{
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import glob, os, re, json, pickle\n",
"import pandas as pd\n",
"from classifier import *\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_rows', 200)\n",
"pd.set_option('display.max_columns', 200)\n",
"\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Human labeled data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>text</th>\n",
" <th>manifestolabel_true</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>„Die Digitalisierung der Verwaltung schleift einfach. Keine Verwaltung ist wirklich digital, was auch daran liegt, dass es den Verwaltungen freigestellt wird.“ @AnnaVTreuenfels</td>\n",
" <td>infrastructure +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>723</th>\n",
" <td>Brütende Rotmilane wegen Windkraft erschossen? - es geht bei Energiewende und Klimaschutz nicht um die Umwelt, sondern um Geld und Macht. Wir brauchen Naturschutz statt linker Ideologie!</td>\n",
" <td>environmentalism +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>927</th>\n",
" <td>„Das C in #CDU steht nicht für Christenclub\" - @SerapGueler über unsere #Heimat und unsere Partei. „warum falsch mit den Morden in #Hanau umgeht, welche die Fehler der @CDU bei #Migranten gemacht hat und weshalb @ArminLaschet #Kanzler werden sollte“</td>\n",
" <td>multiculturalism +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93</th>\n",
" <td>Wie ein Post-#Corona-#Konjunkturprogramm den #Klimaschutz mitdenkt: Förderung energetische Gebäudesanierung + klimafreundliches Heizen Senkung #EEG-Umlage #Verkehrswende in Gang setzen Investition in zukunftssichere Industrieanlagen Energiewende europäisch denken</td>\n",
" <td>environmentalism +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>Waschmaschinen von Männer für Frauen gemacht. Schon mal im Flusensieb rumgepfriemelt, Männers?</td>\n",
" <td>ignored</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>Spoiler: Wenn Corona die Menschheit ausrottet ist der #Klimawandel besiegt. Traurig dass ein Virus mehr für den #Klimaschutz tut als die Bundesregierung.</td>\n",
" <td>environmentalism +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Ich mag die vielen Frauen in diesen Führungspositionen. Spiegelt gerecht den Anteil der Frauen in den sozialen Berufen wider. Perfekt.</td>\n",
" <td>social justice +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>Wenn du in das Reich Gottes eintreten willst ... Sie müssen zur Gerechtigkeit des Messias zurückkehren ... Und Dies wird erreicht, indem die wahre Errettung Christi Jesus angenommen wird. #HeavenlyFocusedChristian</td>\n",
" <td>traditional morality +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>830</th>\n",
" <td>Ich* bin von Natur aus rebellisch. *putzt Holztisch mit Glasreiniger</td>\n",
" <td>ignored</td>\n",
" </tr>\n",
" <tr>\n",
" <th>546</th>\n",
" <td>In der #Corona-Pandemie haben ohnehin miserable Arbeitsbedingungen in #Schlachthöfen schwere Konsequenzen. Gesundheit geht vor Profitinteressen. Deshalb besserer #Arbeitsschutz häufigere Kontrolle der Betriebe Arbeitskräfte über Arbeitnehmerrechte informieren</td>\n",
" <td>labour +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>Juten Morjen allerseits Neue Woche &amp; viel Arbeit</td>\n",
" <td>ignored</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>Unser Regierungsrat @MartinNeukom stellt die Weichen für die fossil-freie Beheizung der Gebäude im Kanton Zürich. Mit der Teilrevision des Energiegesetzes will Neukom klar in Richtung null CO2-Emissionen. Ein ganz wichtiger Schritt für den Klimaschutz. http://bit.ly/2zmzR9Q</td>\n",
" <td>environmentalism +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>665</th>\n",
" <td>Er hat versucht eine Sache zu sagen aber hat eigentlich was viel besseres damit gesagt</td>\n",
" <td>social justice +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>Wer die @mopo stilllegt, schwächt die Demokratie. Wir brauchen Journalisten, die politische Zusammenhänge und Entscheidungen erklären und unabhängig sowie kritisch einordnen. Das ganze Statement: #RettetdieMopo</td>\n",
" <td>democracy +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>Wir leben für die Freiheit, wir arbeiten an der Freiheit und wir kämpfen für die Freiheit! Danke @KonstantinKuhle und @johannesvogel für diesen Artikel</td>\n",
" <td>freedom/human rights +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td>Das liegt doch an uns! So wie Deutschland jetzt aussieht, gehört die CDU/CSU 30 Jahre in die Opposition. Wenn wir die Altparteien erneut wählen, glaubt man da tatsächlich an eine Verbesserung der Politik? Lassen wir doch mal die anderen ran! Jedem seine Chance. ...</td>\n",
" <td>political authority +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>602</th>\n",
" <td>Was ist die Position der SPD? Warum hat die SPD den Grünen-Antrag abgelehnt? @EskenSaskia fasst das ganz gut zusammen.</td>\n",
" <td>national way of life -</td>\n",
" </tr>\n",
" <tr>\n",
" <th>692</th>\n",
" <td>Gerade jetzt wäre die Stunde für Europa. Ein Europa, das in Anbetracht einer globalen Krise zusammen arbeitet. Ein Europa der Solidarität, gerade mit den Ländern, die die Krise am härtesten trifft. Ein Europa, das niemand zurück lässt. #LeaveNoOneBehind</td>\n",
" <td>europe +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>634</th>\n",
" <td>#NieMehrCDU daran ändert sich auch nach wie vor nichts. Ja, sie handhabt die Krise aktuell gut. Aber das erwarte ich von einer Regierung, egal welche Partei diese bildet. Bei nie mehr cdu geht es darum, dass Deutschland die Digitalisierung verschlafen hat. Umweltschutz nicht</td>\n",
" <td>infrastructure +</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>#Klimaschutz kam 2019 durch CO2-Preis und weniger Kohlekraftwerke ETWAS (zu wenig) voran, Gebäude &amp; Mobilität bleiben Sorgenkinder. 2020 wird vermutlich #Corona die Einhaltung des Pariser Klimaabkommens ermöglichen. Soeben Vorstellung der #Klimabilanz durch @SvenjaSchulze68 @bmu</td>\n",
" <td>environmentalism +</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" text \\\n",
"46 „Die Digitalisierung der Verwaltung schleift einfach. Keine Verwaltung ist wirklich digital, was auch daran liegt, dass es den Verwaltungen freigestellt wird.“ @AnnaVTreuenfels \n",
"723 Brütende Rotmilane wegen Windkraft erschossen? - es geht bei Energiewende und Klimaschutz nicht um die Umwelt, sondern um Geld und Macht. Wir brauchen Naturschutz statt linker Ideologie! \n",
"927 „Das C in #CDU steht nicht für Christenclub\" - @SerapGueler über unsere #Heimat und unsere Partei. „warum falsch mit den Morden in #Hanau umgeht, welche die Fehler der @CDU bei #Migranten gemacht hat und weshalb @ArminLaschet #Kanzler werden sollte“ \n",
"93 Wie ein Post-#Corona-#Konjunkturprogramm den #Klimaschutz mitdenkt: Förderung energetische Gebäudesanierung + klimafreundliches Heizen Senkung #EEG-Umlage #Verkehrswende in Gang setzen Investition in zukunftssichere Industrieanlagen Energiewende europäisch denken \n",
"280 Waschmaschinen von Männer für Frauen gemacht. Schon mal im Flusensieb rumgepfriemelt, Männers? \n",
"341 Spoiler: Wenn Corona die Menschheit ausrottet ist der #Klimawandel besiegt. Traurig dass ein Virus mehr für den #Klimaschutz tut als die Bundesregierung. \n",
"31 Ich mag die vielen Frauen in diesen Führungspositionen. Spiegelt gerecht den Anteil der Frauen in den sozialen Berufen wider. Perfekt. \n",
