{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "kmQoy8B64bpZ" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "f5lduCKj4bpi" }, "source": [ "# Intro to LifeCycles\n", "\n", "``LifeCycles`` is a python library designed track and analyze the longitudinal evolution of sets (e.g., data clusters, graph communities...)\n", "\n", "In this notebook are introduced some of the main features of the library and an overview of its functionalities.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "wxK7cEmr4bpj" }, "source": [] }, { "cell_type": "markdown", "metadata": { "id": "hM7cC4hC4bpl" }, "source": [ "## 1. Installing LifeCycles" ] }, { "cell_type": "markdown", "metadata": { "id": "2Np6XG4L4bpn" }, "source": [ "As a first step, we need to make sure that ``LifeCycles`` is installed and working.\n", "\n", "The library is available for python 3.9, and its stable version can be installed using ``pip``:" ] }, { "cell_type": "markdown", "metadata": { "id": "ASHO-3yU4bpp" }, "source": [ "!pip install lifecycles" ] }, { "cell_type": "markdown", "metadata": { "id": "r58mJg414bpr" }, "source": [ "In order to check if ``LifeCycles`` has been correctly installed just try to import it" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "Oj_-Xhh_4bps", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:19.886203Z", "start_time": "2024-10-24T10:54:19.879975Z" } }, "outputs": [], "source": [ "import lifecycles as lcs" ] }, { "cell_type": "markdown", "metadata": { "id": "w-EoeS7r4bpu" }, "source": [ "## 2. Creating LifeCycles\n", "\n", "``LifeCycles`` allows to represent and analyze the evolution of clusters in time.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "KpUSn8jg4bpv" }, "source": [ "As a first step we need to instantiate the ``LifeCycle`` object. The object takes the datatype of the clusters' members as an input.\n", "\n", "The default data type is ``int``, but others like ``str``, ``list`` and ``dict`` are also available." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "ZCecgAep4bpw", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:24.773859Z", "start_time": "2024-10-24T10:54:24.769342Z" } }, "outputs": [], "source": [ "lc = lcs.LifeCycle(int)" ] }, { "cell_type": "markdown", "metadata": { "id": "MAcV1ChP4bpz" }, "source": [ "Then, we must add temporally-ordered data partitions. We will generate some random data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "tkWDOaFb4bp0", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:28.270343Z", "start_time": "2024-10-24T10:54:28.257723Z" } }, "outputs": [ { "data": { "text/plain": "[[[0], [1, 2, 3, 4, 5, 6]],\n [[0, 1], [2, 3], [4, 5], [6, 7, 8, 9, 10, 11], [12]],\n [[0, 1, 2, 3, 4], [5, 6, 7, 8]],\n [[0]],\n [[0, 1], [2, 3]]]" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import random\n", "def generate_random_lifecycle(n_snaps=10, max_groups_in_t=7, max_group_size=5, seed=42):\n", " random.seed(seed)\n", " snaps = []\n", " for t in range(5):\n", " snap_t = [] # snapshot in t\n", " present_in_t = 0 # entities present in t\n", " n_groups_t = random.randint(1,max_groups_in_t+1)\n", " for g in range(n_groups_t):\n", " size = random.randint(1, max_group_size+1)\n", " group = list(range(present_in_t, present_in_t+size))\n", " snap_t.append(group)\n", " present_in_t += size\n", " snaps.append(snap_t)\n", " return snaps\n", " \n", "snaps = generate_random_lifecycle()\n", "snaps" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "BDdaqGm84bp3", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:31.317339Z", "start_time": "2024-10-24T10:54:31.315141Z" } }, "outputs": [], "source": [ "# lc.add_partition(snaps[0]) # to add one partition at a time\n", "lc.add_partitions_from(snaps) # to add multiple partitions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When partitions are added, they are automatically assigned a progressive temporal id (integer). The same happens for individual sets, whose id is a string of the form tid_sid, where:\n", "- tid is a number indicating temporal id of the partition \n", "- sid is a number indicatin the set within that partition \n", "\n", "One can retrieve the list of ids as follows:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:34.564369Z", "start_time": "2024-10-24T10:54:34.560268Z" } }, "outputs": [ { "data": { "text/plain": "['0_0',\n '0_1',\n '1_0',\n '1_1',\n '1_2',\n '1_3',\n '1_4',\n '2_0',\n '2_1',\n '3_0',\n '4_0',\n '4_1']" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.groups_ids()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basic functionalities include the possibility to get:\n", "- the universe set (i.e., the set of unique elements observed across all partitions), \n", "- the list of temporal ids associated with each partition, \n", "- the elements in a specific set\n", "- the set ids associated with a specific partition" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "OppJzeZJ4bp5", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:35.902065Z", "start_time": "2024-10-24T10:54:35.898529Z" } }, "outputs": [ { "data": { "text/plain": "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(lc.universe_set())[:10] # ten elements" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:36.685447Z", "start_time": "2024-10-24T10:54:36.682291Z" } }, "outputs": [ { "data": { "text/plain": "[0, 1, 2, 3, 4]" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.temporal_ids()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:37.330225Z", "start_time": "2024-10-24T10:54:37.326911Z" } }, "outputs": [ { "data": { "text/plain": "{1, 2, 3, 4, 5, 6}" }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.get_group('0_1')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:38.141978Z", "start_time": "2024-10-24T10:54:38.138812Z" } }, "outputs": [ { "data": { "text/plain": "['0_0', '0_1']" }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.get_partition_at(0) # returns the group id; \n", " # you can get the corresponding group with \n", " # lc.get_group(group_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another possibility is to remove sets above or below certain thresholds. This allows to reduce noise in the analyses." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "TU6zPOrS4bp8", "outputId": "bea6a090-892f-4f01-f9c1-eed7cf9bbb03", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:39.620445Z", "start_time": "2024-10-24T10:54:39.615335Z" } }, "outputs": [], "source": [ "lc.filter_on_group_size(min_size=2, max_size=None)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, you can slice the Lifecycle via temporal ids. This method wil return a new object with data in [start, end[ timestamps" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-10-24T10:54:41.328143Z", "start_time": "2024-10-24T10:54:41.322520Z" } }, "outputs": [ { "data": { "text/plain": "[0, 1, 2]" }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sliced = lc.slice(start=0, end=3)\n", "sliced.temporal_ids()" ] }, { "cell_type": "markdown", "metadata": { "id": "2bmTLTl-4bqJ" }, "source": [ "## 3. Flows\n", "\n", "In this section we provide basic functionalities for anlytical purposes. We start by introducing the concept of group flow. \n", "\n", "The idea behind a group's flow(s) is that a group can contain element that can be allocated in the same/another group in the future. As a group evolves in time, its elements spread to one or multiple groups. So, if a group X_t1 gives elements to groups Y_t2 and Z_t2, its flow towards the future will be described by the elements moving from X_t1 to Y_t2 and from X_t1 to Z_t2.\n", "The same holds in the opposite temporal direction, i.e., X_t1 can recieve elements from Y_t0, Z_t0, and so on..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``LifeCycle`` object allows to easily retrieve the past and future flows of its groups, by specifying group id and temporal direction. Moreover, specifying the ``min_branch_size`` parameter restricts the result to those branches (i.e., the collection of elements that move from one group to another, for instance from X_t1 to Y_t2) whose size is equal or above a minimum threshold:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "Gf7P13nz4bqK", "outputId": "520533cc-471a-45f9-df6a-2b77a22f0a1a", "scrolled": true, "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:43.640513Z", "start_time": "2024-10-24T10:54:43.635213Z" } }, "outputs": [ { "data": { "text/plain": "{'1_0': {0, 1}, '1_1': {2, 3}, '1_2': {4}}" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.group_flow('2_0', direction='-') # past flow" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "Vc7xFELi4bqM", "outputId": "cefb6441-23ea-4411-cbc1-a454d1598c01", "tags": [], "ExecuteTime": { "end_time": "2024-10-24T10:54:47.877992Z", "start_time": "2024-10-24T10:54:47.874128Z" } }, "outputs": [ { "data": { "text/plain": "{'1_0': {0, 1}, '1_1': {2, 