lab/Gensim_word_vector_visualization.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"iQGwGMFs7dRx"},"source":["# Gensim word vector visualization of various word vectors"]},{"cell_type":"markdown","metadata":{"id":"6T8PE_5X7dRz"},"source":["### Original downloaded notebook version"]},{"cell_type":"markdown","metadata":{"id":"ehd7m6mY7dR0"},"source":["```python\n","import numpy as np\n","\n","# Get the interactive Tools for Matplotlib\n","%matplotlib notebook\n","import matplotlib.pyplot as plt\n","plt.style.use('ggplot')\n","\n","from sklearn.decomposition import PCA\n","\n","from gensim.test.utils import datapath, get_tmpfile\n","from gensim.models import KeyedVectors\n","from gensim.scripts.glove2word2vec import glove2word2vec\n","```"]},{"cell_type":"markdown","metadata":{"id":"OHZNx_r77dR0"},"source":["For looking at word vectors, I'll use Gensim. We also use it in hw1 for word vectors. Gensim isn't really a deep learning package. It's a package for for word and text similarity modeling, which started with (LDA-style) topic models and grew into SVD and neural word representations. But its efficient and scalable, and quite widely used."]},{"cell_type":"markdown","metadata":{"id":"OGOtzLoL7dR1"},"source":["Our homegrown Stanford offering is GloVe word vectors. Gensim doesn't give them first class support, but allows you to convert a file of GloVe vectors into word2vec format. You can download the GloVe vectors from [the Glove page](https://nlp.stanford.edu/projects/glove/). They're inside [this zip file](https://nlp.stanford.edu/data/glove.6B.zip)"]},{"cell_type":"markdown","metadata":{"id":"NFGmoEpo7dR1"},"source":["(I use the 100d vectors below as a mix between speed and smallness vs. quality. If you try out the 50d vectors, they basically work for similarity but clearly aren't as good for analogy problems. If you load the 300d vectors, they're even better than the 100d vectors.)"]},{"cell_type":"markdown","metadata":{"id":"91Kj_Mjh7dR1"},"source":["```python\n","glove_file = datapath('/Users/manning/Corpora/GloVe/glove.6B.100d.txt')\n","word2vec_glove_file = get_tmpfile(\"glove.6B.100d.word2vec.txt\")\n","glove2word2vec(glove_file, word2vec_glove_file)\n","\n","model = KeyedVectors.load_word2vec_format(word2vec_glove_file)\n","```"]},{"cell_type":"markdown","metadata":{"id":"OtTVY8ng7dR2"},"source":["### Lecture video version "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iGDapn-07dR2"},"outputs":[],"source":["import numpy as np\n","\n","# Get the interactive Tools for Matplotlib\n","# %matplotlib notebook\n","%matplotlib notebook\n","import matplotlib.pyplot as plt\n","plt.style.use('ggplot')\n","\n","from sklearn.decomposition import PCA\n","\n","import gensim.downloader as api\n","from gensim.models import KeyedVectors"]},{"cell_type":"markdown","metadata":{"id":"dDqQZ6hC7dR4"},"source":["For looking at word vectors, I'll use Gensim. We also use it in hw1 for word vectors. Gensim isn't really a deep learning package. It's a package for for word and text similarity modeling, which started with (LDA-style) topic models and grew into SVD and neural word representations. But its efficient and scalable, and quite widely used."]},{"cell_type":"markdown","metadata":{"id":"dRgmUWjI7dR4"},"source":["I'll use our homegrown Stanford offering of GloVe word vectors. Gensim provides a library of several sets of word vectors that you can easily load. You can find out more about GloVe on [the Glove page](https://nlp.stanford.edu/projects/glove/). I use the 100d vectors below as a balance between speed and smallness vs. quality. If you try out the 50d vectors, they basically work for similarity but clearly aren't as good for analogy problems. If you load the 300d vectors, you'll wait longer, but they're even better than the 100d vectors."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yL0mqt847dR5","outputId":"948b5167-52c4-46c3-8fdd-4fc88e756d45"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'gensim.models.keyedvectors.KeyedVectors'>\n"]}],"source":["model = api.load(\"glove-wiki-gigaword-100\") # downloads in /root/gensim.. if none\n","print(type(model))"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true,"id":"rgn-aCfO7dR5","outputId":"5fe3dba1-a1a2-4b11-c4d2-29d193f9fac2"},"outputs":[{"data":{"text/plain":["array([-0.66146  ,  0.94335  , -0.72214  ,  0.17403  , -0.42524  ,\n","        0.36303  ,  1.0135   , -0.14802  ,  0.25817  , -0.20326  ,\n","       -0.64338  ,  0.16632  ,  0.61518  ,  1.397    , -0.094506 ,\n","        0.0041843, -0.18976  , -0.55421  , -0.39371  , -0.22501  ,\n","       -0.34643  ,  0.32076  ,  0.34395  , -0.7034   ,  0.23932  ,\n","        0.69951  , -0.16461  , -0.31819  , -0.34034  , -0.44906  ,\n","       -0.069667 ,  0.35348  ,  0.17498  , -0.95057  , -0.2209   ,\n","        1.0647   ,  0.23231  ,  0.32569  ,  0.47662  , -1.1206   ,\n","        0.28168  , -0.75172  , -0.54654  , -0.66337  ,  0.34804  ,\n","       -0.69058  , -0.77092  , -0.40167  , -0.069351 , -0.049238 ,\n","       -0.39351  ,  0.16735  , -0.14512  ,  1.0083   , -1.0608   ,\n","       -0.87314  , -0.29339  ,  0.68278  ,  0.61634  , -0.088844 ,\n","        0.88094  ,  0.099809 , -0.27161  , -0.58026  ,  0.50364  ,\n","       -0.93814  ,  0.67576  , -0.43124  , -0.10517  , -1.2404   ,\n","       -0.74353  ,  0.28637  ,  0.29012  ,  0.89377  ,  0.67406  ,\n","        0.86422  , -0.30693  , -0.14718  ,  0.078353 ,  0.74013  ,\n","        0.32658  , -0.052579 , -1.1665   ,  0.87079  , -0.69402  ,\n","       -0.75977  , -0.37164  , -0.11887  ,  0.18551  ,  0.041883 ,\n","        0.59352  ,  0.30519  , -0.54819  , -0.29424  , -1.4912   ,\n","       -1.6548   ,  0.98982  ,  0.27325  ,  1.009    ,  0.94544  ],\n","      dtype=float32)"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["model['bread']"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true,"id":"07Gd0J6E7dR6","outputId":"d26e22f2-4f78-4e97-b072-97da3781b723"},"outputs":[{"data":{"text/plain":["array([-0.25144  ,  0.52157  , -0.75452  ,  0.28039  , -0.31388  ,\n","        0.274    ,  1.1971   , -0.10519  ,  0.82544  , -0.33398  ,\n","       -0.21417  ,  0.22216  ,  0.14982  ,  0.47384  ,  0.41984  ,\n","        0.69397  , -0.25999  , -0.44414  ,  0.58296  , -0.30851  ,\n","       -0.076455 ,  0.33468  ,  0.28055  , -0.99012  ,  0.30349  ,\n","        0.39128  ,  0.031526 , -0.095395 , -0.004745 , -0.81347  ,\n","        0.27869  , -0.1812   ,  0.14632  , -0.42186  ,  0.13857  ,\n","        1.139    ,  0.14925  , -0.051459 ,  0.37875  , -0.2613   ,\n","        0.011081 , -0.28881  , -0.38662  , -0.3135   , -0.1954   ,\n","        0.19248  , -0.52995  , -0.40674  , -0.25159  ,  0.06272  ,\n","       -0.32724  ,  0.28374  , -0.2155   , -0.061832 , -0.50134  ,\n","        0.0093959,  0.30715  ,  0.3873   , -0.74554  , -0.45947  ,\n","        0.40032  , -0.1378   , -0.26968  , -0.3946   , -0.64876  ,\n","       -0.47149  , -0.085536 ,  0.092795 , -0.034018 , -0.61906  ,\n","        0.19123  ,  0.20563  ,  0.29056  , -0.010908 ,  0.15313  ,\n","        0.33144  ,  0.33806  ,  0.061708 ,  0.20785  ,  0.65348  ,\n","       -0.053222 ,  0.18589  ,  0.32647  , -0.11923  ,  0.42008  ,\n","       -0.26931  ,  0.025489 ,  0.0036535,  0.1327   , -0.22763  ,\n","        0.07564  ,  0.55773  ,  0.2978   ,  0.28144  ,  0.19775  ,\n","       -0.23582  ,  0.65303  ,  0.089897 ,  0.35844  ,  0.14304  ],\n","      dtype=float32)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["model['croissant']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BqdUys6U7dR6","outputId":"5ff092c2-20b1-49c7-f805-f04f872809bf"},"outputs":[{"data":{"text/plain":["[('croissants', 0.6829845309257507),\n"," ('brioche', 0.6283302307128906),\n"," ('baguette', 0.5968102812767029),\n"," ('focaccia', 0.5876684188842773),\n"," ('pudding', 0.5803956985473633),\n"," ('souffle', 0.5614768862724304),\n"," ('baguettes', 0.5558240413665771),\n"," ('tortilla', 0.5449502468109131),\n"," ('pastries', 0.5427731275558472),\n"," ('calzone', 0.5374531745910645)]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["model.most_similar('croissant')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mo_m1JDk7dR6","outputId":"14a24a4b-034d-42a2-e725-dc2275939c0e"},"outputs":[{"data":{"text/plain":["[('coconut', 0.7097253203392029),\n"," ('mango', 0.7054824233055115),\n"," ('bananas', 0.6887733936309814),\n"," ('potato', 0.6629636287689209),\n"," ('pineapple', 0.6534532308578491),\n"," ('fruit', 0.6519854664802551),\n"," ('peanut', 0.6420575976371765),\n"," ('pecan', 0.6349172592163086),\n"," ('cashew', 0.6294420957565308),\n"," ('papaya', 0.6246591210365295)]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["model.most_similar('banana')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2FPhyj8M7dR6","outputId":"60acdfcd-36c4-4227-85f0-7f31048363e0"},"outputs":[{"data":{"text/plain":["[('shunichi', 0.49618104100227356),\n"," ('ieronymos', 0.4736502170562744),\n"," ('pengrowth', 0.4668096601963043),\n"," ('höss', 0.4636845588684082),\n"," ('damaskinos', 0.46178483963012695),\n"," ('yadin', 0.4617374539375305),\n"," ('hundertwasser', 0.4588957130908966),\n"," ('ncpa', 0.45773395895957947),\n"," ('maccormac', 0.4566109776496887),\n"," ('rothfeld', 0.4523947834968567)]"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["model.most_similar(negative='banana')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mBeoUW8A7dR7","outputId":"81c1f76c-3c44-4357-f220-c5f18aa3fa79"},"outputs":[{"name":"stdout","output_type":"stream","text":["queen: 0.7699\n"]}],"source":["result = model.most_similar(positive=['woman', 'king'], negative=['man'])\n","print(\"{}: {:.4f}\".format(*result[0]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NVwbllOh7dR7"},"outputs":[],"source":["# vector composition\n","def analogy(x1, x2, y1):\n","    result = model.most_similar(positive=[y1, x2], negative=[x1])\n","    return result[0][0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NhpI23FH7dR7","outputId":"3c4c6537-e387-4ed5-f981-5488d87b903c"},"outputs":[{"data":{"text/plain":["'australian'"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["analogy('japan', 'japanese', 'australia')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2IY5U6ta7dR7","outputId":"e7ffd29d-f043-4488-b5d3-49c57d458bbc"},"outputs":[{"data":{"text/plain":["'champagne'"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["analogy('australia', 'beer', 'france')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gnfc0H6D7dR7","outputId":"ddd1a3b2-14cb-4270-9b1d-4fd329e33d58"},"outputs":[{"data":{"text/plain":["'nixon'"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["analogy('obama', 'clinton', 'reagan')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TanF7W6W7dR8","outputId":"3a89e1e6-0cf8-49df-c4e0-4d1d34da548d"},"outputs":[{"data":{"text/plain":["'longest'"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["analogy('tall', 'tallest', 'long')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wEjW0ZkE7dR8","outputId":"df21caf4-bdf6-4d86-9bd3-6f20b1d73a29"},"outputs":[{"data":{"text/plain":["'terrible'"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["analogy('good', 'fantastic', 'bad')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KEz_Tiqh7dR8","outputId":"40d82090-e6ac-45c4-d194-7e13cc899e85"},"outputs":[{"name":"stdout","output_type":"stream","text":["cereal\n"]}],"source":["print(model.doesnt_match(\"breakfast cereal dinner lunch\".split()))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yqd3sp6K7dR8"},"outputs":[],"source":["def display_pca_scatterplot(model, words=None, sample=0):\n","    if words == None:\n","        if sample > 0:\n","            words = np.random.choice(list(model.vocab.keys()), sample)\n","        else:\n","            words = [ word for word in model.vocab ]\n","        \n","    word_vectors = np.array([model[w] for w in words])\n","\n","    twodim = PCA().fit_transform(word_vectors)[:,:2]\n","    \n","    plt.figure(figsize=(6,6))\n","    plt.scatter(twodim[:,0], twodim[:,1], edgecolors='k', c='r')\n","    for word, (x,y) in zip(words, twodim):\n","        plt.text(x+0.05, y+0.05, word)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3VGfSq9G7dR8","outputId":"0f5a2776-6f05-436c-af68-ba67fd37e87a"},"outputs":[{"data":{"application/javascript":["/* Put everything inside the global mpl namespace */\n","/* global mpl */\n","window.mpl = {};\n","\n","mpl.get_websocket_type = function () {\n","    if (typeof WebSocket !== 'undefined') {\n","        return WebSocket;\n","    } 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height: ' + height + 'px;'\n","            );\n","\n","            rubberband_canvas.setAttribute('width', width);\n","            rubberband_canvas.setAttribute('height', height);\n","\n","            // And update the size in Python. We ignore the initial 0/0 size\n","            // that occurs as the element is placed into the DOM, which should\n","            // otherwise not happen due to the minimum size styling.\n","            if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n","                fig.request_resize(width, height);\n","            }\n","        }\n","    });\n","    this.resizeObserverInstance.observe(canvas_div);\n","\n","    function on_mouse_event_closure(name) {\n","        return function (event) {\n","            return fig.mouse_event(event, name);\n","        };\n","    }\n","\n","    rubberband_canvas.addEventListener(\n","        'mousedown',\n","        on_mouse_event_closure('button_press')\n","    );\n","    rubberband_canvas.addEventListener(\n","        'mouseup',\n","        on_mouse_event_closure('button_release')\n","    );\n","    rubberband_canvas.addEventListener(\n","        'dblclick',\n","        on_mouse_event_closure('dblclick')\n","    );\n","    // Throttle sequential mouse events to 1 every 20ms.\n","    rubberband_canvas.addEventListener(\n","        'mousemove',\n","        on_mouse_event_closure('motion_notify')\n","    );\n","\n","    rubberband_canvas.addEventListener(\n","        'mouseenter',\n","        on_mouse_event_closure('figure_enter')\n","    );\n","    rubberband_canvas.addEventListener(\n","        'mouseleave',\n","        on_mouse_event_closure('figure_leave')\n","    );\n","\n","    canvas_div.addEventListener('wheel', function (event) {\n","        if (event.deltaY < 0) {\n","            event.step = 1;\n","        } else {\n","            event.step = -1;\n","        }\n","        on_mouse_event_closure('scroll')(event);\n","    });\n","\n","    canvas_div.appendChild(canvas);\n","    canvas_div.appendChild(rubberband_canvas);\n","\n","    this.rubberband_context = rubberband_canvas.getContext('2d');\n","    this.rubberband_context.strokeStyle = '#000000';\n","\n","    this._resize_canvas = function (width, height, forward) {\n","        if (forward) {\n","            canvas_div.style.width = width + 'px';\n","            canvas_div.style.height = height + 'px';\n","        }\n","    };\n","\n","    // Disable right mouse context menu.