Google BigQuery and Vertex AI are powerful components of Google Cloud’s AI/ML ecosystem and are designed to work seamlessly together to facilitate the entire machine learning lifecycle. Here’s how they integrate and how you can leverage them together:

Key Integration Points and Use Cases:

1. Data Preparation and Feature Engineering (BigQuery to Vertex AI):
   • Data Storage: BigQuery serves as an excellent data warehouse to store and manage the large datasets needed for training ML models in Vertex AI.
   • Data Exploration and Analysis: You can use BigQuery’s SQL capabilities to explore, clean, and analyze your data before training.
   • Feature Engineering: Perform feature engineering directly within BigQuery using SQL or User-Defined Functions (UDFs). This allows you to create the features needed for your models at scale.
   • Exporting Data for Training: Easily query and export prepared feature data from BigQuery to Cloud Storage, which Vertex AI can then access for training. Vertex AI’s managed datasets can directly connect to BigQuery tables.
2. Model Training (Vertex AI using BigQuery Data):
   • Managed Datasets: Vertex AI allows you to create managed datasets directly from BigQuery tables. This simplifies the process of accessing and using your BigQuery data for training AutoML models or custom-trained models.
   • AutoML Training: Train AutoML models (for tabular data, images, text, video) directly on BigQuery tables without writing any training code. Vertex AI handles data splitting, model selection, hyperparameter tuning, and evaluation.
   • Custom Training: When using custom training jobs in Vertex AI (with TensorFlow, PyTorch, scikit-learn, etc.), you can configure your training script to read data directly from BigQuery using the BigQuery Python client library or by staging data in Cloud Storage first.
3. Feature Store (Vertex AI Feature Store with BigQuery):
   • Centralized Feature Management: Vertex AI Feature Store can use BigQuery as its online and offline storage backend. This allows you to:
     • Store and serve features consistently for both training and online/batch inference.
     • Manage feature metadata and track feature lineage.
     • Easily access features prepared in BigQuery for model training in Vertex AI.
4. Model Deployment and Prediction (Vertex AI using Models Trained on BigQuery Data):
   • Deploy Models: Once you’ve trained a model in Vertex AI (whether using AutoML or custom training with BigQuery data), you can deploy it to Vertex AI Endpoints for online or batch predictions.
   • Batch Prediction: Vertex AI Batch Prediction jobs can read input data directly from BigQuery tables and write predictions back to BigQuery tables, making it easy to process large volumes of data.
   • Online Prediction: For real-time predictions, your deployed Vertex AI Endpoint can receive prediction requests. The features used for these predictions can be retrieved from Vertex AI Feature Store (which might be backed by BigQuery).
5. MLOps and Monitoring:
   • Data Monitoring: You can use BigQuery to analyze logs and monitoring data from your deployed Vertex AI models to track performance, detect drift, and troubleshoot issues.
   • Pipeline Orchestration (Vertex AI Pipelines): Vertex AI Pipelines can include steps that interact with BigQuery (e.g., data extraction, feature engineering) and steps that involve model training and deployment in Vertex AI.

Example Workflow:

1. Store raw data in BigQuery.
2. Use BigQuery SQL to explore, clean, and engineer features.
3. Create a Vertex AI Managed Dataset directly from the BigQuery table.
4. Train an AutoML Tabular model in Vertex AI using the Managed Dataset.
5. Deploy the trained model to a Vertex AI Endpoint.
6. For batch predictions, provide input data as a BigQuery table and configure the Vertex AI Batch Prediction job to write results back to BigQuery.
7. Monitor model performance using logs stored and analyzed in BigQuery.