3}}" }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.group_flow('2_0', direction='-', min_branch_size=2) " ] }, { "cell_type": "markdown", "metadata": { "id": "JOgzNuWZ4bqO" }, "source": [ "Also, the ``all_flows()`` method allows to get the past or future flows of all the sets in the ``LifeCycle:``" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "AGNJuZUs4bqP", "outputId": "f0be2b09-11ca-4daf-ccb8-d53426d5947b", "tags": [] }, "outputs": [], "source": [ "#lc.all_flows(direction='+', min_branch_size = 19) " ] }, { "cell_type": "markdown", "metadata": { "id": "sJ48In4F4bqR" }, "source": [ "### 3.1. Measures and Events \n", "\n", "``LifeCycles`` provides measures to characterize the themporal evolution of groups according to ''facets''.\n", "For further details, please refer to the [original paper]().\n", "\n", "\n", "Note: in the following, \n", "- $X$ identifies the target set that we want to analyze;\n", "- $\\mathcal{R}$ identifies the reference partition (i.e., the groups at the previous/next timestamp w.r.t.$X$)\n", "- $R$ identifies a group/cluster in $\\mathcal{R}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unicity Facet:\n", "\n", "$$\n", " \\begin{equation}\n", " \\mathcal{U} = \n", " \\begin{cases}\n", " \\frac{|R_1\\cap X|-|R_2\\cap X|}{|\\bigcup_{R \\in \\mathcal{R}} R|} & \\text{if } |\\mathcal{R}| \\geq 2\\\\\n", " 1 & \\text{otherwise}\n", " \\end{cases}\n", " \\end{equation}\n", "$$\n", "\n", "This score guarantees that a large value corresponds to (e.g., in the backward perspective) having one dominating source, and reciprocally, that having a dominating souce leads to a high score. Conversely, having a low score corresponds to having no dominating unique source, and having no dominating source leads to a low score." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Identity Facet:\n", "$$\n", " \\begin{equation}\n", " \\label{eq:contr}\n", " \\mathcal{I} = \\begin{cases}\n", " \\frac{1}{|\\bigcup_{R \\in \\mathcal{R}} R|} \\sum_{R \\in \\mathcal{R}} |R \\cap X | \\frac{|R \\cap X|}{|R|} & \\text{if } |\\mathcal{R}| > 0\\\\\n", " 0 & \\text{otherwise}\n", " \\end{cases}\n", " \\end{equation}\n", "$$\n", "As an illustrative example, assume that a single set of 10 elements, $R$, provides elements to $X$ with two alternative scenarios: a) it provides a single element, and b) it provides 9 out of 10 elements.\n", "In the former scenario, the contribution $\\mathcal{I}$ will approach 0; in the latter, it will approach 1 (reaching those extreme values only when none or all elements of $\\mathcal{R}$ are present in $X$).\n", "\n", "Note that $\\mathcal{I}=0$ if there is no related set: in that case, the group identity is completely lost (forward) or completely new (backward)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Outflow Facet:\n", "$$ \\begin{equation}\n", " \\mathcal{O} = \\frac{|X - \\bigcup_{R \\in \\mathcal{R}}R|}{|X|}\n", " \\end{equation}$$\n", " \n", "This facet quantifies the fraction of members in $X$ that are not observed in the previous timestamp, i.e., the new, never-before-seen elements. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These facets can be computed for a group's past or future flow as follows. \n", "\n", "Note that also the size of the group is returned " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "pOw3TWf64bqR", "outputId": "207f3a23-dd9c-4904-d60f-317233adbf9b", "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'U': 0.0, 'I': 0.9, 'O': 0.0, 'size': 5}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lcs.analyze_flow(lc, target='2_0', direction='-')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [], "source": [ "complete_flow_analysis = lcs.analyze_all_flows(lc, direction='-') # computes for all flows" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "Then, it is possible to obtain the event weights as defined in the paper" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "lcXJmlJ04bqU", "outputId": "c6a2bdcb-d7a1-4704-ddcf-5ae82d45388d", "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'Birth': 0.4166666666666667,\n", " 'Accumulation': 0.0,\n", " 'Growth': 0.08333333333333333,\n", " 'Expansion': 0.0,\n", " 'Continuation': 0.08333333333333333,\n", " 'Merge': 0.0,\n", " 'Offspring': 0.4166666666666667,\n", " 'Reorganization': 0.0}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e_ws = lcs.events_all(lc)\n", "past = e_ws['-'] # or +\n", "fut = e_ws['+']\n", "\n", "past['1_0']" ] }, { "cell_type": "markdown", "metadata": { "id": "Yot8kIz14bqX" }, "source": [ "### 3.2. Validation\n", " TO BE REVISED \n", "\n", "It is also possible to obtain statistically significant flows only, by comparing them to a null distribution.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "jZQikIeL4bqf", "outputId": "37132171-d78f-4a32-8b29-48f1d42f6e17", "tags": [] }, "outputs": [], "source": [ "#one_validated = lcs.validated_flow(lc, target='3_3', direction='-', min_branch_size=1,\n", "# num_permutations=1000, significance_level=.05)\n", "validated = lcs.validate_all_flows(lc, direction='-', min_branch_size=1,\n", " iterations=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### 3.3. Attributes \n", "\n", "``LifeCycles`` provides support for studying flows along with element metadata. To attach such metadata to the elements we use the set_attributes method" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "random.seed(42)\n", "cls = dict()\n", "for e in lc.universe_set():\n", " cls[e] = random.choice(['red', 'blue'])" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from collections import defaultdict\n", "attrs = defaultdict(dict) # {n: {t: 'value', ...}, ...}\n", "for e in lc.universe_set():\n", " for t in lc.temporal_ids():\n", " attrs[e][t] = cls[e] \n", " \n", "lc.set_attributes(attrs, 'color')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can retrieve the attributes with a get method" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{0: 'red', 1: 'red', 2: 'red', 3: 'red', 4: 'red'}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# lc.get_attributes(attr_name='class') # get all attributes for all elements\n", "lc.get_attributes(attr_name='color', of=0) # get attribute of a specific element, by timestamp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When analyzing flows, adding the attribute as a parameter results in additional information, namely, (i) the entropy of the set w.r.t. the attribute; (ii) the change w.r.t. the past/future average entropy; (iii) the purity ,i.e., the relative frequency of the most frequent attribute value; (iv) the most common attribute value" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'U': 0.0,\n", " 'I': 0.9,\n", " 'O': 0.0,\n", " 'size': 5,\n", " 'color_H': 0.7219280948873623,\n", " 'color_H_change': 0.388594761554029,\n", " 'color_purity': 0.8,\n", " 'color_mca': 'red'}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = lcs.analyze_flow(lc, target='2_0', direction='-', attr='color')\n", "res" ] }, { "cell_type": "markdown", "metadata": { "id": "lnVi52Uo4bqr" }, "source": [ "\n", "### 4. Visualization \n", "\n", "Lifecycles offers several visualization facilities" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "tYP1syJ84bqt", "outputId": "f03d44ad-49c6-4705-f2b4-3f4400ff8fe6", "tags": [] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "arrangement": "snap", "link": { "color": [ "lightblue", "lightblue", "lightblue", "lightblue", "lightblue", "lightblue", "rgba(0, 0, 0, 0)", "rgba(0, 0, 0, 0)", "rgba(0, 0, 0, 0)", "rgba(0, 0, 0, 0)", "rgba(0, 0, 0, 0)" ], "label": [ 1, 1, 2, 2, 1, 1, 5, 1, 6, 1, 4 ], "source": [ 0, 1, 2, 3, 4, 7, 1, 4, 5, 6, 8 ], "target": [ 2, 2, 7, 7, 7, 9, 1, 4, 5, 6, 8 ], "value": [ 1, 1, 2, 2, 1, 1, 5, 1, 6, 1, 4 ] }, "node": { "color": [ "green", "lightgrey", "green", "lightgrey", "lightgrey", "lightgrey", "lightgrey", "green", "lightgrey", "green" ], 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lc = lcs.LifeCycle(int)\n", "lc.add_partitions_from(snaps)\n", "lcs.plot_flow(lc, \n", " slice=(0,4), # slice according to temporal ids\n", " node_focus=0 # focus on specific nodes (communities will be highlighted)\n", " )" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "lcs.plot_event_radars(lc, '2_0', min_branch_size=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "FIMp6HKZ4brj" }, "source": [ "\n", "### 4. IO \n", "\n", "To read/write a LifeCycle object on your machine:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# write\n", "filename = 'data/my-lc.json'\n", "lc.write_json(filename)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded LifeCycle from data/my-lc.json\n" ] } ], "source": [ "# read\n", "lc = lcs.LifeCycle()\n", "lc.read_json(filename)" ] } ], "metadata": { "colab": { "name": "CDlib.ipynb", "provenance": [] }, "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": "3.11.9" }, "nteract": { "version": "0.22.0" } }, "nbformat": 4, "nbformat_minor": 4 }