{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering\n", "This notebook includes the computation of the full affinity matrix \n", "(containing LOS vlaues between all SocialSent lexicons), \n", "aswell as the clustering. Furthermore, the graphic for the LOS distributions\n", "as given in the usage examples is created here. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from tqdm.notebook import tqdm, trange\n", "from collections import Counter\n", "\n", "from sklearn.cluster import DBSCAN, SpectralClustering, OPTICS" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from qlex import los" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"1001\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; 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If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. 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function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1001\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.plotting import ColumnDataSource, figure, output_notebook, show\n", "from bokeh.models.widgets import DataTable\n", "from bokeh.models import TableColumn\n", "from bokeh.layouts import Column, Row\n", "output_notebook()\n", "\n", "from IPython.display import display " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from social_sent_helpers import list_lexicons, read_lexicon, games_list, sports_list" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c9efabdd185f4c9ea3d2e6722aabb990", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "lexicons = [read_lexicon(name) for name in tqdm(list_lexicons())]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Cluster sentiment lexicons based on their words and semantic orientation into groups\n", "# For SocialSent e.g. into a gaming group, a sports group (presumable)\n", "# First Step Cluster only based on count of overlapping words (see below)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4985" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max_lex_length = max(len(lex) for lex in lexicons)\n", "max_lex_length" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6e619a90f8384911acd47e57d3d24fa9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=250.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=249.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", 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"version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "simple_list = np.zeros((len(lexicons), len(lexicons)))\n", "binary_list = np.zeros((len(lexicons), len(lexicons)))\n", "score_list = np.zeros((len(lexicons), len(lexicons)))\n", "\n", "all_scores = []\n", "\n", "lex_names = list_lexicons()\n", "\n", "for i, (l1, name1) in enumerate(zip(tqdm(lexicons), lex_names)):\n", " for j, (l2, name2) in enumerate(zip(tqdm(lexicons[i:], leave=False), lex_names[i:])):\n", " if name1 != name2:\n", " si = los.simple(l1, l2)\n", " bi = los.binary(l1, l2)\n", " sc = los.score(l1, l2)\n", " all_scores.append([name1, name2, si, bi, sc])\n", " simple_list[i, j+i] = si\n", " simple_list[j+i, i] = si\n", " binary_list[i, j+i] = bi\n", " binary_list[j+i, i] = bi\n", " score_list[i, j+i] = sc\n", " score_list[j+i, i] = sc" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(all_scores, columns=[\"l1\", \"l2\", \"simple\", \"binary\", \"score\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df.to_pickle(\"social_sent_lexicon_los_similarities.pickle\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# df = pd.read_pickle(\"social_sent_lexicon_los_similarities.pickle\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 31125.000000\n", "mean 0.043792\n", "std 0.030751\n", "min -0.030761\n", "25% 0.022759\n", "50% 0.039521\n", "75% 0.058742\n", "max 0.341709\n", "Name: score, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.score.describe()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>l1</th>\n", " <th>l2</th>\n", " <th>simple</th>\n", " <th>binary</th>\n", " <th>score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>30305</th>\n", " <td>relationship_advice</td>\n", " <td>relationships</td>\n", " <td>0.901</td>\n", " <td>0.708</td>\n", " <td>0.342</td>\n", " </tr>\n", " <tr>\n", " <th>26009</th>\n", " <td>fantasyfootball</td>\n", " <td>nba</td>\n", " <td>0.501</td>\n", " <td>0.212</td>\n", " <td>-0.030</td>\n", " </tr>\n", " <tr>\n", " <th>29028</th>\n", " <td>nba</td>\n", " <td>tipofmytongue</td>\n", " <td>0.343</td>\n", " <td>0.145</td>\n", " <td>-0.031</td>\n", " </tr>\n", " <tr>\n", " <th>3489</th>\n", " <td>CasualPokemonTrades</td>\n", " <td>askscience</td>\n", " <td>0.190</td>\n", " <td>0.102</td>\n", " <td>0.008</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " l1 l2 simple binary score\n", "30305 relationship_advice relationships 0.901 0.708 0.342\n", "26009 fantasyfootball nba 0.501 0.212 -0.030\n", "29028 nba tipofmytongue 0.343 0.145 -0.031\n", "3489 CasualPokemonTrades askscience 0.190 0.102 0.008" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[[30305, 26009, 29028, 