notebook.ipynb

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   "source": [
    "## 1. Book recommendation system\n",
    "In this project I built a a content-based book recommendation system to determine which books are most similar to each other by building a tf-idf model. Cosine similarity for similarity metric.\n",
    "Our dataset are multiple notebook written by Charles Darwin\n",
    "\n",
    "This project was built in a DataCamp course"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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    {
     "data": {
      "text/plain": [
       "['datasets/Autobiography.txt',\n",
       " 'datasets/CoralReefs.txt',\n",
       " 'datasets/DescentofMan.txt',\n",
       " 'datasets/DifferentFormsofFlowers.txt',\n",
       " 'datasets/EffectsCrossSelfFertilization.txt',\n",
       " 'datasets/ExpressionofEmotionManAnimals.txt',\n",
       " 'datasets/FormationVegetableMould.txt',\n",
       " 'datasets/FoundationsOriginofSpecies.txt',\n",
       " 'datasets/GeologicalObservationsSouthAmerica.txt',\n",
       " 'datasets/InsectivorousPlants.txt',\n",
       " 'datasets/LifeandLettersVol1.txt',\n",
       " 'datasets/LifeandLettersVol2.txt',\n",
       " 'datasets/MonographCirripedia.txt',\n",
       " 'datasets/MonographCirripediaVol2.txt',\n",
       " 'datasets/MovementClimbingPlants.txt',\n",
       " 'datasets/OriginofSpecies.txt',\n",
       " 'datasets/PowerMovementPlants.txt',\n",
       " 'datasets/VariationPlantsAnimalsDomestication.txt',\n",
       " 'datasets/VolcanicIslands.txt',\n",
       " 'datasets/VoyageBeagle.txt']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library\n",
    "import glob\n",
    "\n",
    "# The books files are contained in this folder\n",
    "folder = \"datasets/\"\n",
    "\n",
    "# List all the .txt files and sort them alphabetically\n",
    "files = sorted(glob.glob(folder + '*.txt'))\n",
    "# ... YOUR CODE FOR TASK 1 ...\n",
    "files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "10"
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    "deletable": false,
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   "source": [
    "## 2. Load the contents of each book into Python\n",
    "<p>As a first step, we need to load the content of these books into Python and do some basic pre-processing to facilitate the downstream analyses. We call such a collection of texts <strong>a corpus</strong>. We will also store the titles for these books for future reference and print their respective length to get a gauge for their contents.</p>"
   ]
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   "source": [
    "# Import libraries\n",
    "import re, os\n",
    "\n",
    "# Initialize the object that will contain the texts and titles\n",
    "txts = []\n",
    "titles = []\n",
    "\n",
    "for n in files:\n",
    "    # Open each file\n",
    "    f = open(n, encoding='utf-8-sig')\n",
    "    # Remove all non-alpha-numeric characters\n",
    "    f_cleaned = re.sub('[^a-zA-Z0-9 -]','',f.read())\n",
    "    # ... YOUR CODE FOR TASK 2 ...\n",
    "    # Store the texts and titles of the books in two separate lists\n",
    "    txts.append(f_cleaned)\n",
    "    titles.append(os.path.basename(n).replace('.txt',''))\n",
    "\n",
    "# Print the length, in characters, of each book\n",
    "[len(t) for t in txts]"
   ]
  },
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   "source": [
    "## 3. Find \"On the Origin of Species\"\n",
    "<p>For the next parts of this analysis, we will often check the results returned by our method for a given book. For consistency, we will refer to Darwin's most famous book: \"<em>On the Origin of Species</em>.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "dc": {
     "key": "17"
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     "sample_code"
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    {
     "name": "stdout",
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     "text": [
      "15\n"
     ]
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   "source": [
    "# Browse the list containing all the titles\n",
    "for i in range(len(titles)):\n",
    "    # Store the index if the title is \"OriginofSpecies\"\n",
    "    # ... YOUR CODE FOR TASK 3 ...\n",
    "    if 'OriginofSpecies' in titles[i]:\n",
    "        ori = i\n",
    "# Print the stored index\n",
    "# ... YOUR CODE FOR TASK 3 ...\n",
    "print(ori)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## 4. Tokenize the corpus\n",
    "<p>As a next step, we need to transform the corpus into a format that is easier to deal with for the downstream analyses. We will tokenize our corpus, i.e., transform each text into a list of the individual words (called tokens) it is made of. To check the output of our process, we will print the first 20 tokens of \"<em>On the Origin of Species</em>\".</p>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['on', 'theorigin', 'species', 'but', 'with', 'regard', 'material', 'world', 'can', 'least', 'go', 'so', 'far', 'asthis--we', 'can', 'perceive', 'events', 'are', 'brought', 'about']\n"
     ]
    }
   ],
   "source": [
    "# Define a list of stop words\n",
    "stoplist = set('for a of the and to in to be which some is at that we i who whom show via may my our might as well'.split())\n",
    "\n",
    "# Convert the text to lower case \n",
    "txts_lower_case = [txt.lower() for txt in txts]\n",
    "\n",
    "# Transform the text into tokens \n",
    "txts_split = [txt.split() for txt in txts_lower_case]\n",
    "\n",
    "# Remove tokens which are part of the list of stop words\n",
    "texts = [[t for t in tokens if t not in stoplist] for tokens in txts_split]\n",
    "\n",
    "# Print the first 20 tokens for the \"On the Origin of Species\" book\n",
    "# ... YOUR CODE FOR TASK 4 ...\n",
    "print(texts[ori][:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "31"
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    "deletable": false,
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   "source": [
    "## 5. Stemming of the tokenized corpus\n",
    "<p>If you have read <em>On the Origin of Species</em>, you will have noticed that Charles Darwin can use different words to refer to a similar concept. For example, the concept of selection can be described by words such as <em>selection</em>, <em>selective</em>, <em>select</em> or <em>selects</em>. This will dilute the weight given to this concept in the book and potentially bias the results of the analysis.</p>\n",
