diff --git a/financial-payment-pipeline.ipynb b/financial-payment-pipeline.ipynb index f9c0968..da363f8 100644 --- a/financial-payment-pipeline.ipynb +++ b/financial-payment-pipeline.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "e34f8c80-6584-4e80-981c-0f17e1584ebf", "metadata": { "tags": [] @@ -36,7 +36,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "> 2024-01-30 08:51:04,839 [info] Project loaded successfully: {'project_name': 'sagemaker'}\n" + "> 2024-02-13 10:14:19,611 [info] Project loaded successfully: {'project_name': 'sagemaker'}\n" ] } ], @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "9fb9bf8f-dfc5-4b9f-8d63-d423ae326630", "metadata": { "tags": [] @@ -70,7 +70,7 @@ { "data": { "text/html": [ - "
Pipeline running (id=c5935692-c4e0-4bb9-92a7-b1d60e74bedd), click here to view the details in MLRun UI
" + "
Pipeline running (id=24e028c0-cc0d-45c3-bdc7-ae29c02d1ee1), click here to view the details in MLRun UI
" ], "text/plain": [ "" @@ -85,52 +85,52 @@ "\n", "\n", - "\n", "\n", - "\n", + "\n", "\n", "kfp\n", - "\n", - "\n", + "\n", + "\n", "\n", - "fraud-detection-pipeline-6lj7n-2077403528\n", - "\n", - "\n", - "\n", - "\n", - "deploy-serving\n", + "fraud-detection-pipeline-47fhj-3902555814\n", + "\n", + "train\n", "\n", - "\n", + "\n", "\n", - "fraud-detection-pipeline-6lj7n-3541775745\n", - "\n", - "evaluate\n", + "fraud-detection-pipeline-47fhj-973894202\n", + "\n", + "\n", + "\n", + "\n", + "deploy-serving\n", "\n", - "\n", - "\n", - "fraud-detection-pipeline-6lj7n-4078108240\n", - "\n", - "train\n", - "\n", - "\n", + "\n", "\n", - "fraud-detection-pipeline-6lj7n-4078108240->fraud-detection-pipeline-6lj7n-2077403528\n", - "\n", - "\n", + "fraud-detection-pipeline-47fhj-3902555814->fraud-detection-pipeline-47fhj-973894202\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "fraud-detection-pipeline-47fhj-4183868495\n", + "\n", + "evaluate\n", "\n", - "\n", + "\n", "\n", - "fraud-detection-pipeline-6lj7n-4078108240->fraud-detection-pipeline-6lj7n-3541775745\n", - "\n", - "\n", + "fraud-detection-pipeline-47fhj-3902555814->fraud-detection-pipeline-47fhj-4183868495\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -139,7 +139,7 @@ { "data": { "text/html": [ - "

Run Results

[info] Workflow c5935692-c4e0-4bb9-92a7-b1d60e74bedd finished, state=Succeeded


click the hyper links below to see detailed results
\n", + "

Run Results

[info] Workflow 24e028c0-cc0d-45c3-bdc7-ae29c02d1ee1 finished, state=Succeeded


click the hyper links below to see detailed results
\n", " \n", " \n", " \n", @@ -152,16 +152,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -180,10 +180,10 @@ { "data": { "text/plain": [ - "c5935692-c4e0-4bb9-92a7-b1d60e74bedd" + "24e028c0-cc0d-45c3-bdc7-ae29c02d1ee1" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "1421e3e9-dad2-4983-88d7-c9d48cb49fb2", "metadata": {}, "outputs": [], @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "fff74774-7422-4c8f-af9f-e39ee2505f08", "metadata": {}, "outputs": [], @@ -255,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "5716bca9-ac11-44cf-b9da-b895dba9055f", "metadata": {}, "outputs": [ @@ -263,19 +263,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "> 2024-01-30 09:00:00,436 [info] invoking function: {'method': 'POST', 'path': 'http://nuclio-sagemaker-yoni-serving.default-tenant.svc.cluster.local:8080/predict'}\n" + "> 2024-02-13 10:23:44,657 [info] Invoking function: {'method': 'POST', 'path': 'http://sagemaker-admin-serving-sagemaker-admin.default-tenant.app.cust-cs-il-353.iguazio-cd2.com//predict'}\n" ] }, { "data": { "text/plain": [ - "{'id': 'cce12b91-6890-4de4-a584-0b23aa27aaac',\n", + "{'id': 'f0d430c4-79e8-426a-bd15-9acc0941bf84',\n", " 'model_name': 'xgboost-model',\n", - " 'predictions': [1],\n", - " 'confidences': [0.43330907821655273]}" + " 'predictions': [17],\n", + " 'confidences': [0.3079691231250763]}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -287,9 +287,9 @@ ], "metadata": { "kernelspec": { - "display_name": "mlrun-base", + "display_name": "smdemo", "language": "python", - "name": "conda-env-mlrun-base-py" + "name": "smdemo" }, "language_info": { "codemirror_mode": { @@ -301,7 +301,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/financial_payment_classification.ipynb b/financial_payment_classification.ipynb index b0d8037..1d32fd0 100644 --- a/financial_payment_classification.ipynb +++ b/financial_payment_classification.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "attachments": {}, "cell_type": "markdown", "id": "01b5c703", "metadata": {}, @@ -9,6 +10,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "6498f087", "metadata": {}, @@ -23,6 +25,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "c2e49281", "metadata": {}, @@ -67,63 +70,29 @@ "cell_type": "code", "execution_count": 1, "id": "fff19d6b", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "outputs": [], - "source": [ - "import sys\n", - "!{sys.executable} -m pip install --upgrade pip --quiet # upgrade pip to the latest vesion\n", - "!{sys.executable} -m pip install --upgrade sagemaker --quiet # upgrade SageMaker to the latest vesion\n", - "!{sys.executable} -m pip install --upgrade boto --quiet # upgrade boto to the latest vesion" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "32a9c9d4-1515-4d8e-ad4c-e2f88544e67f", "metadata": {}, - "outputs": [], - "source": [ - "import mlrun" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "1243f1c6-8043-41f6-a64f-1da7017622ac", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2024-01-25 14:10:06,832 [info] Project loaded successfully: {'project_name': 'sagemaker'}\n" + "\u001b[33mWARNING: Ignoring invalid distribution -yyaml (/home/sagemaker-user/.conda/envs/smdemo/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -yyaml (/home/sagemaker-user/.conda/envs/smdemo/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -yyaml (/home/sagemaker-user/.conda/envs/smdemo/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -yyaml (/home/sagemaker-user/.conda/envs/smdemo/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n", + "\u001b[0m" ] } ], "source": [ - "project = mlrun.get_or_create_project(\n", - " name=\"sagemaker\", \n", - " user_project=True,\n", - " parameters={\n", - " # \"source\" : \"git://github.com/mlrun/demo-sagemaker#main\",\n", - " \"default_image\" : \"yonishelach/sagemaker-demo\"\n", - " }\n", - ")" + "import sys\n", + "\n", + "!{sys.executable} -m pip install --upgrade pip --quiet # upgrade pip to the latest vesion\n", + "!{sys.executable} -m pip install --upgrade sagemaker --quiet # upgrade SageMaker to the latest vesion" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "1b17a94d", "metadata": {}, @@ -133,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "42c5d6d0", "metadata": {}, "outputs": [ @@ -142,7 +111,7 @@ "output_type": "stream", "text": [ "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n", - "sagemaker.config INFO - Not applying SDK defaults from location: /User/.config/sagemaker/config.yaml\n" + "sagemaker.config INFO - Not applying SDK defaults from location: /home/sagemaker-user/.config/sagemaker/config.yaml\n" ] } ], @@ -152,36 +121,19 @@ "import sagemaker\n", "import time\n", "import os\n", + "\n", "from time import sleep\n", "from sklearn.metrics import classification_report\n", "from sagemaker.feature_store.feature_group import FeatureGroup\n", + "\n", "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "6406c0df-e745-4e3d-ad98-7d4504ff8b07", - "metadata": {}, - "outputs": [], - "source": [ - "sagemaker_role = os.environ[\"SAGEMAKER-ROLE\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "b0f0ea71-1c48-4174-a0bd-e1b4c0137d25", - "metadata": {}, - "outputs": [], - "source": [ - "sess = sagemaker.Session()\n", - "write_bucket = sess.default_bucket()\n", - "write_prefix = \"sagemaker-app-lab\"" + "import numpy as np\n", + "\n", + "import mlrun" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "3af7c33d", "metadata": {}, @@ -191,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "c0e4db17", "metadata": {}, "outputs": [], @@ -200,13 +152,13 @@ "sm_client = boto3.client(\"sagemaker\")\n", "boto_session = boto3.Session(region_name=region)\n", "sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client)\n", - "#role = sagemaker.get_execution_role()\n", - "role = sagemaker_role\n", + "role = os.environ[\"SAGEMAKER-ROLE\"]\n", "bucket_prefix = \"payment-classification\"\n", "s3_bucket = sagemaker_session.default_bucket()" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "4fe6a975", "metadata": {}, @@ -216,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "43946b9f", "metadata": {}, "outputs": [], @@ -241,10 +193,13 @@ " \"Gambling\": 16,\n", " \"Home\": 17,\n", " \"Pension and insurances\": 18,\n", - "}" + "}\n", + "\n", + "factorize_key = {key: str(value) for key, value in factorize_key.items()}" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "5e3dc3c4", "metadata": {}, @@ -256,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "5ff0d280", "metadata": {}, "outputs": [], @@ -270,6 +225,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "08578d93", "metadata": {}, @@ -279,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "a477abd7", "metadata": {}, "outputs": [], @@ -293,6 +249,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "cf6be447", "metadata": {}, @@ -314,6 +271,161 @@ ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "558fa01c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Jan 30 08:58:05Feb 13 10:21:33completedevaluate
model_path=store://artifacts/sagemaker-yoni/train_model_path@c5935692-c4e0-4bb9-92a7-b1d60e74bedd
model_name=xgboost-model
label_column=transaction_category
model_path=store://artifacts/sagemaker-admin/train_model_path@24e028c0-cc0d-45c3-bdc7-ae29c02d1ee1
model_name=xgboost-model
label_column=transaction_category
Jan 30 08:51:15Feb 13 10:14:27completedtrain
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transaction_categoryreceiver_idsender_idamounttimestamp
35092Shopping44493690936506794778391406636584190.962021-01-10 10:31:30
70144Bills and Utilities441912737059337645688600100529377.152021-01-11 19:13:36
11607Entertainment4052325679412775488620745266488326.102021-04-25 21:09:54
8677Entertainment4146991665749724448456489451899482.072021-02-27 23:21:54
52661Health and Fitness40850203209696504412524886191272196.822021-01-11 10:25:08
92692Travel42506521349983574859762416651197368.352021-01-12 12:02:25
68488Investments464946539013022147183611887297288468.552021-01-05 15:34:07
29134Shopping40761475126915854427561275309035116.842021-02-15 20:25:00
87164Auto and Transport45849983355594734138682268863182110.642021-04-01 09:13:05
83613Auto and Transport4627849002038847432661245455594888.872021-03-15 10:04:38
\n", + "" + ], + "text/plain": [ + " transaction_category receiver_id sender_id amount \\\n", + "35092 Shopping 4449369093650679 4778391406636584 190.96 \n", + "70144 Bills and Utilities 4419127370593376 4568860010052937 7.15 \n", + "11607 Entertainment 4052325679412775 4886207452664883 26.10 \n", + "8677 Entertainment 4146991665749724 4484564894518994 82.07 \n", + "52661 Health and Fitness 4085020320969650 4412524886191272 196.82 \n", + "92692 Travel 4250652134998357 4859762416651197 368.35 \n", + "68488 Investments 4649465390130221 4718361188729728 8468.55 \n", + "29134 Shopping 4076147512691585 4427561275309035 116.84 \n", + "87164 Auto and Transport 4584998335559473 4138682268863182 110.64 \n", + "83613 Auto and Transport 4627849002038847 4326612454555948 88.87 \n", + "\n", + " timestamp \n", + "35092 2021-01-10 10:31:30 \n", + "70144 2021-01-11 19:13:36 \n", + "11607 2021-04-25 21:09:54 \n", + "8677 2021-02-27 23:21:54 \n", + "52661 2021-01-11 10:25:08 \n", + "92692 2021-01-12 12:02:25 \n", + "68488 2021-01-05 15:34:07 \n", + "29134 2021-02-15 20:25:00 \n", + "87164 2021-04-01 09:13:05 \n", + "83613 2021-03-15 10:04:38 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.sample(10)" + ] + }, + { + "attachments": {}, "cell_type": "markdown", "id": "b5492919", "metadata": {}, @@ -323,7 +435,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "24f6090e", "metadata": {}, "outputs": [], @@ -338,240 +450,586 @@ "del data[\"timestamp\"]" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f7314f8a", + "metadata": {}, + "source": [ + "We'll transform the transaction categories to numeric targets for the classification by factorization." + ] + }, { "cell_type": "code", - "execution_count": 12, - "id": "79b0854f-c209-4092-ac0f-a680f35c2c74", + "execution_count": 9, + "id": "ea2ebdd5", "metadata": {}, "outputs": [], "source": [ - "for key, val in factorize_key.items():\n", - " factorize_key[key] = str(val)" + "data[\"transaction_category\"] = data[\"transaction_category\"].replace(factorize_key)" ] }, { - "cell_type": "code", - "execution_count": 13, - "id": "0ee06b1d-0cfb-4242-a7e7-2443a0377d99", + "attachments": {}, + "cell_type": "markdown", + "id": "9b9ed583", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "[ 'Uncategorized', 'Entertainment', 'Education',\n", - " 'Shopping', 'Personal Care', 'Health and Fitness',\n", - " 'Food and Dining', 'Gifts and Donations', 'Investments',\n", - " 'Bills and Utilities', 'Auto and Transport', 'Travel',\n", - " 'Fees and Charges', 'Business Services', 'Personal Services',\n", - " 'Taxes', 'Gambling', 'Home',\n", - " 'Pension and insurances']\n", - "Length: 19, dtype: string" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "data[\"transaction_category\"].unique()" + "### 3. Create feature store \n", + "\n", + "To enrich dataset we will use the [Feature Store](https://aws.amazon.com/sagemaker/feature-store/). " ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "f7314f8a", + "id": "233b862a", "metadata": {}, "source": [ - "We'll transform the transaction categories to numeric targets for the classification by factorization." + "Before creating the feature store itself we need to set a name for the feature group and identifier used" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "ea2ebdd5", + "execution_count": 10, + "id": "9df53a9c", "metadata": {}, "outputs": [], "source": [ - "data[\"transaction_category\"] = data[\"transaction_category\"].replace(factorize_key)" + "feature_group_name = \"feature-group-payment-classification\"\n", + "record_identifier_feature_name = \"identifier\"" ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "ce6d007f-4be8-47be-a993-6c0e6a217d86", - "metadata": { - "tags": [] - }, + "id": "8d9b663f", + "metadata": {}, "source": [ - "### 3. Create feature store \n", + "With the name we defined we create the feature group, runtime and session" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1aef7f91", + "metadata": {}, + "outputs": [], + "source": [ + "feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=sagemaker_session)\n", "\n", - "To enrich dataset we will use the [Feature Store](https://aws.amazon.com/sagemaker/feature-store/). " + "featurestore_runtime = boto_session.client(\n", + " service_name=\"sagemaker-featurestore-runtime\", region_name=region\n", + ")\n", + "\n", + "feature_store_session = sagemaker.Session(\n", + " boto_session=boto_session,\n", + " sagemaker_client=sm_client,\n", + " sagemaker_featurestore_runtime_client=featurestore_runtime,\n", + ")" ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "7fa840f3-e226-4e6a-9159-748b5dd77f8d", + "id": "3f3d69f5", "metadata": {}, "source": [ - "#### feature-group-payment-classification" + "Once we have defined our feature store we need to put some data in it. We create a Pandas dataframe with the columns mean_amount, count, identifier and event time to store in the feature store" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "3c621044-681a-4e1a-9968-f637ed992539", + "execution_count": 12, + "id": "a1a250da", "metadata": {}, "outputs": [], "source": [ - "def add_grouped_features(df):\n", - " feature_store_data = pd.DataFrame()\n", - " feature_store_data[\"mean_amount\"] = df.groupby([\"transaction_category\"]).mean()[\"amount\"]\n", - " feature_store_data[\"count\"] = df.groupby([\"transaction_category\"]).count()[\"amount\"]\n", - " feature_store_data[\"identifier\"] = feature_store_data.index\n", - " feature_store_data[\"EventTime\"] = time.time()\n", - " \n", - " \n", - " \n", - " additional_features = pd.pivot_table(\n", - " feature_store_data, values=[\"mean_amount\"], index=[\"identifier\"]\n", - " ).T.add_prefix(\"dist_\")\n", - " additional_features_columns = list(additional_features.columns)\n", - " df2 = df.copy()\n", - " df2 = pd.concat([df2, pd.DataFrame(columns=additional_features_columns, dtype=object)])\n", - " df2[additional_features_columns] = additional_features.values[0]\n", - " for col in additional_features_columns:\n", - " df2[col] = abs(df2[col] - df2[\"amount\"]) \n", - " df2['transaction_id']= df2.reset_index().index \n", - " return df2" + "columns = [\"mean_amount\", \"count\", \"identifier\", \"EventTime\"]\n", + "feature_store_data = pd.DataFrame(columns=columns, dtype=object)\n", + "\n", + "feature_store_data[\"identifier\"] = range(19)\n", + "feature_store_data[\"mean_amount\"] = 0.0\n", + "feature_store_data[\"count\"] = 1\n", + "feature_store_data[\"EventTime\"] = time.time()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "dea2565b", + "metadata": {}, + "source": [ + "Using the created dataframe we set the feature definitions" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "c71af4a9-f2d8-40ca-b0bf-3ef67c5b69d9", + "execution_count": 13, + "id": "292571c7", "metadata": {}, "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "mlrun-flow\n", - "\n", - "\n", - "\n", - "_start\n", - "\n", - "start\n", - "\n", - "\n", - "\n", - "add_grouped_features\n", - "\n", - "add_grouped_features\n", - "\n", - "\n", - "\n", - "_start->add_grouped_features\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "parquet/parquet\n", - "\n", - "\n", - "parquet\n", - "\n", - "\n", - "\n", - "add_grouped_features->parquet/parquet\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "nosql/nosql\n", - "\n", - "\n", - "nosql\n", - "\n", - "\n", - "\n", - "add_grouped_features->nosql/nosql\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], "text/plain": [ - "" + "[FeatureDefinition(feature_name='mean_amount', feature_type=, collection_type=None),\n", + " FeatureDefinition(feature_name='count', feature_type=, collection_type=None),\n", + " FeatureDefinition(feature_name='identifier', feature_type=, collection_type=None),\n", + " FeatureDefinition(feature_name='EventTime', feature_type=, collection_type=None)]" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import mlrun.feature_store as fstore\n", - "from mlrun.datastore.targets import ParquetTarget\n", - "\n", - "# creating feature set\n", - "extended_transactions_set = fstore.FeatureSet(\"transactions\",\n", - " entities=[fstore.Entity(\"transaction_id\")],\n", - " engine=\"pandas\",\n", - " description=\"transactions feature set\")\n", - "\n", - "# setting up the graph\n", - "extended_transactions_set.graph \\\n", - " .to(name='add_grouped_features', handler='add_grouped_features')\n", - " # Add aggregations for 2, 12, and 24 hour time windows\n", - " \n", - " \n", - "\n", - "\n", - "extended_transactions_set.set_targets()\n", - "\n", - "extended_transactions_set.plot(rankdir=\"LR\", with_targets=True)" + "feature_group.load_feature_definitions(data_frame=feature_store_data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "2a845e07", + "metadata": {}, + "source": [ + "With these definitions ready we can create the feature group itself" ] }, { "cell_type": "code", - "execution_count": 17, - "id": "2085e0a9-56e1-4641-a4a6-64e2124d9c15", + "execution_count": 14, + "id": "d046eeb4", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-01-25 14:11:30,483 [warning] Overriding type of entity 'transaction_id' from 'str' to 'int'. This may result in errors or unusable data.\n" - ] - }, { "data": { - "text/html": [ - "
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" + ], + "text/plain": [ + " mean_amount count identifier EventTime\n", + "2 495.837355 465 0 1.707813e+09\n", + "15 51.209382 14512 1 1.707813e+09\n", + "11 851.153495 744 2 1.707813e+09\n", + "14 100.917503 33953 3 1.707813e+09\n", + "5 31.504036 1209 4 1.707813e+09\n", + "8 119.596405 4837 5 1.707813e+09\n", + "4 93.249393 9674 6 1.707813e+09\n", + "16 51.072447 2791 7 1.707813e+09\n", + "17 6024.547484 930 8 1.707813e+09\n", + "10 114.779558 3349 9 1.707813e+09\n", + "7 100.977110 19349 10 1.707813e+09\n", + "9 351.807588 1302 11 1.707813e+09\n", + "0 26.950559 930 12 1.707813e+09\n", + "3 204.739197 1395 13 1.707813e+09\n", + "18 496.766086 930 14 1.707813e+09\n", + "12 2913.673247 465 15 1.707813e+09\n", + "13 375.228871 186 16 1.707813e+09\n", + "6 781.392199 1860 17 1.707813e+09\n", + "1 205.457151 1116 18 1.707813e+09" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_store_data = get_feature_store_values()\n", + "feature_store_data" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "cf148985", + "metadata": {}, + "source": [ + "We use the feature store to calculate the distance between the average of every category and the current amount" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4a3e85de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" ], "text/plain": [ @@ -885,84 +1343,68 @@ "99995 18 4.262047e+15 4.017367e+15 204.26 2021.0 2.0 \n", "99996 18 4.627517e+15 4.250421e+15 207.92 2021.0 4.0 \n", "\n", - " day hour minute second ... dist_18 dist_2 dist_3 \\\n", - "0 10.0 19.0 57.0 42.0 ... 627.802849 17.893495 732.342497 \n", - "1 11.0 17.0 53.0 32.0 ... 391.172849 254.523495 495.712497 \n", - "2 21.0 18.0 29.0 32.0 ... 28.697151 674.393495 75.842497 \n", - "3 9.0 16.0 14.0 19.0 ... 674.322849 28.626505 778.862497 \n", - "4 4.0 15.0 50.0 16.0 ... 536.792849 108.903495 641.332497 \n", - "... ... ... ... ... ... ... ... ... \n", - "99992 20.0 12.0 23.0 53.0 ... 0.027151 645.723495 104.512497 \n", - "99993 24.0 19.0 30.0 18.0 ... 53.967151 699.663495 50.572497 \n", - "99994 8.0 19.0 51.0 10.0 ... 17.177151 662.873495 87.362497 \n", - "99995 14.0 23.0 25.0 7.0 ... 1.197151 646.893495 103.342497 \n", - "99996 14.0 0.0 42.0 0.0 ... 2.462849 643.233495 107.002497 \n", + " day hour minute second ... 9_dist 10_dist 11_dist \\\n", + "0 10.0 19.0 57.0 42.0 ... 718.480442 732.28289 481.452412 \n", + "1 11.0 17.0 53.0 32.0 ... 481.850442 495.65289 244.822412 \n", + "2 21.0 18.0 29.0 32.0 ... 61.980442 75.78289 175.047588 \n", + "3 9.0 16.0 14.0 19.0 ... 765.000442 778.80289 527.972412 \n", + "4 4.0 15.0 50.0 16.0 ... 627.470442 641.27289 390.442412 \n", + "... ... ... ... ... ... ... ... ... \n", + "99992 20.0 12.0 23.0 53.0 ... 90.650442 104.45289 146.377588 \n", + "99993 24.0 19.0 30.0 18.0 ... 36.710442 50.51289 200.317588 \n", + "99994 8.0 19.0 51.0 10.0 ... 73.500442 87.30289 163.527588 \n", + "99995 14.0 23.0 25.0 7.0 ... 89.480442 103.28289 147.547588 \n", + "99996 14.0 0.0 42.0 0.0 ... 93.140442 106.94289 143.887588 \n", "\n", - " dist_4 dist_5 dist_6 dist_7 dist_8 \\\n", - "0 801.755964 713.663595 740.010607 782.187553 5191.287484 \n", - "1 565.125964 477.033595 503.380607 545.557553 5427.917484 \n", - "2 145.255964 57.163595 83.510607 125.687553 5847.787484 \n", - "3 848.275964 760.183595 786.530607 828.707553 5144.767484 \n", - "4 710.745964 622.653595 649.000607 691.177553 5282.297484 \n", - "... ... ... ... ... ... \n", - "99992 173.925964 85.833595 112.180607 154.357553 5819.117484 \n", - "99993 119.985964 31.893595 58.240607 100.417553 5873.057484 \n", - "99994 156.775964 68.683595 95.030607 137.207553 5836.267484 \n", - "99995 172.755964 84.663595 111.010607 153.187553 5820.287484 \n", - "99996 176.415964 88.323595 114.670607 156.847553 5816.627484 \n", + " 12_dist 13_dist 14_dist 15_dist 16_dist \\\n", + "0 806.309441 628.520803 336.493914 2080.413247 458.031129 \n", + "1 569.679441 391.890803 99.863914 2317.043247 221.401129 \n", + "2 149.809441 27.979197 320.006086 2736.913247 198.468871 \n", + "3 852.829441 675.040803 383.013914 2033.893247 504.551129 \n", + "4 715.299441 537.510803 245.483914 2171.423247 367.021129 \n", + "... ... ... ... ... ... \n", + "99992 178.479441 0.690803 291.336086 2708.243247 169.798871 \n", + "99993 124.539441 53.249197 345.276086 2762.183247 223.738871 \n", + "99994 161.329441 16.459197 308.486086 2725.393247 186.948871 \n", + "99995 177.309441 0.479197 292.506086 2709.413247 170.968871 \n", + "99996 180.969441 