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asvnpr/similarity-search-demo.ipynb

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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
},
"colab": {
"name": "Similarity Search Demo.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/asvnpr/38602b91b4bf69f748cb3a6ef31a6a0c/similarity-search-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wehaECjm8Z4t",
"colab_type": "text"
},
"source": [
"# Services Content Embedding: Similarity Search\n",
"\n",
"- uses our [Universal Sentence Encoder](https://tfhub.dev/google/universal-sentence-encoder/4) embeddings of Services description and other auxilary data\n",
"- embed the user given query and find the top-k most similar services\n",
"\n",
"[Try your own query](#User-Query) \n",
"[Explore Services Similarity](#Service-Similarity-Heatmap)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "k7aPQSzCj9jr",
"colab_type": "text"
},
"source": [
"## Imports and Global Config\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a73qer_zPJLy",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip3 install annoy h2o4gpu tqdm tensorflow_text==2.0rc0 gdown"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fidU1Ta38Z45",
"colab_type": "code",
"outputId": "5c18bf90-9082-4cbb-ca7a-a2551d05af9a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"#following this guide: https://colab.research.google.com/drive/1t4bi7X7zRzwIjdxUrU2hUs7LneqgYLVK#scrollTo=a73qer_zPJLy\n",
"# and: https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_semantic_approximate_nearest_neighbors.ipynb\n",
"\n",
"try:\n",
" # %tensorflow_version only exists in Colab.\n",
" %tensorflow_version 2.x\n",
"except Exception:\n",
" pass\n",
" \n",
"import tensorflow as tf\n",
"import tensorflow_text\n",
"import tensorflow_hub as hub\n",
"import numpy as np\n",
"from h2o4gpu.metrics.pairwise import cosine_similarity\n",
"import seaborn as sns\n",
"import annoy\n",
"import gdown\n",
"import json\n",
"import os\n",
"import pickle\n",
"import pandas as pd\n",
"import random\n",
"from tqdm import tqdm, trange\n",
"import logging"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"TensorFlow 2.x selected.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EgvycbQTG7Lj",
"colab_type": "code",
"outputId": "6311573c-709a-42ab-a329-b9af557a2499",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 108
}
},
"source": [
"# make sure we're using a gpu or hw acceleration before we start embedding!!\n",
"# NOTE: works inconsistently \n",
"if not tf.test.is_gpu_available() and 'COLAB_TPU_ADDR' not in os.environ:\n",
" print(\"WARNING!: This notebook is not connected to a GPU nor TPU runtime.\")\n",
"else:\n",
" print(\"HW Acceleration should work :)\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-2-d6bac8f179d3>:1: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.config.list_physical_devices('GPU')` instead.\n",
"HW Acceleration should work :)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "W_32VQBkkQWP",
"colab_type": "code",
"outputId": "b02333b5-ff98-476d-8169-a752bbc4a221",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"NODE_TYPE = 'services'\n",
"\n",
"MODEL = 'USE'\n",
"MODEL_URL = 'https://tfhub.dev/google/universal-sentence-encoder-large/5'\n",
"MODEL_TYPE = MODEL_URL.split('/')[-2]\n",
"MODEL_VER = MODEL_URL.split('/')[-1]\n",
"\n",
