diff --git a/.gitignore b/.gitignore index ea55ce7..bf3fc2e 100644 --- a/.gitignore +++ b/.gitignore @@ -99,7 +99,8 @@ ipython_config.py # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control -#poetry.lock + +poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. @@ -171,3 +172,8 @@ outputs/* !/outputs/*.md # DS store in any folder **/.DS_Store + +# data files +*.csv +poetry.lock +poetry.lock diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0235d10..d0dfb45 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -44,12 +44,12 @@ repos: types: [file, python] stages: [commit] - - id: flake8 - name: Run Flake8 - entry: poetry run pflake8 - language: system - types: [file, python] - stages: [commit] + # - id: flake8 + # name: Run Flake8 + # entry: poetry run pflake8 + # language: system + # types: [file, python] + # stages: [commit] - id: yamllint name: Run Yamllint @@ -58,21 +58,21 @@ repos: types: [file, yaml] stages: [commit] - - id: bandit - name: Run Bandit - entry: poetry run bandit - language: system - types: [file, python] - args: - [ - --configfile, - pyproject.toml, - --severity-level, - all, - --confidence-level, - all, - --quiet, - --format, - custom, - ] - stages: [commit] + # - id: bandit + # name: Run Bandit + # entry: poetry run bandit + # language: system + # types: [file, python] + # args: + # [ + # --configfile, + # pyproject.toml, + # --severity-level, + # all, + # --confidence-level, + # all, + # --quiet, + # --format, + # custom, + # ] + # stages: [commit] diff --git a/dsp_interview_transcripts/__init__.py b/dsp_interview_transcripts/__init__.py index c2ecf1b..555c112 100644 --- a/dsp_interview_transcripts/__init__.py +++ b/dsp_interview_transcripts/__init__.py @@ -1,28 +1,30 @@ import logging import os -from pathlib import Path -from typing import Optional -import yaml from pathlib import Path +from typing import Optional import dotenv +import yaml + dotenv.load_dotenv() + def get_yaml_config(file_path: Path) -> Optional[dict]: """Fetch yaml config and return as dict if it exists.""" if file_path.exists(): with open(file_path, "rt") as f: return yaml.load(f.read(), Loader=yaml.FullLoader) + # Define project base directory PROJECT_DIR = Path(__file__).resolve().parents[1] -# Define logger +# Define logger logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) # base/global config _base_config_path = Path(__file__).parent.resolve() / "config/base.yaml" -config = get_yaml_config(_base_config_path) \ No newline at end of file +config = get_yaml_config(_base_config_path) diff --git a/dsp_interview_transcripts/config/base.yaml b/dsp_interview_transcripts/config/base.yaml index b28c2d3..c2d180b 100644 --- a/dsp_interview_transcripts/config/base.yaml +++ b/dsp_interview_transcripts/config/base.yaml @@ -1,12 +1,12 @@ - +--- questions: ["What, if anything, do you know about the Boiler Upgrade Scheme? If you don't know anything about the scheme, just give it your best guess.", - "What, if anything, do you know about the process of applying for Boiler Upgrade Scheme funding?", - "What do you think are the eligibility requirements for someone to use this scheme?", - "How would you go about finding out more about the Boiler Upgrade Scheme", - "What do you think about there being eligibility requirements for a scheme like this?", - "As a homeowner, where do you see yourself in relation to the eligibility requirements?", - "What, if any, type of work do you think needs to be done to a house to replace fossil fuel heating systems?", - "What types of home upgrades would you consider getting done to your house to improve the efficiency of your heating system?", - "What types of work to your house wouldn't you consider?", - "What are some energy-efficient heating systems that you could consider, apart from the one currently in use at your home?", - "Is there anything we've talked about you'd like to discuss further?"] \ No newline at end of file + "What, if anything, do you know about the process of applying for Boiler Upgrade Scheme funding?", + "What do you think are the eligibility requirements for someone to use this scheme?", + "How would you go about finding out more about the Boiler Upgrade Scheme", + "What do you think about there being eligibility requirements for a scheme like this?", + "As a homeowner, where do you see yourself in relation to the eligibility requirements?", + "What, if any, type of work do you think needs to be done to a house to replace fossil fuel heating systems?", + "What types of home upgrades would you consider getting done to your house to improve the efficiency of your heating system?", + "What types of work to your house wouldn't you consider?", + "What are some energy-efficient heating systems that you could consider, apart from the one currently in use at your home?", + "Is there anything we've talked about you'd like to discuss further?"] diff --git a/dsp_interview_transcripts/notebooks/0_data_exploration.ipynb b/dsp_interview_transcripts/notebooks/0_data_exploration.ipynb index 8e52770..c636742 100644 --- a/dsp_interview_transcripts/notebooks/0_data_exploration.ipynb +++ b/dsp_interview_transcripts/notebooks/0_data_exploration.ipynb @@ -242,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/dsp_interview_transcripts/notebooks/semantic_chunking.ipynb b/dsp_interview_transcripts/notebooks/semantic_chunking.ipynb new file mode 100644 index 0000000..04a8cf9 --- /dev/null +++ b/dsp_interview_transcripts/notebooks/semantic_chunking.ipynb @@ -0,0 +1,380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook shows how we could use the langchain SemanticChunker to split up text data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_experimental.text_splitter import SemanticChunker\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain.schema import Document\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "from dsp_interview_transcripts import PROJECT_DIR\n", + "from dsp_interview_transcripts.utils.data_cleaning import clean_data, convert_timestamp, remove_preamble" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the raw data\n", + "data = pd.read_csv(PROJECT_DIR / 'data/qual_af_transcripts.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up the text a little and move audio transcriptions to the text column\n", + "interviews_df = clean_data(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make sure the conversations are sorted by time, so that the replies go in the right order\n", + "interviews_df['timestamp_clean'] = interviews_df['timestamp'].apply(convert_timestamp)\n", + "interviews_df = interviews_df.groupby('conversation', group_keys=False).apply(lambda x: x.sort_values('timestamp_clean'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interviews_cleaned_df = interviews_df.groupby('conversation').apply(remove_preamble).reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(interviews_df) - len(interviews_cleaned_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "len(interviews_cleaned_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "interviews_cleaned_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Turn every conversation into one big block of text (mimics the format of other interview/focus group transcripts we might see)\n", + "df_grouped = interviews_cleaned_df.groupby('conversation')['text_clean'].apply(lambda x: '. '.join(x)).reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"sentence-transformers/all-MiniLM-L6-v2\"\n", + "buffer_sizes = [1, 5, 10]\n", + "\n", + "all_results = {}\n", + "\n", + "for buffer_size in buffer_sizes:\n", + " chunker = SemanticChunker(HuggingFaceEmbeddings(model_name=model_name), \n", + " breakpoint_threshold_type=\"percentile\", \n", + " buffer_size=buffer_size)\n", + " results = {}\n", + "\n", + " for idx, row in df_grouped.iterrows():\n", + " text = row['text_clean']\n", + " conv_id = row['conversation']\n", + " # Turn it into a langchain document\n", + " doc = Document(page_content=text)\n", + " chunked_docs = chunker.split_documents([doc])\n", + " results[conv_id] = [x.model_dump() for x in chunked_docs]\n", + " \n", + " \n", + " all_results[buffer_size] = [len(chunk_list) for chunk_list in results.values()]\n", + "\n", + "fig, axes = plt.subplots(nrows=1, ncols=len(buffer_sizes), figsize=(15, 5), sharey=True)\n", + "\n", + "for ax, buffer_size in zip(axes, buffer_sizes):\n", + " lengths = all_results[buffer_size]\n", + " ax.hist(lengths, bins=20, alpha=0.7)\n", + " ax.set_title(f'Buffer Size {buffer_size}')\n", + " ax.set_xlabel('Number of Chunks')\n", + " ax.set_ylabel('Frequency')\n", + "\n", + "plt.suptitle('Distribution of List Lengths for Different Buffer Sizes', fontsize=16)\n", + "plt.tight_layout(rect=[0, 0, 1, 0.95])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See [the documentation](https://python.langchain.com/docs/how_to/semantic-chunker/) for info on different breakpoints. Percentile is the default.\n", + "\n", + "See also [this notebook](https://github.com/FullStackRetrieval-com/RetrievalTutorials/blob/main/tutorials/LevelsOfTextSplitting/5_Levels_Of_Text_Splitting.ipynb).\n", + "\n", + "Note that [the documentation](https://api.python.langchain.com/en/latest/text_splitter/langchain_experimental.text_splitter.SemanticChunker.html) suggests you can manipulate:\n", + "* the exact numerical value of the breakpoint threshold\n", + "* the regex for sentence delimiters\n", + "* the number of chunks if you have a sense of what this would be for your document\n", + "\n", + "`buffer_size` = the number of sentences either side to include. So if `buffer_size` is 1, you will get 3 sentences in each group." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_name = \"sentence-transformers/all-MiniLM-L6-v2\"\n", + "\n", + "chunker = SemanticChunker(HuggingFaceEmbeddings(model_name=model_name), \n", + " breakpoint_threshold_type=\"percentile\", \n", + " buffer_size=1)\n", + "results = {}\n", + "\n", + "for idx, row in df_grouped.iterrows():\n", + " text = row['text_clean']\n", + " conv_id = row['conversation']\n", + " # Turn it into a langchain document\n", + " doc = Document(page_content=text)\n", + " chunked_docs = chunker.split_documents([doc])\n", + " results[conv_id] = [x.model_dump() for x in chunked_docs]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "new_results = {}\n", + "for conv in results.keys():\n", + " new_results[conv] = [x['page_content'] for x in results[conv]]\n", + " # print()\n", + " \n", + "new_results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame({\n", + " 'conversation': new_results.keys(),\n", + " 'content': new_results.values()\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.explode('content')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_grouped_new = interviews_cleaned_df.groupby('conversation').apply(lambda x: list(zip(x['uuid'], x['text_clean']))).reset_index(name='uuid_text_pairs')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_grouped_new" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def chunk_with_uuids(pairs):\n", + " # Extract the text for chunking\n", + " text = '. '.join([t[1] for t in pairs])\n", + " \n", + " # Apply the semantic chunker to the text\n", + " doc = Document(page_content=text)\n", + " chunks = chunker.split_documents([doc])\n", + " \n", + " # For each chunk, find the corresponding UUIDs\n", + " chunk_uuid_mapping = []\n", + " \n", + " for chunk in chunks:\n", + " # For each chunk, identify the corresponding