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app.py
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import numpy as np
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import pandas as pd
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import torch
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@@ -62,16 +62,8 @@ def down_samp(embedding):
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def data_comparison(df):
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# set up a dropdown select bindinf
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# input_dropdown = alt.binding_select(options=['Negative Sentiment','Positive Sentiment'])
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#data_kmeans['distance_from_centroid'] = data_kmeans.apply(distance_from_centroid, axis=1)
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selection = alt.selection_multi(fields=['cluster','label'])
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color = alt.condition(alt.datum.slice == 'high-loss', alt.Color('cluster:N', scale = alt.Scale(domain=df.cluster.tolist())), alt.value("lightgray"))
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# color = alt.condition(selection,
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# alt.Color('cluster:Q', legend=None),
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# # scale = alt.Scale(domain = pop_domain,range=color_range)),
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# alt.value('lightgray'))
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opacity = alt.condition(selection, alt.value(0.7), alt.value(0.25))
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# basic chart
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@@ -97,7 +89,7 @@ def data_comparison(df):
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selection
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)
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layered =
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layered = layered.configure_axis(
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grid=False
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@@ -112,14 +104,12 @@ def quant_panel(embedding_df):
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""" Quantitative Panel Layout"""
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all_metrics = {}
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# " * the **shape** of each point reflects whether it a positive (diamond) or negative sentiment (circle)")
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# st.markdown("* the **color** of each point is the ")
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st.altair_chart(data_comparison(down_samp(embedding_df)))
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def frequent_tokens(data, tokenizer, loss_quantile=0.95, top_k=200, smoothing=0.005):
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@@ -246,7 +236,7 @@ if __name__ == "__main__":
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)
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loss_quantile = st.sidebar.slider(
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"Loss Quantile", min_value=0.0, max_value=1.0,step=0.
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)
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run_kmeans = st.sidebar.radio("Cluster error slice?", ('True', 'False'), index=0)
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@@ -280,15 +270,16 @@ if __name__ == "__main__":
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table_html = dataframe.to_html(
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columns=['content', 'label', 'pred', 'loss', 'cluster'], max_rows=50)
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# table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers
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st.
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# st.write(table_html)
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with rcol:
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st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)
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commontokens = frequent_tokens(merged, tokenizer, loss_quantile=loss_quantile)
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st.write(commontokens)
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quant_panel(merged)
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## LIBRARIES ###
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## Data
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import numpy as np
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import pandas as pd
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import torch
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def data_comparison(df):
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selection = alt.selection_multi(fields=['cluster','label'])
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color = alt.condition(alt.datum.slice == 'high-loss', alt.Color('cluster:N', scale = alt.Scale(domain=df.cluster.unique().tolist())), alt.value("lightgray"))
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opacity = alt.condition(selection, alt.value(0.7), alt.value(0.25))
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# basic chart
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selection
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)
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layered = scatter | legend
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layered = layered.configure_axis(
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grid=False
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""" Quantitative Panel Layout"""
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all_metrics = {}
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st.warning("**Error slice visualization**")
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with st.expander("How to read this chart:"):
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st.markdown("* Each **point** is an input example.")
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st.markdown("* Gray points have low-loss and the colored have high-loss. High-loss instances are clustered using **kmeans** and each color represents a cluster.")
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st.markdown("* The **shape** of each point reflects the label category -- positive (diamond) or negative sentiment (circle).")
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st.altair_chart(data_comparison(down_samp(embedding_df)))
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def frequent_tokens(data, tokenizer, loss_quantile=0.95, top_k=200, smoothing=0.005):
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)
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loss_quantile = st.sidebar.slider(
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"Loss Quantile", min_value=0.0, max_value=1.0,step=0.01,value=0.95
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)
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run_kmeans = st.sidebar.radio("Cluster error slice?", ('True', 'False'), index=0)
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table_html = dataframe.to_html(
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columns=['content', 'label', 'pred', 'loss', 'cluster'], max_rows=50)
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# table_html = table_html.replace("<th>", '<th align="left">') # left-align the headers
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with st.expander("How to read the table:"):
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st.markdown("* The table displays model error slices on the test set, sorted by loss.")
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st.markdown("* Each row is an input example that includes the label, model pred, loss, and error cluster.")
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st.write(dataframe,width=900, height=300)
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with rcol:
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st.markdown('<h3>Word Distribution in Error Slice</h3>', unsafe_allow_html=True)
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commontokens = frequent_tokens(merged, tokenizer, loss_quantile=loss_quantile)
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with st.expander("How to read the table:"):
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st.markdown("* The table displays the most frequent tokens in error slices, relative to their frequencies in the val set.")
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st.write(commontokens)
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quant_panel(merged)
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error_analysis/utils/__pycache__/__init__.cpython-39.pyc
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Binary file (204 Bytes). View file
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error_analysis/utils/__pycache__/style_hacks.cpython-39.pyc
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Binary file (2.16 kB). View file
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error_analysis/utils/style_hacks.py
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"""
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streamlit style hacks
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"""
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import streamlit as st
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@@ -10,12 +10,13 @@ def init_style():
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<style>
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/* Side Bar */
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
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width:
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margin-left: -500px;
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
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width:
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}
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.css-1outpf7 {
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background-color:rgb(254 244 219);
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padding:10px 10px 10px 10px;
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}
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/* Main Panel*/
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[data-testid="stVerticalBlock"]{
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margin-left: -200px;
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padding:10px 10px 10px -200px;
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}
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.css-18e3th9 {
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padding:10px 10px 10px -200px;
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}
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"""
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placeholder for all streamlit style hacks
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"""
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import streamlit as st
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<style>
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/* Side Bar */
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[data-testid="stSidebar"][aria-expanded="true"] > div:first-child {
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width: 300px;
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}
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[data-testid="stSidebar"][aria-expanded="false"] > div:first-child {
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width: 300px;
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}
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[data-testid="stSidebar"]{
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flex-basis: unset;
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}
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.css-1outpf7 {
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background-color:rgb(254 244 219);
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padding:10px 10px 10px 10px;
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}
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/* Main Panel*/
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.css-18e3th9 {
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padding:10px 10px 10px -200px;
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}
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