\n","    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n","        event.preventDefault();\n","        return false;\n","    });\n","\n","    function set_focus() {\n","        canvas.focus();\n","        canvas_div.focus();\n","    }\n","\n","    window.setTimeout(set_focus, 100);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n","    var fig = this;\n","\n","    var toolbar = document.createElement('div');\n","    toolbar.classList = 'mpl-toolbar';\n","    this.root.appendChild(toolbar);\n","\n","    function on_click_closure(name) {\n","        return function (_event) {\n","            return fig.toolbar_button_onclick(name);\n","        };\n","    }\n","\n","    function on_mouseover_closure(tooltip) {\n","        return function (event) {\n","            if (!event.currentTarget.disabled) {\n","                return fig.toolbar_button_onmouseover(tooltip);\n","            }\n","        };\n","    }\n","\n","    fig.buttons = {};\n","    var buttonGroup = document.createElement('div');\n","    buttonGroup.classList = 'mpl-button-group';\n","    for (var toolbar_ind in mpl.toolbar_items) {\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) {\n","            /* Instead of a spacer, we start a new button group. */\n","            if (buttonGroup.hasChildNodes()) {\n","                toolbar.appendChild(buttonGroup);\n","            }\n","            buttonGroup = document.createElement('div');\n","            buttonGroup.classList = 'mpl-button-group';\n","            continue;\n","        }\n","\n","        var button = (fig.buttons[name] = document.createElement('button'));\n","        button.classList = 'mpl-widget';\n","        button.setAttribute('role', 'button');\n","        button.setAttribute('aria-disabled', 'false');\n","        button.addEventListener('click', on_click_closure(method_name));\n","        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n","\n","        var icon_img = document.createElement('img');\n","        icon_img.src = '_images/' + image + '.png';\n","        icon_img.srcset = '_images/' + image + '_large.png 2x';\n","        icon_img.alt = tooltip;\n","        button.appendChild(icon_img);\n","\n","        buttonGroup.appendChild(button);\n","    }\n","\n","    if (buttonGroup.hasChildNodes()) {\n","        toolbar.appendChild(buttonGroup);\n","    }\n","\n","    var fmt_picker = document.createElement('select');\n","    fmt_picker.classList = 'mpl-widget';\n","    toolbar.appendChild(fmt_picker);\n","    this.format_dropdown = fmt_picker;\n","\n","    for (var ind in mpl.extensions) {\n","        var fmt = mpl.extensions[ind];\n","        var option = document.createElement('option');\n","        option.selected = fmt === mpl.default_extension;\n","        option.innerHTML = fmt;\n","        fmt_picker.appendChild(option);\n","    }\n","\n","    var status_bar = document.createElement('span');\n","    status_bar.classList = 'mpl-message';\n","    toolbar.appendChild(status_bar);\n","    this.message = status_bar;\n","};\n","\n","mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n","    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n","    // which will in turn request a refresh of the image.\n","    this.send_message('resize', { width: x_pixels, height: y_pixels });\n","};\n","\n","mpl.figure.prototype.send_message = function (type, properties) {\n","    properties['type'] = type;\n","    properties['figure_id'] = this.id;\n","    this.ws.send(JSON.stringify(properties));\n","};\n","\n","mpl.figure.prototype.send_draw_message = function () {\n","    if (!this.waiting) {\n","        this.waiting = true;\n","        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n","    }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n","    var format_dropdown = fig.format_dropdown;\n","    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n","    fig.ondownload(fig, format);\n","};\n","\n","mpl.figure.prototype.handle_resize = function (fig, msg) {\n","    var size = msg['size'];\n","    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n","        fig._resize_canvas(size[0], size[1], msg['forward']);\n","        fig.send_message('refresh', {});\n","    }\n","};\n","\n","mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n","    