The model.build() method requires data to train and evaluate the machine learning model. You can provide this data in two main ways:

  1. List of Pandas DataFrames: The simplest way for tabular data.
  2. List of plexe.DatasetGenerator objects: Useful for generating synthetic data or augmenting existing datasets.

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Using Pandas DataFrames

If your data is already loaded into Pandas DataFrames, pass a list containing these DataFrames to the datasets argument of model.build().

import plexe
import pandas as pd

# Load your data into DataFrames
try:
    train_df = pd.read_csv("train_data.csv")
    # val_df = pd.read_csv("validation_data.csv") # Optional validation set
except FileNotFoundError:
    print("Warning: Data files not found, using dummy data.")
    train_df = pd.DataFrame({
        'feature1': [1, 2, 3, 4, 5], 'feature2': [10, 12, 11, 14, 13], 'target': [0, 1, 0, 1, 0]
    })
    # val_df = pd.DataFrame({ # Dummy validation data
    #    'feature1': [6, 7], 'feature2': [15, 16], 'target': [1, 0]
    # })


# --- Define Model ---
model = plexe.Model(
    intent="Classify target based on feature1 and feature2."
    # Schemas might be inferred if not provided explicitly
)
# --------------------

# Provide the DataFrame(s) in a list
datasets_to_use = [train_df]
# datasets_to_use = [train_df, val_df] # If you have a separate validation set

print("Building model using Pandas DataFrame(s)...")
model.build(
    datasets=datasets_to_use,
    provider="openai/gpt-4o-mini",
    max_iterations=1
)

print(f"Model build finished. State: {model.get_state()}")

Plexe will internally assign default names (like dataset_0, dataset_1) to these DataFrames and use them during the build process. If schemas are not provided, they will be inferred from these DataFrames.

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Using DatasetGenerator

The DatasetGenerator class allows you to define requirements for a dataset, potentially generating synthetic data or augmenting existing data using an LLM provider.

Use Case 1: Generating purely synthetic data

import plexe
from pydantic import BaseModel

# Define the desired schema for synthetic data
class SyntheticDataSchema(BaseModel):
    user_query: str
    intent_category: str
    sentiment_score: float

# Create a DatasetGenerator instance
synthetic_dataset_gen = plexe.DatasetGenerator(
    description="Generate synthetic user support queries with intent and sentiment.",
    provider="openai/gpt-4o-mini", # LLM used for generation
    schema=SyntheticDataSchema
    # data=None # No existing data provided
)

# Generate synthetic samples (this happens during the build process)
# The 'num_samples' argument inside build tells the generator how many to create.
# Note: The old API had a .generate() method, the new API integrates generation
# directly into the .build() call using the DatasetGenerator object.
# (Actual generation mechanism within build needs confirmation from codebase analysis,
# assuming it uses the generator object passed in datasets)

# --- Define Model ---
model = plexe.Model(
    intent="Classify user query intent and predict sentiment.",
    input_schema={"user_query": str},
    output_schema={"intent_category": str, "sentiment_score": float}
)
# --------------------

print("Building model using DatasetGenerator for synthetic data...")
# Pass the generator object in the list
model.build(
    datasets=[synthetic_dataset_gen], # Pass the generator here
    provider="openai/gpt-4o-mini",
    max_iterations=1
    # Add num_samples if required by the internal build logic using DatasetGenerator
    # e.g., build_args={"synthetic_dataset_gen_0": {"num_samples": 500}} ??? -> Needs clarification from code how generation count is passed
)

print(f"Model build finished. State: {model.get_state()}")

Use Case 2: Augmenting existing data

You can provide an existing DataFrame to DatasetGenerator and potentially use it as a base for generating more samples (although the exact augmentation mechanism within build needs confirmation).

import plexe
import pandas as pd
from pydantic import BaseModel

# --- Load existing data ---
try:
    existing_df = pd.read_csv("partial_data.csv")
except FileNotFoundError:
     print("Warning: Partial data file not found, using dummy data.")
     existing_df = pd.DataFrame({'text': ['good service', 'bad experience'], 'label': [1, 0]})
# --------------------------

# Define schema matching existing data
class TextFeedback(BaseModel):
    text: str
    label: int

# Create generator, providing existing data
augmented_dataset_gen = plexe.DatasetGenerator(
    description="User feedback text for classification, augmenting existing samples.",
    provider="openai/gpt-4o-mini",
    schema=TextFeedback,
    data=existing_df # Provide existing data here
)

# --- Define Model ---
model = plexe.Model(
    intent="Classify user feedback text.",
    input_schema={"text": str},
    output_schema={"label": int}
)
# --------------------

print("Building model using DatasetGenerator with existing data...")
model.build(
    datasets=[augmented_dataset_gen], # Pass the generator
    provider="openai/gpt-4o-mini",
    max_iterations=1
    # Again, clarification needed on how augmentation count is controlled within build.
)

print(f"Model build finished. State: {model.get_state()}")

Choose the method that best fits how your data is structured and whether you need synthetic data generation capabilities.