Corey Morris
for the custom charts, remove columns with NaN values
b94ee8f
raw
history blame
3.58 kB
import streamlit as st
import pandas as pd
import plotly.express as px
from result_data_processor import ResultDataProcessor
data_provider = ResultDataProcessor()
st.title('Model Evaluation Results including MMLU by task')
filters = st.checkbox('Select Models and Evaluations')
# Create defaults for selected columns and models
selected_columns = data_provider.data.columns.tolist()
selected_models = data_provider.data.index.tolist()
if filters:
# Create checkboxes for each column
selected_columns = st.multiselect(
'Select Columns',
data_provider.data.columns.tolist(),
default=selected_columns
)
selected_models = st.multiselect(
'Select Models',
data_provider.data.index.tolist(),
default=selected_models
)
# Get the filtered data
st.header('Sortable table')
filtered_data = data_provider.get_data(selected_models)
# sort the table by the MMLU_average column
filtered_data = filtered_data.sort_values(by=['MMLU_average'], ascending=False)
st.dataframe(filtered_data[selected_columns])
# CSV download
csv = filtered_data.to_csv(index=True)
st.download_button(
label="Download data as CSV",
data=csv,
file_name="model_evaluation_results.csv",
mime="text/csv",
)
def create_plot(df, arc_column, moral_column, models=None):
if models is not None:
df = df[df.index.isin(models)]
# remove rows with NaN values
df = df.dropna(subset=[arc_column, moral_column])
plot_data = pd.DataFrame({
'Model': df.index,
arc_column: df[arc_column],
moral_column: df[moral_column],
})
plot_data['color'] = 'purple'
fig = px.scatter(plot_data, x=arc_column, y=moral_column, color='color', hover_data=['Model'], trendline="ols")
fig.update_layout(showlegend=False,
xaxis_title=arc_column,
yaxis_title=moral_column,
xaxis = dict(),
yaxis = dict())
return fig
st.header('Custom scatter plots')
selected_x_column = st.selectbox('Select x-axis', filtered_data.columns.tolist(), index=0)
selected_y_column = st.selectbox('Select y-axis', filtered_data.columns.tolist(), index=1)
if selected_x_column != selected_y_column: # Avoid creating a plot with the same column on both axes
fig = create_plot(filtered_data, selected_x_column, selected_y_column)
st.plotly_chart(fig)
else:
st.write("Please select different columns for the x and y axes.")
st.header('Overall evaluation comparisons')
fig = create_plot(filtered_data, 'arc:challenge|25', 'hellaswag|10')
st.plotly_chart(fig)
fig = create_plot(filtered_data, 'arc:challenge|25', 'MMLU_average')
st.plotly_chart(fig)
fig = create_plot(filtered_data, 'hellaswag|10', 'MMLU_average')
st.plotly_chart(fig)
st.header('Top 50 models on MMLU_average')
top_50 = filtered_data.nlargest(50, 'MMLU_average')
fig = create_plot(top_50, 'arc:challenge|25', 'MMLU_average')
st.plotly_chart(fig)
st.header('Moral Reasoning')
fig = create_plot(filtered_data, 'arc:challenge|25', 'MMLU_moral_scenarios')
st.plotly_chart(fig)
fig = create_plot(filtered_data, 'MMLU_moral_disputes', 'MMLU_moral_scenarios')
st.plotly_chart(fig)
fig = create_plot(filtered_data, 'MMLU_average', 'MMLU_moral_scenarios')
st.plotly_chart(fig)
fig = px.histogram(filtered_data, x="MMLU_moral_scenarios", marginal="rug", hover_data=filtered_data.columns)
st.plotly_chart(fig)
fig = px.histogram(filtered_data, x="MMLU_moral_disputes", marginal="rug", hover_data=filtered_data.columns)
st.plotly_chart(fig)