"98 Wenn du in das Reich Gottes eintreten willst ... Sie müssen zur Gerechtigkeit des Messias zurückkehren ... Und Dies wird erreicht, indem die wahre Errettung Christi Jesus angenommen wird. #HeavenlyFocusedChristian \n",
"830 Ich* bin von Natur aus rebellisch. *putzt Holztisch mit Glasreiniger \n",
"546 In der #Corona-Pandemie haben ohnehin miserable Arbeitsbedingungen in #Schlachthöfen schwere Konsequenzen. Gesundheit geht vor Profitinteressen. Deshalb besserer #Arbeitsschutz häufigere Kontrolle der Betriebe Arbeitskräfte über Arbeitnehmerrechte informieren \n",
"499 Juten Morjen allerseits Neue Woche & viel Arbeit \n",
"86 Unser Regierungsrat @MartinNeukom stellt die Weichen für die fossil-freie Beheizung der Gebäude im Kanton Zürich. Mit der Teilrevision des Energiegesetzes will Neukom klar in Richtung null CO2-Emissionen. Ein ganz wichtiger Schritt für den Klimaschutz. http://bit.ly/2zmzR9Q \n",
"665 Er hat versucht eine Sache zu sagen aber hat eigentlich was viel besseres damit gesagt \n",
"392 Wer die @mopo stilllegt, schwächt die Demokratie. Wir brauchen Journalisten, die politische Zusammenhänge und Entscheidungen erklären und unabhängig sowie kritisch einordnen. Das ganze Statement: #RettetdieMopo \n",
"110 Wir leben für die Freiheit, wir arbeiten an der Freiheit und wir kämpfen für die Freiheit! Danke @KonstantinKuhle und @johannesvogel für diesen Artikel \n",
"154 Das liegt doch an uns! So wie Deutschland jetzt aussieht, gehört die CDU/CSU 30 Jahre in die Opposition. Wenn wir die Altparteien erneut wählen, glaubt man da tatsächlich an eine Verbesserung der Politik? Lassen wir doch mal die anderen ran! Jedem seine Chance. ... \n",
"602 Was ist die Position der SPD? Warum hat die SPD den Grünen-Antrag abgelehnt? @EskenSaskia fasst das ganz gut zusammen. \n",
"692 Gerade jetzt wäre die Stunde für Europa. Ein Europa, das in Anbetracht einer globalen Krise zusammen arbeitet. Ein Europa der Solidarität, gerade mit den Ländern, die die Krise am härtesten trifft. Ein Europa, das niemand zurück lässt. #LeaveNoOneBehind \n",
"634 #NieMehrCDU daran ändert sich auch nach wie vor nichts. Ja, sie handhabt die Krise aktuell gut. Aber das erwarte ich von einer Regierung, egal welche Partei diese bildet. Bei nie mehr cdu geht es darum, dass Deutschland die Digitalisierung verschlafen hat. Umweltschutz nicht \n",
"691 #Klimaschutz kam 2019 durch CO2-Preis und weniger Kohlekraftwerke ETWAS (zu wenig) voran, Gebäude & Mobilität bleiben Sorgenkinder. 2020 wird vermutlich #Corona die Einhaltung des Pariser Klimaabkommens ermöglichen. Soeben Vorstellung der #Klimabilanz durch @SvenjaSchulze68 @bmu \n",
"\n",
" manifestolabel_true \n",
"46 infrastructure + \n",
"723 environmentalism + \n",
"927 multiculturalism + \n",
"93 environmentalism + \n",
"280 ignored \n",
"341 environmentalism + \n",
"31 social justice + \n",
"98 traditional morality + \n",
"830 ignored \n",
"546 labour + \n",
"499 ignored \n",
"86 environmentalism + \n",
"665 social justice + \n",
"392 democracy + \n",
"110 freedom/human rights + \n",
"154 political authority + \n",
"602 national way of life - \n",
"692 europe + \n",
"634 infrastructure + \n",
"691 environmentalism + "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_human = pd.read_json('human_labeled_data.json')\n",
"df_human['text'] = df_human['text_data'].str.replace('https[^\\s]*\\s', '')\n",
"df_human['manifestolabel_true'] = df_human['major_label'].str.replace('\\d\\d\\d ','')\n",
"# df_human['manifestolabel_true'] = df_human['manifestolabel_true'].replace('ignored','undefined')\n",
"df_human = df_human.drop(['text_data','labeled','major_label','selected','taught','labels','users','uncertainty','text_id','predicted_label'],axis=1)\n",
"# df_human.to_json('human_labeled_anonymized.json',orient='records')\n",
"df_human.sample(n=20)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ignored 175\n",
"environmentalism + 156\n",
"political authority + 130\n",
"democracy + 112\n",
"social justice + 79 \n",
"freedom/human rights + 49 \n",
"education + 43 \n",
"infrastructure + 36 \n",
"welfare + 34 \n",
"europe + 19 \n",
"culture + 17 \n",
"anti-growth economy + 16 \n",
"agriculture + 12 \n",
"free enterprise + 12 \n",
"national way of life + 11 \n",
"social harmony + 9 \n",
"labour + 9 \n",
"law and order + 7 \n",
"market regulation + 7 \n",
"multiculturalism + 7 \n",
"military + 7 \n",
"productivity + 7 \n",
"traditional morality + 6 \n",
"europe - 5 \n",
"multiculturalism - 5 \n",
"national way of life - 5 \n",
"non economic groups + 4 \n",
"traditional morality - 4 \n",
"internationalism + 3 \n",
"foreign special + 3 \n",
"nationalization + 3 \n",
"political corruption - 2 \n",
"military - 2 \n",
"controlled economy + 2 \n",
"education - 2 \n",
"peace + 2 \n",
"gov-admin efficiency + 2 \n",
"incentives + 2 \n",
"marxist analysis + 1 \n",
"constitution + 1 \n",
"economic goals 1 \n",
"constitution - 1 \n",
"centralism + 1 \n",
"Name: manifestolabel_true, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_human['manifestolabel_true'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classification performance on tweets\n",
"\n",
"* we want to predict manifesto codes on tweets well\n",
"* but we only have few tweets labeled\n",
"* we'd like to use as much training data as possible\n",
"* but we don't want to degrade manifesto-prediction performance on tweets by adding irrelevant manifesto training data. \n",
"\n",
"$\\rightarrow$ How many manifesto training data should be have in our training set to achieve high tweet classification performance?\n",
"\n",
"$\\Rightarrow$ **Crossvalidation with blending in manifesto data**:\n",
"* We start with 0 manifesto data and all labeled tweets and add manifesto data\n",
"* We evaluate on held-out tweet data "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.1s remaining: 2.1s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.1s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.750000 0.750000 0.750000 4.000000 \n",
"democracy + 0.846154 0.846154 0.846154 26.000000 \n",
"education + 0.636364 0.777778 0.700000 9.000000 \n",
"environmentalism + 0.846154 0.846154 0.846154 26.000000 \n",
"europe + 1.000000 1.000000 1.000000 6.000000 \n",
"ignored 0.760000 0.542857 0.633333 35.000000 \n",
"infrastructure + 0.500000 0.166667 0.250000 6.000000 \n",
"political authority + 0.814815 0.687500 0.745763 32.000000 \n",
"social justice + 0.833333 0.833333 0.833333 12.000000 \n",
"accuracy 0.551724 0.551724 0.551724 0.551724 \n",
"macro avg 0.249529 0.230373 0.235883 203.000000\n",
"weighted avg 0.612815 0.551724 0.575523 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.9s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"democracy + 0.931034 0.843750 0.885246 32.000000 \n",
"education + 0.750000 1.000000 0.857143 6.000000 \n",
"environmentalism + 0.818182 0.600000 0.692308 30.000000 \n",
"europe + 0.666667 0.666667 0.666667 3.000000 \n",
"freedom/human rights + 0.555556 0.500000 0.526316 10.000000 \n",
"ignored 0.600000 0.264706 0.367347 34.000000 \n",
"political authority + 0.589744 0.741935 0.657143 31.000000 \n",
"social justice + 0.833333 0.909091 0.869565 11.000000 \n",
"welfare + 1.000000 0.166667 0.285714 6.000000 \n",
"accuracy 0.497537 0.497537 0.497537 0.497537 \n",
"macro avg 0.217565 0.183639 0.187337 203.000000\n",
"weighted avg 0.592329 0.497537 0.520413 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"democracy + 0.869565 0.869565 0.869565 23.000000 \n",
"education + 0.750000 0.545455 0.631579 11.000000 \n",
