Plexe provides robust capabilities for monitoring and interacting with the model building process through its callback system and logging features. These tools give you visibility into what’s happening during model creation and allow you to integrate with external systems.

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The Callback System

Callbacks in Plexe let you hook into key points in the model building lifecycle. They’re useful for:

  • Logging progress and results
  • Integrating with experiment tracking systems
  • Implementing custom monitoring solutions
  • Saving artifacts
  • Event triggering for workflows

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Callback Lifecycle Events

The Callback base class defines four key methods that are triggered at different points:

  1. on_build_start(info): Called once when model.build() begins
  2. on_iteration_start(info): Called at the start of each build iteration
  3. on_iteration_end(info): Called at the end of each build iteration
  4. on_build_end(info): Called once when the entire build process completes

The info parameter provides context about the current state, including:

  • The model’s intent and configuration
  • The current iteration number
  • The datasets being used
  • Current schemas
  • Performance metrics (in on_iteration_end)

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Built-in Callbacks

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Chain of Thought Callback

This callback is automatically added when you set chain_of_thought=True in model.build(). It captures the detailed reasoning steps and decision-making process of the AI agents.

import plexe

model = plexe.Model(intent="Predict customer churn")
model.build(
    datasets=[df],
    chain_of_thought=True  # Enables verbose output
)

The output shows the step-by-step thought process of the agents, including:

  • Problem analysis
  • Solution planning
  • Code development
  • Debugging
  • Evaluation

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MLflow Callback

This integrates with MLflow, a popular platform for tracking ML experiments:

import plexe
from plexe.callbacks import MLFlowCallback

# Set up MLflow callback
mlflow_callback = MLFlowCallback(
    tracking_uri="http://localhost:5000",
    experiment_name="Customer Churn Models"
)

# Build model with MLflow tracking
model = plexe.Model(intent="Predict customer churn")
model.build(
    datasets=[df],
    callbacks=[mlflow_callback]
)

The MLflow callback:

  • Creates runs for each iteration
  • Logs parameters (intent, provider, iteration number)
  • Records metrics (accuracy, RMSE, etc.)
  • Saves artifacts (code, model files)
  • Tags runs with relevant metadata

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Creating Custom Callbacks

You can create your own callbacks by subclassing Callback:

import plexe
from plexe.callbacks import Callback
import time

class TimingCallback(Callback):
    def __init__(self):
        self.start_time = None
        self.iteration_times = {}
        
    def on_build_start(self, info):
        self.start_time = time.time()
        print(f"Build started for model with intent: {info.intent[:50]}...")
        
    def on_iteration_start(self, info):
        iteration = info.iteration
        self.iteration_times[iteration] = time.time()
        print(f"Starting iteration {iteration + 1}")
        
    def on_iteration_end(self, info):
        iteration = info.iteration
        duration = time.time() - self.iteration_times[iteration]
        print(f"Iteration {iteration + 1} completed in {duration:.2f} seconds")
        
        # Check for metrics if available
        if hasattr(info, "node") and info.node and hasattr(info.node, "performance"):
            perf = info.node.performance
            if perf:
                print(f"  Performance: {perf.name} = {perf.value:.4f}")
        
    def on_build_end(self, info):
        total_time = time.time() - self.start_time
        print(f"Build finished in {total_time:.2f} seconds")
        if hasattr(info, "model"):
            print(f"Final model state: {info.model.get_state()}")

Usage:

# Create and use the custom callback
timing_callback = TimingCallback()
model.build(datasets=[df], callbacks=[timing_callback])

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Logging System

In addition to callbacks, Plexe has a built-in logging system for observing the inner workings of the library.

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Configuring Logging

You can configure Plexe’s logging system to adjust verbosity:

import plexe
import logging

# Set up logging with desired verbosity
plexe.configure_logging(level=logging.DEBUG)

For more control over logging format:

import logging
from plexe.config import configure_logging

# Create a custom handler
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))

# Configure with more options
configure_logging(
    level=logging.INFO,
    file="plexe_build.log"  # Optional: also log to a file
)

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Logging Levels

  • DEBUG: Very detailed information, useful for diagnosing problems
  • INFO: Confirmation that things are working as expected
  • WARNING: Indication of potential issues
  • ERROR: Serious problems that prevented operations
  • CRITICAL: Very serious errors

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Log Output Destinations

By default, Plexe logs to the console (stdout), but you can redirect logs:

import logging
from plexe.config import configure_logging

# Configure Plexe logging to a file
configure_logging(
    level=logging.DEBUG,
    file="plexe_build.log"  # Will create a file handler automatically
)

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Integrating Callbacks and Logging

For comprehensive monitoring, combine callbacks and logging:

import plexe
import logging
from plexe.callbacks import MLFlowCallback

# Set up logging
plexe.configure_logging(level=logging.INFO)

# Set up MLflow callback
mlflow_callback = MLFlowCallback(
    tracking_uri="http://localhost:5000",
    experiment_name="Customer Churn Models"
)

# Create custom callback
timing_callback = TimingCallback()

# Build with both systems
model = plexe.Model(intent="Predict customer churn")
model.build(
    datasets=[df],
    callbacks=[mlflow_callback, timing_callback],
    chain_of_thought=True  # Also get agent reasoning steps
)

This approach gives you:

  1. Standard logging for library operations
  2. MLflow integration for experiment tracking
  3. Custom timing logs for performance monitoring
  4. Detailed agent reasoning via chain-of-thought

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Best Practices

  1. Start Simple: Begin with chain_of_thought=True to see what’s happening
  2. Use Callbacks for Integration: Connect Plexe to your existing ML infrastructure
  3. Create Custom Callbacks: Build callbacks tailored to your workflow needs
  4. Adjust Log Levels: Set appropriate verbosity based on your needs
  5. Combine Approaches: Use both logging and callbacks for comprehensive monitoring

By leveraging Plexe’s callback system and logging capabilities, you can gain insights into the model building process, track experiments systematically, and integrate Plexe into your broader ML workflows.