Code Snippet (Conceptual – Python with Vertex AI SDK and BigQuery Client):

Python

from google.cloud import bigquery
from google.cloud import aiplatform

# Initialize BigQuery client
bq_client = bigquery.Client(location="US")  # Adjust location as needed

# Initialize Vertex AI client
aiplatform.init(location="us-central1")  # Adjust location as needed

# --- Data Preparation in BigQuery ---
query = """
SELECT
    feature1,
    feature2,
    target
FROM
    your_project.your_dataset.your_table
WHERE
    split = 'train'
"""
train_table = bq_client.query(query).result().to_dataframe()

# --- Upload data to GCS (if not using Managed Datasets) ---
# from google.cloud import storage
# gcs_client = storage.Client()
# bucket = gcs_client.bucket("your-gcs-bucket")
# blob = bucket.blob("training_data.csv")
# blob.upload_from_string(train_table.to_csv(index=False), "text/csv")
# train_uri = "gs://your-gcs-bucket/training_data.csv"

# --- Create Vertex AI Managed Dataset from BigQuery ---
dataset = aiplatform.TabularDataset.create(
    display_name="your_dataset_name",
    bq_source="bq://your_project.your_dataset.your_table",
)

# --- Train AutoML Tabular Model ---
job = aiplatform.AutoMLTabularTrainingJob(
    display_name="automl_model_training",
    objective_column="target",
)
model = job.run(
    dataset=dataset,
    target_column="target",
    # ... other training parameters
)

# --- Deploy the Model to an Endpoint ---
endpoint = aiplatform.Endpoint.create(
    display_name="your_endpoint_name",
)
deployed_model = endpoint.deploy(model=model)

# --- Get Batch Predictions from BigQuery Data ---
batch_prediction_job = aiplatform.BatchPredictionJob.create(
    display_name="batch_prediction_job",
    model=deployed_model,
    bigquery_source="bq://your_project.your_dataset.prediction_input_table",
    bigquery_destination="bq://your_project.your_dataset.prediction_output_table",
)

print(f"Batch Prediction Job: {batch_prediction_job.name}")

In essence, BigQuery provides the scalable and efficient data foundation for your ML workflows in Vertex AI, while Vertex AI offers the tools and services for building, training, deploying, and managing your models. Their tight integration streamlines the entire process and allows you to leverage the strengths of both platforms.

You can train image classification and object detection models using Vertex AI. Here’s a comprehensive overview of the process:

1. Data Preparation

• Supported Formats: Vertex AI supports common image formats like JPEG, PNG, and TIFF. The maximum file size per image is 30MB for training data and 1.5MB for prediction data.
• Data Quality: Ensure your training data is representative of the data you’ll use for predictions. Consider including variations in angle, resolution, and background.
• Labeling: You’ll need to label your images. For classification, this means assigning categories to each image. For object detection, you’ll need to draw bounding boxes around objects of interest and assign labels to them.
• Dataset Size: Google recommends about 1000 training images per label, with a minimum of 10.
• Data Split: Divide your data into training, validation, and test sets. Vertex AI allows you to control the split ratios.
• Storage: Store your images in Google Cloud Storage (GCS).

2. Training Options

Vertex AI offers two main approaches for image model training:

• AutoML: This option is suitable if you want to train a model with minimal code. Vertex AI handles the model selection, architecture, and hyperparameter tuning automatically.
  • How it works: You upload your labeled image data to Vertex AI Datasets, select AutoML as the training method, and configure training parameters like the training budget (node hours).
  • Model types: AutoML supports image classification (single-label and multi-label) and object detection.
• Custom Training: This option gives you more control and flexibility. You can use any ML framework (TensorFlow, PyTorch, etc.) and customize the training process.
  • How it works: You provide your own training script, which defines the model architecture, training loop, and any custom preprocessing or evaluation steps. You can package your training code into a Docker container.
  • Use cases: Custom training is ideal for complex models, specialized architectures, or when you need fine-grained control over the training process.