"environmentalism + 0.875000 0.700000 0.777778 40.000000 \n",
"ignored 0.608696 0.560000 0.583333 25.000000 \n",
"infrastructure + 0.666667 0.400000 0.500000 5.000000 \n",
"political authority + 0.666667 0.952381 0.784314 21.000000 \n",
"social justice + 1.000000 0.583333 0.736842 12.000000 \n",
"accuracy 0.477833 0.477833 0.477833 0.477833 \n",
"macro avg 0.169894 0.144085 0.152607 203.000000\n",
"weighted avg 0.531038 0.477833 0.494850 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 6.00000 \n",
"democracy + 0.777778 0.736842 0.756757 19.00000 \n",
"education + 1.000000 0.692308 0.818182 13.00000 \n",
"environmentalism + 0.805556 0.805556 0.805556 36.00000 \n",
"europe + 0.500000 0.500000 0.500000 6.00000 \n",
"freedom/human rights + 0.428571 0.375000 0.400000 8.00000 \n",
"ignored 0.500000 0.225806 0.311111 31.00000 \n",
"infrastructure + 1.000000 0.400000 0.571429 5.00000 \n",
"political authority + 0.586207 0.772727 0.666667 22.00000 \n",
"social justice + 0.777778 0.500000 0.608696 14.00000 \n",
"welfare + 1.000000 0.166667 0.285714 6.00000 \n",
"accuracy 0.467980 0.467980 0.467980 0.46798 \n",
"macro avg 0.261747 0.177341 0.199712 203.00000\n",
"weighted avg 0.588630 0.467980 0.500586 203.00000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 6.000000 \n",
"democracy + 0.888889 0.800000 0.842105 20.000000 \n",
"education + 0.700000 0.777778 0.736842 9.000000 \n",
"environmentalism + 0.827586 0.750000 0.786885 32.000000 \n",
"freedom/human rights + 1.000000 0.384615 0.555556 13.000000 \n",
"ignored 0.600000 0.500000 0.545455 30.000000 \n",
"political authority + 0.750000 0.782609 0.765957 23.000000 \n",
"social justice + 0.764706 0.866667 0.812500 15.000000 \n",
"accuracy 0.497537 0.497537 0.497537 0.497537 \n",
"macro avg 0.217706 0.178722 0.190399 203.000000\n",
"weighted avg 0.572813 0.497537 0.522386 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.666667 0.800000 3.000000 \n",
"democracy + 0.800000 0.666667 0.727273 24.000000 \n",
"education + 0.444444 0.666667 0.533333 6.000000 \n",
"environmentalism + 0.694444 0.862069 0.769231 29.000000 \n",
"europe + 0.400000 0.666667 0.500000 3.000000 \n",
"freedom/human rights + 0.800000 0.266667 0.400000 15.000000 \n",
"ignored 0.933333 0.437500 0.595745 32.000000 \n",
"infrastructure + 0.500000 0.166667 0.250000 6.000000 \n",
"political authority + 0.534884 0.851852 0.657143 27.000000 \n",
"social justice + 0.900000 0.500000 0.642857 18.000000 \n",
"welfare + 1.000000 0.166667 0.285714 12.000000 \n",
"accuracy 0.502463 0.502463 0.502463 0.502463 \n",
"macro avg 0.276107 0.204072 0.212458 203.000000\n",
"weighted avg 0.658690 0.502463 0.523000 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"democracy + 0.875000 0.736842 0.800000 19.000000 \n",
"education + 0.428571 0.750000 0.545455 4.000000 \n",
"environmentalism + 0.814815 0.666667 0.733333 33.000000 \n",
"freedom/human rights + 0.818182 0.900000 0.857143 10.000000 \n",
"ignored 0.739130 0.459459 0.566667 37.000000 \n",
"infrastructure + 0.750000 0.375000 0.500000 8.000000 \n",
"political authority + 0.571429 0.714286 0.634921 28.000000 \n",
"social justice + 0.722222 0.764706 0.742857 17.000000 \n",
"welfare + 1.000000 0.250000 0.400000 8.000000 \n",
"accuracy 0.507389 0.507389 0.507389 0.507389 \n",
"macro avg 0.191981 0.160485 0.165154 203.000000\n",
"weighted avg 0.606087 0.507389 0.535597 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 2.000000 \n",
"democracy + 0.826087 0.760000 0.791667 25.000000 \n",
"education + 0.428571 0.750000 0.545455 4.000000 \n",
"environmentalism + 0.852941 0.783784 0.816901 37.000000 \n",
"freedom/human rights + 0.444444 0.666667 0.533333 6.000000 \n",
"ignored 0.695652 0.470588 0.561404 34.000000 \n",
"infrastructure + 1.000000 0.600000 0.750000 5.000000 \n",
"political authority + 0.657143 0.718750 0.686567 32.000000 \n",
"social justice + 0.666667 0.571429 0.615385 14.000000 \n",
"welfare + 1.000000 0.375000 0.545455 8.000000 \n",
"accuracy 0.536946 0.536946 0.536946 0.536946 \n",
"macro avg 0.252384 0.206541 0.217094 203.000000\n",
"weighted avg 0.618749 0.536946 0.564133 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.714286 0.833333 7.000000 \n",
"democracy + 0.952381 0.952381 0.952381 21.000000 \n",
"education + 0.500000 0.500000 0.500000 10.000000 \n",
"environmentalism + 0.812500 0.722222 0.764706 36.000000 \n",
"ignored 0.772727 0.425000 0.548387 40.000000 \n",
"law and order + 0.333333 1.000000 0.500000 1.000000 \n",
"political authority + 0.583333 0.777778 0.666667 18.000000 \n",
"social justice + 0.842105 0.727273 0.780488 22.000000 \n",
"accuracy 0.512315 0.512315 0.512315 0.512315 \n",
"macro avg 0.186980 0.187708 0.178902 203.000000\n",
"weighted avg 0.598614 0.512315 0.541719 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.833333 1.000000 0.909091 5.000000 \n",
"democracy + 0.894737 0.809524 0.850000 21.000000 \n",
"education + 0.750000 1.000000 0.857143 9.000000 \n",
"environmentalism + 0.714286 0.869565 0.784314 23.000000 \n",
"europe + 0.428571 1.000000 0.600000 3.000000 \n",
"freedom/human rights + 0.714286 0.625000 0.666667 8.000000 \n",
"ignored 0.937500 0.348837 0.508475 43.000000 \n",
"infrastructure + 0.500000 0.166667 0.250000 6.000000 \n",
"political authority + 0.540541 0.869565 0.666667 23.000000 \n",
"social justice + 0.722222 0.812500 0.764706 16.000000 \n",
"welfare + 0.666667 0.285714 0.400000 7.000000 \n",
"accuracy 0.541872 0.541872 0.541872 0.541872 \n",
"macro avg 0.248456 0.251206 0.234099 203.000000\n",
"weighted avg 0.616265 0.541872 0.537021 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 0.500000 1.000000 0.666667 2.000000 \n",
"culture + 1.000000 0.500000 0.666667 2.000000 \n",
"democracy + 0.800000 0.842105 0.820513 19.000000 \n",
"environmentalism + 0.904762 0.633333 0.745098 30.000000 \n",
"europe + 0.555556 0.833333 0.666667 6.000000 \n",
"freedom/human rights + 0.600000 0.272727 0.375000 11.000000 \n",
"ignored 0.684211 0.361111 0.472727 36.000000 \n",
"infrastructure + 0.600000 0.333333 0.428571 9.000000 \n",
"political authority + 0.617647 0.700000 0.656250 30.000000 \n",
"social justice + 0.750000 0.562500 0.642857 16.000000 \n",
"accuracy 0.453202 0.453202 0.453202 0.453202 \n",
"macro avg 0.241799 0.208222 0.211759 203.000000\n",
"weighted avg 0.570627 0.453202 0.490556 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"democracy + 0.933333 0.608696 0.736842 23.000000 \n",
"education + 0.333333 0.428571 0.375000 7.000000 \n",
"environmentalism + 0.896552 0.666667 0.764706 39.000000 \n",
"freedom/human rights + 0.500000 0.285714 0.363636 7.000000 \n",
"ignored 0.705882 0.387097 0.500000 31.000000 \n",
"law and order + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.583333 0.807692 0.677419 26.000000 \n",
"social justice + 0.785714 0.523810 0.628571 21.000000 \n",
"welfare + 1.000000 0.250000 0.400000 8.000000 \n",
"accuracy 0.453202 0.453202 0.453202 0.453202 \n",
"macro avg 0.232350 0.170974 0.187799 203.000000\n",
"weighted avg 0.614850 0.453202 0.504701 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.285714 0.444444 7.000000 \n",
"democracy + 0.666667 0.777778 0.717949 18.000000 \n",
"education + 0.750000 0.900000 0.818182 10.000000 \n",
"environmentalism + 0.736842 0.777778 0.756757 18.000000 \n",
"europe + 0.800000 0.800000 0.800000 5.000000 \n",
"freedom/human rights + 0.800000 0.444444 0.571429 9.000000 \n",
"ignored 0.666667 0.352941 0.461538 34.000000 \n",
