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9
cw/04_wyszukiwarki.ipynb
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@ -44,12 +44,12 @@
"source": [
"# Zadanie domowe (maksymalnie 160 punktów)\n",
"\n",
"Proszę stworzyć wyszukiwarkę dla wybranej kolekcji dokumentów (innej niż przykładowe w solr).\n",
"Proszę stworzyć wyszukiwarkę dla wybranej kolekcji dokumentów (innej niż przykładowe w Solr).\n",
"\n",
"## Warunki konieczne do zaliczenia zadania\n",
" \n",
" * użycie gotowego silnika wuszukiwarki (np. Solr lub Elastisearch)\n",
" * zaindeksowanie conajmniej 40 tys. dokumentów\n",
" * użycie gotowego silnika wyszukiwarki (np. Solr lub Elasticsearch)\n",
" * zaindeksowanie co najmniej 40 tys. dokumentów\n",
" * zaimplementowanie frontendu w postaci aplikacji webowej. Użytkownik nie ma korzystać z panelu admina. Aplikacja webowa może być napisana w dowolnym języku/frameworku.\n",
" * dokumenty powinny stanowić sensowną, rzeczywistą kolekcję. Proszę nie losować dokumentów, powielać, itp.\n",
" \n",
@ -63,10 +63,11 @@
" * zindeksowanie > 0,5 mln dokumentów: +20 punktów, > 5 mln - +40 punktów\n",
" * wizualizacja (wykres, mapa): +20 punktów\n",
" * użycie ciekawej funkcji niewymienionej wyżej: +20 punktów\n",
" * za oddanie do 21.04 - +10 punktów\n",
" * maksimum do zdobycia z tego zadania: 160 punktów\n",
" \n",
"## Zaliczenie\n",
" * termin zaliczenia to 20.04 (na zajęciach)\n",
" * termin zaliczenia to 21.04 lub 28.04 (na zajęciach)\n",
" * proszę zaznaczyć w MS TEAMS, że Państwo zrobili zadanie w assigments\n",
" * zdawanie zadania będzie na zajęciach. Proszę przygotować prezentację do 5 minut"
]

495
wyk/05_Geste_wektory.ipynb
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@ -10,11 +10,13 @@
"\n",
"## _Hashing trick_\n",
"\n",
"Powierzchownie problem możemy rozwiązać przez użycie tzw. _sztuczki z haszowaniem_ (_hashing trick_). Będziemy potrzebować funkcji mieszającej (haszującej) $H$, która rzutuje na napisy na liczby, których reprezentacja binarna składa się z $b$ bitów:\n",
"Powierzchownie problem możemy rozwiązać przez użycie tzw. _sztuczki z haszowaniem_ (_hashing trick_). Będziemy potrzebować funkcji mieszającej (haszującej) $H$, która rzutuje napisy na liczby, których reprezentacja binarna składa się z $b$ bitów:\n",
"\n",
"$$H : V \\rightarrow \\{0,\\dots,2^b-1\\}$$\n",
"$$H : \\Sigma^{*} \\rightarrow \\{0,\\dots,2^b-1\\}$$\n",
"\n",
"($\\Sigma^{*}$ to zbiór wszystkich napisów.)\n",
"\n",
"**Pytanie:** Czy funkcja $H$ może być różnowartościowa?\n",
"\n"
]
},
@ -22,14 +24,32 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Jako funkcji $H$ możemy np. użyć funkcji MurmurHash3."
"Jako funkcji $H$ możemy np. użyć funkcji MurmurHash2 lub 3."
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Hash64 0x4a80abc136f926e7"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Hash64 0x6c3a641663470e2c"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
@ -65,15 +85,37 @@
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Hash64 0xb04ce6229407c882"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Hash64 0x6ecd7bae29ae0450"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import Data.Digest.Murmur64\n",
"\n",
"hash64 \"Komputer\"\n",
"hash64 \"komputer\"\n",
"hash64 \"komputer\"\n",
"hash64 \"komputerze\"\n",
"hash64 \"komputerek\"\n",
"hash64 \"abrakadabra\"\n"
"hash64 \"abrakadabra\"\n",
"hash64 \"\"\n",
"hash64 \" \"\n"
]
},
{
@ -514,18 +556,42 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Stwórzmy najpierw funkcję, która będzie wektoryzowała pojedynczy term $t$. Po prostu stworzymy wektor, które będzie miał rozmiar $2^b$, wszędzie będzie miał 0 z wyjątkiem pozycji o numerze $H_b(t)$ - tam wpiszmy odwrotną częstość dokumentową."
