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update demo

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Files changed (11) hide show
  1. app.py +38 -0
  2. catboost_info/catboost_training.json +1004 -0
  3. catboost_info/learn/events.out.tfevents +0 -0
  4. catboost_info/learn_error.tsv +1003 -0
  5. catboost_info/time_left.tsv +1055 -0
  6. images/AUC.png +0 -0
  7. images/Confusion Matrix.png +0 -0
  8. images/Prediction Error.png +0 -0
  9. images/Residuals.png +0 -0
  10. logs.log +0 -0
  11. pages/regression.py +26 -0
app.py ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ from pycaret.datasets import get_data
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+ from pycaret.classification import *
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+ import streamlit as st
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+ from pathlib import Path
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+
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+ path_root = Path(Path.cwd())
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+ st.sidebar.markdown("# Main page 🎈")
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+ st.title("Classification")
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+ @st.cache
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+ def load_data():
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+ data = get_data('diabetes')
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+ return data
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+ data = load_data()
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+ s = setup(data, target = 'Class variable')
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+
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+ best = compare_models()
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+ df_metric = pull()
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+ st.markdown("Compare model")
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+ st.dataframe(df_metric)
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+
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+ evaluate_model(best)
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+ cols = st.columns(2)
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+ with cols[0]:
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+ try:
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+ st.markdown("## AUC")
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+ plot_model(best, plot = 'auc', save = 'images')
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+ st.image(str(path_root.joinpath("images/AUC.png")))
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+ except e:
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+ st.text(e)
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+ with cols[1]:
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+ st.markdown("## Confusion Matrix")
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+ try:
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+ plot_model(best, plot = 'confusion_matrix', save = 'images')
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+ st.image(str(path_root.joinpath("images/Confusion Matrix.png")))
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+ except e:
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+ st.text(e)
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+
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+
catboost_info/catboost_training.json ADDED
@@ -0,0 +1,1004 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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images/AUC.png ADDED
images/Confusion Matrix.png ADDED
images/Prediction Error.png ADDED
images/Residuals.png ADDED
logs.log ADDED
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pages/regression.py ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ from pycaret.regression import *
3
+ from pathlib import Path
4
+
5
+ path_root = Path(Path.cwd())
6
+
7
+ from pycaret.datasets import get_data
8
+ data = get_data('insurance')
9
+ s = setup(data, target = 'charges')
10
+ best = compare_models()
11
+ evaluate_model(best)
12
+
13
+ with st.sidebar:
14
+ st.sidebar.markdown("# Regression Feature ❄️")
15
+ evaluation_option = st.selectbox("evaluation", ["residuals", "error", "cooks"])
16
+
17
+ st.markdown("# Regression❄️")
18
+ st.markdown("## Evaluation")
19
+ image_options = {"error": "Prediction Error.png", "residuals": "Residuals.png"}
20
+ try:
21
+ st.markdown(f"## {evaluation_option}")
22
+ plot_model(best, plot = evaluation_option, save = 'images')
23
+ st.image(str(path_root.joinpath(f"images/{image_options[evaluation_option]}")))
24
+ except e:
25
+ st.text(e)
26
+ print(evaluation_option)