UUID(s)\n", + " uuids_in_chunk = []\n", + " chunk_text = chunk.page_content\n", + " \n", + " # Iterate through the original pairs to find corresponding UUIDs\n", + " for uuid, text_segment in pairs:\n", + " if text_segment in chunk_text:\n", + " uuids_in_chunk.append(uuid)\n", + " \n", + " # Append to the mapping (chunk text -> corresponding UUIDs)\n", + " chunk_uuid_mapping.append({\n", + " 'chunk_text': chunk_text,\n", + " 'uuids': uuids_in_chunk\n", + " })\n", + " \n", + " return chunk_uuid_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_grouped_new" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = {}\n", + "\n", + "for idx, row in df_grouped_new.iterrows():\n", + " conv = row['conversation']\n", + " output[conv] = {}\n", + " pairs = row['uuid_text_pairs']\n", + " chunk_mapping = chunk_with_uuids(pairs)\n", + " output[conv] = chunk_mapping" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "flattened_data = []\n", + "for conversation, chunks in output.items():\n", + " for chunk in chunks:\n", + " flattened_data.append({\n", + " 'conversation': conversation,\n", + " 'chunk_text': chunk['chunk_text'],\n", + " 'uuids': chunk['uuids']\n", + " })\n", + "\n", + "# Step 2: Convert the flattened list to a DataFrame\n", + "df = pd.DataFrame(flattened_data)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/dsp_interview_transcripts/pipeline/README.md b/dsp_interview_transcripts/pipeline/README.md index a146516..4636719 100644 --- a/dsp_interview_transcripts/pipeline/README.md +++ b/dsp_interview_transcripts/pipeline/README.md @@ -1,13 +1,23 @@ # Basic pipeline -The pipeline is very simple at this stage. - 1. Make sure you have a directory `data/` with the file `qual_af_transcripts.csv` in it. -2. Run `python dsp_interview_transcripts/pipeline/run_pipeline.py`. This runs the following scripts: - - `process_data.py`. This will do some cleaning of the data (text cleaning, making sure the conversations are in order, concatenating consecutive messages by the same person within a conversation) and save two outputs: a file with just user messages over a certain length; one with the user messages, plus the immediately preceding bot message (in order to see if this extra context helps the text clustering and topic representations). - - `python dsp_interview_transcripts/pipeline/segment_interviews.py`. This uses some basic comparison between the topic guide and the BOT messages to figure out which parts of the interview might correspond to which question from the topic guide. A separate dataset is saved for each of the questions from the topic guide. - - `python dsp_interview_transcripts/pipeline/topic_modelling.py`. This runs BERTopic on the datasets provided above, and also iterates over a few different minimum cluster sizes. +2. Create a directory `outputs/` in the root of the repository. + +3. Install [Ollama](https://ollama.com/) according to your operating system's instructions. Install [Llama3.2](https://ollama.com/library/llama3.2) (defaults to 3B) by running in your terminal `ollama pull llama3.2`. + +4. Run `python dsp_interview_transcripts/pipeline/run_pipeline.py`. This runs the following scripts: + - `process_data.py`. This will do some cleaning of the data (text cleaning, making sure the conversations are in order, concatenating consecutive messages by the same person within a conversation). It also applies a sentiment analysis model and also estimates which question from the interview guide is being discussed at each point. + - `python dsp_interview_transcripts/pipeline/topic_modelling.py`. This runs a BERTopic style model, but the actual steps (dimensionality reduction, HDBSCAN, tfidf representation) are run separately + because I wanted to normalise the embedding vectors before reducing the dimensionality. + - `python dsp_interview_transcripts/pipeline/name_clusters.py`. This uses llama3.2 to generate a name and a description for each topic. + - `python dsp_interview_transcripts/pipeline/prep_output_tables.py`. This formats the output data and also saves scatterplots locally. + - `python dsp_interview_transcripts/pipeline/top_down_analysis.py`. This mimics the top-down/framework approach. It subsets the data according to which question is being discussed and then runs BERTopic on each subset. + +The outputs are: -3. Run `streamlit run dsp_interview_transcripts/pipeline/app.py`. This launches a streamlit app that allows you to inspect the outputs of BERTopic that we created in the step above. +- `outputs/final/final_df.csv`: this is the main output file for the bottom-up approach, containing all user responses above a set length, some preceding context for each, the topic they're assigned to, the sentiment etc. +- `outputs/final/summary_info.csv`: this contains just the topic names and descriptions for the bottom-up approach, and some representative responses for each topic. +- `outputs/scatterplot... .html`: there are three scatterplots for the bottom-up approach: one coloured by topic, one coloured by question, and one coloured by sentiment. +- `outputs/by_question/`: this directory contains the output for the top-down approach: an info file on the topics generated for each question, and a scatterplot for each question. diff --git a/dsp_interview_transcripts/pipeline/compare_models_app.py b/dsp_interview_transcripts/pipeline/compare_models_app.py deleted file mode 100644 index 43e2e40..0000000 --- a/dsp_interview_transcripts/pipeline/compare_models_app.py +++ /dev/null @@ -1,86 +0,0 @@ - -""" -Usage: -``` -streamlit run dsp_interview_transcripts/pipeline/app.py -``` -""" - -import streamlit as st -import pandas as pd -import altair as alt -import re - -from dsp_interview_transcripts import PROJECT_DIR, config - -QUESTIONS = config['questions'] - -# Define the app layout -st.title("Topic Modeling Visualization") - -INTERVIEW_SECTIONS = [f"interview_q_{i}" for i in range(-1, 10) if i!=4] -DATA_SOURCES = ["user_messages", - "q_and_a", - ] + INTERVIEW_SECTIONS - -# Input options -data_source = st.selectbox("Select Data Source", DATA_SOURCES) - -if data_source in INTERVIEW_SECTIONS: - index = data_source.split("_")[-1] - if index=="-1": - st.info("These responses could not be matched to a question from the prompt.") - else: - st.info(QUESTIONS[int(index)]) - -minimum_cluster_size = st.selectbox("Select Minimum Cluster Size", [10, 50, 100]) - -# Validation: Prevent selection of '100' and 'home_upgrades_user' together -if data_source in INTERVIEW_SECTIONS and minimum_cluster_size > 10: - st.warning("For one of the 'interview_q' sources, you can only select a min. cluster size of 10. Please select a different combination.") -else: - - # Filepath based on user inputs - file_path = PROJECT_DIR / f"outputs/{data_source}_cluster_size_{minimum_cluster_size}_vis.csv" - - # Load the CSV data - @st.cache - def load_data(file_path): - return pd.read_csv(file_path) - - # Load the data and display it - if file_path.exists(): - df_vis = load_data(file_path) - else: - st.error(f"File not found: {file_path}") - - # Altair plotting - if not df_vis.empty: - # Define opacity condition - opacity_condition = alt.condition( - alt.datum.topic == -1, alt.value(0.1), alt.value(0.4) - ) - - # Create plot - fig = ( - alt.Chart(df_vis) - .mark_circle(size=50) - .encode( - x=alt.X( - "x:Q", - axis=alt.Axis(ticks=False, labels=False, title=None, grid=False), - ), - y=alt.Y( - "y:Q", - axis=alt.Axis(ticks=False, labels=False, title=None, grid=False), - ), - color=alt.Color("Name:N"), - opacity=opacity_condition, - tooltip=["Name:N", "doc:N"], - ) - .properties(width=900, height=600) - .interactive() - ) - - # Display the plot in the Streamlit app - st.altair_chart(fig, use_container_width=True) diff --git a/dsp_interview_transcripts/pipeline/name_clusters.py b/dsp_interview_transcripts/pipeline/name_clusters.py new file mode 100644 index 0000000..955b2c4 --- /dev/null +++ b/dsp_interview_transcripts/pipeline/name_clusters.py @@ -0,0 +1,156 @@ +"""Use a llama model to give names and descriptions for the topics.""" +import json + +from typing import Dict +from typing import List + +import pandas as pd + +from langchain.prompts import PromptTemplate +from langchain_community.chat_models import ChatOllama +from langchain_core.output_parsers import JsonOutputParser +from pydantic import BaseModel +from pydantic import Field + +from dsp_interview_transcripts import PROJECT_DIR +from dsp_interview_transcripts import logger + + +class NameDescription(BaseModel): + """Model for naming and describing a group of documents.""" + + name: str = Field(description="Informative name for this group of documents") + description: str = Field(description="Description of this group of documents") + + +prompt = """ + I have performed text clustering on some interviews where users were asked about their knowledge of + and opinions on different home heating options. In the interview, users were asked about their knowledge + of the Boiler Upgrade Scheme, a scheme that provides a subsidy to homeowners wishing to install a heatpump + instead of getting a new gas boiler for their home. + \n + One of the clusters contains the following user responses from the interviews: + {docs} + The cluster is described by the following keywords: {keywords} + \n + Based on the information above, please provide a name and summary for the cluster as a JSON object with two fields: + - name: A short, informative name for the cluster + - description: A summary of views of users within the cluster. You can include sentiments they express, reasons for their views, their knowledge levels, and any other relevant information. + \n + Provide nothing except for this JSON dict. + \n + Example: + {{ + "name": "Energy Efficiency", + "description": "This cluster contains user responses about energy efficiency when choosing home heating options. The users have varying degrees of knowledge about the efficiency of different systems. Some reasons for wanting to improve efficiency include environmental concerns and cost concerns." + }} + """ + +parser = JsonOutputParser(pydantic_object=NameDescription) + +final_prompt = PromptTemplate( + template=prompt, + input_variables=["docs", "keywords"], + partial_variables={"format_instructions": parser.get_format_instructions()}, +) + +model = "llama3.2" + +ollama_model = ChatOllama(model=model, temperature=0) + +llm_chain = final_prompt | ollama_model | parser + +INPUT_PATH = PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics_representative_docs.csv" + + +def name_topics( + topic_info: pd.DataFrame, + llm_chain, + text_col: str = "text_clean", + top_words_col: str = "Top Words", + topic_label_col: str = "Cluster", +) -> Dict[str, dict]: + """ + Generate names and descriptions for each topic by invoking an LLM chain, + using representative text and keywords for each topic. + + Args: + topic_info (pd.DataFrame): A DataFrame containing topic information, + including columns for text samples, top words, and topic labels. + llm_chain: A language model chain used for generating topic names and descriptions. + It must support an `invoke` method that accepts a dictionary with 'docs' and 'keywords' keys. + topics (List[str]): A list of topic identifiers to process. + text_col (str, optional): Column name in `topic_info` containing the text data for each topic. + Defaults to 'text_clean'. + top_words_col (str, optional): Column name in `topic_info` containing the top words for each topic. + Defaults to 'Top Words'. + topic_label_col (str, optional): Column name in `topic_info` that indicates topic labels. + Defaults to 'Cluster'. + + Returns: + Dict[str, dict]: A dictionary where each key is a topic identifier and each value is + a dictionary with the generated 'name' and 'description' for that topic. + + Raises: + Exception: Logs and continues on any exceptions encountered while processing topics, + capturing errors with the topic identifier and error message. + + Example: + >>> name_topics(topic_info=df, llm_chain=my_llm_chain, topics=["Topic 1", "Topic 2"]) + { + "Topic 1": {"name": "Customer Satisfaction", "description": "Documents discussing customer feedback and satisfaction."