var x0 = msg['x0'] / fig.ratio;\n","    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n","    var x1 = msg['x1'] / fig.ratio;\n","    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n","    x0 = Math.floor(x0) + 0.5;\n","    y0 = Math.floor(y0) + 0.5;\n","    x1 = Math.floor(x1) + 0.5;\n","    y1 = Math.floor(y1) + 0.5;\n","    var min_x = Math.min(x0, x1);\n","    var min_y = Math.min(y0, y1);\n","    var width = Math.abs(x1 - x0);\n","    var height = Math.abs(y1 - y0);\n","\n","    fig.rubberband_context.clearRect(\n","        0,\n","        0,\n","        fig.canvas.width / fig.ratio,\n","        fig.canvas.height / fig.ratio\n","    );\n","\n","    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n","};\n","\n","mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n","    // Updates the figure title.\n","    fig.header.textContent = msg['label'];\n","};\n","\n","mpl.figure.prototype.handle_cursor = function (fig, msg) {\n","    fig.rubberband_canvas.style.cursor = msg['cursor'];\n","};\n","\n","mpl.figure.prototype.handle_message = function (fig, msg) {\n","    fig.message.textContent = msg['message'];\n","};\n","\n","mpl.figure.prototype.handle_draw = function (fig, _msg) {\n","    // Request the server to send over a new figure.\n","    fig.send_draw_message();\n","};\n","\n","mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n","    fig.image_mode = msg['mode'];\n","};\n","\n","mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n","    for (var key in msg) {\n","        if (!(key in fig.buttons)) {\n","            continue;\n","        }\n","        fig.buttons[key].disabled = !msg[key];\n","        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n","    }\n","};\n","\n","mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n","    if (msg['mode'] === 'PAN') {\n","        fig.buttons['Pan'].classList.add('active');\n","        fig.buttons['Zoom'].classList.remove('active');\n","    } else if (msg['mode'] === 'ZOOM') {\n","        fig.buttons['Pan'].classList.remove('active');\n","        fig.buttons['Zoom'].classList.add('active');\n","    } else {\n","        fig.buttons['Pan'].classList.remove('active');\n","        fig.buttons['Zoom'].classList.remove('active');\n","    }\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n","    // Called whenever the canvas gets updated.\n","    this.send_message('ack', {});\n","};\n","\n","// A function to construct a web socket function for onmessage handling.\n","// Called in the figure constructor.\n","mpl.figure.prototype._make_on_message_function = function (fig) {\n","    return function socket_on_message(evt) {\n","        if (evt.data instanceof Blob) {\n","            var img = evt.data;\n","            if (img.type !== 'image/png') {\n","                /* FIXME: We get \"Resource interpreted as Image but\n","                 * transferred with MIME type text/plain:\" errors on\n","                 * Chrome.  But how to set the MIME type?  It doesn't seem\n","                 * to be part of the websocket stream */\n","                img.type = 'image/png';\n","            }\n","\n","            /* Free the memory for the previous frames */\n","            if (fig.imageObj.src) {\n","                (window.URL || window.webkitURL).revokeObjectURL(\n","                    fig.imageObj.src\n","                );\n","            }\n","\n","            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n","                img\n","            );\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        } else if (\n","            typeof evt.data === 'string' &&\n","            evt.data.slice(0, 21) === 'data:image/png;base64'\n","        ) {\n","            fig.imageObj.src = evt.data;\n","            fig.updated_canvas_event();\n","            fig.waiting = false;\n","            return;\n","        }\n","\n","        var msg = JSON.parse(evt.data);\n","        var msg_type = msg['type'];\n","\n","        // Call the  \"handle_{type}\" callback, which takes\n","        // the figure and JSON message as its only arguments.\n","        try {\n","            var callback = fig['handle_' + msg_type];\n","        } catch (e) {\n","            console.log(\n","                \"No handler for the '\" + msg_type + \"' message type: \",\n","                msg\n","            );\n","            return;\n","        }\n","\n","        if (callback) {\n","            try {\n","                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n","                callback(fig, msg);\n","            } catch (e) {\n","                console.log(\n","                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n","                    e,\n","                    e.stack,\n","                    msg\n","                );\n","            }\n","        }\n","    };\n","};\n","\n","// from https://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n","mpl.findpos = function (e) {\n","    //this section is from http://www.quirksmode.org/js/events_properties.html\n","    var targ;\n","    if (!e) {\n","        e = window.event;\n","    }\n","    if (e.target) {\n","        targ = e.target;\n","    } else if (e.srcElement) {\n","        targ = e.srcElement;\n","    }\n","    if (targ.nodeType === 3) {\n","        // defeat Safari bug\n","        targ = targ.parentNode;\n","    }\n","\n","    // pageX,Y are the mouse positions relative to the document\n","    var boundingRect = targ.getBoundingClientRect();\n","    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n","    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n","\n","    return { x: x, y: y };\n","};\n","\n","/*\n"," * return a copy of an object with only non-object keys\n"," * we need this to avoid circular references\n"," * https://stackoverflow.com/a/24161582/3208463\n"," */\n","function simpleKeys(original) {\n","    return Object.keys(original).reduce(function (obj, key) {\n","        if (typeof original[key] !== 'object') {\n","            obj[key] = original[key];\n","        }\n","        return obj;\n","    }, {});\n","}\n","\n","mpl.figure.prototype.mouse_event = function (event, name) {\n","    var canvas_pos = mpl.findpos(event);\n","\n","    if (name === 'button_press') {\n","        this.canvas.focus();\n","        this.canvas_div.focus();\n","    }\n","\n","    var x = canvas_pos.x * this.ratio;\n","    var y = canvas_pos.y * this.ratio;\n","\n","    this.send_message(name, {\n","        x: x,\n","        y: y,\n","        button: event.button,\n","        step: event.step,\n","        guiEvent: simpleKeys(event),\n","    });\n","\n","    /* This prevents the web browser from automatically changing to\n","     * the text insertion cursor when the button is pressed.  We want\n","     * to control all of the cursor setting manually through the\n","     * 'cursor' event from matplotlib */\n","    event.preventDefault();\n","    return false;\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (_event, _name) {\n","    // Handle any extra behaviour associated with a key event\n","};\n","\n","mpl.figure.prototype.key_event = function (event, name) {\n","    // Prevent repeat events\n","    if (name === 'key_press') {\n","        if (event.key === this._key) {\n","            return;\n","        } else {\n","            this._key = event.key;\n","        }\n","    }\n","    if (name === 'key_release') {\n","        this._key = null;\n","    }\n","\n","    var value = '';\n","    if (event.ctrlKey && event.key !== 'Control') {\n","        value += 'ctrl+';\n","    }\n","    else if (event.altKey && event.key !== 'Alt') {\n","        value += 'alt+';\n","    }\n","    else if (event.shiftKey && event.key !== 'Shift') {\n","        value += 'shift+';\n","    }\n","\n","    value += 'k' + event.key;\n","\n","    this._key_event_extra(event, name);\n","\n","    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n","    return false;\n","};\n","\n","mpl.figure.prototype.toolbar_button_onclick = function (name) {\n","    if (name === 'download') {\n","        this.handle_save(this, null);\n","    } else {\n","        this.send_message('toolbar_button', { name: name });\n","    }\n","};\n","\n","mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n","    this.message.textContent = tooltip;\n","};\n","\n","///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n","// prettier-ignore\n","var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n","mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n","\n","mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n","\n","mpl.default_extension = \"png\";/* global mpl */\n","\n","var comm_websocket_adapter = function (comm) {\n","    // Create a \"websocket\"-like object which calls the given IPython comm\n","    // object with the appropriate methods. Currently this is a non binary\n","    // socket, so there is still some room for performance tuning.\n","    var ws = {};\n","\n","    ws.binaryType = comm.kernel.ws.binaryType;\n","    ws.readyState = comm.kernel.ws.readyState;\n","    function updateReadyState(_event) {\n","        if (comm.kernel.ws) {\n","            ws.readyState = comm.kernel.ws.readyState;\n","        } else {\n","            ws.readyState = 3; // Closed state.\n","        }\n","    }\n","    comm.kernel.ws.addEventListener('open', updateReadyState);\n","    comm.kernel.ws.addEventListener('close', updateReadyState);\n","    comm.kernel.ws.addEventListener('error', updateReadyState);\n","\n","    ws.close = function () {\n","        comm.close();\n","    };\n","    ws.send = function (m) {\n","        //console.log('sending', m);\n","        comm.send(m);\n","    };\n","    // Register the callback with on_msg.