"print(\"Using Model {}_{}_v{}\".format(MODEL, MODEL_TYPE, MODEL_VER))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Using Model USE_universal-sentence-encoder-large_v5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UcHy7er_1P3Z",
"colab_type": "text"
},
"source": [
"## Get our service texts, service embeddings, and taxonomy codes"
]
},
{
"cell_type": "code",
"metadata": {
"id": "A4spVCrY8Z5W",
"colab_type": "code",
"outputId": "134fb7e1-997f-4cdd-961f-1677e4765aef",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
}
},
"source": [
"texts_url = 'https://drive.google.com/uc?id=1-4eT0u1Cdg4fE8MLXdfEsaLWeljXDKeE'\n",
"texts_file = 'tagged_texts.json'\n",
"tagged_texts = {}\n",
"gdown.download(texts_url, texts_file, quiet=False)\n",
"\n",
"with open(texts_file) as tf:\n",
" tagged_texts = json.load(tf)\n",
" print(\"Loaded {} Services text docs...\".format(len(tagged_texts)))\n",
"\n",
"embed_url = 'https://drive.google.com/uc?id=1li8eF5VCJ2wxl9FpfeQky8Z_1K3gx-bf'\n",
"embed_file = \"{}_{}_v{}.pkl\".format(NODE_TYPE, MODEL_TYPE, MODEL_VER)\n",
"gdown.download(embed_url, embed_file, quiet=False)\n",
"\n",
"with open(embed_file, 'rb') as f:\n",
" tagged_embeds = pickle.load(f)\n",
" e_key = list(tagged_embeds.keys())[0]\n",
" e_smpl = tagged_embeds[e_key]['embed']\n",
" print(\"Loaded {} {}-dimensional embeddings...\".format(len(tagged_embeds), len(e_smpl)))\n",
"\n",
"taxo_url = 'https://drive.google.com/uc?id=10gfognNI2_Q4epqApYVrFSemIZblQKjy'\n",
"taxo_file = 'HIN_nodes.json'\n",
"gdown.download(taxo_url, taxo_file, quiet=False)\n",
"\n",
"with open(taxo_file) as taxo:\n",
" taxo_nodes = json.load(taxo)\n",
" print(\"Loaded taxonomy codes...\")"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1-4eT0u1Cdg4fE8MLXdfEsaLWeljXDKeE\n",
"To: /content/tagged_texts.json\n",
"13.2MB [00:00, 247MB/s]\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Loaded 16547 Services text docs...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1li8eF5VCJ2wxl9FpfeQky8Z_1K3gx-bf\n",
"To: /content/services_universal-sentence-encoder-large_v5.pkl\n",
"35.2MB [00:00, 188MB/s]\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Loaded 16547 512-dimensional embeddings...\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=10gfognNI2_Q4epqApYVrFSemIZblQKjy\n",
"To: /content/HIN_nodes.json\n",
"59.9MB [00:00, 233MB/s]\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Loaded taxonomy codes...\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XkxKDM_AKVsN",
"colab_type": "code",
"colab": {}
},
"source": [
"# reformat data for easier use\n",
"node_names = [taxo_nodes[idx]['name'] for idx in tagged_texts]\n",
"node_embeds = [tagged_embeds[idx]['embed'] for idx in tagged_embeds]\n",
"node_texts = ['\\n'.join(tagged_texts[idx]) for idx in tagged_embeds]\n",
"node_ids = list(tagged_embeds.keys())\n",
"# get size of vectors\n",
"embed_dim = len(e_smpl)\n",
"# consolidate all our data into a single var\n",
"embed_data = list(zip(node_ids, node_names, node_texts, node_embeds))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "TzIZfPh81YzP",
"colab_type": "text"
},
"source": [
"## Semantic Similarity Search: \n",
"get the most similar items for each of our queries"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4QbVvDrLFxZB",
"colab_type": "text"
},
"source": [
"### Approximate Nearest Neighbors\n",
"\n",
"Using [ANNOY](https://github.com/spotify/annoy) to build a search index so we can have a real-time search instead of searching through the 10K+ embeddings for every query.\n",
