diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index abd8108a..96bf6f56 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -52,4 +52,4 @@ jobs: GCP_REGION: ${{ secrets.GCP_REGION }} GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }} run: | - pytest --verbose --nbval-lax python-recipes/RAG/ python-recipes/vector-search python-recipes/redis-intro python-recipes/recommendation-systems python-recipes/agents python-recipes/computer-vision --ignore python-recipes/agents/01_crewai_langgraph_redis.ipynb --ignore python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb --ignore python-recipes/semantic-cache/doc2cache_llama3_1.ipynb + pytest --verbose --nbval-lax python-recipes/ --ignore python-recipes/agents/01_crewai_langgraph_redis.ipynb --ignore python-recipes/RAG/05_nvidia_ai_rag_redis.ipynb --ignore python-recipes/semantic-cache/doc2cache_llama3_1.ipynb diff --git a/README.md b/README.md index f423abef..8bb35167 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,10 @@ An estimated 31% of LLM queries are potentially redundant ([source](https://arxi | ------ | ----------- | | [/computer-vision/00_facial_recognition_facenet.ipynb](python-recipes/computer-vision/00_facial_recognition_facenet.ipynb) | Build a facial recognition system using the Facenet embedding model and RedisVL. +### Feature Store +| Recipe | Description | +| ------ | ----------- | +| [/feature-store/00_feast_credit_score.ipynb](python-recipes/feature-store/00_feast_credit_score.ipynb) | Credit scoring system using Feast with Redis as the online store. ### Recommendation Systems diff --git a/python-recipes/RAG/01_redisvl.ipynb b/python-recipes/RAG/01_redisvl.ipynb index f94c63df..99daa73c 100644 --- a/python-recipes/RAG/01_redisvl.ipynb +++ b/python-recipes/RAG/01_redisvl.ipynb @@ -37,16 +37,16 @@ "cell_type": "code", "execution_count": 1, "metadata": { - "id": "AJJ2UW6M1ui0", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "AJJ2UW6M1ui0", "outputId": "0f5773b7-a292-4ee6-f4bd-20dc40ca2aba" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Cloning into 'temp_repo'...\n", "remote: Enumerating objects: 384, done.\u001b[K\n", @@ -78,16 +78,16 @@ "cell_type": "code", "execution_count": 2, "metadata": { - "id": "DgxBQFXQ1ui2", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "DgxBQFXQ1ui2", "outputId": "c3c399d6-e294-4a3a-a0a3-82d818509991" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/261.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m256.0/261.4 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m96.1/96.1 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", @@ -116,8 +116,7 @@ "### Install Redis Stack\n", "\n", "Later in this tutorial, Redis will be used to store, index, and query vector\n", - "embeddings created from PDF document chunks. **We need to make sure we have a Redis\n", - "instance available.**" + "embeddings created from PDF document chunks. **We need to make sure we have a Redis instance available.**" ] }, { @@ -142,8 +141,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n", "Starting redis-stack-server, database path /var/lib/redis-stack\n" @@ -168,10 +167,14 @@ "source": [ "#### For Alternative Environments\n", "There are many ways to get the necessary redis-stack instance running\n", - "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.com/try-free/). Or, if you have your\n", + "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.io/cloud/). Or, if you have your\n", "own version of Redis Enterprise running, that works too!\n", "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n", - "3. With docker: `docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest`" + "3. With docker:\n", + "\n", + " ```bash\n", + " docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest\n", + " ```" ] }, { @@ -195,7 +198,7 @@ "source": [ "import os\n", "import warnings\n", - "#warnings.filterwarnings('ignore')\n", + "warnings.filterwarnings('ignore')\n", "\n", "# Replace values below with your own if using Redis Cloud instance\n", "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\") # ex: \"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n", @@ -234,16 +237,16 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "id": "uijl2qFH1ui3", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "uijl2qFH1ui3", "outputId": "a99b3fcb-7cfd-4dbd-f258-57779cfcae3c" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Listing available documents ... ['resources/jnj-10k-2023.pdf', 'resources/retrieval_basic_rag_test.csv', 'resources/aapl-10k-2023.pdf', 'resources/nke-10k-2023.pdf', 'resources/amzn-10k-2023.pdf', 'resources/testset_15.csv', 'resources/generation_basic_rag_test.csv', 'resources/testset.csv', 'resources/msft-10k-2023.pdf', 'resources/propositions.json', 'resources/nvd-10k-2023.pdf']\n" ] @@ -264,16 +267,16 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "id": "anya8hVnT6K_", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "anya8hVnT6K_", "outputId": "a8430acc-2e6d-45fd-fc8b-601fbbd8289b" }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Done preprocessing. Created 211 chunks of the original pdf resources/nke-10k-2023.pdf\n" ] @@ -463,8 +466,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "20:48:16 numexpr.utils INFO NumExpr defaulting to 2 threads.\n", "20:48:30 sentence_transformers.SentenceTransformer INFO Use pytorch device_name: cuda\n", @@ -472,8 +475,8 @@ ] }, { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", @@ -484,204 +487,204 @@ ] }, { - "output_type": "display_data", "data": { - "text/plain": [ - "modules.json: 0%| | 0.00/349 [00:00[KNN 3 @text_embedding $vector AS vector_distance] RETURN 3 chunk_id content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3'" - ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" - } + }, + "text/plain": [ + "'*=>[KNN 3 @text_embedding $vector AS vector_distance] RETURN 3 chunk_id content vector_distance SORTBY vector_distance ASC DIALECT 2 LIMIT 0 3'" + ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } ], "source": [ @@ -957,28 +960,20 @@ "cell_type": "code", "execution_count": 14, "metadata": { - "id": "5reL5qTW1ujC", "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, + "id": "5reL5qTW1ujC", "outputId": "dd58f191-54f5-4226-c4e1-70207d58f2dc" }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " id vector_distance chunk_id \\\n", - "0 chunk:88 0.337694585323 88 \n", - "1 chunk:80 0.342052936554 80 \n", - "2 chunk:87 0.35776078701 87 \n", - "\n", - " content \n", - "0 Asia Pacific & Latin America 1,932 1,896 2 % 1... \n", - "1 Table of Contents\\nCONSOLIDATED OPERATING RESU... \n", - "2 Table of Contents\\nOPERATING SEGMENTS\\nAs disc... 