3489]].round(3)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>l1</th>\n", " <th>l2</th>\n", " <th>simple</th>\n", " <th>binary</th>\n", " <th>score</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>30305</th>\n", " <td>relationship_advice</td>\n", " <td>relationships</td>\n", " <td>0.901489</td>\n", " <td>0.707904</td>\n", " <td>0.341709</td>\n", " </tr>\n", " <tr>\n", " <th>26796</th>\n", " <td>funny</td>\n", " <td>pics</td>\n", " <td>0.825513</td>\n", " <td>0.615652</td>\n", " <td>0.246467</td>\n", " </tr>\n", " <tr>\n", " <th>1722</th>\n", " <td>AskMen</td>\n", " <td>AskWomen</td>\n", " <td>0.817187</td>\n", " <td>0.572888</td>\n", " <td>0.203180</td>\n", " </tr>\n", " <tr>\n", " <th>15192</th>\n", " <td>PS4</td>\n", " <td>xboxone</td>\n", " <td>0.812261</td>\n", " <td>0.546298</td>\n", " <td>0.176759</td>\n", " </tr>\n", " <tr>\n", " <th>12163</th>\n", " <td>Libertarian</td>\n", " <td>politics</td>\n", " <td>0.810433</td>\n", " <td>0.574700</td>\n", " <td>0.210586</td>\n", " </tr>\n", " <tr>\n", " <th>2052</th>\n", " <td>AskWomen</td>\n", " <td>TrollXChromosomes</td>\n", " <td>0.800108</td>\n", " <td>0.574553</td>\n", " <td>0.222573</td>\n", " </tr>\n", " <tr>\n", " <th>10225</th>\n", " <td>Gunners</td>\n", " <td>LiverpoolFC</td>\n", " <td>0.788793</td>\n", " <td>0.518319</td>\n", " <td>0.149748</td>\n", " </tr>\n", " <tr>\n", " <th>18883</th>\n", " <td>SubredditDrama</td>\n", " <td>TumblrInAction</td>\n", " <td>0.785266</td>\n", " <td>0.596343</td>\n", " <td>0.267755</td>\n", " </tr>\n", " <tr>\n", " <th>21442</th>\n", " <td>asoiaf</td>\n", " <td>gameofthrones</td>\n", " <td>0.783987</td>\n", " <td>0.567974</td>\n", " <td>0.208325</td>\n", " </tr>\n", " <tr>\n", " <th>20048</th>\n", " <td>WTF</td>\n", " <td>pics</td>\n", " <td>0.782072</td>\n", " <td>0.572195</td>\n", " <td>0.218170</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " l1 l2 simple binary score\n", "30305 relationship_advice relationships 0.901489 0.707904 0.341709\n", "26796 funny pics 0.825513 0.615652 0.246467\n", "1722 AskMen AskWomen 0.817187 0.572888 0.203180\n", "15192 PS4 xboxone 0.812261 0.546298 0.176759\n", "12163 Libertarian politics 0.810433 0.574700 0.210586\n", "2052 AskWomen TrollXChromosomes 0.800108 0.574553 0.222573\n", "10225 Gunners LiverpoolFC 0.788793 0.518319 0.149748\n", "18883 SubredditDrama TumblrInAction 0.785266 0.596343 0.267755\n", "21442 asoiaf gameofthrones 0.783987 0.567974 0.208325\n", "20048 WTF pics 0.782072 0.572195 0.218170" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(\"simple\", ascending=False).head(10)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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<th>206</th>\n", " <td>randomsuperpowers</td>\n", " <td>42</td>\n", " </tr>\n", " <tr>\n", " <th>233</th>\n", " <td>tipofmytongue</td>\n", " <td>42</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lex group\n", "206 randomsuperpowers 42\n", "233 tipofmytongue 42" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] }, { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>lex</th>\n", " <th>group</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>222</th>\n", " <td>summonerschool</td>\n", " <td>50</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " lex group\n", "222 summonerschool 50" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "res = SpectralClustering(100, affinity='precomputed', n_init=100, assign_labels='discretize').fit_predict(score_list + abs(np.min(score_list)))\n", "\n", "df = pd.DataFrame({\"lex\": list_lexicons().values, \"group\": res})\n", "\n", "for x in df.group.unique():\n", " ds = df[df.group == x]\n", " print(len(ds))\n", " display(ds)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import silhouette_samples\n", "\n", "non_neg_score_list = score_list + 1 # Make copy\n", "non_neg_score_list = non_neg_score_list - 1\n", "non_neg_score_list[non_neg_score_list < 0] = 0\n", "\n", "sil_scores = silhouette_samples(non_neg_score_list, res, metric=\"precomputed\")\n", "df[\"sil\"] = sil_scores" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "group\n", "79 0.000000\n", "10 0.000000\n", "36 -0.893295\n", "34 -0.897883\n", "86 -0.903746\n", "85 -0.908606\n", "32 -0.920206\n", "82 -0.927908\n", "17 -0.931422\n", "1 -0.935976\n", "Name: sil, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.groupby(\"group\")[\"sil\"].mean().sort_values(ascending=False).tail(70).head(10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "41961fcec3284fa9a5d1058895b546f0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, max=198.0), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1c1aa648a88>]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.cluster import SpectralClustering\n", "from sklearn.metrics import silhouette_score\n", "\n", "sil_score_list = []\n", "\n", "non_neg_score_list = score_list + 1 # Make copy\n", "non_neg_score_list = non_neg_score_list - 1\n", "non_neg_score_list[non_neg_score_list < 0] = 0\n", "\n", "for n in trange(2, 200):\n", " res = SpectralClustering(n, affinity='precomputed', n_init=100, assign_labels='discretize').fit_predict(non_neg_score_list)\n", " sil_score = silhouette_score(non_neg_score_list, res, metric=\"precomputed\")\n", " sil_score_list.append(sil_score)\n", "\n", "plt.plot(sil_score_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }