diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/03-AutoML-PyCaret-classification.ipynb b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/03-AutoML-PyCaret-classification.ipynb index da3fed17..d5d91698 100644 --- a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/03-AutoML-PyCaret-classification.ipynb +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/03-AutoML-PyCaret-classification.ipynb @@ -84,11 +84,11 @@ "# print(\"Downloading: %d%% [%d / %d] bytes\" % (current / total * 100, current, total))\n", "\n", "\n", - "# url = \"https://data.atoti.io/notebooks/credit-card-fraud/output.zip\"\n", + "# url = \"https://data.atoti.io/notebooks/credit-card-fraud/data.zip\"\n", "# filename = wget.download(url, bar=bar_custom)\n", "\n", "# # unzipping the file\n", - "# with ZipFile(\"output.zip\", \"r\") as zipObj:\n", + "# with ZipFile(\"data.zip\", \"r\") as zipObj:\n", "# # Extract all the contents of zip file in current directory\n", "# zipObj.extractall(data_path)" ] @@ -5109,7 +5109,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.13" } }, "nbformat": 4, diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/README.md b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/README.md index 5e0a9842..20cbc9cf 100644 --- a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/README.md +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/README.md @@ -6,6 +6,15 @@ This use case comprises of 3 main sections: - Detect credit card fraud with autoML, choosing the best model with PyCaret. - Real-time credit card analysis to investigate suspicious transaction flagged by the ML. +Below depicts the flow between the libraries and their usage: + + +This use case uses [PyCaret 2.3.4](https://pycaret.org/). +As the latest version of atoti has conflicting dependencies with PyCaret, the two programs are running on separate virtual environment, communicating through endpoints. + + + + ## 1 Synthetic data generation [01-Synthetic-data-generation.ipynb](./01-Synthetic-data-generation.ipynb) is adapted from the GitHub repository [Sparkov_Data_Generation](https://github.com/namebrandon/Sparkov_Data_Generation). It makes use of Faker to generate customers and credit card transactions with varying profiles: @@ -70,9 +79,28 @@ This notebook covers the following: - we used the LGBM model to perform fraud prediction - load transactions and its prediction into atoti - Evaluate incoming transaction from atoti web application on http://localhost:10327. -- Create **source simulation** in atoti with prediction (with and without cumulative features) from: - - LGBM - - DT - - anomaly detection -This allows us to compare the performance of the models and also decide if the additional cumulative features are necessary. +### Real-time fraud prediction + +To test the real-time fraud detection, start the Flask application included under the `atoti-pycaret` package. Follow the [README.md](./atoti-pycaret/README.md) included under the package on how to start the application. + +Alternatively, you can always integrate your own machine learning models and update the REST URI under the function `get_prediction` in the [main.ipynb](./main.ipynb): + +``` +def get_prediction(features_df): + url = "http://127.0.0.1:105/predict" + header = {"Content-Type": "application/json"} + + payload = { + "features": features_df.to_json(orient="records"), + } + + try: + response = requests.post(url, json=payload) + + prediction = pd.DataFrame.from_dict(response.json()) + return prediction + + except requests.exceptions.HTTPError as e: + print(e.response.text) +``` \ No newline at end of file diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/.python-version b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/.python-version new file mode 100644 index 00000000..4351a7e3 --- /dev/null +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/.python-version @@ -0,0 +1 @@ +3.8.7 diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/README.md b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/README.md new file mode 100644 index 00000000..733ce97b --- /dev/null +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/README.md @@ -0,0 +1,53 @@ +# Endpoint for Credit Card Fraud