{ "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 functools\n", "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", "\n", "def sha256sum(filename, bufsize=32768):\n", " s = hashlib.sha256()\n", " with io.open(filename, 'rb') as fd:\n", " for data in iter(functools.partial(fd.read, bufsize), b''):\n", " s.update(data)\n", " return s.hexdigest()\n", "\n", "\n", "if not CSV.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 i.filename.endswith(CSV.name))\n", " i.filename = CSV.name\n", " z.extract(i)\n", "\n", "sha256sum(CSV)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import scipy.stats\n", "\n", "\n", "def pearsonr(df, left, right, func=scipy.stats.pearsonr):\n", " df = df[[left, right]].dropna()\n", " name = f'{left} & {right}'\n", " with np.errstate(invalid='ignore'):\n", " result = func(df[left], df[right])\n", " return pd.Series(result, index=('r', 'p'), name=name)\n", "\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]
\n", "
" ], "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', 'Tlachichilco Tepehuan': 'Tepehua', 'Lakhota': 'Lakota'}\n", "\n", "\n", "cf = pd.read_csv(CSV, encoding='utf-8')\n", "\n", "cf['Language'] = cf['Language'].replace(RENAME)\n", "cf = cf.sort_values(by='Language', kind='mergesort').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
1s->3pkuΣ
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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", "\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
1s->2pTrueeciΣ
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1s->3pFalsekuΣ
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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", "\n", "def is_distinct_local(cellkey, sep=SEP, persons=('1', '2')):\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", "\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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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(xf.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": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(xf['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 neutralization differences" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " r p\n", "d_local & ratio -0.043886 0.757379\n", "d_local & ratio (norm) -0.159971 0.257281" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame([pearsonr(xfp, 'd_local', c) for c in ['ratio', 'ratio (norm)']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Count 1/2 subcategory (number, gender) neutralizations" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['1s' '1p' '2s' '2p' '3s' '3p' 'x' '1d' '2d' '3d' '1pe' '1pi' '1' '12' '2'\n", " '3' '1di' '3s.m' '3s.f' '3s.n' '3p.m' '3p.f' '3p.n' '1de' '3.I' '3.III'\n", " '3.II' '3.IV' '3.V' '3.VI']\n" ] }, { "data": { "text/html": [ "
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d_localsizenuniqueneutratio
LanguagegroupX
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xFalse2200.0
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2->X:P3pFalse2200.0
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xFalse2200.0
\n", "
" ], "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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" ] }, "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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" ], "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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