"infrastructure + 0.666667 0.285714 0.400000 7.000000 \n",
"political authority + 0.583333 0.840000 0.688525 25.000000 \n",
"social justice + 0.750000 0.600000 0.666667 15.000000 \n",
"accuracy 0.448276 0.448276 0.448276 0.448276 \n",
"macro avg 0.218240 0.178364 0.186044 203.000000\n",
"weighted avg 0.512955 0.448276 0.456581 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 1.000000 1.000000 1.000000 1.00000 \n",
"culture + 1.000000 0.666667 0.800000 3.00000 \n",
"democracy + 0.842105 0.842105 0.842105 19.00000 \n",
"education + 0.555556 0.555556 0.555556 9.00000 \n",
"environmentalism + 0.848485 0.800000 0.823529 35.00000 \n",
"europe + 0.666667 1.000000 0.800000 4.00000 \n",
"freedom/human rights + 0.500000 0.555556 0.526316 9.00000 \n",
"ignored 0.750000 0.357143 0.483871 42.00000 \n",
"political authority + 0.750000 0.777778 0.763636 27.00000 \n",
"social justice + 0.846154 0.687500 0.758621 16.00000 \n",
"accuracy 0.532020 0.532020 0.532020 0.53202 \n",
"macro avg 0.277106 0.258654 0.262630 203.00000\n",
"weighted avg 0.626365 0.532020 0.562754 203.00000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 4.000000 \n",
"democracy + 0.884615 0.793103 0.836364 29.000000 \n",
"education + 0.583333 0.700000 0.636364 10.000000 \n",
"environmentalism + 0.787879 0.787879 0.787879 33.000000 \n",
"europe + 0.666667 1.000000 0.800000 2.000000 \n",
"freedom/human rights + 0.333333 0.142857 0.200000 7.000000 \n",
"ignored 0.736842 0.318182 0.444444 44.000000 \n",
"political authority + 0.650000 0.590909 0.619048 22.000000 \n",
"social justice + 0.916667 0.647059 0.758621 17.000000 \n",
"accuracy 0.497537 0.497537 0.497537 0.497537 \n",
"macro avg 0.234262 0.213571 0.217240 203.000000\n",
"weighted avg 0.627873 0.497537 0.540342 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.400000 0.571429 5.000000 \n",
"democracy + 0.840000 0.777778 0.807692 27.000000 \n",
"education + 0.800000 0.800000 0.800000 10.000000 \n",
"environmentalism + 0.866667 0.722222 0.787879 36.000000 \n",
"freedom/human rights + 0.833333 0.333333 0.476190 15.000000 \n",
"ignored 0.933333 0.518519 0.666667 27.000000 \n",
"political authority + 0.680000 0.680000 0.680000 25.000000 \n",
"social justice + 0.818182 0.500000 0.620690 18.000000 \n",
"accuracy 0.502463 0.502463 0.502463 0.502463 \n",
"macro avg 0.241840 0.168995 0.193234 203.000000\n",
"weighted avg 0.671464 0.502463 0.563270 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.772727 0.871795 22.000000 \n",
"education + 0.400000 1.000000 0.571429 4.000000 \n",
"environmentalism + 0.648649 0.800000 0.716418 30.000000 \n",
"europe + 0.600000 0.600000 0.600000 5.000000 \n",
"freedom/human rights + 0.600000 0.375000 0.461538 8.000000 \n",
"ignored 0.722222 0.371429 0.490566 35.000000 \n",
"infrastructure + 1.000000 0.111111 0.200000 9.000000 \n",
"law and order + 1.000000 0.500000 0.666667 2.000000 \n",
"political authority + 0.631579 0.800000 0.705882 30.000000 \n",
"social justice + 0.666667 0.315789 0.428571 19.000000 \n",
"accuracy 0.472906 0.472906 0.472906 0.472906 \n",
"macro avg 0.242304 0.188202 0.190429 203.000000\n",
"weighted avg 0.584982 0.472906 0.489027 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 1.000000 \n",
"democracy + 0.894737 0.739130 0.809524 23.000000 \n",
"education + 0.555556 0.714286 0.625000 7.000000 \n",
"environmentalism + 0.750000 0.750000 0.750000 24.000000 \n",
"europe + 1.000000 0.200000 0.333333 5.000000 \n",
"freedom/human rights + 0.428571 0.250000 0.315789 12.000000 \n",
"ignored 0.833333 0.348837 0.491803 43.000000 \n",
"infrastructure + 1.000000 0.285714 0.444444 7.000000 \n",
"political authority + 0.575000 0.741935 0.647887 31.000000 \n",
"social justice + 0.625000 0.625000 0.625000 8.000000 \n",
"welfare + 0.500000 0.142857 0.222222 7.000000 \n",
"accuracy 0.448276 0.448276 0.448276 0.448276 \n",
"macro avg 0.291507 0.207063 0.223750 203.000000\n",
"weighted avg 0.604774 0.448276 0.484477 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.2s remaining: 0.2s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.750000 1.000000 0.857143 3.000000 \n",
"democracy + 0.941176 0.695652 0.800000 23.000000 \n",
"education + 0.833333 1.000000 0.909091 5.000000 \n",
"environmentalism + 0.843750 0.818182 0.830769 33.000000 \n",
"ignored 0.900000 0.391304 0.545455 46.000000 \n",
"political authority + 0.541667 0.520000 0.530612 25.000000 \n",
"social justice + 0.600000 0.500000 0.545455 12.000000 \n",
"accuracy 0.433498 0.433498 0.433498 0.433498 \n",
"macro avg 0.200368 0.182413 0.185871 203.000000\n",
"weighted avg 0.581523 0.433498 0.481941 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=100\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.500000 0.333333 0.400000 3.000000 \n",
"democracy + 0.894737 0.653846 0.755556 26.000000 \n",
"education + 0.857143 0.750000 0.800000 8.000000 \n",
"environmentalism + 0.823529 0.823529 0.823529 34.000000 \n",
"europe + 1.000000 1.000000 1.000000 2.000000 \n",
"freedom/human rights + 0.666667 0.545455 0.600000 11.000000 \n",
"ignored 0.900000 0.500000 0.642857 36.000000 \n",
"infrastructure + 0.500000 0.285714 0.363636 7.000000 \n",
"political authority + 0.593750 0.826087 0.690909 23.000000 \n",
"social justice + 0.923077 0.666667 0.774194 18.000000 \n",
"accuracy 0.546798 0.546798 0.546798 0.546798 \n",
"macro avg 0.255297 0.212821 0.228356 203.000000\n",
"weighted avg 0.665642 0.546798 0.587976 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.250000 1.000000 0.400000 2.000000 \n",
"democracy + 0.900000 0.500000 0.642857 18.000000 \n",
"education + 0.666667 0.800000 0.727273 10.000000 \n",
"environmentalism + 0.695652 0.516129 0.592593 31.000000 \n",
"freedom/human rights + 0.625000 0.833333 0.714286 6.000000 \n",
"ignored 0.769231 0.270270 0.400000 37.000000 \n",
"infrastructure + 0.500000 0.200000 0.285714 5.000000 \n",
"political authority + 0.518519 0.608696 0.560000 23.000000 \n",
"social justice + 0.857143 0.300000 0.444444 20.000000 \n",
"accuracy 0.349754 0.349754 0.349754 0.349754 \n",
"macro avg 0.180694 0.157138 0.148974 203.000000\n",
"weighted avg 0.535528 0.349754 0.395555 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 1.000000 0.500000 0.666667 2.000000 \n",
"culture + 0.666667 0.666667 0.666667 6.000000 \n",
"democracy + 0.937500 0.652174 0.769231 23.000000 \n",
"education + 0.777778 0.500000 0.608696 14.000000 \n",
"environmentalism + 0.821429 0.718750 0.766667 32.000000 \n",
"freedom/human rights + 0.750000 0.300000 0.428571 10.000000 \n",
"ignored 1.000000 0.259259 0.411765 27.000000 \n",
"infrastructure + 1.000000 0.500000 0.666667 6.000000 \n",
"law and order + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.600000 0.692308 0.642857 26.000000 \n",
"social justice + 0.769231 0.454545 0.571429 22.000000 \n",
"welfare + 1.000000 0.142857 0.250000 7.000000 \n",
"accuracy 0.458128 0.458128 0.458128 0.458128 \n",
"macro avg 0.355952 0.220226 0.256869 203.000000\n",
"weighted avg 0.718030 0.458128 0.529654 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 4.000000 \n",
"democracy + 0.833333 0.800000 0.816327 25.000000 \n",
"education + 0.666667 0.750000 0.705882 8.000000 \n",
"environmentalism + 0.806452 0.806452 0.806452 31.000000 \n",
"europe + 0.666667 0.666667 0.666667 6.000000 \n",
"freedom/human rights + 0.666667 0.444444 0.533333 9.000000 \n",
"ignored 0.800000 0.222222 0.347826 36.000000 \n",
"infrastructure + 0.714286 0.555556 0.625000 9.000000 \n",
