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{ "cells": [ { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2018-03-22T11:58:38.092638Z", "start_time": "2018-03-22T11:58:38.066875Z" } }, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ], "text/vnd.plotly.v1+html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "/Users/jacquesfize/nas_cloud/Code/str-python\n" ] } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "import json,glob,re\n", "from scipy.spatial.distance import jaccard\n", "import numpy as np\n", "from math import*\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "#%matplotlib inline\n", "#from IPython.display import set_matplotlib_formats\n", "#set_matplotlib_formats('pdf', 'svg')\n", "#sns.set_palette(sns.color_palette(\"hls\", 8))\n", "\n", "\n", "import plotly.graph_objs as go\n", "import plotly.offline as py\n", "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n", "init_notebook_mode(connected=True)\n", "\n", "\n", "def jaccard_similarity(x,y): \n", " intersection_cardinality = len(set.intersection(*[set(x), set(y)]))\n", " union_cardinality = len(set.union(*[set(x), set(y)]))\n", " return intersection_cardinality/float(union_cardinality)\n", "%cd .." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2018-03-22T11:58:39.805564Z", "start_time": "2018-03-22T11:58:38.559018Z" }7172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140
},
"outputs": [],
"source": [
"df=pd.read_csv(\"resources/results_graph_exp18fev.tsv\",delimiter=\"\\t\",index_col=0)\n",
"new_df=pd.DataFrame(columns=df.columns)\n",
"\n",
"selected_graph=json.load(open(\"data/graph_exp_fev_18/selected.json\"))\n",
"types=df.type.unique()\n",
"graph_size={}\n",
"graphs_={}\n",
"\n",
"files_glob= glob.glob(\"data/graph_exp_fev_18/normal/*.gexf\")\n",
"for fn in files_glob:\n",
" id_ = int(re.findall(\"\\d+\", fn)[-1])\n",
" graphs_[id_]=nx.read_gexf(fn)\n",
" graph_size[id_]=len(graphs_[id_])\n",
"graph_size[999]=0\n",
"nb_of_g_w_es_com={}\n",
"for g in graphs_:\n",
" if not g in nb_of_g_w_es_com:\n",
" nb_of_g_w_es_com[g]=0\n",
" for g2 in graphs_:\n",
" if not g2 == g:\n",
" if set(graphs_[g].nodes()).intersection(set(graphs_[g2].nodes())):\n",
" nb_of_g_w_es_com[g]+=1 \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:40.305156Z",
"start_time": "2018-03-22T11:58:40.299648Z"
}
},
"outputs": [],
"source": [
"df_mesure=pd.read_csv(\"resources/mesures.tsv\",delimiter=\"\\t\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:44.001448Z",
"start_time": "2018-03-22T11:58:40.770565Z"
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},
"outputs": [],
"source": [
"n=3\n",
"df_copy=pd.DataFrame(columns=df.columns)\n",
"for t in types:\n",
" mesures=df[df.type == t].mesure.unique()\n",
" for m in mesures:\n",
" data=df[(df.mesure == m) & (df.type == t)]\n",
" for g in selected_graph:\n",
" subset=data[data.id_g1 == g].iloc[:n]\n",
" if len(subset)<1:#No graph found\n",
" df_2=pd.DataFrame([[g,999,m,t,3,0,0,0,0]],columns=df.columns)\n",
" for i in range(n):df_copy=df_copy.append(df_2)\n",
" elif len(subset)<n: # not 5 associated graphs\n",
" df_2=pd.DataFrame([[g,999,m,t,3,0,0,0,0]],columns=df.columns)\n",
" for i in range(n-len(subset)):df_copy=df_copy.append(df_2)\n",
" else:# perfecto ! :P\n",
" df_copy=df_copy.append(subset)\n",
"df=df_copy"
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" <th></th>\n",
" <th>id_g1</th>\n",
" <th>id_g2</th>\n",
" <th>mesure</th>\n",
" <th>type</th>\n",
" <th>id_user</th>\n",
" <th>c1_val</th>\n",
" <th>c2_val</th>\n",
" <th>c3_val</th>\n",
" <th>c4_val</th>\n",
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" <th>1</th>\n",
" <td>2.0</td>\n",
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" <td>3.0</td>\n",
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" <td>1.0</td>\n",
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" <td>7.0</td>\n",
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" <td>3.0</td>\n",
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" <td>14.0</td>\n",
" <td>437.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <th>13</th>\n",
" <td>14.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>27.0</td>\n",
" <td>26.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
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" <th>17</th>\n",
" <td>27.0</td>\n",
" <td>33.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>40.0</td>\n",
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" <td>3.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
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" <td>45.0</td>\n",