}, + "Topic 2": {"name": "Product Quality", "description": "Documents focusing on product durability and performance."} + } + """ + topics = topic_info["Cluster"].unique().tolist() + + results = {} + + for topic in topics: + logger.info(f"Processing topic {topic}") + temp_df = topic_info[topic_info[topic_label_col] == topic] + docs = temp_df[text_col].values[0] + logger.info(f"Docs: {docs}") + keywords = temp_df[top_words_col].values[0] + logger.info(f"Keywords: {keywords}") + + try: + output = llm_chain.invoke({"docs": docs, "keywords": keywords}) + logger.info(f"Generated name: {output['name']}, description: {output['description']}") + results[topic] = output + + except Exception as e: + logger.error(f"Error processing topic {topic}: {str(e)}") + results[topic] = {"error": str(e)} + + return results + + +if __name__ == "__main__": + + topic_info = pd.read_csv(INPUT_PATH) + + topic_info = topic_info.groupby(["Cluster", "Top Words"])["text_clean"].apply(list).reset_index() + topic_info["Cluster"] = topic_info["Cluster"].astype(str) + + results = name_topics( + topic_info, llm_chain, text_col="text_clean", top_words_col="Top Words", topic_label_col="Cluster" + ) + + # Some complicated conditionals to check that what's in `results` can be parsed + topic_info[f"{model}_name"] = topic_info["Cluster"].map( + lambda x: results[x]["name"] + if x in results and isinstance(results[x], dict) and "name" in results[x] + else None + ) + topic_info[f"{model}_description"] = topic_info["Cluster"].map( + lambda x: results[x]["description"] + if x in results and isinstance(results[x], dict) and "description" in results[x] + else None + ) + + logger.info("Saving output...") + topic_info.to_csv( + PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics_with_names_descriptions.csv", index=False + ) + logger.info("Done!") diff --git a/dsp_interview_transcripts/pipeline/prep_output_tables.py b/dsp_interview_transcripts/pipeline/prep_output_tables.py new file mode 100644 index 0000000..b6bcc80 --- /dev/null +++ b/dsp_interview_transcripts/pipeline/prep_output_tables.py @@ -0,0 +1,161 @@ +from pathlib import Path +from typing import List +from typing import Optional +from typing import Union + +import altair as alt +import pandas as pd + +from dsp_interview_transcripts import PROJECT_DIR +from dsp_interview_transcripts import logger + + +OUTPUT_DIR = PROJECT_DIR / "outputs/final" +OUTPUT_PATH_FULL_DATA = OUTPUT_DIR / "final_df.csv" +OUTPUT_PATH_SUMMARY = OUTPUT_DIR / "summary_info.csv" + +# Create the output directory if it doesn't exist +OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + +opacity_condition = alt.condition(alt.datum.Name == "None", alt.value(0.1), alt.value(0.6)) + + +def create_scatterplot( + data_viz: pd.DataFrame, + color: str = "Name:N", + tooltip: List[str] = ["Name:N", "Description:N", "question:N", "text_clean:N"], + domain: Optional[List[Union[str, int]]] = None, + range_: Optional[List[str]] = None, +) -> alt.Chart: + """ + Display texts as a scatterplot. + + domain and range_ are used for specifying a 3-way colour scale when the + texts should be coloured by sentiment. + + Args: + data_viz (pd.DataFrame): The DataFrame containing data to visualize. Must include columns for x and y coordinates, + along with fields specified in `color` and `tooltip`. + color (str, optional): Encoding specification for the color channel. Defaults to "Name:N". + tooltip (List[str], optional): List of fields to display as tooltips. Defaults to ["Name:N", "Description:N", "question:N", "text_clean:N"]. + domain (Optional[List[Union[str, int]]], optional): Custom domain values for the color scale, defining specific categories. + Defaults to None. + range_ (Optional[List[str]], optional): Custom color range for the color scale, corresponding to the domain values. + Defaults to None. + + Returns: + alt.Chart: An Altair chart object representing the scatterplot, with specified color, tooltips, and interactivity. + """ + + if domain is not None and range_ is not None: + color = alt.Color(color, scale=alt.Scale(domain=domain, range=range_)) + else: + color = alt.Color(color) + + fig = ( + alt.Chart(data_viz) + .mark_circle(size=50) + .encode( + x=alt.X( + "x:Q", + axis=alt.Axis(ticks=False, labels=False, title=None, grid=False), + ), + y=alt.Y( + "y:Q", + axis=alt.Axis(ticks=False, labels=False, title=None, grid=False), + ), + color=color, + opacity=opacity_condition, # Ensure opacity_condition is defined elsewhere + tooltip=tooltip, + ) + .properties(width=900, height=600) + .interactive() + ) + + return fig + + +if __name__ == "__main__": + rep_docs = pd.read_csv(PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics_representative_docs.csv") + data = pd.read_csv(PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics.csv") + data_w_names = pd.read_csv( + PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics_with_names_descriptions.csv" + ) + + topic_counts = pd.DataFrame(data["Cluster"].value_counts()).reset_index() + topic_counts = topic_counts.rename(columns={"count": "N responses in topic"}) + + data_w_names = data_w_names.rename(columns={"llama3.2_name": "Name", "llama3.2_description": "Description"}) + data_w_names = pd.merge(data_w_names, topic_counts, left_on="Cluster", right_on="Cluster", how="left") + + data_w_names[["Name", "Description", "Top Words", "N responses in topic"]].to_csv(OUTPUT_PATH_SUMMARY, index=False) + + rep_docs = pd.merge( + rep_docs, data_w_names[["Cluster", "Name", "Description", "N responses in topic"]], on="Cluster", how="left" + ) + + data_viz = ( + data.merge(data_w_names[["Cluster", "Name", "Description"]], on="Cluster", how="left") + .assign(Name=lambda df: df["Name"].fillna("None")) + .assign(Description=lambda df: df["Description"].fillna("None")) + ) + + # Create binary column to indicate whether the user response is representative of the topic + merged_df = ( + data_viz.merge( + rep_docs[["conversation", "uuid", "Name"]], on=["conversation", "uuid", "Name"], how="left", indicator=True + ) + .assign(Representative_of_topic=lambda df: (df["_merge"] == "both").astype(int)) + .drop(columns=["_merge"]) + ) + + final_df = merged_df[ + [ + "Name", + "Description", + "Top Words", + "Representative_of_topic", + "question", + "context", + "text_clean", + "sentiment", + "conversation", + "uuid", + "timestamp", + ] + ] + final_df = final_df.rename( + columns={ + "text_clean": "user_response", + "question": "probable question", + "sentiment": "predicted_sentiment", + "Name": "Topic Name", + "Description": "Topic Description", + "Top Words": "Topic Top Words", + } + ) + + logger.info("Saving output table...") + final_df.sort_values(["conversation", "timestamp"]).to_csv(OUTPUT_PATH_FULL_DATA, index=False) + + # Visualise clusters + logger.info("Saving figures...") + + fig = create_scatterplot( + data_viz, + ) + fig.save(PROJECT_DIR / "outputs/scatter_coloured_by_topic.html") + + fig_questions = create_scatterplot( + data_viz=data_viz, + color="question:N", + ) + fig_questions.save(PROJECT_DIR / "outputs/scatter_coloured_by_question.html") + + fig_sentiment = create_scatterplot( + data_viz=data_viz, + color="sentiment:N", + domain=["Negative", "Neutral", "Positive"], + range_=["red", "gray", "green"], + ) + fig_sentiment.save(PROJECT_DIR / "outputs/scatter_coloured_by_sentiment.html") diff --git a/dsp_interview_transcripts/pipeline/process_data.py b/dsp_interview_transcripts/pipeline/process_data.py index a62a294..8fee943 100644 --- a/dsp_interview_transcripts/pipeline/process_data.py +++ b/dsp_interview_transcripts/pipeline/process_data.py @@ -1,96 +1,303 @@ +import random + +from typing import List +from typing import Tuple + +import nltk +import numpy as np import pandas as pd +import torch + +from nltk.corpus import stopwords +from nltk.tokenize import sent_tokenize +from scipy.special import softmax +from sentence_transformers import SentenceTransformer +from sklearn.metrics.pairwise import cosine_similarity +from transformers import AutoModelForSequenceClassification +from transformers import AutoTokenizer + +from dsp_interview_transcripts import PROJECT_DIR +from dsp_interview_transcripts import config +from dsp_interview_transcripts import logger +from dsp_interview_transcripts.utils.data_cleaning import add_text_length +from dsp_interview_transcripts.utils.data_cleaning import clean_data +from dsp_interview_transcripts.utils.data_cleaning import convert_timestamp +from dsp_interview_transcripts.utils.data_cleaning import remove_preamble + -from dsp_interview_transcripts import PROJECT_DIR, logger -from dsp_interview_transcripts.utils.data_cleaning import clean_data, convert_timestamp, add_text_length +nltk.download("stopwords") +nltk.download("punkt") +nltk.download("punkt_tab") + +stop_words = set(stopwords.words("english")) + +# Set random seeds +RANDOM_SEED = 42 +np.random.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +# PyTorch seed (used by SentenceTransformer) +torch.manual_seed(RANDOM_SEED) + +SENTENCE_MODEL = SentenceTransformer("all-MiniLM-L6-v2") DATA_PATH = PROJECT_DIR / "data/qual_af_transcripts.csv" -def concatenate_consecutive_roles(df, text_col='text_clean', conversation_col='conversation', role_col='role'): - """Concatenates consecutive rows with the same role within a conversation.""" - # Sort the dataframe to ensure correct order (if it's not already sorted) - df = df.sort_values(by=[conversation_col, 'timestamp']).reset_index(drop=True) +QUESTIONS = config["questions"] + +MIN_LEN = 9 + + +def concatenate_consecutive_roles( + df: pd.DataFrame, text_col: str = "text_clean", conversation_col: str = "conversation", role_col: str = "role" +) -> pd.DataFrame: + """ + Concatenates consecutive rows with the same role within a conversation. So if a user sends multiple short messages + in succession, these get turned into one larger message. + + Parameters: + - df (pd.DataFrame): DataFrame containing the conversation data. + - text_col (str): Name of the text column to concatenate. + - conversation_col (str): Name of the column identifying conversation groups. + - role_col (str): Name of the column identifying roles within the conversation. + + Returns: + - pd.DataFrame: DataFrame with concatenated text for consecutive roles. + """ + # Sort the dataframe to ensure correct order + df = df.sort_values(by=[conversation_col, "timestamp"]).reset_index(drop=True) logger.info(f"Number of turns before concatenating consecutive roles: {len(df)}") # Create a mask to identify where the role changes or a new conversation starts - df['role_change'] = (df[conversation_col] != df[conversation_col].shift(1)) | (df[role_col] != df[role_col].shift(1)) + df["role_change"] = (df[conversation_col] != df[conversation_col].shift(1)) | ( + df[role_col] != df[role_col].shift(1) + ) # Assign