"political authority + 0.666667 0.620690 0.642857 29.000000 \n",
"social justice + 0.900000 0.600000 0.720000 15.000000 \n",
"accuracy 0.497537 0.497537 0.497537 0.497537 \n",
"macro avg 0.275741 0.213073 0.233250 203.000000\n",
"weighted avg 0.656298 0.497537 0.542421 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.750000 0.857143 4.000000 \n",
"democracy + 0.937500 0.714286 0.810811 21.000000 \n",
"education + 0.444444 0.571429 0.500000 7.000000 \n",
"environmentalism + 0.857143 0.731707 0.789474 41.000000 \n",
"europe + 0.500000 1.000000 0.666667 2.000000 \n",
"freedom/human rights + 0.545455 0.600000 0.571429 10.000000 \n",
"ignored 0.833333 0.312500 0.454545 32.000000 \n",
"infrastructure + 1.000000 0.333333 0.500000 12.000000 \n",
"military + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.478261 0.578947 0.523810 19.000000 \n",
"social justice + 0.846154 0.578947 0.687500 19.000000 \n",
"welfare + 1.000000 0.142857 0.250000 7.000000 \n",
"accuracy 0.482759 0.482759 0.482759 0.482759 \n",
"macro avg 0.314743 0.243800 0.253713 203.000000\n",
"weighted avg 0.690771 0.482759 0.540305 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.833333 0.909091 6.00000 \n",
"democracy + 0.782609 0.782609 0.782609 23.00000 \n",
"education + 0.714286 1.000000 0.833333 5.00000 \n",
"environmentalism + 0.760000 0.612903 0.678571 31.00000 \n",
"europe + 0.800000 0.666667 0.727273 6.00000 \n",
"freedom/human rights + 1.000000 0.400000 0.571429 10.00000 \n",
"ignored 0.733333 0.314286 0.440000 35.00000 \n",
"infrastructure + 0.833333 0.625000 0.714286 8.00000 \n",
"political authority + 0.545455 0.571429 0.558140 21.00000 \n",
"social justice + 0.777778 0.437500 0.560000 16.00000 \n",
"accuracy 0.443350 0.443350 0.443350 0.44335 \n",
"macro avg 0.294326 0.231249 0.250916 203.00000\n",
"weighted avg 0.601792 0.443350 0.495222 203.00000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.750000 1.000000 0.857143 3.000000 \n",
"democracy + 0.857143 0.545455 0.666667 22.000000 \n",
"education + 0.615385 0.888889 0.727273 9.000000 \n",
"environmentalism + 0.888889 0.600000 0.716418 40.000000 \n",
"europe + 0.666667 1.000000 0.800000 2.000000 \n",
"freedom/human rights + 0.600000 0.333333 0.428571 9.000000 \n",
"ignored 0.720000 0.545455 0.620690 33.000000 \n",
"infrastructure + 0.750000 0.333333 0.461538 9.000000 \n",
"political authority + 0.633333 0.633333 0.633333 30.000000 \n",
"social justice + 0.727273 0.533333 0.615385 15.000000 \n",
"welfare + 0.666667 0.500000 0.571429 4.000000 \n",
"accuracy 0.502463 0.502463 0.502463 0.502463 \n",
"macro avg 0.262512 0.230438 0.236615 203.000000\n",
"weighted avg 0.650346 0.502463 0.556899 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.500000 0.500000 0.500000 2.000000 \n",
"democracy + 0.909091 0.476190 0.625000 21.000000 \n",
"education + 0.600000 0.857143 0.705882 7.000000 \n",
"environmentalism + 0.760000 0.760000 0.760000 25.000000 \n",
"europe + 0.750000 0.500000 0.600000 6.000000 \n",
"freedom/human rights + 0.125000 0.166667 0.142857 6.000000 \n",
"ignored 0.909091 0.250000 0.392157 40.000000 \n",
"infrastructure + 0.666667 0.285714 0.400000 7.000000 \n",
"political authority + 0.740741 0.689655 0.714286 29.000000 \n",
"social justice + 0.733333 0.647059 0.687500 17.000000 \n",
"welfare + 1.000000 0.333333 0.500000 9.000000 \n",
"accuracy 0.423645 0.423645 0.423645 0.423645 \n",
"macro avg 0.248191 0.176315 0.194441 203.000000\n",
"weighted avg 0.652805 0.423645 0.482322 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 1.000000 0.250000 0.400000 4.000000 \n",
"culture + 1.000000 0.800000 0.888889 5.000000 \n",
"democracy + 0.625000 0.909091 0.740741 11.000000 \n",
"education + 0.500000 0.555556 0.526316 9.000000 \n",
"environmentalism + 0.700000 0.700000 0.700000 30.000000 \n",
"freedom/human rights + 0.714286 0.555556 0.625000 9.000000 \n",
"ignored 0.900000 0.257143 0.400000 35.000000 \n",
"market regulation + 0.500000 1.000000 0.666667 1.000000 \n",
"political authority + 0.800000 0.645161 0.714286 31.000000 \n",
"social justice + 0.928571 0.650000 0.764706 20.000000 \n",
"accuracy 0.438424 0.438424 0.438424 0.438424 \n",
"macro avg 0.255595 0.210750 0.214220 203.000000\n",
"weighted avg 0.606773 0.438424 0.481074 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.3s remaining: 0.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 2.000000 \n",
"democracy + 0.933333 0.608696 0.736842 23.000000 \n",
"education + 0.777778 0.583333 0.666667 12.000000 \n",
"environmentalism + 0.714286 0.689655 0.701754 29.000000 \n",
"europe + 0.666667 0.666667 0.666667 3.000000 \n",
"freedom/human rights + 0.500000 0.600000 0.545455 10.000000 \n",
"ignored 0.666667 0.242424 0.355556 33.000000 \n",
"political authority + 0.538462 0.736842 0.622222 19.000000 \n",
"social justice + 0.769231 0.555556 0.645161 18.000000 \n",
"welfare + 0.500000 0.111111 0.181818 9.000000 \n",
"accuracy 0.413793 0.413793 0.413793 0.413793 \n",
"macro avg 0.207836 0.170420 0.180063 203.000000\n",
"weighted avg 0.547247 0.413793 0.451023 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=500\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 2.000000 \n",
"democracy + 0.900000 0.782609 0.837209 23.000000 \n",
"education + 0.500000 0.250000 0.333333 12.000000 \n",
"environmentalism + 0.774194 0.705882 0.738462 34.000000 \n",
"europe + 0.750000 0.750000 0.750000 4.000000 \n",
"freedom/human rights + 1.000000 0.454545 0.625000 11.000000 \n",
"ignored 0.611111 0.379310 0.468085 29.000000 \n",
"infrastructure + 1.000000 0.571429 0.727273 7.000000 \n",
"political authority + 0.608696 0.538462 0.571429 26.000000 \n",
"social justice + 0.666667 0.444444 0.533333 18.000000 \n",
"accuracy 0.448276 0.448276 0.448276 0.448276 \n",
"macro avg 0.251957 0.173441 0.201638 203.000000\n",
"weighted avg 0.598871 0.448276 0.505884 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.4s remaining: 0.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 2.00000 \n",
"democracy + 1.000000 0.727273 0.842105 22.00000 \n",
"education + 0.500000 0.300000 0.375000 10.00000 \n",
"environmentalism + 0.714286 0.483871 0.576923 31.00000 \n",
"europe + 0.500000 0.666667 0.571429 3.00000 \n",
"freedom/human rights + 1.000000 0.250000 0.400000 8.00000 \n",
"ignored 1.000000 0.250000 0.400000 32.00000 \n",
"infrastructure + 1.000000 0.285714 0.444444 7.00000 \n",
"political authority + 0.666667 0.666667 0.666667 21.00000 \n",
"social justice + 1.000000 0.421053 0.592593 19.00000 \n",
"accuracy 0.354680 0.354680 0.354680 0.35468 \n",
"macro avg 0.246499 0.148566 0.172622 203.00000\n",
"weighted avg 0.653413 0.354680 0.434707 203.00000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 0.500000 0.500000 0.500000 2.000000 \n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 0.954545 0.807692 0.875000 26.000000 \n",
"education + 0.636364 0.777778 0.700000 9.000000 \n",
"environmentalism + 0.913043 0.617647 0.736842 34.000000 \n",
"europe + 0.666667 1.000000 0.800000 2.000000 \n",
"freedom/human rights + 0.888889 0.571429 0.695652 14.000000 \n",
"ignored 1.000000 0.272727 0.428571 33.000000 \n",
"infrastructure + 1.000000 0.250000 0.400000 8.000000 \n",
"military + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.750000 0.461538 0.571429 26.000000 \n",
"social justice + 0.857143 0.461538 0.600000 13.000000 \n",
"accuracy 0.448276 0.448276 0.448276 0.448276 \n",
"macro avg 0.327956 0.227538 0.251855 203.000000\n",