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" <td>1.0</td>\n",
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" <td>45.0</td>\n",
" <td>44.0</td>\n",
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" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>47.0</td>\n",
" <td>81.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
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" <td>170.0</td>\n",
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" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
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" <td>47.0</td>\n",
" <td>488.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <th>31</th>\n",
" <td>53.0</td>\n",
" <td>154.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>32</th>\n",
" <td>53.0</td>\n",
" <td>512.0</td>\n",
" <td>1.0</td>\n",
" <td>normal</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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"text/plain": [
" id_g1 id_g2 mesure type id_user c1_val c2_val c3_val c4_val\n",
"1 2.0 230.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
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"2 2.0 275.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
"3 2.0 269.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
"6 7.0 437.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"7 7.0 4.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"8 7.0 371.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"11 14.0 443.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
"12 14.0 437.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
"13 14.0 4.0 1.0 normal 3.0 1.0 1.0 1.0 1.0\n",
"16 27.0 26.0 1.0 normal 3.0 1.0 0.0 0.0 0.0\n",
"17 27.0 33.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"18 27.0 40.0 1.0 normal 3.0 1.0 0.0 0.0 0.0\n",
"21 45.0 21.0 1.0 normal 3.0 1.0 1.0 0.0 1.0\n",
"22 45.0 23.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"23 45.0 44.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"26 47.0 81.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"27 47.0 170.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"28 47.0 488.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"31 53.0 154.0 1.0 normal 3.0 1.0 1.0 0.0 0.0\n",
"32 53.0 512.0 1.0 normal 3.0 1.0 1.0 0.0 0.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:49.010880Z",
"start_time": "2018-03-22T11:58:46.509434Z"
}
},
"outputs": [],
"source": [
"df[\"g1_size\"]=df[\"id_g1\"].apply(lambda x:graph_size[int(x)])\n",
"df[\"g2_size\"]=df[\"id_g2\"].apply(lambda x:graph_size[int(x)])\n",
"df[\"mesure\"]=df[\"mesure\"].apply(lambda x:df_mesure[df_mesure.id==x].values[0][-1])\n",
"df['c1*c2']=df.c1_val*df.c2_val\n",
"df['c1+c2']=(df.c1_val+df.c2_val).apply(lambda x:x if x<2 else 1)\n",
"df['c1*c2*c3']=df.c1_val*df.c2_val*df.c3_val\n",
"df['c1*c3']=df.c1_val*df.c3_val\n",
"df['c1+c3']=(df.c1_val+df.c3_val).apply(lambda x:x if x<2 else 1)\n",
"df['c2*c3']=df.c2_val*df.c3_val\n",
"df[\"for_c\"]=df[\"id_g2\"].apply(lambda x:1)\n",
"df[\"es_in_common\"]=df[\"id_g1\"].apply(lambda x:nb_of_g_w_es_com[x])\n",
"normal=df[df.type == \"normal\"]\n",
"gen_country=df[df.type == \"gen_country\"]\n",
"gen_region=df[df.type == \"gen_region\"]\n",
"extension_1=df[df.type == \"extension_1\"]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:52.709983Z",
"start_time": "2018-03-22T11:58:52.706291Z"
}
},
"outputs": [],
"source": [
"colorized_subset=['c1_val', 'c2_val', 'c3_val',\n",
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" 'c4_val', 'c1*c2', 'c1+c2', 'c1*c2*c3', 'c1*c3', 'c1+c3', 'c2*c3']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Raw Performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Normal"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:56.460440Z",
"start_time": "2018-03-22T11:58:56.295334Z"
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},
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" 70.32\n",
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" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col0\"\n",
" class=\"data row3 col0\" >\n",
" GREEDY\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col1\"\n",
" class=\"data row3 col1\" >\n",
" 241.38\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col2\"\n",
" class=\"data row3 col2\" >\n",
1191119211931194119511961197119811991200120112021203120412051206120712081209121012111212121312141215121612171218121912201221122212231224122512261227122812291230123112321233123412351236123712381239124012411242124312441245124612471248124912501251125212531254125512561257125812591260