3. Training Steps

Here’s a general outline of the steps involved in training an image model on Vertex AI:

1. Create a Dataset: In the Vertex AI section of the Google Cloud Console, create a new Dataset and select the appropriate data type (e.g., “Images”).
2. Import Data: Import your labeled image data from Google Cloud Storage into the Dataset. You can use JSON Lines or CSV files to specify the image paths and labels.
3. Train a Model:
   • AutoML: Select “Train new model” from the Dataset page, choose “AutoML” as the training method, and configure the training job.
   • Custom Training: Create a custom training job, specify your training script or container, and configure the compute resources (machine type, accelerators, etc.).
4. Evaluate the Model: After training, Vertex AI provides tools to evaluate your model’s performance (e.g., confusion matrices, precision-recall curves).
5. Deploy the Model (Optional): If you want to serve predictions online, you can deploy your trained model to a Vertex AI Endpoint.
6. Get Predictions:
   • Online Predictions: Send individual image requests to your deployed endpoint and get real-time predictions.
   • Batch Predictions: Process a large batch of images and store the predictions in a BigQuery table or Cloud Storage.

4. Code Examples (Conceptual)

Here are some conceptual code snippets (using the Vertex AI SDK for Python) to illustrate the process:

AutoML Image Classification:

Python

from google.cloud import aiplatform

aiplatform.init(project="your-project-id", location="us-central1")

dataset = aiplatform.ImageDataset.create(
    display_name="my-image-dataset",
    gcs_source=["gs://your-bucket/data/image_classification_data.jsonl"],
)

model = aiplatform.AutoMLImageTrainingJob(
    display_name="my-image-classification-model",
    prediction_type="classification",
    multi_label=False,  # Set to True for multi-label classification
).run(
    dataset=dataset,
    model_display_name="my-trained-model",
    training_fraction_split=0.8,
    validation_fraction_split=0.1,
    test_fraction_split=0.1,
    # Add more parameters as needed
)

Custom Training (Simplified):

Python

from google.cloud import aiplatform

aiplatform.init(project="your-project-id", location="us-central1")

job = aiplatform.CustomContainerTrainingJob(
    display_name="my-custom-image-training-job",
    container_uri="us-docker.pkg.dev/your-project/your-container-registry/your-image:latest",  # Your Docker image
    model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-7:latest",  # Example for TensorFlow
)

model = job.run(
    dataset="your-dataset-resource-name",  # Or specify training data directly
    model_display_name="my-custom-trained-model",
    # Add more parameters as needed
)

5. Key Considerations

• Compute Resources: Choose appropriate machine types and accelerators (GPUs, TPUs) based on your model complexity and dataset size.
• Training Budget: For AutoML, set a training budget (node hours) to control costs.
• Model Evaluation: Carefully evaluate your model’s performance on the test set.
• Prediction: Choose the appropriate prediction method (online or batch) based on your application’s requirements.
• Vertex AI Feature Store: Consider using Feature Store to manage and serve features for your image models.

By following these guidelines and leveraging Vertex AI’s capabilities, you can efficiently train and deploy image models for various applications. Remember to consult the official Google Cloud documentation for the most up-to-date information and best practices.

Okay, here’s a sample dataset for a house price prediction model, incorporating many of the features we discussed. This data is synthetic and intended to illustrate the variety of features.