"weighted avg 0.748816 0.448276 0.536659 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 0.750000 0.714286 0.731707 21.000000 \n",
"education + 0.555556 0.555556 0.555556 9.000000 \n",
"environmentalism + 0.809524 0.566667 0.666667 30.000000 \n",
"europe + 1.000000 0.500000 0.666667 2.000000 \n",
"freedom/human rights + 0.800000 0.400000 0.533333 10.000000 \n",
"ignored 1.000000 0.209302 0.346154 43.000000 \n",
"political authority + 0.600000 0.461538 0.521739 26.000000 \n",
"social justice + 0.714286 0.454545 0.555556 11.000000 \n",
"accuracy 0.339901 0.339901 0.339901 0.339901 \n",
"macro avg 0.225918 0.131101 0.158668 203.000000\n",
"weighted avg 0.613265 0.339901 0.409327 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 0.857143 0.631579 0.727273 19.000000 \n",
"education + 0.857143 0.666667 0.750000 9.000000 \n",
"environmentalism + 0.846154 0.611111 0.709677 36.000000 \n",
"europe + 0.666667 0.800000 0.727273 5.000000 \n",
"freedom/human rights + 0.571429 0.333333 0.421053 12.000000 \n",
"ignored 0.750000 0.162162 0.266667 37.000000 \n",
"infrastructure + 1.000000 0.333333 0.500000 6.000000 \n",
"military + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.681818 0.681818 0.681818 22.000000 \n",
"social justice + 0.777778 0.388889 0.518519 18.000000 \n",
"accuracy 0.394089 0.394089 0.394089 0.394089 \n",
"macro avg 0.300271 0.198074 0.226743 203.000000\n",
"weighted avg 0.647301 0.394089 0.465545 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 3.000000 \n",
"democracy + 1.000000 0.666667 0.800000 18.000000 \n",
"education + 0.500000 0.800000 0.615385 10.000000 \n",
"environmentalism + 0.800000 0.571429 0.666667 35.000000 \n",
"europe + 0.750000 0.750000 0.750000 4.000000 \n",
"freedom/human rights + 0.500000 0.333333 0.400000 6.000000 \n",
"ignored 0.750000 0.243243 0.367347 37.000000 \n",
"infrastructure + 0.500000 0.200000 0.285714 5.000000 \n",
"political authority + 0.611111 0.523810 0.564103 21.000000 \n",
"social justice + 0.750000 0.461538 0.571429 13.000000 \n",
"accuracy 0.369458 0.369458 0.369458 0.369458 \n",
"macro avg 0.217003 0.168182 0.182444 203.000000\n",
"weighted avg 0.555829 0.369458 0.426514 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 2.000000 \n",
"democracy + 0.869565 0.714286 0.784314 28.000000 \n",
"education + 0.750000 0.500000 0.600000 6.000000 \n",
"environmentalism + 0.900000 0.545455 0.679245 33.000000 \n",
"freedom/human rights + 0.500000 0.428571 0.461538 7.000000 \n",
"ignored 0.909091 0.222222 0.357143 45.000000 \n",
"political authority + 0.687500 0.550000 0.611111 20.000000 \n",
"social justice + 0.666667 0.285714 0.400000 14.000000 \n",
"accuracy 0.344828 0.344828 0.344828 0.344828 \n",
"macro avg 0.209427 0.124875 0.152001 203.000000\n",
"weighted avg 0.630740 0.344828 0.425781 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.750000 0.857143 4.000000 \n",
"democracy + 0.941176 0.842105 0.888889 19.000000 \n",
"education + 0.250000 0.111111 0.153846 9.000000 \n",
"environmentalism + 0.812500 0.433333 0.565217 30.000000 \n",
"europe + 0.833333 0.833333 0.833333 6.000000 \n",
"freedom/human rights + 1.000000 0.333333 0.500000 6.000000 \n",
"ignored 0.666667 0.250000 0.363636 40.000000 \n",
"infrastructure + 0.800000 0.500000 0.615385 8.000000 \n",
"political authority + 0.666667 0.518519 0.583333 27.000000 \n",
"social justice + 0.909091 0.588235 0.714286 17.000000 \n",
"accuracy 0.384236 0.384236 0.384236 0.384236 \n",
"macro avg 0.254175 0.166451 0.195970 203.000000\n",
"weighted avg 0.620830 0.384236 0.463153 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.750000 1.000000 0.857143 3.000000 \n",
"democracy + 0.947368 0.857143 0.900000 21.000000 \n",
"education + 0.666667 0.500000 0.571429 12.000000 \n",
"environmentalism + 0.774194 0.666667 0.716418 36.000000 \n",
"europe + 0.750000 0.600000 0.666667 5.000000 \n",
"freedom/human rights + 0.727273 0.666667 0.695652 12.000000 \n",
"ignored 0.857143 0.193548 0.315789 31.000000 \n",
"infrastructure + 0.500000 0.142857 0.222222 7.000000 \n",
"political authority + 0.750000 0.480000 0.585366 25.000000 \n",
"social justice + 0.785714 0.647059 0.709677 17.000000 \n",
"welfare + 1.000000 0.125000 0.222222 8.000000 \n",
"accuracy 0.458128 0.458128 0.458128 0.458128 \n",
"macro avg 0.265886 0.183717 0.201956 203.000000\n",
"weighted avg 0.692963 0.458128 0.520307 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.5s remaining: 0.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 4.000000 \n",
"democracy + 0.866667 0.565217 0.684211 23.000000 \n",
"education + 0.833333 0.714286 0.769231 7.000000 \n",
"environmentalism + 0.772727 0.653846 0.708333 26.000000 \n",
"europe + 0.800000 0.800000 0.800000 5.000000 \n",
"ignored 0.888889 0.275862 0.421053 29.000000 \n",
"infrastructure + 0.600000 0.300000 0.400000 10.000000 \n",
"political authority + 0.576923 0.517241 0.545455 29.000000 \n",
"social justice + 0.750000 0.428571 0.545455 14.000000 \n",
"accuracy 0.359606 0.359606 0.359606 0.359606 \n",
"macro avg 0.244432 0.163966 0.191048 203.000000\n",
"weighted avg 0.555991 0.359606 0.423004 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 0.6s remaining: 0.6s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 0.8s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=1000\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 0.333333 0.500000 0.400000 2.000000 \n",
"culture + 1.000000 0.750000 0.857143 4.000000 \n",
"democracy + 0.882353 0.714286 0.789474 21.000000 \n",
"education + 0.714286 0.555556 0.625000 9.000000 \n",
"environmentalism + 0.777778 0.840000 0.807692 25.000000 \n",
"europe + 0.750000 0.750000 0.750000 4.000000 \n",
"freedom/human rights + 0.666667 0.400000 0.500000 10.000000 \n",
"ignored 0.750000 0.166667 0.272727 36.000000 \n",
"infrastructure + 1.000000 0.125000 0.222222 8.000000 \n",
"political authority + 0.760000 0.655172 0.703704 29.000000 \n",
"social justice + 0.909091 0.588235 0.714286 17.000000 \n",
"accuracy 0.433498 0.433498 0.433498 0.433498 \n",
"macro avg 0.275597 0.194997 0.214266 203.000000\n",
"weighted avg 0.646455 0.433498 0.486557 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.1s remaining: 2.1s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.2s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 0.777778 0.350000 0.482759 20.000000 \n",
"environmentalism + 0.545455 0.176471 0.266667 34.000000 \n",
"europe + 1.000000 0.600000 0.750000 5.000000 \n",
"infrastructure + 1.000000 0.200000 0.333333 5.000000 \n",
"military + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 0.636364 0.280000 0.388889 25.000000 \n",
"social justice + 1.000000 0.333333 0.500000 15.000000 \n",
"accuracy 0.147783 0.147783 0.147783 0.147783 \n",
"macro avg 0.175282 0.086465 0.109460 203.000000\n",
"weighted avg 0.374434 0.147783 0.208674 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 3.5s remaining: 3.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 4.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.250000 0.400000 4.000000 \n",
"democracy + 1.000000 0.380952 0.551724 21.000000 \n",
"education + 1.000000 0.125000 0.222222 8.000000 \n",
"environmentalism + 0.600000 0.171429 0.266667 35.000000 \n",
"infrastructure + 0.500000 0.142857 0.222222 7.000000 \n",
"political authority + 0.777778 0.269231 0.400000 26.000000 \n",
"social justice + 1.000000 0.380952 0.551724 21.000000 \n",
"accuracy 0.157635 0.157635 0.157635 0.157635 \n",
"macro avg 0.209921 0.061444 0.093377 203.000000\n",