3.180803 288.846086 2705.753247 167.308871 \n", "\n", - " dist_9 transaction_id \n", - "0 718.480442 0 \n", - "1 481.850442 1 \n", - "2 61.980442 2 \n", - "3 765.000442 3 \n", - "4 627.470442 4 \n", - "... ... ... \n", - "99992 90.650442 99992 \n", - "99993 36.710442 99993 \n", - "99994 73.500442 99994 \n", - "99995 89.480442 99995 \n", - "99996 93.140442 99996 \n", + " 17_dist 18_dist \n", + "0 51.867801 627.802849 \n", + "1 184.762199 391.172849 \n", + "2 604.632199 28.697151 \n", + "3 98.387801 674.322849 \n", + "4 39.142199 536.792849 \n", + "... ... ... \n", + "99992 575.962199 0.027151 \n", + "99993 629.902199 53.967151 \n", + "99994 593.112199 17.177151 \n", + "99995 577.132199 1.197151 \n", + "99996 573.472199 2.462849 \n", "\n", - "[99997 rows x 30 columns]" + "[99997 rows x 29 columns]" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import mlrun.feature_store as fstore\n", - "data = extended_transactions_set.ingest(data, overwrite=True)\n", + "additional_features = pd.pivot_table(\n", + " feature_store_data, values=[\"mean_amount\"], index=[\"identifier\"]\n", + ").T.add_suffix(\"_dist\")\n", + "additional_features_columns = list(additional_features.columns)\n", + "data = pd.concat([data, pd.DataFrame(columns=additional_features_columns, dtype=object)])\n", + "data[additional_features_columns] = additional_features.values[0]\n", + "for col in additional_features_columns:\n", + " data[col] = abs(data[col] - data[\"amount\"])\n", + "\n", "data" ] }, { - "cell_type": "markdown", - "id": "b5e4834e", - "metadata": {}, - "source": [ - "We update the values in the feature store with the real values of our data" - ] - }, - { - "cell_type": "markdown", - "id": "e2f6395f", - "metadata": {}, - "source": [ - "And display them after getting them from the feature store" - ] - }, - { - "cell_type": "markdown", - "id": "cf148985", - "metadata": {}, - "source": [ - "We use the feature store to calculate the distance between the average of every category and the current amount" - ] - }, - { + "attachments": {}, "cell_type": "markdown", "id": "289eeca6", "metadata": {}, @@ -977,7 +1419,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "id": "bb4bdd8d", "metadata": {}, "outputs": [], @@ -989,6 +1431,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "f81f65b9", "metadata": {}, @@ -998,7 +1441,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "id": "f849a7a9", "metadata": {}, "outputs": [], @@ -1009,6 +1452,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "de669936", "metadata": {}, @@ -1018,7 +1462,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "id": "e1ca2543", "metadata": {}, "outputs": [], @@ -1035,6 +1479,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "22de532f", "metadata": {}, @@ -1044,7 +1489,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "id": "a41b6a7d", "metadata": {}, "outputs": [], @@ -1053,6 +1498,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "66cae2a9", "metadata": {}, @@ -1062,7 +1508,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "id": "e51c917a", "metadata": {}, "outputs": [], @@ -1076,6 +1522,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "6f2985d8", "metadata": {}, @@ -1085,7 +1532,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "id": "92c1fe8c", "metadata": {}, "outputs": [], @@ -1101,6 +1548,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "ecafdfe8", "metadata": {}, @@ -1110,7 +1558,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "id": "582adc6c", "metadata": {}, "outputs": [], @@ -1129,6 +1577,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "b36463dd", "metadata": {}, @@ -1138,7 +1587,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "id": "c24e06fc", "metadata": { "scrolled": true @@ -1148,164 +1597,192 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-01-25-14-12-01-149\n" + "INFO:sagemaker:Creating training-job with name: sagemaker-xgboost-2024-02-13-08-29-45-600\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-01-25 14:12:01 Starting - Starting the training job...\n", - "2024-01-25 14:12:18 Starting - Preparing the instances for training.........\n", - "2024-01-25 14:13:58 Downloading - Downloading input data......\n", - "2024-01-25 14:14:34 Downloading - Downloading the training image...\n", - "2024-01-25 14:15:29 Training - Training image download completed. Training in progress...\u001B[34m[2024-01-25 14:15:41.041 ip-10-2-106-129.ec2.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Imported framework sagemaker_xgboost_container.training\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Failed to parse hyperparameter objective value multi:softprob to Json.\u001B[0m\n", - "\u001B[34mReturning the value itself\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] No GPUs detected (normal if no gpus installed)\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Running XGBoost Sagemaker in algorithm mode\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Determined delimiter of CSV input is ','\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Determined delimiter of CSV input is ','\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Determined delimiter of CSV input is ','\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Determined delimiter of CSV input is ','\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Single node training.\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Train matrix has 69997 rows and 29 columns\u001B[0m\n", - "\u001B[34m[2024-01-25:14:15:41:INFO] Validation matrix has 20000 rows\u001B[0m\n", - "\u001B[34m[2024-01-25 14:15:41.342 ip-10-2-106-129.ec2.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001B[0m\n", - "\u001B[34m[2024-01-25 14:15:41.343 ip-10-2-106-129.ec2.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. 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"\u001B[34m[86]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[87]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[88]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[89]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", + "2024-02-13 08:29:45 Starting - Starting the training job...\n", + "2024-02-13 08:30:00 Starting - Preparing the instances for training......\n", + "2024-02-13 08:31:05 Downloading - Downloading input data...\n", + "2024-02-13 08:31:45 Downloading - Downloading the training image......\n", + "2024-02-13 08:32:35 Training - Training image download completed. Training in progress...\u001b[34m[2024-02-13 08:32:46.926 ip-10-0-78-133.us-east-2.compute.internal:7 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Imported framework sagemaker_xgboost_container.training\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Failed to parse hyperparameter objective value multi:softprob to Json.\u001b[0m\n", + "\u001b[34mReturning the value itself\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] No GPUs detected (normal if no gpus installed)\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Running XGBoost Sagemaker in algorithm mode\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:46:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:47:INFO] Determined delimiter of CSV input is ','\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:47:INFO] Single node training.\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:47:INFO] Train matrix has 69997 rows and 28 columns\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:47:INFO] Validation matrix has 20000 rows\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:47.275 ip-10-0-78-133.us-east-2.compute.internal:7 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:47.276 ip-10-0-78-133.us-east-2.compute.internal:7 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:47.276 ip-10-0-78-133.us-east-2.compute.internal:7 INFO profiler_config_parser.py:102] User has disabled profiler.