prediction + +The package automl consists of machine learning models that we have trained using [PyCaret](https://pycaret.org/). + +By creating a small [Flask application](https://flask.palletsprojects.com/en/2.2.x/), we are able to create an endpoint that takes in the features for the model to perform fraud prediction. + +# Installation + +Set up the virtual environment for the project using the below command: +``` +poetry install +``` + +Refer to the [poetry documentation](https://python-poetry.org/docs/master/#installing-with-the-official-installer) for more information on the package manager. + + +# Runtime +To launch the Flask application, run the following command: +``` +poetry run python .\automl\prediction.py +``` + +You should able to see the following: + + + +We can post requests to the endpoint at http://127.0.0.1:105/predict, e.g. + +``` +def get_prediction(features_df): + url = "http://127.0.0.1:105/predict" + header = {"Content-Type": "application/json"} + + payload = { + "features": features_df.to_json(orient="records"), + } + + try: + response = requests.post(url, json=payload) + + prediction = pd.DataFrame.from_dict(response.json()) + return prediction + + except requests.exceptions.HTTPError as e: + print(e.response.text) +``` + +You can verify that the requests are received by the endpoint through the shell running this program: + + + + +The endpoint returns a Pandas Dataframe containing the features and their corresponding prediction. diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/models/Final_DT_Model_20211130.pkl b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/models/Final_DT_Model_20211130.pkl new file mode 100644 index 00000000..45f6493b Binary files /dev/null and b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/models/Final_DT_Model_20211130.pkl differ diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/models/Final_ET_Model_20211130.pkl 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b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/models/Final_LGBM_Model_20211130.pkl differ diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/prediction.py b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/prediction.py new file mode 100644 index 00000000..f6e0bcb5 --- /dev/null +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/automl/prediction.py @@ -0,0 +1,33 @@ +from flask import Flask, jsonify, request +import pandas as pd +import pycaret.classification as pyc +import pickle +import os + +app = Flask(__name__) + +dir_path = os.path.dirname(os.path.realpath(__file__)) +print(dir_path) + + +def predict(df): + model = pyc.load_model("./automl/models/Final_LGBM_Model_20211130") + return pyc.predict_model(model, data=df) + + +@app.route("/predict", methods=["POST"]) +def predict_model(): + test = request.json + + features_json = test["features"] + features_df = pd.read_json(features_json) + print(f"Features received: {len(features_df)}") + + model_prediction = predict(features_df) + print(f"Prediction completed for {len(model_prediction)}") + + return model_prediction.to_json(orient="records") + + +if __name__ == "__main__": + app.run(host="0.0.0.0", port=105) diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/poetry.lock b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/poetry.lock new file mode 100644 index 00000000..b4644c99 --- /dev/null +++ b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/atoti-pycaret/poetry.lock @@ -0,0 +1,4515 @@ +[[package]] +name = "alembic" +version = "1.8.1" +description = "A database migration tool for 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"^2.2.2" +pycaret = "2.3.4" + + +[tool.poetry.dev-dependencies] +black-nb = "^0.5.0" +black = {extras = ["jupyter"], version = "^21.6b0"} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/content/content.mv.db b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/content/content.mv.db index d37d6733..94f59fbb 100644 Binary files a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/content/content.mv.db and b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/content/content.mv.db differ diff --git a/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/img/app_flow.png