"\n",
"Load our Search Index file. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "qN7bXLJYlMIJ",
"colab_type": "code",
"outputId": "20de3850-277c-4d33-da82-72be7c5dbd80",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
}
},
"source": [
"index_url = 'https://drive.google.com/uc?id=1--4qmz9oaP-AgWZxgt9gp_qmn7X0oYul'\n",
"index_file = \"{}_{}_{}_ANNOY_index\".format(NODE_TYPE, MODEL, MODEL_TYPE)\n",
"gdown.download(index_url, index_file, quiet=False)\n",
"\n",
"mapping_url = 'https://drive.google.com/uc?id=1--H7X_OErsUypKoaBSACE1TVtSDbqlO2'\n",
"mapping_file = index_file + '.mapping'\n",
"gdown.download(mapping_url, mapping_file, quiet=False)\n",
"\n",
"# adapted from: https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_semantic_approximate_nearest_neighbors.ipynb\n",
"# uses an approximate nearest neighbor index (with ANNOY lib)\n",
"# used to avoid searching through the entirety of our data when comparing queries\n",
"# TODO: experiment with param values\n",
"\n",
"# load the index and mapping files\n",
"index = annoy.AnnoyIndex(embed_dim, metric='angular')\n",
"index.load(index_file, prefault=True)\n",
"print('Annoy index is loaded.')\n",
"with open(mapping_file, 'rb') as handle:\n",
" mapping = pickle.load(handle)\n",
"print('Mapping file is loaded.')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1--4qmz9oaP-AgWZxgt9gp_qmn7X0oYul\n",
"To: /content/services_USE_universal-sentence-encoder-large_ANNOY_index\n",
"63.1MB [00:00, 248MB/s]\n",
"Downloading...\n",
"From: https://drive.google.com/uc?id=1--H7X_OErsUypKoaBSACE1TVtSDbqlO2\n",
"To: /content/services_USE_universal-sentence-encoder-large_ANNOY_index.mapping\n",
"13.3MB [00:00, 274MB/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Annoy index is loaded.\n",
"Mapping file is loaded.\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "BIX3NbLblVoo",
"colab_type": "code",
"colab": {}
},
"source": [
"# Finds similar items to a given embedding in the ANN index\n",
"def find_similar_items_filter(embedding, num_matches=100, k=10):\n",
" ids, distances = index.get_nns_by_vector(\n",
" embedding, num_matches, search_k=-1, include_distances=True)\n",
" items = [mapping[i] for i in ids]\n",
"\n",
" uniq_names = set()\n",
" uniq_descrs = set()\n",
" uniq_codes = set()\n",
" filter_items = []\n",
" filter_dists = []\n",
" i = 0\n",
" while len(uniq_names) < num_matches and i < len(items):\n",
" name = items[i]['name']\n",
" # get node with item info \n",
" node = taxo_nodes[items[i]['id']]\n",
" descr = node['description']\n",
" codes = node['codes']\n",
" codes = [c.split('.')[0] for c in codes] \n",
" new_codes = [c not in uniq_codes for c in codes]\n",
" if name not in uniq_names and descr not in uniq_descrs and all(new_codes):\n",
" # print(name)\n",
" uniq_names.add(name)\n",
" uniq_descrs.add(descr)\n",
" # print(new_codes)\n",
" # print(uniq_codes)\n",
" for idx, nc in enumerate(new_codes):\n",
" if nc:\n",
" uniq_codes.add(codes[idx])\n",
" filter_items.append(items[i])\n",
" filter_dists.append(distances[i])\n",
" i += 1\n",
" return filter_items[:k], filter_dists[:k]\n",
"\n",
"def find_similar_items(embedding, num_matches=100, k=10):\n",
" ids, distances = index.get_nns_by_vector(\n",
" embedding, num_matches, search_k=-1, include_distances=True)\n",
" items = [mapping[i] for i in ids]\n",
"\n",
