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Fix cURL

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This commit is contained in:
yagudin 2021-05-31 22:33:53 +03:00
parent 949438203c
commit fb53c015f6
1 changed files with 51 additions and 46 deletions
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97
strmlt.py
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@ -32,69 +32,74 @@ if __name__ == "__main__":
uid = st.number_input("What is your user ID?", min_value=1, value=28899) uid = st.number_input("What is your user ID?", min_value=1, value=28899)
uid = str(uid) uid = str(uid)
curl_value = "" curl_value = """curl 'https://www.gjopen.com/' \\
-H 'authority: www.gjopen.com' \\
-H '...: ...'"""
curl_command = st.text_area( curl_command = st.text_area(
"Ugh... Gimme your cURL info...", value=curl_value.strip() "Ugh... Gimme your cURL info...", value=curl_value
) )
curl_content = uncurl.parse_context(curl_command) curl_command = "".join(curl_command.split("\\\n"))
headers, cookies = curl_content.headers, curl_content.cookies
if curl_command != curl_value:
# --- curl_content = uncurl.parse_context(curl_command)
headers, cookies = curl_content.headers, curl_content.cookies
questions = get_resolved_questions(uid, platform_url, headers, cookies) # ---
st.write(f"{len(questions)} questions you forecasted on have resolved.") questions = get_resolved_questions(uid, platform_url, headers, cookies)
# --- st.write(f"{len(questions)} questions you forecasted on have resolved.")
# TODO: Make a progress bar..?
forecasts = get_forecasts(uid, questions, platform_url, headers, cookies) # ---
resolutions = get_resolutions(questions, platform_url, headers, cookies) # TODO: Make a progress bar..?
# --- forecasts = get_forecasts(uid, questions, platform_url, headers, cookies)
resolutions = get_resolutions(questions, platform_url, headers, cookies)
num_forecasts = sum(len(f) for f in forecasts.values()) # ---
st.write(
f"On these {len(questions)} questions you've made {num_forecasts} forecasts."
)
flatten = lambda t: [item for sublist in t for item in sublist] num_forecasts = sum(len(f) for f in forecasts.values())
y_true = flatten(resolutions[q]["y_true"] for q in questions for _ in forecasts[q]) st.write(
y_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q]) f"On these {len(questions)} questions you've made {num_forecasts} forecasts."
# Note that I am "double counting" each prediction.
if st.checkbox("Drop last"):
y_true = flatten(
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_true, y_pred = np.array(y_true), np.array(y_pred) 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_pred = flatten(f["y_pred"] for q in questions for f in forecasts[q])
st.write(f"Which gives us {len(y_pred)} datapoints to work with.") # Note that I am "double counting" each prediction.
if st.checkbox("Drop last"):
y_true = flatten(
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_true, y_pred = np.array(y_true), np.array(y_pred)
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 = st.selectbox(
st.plotly_chart(fig, use_container_width=True) "Which binning stranegy do you prefer?",
["uniform", "quantile"],
)
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(y_true, y_pred, n_bins=n_bins, strategy=strategy)
st.plotly_chart(fig, use_container_width=True)
fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy) overconf = overconfidence(y_true, y_pred)
st.plotly_chart(fig, use_container_width=True) st.write(f"Your over/under- confidence score is {overconf:.2f}.")
# ---
fig = plotly_calibration_odds(y_true, y_pred, n_bins=n_bins, strategy=strategy)
st.plotly_chart(fig, use_container_width=True)