Lecture 07 - Loading and Visualizing GloVe.ipynb

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    "colab": {
      "name": "Lecture 10 - Loading and Visualizing GloVe.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s8ut5-fUttII"
      },
      "source": [
        "# Lecture 10 - Loading and Visualizing GloVe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJc6VipHHZY2"
      },
      "source": [
        "import os\n",
        "import os.path\n",
        "import numpy as np\n",
        "\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyU9mFPq2smr"
      },
      "source": [
        "I'm loading [GloVe](https://nlp.stanford.edu/projects/glove/) from google drive since it is attached to colab. You can find a url to the pre-trained embedding below or go to the GloVe's website to download it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0SuG7MQHa4V",
        "outputId": "e3e1df55-c424-4e81-f2e5-dee8a5145736"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "base = \"\"\"/content/drive/MyDrive/GloVe\"\"\"\n",
        "assert os.path.exists(base)\n",
        "\n",
        "if not os.path.exists(os.path.join(base, \"glove.6B.zip\")):\n",
        "  !wget http://nlp.stanford.edu/data/glove.6B.zip\n",
        "  !mv glove.6B.zip drive/MyDrive/GloVe/\n",
        "\n",
        "!unzip -l drive/MyDrive/GloVe/glove.6B.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "Archive:  drive/MyDrive/GloVe/glove.6B.zip\n",
            "  Length      Date    Time    Name\n",
            "---------  ---------- -----   ----\n",
            "171350079  2014-08-04 20:15   glove.6B.50d.txt\n",
            "347116733  2014-08-04 20:14   glove.6B.100d.txt\n",
            "693432828  2014-08-04 20:14   glove.6B.200d.txt\n",
            "1037962819  2014-08-27 19:19   glove.6B.300d.txt\n",
            "---------                     -------\n",
            "2249862459                     4 files\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J61lALYg3O-J"
      },
      "source": [
        "There are several versions avalible, so I have to choose one."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vv33Fi5UICsy",
        "outputId": "7a00e922-f24b-4bca-b6a2-f796db55f42b"
      },
      "source": [
        "n_vocab = 400000\n",
        "n_dim = 200\n",
        "\n",
        "import zipfile\n",
        "import io\n",
        "\n",
        "idx2voc = list()\n",
        "GloVe = np.zeros((n_vocab, n_dim))\n",
        "with zipfile.ZipFile(os.path.join(base, \"glove.6B.zip\")) as archive:\n",
        "  with archive.open(\"glove.6B.%id.txt\" % n_dim) as raw:\n",
        "    for n, line in tqdm(enumerate(io.TextIOWrapper(raw, encoding=None, newline='\\n')), \n",
        "                     total=n_vocab, desc=\"Loading vectors\"):\n",
        "      tokens = line.split(' ')\n",
        "      idx2voc.append(tokens[0])\n",
        "      GloVe[n, :] = np.asarray(tokens[1:], dtype=np.float32)\n",
        "vocabulary = set(idx2voc)\n",
        "voc2idx = {word:n for n, word in enumerate(idx2voc)}"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading vectors: 100%|██████████| 400000/400000 [00:31<00:00, 12734.32it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHTXEehDdLmF",
        "outputId": "fc29a984-619f-4f35-cf11-8bd611b45530"
      },
      "source": [
        "GloVe[voc2idx['silence'], :]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VYw27EuI5Gpk"
      },
      "source": [
        "## Word analogies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rnNiCsEZ5WOV"
      },
      "source": [
        "from sklearn.metrics import pairwise_distances"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hu_LDSUedlHC",
        "outputId": "3521bc8f-0789-485a-bdc2-624f6e55d598"
      },
      "source": [
        "vec = lambda token: np.vstack(GloVe[voc2idx[token], :]).T\n",
        "\n",
        "v = vec('star')\n",
        "\n",
        "distances = pairwise_distances(GloVe, v, metric='euclidean').ravel()\n",
