{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Load segmented affix occurrences from supplement" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'6e6389a913cc01020d03ac16217bc1c63c9d0e16b78179b4c931741c0d5a69cf'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import hashlib\n", "import io\n", "import pathlib\n", "import shutil\n", "import urllib.request\n", "import zipfile\n", "\n", "URL = 'https://zenodo.org/record/841982/files/xflr6/portmanteaus-v1.0.zip'\n", "\n", "CSV = pathlib.Path('esm3-analyses.csv')\n", "\n", "def sha256sum(filepath: pathlib.Path | str, /) -> str:\n", " with open(filepath, 'rb') as f:\n", " return hashlib.file_digest(f, hashlib.sha256).hexdigest()\n", "\n", "def download_archive_path(url: str, /, filename: str, *, target: pathlib.Path,\n", " clear_cache: bool = False) -> pathlib.Path:\n", " if clear_cache or not target.exists():\n", " with io.BytesIO() as b:\n", " with urllib.request.urlopen(url) as u:\n", " shutil.copyfileobj(u, b)\n", " with zipfile.ZipFile(b) as z:\n", " (i,) = (i for i in z.infolist() if pathlib.Path(i.filename).name == filename)\n", " i.filename = target.name\n", " z.extract(i)\n", " assert target.exists()\n", " return target\n", "\n", "sha256sum(download_archive_path(URL, CSV.name, target=CSV))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import scipy.stats\n", "\n", "def pearsonr(df, /, left, right, *, func=scipy.stats.pearsonr) -> pd.Series:\n", " df = df[[left, right]].dropna()\n", " name = f'{left} & {right}'\n", " result = func(df[left], df[right])\n", " return pd.Series(result, index=('r', 'p'), name=name)\n", "\n", "plt.style.use('classic')\n", "plt.rcParams.update({'figure.figsize': (6, 4), 'figure.facecolor': 'w',\n", " 'figure.subplot.bottom': .125, 'font.size': 10, 'savefig.dpi': 72})\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2818 entries, 0 to 2817\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Language 2818 non-null object\n", " 1 Cell 2818 non-null object\n", " 2 Position 2818 non-null int64 \n", " 3 Form 2818 non-null object\n", " 4 Meaning 2818 non-null object\n", "dtypes: int64(1), object(4)\n", "memory usage: 110.2+ KB\n" ] }, { "data": { "text/html": [ "
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LanguageCellPositionFormMeaning
0Ainu1s-1kuSA[+1 +sg]
1Ainu1p1asS[+1 +pl]
2Ainu2s-1eSAP[-3 +sg]
3Ainu2p-1eciSAP[+2]
4Ainux1anS[-1 -2 -3]
5Ainu1s->2s-1eciSAP[+2]
6Ainu1s->2p-1eciSAP[+2]
7Ainu1s->3s-1kuSA[+1 +sg]
8Ainu1s->3p-1kuSA[+1 +sg]
9Ainu1s->x-2kuSA[+1 +sg]
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" ], "text/plain": [ " Language Cell Position Form Meaning\n", "0 Ainu 1s -1 ku SA[+1 +sg]\n", "1 Ainu 1p 1 as S[+1 +pl]\n", "2 Ainu 2s -1 e SAP[-3 +sg]\n", "3 Ainu 2p -1 eci SAP[+2]\n", "4 Ainu x 1 an S[-1 -2 -3]\n", "5 Ainu 1s->2s -1 eci SAP[+2]\n", "6 Ainu 1s->2p -1 eci SAP[+2]\n", "7 Ainu 1s->3s -1 ku SA[+1 +sg]\n", "8 Ainu 1s->3p -1 ku SA[+1 +sg]\n", "9 Ainu 1s->x -2 ku SA[+1 +sg]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "RENAME = {'Quechua (Ayacucho)': 'Ayacucho',\n", " 'Tlachichilco Tepehuan': 'Tepehua',\n", " 'Lakhota': 'Lakota'}\n", "\n", "cf = (pd.read_csv(CSV, encoding='utf-8')\n", " .assign(Language=lambda x: x['Language'].replace(RENAME))\n", " .sort_values(by='Language', kind='mergesort')\n", " .reset_index(drop=True))\n", "\n", "cf.info()\n", "assert cf.set_index(['Language', 'Cell', 'Position']).index.is_unique\n", "cf.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reconcatenate word-forms with stem symbol" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MultiIndex: 1317 entries, ('Ainu', '1s') to ('Yimas', '3p->3p')\n", "Data columns (total 1 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Form 1317 non-null object\n", "dtypes: object(1)\n", "memory usage: 17.3+ KB\n" ] }, { "data": { "text/html": [ "
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Form
LanguageCell
Ainu1skuΣ
1pΣas
2seΣ
2peciΣ
xΣan
1s->2seciΣ
1s->2peciΣ
1s->3skuΣ
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" ], "text/plain": [ " Form\n", "Language Cell \n", "Ainu 1s kuΣ\n", " 1p Σas\n", " 2s eΣ\n", " 2p eciΣ\n", " x Σan\n", " 1s->2s eciΣ\n", " 1s->2p eciΣ\n", " 1s->3s kuΣ\n", " 1s->3p kuΣ\n", " 1s->x kuiΣ" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "STEM = 'Σ'\n", "\n", "assert not cf['Form'].str.contains(STEM).any()\n", "\n", "_cf = (cf.drop('Meaning', axis=1)\n", " .assign(cell_index=lambda x: x.groupby(['Language', 'Cell'], sort=False).ngroup()))\n", "\n", "_sf = (_cf.drop_duplicates('cell_index')\n", " .assign(Position=0, Form=STEM))\n", "\n", "df = (pd.concat([_cf, _sf])\n", " .sort_values(by=['cell_index', 'Position'])\n", " .groupby(['cell_index', 'Language', 'Cell'])[['Form']]\n", " .agg(''.join)\n", " .reset_index('cell_index', drop=True))\n", "\n", "df.info()\n", "assert df.index.is_unique\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tag cells as 1/2<->1/2, 1/2<->3, and other" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MultiIndex: 1317 entries, ('Ainu', '1s') to ('Yimas', '3p->3p')\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 d_local 912 non-null object\n", " 1 Form 1317 non-null object\n", "dtypes: object(2)\n", "memory usage: 59.9+ KB\n" ] }, { "data": { "text/html": [ "