" 197.233\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col3\"\n",
" class=\"data row3 col3\" >\n",
" 3\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col4\"\n",
" class=\"data row3 col4\" >\n",
" 0.446667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col5\"\n",
" class=\"data row3 col5\" >\n",
" 0.373333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col6\"\n",
" class=\"data row3 col6\" >\n",
" 0.26\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col7\"\n",
" class=\"data row3 col7\" >\n",
" 0.22\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col8\"\n",
" class=\"data row3 col8\" >\n",
" 4.92\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col9\"\n",
" class=\"data row3 col9\" >\n",
" 3.82\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col10\"\n",
" class=\"data row3 col10\" >\n",
" 0.36\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col11\"\n",
" class=\"data row3 col11\" >\n",
" 0.46\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col12\"\n",
" class=\"data row3 col12\" >\n",
" 0.26\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col13\"\n",
" class=\"data row3 col13\" >\n",
" 0.26\n",
" \n",
" \n",
" \n",
1261126212631264126512661267126812691270127112721273127412751276127712781279128012811282128312841285128612871288128912901291129212931294129512961297129812991300130113021303130413051306130713081309131013111312131313141315131613171318131913201321132213231324132513261327132813291330
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col14\"\n",
" class=\"data row3 col14\" >\n",
" 0.446667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col15\"\n",
" class=\"data row3 col15\" >\n",
" 0.26\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col16\"\n",
" class=\"data row3 col16\" >\n",
" 1\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow3_col17\"\n",
" class=\"data row3 col17\" >\n",
" 70.32\n",
" \n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" \n",
" \n",
" <th id=\"T_66147f4c_2dc8_11e8_a974_4c327598678b\"\n",
" class=\"row_heading level0 row4\" rowspan=1>\n",
" 4\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col0\"\n",
" class=\"data row4 col0\" >\n",
" HED\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col1\"\n",
" class=\"data row4 col1\" >\n",
" 241.38\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col2\"\n",
" class=\"data row4 col2\" >\n",
" 547.36\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col3\"\n",
" class=\"data row4 col3\" >\n",
" 3\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col4\"\n",
" class=\"data row4 col4\" >\n",
" 0.593333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col5\"\n",
" class=\"data row4 col5\" >\n",
" 0.446667\n",
" \n",
" \n",
" \n",
1331133213331334133513361337133813391340134113421343134413451346134713481349135013511352135313541355135613571358135913601361136213631364136513661367136813691370137113721373137413751376137713781379138013811382138313841385138613871388138913901391139213931394139513961397139813991400
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col6\"\n",
" class=\"data row4 col6\" >\n",
" 0.213333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col7\"\n",
" class=\"data row4 col7\" >\n",
" 0.206667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col8\"\n",
" class=\"data row4 col8\" >\n",
" 4.92\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col9\"\n",
" class=\"data row4 col9\" >\n",
" 1.82\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col10\"\n",
" class=\"data row4 col10\" >\n",
" 0.446667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col11\"\n",
" class=\"data row4 col11\" >\n",
" 0.593333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col12\"\n",
" class=\"data row4 col12\" >\n",
" 0.213333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col13\"\n",
" class=\"data row4 col13\" >\n",
" 0.213333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col14\"\n",
" class=\"data row4 col14\" >\n",
" 0.593333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col15\"\n",
" class=\"data row4 col15\" >\n",
" 0.213333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col16\"\n",
" class=\"data row4 col16\" >\n",
" 1\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow4_col17\"\n",
" class=\"data row4 col17\" >\n",
" 70.32\n",
" \n",
1401140214031404140514061407140814091410141114121413141414151416141714181419142014211422142314241425142614271428142914301431143214331434143514361437143814391440144114421443144414451446144714481449145014511452145314541455145614571458145914601461146214631464146514661467146814691470
