{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "import os\n", "import pickle\n", "import gzip\n", "\n", "from tf.app import use\n", "from tf.fabric import Fabric" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ghBase = os.path.expanduser(\"~/github\")\n", "org = \"etcbc\"\n", "repo = \"dss\"\n", "subdir = \"parallels\"\n", "mainpath = f\"{org}/{repo}/tf\"\n", "path = f\"{org}/{repo}/{subdir}/tf\"\n", "location = f\"{ghBase}/{path}\"\n", "mainlocation = f\"{ghBase}/{mainpath}\"\n", "version = \"1.6\"\n", "module = version\n", "tempdir = f\"{ghBase}/{org}/{repo}/_temp\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 10.0.3\n", "Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html\n", "\n", "70 features found and 0 ignored\n" ] } ], "source": [ "TF = Fabric(locations=mainlocation, modules=module)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.37s Dataset without structure sections in otext:no structure functions in the T-API\n", " 3.75s All features loaded/computed - for details use TF.isLoaded()\n" ] } ], "source": [ "api = TF.load(\"lex type\")\n", "docs = api.makeAvailableIn(globals())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parallels\n", "\n", "We make edges between similar lines.\n", "\n", "When are lines similar?\n", "\n", "If a certain distance metric is above a certain threshold.\n", "\n", "We choose this metric:\n", "\n", "* we reduce a line to the set of lexemes in it.\n", "* the similarity between two lines is the length of the intersection divided by the length of the union of their sets times 100." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation\n", "\n", "We pre-compute all sets for lines.\n", "\n", "But because not all lines are filled with definite material, we exclude lines with 5 or less consonants." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.49s 37106 contentful lines out of 52895\n" ] } ], "source": [ "CONS = \"cons\"\n", "\n", "valid = set()\n", "\n", "allLines = F.otype.s(\"line\")\n", "\n", "TF.indent(reset=True)\n", "for ln in F.otype.s(\"line\"):\n", " if ln in valid:\n", " continue\n", " if sum(1 for s in L.d(ln, otype=\"sign\") if F.type.v(s) == CONS) >= 5:\n", " valid.add(ln)\n", "\n", "TF.info(f\"{len(valid)} contentful lines out of {len(allLines)}\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def makeSet(ln):\n", " lineSet = set()\n", " for s in L.d(ln, otype=\"word\"):\n", " r = F.lex.v(s)\n", " if r:\n", " lineSet.add(r)\n", " return lineSet" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.36s 37106 lines\n" ] } ], "source": [ "lines = {}\n", "\n", "TF.indent(reset=True)\n", "for ln in valid:\n", " lineSet = makeSet(ln)\n", " if lineSet:\n", " lines[ln] = lineSet\n", "\n", "nLines = len(lines)\n", "TF.info(f\"{nLines} lines\")" ] }, { "cell_type": "markdown", "metadata": { "lines_to_next_cell": 2 }, "source": [ "# Measure" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def sim(lSet, mSet):\n", " return int(round(100 * len(lSet & mSet) / len(lSet | mSet)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Compute all similarities\n", "\n", "We are going to perform more than half a billion of comparisons, each of which is more than an elementary operation.\n", "\n", "Let's measure time." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "THRESHOLD = 60\n", "\n", "\n", "def computeSim(limit=None):\n", " similarity = {}\n", "\n", " lineNodes = sorted(lines.keys())\n", " nLines = len(lineNodes)\n", "\n", " nComparisons = nLines * (nLines - 1) // 2\n", "\n", " print(f\"{nComparisons} comparisons to make\")\n", " chunkSize = nComparisons // 1000\n", "\n", " co = 0\n", " b = 0\n", " si = 0\n", " p = 0\n", "\n", " TF.indent(reset=True)\n", "\n", " stop = False\n", " for i in range(nLines):\n", " nodeI = lineNodes[i]\n", " lineI = lines[nodeI]\n", " for j in range(i + 1, nLines):\n", " nodeJ = lineNodes[j]\n", " lineJ = lines[nodeJ]\n", " s = sim(lineI, lineJ)\n", " co += 1\n", " b += 1\n", " if b == chunkSize:\n", " p += 1\n", " TF.info(f\"{p:>3}‰ - {co:>12} comparisons and {si:>10} similarities\")\n", " b = 0\n", " if limit is not None and p >= limit:\n", " stop = True\n", " break\n", "\n", " if s < THRESHOLD:\n", " continue\n", " similarity[(nodeI, nodeJ)] = sim(lineI, lineJ)\n", " si += 1\n", " if stop:\n", " break\n", "\n", " TF.info(f\"{p:>3}% - {co:>12} comparisons and {si:>10} similarities\")\n", " return similarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going to run it to several ‰ first and do some checks then." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "688409065 comparisons to make\n", " 0.75s 1‰ - 688409 comparisons and 12 similarities\n", " 1.52s 2‰ - 1376818 comparisons and 20 similarities\n", " 2.28s 3‰ - 2065227 comparisons and 28 similarities\n", " 2.28s 3% - 2065227 comparisons and 28 similarities\n" ] } ], "source": [ "similarity = computeSim(limit=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We check the sanity of the results." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "60\n", "100\n" ] } ], "source": [ "print(min(similarity.values()))\n", "print(max(similarity.values()))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "eq = [x for x in similarity.items() if x[1] >= 100]\n", "neq = [x for x in similarity.items() if x[1] <= 70]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "9\n" ] } ], "source": [ "print(len(eq))\n", "print(len(neq))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((1552980, 1563775), 100)\n", "((1552973, 1563769), 69)\n" ] } ], "source": [ "print(eq[0])\n", "print(neq[0])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "את ארצ׳ו ולדשן בטוב אדמת׳ו ׃ ויבינו בעונ׳ם וידעו כי \n", "את ארצ׳ו ולדשן בטוב אדמת׳ו ׃ ויבינו בעוונ׳מה וידעו כי \n" ] } ], "source": [ "print(T.text(eq[0][0][0]))\n", "print(T.text(eq[0][0][1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good.\n", "\n", "Now the whole computation.\n", "\n", "But if we have done this before, and nothing has changed, we load previous results from disk.\n", "\n", "If we do not find previous results, we compute them and save the results to disk." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "PARA_DIR = f\"{tempdir}/parallels\"\n", "\n", "\n", "def writeResults(data, location, name):\n", " if not os.path.exists(location):\n", " os.makedirs(location, exist_ok=True)\n", " path = f\"{location}/{name}\"\n", " with gzip.open(path, \"wb\") as f:\n", " pickle.dump(data, f)\n", " TF.info(f\"Data written to {path}\")\n", "\n", "\n", "def readResults(location, name):\n", " TF.indent(reset=True)\n", " path = f\"{location}/{name}\"\n", " if not os.path.exists(path):\n", " print(f\"File not found: {path}\")\n", " return None\n", " with gzip.open(path, \"rb\") as f:\n", " data = pickle.load(f)\n", " TF.info(f\"Data read from {path}\")\n", " return data" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "File not found: C:\\Users\\geitb/github/etcbc/dss/_temp/parallels/sim-1.6.zip\n", "688409065 comparisons to make\n", " 0.71s 1‰ - 688409 comparisons and 12 similarities\n", " 1.44s 2‰ - 1376818 comparisons and 20 similarities\n", " 2.20s 3‰ - 2065227 comparisons and 28 similarities\n", " 2.95s 4‰ - 2753636 comparisons and 34 similarities\n", " 3.70s 5‰ - 3442045 comparisons and 40 similarities\n", " 4.45s 6‰ - 