"""Show every example sorted by loss (descending) for close inspection.""" import pandas as pd import streamlit as st from src.subpages.page import Context, Page from src.utils import ( colorize_classes, get_bg_color, get_fg_color, htmlify_labeled_example, ) class LossySamplesPage(Page): name = "Samples by Loss" icon = "sort-numeric-down-alt" def _get_widget_defaults(self): return { "skip_correct": True, "samples_by_loss_show_df": True, } def render(self, context: Context): st.title(self.name) with st.expander("💡", expanded=True): st.write("Show every example sorted by loss (descending) for close inspection.") st.write( "The **dataframe** is mostly self-explanatory. The cells are color-coded by label, a lighter color signifies a continuation label. Cells in the loss row are filled red from left to right relative to the top loss." ) st.write( "The **numbers to the left**: Top (black background) are sample number (listed here) and sample index (from the dataset). Below on yellow background is the total loss for the given sample." ) st.write( "The **annotated sample**: Every predicted entity (every token, really) gets a black border. The text color signifies the predicted label, with the first token of a sequence of token also showing the label's icon. If (and only if) the prediction is wrong, a small little box after the entity (token) contains the correct target class, with a background color corresponding to that class." ) st.subheader("💥 Samples ⬇loss") skip_correct = st.checkbox("Skip correct examples", value=True, key="skip_correct") show_df = st.checkbox("Show dataframes", key="samples_by_loss_show_df") st.write( """""", unsafe_allow_html=True, ) top_indices = ( context.df.sort_values(by="total_loss", ascending=False) .query("total_loss > 0.5") .index ) cnt = 0 for idx in top_indices: sample = context.df_tokens_merged.loc[idx] if isinstance(sample, pd.Series): continue if skip_correct and sum(sample.labels != sample.preds) == 0: continue if show_df: def colorize_col(col): if col.name == "labels" or col.name == "preds": bgs = [] fgs = [] ops = [] for v in col.values: bgs.append(get_bg_color(v.split("-")[1]) if "-" in v else "#ffffff") fgs.append(get_fg_color(bgs[-1])) ops.append("1" if v.split("-")[0] == "B" or v == "O" else "0.5") return [ f"background-color: {bg}; color: {fg}; opacity: {op};" for bg, fg, op in zip(bgs, fgs, ops) ] return [""] * len(col) df = sample.reset_index().drop(["index", "hidden_states", "ids"], axis=1).round(3) losses_slice = pd.IndexSlice["losses", :] # x = df.T.astype(str) # st.dataframe(x) # st.dataframe(x.loc[losses_slice]) styler = ( df.T.style.apply(colorize_col, axis=1) .bar(subset=losses_slice, axis=1) .format(precision=3) ) # styler.data = styler.data.astype(str) st.write(styler.to_html(), unsafe_allow_html=True) st.write("") # st.dataframe(colorize_classes(sample.drop("hidden_states", axis=1)))#.bar(subset='losses')) # type: ignore # st.write( # colorize_errors(sample.round(3).drop("hidden_states", axis=1).astype(str)) # ) col1, _, col2 = st.columns([3.5 / 32, 0.5 / 32, 28 / 32]) cnt += 1 counter = f"[{cnt} | {idx}]" loss = f"𝐿 {sample.losses.sum():.3f}" col1.write(f"{counter}{loss}", unsafe_allow_html=True) col1.write("") col2.write(htmlify_labeled_example(sample), unsafe_allow_html=True) # st.write(f"[{i};{idx}] " + htmlify_corr_sample(sample), unsafe_allow_html=True)