        "\n",
        "for idx in np.argsort(distances)[:20]:\n",
        "  print(idx2voc[idx], '\\t', distances[idx])"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "star \t 0.0\n",
            "stars \t 3.8225903699707438\n",
            "superstar \t 5.220676558076345\n",
            "alongside \t 6.026981028914708\n",
            "veteran \t 6.037169848629846\n",
            "legend \t 6.060596367666348\n",
            "once \t 6.071650549231439\n",
            "like \t 6.076475804202069\n",
            "named \t 6.07712250346828\n",
            "starred \t 6.077577935783381\n",
            "turned \t 6.092562773162954\n",
            "appeared \t 6.0965328280276\n",
            "appears \t 6.099973619906431\n",
            "hollywood \t 6.113614081243547\n",
            "as \t 6.113906895069805\n",
            "newcomer \t 6.11569846998341\n",
            "hero \t 6.116005740568125\n",
            "fame \t 6.130075701371151\n",
            "best \t 6.148700905217092\n",
            "man \t 6.156803785683631\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qFxZ6fipeD_V",
        "outputId": "f638266e-da3b-4d3f-800f-0a47cae241a7"
      },
      "source": [
        "import random\n",
        "random.choices(list(vocabulary), k=20)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['admitting',\n",
              " 'integra',\n",
              " 'initializing',\n",
              " 'monsoonal',\n",
              " 'hizbullah',\n",
              " 'public-school',\n",
              " 'casco',\n",
              " 'medium-pace',\n",
              " 'doncasters',\n",
              " 'stratos',\n",
              " 'magmas',\n",
              " 'parting',\n",
              " 'isesaki',\n",
              " 'eerdekens',\n",
              " 'fleisig',\n",
              " 'sup',\n",
              " 'kaiserlautern',\n",
              " '73-75',\n",
              " 'liana',\n",
              " 'nastasia']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-bVXoDoVzZz",
        "outputId": "f53d1ad3-93bb-48ec-eb5f-9cfc9f24cd8f"
      },
      "source": [
        "# v = vec('king')\n",
        "v = vec('king') - vec('man') + vec('woman')\n",
        "# v = vec('queen') + vec('man') - vec('woman')\n",
        "\n",
        "distances = pairwise_distances(GloVe, v, metric='euclidean').ravel()\n",
        "\n",
        "for idx in np.argsort(distances)[:20]:\n",
        "  print(idx2voc[idx], '\\t', distances[idx])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "king \t 0.7042604306040586\n",
            "queen \t 5.256927584849657\n",
            "prince \t 5.415242229239351\n",
            "monarch \t 5.463253010710028\n",
            "kingdom \t 5.621511180803411\n",
            "throne \t 5.808637217409268\n",
            "reign \t 5.886935461583319\n",
            "uncle \t 5.982255724499488\n",
            "ii \t 5.988985645435528\n",
            "crown \t 6.012373561394796\n",
            "nephew \t 6.0877090368285165\n",
            "cousin \t 6.161330447399032\n",
            "iii \t 6.1692461766340365\n",
            "brother \t 6.21298971862443\n",
            "later \t 6.295843975895495\n",
            "afterwards \t 6.300898549205696\n",
            "latter \t 6.31596081226383\n",
            "iv \t 6.322683596804751\n",
            "mentioned \t 6.3288909280658014\n",
            "accompanied \t 6.329484272009041\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLhj-Dqd6rWb"
      },
      "source": [
        "## Load a book"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nU1Etp-t6rdF",
        "outputId": "d8209155-b11d-465b-8e03-c2566172b352"
      },
      "source": [
        "import nltk\n",
        "nltk.download('gutenberg')\n",
        "print(nltk.corpus.gutenberg.fileids())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package gutenberg to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/gutenberg.zip.\n",