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" \n",
" \n",
" <th id=\"T_66147f4c_2dc8_11e8_a974_4c327598678b\"\n",
" class=\"row_heading level0 row5\" rowspan=1>\n",
" 5\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col0\"\n",
" class=\"data row5 col0\" >\n",
" JACCARD\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col1\"\n",
" class=\"data row5 col1\" >\n",
" 241.38\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col2\"\n",
" class=\"data row5 col2\" >\n",
" 290.133\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col3\"\n",
" class=\"data row5 col3\" >\n",
" 3\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col4\"\n",
" class=\"data row5 col4\" >\n",
" 0.466667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col5\"\n",
" class=\"data row5 col5\" >\n",
" 0.473333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col6\"\n",
" class=\"data row5 col6\" >\n",
" 0.266667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col7\"\n",
" class=\"data row5 col7\" >\n",
" 0.26\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col8\"\n",
" class=\"data row5 col8\" >\n",
" 4.92\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col9\"\n",
" class=\"data row5 col9\" >\n",
" 5.19333\n",
" \n",
1471147214731474147514761477147814791480148114821483148414851486148714881489149014911492149314941495149614971498149915001501150215031504150515061507150815091510151115121513151415151516151715181519152015211522152315241525152615271528152915301531153215331534153515361537153815391540
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col10\"\n",
" class=\"data row5 col10\" >\n",
" 0.46\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col11\"\n",
" class=\"data row5 col11\" >\n",
" 0.48\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col12\"\n",
" class=\"data row5 col12\" >\n",
" 0.266667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col13\"\n",
" class=\"data row5 col13\" >\n",
" 0.266667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col14\"\n",
" class=\"data row5 col14\" >\n",
" 0.466667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col15\"\n",
" class=\"data row5 col15\" >\n",
" 0.266667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col16\"\n",
" class=\"data row5 col16\" >\n",
" 1\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow5_col17\"\n",
" class=\"data row5 col17\" >\n",
" 70.32\n",
" \n",
" \n",
" </tr>\n",
" \n",
" <tr>\n",
" \n",
" \n",
" <th id=\"T_66147f4c_2dc8_11e8_a974_4c327598678b\"\n",
" class=\"row_heading level0 row6\" rowspan=1>\n",
" 6\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col0\"\n",
" class=\"data row6 col0\" >\n",
" MCS\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col1\"\n",
" class=\"data row6 col1\" >\n",
" 241.38\n",
" \n",
1541154215431544154515461547154815491550155115521553155415551556155715581559156015611562156315641565156615671568156915701571157215731574157515761577157815791580158115821583158415851586158715881589159015911592159315941595159615971598159916001601160216031604160516061607160816091610
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col2\"\n",
" class=\"data row6 col2\" >\n",
" 280.633\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col3\"\n",
" class=\"data row6 col3\" >\n",
" 3\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col4\"\n",
" class=\"data row6 col4\" >\n",
" 0.906667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col5\"\n",
" class=\"data row6 col5\" >\n",
" 0.833333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col6\"\n",
" class=\"data row6 col6\" >\n",
" 0.48\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col7\"\n",
" class=\"data row6 col7\" >\n",
" 0.42\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col8\"\n",
" class=\"data row6 col8\" >\n",
" 4.92\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col9\"\n",
" class=\"data row6 col9\" >\n",
" 4.89333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col10\"\n",
" class=\"data row6 col10\" >\n",
" 0.82\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col11\"\n",
" class=\"data row6 col11\" >\n",
" 0.92\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col12\"\n",
" class=\"data row6 col12\" >\n",
" 0.46\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col13\"\n",
" class=\"data row6 col13\" >\n",
1611161216131614161516161617161816191620162116221623162416251626162716281629163016311632163316341635163616371638163916401641164216431644164516461647164816491650165116521653165416551656165716581659166016611662166316641665166616671668166916701671167216731674167516761677167816791680