"Stwórzmy najpierw funkcję, która będzie wektoryzowała pojedynczy term $t$. Po prostu stworzymy wektor, które będzie miał rozmiar $2^b$, wszędzie będzie miał 0 z wyjątkiem pozycji o numerze $H_b(t)$ - tam wpiszmy odwrotną częstość dokumentową.\n",
"\n",
"$$\\vec{t} = [0,\\dots,\\idf_c t,\\dots,0]$$\n",
"\n",
"Teraz dla dokumentu $d = (t_1,\\dots,t_n)$ i dla schematu ważenia tf-idf:\n",
"\n",
"$$\\vec{d} = \\sum \\vec{t_i}$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
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"[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3.7727609380946383,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]"
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@ -541,7 +607,9 @@
" | i == wordFingerprint = idf coll term\n",
" | otherwise = 0.0\n",
"\n",
"wordVector 6 collectionLNormalized \"ameryk\""
"wordVector 6 collectionLNormalized \"aromat\"\n",
"wordVector 6 collectionLNormalized \"albo\"\n",
"wordVector 6 collectionLNormalized \"akcja\""
]
},
{
@ -553,13 +621,13 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1.2,4.0]"
"[1.2,4.0,3.0]"
]
},
"metadata": {},
@ -570,7 +638,7 @@
"(+++) :: [Double] -> [Double] -> [Double]\n",
"(+++) = Prelude.zipWith (+)\n",
"\n",
"[0.2, 0.5] +++ [1.0, 3.5]"
"[0.2, 0.5, 1.0] +++ [1.0, 3.5, 2.0]"
]
},
{
@ -582,7 +650,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 33,
"metadata": {},
"outputs": [
{
@ -604,9 +672,110 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/* Styles used for the Hoogle display in the pager */\n",
".hoogle-doc {\n",
"display: block;\n",
"padding-bottom: 1.3em;\n",
"padding-left: 0.4em;\n",
"}\n",
".hoogle-code {\n",
"display: block;\n",
"font-family: monospace;\n",
"white-space: pre;\n",
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".hoogle-text {\n",
"display: block;\n",
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".hoogle-name {\n",
"color: green;\n",
"font-weight: bold;\n",
"}\n",
".hoogle-head {\n",
"font-weight: bold;\n",
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".hoogle-sub {\n",
"display: block;\n",
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"}\n",
".mono {\n",
"font-family: monospace;\n",
"display: block;\n",
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".err-msg {\n",
"color: red;\n",
"font-style: italic;\n",
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"#unshowable {\n",
"color: red;\n",
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".err-msg.in.collapse {\n",
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".suggestion-warning { \n",
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".suggestion-error { \n",
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".suggestion-name {\n",
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"</style><div class=\"suggestion-name\" style=\"clear:both;\">Eta reduce</div><div class=\"suggestion-row\" style=\"float: left;\"><div class=\"suggestion-warning\">Found:</div><div class=\"highlight-code\" id=\"haskell\">vectorizeWithHashingTrick b coll doc\n",
" = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2 ^ b) doc</div></div><div class=\"suggestion-row\" style=\"float: left;\"><div class=\"suggestion-warning\">Why Not:</div><div class=\"highlight-code\" id=\"haskell\">vectorizeWithHashingTrick b coll\n",
" = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2 ^ b)</div></div>"
],
"text/plain": [
"Line 3: Eta reduce\n",
"Found:\n",
"vectorizeWithHashingTrick b coll doc\n",
" = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2 ^ b) doc\n",
"Why not:\n",
"vectorizeWithHashingTrick b coll\n",
" = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2 ^ b)"
]
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"source": [
"\n",
"vectorizeWithHashingTrick :: Integer -> [[Text]] -> [Text] -> [Double]\n",
"vectorizeWithHashingTrick b coll = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2^b)\n",
"vectorizeWithHashingTrick b coll doc = Prelude.foldr ((+++) . wordVector b coll) (zero $ 2^b) doc\n",
"\n",
"vectorizeWithHashingTrick 6 collectionLNormalized $ collectionLNormalized !! 3\n"
"vectorizeWithHashingTrick 6 collectionLNormalized $ collectionLNormalized !! 3\n",
"vectorizeWithHashingTrick 6 collectionLNormalized [\"aromat\", \"albo\", \"akcja\"]\n",
"vectorizeWithHashingTrick 6 collectionLNormalized [\"akcja\", \"aromat\", \"albo\"]\n",
"vectorizeWithHashingTrick 6 collectionLNormalized [\"akcja\", \"aromat\", \"albo\", \"albo\"]\n",
"vectorizeWithHashingTrick 6 collectionLNormalized [\"akcja\", \"aromat\", \"09\"]\n"
]
},
{
@ -634,26 +843,26 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" na_ak w_lud ba_hy w_lap ne_dz be_wy zw_oz mo_zu be_wy ba_hy mo_zu be_wy w_lud\n",
"na_ak 1.00 0.66 0.40 0.58 0.65 0.52 0.66 0.79 0.76 0.41 0.59 0.44 0.72\n",
"w_lud 0.66 1.00 0.51 0.53 0.66 0.43 0.57 0.76 0.68 0.38 0.47 0.42 0.62\n",
"ba_hy 0.40 0.51 1.00 0.42 0.55 0.29 0.41 0.54 0.58 0.54 0.47 0.24 0.50\n",
"w_lap 0.58 0.53 0.42 1.00 0.41 0.35 0.54 0.59 0.53 0.19 0.47 0.34 0.53\n",
"ne_dz 0.65 0.66 0.55 0.41 1.00 0.56 0.56 0.79 0.74 0.55 0.68 0.57 0.69\n",
"be_wy 0.52 0.43 0.29 0.35 0.56 1.00 0.51 0.54 0.64 0.28 0.59 0.61 0.49\n",
"zw_oz 0.66 0.57 0.41 0.54 0.56 0.51 1.00 0.72 0.61 0.29 0.55 0.48 0.63\n",
"mo_zu 0.79 0.76 0.54 0.59 0.79 0.54 0.72 1.00 0.79 0.49 0.73 0.58 0.79\n",
"be_wy 0.76 0.68 0.58 0.53 0.74 0.64 0.61 0.79 1.00 0.49 0.72 0.61 0.74\n",
"ba_hy 0.41 0.38 0.54 0.19 0.55 0.28 0.29 0.49 0.49 1.00 0.37 0.32 0.48\n",
"mo_zu 0.59 0.47 0.47 0.47 0.68 0.59 0.55 0.73 0.72 0.37 1.00 0.53 0.71\n",
"be_wy 0.44 0.42 0.24 0.34 0.57 0.61 0.48 0.58 0.61 0.32 0.53 1.00 0.54\n",
"w_lud 0.72 0.62 0.50 0.53 0.69 0.49 0.63 0.79 0.74 0.48 0.71 0.54 1.00"
"na_ak 1.00 0.37 0.21 0.28 0.35 0.22 0.32 0.45 0.47 0.21 0.25 0.20 0.39\n",
"w_lud 0.37 1.00 0.28 0.18 0.38 0.15 0.20 0.35 0.36 0.14 0.17 0.19 0.33\n",
"ba_hy 0.21 0.28 1.00 0.08 0.20 0.18 0.24 0.29 0.30 0.27 0.17 0.15 0.24\n",
"w_lap 0.28 0.18 0.08 1.00 0.10 0.11 0.11 0.30 0.17 0.06 0.07 0.13 0.21\n",
"ne_dz 0.35 0.38 0.20 0.10 1.00 0.32 0.30 0.52 0.44 0.27 0.36 0.26 0.41\n",
"be_wy 0.22 0.15 0.18 0.11 0.32 1.00 0.26 0.26 0.39 0.15 0.23 0.43 0.22\n",
"zw_oz 0.32 0.20 0.24 0.11 0.30 0.26 1.00 0.38 0.36 0.06 0.18 0.20 0.29\n",
"mo_zu 0.45 0.35 0.29 0.30 0.52 0.26 0.38 1.00 0.54 0.23 0.39 0.38 0.51\n",
"be_wy 0.47 0.36 0.30 0.17 0.44 0.39 0.36 0.54 1.00 0.26 0.37 0.42 0.48\n",
"ba_hy 0.21 0.14 0.27 0.06 0.27 0.15 0.06 0.23 0.26 1.00 0.24 0.10 0.27\n",
"mo_zu 0.25 0.17 0.17 0.07 0.36 0.23 0.18 0.39 0.37 0.24 1.00 0.20 0.34\n",
"be_wy 0.20 0.19 0.15 0.13 0.26 0.43 0.20 0.38 0.42 0.10 0.20 1.00 0.29\n",
