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import os |
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import re |
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import pandas as pd |
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import plotly.express as px |
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import streamlit as st |
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st.set_page_config(layout="wide") |
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DATA_FILE = "data/aclanthology2016-23_specter2_base.json" |
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THEMES = {"cluster": "fall", "year": "mint", "source": "phase"} |
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def to_string_authors(list_of_authors): |
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if len(list_of_authors) > 5: |
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return ", ".join(list_of_authors[:5]) + ", et al." |
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elif len(list_of_authors) > 2: |
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return ", ".join(list_of_authors[:-1]) + ", and " + list_of_authors[-1] |
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else: |
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return " and ".join(list_of_authors) |
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def load_df(data_file: os.PathLike): |
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df = pd.read_json(data_file, orient="records") |
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df["x"] = df["point2d"].apply(lambda x: x[0]) |
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df["y"] = df["point2d"].apply(lambda x: x[1]) |
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df["authors_trimmed"] = df.authors.apply( |
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lambda row: to_string_authors( |
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[(x[x.index(",") + 1 :].strip() + " " + x.split(",")[0].strip()) if "," in x else x for x in row] |
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) |
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) |
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if "publication_type" in df.columns: |
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df["type"] = df["publication_type"] |
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df = df.drop(columns=["point2d", "publication_type"]) |
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else: |
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df = df.drop(columns=["point2d"]) |
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return df |
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@st.cache_data |
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def load_dataframe(): |
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return load_df(DATA_FILE) |
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DF = load_dataframe() |
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DF["opacity"] = 0.04 |
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min_year, max_year = DF["year"].min(), DF["year"].max() |
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with st.sidebar: |
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venues = st.multiselect( |
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"Venues", |
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["ACL", "EMNLP", "NAACL", "TACL"], |
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["ACL", "EMNLP", "NAACL", "TACL"], |
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) |
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start_year, end_year = st.select_slider( |
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"Publication year", |
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options=[str(y) for y in range(min_year, max_year + 1)], |
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value=(str(min_year), str(max_year)), |
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) |
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author_names = st.text_input("Author names (separated by comma)") |
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title = st.text_input("Title") |
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start_year = int(start_year) |
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end_year = int(end_year) |
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df_mask = (DF["year"] >= start_year) & (DF["year"] <= end_year) |
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if 0 < len(venues) < 4: |
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selected_venues = [v.lower() for v in venues] |
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df_mask = df_mask & DF["source"].isin(selected_venues) |
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elif not venues: |
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st.write(":red[Please select a venue]") |
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if author_names: |
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authors = [a.strip() for a in author_names.split(",")] |
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author_mask = DF.authors.apply( |
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lambda row: all(any(re.match(rf".*{a}.*", x, re.IGNORECASE) for x in row) for a in authors) |
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) |
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df_mask = df_mask & author_mask |
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if title: |
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df_mask = df_mask & DF.title.apply(lambda x: title.lower() in x.lower()) |
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DF.loc[df_mask, "opacity"] = 1.0 |
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st.write(f"Number of points: {DF[df_mask].shape[0]}") |
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color = st.selectbox("Color", ("cluster", "year", "source")) |
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fig = px.scatter( |
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DF, |
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x="x", |
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y="y", |
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opacity=DF["opacity"], |
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color=color, |
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width=1000, |
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height=800, |
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custom_data=("title", "authors_trimmed", "year", "source", "type"), |
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color_continuous_scale=THEMES[color], |
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) |
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fig.update_traces( |
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hovertemplate="<b>%{customdata[0]}</b><br>%{customdata[1]}<br>%{customdata[2]}<br><i>%{customdata[3]}</i>" |
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) |
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fig.update_layout( |
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showlegend=False, |
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font=dict( |
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family="Times New Roman", |
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size=30, |
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), |
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hoverlabel=dict( |
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align="left", |
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font_size=14, |
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font_family="Rockwell", |
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namelength=-1, |
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), |
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) |
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fig.update_xaxes(title="") |
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fig.update_yaxes(title="") |
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st.plotly_chart(fig, use_container_width=True) |
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