Handling Big Data with XGBoost and Azure Databricks: From EDA to Deployment

Hi Connections!

Today, we're diving deep into handling a big data problem using XGBoost and Azure Databricks. I'll guide you through exploratory data analysis (EDA), hyperparameter tuning, model deployment, and saving predictions. Let's begin!

Part 1: Importing the Data

1. Pull Data from Azure Delta Table

Connect to your Delta table and load the data into a PySpark dataframe.

from pyspark.sql import SparkSession


spark = SparkSession.builder.getOrCreate()
df = spark.read.format("delta").load("path_to_your_delta_table")
        

2. Convert PySpark Dataframe to Pandas

With a 40 billion rows dataset (example size), we must downsample or use Dask for parallel computing.

pandas_df = df.sample(False, fraction=0.001).toPandas()? # adjust fraction as per your needs        

Part 2: Exploratory Data Analysis (EDA)

1. Perform EDA on the numerical variables.

pandas_df.describe()        

2. Perform EDA on the categorical variables.

pandas_df['your_categorical_column'].value_counts()        

Part 3: Data Preprocessing

1. Handle missing values, one-hot encode categorical variables, and check class imbalance. Let's say 'target' is your target column.

from sklearn.impute import SimpleImpute
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split


# Handle missing values
imputer = SimpleImputer(strategy='most_frequent')
pandas_df = pd.DataFrame(imputer.fit_transform(pandas_df), columns = pandas_df.columns)


# One-hot encode categorical variables
pandas_df = pd.get_dummies(pandas_df)


# Check class imbalance
target_counts = pandas_df['target'].value_counts(normalize=True)
print("Class distribution:\n", target_counts)


# Prepare data for training
X = pandas_df.drop('target', axis=1)
y = pandas_df['target']


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)        

Part 4: Model Building

1. Set up the XGBoost model and GridSearchCV. Link to Detailed List and description of hyperparameters - XGBoost Parameters — xgboost 2.0.0-dev documentation

from xgboost import XGBClassifier
from sklearn.model_selection import GridSearchCV


model = XGBClassifier()


param_grid = {
? ? 'objective': ['multi:softmax', 'multi:softprob'],
? ? 'learning_rate': [0.01, 0.1, 0.2],
? ? 'max_depth': [5, 10, 15],
? ? 'n_estimators': [50, 100, 200],
? ? 'num_class': 10? # update this with the number of your classes
}


grid_search = GridSearchCV(model, param_grid, cv=10, scoring='accuracy')
        

2. Fit the GridSearchCV object on your training data.

grid_search.fit(X_train, y_train)        

3. Make predictions.

best_model = grid_search.best_estimator_
predictions = best_model.predict(X_test)        

Part 5: Saving the Model

1. Connect to your Azure ML workspace.

from azureml.core import Workspace


ws = Workspace.get(name='your_workspace',
? ? ? ? ? ? ? ? ? ?subscription_id='your_subscription_id',
? ? ? ? ? ? ? ? ? ?resource_group='your_resource_group')        

2. Save the best model to the workspace.

from azureml.core.model import Model


Model.register(ws, model_path='path_to_your_model', model_name='your_model_name')        

3. Convert predictions to PySpark dataframe and save them back to a new Delta table.

predictions_df = spark.createDataFrame(predictions.tolist(), "prediction: double"
predictions_df.write.format("delta").save("path_to_new_delta_table"))        

Remember, this guide might need adjustments based on your specific problem and data. The key is to experiment, learn, and iterate. Happy modeling!

good luck!