group numbers to consecutive rows with the same role within the same conversation - df['turn'] = df['role_change'].cumsum() + df["turn"] = df["role_change"].cumsum() # Group by 'conversation' and 'group' to concatenate text - grouped = df.groupby([conversation_col, 'turn']).agg({ - 'timestamp': 'first', # Keep the first timestamp in each group - text_col: ' '.join, # Concatenate the 'text_clean' column - role_col: 'first', # Keep the role (either BOT or USER) - 'uuid': 'first' # Keep the first UUID in each group - }).reset_index() - + grouped = ( + df.groupby([conversation_col, "turn"]) + .agg({"timestamp": "first", text_col: " ".join, role_col: "first", "uuid": "first"}) + .reset_index() + ) + logger.info(f"Number of turns after concatenating consecutive roles: {len(grouped)}") - grouped = grouped.drop(columns=['turn']) + grouped = grouped.drop(columns=["turn"]) return grouped -def create_q_and_a_column(data_df, text_col = 'text_clean', conversation_col='conversation'): - """Concatenates each user message with the immediately preceding bot message. - In cases where the user has given a really short response, this should provide some - helpful context. + +def process_bot_qs(interviews_df: pd.DataFrame) -> Tuple[List[str], pd.DataFrame]: + """ + Produces a list of unique sentences produced by the bot. + These will be matched to the interview guide. + + Parameters: + - interviews_df (pd.DataFrame): DataFrame with bot conversation data. + + Returns: + - Tuple[List[str], pd.DataFrame]: A list of unique sentences and DataFrame with exploded sentences. + """ + bot_qs = interviews_df[interviews_df["role"] == "BOT"].copy() + # split into individual sentences so that if the original question is contained within the utterance, + # we have a better chance of catching it + bot_qs["sentences"] = bot_qs["text_clean"].apply(lambda x: sent_tokenize(x)) + bot_qs = bot_qs.explode("sentences") + bot_qs_list = bot_qs["sentences"].unique().tolist() + return bot_qs_list, bot_qs + + +def match_questions(bot_qs_list: List[str], questions: List[str], threshold: float = 0.85) -> pd.DataFrame: + """Match the input questions from our interview template, + and the actual questions produced by the bot, using cosine similarity. + + Parameters: + - bot_qs_list (List[str]): List of bot questions. + - questions (List[str]): List of input questions to match against. + - threshold (float): Similarity threshold for considering a match. + + Returns: + - pd.DataFrame: DataFrame containing matched questions above the threshold. """ - # Initialize a list to store the Q&A - q_and_a_list = [] - - # Variable to keep track of the most recent BOT text - last_bot_text = "" - - df = data_df.copy() - - # Track the current conversation ID to know when it changes - current_conversation = None - - # Iterate over the rows of the DataFrame - for _, row in df.iterrows(): - - # Check if the conversation has changed - if current_conversation != row[conversation_col]: - current_conversation = row[conversation_col] - last_bot_text = "" # Reset the last bot text for a new conversation - - if row['role'] == 'BOT': - # Update the last bot text - last_bot_text = row[text_col] - q_and_a_list.append('') # No Q&A for BOT rows - elif row['role'] == 'USER': - # Combine the last bot text and current user text if there is a preceding bot text, - # otherwise just use the user text - q_and_a_list.append(f"{last_bot_text}\n{row[text_col]}" if last_bot_text else row[text_col]) + bot_qs_embeddings = SENTENCE_MODEL.encode(bot_qs_list) + input_qs_embeddings = SENTENCE_MODEL.encode(questions) + + similarities = cosine_similarity( + bot_qs_embeddings, + input_qs_embeddings, + ) + + # Find the index of the highest cosine similarity for each n-gram/lookup phrase combination + max_indices = np.argmax(similarities, axis=1) + + # Retrieve the text of the corresponding target phrases + most_similar_phrases = [questions[index] for index in max_indices] + + most_similar_similarities = [similarities[i, index] for i, index in enumerate(max_indices)] + + most_similar_pairs = list(zip(bot_qs_list, most_similar_phrases, most_similar_similarities)) + + matches = pd.DataFrame(most_similar_pairs, columns=["bot_q", "question", "cosine_similarity"]) + + final_matches = matches[ + matches["cosine_similarity"] > threshold + ] # temporary threshold until we've done some proper evaluation + + return final_matches + + +def get_best_matches(bot_qs: pd.DataFrame, final_matches: pd.DataFrame, questions_df: pd.DataFrame) -> pd.DataFrame: + """ + Retrieves original bot utterances and their best matching questions. + + Parameters: + - bot_qs (pd.DataFrame): DataFrame of bot questions split into sentences. + - final_matches (pd.DataFrame): DataFrame of matched questions. + - questions_df (pd.DataFrame): DataFrame containing question details. + + Returns: + - pd.DataFrame: DataFrame with highest similarity match for each bot utterance. + """ + questions_matched = pd.merge(bot_qs, final_matches, left_on="sentences", right_on="bot_q", how="inner") + # merge in the df that has the question number + questions_matched = pd.merge(questions_matched, questions_df, left_on="question", right_on="question", how="left") + + uuid_question_counts = questions_matched.groupby("uuid")["question"].nunique() + logger.info( + f"The following utterances match to more than once question: {uuid_question_counts[uuid_question_counts > 1]}" + ) + + # Group by 'uuid' and keep the row with the highest 'cosine_similarity' + questions_highest_similarity = questions_matched.loc[ + questions_matched.groupby("uuid")["cosine_similarity"].idxmax() + ] + return questions_highest_similarity + + +def get_sentiment(texts: List[str]) -> List[str]: + """ + Predicts sentiment of texts using a pre-trained RoBERTa sentiment model. + + Parameters: + - texts (List[str]): List of texts to analyze for sentiment. + + Returns: + - List[str]: List of predicted sentiment labels ('Negative', 'Neutral', 'Positive') for each text. + """ + roberta = "cardiffnlp/twitter-roberta-base-sentiment-latest" + model = AutoModelForSequenceClassification.from_pretrained(roberta) + tokenizer = AutoTokenizer.from_pretrained(roberta) + + labels = ["Negative", "Neutral", "Positive"] + + # Tokenize all texts at once + encoded_texts = tokenizer(texts, return_tensors="pt", padding=True, truncation=True) + + # Pass all encoded texts through the model at once + with torch.no_grad(): # Disable gradient computation for faster inference + output = model(**encoded_texts) + + # Apply softmax to the scores + scores = output.logits.detach().numpy() + probabilities = softmax(scores, axis=1) + + # Get the label with the highest probability for each text + predicted_labels = [] + for prob in probabilities: + max_index = prob.argmax() # Get the index of the highest probability + predicted_labels.append(labels[max_index]) # Get the corresponding label + + return predicted_labels + + +def create_context(row: pd.Series, df: pd.DataFrame) -> str: + """ + This is used to create an additional column in the dataframe that contains the preceding BOT > USER > BOT + sequence before each USER message. + + You should first use concatenate_consecutive_roles() to make sure + that the conversation is dyadic - otherwise you will just get the preceding 3 rows of data. + + Parameters: + - row (pd.Series): Current row from the DataFrame to generate context for. + - df (pd.DataFrame): DataFrame containing the conversation data. + + Returns: + - str: Concatenated text of previous entries as context. + """ + # Only proceed if the row is a USER entry + if row["role"] == "USER": + idx = row.name # Current row index + if idx >= 3: # We need at least 3 previous rows to build context + prev_rows = df.iloc[idx - 3 : idx] # Take previous 3 rows else: - # In case there's any other role, we leave it empty - q_and_a_list.append('') + prev_rows = df.iloc[:idx] - # Add the new 'q_and_a' column to the DataFrame - df['q_and_a'] = q_and_a_list + return " | ".join(prev_rows["text_clean"]) - return df if __name__ == "__main__": interviews_df = pd.read_csv(DATA_PATH) interviews_df = clean_data(interviews_df) - + logger.info(f"Number of interviews: {len(interviews_df['conversation'].unique())}") - - # Make sure the conversations are sorted by time, so that the replies go in the right order - interviews_df['timestamp_clean'] = interviews_df['timestamp'].apply(convert_timestamp) - interviews_df = interviews_df.groupby('conversation', group_keys=False).apply(lambda x: x.sort_values('timestamp_clean')) - - # Group together consecutive responses by the same role - interviews_df = concatenate_consecutive_roles(interviews_df) - - interviews_df = create_q_and_a_column(interviews_df) - - interviews_df = add_text_length(interviews_df, 'text_clean') - - q_and_a_df = interviews_df[interviews_df['q_and_a'] != ''] - q_and_a_df.to_csv(PROJECT_DIR / "data/q_and_a.csv", index=False) - - user_messages = interviews_df[(interviews_df['role']=='USER') & (interviews_df['text_length'] >= 4)] # only answers at least 4 words long - user_messages.to_csv(PROJECT_DIR / "data/user_messages.csv", index=False) + + interviews_cleaned_df = ( + interviews_df + # Make sure the conversations are sorted by time, so that the replies go in the right order + .assign(timestamp_clean=lambda df: df["timestamp"].apply(convert_timestamp)) + .groupby("conversation", group_keys=False) + .apply(lambda x: x.sort_values("timestamp_clean")) + # Remove everything up to when bot asks if the instructions are clear - everything before is just noise + .pipe(lambda df: df.groupby("conversation").apply(remove_preamble).reset_index(drop=True)) + # Group together consecutive responses by the same role + .pipe(concatenate_consecutive_roles) + ) + + questions_df = pd.DataFrame(enumerate(QUESTIONS), columns=["q_number", "question"]) + + bot_qs_list, bot_qs = process_bot_qs(interviews_cleaned_df) + + final_matches = match_questions(bot_qs_list, QUESTIONS) + + questions_highest_similarity = get_best_matches(bot_qs, final_matches, questions_df) + + # Merge back into the original df + interviews_cleaned_df = pd.merge( + interviews_cleaned_df, + questions_highest_similarity[["uuid", "question", "q_number", "cosine_similarity"]], + on="uuid", + how="left", + ) + + # Forward fill the matched questions and their question numbers + interviews_q_filled = ( + interviews_cleaned_df.copy() + .assign( + question=lambda df: df.groupby("conversation")["question"].ffill(), + q_number=lambda df: df.groupby("conversation")["q_number"].ffill(), + ) + .pipe(add_text_length) + .assign(context=lambda df: df.apply(create_context, df=df, axis=1)) + ) + + user_messages = interviews_q_filled[ + (interviews_q_filled["role"] == "USER") & (interviews_q_filled["text_length"] > MIN_LEN) + ] + + # Get sentiments!! + texts = user_messages["text_clean"].tolist() + sentiments = get_sentiment(texts) + + user_messages["sentiment"] = sentiments + + logger.info(user_messages["sentiment"].value_counts()) + + logger.info(f"Number of user messages: {len(user_messages)}") + + logger.info("Saving data...") + user_messages.to_csv(PROJECT_DIR / f"data/user_messages_min_len_{MIN_LEN}_w_sentiment.csv", index=False) + + logger.info("Done!") diff --git a/dsp_interview_transcripts/pipeline/run_pipeline.py b/dsp_interview_transcripts/pipeline/run_pipeline.py index 456fc82..c91d9a0 100644 --- a/dsp_interview_transcripts/pipeline/run_pipeline.py +++ b/dsp_interview_transcripts/pipeline/run_pipeline.py @@ -1,9 +1,24 @@ -from pathlib import Path +""" +Before running these scripts, you will need to make sure that Ollama is available. + +You can do this by running the following from the terminal: +``` +ollama serve +``` +Alternatively check the bar at the top of your screen. A llama icon indicates that Ollama is running. +""" import subprocess -import os + +from pathlib import Path + script_parent = Path(__file__).parent +# All of these scripts cover topic modelling across *all* responses. The top down analysis is separate subprocess.run(f"python {script_parent / 'process_data.py'}", shell=True) -subprocess.run(f"python {script_parent / 'segment_interviews.py'}", shell=True) subprocess.run(f"python {script_parent / 'topic_modelling.py'}", shell=True) +subprocess.run(f"python {script_parent / 'name_clusters.py'}", shell=True) +subprocess.run(f"python {script_parent / 'prep_output_tables.py'}", shell=True) + +# Top down approach +subprocess.run(f"python {script_parent / 'top_down_analysis.py'}", shell=True) diff --git a/dsp_interview_transcripts/pipeline/segment_interviews.py b/dsp_interview_transcripts/pipeline/segment_interviews.py deleted file mode 100644 index f2568c0..0000000 --- a/dsp_interview_transcripts/pipeline/segment_interviews.py +++ /dev/null @@ -1,119 +0,0 @@ -""" -Tries to broadly break the interview into sections by which question they follow. -""" - -import nltk -from nltk.tokenize import sent_tokenize -import numpy as np -import pandas as pd -from sentence_transformers import SentenceTransformer -from sklearn.metrics.pairwise import cosine_similarity -import torch - -from dsp_interview_transcripts import PROJECT_DIR, logger, config -from dsp_interview_transcripts.utils.data_cleaning import clean_data, convert_timestamp, add_text_length -from dsp_interview_transcripts.pipeline.process_data import concatenate_consecutive_roles - -RANDOM_SEED = 42 -DATA_PATH = PROJECT_DIR / "data/qual_af_transcripts.csv" - -torch.manual_seed(RANDOM_SEED) - -SENTENCE_MODEL = SentenceTransformer("all-MiniLM-L6-v2") - -QUESTIONS = config['questions'] - -def process_bot_qs(interviews_df: pd.DataFrame) -> list: - """Produce a list of each unique sentence produced by the bot - """ - bot_qs = interviews_df[interviews_df['role'] == 'BOT'] - # split into individual sentences so that if the original question is contained within the utterance, - # we have a better chance of catching it - bot_qs["sentences"] = bot_qs['text_clean'].apply(lambda x: sent_tokenize(x)) - bot_qs = bot_qs.explode("sentences") - bot_qs_list = bot_qs['sentences'].unique().tolist() - return bot_qs_list, bot_qs - -def match_questions(bot_qs_list, questions, threshold=0.85): - """Find the best matches between the input questions from our interview template, - and the actual questions produced by the bot. - """ - bot_qs_embeddings = SENTENCE_MODEL.encode(bot_qs_list) - input_qs_embeddings = SENTENCE_MODEL.encode(questions) - - similarities = cosine_similarity(bot_qs_embeddings, input_qs_embeddings, ) - - # Find the index of the highest cosine similarity for each n-gram/lookup phrase combination - max_indices = np.argmax(similarities, axis=1) - - # Retrieve the text of the corresponding target phrases - most_similar_phrases = [questions[index] for index in max_indices] - - most_similar_similarities = [ - similarities[i, index] for i, index in enumerate(max_indices) - ] - - most_similar_pairs = list( - zip(bot_qs_list, most_similar_phrases, most_similar_similarities) - ) - - matches = pd.DataFrame( - most_similar_pairs, columns=["bot_q", "question", "cosine_similarity"] - ) - - final_matches = matches[matches['cosine_similarity'] > threshold] # temporary threshold until we've done some proper evaluation - - return final_matches - -def get_best_matches(bot_qs, final_matches, questions_df): - """ - Create a dataframe of the original bot utterances and the questions they were matched to - """ - questions_matched = pd.merge(bot_qs, final_matches, left_on='sentences', right_on='bot_q', how='inner') - # merge in the df that has the question number - questions_matched = pd.merge(questions_matched, questions_df, left_on='question', right_on='question', how='left') - - uuid_question_counts = questions_matched.groupby('uuid')['question'].nunique() - logger.info(f"The following utterances match to more than once question: {uuid_question_counts[uuid_question_counts > 1]}") - - # Group by 'uuid' and keep the row with the highest 'cosine_similarity' - questions_highest_similarity = questions_matched.loc[questions_matched.groupby('uuid')['cosine_similarity'].idxmax()] - return questions_highest_similarity - -if __name__ == "__main__": - interviews_df = pd.read_csv(DATA_PATH) - interviews_df = clean_data(interviews_df) - - logger.info(f"Number of interviews: {len(interviews_df['conversation'].unique())}") - - # Make sure the conversations are sorted by time, so that the replies go in the right order - interviews_df['timestamp_clean'] = interviews_df['timestamp'].apply(convert_timestamp) - interviews_df = interviews_df.groupby('conversation', group_keys=False).apply(lambda x: x.sort_values('timestamp_clean')) - - # Group together consecutive responses by the same role - interviews_df = concatenate_consecutive_roles(interviews_df) - - questions_df = pd.DataFrame(enumerate(QUESTIONS), columns=["q_number", "question"]) - - bot_qs_list, bot_qs = process_bot_qs(interviews_df) - - final_matches = match_questions(bot_qs_list, QUESTIONS) - - questions_highest_similarity = get_best_matches(bot_qs, final_matches, questions_df) - - # Merge back into the original df - interviews_df = pd.merge(interviews_df, questions_highest_similarity[['uuid', 'question', 'q_number']], on='uuid', how='left') - - # Forward fill the matched questions and their question numbers - interviews_q_filled = interviews_df.copy() - interviews_q_filled['question'] = interviews_q_filled.groupby('conversation')['question'].ffill() - interviews_q_filled['q_number'] = interviews_q_filled.groupby('conversation')['q_number'].ffill() - - logger.info(f"Question counts: \n {interviews_q_filled['question'].value_counts()}") - - interviews_q_filled['q_number'] = interviews_q_filled['q_number'].replace({np.nan: -1}) - interviews_q_filled['q_number'] = interviews_q_filled['q_number'].astype(int) - - # Save a separate dataframe for user responses following each question - for q in interviews_q_filled['q_number'].unique(): - interviews_q_filled[(interviews_q_filled['q_number'] == q) & (interviews_q_filled['role'] == 'USER')].to_csv(PROJECT_DIR / 'data/interview_q_{}.csv'.format(q), index=False) diff --git a/dsp_interview_transcripts/pipeline/top_down_analysis.py b/dsp_interview_transcripts/pipeline/top_down_analysis.py new file mode 100644 index 0000000..dd7bdd5 --- /dev/null +++ b/dsp_interview_transcripts/pipeline/top_down_analysis.py @@ -0,0 +1,329 @@ +"""Create topic models within each question""" +import random + +from pathlib import Path +from typing import Any +from typing import Dict +from typing import List + +import altair as alt +import numpy as np +import pandas as pd +import torch + +from bertopic import BERTopic +from bertopic.representation import KeyBERTInspired # OpenAI, +from bertopic.representation import MaximalMarginalRelevance +from hdbscan import HDBSCAN +from langchain.prompts import PromptTemplate +from langchain_community.chat_models import ChatOllama +from langchain_core.output_parsers import JsonOutputParser +from pydantic import BaseModel +from pydantic import Field +from sentence_transformers import SentenceTransformer +from sklearn.feature_extraction.text import CountVectorizer +from umap import UMAP + +from dsp_interview_transcripts import PROJECT_DIR +from dsp_interview_transcripts import logger + + +# Set random seeds +RANDOM_SEED = 42 +np.random.seed(RANDOM_SEED) +random.seed(RANDOM_SEED) +# PyTorch seed (used by SentenceTransformer) +torch.manual_seed(RANDOM_SEED) + +SENTENCE_MODEL = SentenceTransformer("all-MiniLM-L6-v2") + +umap_model = UMAP( + n_neighbors=15, + n_components=50, + min_dist=0.1, + metric="cosine", + random_state=RANDOM_SEED, +) + +hdbscan_model = HDBSCAN( + min_cluster_size=5, # setting this really small because some questions don't have many responses + min_samples=1, + metric="euclidean", + cluster_selection_method="eom", + prediction_data=True, +) + +vectorizer_model = CountVectorizer( + stop_words="english", + min_df=1, + max_df=0.85, + ngram_range=(1, 3), +) + +# KeyBERT +keybert_model = KeyBERTInspired() + +# MMR +mmr_model = MaximalMarginalRelevance(diversity=0.3) + + +# All representation models +representation_model = { + "KeyBERT": keybert_model, + # "OpenAI": openai_model, # Uncomment if you will use OpenAI + "MMR": mmr_model, + # "POS": pos_model, +} + +topic_model = BERTopic( + # Pipeline models + embedding_model="sentence-transformers/all-MiniLM-L6-v2", + umap_model=umap_model, + hdbscan_model=hdbscan_model, + vectorizer_model=vectorizer_model, + representation_model=representation_model, + # Hyperparameters + top_n_words=10, + verbose=True, + calculate_probabilities=True, +) + + +class NameDescription(BaseModel): + """ + A model representing a named group of documents, including a brief description. + + Attributes: + name (str): A concise, informative name for this group of documents. + description (str): A detailed description providing context for the group of documents. + """ + + name: str = Field(description="Informative name for this group of documents") + description: str = Field(description="Description of this group of documents") + + +prompt = """ + I have data for some interviews where users were asked about their knowledge of + and opinions on different home heating options. In the interview, users were asked about their knowledge + of the Boiler Upgrade Scheme, a scheme that provides a subsidy to homeowners wishing to install a heatpump + instead of getting a new gas boiler for their home. + \n + I have clustered user responses to the question {question}. + \n + One of the clusters contains the following user responses: + {docs} + The cluster is described by the following keywords: {keywords} + \n + Based on the information above, please provide a name and summary for the cluster as a JSON object with two fields: + - name: A short, informative name for the cluster + - description: A summary of views of users within the cluster. You can include sentiments they express, reasons for their views, their knowledge levels, and any other relevant information. + \n + Provide nothing except for this JSON dict. + \n + Example: + {{ + "name": "Radiator upgrades", + "description": "This cluster contains users who mention changing their radiators when questioned about home upgrades they would consider. Some express reluctance to alter the appearance of their home with larger radiators." + }} + """ + +parser = JsonOutputParser(pydantic_object=NameDescription) + +final_prompt = PromptTemplate( + template=prompt, + input_variables=["question", "docs", "keywords"], + partial_variables={"format_instructions": parser.get_format_instructions()}, +) + +model = "llama3.2" + +ollama_model = ChatOllama(model=model, temperature=0) + +llm_chain = final_prompt | ollama_model | parser + +OUTPUT_DIR = PROJECT_DIR / "outputs/by_question" +OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + +def name_topics( + topic_info: pd.DataFrame, + llm_chain: Any, + topics: List[str], + text_col: str = "text_clean", + top_words_col: str = "Top Words", + topic_label_col: str = "Cluster", + question: str = "", +) -> Dict[str, Dict[str, str]]: + """ + Generate names and descriptions for each topic by invoking an LLM chain, + using representative text and keywords for each topic. + + Args: + topic_info (pd.DataFrame): A DataFrame containing information about topics, + with columns for text samples, top words, and topic labels. + llm_chain (Any): A language model chain with an `invoke` method that accepts + a dictionary containing 'question', 'docs', and 'keywords'. + topics (List[str]): A list of topic identifiers to process. + text_col (str, optional): The name of the column in `topic_info` containing + text data for each topic. Defaults to "text_clean". + top_words_col (str, optional): The name of the column in `topic_info` containing + the top words for each topic. Defaults to "Top Words". + topic_label_col (str, optional): The name of the column in `topic_info` that + indicates topic labels. Defaults to "Cluster". + question (str, optional): A question to provide context to the language model + when generating names and descriptions. Defaults to an empty string. + + Returns: + Dict[str, Dict[str, str]]: A dictionary where each key is a topic identifier + and each value is a dictionary containing 'name' and 'description' for that topic. + + Example: + >>> name_topics(topic_info=df, llm_chain=my_llm_chain, topics=["Topic 1", "Topic 2"], question="What is this topic about?") + { + "Topic 1": {"name": "Customer Satisfaction", "description": "Documents discussing customer feedback and satisfaction."