" 0.473333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col14\"\n",
" class=\"data row6 col14\" >\n",
" 0.913333\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col15\"\n",
" class=\"data row6 col15\" >\n",
" 0.466667\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col16\"\n",
" class=\"data row6 col16\" >\n",
" 1\n",
" \n",
" \n",
" \n",
" <td id=\"T_66147f4c_2dc8_11e8_a974_4c327598678brow6_col17\"\n",
" class=\"data row6 col17\" >\n",
" 70.32\n",
" \n",
" \n",
" </tr>\n",
" \n",
" </tbody>\n",
" </table>\n",
" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x10b982cf8>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_per_mesure=normal.groupby([\"id_g1\",\"mesure\"], as_index=False).mean()\n",
"score_normal=score_per_mesure.groupby([\"mesure\"],as_index=False).mean()\n",
"def highlight_max(s):\n",
" '''\n",
" highlight the maximum in a Series yellow.\n",
" '''\n",
" is_max = s == s.max()\n",
" return ['background-color: yellow' if v else '' for v in is_max]\n",
"def highlight_min(s):\n",
" '''\n",
" highlight the maximum in a Series yellow.\n",
" '''\n",
" is_max = s == s.min()\n",
" return ['background-color: red;color:white;' if v else '' for v in is_max]\n",
"score_normal.style.apply(highlight_max,subset=colorized_subset).apply(highlight_min,subset=colorized_subset)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:58:56.899872Z",
"start_time": "2018-03-22T11:58:56.463716Z"
}
},
"outputs": [],
1681168216831684168516861687168816891690169116921693169416951696169716981699170017011702170317041705170617071708170917101711171217131714171517161717171817191720172117221723172417251726172717281729173017311732173317341735173617371738173917401741174217431744174517461747174817491750
"source": [
"score_normal[[\"c1_val\",\"c2_val\",\"c3_val\",\"c4_val\"]].plot.bar(x=score_normal[\"mesure\"].unique())\n",
"plt.show()\n",
"score_normal[[\"c1*c2\",\"c1+c2\",\"c1*c2*c3\",\"c1+c3\"]].plot.bar(x=score_normal[\"mesure\"].unique())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generalisation Pays"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
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"start_time": "2018-03-22T11:59:05.832446Z"
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" \n",
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" class=\"data row6 col16\" >\n",
" 1\n",
" \n",
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" \n",
" <td id=\"T_6bb99d4c_2dc8_11e8_9f5e_4c327598678brow6_col17\"\n",
" class=\"data row6 col17\" >\n",
" 70.32\n",
" \n",
" \n",
" </tr>\n",
" \n",
" </tbody>\n",
" </table>\n",
" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x10dd8ceb8>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_per_mesure=gen_country.groupby([\"id_g1\",\"mesure\"], as_index=False).mean()\n",
"score_gen_country=score_per_mesure.groupby([\"mesure\"],as_index=False).mean()\n",
"score_gen_country.style.apply(highlight_max,subset=colorized_subset).apply(highlight_min,subset=colorized_subset)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:10.969020Z",
"start_time": "2018-03-22T11:59:10.461280Z"
}
},
"outputs": [],
"source": [
"score_gen_country[[\"c1_val\",\"c2_val\",\"c3_val\",\"c4_val\"]].plot.bar(x=score_gen_country[\"mesure\"].unique())\n",
"plt.show()\n",
"score_gen_country[[\"c1*c2\",\"c1+c2\",\"c1*c2*c3\",\"c1+c3\"]].plot.bar(x=score_gen_country[\"mesure\"].unique())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generalisation region"
]
},
{
2871287228732874287528762877287828792880288128822883288428852886288728882889289028912892289328942895289628972898289929002901290229032904290529062907290829092910291129122913291429152916291729182919292029212922292329242925292629272928292929302931293229332934293529362937293829392940
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:15.025151Z",
"start_time": "2018-03-22T11:59:14.954115Z"
}
},
"outputs": [
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" \n",
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" </tr>\n",
" \n",
" </tbody>\n",
" </table>\n",
" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x10d5d2080>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_per_mesure=gen_region.groupby([\"id_g1\",\"mesure\"], as_index=False).mean()\n",
"score_gen_region=score_per_mesure.groupby([\"mesure\"],as_index=False).mean()\n",
"score_gen_region.style.apply(highlight_max,subset=colorized_subset).apply(highlight_min,subset=colorized_subset)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:19.770228Z",