b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/img/app_flow.png new file 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b/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/main.ipynb @@ -8,14 +8,16 @@ "# PyCaret with Synthetic Data for Real-Time Credit Card Fraud detection\n", "\\[_In case you’re unable to see the atoti visualizations in GitHub, try viewing the notebook in [nbviewer](https://nbviewer.org/github/atoti/notebooks/blob/main/notebooks/01-use-cases/finance/risk-management/operational-risk/credit-card-fraud-detection/main.ipynb)._] \n", "\n", - "__NOTE: As of [PyCaret v2.3.9](https://pycaret.gitbook.io/docs/get-started/installation#dependencies) and [atoti v0.6.5](https://docs.atoti.io/latest/installation.html), there are some dependencies conflict in the version of NumPy used. Hence, the machine learning aspect in this notebook is used for reference purposes only.__ \n", + "__NOTE: As of [atoti v0.6.5](https://docs.atoti.io/latest/installation.html), there are some dependencies conflict in the version of NumPy used by atoti and PyCaret. We have included a small flask api under the _atoti-pycaret_ folder that allow us to execute the PyCaret model under a different environment from atoti.__\n", "\n", "[Credit card fraud is on the rise](https://www.forbes.com/sites/billhardekopf/2021/09/17/this-week-in-credit-card-news-huge-increase-in-fraudulent-mobile-apps-a-rosy-holiday-sales-outlook/?sh=385215f866a1) with the increase of online transactions due to COVID-19. \n", "\n", "In this use case, we will attempt a couple of fun things:\n", "- [Generate synthetic data for machine learning](./01-Synthetic-data-generation.ipynb)\n", "- Test out the autoML PyCaret in predicting the fraudulent cases with the [Anomaly Detection](./02-AutoML-PyCaret-anomaly.ipynb) and [Classification module](./03-AutoML-PyCaret-classification.ipynb)\n", - "- Integrate the prediction into atoti for real-time credit card reporting\n", + "- Integrate the prediction into atoti for real-time credit card reporting \n", + "\n", + "\n", "\n", "The use case is only for illustrative purpose to show how atoti can be integrated with machine learning to achieve monitoring objective. \n", "\n", @@ -41,17 +43,16 @@ "metadata": {}, "outputs": [], "source": [ + "import json\n", "import os\n", "import time\n", "from zipfile import ZipFile\n", "\n", "import atoti as tt\n", - "\n", - "# import faker\n", + "import numpy as np\n", "import pandas as pd\n", + "import requests\n", "import wget\n", - "\n", - "# from faker import Faker\n", "from IPython.display import clear_output, display\n", "from utils import data_prep" ] @@ -966,29 +967,29 @@ " \n", " \n", " \n", - " 2fe46964e51bfcefbed728b9e33fc1cf\n", + " 9dde534c5c9213ba4c10014c6a88f891\n", " 0\n", - " 0.9994\n", + " 0.9997\n", " \n", " \n", - " 5ebf9c43d30d0ed15e993578df14a4c5\n", + " d23f920e3e226cf58000ebfb126e5f56\n", " 0\n", - " 0.9994\n", + " 0.9998\n", " \n", " \n", - " a3c0100cb4cfb7a5c4ffc90faaf7a6d8\n", + " 064ce67c7a0cfa99d6ff72dadafde827\n", " 0\n", - " 0.9998\n", + " 0.9997\n", " \n", " \n", - " a1ced786014c2a12a51ee8938d30e866\n", - " 1\n", - " 0.9792\n", + " 256a58ebcd203ac80eec91777f7648fd\n", + " 0\n", + " 1.0000\n", " \n", " \n", - " 933b03044fdeddc8029233e18ae8efbf\n", + " 677588af0956a1704ec59e9fc051e99b\n", " 0\n", - " 0.9998\n", + " 1.0000\n", " \n", " \n", "\n", @@ -997,11 +998,11 @@ "text/plain": [ " Label Score\n", "trans_num \n", - "2fe46964e51bfcefbed728b9e33fc1cf 0 0.9994\n", - "5ebf9c43d30d0ed15e993578df14a4c5 0 0.9994\n", - "a3c0100cb4cfb7a5c4ffc90faaf7a6d8 0 0.9998\n", - "a1ced786014c2a12a51ee8938d30e866 1 0.9792\n", - "933b03044fdeddc8029233e18ae8efbf 0 0.9998" + "9dde534c5c9213ba4c10014c6a88f891 0 0.9997\n", + "d23f920e3e226cf58000ebfb126e5f56 0 0.9998\n", + "064ce67c7a0cfa99d6ff72dadafde827 0 0.9997\n", + "256a58ebcd203ac80eec91777f7648fd 0 1.0000\n", + "677588af0956a1704ec59e9fc051e99b 0 1.0000" ] }, "execution_count": 17, @@ -1451,7 +1452,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "0 - Non-fraudulent, 1 - Fraudulent", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -1459,7 +1460,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -1527,7 +1528,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Merchants