"w_lud 0.39 0.33 0.24 0.21 0.41 0.22 0.29 0.51 0.48 0.27 0.34 0.29 1.00"
]
},
"metadata": {},
@ -661,7 +870,7 @@
}
],
"source": [
"lVectorized' = map (vectorizeWithHashingTrick 6 collectionLNormalized) collectionLNormalized\n",
"lVectorized' = map (vectorizeWithHashingTrick 8 collectionLNormalized) collectionLNormalized\n",
"limitedL' = Data.List.take limit lVectorized'\n",
"\n",
"paintMatrix cosineSim labelsLimited limitedL'"
@ -1071,7 +1280,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 16,
"metadata": {},
"outputs": [
{
@ -1117,7 +1326,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 17,
"metadata": {},
"outputs": [
{
@ -1136,7 +1345,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 18,
"metadata": {},
"outputs": [
{
@ -1168,6 +1377,224 @@
"paintMatrix cosineSim labelsLimited limitedL''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Możemy próbować mnożyć wektory z modelu Word2vec z idf. Najpierw zdefiniujmy mnożenie przez skalar."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2.5,0.0,5.0]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(***) :: Double -> [Double] -> [Double]\n",
"(***) s = map (*s)\n",
"\n",
"2.5 *** [1.0, 0.0, 2.0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Teraz będziemy przemnażali wektory Word2vec przez idf (jako skalar)."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>/* Styles used for the Hoogle display in the pager */\n",
".hoogle-doc {\n",
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"</style><div class=\"suggestion-name\" style=\"clear:both;\">Fuse foldr/map</div><div class=\"suggestion-row\" style=\"float: left;\"><div class=\"suggestion-warning\">Found:</div><div class=\"highlight-code\" id=\"haskell\">Prelude.foldr (+++) (zero 100)\n",
" $ map (\\ (t, Just v) -> idf coll t *** toOurVector v)\n",
" $ Prelude.filter (\\ (_, v) -> isJust v)\n",
" $ map (\\ t -> (t, getVector model t)) d</div></div><div class=\"suggestion-row\" style=\"float: left;\"><div class=\"suggestion-warning\">Why Not:</div><div class=\"highlight-code\" id=\"haskell\">foldr\n",
" ((+++) . (\\ (t, Just v) -> idf coll t *** toOurVector v))\n",
" (zero 100)\n",
" (Prelude.filter (\\ (_, v) -> isJust v)\n",
" $ map (\\ t -> (t, getVector model t)) d)</div></div>"
],
"text/plain": [
"Line 4: Fuse foldr/map\n",
"Found:\n",
"Prelude.foldr (+++) (zero 100)\n",
" $ map (\\ (t, Just v) -> idf coll t *** toOurVector v)\n",
" $ Prelude.filter (\\ (_, v) -> isJust v)\n",
" $ map (\\ t -> (t, getVector model t)) d\n",
"Why not:\n",
"foldr\n",
" ((+++) . (\\ (t, Just v) -> idf coll t *** toOurVector v))\n",
" (zero 100)\n",
" (Prelude.filter (\\ (_, v) -> isJust v)\n",
" $ map (\\ t -> (t, getVector model t)) d)"
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"import Data.Maybe (isJust)\n",
"\n",
"vectorizeWord2vecIdf model coll d = \n",
" Prelude.foldr (+++) (zero 100) \n",
" $ map (\\(t, Just v) -> idf coll t *** toOurVector v) \n",
" $ Prelude.filter (\\(_, v) -> isJust v)\n",
" $ map (\\t -> (t, getVector model t)) d\n",
"\n",