            "['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccyKh3vP9T7u"
      },
      "source": [
        "books = ['carroll-alice.txt']\n",
        "words = nltk.corpus.gutenberg.words(books)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EksVFeCK8o-y"
      },
      "source": [
        "text = \"\"\"No more be grieved at that which thou hast done:\n",
        "Roses have thorns, and silver fountains mud:\n",
        "Clouds and eclipses stain both moon and sun,\n",
        "And loathsome canker lives in sweetest bud.\n",
        "All men make faults, and even I in this,\n",
        "Authorizing thy trespass with compare,\n",
        "Myself corrupting, salving thy amiss,\n",
        "Excusing thy sins more than thy sins are;\n",
        "For to thy sensual fault I bring in sense,\n",
        "Thy adverse party is thy advocate,\n",
        "And ‘gainst myself a lawful plea commence:\n",
        "Such civil war is in my love and hate,\n",
        "That I an accessary needs must be,\n",
        "To that sweet thief which sourly robs from me.\"\"\"\n",
        "\n",
        "words = nltk.tokenize.wordpunct_tokenize(text)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vRw51w9Cy10",
        "outputId": "944ad459-8f98-4de3-a7f7-97512f7adb1f"
      },
      "source": [
        "words = [word.lower() for word in words if word.isalpha()]\n",
        "\n",
        "tokencounts = dict()\n",
        "for word in words:\n",
        "  if word not in tokencounts.keys():\n",
        "    tokencounts[word] = 1\n",
        "  else:\n",
        "    tokencounts[word] += 1\n",
        "\n",
        "print(\"%i tokens in the text(s)\" % len(tokencounts.keys()))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "80 tokens in the text(s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c65yxMsh43rR"
      },
      "source": [
        "### POS tags for colour\n",
        "\n",
        "We know that baseline POS taggers do quite well. Let's make one for the \n",
        "visualisation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rhw9SpQ0GrCX",
        "outputId": "519c7500-d180-4758-c3e1-0f98a0c2af30"
      },
      "source": [
        "nltk.download('brown')\n",
        "from nltk.corpus import brown\n",
        "nltk.download('universal_tagset')\n",
        "\n",
        "pos = dict()\n",
        "for sentence in brown.tagged_sents(tagset='universal'):\n",
        "  for n, (word, tag) in enumerate(sentence):\n",
        "    word = word.lower()\n",
        "    if word not in pos.keys():\n",
        "      pos[word] = {tag: 1}\n",
        "    elif tag not in pos[word].keys():\n",
        "      pos[word][tag] = 1\n",
        "    else:\n",
        "      pos[word][tag] += 1\n",
        "for word in pos.keys():\n",
        "  pos[word] = sorted(pos[word])[0]\n",
        "\n",
        "missing = set(tokencounts.keys()).difference(set(pos.keys()))\n",
        "print(\"Tags are missing for %.1f%% of the words\" % (100*np.sum([tokencounts[token] for token in missing])/len(tokencounts.keys())))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package brown to /root/nltk_data...\n",
            "[nltk_data]   Package brown is already up-to-date!\n",
            "[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n",
            "[nltk_data]   Package universal_tagset is already up-to-date!\n",
            "Tags are missing for 7.5% of the words\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kE8RIK4YaXF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "6e32a159-1bbe-478a-c8ae-f92cf1baabb0"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=min(100, GloVe.shape[1]//2))\n",
        "V_pca = pca.fit_transform(GloVe)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKzfmkQKOuxE"
      },
      "source": [
        "idx_tag = dict()\n",
        "tag2idx = {tag:n for n, tag in enumerate(set(pos.values()).union({'UNK'}))}\n",
        "for word in set(tokencounts.keys()).intersection(vocabulary):\n",