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EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\",\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nOPERATING SEGMENTS\\nAs discussed in Note 15 \\u2014 Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments\\nare evidence of the structure of the Company's internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE\\nBrand sales activity.\\nThe breakdown of Revenues is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL 2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNorth America $ 21,608 $ 18,353 18 % 18 %$ 17,179 7 % 7 %\\nEurope, Middle East & Africa 13,418 12,479 8 % 21 % 11,456 9 % 12 %\\nGreater China 7,248 7,547 -4 % 4 % 8,290 -9 % -13 %\\nAsia Pacific & Latin America 6,431 5,955 8 % 17 % 5,343 11 % 16 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\n(1) The percent change excluding currency changes represents a non-GAAP financial measure. 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EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\",\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nOPERATING SEGMENTS\\nAs discussed in Note 15 \\u2014 Operating Segments and Related Information in the accompanying Notes to the Consolidated Financial Statements, our operating segments\\nare evidence of the structure of the Company's internal organization. The NIKE Brand segments are defined by geographic regions for operations participating in NIKE\\nBrand sales activity.\\nThe breakdown of Revenues is as follows:\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL 2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNorth America $ 21,608 $ 18,353 18 % 18 %$ 17,179 7 % 7 %\\nEurope, Middle East & Africa 13,418 12,479 8 % 21 % 11,456 9 % 12 %\\nGreater China 7,248 7,547 -4 % 4 % 8,290 -9 % -13 %\\nAsia Pacific & Latin America 6,431 5,955 8 % 17 % 5,343 11 % 16 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\n(1) The percent change excluding currency changes represents a non-GAAP financial measure. For further information, see \\\"Use of Non-GAAP Financial Measures\\\".\\n(2) For additional information on the transition of our NIKE Brand businesses within our CASA territory to a third-party distributor, see Note 18 \\u2014 Acquisitions and Divestitures of the Notes to ConsolidatedFinancial Statements contained in Item 8 of this Annual Report.\\n(3) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(4) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, butmanaged through our central foreign exchange risk management program.\\nThe primary financial measure used by the Company to evaluate performance is Earnings Before Interest and Taxes (\\\"EBIT\\\"). 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However, lead times for many of our products may make it more difficult for us to respond rapidly to new or changing product trends or consumer preferences. All\\nof our products are subject to changing consumer preferences that cannot be predicted with certainty. Our new products may not receive consumer acceptance as\\nconsumer preferences could shift rapidly to different types of performance products or away from these types of products altogether, and our future success depends in\\npart on our ability to anticipate and respond to these changes. If we fail to anticipate accurately and respond to trends and shifts in consumer preferences by adjusting the\\nmix of existing product offerings, developing new products, designs, styles and categories, and influencing sports and fitness preferences through extensive marketing, we\\ncould experience lower sales, excess inventories or lower profit margins, any of which could have an adverse effect on our results of operations and financial condition. In\\naddition, we market our products globally through a diverse spectrum of advertising and promotional programs and campaigns, including social media and other digital\\nadvertising networks. 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Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. 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Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. 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Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. 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Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. 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Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " id vector_distance content\n", + "0 chunk:88 0.337694585323 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n", + "1 chunk:80 0.342052936554 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n", + "2 chunk:87 0.35776078701 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n", + "3 chunk:83 0.37876611948 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..." + ] }, + "execution_count": 19, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], "source": [ @@ -2280,24 +2283,20 @@ "cell_type": "code", "execution_count": 20, "metadata": { - "id": "YZg4U21r1ujD", "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, + "id": "YZg4U21r1ujD", "outputId": "d3db5ac3-6ae9-42c4-aaee-874cecafe3ad" }, "outputs": [ { - "output_type": "execute_result", "data": { - "text/plain": [ - " id vector_distance content\n", - "0 chunk:88 0.337694585323 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n", - "1 chunk:80 0.342052936554 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n", - "2 chunk:87 0.35776078701 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n", - "3 chunk:83 0.37876611948 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..." - ], + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"pd\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"chunk:80\",\n \"chunk:83\",\n \"chunk:88\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vector_distance\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"0.342052936554\",\n \"0.37876611948\",\n \"0.337694585323\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"content\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"Table of Contents\\nCONSOLIDATED OPERATING RESULTS\\nREVENUES\\n(Dollars in millions) FISCAL2023 FISCAL2022 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES FISCAL2021 % CHANGE\\n% CHANGEEXCLUDINGCURRENCYCHANGES\\nNIKE, Inc. 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For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. 