4061406240634064406540664067406840694070407140724073407440754076407740784079408040814082408340844085408640874088408940904091409240934094409540964097409840994100410141024103410441054106410741084109411041114112411341144115411641174118411941204121412241234124412541264127412841294130
"start_time": "2018-03-22T11:59:19.440447Z"
},
"format": "row"
},
"outputs": [],
"source": [
"score_gen_region[[\"c1_val\",\"c2_val\",\"c3_val\",\"c4_val\"]].plot.bar(x=score_gen_region[\"mesure\"].unique())\n",
"plt.show()\n",
"score_gen_region[[\"c1*c2\",\"c1+c2\",\"c1*c2*c3\",\"c1+c3\"]].plot.bar(x=score_gen_region[\"mesure\"].unique())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extension 1"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:24.645950Z",
"start_time": "2018-03-22T11:59:24.580171Z"
}
},
"outputs": [
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" class=\"data row5 col9\" >\n",
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" \n",
" \n",
" \n",
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" \n",
" \n",
" \n",
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" \n",
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" \n",
" \n",
" \n",
" <td id=\"T_76e49e58_2dc8_11e8_a35f_4c327598678brow5_col14\"\n",
" class=\"data row5 col14\" >\n",
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" \n",
" \n",
" \n",
" <td id=\"T_76e49e58_2dc8_11e8_a35f_4c327598678brow5_col15\"\n",
" class=\"data row5 col15\" >\n",
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" \n",
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" \n",
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" 1\n",
" \n",
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" \n",
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" class=\"data row5 col17\" >\n",
" 70.32\n",
" \n",
" \n",
" </tr>\n",
" \n",
" </tbody>\n",
5111511251135114511551165117511851195120512151225123512451255126512751285129513051315132513351345135513651375138513951405141514251435144514551465147514851495150515151525153515451555156515751585159516051615162516351645165516651675168516951705171517251735174517551765177517851795180
" </table>\n",
" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x10e84c630>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_per_mesure=extension_1.groupby([\"id_g1\",\"mesure\"], as_index=False).mean()\n",
"score_ext_1=score_per_mesure.groupby([\"mesure\"],as_index=False).mean()\n",
"score_ext_1.style.apply(highlight_max,subset=colorized_subset).apply(highlight_min,subset=colorized_subset)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:29.624155Z",
"start_time": "2018-03-22T11:59:29.189251Z"
}
},
"outputs": [],
"source": [
"score_ext_1[[\"c1_val\",\"c2_val\",\"c3_val\",\"c4_val\"]].plot.bar(x=score_ext_1[\"mesure\"].unique())\n",
"plt.show()\n",
"score_ext_1[[\"c1*c2\",\"c1+c2\",\"c1*c2*c3\",\"c1+c3\"]].plot.bar(x=score_ext_1[\"mesure\"].unique())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Impact of the graph size over criteria"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:34.098799Z",
"start_time": "2018-03-22T11:59:34.094114Z"
}
},
"outputs": [],
"source": [
"df=df[df[\"id_g2\"] != 999]\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:40.773952Z",
"start_time": "2018-03-22T11:59:40.770620Z"
}
},
"outputs": [],
"source": [
"x_label=\"g1_size\""
]
},
5181518251835184518551865187518851895190519151925193519451955196519751985199520052015202520352045205520652075208520952105211521252135214521552165217521852195220522152225223522452255226522752285229523052315232523352345235523652375238523952405241524252435244524552465247524852495250
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Score Gradient over size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selon la taille sans prendre en compte mesure\n",
"#### Normal"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:45.289041Z",
"start_time": "2018-03-22T11:59:45.198099Z"
}
},
"outputs": [
{
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" "
],
"text/plain": [
"<pandas.formats.style.Styler at 0x10eb26a58>"
]
},
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"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"score_normal_size=normal.groupby([x_label],as_index=False).mean()\n",
"score_normal_size.style.background_gradient(subset=colorized_subset,low=0,high=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Extension 1"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2018-03-22T11:59:49.867527Z",
"start_time": "2018-03-22T11:59:49.778871Z"
}
},
"outputs": [
{
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