initial commit

app.py

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+import streamlit as st
+import pandas as pd
+import sys
+import os
+from datasets import load_from_disk, load_dataset
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+import time
+from annotated_text import annotated_text
+
+ORG_ID = "cornell-authorship"
+
+@st.cache
+def preprocess_text(s):
+    return list(filter(lambda x: x!= '', (''.join(c if c.isalnum() or c == ' ' else ' ' for c in s)).split(' ')))
+
+@st.cache
+def get_pairwise_distances(model):
+    dataset = load_dataset(f"{ORG_ID}/{model}_distance")["train"]
+    df = pd.DataFrame(dataset).set_index('index')
+    return df
+
+@st.cache
+def get_pairwise_distances_chunked(model, chunk):
+    # for df in pd.read_csv(f"{ASSETS_PATH}/{model}/pairwise_distances.csv", chunksize = 16):
+    # 	print(df.iloc[0]['queries'])
+    # 	if chunk == int(df.iloc[0]['queries']):
+    # 		return df
+    return get_pairwise_distances(model)
+
+@st.cache
+def get_query_strings():
+    # df = pd.read_json(hf_hub_download(repo_id=repo_id, filename="IUR_Reddit_test_queries_english.jsonl"), lines = True)
+    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_queries_english")["train"]
+    df = pd.DataFrame(dataset)
+    df['index'] = df.reset_index().index
+    return df
+    # df['partition'] = df['index']%100
+    # df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", index = 'index', partition_cols = 'partition')
+    
+    # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_queries_english.parquet", columns=['fullText', 'index', 'authorIDs'])
+
+@st.cache
+def get_candidate_strings():
+    # df = pd.read_json(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.jsonl", lines = True)
+    dataset = load_dataset(f"{ORG_ID}/IUR_Reddit_test_candidates_english")["train"]
+    df = pd.DataFrame(dataset)
+    df['index'] = df.reset_index().index
+    return df
+    # df['partition'] = df['index']%100
+    # df.to_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", index = 'index', partition_cols = 'partition')
+    # return pd.read_parquet(f"{ASSETS_PATH}/IUR_Reddit_test_candidates_english.parquet", columns=['fullText', 'index', 'authorIDs'])
+
+@st.cache
+def get_embedding_dataset(model):
+    # data = load_from_disk(f"{ASSETS_PATH}/{model}/embedding")
+    data = load_dataset(f"{ORG_ID}/{model}_embedding")
+    return data
+
+@st.cache
+def get_bad_queries(model):
+    df = get_query_strings().iloc[list(get_pairwise_distances(model)['queries'].unique())][['fullText', 'index', 'authorIDs']]
+    return df
+
+@st.cache
+def get_gt_candidates(model, author):
+    gt_candidates = get_candidate_strings()
+    df = gt_candidates[gt_candidates['authorIDs'].apply(lambda x: x[0]) == author]
+    return df
+
+@st.cache
+def get_candidate_text(l):
+    return get_candidate_strings().at[l,'fullText']
+
+@st.cache
+def get_annotated_text(text, word, pos):
+    # print("here", word, pos)
+    start= text.index(word, pos)
+    end = start+len(word)
+    return (text[:start], (text[start:end ], 'SELECTED'), text[end:]), end
+
+class AgGridBuilder:
+    __static_key = 0
+    def build_ag_grid(table, display_columns):
+        AgGridBuilder.__static_key += 1
+        options_builder = GridOptionsBuilder.from_dataframe(table[display_columns])
+        options_builder.configure_pagination(paginationAutoPageSize=False, paginationPageSize=10)
+        options_builder.configure_selection(selection_mode= 'single', pre_selected_rows = [0])
+        options = options_builder.build()
+        return AgGrid(table, gridOptions = options, fit_columns_on_grid_load=True, key = AgGridBuilder.__static_key, reload_data = True, update_mode = GridUpdateMode.SELECTION_CHANGED | GridUpdateMode.VALUE_CHANGED)
+
+if __name__ == '__main__':
+    st.set_page_config(layout="wide")
+
+    # models = filter(lambda file_name: os.path.isdir(f"{ASSETS_PATH}/{file_name}") and not file_name.endswith(".parquet"), os.listdir(ASSETS_PATH))
+    models = ['luar_clone2_top_100']
+ 
+    with st.sidebar:
+        current_model = st.selectbox(
+            "Select Model to analyze", 
+            models
+            )
+
+    pairwise_distances = get_pairwise_distances(current_model)
+    embedding_dataset = get_embedding_dataset(current_model)
+
+    candidate_string_grid = None
+    gt_candidate_string_grid  = None
+    with st.container():
+        t1 = time.time()
+        st.title("Full Text")
+        col1, col2 = st.columns([14, 2])
+        t2 = time.time()
+        query_table = get_bad_queries(current_model)
+        t3 = time.time()
+        # print(query_table)
+        with col2:
+            index = st.number_input('Enter Query number to inspect', min_value = 0, max_value = query_table.shape[0], step = 1)
+            query_text = query_table.loc[index]['fullText']
+            preprocessed_query_text = preprocess_text(query_text)
+            text_highlight_index = st.number_input('Enter word #', min_value = 0, max_value = len(preprocessed_query_text), step = 1)
+            query_index = int(query_table.iloc[index]['index'])
+
+        with col1:
+            if 'pos_highlight' not in st.session_state or text_highlight_index == 0:
+                st.session_state['pos_highlight'] = text_highlight_index
+                st.session_state['pos_history'] = [0]
+
+            if st.session_state['pos_highlight'] > text_highlight_index:
+                st.session_state['pos_history'] = st.session_state['pos_history'][:-2]
+                if len(st.session_state['pos_history']) == 0:
+                    st.session_state['pos_history'] = [0]
+            # print("pos", st.session_state['pos_history'], st.session_state['pos_highlight'], text_highlight_index)
+            anotated_text_, pos = get_annotated_text(query_text, preprocessed_query_text[text_highlight_index-1], st.session_state['pos_history'][-1]) if text_highlight_index >= 1 else ((query_text), 0)
+            if st.session_state['pos_highlight'] < text_highlight_index:
+                st.session_state['pos_history'].append(pos)
+            st.session_state['pos_highlight'] = text_highlight_index
+            annotated_text(*anotated_text_)
+            # annotated_text("Lol, this" , ('guy', 'SELECTED') , "is such a PR chameleon. \n\n In the Chan Zuckerberg Initiative announcement, he made it sound like he was giving away all his money to charity <PERSON> or <PERSON>. http://www.businessinsider.in/Mark-Zuckerberg-says-hes-giving-99-of-his-Facebook-shares-45-billion-to-charity/articleshow/50005321.cms Apparently, its just a VC fund. And there are still people out there who believe Facebook.org was an initiative to bring Internet to the poor.")
+            t4 = time.time()
+
+        # print(f"query time query text: {t3-t2}, total time: {t4-t1}")
+    with st.container():
+        st.title("Top 16 Recommended Candidates")
+        col1, col2, col3 = st.columns([10, 4, 2])	
+        rec_candidates = pairwise_distances[pairwise_distances["queries"]==query_index]['candidates']
+        # print(rec_candidates)
+        l = list(rec_candidates)
+        with col3:
+            candidate_rec_index = st.number_input('Enter recommended candidate number to inspect', min_value = 0, max_value = len(l), step = 1)
+            print("l:",l, query_index)
+            pairwise_candidate_index = int(l[candidate_rec_index])
+        with col1:
+            st.header("Text")
+            t1 = time.time()
+            st.write(get_candidate_text(pairwise_candidate_index))
+            t2 = time.time()
+        with col2:
+            st.header("Cosine Distance")
+            st.write(float(pairwise_distances[\
+                ( pairwise_distances['queries'] == query_index ) \
+                &
+                ( pairwise_distances['candidates'] == pairwise_candidate_index)]['distances']))
+        print(f"candidate string retreival: {t2-t1}")
+    with st.container():
+        t1 = time.time()
+        st.title("Candidates With Same Authors As Query")
+        col1, col2, col3 = st.columns([10, 4, 2])
+        t2 = time.time()
+        gt_candidates = get_gt_candidates(current_model, query_table.iloc[query_index]['authorIDs'][0])
+        t3 = time.time()
+
+        with col3:
+            candidate_index = st.number_input('Enter ground truthnumber to inspect', min_value = 0, max_value = gt_candidates.shape[0], step = 1)
+            gt_candidate_index = int(gt_candidates.iloc[candidate_index]['index'])
+        with col1:
+            st.header("Text")
+            st.write(gt_candidates.iloc[candidate_index]['fullText'])
+        with col2:
+            t4 = time.time()
+            st.header("Cosine Distance")
+            st.write(1-cosine_similarity(np.array([embedding_dataset['queries'][query_index]['embedding']]), np.array([embedding_dataset['candidates'][gt_candidate_index]['embedding']]))[0,0])
+            t5 = time.time()
+        print(f"find gt candidates: {t3-t2}, find cosine: {t5-t4}, total: {t5-t1}")

