106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
import numpy as np
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import pandas as pd
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import streamlit as st
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import uncurl
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from calibration import overconfidence
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from firebase_requests import get_forecasts, get_resolutions
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from gjo_requests import get_resolved_questions
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from plotting import plotly_calibration, plotly_calibration_odds
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if __name__ == "__main__":
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st.title("Learn how calibrated are you?")
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# ---
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# if st.checkbox('I am new! Show me instructions.'):
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# st.write("""
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# Hey!
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# """)
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# ---
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platform = st.selectbox(
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"Which platform are you using?",
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["Good Judgement Open", "CSET Foretell"],
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)
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platform_url = {
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"Good Judgement Open": "https://www.gjopen.com",
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"CSET Foretell": "https://www.cset-foretell.com",
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}[platform]
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uid = st.number_input("What is your user ID?", min_value=1, value=28899)
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uid = str(uid)
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curl_value = """curl 'https://www.gjopen.com/' \\
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-H 'authority: www.gjopen.com' \\
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-H '...: ...'"""
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curl_command = st.text_area(
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"Ugh... Gimme your cURL info...", value=curl_value
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)
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curl_command = "".join(curl_command.split("\\\n"))
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if curl_command != curl_value:
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curl_content = uncurl.parse_context(curl_command)
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headers, cookies = curl_content.headers, curl_content.cookies
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# ---
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questions = get_resolved_questions(uid, platform_url, headers, cookies)
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st.write(f"{len(questions)} questions you forecasted on have resolved.")
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# ---
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# TODO: Make a progress bar..?
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forecasts = get_forecasts(uid, questions, platform_url, headers, cookies)
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resolutions = get_resolutions(questions, platform_url, headers, cookies)
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# ---
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num_forecasts = sum(len(f) for f in forecasts.values())
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st.write(
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f"On these {len(questions)} questions you've made {num_forecasts} forecasts."
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)
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flatten = lambda t: [item for sublist in t for item in sublist]
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y_true = flatten(resolutions[q]["y_true"] for q in questions for _ in forecasts[q])
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y_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q])
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# Note that I am "double counting" each prediction.
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if st.checkbox("Drop last"):
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y_true = flatten(
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resolutions[q]["y_true"][:-1] for q in questions for _ in forecasts[q]
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)
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y_pred = flatten(f["y_pred"][:-1] for q in questions for f in forecasts[q])
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y_true, y_pred = np.array(y_true), np.array(y_pred)
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st.write(f"Which gives us {len(y_pred)} datapoints to work with.")
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# ---
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strategy = st.selectbox(
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"Which binning stranegy do you prefer?",
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["uniform", "quantile"],
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)
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recommended_n_bins = int(np.sqrt(len(y_pred))) if strategy == "quantile" else 20 + 1
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n_bins = st.number_input(
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"How many bins do you want me to display?",
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min_value=1,
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value=recommended_n_bins,
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)
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fig = plotly_calibration(y_true, y_pred, n_bins=n_bins, strategy=strategy)
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st.plotly_chart(fig, use_container_width=True)
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overconf = overconfidence(y_true, y_pred)
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st.write(f"Your over/under- confidence score is {overconf:.2f}.")
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# ---
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fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy)
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st.plotly_chart(fig, use_container_width=True)
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