\n","    comm.on_msg(function (msg) {\n","        //console.log('receiving', msg['content']['data'], msg);\n","        var data = msg['content']['data'];\n","        if (data['blob'] !== undefined) {\n","            data = {\n","                data: new Blob(msg['buffers'], { type: data['blob'] }),\n","            };\n","        }\n","        // Pass the mpl event to the overridden (by mpl) onmessage function.\n","        ws.onmessage(data);\n","    });\n","    return ws;\n","};\n","\n","mpl.mpl_figure_comm = function (comm, msg) {\n","    // This is the function which gets called when the mpl process\n","    // starts-up an IPython Comm through the \"matplotlib\" channel.\n","\n","    var id = msg.content.data.id;\n","    // Get hold of the div created by the display call when the Comm\n","    // socket was opened in Python.\n","    var element = document.getElementById(id);\n","    var ws_proxy = comm_websocket_adapter(comm);\n","\n","    function ondownload(figure, _format) {\n","        window.open(figure.canvas.toDataURL());\n","    }\n","\n","    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n","\n","    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n","    // web socket which is closed, not our websocket->open comm proxy.\n","    ws_proxy.onopen();\n","\n","    fig.parent_element = element;\n","    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n","    if (!fig.cell_info) {\n","        console.error('Failed to find cell for figure', id, fig);\n","        return;\n","    }\n","    fig.cell_info[0].output_area.element.on(\n","        'cleared',\n","        { fig: fig },\n","        fig._remove_fig_handler\n","    );\n","};\n","\n","mpl.figure.prototype.handle_close = function (fig, msg) {\n","    var width = fig.canvas.width / fig.ratio;\n","    fig.cell_info[0].output_area.element.off(\n","        'cleared',\n","        fig._remove_fig_handler\n","    );\n","    fig.resizeObserverInstance.unobserve(fig.canvas_div);\n","\n","    // Update the output cell to use the data from the current canvas.\n","    fig.push_to_output();\n","    var dataURL = fig.canvas.toDataURL();\n","    // Re-enable the keyboard manager in IPython - without this line, in FF,\n","    // the notebook keyboard shortcuts fail.\n","    IPython.keyboard_manager.enable();\n","    fig.parent_element.innerHTML =\n","        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n","    fig.close_ws(fig, msg);\n","};\n","\n","mpl.figure.prototype.close_ws = function (fig, msg) {\n","    fig.send_message('closing', msg);\n","    // fig.ws.close()\n","};\n","\n","mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n","    // Turn the data on the canvas into data in the output cell.\n","    var width = this.canvas.width / this.ratio;\n","    var dataURL = this.canvas.toDataURL();\n","    this.cell_info[1]['text/html'] =\n","        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n","    // Tell IPython that the notebook contents must change.\n","    IPython.notebook.set_dirty(true);\n","    this.send_message('ack', {});\n","    var fig = this;\n","    // Wait a second, then push the new image to the DOM so\n","    // that it is saved nicely (might be nice to debounce this).\n","    setTimeout(function () {\n","        fig.push_to_output();\n","    }, 1000);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n","    var fig = this;\n","\n","    var toolbar = document.createElement('div');\n","    toolbar.classList = 'btn-toolbar';\n","    this.root.appendChild(toolbar);\n","\n","    function on_click_closure(name) {\n","        return function (_event) {\n","            return fig.toolbar_button_onclick(name);\n","        };\n","    }\n","\n","    function on_mouseover_closure(tooltip) {\n","        return function (event) {\n","            if (!event.currentTarget.disabled) {\n","                return fig.toolbar_button_onmouseover(tooltip);\n","            }\n","        };\n","    }\n","\n","    fig.buttons = {};\n","    var buttonGroup = document.createElement('div');\n","    buttonGroup.classList = 'btn-group';\n","    var button;\n","    for (var toolbar_ind in mpl.toolbar_items) {\n","        var name = mpl.toolbar_items[toolbar_ind][0];\n","        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n","        var image = mpl.toolbar_items[toolbar_ind][2];\n","        var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n","        if (!name) {\n","            /* Instead of a spacer, we start a new button group. */\n","            if (buttonGroup.hasChildNodes()) {\n","                toolbar.appendChild(buttonGroup);\n","            }\n","            buttonGroup = document.createElement('div');\n","            buttonGroup.classList = 'btn-group';\n","            continue;\n","        }\n","\n","        button = fig.buttons[name] = document.createElement('button');\n","        button.classList = 'btn btn-default';\n","        button.href = '#';\n","        button.title = name;\n","        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n","        button.addEventListener('click', on_click_closure(method_name));\n","        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n","        buttonGroup.appendChild(button);\n","    }\n","\n","    if (buttonGroup.hasChildNodes()) {\n","        toolbar.appendChild(buttonGroup);\n","    }\n","\n","    // Add the status bar.