requirements.txt

@@ -0,0 +1,74 @@
+absl-py==1.3.0
+aiohttp==3.8.3
+aiosignal==1.3.1
+async-timeout==4.0.2
+attrs==22.1.0
+blis==0.7.9
+catalogue==2.0.8
+certifi==2022.12.7
+charset-normalizer==2.1.1
+click==8.1.3
+confection==0.0.3
+cymem==2.0.7
+datasets==2.7.1
+dill==0.3.6
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.4.1/en_core_web_sm-3.4.1-py3-none-any.whl
+einops==0.6.0
+filelock==3.8.2
+frozenlist==1.3.3
+fsspec==2022.11.0
+huggingface-hub==0.13.1
+idna==3.4
+Jinja2==3.1.2
+joblib==1.2.0
+langcodes==3.3.0
+MarkupSafe==2.1.1
+multidict==6.0.3
+multiprocess==0.70.14
+murmurhash==1.0.9
+numpy==1.23.5
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+packaging==22.0
+pandas==1.5.2
+pathy==0.10.1
+Pillow==9.3.0
+preshed==3.0.8
+pyarrow==10.0.1
+pydantic==1.10.2
+python-dateutil==2.8.2
+pytorch-lightning==1.8.5.post0
+pytorch-metric-learning==1.6.3
+pytz==2022.6
+PyYAML==6.0
+regex==2022.10.31
+requests==2.28.1
+responses==0.18.0
+sacremoses==0.0.53
+scikit-learn==1.2.0
+scipy==1.9.3
+six==1.16.0
+smart-open==6.3.0
+spacy==3.4.3
+spacy-legacy==3.0.10
+spacy-loggers==1.0.4
+srsly==2.4.5
+thinc==8.1.5
+threadpoolctl==3.1.0
+tokenizers==0.10.3
+torch==1.13.0
+torchvision==0.14.0
+tqdm==4.64.1
+transformers==4.14.1
+typer==0.7.0
+typing_extensions==4.4.0
+urllib3==1.26.13
+wasabi==0.10.1
+xxhash==3.1.0
+yarl==1.8.2
+streamlit==1.17.0
+streamlit-aggrid==0.3.4.post3
+--extra-index-url http://download.pytorch.org/whl/cu116
+--trusted-host download.pytorch.org