\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:47.277 ip-10-0-78-133.us-east-2.compute.internal:7 INFO hook.py:255] Saving to /opt/ml/output/tensors\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:47.277 ip-10-0-78-133.us-east-2.compute.internal:7 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n", + "\u001b[34m[2024-02-13:08:32:47:INFO] Debug hook created from config\u001b[0m\n", + "\u001b[34m[0]#011train-merror:0.54809#011validation-merror:0.56205\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:49.057 ip-10-0-78-133.us-east-2.compute.internal:7 INFO hook.py:423] Monitoring the collections: metrics\u001b[0m\n", + "\u001b[34m[2024-02-13 08:32:49.060 ip-10-0-78-133.us-east-2.compute.internal:7 INFO hook.py:486] Hook is writing from the hook with pid: 7\u001b[0m\n", + "\u001b[34m[1]#011train-merror:0.52881#011validation-merror:0.54025\u001b[0m\n", + "\u001b[34m[2]#011train-merror:0.51631#011validation-merror:0.53010\u001b[0m\n", + "\u001b[34m[3]#011train-merror:0.50496#011validation-merror:0.52125\u001b[0m\n", + "\u001b[34m[4]#011train-merror:0.49649#011validation-merror:0.51125\u001b[0m\n", + "\u001b[34m[5]#011train-merror:0.49515#011validation-merror:0.51135\u001b[0m\n", + "\u001b[34m[6]#011train-merror:0.49075#011validation-merror:0.50570\u001b[0m\n", + "\u001b[34m[7]#011train-merror:0.48436#011validation-merror:0.50025\u001b[0m\n", + "\u001b[34m[8]#011train-merror:0.47908#011validation-merror:0.49355\u001b[0m\n", + "\u001b[34m[9]#011train-merror:0.47491#011validation-merror:0.48925\u001b[0m\n", + "\u001b[34m[10]#011train-merror:0.47348#011validation-merror:0.48725\u001b[0m\n", + "\u001b[34m[11]#011train-merror:0.47023#011validation-merror:0.48375\u001b[0m\n", + "\u001b[34m[12]#011train-merror:0.46452#011validation-merror:0.47650\u001b[0m\n", + "\u001b[34m[13]#011train-merror:0.45632#011validation-merror:0.46575\u001b[0m\n", + "\u001b[34m[14]#011train-merror:0.45106#011validation-merror:0.46095\u001b[0m\n", + "\u001b[34m[15]#011train-merror:0.44463#011validation-merror:0.45505\u001b[0m\n", + "\u001b[34m[16]#011train-merror:0.44115#011validation-merror:0.45115\u001b[0m\n", + "\u001b[34m[17]#011train-merror:0.43572#011validation-merror:0.44560\u001b[0m\n", + "\u001b[34m[18]#011train-merror:0.43310#011validation-merror:0.44285\u001b[0m\n", + "\u001b[34m[19]#011train-merror:0.42675#011validation-merror:0.43620\u001b[0m\n", + "\u001b[34m[20]#011train-merror:0.41697#011validation-merror:0.42690\u001b[0m\n", + "\u001b[34m[21]#011train-merror:0.40773#011validation-merror:0.41710\u001b[0m\n", + "\u001b[34m[22]#011train-merror:0.39490#011validation-merror:0.40325\u001b[0m\n", + "\u001b[34m[23]#011train-merror:0.38994#011validation-merror:0.39810\u001b[0m\n", + "\u001b[34m[24]#011train-merror:0.38683#011validation-merror:0.39415\u001b[0m\n", + "\u001b[34m[25]#011train-merror:0.37924#011validation-merror:0.38710\u001b[0m\n", + "\u001b[34m[26]#011train-merror:0.37102#011validation-merror:0.37915\u001b[0m\n", + "\u001b[34m[27]#011train-merror:0.36164#011validation-merror:0.36975\u001b[0m\n", + "\u001b[34m[28]#011train-merror:0.35730#011validation-merror:0.36610\u001b[0m\n", + "\u001b[34m[29]#011train-merror:0.35083#011validation-merror:0.36000\u001b[0m\n", + "\u001b[34m[30]#011train-merror:0.34436#011validation-merror:0.35325\u001b[0m\n", + "\u001b[34m[31]#011train-merror:0.33466#011validation-merror:0.34165\u001b[0m\n", + "\u001b[34m[32]#011train-merror:0.32956#011validation-merror:0.33610\u001b[0m\n", + "\u001b[34m[33]#011train-merror:0.32551#011validation-merror:0.33195\u001b[0m\n", + "\u001b[34m[34]#011train-merror:0.31620#011validation-merror:0.32330\u001b[0m\n", + "\u001b[34m[35]#011train-merror:0.31168#011validation-merror:0.31940\u001b[0m\n", + "\u001b[34m[36]#011train-merror:0.30877#011validation-merror:0.31530\u001b[0m\n", + "\u001b[34m[37]#011train-merror:0.30557#011validation-merror:0.31230\u001b[0m\n", + "\u001b[34m[38]#011train-merror:0.29613#011validation-merror:0.30190\u001b[0m\n", + "\u001b[34m[39]#011train-merror:0.29147#011validation-merror:0.29620\u001b[0m\n", + "\u001b[34m[40]#011train-merror:0.28283#011validation-merror:0.28695\u001b[0m\n", + "\u001b[34m[41]#011train-merror:0.27641#011validation-merror:0.28100\u001b[0m\n", + "\u001b[34m[42]#011train-merror:0.27238#011validation-merror:0.27690\u001b[0m\n", + "\u001b[34m[43]#011train-merror:0.26728#011validation-merror:0.27205\u001b[0m\n", + "\u001b[34m[44]#011train-merror:0.26151#011validation-merror:0.26660\u001b[0m\n", + "\u001b[34m[45]#011train-merror:0.25514#011validation-merror:0.25815\u001b[0m\n", + "\u001b[34m[46]#011train-merror:0.25094#011validation-merror:0.25390\u001b[0m\n", + "\u001b[34m[47]#011train-merror:0.24552#011validation-merror:0.24770\u001b[0m\n", + "\u001b[34m[48]#011train-merror:0.24063#011validation-merror:0.24275\u001b[0m\n", + "\u001b[34m[49]#011train-merror:0.23564#011validation-merror:0.23870\u001b[0m\n", + "\u001b[34m[50]#011train-merror:0.23431#011validation-merror:0.23740\u001b[0m\n", + "\u001b[34m[51]#011train-merror:0.22709#011validation-merror:0.23020\u001b[0m\n", + "\u001b[34m[52]#011train-merror:0.22091#011validation-merror:0.22380\u001b[0m\n", + "\u001b[34m[53]#011train-merror:0.21611#011validation-merror:0.21880\u001b[0m\n", + "\u001b[34m[54]#011train-merror:0.20878#011validation-merror:0.21190\u001b[0m\n", + "\u001b[34m[55]#011train-merror:0.20377#011validation-merror:0.20710\u001b[0m\n", + "\u001b[34m[56]#011train-merror:0.20087#011validation-merror:0.20430\u001b[0m\n", + "\u001b[34m[57]#011train-merror:0.19719#011validation-merror:0.20015\u001b[0m\n", + "\u001b[34m[58]#011train-merror:0.19445#011validation-merror:0.19750\u001b[0m\n", + "\u001b[34m[59]#011train-merror:0.19131#011validation-merror:0.19420\u001b[0m\n", + "\u001b[34m[60]#011train-merror:0.18908#011validation-merror:0.19210\u001b[0m\n", + "\u001b[34m[61]#011train-merror:0.18516#011validation-merror:0.18885\u001b[0m\n", + "\u001b[34m[62]#011train-merror:0.18132#011validation-merror:0.18475\u001b[0m\n", + "\u001b[34m[63]#011train-merror:0.17795#011validation-merror:0.18185\u001b[0m\n", + "\u001b[34m[64]#011train-merror:0.17456#011validation-merror:0.17820\u001b[0m\n", + "\u001b[34m[65]#011train-merror:0.17232#011validation-merror:0.17655\u001b[0m\n", + "\u001b[34m[66]#011train-merror:0.16769#011validation-merror:0.17095\u001b[0m\n", + "\u001b[34m[67]#011train-merror:0.16395#011validation-merror:0.16710\u001b[0m\n", + "\u001b[34m[68]#011train-merror:0.16091#011validation-merror:0.16495\u001b[0m\n", + "\u001b[34m[69]#011train-merror:0.15839#011validation-merror:0.16225\u001b[0m\n", + "\u001b[34m[70]#011train-merror:0.15706#011validation-merror:0.16140\u001b[0m\n", + "\u001b[34m[71]#011train-merror:0.15382#011validation-merror:0.15725\u001b[0m\n", + "\u001b[34m[72]#011train-merror:0.15138#011validation-merror:0.15495\u001b[0m\n", + "\u001b[34m[73]#011train-merror:0.14889#011validation-merror:0.15315\u001b[0m\n", + "\u001b[34m[74]#011train-merror:0.14601#011validation-merror:0.14925\u001b[0m\n", + "\u001b[34m[75]#011train-merror:0.14246#011validation-merror:0.14550\u001b[0m\n", + "\u001b[34m[76]#011train-merror:0.13822#011validation-merror:0.14125\u001b[0m\n", + "\u001b[34m[77]#011train-merror:0.13733#011validation-merror:0.14060\u001b[0m\n", + "\u001b[34m[78]#011train-merror:0.13628#011validation-merror:0.13935\u001b[0m\n", + "\u001b[34m[79]#011train-merror:0.13438#011validation-merror:0.13840\u001b[0m\n", + "\u001b[34m[80]#011train-merror:0.13035#011validation-merror:0.13430\u001b[0m\n", + "\u001b[34m[81]#011train-merror:0.12919#011validation-merror:0.13325\u001b[0m\n", + "\u001b[34m[82]#011train-merror:0.12453#011validation-merror:0.12970\u001b[0m\n", + "\u001b[34m[83]#011train-merror:0.12369#011validation-merror:0.12905\u001b[0m\n", + "\u001b[34m[84]#011train-merror:0.12163#011validation-merror:0.12690\u001b[0m\n", + "\u001b[34m[85]#011train-merror:0.12063#011validation-merror:0.12595\u001b[0m\n", + "\u001b[34m[86]#011train-merror:0.11888#011validation-merror:0.12405\u001b[0m\n", + "\u001b[34m[87]#011train-merror:0.11849#011validation-merror:0.12330\u001b[0m\n", + "\u001b[34m[88]#011train-merror:0.11685#011validation-merror:0.12215\u001b[0m\n", + "\u001b[34m[89]#011train-merror:0.11521#011validation-merror:0.12095\u001b[0m\n", + "\u001b[34m[90]#011train-merror:0.11383#011validation-merror:0.11945\u001b[0m\n", + "\u001b[34m[91]#011train-merror:0.11163#011validation-merror:0.11725\u001b[0m\n", + "\u001b[34m[92]#011train-merror:0.11035#011validation-merror:0.11615\u001b[0m\n", + "\u001b[34m[93]#011train-merror:0.10860#011validation-merror:0.11480\u001b[0m\n", + "\u001b[34m[94]#011train-merror:0.10619#011validation-merror:0.11245\u001b[0m\n", + "\u001b[34m[95]#011train-merror:0.10453#011validation-merror:0.11095\u001b[0m\n", + "\u001b[34m[96]#011train-merror:0.10273#011validation-merror:0.10900\u001b[0m\n", + "\u001b[34m[97]#011train-merror:0.10186#011validation-merror:0.10835\u001b[0m\n", + "\u001b[34m[98]#011train-merror:0.10068#011validation-merror:0.10740\u001b[0m\n", + "\u001b[34m[99]#011train-merror:0.09996#011validation-merror:0.10595\u001b[0m\n", "\n", - "2024-01-25 