    "<p>To solve this issue, it is a common practice to use a <strong>stemming process</strong>, which will group together the inflected forms of a word so they can be analysed as a single item: <strong>the stem</strong>. In our <em>On the Origin of Species</em> example, the words related to the concept of selection would be gathered under the <em>select</em> stem.</p>\n",
    "<p>As we are analysing 20 full books, the stemming algorithm can take several minutes to run and, in order to make the process faster, we will directly load the final results from a pickle file and review the method used to generate it.</p>"
   ]
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
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     "key": "31"
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    {
     "data": {
      "text/plain": [
       "['on',\n",
       " 'origin',\n",
       " 'speci',\n",
       " 'but',\n",
       " 'with',\n",
       " 'regard',\n",
       " 'materi',\n",
       " 'world',\n",
       " 'can',\n",
       " 'least',\n",
       " 'go',\n",
       " 'so',\n",
       " 'far',\n",
       " 'thi',\n",
       " 'can',\n",
       " 'perceiv',\n",
       " 'event',\n",
       " 'are',\n",
       " 'brought',\n",
       " 'about']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "# Load the stemmed tokens list from the pregenerated pickle file\n",
    "texts_stem = pickle.load( open( \"datasets/texts_stem.p\", \"rb\" ) )\n",
    "\n",
    "# Print the 20 first stemmed tokens from the \"On the Origin of Species\" book\n",
    "# ... YOUR CODE FOR TASK 5 ...\n",
    "texts_stem[ori][:20]"
   ]
  },
  {
   "cell_type": "markdown",
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     "key": "38"
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   "source": [
    "## 6. Building a bag-of-words model\n",
    "<p>Now that we have transformed the texts into stemmed tokens, we need to build models that will be useable by downstream algorithms.</p>\n",
    "<p>First, we need to will create a universe of all words contained in our corpus of Charles Darwin's books, which we call <em>a dictionary</em>. Then, using the stemmed tokens and the dictionary, we will create <strong>bag-of-words models</strong> (BoW) of each of our texts. The BoW models will represent our books as a list of all uniques tokens they contain associated with their respective number of occurrences. </p>\n",
    "<p>To better understand the structure of such a model, we will print the five first elements of one of the \"<em>On the Origin of Species</em>\" BoW model.</p>"
   ]
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   "metadata": {
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    {
     "data": {
      "text/plain": [
       "[(0, 11), (5, 51), (6, 1), (8, 2), (21, 1)]"
      ]
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     "execution_count": 37,
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   "source": [
    "# Load the functions allowing to create and use dictionaries\n",
    "from gensim import corpora\n",
    "\n",
    "# Create a dictionary from the stemmed tokens\n",
    "dictionary = corpora.Dictionary(texts_stem)\n",
    "\n",
    "# Create a bag-of-words model for each book, using the previously generated dictionary\n",
    "bows = [dictionary.doc2bow(text) for text in texts_stem]\n",
    "\n",
    "# Print the first five elements of the On the Origin of species' BoW model\n",
    "# ... YOUR CODE FOR TASK 6 ...\n",
    "bows[ori][:5]"
   ]
  },
  {
   "cell_type": "markdown",
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     "key": "45"
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   "source": [
    "## 7. The most common words of a given book\n",
    "<p>The results returned by the bag-of-words model is certainly easy to use for a computer but hard to interpret for a human. It is not straightforward to understand which stemmed tokens are present in a given book from Charles Darwin, and how many occurrences we can find.</p>\n",
    "<p>In order to better understand how the model has been generated and visualize its content, we will transform it into a DataFrame and display the 10 most common stems for the book \"<em>On the Origin of Species</em>\".</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
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     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>occurrences</th>\n",
       "      <th>token</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>1168</td>\n",
       "      <td>2023</td>\n",
       "      <td>have</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>1736</td>\n",
       "      <td>1558</td>\n",
       "      <td>on</td>\n",
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       "    <tr>\n",
       "      <th>1489</th>\n",
       "      <td>2288</td>\n",
       "      <td>1543</td>\n",
       "      <td>speci</td>\n",
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       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>1366</td>\n",
       "      <td>1480</td>\n",
       "      <td>it</td>\n",
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       "      <th>239</th>\n",
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       "      <td>or</td>\n",
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       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>218</td>\n",
       "      <td>1140</td>\n",
       "      <td>are</td>\n",
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       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>1043</td>\n",
       "      <td>1137</td>\n",
       "      <td>from</td>\n",
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       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>2703</td>\n",
       "      <td>1000</td>\n",
       "      <td>with</td>\n",
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       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>2452</td>\n",
       "      <td>962</td>\n",
       "      <td>thi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "      index  occurrences  token\n",
       "748    1168         2023   have\n",
       "1119   1736         1558     on\n",
       "1489   2288         1543  speci\n",
       "892    1366         1480     it\n",
       "239     393         1362     by\n",
       "1128   1747         1201     or\n",
       "125     218         1140    are\n",
       "665    1043         1137   from\n",
       "1774   2703         1000   with\n",
       "1609   2452          962    thi"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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    }
   ],
   "source": [
    "# Import pandas to create and manipulate DataFrames\n",
    "import pandas as pd\n",