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d_localForm
LanguageCell
Ainu1sNonekuΣ
1pNoneΣas
2sNoneeΣ
2pNoneeciΣ
xNoneΣan
1s->2sTrueeciΣ
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1p->2pTrueeciΣ
1p->3sFalseciΣ
1p->3pFalseciΣ
1p->xFalseaiΣ
2s->1sTrueenΣ
2s->1pTrueunΣ
2s->3sFalseeΣ
2s->3pFalseeΣ
2s->xFalseeiΣ
2p->1sTrueecienΣ
2p->1pTrueeciunΣ
2p->3sFalseeciΣ
2p->3pFalseeciΣ
2p->xFalseeciiΣ
3s->1sFalseenΣ
3s->1pFalseunΣ
3s->2sFalseeΣ
3s->2pFalseeciΣ
3s->xNoneiΣ
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" ], "text/plain": [ " d_local Form\n", "Language Cell \n", "Ainu 1s None kuΣ\n", " 1p None Σas\n", " 2s None eΣ\n", " 2p None eciΣ\n", " x None Σan\n", " 1s->2s True eciΣ\n", " 1s->2p True eciΣ\n", " 1s->3s False kuΣ\n", " 1s->3p False kuΣ\n", " 1s->x False kuiΣ\n", " 1p->2s True eciΣ\n", " 1p->2p True eciΣ\n", " 1p->3s False ciΣ\n", " 1p->3p False ciΣ\n", " 1p->x False aiΣ\n", " 2s->1s True enΣ\n", " 2s->1p True unΣ\n", " 2s->3s False eΣ\n", " 2s->3p False eΣ\n", " 2s->x False eiΣ\n", " 2p->1s True ecienΣ\n", " 2p->1p True eciunΣ\n", " 2p->3s False eciΣ\n", " 2p->3p False eciΣ\n", " 2p->x False eciiΣ\n", " 3s->1s False enΣ\n", " 3s->1p False unΣ\n", " 3s->2s False eΣ\n", " 3s->2p False eciΣ\n", " 3s->x None iΣ" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SEP = '->'\n", "\n", "def is_distinct_local(cellkey, /, *, sep=SEP, persons=('1', '2')) -> bool | None:\n", " (subj, trans, obj) = cellkey.partition(sep)\n", " (local_subj, local_obj) = (any(p in arg for p in persons) for arg in (subj, obj))\n", " if local_subj and local_obj:\n", " return True\n", " elif trans and (local_subj or local_obj):\n", " return False\n", " else:\n", " return None\n", "\n", "df.insert(0, 'd_local', df.index.get_level_values('Cell').map(is_distinct_local))\n", "\n", "df.info()\n", "assert df.index.is_unique\n", "df.head(30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Neutralization ratios (1/2<->3 vs. 1/2<->1/2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sizenuniqueneutratioratio (norm)
d_localFalseTrueFalseTrueFalseTrueFalseTrueFalseTrue
Language
Ainu24814510343.47826142.8571430.5035970.496403
Aleut3618135231365.71428676.4705880.4621750.537825
Ayacucho2081169247.36842128.5714290.6237620.376238
Bella Coola1681363220.00000028.5714290.4117650.588235
Chuckchi158768257.14285728.5714290.6666670.333333
Darai1681155333.33333342.8571430.4375000.562500
Fox2081664221.05263228.5714290.4242420.575758
Hixkaryana1867311364.70588260.0000000.5188680.481132
Jaqaru63531020.0000000.0000001.0000000.000000
Jumjum2481787030.4347830.0000001.0000000.000000
Karuk168957346.66666742.8571430.5212770.478723
Ket48822826055.3191490.0000001.0000000.000000
Kunama4818227261155.31914964.7058820.4608970.539103
Lakota2081367236.84210528.5714290.5632180.436782
Maricopa1683213686.66666785.7142860.5027620.497238
Maung60845415425.42372957.1428570.3079180.692082
Mordvin1681046440.00000057.1428570.4117650.588235
Nocte1686310566.66666771.4285710.4827590.517241
Reyesano168828653.33333385.7142860.3835620.616438
Sahu60830830050.8474580.0000001.0000000.000000
Siuslawan48184817010.0000005.8823530.0000001.000000
Tepehua2081545426.31578957.1428570.3153150.684685
Thangmi1681066240.00000028.5714290.5833330.416667
Turkana168739560.00000071.4285710.4565220.543478
Wardaman248237114.34782614.2857140.2333330.766667
Yimas361830176117.1428575.8823530.7445260.255474
\n", "
" ], "text/plain": [ " size nunique neut ratio \\\n", "d_local False True False True False True False True \n", "Language \n", "Ainu 24 8 14 5 10 3 43.478261 42.857143 \n", "Aleut 36 18 13 5 23 13 65.714286 76.470588 \n", "Ayacucho 20 8 11 6 9 2 47.368421 28.571429 \n", "Bella Coola 16 8 13 6 3 2 20.000000 28.571429 \n", "Chuckchi 15 8 7 6 8 2 57.142857 28.571429 \n", "Darai 16 8 11 5 5 3 33.333333 42.857143 \n", "Fox 20 8 16 6 4 2 21.052632 28.571429 \n", "Hixkaryana 18 6 7 3 11 3 64.705882 60.000000 \n", "Jaqaru 6 3 5 3 1 0 20.000000 0.000000 \n", "Jumjum 24 8 17 8 7 0 30.434783 0.000000 \n", "Karuk 16 8 9 5 7 3 46.666667 42.857143 \n", "Ket 48 8 22 8 26 0 55.319149 0.000000 \n", "Kunama 48 18 22 7 26 11 55.319149 64.705882 \n", "Lakota 20 8 13 6 7 2 36.842105 28.571429 \n", "Maricopa 16 8 3 2 13 6 86.666667 85.714286 \n", "Maung 60 8 45 4 15 4 