14:17:00 Uploading - Uploading generated training model\u001B[34m[90]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[91]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[92]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[93]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[94]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[95]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[96]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[97]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[98]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\u001B[34m[99]#011train-merror:0.00000#011validation-merror:0.00010\u001B[0m\n", - "\n", - "2024-01-25 14:17:16 Completed - Training job completed\n", - "Training seconds: 198\n", - "Billable seconds: 198\n" + "2024-02-13 08:35:41 Uploading - Uploading generated training model\n", + "2024-02-13 08:35:57 Completed - Training job completed\n", + "Training seconds: 293\n", + "Billable seconds: 293\n" + ] + } + ], + "source": [ + "xgb.fit({\"train\": s3_input_train, \"validation\": s3_input_validation})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8b716cd7", + "metadata": {}, + "source": [ + "### 5. Using the endpoint \n", + "\n", + "Deploy the model to an endpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ded8301b-2523-4ad7-bba2-30e0ca748253", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "> 2024-02-13 08:36:31,116 [info] Identified pre-initialized git repo, using it: {'url': 'git://github.com/mlrun/demo-sagemaker.git#refs/heads/development'}\n", + "> 2024-02-13 08:36:46,461 [info] Created and saved project: {'name': 'sagemaker-admin', 'from_template': None, 'overwrite': False, 'context': './', 'save': True}\n", + "> 2024-02-13 08:36:47,244 [info] Project created successfully: {'project_name': 'sagemaker', 'stored_in_db': True}\n" ] } ], "source": [ - "xgb.fit({\"train\": s3_input_train, \"validation\": s3_input_validation})" - ] - }, - { - "cell_type": "markdown", - "id": "8b716cd7", - "metadata": {}, - "source": [ - "### 5. Using the endpoint \n", - "\n", - "Deploy the model to an endpoint" + "project = mlrun.get_or_create_project(\n", + " name=\"sagemaker\", \n", + " user_project=True,\n", + " parameters={\n", + " # \"source\" : \"git://github.com/mlrun/demo-sagemaker#main\",\n", + " \"default_image\" : \"yonishelach/sagemaker-demo\"\n", + " }\n", + ")" ] }, { "cell_type": "code", - "execution_count": 26, - "id": "78444d49-4ad3-49e4-a579-19b173facb26", + "execution_count": 30, + "id": "fcb620d5-0697-4860-adc3-2696a9e1fa66", "metadata": {}, "outputs": [], "source": [ @@ -1314,8 +1791,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "bd061d8f-82e5-4ed2-b3ed-d994fadaceaa", + "execution_count": 31, + "id": "e1b53d0c-baee-45f3-be07-6c02af75a6c8", "metadata": {}, "outputs": [ { @@ -1324,52 +1801,52 @@ "\n", "\n", - "\n", "\n", - "\n", + "\n", "\n", "mlrun-flow\n", - "\n", + "\n", "\n", "\n", "_start\n", - "\n", - "start\n", + "\n", + "start\n", "\n", "\n", "\n", "xgboost-model\n", - "\n", - "xgboost-model\n", + "\n", + "xgboost-model\n", "\n", "\n", "\n", "_start->xgboost-model\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "postprocess\n", - "\n", - "postprocess\n", + "\n", + "postprocess\n", "\n", "\n", "\n", "xgboost-model->postprocess\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1390,43 +1867,34 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "ceae49b8-98d1-4c00-8bde-597ee80cf6e3", - "metadata": {}, + "execution_count": 32, + "id": "2963f0b2-5169-4138-aefb-cef8813b0e0e", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2024-01-25 14:17:46,696 [info] Starting remote function deploy\n", - "2024-01-25 14:17:46 (info) Deploying function\n", - "2024-01-25 14:17:46 (info) Building\n", - "2024-01-25 14:17:47 (info) Staging files and preparing base images\n", - "2024-01-25 14:17:47 (info) Building processor image\n", - "2024-01-25 14:19:32 (info) Build complete\n", - "2024-01-25 14:19:40 (info) Function deploy complete\n", - "> 2024-01-25 14:19:48,105 [info] successfully deployed function: {'internal_invocation_urls': ['nuclio-sagemaker-yoni-serving.default-tenant.svc.cluster.local:8080'], 'external_invocation_urls': ['sagemaker-yoni-serving-sagemaker-yoni.default-tenant.app.app-lab-2-b688.iguazio-cd2.com/']}\n" + "> 2024-02-13 08:36:47,792 [info] model xgboost-model was loaded\n", + "{'inputs': [[4630518417004166.0, 4898290331783278.0, 67.18, 2021.0, 1.0, 10.0, 23.0, 57.0, 10.0, 428.65735483870964, 15.970618109151054, 783.9734946236558, 33.73750272435426, 35.675963606286196, 52.41640479636139, 26.06939321893735, 16.107552848441422, 5957.367483870968, 47.59955807703791, 33.79710992816166, 284.6275883256528, 40.229440860215064, 137.55919713261648, 429.58608602150537, 2846.4932473118283, 308.04887096774195, 714.2121989247312, 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"output_type": "execute_result" } ], "source": [ - "project.deploy_function(\"serving\")" + "server = serving_function.to_mock_server()" ] }, { "cell_type": "code", - "execution_count": 29, - "id": "c858e3e9-9e43-4148-8015-6047565db456", + "execution_count": 35, + "id": "ffa8dda2-ce3d-459c-8a93-f7ebaee7b7f8", "metadata": {}, "outputs": [], "source": [ @@ -1435,25 +1903,70 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "de741da6-8ff6-4f60-bae4-3c1d11df87c4", + "execution_count": 42, + "id": "5b1f27eb-a2a0-457e-9291-4b61d40e4d0c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 1, 11, 3, 1, 3, 3, 5, 1, 1, 3, 3, 17, 7, 2, 13, 9, 7, 3, 10, 1, 7, 1, 10, 10, 6, 3, 5, 3, 3, 1, 12, 9, 12, 6, 1, 10, 10, 10, 10, 1, 3, 3, 1, 3, 9, 1, 10, 3, 4, 3, 3, 10, 1, 3, 10, 3, 3, 17, 3, 6, 3, 3, 3, 3, 1, 3, 1, 1, 6, 3, 10, 3, 5, 1, 10, 3, 10, 10, 10, 10, 14, 1, 12, 3, 7, 1, 3, 12, 3, 10, 5, 3, 3, 1, 1, 6, 1, 10, 10, 3, 3, 1, 3, 3, 6, 3, 3, 3, 1, 10, 3, 3, 1, 5, 10, 1, 3, 1, 3, 10, 3, 3, 11, 12, 3, 3, 5, 1, 3, 3, 1, 3, 1, 10, 3, 1, 3, 3, 18, 13, 3, 1, 1, 10, 3, 1, 3, 1, 10, 6, 3, 10, 10, 3, 3, 3, 6, 1, 3, 10, 10, 1, 3, 17, 1, 3, 3, 3, 3, 17, 3, 3, 7, 3, 6, 10, 3, 3, 3, 10, 3, 6, 3, 3, 10, 11, 10, 10, 1, 10, 10, 5, 12, 5, 13, 3, 10, 3, 3, 5, 1, 9, 1, 3, 9, 3, 10, 10, 10, 9, 3, 3, 3, 10, 1, 2, 1, 1, 6, 3, 1, 6, 6, 7, 10, 10, 3, 3, 6, 1, 9, 3, 3, 3, 3, 10, 3, 6, 10, 1, 3, 17, 1, 0, 10, 3, 9, 17, 2, 1, 10, 3, 3, 3, 10, 6, 3, 3, 3, 3, 3, 14, 1, 3, 6, 10, 1, 3, 3, 1, 3, 10, 3, 3, 10, 7, 10, 10, 10, 18, 5, 13, 3, 10, 3, 10, 3, 3, 3, 10, 1, 5, 3, 1, 3, 1, 3, 3, 5, 3, 1, 3, 3, 1, 10, 18, 6, 3, 3, 10, 3, 3, 1, 7, 3, 1, 3, 6, 1, 9, 3, 3, 3, 10, 10, 3, 17, 3, 1, 10, 1, 10, 15, 1, 3, 3, 1, 1, 14, 11, 9, 10, 10, 6, 1, 4, 1, 3, 17, 3, 3, 3, 1, 6, 14, 3, 3, 9, 1, 10, 6, 5, 3, 13, 3, 1, 3, 3, 6, 3, 17, 13, 17, 12, 1, 1, 10, 13, 5, 3, 3, 3, 3, 10, 13, 18, 3, 3, 3, 3, 10, 10, 3, 8, 1, 3, 9, 3, 10, 3, 1, 10, 10, 1, 3, 3, 3, 10, 1, 1, 3, 7, 1, 10, 6, 3, 3, 3, 3, 3, 3, 9, 10, 3, 10, 9, 3, 3, 17, 10, 3, 7, 1, 1, 3, 1, 17, 7, 3, 3, 3, 3, 9, 3, 3, 1, 10, 10, 3, 3, 1, 7, 10, 10, 10, 12, 6, 3, 6, 16, 10, 3, 1, 18, 8, 10, 6, 10, 17, 6, 1, 10, 1, 3, 10, 1, 3, 3, 10, 3, 10, 3, 3, 5, 7, 6, 3, 6, 5, 3, 3, 3, 3, 2, 10, 12, 9, 1, 3]\n" + ] + } + ], + "source": [ + "response = server.test(body={\"inputs\": samples})\n", + "print(response['predictions'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "815fd915-e8ff-455a-a16b-dbf470f8d825", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "> 2024-01-25 14:19:48,167 [info] invoking function: {'method': 'POST', 'path': 'http://nuclio-sagemaker-yoni-serving.default-tenant.svc.cluster.local:8080/predict'}\n" + "> 