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Revenues:\\nNIKE Brand Revenues by:\\nFootwear $ 33,135 $ 29,143 14 % 20 %$ 28,021 4 % 4 %\\nApparel 13,843 13,567 2 % 8 % 12,865 5 % 6 %\\nEquipment 1,727 1,624 6 % 13 % 1,382 18 % 18 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTotal NIKE Brand Revenues $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nConverse 2,427 2,346 3 % 8 % 2,205 6 % 7 %\\nCorporate 27 (72) \\u2014 \\u2014 40 \\u2014 \\u2014 \\nTOTAL NIKE, INC. REVENUES $ 51,217 $ 46,710 10 % 16 %$ 44,538 5 % 6 %\\nSupplemental NIKE Brand Revenues Details:\\nNIKE Brand Revenues by:\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales through NIKE Direct 21,308 18,726 14 % 20 % 16,370 14 % 15 %\\nGlobal Brand Divisions 58 102 -43 % -43 % 25 308 % 302 %\\nTOTAL NIKE BRAND REVENUES $ 48,763 $ 44,436 10 % 16 %$ 42,293 5 % 6 %\\nNIKE Brand Revenues on a Wholesale Equivalent Basis :\\nSales to Wholesale Customers $ 27,397 $ 25,608 7 % 14 %$ 25,898 -1 % -1 %\\nSales from our Wholesale Operations to NIKE Direct Operations 12,730 10,543 21 % 27 % 9,872 7 % 7 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\nNIKE Brand Wholesale Equivalent Revenues by:\\nMen's $ 20,733 $ 18,797 10 % 17 %$ 18,391 2 % 3 %\\nWomen's 8,606 8,273 4 % 11 % 8,225 1 % 1 %\\nNIKE Kids' 5,038 4,874 3 % 10 % 4,882 0 % 0 %\\nJordan Brand 6,589 5,122 29 % 35 % 4,780 7 % 7 %\\nOthers (839) (915) 8 % -3 % (508) -80 % -79 %\\nTOTAL NIKE BRAND WHOLESALE EQUIVALENT REVENUES $ 40,127 $ 36,151 11 % 18 %$ 35,770 1 % 1 %\\n(1) The percent change excluding currency changes and the presentation of wholesale equivalent revenues represent non-GAAP financial measures. For further information, see \\\"Use of Non-GAAPFinancial Measures\\\".\\n(2) Global Brand Divisions revenues include NIKE Brand licensing and other miscellaneous revenues that are not part of a geographic operating segment.\\n(3) Corporate revenues primarily consist of foreign currency hedge gains and losses related to revenues generated by entities within the NIKE Brand geographic operating segments and Converse, but\\nmanaged through our central foreign exchange risk management program.\",\n \"Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nFor fiscal 2023, our consolidated gross profit increased 4% to $22,292 million compared to $21,479 million for fiscal 2022. Gross margin decreased 250 basis points to\\n43.5% for fiscal 2023 compared to 46.0% for fiscal 2022 due to the following:\\n*Wholesale equivalent\\nThe decrease in gross margin for fiscal 2023 was primarily due to:\\n\\u2022 Higher NIKE Brand product costs, on a wholesale equivalent basis, primarily due to higher input costs and elevated inbound freight and logistics costs as well as\\nproduct mix;\\n\\u2022 Lower margin in our NIKE Direct business, driven by higher promotional activity to liquidate inventory in the current period compared to lower promotional activity in\\nthe prior period resulting from lower available inventory supply;\\n\\u2022 Unfavorable changes in net foreign currency exchange rates, including hedges; and\\n\\u2022 Lower off-price margin, on a wholesale equivalent basis.\\nThis was partially offset by:\\n\\u2022 Higher NIKE Brand full-price ASP, net of discounts, on a wholesale equivalent basis, due primarily to strategic pricing actions and product mix; and\\n\\u2022 Lower other costs, primarily due to higher inventory obsolescence reserves recognized in Greater China in the fourth quarter of fiscal 2022.\\nTOTAL SELLING AND ADMINISTRATIVE EXPENSE\\n(Dollars in millions) FISCAL 2023 FISCAL 2022 % CHANGE FISCAL 2021 % CHANGE\\nDemand creation expense $ 4,060 $ 3,850 5 % $ 3,114 24 %\\nOperating overhead expense 12,317 10,954 12 % 9,911 11 %\\nTotal selling and administrative expense $ 16,377 $ 14,804 11 % $ 13,025 14 %\\n% of revenues 32.0 % 31.7 % 30 bps 29.2 % 250 bps\\n(1) Demand creation expense consists of advertising and promotion costs, including costs of endorsement contracts, complimentary product, television, digital and print advertising and media costs, brandevents and retail brand presentation.\\nFISCAL 2023 COMPARED TO FISCAL 2022\\nDemand creation expense increased 5% for fiscal 2023, primarily due to higher advertising and marketing expense and higher sports marketing expense. Changes in\\nforeign currency exchange rates decreased Demand creation expense by approximately 4 percentage points.\\nOperating overhead expense increased 12%, primarily due to higher wage-related expenses, NIKE Direct variable costs, strategic technology enterprise investments and\\nother administrative costs. Changes in foreign currency exchange rates decreased Operating overhead expense by approximately 3 percentage points.\\n(1)\",\n \"Asia Pacific & Latin America 1,932 1,896 2 % 1,530 24 %\\nGlobal Brand Divisions (4,841) (4,262) -14 % (3,656) -17 %\\nTOTAL NIKE BRAND $ 8,359 $ 8,406 -1 % $ 8,641 -3 %\\nConverse 676 669 1 % 543 23 %\\nCorporate (2,840) (2,219) -28 % (2,261) 2 %\\nTOTAL NIKE, INC. EARNINGS BEFORE INTEREST ANDTAXES $ 6,195 $ 6,856 -10 % $ 6,923 -1 %\\nEBIT margin 12.1 % 14.7 % 15.5 %\\nInterest expense (income), net (6) 205 \\u2014 262 \\u2014 \\nTOTAL NIKE, INC. INCOME BEFORE INCOME TAXES $ 6,201 $ 6,651 -7 % $ 6,661 0 %\\n(1) Total NIKE Brand EBIT, Total NIKE, Inc. EBIT and EBIT Margin represent non-GAAP financial measures. See \\\"Use of Non-GAAP Financial Measures\\\" for further information.\\n(1) (1)\\n(2)\\n(3)\\n(4)\\n(1)\\n(1)\\n(1)\\n2023 FORM 10-K 36\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } + "text/plain": [ + " id vector_distance content\n", + "0 chunk:88 0.337694585323 Asia Pacific & Latin America 1,932 1,896 2 % 1...\n", + "1 chunk:80 0.342052936554 Table of Contents\\nCONSOLIDATED OPERATING RESU...\n", + "2 chunk:87 0.35776078701 Table of Contents\\nOPERATING SEGMENTS\\nAs disc...\n", + "3 chunk:83 0.37876611948 Table of Contents\\nGROSS MARGIN\\nFISCAL 2023 C..." + ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], "source": [ @@ -2606,22 +2609,22 @@ "cell_type": "code", "execution_count": 21, "metadata": { - "id": "_esLGYzbT6LG", "colab": { "base_uri": "https://localhost:8080/" }, + "id": "_esLGYzbT6LG", "outputId": "d3314a08-8746-4239-dcb2-e7e41b51c640" }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 21, "metadata": {}, - "execution_count": 21 + "output_type": "execute_result" } ], "source": [ @@ -2785,7 +2788,6 @@ "cell_type": "code", "execution_count": 25, "metadata": { - "id": "9M_iU6_hbv0J", "colab": { "base_uri": "https://localhost:8080/", "height": 264, @@ -2847,82 +2849,83 @@ "fe546bd8269d48eba90fb932784eea43" ] }, + "id": "9M_iU6_hbv0J", "outputId": "b9fc43d9-883a-4795-8a37-8a2f4c545892" }, "outputs": [ { - "output_type": "display_data", "data": { - "text/plain": [ - "Batches: 0%| | 0/1 [00:00\"Open\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sBIoQ08FI_d_" + }, + "source": [ + "## Environment Setup\n", + "\n", + "### Install Python Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pf1wE6aXvofJ", + "outputId": "cf0247c6-03b0-4314-c389-96867b60fc1a" + }, + "outputs": [ + { + 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available.