4130454 comparisons and 57 similarities\n", " 5.19s 7‰ - 4818863 comparisons and 70 similarities\n", " 5.92s 8‰ - 5507272 comparisons and 81 similarities\n", " 6.66s 9‰ - 6195681 comparisons and 92 similarities\n", " 7.40s 10‰ - 6884090 comparisons and 107 similarities\n", " 8.13s 11‰ - 7572499 comparisons and 122 similarities\n", " 8.86s 12‰ - 8260908 comparisons and 135 similarities\n", " 9.59s 13‰ - 8949317 comparisons and 148 similarities\n", " 10s 14‰ - 9637726 comparisons and 159 similarities\n", " 11s 15‰ - 10326135 comparisons and 169 similarities\n", " 12s 16‰ - 11014544 comparisons and 189 similarities\n", " 13s 17‰ - 11702953 comparisons and 200 similarities\n", " 13s 18‰ - 12391362 comparisons and 240 similarities\n", " 14s 19‰ - 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663626276 comparisons and 50037 similarities\n", "11m 23s 965‰ - 664314685 comparisons and 50048 similarities\n", "11m 23s 966‰ - 665003094 comparisons and 50049 similarities\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "11m 24s 967‰ - 665691503 comparisons and 50072 similarities\n", "11m 25s 968‰ - 666379912 comparisons and 50105 similarities\n", "11m 26s 969‰ - 667068321 comparisons and 50113 similarities\n", "11m 27s 970‰ - 667756730 comparisons and 50126 similarities\n", "11m 27s 971‰ - 668445139 comparisons and 50135 similarities\n", "11m 28s 972‰ - 669133548 comparisons and 50150 similarities\n", "11m 29s 973‰ - 669821957 comparisons and 50161 similarities\n", "11m 30s 974‰ - 670510366 comparisons and 50176 similarities\n", "11m 31s 975‰ - 671198775 comparisons and 50179 similarities\n", "11m 31s 976‰ - 671887184 comparisons and 50183 similarities\n", "11m 32s 977‰ - 672575593 comparisons and 50191 similarities\n", "11m 33s 978‰ - 673264002 comparisons and 50195 similarities\n", "11m 34s 979‰ - 673952411 comparisons and 50199 similarities\n", "11m 34s 980‰ - 674640820 comparisons and 50248 similarities\n", "11m 35s 981‰ - 675329229 comparisons and 50286 similarities\n", "11m 36s 982‰ - 676017638 comparisons and 50307 similarities\n", "11m 37s 983‰ - 676706047 comparisons and 50347 similarities\n", "11m 37s 984‰ - 677394456 comparisons and 50431 similarities\n", "11m 38s 985‰ - 678082865 comparisons and 50452 similarities\n", "11m 39s 986‰ - 678771274 comparisons and 50498 similarities\n", "11m 39s 987‰ - 679459683 comparisons and 50539 similarities\n", "11m 40s 988‰ - 680148092 comparisons and 50587 similarities\n", "11m 41s 989‰ - 680836501 comparisons and 50612 similarities\n", "11m 42s 990‰ - 681524910 comparisons and 50621 similarities\n", "11m 42s 991‰ - 682213319 comparisons and 50633 similarities\n", "11m 43s 992‰ - 682901728 comparisons and 50652 similarities\n", "11m 44s 993‰ - 683590137 comparisons and 51146 similarities\n", "11m 45s 994‰ - 684278546 comparisons and 51414 similarities\n", "11m 45s 995‰ - 684966955 comparisons and 51547 similarities\n", "11m 46s 996‰ - 685655364 comparisons and 51673 similarities\n", "11m 47s 997‰ - 686343773 comparisons and 51707 similarities\n", "11m 47s 998‰ - 687032182 comparisons and 51768 similarities\n", "11m 48s 999‰ - 687720591 comparisons and 51831 similarities\n", "11m 49s 1000‰ - 688409000 comparisons and 51862 similarities\n", "11m 49s 1000% - 688409065 comparisons and 51862 similarities\n", "11m 49s Data written to ~/github/etcbc/dss/_temp/parallels/sim-1.6.zip\n" ] } ], "source": [ "similarity = readResults(PARA_DIR, f\"sim-{version}.zip\")\n", "if not similarity:\n", " similarity = computeSim()\n", " writeResults(similarity, PARA_DIR, f\"sim-{version}.zip\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51862" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(similarity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, just over 50,000 pairs of similar lines." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Add parallels to the TF dataset\n", "\n", "We can add this information to the DSS dataset as an *edge feature*.\n", "\n", "An edge feature links two nodes and may annotate that link with a value.\n", "\n", "For parallels, we link each line to each of its parallel lines and we annotate that link with the similarity between\n", "the two lines. The similarity is a percentage, and we round it to integer values.\n", "\n", "If `n1` is similar to `n2`, then `n2` is similar to `n1`.\n", "In order to save space, we only add such links once.\n", "\n", "We can then use\n", "[`E.sim.b(node)`](https://annotation.github.io/text-fabric/tf/core/edgefeature.html#tf.core.edgefeature)\n", "to find all nodes that are parallel to node.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "metaData = {\n", " \"\": {\n", " \"acronym\": \"dss\",\n", " \"description\": \"parallel lines in the DSS (computed)\",\n", " \"createdBy\": \"Dirk Roorda\",\n", " \"createdDate\": \"2022-09-29\",\n", " \"sourceCreatedDate\": \"2015\",\n", " \"sourceCreatedBy\": \"Martin G. Abegg, Jr., James E. Bowley, and Edward M. Cook\",\n", " \"convertedBy\": \"Jarod Jacobs, Martijn Naaijer and Dirk Roorda\",\n", " \"source\": \"Martin Abegg's data files, personal communication\",\n", " \"license\": \"Creative Commons Attribution-NonCommercial 4.0 International License\",\n", " \"licenseUrl\": \"http://creativecommons.org/licenses/by-nc/4.0/\",\n", " \"sourceDescription\": \"Dead Sea Scrolls: biblical and non-biblical scrolls\",\n", " },\n", " \"sim\": {\n", " \"valueType\": \"int\",\n", " \"edgeValues\": True,\n", " \"description\": \"similarity between lines, as a percentage of the common material wrt the combined material\",\n", " },\n", "}" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "simData = {}\n", "\n", "for ((f, t), d) in similarity.items():\n", " simData.setdefault(f, {})[t] = d" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.00s Exporting 0 node and 1 edge and 0 config features to ~/github/etcbc/dss/tf/1.6:\n", " | 0.06s T sim to ~/github/etcbc/dss/tf/1.6\n", " 0.06s Exported 0 node features and 1 edge features and 0 config features to ~/github/etcbc/dss/tf/1.6\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "TF.save(\n", " edgeFeatures=dict(sim=simData), metaData=metaData, module=module\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Turn the parallels feature into a module\n", "\n", "Here we show how to turn the new feature `sim` into a module, so that users can easily load it in a Jupyter notebook or in the TF browser." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a TF dataset\n", "Create release data for etcbc/dss/parallels/tf\n", "Found 5 versions\n", "zip files end up in ~/Downloads/None/etcbc-release/dss\n", "zipping etcbc/dss 1.4 with 1 features ==> parallels-tf-1.4.zip\n", "zipping etcbc/dss 1.5 with 1 features ==> parallels-tf-1.5.zip\n", "zipping etcbc/dss 1.6 with 1 features ==> parallels-tf-1.6.zip\n", "zipping etcbc/dss 1.7 with 1 features ==> parallels-tf-1.7.zip\n", "zipping etcbc/dss 1.7.1 with 1 features ==> parallels-tf-1.7.1.zip\n" ] } ], "source": [ "#%%bash\n", "!text-fabric-zip etcbc/dss/parallels/tf" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is a TF dataset\n", "Create release data for etcbc/dss/tf\n", "Found 3 versions\n", "zip files end up in ~/Downloads/None/etcbc-release/dss\n", "zipping etcbc/dss 1.6 with 70 features ==> tf-1.6.zip\n", "zipping etcbc/dss 1.7 with 70 features ==> tf-1.7.zip\n", "zipping etcbc/dss 1.7.1 with 70 features ==> tf-1.7.1.zip\n" ] } ], "source": [ "!text-fabric-zip etcbc/dss/tf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I have added this file to a new release of the DSS GitHub repo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use the parallels module\n", "\n", "We load the DSS corpus again, but now with the parallels module." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The requested TF-app is not available offline\n", "\t~/github/annotation/app-dss/code not found\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "This is Text-Fabric 10.0.3\n", "Api reference : https://annotation.github.io/text-fabric/tf/cheatsheet.html\n", "\n", "0 features found and 0 ignored\n", " 0.00s Not all of the warp features otype and oslots are present in\n", "None/dss/\n", " 0.00s Only the Feature and Edge APIs will be enabled\n", " 0.00s Warp feature \"otext\" not found. Working without Text-API\n", "\n" ] }, { "data": { "text/html": [ "Text-Fabric: Text-Fabric API 10.0.3, no app configured
Data: None/dss/
Features:
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
Text-Fabric API: names N F E L T S C TF directly usable
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = use(\"ETCBC/dss:clone\", checkout=\"clone\", hoist=globals())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lo and behold: you see the parallels module listed with one feature: `sim`. It is in *italics*, which indicates\n", "it is an edge feature.\n", "\n", "We just do a quick check here and in another notebook we study parallels a bit more, using the feature `sim`.\n", "\n", "We count how many similar pairs their are, and how many 100% similar pairs there are." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.25s 51862 results\n" ] }, { "data": { "text/plain": [ "1565737" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"\"\"\n", "line\n", "-sim> line\n", "\"\"\"\n", "results = A.search(query)\n", "refNode = results[20000][0]\n", "refNode" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.14s 3646 results\n" ] } ], "source": [ "query = \"\"\"\n", "line\n", "-sim=100> line\n", "\"\"\"\n", "results = A.search(query)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's show a few of the pairs are 100 percent similar." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
nplineline
1 4Q268 f1:15את ארצ׳ו ולדשן בטוב אדמת׳ו ׃ ויבינו בעונ׳ם וידעו כי 1552980 את ארצ׳ו ולדשן בטוב אדמת׳ו ׃ ויבינו בעוונ׳מה וידעו כי 1563775
2 4Q267 f2:4ובקץ חרבן הארץ עמדו מסיגי הגבול ויתעו את ישראל ׃ 1553076 ובקץ חורבן הארץ עמדו מסיגי גבול ויתעו את ישראל 1563610
3 4Q266 f3iii:19אשר בזה ישראל את דברי׳הם ׃ והכוכב הוא דורש התורה 1553116 אשר בזה ישראל את דברי׳הם ׃ והכוכב הוא דורש התורה 1563157
4 4Q266 f9i:3אל יעל׳ה איש בסולם וחבל וכלי ׃ אל יעל איש למזבח בשבת 1553203 בסולם וחבל וכלי ׃ אל יעל איש למזבח בשבת 1563345
5 4Q464b f1:1ε אמר ל׳הם 1553296 ε לאמר ל ε ׃ 1575440
6 PAM43663 f43:1ε אמר ל׳הם 1553296 ε # אמר ל׳י ε 1588433
7 1Q4 f18:1ε אמר ל׳הם 1553296 ε לאמור ε ׃ 1589783
8 4Q264 f1:10חמר קורצ ולעפר תשוקת׳ו ׃ מה ישיב חמר ויוצר יד ולעצת מה יבין ׃ 1553669 קורץ ולעפר תשוקת׳ו ׃ מה ישיב חמר ויוצר יד לעצת מה יבין ׃ 1562924
9 4Q286 f11:3ε קודש׳ו # ε ׃ 1553730 ε ת קודש ε ׃ 1564740
10 4Q401 f31:3ε קודש׳ו # ε ׃ 1553730 ε י קודש ε ׃ 1571118