2024-02-13 08:57:37,720 [info] Starting remote function deploy\n", + "2024-02-13 08:57:38 (info) Deploying function\n", + "2024-02-13 08:57:38 (info) Building\n", + "2024-02-13 08:57:38 (info) Staging files and preparing base images\n", + "2024-02-13 08:57:38 (info) Building processor image\n" ] } ], + "source": [ + "project.deploy_function(\"serving\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0bd727dc-7243-4d0c-94b7-91dcaeca3fae", + "metadata": {}, + "outputs": [], "source": [ "response = serving_function.invoke(path='/predict', body={\"inputs\": samples})" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6e22e08-9fa3-4fc9-a084-652edf9fdfb7", + "metadata": {}, + "outputs": [], + "source": [ + "print(response['predictions'])" + ] + }, { "cell_type": "markdown", - "id": "712f4d35", + "id": "7a93919f-da79-455c-a8c2-eab5deece6d9", "metadata": {}, "source": [ "### 6. Evaluate performance \n", @@ -1463,8 +1976,8 @@ }, { "cell_type": "code", - "execution_count": 31, - "id": "2e863ea7-5804-4637-b677-390c305cabfe", + "execution_count": null, + "id": "6b6a92c2-09f2-4e4a-9c3a-1c005e231d1b", "metadata": {}, "outputs": [], "source": [ @@ -1472,284 +1985,41 @@ ] }, { - "cell_type": "markdown", - "id": "507de272-df4e-4fbe-be2e-cd99fae1b63a", + "cell_type": "code", + "execution_count": null, + "id": "84c6a5b7", "metadata": {}, + "outputs": [], "source": [ - "Add the evaluation function to our project" + "evaluate_function = project.get_function(\"evaluate\")" ] }, { "cell_type": "code", - "execution_count": 32, - "id": "ca4f7e49", + "execution_count": null, + "id": "cdb2def1-c771-4757-a581-41c0014f2678", "metadata": {}, "outputs": [], "source": [ - "evaluate_function = project.get_function(\"evaluate\")" + "xgb.model_data" ] }, { - "cell_type": "markdown", - "id": "9ba13872-7f0e-4033-96ce-ad8cde950442", + "cell_type": "code", + "execution_count": null, + "id": "fe92026c-c9e3-4e06-8b75-894db62f8f6a", "metadata": {}, + "outputs": [], "source": [ - "Run the evaluation job" + "s3_data" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "b6eab7af-e967-4e22-9817-18cc4bf2db0c", + "execution_count": null, + "id": "62b14992-24db-4be2-bcb3-dbd115fa7544", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-01-25 14:19:48,410 [info] Storing function: {'name': 'evaluate-evaluate', 'uid': 'cac9cd3c55ba40d58fbe1156d4861e79', 'db': 'http://mlrun-api:8080'}\n", - "> 2024-01-25 14:19:48,708 [info] Job is running in the background, pod: evaluate-evaluate-5rrtk\n", - "[14:19:52] WARNING: /workspace/src/common/error_msg.h:80: If you are loading a serialized model (like pickle in Python, RDS in R) or\n", - "configuration generated by an older version of XGBoost, please export the model by calling\n", - "`Booster.save_model` from that version first, then load it back in current version. See:\n", - "\n", - " https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html\n", - "\n", - "for more details about differences between saving model and serializing.\n", - "\n", - "> 2024-01-25 14:19:53,802 [info] To track results use the CLI: {'info_cmd': 'mlrun get run cac9cd3c55ba40d58fbe1156d4861e79 -p sagemaker-yoni', 'logs_cmd': 'mlrun logs cac9cd3c55ba40d58fbe1156d4861e79 -p sagemaker-yoni'}\n", - "> 2024-01-25 14:19:53,802 [info] Or click for UI: {'ui_url': 'https://dashboard.default-tenant.app.app-lab-2-b688.iguazio-cd2.com/mlprojects/sagemaker-yoni/jobs/monitor/cac9cd3c55ba40d58fbe1156d4861e79/overview'}\n", - "> 2024-01-25 14:19:53,803 [info] Run execution finished: {'status': 'completed', 'name': 'evaluate-evaluate'}\n" - ] - }, - { - "data": { - "text/html": [ - "
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projectuiditerstartstatenamelabelsinputsparametersresultsartifacts
sagemaker-yoni0Jan 25 14:19:51completedevaluate-evaluate
v3io_user=yoni
kind=job
owner=yoni
mlrun/client_version=1.6.0-rc21
mlrun/client_python_version=3.9.16
host=evaluate-evaluate-5rrtk
model_path=s3://sagemaker-us-east-1-934638699319/payment-classification/output/sagemaker-xgboost-2024-01-25-14-12-01-149/output/model.tar.gz
model_name=xgboost-model
test_set=s3://sagemaker-us-east-1-934638699319/payment-classification/test/test.csv
label_column=transaction_category
factorize_key={'Uncategorized': '0', 'Entertainment': '1', 'Education': '2', 'Shopping': '3', 'Personal Care': '4', 'Health and Fitness': '5', 'Food and Dining': '6', 'Gifts and Donations': '7', 'Investments': '8', 'Bills and Utilities': '9', 'Auto and Transport': '10', 'Travel': '11', 'Fees and Charges': '12', 'Business Services': '13', 'Personal Services': '14', 'Taxes': '15', 'Gambling': '16', 'Home': '17', 'Pension and insurances': '18'}
classification_report
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\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "text/html": [ - " > to track results use the .show() or .logs() methods or click here to open in UI" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "> 2024-01-25 14:19:59,831 [info] Run execution finished: {'status': 'completed', 'name': 'evaluate-evaluate'}\n" - ] - } - ], + "outputs": [], "source": [ "evaluate_run = evaluate_function.run(\n", " handler=\"evaluate\",\n", @@ -1763,251 +2033,21 @@ " returns=[\"classification_report: dataset\"])" ] }, - { - "cell_type": "markdown", - "id": "ffc4326e-3085-47e1-b1f6-97d5eceba893", - "metadata": {}, - "source": [ - "See the evaluation result" - ] - }, { "cell_type": "code", - "execution_count": 34, - "id": "3a9c30bd-a3bf-49f1-b57e-1490f3da00f2", + "execution_count": null, + "id": "a40da196-0ee8-4ac6-9f7b-bea35ab181c9", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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precisionrecallf1-scoresupport
Uncategorized1.0000001.0000001.00000051.0000
Entertainment1.0000001.0000001.0000001486.0000
Education1.0000001.0000001.00000080.0000
Shopping1.0000001.0000001.0000003441.0000
Personal Care1.0000001.0000001.000000132.0000
Health and Fitness1.0000001.0000001.000000443.0000
Food and Dining1.0000001.0000001.000000918.0000
Gifts and Donations1.0000001.0000001.000000275.0000
Investments1.0000001.0000001.00000088.0000
Bills and Utilities1.0000001.0000001.000000332.0000
Auto and Transport1.0000001.0000001.0000001967.0000
Travel1.0000001.0000001.000000120.0000
Fees and Charges1.0000001.0000001.000000106.0000
Business Services1.0000001.0000001.000000146.0000
Personal Services1.0000001.0000001.00000075.0000
Taxes1.0000000.9787230.98924747.0000
Gambling0.9375001.0000000.96774215.0000
Home1.0000001.0000001.000000168.0000
Pension and insurances1.0000001.0000001.000000110.0000
accuracy0.9999000.9999000.9999000.9999
macro avg0.9967110.9988800.99773610000.0000
weighted avg0.9999060.9999000.99990110000.0000
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" - ], - "text/plain": [ - " precision recall f1-score support\n", - "Uncategorized 1.000000 1.000000 1.000000 51.0000\n", - "Entertainment 1.000000 1.000000 1.000000 1486.0000\n", - "Education 1.000000 1.000000 1.000000 80.0000\n", - "Shopping 1.000000 1.000000 1.000000 3441.0000\n", - "Personal Care 1.000000 1.000000 1.000000 132.0000\n", - "Health and Fitness 1.000000 1.000000 1.000000 443.0000\n", - "Food and Dining 1.000000 1.000000 1.000000 918.0000\n", - "Gifts and Donations 1.000000 1.000000 1.000000 275.0000\n", - "Investments 1.000000 1.000000 1.000000 88.0000\n", - "Bills and Utilities 1.000000 1.000000 1.000000 332.0000\n", - "Auto and Transport 1.000000 1.000000 1.000000 1967.0000\n", - "Travel 1.000000 1.000000 1.000000 120.0000\n", - "Fees and Charges 1.000000 1.000000 1.000000 106.0000\n", - "Business Services 1.000000 1.000000 1.000000 146.0000\n", - "Personal Services 1.000000 1.000000 1.000000 75.0000\n", - "Taxes 1.000000 0.978723 0.989247 47.0000\n", - "Gambling 0.937500 1.000000 0.967742 15.0000\n", - "Home 1.000000 1.000000 1.000000 168.0000\n", - "Pension and insurances 1.000000 1.000000 1.000000 110.0000\n", - "accuracy 0.999900 0.999900 0.999900 0.9999\n", - "macro avg 0.996711 0.998880 0.997736 10000.0000\n", - "weighted avg 0.999906 0.999900 0.999901 