" return items[:k], distances[:k]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "AIxHISIVui6P",
"colab_type": "code",
"outputId": "706170c3-2242-4068-cfb1-6c8527c4a94c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
}
},
"source": [
"# Load the TF-Hub module\n",
"print(\"Loading the TF-Hub {} module...\".format(MODEL))\n",
"%time embed_fn = hub.load(MODEL_URL)\n",
"print(\"TF-Hub module is loaded.\")\n",
"\n",
"def extract_embed(queries):\n",
" # Generates the embedding for the query\n",
" query_embedding = embed_fn(queries).numpy()\n",
" # print(len(query_embedding))\n",
" return query_embedding\n",
"\n",
"def k_query_matches(queries, sample_size=10, num_res=100, k=10, all_queries=False, out=True, filtr=True, dist_var=False, dl=0.5, du=0.8):\n",
" if all_queries:\n",
" sample_size = len(queries)\n",
"\n",
" query_samples = random.choices(queries, k=sample_size)\n",
" for query in query_samples:\n",
" # print(query)\n",
" query_embed = extract_embed([query])[0]\n",
"\n",
" items, dists = None, None\n",
" if filtr:\n",
" items, dists = find_similar_items_filter(query_embed, num_res, k)\n",
" elif dist_var:\n",
" # try to add variety by getting items where similarity between \n",
" # new candidate and all currently selected items statisfies\n",
" # dl <= dist <= du\n",
" items, dists = find_similar_items(query_embed, num_res, k)\n",
" # add the first embedding since it is excempt from the condition\n",
" var_items, var_dists, res_embeds = [], [], [tagged_embeds[items[0]['id']]['embed']]\n",
" i = 1\n",
" while len(var_items) < k and i < len(items):\n",
" embed = tagged_embeds[items[i]['id']]['embed']\n",
" # TODO: maybe change to cosine distance to be consistent\n",
" e_dists = [cosine_similarity([e], [embed]) for e in res_embeds]\n",
" if all(ed >= dl and ed <= du for ed in e_dists):\n",
" var_items.append(items[i])\n",
" var_dists.append(dists[i])\n",
" res_embeds.append(embed) \n",
" items = var_items\n",
" dists = var_dists\n",
" else:\n",
" items, dists = find_similar_items(query_embed, num_matches=k)\n",
"\n",
" if out:\n",
" print(\"Top-{} most similar items (w.o. duplicates) to query \\\"{}\\\":\".format(len(items), query))\n",
" for i in range(len(items)):\n",
" item = items[i]\n",
" dist = dists[i]\n",
" name = item['name']\n",
" print(\"({}) {} (dist={})\".format(i+1, name, dist))\n",
" print()\n",
" return items, dists"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Loading the TF-Hub USE module...\n",
"CPU times: user 18.6 s, sys: 3.24 s, total: 21.9 s\n",
"Wall time: 28 s\n",
"TF-Hub module is loaded.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vQ3O1Gw7MVCD",
"colab_type": "text"
},
"source": [
"## User Query\n",
"Make sure to **run all the code cells above** and enter your own relevant query to find similar social services"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XEf54OEjBI8X",
"colab_type": "code",
"outputId": "eae0f24d-c121-4d6a-c2c2-f04578def6c2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
}
},
"source": [
"user_query = [input(\"Write a query to get recommended services: \")]\n",
"\n",
"# %time matches, distances = k_query_matches(user_query, sample_size=len(user_query), filtr=False)\n",
"%time matches, distances = k_query_matches(user_query, sample_size=len(user_query), filtr=True)\n",
"# %time matches, distances = k_query_matches(user_query, sample_size=len(user_query), dist_var=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Write a query to get recommended services: adoption\n",
