Clustering for Transaction Classification

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Colin Jarvis, Ted Sanders
Oct 20, 2022
Open in Github

This notebook covers use cases where your data is unlabelled but has features that can be used to cluster them into meaningful categories. The challenge with clustering is making the features that make those clusters stand out human-readable, and that is where we'll look to use GPT-3 to generate meaningful cluster descriptions for us. We can then use these to apply labels to a previously unlabelled dataset.

To feed the model we use embeddings created using the approach displayed in the notebook Multiclass classification for transactions Notebook, applied to the full 359 transactions in the dataset to give us a bigger pool for learning

# imports
import openai
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE
import matplotlib
import matplotlib.pyplot as plt
import os
from ast import literal_eval

openai.api_key = os.getenv("OPENAI_API_KEY")
COMPLETIONS_MODEL = "text-davinci-003"

# This path leads to a file with data and precomputed embeddings
embedding_path = "data/library_transactions_with_embeddings_359.csv"

Clustering

We'll reuse the approach from the Clustering Notebook, using K-Means to cluster our dataset using the feature embeddings we created previously. We'll then use the Completions endpoint to generate cluster descriptions for us and judge their effectiveness

df = pd.read_csv(embedding_path)
embedding_df = pd.read_csv(embedding_path)
embedding_df["embedding"] = embedding_df.embedding.apply(literal_eval).apply(np.array)
matrix = np.vstack(embedding_df.embedding.values)
matrix.shape
(359, 1536)
n_clusters = 5

kmeans = KMeans(n_clusters=n_clusters, init="k-means++", random_state=42)
kmeans.fit(matrix)
labels = kmeans.labels_
embedding_df["Cluster"] = labels
/Users/ted/.virtualenvs/openai/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  warnings.warn(

tsne = TSNE(
    n_components=2, perplexity=15, random_state=42, init="random", learning_rate=200
)
vis_dims2 = tsne.fit_transform(matrix)

x = [x for x, y in vis_dims2]
y = [y for x, y in vis_dims2]

for category, color in enumerate(["purple", "green", "red", "blue","yellow"]):
    xs = np.array(x)[embedding_df.Cluster == category]
    ys = np.array(y)[embedding_df.Cluster == category]
    plt.scatter(xs, ys, color=color, alpha=0.3)

    avg_x = xs.mean()
    avg_y = ys.mean()

    plt.scatter(avg_x, avg_y, marker="x", color=color, s=100)
plt.title("Clusters identified visualized in language 2d using t-SNE")

Text(0.5, 1.0, 'Clusters identified visualized in language 2d using t-SNE')
image generated by notebook
# We'll read 10 transactions per cluster as we're expecting some variation
transactions_per_cluster = 10

for i in range(n_clusters):
    print(f"Cluster {i} Theme:", end=" ")

    transactions = "\n".join(
        embedding_df[embedding_df.Cluster == i]
        .combined.str.replace("Supplier: ", "")
        .str.replace("Description: ", ":  ")
        .str.replace("Value: ", ":  ")
        .sample(transactions_per_cluster, random_state=42)
        .values
    )
    response = openai.Completion.create(
        model=COMPLETIONS_MODEL,
        # We'll include a prompt to instruct the model what sort of description we're looking for
        prompt=f'''We want to group these transactions into meaningful clusters so we can target the areas we are spending the most money. 
                What do the following transactions have in common?\n\nTransactions:\n"""\n{transactions}\n"""\n\nTheme:''',
        temperature=0,
        max_tokens=100,
        top_p=1,
        frequency_penalty=0,
        presence_penalty=0,
    )
    print(response["choices"][0]["text"].replace("\n", ""))

    sample_cluster_rows = embedding_df[embedding_df.Cluster == i].sample(transactions_per_cluster, random_state=42)
    for j in range(transactions_per_cluster):
        print(sample_cluster_rows.Supplier.values[j], end=", ")
        print(sample_cluster_rows.Description.values[j], end="\n   ")
        #print(str(sample_cluster_rows.Value).values[j])

    print("-" * 100)