10000.0000" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evaluate_run.artifact(\"classification_report\").as_df()" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "98d0b67e", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "metadata": {}, "source": [ "You should see results similar to this:\n", "\n", @@ -2041,6 +2081,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "49fdc82d", "metadata": {}, @@ -2052,7 +2093,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "f79b1164", "metadata": {}, "outputs": [], @@ -2062,15 +2103,10 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "e04b6fa6", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "metadata": {}, "source": [ "## Notebook CI Test Results\n", "\n", @@ -2111,9 +2147,9 @@ "metadata": { "instance_type": "ml.t3.medium", "kernelspec": { - "display_name": "mlrun-base", + "display_name": "smdemo", "language": "python", - "name": "conda-env-mlrun-base-py" + "name": "smdemo" }, "language_info": { "codemirror_mode": { @@ -2125,7 +2161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/src/functions/evaluate.py b/src/functions/evaluate.py index 34a7359..83e205a 100644 --- a/src/functions/evaluate.py +++ b/src/functions/evaluate.py @@ -56,6 +56,8 @@ def evaluate( # convert to pandas dataframe: test_set = pd.read_csv(test_set_temp_path) + print(test_set) + # convert to xgboost object: test_data = xgb.DMatrix(test_set.drop(columns=[label_column], axis=1)) diff --git a/src/functions/serving.py b/src/functions/serving.py index 6a61a72..9a56ac7 100644 --- a/src/functions/serving.py +++ b/src/functions/serving.py @@ -6,6 +6,8 @@ import numpy as np import xgboost as xgb from cloudpickle import load +import mlrun.feature_store as fstore + warnings.filterwarnings("ignore") @@ -28,6 +30,13 @@ def load(self): def predict(self, body: dict) -> List: """Generate model predictions from sample.""" + print(body) + # body['inputs'][0] = body['inputs'][0][1:] + + # print(body) + + + # Convert input to numpy array: data = np.asarray(body["inputs"]) @@ -71,4 +80,4 @@ def postprocess(inputs: dict) -> dict: inputs["predictions"] = predictions inputs["confidences"] = confidences - return inputs + return inputs \ No newline at end of file diff --git a/src/functions/train.py b/src/functions/train.py index 86e1e9b..4cce66f 100644 --- a/src/functions/train.py +++ b/src/functions/train.py @@ -4,14 +4,176 @@ import mlrun.feature_store as fs import numpy as np import sagemaker +import pandas as pd +from sagemaker.feature_store.feature_group import FeatureGroup +import time + + def train(context): # Set AWS environment variables: _set_envars(context) - # Get data from feature-store: - data = _get_feature_store_data(context) + + region = sagemaker.Session().boto_region_name + sm_client = boto3.client("sagemaker") + boto_session = boto3.Session(region_name=region) + sagemaker_session = sagemaker.session.Session(boto_session=boto_session, sagemaker_client=sm_client) + role = os.environ["SAGEMAKER-ROLE"] + bucket_prefix = "payment-classification" + s3_bucket = sagemaker_session.default_bucket() + + factorize_key = { + "Uncategorized": 0, + "Entertainment": 1, + "Education": 2, + "Shopping": 3, + "Personal Care": 4, + "Health and Fitness": 5, + "Food and Dining": 6, + "Gifts and Donations": 7, + "Investments": 8, + "Bills and Utilities": 9, + "Auto and Transport": 10, + "Travel": 11, + "Fees and Charges": 12, + "Business Services": 13, + "Personal Services": 14, + "Taxes": 15, + "Gambling": 16, + "Home": 17, + "Pension and insurances": 18, + } + + factorize_key = {key: str(value) for key, value in factorize_key.items()} + + s3 = boto3.client("s3") + s3.download_file( + f"sagemaker-example-files-prod-{region}", + "datasets/tabular/synthetic_financial/financial_transactions_mini.csv", + "financial_transactions_mini.csv", + ) + + data = pd.read_csv( + "financial_transactions_mini.csv", + parse_dates=["timestamp"], + infer_datetime_format=True, + dtype={"transaction_category": "string"}, + ) + + data["year"] = data["timestamp"].dt.year + data["month"] = data["timestamp"].dt.month + data["day"] = data["timestamp"].dt.day + data["hour"] = data["timestamp"].dt.hour + data["minute"] = data["timestamp"].dt.minute + data["second"] = data["timestamp"].dt.second + + del data["timestamp"] + + data["transaction_category"] = data["transaction_category"].replace(factorize_key) + + feature_group_name = "feature-group-payment-classification" + record_identifier_feature_name = "identifier" + + feature_group = FeatureGroup(name=feature_group_name, sagemaker_session=sagemaker_session) + + featurestore_runtime = boto_session.client( + service_name="sagemaker-featurestore-runtime", region_name=region + ) + + feature_store_session = sagemaker.Session( + boto_session=boto_session, + sagemaker_client=sm_client, + sagemaker_featurestore_runtime_client=featurestore_runtime, + ) + + columns = ["mean_amount", "count", "identifier", "EventTime"] + feature_store_data = pd.DataFrame(columns=columns, dtype=object) + + feature_store_data["identifier"] = range(19) + feature_store_data["mean_amount"] = 0.0 + feature_store_data["count"] = 1 + feature_store_data["EventTime"] = time.time() + + feature_group.load_feature_definitions(data_frame=feature_store_data) + + status = feature_group.describe().get("FeatureGroupStatus") + + if status!='Created': + feature_group.create( + s3_uri=f"s3://{s3_bucket}/{bucket_prefix}", + record_identifier_name=record_identifier_feature_name, + event_time_feature_name="EventTime", + role_arn=role, + enable_online_store=True, + ) + + status = feature_group.describe().get("FeatureGroupStatus") + while status == "Creating": + print("Waiting for Feature Group to be Created") + time.sleep(5) + status = feature_group.describe().get("FeatureGroupStatus") + print(f"FeatureGroup {feature_group.name} successfully created.") + + feature_group.ingest(data_frame=feature_store_data, max_workers=3, wait=True) + + def get_feature_store_values(): + response = featurestore_runtime.batch_get_record( + Identifiers=[ + { + "FeatureGroupName": feature_group_name, + "RecordIdentifiersValueAsString": [str(i) for i in range(19)], + } + ] + ) + + columns = ["mean_amount", "count", "identifier", "EventTime"] + + feature_store_resp = pd.DataFrame( + data=[ + [resp["Record"][i]["ValueAsString"] for i in range(len(columns))] + for resp in response["Records"] + ], + columns=columns, + ) + feature_store_resp["identifier"] = feature_store_resp["identifier"].astype(int) + feature_store_resp["count"] = feature_store_resp["count"].astype(int) + feature_store_resp["mean_amount"] = feature_store_resp["mean_amount"].astype(float) + feature_store_resp["EventTime"] = feature_store_resp["EventTime"].astype(float) + feature_store_resp = feature_store_resp.sort_values(by="identifier") + + return feature_store_resp + + feature_store_resp = get_feature_store_values() + + feature_store_data = pd.DataFrame() + feature_store_data["mean_amount"] = data.groupby(["transaction_category"]).mean()["amount"] + feature_store_data["count"] = data.groupby(["transaction_category"]).count()["amount"] + feature_store_data["identifier"] = feature_store_data.index + feature_store_data["EventTime"] = time.time() + + feature_store_data["mean_amount"] = ( + pd.concat([feature_store_resp, feature_store_data]) + .groupby("identifier") + .apply(lambda x: np.average(x["mean_amount"], weights=x["count"])) + ) + feature_store_data["count"] = ( + pd.concat([feature_store_resp, feature_store_data]).groupby("identifier").sum()["count"] + ) + + feature_group.ingest(data_frame=feature_store_data, max_workers=3, wait=True) + + feature_store_data = get_feature_store_values() + + additional_features = pd.pivot_table( + feature_store_data, values=["mean_amount"], index=["identifier"] + ).T.add_suffix("_dist") + additional_features_columns = list(additional_features.columns) + data = pd.concat([data, pd.DataFrame(columns=additional_features_columns, dtype=object)]) + data[additional_features_columns] = additional_features.values[0] + for col in additional_features_columns: + data[col] = abs(data[col] - data["amount"]) # Randomly sort the data then split out first 70%, second 20%, and last 10% train_data, validation_data, test_data = np.split(