with highest fraudulent occurence on 29th Aug", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -1535,7 +1536,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -1639,7 +1640,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Number of transactions by customer in 1 day on 29th Aug - (x-axis: longitude, y-axis: latitude)", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -1647,7 +1648,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -1678,7 +1679,7 @@ "metadata": {}, "outputs": [], "source": [ - "m[\"score\"] = tt.agg.sum(prediction_tbl[\"Score\"])\n", + "m[\"score\"] = tt.where(l[\"trans_num\"] != None, tt.agg.sum(prediction_tbl[\"Score\"]))\n", "m[\"score\"].formatter = \"DOUBLE[#,###.000]\"" ] }, @@ -1851,7 +1852,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Cumulative values for a customer account - SSN (862-79-0516)", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -1859,7 +1860,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -1922,7 +1923,7 @@ "data": { "application/vnd.atoti.convert-query-result-to-widget.v0+json": { "mdx": "SELECT {[Measures].[nb_txns_1_days], [Measures].[nb_txns_7_days], [Measures].[nb_txns_30_days]} ON COLUMNS, NON EMPTY [transactions].[trans_date].[trans_date].Members ON ROWS FROM (SELECT [transactions].[trans_date].[AllMember].[2021-07-12] ON COLUMNS FROM (SELECT [transactions].[ssn].[AllMember].[862-79-0516] ON COLUMNS FROM [credit_card_trans]))", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "widgetCreationCode": "session.visualize()" }, "text/html": [ @@ -2123,7 +2124,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Merchants with more than 25% percentage fraudulent transactions", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -2131,7 +2132,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -2218,7 +2219,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Fraud trend", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -2226,7 +2227,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -2291,7 +2292,7 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Drillthrough on 29th Aug", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 @@ -2299,7 +2300,7 @@ "widgetCreationCode": "session.visualize()" }, "text/html": [ - "" + "" ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." @@ -2399,16 +2400,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Amount loss to fraud comparison in last 2 months", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -2465,16 +2463,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Amount loss to fraud", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -2840,16 +2835,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "% loss to fraud by age group", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -2872,7 +2864,14 @@ "## 3. Integrate prediction from AutoML (PyCaret) into atoti\n", "\n", "Using [PyCaret](https://pycaret.org/), we trained and tested 3 months worth of synthetic credit card transactions. \n", - "The trained models are being saved under the __models__ folder. " + "The trained models are being saved under the __models__ folder. \n", + "\n", + " \n", + "\n", + "The diagram above shows how we can integrate the PyCaret's prediction back into atoti. \n", + "There are many ways to do so, among them are the following:\n", + "1. if we have the predictions output in CSV, we can use the connector to load predictions into atoti\n", + "2. invoking rest api integrated with the trained model to perform real-time prediction and loading the returned results into atoti" ] }, { @@ -2994,16 +2993,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Difference between actual and predicted", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3067,16 +3063,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Confusion matrix", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3180,16 +3173,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Performance of the LGBM model against July prediction", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3291,16 +3281,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Transaction amount saved/lost to fraud after preventive actions", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3491,16 +3478,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Confusion matrix", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3568,16 +3552,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Performance comparison for models against July and August prediction", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3638,16 +3619,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Amount of money saved by each model", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3735,16 +3713,13 @@ "data": { "application/vnd.atoti.widget.v0+json": { "name": "Anomaly detected on 27th Aug but not identified as fraud by classification", - "sessionId": "1664506742_864XYW", + "sessionId": "1664845842_PSHMNZ", "sessionLocation": { "https": null, "port": 10327 }, "widgetCreationCode": "session.visualize()" }, - "text/html": [ - "" - ], "text/plain": [ "Open the notebook in JupyterLab with the atoti extension enabled to build this widget." ] @@ -3800,20 +3775,7 @@ "id": "6c780a37-5500-4403-99b4-6541a0d44745", "metadata": {}, "source": [ - "### 3.3 Launch real-time dashboard \n", - "\n", - "__NOTE: We have disabled the integration of real-time prediction into atoti due to the dependency conflict between atoti and PyCaret. \n", - "Feel free to enable the prediction once the conflict is resolved.__" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "a2abec37-0835-4cff-a363-1e50c049f3c5", - "metadata": {}, - "outputs": [], - "source": [ - "disable_prediction = True" + "### 3.3 Launch real-time dashboard " ] }, { @@ -3826,7 +3788,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "id": "17e99937-4479-4b43-8485-a481b2c6acd0", "metadata": {}, "outputs": [ @@ -3843,7 +3805,7 @@ "Open the notebook in JupyterLab with the atoti extension enabled to see this link." ] }, - "execution_count": 68, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -3857,25 +3819,49 @@ "id": "019a3643-0e7d-4b39-9711-f3161a4fafd8", "metadata": {}, "source": [ - "### 3.4 Load saved ML model\n", + "### 3.4 PyCaret prediction endpoint\n", + "\n", + "As we can see from the previous section, the Light Gradient Boosting Machine (LGBM) model with full features has the best performance. \n", + "Refer to [03-AutoML-PyCaret-classification.ipynb](./03-AutoML-PyCaret-classification.ipynb) for the model evaluation.\n", + "\n", + "Under the _atoti-pycaret_ folder, we have a small script that's running on [Flask](https://flask.palletsprojects.com/en/2.2.x/). It allows us to load the saved LGBM model and perform our real-time prediction. \n", "\n", - "As we can see from the previous section, the Light Gradient Boosting Machine (LGBM) model with full features has the best performance. We will load its saved model for real-time fraud detection simulation below.\n", + "To get it running, perform the following:\n", + "- Navigate to _atoti-pycaret_ folder in your terminal\n", + "- run `poetry install`\n", + "- run `poetry run python ./automl/prediction.py`\n", "\n", - "Refer to [03-AutoML-PyCaret-classification.ipynb](./03-AutoML-PyCaret-classification.ipynb) for the model evaluation." + "\n", + "\n", + "The following function allows us to send our request to the endpoint." ] }, { "cell_type": "code", - "execution_count": 69, - "id": "93944bea-fb85-4519-b405-971f1e7855fb", + "execution_count": 68, + "id": "e843ed52-f64c-4288-903b-49f090458534", "metadata": {}, "outputs": [], "source": [ - "if not disable_prediction:\n", - " from pycaret.classification import *\n", + "def get_prediction(features_df):\n", + " url = \"http://127.0.0.1:105/predict\"\n", + " header = {\"Content-Type\": \"application/json\"}\n", + "\n", + " print(f\"Received features length: {len(features_df)}\")\n", + " payload = {\n", + " \"features\": features_df.to_json(orient=\"records\"),\n", + " }\n", + "\n", + " try:\n", + " response = requests.post(url, json=payload)\n", + " print(response)\n", "\n", - " model_path = \"./models/\"\n", - " saved_final_lgbm = load_model(f\"{model_path}Final LGBM Model 20211130\")" + " prediction = pd.DataFrame.from_dict(response.json())\n", + " print(f\"Returned prediction length: {len(prediction)}\")\n", + " return prediction\n", + "\n", + " except requests.exceptions.HTTPError as