"weighted avg 0.486316 0.157635 0.235660 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.9s remaining: 1.9s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.666667 0.400000 0.500000 5.000000 \n",
"democracy + 1.000000 0.444444 0.615385 18.000000 \n",
"environmentalism + 0.900000 0.281250 0.428571 32.000000 \n",
"europe + 0.333333 1.000000 0.500000 1.000000 \n",
"freedom/human rights + 0.714286 0.357143 0.476190 14.000000 \n",
"ignored 1.000000 0.071429 0.133333 28.000000 \n",
"political authority + 0.375000 0.142857 0.206897 21.000000 \n",
"social justice + 1.000000 0.277778 0.434783 18.000000 \n",
"accuracy 0.172414 0.172414 0.172414 0.172414 \n",
"macro avg 0.181494 0.090149 0.099853 203.000000\n",
"weighted avg 0.563259 0.172414 0.248089 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.3s remaining: 1.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 1.9s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.538462 0.700000 26.000000 \n",
"education + 0.625000 0.416667 0.500000 12.000000 \n",
"environmentalism + 0.666667 0.270270 0.384615 37.000000 \n",
"europe + 0.500000 0.200000 0.285714 5.000000 \n",
"freedom/human rights + 1.000000 0.125000 0.222222 8.000000 \n",
"political authority + 0.833333 0.217391 0.344828 23.000000 \n",
"social justice + 1.000000 0.071429 0.133333 14.000000 \n",
"accuracy 0.182266 0.182266 0.182266 0.182266 \n",
"macro avg 0.187500 0.061307 0.085690 203.000000\n",
"weighted avg 0.501642 0.182266 0.253373 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.3s remaining: 1.3s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 1.8s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 0.500000 0.333333 0.400000 3.00000 \n",
"democracy + 0.894737 0.708333 0.790698 24.00000 \n",
"environmentalism + 0.818182 0.281250 0.418605 32.00000 \n",
"europe + 0.500000 0.200000 0.285714 5.00000 \n",
"freedom/human rights + 1.000000 0.125000 0.222222 8.00000 \n",
"labour + 1.000000 1.000000 1.000000 1.00000 \n",
"military + 1.000000 0.333333 0.500000 3.00000 \n",
"political authority + 0.600000 0.136364 0.222222 22.00000 \n",
"social justice + 1.000000 0.111111 0.200000 18.00000 \n",
"accuracy 0.177340 0.177340 0.177340 0.17734 \n",
"macro avg 0.215086 0.094962 0.118808 203.00000\n",
"weighted avg 0.467268 0.177340 0.235307 203.00000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.6s remaining: 1.6s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.250000 0.400000 4.000000 \n",
"democracy + 0.785714 0.423077 0.550000 26.000000 \n",
"environmentalism + 1.000000 0.200000 0.333333 35.000000 \n",
"europe + 1.000000 0.500000 0.666667 2.000000 \n",
"ignored 1.000000 0.030303 0.058824 33.000000 \n",
"political authority + 0.500000 0.107143 0.176471 28.000000 \n",
"social justice + 0.666667 0.095238 0.166667 21.000000 \n",
"accuracy 0.128079 0.128079 0.128079 0.128079 \n",
"macro avg 0.212585 0.057349 0.083999 203.000000\n",
"weighted avg 0.603096 0.128079 0.193509 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.6s remaining: 1.6s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.4s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 1.000000 0.368421 0.538462 19.000000 \n",
"education + 0.666667 0.285714 0.400000 7.000000 \n",
"environmentalism + 0.777778 0.259259 0.388889 27.000000 \n",
"europe + 0.500000 0.666667 0.571429 3.000000 \n",
"freedom/human rights + 0.666667 0.250000 0.363636 8.000000 \n",
"infrastructure + 0.666667 0.285714 0.400000 7.000000 \n",
"law and order + 1.000000 0.500000 0.666667 2.000000 \n",
"political authority + 0.700000 0.205882 0.318182 34.000000 \n",
"social justice + 1.000000 0.153846 0.266667 13.000000 \n",
"accuracy 0.162562 0.162562 0.162562 0.162562 \n",
"macro avg 0.249306 0.103401 0.137935 203.000000\n",
"weighted avg 0.482594 0.162562 0.236809 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.1s remaining: 2.1s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 2.000000 \n",
"democracy + 0.857143 0.521739 0.648649 23.000000 \n",
"education + 0.166667 0.142857 0.153846 7.000000 \n",
"environmentalism + 0.571429 0.307692 0.400000 26.000000 \n",
"europe + 0.500000 0.200000 0.285714 5.000000 \n",
"infrastructure + 0.666667 0.200000 0.307692 10.000000 \n",
"political authority + 0.714286 0.192308 0.303030 26.000000 \n",
"social justice + 1.000000 0.200000 0.333333 20.000000 \n",
"accuracy 0.172414 0.172414 0.172414 0.172414 \n",
"macro avg 0.176651 0.089181 0.110718 203.000000\n",
"weighted avg 0.421065 0.172414 0.233728 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.9s remaining: 1.9s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.200000 0.333333 5.000000 \n",
"democracy + 0.846154 0.478261 0.611111 23.000000 \n",
"environmentalism + 0.900000 0.272727 0.418605 33.000000 \n",
"europe + 0.500000 0.200000 0.285714 5.000000 \n",
"freedom/human rights + 0.500000 0.153846 0.235294 13.000000 \n",
"political authority + 0.600000 0.111111 0.187500 27.000000 \n",
"social justice + 1.000000 0.166667 0.285714 18.000000 \n",
"accuracy 0.147783 0.147783 0.147783 0.147783 \n",
"macro avg 0.178205 0.052754 0.078576 203.000000\n",
"weighted avg 0.479613 0.147783 0.217876 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 1.5s remaining: 1.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 2.2s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=5000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.428571 0.600000 21.000000 \n",
"environmentalism + 0.875000 0.280000 0.424242 25.000000 \n",
"freedom/human rights + 0.666667 0.307692 0.421053 13.000000 \n",
"political authority + 0.600000 0.120000 0.200000 25.000000 \n",
"social justice + 0.666667 0.333333 0.444444 12.000000 \n",
"accuracy 0.133005 0.133005 0.133005 0.133005 \n",
"macro avg 0.122849 0.047406 0.067411 203.000000\n",
"weighted avg 0.367200 0.133005 0.192183 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 3.4s remaining: 3.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 4.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"agriculture + 1.000000 0.250000 0.400000 4.000000 \n",
"democracy + 0.857143 0.315789 0.461538 19.000000 \n",
"education + 0.666667 0.222222 0.333333 9.000000 \n",
"environmentalism + 0.833333 0.142857 0.243902 35.000000 \n",
"political authority + 1.000000 0.166667 0.285714 18.000000 \n",
"social justice + 0.666667 0.105263 0.181818 19.000000 \n",
"accuracy 0.093596 0.093596 0.093596 0.093596 \n",
"macro avg 0.167460 0.040093 0.063544 203.000000\n",
"weighted avg 0.424232 0.093596 0.150262 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.9s remaining: 2.9s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 4.0s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 1.000000 0.136364 0.240000 22.000000 \n",
"education + 1.000000 0.066667 0.125000 15.000000 \n",
"environmentalism + 1.000000 0.103448 0.187500 29.000000 \n",
"europe + 0.500000 0.166667 0.250000 6.000000 \n",
"political authority + 0.333333 0.050000 0.086957 20.000000 \n",
"social justice + 1.000000 0.071429 0.133333 14.000000 \n",
"accuracy 0.054187 0.054187 0.054187 0.054187 \n",
"macro avg 0.194444 0.030930 0.050760 203.000000\n",
"weighted avg 0.456486 0.054187 0.094573 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.5s remaining: 2.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.5s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.318182 0.482759 22.000000 \n",
"education + 1.000000 0.090909 0.166667 11.000000 \n",
"freedom/human rights + 0.666667 0.222222 0.333333 9.000000 \n",