    "\n",
    "# Convert the BoW model for \"On the Origin of Species\" into a DataFrame\n",
    "df_bow_origin = pd.DataFrame(bows[ori])\n",
    "\n",
    "# Add the column names to the DataFrame\n",
    "# ... YOUR CODE FOR TASK 7 ...\n",
    "df_bow_origin.columns=['index','occurrences']\n",
    "\n",
    "# Add a column containing the token corresponding to the dictionary index\n",
    "# ... YOUR CODE FOR TASK 7 ...\n",
    "df_bow_origin['token'] = df_bow_origin['index'].apply(lambda i: dictionary.get(i))\n",
    "\n",
    "# Sort the DataFrame by descending number of occurrences and print the first 10 values\n",
    "# ... YOUR CODE FOR TASK 7 ...\n",
    "df_bow_origin.sort_values(by='occurrences', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "52"
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    "deletable": false,
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   "source": [
    "## 8. Build a tf-idf model\n",
    "<p>If it wasn't for the presence of the stem \"<em>speci</em>\", we would have a hard time to guess this BoW model comes from the <em>On the Origin of Species</em> book. The most recurring words are, apart from few exceptions, very common and unlikely to carry any information peculiar to the given book. We need to use an additional step in order to determine which tokens are the most specific to a book.</p>\n",
    "<p>To do so, we will use a <strong>tf-idf model</strong> (term frequency–inverse document frequency). This model defines the importance of each word depending on how frequent it is in this text and how infrequent it is in all the other documents. As a result, a high tf-idf score for a word will indicate that this word is specific to this text.</p>\n",
    "<p>After computing those scores, we will print the 10 words most specific to the \"<em>On the Origin of Species</em>\" book (i.e., the 10 words with the highest tf-idf score).</p>"
   ]
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     ]
    }
   ],
   "source": [
    "# Load the gensim functions that will allow us to generate tf-idf models\n",
    "from gensim.models import TfidfModel\n",
    "\n",
    "# Generate the tf-idf model\n",
    "model = TfidfModel(bows)\n",
    "\n",
    "# Print the model for \"On the Origin of Species\"\n",
    "# ... YOUR CODE FOR TASK 8 ...\n",
    "print(model[bows[ori]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "59"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. The results of the tf-idf model\n",
    "<p>Once again, the format of those results is hard to interpret for a human. Therefore, we will transform it into a more readable version and display the 10 most specific words for the \"<em>On the Origin of Species</em>\" book.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "dc": {
     "key": "59"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>token</th>\n",
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       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>2164</td>\n",
       "      <td>0.327414</td>\n",
       "      <td>select</td>\n",
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       "    <tr>\n",
       "      <th>3106</th>\n",
       "      <td>10108</td>\n",
       "      <td>0.203908</td>\n",
       "      <td>pigeon</td>\n",
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       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>369</td>\n",
       "      <td>0.197721</td>\n",
       "      <td>breed</td>\n",
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       "    <tr>\n",
       "      <th>2988</th>\n",
       "      <td>9396</td>\n",
       "      <td>0.167496</td>\n",
       "      <td>migrat</td>\n",
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       "      <th>945</th>\n",
       "      <td>2325</td>\n",
       "      <td>0.148186</td>\n",
       "      <td>steril</td>\n",
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       "      <th>284</th>\n",
       "      <td>752</td>\n",
       "      <td>0.145990</td>\n",
       "      <td>domest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>1255</td>\n",
       "      <td>0.128272</td>\n",
       "      <td>hybrid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>966</td>\n",
       "      <td>0.127534</td>\n",
       "      <td>fertil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3889</th>\n",
       "      <td>16210</td>\n",
       "      <td>0.124392</td>\n",
       "      <td>rtner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <td>12715</td>\n",
       "      <td>0.121197</td>\n",
       "      <td>naturalis</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id     score      token\n",
       "878    2164  0.327414     select\n",
       "3106  10108  0.203908     pigeon\n",
       "128     369  0.197721      breed\n",
       "2988   9396  0.167496     migrat\n",
       "945    2325  0.148186     steril\n",
       "284     752  0.145990     domest\n",
       "503    1255  0.128272     hybrid\n",
       "370     966  0.127534     fertil\n",
       "3889  16210  0.124392      rtner\n",
       "3540  12715  0.121197  naturalis"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the tf-idf model for \"On the Origin of Species\" into a DataFrame\n",
    "df_tfidf = pd.DataFrame(model[bows[ori]])\n",
    "\n",
    "# Name the columns of the DataFrame id and score\n",
    "# ... YOUR CODE FOR TASK 9 ...\n",
    "df_tfidf.columns=['id','score']\n",
    "\n",
    "# Add the tokens corresponding to the numerical indices for better readability\n",
    "# ... YOUR CODE FOR TASK 9 ...\n",
    "df_tfidf['token'] = df_tfidf['id'].apply(lambda i: dictionary.get(i))\n",
    "\n",
    "# Sort the DataFrame by descending tf-idf score and print the first 10 rows.\n",
    "# ... YOUR CODE FOR TASK 9 ...\n",
    "df_tfidf.sort_values(by='score', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "66"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 10. Compute distance between texts\n",
    "<p>The results of the tf-idf algorithm now return stemmed tokens which are specific to each book. We can, for example, see that topics such as selection, breeding or domestication are defining \"<em>On the Origin of Species</em>\" (and yes, in this book, Charles Darwin talks quite a lot about pigeons too). Now that we have a model associating tokens to how specific they are to each book, we can measure how related to books are between each other.</p>\n",
    "<p>To this purpose, we will use a measure of similarity called <strong>cosine similarity</strong> and we will visualize the results as a distance matrix, i.e., a matrix showing all pairwise distances between Darwin's books.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "dc": {