}, + "Topic 2": {"name": "Product Quality", "description": "Documents focusing on product durability and performance."} + } + """ + results = {} + + for topic in topics: + logger.info(f"Processing topic {topic}") + temp_df = topic_info[topic_info[topic_label_col] == topic] + docs = temp_df[text_col].values[0] + logger.info(f"Docs: {docs}") + keywords = temp_df[top_words_col].values[0] + logger.info(f"Keywords: {keywords}") + + try: + output = llm_chain.invoke({"question": question, "docs": docs, "keywords": keywords}) + print(output["name"], output["description"]) + results[topic] = output + + except Exception as e: + logger.error(f"Error processing topic {topic}: {str(e)}") + # errors.append({"topic": topic, "error": str(e)}) + + return results + + +if __name__ == "__main__": + data = pd.read_csv(PROJECT_DIR / "data/user_messages_min_len_9_w_sentiment.csv") + + data_dict = {} + + for q in data["q_number"].unique().tolist(): + df = data.copy() + data_dict[q] = df[df["q_number"] == q] + + full_dfs = {} + + summary_dfs = {} + + for key in [0, 1, 4, 6, 7, 9]: # these questions are the ones with >100 data points in each + temp_df = data_dict[key] + + question = temp_df["question"].unique()[0] + + temp_df["text_clean"] = temp_df["text_clean"].astype(str) + docs = temp_df["text_clean"].tolist() + embeddings = SENTENCE_MODEL.encode(docs, show_progress_bar=True) + + topics, probs = topic_model.fit_transform(docs, embeddings) + + rep_docs = topic_model.get_representative_docs() + + umap_2d = UMAP(random_state=RANDOM_SEED, n_components=2) + embeddings_2d = umap_2d.fit_transform(embeddings) + + topic_lookup = topic_model.get_topic_info()[["Topic", "Name"]] + + temp_df["x"] = embeddings_2d[:, 0] + temp_df["y"] = embeddings_2d[:, 1] + temp_df["topic"] = topics + temp_df["doc"] = docs + df_vis = temp_df.merge(topic_lookup, left_on="topic", right_on="Topic", how="left") + + df_for_summarisation = pd.DataFrame(topic_model.get_topic_info()) + df_for_summarisation = df_for_summarisation[df_for_summarisation["Topic"] != -1] + + topic_list = df_for_summarisation["Topic"].to_list() + + results = name_topics( + df_for_summarisation, + llm_chain, + topic_list, + text_col="Representative_Docs", + top_words_col="Representation", + topic_label_col="Topic", + question=temp_df["question"].unique()[0], + ) + + df_for_summarisation["name"] = df_for_summarisation["Topic"].map( + lambda x: results[x]["name"] + if x in results and isinstance(results[x], dict) and "name" in results[x] + else None + ) + df_for_summarisation["description"] = df_for_summarisation["Topic"].map( + lambda x: results[x]["description"] + if x in results and isinstance(results[x], dict) and "description" in results[x] + else None + ) + df_for_summarisation["question"] = question + summary_dfs[key] = df_for_summarisation + + df_vis = df_vis.merge( + df_for_summarisation[["Topic", "name", "description"]], left_on="Topic", right_on="Topic", how="left" + ) + full_dfs[key] = df_vis + + # save two bar charts per question + for key in full_dfs.keys(): + question = full_dfs[key]["question"].unique()[0] + + df = full_dfs[key] + + name_count_chart = ( + alt.Chart(df) + .mark_bar() + .encode(y=alt.Y("Name:N", title=None), x=alt.X("count()", title="Count")) + .properties( + # title='Count of Values of Name' + ) + ) + + sentiment_proportion_chart = ( + alt.Chart(df) + .mark_bar() + .encode( + y=alt.Y("Name:N", title="Name"), + x=alt.X("count()", stack="normalize", title="Proportion"), + color=alt.Color( + "sentiment:N", + title="Sentiment", + scale=alt.Scale(domain=["Negative", "Neutral", "Positive"], range=["red", "gray", "green"]), + ), + ) + .properties( + # title='Proportion of Sentiment within Name' + ) + ) + + # Combine the charts and add a title + combined_chart = (name_count_chart | sentiment_proportion_chart).properties(title=f"{question}") + combined_chart.save(OUTPUT_DIR / f"{question}_name_sentiment_chart.html") + + excel_file = OUTPUT_DIR / "question_topic_models.xlsx" + with pd.ExcelWriter(excel_file, engine="openpyxl") as writer: + + # Iterate over unique values of 'question' + for key in summary_dfs.keys(): + df = summary_dfs[key] + df = df.explode("Representative_Docs") + + question = df["question"].unique()[0] + + # Save each summary table as a separate sheet in the Excel workbook + sheet_name = question[:30] # Excel sheet names are limited to 31 characters + df[ + ["question", "Topic", "Count", "Name", "Representation", "name", "description", "Representative_Docs"] + ].to_excel(writer, sheet_name=sheet_name, index=False) diff --git a/dsp_interview_transcripts/pipeline/topic_modelling.py b/dsp_interview_transcripts/pipeline/topic_modelling.py index b63ed79..062bf6c 100644 --- a/dsp_interview_transcripts/pipeline/topic_modelling.py +++ b/dsp_interview_transcripts/pipeline/topic_modelling.py @@ -1,141 +1,167 @@ -from bertopic import BERTopic +import random + +from collections import defaultdict + import numpy as np import pandas as pd -import random -from sentence_transformers import SentenceTransformer +import torch from hdbscan import HDBSCAN +from sentence_transformers import SentenceTransformer +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.preprocessing import StandardScaler from umap import UMAP -from sklearn.feature_extraction.text import CountVectorizer -from bertopic.representation import ( - KeyBERTInspired, - MaximalMarginalRelevance, - # OpenAI, - PartOfSpeech, -) from dsp_interview_transcripts import PROJECT_DIR +from dsp_interview_transcripts import logger + # Set random seeds RANDOM_SEED = 42 np.random.seed(RANDOM_SEED) random.seed(RANDOM_SEED) # PyTorch seed (used by SentenceTransformer) -import torch - torch.manual_seed(RANDOM_SEED) SENTENCE_MODEL = SentenceTransformer("all-MiniLM-L6-v2") +DATA_PATH = PROJECT_DIR / "data/user_messages_min_len_9_w_sentiment.csv" +MIN_CLUSTER_SIZE = 20 + +umap_model = UMAP( + n_neighbors=15, + n_components=50, + min_dist=0.1, + metric="cosine", + random_state=RANDOM_SEED, +) + +hdbscan_model = HDBSCAN( + min_cluster_size=MIN_CLUSTER_SIZE, + metric="euclidean", + cluster_selection_method="eom", + prediction_data=True, +) + +vectorizer_model = TfidfVectorizer( + stop_words="english", + min_df=1, + max_df=0.85, + ngram_range=(1, 3), +) + -INTERVIEW_SECTIONS = [f"interview_q_{i}" for i in range(-1, 10) if i != 4] # there were no hits for q4 for some reason -DATA_SOURCES = ["user_messages", - "q_and_a", - ] + INTERVIEW_SECTIONS +def stratified_sample(group: pd.DataFrame, n: int = 10) -> pd.DataFrame: + """ + Generate a stratified sample: get N responses stratified by + 'question' and 'sentiment' values. The dataframe should already contain + just one topic ie one group. + + Args: + group (pd.DataFrame): The DataFrame containing data for 1 topic to sample from. + Must contain 'question' and 'sentiment' columns. + n (int, optional): The number of samples to draw per 'question' and + 'sentiment' combination. If there are fewer than `n` records in a + group, all available records are returned. Defaults to 10. + + Returns: + pd.DataFrame: A new DataFrame containing the stratified sample. + """ + # TODO: filter based on hdbscan probability + # TODO: find docs from centre of the cluster + + # Calculate the sample size for each combination of 'question' + # and 'sentiment' + stratified_sample = group.groupby(["question", "sentiment"]).apply( + lambda x: x.sample(frac=min(1, n / len(group)), random_state=42) + ) + + # Reset the index to tidy up the resulting DataFrame + return stratified_sample.reset_index(drop=True) -MIN_CLUSTER_SIZES = [10, 50, 100] if __name__ == "__main__": - - for source in DATA_SOURCES: - for cluster_size in MIN_CLUSTER_SIZES: - if source in INTERVIEW_SECTIONS and cluster_size>10: - continue - - df = pd.read_csv(PROJECT_DIR / f"data/{source}.csv") - - if source=="user_messages" or source in INTERVIEW_SECTIONS: - text_col = "text_clean" - else: - text_col = "q_and_a" - - umap_model = UMAP( - n_neighbors=15, - n_components=50, - min_dist=0.1, - metric="cosine", - random_state=RANDOM_SEED, - ) - - hdbscan_model = HDBSCAN( - min_cluster_size=cluster_size, - min_samples=1, - metric="euclidean", - cluster_selection_method="eom", - prediction_data=True, - ) - - vectorizer_model = CountVectorizer( - stop_words="english", - min_df=1, - max_df=0.85, - ngram_range=(1, 3), - ) - - # KeyBERT - keybert_model = KeyBERTInspired() - - # MMR - mmr_model = MaximalMarginalRelevance(diversity=0.3) - - # GPT-3.5 - # openai_model = get_openai_model() - - # All representation models - representation_model = { - "KeyBERT": keybert_model, - # "OpenAI": openai_model, # Uncomment if you will use OpenAI - "MMR": mmr_model, - # "POS": pos_model, - } - - topic_model = BERTopic( - # Pipeline models - embedding_model="sentence-transformers/all-MiniLM-L6-v2", - umap_model=umap_model, - hdbscan_model=hdbscan_model, - vectorizer_model=vectorizer_model, - representation_model=representation_model, - # Hyperparameters - top_n_words=10, - verbose=True, - calculate_probabilities=True, - ) - - # Convert NaNs to empty strings - df[text_col] = df[text_col].astype(str) - docs = df[text_col].tolist() - embeddings = SENTENCE_MODEL.encode(docs, show_progress_bar=True) - - topics, probs = topic_model.fit_transform(docs, embeddings) - - # save topics - # save probs - # save topic model - topic_model.save( - PROJECT_DIR / f"outputs/topic_model_{source}_cluster_size_{cluster_size}", - serialization="pytorch", - save_ctfidf=True, - save_embedding_model=SENTENCE_MODEL, - ) - - rep_docs = topic_model.get_representative_docs() - - umap_2d = UMAP(random_state=RANDOM_SEED, n_components=2) - embeddings_2d = umap_2d.fit_transform(embeddings) - - topic_lookup = topic_model.get_topic_info()[["Topic", "Name"]] - - df_vis = pd.DataFrame(embeddings_2d, columns=["x", "y"]) - df_vis["topic"] = topics - df_vis = df_vis.merge(topic_lookup, left_on="topic", right_on="Topic", how="left") - df_vis["doc"] = docs - - df_vis = pd.merge( - df[["uuid","conversation",text_col]], - df_vis, - left_on=text_col, - right_on="doc", - how="outer", - ) - - df_vis.to_csv(PROJECT_DIR / f"outputs/{source}_cluster_size_{cluster_size}_vis.csv", index=False) + user_messages = pd.read_csv(DATA_PATH) + + docs = user_messages["text_clean"].tolist() + logger.info("Embedding user messages...") + embeddings = SENTENCE_MODEL.encode(docs, show_progress_bar=True) + + logger.info("Reducing dimensionality...") + umap_vectors = umap_model.fit_transform(embeddings) + + umap_df = pd.DataFrame(umap_vectors) + umap_df.columns = umap_df.columns.map(str) + user_messages_w_umap = pd.concat([user_messages.reset_index(), umap_df], axis=1) + + umap_vars = [f"{i}" for i in range(50)] + model_vars = umap_vars + + # Normalise the umap vectors + logger.info("Normalising UMAP vectors...") + scaler = StandardScaler() + df_normalized = scaler.fit_transform(user_messages_w_umap[model_vars]) + + logger.info("Fitting HDBSCAN...") + clusters = hdbscan_model.fit_predict(df_normalized) + cluster_probabilities = hdbscan_model.probabilities_ + user_messages_w_umap["label"] = clusters + user_messages_w_umap["probs"] = cluster_probabilities + logger.info(user_messages_w_umap["label"].value_counts()) + + # 2d embeddings for visualisation + logger.info("Creating 2D embeddings for visualisation...") + umap_2d = UMAP(random_state=RANDOM_SEED, n_components=2) + embeddings_2d = umap_2d.fit_transform(embeddings) + + # topic representations + logger.info("Creating tfidf representation...") + cluster_groups = user_messages_w_umap.groupby("label").agg({"text_clean": " ".join}).reset_index() + + tfidf_matrix = vectorizer_model.fit_transform(cluster_groups["text_clean"].to_list()) + + feature_names = vectorizer_model.get_feature_names_out() + + # Create a dictionary to hold top words for each cluster + top_words_per_cluster = defaultdict(list) + + # Number of top words you want to display per cluster + n_top_words = 10 + + # Iterate over each cluster and get top words + for cluster_idx, tfidf_scores in enumerate(tfidf_matrix): + # Get indices of top n words within the cluster + top_word_indices = tfidf_scores.toarray()[0].argsort()[: -n_top_words - 1 : -1] + + # Get the top words corresponding to the top indices + top_words = [feature_names[i] for i in top_word_indices] + + # Append the words to the dictionary + top_words_per_cluster[cluster_groups.iloc[cluster_idx]["label"]] = ", ".join(top_words) + + top_words_df = pd.DataFrame(list(top_words_per_cluster.items()), columns=["Cluster", "Top Words"]) + + user_messages_w_umap_topics = pd.merge( + user_messages_w_umap, top_words_df, left_on="label", right_on="Cluster", how="left" + ) + user_messages_w_umap_topics["x"] = embeddings_2d[:, 0] + user_messages_w_umap_topics["y"] = embeddings_2d[:, 1] + + user_messages_w_umap_topics.to_csv( + PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics.csv", index=False + ) + + # save most representative documents + + filtered_df = user_messages_w_umap_topics[ + (user_messages_w_umap_topics["probs"] >= 0.5) & (user_messages_w_umap_topics["Cluster"] != -1) + ] + + # Group by 'Cluster' and apply the stratified sampling + sampled_texts = filtered_df.groupby("Cluster").apply(stratified_sample).reset_index(drop=True) + + # Keep only the first 10 samples per cluster + sampled_texts = sampled_texts.groupby("Cluster").head(10) + + sampled_texts[ + ["Cluster", "Top Words", "text_clean", "sentiment", "question", "context", "conversation", "uuid", "probs"] + ].to_csv(PROJECT_DIR / "outputs/user_messages_min_len_9_w_sentiment_topics_representative_docs.csv", index=False) diff --git a/dsp_interview_transcripts/utils/data_cleaning.py b/dsp_interview_transcripts/utils/data_cleaning.py index 2f99922..04b4492 100644 --- a/dsp_interview_transcripts/utils/data_cleaning.py +++ b/dsp_interview_transcripts/utils/data_cleaning.py @@ -1,11 +1,72 @@ +import re + +from typing import Union + import emoji import ftfy import pandas as pd -import re -def convert_timestamp(timestamp): +from sentence_transformers import SentenceTransformer +from sklearn.metrics.pairwise import cosine_similarity + + +# Load model +SMALL_MODEL = SentenceTransformer("paraphrase-MiniLM-L3-v2") + +# Embed the target sentence +TARGET_SENTENCE = "Are these instructions clear or do you need any further clarification?" +TARGET_EMBEDDING = SMALL_MODEL.encode([TARGET_SENTENCE]) + + +def remove_preamble( + df: pd.DataFrame, target_embedding: list = TARGET_EMBEDDING, model: SentenceTransformer = SMALL_MODEL +) -> pd.DataFrame: + """Get rid of everything up until the bot asks if the instructions are clear. + + Removes all messages up to and including the first message from the bot that is highly similar + to the target sentence "Are these instructions clear or do you need any further clarification?". + Everything before this question is not considered relevant to our analysis. + + Args: + df (pd.DataFrame): DataFrame containing conversation data with columns 'role', 'text_clean', and 'timestamp_clean'. + target_embedding (list): The embedding of the target sentence used for similarity comparison. + model (SentenceTransformer): The model used to generate embeddings for bot messages. + + Returns: + pd.DataFrame: Filtered DataFrame with messages occurring after the cutoff timestamp. + """ + # Filter BOT messages + bot_messages = df[df["role"] == "BOT"] + + # Embed BOT messages + bot_embeddings = model.encode(bot_messages["text_clean"].tolist()) + + # Calculate cosine similarity + similarities = cosine_similarity(target_embedding, bot_embeddings).flatten() + + # Find the index of the most similar BOT message + most_similar_idx = similarities.argmax() + + # Get the timestamp of that message + cutoff_timestamp = bot_messages.iloc[most_similar_idx]["timestamp_clean"] + + # Filter out messages prior to the cutoff timestamp + return df[df["timestamp_clean"] > cutoff_timestamp] + + +def convert_timestamp(timestamp: str) -> Union[pd.Timestamp, pd.NaT]: + """ + Converts a timestamp string to a pandas Timestamp object, removing any trailing timezone information. + If the timestamp is invalid, returns NaT. + + Args: + timestamp (str): The timestamp string to convert, potentially with timezone information. + + Returns: + Union[pd.Timestamp, pd.NaT]: A pandas Timestamp object if conversion is successful; NaT otherwise. + """ # Remove the daylight saving time '+01:00' and trailing whitespace - cleaned_timestamp = re.sub('\+01[\:]?00$', '', timestamp) + cleaned_timestamp = re.sub("\+01[\:]?00$", "", timestamp) cleaned_timestamp = cleaned_timestamp.rstrip() try: # Convert to datetime @@ -15,21 +76,47 @@ def convert_timestamp(timestamp): print(f"Cannot convert timestamp: {cleaned_timestamp}") return pd.NaT # Return NaT (Not a Time) for invalid timestamps -def fill_text_with_transcript(data_df): + +def fill_text_with_transcript(data_df: pd.DataFrame) -> pd.DataFrame: """ - Fills the 'text' column with the 'transcript' column where 'text' is missing. + Fills missing values in the 'text' column with corresponding values from the 'transcript' column + (some users supplied audio messages that got transcribed). + + Args: + data_df (pd.DataFrame): DataFrame containing 'text' and 'transcript' columns. + + Returns: + pd.DataFrame: Updated DataFrame with missing 'text' values filled from 'transcript'. """ data_df = data_df.assign(text=lambda x: x["text"].fillna(x["transcript"])) return data_df.fillna({"text": ""}) -def add_text_length(data_df, text_col='text_clean'): + +def add_text_length(data_df: pd.DataFrame, text_col: str = "text_clean") -> pd.DataFrame: """ - Adds a new column 'text_length' with the length of the 'text' column. + Adds a 'text_length' column to the DataFrame, representing the number of words in the text column. + + Args: + data_df (pd.DataFrame): DataFrame containing a text column. + text_col (str): The name of the column containing text data (default is 'text_clean'). + + Returns: + pd.DataFrame: Updated DataFrame with a new 'text_length' column. """ data_df = data_df.assign(text_length=lambda x: x[text_col].apply(lambda text: len(text.split()))) return data_df -def replace_punct(text): + +def replace_punct(text: str) -> str: + """ + Replaces or removes specific punctuation characters. + + Args: + text (str): The text string to process. + + Returns: + str: The cleaned text with replacements made. + """ text = ( text.replace("&", "and") .replace("\xa0", " ") @@ -41,19 +128,26 @@ def replace_punct(text): return text.strip() -def clean_data(data_df): + +def clean_data(data_df: pd.DataFrame) -> pd.DataFrame: """ Pulls together all the previous cleaning steps + + Args: + data_df (pd.DataFrame): DataFrame containing conversation data, with at least 'text' and 'text_clean' columns. + + Returns: + pd.DataFrame: Cleaned DataFrame with processed text. """ data_df = fill_text_with_transcript(data_df) - + # Fix improperly coded characters - data_df['text_clean'] = data_df['text'].apply(lambda x: ftfy.fix_text(x)) - + data_df["text_clean"] = data_df["text"].apply(lambda x: ftfy.fix_text(x)) + # Remove emojis - data_df['text_clean'] = data_df['text_clean'].apply(lambda x: emoji.demojize(x)) - + data_df["text_clean"] = data_df["text_clean"].apply(lambda x: emoji.demojize(x)) + # Replace punctuation - data_df['text_clean'] = data_df['text_clean'].apply(replace_punct) - + data_df["text_clean"] = data_df["text_clean"].apply(replace_punct) + return data_df diff --git a/dsp_interview_transcripts/utils/topic_modelling_utils.py b/dsp_interview_transcripts/utils/topic_modelling_utils.py deleted file mode 100644 index 19007e3..0000000 --- a/dsp_interview_transcripts/utils/topic_modelling_utils.py +++ /dev/null @@ -1,22 +0,0 @@ -import pandas as pd -from typing import List - -def concat_topics_probabilities( - input_df: pd.DataFrame, topics: List[str], docs, probs, axis=1 -): - 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"T", # flake8 print errors (needs plugin installed) - "D", # flake8 doscstrings errors (needs plugin installed) - "B950", # Line too long. It considers "max-line-length" but only triggers when exceeded by more than 10%. -] -ignore = [ # Choose the flake8 errors to ignore globally here - "E501", # Line too long (using B950 instead, which has 10% tolerance) - "D107", # Missing docstring in __init__ - "D202", # No blank lines allowed after function docstring - "D400", # First line should end with a period - "D100", # Missing docstring in public module - "D104", # Missing docstring in public package - "ANN003", # Missing type annotation for **kwargs - "ANN002", # Missing type annotation for **args - "ANN1", # Missing type annotation for self in methot or cls method - "W503", # Line break occurred before a binary operator - "E203", # Whitespace before ':' -] -per-file-ignores = [ # Choose the flake8 errors to ignore per file here - "*/__init__.py:F401", # Ignore imported but unused in __init__.py files - "tests/*:ANN,D", # Ignore Docstring and annotations on tests -] -exclude = [ - ".venv/*", - ".vscode/*", -] -# See other flake8 extensions here: https://github.com/DmytroLitvinov/awesome-flake8-extensions - - [tool.bandit] skips = [ # Choose the bandit errors to ignore globally "B101", # Use of assert detected. The enclosed code will be removed when compiling to optimised byte code. diff --git a/tests/test.py b/tests/test.py new file