25.423729 57.142857 \n", "Mordvin 16 8 10 4 6 4 40.000000 57.142857 \n", "Nocte 16 8 6 3 10 5 66.666667 71.428571 \n", "Reyesano 16 8 8 2 8 6 53.333333 85.714286 \n", "Sahu 60 8 30 8 30 0 50.847458 0.000000 \n", "Siuslawan 48 18 48 17 0 1 0.000000 5.882353 \n", "Tepehua 20 8 15 4 5 4 26.315789 57.142857 \n", "Thangmi 16 8 10 6 6 2 40.000000 28.571429 \n", "Turkana 16 8 7 3 9 5 60.000000 71.428571 \n", "Wardaman 24 8 23 7 1 1 4.347826 14.285714 \n", "Yimas 36 18 30 17 6 1 17.142857 5.882353 \n", "\n", " ratio (norm) \n", "d_local False True \n", "Language \n", "Ainu 0.503597 0.496403 \n", "Aleut 0.462175 0.537825 \n", "Ayacucho 0.623762 0.376238 \n", "Bella Coola 0.411765 0.588235 \n", "Chuckchi 0.666667 0.333333 \n", "Darai 0.437500 0.562500 \n", "Fox 0.424242 0.575758 \n", "Hixkaryana 0.518868 0.481132 \n", "Jaqaru 1.000000 0.000000 \n", "Jumjum 1.000000 0.000000 \n", "Karuk 0.521277 0.478723 \n", "Ket 1.000000 0.000000 \n", "Kunama 0.460897 0.539103 \n", "Lakota 0.563218 0.436782 \n", "Maricopa 0.502762 0.497238 \n", "Maung 0.307918 0.692082 \n", "Mordvin 0.411765 0.588235 \n", "Nocte 0.482759 0.517241 \n", "Reyesano 0.383562 0.616438 \n", "Sahu 1.000000 0.000000 \n", "Siuslawan 0.000000 1.000000 \n", "Tepehua 0.315315 0.684685 \n", "Thangmi 0.583333 0.416667 \n", "Turkana 0.456522 0.543478 \n", "Wardaman 0.233333 0.766667 \n", "Yimas 0.744526 0.255474 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xf = (df.dropna(subset=['d_local'])\n", " .groupby(['Language', 'd_local'])['Form']\n", " .agg(['size', 'nunique']))\n", "\n", "xf['neut'] = xf['size'] - xf['nunique']\n", "xf['ratio'] = 100 * xf['neut'] / (xf['size'] - 1)\n", "xf['ratio (norm)'] = xf['ratio'] / xf['ratio'].groupby(level='Language').sum()\n", "xf.loc[xf['ratio (norm)'].isnull(), 'ratio'] = None\n", "\n", "xfp = xf.reset_index('d_local')\n", "xf = xf.unstack()\n", "\n", "xf" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ], "text/plain": [ " d_local size nunique neut ratio\n", "Language group X \n", "Ainu 1->2:A 1p True 2 1 1 100.0\n", " 1s True 2 1 1 100.0\n", " 1->2:P 2p True 2 1 1 100.0\n", " 2s True 2 1 1 100.0\n", " 1->X:P 3p False 2 2 0 0.0\n", " 3s False 2 2 0 0.0\n", " x False 2 2 0 0.0\n", " 2->1:A 2p True 2 2 0 0.0\n", " 2s True 2 2 0 0.0\n", " 2->1:P 1p True 2 2 0 0.0\n", " 1s True 2 2 0 0.0\n", " 2->X:P 3p False 2 2 0 0.0\n", " 3s False 2 2 0 0.0\n", " x False 2 2 0 0.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ff = df.dropna(subset=['d_local']).reset_index()\n", "ff[['A', 'P']] = ff['Cell'].str.partition(SEP)[[0, 2]]\n", "print(pd.concat([ff['A'], ff['P']]).unique())\n", "\n", "(a_first, p_first) = (ff[x].str.contains(r'1') for x in ('A', 'P'))\n", "(a_second, p_second) = (ff[x].str.contains(r'2|[dp]i') for x in ('A', 'P'))\n", "(a_third, p_third) = ~a_first & ~a_second, ~p_first & ~p_second\n", "\n", "# treat inclusive cells as first person only\n", "a_second &= ~a_first; p_second &= ~p_first\n", "assert (pd.concat([a_first, a_second, a_third], axis=1).sum(axis=1) == 1).all()\n", "assert (pd.concat([p_first, p_second, p_third], axis=1).sum(axis=1) == 1).all()\n", "\n", "groups = {'1->X:P': a_first & p_third,\n", " '2->X:P': a_second & p_third,\n", " 'X->1:A': a_third & p_first,\n", " 'X->2:A': a_third & p_second,\n", " #\n", " '1->2:A': a_first & p_second,\n", " '2->1:A': a_second & p_first,\n", " '1->2:P': a_first & p_second,\n", " '2->1:P': a_second & p_first}\n", "\n", "lf = (pd.concat([ff[c].groupby(['Language', 'd_local', g.rpartition(':')[-1]])['Form']\n", " .agg(['size', 'nunique'])\n", " .assign(group=g)\n", " .set_index('group', append=True)\n", " .swaplevel()\n", " .reset_index('d_local')\n", " for g, c in groups.items()])\n", " .sort_index())\n", "lf.index.rename('X', level=2, inplace=True)\n", "\n", "lf['neut'] = lf['size'] - lf['nunique']\n", "lf['ratio'] = 100 * lf['neut'] / (lf['size'] - 1)\n", "\n", "lf.head(14)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1/2 subcategory neutralization ratios (1/2<->3 vs. 1/2<->1/2)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ratioratio (norm)
d_localFalseTrueFalseTrue
Language
Ainu0.00000050.0000000.0000001.000000
Aleut62.50000062.5000000.5000000.500000
Ayacucho6.25000025.0000000.2000000.800000
Bella Coola0.00000012.5000000.0000001.000000
Chuckchi28.57142925.0000000.5333330.466667
Darai25.00000037.5000000.4000000.600000
Fox12.50000025.0000000.3333330.666667
Hixkaryana50.00000060.0000000.4545450.545455
JaqaruNaNNaNNaNNaN
JumjumNaNNaNNaNNaN
Karuk0.00000037.5000000.0000001.000000
KetNaNNaNNaNNaN
Kunama31.25000054.1666670.3658540.634146
Lakota31.25000025.0000000.5555560.444444
Maricopa100.000000100.0000000.5000000.500000
Maung0.00000050.0000000.0000001.000000
Mordvin25.00000050.0000000.3333330.666667
