifted1  sentB_pos_roll3_weighted_shifted1  \\\n",
       "4                            0.047919                          -0.031626   \n",
       "5                            0.048227                          -0.047672   \n",
       "6                            0.038685                          -0.034898   \n",
       "7                            0.045650                          -0.026328   \n",
       "8                            0.039750                          -0.024410   \n",
       "9                            0.021497                          -0.008579   \n",
       "10                           0.021386                          -0.007287   \n",
       "11                           0.014635                           0.022042   \n",
       "12                          -0.002227                           0.010831   \n",
       "13                          -0.007031                           0.045811   \n",
       "14                          -0.008032                           0.080720   \n",
       "15                           0.007246                           0.092164   \n",
       "16                          -0.024864                           0.083797   \n",
       "17                          -0.012542                           0.023842   \n",
       "18                          -0.001215                           0.008928   \n",
       "19                           0.013245                          -0.026783   \n",
       "20                           0.002762                          -0.001781   \n",
       "21                           0.003169                           0.038753   \n",
       "22                          -0.012800                           0.049621   \n",
       "23                          -0.001375                           0.033053   \n",
       "\n",
       "    sentB_neg_roll3_weighted_shifted1  \n",
       "4                            0.007935  \n",
       "5                            0.007511  \n",
       "6                            0.030279  \n",
       "7                            0.053030  \n",
       "8                            0.052222  \n",
       "9                            0.025936  \n",
       "10                          -0.004483  \n",
       "11                          -0.005455  \n",
       "12                          -0.001569  \n",
       "13                           0.012940  \n",
       "14                           0.016344  \n",
       "15                           0.012436  \n",
       "16                          -0.005235  \n",
       "17                          -0.019880  \n",
       "18                          -0.014481  \n",
       "19                          -0.013875  \n",
       "20                           0.005571  \n",
       "21                           0.013463  \n",
       "22                           0.016757  \n",
       "23                          -0.007852  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from https://stackoverflow.com/questions/40996175/loading-a-rds-file-in-pandas\n",
    "\n",
    "# the file is from the github repository of the publication\n",
    "# https://github.com/maxpel/SocialMediaMacroscopes\n",
    "\n",
    "result = pyreadr.read_r('cdplstandard.RDS')\n",
    "\n",
    "# done! \n",
    "# result is a dictionary where keys are the name of objects and the values python\n",
    "# objects. In the case of Rds there is only one object with None as key\n",
    "\n",
    "# extract the pandas data frame \n",
    "standarddf = result[None]\n",
    "\n",
    "# to date object\n",
    "standarddf['date_day'] = pd.to_datetime(standarddf['date_day'])\n",
    "\n",
    "# to time range of survey\n",
    "standarddf = standarddf.loc[(standarddf['date_day']>='2020-11-11') & (standarddf['date_day']<='2020-11-30')]\n",
    "\n",
    "standarddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the survey\n",
    "standarddf.plot(x=\"date_day\", y=[\"prop_good\"], kind=\"line\", figsize=(12, 10))\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the survey and the text sentiment measure\n",
    "stdpl = standarddf.plot(x=\"date_day\", y=[\"prop_good\",\"sent_roll3_weighted\"], kind=\"line\", figsize=(12, 10))\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prop_good</th>\n",
       "      <th>sent_roll3_weighted_shifted1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prop_good</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.895321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sent_roll3_weighted_shifted1</th>\n",
       "      <td>0.895321</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              prop_good  sent_roll3_weighted_shifted1\n",
       "prop_good                      1.000000                      0.895321\n",
       "sent_roll3_weighted_shifted1   0.895321                      1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigate the relationship between survey and text sentiment measure further\n",
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(standarddf[['prop_good']],standarddf[['sent_roll3_weighted_shifted1']])\n",
    "\n",
    "plt.scatter(standarddf[['prop_good']], standarddf[['sent_roll3_weighted_shifted1']],  color='black')\n",
    "plt.plot(standarddf[['prop_good']], reg.predict(standarddf[['prop_good']]), color='blue', linewidth=3)\n",
    "plt.show()\n",
    "\n",
    "standarddf[['prop_good','sent_roll3_weighted_shifted1']].corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.51611238]]\n",
      "[0.64178639]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rescale text sentiment measure to overlay the two lines and inspect them visually\n",
    "\n",
    "reg.fit(standarddf[['sent_roll3_weighted_shifted1']],standarddf[['prop_good']])\n",
    "\n",
    "# that's how much we have to scale the text sentiment measure\n",
    "print(reg.coef_)\n",
    "\n",
    "# that's how much we have to offset the text sentiment measure\n",
    "print(reg.intercept_)\n",
    "\n",
    "# start the plot\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "\n",
    "y=standarddf[['sent_roll3_weighted_shifted1']]\n",
    "\n",
    "# transform according to the linear model\n",
    "y_trans = reg.intercept_+y*reg.coef_\n",
    "\n",
    "ax.plot(standarddf[['date_day']],standarddf[['prop_good']])\n",
    "\n",
    "ax.plot(standarddf[['date_day']],y_trans)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you want to do more\n",
    "\n",
    "* Play around with the huggingface model hub and load different models in pipelines to try them out. Can you think of research questions where a specific model would come in handy?\n",
    "\n",
    "* In the github repository (https://github.com/maxpel/SocialMediaMacroscopes), you find the file pl.RDS, can you repeat the above analysis using \"sent_roll3_weighted\" from that data file? What do you find? If you check in the paper, which figure did you replicate now?\n",
    "\n",
    "* Both data files contain a colum called \"sent\", what do you find when you plot that variable? Can you replicate the smoothing of values over 3 days?\n",
    "\n",
    "* Do you manage to manage to include the second y axis on the right on the last plot? (check out the plot in the paper for reference)"
   ]
  }
 ],
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