        "  if word in pos:\n",
        "    idx_tag[voc2idx[word]] = tag2idx[pos[word]]\n",
        "  else:\n",
        "    idx_tag[voc2idx[word]] = tag2idx['UNK']\n",
        "\n",
        "idx = list(idx_tag.keys())\n",
        "y = np.asarray([idx_tag[i] for i in idx])\n",
        "s = np.asarray([tokencounts[idx2voc[i]] for i in idx])\n",
        "assert len(idx) == len(y)\n",
        "assert len(idx) == len(s)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "LEPgO2J1Yguo",
        "outputId": "4743c248-6874-48c3-ca3c-300187bac979"
      },
      "source": [
        "fig = plt.figure(figsize=(8, 5), dpi=100)\n",
        "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
        "# ax.scatter(V_pca[idx, 0], V_pca[idx, 1], V_pca[idx, 2], c=y, cmap='Pastel1', marker='.', alpha=.8)\n",
        "ax.scatter(V_pca[idx, 0], V_pca[idx, 1], V_pca[idx, 2], c=y, s=s*20, cmap='tab10', marker='.', alpha=.6)\n",
        "ax.axis('off')\n",
        "fig.show()\n",
        "\n",
        "print(\"%i points\" % len(idx))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "79 points\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "rnW3xU1kdxvW",
        "outputId": "dcb38dc2-05c5-44eb-f26e-6bfe6d0a1a55"
      },
      "source": [
        "sentence = \"This parrot is simply pining for the fjords\"\n",
        "# sentence = \"machine learning for language technologists\"\n",
        "\n",
        "words =  [word for word in sentence.lower().split() if word in vocabulary]\n",
        "d = len(words) - len(sentence.split())\n",
        "if d > 0:\n",
        "  print(\"%i word(s) was not in the vocabulary\")\n",
        "path = np.concatenate([np.vstack(V_pca[voc2idx[word]]).T for word in words])\n",
        "\n",
        "fig = plt.figure(figsize=(8, 5), dpi=100)\n",
        "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
        "ax.plot3D(path[:, 0], path[:, 1], path[:, 2], 'o-', alpha=.8)\n",
        "# # ax.scatter(X_pca[idx, 0], X_pca[idx, 1], X_pca[idx, 2], c='b', marker='.', alpha=.3)\n",
        "# # ax.scatter(X_pca[:, 0], X_pca[:, 1], X_pca[:, 2], c='b', cmap='Spectral', alpha=.3)\n",
        "# # ax.view_init(20, 0)\n",
        "fig.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXrYISH_YuKV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ab47881-51d6-46fc-fb4f-c3ed25c7296f"
      },
      "source": [
        "try:\n",
        "  from MulticoreTSNE import MulticoreTSNE as TSNE\n",
        "except:\n",
        "  !pip install MulticoreTSNE\n",
        "finally:\n",
        "  from MulticoreTSNE import MulticoreTSNE as TSNE\n",
        "import os"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting MulticoreTSNE\n",
            "  Downloading https://files.pythonhosted.org/packages/2d/e8/2afa896fa4eebfa1d0d0ba2673fddac45582ec0f06b2bdda88108ced5425/MulticoreTSNE-0.1.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from MulticoreTSNE) (1.19.5)\n",
            "Requirement already satisfied: cffi in /usr/local/lib/python3.7/dist-packages (from MulticoreTSNE) (1.14.5)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi->MulticoreTSNE) (2.20)\n",
            "Building wheels for collected packages: MulticoreTSNE\n",
            "  Building wheel for MulticoreTSNE (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for MulticoreTSNE: filename=MulticoreTSNE-0.1-cp37-cp37m-linux_x86_64.whl size=68502 sha256=74830b24eb11c2d3b5b9007194dd613ce90b20d85e40600443d636e41bc5efee\n",
            "  Stored in directory: /root/.cache/pip/wheels/27/59/53/3b52ee63add3692254c30d687fa4dff4d128d0557861fb028e\n",
            "Successfully built MulticoreTSNE\n",
            "Installing collected packages: MulticoreTSNE\n",
            "Successfully installed MulticoreTSNE-0.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZcx1SGkXzJd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb932d70-2522-4dde-df59-840288c964a2"
      },
      "source": [
        "tsne = TSNE(n_components=3, n_jobs=os.cpu_count(), verbose=True, metric='cosine')\n",