"Top-10 most similar items (w.o. duplicates) to query \"adoption\":\n",
"(1) Adoption Services (dist=0.9472340941429138)\n",
"(2) Domestic Infant And International Adoption (dist=1.0427831411361694)\n",
"(3) Adoption / Foster Care (dist=1.0706366300582886)\n",
"(4) Clerk's Office - Main Street - South Bend (dist=1.088861107826233)\n",
"(5) Birth Parent Network - Indiana Birth Parents (dist=1.1011050939559937)\n",
"(6) Foster Parenting - Mishawaka (dist=1.102400541305542)\n",
"(7) Angels Of Love - (dist=1.1205506324768066)\n",
"(8) Family Planning (dist=1.1461135149002075)\n",
"(9) Pregnancy Services (dist=1.1584712266921997)\n",
"(10) Information and Referral (dist=1.1598827838897705)\n",
"\n",
"CPU times: user 42.2 ms, sys: 2.79 ms, total: 45 ms\n",
"Wall time: 29.5 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NVe1mbYuCo0p",
"colab_type": "text"
},
"source": [
"## Broad Service-Service Comparisons\n",
"- each service has one or more [211 taxonomy codes](https://211taxonomy.org/publicfiles/view/Intro-What_is_the_AIRS.pdf) of the form AZ-1234.\\[5678\\]. The less characters the code has, the more broad is the social service category following a hierarchy\n",
"- We use the broadest codes (1 letter) for the x and y axis which cover 9 categories , and the next level down (2 letters) which cover 96 categories for a more granular comparison \n",
"- We average the embeddigns for the sum of all embeddings in a code and compare them in a heatmap of their cosine similarity"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JdRsPmGCBPJo",
"colab_type": "code",
"outputId": "04a75cb7-f654-4288-fe5e-5dc3d1ebb1b1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
}
},
"source": [
"def node_to_n_degree_code(node_num, code_names, n=2):\n",
" node = taxo_nodes[node_num]\n",
" codes = node['codes']\n",
" main_code = None\n",
" if len(codes) >= 1:\n",
" main_code = codes[0]\n",
" main_code = main_code[:n]\n",
" else:\n",
" main_code = None\n",
"\n",
" return main_code\n",
"\n",
"code_names_url = 'https://drive.google.com/uc?id=1Zummlwwvg3mCKH58Q5uCMtqow2YTjw_e'\n",
"code_names_file = 'code_to_name.json'\n",
"gdown.download(code_names_url, code_names_file, quiet=False)\n",
"\n",
"with open(code_names_file) as ctn:\n",
" code_names = json.loads(ctn.read())\n",
"\n",
"def getLabelEmbeds(code_len='1'):\n",
" tagged_lbl_emb = {}\n",
" lbl_to_avg_emb = {'No Label': []}\n",
" lbl_cnt = {'No Label': 0}\n",
" # TODO change how code_names is saved so it only has the lbls from 2 char codes\n",
"\n",
" for lbl in code_names[code_len].values():\n",
" # print(lbl)\n",
" lbl_to_avg_emb[lbl] = []\n",
" lbl_cnt[lbl] = 0\n",
"\n",
" for node_num in tagged_embeds:\n",
"\n",
" node_codes = taxo_nodes[node_num]['codes']\n",
" main_code = node_to_n_degree_code(node_num, node_codes, n=int(code_len))\n",
" if main_code == None:\n",
" lbl = 'No Label'\n",
" # pseudo label according to 2 first chars of taxonomy code\n",
" else:\n",
" lbl = code_names[code_len][main_code]\n",
" tagged_lbl_emb[node_num] = {'embed': tagged_embeds[node_num]['embed'], 'label': lbl}\n",
" lbl_to_avg_emb[lbl].append(tagged_embeds[node_num]['embed'])\n",
" # print(len(lbl_to_avg_emb[lbl]))\n",
" lbl_cnt[lbl] += 1\n",
" \n",
" # get the mean of each collected label embeddings\n",
" keys_to_del = []\n",
" for lbl in lbl_to_avg_emb:\n",