Code snippet

UniqueID,Size_LivingArea_SqFt,Size_Lot_SqFt,Size_TotalArea_SqFt,Rooms_Total,Bedrooms,Bathrooms_Full,Bathrooms_Half,Basement_Area_SqFt,Basement_Finished,Garage_Cars,Fireplaces,Porch_Area_SqFt,Year_Built,Year_Remodeled,Condition_Overall,Quality_Overall,Building_Type,House_Style,Foundation_Type,Roof_Material,Exterior_Material,Heating_Type,Cooling_Type,Kitchen_Quality,Bathroom_Quality,Fireplace_Quality,Basement_Quality,Stories,Floor_Material,Neighborhood,Proximity_Schools_Miles,Proximity_Parks_Miles,Proximity_PublicTransport_Miles,Proximity_Shopping_Miles,Proximity_Hospitals_Miles,Safety_CrimeRate_Index,Environmental_NoiseLevel_dB,Environmental_AirQuality_Index,Flood_Zone,View,Time_of_Sale,Interest_Rate,Inflation_Rate,Unemployment_Rate,Housing_Inventory,Economic_Growth_Rate,Sale_Price
1,1800,7500,2500,7,3,2,1,700,1,2,1,150,1995,2010,7,7,House,Ranch,Slab,Composition Shingle,Brick,Forced Air,Central AC,Good,Good,Average,Average,1,Hardwood,Bentonville Central,0.5,1.2,0.8,1.5,2.0,65,45,35,No,None,2024-08,6.2,3.5,4.2,0.05,2.5,285000
2,2200,10000,3000,8,4,3,0,800,0,2,1,200,2005,2005,6,6,House,Two-Story,Foundation,Composition Shingle,Siding,Forced Air,Central AC,Average,Average,Average,Poor,2,Carpet,Bentonville West,1.5,0.3,2.5,0.5,0.8,40,55,50,No,Trees,2024-11,6.5,3.8,4.0,0.03,2.8,350000
3,1500,6000,1800,6,3,1,1,0,0,1,0,100,1980,1980,5,5,House,Split-Level,Crawl Space,Asphalt,Vinyl Siding,Baseboard Heat,Window AC,Fair,Fair,None,None,1.5,Carpet,Bella Vista,3.0,0.8,0.5,2.0,5.0,80,35,25,Yes,None,2024-05,5.8,3.2,4.5,0.07,2.2,195000
4,2800,12000,3500,9,4,3,1,1000,1,3,2,250,2015,2018,8,8,House,Traditional,Foundation,Composition Shingle,Brick Veneer,Forced Air,Central AC,Excellent,Excellent,Good,Good,2,Hardwood,Centerton,0.2,2.0,1.0,0.3,1.0,50,40,30,No,Park View,2025-01,6.8,4.0,3.8,0.02,3.0,450000
5,1200,5000,1500,5,2,1,0,0,0,1,0,50,1970,1970,4,4,House,Ranch,Slab,Asphalt,Aluminum Siding,Wall Unit,Window AC,Poor,Fair,None,None,1,Vinyl,Rogers,2.5,1.5,3.5,1.0,3.0,90,60,65,No,None,2024-07,6.0,3.4,4.3,0.06,2.4,150000
6,3200,15000,4000,10,5,4,1,1200,1,3,2,300,2020,2022,9,9,House,Modern,Foundation,Metal,Stucco,Geothermal,Central AC,Excellent,Excellent,Excellent,Excellent,2,Tile,Bentonville Central,0.1,0.5,0.2,0.8,0.5,30,30,20,No,City View,2025-03,7.0,4.2,3.5,0.01,3.2,580000
7,1900,8000,2600,7,3,2,1,750,1,2,1,180,1998,2015,7,8,House,Colonial,Foundation,Composition Shingle,Brick,Forced Air,Central AC,Good,Excellent,Average,Good,2,Hardwood,Bella Vista,2.0,1.0,1.5,1.2,4.0,70,48,38,No,Trees,2024-09,6.3,3.6,4.1,0.04,2.6,310000
8,2500,11000,3300,8,4,2,1,900,1,2,1,220,2010,2010,6,7,House,Ranch,Slab,Composition Shingle,Siding,Forced Air,Central AC,Average,Good,Average,Average,1,Carpet,Rogers,1.0,2.5,2.0,0.7,2.5,55,52,45,No,None,2024-12,6.6,3.9,3.9,0.035,2.9,390000
9,1600,6500,2000,6,3,2,0,0,0,1,0,120,1985,1985,5,5,House,Split-Level,Crawl Space,Asphalt,Vinyl Siding,Baseboard Heat,Window AC,Fair,Fair,None,None,1.5,Vinyl,Centerton,2.8,0.5,0.3,2.5,1.5,85,40,30,Yes,None,2024-06,5.9,3.3,4.4,0.065,2.3,220000
10,3000,13000,3800,9,4,3,1,1100,1,3,2,280,2018,2020,8,9,House,Traditional,Foundation,Composition Shingle,Brick Veneer,Forced Air,Central AC,Excellent,Excellent,Good,Good,2,Hardwood,Bentonville West,0.3,1.8,0.9,0.5,0.7,45,35,28,No,Park View,2025-02,6.9,4.1,3.7,0.015,3.1,510000

Explanation of the Columns:

• UniqueID: A unique identifier for each house.
• Size_LivingArea_SqFt: The square footage of the living space.
• Size_Lot_SqFt: The square footage of the land lot.
• Size_TotalArea_SqFt: The total square footage including basement, etc.
• Rooms_Total: The total number of rooms.
• Bedrooms: The number of bedrooms.
• Bathrooms_Full: The number of full bathrooms.
• Bathrooms_Half: The number of half bathrooms.
• Basement_Area_SqFt: The square footage of the basement.
• Basement_Finished: 1 if the basement is finished, 0 otherwise.
• Garage_Cars: The number of cars the garage can hold.
• Fireplaces: The number of fireplaces.
• Porch_Area_SqFt: The square footage of porches, decks, or patios.
• Year_Built: The year the house was built.
• Year_Remodeled: The year the house was last remodeled (if applicable).
• Condition_Overall: An overall rating of the house’s condition (1-10).
• Quality_Overall: An overall rating of the house’s material and finish quality (1-10).
• Building_Type: The type of building (e.g., House, Townhouse, Condo).
• House_Style: The architectural style of the house (e.g., Ranch, Two-Story).
• Foundation_Type: The type of foundation (e.g., Slab, Foundation, Crawl Space).
• Roof_Material: The material of the roof (e.g., Composition Shingle, Asphalt).
• Exterior_Material: The material of the exterior (e.g., Brick, Siding).
• Heating_Type: The type of heating system (e.g., Forced Air, Baseboard Heat).
• Cooling_Type: The type of cooling system (e.g., Central AC, Window AC).
• Kitchen_Quality: A rating of the kitchen quality (e.g., Poor, Fair, Average, Good, Excellent).
• Bathroom_Quality: A rating of the bathroom quality.
• Fireplace_Quality: A rating of the fireplace quality.
• Basement_Quality: A rating of the basement quality.
• Stories: The number of stories in the house.
• Floor_Material: The primary flooring material (e.g., Hardwood, Carpet, Tile).
• Neighborhood: The name of the neighborhood (using Bentonville, Arkansas area examples).
• Proximity_Schools_Miles: The distance to the nearest good school in miles.
• Proximity_Parks_Miles: The distance to the nearest park in miles.
• Proximity_PublicTransport_Miles: The distance to the nearest public transportation stop in miles.
• Proximity_Shopping_Miles: The distance to the nearest shopping center in miles.
• Proximity_Hospitals_Miles: The distance to the nearest hospital in miles.
• Safety_CrimeRate_Index: A numerical index representing the crime rate (lower is safer).
• Environmental_NoiseLevel_dB: The average noise level in decibels.
• Environmental_AirQuality_Index: An index representing the air quality (lower is better).
• Flood_Zone: “Yes” if in a flood zone, “No” otherwise.
• View: A description of any significant view (e.g., Park View, City View, None).
• Time_of_Sale: The date of the sale.
• Interest_Rate: The prevailing mortgage interest rate at the time of sale.
• Inflation_Rate: The inflation rate at the time of sale.
• Unemployment_Rate: The unemployment rate in the area at the time of sale.
• Housing_Inventory: A measure of the available housing inventory (lower means higher demand).
• Economic_Growth_Rate: The economic growth rate at the time of sale.
• Sale_Price: The target variable – the price the house sold for.

How to Use This Data in Vertex AI:

1. Save as CSV: Save this data as a .csv file (e.g., house_price_data.csv).
2. Upload to GCS: Upload this CSV file to a Google Cloud Storage (GCS) bucket.
3. Create a Vertex AI Dataset: In the Vertex AI console, create a new Tabular Dataset and point it to the CSV file in your GCS bucket.
4. Train Your Model: You can then use this Dataset to train an AutoML Tabular Regression model or use it as the data source for a custom training job.

Remember that this is just a small sample. For a real-world model, you would need a much larger and more diverse dataset. You would also need to handle data cleaning, preprocessing, and feature engineering steps within Vertex AI or your custom training code.

