import xgboost as xgb
from sklearn.model_selection import GridSearchCV

# Optimized Churn Model hyper-parameters
params = {
    'max_depth': [3, 5, 7],
    'learning_rate': [0.01, 0.1, 0.2],
    'n_estimators': [100, 200, 300]
}

xgb_model = xgb.XGBClassifier(use_label_encoder=False)
grid_search = GridSearchCV(xgb_model, params, cv=5, scoring='f1')
grid_search.fit(X_train, y_train)

best_estimator = grid_search.best_estimator_

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")

# Perform financial inference
inputs = tokenizer(["Company revenue surges 25% year-on-year, beating estimates"], 
                   padding=True, truncation=True, return_tensors="pt")
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, window

spark = SparkSession.builder.appName("TransETL").getOrCreate()

# Structured streaming from Kafka
stream_df = spark.readStream.format("kafka").load()

cleaned_stream = stream_df.selectExpr("CAST(value AS STRING)") \
    .groupBy(window(col("timestamp"), "1 hour")) \
    .count()

cleaned_stream.writeStream.format("delta") \
    .option("checkpointLocation", "/delta/checkpoints") \
    .start("/delta/transactions")