     "key": "66"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                     Autobiography  CoralReefs  DescentofMan  \\\n",
      "Autobiography                             1.000000    0.049467      0.080428   \n",
      "CoralReefs                                0.049467    1.000000      0.009480   \n",
      "DescentofMan                              0.080428    0.009480      1.000000   \n",
      "DifferentFormsofFlowers                   0.066482    0.001952      0.072761   \n",
      "EffectsCrossSelfFertilization             0.077184    0.001923      0.029968   \n",
      "ExpressionofEmotionManAnimals             0.088723    0.004999      0.148670   \n",
      "FormationVegetableMould                   0.040678    0.029432      0.027055   \n",
      "FoundationsOriginofSpecies                0.059271    0.022096      0.135775   \n",
      "GeologicalObservationsSouthAmerica        0.030562    0.061027      0.009698   \n",
      "InsectivorousPlants                       0.014878    0.002276      0.009404   \n",
      "LifeandLettersVol1                        0.396709    0.030965      0.059684   \n",
      "LifeandLettersVol2                        0.217129    0.017558      0.080314   \n",
      "MonographCirripedia                       0.005686    0.006324      0.053506   \n",
      "MonographCirripediaVol2                   0.008483    0.010579      0.043275   \n",
      "MovementClimbingPlants                    0.022856    0.001518      0.005146   \n",
      "OriginofSpecies                           0.099991    0.039089      0.267554   \n",
      "PowerMovementPlants                       0.016247    0.002707      0.011357   \n",
      "VariationPlantsAnimalsDomestication       0.049018    0.011586      0.232841   \n",
      "VolcanicIslands                           0.038556    0.057514      0.007882   \n",
      "VoyageBeagle                              0.183507    0.267749      0.123917   \n",
      "\n",
      "                                     DifferentFormsofFlowers  \\\n",
      "Autobiography                                       0.066482   \n",
      "CoralReefs                                          0.001952   \n",
      "DescentofMan                                        0.072761   \n",
      "DifferentFormsofFlowers                             1.000000   \n",
      "EffectsCrossSelfFertilization                       0.391834   \n",
      "ExpressionofEmotionManAnimals                       0.006474   \n",
      "FormationVegetableMould                             0.010585   \n",
      "FoundationsOriginofSpecies                          0.040104   \n",
      "GeologicalObservationsSouthAmerica                  0.002846   \n",
      "InsectivorousPlants                                 0.007502   \n",
      "LifeandLettersVol1                                  0.015933   \n",
      "LifeandLettersVol2                                  0.046523   \n",
      "MonographCirripedia                                 0.009405   \n",
      "MonographCirripediaVol2                             0.005484   \n",
      "MovementClimbingPlants                              0.008151   \n",
      "OriginofSpecies                                     0.128909   \n",
      "PowerMovementPlants                                 0.018964   \n",
      "VariationPlantsAnimalsDomestication                 0.050023   \n",
      "VolcanicIslands                                     0.002611   \n",
      "VoyageBeagle                                        0.013124   \n",
      "\n",
      "                                     EffectsCrossSelfFertilization  \\\n",
      "Autobiography                                             0.077184   \n",
      "CoralReefs                                                0.001923   \n",
      "DescentofMan                                              0.029968   \n",
      "DifferentFormsofFlowers                                   0.391834   \n",
      "EffectsCrossSelfFertilization                             1.000000   \n",
      "ExpressionofEmotionManAnimals                             0.006844   \n",
      "FormationVegetableMould                                   0.032262   \n",
      "FoundationsOriginofSpecies                                0.040288   \n",
      "GeologicalObservationsSouthAmerica                        0.002246   \n",
      "InsectivorousPlants                                       0.006777   \n",
      "LifeandLettersVol1                                        0.019504   \n",
      "LifeandLettersVol2                                        0.046504   \n",
      "MonographCirripedia                                       0.003212   \n",
      "MonographCirripediaVol2                                   0.002962   \n",
      "MovementClimbingPlants                                    0.014932   \n",
      "OriginofSpecies                                           0.146441   \n",
      "PowerMovementPlants                                       0.039770   \n",
      "VariationPlantsAnimalsDomestication                       0.055132   \n",
      "VolcanicIslands                                           0.002178   \n",
      "VoyageBeagle                                              0.017140   \n",
      "\n",
      "                                     ExpressionofEmotionManAnimals  \\\n",
      "Autobiography                                             0.088723   \n",
      "CoralReefs                                                0.004999   \n",
      "DescentofMan                                              0.148670   \n",
      "DifferentFormsofFlowers                                   0.006474   \n",
      "EffectsCrossSelfFertilization                             0.006844   \n",
      "ExpressionofEmotionManAnimals                             1.000000   \n",
      "FormationVegetableMould                                   0.020985   \n",
      "FoundationsOriginofSpecies                                0.047202   \n",
      "GeologicalObservationsSouthAmerica                        0.005217   \n",
      "InsectivorousPlants                                       0.011475   \n",
      "LifeandLettersVol1                                        0.064873   \n",
      "LifeandLettersVol2                                        0.048886   \n",
      "MonographCirripedia                                       0.016825   \n",
      "MonographCirripediaVol2                                   0.029897   \n",
      "MovementClimbingPlants                                    0.005913   \n",
      "OriginofSpecies                                           0.062979   \n",