"political authority + 1.000000 0.066667 0.125000 30.000000 \n",
"social justice + 1.000000 0.083333 0.153846 12.000000 \n",
"accuracy 0.064039 0.064039 0.064039 0.064039 \n",
"macro avg 0.155556 0.026044 0.042053 203.000000\n",
"weighted avg 0.399015 0.064039 0.103695 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.7s remaining: 2.7s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.8s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.166667 0.285714 6.000000 \n",
"democracy + 0.888889 0.421053 0.571429 19.000000 \n",
"education + 0.500000 0.100000 0.166667 10.000000 \n",
"environmentalism + 0.600000 0.115385 0.193548 26.000000 \n",
"freedom/human rights + 0.500000 0.166667 0.250000 12.000000 \n",
"infrastructure + 1.000000 0.100000 0.181818 10.000000 \n",
"law and order + 1.000000 0.333333 0.500000 3.000000 \n",
"political authority + 0.833333 0.217391 0.344828 23.000000 \n",
"social justice + 1.000000 0.105263 0.190476 19.000000 \n",
"accuracy 0.118227 0.118227 0.118227 0.118227 \n",
"macro avg 0.228819 0.053930 0.083890 203.000000\n",
"weighted avg 0.495840 0.118227 0.182949 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.4s remaining: 2.4s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.3s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.500000 0.666667 2.000000 \n",
"democracy + 1.000000 0.315789 0.480000 19.000000 \n",
"education + 0.500000 0.166667 0.250000 6.000000 \n",
"environmentalism + 0.500000 0.024390 0.046512 41.000000 \n",
"political authority + 0.600000 0.115385 0.193548 26.000000 \n",
"social justice + 0.666667 0.133333 0.222222 15.000000 \n",
"accuracy 0.068966 0.068966 0.068966 0.068966 \n",
"macro avg 0.137634 0.040502 0.059966 203.000000\n",
"weighted avg 0.345320 0.068966 0.109487 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.6s remaining: 2.6s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.6s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.458333 0.628571 24.0000 \n",
"environmentalism + 0.666667 0.166667 0.266667 36.0000 \n",
"freedom/human rights + 0.666667 0.181818 0.285714 11.0000 \n",
"political authority + 0.500000 0.133333 0.210526 30.0000 \n",
"accuracy 0.113300 0.113300 0.113300 0.1133 \n",
"macro avg 0.088542 0.029380 0.043484 203.0000\n",
"weighted avg 0.346470 0.113300 0.168199 203.0000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.5s remaining: 2.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.4s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_ranking.py:657: RuntimeWarning: invalid value encountered in true_divide\n",
" recall = tps / tps[-1]\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.352941 0.521739 17.000000 \n",
"education + 0.750000 0.250000 0.375000 12.000000 \n",
"environmentalism + 0.900000 0.214286 0.346154 42.000000 \n",
"military + 1.000000 1.000000 1.000000 1.000000 \n",
"political authority + 1.000000 0.181818 0.307692 22.000000 \n",
"social justice + 0.750000 0.157895 0.260870 19.000000 \n",
"accuracy 0.128079 0.128079 0.128079 0.128079 \n",
"macro avg 0.200000 0.079887 0.104128 203.000000\n",
"weighted avg 0.497783 0.128079 0.200166 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.5s remaining: 2.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.4s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 0.333333 0.500000 3.000000 \n",
"democracy + 0.857143 0.352941 0.500000 17.000000 \n",
"education + 1.000000 0.100000 0.181818 10.000000 \n",
"environmentalism + 0.750000 0.103448 0.181818 29.000000 \n",
"europe + 0.500000 0.500000 0.500000 2.000000 \n",
"political authority + 0.857143 0.181818 0.300000 33.000000 \n",
"social justice + 1.000000 0.125000 0.222222 16.000000 \n",
"accuracy 0.098522 0.098522 0.098522 0.098522 \n",
"macro avg 0.175420 0.049898 0.070172 203.000000\n",
"weighted avg 0.466045 0.098522 0.155401 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.5s remaining: 2.5s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.7s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"culture + 1.000000 1.000000 1.000000 3.000000 \n",
"democracy + 0.875000 0.241379 0.378378 29.000000 \n",
"environmentalism + 1.000000 0.068966 0.129032 29.000000 \n",
"military + 1.000000 0.250000 0.400000 4.000000 \n",
"political authority + 0.500000 0.047619 0.086957 21.000000 \n",
"social justice + 1.000000 0.153846 0.266667 13.000000 \n",
"accuracy 0.078818 0.078818 0.078818 0.078818 \n",
"macro avg 0.179167 0.058727 0.075368 203.000000\n",
"weighted avg 0.418103 0.078818 0.121220 203.000000\n",
"Loading manifesto/manifesto-Germany.csv\n",
"Fitting 2 folds for each of 3 candidates, totalling 6 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 3 out of 6 | elapsed: 2.8s remaining: 2.8s\n",
"[Parallel(n_jobs=-1)]: Done 6 out of 6 | elapsed: 3.9s finished\n",
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=10000\n",
"\n",
" precision recall f1-score support\n",
"democracy + 1.000000 0.300000 0.461538 20.000000 \n",
"education + 1.000000 0.250000 0.400000 8.000000 \n",
"environmentalism + 1.000000 0.051282 0.097561 39.000000 \n",
"freedom/human rights + 0.666667 0.166667 0.266667 12.000000 \n",
"infrastructure + 1.000000 0.125000 0.222222 8.000000 \n",
"political authority + 0.666667 0.066667 0.121212 30.000000 \n",
"social justice + 1.000000 0.052632 0.100000 19.000000 \n",
"accuracy 0.078818 0.078818 0.078818 0.078818 \n",
"macro avg 0.211111 0.033742 0.055640 203.000000\n",
"weighted avg 0.600985 0.078818 0.131772 203.000000\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/felix/anaconda3/envs/pdds_1920/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
}
],
"source": [
"mixin_manifesto = []\n",
"\n",
"for N in [0, 100, 500, 1000, 5000, 10000]:\n",
" for rep in range(10):\n",
" df_manifesto = get_manifesto_data()\n",
" tweets_train, tweets_test, labels_train, labels_test = train_test_split(df_human['text'], \n",
" df_human['manifestolabel_true'],\n",
" test_size=.2)\n",
" df_manifesto = df_manifesto.sample(n=N)\n",
" train_text = pd.concat([df_manifesto['text'],tweets_train])\n",
" train_labels = pd.concat([df_manifesto['manifestolabel'],labels_train]) \n",
"\n",
" enough_samples_per_class = train_labels.value_counts() > 5\n",
" valid = train_labels.isin(enough_samples_per_class[enough_samples_per_class==True].index)\n",
"\n",
" train_single(train_text[valid], train_labels[valid], 'tweets_and_manifesto')\n",
"\n",
" df_test = pd.concat([tweets_test,labels_test],axis=1)\n",
" df_test.columns = ['text','manifestolabel_true']\n",
" tw = score_texts(df_test,['tweets_and_manifesto'])\n",
" results_tweets_and_manifesto_df = pd.DataFrame(classification_report(tw['manifestolabel_true'],tw['tweets_and_manifesto'],output_dict=True,zero_division=0)).T\n",
" print(f'N={N}\\n')\n",
" print(results_tweets_and_manifesto_df[results_tweets_and_manifesto_df['f1-score']>0])\n",
" mixin_manifesto.append(\n",
" {\n",
" 'N': N,\n",
" 'rep': rep,\n",
" 'f1':results_tweets_and_manifesto_df.loc['weighted avg','f1-score']\n",
" })\n",
"\n",
"mixin_manifesto_df = pd.DataFrame(mixin_manifesto)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Results\n",
"\n",
"More than 500 manifesto samples lead to decreased classification performance on held-out tweets "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1b2cce90>"
]
},
"execution_count": 18,
"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": [
"mixin_manifesto_df.groupby('N').agg({'f1':np.median}).plot.bar()"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}