**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wojUwDA6H5GH" + }, + "source": [ + "#### For Colab\n", + "Use the shell script below to download, extract, and install [Redis Stack](https://redis.io/docs/getting-started/install-stack/) directly from the Redis package archive." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZYmnw8E16UvK", + "outputId": "db7b19c1-c9d5-45f2-92f0-caf045216234" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb jammy main\n", + "Starting redis-stack-server, database path /var/lib/redis-stack\n" + ] + } + ], + "source": [ + "# NBVAL_SKIP\n", + "%%sh\n", + "curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg\n", + "echo \"deb [signed-by=/usr/share/keyrings/redis-archive-keyring.gpg] https://packages.redis.io/deb $(lsb_release -cs) main\" | sudo tee /etc/apt/sources.list.d/redis.list\n", + "sudo apt-get update > /dev/null 2>&1\n", + "sudo apt-get install redis-stack-server > /dev/null 2>&1\n", + "redis-stack-server --daemonize yes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OdWGcmVeH-Oy" + }, + "source": [ + "#### For Alternative Environments\n", + "There are many ways to get the necessary redis-stack instance running\n", + "1. On cloud, deploy a [FREE instance of Redis in the cloud](https://redis.io/cloud/). Or, if you have your\n", + "own version of Redis Enterprise running, that works too!\n", + "2. Per OS, [see the docs](https://redis.io/docs/latest/operate/oss_and_stack/install/install-stack/)\n", + "3. With docker:\n", + "\n", + " ```bash\n", + " docker run -d --name redis-stack-server -p 6379:6379 redis/redis-stack-server:latest\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nBgAPO0UIFGd" + }, + "source": [ + "### Define the Redis Connection URL\n", + "\n", + "By default this notebook connects to the local instance of Redis Stack. **If you have your own Redis Enterprise instance** - replace `REDIS_PASSWORD`, `REDIS_HOST` and `REDIS_PORT` values with your own." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "vhPBR4sS6We9" + }, + "outputs": [], + "source": [ + "import os\n", + "import warnings\n", + "\n", + "REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n", + "REDIS_PORT = os.getenv(\"REDIS_PORT\", \"6379\")\n", + "REDIS_PASSWORD = os.getenv(\"REDIS_PASSWORD\", \"\")\n", + "\n", + "# Replace values above with your own if using Redis Cloud instance\n", + "#REDIS_HOST=\"redis-18374.c253.us-central1-1.gce.cloud.redislabs.com\"\n", + "#REDIS_PORT=18374\n", + "#REDIS_PASSWORD=\"1TNxTEdYRDgIDKM2gDfasupCADXXXX\"\n", + "\n", + "# If SSL is enabled on the endpoint, use rediss:// as the URL prefix\n", + "REDIS_URL = f\"redis://:{REDIS_PASSWORD}@{REDIS_HOST}:{REDIS_PORT}\"\n", + "\n", + "# See https://docs.feast.dev/reference/online-stores/redis for details on Feast connection to Redis\n", + "REDIS_URL_FEAST = f\"{REDIS_HOST}:{REDIS_PORT},ssl=false,password={REDIS_PASSWORD}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZSEHUJSVIICm" + }, + "source": [ + "## Load features dataset\n", + "\n", + "Below we will make a `creditscore/` directory which will be the home of our Feast repo. We'll create and store additional files there down the road. For now we are loading dataset files into `creditscore/data`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "v15le9QHUDx1" + }, + "outputs": [], + "source": [ + "%%bash\n", + "mkdir creditscore\n", + "mkdir creditscore/data\n", + "wget https://github.com/antonum/feast-redis/raw/refs/heads/main/creditscore/data/credit_history.parquet -q -P creditscore/data\n", + "wget https://github.com/antonum/feast-redis/raw/refs/heads/main/creditscore/data/zipcode_table.parquet -q -P creditscore/data\n", + "wget https://github.com/antonum/feast-redis/raw/refs/heads/main/creditscore/data/loan_table.parquet -q -P creditscore/data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aPN5iDBmFZvU" + }, + "source": [ + "### Creating feature_store.yaml\n", + "\n", + "`feature_store.yaml` is used to configure a feature store with Feast. The file must be located at the root of a feature repository `creditscore/`.\n", + "\n", + "See [Redis | Feast Documentation](https://docs.feast.dev/reference/online-stores/redis) for the details of configuring Redis as an online store." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "rpL-6kwUWQbN" + }, + "outputs": [], + "source": [ + "feature_store_config = \\\n", + "f\"\"\"project: creditscore\n", + "registry: data/registry.db\n", + "provider: local\n", + "online_store:\n", + " type: redis\n", + " connection_string: {REDIS_URL_FEAST}\n", + "entity_key_serialization_version: 2\n", + "\"\"\"\n", + "\n", + "with open('creditscore/feature_store.yaml', \"w\") as file:\n", + " file.write(feature_store_config)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5iIlkheBMAFj", + "outputId": "e6fbefb8-4661-4b31-dece-53a3c172a491" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "project: creditscore\n", + "registry: data/registry.db\n", + "provider: local\n", + "online_store:\n", + " type: redis\n", + " connection_string: localhost:6379,ssl=false,password=\n", + "entity_key_serialization_version: 2\n" + ] + } + ], + "source": [ + "# Print our feature_store.yaml\n", + "! cat creditscore/feature_store.yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xbl45OPxGbFY" + }, + "source": [ + "### Feature Definitions\n", + "\n", + "A feature repository can also contain one or more Python files that contain feature definitions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W_9xklCQWsQI", + "outputId": "4ad9db17-c6bd-4406-a724-0a5a73f01733" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Writing creditscore/features.py\n" + ] + } + ], + "source": [ + "%%writefile creditscore/features.py\n", + "\n", + "from datetime import timedelta\n", + "\n", + "from feast import (\n", + " Entity,\n", + " Field,\n", + " FeatureView,\n", + " ValueType,\n", + " FileSource\n", + " )\n", + "from feast.types import Float32, Int64, String\n", + "\n", + "\n", + "# Feature Definitions\n", + "\n", + "## Zipcode Features\n", + "zipcode = Entity(\n", + " name=\"zipcode\",\n", + " value_type=ValueType.STRING\n", + ")\n", + "zipcode_source = FileSource(\n", + " path=\"data/zipcode_table.parquet\",\n", + " timestamp_field=\"event_timestamp\",\n", + " #event_timestamp_column=\"event_timestamp\",\n", + " created_timestamp_column=\"created_timestamp\",\n", + ")\n", + "zipcode_features = FeatureView(\n", + " name=\"zipcode_features\",\n", + " entities=[zipcode],\n", + " ttl=timedelta(days=3650),\n", + " schema=[\n", + " Field(name=\"city\", dtype=String),\n", + " Field(name=\"state\", dtype=String),\n", + " Field(name=\"location_type\", dtype=String),\n", + " Field(name=\"tax_returns_filed\", dtype=Int64),\n", + " Field(name=\"population\", dtype=Int64),\n", + " Field(name=\"total_wages\", dtype=Int64),\n", + " ],\n", + " source=zipcode_source,\n", + ")\n", + "\n", + "\n", + "## Credit History Features\n", + "dob_ssn = Entity(\n", + " name=\"dob_ssn\",\n", + " description=\"Date of birth and last four digits of social security number\",\n", + " value_type=ValueType.STRING\n", + ")\n", + "credit_history_source = FileSource(\n", + " path=\"data/credit_history.parquet\",\n", + " timestamp_field=\"event_timestamp\",\n", + " #event_timestamp_column=\"event_timestamp\",\n", + " created_timestamp_column=\"created_timestamp\",\n", + "\n", + ")\n", + "credit_history = FeatureView(\n", + " name=\"credit_history\",\n", + " entities=[dob_ssn],\n", + " ttl=timedelta(days=3650),\n", + " schema=[\n", + " Field(name=\"dob_ssn\", dtype=String), # Add entity column for dob_ssn\n", + " Field(name=\"credit_card_due\", dtype=Int64),\n", + " Field(name=\"mortgage_due\", dtype=Int64),\n", + " Field(name=\"student_loan_due\", dtype=Int64),\n", + " Field(name=\"vehicle_loan_due\", dtype=Int64),\n", + " Field(name=\"hard_pulls\", dtype=Int64),\n", + " Field(name=\"missed_payments_2y\", dtype=Int64),\n", + " Field(name=\"missed_payments_1y\", dtype=Int64),\n", + " Field(name=\"missed_payments_6m\", dtype=Int64),\n", + " Field(name=\"bankruptcies\", dtype=Int64),\n", + " ],\n", + " source=credit_history_source,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Go53F4ZOnkZf" + }, + "source": [ + "### Create Feast repository" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8Ni6sGGjXDks", + "outputId": "d51d4097-2945-4ab8-ba29-813fff333d00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/creditscore\n", + "No project found in the repository. Using project name creditscore defined in feature_store.yaml\n", + "Applying changes for project creditscore\n", + "Deploying infrastructure for \u001b[1m\u001b[32mzipcode_features\u001b[0m\n", + "Deploying infrastructure for \u001b[1m\u001b[32mcredit_history\u001b[0m\n" + ] + } + ], + "source": [ + "%cd creditscore/\n", + "!feast apply" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nZseuwvTnqbH" + }, + "source": [ + "### Materialize features into Redis\n", + "\n", + "Load data from feature views (parquet files) into the online store (Redis). Use `feast materialize-incremental` to update online store with changes since the last `materialize` call." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PH8wOOLUv75g", + "outputId": "18cb39e3-e037-4ff5-982c-55d86bfa3b22" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Materializing \u001b[1m\u001b[32m2\u001b[0m feature views to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m into the \u001b[1m\u001b[32mredis\u001b[0m online store.\n", + "\n", + "\u001b[1m\u001b[32mzipcode_features\u001b[0m from \u001b[1m\u001b[32m2015-01-28 17:19:20+00:00\u001b[0m to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m:\n", + "100%|██████████████████████████████████████████████████████| 28844/28844 [00:02<00:00, 12728.58it/s]\n", + "\u001b[1m\u001b[32mcredit_history\u001b[0m from \u001b[1m\u001b[32m2015-01-28 17:19:23+00:00\u001b[0m to \u001b[1m\u001b[32m2025-01-24 16:57:10+00:00\u001b[0m:\n", + "100%|██████████████████████████████████████████████████████| 28633/28633 [00:02<00:00, 10716.44it/s]\n", + "/content\n" + ] + } + ], + "source": [ + "warnings.simplefilter(\"ignore\", DeprecationWarning)\n", + "\n", + "!feast materialize-incremental 2025-01-24T16:57:10\n", + "%cd .." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e1L8uAaKuoge" + }, + "source": [ + "## Retreive feature vector from the Redis Online Store\n", + "\n", + "`feast apply` and `feast materialize` initialized our feature store, so now we can request features from the Redis online store with `store.get_online_features()` call." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u7Yo-3BBgLFy", + "outputId": "872bb552-bda4-4ccd-f473-6ddee6c692d8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'zipcode': ['76104'],\n", + " 'dob_ssn': ['19630621_4278'],\n", + " 'total_wages': [142325465],\n", + " 'state': ['TX'],\n", + " 'tax_returns_filed': [6058],\n", + " 'city': ['FORT WORTH'],\n", + " 'location_type': ['PRIMARY'],\n", + " 'population': [10534],\n", + " 'hard_pulls': [1],\n", + " 'missed_payments_2y': [0],\n", + " 'bankruptcies': [0],\n", + " 'missed_payments_6m': [0],\n", + " 'credit_card_due': [3343],\n", + " 'student_loan_due': [44375],\n", + " 'mortgage_due': [378847],\n", + " 'vehicle_loan_due': [11506],\n", + " 'missed_payments_1y': [0]}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from feast import FeatureStore\n", + "warnings.simplefilter(\"ignore\", DeprecationWarning)\n", + "\n", + "\n", + "store = FeatureStore(repo_path=\"creditscore/\")\n", + "feast_features = [\n", + " \"zipcode_features:city\",\n", + " \"zipcode_features:state\",\n", + " \"zipcode_features:location_type\",\n", + " \"zipcode_features:tax_returns_filed\",\n", + " \"zipcode_features:population\",\n", + " \"zipcode_features:total_wages\",\n", + " \"credit_history:credit_card_due\",\n", + " \"credit_history:mortgage_due\",\n", + " \"credit_history:student_loan_due\",\n", + " \"credit_history:vehicle_loan_due\",\n", + " \"credit_history:hard_pulls\",\n", + " \"credit_history:missed_payments_2y\",\n", + " \"credit_history:missed_payments_1y\",\n", + " \"credit_history:missed_payments_6m\",\n", + " \"credit_history:bankruptcies\",\n", + " ]\n", + "zipcode = \"76104\"\n", + "dob_ssn = \"19630621_4278\"\n", + "\n", + "feature_vector = store.get_online_features(\n", + " features = feast_features,\n", + " entity_rows = [{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}]\n", + ")\n", + "feature_vector.