Pytorch model for this above dataset

Python

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# 1. Load the dataset
data = """UniqueID,Size_LivingArea_SqFt,Size_Lot_SqFt,Size_TotalArea_SqFt,Rooms_Total,Bedrooms,Bathrooms_Full,Bathrooms_Half,Basement_Area_SqFt,Basement_Finished,Garage_Cars,Fireplaces,Porch_Area_SqFt,Year_Built,Year_Remodeled,Condition_Overall,Quality_Overall,Building_Type,House_Style,Foundation_Type,Roof_Material,Exterior_Material,Heating_Type,Cooling_Type,Kitchen_Quality,Bathroom_Quality,Fireplace_Quality,Basement_Quality,Stories,Floor_Material,Neighborhood,Proximity_Schools_Miles,Proximity_Parks_Miles,Proximity_PublicTransport_Miles,Proximity_Shopping_Miles,Proximity_Hospitals_Miles,Safety_CrimeRate_Index,Environmental_NoiseLevel_dB,Environmental_AirQuality_Index,Flood_Zone,View,Time_of_Sale,Interest_Rate,Inflation_Rate,Unemployment_Rate,Housing_Inventory,Economic_Growth_Rate,Sale_Price
1,1800,7500,2500,7,3,2,1,700,1,2,1,150,1995,2010,7,7,House,Ranch,Slab,Composition Shingle,Brick,Forced Air,Central AC,Good,Good,Average,Average,1,Hardwood,Bentonville Central,0.5,1.2,0.8,1.5,2.0,65,45,35,No,None,2024-08,6.2,3.5,4.2,0.05,2.5,285000
2,2200,10000,3000,8,4,3,0,800,0,2,1,200,2005,2005,6,6,House,Two-Story,Foundation,Composition Shingle,Siding,Forced Air,Central AC,Average,Average,Average,Poor,2,Carpet,Bentonville West,1.5,0.3,2.5,0.5,0.8,40,55,50,No,Trees,2024-11,6.5,3.8,4.0,0.03,2.8,350000
3,1500,6000,1800,6,3,1,1,0,0,1,0,100,1980,1980,5,5,House,Split-Level,Crawl Space,Asphalt,Vinyl Siding,Baseboard Heat,Window AC,Fair,Fair,None,None,1.5,Carpet,Bella Vista,3.0,0.8,0.5,2.0,5.0,80,35,25,Yes,None,2024-05,5.8,3.2,4.5,0.07,2.2,195000
4,2800,12000,3500,9,4,3,1,1000,1,3,2,250,2015,2018,8,8,House,Traditional,Foundation,Composition Shingle,Brick Veneer,Forced Air,Central AC,Excellent,Excellent,Good,Good,2,Hardwood,Centerton,0.2,2.0,1.0,0.3,1.0,50,40,30,No,Park View,2025-01,6.8,4.0,3.8,0.02,3.0,450000
5,1200,5000,1500,5,2,1,0,0,0,1,0,50,1970,1970,4,4,House,Ranch,Slab,Asphalt,Aluminum Siding,Wall Unit,Window AC,Poor,Fair,None,None,1,Vinyl,Rogers,2.5,1.5,3.5,1.0,3.0,90,60,65,No,None,2024-07,6.0,3.4,4.3,0.06,2.4,150000
6,3200,15000,4000,10,5,4,1,1200,1,3,2,300,2020,2022,9,9,House,Modern,Foundation,Metal,Stucco,Geothermal,Central AC,Excellent,Excellent,Excellent,Excellent,2,Tile,Bentonville Central,0.1,0.5,0.2,0.8,0.5,30,30,20,No,City View,2025-03,7.0,4.2,3.5,0.01,3.2,580000
7,1900,8000,2600,7,3,2,1,750,1,2,1,180,1998,2015,7,8,House,Colonial,Foundation,Composition Shingle,Brick,Forced Air,Central AC,Good,Excellent,Average,Good,2,Hardwood,Bella Vista,2.0,1.0,1.5,1.2,4.0,70,48,38,No,Trees,2024-09,6.3,3.6,4.1,0.04,2.6,310000
8,2500,11000,3300,8,4,2,1,900,1,2,1,220,2010,2010,6,7,House,Ranch,Slab,Composition Shingle,Siding,Forced Air,Central AC,Average,Good,Average,Average,1,Carpet,Rogers,1.0,2.5,2.0,0.7,2.5,55,52,45,No,None,2024-12,6.6,3.9,3.9,0.035,2.9,390000
9,1600,6500,2000,6,3,2,0,0,0,1,0,120,1985,1985,5,5,House,Split-Level,Crawl Space,Asphalt,Vinyl Siding,Baseboard Heat,Window AC,Fair,Fair,None,None,1.5,Vinyl,Centerton,2.8,0.5,0.3,2.5,1.5,85,40,30,Yes,None,2024-06,5.9,3.3,4.4,0.065,2.3,220000
10,3000,13000,3800,9,4,3,1,1100,1,3,2,280,2018,2020,8,9,House,Traditional,Foundation,Composition Shingle,Brick Veneer,Forced Air,Central AC,Excellent,Excellent,Good,Good,2,Hardwood,Bentonville West,0.3,1.8,0.9,0.5,0.7,45,35,28,No,Park View,2025-02,6.9,4.1,3.7,0.015,3.1,510000
"""
from io import StringIO
df = pd.read_csv(StringIO(data))