e:\n", + " print(e.response.text)" ] }, { @@ -3895,8 +3881,8 @@ }, { "cell_type": "code", - "execution_count": 70, - "id": "d25fb407-db17-4b83-b04c-a612d32ebe26", + "execution_count": 69, + "id": "408210ea-b39d-4658-80dc-c30d4aac1783", "metadata": {}, "outputs": [], "source": [ @@ -3939,18 +3925,17 @@ }, { "cell_type": "code", - "execution_count": 71, - "id": "19179724-58ee-4276-9c5b-b442f3d0230a", + "execution_count": 70, + "id": "18faf412-961a-4f92-adb8-adbfe1cd46c2", "metadata": {}, "outputs": [], "source": [ - "if not disable_prediction:\n", - " new_txns_df = pd.read_csv(\n", - " # f\"{data_path}/realtime_txn.csv\", # uncomment for more data to have a longer streaming time\n", - " f\"{data_path}/realtime_txn_subset.csv\", # using smaller dataset to facilitate testing in GIT\n", - " parse_dates=[\"trans_date\"],\n", - " chunksize=1000,\n", - " )" + "new_txns_df = pd.read_csv(\n", + " # f\"{data_path}/realtime_txn.csv\", # uncomment for more data to have a longer streaming time\n", + " f\"{data_path}/realtime_txn_subset.csv\", # using smaller dataset to facilitate testing in GIT\n", + " parse_dates=[\"trans_date\"],\n", + " chunksize=1000,\n", + ")" ] }, { @@ -3971,12 +3956,12 @@ }, { "cell_type": "code", - "execution_count": 72, - "id": "bf15da91-dd2e-424a-84be-ad16d84d4d75", + "execution_count": 71, + "id": "6e285f6e-2583-4c6b-ae3a-3b0ac077d1af", "metadata": {}, "outputs": [], "source": [ - "def run_prediction(_txn_df, model, debug=False):\n", + "def run_prediction(_txn_df, debug=False):\n", "\n", " print(\".\", end=\" \")\n", " _txn_df[common_col] = _txn_df[common_col].astype(str)\n", @@ -4015,7 +4000,7 @@ " m[\"avg_txns_amt_7_days\"],\n", " m[\"avg_txns_amt_30_days\"],\n", " levels=[l[\"cc_num\"], l[\"acct_num\"], l[\"trans_date\"]],\n", - " condition=((l[\"cc_num\"].isin(*cc_list)) & (l[\"trans_date\"].isin(*trans_date))),\n", + " filter=((l[\"cc_num\"].isin(*cc_list)) & (l[\"trans_date\"].isin(*trans_date))),\n", " )\n", "\n", " cum_df.reset_index(inplace=True)\n", @@ -4026,10 +4011,23 @@ " print(\"4 ... Performing machine learning prediction\")\n", "\n", " features = features_df[feature_list]\n", - " model_prediction = predict_model(model, data=features)\n", + " # model_prediction = predict_model(model, data=features)\n", + " model_prediction = get_prediction(features)\n", + "\n", + " # cast to the correct types for merging\n", + " nonNumericCols = features_df.select_dtypes(\n", + " exclude=[np.number]\n", + " ).columns.intersection(model_prediction.columns)\n", + " model_prediction[nonNumericCols] = model_prediction[nonNumericCols].astype(str)\n", "\n", " # merge prediction results back to original dataset\n", - " features_df = features_df.merge(model_prediction)\n", + " # can only merge on non-float columns\n", + " nonFloatCol = (\n", + " features_df.select_dtypes(exclude=[\"float64\"])\n", + " .columns.intersection(model_prediction.columns)\n", + " .to_list()\n", + " )\n", + " features_df = features_df.merge(model_prediction, on=nonFloatCol)\n", "\n", " features_df[[\"trans_num\", \"Label\"]] = features_df[[\"trans_num\", \"Label\"]].astype(\n", " str\n", @@ -4057,17 +4055,25 @@ }, { "cell_type": "code", - "execution_count": 73, - "id": "78b96017-dac7-4d0e-98c5-054269df3bed", + "execution_count": 72, + "id": "6e500f72-833f-4720-a36d-86353ef95a33", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ". Received features length: 1000\n", + "\n", + "Returned prediction length: 1000\n" + ] + } + ], "source": [ - "if not disable_prediction:\n", - "\n", - " for chunk in new_txns_df:\n", - " run_prediction(chunk, saved_final_lgbm)" + "for chunk in new_txns_df:\n", + " run_prediction(chunk)" ] }, { @@ -4075,6 +4081,8 @@ "id": "44a99bc9-223c-49f6-b20d-6acfbe2d5f37", "metadata": {}, "source": [ + "## 4. Conclusion\n", + "\n", "While PyCaret allows us to compare multiple machine learning models in one go, atoti translate the predictions and allows us to compare downstream business metrics side-by-side. The ability to compute the different KPIs on the fly reduce the turnaround time needed to discover a potential fraud. Hence, leveraging atoti, we can choose the most efficient machine learning model based on business KPI. \n", "\n", "Have a try with other models."