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tqDuixcKuvYL" + }, + "source": [ + "## Examine source data\n", + "\n", + "`credit_history.parquet` and `zipcode_table.parquet` contains data that would be exposed by our featurestoe as both online and offline features. `loan_table.parquet` is used only to train the model and contains historical loan request submissions and target value as approve/deny in `loan_status`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "H2qtjYqQx01b", + "outputId": "c88c250c-ccfe-4a42-ebf9-41528367589b" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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Load model\n", + " if Path(self.model_filename).exists():\n", + " self.classifier = joblib.load(self.model_filename)\n", + " else:\n", + " self.classifier = tree.DecisionTreeClassifier()\n", + "\n", + " # Load ordinal encoder\n", + " if Path(self.encoder_filename).exists():\n", + " self.encoder = joblib.load(self.encoder_filename)\n", + " else:\n", + " self.encoder = OrdinalEncoder()\n", + "\n", + " # Set up feature store\n", + " self.fs = feast.FeatureStore(repo_path=\"creditscore/\")\n", + " #if secret and (\":\" in secret):\n", + " # self.fs.config.online_store.connection_string=secret\n", + "\n", + " def train(self, loans):\n", + " train_X, train_Y = self._get_training_features(loans)\n", + "\n", + " self.classifier.fit(train_X[sorted(train_X)], train_Y)\n", + " joblib.dump(self.classifier, self.model_filename)\n", + "\n", + " def _get_training_features(self, loans):\n", + " training_df = self.fs.get_historical_features(\n", + " entity_df=loans, features=self.feast_features\n", + " ).to_df()\n", + "\n", + " self._fit_ordinal_encoder(training_df)\n", + " self._apply_ordinal_encoding(training_df)\n", + " #print(training_df.head())\n", + " train_X = training_df[\n", + " training_df.columns.drop(self.target)\n", + " .drop(\"event_timestamp\")\n", + " .drop(\"created_timestamp__\")\n", + " .drop(\"loan_id\")\n", + " .drop(\"zipcode\")\n", + " .drop(\"dob_ssn\")\n", + " ]\n", + " train_X = train_X.reindex(sorted(train_X.columns), axis=1)\n", + " train_Y = training_df.loc[:, self.target]\n", + "\n", + " return train_X, train_Y\n", + "\n", + " def _fit_ordinal_encoder(self, requests):\n", + " self.encoder.fit(requests[self.categorical_features])\n", + " joblib.dump(self.encoder, self.encoder_filename)\n", + "\n", + " def _apply_ordinal_encoding(self, requests):\n", + " requests[self.categorical_features] = self.encoder.transform(\n", + " requests[self.categorical_features]\n", + " )\n", + "\n", + " def predict(self, request):\n", + " # Get online features from Feast\n", + " feature_vector = self._get_online_features_from_feast(request)\n", + "\n", + " # Join features to request features\n", + " features = request.copy()\n", + " features.update(feature_vector)\n", + " features_df = pd.DataFrame.from_dict(features)\n", + "\n", + " # Apply ordinal encoding to categorical features\n", + " self._apply_ordinal_encoding(features_df)\n", + "\n", + " # Sort columns\n", + " features_df = features_df.reindex(sorted(features_df.columns), axis=1)\n", + "\n", + " # Drop unnecessary columns\n", + " features_df = features_df[features_df.columns.drop(\"zipcode\").drop(\"dob_ssn\")]\n", + "\n", + " # Make prediction\n", + " features_df[\"prediction\"] = self.classifier.predict(features_df)\n", + "\n", + " # return result of credit scoring\n", + " return features_df[\"prediction\"].iloc[0]\n", + "\n", + " def _get_online_features_from_feast(self, request):\n", + " zipcode = request[\"zipcode\"][0]\n", + " dob_ssn = request[\"dob_ssn\"][0]\n", + "\n", + " return self.fs.get_online_features(\n", + " entity_rows=[{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}],\n", + " features=self.feast_features,\n", + " ).to_dict()\n", + "\n", + " def is_model_trained(self):\n", + " try:\n", + " check_is_fitted(self.classifier, \"tree_\")\n", + " except NotFittedError:\n", + " return False\n", + " return True\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aJMddzepop_-" + }, + "source": [ + "## Initialize the model\n", + "\n", + "Now we need to train the model and make a sample prediction. After training is completed you'll see `model.bin` and `encoder.bin` files in the filesystem." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Nw99Ey_0EmZ0", + "outputId": "ec762747-da85-4f93-cc98-d165b33258e5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model not trained. Performing training.\n" + ] + } + ], + "source": [ + "# Create model\n", + "model = LoadRequestModel()\n", + "\n", + "# Train model (using Parquet for zipcode and credit history features)\n", + "if not model.is_model_trained():\n", + " print(\"Model not trained. Performing training.\")\n", + " # Get historic loan data\n", + " loans = pd.read_parquet(\"creditscore/data/loan_table.parquet\")\n", + " model.train(loans)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mKhKRnCcLrwo" + }, + "source": [ + "### Make a Loan Request\n", + "\n", + "We will now use our trained ML model and feature store to predict whether or not you would get a loan.\n", + "\n", + "While making a loan request, make sure that `dob_ssn` and `zipcode` values do exist in the source datasets. You can examine source datasets with `pd.read_parquet(\"creditscore/data/credit_history.parquet\")`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 127, + "referenced_widgets": [ + "d9a931929d2b4eda8790379c157d7060", + "e4dbf90e7a1943d6b513ebbaea4620fc", + "70a1a976057f4a7bae49c65b4aed9f4e", + "be98a638e1de496495281282d3b5afa2", + "580c994253f6470a8e140f2cc8370328", + "259feb51fc7a4291b8a0fdeb757dd0af", + "2a017fb94aed4714b39cdb890a72c364", + "3c8eebe78d464e4d913a89628fe1c5dd", + "31603e43689148acad80127c15e2b711" + ] + }, + "id": "28yr7TDhlOfa", + "outputId": "de0b7e4c-26a9-4197-af47-4c05e18bb372" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Select amounts below:\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d9a931929d2b4eda8790379c157d7060", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntSlider(value=159000, description='Income: ', max=1000000)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "be98a638e1de496495281282d3b5afa2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntSlider(value=5000, description='Loan Amount: ', max=1000000)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2a017fb94aed4714b39cdb890a72c364", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "IntSlider(value=16, description='Interest Rate: ', max=90, min=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import ipywidgets as widgets\n", + "\n", + "# initialize loan request with sample data\n", + "loan_request = {\n", + " \"zipcode\": [76104],\n", + " \"dob_ssn\": [\"19630621_4278\"],\n", + " \"person_age\": [63],\n", + " \"person_income\": [159000],\n", + " \"person_home_ownership\": [\"RENT\"],\n", + " \"person_emp_length\": [123.0],\n", + " \"loan_intent\": [\"PERSONAL\"],\n", + " \"loan_amnt\": [5000],\n", + " \"loan_int_rate\": [16.02],\n", + "}\n", + "\n", + "\n", + "slider_income = widgets.IntSlider(loan_request[\"person_income\"][0], max=1000000, min=0, description=\"Income: \")\n", + "slider_amount = widgets.IntSlider(loan_request[\"loan_amnt\"][0], max=1000000, min=0, description=\"Loan Amount: \")\n", + "slider_int_rate = widgets.IntSlider(loan_request[\"loan_int_rate\"][0], max=90, min=1, description=\"Interest Rate: \")\n", + "\n", + "print(\"Select amounts below:\")\n", + "display(slider_income, slider_amount, slider_int_rate)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yFhF1XpWPbTG", + "outputId": "dc18d87b-548e-4097-ce63-d76d97dca85d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan rejected!