# 2. Preprocessing
# Identify numerical and categorical features
numerical_features = ['Size_LivingArea_SqFt', 'Size_Lot_SqFt', 'Size_TotalArea_SqFt', 'Rooms_Total',
                      'Bedrooms', 'Bathrooms_Full', 'Bathrooms_Half', 'Basement_Area_SqFt',
                      'Garage_Cars', 'Fireplaces', 'Porch_Area_SqFt', 'Year_Built', 'Year_Remodeled',
                      'Condition_Overall', 'Quality_Overall', 'Stories',
                      'Proximity_Schools_Miles', 'Proximity_Parks_Miles',
                      'Proximity_PublicTransport_Miles', 'Proximity_Shopping_Miles',
                      'Proximity_Hospitals_Miles', 'Safety_CrimeRate_Index',
                      'Environmental_NoiseLevel_dB', 'Environmental_AirQuality_Index',
                      'Interest_Rate', 'Inflation_Rate', 'Unemployment_Rate',
                      'Housing_Inventory', 'Economic_Growth_Rate']
categorical_features = ['Building_Type', 'House_Style', 'Foundation_Type', 'Roof_Material',
                        'Exterior_Material', 'Heating_Type', 'Cooling_Type', 'Kitchen_Quality',
                        'Bathroom_Quality', 'Fireplace_Quality', 'Basement_Quality',
                        'Floor_Material', 'Neighborhood', 'Flood_Zone', 'View']

# Create preprocessor
preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), numerical_features),
        ('cat', OneHotEncoder(handle_unknown='ignore'), categorical_features)
    ],
    remainder='passthrough'  # Keep other columns (like UniqueID, Time_of_Sale)
)

# Separate features and target
X = df.drop('Sale_Price', axis=1)
y = df['Sale_Price'].values.reshape(-1, 1)

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Fit and transform the data
X_train_processed = preprocessor.fit_transform(X_train)
X_test_processed = preprocessor.transform(X_test)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.float32)

# Convert processed data to PyTorch Tensors
X_train_tensor = torch.tensor(X_train_processed, dtype=torch.float32)
X_test_tensor = torch.tensor(X_test_processed, dtype=torch.float32)

# 3. Define the PyTorch Dataset
class HousePriceDataset(Dataset):
    def __init__(self, features, labels):
        self.features = features
        self.labels = labels
        self.n_samples = features.shape[0]

    def __getitem__(self, index):
        return self.features[index], self.labels[index]

    def __len__(self):
        return self.n_samples

train_dataset = HousePriceDataset(X_train_tensor, y_train_tensor)
test_dataset = HousePriceDataset(X_test_tensor, y_test_tensor)