" if lbl_to_avg_emb[lbl] == []:\n",
" keys_to_del.append(lbl)\n",
" \n",
" else: \n",
" avg_embed = np.mean(lbl_to_avg_emb[lbl], axis=0)\n",
" lbl_to_avg_emb[lbl] = avg_embed\n",
" # print(avg_embed.shape)\n",
" for lbl in keys_to_del:\n",
" try:\n",
" lbl_to_avg_emb.pop(lbl) \n",
" lbl_cnt.pop(lbl) \n",
" except KeyError:\n",
" print(\"Key not found\") \n",
" # for lbl in lbl_cnt:\n",
" # print(\"Label {} has {} elements\".format(lbl, lbl_cnt[lbl]))\n",
" print(\"Nodes have {} labels\".format(len(lbl_to_avg_emb)))\n",
" return tagged_lbl_emb, lbl_to_avg_emb, lbl_cnt"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading...\n",
"From: https://drive.google.com/uc?id=1Zummlwwvg3mCKH58Q5uCMtqow2YTjw_e\n",
"To: /content/code_to_name.json\n",
"100%|██████████| 3.76k/3.76k [00:00<00:00, 5.54MB/s]\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MMo4v6ABBRaA",
"colab_type": "code",
"outputId": "adebfab7-5de5-47aa-d5e3-d2e23adbf375",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
}
},
"source": [
"lvl1_tagged_embs, lvl1_avg_embeds, lvl1_cnt = getLabelEmbeds(code_len='1')\n",
"lvl2_tagged_embs, lvl2_avg_embeds, lvl2_cnt = getLabelEmbeds(code_len='2')\n",
"\n",
"label_hierarchy = {}\n",
"for lbl in lvl1_avg_embeds:\n",
" label_hierarchy[lbl] = set()\n",
"\n",
"# get order of nodes to display their names when hovering\n",
"lbl_node_nums = {}\n",
"for lbl in lvl1_avg_embeds:\n",
" lbl_node_nums[lbl] = []\n",
"\n",
"for n_idx in lvl1_tagged_embs:\n",
" lvl1_lbl = lvl1_tagged_embs[n_idx]['label']\n",
"\n",
" label_hierarchy[lvl1_lbl].add(lvl2_tagged_embs[n_idx]['label'])\n",
" # lbl_node_nums[lvl1_lbl].append(n_idx)\n",
"\n",
"# get the embeddings grouped according to lvl1 labels\n",
"reordered_embeds = []\n",
"reordered_lbls = []\n",
"for lbl in label_hierarchy:\n",
"\n",
" sub_lbls = label_hierarchy[lbl]\n",
" for sub in sub_lbls:\n",
" reordered_embeds.append(lvl2_avg_embeds[sub])\n",
" reordered_lbls.append(sub)\n",
"\n",
"# reordered_names = [taxo_nodes[n_idx]['name'] for n_idx in reordered_node_ids]\n",
"embed_sims = cosine_similarity(reordered_embeds)\n",
"tick_vals = []\n",
"tick_total = 0\n",
"for lbl in lvl1_cnt:\n",
" val = lvl1_cnt[lbl]\n",
" tick_vals.append(val // 2)\n",
" tick_total += val\n",
"\n",
"print(\"Have {} names for x,y axis\".format(len(reordered_lbls)))\n",
"# print(reordered_embeds)\n",
"print(\"Have {}x{} similarity matrix\".format(len(embed_sims), len(embed_sims[0])))\n",
"\n",
"# print(list(lvl1_avg_embeds.keys()))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Nodes have 12 labels\n",
"Nodes have 73 labels\n",
"Have 73 names for x,y axis\n",
"Have 73x73 similarity matrix\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gE8xT2NqM0x3",
"colab_type": "text"
},
"source": [
"## Services Similarity Heatmap"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uYaweLC2Bd0D",
"colab_type": "code",
"outputId": "e5e83847-1335-4a3f-ce78-7a25a9929920",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 853
}
},
"source": [
"import plotly.graph_objects as go\n",
"import datetime\n",
"import numpy as np\n",
"np.random.seed(1)\n",
"\n",
"ticks = list(label_hierarchy.keys())\n",
"sim_threshold = float(input(\"Enter a minimum similarity threshold (0.0 <= x <= 1.0) for service comparison: \"))\n",
"while sim_threshold > 1.0 or sim_threshold < 0.0:\n",
" sim_threshold = float(input(\"Enter a minimum similarity threshold (0.0 <= x <= 1.0) for service comparison: \"))\n",