      "PowerMovementPlants                                       0.011317   \n",
      "VariationPlantsAnimalsDomestication                       0.083847   \n",
      "VolcanicIslands                                           0.005561   \n",
      "VoyageBeagle                                              0.098961   \n",
      "\n",
      "                                     FormationVegetableMould  \\\n",
      "Autobiography                                       0.040678   \n",
      "CoralReefs                                          0.029432   \n",
      "DescentofMan                                        0.027055   \n",
      "DifferentFormsofFlowers                             0.010585   \n",
      "EffectsCrossSelfFertilization                       0.032262   \n",
      "ExpressionofEmotionManAnimals                       0.020985   \n",
      "FormationVegetableMould                             1.000000   \n",
      "FoundationsOriginofSpecies                          0.021470   \n",
      "GeologicalObservationsSouthAmerica                  0.067989   \n",
      "InsectivorousPlants                                 0.035589   \n",
      "LifeandLettersVol1                                  0.027916   \n",
      "LifeandLettersVol2                                  0.023620   \n",
      "MonographCirripedia                                 0.019866   \n",
      "MonographCirripediaVol2                             0.023984   \n",
      "MovementClimbingPlants                              0.038820   \n",
      "OriginofSpecies                                     0.049259   \n",
      "PowerMovementPlants                                 0.040182   \n",
      "VariationPlantsAnimalsDomestication                 0.033147   \n",
      "VolcanicIslands                                     0.059407   \n",
      "VoyageBeagle                                        0.097908   \n",
      "\n",
      "                                     FoundationsOriginofSpecies  \\\n",
      "Autobiography                                          0.059271   \n",
      "CoralReefs                                             0.022096   \n",
      "DescentofMan                                           0.135775   \n",
      "DifferentFormsofFlowers                                0.040104   \n",
      "EffectsCrossSelfFertilization                          0.040288   \n",
      "ExpressionofEmotionManAnimals                          0.047202   \n",
      "FormationVegetableMould                                0.021470   \n",
      "FoundationsOriginofSpecies                             1.000000   \n",
      "GeologicalObservationsSouthAmerica                     0.028028   \n",
      "InsectivorousPlants                                    0.006023   \n",
      "LifeandLettersVol1                                     0.057820   \n",
      "LifeandLettersVol2                                     0.054782   \n",
      "MonographCirripedia                                    0.007618   \n",
      "MonographCirripediaVol2                                0.010883   \n",
      "MovementClimbingPlants                                 0.003973   \n",
      "OriginofSpecies                                        0.322405   \n",
      "PowerMovementPlants                                    0.008788   \n",
      "VariationPlantsAnimalsDomestication                    0.194533   \n",
      "VolcanicIslands                                        0.017590   \n",
      "VoyageBeagle                                           0.089132   \n",
      "\n",
      "                                     GeologicalObservationsSouthAmerica  \\\n",
      "Autobiography                                                  0.030562   \n",
      "CoralReefs                                                     0.061027   \n",
      "DescentofMan                                                   0.009698   \n",
      "DifferentFormsofFlowers                                        0.002846   \n",
      "EffectsCrossSelfFertilization                                  0.002246   \n",
      "ExpressionofEmotionManAnimals                                  0.005217   \n",
      "FormationVegetableMould                                        0.067989   \n",
      "FoundationsOriginofSpecies                                     0.028028   \n",
      "GeologicalObservationsSouthAmerica                             1.000000   \n",
      "InsectivorousPlants                                            0.006879   \n",
      "LifeandLettersVol1                                             0.028551   \n",
      "LifeandLettersVol2                                             0.012104   \n",
      "MonographCirripedia                                            0.009687   \n",
      "MonographCirripediaVol2                                        0.024738   \n",
      "MovementClimbingPlants                                         0.002043   \n",
      "OriginofSpecies                                                0.058046   \n",
      "PowerMovementPlants                                            0.003491   \n",
      "VariationPlantsAnimalsDomestication                            0.014389   \n",
      "VolcanicIslands                                                0.373249   \n",
      "VoyageBeagle                                                   0.260141   \n",
      "\n",
      "                                     InsectivorousPlants  LifeandLettersVol1  \\\n",
      "Autobiography                                   0.014878            0.396709   \n",
      "CoralReefs                                      0.002276            0.030965   \n",
      "DescentofMan                                    0.009404            0.059684   \n",
      "DifferentFormsofFlowers                         0.007502            0.015933   \n",
      "EffectsCrossSelfFertilization                   0.006777            0.019504   \n",
      "ExpressionofEmotionManAnimals                   0.011475            0.064873   \n",
      "FormationVegetableMould                         0.035589            0.027916   \n",
      "FoundationsOriginofSpecies                      0.006023            0.057820   \n",
      "GeologicalObservationsSouthAmerica              0.006879            0.028551   \n",
      "InsectivorousPlants                             1.000000            0.005967   \n",
      "LifeandLettersVol1                              0.005967            1.000000   \n",
      "LifeandLettersVol2                              0.016518            0.885828   \n",
      "MonographCirripedia                             0.019214            0.005752   \n",