"collectionLVectorized''' = map (vectorizeWord2vecIdf model collectionLNormalized') collectionLNormalized'"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[-35.63830397762308,32.606312678971506,102.20663646169147,56.00417395285867,-130.56709475346878,-14.916644370325773,55.15817632053957,83.2241937686228,26.432875116296394,48.94350344147367,11.370669191277202,-59.54579267200742,-116.01687192456801,60.53824040579282,39.84659684249884,-34.37377085402866,104.53525319069323,45.53363024094972,-34.25020197907558,-43.9007702604392,35.36538495508536,-59.81737728971619,-1.5823889595648828,-50.211106838043655,14.83789867297237,-109.45917608219175,86.56767915592452,-32.170794763065615,29.559930839016644,-126.81686726526162,-9.918908360030228,47.14965938694648,5.955083439147183,41.24417782948478,3.592410260515919,72.10649687523313,61.374776273461855,60.28687760276824,-28.886499026001676,-8.710633131022206,-68.73464623080284,-37.95272838994007,-26.390548039392165,-14.241950251566944,74.6286124718925,46.21889022510431,72.23999508751568,-19.597547074284556,-20.160749174807382,99.49036127458763,131.98057386978817,-23.842794956628147,-62.381675411749846,-19.366936151725387,1.4839595614144327,60.40520721416763,-7.70311857607342,-31.75784386529525,48.71818084466781,-202.41827342135582,138.5639100010709,12.447619757719652,-39.38375639132277,27.877688543771935,-87.00559882214534,56.45689362090545,37.89098984507379,103.78465196444151,-166.10094891357176,-50.83382060940457,11.574060187412977,74.00519869734406,-97.00170731343235,32.18159534728971,-11.280059681646494,-40.701643971890256,74.64230137346699,0.7613112917269982,-6.103424218278271,-150.47551072570587,-21.714627635239918,91.26690441786137,62.91576955719526,-92.35700140312395,-25.421583980267307,-67.87480813505826,-120.16245846953592,-68.89155479679258,-122.00206448376261,35.263603445401785,6.416282520155956,203.41225708856086,-62.42983953251155,59.36113672119048,40.00275897200196,-62.55633545667429,89.66866371308245,-42.287712072353834,-72.59490110281287,52.23637641217955]"
]
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"metadata": {},
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],
"source": [
"collectionLVectorized''' !! 3"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" na_ak w_lud ba_hy w_lap ne_dz be_wy zw_oz mo_zu be_wy ba_hy mo_zu be_wy w_lud\n",
"na_ak 1.00 0.83 0.78 0.63 0.78 0.81 0.83 0.76 0.77 0.80 0.77 0.79 0.79\n",
"w_lud 0.83 1.00 0.82 0.60 0.84 0.84 0.84 0.85 0.86 0.74 0.86 0.83 0.90\n",
"ba_hy 0.78 0.82 1.00 0.57 0.78 0.84 0.77 0.79 0.90 0.75 0.74 0.89 0.85\n",
"w_lap 0.63 0.60 0.57 1.00 0.38 0.60 0.50 0.43 0.52 0.45 0.55 0.65 0.47\n",
"ne_dz 0.78 0.84 0.78 0.38 1.00 0.81 0.79 0.90 0.89 0.77 0.81 0.81 0.90\n",
"be_wy 0.81 0.84 0.84 0.60 0.81 1.00 0.82 0.76 0.83 0.74 0.81 0.92 0.88\n",
"zw_oz 0.83 0.84 0.77 0.50 0.79 0.82 1.00 0.77 0.77 0.74 0.82 0.75 0.83\n",
"mo_zu 0.76 0.85 0.79 0.43 0.90 0.76 0.77 1.00 0.93 0.74 0.87 0.80 0.90\n",
"be_wy 0.77 0.86 0.90 0.52 0.89 0.83 0.77 0.93 1.00 0.72 0.81 0.89 0.92\n",
"ba_hy 0.80 0.74 0.75 0.45 0.77 0.74 0.74 0.74 0.72 1.00 0.66 0.73 0.72\n",
"mo_zu 0.77 0.86 0.74 0.55 0.81 0.81 0.82 0.87 0.81 0.66 1.00 0.80 0.88\n",
"be_wy 0.79 0.83 0.89 0.65 0.81 0.92 0.75 0.80 0.89 0.73 0.80 1.00 0.87\n",
"w_lud 0.79 0.90 0.85 0.47 0.90 0.88 0.83 0.90 0.92 0.72 0.88 0.87 1.00"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"limitedL''' = Data.List.take limit collectionLVectorized'''\n",
"\n",
"paintMatrix cosineSim labelsLimited limitedL'''"
]
},
{
"cell_type": "code",
"execution_count": null,