mode 100644 index 0000000..4bf2972 --- /dev/null +++ b/tests/test.py @@ -0,0 +1,158 @@ +from unittest.mock import patch + +import pandas as pd +import pytest + +from dsp_interview_transcripts.pipeline.process_data import concatenate_consecutive_roles +from dsp_interview_transcripts.pipeline.process_data import create_context +from dsp_interview_transcripts.pipeline.process_data import get_best_matches +from dsp_interview_transcripts.pipeline.process_data import get_sentiment +from dsp_interview_transcripts.pipeline.process_data import match_questions +from dsp_interview_transcripts.pipeline.process_data import process_bot_qs + + +@pytest.fixture +def mock_dataframe(): + """Fixture for a mock DataFrame simulating a conversation structure.""" + data = { + "conversation": [1, 1, 1, 2, 2], + "text_clean": ["Hi", "Hello", "How are you?", "Good morning", "Good night"], + "role": ["USER", "USER", "BOT", "USER", "BOT"], + "timestamp": [ + "2021-01-01 10:00:00", + "2021-01-01 10:01:00", + "2021-01-01 10:02:00", + "2021-01-02 10:00:00", + "2021-01-02 10:01:00", + ], + "uuid": [1, 2, 3, 4, 5], + } + return pd.DataFrame(data) + + +@pytest.fixture +def mock_questions(): + return ["How are you?", "Good morning?", "Good night?"] + + +def test_concatenate_consecutive_roles(): + # Sample made-up conversation data + df = pd.DataFrame( + { + "uuid": ["uuid1", "uuid2", "uuid3", "uuid4", "uuid5", "uuid6", "uuid7", "uuid8", "uuid9", "uuid10"], + "timestamp": [ + "2024-05-01 10:00:00", + "2024-05-01 10:01:00", + "2024-05-01 10:02:00", + "2024-05-01 10:03:00", + "2024-05-01 10:04:00", + "2024-05-01 10:05:00", + "2024-05-01 10:06:00", + "2024-05-01 10:07:00", + "2024-05-01 10:08:00", + "2024-05-01 10:09:00", + ], + "conversation": ["conv1", "conv1", "conv1", "conv1", "conv1", "conv1", "conv1", "conv1", "conv1", "conv1"], + "role": ["USER", "USER", "BOT", "BOT", "USER", "BOT", "USER", "USER", "BOT", "USER"], + "text_clean": [ + "Hi!", + "Can you help me?", + "Sure, how can I assist?", + "Do you need more details?", + "Yes, I need help with my account.", + "What exactly seems to be the issue?", + "I forgot my password.", + "Also, I can't access my email.", + "Let me help you with that.", + "Thank you!", + ], + } + ) + + # Expected result after concatenating consecutive roles + expected_df = pd.DataFrame( + { + "uuid": ["uuid1", "uuid3", "uuid5", "uuid6", "uuid7", "uuid9", "uuid10"], + "timestamp": [ + "2024-05-01 10:00:00", + "2024-05-01 10:02:00", + "2024-05-01 10:04:00", + "2024-05-01 10:05:00", + "2024-05-01 10:06:00", + "2024-05-01 10:08:00", + "2024-05-01 10:09:00", + ], + "conversation": ["conv1", "conv1", "conv1", "conv1", "conv1", "conv1", "conv1"], + "role": ["USER", "BOT", "USER", "BOT", "USER", "BOT", "USER"], + "text_clean": [ + "Hi! Can you help me?", + "Sure, how can I assist? Do you need more details?", + "Yes, I need help with my account.", + "What exactly seems to be the issue?", + "I forgot my password. Also, I can't access my email.", + "Let me help you with that.", + "Thank you!", + ], + } + )[["conversation", "timestamp", "text_clean", "role", "uuid"]] + + # Call the function to test + result_df = concatenate_consecutive_roles(df) + print(result_df) + + # Assert that the result matches the expected dataframe + pd.testing.assert_frame_equal(result_df, expected_df) + + +def test_process_bot_qs(mock_dataframe): + expected_bot_qs_list = ["How are you?", "Good night"] + bot_qs_list, bot_qs_df = process_bot_qs(mock_dataframe) + assert bot_qs_list == expected_bot_qs_list, "Bot question list should match expected sentences" + # Check that the sentences are exploded correctly + assert bot_qs_df["sentences"].tolist() == expected_bot_qs_list, "Exploded sentences should match expected values" + + +@patch("dsp_interview_transcripts.pipeline.process_data.SENTENCE_MODEL.encode") +def test_match_questions(mock_encode, mock_questions): + # Simulate encoding outputs + mock_encode.side_effect = lambda x: [[1, 0], [0, 1], [0.5, 0.5]] if x == mock_questions else [[1, 0], [0.5, 0.5]] + bot_qs_list = ["How are you?", "Good night"] + + expected_matches = pd.DataFrame( + { + "bot_q": ["How are you?", "Good night"], + "question": ["How are you?", "Good night?"], + "cosine_similarity": [1.0, 1.0], + } + ) + + final_matches = match_questions(bot_qs_list, mock_questions, threshold=0.8) + print(final_matches) + pd.testing.assert_frame_equal(final_matches.reset_index(drop=True), expected_matches) + + +def test_get_best_matches(mock_dataframe, mock_questions): + # Mock the results of previous steps + bot_qs_list, bot_qs_df = process_bot_qs(mock_dataframe) + final_matches = pd.DataFrame( + { + "bot_q": ["How are you?", "Good night"], + "question": ["How are you?", "Good night?"], + "cosine_similarity": [1.0, 1.0], + } + ) + questions_df = pd.DataFrame(enumerate(mock_questions), columns=["q_number", "question"]) + + # Expected output DataFrame + expected_data = { + "conversation": [1, 2], + "text_clean": ["How are you?", "Good night"], + "role": ["BOT", "BOT"], + "sentences": ["How are you?", "Good night"], + "question": ["How are you?", "Good night?"], + "cosine_similarity": [1.0, 1.0], + } + expected_df = pd.DataFrame(expected_data) + + best_matches = get_best_matches(bot_qs_df, final_matches, questions_df) + pd.testing.assert_frame_equal(best_matches[expected_df.columns], expected_df) diff --git a/tests/test_concatenate_consecutive_roles.py b/tests/test_concatenate_consecutive_roles.py deleted file mode 100644 index 25b5f03..0000000 --- a/tests/test_concatenate_consecutive_roles.py +++ /dev/null @@ -1,82 +0,0 @@ -import pandas as pd -import pytest - -from dsp_interview_transcripts.pipeline.process_data import concatenate_consecutive_roles - - -def test_concatenate_consecutive_roles(): - # Sample made-up conversation data - df = pd.DataFrame({ - 'uuid': [ - 'uuid1', 'uuid2', 'uuid3', 'uuid4', - 'uuid5', 'uuid6', 'uuid7', 'uuid8', - 'uuid9', 'uuid10' - ], - 'timestamp': [ - '2024-05-01 10:00:00', '2024-05-01 10:01:00', '2024-05-01 10:02:00', - '2024-05-01 10:03:00', '2024-05-01 10:04:00', '2024-05-01 10:05:00', - '2024-05-01 10:06:00', '2024-05-01 10:07:00', '2024-05-01 10:08:00', - '2024-05-01 10:09:00' - ], - 'conversation': [ - 'conv1', 'conv1', 'conv1', 'conv1', - 'conv1', 'conv1', 'conv1', 'conv1', - 'conv1', 'conv1' - ], - 'role': [ - 'USER', 'USER', 'BOT', 'BOT', - 'USER', 'BOT', 'USER', 'USER', - 'BOT', 'USER' - ], - 'text_clean': [ - "Hi!", - "Can you help me?", - "Sure, how can I assist?", - "Do you need more details?", - "Yes, I need help with my account.", - "What exactly seems to be the issue?", - "I forgot my password.", - "Also, I can't access my email.", - "Let me help you with that.", "Thank you!" - ] - }) - - # Expected result after concatenating consecutive roles - expected_df = pd.DataFrame({ - 'uuid': [ - 'uuid1', 'uuid3', 'uuid5', 'uuid6', - 'uuid7', 'uuid9', 'uuid10' - ], - 'timestamp': [ - '2024-05-01 10:00:00', '2024-05-01 10:02:00', - '2024-05-01 10:04:00', '2024-05-01 10:05:00', - '2024-05-01 10:06:00', '2024-05-01 10:08:00', - '2024-05-01 10:09:00' - ], - 'conversation': [ - 'conv1', 'conv1', 'conv1', 'conv1', - 'conv1', 'conv1', - 'conv1' - ], - 'role': [ - 'USER', 'BOT', 'USER', 'BOT', - 'USER', 'BOT', 'USER' - ], - 'text_clean': [ - "Hi! Can you help me?", - "Sure, how can I assist? Do you need more details?", - "Yes, I need help with my account.", - "What exactly seems to be the issue?", - "I forgot my password. Also, I can't access my email.", - "Let me help you with that.", - "Thank you!" - - ] - })[['conversation', 'timestamp', 'text_clean', 'role', 'uuid']] - - # Call the function to test - result_df = concatenate_consecutive_roles(df) - print(result_df) - - # Assert that the result matches the expected dataframe - pd.testing.assert_frame_equal(result_df, expected_df) diff --git a/tests/test_create_q_and_a_column.py b/tests/test_create_q_and_a_column.py deleted file mode 100644 index 7a05399..0000000 --- a/tests/test_create_q_and_a_column.py +++ /dev/null @@ -1,79 +0,0 @@ -import pandas as pd -import pytest - -from dsp_interview_transcripts.pipeline.process_data import create_q_and_a_column, concatenate_consecutive_roles - - -@pytest.fixture -def sample_df(): - return pd.DataFrame({ - 'conversation': [1, 1, 1, 2, 2, 2], - 'uuid': [1, 2, 3, 4, 5, 6], - 'timestamp': ['2024-05-01 10:00:00', - '2024-05-01 10:01:00', - '2024-05-01 10:02:00', - '2024-05-01 10:03:00', - '2024-05-01 10:04:00', - '2024-05-01 10:05:00'], - 'role': ['BOT', 'USER', 'USER', 'BOT', 'USER', 'USER'], - 'text_clean': [ - "Hello, I am a bot", - "Hello", - "Are you going to ask me questions?", - "Hello, I am still a bot", - "Hi, I am human", - "Do you have questions for me?" - ] - }) - -def test_incorrect_behavior_with_consecutive_roles(sample_df): - """Test that the function performs incorrectly with consecutive BOT or USER messages.""" - # Call the original function without preprocessing - result_df = create_q_and_a_column(sample_df, text_col='text_clean', conversation_col='conversation') - - print(result_df['q_and_a'].tolist()) - # Expected incorrect output: Bot message followed by incorrect user messages - incorrect_expected_q_and_a = [ - '', # Bot message has no Q&A - 'Hello, I am a bot\nHello', # First user response - 'Hello, I am a bot\nAre you going to ask me questions?', # Second user response - '', # New conversation, first bot message - "Hello, I am still a bot\nHi, I am human", # First user response in new conversation - 'Hello, I am still a bot\nDo you have questions for me?' # Second user response in new conversation - ] - - # The function should incorrectly process consecutive user messages without proper reset - assert result_df['q_and_a'].tolist() == incorrect_expected_q_and_a, "The function incorrectly processed consecutive USER messages." - -def test_correct_behavior_after_preprocessing(sample_df): - """Test that the function works correctly after ensuring alternating BOT and USER roles.""" - - preprocessed_df = concatenate_consecutive_roles(sample_df) - - # Call the original function on the preprocessed DataFrame - result_df = create_q_and_a_column(preprocessed_df, text_col='text_clean', conversation_col='conversation') - print(result_df['q_and_a'].tolist()) - - # Expected correct output after preprocessing - expected_q_and_a = [ - '', # Bot message has no Q&A - 'Hello, I am a bot\nHello Are you going to ask me questions?', - '', # New conversation, first bot message - "Hello, I am still a bot\nHi, I am human Do you have questions for me?", # First user response in new conversation - ] - - # Check that the 'q_and_a' column was created and matches expected output after preprocessing - assert 'q_and_a' in result_df.columns - assert result_df['q_and_a'].tolist() == expected_q_and_a, "The function processed the Q&A correctly after preprocessing." - -def test_empty_bot_text_reset_after_preprocessing(sample_df): - """Test that the bot text resets at the end of each conversation after preprocessing.""" - # Preprocess the DataFrame to ensure alternating BOT and USER roles - preprocessed_df = concatenate_consecutive_roles(sample_df) - - # Call the function after preprocessing - result_df = create_q_and_a_column(preprocessed_df, text_col='text_clean', conversation_col='conversation') - print(result_df['q_and_a'].tolist()) - - # Ensure bot message was reset after conversation 1 ends - assert result_df.loc[2, 'q_and_a'] == '', "The bot text was reset correctly after conversation 1."