      "MonographCirripediaVol2                         0.020023            0.012772   \n",
      "MovementClimbingPlants                          0.249814            0.005388   \n",
      "OriginofSpecies                                 0.014961            0.097457   \n",
      "PowerMovementPlants                             0.023056            0.009505   \n",
      "VariationPlantsAnimalsDomestication             0.010522            0.055259   \n",
      "VolcanicIslands                                 0.008544            0.026374   \n",
      "VoyageBeagle                                    0.014776            0.171708   \n",
      "\n",
      "                                     LifeandLettersVol2  MonographCirripedia  \\\n",
      "Autobiography                                  0.217129             0.005686   \n",
      "CoralReefs                                     0.017558             0.006324   \n",
      "DescentofMan                                   0.080314             0.053506   \n",
      "DifferentFormsofFlowers                        0.046523             0.009405   \n",
      "EffectsCrossSelfFertilization                  0.046504             0.003212   \n",
      "ExpressionofEmotionManAnimals                  0.048886             0.016825   \n",
      "FormationVegetableMould                        0.023620             0.019866   \n",
      "FoundationsOriginofSpecies                     0.054782             0.007618   \n",
      "GeologicalObservationsSouthAmerica             0.012104             0.009687   \n",
      "InsectivorousPlants                            0.016518             0.019214   \n",
      "LifeandLettersVol1                             0.885828             0.005752   \n",
      "LifeandLettersVol2                             1.000000             0.004967   \n",
      "MonographCirripedia                            0.004967             1.000001   \n",
      "MonographCirripediaVol2                        0.010843             0.522273   \n",
      "MovementClimbingPlants                         0.017565             0.012441   \n",
      "OriginofSpecies                                0.096955             0.029902   \n",
      "PowerMovementPlants                            0.012099             0.018694   \n",
      "VariationPlantsAnimalsDomestication            0.050764             0.023460   \n",
      "VolcanicIslands                                0.011806             0.010754   \n",
      "VoyageBeagle                                   0.089947             0.014342   \n",
      "\n",
      "                                     MonographCirripediaVol2  \\\n",
      "Autobiography                                       0.008483   \n",
      "CoralReefs                                          0.010579   \n",
      "DescentofMan                                        0.043275   \n",
      "DifferentFormsofFlowers                             0.005484   \n",
      "EffectsCrossSelfFertilization                       0.002962   \n",
      "ExpressionofEmotionManAnimals                       0.029897   \n",
      "FormationVegetableMould                             0.023984   \n",
      "FoundationsOriginofSpecies                          0.010883   \n",
      "GeologicalObservationsSouthAmerica                  0.024738   \n",
      "InsectivorousPlants                                 0.020023   \n",
      "LifeandLettersVol1                                  0.012772   \n",
      "LifeandLettersVol2                                  0.010843   \n",
      "MonographCirripedia                                 0.522273   \n",
      "MonographCirripediaVol2                             1.000000   \n",
      "MovementClimbingPlants                              0.006802   \n",
      "OriginofSpecies                                     0.036755   \n",
      "PowerMovementPlants                                 0.022376   \n",
      "VariationPlantsAnimalsDomestication                 0.030669   \n",
      "VolcanicIslands                                     0.017952   \n",
      "VoyageBeagle                                        0.025047   \n",
      "\n",
      "                                     MovementClimbingPlants  OriginofSpecies  \\\n",
      "Autobiography                                      0.022856         0.099991   \n",
      "CoralReefs                                         0.001518         0.039089   \n",
      "DescentofMan                                       0.005146         0.267554   \n",
      "DifferentFormsofFlowers                            0.008151         0.128909   \n",
      "EffectsCrossSelfFertilization                      0.014932         0.146441   \n",
      "ExpressionofEmotionManAnimals                      0.005913         0.062979   \n",
      "FormationVegetableMould                            0.038820         0.049259   \n",
      "FoundationsOriginofSpecies                         0.003973         0.322405   \n",
      "GeologicalObservationsSouthAmerica                 0.002043         0.058046   \n",
      "InsectivorousPlants                                0.249814         0.014961   \n",
      "LifeandLettersVol1                                 0.005388         0.097457   \n",
      "LifeandLettersVol2                                 0.017565         0.096955   \n",
      "MonographCirripedia                                0.012441         0.029902   \n",
      "MonographCirripediaVol2                            0.006802         0.036755   \n",
      "MovementClimbingPlants                             1.000000         0.008802   \n",
      "OriginofSpecies                                    0.008802         1.000000   \n",
      "PowerMovementPlants                                0.104966         0.018266   \n",
      "VariationPlantsAnimalsDomestication                0.011530         0.405333   \n",
      "VolcanicIslands                                    0.002832         0.036014   \n",
      "VoyageBeagle                                       0.012282         0.164661   \n",
      "\n",
      "                                     PowerMovementPlants  \\\n",
      "Autobiography                                   0.016247   \n",
      "CoralReefs                                      0.002707   \n",
      "DescentofMan                                    0.011357   \n",
      "DifferentFormsofFlowers                         0.018964   \n",
      "EffectsCrossSelfFertilization                   0.039770   \n",
      "ExpressionofEmotionManAnimals                   0.011317   \n",
      "FormationVegetableMould                         0.040182   \n",
      "FoundationsOriginofSpecies                      0.008788   \n",
