| import streamlit as st | |
| import pandas as pd | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| covariate_columns = { | |
| 'content_domain': 'Content Domain', | |
| 'language': 'Language', | |
| 'rater_group': 'Rater Group', | |
| } | |
| id_vars = [ | |
| 'mean_z', 'text', 'content_domain', 'language', | |
| 'rater_group', 'study', 'instrument' | |
| ] | |
| if 'df' not in st.session_state: | |
| st.session_state.df = ( | |
| pd | |
| .read_feather(path='data.feather') | |
| .query('partition == "test" | partition == "dev"') | |
| .melt( | |
| value_vars=['sentiment_model', 'desirability_model'], | |
| var_name='x_group', | |
| value_name='x', | |
| id_vars=id_vars | |
| ) | |
| .replace( | |
| to_replace={ | |
| 'en': 'English', | |
| 'de': 'German', | |
| 'other': 'Other', | |
| 'personality': 'Personality', | |
| 'laypeople': 'Laypeople', | |
| 'students': 'Students', | |
| 'sentiment_model': 'Sentiment Model', | |
| 'desirability_model': 'Desirability Model' | |
| } | |
| ) | |
| .rename(columns=covariate_columns) | |
| .rename( | |
| columns={ | |
| 'mean_z': 'Human-ratings', | |
| 'x': 'Machine-ratings', | |
| } | |
| ) | |
| ) | |
| def scatter_plot(df, group_var): | |
| colors = ['#36def1', '#4361ee'] if group_var else ['#4361ee'] | |
| plot = px.scatter( | |
| df, | |
| x='Machine-ratings', | |
| y='Human-ratings', | |
| color=group_var, | |
| facet_col='x_group', | |
| facet_col_wrap=2, | |
| trendline='ols', | |
| trendline_scope='trace', | |
| hover_data={ | |
| 'Text': df.text, | |
| 'Language': False, | |
| 'x_group': False, | |
| 'Human-ratings': ':.2f', | |
| 'Machine-ratings': ':.2f', | |
| 'Study': df.study, | |
| 'Instrument': df.instrument, | |
| }, | |
| width=400, | |
| height=400, | |
| color_discrete_sequence=colors | |
| ) | |
| plot.for_each_annotation(lambda a: a.update(text=a.text.split('=')[-1])) | |
| plot.update_layout( | |
| legend={ | |
| 'orientation':'h', | |
| 'yanchor': 'bottom', | |
| 'y': -.30 | |
| }) | |
| plot.update_xaxes(title_standoff = 0) | |
| return plot | |
| def show(): | |
| st.markdown(""" | |
| ## Explore the data | |
| Figures show the accuarcy in precitions of human-rated item desirability by the sentiment model (left) and the desirability model (right), using `test`-partition data only. | |
| """) | |
| show_covariates = st.checkbox('Show covariates', value=True) | |
| if show_covariates: | |
| option = st.selectbox('Group by', options=list(covariate_columns.values())) | |
| else: | |
| option = None | |
| if 'df' in st.session_state: | |
| plot = scatter_plot(st.session_state.df, option) | |
| st.plotly_chart(plot, theme=None, use_container_width=True) |