Cluster 0 Theme:  All of the transactions have to do with legal deposit services from the Agency for Legal Deposit Libraries (ALDL).
ALDL, ALDL Charges
   Agency for Legal Deposit Libaries, Agency services
   ALDL, Legal Deposit Services
   ALDL, ALDL Charges
   ALDL, Legal deposit services
   British Library, Legal Deposit Services
   ALDL, Legal deposit materials April to July 22
   ALDL, Jan20-Mar 20 charge from Agency for Legal Deposit Libraries
   ALDL, ALDL Charges
   ALDL, Legal Deposit Services
   ----------------------------------------------------------------------------------------------------
Cluster 1 Theme:  These transactions all involve the purchase of goods or services from various vendors.
GARDINER AND THEOBALD LLP, GB Feasibility Study
   BSI, Annual subscription
   EX LIBRIS, IT equipment
   Private Sale, Literary & Archival Items
   XMA SCOTLAND LTD, 2 invoices one for the replacement of obsolete hardware and the other for a new laptop
   JISC SERVICES LTD SUBSCRIPTION ACCOUNT, Annual Subscription
   EDF Energy, Utilities
   JISC Services Ltd, Managed router service charge annual subscription 01/04/22 to 31/03/23
   RICK GEKOSKI, Papers 1970's to 2019 Alisdair Gray
   Trustmarque, Miscrosoft Software Licenses
   ----------------------------------------------------------------------------------------------------
Cluster 2 Theme:  All of these transactions are related to ECG Facilities Service, and involve payments for facility management fees, inspections and maintenance, maintenance contracts, electrical and mechanical works, boiler replacements, USP batteries, gutter works, and cleaning of pigeon fouling.
ECG FACILITIES SERVICE, This payment covers multiple invoices for facility management fees
   ECG FACILITIES SERVICE, Facilities Management Charge
   ECG FACILITIES SERVICE, Inspection and Maintenance of all Library properties
   ECG Facilities Service, Facilities Management Charge
   ECG FACILITIES SERVICE, Maintenance contract - October
   ECG FACILITIES SERVICE, Electrical and mechanical works
   ECG FACILITIES SERVICE, This payment covers multiple invoices for facility management fees
   ECG FACILITIES SERVICE, CB Bolier Replacement (1),USP Batteries,Gutter Works & Cleaning of pigeon fouling
   ECG Facilities Service, Facilities Management Charge
   ECG Facilities Service, Facilities Management Charge
   ----------------------------------------------------------------------------------------------------
Cluster 3 Theme:  All of these transactions are related to Kelvin Hall and involve spending money on services or goods.
CBRE, Kelvin Hall
   GLASGOW CITY COUNCIL, Kelvin Hall
   University Of Glasgow, Kelvin Hall
   GLASGOW LIFE, Oct 20 to Dec 20 service charge - Kelvin Hall
   Computacenter Uk, Kelvin Hall
   XMA Scotland Ltd, Kelvin Hall
   GLASGOW LIFE, Service Charges Kelvin Hall 01/07/19-30/09/19
   Glasgow Life, Kelvin Hall Service Charges
   Glasgow City Council, Kelvin Hall
   GLASGOW LIFE, Quarterly service charge KH
   ----------------------------------------------------------------------------------------------------
Cluster 4 Theme:  These transactions all involve construction and refurbishment work.
M & J Ballantyne Ltd, George IV Bridge Work
   John Graham Construction Ltd, Causewayside Refurbishment
   John Graham Construction Ltd, Causewayside Refurbishment
   John Graham Construction Ltd, Causewayside Refurbishment
   John Graham Construction Ltd, Causewayside Refurbishment
   ARTHUR MCKAY BUILDING SERVICES, Causewayside Work
   John Graham Construction Ltd, Causewayside Refurbishment
   Morris & Spottiswood Ltd, George IV Bridge Work
   ECG FACILITIES SERVICE, Causewayside IT Work
   John Graham Construction Ltd, Causewayside Refurbishment
   ----------------------------------------------------------------------------------------------------

Conclusion

We now have five new clusters that we can use to describe our data. Looking at the visualisation some of our clusters have some overlap and we'll need some tuning to get to the right place, but already we can see that GPT-3 has made some effective inferences. In particular, it picked up that items including legal deposits were related to literature archival, which is true but the model was given no clues on. Very cool, and with some tuning we can create a base set of clusters that we can then use with a multiclass classifier to generalise to other transactional datasets we might use.