Nocte50.00000075.0000000.4000000.600000
Reyesano0.00000050.0000000.0000001.000000
SahuNaNNaNNaNNaN
SiuslawanNaNNaNNaNNaN
Tepehua25.00000050.0000000.3333330.666667
Thangmi0.00000025.0000000.0000001.000000
Turkana25.00000050.0000000.3333330.666667
Wardaman0.00000012.5000000.0000001.000000
Yimas0.0000004.1666670.0000001.000000
\n", "
" ], "text/plain": [ " ratio ratio (norm) \n", "d_local False True False True \n", "Language \n", "Ainu 0.000000 50.000000 0.000000 1.000000\n", "Aleut 62.500000 62.500000 0.500000 0.500000\n", "Ayacucho 6.250000 25.000000 0.200000 0.800000\n", "Bella Coola 0.000000 12.500000 0.000000 1.000000\n", "Chuckchi 28.571429 25.000000 0.533333 0.466667\n", "Darai 25.000000 37.500000 0.400000 0.600000\n", "Fox 12.500000 25.000000 0.333333 0.666667\n", "Hixkaryana 50.000000 60.000000 0.454545 0.545455\n", "Jaqaru NaN NaN NaN NaN\n", "Jumjum NaN NaN NaN NaN\n", "Karuk 0.000000 37.500000 0.000000 1.000000\n", "Ket NaN NaN NaN NaN\n", "Kunama 31.250000 54.166667 0.365854 0.634146\n", "Lakota 31.250000 25.000000 0.555556 0.444444\n", "Maricopa 100.000000 100.000000 0.500000 0.500000\n", "Maung 0.000000 50.000000 0.000000 1.000000\n", "Mordvin 25.000000 50.000000 0.333333 0.666667\n", "Nocte 50.000000 75.000000 0.400000 0.600000\n", "Reyesano 0.000000 50.000000 0.000000 1.000000\n", "Sahu NaN NaN NaN NaN\n", "Siuslawan NaN NaN NaN NaN\n", "Tepehua 25.000000 50.000000 0.333333 0.666667\n", "Thangmi 0.000000 25.000000 0.000000 1.000000\n", "Turkana 25.000000 50.000000 0.333333 0.666667\n", "Wardaman 0.000000 12.500000 0.000000 1.000000\n", "Yimas 0.000000 4.166667 0.000000 1.000000" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf = lf.pivot_table('ratio', ['Language', 'd_local'], aggfunc='mean')\n", "\n", "rf['ratio (norm)'] = rf['ratio'] / rf['ratio'].groupby(level='Language').sum()\n", "rf.loc[rf['ratio (norm)'].isnull(), 'ratio'] = None\n", "\n", "rfp = rf.reset_index('d_local')\n", "rf = rf.unstack()\n", "\n", "rf" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(rf.sort_values(by=('ratio (norm)', True), kind='mergesort')['ratio']\n", " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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tzyDXq1fP2rdvb1OnTnXNuaZPn+4eMBwcB2uTu3btaiNGjLD69eu7QPnMM8/c7PEaNWrkDkoRvsq1AQAAAABIJyV6lalWPLpDGWT56KOPXHD85ZdfurT1pEmT7MADD7S+ffu6xly6yQMPPGC33HKL+/qoo45yQfLOO+9cWf9PAAAAAACkRYUDZAAAAAAAqrIK74MMAAAAAEBVRoAMAAAAAICvbZ4AAFXHjz/+aPfcc4+9++67tnHjxtT9Tz75pJfj0c4Lf/zjH+29994rcTz//Oc/zZdgNRONKTe3Zs0aW7hwYYnXKuhrApR25JFH2iuvvGJ77LFHic+TPmP6XjuuZDs1wlWz3OrVq9trr71m77zzjp133nmuOa4Pe++9d5nnvo8//tjL8cycOdOWLl1qmzZtSt131VVXeTkWxBMBchU7Qffo0cOeeuqprd4HYNvoYrrffvuVuG/+/PnWuXPnrH8q+/Xr55o3zps3z66++mqbPHmyHXHEEd6elwsuuMDtqPDiiy/a//7v/9q4cePs4IMP9nIsa9eudcfz0ksvuWvDscce65pZaqtEH3JycsocqP78889ejucvf/mL3Xjjje6aqc+XJjUOO+wwAmSU67HHHnP/1YRc3Lzxxhtuu9Nw4HXuuedGfhzdu3d352PtjazdZHQ+1Jj1iSeeMB+effbZ1NeaCHvooYdszz339HIs55xzji1evNhdE3baaafYTFxedNFF9uc//3mr90Xh1VdfLfN+n9d1XUuXL19e4rOVzuPJ2CZdP/30k3uiwjPOv/zlLyN9oTTA+eSTT8r8ebNmzcwHbcdVOkui5+X99983n4HFZZddtlk2x9ckQhyyFXGdYNGs84UXXuj2KtfgQ7eXX37ZrrzySvPl8ccf3ywzefvtt0d+HNq7XcHWGWec4b7/n//5Hxfo6P3tw4oVK+zWW2/dbDCmQCxq2tHgX//6V+pco+0TTjzxxHIvsunWrl07954JjquoqMh95jShEbWTTz7ZTaJooCPa2UEZnWeeecZ8+P7770tk/qdMmeKC48GDB3s5Hr1Gep8cc8wxLsulrzXBosDZF/197doRNnToUPd5i0v2rXbt2u59ddNNN1mdOnUiPZ4333zTXnjhBff1r3/9a+vYsWOkfz+udO2cNWuWO/+EAy9dw3yNBcePH+8qaq677jo76KCD3DgsLrp06eKC+Kjtv//+tmjRotRrFBdljd+Da1nUOoY+0xp7aSejAw44wFsV1qhRo+y2226zffbZp8RnS+eidMnIDLJmopSx+Prrr23XXXe1b775xpo2beoC5qgEs/++AuHSlCHRwGvJkiXuQxbYsGGDtW3b1uux6bXShUNZAs38qhSzefPmWZ2tiOsM+IABA+zss892JyLRCbF3797eAmRNrOhz/fbbb9tZZ53lZr81IPNh9uzZ9vvf/94FoJqVD7IFvihQVzbykksu8X6h32WXXdx/c3NzXQCmMr7PP//c2/GorFBq1KjhtibURNQXX3zhbdIpHAwPGzbMDXp80TUz/LXKCg899FBvAbJeK70+wSSPMgJXXHGF+aSMjSbE9PkSBaE+J5l79erlzjmqRBBts6kAWROqAwcOjDQAU8B1880322mnneYGqKeffrpdf/31bsvPqGkyZdCgQbZs2TL3/gkmmAsKCswHTRooM6nzjm+FhYXu9o9//MP756ksOi9/9tlnXv62xp96bn7xi19YHPz1r391Y0KNdfS5Co/fd9ttNy/H9NZbb5X4XoGoJg590dhdyYAoqw4yMkDWyfj11193pcOacZ46daq3WTHNpgwfPtytowhncaJeV3H88cdbq1atXCB6xx13pO5X2WOUmfWy6GKlwEIXVWULFMxrQKbnLWp6bvSeUbZCQVeQrYha3CZYAppp1mBMmdIg4NHNF2Wv9dlWKZSOSYN4raPyYd9993XH8Jvf/Mb22msv9z6KOnMTplndP/3pTxYHeh40Yfnb3/7WunXr5p6fJk2aeDueli1bugGY3ss61+g8eMghh3g5Fg3aNRBs0KCB+15fx6lw68MPP/Q2eSB5eXnu+dBrduedd7pz4nfffWc+aUmSPuePPvqoK9PX7bnnnvN2PM8//3yJyThl3vS+1qC1TZs2kR7Lvffe666ddevWdd/rOq6JBB8BsibflVnq1KmT90nC4Lqu93McaEJZ5xx9rvR+UdWjz4BQ1/CgCkIVK6q+HDJkiJdj0XX8uOOOs6OOOqrEZMYNN9zg5XiU0VZJvOIJ/Teg61YwSedbp06dXALFF62nj7okPyMDZK2h0kU0CEg1CAoHhVHSYF0ZHJU6+TxB6/nQ7YMPPrC42Xnnnd1/lVVSWahO2r4GZHHLVsStmZCC4fDgXUGPz8G8Ll7Bmkktq9B7RyXyvi6qyiwpaNfro8oDZXJUuuuDsvsrV6501TO+/f3vf3fnP2XaHn74YVfV42PdXUCTpnL55Ze7wFjHo0lEH5Th0uDwhBNOcN8r0AoqNHwIL+vQQFWfb1X1+KKJU02iasmCsqF6rXysuQtr1KiRy+ioskjnHGXhfGYFVfH0ww8/pAIcfa3nSXwcVxAcl/46agoglMGOC01a6HiUEAi/Lj4azqmkWmNTPUf6vGv8NW3aNPNFk1/hcYZKZX31YbjmmmvcWFBjLo0rfFPpu25alqTPk7Lbvida3g9VzOg6oQk6n8+VKgc1VleFY/izlc4EYEYGyEHApQzF3/72N1cuoYG8DxoU+pxVKe3oo48us9mAj3WJAQWhyuboZK3Bqk5MahrhQ9yyFXFqJiS/+93v3PtZA9YJEya4sn0fmYGALuoaDOo50kSYBqu+ZsFVPqeMjWYxNSOvm07WysD5oBJmXVQ1ORe+YPjoHB1eE6TXKU703vFJSxT0mVaJvqiJmc9lL+FlHRqo6jPlc3JX1Tyy++67u0DUp1NPPbXE9VPPj65Xeg19dmXXeUYTcjo/y/Tp012GUNeuqJcraWnStddemxr3qA9D6eaFUenZs6dr9qSANFhW4dOCBQvcf++7777UfXo/+erIriUUKj8PT743btw48uNQgKXxhF6rONB6Wt3iRtVFGsNr8kt9YFSpofJrTR5GrXsok63zoD7jDz74oPmiXhnyf//3fyU+W+ms1s3IJl0qe1I2QE+MAi29mRTsqDNd1C6++GL7wx/+YB06dLA4UCYnoJPiI4884oLBuJRiaj2e1lUo++WDJgoUpCuDHWQr9Nyo3CbbmwmFP18qMdSpQcsYNDjzZd26dW6tXXFxsZtA0OulrKDWB0YtWN8WZLCVZVIzKl/bZpR3sfJRgh63LTyUpdVss/5+kCXV8fnq1IzN3X333VvtPxC1rQ0AfS3vCK7twUS3JhWUbfI1MXfppZeWaNJ11113Wb169SI/Fg2WNSGnSVThcx6vXj2ls+s+e3aEacJCVUbKrseJSr5VUaPPl5Zw6f2ssbIaiiF6GRkgK7jRGret3RfFegqVHGgmqkWLFiWyOD732wxTKbEupr66yQa0ZlwX1GCbE82GI7muQ1lJdQzUoF7lj1pL7qszso6hdClqWfdlI2WKlbEIAmRVsKhkTa9XtgtfwMNbeKhfhA+aFFTZcOmlL+EGVdnWp6K84wmCiqiPRxPL5dHx+OxijcygMl1lJpWg8P05j8sOK+Exqia6S/fq8bW8Q80JtVRA3eHDjad8PDeqOFB2Vn0GwuN2H7tjhOl9rCoEvXZ6zST8ddSKi4tdVjt83fK9pCtoPhdI5yRHRpZY601dOgAt676o1lPEmTImvtZsBsaMGeMGq0F3PmX9lR3wsSm7PugqTyu9NY6v5gxxaiYkGjiXDobLui8q6gg6YsSIVJfSgI+OsmqAp9LCIKOuNYqqQtCa5Cgpk64y3fI+Pz4u8qVLhvUeVgm6rwBZnyM1C4uDuPSpiNvxaP1+XJ1//vll3u8raI/Tlm5x2utXWeugRD+OWVv9V8u4fGRt49SrR1QqLOFlFD4m5kSN7aJublcRKmXWBEtQjaWKS1/n6MmTJ7txupa06r0kOi71zfGVZNOkqnbqCUtnVVhGBcgqP9WsnJ4QlTYGyW+V7Ib3doxC0JhHx6N1rcEbWjMuOs44rKHS86RAQp1lfdK2EJq8CDrQadCsDLKPAFnBuWbE4tL1MtxMSLOHuqD6CEZ10lGGVJ+lp59+OnW/vg/K13zQ66WBVxy2MtJrEy4317HdcsstkR9HMPuuNZtx5XMLDznppJNS2RPf4tanIm7HowG8Bu7BwFkTGzof+uyeH56k1DVek6rh7ROzeUu38vb69REgq1RWXbX1/IQzgb5KZ+O0w0qcevWIr9LusqgxapzoHKjznT7feu9oGYOarKkS66KLLvJyTDfddJPb6ikuFXK6JihoV1JCFbFaopPuBoUZVWI9cuRId9PJOHzYOhkqo+IjW6EsycyZM1ODVQUUWhs0d+5c8yG8hkofOJV+KzMZh7KRrd0XBX3YFQiWtV4yaprA0NpjZUl90/tGJx+9JuH19Pps9e/f39t6N5/lRWUNmnXBCmae9bqpeY9KtXy9f3wPlre2hYePrdxESxV0Ltb+zEFjPh2fSvyyvU9F3I5HWQplI5V502ukklmtad/aGuUoaZJQwViw7jZqKkP1uQ9zmJr1qF9GHPb6DTJbEowLffYa0DVC14Ogp0j4vmzu1eOrRLY8N954Y5n3+6okVMl3kGGfN2+eW1uv97ImelWS7iPLfmiM1oyLJiiVaAt/trQ0sfR+zVmbQdasj26awQx3CfR94QxncvS1z67IQROROLSJD6gESh9ydWwWBWJqihVc8KNcg6LmTsrwx+G5UXCjlv7h7Tt8vm90mzhxYup1igN1dNRsoTqh+zZ69Gh3HMH7VSdpbWnkizIDCtAV7LRu3dp8itMWHqU7NfsS7lOhTr9x6VOhz1OcjkfdvfV6BcGOJuN8ZmvLoudJXWV9idOWbnHa61cVe3ESp6ytupwHAbqvnia+S2TLowrUcIXIjBkz3JITXzReD+KaYIcMlVdr/BN1paV2MJEePXq463rpbZV8VWcEny1VoupapbG8Mu3plFEBckBvIr155syZkyp39tG6PjhBKyAOyh715gqvy4maBu06McahTXzgiSeecP995ZVXStyv44p6DYoGheoUqFL08IfeR8fU4HgOP/xwt31HuHGFr+PRxUpZtjp16qRKZbW1ibI7PmhPSZVcqlO0Xi8fDYWUQdcyAR2H9mcNJjO0TCDKxoClqdO5JpuCvRO1blKffR8XMF97QZdHa+98i2ufCpWkxok+07qOBgGyvvdd2HbllVeWqIhQZY2vnRfitqVbnPb6jRuVgSogVificNbWF1U3KjgOj0l9LG3zVSJbntKNytTnREkcXzQWPuGEE1yPk1GjRrmxuxJL6matsusoadeQcJXuVVddFYvqDH2eNB5VVZrGG5p41ucsnTKqxDqg8oNg/1i9YK+99prLfJ188smRH4saZ2imUB960b6x6nQ7ePBg84E28dveOdVnx9S4HU+w7VRcypxVEj906NDNupRGuY9sUNpT+us4URZO555nnnkm8n4McWwipEYiqjbS+r9wN1kfr115fSriUKIaB7pWaiI3GKBqv0t9znxO6mopV+mlSmoyGWQxsnlLN2W1StN728dnXZMqpZdLqYpPEwljx46NfI/oOFEptZYB6fodXiuuyd5sKZHdFhpT+NxOSVlt9RnQtmnaHUMxhSbqsDkFx7qupnuLzYzMIOvipXINXbREHW7VpMFHgKzBe4MGDVL7D2u2R90CfVE2WxMHAZ0Qq1evbr5pK6Ng/ZY6jvta/xa3zqlxO56y5st87h2rrHp5HWV9iON8ooILVWlogFrW4DXbmghJMIH64osvuq7f48aNcwNFH5QJCPep0EDIR58K9enQcxFu5BjmIxspmljR6xM0B1R2UlUbvuh8pzXsvqp44rb/cmlRd+3f2lpSTcgFFU5KlKhctX79+q4RnZqJZWvWVtcFBXxxOB/7KpEtT7i/gT7vGp9qHO9LsNxQTT9VmaG1x7qe+liGGFd9+/Z1SzN1PVX3+igmKzMyQNYbOgiORV/7WouilH+wfjOgNRbavifb28QHNGOpUgjNwOu4lGFXQzW94X3QtlcLFy4skVnyWR4Wp+PR+rLHH3/cBTxB6Y/PtaQKJJQV9TH5Ffjxxx/djLeCY71GwdcBXxcvBTsqWdP5UJUIypb6eq30vPzpT3+yuNB5T5OX6iKr947K41WWpc6c2dqnQtVFUrqzt64XPvtmKAuo9XdBFZbvBoq6XiqLHacAOW7bTsVlr191qg83+1STpaABqI9MaXlZWx+UPddkge/+Jj5LZMsTrojTmFmVcz4n5bp3714iKaCKMN18boW1dOlSdw4sXYXlo9Gl6D2jteKDBg1ySwAVLOuW1l16EhnouOOOSzzwwAOJn3/+2d0mTJjg7vOha9euicLCwtT3y5cvT7Rs2TLhy0MPPZT47W9/m2jSpEni2muvTTRr1izx17/+NeHTgQcemFi/fn3qe32t+3yYOHGie05q1qyZaN++fWKnnXZKHH744V6OJY7H88EHH7j3r45Jt9atWyeWLFni7Xhq166dqFatWuIXv/hFYo899nDf679R0vPQvHnzMm977713wpd+/fol5s2bl4iDs846K/HJJ58k4qJjx47uvx06dEh88cUX7jrRokULL8eic923336b+n7Dhg2Jtm3bJnz78MMPE4MGDUrUr18/ccghh3g7jtWrVydOOOGERI0aNdztxBNPTKxZsybh09ChQxNTp05NxMW9996buo0ZMybRuXPnxMUXX+zlWJ555plEgwYNEnl5eYk6deq487POhT7oWlV6bBGMv9q1axf58bRq1SqxadOmRBwsXLjQvU+uueaaxMiRI1O3OCgqKkoUFBT4PgxswZFHHpl47LHHEm3atEm8//77brwxatQo789ZcXFxYsqUKe6ck5OTk9a/lZEZZK2100ydtqsI1jb46iarTJsa42ifxLVr17qF9j43Y1d5tzrIap221rkpgxIuufZFTYTK+jpqem00e6gyDZUgqVmEsnAcT9L+++/vZsA/+uij1BpgnxUIcehGrPW1ceRrLVlYUK6rsuG4NBESVfAoW6HzoZoKqXFZeG/bKOlapZnucJ8KXyWzymarKkSlqMpKqDpCzd70ufdFpbC6Rj3yyCOp50fZHFWO+KKSb20TplJ9ZeB8bhMmwVgnoIy7ryqjOO31q9JlnXc07hKVVGv/WFVEqPllNmdtr7nmGre8Ttk/ZWt901hrS6LYqUKNYpWJDJZzhOnzraabar7pu4olDgoKCly5t7L8Wjeuc6Kupb62btT2f1qmGaxjV+8KrddOp4xs0qWBj9YxBGVhKknwWdastV06Fu0ZpouHSojxX3o+NADTQEi0xYiCME0qxGGfQp+Nl+J2PHFaL46yaY/YLV3AoyzHKq95UJzWTqqJozrLahDta7JHz1PQp0KBjY8+FVqnqQkLDURVsqvnQ3vaqlTWp7IaA5Z1X5S0j3dcu6OLlpRpP/YPP/wwq/f6FS1PCtZFqweDz27jWvOrz5mWM4QnCn3sr6vJ7WCiOw5Udq41tcH1S9cpTW7onKzvNe5IN702Gn+W16tDa6L1vPkYm8bNof/ZB1lbTmnyUmu0dc7xUe4tmuzp1KmTDRs2zK3P3mWXXdL+NzMyg6yZSzVfCbbF0cVM68yiPBkEi+dFgx3NeGs2QwMO/Szq9TjlNV7xnckJMgJqV6/AT8eobIqvfazVTVZzQppM0fYLGvD4XH8Xt+OJ23rxOHUjjotnn33W/fexxx5z2e3wxFPUA/jSAXBc9l/Xe1iZJFEmSZ8xnad9VRqV7lPhg94vmuzS+0VrsvX5jkOmRK/NZ599lmqSo699z9vHJRAuq8mT722n4rTXr+h58BkUxzVrq0BPWUBf+9aWFSCrM32Q9dMkvM5JygxGRdfIrTWa81lNEydHHHGES0aq8aYmwPS+VkbZFx2LXrfnn3/ejUm1HZViCW2NlS4ZmUG+55573AL2oKxZ5bJqWJPWxdqlaBasPD4W1ZeXyQkasFxxxRWRHk9cqdOvPuxffPGFK3nUPoVqLqQPGseTbLSiyaegDF4zquFuilHTxJfKL1USGu5G7KPZUtyU3iJDp3LNsPrYNiNu+68rADz33HNdebNoAlNdZX00NVIDtbIC0aiPRdeBoLxar5GeH1031NDMp4ceesiGDBmSKpN97rnn3D6lwWvnQ9wm5uK07dSjjz5qxx9/vBvjBHv93nXXXXb22WdHfix6PVTyqWMJd432leWKU9ZWwYzOw6oCC2ezb7/9dm9ji9LjCGWQfZXnf/DBB5t1G9dnCiV9/PHHbmJZAakSXT4pntHuD5pcUVZbE0AKnNMlIwNkoax5y3SS1sycBh+a6Q13eoyaBhrqhqeyeFFwqj0Kdb+P0jR1TY0DBTIaCKrcXDQLrve1Ah9fyrqIlXVfVIJSy6CcT+vqtYZI6yaznaoOtAZw1113TQVAqtLQcpNs339dF05NnOrzpbWs6hSvC6qPz77OdQEFW5rY1et07733mi865yhA1/Vh3333ddn1iy66yNvx6H0SLpONcp/zsjAxlxl0XVCGS70PwssnfL1/tHxCa7LjkLUNT6qE+Rh3iSa2FZwH5c1aD6zETbijdFQ0zlLQp5LhcLdxZSeznZJFY8aMcWMvXTd1HVe5tZbiaLJXu0P4mvTWhI+OS8eoxI0qotJZBZVRAXJ4kK4yI2UFNDDr3bu3t20GNPj61a9+5WZXRKVG8+bNc9vTRC2ODVjKW0/ma51t06ZNXeZYa1F8NgvT66KKBx2LTj76GCpgVjm69lHUfXFYL66Sa820+lqTo4yo1iZp0kCZJe1Pqll6zfxmuxEjRrjXJdiSS3shaw9ZH+vdgq1VNAgKBjzhr31QllRlYhpEa4mJ7+3uwudpDaSDdf4+KXuiho4KloM10lG+PiqnLt3jQO8jbVfWuHFj8yWOE3M6D+qYwhltH1tRqYpHmWNtV6bgVA27FPhE0WSpNN9r1eOetY0TjYv1vgmqHnTu0XhVTbGipgoMxRNxaKYWN61bt3ZjPlGgrB4eWkqh2EbnQF/Jkn/84x8u1gp/rtIto9Ygh/cKC0ybNs3dfO0Vplr48AlagbLuizpADjdg0QxP0IDFd3AsZe1RrQGHrw+Z1j9rVkwXMV3gfQSjKj3VoFRrxwP6Wseikm/t7xiX9eK6z5c4dSOOY4CsiQMtG5Bbb701VaaarfuvKygPzygrmNBkSlCVEYe167rAKziMA71umhTz0VhS16myehuoEiLYw9oXrbcLXiudfzQxp8onX0aPHu3GOStXrnSDVF3HlEHxESCrIkITqBo4a/Jg1KhRbm/SKJoslab+AppQiUsjSWUkdfNJJfBa7nL33XeX+XNf+3ur2dO///3vVGM5jU19LBGQ/Pz8SAOtTBJufjVv3rzU0lWdA3W98CVYu64+J7oF0lmtkVEZ5DgqawbTx7qKmjVruouEBhZBAxZt9+RrLU7Y7373OzdAVVmL3m5aS6psqS74PrM4GoDp4l6vXj278sor3UUlqqY1CvzKK4Xd0s+i2oIhOC0Ez4eP7EBpWnui9W5a/+bzRB0HqqBRdisoz/dNnyUNzDS7rGZU+l6TQEF2Oyoq29sSBRdxaLCkDKnP818cBFUHZdEEproTR01ZLZ1bNCGndbV6H6vniQZhqlzRe9wHPR96rpRt03hDS6i09tZHZU9Q/aU+EI0aNXJbYfmqCNM5UM+FMoLhgCcOE2G+qHmtJplUDluaruc++jCERRnglEfjz1tuucWNlcPvG/VlyHY6LytBU7t2bdesUFUzwQ5BmtTw0Tlf1FG7T58+m42NdU1Nl4weZcbhg6bAVLMsmh0TzarqvqipWZnKVW688UZXeq4Puu8uigENNDTgUFdZnaAVbE2ZMsXb8Sj401oTPV+6mCgw1sBHJaoqJYnClkp7gjWlUdLkRWl6rbQGRe+tdJ6EKioO+3nHhbKzWiKgiZ44lInFZf91BcB6r+pCqvW1caBS1ICCL2VwaAZjbglQeXzN26sxmK4LQfb68ssvdwNGZb7S2S11azSI103VWHpuFKzrmHzQOn49R7oFZfm+KsJ8ruOPaym8zoG6qQGfOozHhUrxFbRHGeCUR1VxmszVZym8BpkA2dzEmyqxcnNz3XrxIDhWnOPz/aTP0OTJk92yRCV0VCGR9iqARAaaP39+Yv/990/k5OSUuPkwb968RP369RNHHnmkuzVq1CjxxhtvJHxatGhR4uqrr07Uq1cv0blz58TYsWMTcfDdd9+5m0+jR49ONG/ePHHiiScmZs2aVeJnLVq0iOw49P59//33E++9995mN/3Mty+//DJx5ZVXJurWrZu46aabvB3HzJkzE61atUrsvPPO7jNerVo1b5/1uDn//PMT7dq1S4waNSpx1113pW4+rVq1KvHpp58mfOvUqZPvQ8BWHHDAAYk1a9Zsdv/q1avdz3zo3r17YuDAgSXuW7lyZWKfffZJ3HPPPQlfunbtmigqKkr07t07cdVVVyVuv/12b8/R66+/7p6nO++8033/0UcfJS699FIvxxI3OhcffPDBiT333DNx2mmnJWrWrJno0aNH5McxYcKERJMmTRIPPPBAIi50TtZ7R9esgoKCxM0335wYM2aMl2PR5/mnn37y8rczwdq1axPvvvtuori4uMR5+ZNPPvF2TPpcSfi816FDh7T+zYwssdZaRM0elJ5JKCsLFgUtXg+adyiTHDTsyuYGLKK1f8G+0GXx0VRN62t1C2bFwtRcI6r1rZqJK6+c29d6etGs9x133JHatkNZk6D7uA96nVTiWLpLqY8se9zEqYROS0rUgGXdunXue+1pq6oMLTfxYdiwYfbVV1+5TLKqRHyec7TOVuvDw1389blSs6NsprWs6iyu96syoqLyPfXTUGm+jy1FVJGmdfw632j5jdaKK4uiXhXKJvuicnNtLamKEWV4tNREFVm+Pl9x2fNcVQi6PpTO2KofS7aXwi9btswtbdMQX1l//VfXB50XfQjK8IPGd2VtVRjletYZM2Z4WwON7Yv7VGatxsxqeqd15Hr/rFixwtIlI0usVTqsJ0sBoMqZNdgI1rj6oMXrUe7BnAkNWETrep999tkym6v5CAJVyqPufGUFxxJl86d0fqi3h0r3tC2Y1pVpexydiLT+xDctm9A6IWxu0qRJsXlaFARqeYf6DYjW1+o+H4MfUfmnqJmR74knTbyFJ5n22msvb89LnFx88cVuv2GV8wWBlkp11fDJ136bOg5NKqsBVvA+1vZXvoLj8Pr1gMY8mvRRCa2P7sia8NbkaRz2PNdkiq4RKv/U+E8lmD77ZcSlFF6viSYHNWk5ePDgWHTwD/qG6FyoQFkBzueff+7lWLQcSIGWxqbhMl1fDcywdXovq2GiJpy0hEBxoLaWTKeMzSBHPZNQHs3MlZUJjMOazTjQ20trWNXMIw6U4Vezp7jshRwX6rypjIA6I5eVlfCReRMdjxrh9ejRw8vfjzNNECrbHwSBmkjQQN5HA7NwViAO+2fHSennRudE3eejCVUcKSsa7Jet85DP6pDg/aosv7bsOemkk9xEr6/zoK5TGtuoMWFZ1ywfe9rGac/z4LMVnGu+/fZbt4NI6aaTUdE2NNpVQI3L1COiSZMmrkKi9LkxnZQwUmWG1tnGaXJZ1yqt8VXwrqRNEOBoQiybq6+w7fTeUcVIuvs95VaVmQSVG/mgE3K43EfNpwiOS9J2SnEZDGqwoUGPmgqFSy+1L2m2D1J1gShrD12fJd8q9d6wYYPbekDZHd9lYnGi7JKyE8pw6TlRBcAnn3xS7vYe6S6fmz17ths8B92kfW7Hpe1wytsHPWoqt1SJrjI5ev9qb0kfe3/GlZrMBdtw+RaudtL1Qe9p3XydB1988UU3aFfAo7Lz888/3/bdd1/zSR2Sww349LwE22L52pJGk4Lff/+9GzD7ykqKtpBUFYR26tD4VE1bo24WqGuAJi7istQvmHRSVcbq1avd8htNIijAqV+/vvtv1Fsuxan6Clu2tUn2dE5aZmQGOUzBsQJTzWCWNyjyleFGkma/1RVUpYW+aT1ZabrAB3vJIl50sS9LHMq/fdOFQevcgsySMsoKVH1kbYMtp4Iul6rmUTYwWOMV9bYryt7oc63LmwZgmgBSaZ9KeqNWUFBgV1xxhVtuomPSZJwqn3zsdoDMpPeQ1vRrYK+gUFvUaJzhgyZ35syZ4/6+Ptfa8/zUU08td8uudI8t9LwoINVEhsYYCpTDSyvgl9bOlxZUXWr8riSTXj9l3aNS3i4qdLHOjPdPIN2TlhmZQQ7TAEy3uMT5ajKi8ixYiZl4lclqnXY4a+tj/dTLL7/MS5NBCITLp3Oe1roFAbK+93UejNt2K6WzSGraE/Xe9AGtkaR0Dzv6HlJ2W5UzqhDROMNXgKxqCC150WdMlXvBnuc+qPmo1teqd8bDDz/s1kX7CHJUslte003J5s//8uXLt/hzLcFT35MoA+Rnnnkm9bUmULXsThM/BMiZ9/5Jp4wPkANbOjmlu0FX8LdVWq0BqroqomR2Sbe40KylPnThrpe+1thi67OHZX22fZV8x4myJ1q+oGYsway47vMhWOqiKh7fJaBl0b7Do0ePtpEjR3r5+9pPXMtMwuecbF/Wga3TmOLpp5+2iRMnumqa3r17u6xtw4YNLdv3PJfwHrY6Ll+0V7ZorbHWP6uJmY5J2W2tS0b59F7u379/pE/RE088UeJ7jQd97nOOrVNjy7Zt27olOY8//rjbb1zLzNLZ3yijSqy3VDqoZgSaifJZAqp1MFpfEYeOgSibyhzV+VJbc6kZjP6rLKXPWSqUL9z4RcGF1nOpVPb666/P+qdN2ePx48fbCy+84AZjxx13nBto+JgsVHmjBoU6BypI1sVM68c1ePZVkhoOMrTkRR1KlyxZEvmxKHukDt/K/mnbO2Wyla1Q1gJJGvCU3qrHR4VR3Gg8oXXzylAefvjhm/3cx8Sustf777+/xYEmC7TWVxOmWmLiewJVHbQ1xlDGPzgPqeeJr6ZhqDg1J/VVZYSKvT76vOuzrWrU008/3X0/a9YsS5eMCpB91qJjx2i2p/QAyEdLfW0p8tRTT7kSMTWy0ABeJ8Xbbrst8mPB9nci17Ye2ar01i/BKTwIjH0EFgr4VOKoi5Y+V6LZ3qg725511lkuaxPsLqDnRhOWCkz1vPjIsKt6RgNklRHqudHX2o4mm8suw3Qd0ASlutvq9VN2R/uUKmua7bSmP/hcB+9n32MeHZM+T+piffLJJ3ur3gs+Wyr51r7V4cSEzj0+tG7d2m0lubX74JeqMsITqK+//rpbfqdxKuKp/X/20dYSE02GaRyk8Xww3rBsL7GOY5av9Axm0GWXYP2/VNqo/SSVWVIpphpoqKOhjwBZA2dljIPZZpVlafsBZAZ1r//ss88sm915551b3PrFBw0ySpdW++hsq+xWkF2PCz0PWooTnHOUZVLTLiRpYKpJSg121KxH3b7PO+88np7/NLuLG41tVF6tpWR6Hw8cONDteV6nTp3Ij0VB8YABAywulNHXkpdgPa2aqpW1bSL8Co/5VPXUokULt5c34quwsNDWrVvn1o/feuut7r507xiUUQFyHOlCXtYMJv5LW1Sow6WyTNOnT7ePPvrITSr4EHTV1f6Ef/vb39xsuMqsES8KJnTh0qA5vMZfSxo0gM5mcdz6Rdt0aPuX4LXSWrxgC5Yo+cxmlSfYnqxly5ZuckMTdHqu8N/3TpDx1zp2lRVrzTbiSa+Vulbrpqy//qv96s855xy3xj+dawJLU9m5xhbBGmDfVPWg5RTBBJiWvbAcKH7LNLX8p7y9daPecgoVo/3oW7Vq5T5TyiZri0tNPKdTRpVYx5G6M6t0GOXTxUsXMc2uKlOggZCv503ll8q8aRZc+2mr66VOllo/ifj4/e9/72Z0tZ9uQAFzfn6+22uXCo14bP0SlDRrHZAGx7poqTxW66IVwKusOEp6jwTr/8J87p+tLeS0J7R2N1C2TeecP/3pT+5CD3PvEa3bHDJkiOuMrABZJY9slRhfS5cutbFjx7pyeDWbU18Pvc/VKDCKbeaCiVMFNZpwVwYwHNhEva1cMIGr7HHU+x5j25dplp5I1fVBk/I+tpzC9lGVmF6zdFaqkUGuYjOYcaTBuy5kCooHDRrksrfpLo0oj7r+atZJA1Zd5MVH4x5svZzmwgsvtPvuuy91n/bb1D7WpdfgZqs4bP0SlDSrSaIypM8995wbbChY9pHV1gzzjBkzLE40oaOs2+67787+rGXQBIuqr9QHQuvEVdGjJTmIJ33Wde3U+Vk9BmrXru3uV1ZHa+ujoEqMuNF7mLFEvMVxyynEd+9qMshVaAYzrrS9iWbufvjhB1darQyK9k/0sTZHWy6oRDWYdVLJroJmvYaIV4B8wgknuKULo0aNsk8//dQFx1rOcPnll1s2K2vrF918bP0SNM6Ii7gdj6gLsTLHyrLVrVvX9+EAO0TLpFRW7bP/QVC5EjfDhg1zE5bKJO+2226p+9lGMrPWJ6ucF/Hyu9/9rsy9q2fOnJm2v0mAvJ3UpEInQu0HGKbSTzWrUGYn25WX6QvKHX1021VjEW1Jo4t8MFuokhq1jUe8qNxJzdxUsquMkoIMLlzx2volbiXN6e5quT00+aZKCA3oNRmnSZ6oM/1xpCBrS2vGn3zyyUiPBxUX7iWyevVq9zlXR+lsnggrb6cVmrYClS/Yu1pLudKFAHk7abCugXvPnj03u6irS3O4NDRbaYZ5S912//jHP3o5rquvvto1ydEaNzXQKP0awr9gHZvWbWo9svaSDAfH2TwjH6etX7SdypZKmtWQCkmqoNG2cqqIqFevnns/KxMWx8ZiUXjwwQe3+HM6WcfTDTfc4PY5V78BlRRrolnX0Ci33otrgAyg6uxdTYCchhO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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(rf['ratio (norm)'].sort_values(by=True, kind='mergesort')\n", " .plot.bar(stacked=True, figsize=(15, 5), cmap=plt.cm.gray));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test for 1/2 subcategory neutralization differences" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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d_local & ratio0.3759821.413199e-02
d_local & ratio (norm)0.7656243.494391e-09
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" ], "text/plain": [ " r p\n", "d_local & ratio 0.375982 1.413199e-02\n", "d_local & ratio (norm) 0.765624 3.494391e-09" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame([pearsonr(rfp, 'd_local', c) for c in ['ratio', 'ratio (norm)']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Absense of non-person features in learned meanings" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " n\n", "0 \n", "+pl 638\n", "+1 622\n", "+3 618\n", "+2 595\n", "+sg 475\n", "-1 437\n", "-3 366\n", "-2 359\n", "-sg 243\n", "+an 105\n", "-pl 83\n", "+du 73\n", "-du 47\n", "-obv 42\n", "-masc 40\n", "+masc 34\n", "+hum 27\n", "+obv 20\n", "+fem 13\n", "-hum 9\n", "-fem 3" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cf['Meaning'].str.extractall(r'([+-]\\w+)')[0].value_counts().to_frame('n')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LanguageCellPositionFormMeaningPersonOnly
0Ainu1s-1kuSA[+1 +sg]False
1Ainu1p1asS[+1 +pl]False
2Ainu2s-1eSAP[-3 +sg]False
3Ainu2p-1eciSAP[+2]True
4Ainux1anS[-1 -2 -3]True
5Ainu1s->2s-1eciSAP[+2]True
6Ainu1s->2p-1eciSAP[+2]True
7Ainu1s->3s-1kuSA[+1 +sg]False
8Ainu1s->3p-1kuSA[+1 +sg]False
9Ainu1s->x-2kuSA[+1 +sg]False
10Ainu1s->x-1iP[-1 -2 -3]True
11Ainu1p->2s-1eciSAP[+2]True
12Ainu1p->2p-1eciSAP[+2]True
