wordvector_retrofitting.ipynb

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    "# Code to test the algorithm from the Retrofitting word vectors to semantic lexicons paper by Faruqui et al. (2015) \n",
    "# uncomment if gensim is installed\n",
    "#!pip install gensim\n",
    "import gensim\n",
    "# Need the interactive Tools for Matplotlib\n",
    "%matplotlib notebook\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE"
   ]
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    "# Method to plot the top no_similar_words in 2D using TSNE\n",
    "def display_closestwords_tsnescatterplot(model, word, word_vector_dimension, no_similar_words, plot_title):\n",
    "    \n",
    "    arr = np.empty((0,word_vector_dimension), dtype='f')\n",
    "    word_labels = [word]\n",
    "\n",
    "    # get close words\n",
    "    close_words = model.similar_by_word(word, topn=no_similar_words)\n",
    "    \n",
    "    # add the vector for each of the closest words to the array\n",
    "    arr = np.append(arr, np.array([model[word]]), axis=0)\n",
    "    for wrd_score in close_words:\n",
    "        wrd_vector = model[wrd_score[0]]\n",
    "        word_labels.append(wrd_score[0])\n",
    "        arr = np.append(arr, np.array([wrd_vector]), axis=0)\n",
    "        \n",
    "    # find tsne coords for 2 dimensions\n",
    "    tsne = TSNE(n_components=2, random_state=0)\n",
    "    np.set_printoptions(suppress=True)\n",
    "    Y = tsne.fit_transform(arr)\n",
    "\n",
    "    x_coords = Y[:, 0]\n",
    "    y_coords = Y[:, 1]\n",
    "    # display scatter plot\n",
    "    plt.scatter(x_coords, y_coords)\n",
    "\n",
    "    for label, x, y in zip(word_labels, x_coords, y_coords):\n",
    "        plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')\n",
    "    plt.xlim(x_coords.min()+0.00005, x_coords.max()+0.00005)\n",
    "    plt.ylim(y_coords.min()+0.00005, y_coords.max()+0.00005)\n",
    "    plt.xticks(rotation=35)\n",
    "    plt.title(plot_title)\n",
    "    plt.show()\n"
   ]
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    "# git clone https://github.com/mfaruqui/retrofitting.git\n",
    "# Run retrofit.py with arguments to set the word vectors file, the lexicon file, the number of iterations\n",
    "# and the output word vectors. The word vectors must be in text format\n",
    "# Eg:\n",
    "# python retrofit.py -i word_vec_file -l lexicon_file -n num_iter -o out_vec_file\n",
    "# python retrofit.py -i /data/glove.6B.50d.txt -l /retrofitting/lexicons/ppdb-xl.txt -n 10 -o retrofittedglove.txt\n",
    "\n",
    "# Convert txt based GLOVE word vectors to Word2Vec format\n",
    "from gensim.scripts.glove2word2vec import glove2word2vec\n",
    "glove2word2vec(glove_input_file=\"/data/glove.6B.50d.txt\", word2vec_output_file=\"glove.6B.50d.word2vec.txt\")\n",
    "glove2word2vec(glove_input_file=\"/data/retrofittedglove.txt\", word2vec_output_file=\"retrofittedglove.word2vec.txt\")"
   ]
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    "# load the original word vectors and the retrofitted word vectors as separate gensim models\n",
    "\n",
    "original_glove_model = gensim.models.KeyedVectors.load_word2vec_format('glove.6B.50d.word2vec.txt', binary=False)\n",
    "retrofitted_glove_model = gensim.models.KeyedVectors.load_word2vec_format('retrofittedglove.word2vec.txt', binary=False)\n"
   ]
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       "\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(\"No handler for the '\" + msg_type + \"' message type: \", msg);\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(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://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",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\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",
       " * http://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",
       "    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",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\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",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             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",
       "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\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";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.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",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['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 = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\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.get(0);\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",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\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).html('<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/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<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 () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\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) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('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",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\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",
       "\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('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display the words closest to 'happy' using the original GLOVE vectors\n",
    "display_closestwords_tsnescatterplot(original_glove_model, 'happy', 50, 10, \"Original Glove Word Vectors - 'Happy'\")\n",
    "# display the words closest to 'happy' using the GLOVE vectors retrofitted with the Paraphrase lexicons\n",
    "display_closestwords_tsnescatterplot(retrofitted_glove_model, 'happy', 50, 10, \"Retroffited Glove Word Vectors - 'Happy'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# More examples to try\n",
    "display_closestwords_tsnescatterplot(original_glove_model, 'refunding', 50, 10, \"Original Glove Word Vectors - 'Refunding'\")\n",
    "display_closestwords_tsnescatterplot(retrofitted_glove_model, 'refunding', 50, 10, \"Retroffited Glove Word Vectors - 'Refunding'\")\n",
    "\n",
    "display_closestwords_tsnescatterplot(original_glove_model, 'vouchers', 50, 20, \"Original Glove Word Vectors - 'Vouchers'\")\n",
    "display_closestwords_tsnescatterplot(retrofitted_glove_model, 'vouchers', 50, 20, \"Retroffited Glove Word Vectors - 'Vouchers'\")\n",
    "\n",
    "display_closestwords_tsnescatterplot(original_glove_model, 'computer', 50, 20, \"Original Glove Word Vectors - 'Computer'\")\n",
    "display_closestwords_tsnescatterplot(retrofitted_glove_model, 'computer', 50, 20, \"Retroffited Glove Word Vectors - 'Computer'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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