\n","    var status_bar = document.createElement('span');\n","    status_bar.classList = 'mpl-message pull-right';\n","    toolbar.appendChild(status_bar);\n","    this.message = status_bar;\n","\n","    // Add the close button to the window.\n","    var buttongrp = document.createElement('div');\n","    buttongrp.classList = 'btn-group inline pull-right';\n","    button = document.createElement('button');\n","    button.classList = 'btn btn-mini btn-primary';\n","    button.href = '#';\n","    button.title = 'Stop Interaction';\n","    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n","    button.addEventListener('click', function (_evt) {\n","        fig.handle_close(fig, {});\n","    });\n","    button.addEventListener(\n","        'mouseover',\n","        on_mouseover_closure('Stop Interaction')\n","    );\n","    buttongrp.appendChild(button);\n","    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n","    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n","};\n","\n","mpl.figure.prototype._remove_fig_handler = function (event) {\n","    var fig = event.data.fig;\n","    if (event.target !== this) {\n","        // Ignore bubbled events from children.\n","        return;\n","    }\n","    fig.close_ws(fig, {});\n","};\n","\n","mpl.figure.prototype._root_extra_style = function (el) {\n","    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n","};\n","\n","mpl.figure.prototype._canvas_extra_style = function (el) {\n","    // this is important to make the div 'focusable\n","    el.setAttribute('tabindex', 0);\n","    // reach out to IPython and tell the keyboard manager to turn it's self\n","    // off when our div gets focus\n","\n","    // location in version 3\n","    if (IPython.notebook.keyboard_manager) {\n","        IPython.notebook.keyboard_manager.register_events(el);\n","    } else {\n","        // location in version 2\n","        IPython.keyboard_manager.register_events(el);\n","    }\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (event, _name) {\n","    // Check for shift+enter\n","    if (event.shiftKey && event.which === 13) {\n","        this.canvas_div.blur();\n","        // select the cell after this one\n","        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n","        IPython.notebook.select(index + 1);\n","    }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n","    fig.ondownload(fig, null);\n","};\n","\n","mpl.find_output_cell = function (html_output) {\n","    // Return the cell and output element which can be found *uniquely* in the notebook.\n","    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n","    // IPython event is triggered only after the cells have been serialised, which for\n","    // our purposes (turning an active figure into a static one), is too late.\n","    var cells = IPython.notebook.get_cells();\n","    var ncells = cells.length;\n","    for (var i = 0; i < ncells; i++) {\n","        var cell = cells[i];\n","        if (cell.cell_type === 'code') {\n","            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n","                var data = cell.output_area.outputs[j];\n","                if (data.data) {\n","                    // IPython >= 3 moved mimebundle to data attribute of output\n","                    data = data.data;\n","                }\n","                if (data['text/html'] === html_output) {\n","                    return [cell, data, j];\n","                }\n","            }\n","        }\n","    }\n","};\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel !== null) {\n","    IPython.notebook.kernel.comm_manager.register_target(\n","        'matplotlib',\n","        mpl.mpl_figure_comm\n","    );\n","}\n"],"text/plain":["<IPython.core.display.Javascript object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["<img 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\" 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'institute'])"]}],"metadata":{"kernelspec":{"display_name":"cs224n","language":"python","name":"cs224n"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.11"},"colab":{"name":"Gensim_word_vector_visualization.ipynb","provenance":[],"collapsed_sections":["6T8PE_5X7dRz"]}},"nbformat":4,"nbformat_minor":0}