13Ainu1p->3s-1ci[+1 +pl]A->P[+3]False
\n", "
" ], "text/plain": [ " Language Cell Position Form Meaning PersonOnly\n", "0 Ainu 1s -1 ku SA[+1 +sg] False\n", "1 Ainu 1p 1 as S[+1 +pl] False\n", "2 Ainu 2s -1 e SAP[-3 +sg] False\n", "3 Ainu 2p -1 eci SAP[+2] True\n", "4 Ainu x 1 an S[-1 -2 -3] True\n", "5 Ainu 1s->2s -1 eci SAP[+2] True\n", "6 Ainu 1s->2p -1 eci SAP[+2] True\n", "7 Ainu 1s->3s -1 ku SA[+1 +sg] False\n", "8 Ainu 1s->3p -1 ku SA[+1 +sg] False\n", "9 Ainu 1s->x -2 ku SA[+1 +sg] False\n", "10 Ainu 1s->x -1 i P[-1 -2 -3] True\n", "11 Ainu 1p->2s -1 eci SAP[+2] True\n", "12 Ainu 1p->2p -1 eci SAP[+2] True\n", "13 Ainu 1p->3s -1 ci [+1 +pl]A->P[+3] False" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NONPERSON = r'[+-]\\D+\\b'\n", "\n", "nf = cf.assign(PersonOnly=lambda x: ~x['Meaning'].str.contains(NONPERSON))\n", "\n", "nf.head(14)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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d_localPersonOnly
LanguageCell
Ainu1sNoneFalse
1pNoneFalse
2sNoneFalse
2pNoneTrue
xNoneTrue
1s->2sTrueTrue
1s->2pTrueTrue
1s->3sFalseFalse
1s->3pFalseFalse
1s->xFalseFalse
1p->2sTrueTrue
1p->2pTrueTrue
1p->3sFalseFalse
1p->3pFalseFalse
\n", "
" ], "text/plain": [ " d_local PersonOnly\n", "Language Cell \n", "Ainu 1s None False\n", " 1p None False\n", " 2s None False\n", " 2p None True\n", " x None True\n", " 1s->2s True True\n", " 1s->2p True True\n", " 1s->3s False False\n", " 1s->3p False False\n", " 1s->x False False\n", " 1p->2s True True\n", " 1p->2p True True\n", " 1p->3s False False\n", " 1p->3p False False" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cnf = (nf.groupby(['Language', 'Cell'], sort=False)['PersonOnly'].all()\n", " .to_frame('PersonOnly'))\n", "\n", "cnf.insert(0, 'd_local', cnf.index.get_level_values('Cell').map(is_distinct_local))\n", "\n", "cnf.head(14)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ratioratio (norm)
d_localFalseTrueFalseTrue
Language
Ainu0.2916670.5000000.3684210.631579
Aleut0.3333330.0000001.0000000.000000
Ayacucho0.2500000.1250000.6666670.333333
Bella CoolaNaNNaNNaNNaN
ChuckchiNaNNaNNaNNaN
Darai0.1250000.0000001.0000000.000000
Fox0.0000000.1250000.0000001.000000
Hixkaryana0.7777780.6666670.5384620.461538
Jaqaru0.6666670.0000001.0000000.000000
Jumjum0.0416670.0000001.0000000.000000
Karuk0.1250000.2500000.3333330.666667
Ket0.0833330.0000001.0000000.000000
Kunama0.0416670.0000001.0000000.000000
Lakota0.2000000.1250000.6153850.384615
Maricopa1.0000001.0000000.5000000.500000
Maung0.0166670.0000001.0000000.000000
Mordvin0.4375000.7500000.3684210.631579
Nocte0.6250000.7500000.4545450.545455
Reyesano0.2500000.5000000.3333330.666667
Sahu0.2666670.0000001.0000000.000000
Siuslawan0.0416670.0000001.0000000.000000
Tepehua0.6000000.2500000.7058820.294118
Thangmi0.1250000.0000001.0000000.000000
Turkana0.4375000.5000000.4666670.533333
Wardaman0.3333330.5000000.4000000.600000
YimasNaNNaNNaNNaN
\n", "
" ], "text/plain": [ " ratio ratio (norm) \n", "d_local False True False True \n", "Language \n", "Ainu 0.291667 0.500000 0.368421 0.631579\n", "Aleut 0.333333 0.000000 1.000000 0.000000\n", "Ayacucho 0.250000 0.125000 0.666667 0.333333\n", "Bella Coola NaN NaN NaN NaN\n", "Chuckchi NaN NaN NaN NaN\n", "Darai 0.125000 0.000000 1.000000 0.000000\n", "Fox 0.000000 0.125000 0.000000 1.000000\n", "Hixkaryana 0.777778 0.666667 0.538462 0.461538\n", "Jaqaru 0.666667 0.000000 1.000000 0.000000\n", "Jumjum 0.041667 0.000000 1.000000 0.000000\n", "Karuk 0.125000 0.250000 0.333333 0.666667\n", "Ket 0.083333 0.000000 1.000000 0.000000\n", "Kunama 0.041667 0.000000 1.000000 0.000000\n", "Lakota 0.200000 0.125000 0.615385 0.384615\n", "Maricopa 1.000000 1.000000 0.500000 0.500000\n", "Maung 0.016667 0.000000 1.000000 0.000000\n", "Mordvin 0.437500 0.750000 0.368421 0.631579\n", "Nocte 0.625000 0.750000 0.454545 0.545455\n", "Reyesano 0.250000 0.500000 0.333333 0.666667\n", "Sahu 0.266667 0.000000 1.000000 0.000000\n", "Siuslawan 0.041667 0.000000 1.000000 0.000000\n", "Tepehua 0.600000 0.250000 0.705882 0.294118\n", "Thangmi 0.125000 0.000000 1.000000 0.000000\n", "Turkana 0.437500 0.500000 0.466667 0.533333\n", "Wardaman 0.333333 0.500000 0.400000 0.600000\n", "Yimas NaN NaN NaN NaN" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xnf = (cnf.dropna(subset=['d_local'])\n", " .groupby(['Language', 'd_local']).mean()\n", " .rename(columns={'PersonOnly': 'ratio'}))\n", "\n", "xnf['ratio (norm)'] = xnf['ratio'] / xnf['ratio'].groupby(level='Language').sum()\n", "xnf.loc[xnf['ratio (norm)'].isnull(), 'ratio'] = None\n", "\n", "xnfp = xnf.reset_index('d_local')\n", "xnf = xnf.unstack()\n", "\n", "xnf" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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