      "GeologicalObservationsSouthAmerica              0.003491   \n",
      "InsectivorousPlants                             0.023056   \n",
      "LifeandLettersVol1                              0.009505   \n",
      "LifeandLettersVol2                              0.012099   \n",
      "MonographCirripedia                             0.018694   \n",
      "MonographCirripediaVol2                         0.022376   \n",
      "MovementClimbingPlants                          0.104966   \n",
      "OriginofSpecies                                 0.018266   \n",
      "PowerMovementPlants                             1.000000   \n",
      "VariationPlantsAnimalsDomestication             0.020589   \n",
      "VolcanicIslands                                 0.003819   \n",
      "VoyageBeagle                                    0.024149   \n",
      "\n",
      "                                     VariationPlantsAnimalsDomestication  \\\n",
      "Autobiography                                                   0.049018   \n",
      "CoralReefs                                                      0.011586   \n",
      "DescentofMan                                                    0.232841   \n",
      "DifferentFormsofFlowers                                         0.050023   \n",
      "EffectsCrossSelfFertilization                                   0.055132   \n",
      "ExpressionofEmotionManAnimals                                   0.083847   \n",
      "FormationVegetableMould                                         0.033147   \n",
      "FoundationsOriginofSpecies                                      0.194533   \n",
      "GeologicalObservationsSouthAmerica                              0.014389   \n",
      "InsectivorousPlants                                             0.010522   \n",
      "LifeandLettersVol1                                              0.055259   \n",
      "LifeandLettersVol2                                              0.050764   \n",
      "MonographCirripedia                                             0.023460   \n",
      "MonographCirripediaVol2                                         0.030669   \n",
      "MovementClimbingPlants                                          0.011530   \n",
      "OriginofSpecies                                                 0.405333   \n",
      "PowerMovementPlants                                             0.020589   \n",
      "VariationPlantsAnimalsDomestication                             1.000000   \n",
      "VolcanicIslands                                                 0.012620   \n",
      "VoyageBeagle                                                    0.114134   \n",
      "\n",
      "                                     VolcanicIslands  VoyageBeagle  \n",
      "Autobiography                               0.038556      0.183507  \n",
      "CoralReefs                                  0.057514      0.267749  \n",
      "DescentofMan                                0.007882      0.123917  \n",
      "DifferentFormsofFlowers                     0.002611      0.013124  \n",
      "EffectsCrossSelfFertilization               0.002178      0.017140  \n",
      "ExpressionofEmotionManAnimals               0.005561      0.098961  \n",
      "FormationVegetableMould                     0.059407      0.097908  \n",
      "FoundationsOriginofSpecies                  0.017590      0.089132  \n",
      "GeologicalObservationsSouthAmerica          0.373249      0.260141  \n",
      "InsectivorousPlants                         0.008544      0.014776  \n",
      "LifeandLettersVol1                          0.026374      0.171708  \n",
      "LifeandLettersVol2                          0.011806      0.089947  \n",
      "MonographCirripedia                         0.010754      0.014342  \n",
      "MonographCirripediaVol2                     0.017952      0.025047  \n",
      "MovementClimbingPlants                      0.002832      0.012282  \n",
      "OriginofSpecies                             0.036014      0.164661  \n",
      "PowerMovementPlants                         0.003819      0.024149  \n",
      "VariationPlantsAnimalsDomestication         0.012620      0.114134  \n",
      "VolcanicIslands                             1.000000      0.138323  \n",
      "VoyageBeagle                                0.138323      1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Load the library allowing similarity computations\n",
    "from gensim import similarities\n",
    "\n",
    "# Compute the similarity matrix (pairwise distance between all texts)\n",
    "sims = similarities.MatrixSimilarity(model[bows])\n",
    "\n",
    "# Transform the resulting list into a dataframe\n",
    "sim_df = pd.DataFrame(list(sims))\n",
    "\n",
    "# Add the titles of the books as columns and index of the dataframe\n",
    "# ... YOUR CODE FOR TASK 10 ...\n",
    "sim_df.columns=titles\n",
    "sim_df.set_index(pd.Index(titles), inplace=True)\n",
    "\n",
    "# Print the resulting matrix\n",
    "# ... YOUR CODE FOR TASK 10 ..\n",
    "print(sim_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "73"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 11. The book most similar to \"On the Origin of Species\"\n",
    "<p>We now have a matrix containing all the similarity measures between any pair of books from Charles Darwin! We can now use this matrix to quickly extract the information we need, i.e., the distance between one book and one or several others. </p>\n",
    "<p>As a first step, we will display which books are the most similar to \"<em>On the Origin of Species</em>,\" more specifically we will produce a bar chart showing all books ranked by how similar they are to Darwin's landmark work.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "dc": {
     "key": "73"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f60cf681b70>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60ec496978>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is needed to display plots in a notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# Import libraries\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Select the column corresponding to \"On the Origin of Species\" and \n",
    "v = sim_df['OriginofSpecies']\n",
    "\n",
    "# Sort by ascending scores\n",
    "v_sorted = v.sort_values(ascending=True)\n",
    "\n",
    "# Plot this data has a horizontal bar plot\n",
    "# ... YOUR CODE FOR TASK 11 ...\n",
    "v_sorted.plot.barh()\n",
    "\n",
    "# Modify the axes labels and plot title for a better readability\n",
    "# ... YOUR CODE FOR TASK 11 ...\n",
    "plt.xlabel('similarity')\n",
