diff --git a/README.md b/README.md index 7bf592f..27d9f17 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ podman run -d -p 6379:6379 --name redis -e "ALLOW_EMPTY_PASSWORD=yes" docker.io Install Feast using uv ``` -uv sync +uv sync --all-extras ``` We have already set up a feature repository in [feature_repo/](feature_repo/). As a result, all we have to do is configure the [feature_store.yaml/](feature_repo/feature_store.yaml) in the feature repository. Please set the connection string of the Postgresql and Redis according to your local infra setup. @@ -248,4 +248,17 @@ Example returned feature values: } ] } -``` \ No newline at end of file +``` + + +## Interactive demo (using Streamlit) + +Once the credit scoring model has been trained it can be used for interactive loan applications using Streamlit: + +Simply start the Streamlit application +``` +uv run streamlit run streamlit_app.py +``` +Then navigate to the URL on which Streamlit is being served. You should see a user interface through which loan applications can be made: + +![Streamlit Loan Application](streamlit.png) diff --git a/credit_model.py b/credit_model.py index cd95aef..8c7f5df 100644 --- a/credit_model.py +++ b/credit_model.py @@ -74,7 +74,7 @@ def _get_training_features(self, loans): train_X = training_df[ training_df.columns.drop(self.target) .drop("event_timestamp") - .drop("created_timestamp") + .drop("created_timestamp__") .drop("loan_id") .drop("zipcode") .drop("dob_ssn") diff --git a/pyproject.toml b/pyproject.toml index ef46c95..aa467bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,3 +14,9 @@ dependencies = [ "grpcio-tools", "grpcio-reflection", ] +[project.optional-dependencies] +streamlit = [ + "streamlit>=1.50.0", + "shap>=0.49.0", + "matplotlib>=3.10.7", +] diff --git a/streamlit.png b/streamlit.png new file mode 100644 index 0000000..7965336 Binary files /dev/null and b/streamlit.png differ diff --git a/streamlit_app.py b/streamlit_app.py new file mode 100644 index 0000000..6e5ed71 --- /dev/null +++ b/streamlit_app.py @@ -0,0 +1,132 @@ +import datetime +import logging +import sys +from collections import OrderedDict + +import pandas as pd +import shap +import streamlit as st +from matplotlib import pyplot as plt + +from credit_model import CreditScoringModel + +logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s %(levelname)s %(message)s", + handlers=[logging.StreamHandler(sys.stdout)], +) + +logging.debug("Streamlit app started.") + +st.set_page_config(layout="wide") +model = CreditScoringModel() +if not model.is_model_trained(): + raise Exception("The credit scoring model has not been trained. Please run run.py.") + + +def get_loan_request(): + zipcode = st.sidebar.text_input("Zip code", "94109") + date_of_birth = st.sidebar.date_input( + "Date of birth", value=datetime.date(year=1986, day=19, month=3) + ) + ssn_last_four = st.sidebar.text_input( + "Last four digits of social security number", "3643" + ) + dob_ssn = f"{date_of_birth.strftime('%Y%m%d')}_{str(ssn_last_four)}" + age = st.sidebar.slider("Age", 0, 130, 25) + income = st.sidebar.slider("Yearly Income", 0, 1000000, 120000) + person_home_ownership = st.sidebar.selectbox( + "Do you own or rent your home?", ("RENT", "MORTGAGE", "OWN") + ) + + employment = st.sidebar.slider( + "How long have you been employed (months)?", 0, 120, 12 + ) + + loan_intent = st.sidebar.selectbox( + "Why do you want to apply for a loan?", + ( + "PERSONAL", + "VENTURE", + "HOMEIMPROVEMENT", + "EDUCATION", + "MEDICAL", + "DEBTCONSOLIDATION", + ), + ) + + amount = st.sidebar.slider("Loan amount", 0, 100000, 10000) + interest = st.sidebar.slider("Preferred interest rate", 1.0, 25.0, 12.0, step=0.1) + return OrderedDict( + { + "zipcode": [int(zipcode)], + "dob_ssn": [dob_ssn], + "person_age": [age], + "person_income": [income], + "person_home_ownership": [person_home_ownership], + "person_emp_length": [float(employment)], + "loan_intent": [loan_intent], + "loan_amnt": [amount], + "loan_int_rate": [interest], + } + ) + + +# Application +st.title("Loan Application") + +# Input Side Bar +st.header("User input:") +loan_request = get_loan_request() +df = pd.DataFrame.from_dict(loan_request) + +logging.debug(f"User input: {loan_request}") +st.write(df) + +# Full feature vector +st.header("Feature vector (user input + zipcode features + user features):") +vector = model._get_online_features_from_feast(loan_request) +ordered_vector = loan_request.copy() +key_list = vector.keys() +key_list = sorted(key_list) +for vector_key in key_list: + if vector_key not in ordered_vector: + ordered_vector[vector_key] = vector[vector_key] +df = pd.DataFrame.from_dict(ordered_vector) + +logging.debug(f"Online features from Feast: {vector}") +st.write(df) + +# Results of prediction +st.header("Application Status (model prediction):") +result = model.predict(loan_request) + +if result == 0: + st.success("Your loan has been approved!") +elif result == 1: + st.error("Your loan has been rejected!") + +logging.debug(f"Model prediction result: {result}") + +# Feature importance +st.header("Feature Importance") +# TODO: Load a sample dataset from feature store instead of a static file +X = pd.read_parquet("data/training_dataset_sample.parquet") +X['total_debt_due'] = (X['credit_card_due'] + X['mortgage_due'] + X['student_loan_due'] + X['vehicle_loan_due'] + X['loan_amnt']).astype(float) +explainer = shap.TreeExplainer(model.classifier) +shap_values = explainer.shap_values(X) +left, right = st.columns(2) +with left: + fig, ax = plt.subplots() + plt.title("Feature importance based on SHAP values") + 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