\n" + ] + } + ], + "source": [ + "loan_request[\"person_income\"] = [slider_income.value]\n", + "loan_request[\"loan_amnt\"] = [slider_amount.value]\n", + "loan_request[\"loan_int_rate\"] = [slider_int_rate.value]\n", + "\n", + "\n", + "# Make online prediction (using Redis for retrieving online features)\n", + "result = model.predict(loan_request)\n", + "\n", + "if result == 0:\n", + " print(\"Loan approved!\")\n", + "elif result == 1:\n", + " print(\"Loan rejected!\")\n", + "\n", + "warnings.simplefilter(\"ignore\", DeprecationWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dvWW46znVObR" + }, + "source": [ + "Let's inspect an individual loan request payload." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 332 + }, + "id": "MI6ggOO1pH65", + "outputId": "92368c70-3244-4d91-9bef-c246e81d7c85" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"loan_request_df\",\n \"rows\": 9,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 9,\n \"samples\": [\n 5000,\n \"19630621_4278\",\n 123.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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student_loan_due44375
mortgage_due378847
vehicle_loan_due11506
missed_payments_1y0
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\n" + ], + "text/plain": [ + " 0\n", + "zipcode 76104\n", + "dob_ssn 19630621_4278\n", + "person_age 63\n", + "person_income 159000\n", + "person_home_ownership RENT\n", + "person_emp_length 123.0\n", + "loan_intent PERSONAL\n", + "loan_amnt 5000\n", + "loan_int_rate 16\n", + "total_wages 142325465\n", + "state TX\n", + "tax_returns_filed 6058\n", + "city FORT WORTH\n", + "location_type PRIMARY\n", + "population 10534\n", + "hard_pulls 1\n", + "missed_payments_2y 0\n", + "bankruptcies 0\n", + "missed_payments_6m 0\n", + "credit_card_due 3343\n", + "student_loan_due 44375\n", + "mortgage_due 378847\n", + "vehicle_loan_due 11506\n", + "missed_payments_1y 0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features = loan_request.copy()\n", + "features.update(feature_vector)\n", + "features_df = pd.DataFrame.from_dict(features)\n", + "features_df.transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fiV0HSXNw4MA", + "outputId": "71bff765-0448-4242-c931-45d8a6446faa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loan rejected!\n" + ] + } + ], + "source": [ + "result = model.predict(loan_request)\n", + "\n", + "if result == 0:\n", + " print(\"Loan approved!\")\n", + "elif result == 1:\n", + " print(\"Loan rejected!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c9LUGgDZIN7l" + }, + "source": [ + "## Benchmarking\n", + "\n", + "The key advantage of Redis as a Online feature store is it's ability to very quickly retreive features on request. Below, we'll retreive the same data from Online store (Redis) and from the Offline store (parquet) and measure execution time." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "E0yBnM3VV09H" + }, + "outputs": [], + "source": [ + "store = FeatureStore(repo_path=\"creditscore/\")\n", + "feast_features = [\n", + " \"zipcode_features:city\",\n", + " \"zipcode_features:state\",\n", + " \"zipcode_features:location_type\",\n", + " \"zipcode_features:tax_returns_filed\",\n", + " \"zipcode_features:population\",\n", + " \"zipcode_features:total_wages\",\n", + " \"credit_history:credit_card_due\",\n", + " \"credit_history:mortgage_due\",\n", + " \"credit_history:student_loan_due\",\n", + " \"credit_history:vehicle_loan_due\",\n", + " \"credit_history:hard_pulls\",\n", + " \"credit_history:missed_payments_2y\",\n", + " \"credit_history:missed_payments_1y\",\n", + " \"credit_history:missed_payments_6m\",\n", + " \"credit_history:bankruptcies\",\n", + " ]\n", + "zipcode = \"76104\"\n", + "dob_ssn = \"19630621_4278\"\n", + "\n", + "entity_rows=[{\"zipcode\": zipcode, \"dob_ssn\": dob_ssn}]\n", + "entity_rows_df=pd.DataFrame(entity_rows)\n", + "entity_rows_df[\"event_timestamp\"]=pd.to_datetime(\"2020-04-26 18:01:04.746575\")\n", + "entity_rows_df['zipcode'] = entity_rows_df['zipcode'].astype(int)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EwZW_IdvWPMx" + }, + "source": [ + "Online feature store retrieval benchmark:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "khcx8l4WWFST", + "outputId": "29280860-9f59-41d7-9ee2-df094c69eaf0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18.3 ms ± 4.24 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "online_features = store.get_online_features(\n", + " features = feast_features,\n", + " entity_rows = entity_rows\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "stN6hs52WRWo" + }, + "source": [ + "Offline feature store retrieval benchmark:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cMXzXeJxWTxI", + "outputId": "f6856e54-5bf5-4caf-8681-de6c37372e47" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.06 s ± 874 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + ] + } + ], + "source": [ + "%%timeit\n", + "\n", + "offline_features= store.get_historical_features(\n", + " entity_df = entity_rows_df,\n", + " features = feast_features\n", + ").to_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9RvUwM40W86O" + }, + "source": [ + ">Note: That's more than a 100x difference. (typically)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8vGFAJh58D7I", + "outputId": "c3fc6d99-7f6f-403c-f0d0-054c1815862d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019700708_3658creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019770709_1366creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0034112creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019820223_6526creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0053566creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019740104_7765creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019631107_1473creditscore',\n", + " 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b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019630929_6832creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019460324_1049creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019640825_3358creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0038167creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047106creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019550316_4272creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x002643creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019991228_7925creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019740609_4875creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019820924_1126creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0042303creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019871226_1226creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019590818_8005creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0051466creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0020000319_6455creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0093584creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019911204_8105creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0086403creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019790323_7676creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0092342creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0071742creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0089043creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0046774creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047807creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0091042creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019530105_9941creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019800121_4411creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0038668creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0030006creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019990224_2613creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019690403_1557creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0061319creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019610703_1330creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0093608creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0023609creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0047244creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019990503_5880creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0081141creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0068818creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019800413_2593creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019880505_1690creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0091203creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500511_7145creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019670203_8936creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0032060creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019831003_7385creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019651104_2938creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019790704_4149creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0059802creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0013865creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0011377creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0016827creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0016201creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019510305_7529creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019600707_2475creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019510225_8060creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019710511_1417creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x04\\x00\\x00\\x005069creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0027559creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019571111_4225creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0035051creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0061802creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500919_5275creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019600215_7725creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0019009creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019500124_5400creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0066202creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0058335creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019930811_1809creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019480210_6097creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019700515_8374creditscore',\n", + " b'\\x02\\x00\\x00\\x00dob_ssn\\x02\\x00\\x00\\x00\\r\\x00\\x00\\x0019550528_9708creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0084711creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0071082creditscore',\n", + " b'\\x02\\x00\\x00\\x00zipcode\\x02\\x00\\x00\\x00\\x05\\x00\\x00\\x0027964creditscore',\n", + " ...]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# retreive sample of keys from redis\n", + "from redis import Redis\n", + "\n", + "redis_client = Redis.from_url(REDIS_URL)\n", + "redis_client.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yaoQYnpgEPzm" + }, + "source": [ + "### Cleanup" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "EKqGsgQGEIDP" + }, + "outputs": [], + 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(159/159), done.\n" + "remote: Enumerating objects: 473, done.\u001b[K\n", + "remote: Counting objects: 100% (225/225), done.\u001b[K\n", + "remote: Compressing objects: 100% (141/141), done.\u001b[K\n", + "remote: Total 473 (delta 146), reused 114 (delta 79), pack-reused 248 (from 2)\u001b[K\n", + "Receiving objects: 100% (473/473), 25.68 MiB | 7.02 MiB/s, done.\n", + "Resolving deltas: 100% (212/212), done.\n" ] } ], @@ -78,7 +78,7 @@ "id": "c620286e", "metadata": { "id": "c620286e", - "outputId": "d69d35a0-29b2-4a9c-aa13-acf27d85a414", + "outputId": "f9c571ae-5c44-40eb-b050-bab78ec90fa2", "colab": { "base_uri": "https://localhost:8080/" } @@ -88,10 +88,10 @@ "output_type": "stream", "name": "stdout", "text": [ - "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/261.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m261.4/261.4 kB\u001b[0m \u001b[31m7.7 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