    "plt.ylabel('title')\n",
    "plt.title('Top most simlar books to OriginofSpecies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "80"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 12. Which books have similar content?\n",
    "<p>This turns out to be extremely useful if we want to determine a given book's most similar work. For example, we have just seen that if you enjoyed \"<em>On the Origin of Species</em>,\" you can read books discussing similar concepts such as \"<em>The Variation of Animals and Plants under Domestication</em>\" or \"<em>The Descent of Man, and Selection in Relation to Sex</em>.\" If you are familiar with Darwin's work, these suggestions will likely seem natural to you. Indeed, <em>On the Origin of Species</em> has a whole chapter about domestication and <em>The Descent of Man, and Selection in Relation to Sex</em> applies the theory of natural selection to human evolution. Hence, the results make sense.</p>\n",
    "<p>However, we now want to have a better understanding of the big picture and see how Darwin's books are generally related to each other (in terms of topics discussed). To this purpose, we will represent the whole similarity matrix as a dendrogram, which is a standard tool to display such data. <strong>This last approach will display all the information about book similarities at once.</strong> For example, we can find a book's closest relative but, also, we can visualize which groups of books have similar topics (e.g., the cluster about Charles Darwin personal life with his autobiography and letters). If you are familiar with Darwin's bibliography, the results should not surprise you too much, which indicates the method gives good results. Otherwise, next time you read one of the author's book, you will know which other books to read next in order to learn more about the topics it addressed.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "dc": {
     "key": "80"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  [5.0, 5.0, 20.0, 20.0],\n",
       "  [55.0, 55.0, 65.0, 65.0],\n",
       "  [45.0, 45.0, 60.0, 60.0],\n",
       "  [35.0, 35.0, 52.5, 52.5],\n",
       "  [75.0, 75.0, 85.0, 85.0],\n",
       "  [95.0, 95.0, 105.0, 105.0],\n",
       "  [115.0, 115.0, 125.0, 125.0],\n",
       "  [100.0, 100.0, 120.0, 120.0],\n",
       "  [135.0, 135.0, 145.0, 145.0],\n",
       "  [155.0, 155.0, 165.0, 165.0],\n",
       "  [185.0, 185.0, 195.0, 195.0],\n",
       "  [175.0, 175.0, 190.0, 190.0],\n",
       "  [160.0, 160.0, 182.5, 182.5],\n",
       "  [140.0, 140.0, 171.25, 171.25],\n",
       "  [110.0, 110.0, 155.625, 155.625],\n",
       "  [80.0, 80.0, 132.8125, 132.8125],\n",
       "  [43.75, 43.75, 106.40625, 106.40625],\n",
       "  [12.5, 12.5, 75.078125, 75.078125]],\n",
       " 'dcoord': [[0.0, 0.26140235679572277, 0.26140235679572277, 0.0],\n",
       "  [0.0, 1.3236893228308522, 1.3236893228308522, 0.26140235679572277],\n",
       "  [0.0, 0.8674413447333973, 0.8674413447333973, 0.0],\n",
       "  [0.0, 1.1416962203802432, 1.1416962203802432, 0.8674413447333973],\n",
       "  [0.0, 1.1971849249980662, 1.1971849249980662, 1.1416962203802432],\n",
       "  [0.0, 0.6763235487534763, 0.6763235487534763, 0.0],\n",
       "  [0.0, 0.89514185313472, 0.89514185313472, 0.0],\n",
       "  [0.0, 1.1046848348625196, 1.1046848348625196, 0.0],\n",
       "  [0.89514185313472,\n",
       "   1.5287532852673114,\n",
       "   1.5287532852673114,\n",
       "   1.1046848348625196],\n",
       "  [0.0, 0.8619940529015238, 0.8619940529015238, 0.0],\n",
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       "  [0.0, 1.4204908731559662, 1.4204908731559662, 1.3650118511722933],\n",
       "  [1.064324300046064,\n",
       "   1.548886808090358,\n",
       "   1.548886808090358,\n",
       "   1.4204908731559662],\n",
       "  [0.8619940529015238,\n",
       "   1.8572464326136366,\n",
       "   1.8572464326136366,\n",
       "   1.548886808090358],\n",
       "  [1.5287532852673114,\n",
       "   1.9985744459448571,\n",
       "   1.9985744459448571,\n",
       "   1.8572464326136366],\n",
       "  [0.6763235487534763,\n",
       "   2.0857130884059707,\n",
       "   2.0857130884059707,\n",
       "   1.9985744459448571],\n",
       "  [1.1971849249980662,\n",
       "   2.1421800670544138,\n",
       "   2.1421800670544138,\n",
       "   2.0857130884059707],\n",
       "  [1.3236893228308522,\n",
       "   2.5482369452279703,\n",
       "   2.5482369452279703,\n",
       "   2.1421800670544138]],\n",
       " 'ivl': ['Autobiography',\n",
       "  'LifeandLettersVol1',\n",
       "  'LifeandLettersVol2',\n",
       "  'DescentofMan',\n",
       "  'FoundationsOriginofSpecies',\n",
       "  'OriginofSpecies',\n",
       "  'VariationPlantsAnimalsDomestication',\n",
       "  'MonographCirripedia',\n",
       "  'MonographCirripediaVol2',\n",
       "  'GeologicalObservationsSouthAmerica',\n",
       "  'VolcanicIslands',\n",
       "  'CoralReefs',\n",
       "  'VoyageBeagle',\n",
       "  'DifferentFormsofFlowers',\n",
       "  'EffectsCrossSelfFertilization',\n",
       "  'InsectivorousPlants',\n",
       "  'MovementClimbingPlants',\n",
       "  'ExpressionofEmotionManAnimals',\n",
       "  'FormationVegetableMould',\n",
       "  'PowerMovementPlants'],\n",
       " 'leaves': [0,\n",
       "  10,\n",
       "  11,\n",
       "  2,\n",
       "  7,\n",
       "  15,\n",
       "  17,\n",
       "  12,\n",
       "  13,\n",
       "  8,\n",
       "  18,\n",
       "  1,\n",
       "  19,\n",
       "  3,\n",
       "  4,\n",
       "  9,\n",
       "  14,\n",
       "  5,\n",
       "  6,\n",
       "  16],\n",
       " 'color_list': ['g',\n",
       "  'g',\n",
       "  'r',\n",
       "  'r',\n",
       "  'r',\n",
       "  'c',\n",
       "  'm',\n",
       "  'm',\n",
       "  'm',\n",
       "  'y',\n",
       "  'k',\n",
       "  'k',\n",
       "  'k',\n",
       "  'k',\n",
       "  'b',\n",
       "  'b',\n",
       "  'b',\n",
       "  'b',\n",
       "  'b']}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f60cf5420f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries\n",
    "from scipy.cluster import hierarchy\n",
    "\n",
    "# Compute the clusters from the similarity matrix,\n",
    "# using the Ward variance minimization algorithm\n",
    "Z = hierarchy.linkage(list(sims), 'ward')\n",
    "\n",
    "# Display this result as a horizontal dendrogram\n",
    "# ... YOUR CODE FOR TASK 12 ...\n",
    "hierarchy.dendrogram(Z, leaf_font_size=8, labels=sim_df.index, orientation=\"left\")"
   ]
  }
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