# 4. Define the DataLoader
batch_size = 8
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 5. Define the Neural Network Model
class HousePriceModel(nn.Module):
    def __init__(self, input_size):
        super(HousePriceModel, self).__init__()
        self.linear1 = nn.Linear(input_size, 64)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(64, 32)
        self.relu2 = nn.ReLU()
        self.linear3 = nn.Linear(32, 1)  # Output is a single predicted price

    def forward(self, x):
        out = self.linear1(x)
        out = self.relu(out)
        out = self.linear2(out)
        out = self.relu2(out)
        out = self.linear3(out)
        return out

# Get the input size (number of features after preprocessing)
input_size = X_train_tensor.shape[1]
model = HousePriceModel(input_size)

# 6. Define Loss Function and Optimizer
learning_rate = 0.01
criterion = nn.MSELoss()  # Mean Squared Error for regression
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 7. Training Loop
num_epochs = 100
for epoch in range(num_epochs):
    for batch_idx, (features, labels) in enumerate(train_loader):
        # Forward pass
        outputs = model(features)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (batch_idx + 1) % 10 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Batch [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}')

print('Finished Training')

# 8. Evaluation
with torch.no_grad():
    model.eval()
    test_loss = 0
    for features, labels in test_loader:
        outputs = model(features)
        test_loss += criterion(outputs, labels).item()

    avg_test_loss = test_loss / len(test_loader)
    print(f'Test Loss: {avg_test_loss:.4f}')

# 9. Save the Trained Model (for Vertex AI deployment)
torch.save(model.state_dict(), 'house_price_model.pth')
print('Trained model saved as house_price_model.pth')

# To deploy on Vertex AI, you would typically need to:
# 1. Upload 'house_price_model.pth' and potentially the preprocessing pipeline
#    (saved using pickle) to Google Cloud Storage.
# 2. Create a custom serving container that loads the PyTorch model and the
#    preprocessing steps.
# 3. Deploy the container and model to a Vertex AI Endpoint.

Explanation:

1. Load Data: The provided sample data is loaded using pandas.
2. Preprocessing:
   • Identify Feature Types: Numerical and categorical features are separated.
   • Create Preprocessor: ColumnTransformer from sklearn.compose is used to apply different preprocessing steps to different columns.
   • StandardScaler: Numerical features are scaled to have zero mean and unit variance.
   • OneHotEncoder: Categorical features are converted into a one-hot encoded format. handle_unknown='ignore' is used to avoid errors if unseen categories appear during prediction.
   • Fit and Transform: The preprocessor is fitted on the training data and then used to transform both the training and testing data.
   • Convert to Tensors: The processed NumPy arrays are converted to PyTorch Tensors.
3. PyTorch Dataset: A custom HousePriceDataset class is created to load the features and labels in a PyTorch-friendly way.
4. DataLoader: DataLoader is used to create iterable batches of data for training and evaluation.
5. Neural Network Model (HousePriceModel):
   • A simple feedforward neural network with three linear layers and ReLU activation functions is defined.
   • The output layer has a single neuron for predicting the house price.
6. Loss Function and Optimizer:
   • nn.MSELoss() (Mean Squared Error) is chosen as the loss function, suitable for regression tasks.
   • optim.Adam() is a popular and effective optimization algorithm.
7. Training Loop:
   • The model iterates through the training data for a specified number of epochs.
   • In each batch:
     • The forward pass calculates the model’s predictions.
     • The loss is computed.
     • Gradients are calculated using backpropagation (loss.backward()).
     • The optimizer updates the model’s weights (optimizer.step()).
8. Evaluation:
   • The model is set to evaluation mode (model.eval()).
   • The test loss is calculated without tracking gradients (torch.no_grad()).
   • The average test loss is printed.
9. Save Model: The trained model’s state dictionary (the learned weights and biases) is saved to a .pth file.

To deploy this model on Vertex AI:

1. Save Preprocessor: You would also need to save the fitted preprocessor (using pickle) so you can apply the same transformations to incoming prediction data.
2. Create Serving Container: You would need to create a custom Docker container that includes:
   • Your PyTorch model (house_price_model.pth).
   • The saved preprocessor.