        "V_tsne = tsne.fit_transform(GloVe[idx, :])\n",
        "tsne"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<MulticoreTSNE.MulticoreTSNE at 0x7fa5077c7b90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAN_PLjxZfjm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "outputId": "cc98a4e3-8ae8-425a-ad1a-a6b61ca8ae48"
      },
      "source": [
        "plt.style.use('dark_background')\n",
        "fig = plt.figure(figsize=(8, 5), dpi=100)\n",
        "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
        "ax.scatter(V_tsne[:, 0], V_tsne[:, 1], V_tsne[:, 2], c=y, s=s*60, cmap='tab10', marker='.', alpha=.5)\n",
        "# ax.scatter(V_tsne[:, 0], V_tsne[:, 1], V_tsne[:, 2], c=y, s=s, cmap='Spectral', marker='.', alpha=.3)\n",
        "# ax.view_init(20, 20)\n",
        "ax.axis('off')\n",
        "fig.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lLMNIaWs4jW4",
        "outputId": "dd6cf621-4c0c-4341-90ed-66ae815ef78e"
      },
      "source": [
        "try:\n",
        "  from umap import UMAP\n",
        "except:\n",
        "  !pip install umap-learn\n",
        "finally:\n",
        "  from umap import UMAP"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting umap-learn\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/69/85e7f950bb75792ad5d666d86c5f3e62eedbb942848e7e3126513af9999c/umap-learn-0.5.1.tar.gz (80kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 3.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (1.4.1)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn) (0.51.2)\n",
            "Collecting pynndescent>=0.5\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/af/65/8189298dd3a05bbad716ee8e249764ff8800e365d8dc652ad2192ca01b4a/pynndescent-0.5.2.tar.gz (1.1MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 7.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.22->umap-learn) (1.0.1)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (0.34.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.49->umap-learn) (56.1.0)\n",
            "Building wheels for collected packages: umap-learn, pynndescent\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for umap-learn: filename=umap_learn-0.5.1-cp37-none-any.whl size=76569 sha256=0f2077cd3cc89d6178b2acdc84e9056a01787003bdf6e41c3dcfbcc42c3dee63\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/df/d5/a3691296ff779f25cd1cf415a3af954b987fb53111e3392cf4\n",
            "  Building wheel for pynndescent (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pynndescent: filename=pynndescent-0.5.2-cp37-none-any.whl size=51351 sha256=ab89031b3c203666dadec71238b970b89fad40ed8b4609b067d2661c9ebe51ab\n",
            "  Stored in directory: /root/.cache/pip/wheels/ba/52/4e/4c28d04d144a28f89e2575fb63628df6e6d49b56c5ddd0c74e\n",
            "Successfully built umap-learn pynndescent\n",
            "Installing collected packages: pynndescent, umap-learn\n",
            "Successfully installed pynndescent-0.5.2 umap-learn-0.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QhTa0mP4nPr"
      },
      "source": [
        "umap = UMAP(n_components=3, metric='euclidean')\n",
        "# umap = UMAP(n_components=3, metric='cosine')\n",
        "X_umap3 = umap.fit_transform(GloVe[idx, :])"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "mtbYOhZPjdnK",
        "outputId": "58a03960-938d-4b51-8e15-33962ca7fd40"
      },
      "source": [
        "plt.style.use('dark_background')\n",
        "fig = plt.figure(figsize=(8, 5), dpi=100)\n",
        "ax = fig.add_subplot(1, 1, 1, projection='3d')\n",
        "ax.scatter(X_umap3[:, 0], X_umap3[:, 1], X_umap3[:, 2], c=y, s=s*60, cmap='tab10', marker='.', alpha=.5)\n",
        "# ax.scatter(V_tsne[:, 0], V_tsne[:, 1], V_tsne[:, 2], c=y, s=s, cmap='Spectral', marker='.', alpha=.3)\n",
        "# ax.view_init(20, 20)\n",
        "ax.axis('off')\n",
        "fig.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}