Automating the Machine Learning Lifecycle

The significance of AutoML arises from the complexities and difficulties associated with traditional machine learning, which often requires skilled data scientists for tasks such as feature engineering, model selection, and hyperparameter tuning. AutoML simplifies the machine learning process by automating these tasks, minimizing human intervention while potentially enhancing model performance. Its benefits include increased productivity, reduced mistakes, democratization of machine learning, and cost and time savings, while also making the machine learning process more transparent and accessible.

Here are the stages in the machine learning lifecycle:

OPTION 1: Using PyCaret

PyCaret, a low-code, open-source machine learning library, accelerates the model creation process, saving time and effort.

Key advantages of PyCaret include:

• Automation: It simplifies and automates complex modeling processes. With minimal code, you can automate tasks such as model selection, preprocessing, evaluation, and hyperparameter tuning.
• Data Preprocessing: PyCaret facilitates handling missing values, encoding categorical variables, scaling features, and performing feature engineering with a variety of options for customization.
• Model Evaluation: Renowned for its extensive model evaluation packages, PyCaret offers tools for performance metrics, confusion matrices, feature importance plots, and more.
• Hyperparameter Tuning: PyCaret’s automated hyperparameter tuning methods, including grid search, random search, and Bayesian optimization, simplify optimizing model performance.
• Reproducibility: It enhances reproducibility by logging all experiments, ensuring that results can be consistently replicated.

Let's dive into the coding process (if you are new to PyCaret, please refer to the link):

1. Install the Libraries:

pip install -qqq ydata-profiling

pip install mlflow

2. Import the Libraries:

import pandas as pd

import numpy as np

from pycaret.regression import *

from datetime import datetime

from sklearn.model_selection import train_test_split

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

from pycaret.datasets import get_data

3. Load a Sample Dataset:

data = get_data('diamond')

4. Data Preparation:

target = 'Price'

X = data.drop(columns=target)

y = data[target]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

5. Set Up the PyCaret Environment:

exp_reg102 = setup(data=data, target='Price', session_id=123, normalize=True,

transformation=True, transform_target=True, remove_multicollinearity=True,

multicollinearity_threshold=0.95, bin_numeric_features=['Carat Weight'],

experiment_name='diamond1')

6. Compare Models:

best_model = compare_models(include=['rf', 'xgboost', 'lightgbm', 'gbr', 'ada'], errors='raise', n_select=3)

7. Tune Hyperparameters:

tuned_model = tune_model(best_model)

8. Make Predictions:

predictions = predict_model(tuned_model)

9. Plot Feature Importance:

plot_model(tuned_model, plot='feature')

10. Save the Model:

final_best = finalize_model(tuned_model)

save_model(final_best, 'diamond-pipeline')

11. Make Predictions with Unseen Data:

unseen_data = pd.read_csv('diamond_new_data.csv', header=0, sep=';')

saved_model = load_model('diamond-pipeline')

new_predictions = predict_model(saved_model, data=unseen_data)

12. Optional ONNX Conversion:

pip install skl2onnx

pip install onnxruntime

from skl2onnx import to_onnx

X_sample = get_config('X_train')[:1]

model_onnx = to_onnx(final_best, X_sample.to_numpy())

OPTION 2: Leveraging LLMs with FLAML

In this section, we will implement a hyperparameter tuning workflow utilizing Large Language Models (LLMs). The focus will be on creating a process that merges LLM guidance with a basic random search approach and evaluating its performance against FLAML, a well-known AutoML tool developed by Microsoft Research.

Let's dive into the coding process:

1. Clone the Repository:

pip install -q "flaml[autogen]" langchain openai tiktoken

2. Import Required Libraries:

import pandas as pd

import numpy as np

from collections import defaultdict

from flaml import tune, AutoML

3. Data Preparation:

train = pd.read_csv('dataset/train_AutoML_probe.csv')

train.rename(columns={'attack_category': 'attack'}, inplace=True)

test = pd.read_csv('dataset/test_AutoML_probe.csv')

test.rename(columns={'attack_category': 'attack'}, inplace=True)

4. Configure the Chatbot:

from langchain.chains import ConversationChain

from langchain.chat_models import ChatOpenAI

memory = ConversationBufferMemory(return_messages=True)

conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm, verbose=False)

5. Suggest Metrics:

prompt = suggest_metrics(report)

response = conversation.predict(input=prompt)

6. Set Initial Search Parameter Range:

prompt = suggest_initial_search_space()

response = conversation.predict(input=prompt)

7. Hyperparameter Tuning:

clf = xgb.XGBClassifier(seed=42, objective='binary:logistic', eval_metric='logloss', n_jobs=-1, use_label_encoder=False)

search = HalvingRandomSearchCV(clf, search_space, scoring='f1', n_candidates=500, cv=5, min_resources='exhaust', factor=3, verbose=1).fit(X_train, y_train)

The second video, "Evaluating the Effectiveness of Large Language Models // Aniket Singh // MLOps Podcast #248," assesses how effectively LLMs can enhance machine learning workflows and their impact on model performance.