Pushed from Domino: https://ykb.domino-eval.com/workspace/petter/mlop…

…s-best-practices?executionId=673b6ddd011f5130a7ff00d2
dominodatalab · Nov 18, 2024 · 90a2d1c · 90a2d1c
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diff --git a/.Trash-12574/info/Untitled Folder.trashinfo b/.Trash-12574/info/Untitled Folder.trashinfo
@@ -0,0 +1,3 @@
+[Trash Info]
+Path=Untitled%20Folder
+DeletionDate=2024-11-18T16:45:58
diff --git a/.ipynb_checkpoints/app-checkpoint.sh b/.ipynb_checkpoints/app-checkpoint.sh
@@ -1,20 +1,15 @@
-#!/usr/bin/env bash
-
-# This is a bash script for Domino's App publishing feature
-# Learn more at http://support.dominodatalab.com/hc/en-us/articles/209150326
-
-## R/Shiny Example
-## This is an example of the code you would need in this bash script for a R/Shiny app
-R -e 'shiny::runApp("./apps/wine.R", port=8888, host="0.0.0.0")'
-
-## Flask example
-## This is an example of the code you would need in this bash script for a Python/Flask app
-#export LC_ALL=C.UTF-8
-#export LANG=C.UTF-8
-#export FLASK_APP=app-flask.py
-#export FLASK_DEBUG=1
-#python -m flask run --host=0.0.0.0 --port=8888
-
-## Dash Example
-## This is an example of the code you would need in this bash script for a Dash app
-#python app-dash.py
+mkdir ~/.streamlit
+echo "[browser]" > ~/.streamlit/config.toml
+echo "gatherUsageStats = true" >> ~/.streamlit/config.toml
+echo "serverAddress = \"0.0.0.0\"" >> ~/.streamlit/config.toml
+echo "serverPort = 8501" >> ~/.streamlit/config.toml
+echo "[server]" >> ~/.streamlit/config.toml
+echo "port = 8501" >> ~/.streamlit/config.toml
+echo "enableCORS = false" >> ~/.streamlit/config.toml
+echo "enableXsrfProtection = false" >> ~/.streamlit/config.toml
+
+streamlit run apps/streamlit_app.py
+
+# python app/rai.py
+
+# R -e 'shiny::runApp("/mnt/scripts/shiny_app.R", port=8888, host="0.0.0.0")'
diff --git a/app.sh b/app.sh
@@ -1,20 +1,15 @@
-#!/usr/bin/env bash
-
-# This is a bash script for Domino's App publishing feature
-# Learn more at http://support.dominodatalab.com/hc/en-us/articles/209150326
-
-## R/Shiny Example
-## This is an example of the code you would need in this bash script for a R/Shiny app
-R -e 'shiny::runApp("./apps/lead_gen.R", port=8888, host="0.0.0.0")'
-
-## Flask example
-## This is an example of the code you would need in this bash script for a Python/Flask app
-#export LC_ALL=C.UTF-8
-#export LANG=C.UTF-8
-#export FLASK_APP=app-flask.py
-#export FLASK_DEBUG=1
-#python -m flask run --host=0.0.0.0 --port=8888
-
-## Dash Example
-## This is an example of the code you would need in this bash script for a Dash app
-#python app-dash.py
+mkdir ~/.streamlit
+echo "[browser]" > ~/.streamlit/config.toml
+echo "gatherUsageStats = true" >> ~/.streamlit/config.toml
+echo "serverAddress = \"0.0.0.0\"" >> ~/.streamlit/config.toml
+echo "serverPort = 8501" >> ~/.streamlit/config.toml
+echo "[server]" >> ~/.streamlit/config.toml
+echo "port = 8501" >> ~/.streamlit/config.toml
+echo "enableCORS = false" >> ~/.streamlit/config.toml
+echo "enableXsrfProtection = false" >> ~/.streamlit/config.toml
+
+streamlit run apps/streamlit_app.py
+
+# python app/rai.py
+
+# R -e 'shiny::runApp("/mnt/scripts/shiny_app.R", port=8888, host="0.0.0.0")'
diff --git a/apps/.ipynb_checkpoints/streamlit_app-checkpoint.py b/apps/.ipynb_checkpoints/streamlit_app-checkpoint.py
@@ -0,0 +1,157 @@
+###########################################################################################################
+###########################################################################################################
+###########################################################################################################
+###########################################################################################################
+###########################################################################################################
+###########################################################################################################
+
+import streamlit as st
+from streamlit import components
+import numpy as np
+import pandas as pd
+import pickle
+import time
+from matplotlib import pyplot as plt
+from  matplotlib.ticker import FuncFormatter
+import seaborn as sns
+import requests
+import eli5
+import xgboost as xgb
+import seaborn as sns
+import os
+
+xgc = xgb.Booster(model_file="/mnt/code/models/xgb_clf.xgb")
+
+
+st.set_page_config(layout="wide")
+
+
+
+####################
+### INTRODUCTION ###
+####################
+
+row0_spacer1, row0_1, row0_spacer2, row0_2, row0_spacer3 = st.columns((.1, 2.3, .1, 1.3, .1))
+with row0_1:
+    st.title('Credit Card Default Dashboard')
+with row0_2:
+    st.text("")
+row3_spacer1, row3_1, row3_spacer2 = st.columns((.1, 3.2, .1))
+with row3_1:
+    st.markdown("")
+
+#################
+### SELECTION ###
+#################
+
+
+st.sidebar.text('')
+st.sidebar.text('')
+st.sidebar.text('')
+
+### SEASON RANGE ###
+st.sidebar.subheader("**Enter the application inputs to view the default scores.**")
+st.sidebar.subheader("")
+with st.sidebar.form("my_form"):
+    PAY_0 = st.number_input('Repayment status last month', min_value = -1, max_value = 9)
+    PAY_2 = st.number_input('Repayment status 2 months ago', min_value = -1, max_value = 9)
+    PAY_3 = st.number_input('Repayment status 3 months ago', min_value = -1, max_value = 9)
+    PAY_4 = st.number_input('Repayment status 4 months ago', min_value = -1, max_value = 9)
+    LIMIT_BAL = st.number_input('Credit Limit', min_value=0)
+    BILL_AMT1 = st.number_input('Current Balance', min_value=0)
+    AGE = st.number_input('Insert applicant age', min_value = 20, max_value = 115)
+    scored = st.form_submit_button("Score")
+
+# baseline = '/domino/datasets/local/CreditCard-Approval/data/train_data_0.csv'
+# df_cr = pd.read_csv(baseline)
+
+# age_min, age_max = 21, 115
+# age_std = (age - age_min) / (age_max - age_min)
+
+column_names = ["PAY_0", "PAY_2", "PAY_3", "PAY_4", "LIMIT_BAL", "BILL_AMT1"]
+
+# column_names_all = ['PAY_0', 'PAY_2', 'PAY_4', 'LIMIT_BAL', 'PAY_3', 'BILL_AMT1']
+
+# Set some default values
+sample_data = [[-1, 2, 0, 12000, 0, 2682]]
+
+df_all = pd.DataFrame(sample_data, columns=column_names)
+
+# Override the values with what was passed to the scoring function
+df_all[["PAY_0"]] = PAY_0
+df_all[["PAY_2"]] = PAY_2
+df_all[["PAY_3"]] = PAY_3
+df_all[["PAY_4"]] = PAY_4
+df_all[["LIMIT_BAL"]] = LIMIT_BAL
+df_all[["BILL_AMT1"]] = BILL_AMT1
+
+for col in ["PAY_0", "PAY_2", "PAY_3", "PAY_4", "LIMIT_BAL", "BILL_AMT1"]:
+    df_all[col] = df_all[col].astype('int')
+
+df = pd.DataFrame(columns=column_names, 
+                 data=[[PAY_0, PAY_2, PAY_3, PAY_4, LIMIT_BAL, BILL_AMT1]])
+
+setup_dict = {}
+scoring_request = {}
+results = list()
+
+for n in range(df.shape[0]):
+    for i in list(df.columns):
+        setup_dict.update({i :list(df[n:n+1].to_dict().get(i).values())[0]})
+        scoring_request = {'data' : setup_dict}
+
+        response = requests.post(os.environ['API_URL'],
+    auth=(
+        os.environ['API_PASSWORD'],
+        os.environ['API_PASSWORD']
+    ),
+        json=scoring_request
+    )
+    results.append(response.json().get('result'))
+
+
+### Results ###
+
+probability = results[0][0]
+
+if probability == 1:
+    result_text = ":green[REPAYMENT LIKELY]"
+else:
+    result_text = ":red[HIGH RISK OF DEFAULT]"
+
+try:
+    try_df = eli5.explain_prediction_df(xgc, df_all.iloc[0], 
+                         feature_names=list(df_all.columns))
+except:
+    print("An exception occurred")
+
+row4_spacer1, row4_1, row4_spacer2 = st.columns((.2, 7.1, .2))
+with row4_1:
+    st.subheader('After scoring this application, the model suggests that the application be:')
+    st.subheader(' ')
+    st.subheader(result_text)
+    st.subheader(' ')
+    st.subheader("The following table provides insights into the model's prediction:")
+    st.subheader(' ')
+    col1, col2 = st.columns(2)
+    with col1:
+        st.subheader('Model Weights:')
+        df_weights = eli5.explain_weights_df(xgc, 
+                        feature_names=list(df_all.columns))
+        df_weights.columns = ['Feature', 'Weight']
+        st.dataframe(df_weights.style.background_gradient(axis=0, 
+                                                             gmap=df_weights['Weight'], 
+                                                             subset=['Feature', 'Weight'], 
+                                                             cmap = 'Greens').hide_index())
+
+
+    with col2:
+        st.subheader('Model Prediction:')
+        df_prediction = eli5.explain_prediction_df(xgc, df_all.iloc[0], 
+                         feature_names=list(df_all.columns))
+        df_prediction.drop("target", axis=1, inplace=True)
+        df_prediction.columns = ['Feature', 'Weight', 'value']
+        st.dataframe(df_prediction.style.background_gradient(axis=0, 
+                                                             gmap=df_prediction['Weight'], 
+                                                             subset=['Feature', 'Weight'], 
+                                                             cmap = 'RdYlGn').hide_index())
diff --git a/apps/streamlit_app.py b/apps/streamlit_app.py
@@ -18,8 +18,9 @@
 import eli5
 import xgboost as xgb
 import seaborn as sns
+import os
 
-#xgc = xgb.Booster(model_file="/mnt/models/xgb_clf.xgb")
+xgc = xgb.Booster(model_file="/mnt/code/models/xgb_clf.xgb")
 
 
 st.set_page_config(layout="wide")
@@ -99,10 +100,10 @@
         setup_dict.update({i :list(df[n:n+1].to_dict().get(i).values())[0]})
         scoring_request = {'data' : setup_dict}
 
-        response = requests.post("https://ykb.domino-eval.com:443/models/673b5499011f5130a7ff0036/latest/model",
+        response = requests.post(os.environ['API_URL'],
     auth=(
-        "WCkJfQNr8MxoLcG277lCBCqWqZClns6GDMM6Kzs65WwnX9lIMDiA5Y76FKWh2lQJ",
-        "WCkJfQNr8MxoLcG277lCBCqWqZClns6GDMM6Kzs65WwnX9lIMDiA5Y76FKWh2lQJ"
+        os.environ['API_PASSWORD'],
+        os.environ['API_PASSWORD']
     ),
         json=scoring_request
     )
@@ -113,12 +114,10 @@
 
 probability = results[0][0]
 
-if probability >= 0.6:
+if probability == 1:
     result_text = ":green[REPAYMENT LIKELY]"
-elif probability >= 0.4:
-    result_text = ":yellow[RISK OF DEFAULT - LEVEL 1 OUTREACH]"
 else:
-    result_text = ":red[HIGH RISK OF DEFAULT - LEVEL 2 OUTREACH]"
+    result_text = ":red[HIGH RISK OF DEFAULT]"
 
 try:
     try_df = eli5.explain_prediction_df(xgc, df_all.iloc[0], 
@@ -132,8 +131,6 @@
     st.subheader(' ')
     st.subheader(result_text)
     st.subheader(' ')
-    st.plotly_chart(fig, use_container_width=True)
-    st.subheader(' ')
     st.subheader("The following table provides insights into the model's prediction:")
     st.subheader(' ')
     col1, col2 = st.columns(2)
@@ -147,10 +144,6 @@
                                                              subset=['Feature', 'Weight'], 
                                                              cmap = 'Greens').hide_index())
 
-        #html_object = eli5.show_weights(xgc, 
-        #                 feature_names=list(df_all.columns))
-        #raw_html = html_object._repr_html_()
-        #components.v1.html(raw_html)
 
     with col2:
         st.subheader('Model Prediction:')
@@ -161,9 +154,4 @@
         st.dataframe(df_prediction.style.background_gradient(axis=0, 
                                                              gmap=df_prediction['Weight'], 
                                                              subset=['Feature', 'Weight'], 
-                                                             cmap = 'RdYlGn').hide_index())
-        # html_object2 = eli5.show_prediction(xgc, df_all.iloc[0], 
-        #                  feature_names=list(df_all.columns),
-        #                  show_feature_values=True)
-        # raw_html2 = html_object2._repr_html_()
-        # components.v1.html(raw_html2)
+                                                             cmap = 'RdYlGn').hide_index())
diff --git a/models/xgb_clf.xgb b/models/xgb_clf.xgb