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A.table(results, start=1, end=10, withNodes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is also a lower level way to work with edge features.\n", "\n", "We can list all edges going out from a reference node.\n", "What we see is tuple of pairs: the target node and the similarity between the reference node and that target node." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1565738, 100),\n", " (1565739, 100),\n", " (1565740, 100),\n", " (1565741, 100),\n", " (1565742, 100),\n", " (1565744, 67),\n", " (1565745, 62),\n", " (1565781, 60),\n", " (1565782, 60),\n", " (1565783, 60),\n", " (1565791, 67),\n", " (1565792, 67),\n", " (1565793, 67),\n", " (1565794, 60),\n", " (1565796, 60),\n", " (1565808, 60),\n", " (1565809, 71),\n", " (1565811, 71),\n", " (1565813, 71),\n", " (1565815, 71),\n", " (1565819, 67),\n", " (1565847, 67),\n", " (1565848, 67),\n", " (1565854, 75),\n", " (1565855, 75),\n", " (1565856, 67),\n", " (1565857, 67),\n", " (1565858, 67),\n", " (1565859, 67),\n", " (1565887, 71),\n", " (1565900, 71),\n", " (1565901, 71),\n", " (1565902, 71),\n", " (1565903, 71),\n", " (1565905, 71),\n", " (1565906, 71),\n", " (1565907, 71),\n", " (1565974, 100),\n", " (1565976, 67),\n", " (1565980, 67),\n", " (1565982, 67),\n", " (1566001, 67),\n", " (1566018, 71),\n", " (1566032, 71),\n", " (1573353, 67))" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E.sim.f(refNode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Likewise, we can observe the nodes that target the reference node:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1565730, 100),\n", " (1565731, 100),\n", " (1565732, 100),\n", " (1565733, 100),\n", " (1565734, 100),\n", " (1565735, 100),\n", " (1565736, 100))" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E.sim.t(refNode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both sets of nodes are similar to the reference node and it is inconvenient to use both `.f()` and `.t()` to get the similar lines.\n", "\n", "But there is another way:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((1565730, 100),\n", " (1565731, 100),\n", " (1565732, 100),\n", " (1565733, 100),\n", " (1565734, 100),\n", " (1565735, 100),\n", " (1565736, 100),\n", " (1565738, 100),\n", " (1565739, 100),\n", " (1565740, 100),\n", " (1565741, 100),\n", " (1565742, 100),\n", " (1565744, 67),\n", " (1565745, 62),\n", " (1565781, 60),\n", " (1565782, 60),\n", " (1565783, 60),\n", " (1565791, 67),\n", " (1565792, 67),\n", " (1565793, 67),\n", " (1565794, 60),\n", " (1565796, 60),\n", " (1565808, 60),\n", " (1565809, 71),\n", " (1565811, 71),\n", " (1565813, 71),\n", " (1565815, 71),\n", " (1565819, 67),\n", " (1565847, 67),\n", " (1565848, 67),\n", " (1565854, 75),\n", " (1565855, 75),\n", " (1565856, 67),\n", " (1565857, 67),\n", " (1565858, 67),\n", " (1565859, 67),\n", " (1565887, 71),\n", " (1565900, 71),\n", " (1565901, 71),\n", " (1565902, 71),\n", " (1565903, 71),\n", " (1565905, 71),\n", " (1565906, 71),\n", " (1565907, 71),\n", " (1565974, 100),\n", " (1565976, 67),\n", " (1565980, 67),\n", " (1565982, 67),\n", " (1566001, 67),\n", " (1566018, 71),\n", " (1566032, 71),\n", " (1573353, 67))" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "E.sim.b(refNode)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make sure that `.b()` gives the combination of `.f()` and `.t()`." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the intersection of f and t is set()\n", "t | f = b ? True\n" ] } ], "source": [ "f = {x[0] for x in E.sim.f(refNode)}\n", "b = {x[0] for x in E.sim.b(refNode)}\n", "t = {x[0] for x in E.sim.t(refNode)}\n", "\n", "# are f and t disjoint ?\n", "\n", "print(f\"the intersection of f and t is {f & t}\")\n", "\n", "# is b the union of f and t ?\n", "\n", "print(f\"t | f = b ? {f | t == b}\")" ] } ], "metadata": { "jupytext": { "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }