Improve application workflow and control flow

This commit is contained in:
yagudin 2021-06-01 17:31:04 +03:00
parent 192c2c6037
commit b86157e592

106
strmlt.py
View File

@ -65,58 +65,68 @@ if __name__ == "__main__":
curl_value = """curl 'https://www.gjopen.com/' \\ curl_value = """curl 'https://www.gjopen.com/' \\
-H 'authority: www.gjopen.com' \\ -H 'authority: www.gjopen.com' \\
-H 'cache-control: max-age=0' \\ -H 'cache-control: max-age=0' \\
-H 'sec-ch-ua: " Not A;Brand";v="99", "Chromium";v="90", "Google Chrome";v="90"' \\ -H 'sec-ch-ua: "something-something-about-your-browser"' \\
-H 'sec-ch-ua-mobile: ?0' \\ -H 'sec-ch-ua-mobile: ?0' \\
-H 'dnt: 1' \\ -H 'dnt: 1' \\
-H 'upgrade-insecure-requests: 1' \ -H 'upgrade-insecure-requests: 1' \
-H 'user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36' \ -H 'user-agent: Mozilla/5.0 something-something-about-your-PC' \
-H 'accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9' \ -H 'accept: text/html...' \
-H 'sec-fetch-site: none' \\ -H 'sec-fetch-site: none' \\
-H 'sec-fetch-mode: navigate' \\ -H 'sec-fetch-mode: navigate' \\
-H 'sec-fetch-user: ?1' \\ -H 'sec-fetch-user: ?1' \\
-H 'sec-fetch-dest: document' \ -H 'sec-fetch-dest: document' \\
-H 'accept-language: en-US,en;q=0.9,ru;q=0.8' \\ -H 'accept-language: en-US,en;q=0.9,ru;q=0.8' \\
-H 'cookie: a-very-long-mysterious-string' \\ -H 'cookie: a-very-long-mysterious-string' \\
--compressed""" --compressed"""
curl_command = st.text_area( curl_command = st.text_area(
"Ugh... Gimme your cURL info...", value=curl_value "Om Nom Nom Nom... Paste cURL here, if confued see the sidebar for the instrucitons.", value=curl_value
) )
curl_command = "".join(curl_command.split("\\\n"))
if curl_command != curl_value: if curl_command == curl_value:
st.warning('Please input your cURL (see the sidebar for instrucitons :-) ')
st.stop()
try:
curl_command = curl_command.replace("\\", "")
curl_content = uncurl.parse_context(curl_command) curl_content = uncurl.parse_context(curl_command)
headers, cookies = curl_content.headers, curl_content.cookies headers, cookies = curl_content.headers, curl_content.cookies
except SystemExit:
st.warning("It seems like something is wrong with the cURL you provided: see the sidebar for the instrucitons.")
st.stop()
# --- # ---
with st.spinner('Loading resolved questions...'):
questions = get_resolved_questions(uid, platform_url, headers, cookies) questions = get_resolved_questions(uid, platform_url, headers, cookies)
st.write(f"{len(questions)} questions you forecasted on have resolved.") st.write(f"- {len(questions)} questions you forecasted on have resolved.")
# --- # ---
# TODO: Make a progress bar..? # TODO: Make a progress bar..?
with st.spinner('Loading your forecasts...'):
forecasts = get_forecasts(uid, questions, platform_url, headers, cookies) forecasts = get_forecasts(uid, questions, platform_url, headers, cookies)
with st.spinner("Loading questions's resolutions..."):
resolutions = get_resolutions(questions, platform_url, headers, cookies) resolutions = get_resolutions(questions, platform_url, headers, cookies)
# --- # ---
num_forecasts = sum(len(f) for f in forecasts.values()) num_forecasts = sum(len(f) for f in forecasts.values())
st.write( st.write(
f"On these {len(questions)} questions you've made {num_forecasts} forecasts." f"- You've made {num_forecasts} forecasts on these {len(questions)} questions."
) )
flatten = lambda t: [item for sublist in t for item in sublist] flatten = lambda t: [item for sublist in t for item in sublist]
y_true = flatten(resolutions[q]["y_true"] for q in questions for _ in forecasts[q]) # y_true = flatten(resolutions[q]["y_true"] for q in questions for _ in forecasts[q])
y_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q]) # y_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q])
# Note that I am "double counting" each prediction. # Note that I am "double counting" each prediction.
if st.checkbox("Drop last"): # if st.checkbox("Drop last"):
y_true = flatten( y_true = flatten(
resolutions[q]["y_true"][:-1] for q in questions for _ in forecasts[q] resolutions[q]["y_true"][:-1] for q in questions for _ in forecasts[q]
) )
y_pred = flatten(f["y_pred"][:-1] for q in questions for f in forecasts[q]) y_pred = flatten(f["y_pred"][:-1] for q in questions for f in forecasts[q])
y_true, y_pred = np.array(y_true), np.array(y_pred) y_true, y_pred = np.array(y_true), np.array(y_pred)
@ -124,27 +134,37 @@ if __name__ == "__main__":
np.random.default_rng(0).shuffle(order) np.random.default_rng(0).shuffle(order)
y_true, y_pred = y_true[order], y_pred[order] y_true, y_pred = y_true[order], y_pred[order]
# ---
strategy = st.selectbox( st.write(f"- Which gives us {len(y_pred)} datapoints to work with.")
"Which binning stranegy do you prefer?",
["uniform", "quantile"],
)
recommended_n_bins = int(np.sqrt(len(y_pred))) if strategy == "quantile" else 20 + 1 # ---
n_bins = st.number_input(
"How many bins do you want me to display?",
min_value=1,
value=recommended_n_bins,
)
fig = plotly_calibration(y_true, y_pred, n_bins=n_bins, strategy=strategy) strategy_select = st.selectbox(
st.plotly_chart(fig, use_container_width=True) "Which binning stranegy do you prefer?",
[
"I want bins to have identical widths",
"I want bins to have the same number of samples",
],
)
strategy = {
"I want bins to have identical widths": "uniform",
"I want bins to have the same number of samples": "quantile",
}[strategy_select]
overconf = overconfidence(y_true, y_pred) recommended_n_bins = int(np.sqrt(len(y_pred))) if strategy == "quantile" else 20 + 1
st.write(f"Your over/under- confidence score is {overconf:.2f}.") n_bins = st.number_input(
"How many bins do you want me to display?",
min_value=1,
value=recommended_n_bins,
)
# --- # ---
fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy) fig = plotly_calibration(y_true, y_pred, n_bins=n_bins, strategy=strategy)
st.plotly_chart(fig, use_container_width=True) st.plotly_chart(fig, use_container_width=True)
fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy)
st.plotly_chart(fig, use_container_width=True)
# overconf = overconfidence(y_true, y_pred)
# st.write(f"Your over/under- confidence score is {overconf:.2f}.")