Add sidebar

This commit is contained in:
yagudin 2021-06-01 17:28:35 +03:00
parent c9a127da44
commit 1573598c53

View File

@ -13,13 +13,43 @@ if __name__ == "__main__":
# --- # ---
# if st.checkbox('I am new! Show me instructions.'): st.sidebar.header("Welcome!")
# st.write("""
# Hey! st.sidebar.write("Good calibration is vital for good judgemental forecasting. "
# """) "When a calibrated forecaster predicts 70% on 10 quesions, we actually expect "
"around 7 of these to resolve positively. Unfortunately, there is "
"no easy way to see which fractoin of our 70% forecasts resolves "
"positively on Good Judgement Open. Hence I made this web app.")
st.sidebar.subheader("On cURL")
st.sidebar.write("I use your cookies for gathering information from GJO: which questions did you forecast on; what did you forecast on; how did they resolve.")
st.sidebar.write("I do not use them for other purposes, neither I store them. The code is on [github](https://github.com/yagudin/gjo-calibration).")
st.sidebar.write("""
1. Go to e.g [gjopen.com/questions](gjopen.com/questions) in a new tab in Chrome or in Firefox.
2. Press `Ctrl + Shift + I`, and then navigate to the "Network" tab.
3. Click on Reload, or reload the page.
4. Right click on the first request, which loads the "questions" document. Click Copy, then "copy as cURL". Paste the results here.
""")
# st.sidebar.subheader("On plots and methodology")
# st.sidebar.write("""
# - I generate two calibration curves: one in linear space and another one in 'odds' space (hopefully it will be easier to see how well calibrated you are around probabilities close to 0 and 1).
# - I generate plots with a modified [sklearn.calibration.calibration_curve](https://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html), basically it groups points into bins and computes the proportions of samples resolving positively and the mean predicted probabilities.
# - The confidence intervals are a standart deviations wide.
# - If you hover over a datapoint you can see precise coordinates (x, y) and number of samples (N) contributing to it.
# """)
st.sidebar.subheader("Authorship and acknowledgments")
st.sidebar.write("This web app was built by [Misha Yagudin](mailto:mike.yagudin@gmail.com). I am grateful to [Nuño Sempere](https://nunosempere.github.io/) for providing feedback. All errors are mine.")
# --- # ---
platform = st.selectbox( platform = st.selectbox(
"Which platform are you using?", "Which platform are you using?",
["Good Judgement Open", "CSET Foretell"], ["Good Judgement Open", "CSET Foretell"],