"threshold_sims = [list(map(lambda x: x if x >= sim_threshold else 0.0, row)) for row in embed_sims]\n",
"\n",
"y = reordered_lbls\n",
"x = reordered_lbls\n",
"z = threshold_sims\n",
"\n",
"def split_text(text, delim=' '):\n",
" txt = ''\n",
" for j, word in enumerate(text.split(delim)):\n",
" if j % 2 == 0:\n",
" txt += word + ' <br>'\n",
" else:\n",
" txt += word + ''\n",
" tick_text[i] = txt\n",
"\n",
"tick_text = list(lvl1_avg_embeds.keys())\n",
"for i, text in enumerate(tick_text):\n",
" if '/' in text:\n",
" split_text(text, delim='/')\n",
" else:\n",
" split_text(text)\n",
"\n",
"\n",
"dx_dy = 6\n",
"\n",
"x_axis = dict(\n",
" tickmode = 'array',\n",
" tickvals = list(range(0, len(tick_text)*dx_dy, dx_dy)),\n",
" ticktext = tick_text\n",
")\n",
"\n",
"y_axis = dict(\n",
" tickmode = 'array',\n",
" tickvals = list(range(0, len(tick_text)*dx_dy, dx_dy)),\n",
" ticktext = tick_text\n",
")\n",
"\n",
"color_scales = ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',\n",
" 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',\n",
" 'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',\n",
" 'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',\n",
" 'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',\n",
" 'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',\n",
" 'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',\n",
" 'orrd', 'oryel', 'peach', 'phase', 'picnic', 'pinkyl', 'piyg',\n",
" 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn', 'puor',\n",
" 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu', 'rdgy',\n",
" 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar', 'spectral',\n",
" 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn', 'tealrose',\n",
" 'tempo', 'temps', 'thermal', 'tropic', 'turbid', 'twilight',\n",
" 'viridis', 'ylgn', 'ylgnbu', 'ylorbr', 'ylorrd']\n",
"\n",
"color_scale = random.choice(color_scales)\n",
"print(\"Using color scale: {}\".format(color_scale))\n",
"\n",
"\n",
"fig = go.Figure(data=go.Heatmap(\n",
" z=z,\n",
" x=x,\n",
" y=y,\n",
" colorscale = color_scale))\n",
"\n",
"fig.update_layout(\n",
" title='Service-to-Service Similarity',\n",
" xaxis_nticks=len(tick_text),\n",
" yaxis_nticks=len(tick_text),\n",
" width = 1000,\n",
" height = 800,\n",
" xaxis_type = 'category',\n",
" yaxis_type = 'category',\n",
" xaxis = x_axis,\n",
" yaxis = y_axis,\n",
" margin=dict(l=20, r=30, t=50, b=40),\n",
" paper_bgcolor=\"LightSteelBlue\",\n",
" font=dict(\n",
" family=\"Courier New, monospace\",\n",
" size=12,\n",
" # color=\"#7f7f7f\"\n",
" )\n",
" )\n",
" \n",
"\n",
"fig.show()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Enter a minimum similarity threshold (0.0 <= x <= 1.0) for service comparison: 0.65\n",
"Using color scale: speed\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
"<body>\n",
" <div>\n",
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" <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
" <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script> \n",
" <div id=\"c559721a-70c2-4189-b1ba-d52990f8089d\" class=\"plotly-graph-div\" style=\"height:800px; width:1000px;\"></div>\n",
" <script type=\"text/javascript\">\n",
" \n",
" window.PLOTLYENV=window.PLOTLYENV || {};\n",
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},
{
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""
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