Spaces:
Paused
Paused
File size: 2,006 Bytes
e60235b 969e123 e60235b 969e123 e60235b 969e123 e60235b 97fcb64 969e123 97fcb64 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
import streamlit as st
import pandas as pd
import opendashboards.utils.utils as utils
def clean_data(df):
return df.dropna(subset=df.filter(regex='completions|rewards').columns, how='all')
@st.cache_data
def explode_data(df):
list_cols = utils.get_list_col_lengths(df)
try:
return utils.explode_data(df, list(list_cols.keys())).apply(pd.to_numeric, errors='ignore')
except Exception as e:
st.error(f'Error exploding data with columns')
st.write(list_cols)
st.exception(e)
st.dataframe(df)
st.stop()
@st.cache_data
def completions(df_long, col):
return df_long[col].value_counts()
@st.cache_data
def weights(df, index='_timestamp'):
# Create a column for each UID and show most recent rows
scores = df['moving_averaged_scores'].apply(pd.Series).fillna(method='ffill')
if index in df.columns:
scores.index = df[index]
# rename columns
scores.rename({i: f'UID-{i}' for i in range(scores.shape[1])}, axis=1, inplace=True)
return scores
def run_event_data(df_runs, df, selected_runs):
st.markdown('#')
show_col1, show_col2 = st.columns(2)
show_runs = show_col1.checkbox('Show runs', value=True)
show_events = show_col2.checkbox('Show events', value=False)
if show_runs:
st.markdown(f'Wandb info for **{len(selected_runs)} selected runs**:')
st.dataframe(df_runs.loc[df_runs.id.isin(selected_runs)],
column_config={
"url": st.column_config.LinkColumn("URL"),
}
)
if show_events:
st.markdown(f'Raw events for **{len(selected_runs)} selected runs**:')
st.dataframe(df.head(50),
column_config={
"url": st.column_config.LinkColumn("URL"),
}
)
def highlight_row(row, expr, color='lightgrey', bg_color='white'):
return [f'background-color:{color}' if expr else f'background-color:{bg_color}'] * len(row) |