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import pandas as pd
import streamlit as st
import config
from pathlib import Path as P
import json
nbow_results_path = P("assets").glob("nbow*")
def display_metrics_dict(metrics, display_only_accuracy):
model_name = metrics.pop("model_name")
columns = metrics.pop("columns").split("_")
st.markdown(f"### columns: {columns}")
st.markdown(f"best model {model_name}")
if not display_only_accuracy:
st.json(metrics)
else:
st.json({"accuracy@10": metrics["accuracy@k"]["10"]})
def display_metrics():
display_only_accuracy = st.sidebar.checkbox("display only accuracy@10", value=True)
st.markdown("## Test metrics for best validation modelon given columns")
for p in nbow_results_path:
metrics = json.loads(open(p, "r").read())
display_metrics_dict(metrics, display_only_accuracy)
display_metrics()
best_results_df = pd.read_csv(config.best_tasks_path)
worst_results_df = pd.read_csv(config.worst_tasks_path)
show_worst_best_statistics = st.sidebar.checkbox(
label="show worst/best statistics grouped by area"
)
show_area_aggregated_results = st.sidebar.checkbox(
label="show results aggregated by area"
)
if show_worst_best_statistics:
st.markdown(
"""
## Worst/best queries
The following are top 10 worst/best queries per area by number of hits.
There are at least 10 documents per query in the test set, so number of hits/10 is the accuracy.
"""
)
sort_key = st.selectbox("sort by", list(best_results_df.columns))
st.markdown("## Queries with best results")
st.table(best_results_df.sort_values(sort_key, ascending=False))
st.markdown("## Queries with worst results")
st.table(worst_results_df.sort_values(sort_key, ascending=False))
if show_area_aggregated_results:
st.markdown("## Area aggregated results")
best_results_agg = best_results_df.groupby("area").agg("mean").reset_index()
worst_results_agg = worst_results_df.groupby("area").agg("mean").reset_index()
sort_key = st.selectbox("sort by", list(best_results_agg.columns))
st.markdown("Best results")
st.table(best_results_agg.sort_values(sort_key, ascending=False))
st.markdown("Worst results")
st.table(worst_results_agg.sort_values(sort_key, ascending=False))
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