diff --git a/dags/data_drift_simulation.py b/dags/data_drift_simulation.py
index 2491c37354351710ca9031c4a51f36be6b599392..8b6dc1cd80a73a1895740acf77f2d0468f9f5363 100644
--- a/dags/data_drift_simulation.py
+++ b/dags/data_drift_simulation.py
@@ -1,40 +1,59 @@
-import airflow
-from airflow import DAG
-from airflow.operators.python import PythonOperator
-from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
-
-dag = DAG(
- dag_id="data_drift_simulation",
- default_args={
- "owner": "admin",
- "start_date": airflow.utils.dates.days_ago(1)
- },
- schedule_interval="@daily"
-)
-
-start = PythonOperator(
- task_id="start",
- python_callable=lambda: print("Data Drift Simulation started"),
- dag=dag
-)
-
-data_drift_job = SparkSubmitOperator(
- task_id="data_drift",
- conn_id="spark_conn",
- application="jobs/python/data_drift.py",
+import airflow
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
+from airflow.operators.dagrun_operator import TriggerDagRunOperator
+from airflow.models import Variable
+
+dag = DAG(
+ dag_id="data_drift_simulation",
+ default_args={
+ "owner": "admin",
+ "start_date": airflow.utils.dates.days_ago(1)
+ },
+ schedule_interval="@daily"
+)
+
+start = PythonOperator(
+ task_id="start",
+ python_callable=lambda: print("Data Drift Simulation started"),
+ dag=dag
+)
+
+data_drift_job = SparkSubmitOperator(
+ task_id="data_drift",
+ conn_id="spark_conn",
+ application="jobs/python/data_drift.py",
+ dag=dag,
+)
+
+def check_accuracy(**kwargs):
+ accuracy_data = Variable.get("data_drift_accuracy", deserialize_json=True)
+ accuracy = accuracy_data.get("accuracy", 0)
+ if accuracy < 0.70:
+ return 'trigger_sparking_flow'
+ else:
+ return 'end'
+
+branch_task = PythonOperator(
+ task_id='check_accuracy',
+ python_callable=check_accuracy,
+ provide_context=True,
dag=dag,
)
-
-trigger_sparking_flow = PythonOperator(
+
+trigger_sparking_flow = TriggerDagRunOperator(
task_id="trigger_sparking_flow",
- python_callable=lambda: print("Triggering sparking_flow DAG"),
+ trigger_dag_id="sparking_flow",
dag=dag,
)
-
-end = PythonOperator(
- task_id="end",
- python_callable=lambda: print("Data Drift Simulation completed successfully"),
- dag=dag
-)
-
-start >> data_drift_job >> trigger_sparking_flow >> end
+
+end = PythonOperator(
+ task_id="end",
+ python_callable=lambda: print("Data Drift Simulation completed successfully"),
+ dag=dag
+)
+
+start >> data_drift_job >> branch_task
+branch_task >> trigger_sparking_flow >> end
+branch_task >> end
diff --git a/jobs/python/data_drift.py b/jobs/python/data_drift.py
index bd6a7a0569f9bb10d493dbf26498d8e7faef87d4..f9ada4b07a809cad6d65d38e9010f47a57332139 100644
--- a/jobs/python/data_drift.py
+++ b/jobs/python/data_drift.py
@@ -1,106 +1,95 @@
-from pyspark.sql import SparkSession
-from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
-from pyspark.sql.functions import when, col, isnull, monotonically_increasing_id, rand
-from pyspark.ml.classification import LogisticRegressionModel
-from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.ml.feature import VectorAssembler, StringIndexer
-
-spark = SparkSession.builder.master("local").appName("DataDriftSimulation").getOrCreate()
-
-schema = StructType([
- StructField("customerID", StringType(), True),
- StructField("gender", StringType(), True),
- StructField("SeniorCitizen", StringType(), True),
- StructField("Partner", StringType(), True),
- StructField("Dependents", StringType(), True),
- StructField("tenure", DoubleType(), True),
- StructField("PhoneService", StringType(), True),
- StructField("MultipleLines", StringType(), True),
- StructField("InternetService", StringType(), True),
- StructField("OnlineSecurity", StringType(), True),
- StructField("OnlineBackup", StringType(), True),
- StructField("DeviceProtection", StringType(), True),
- StructField("TechSupport", StringType(), True),
- StructField("StreamingTV", StringType(), True),
- StructField("StreamingMovies", StringType(), True),
- StructField("Contract", StringType(), True),
- StructField("PaperlessBilling", StringType(), True),
- StructField("PaymentMethod", StringType(), True),
- StructField("MonthlyCharges", DoubleType(), True),
- StructField("TotalCharges", DoubleType(), True),
- StructField("Churn", StringType(), True)
-])
-
-original_data_path = "dataset/cleaned_data.csv"
-original_data_df = spark.read.csv(original_data_path, header=True, schema=schema)
-
-for column in ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']:
- if original_data_df.filter(isnull(col(column))).count() > 0:
- print(f"Null values found in {column}")
-
-original_data_df = original_data_df.withColumn("SeniorCitizen", col("SeniorCitizen").cast(IntegerType()))
-
-data_drift_df = original_data_df.sample(fraction=0.2, seed=42)
-
-if data_drift_df.count() == 0:
- print("data_drift_df is empty after sampling.")
-
-data_drift_df = data_drift_df.withColumn('Churn', when(data_drift_df['Churn'] == 'Yes', 1).otherwise(0).cast(IntegerType()))
-
-data_drift_df = data_drift_df.withColumn("customerID", monotonically_increasing_id().cast(StringType()))
-
-# Introduce significant changes to the new data to drop accuracy
-data_drift_df = data_drift_df.withColumn("MonthlyCharges", col("MonthlyCharges") * (1 + rand() * 0.5))
-data_drift_df = data_drift_df.withColumn("TotalCharges", col("TotalCharges") * (1 + rand() * 0.5))
-data_drift_df = data_drift_df.withColumn("Churn", when(rand() > 0.5, 1).otherwise(0))
-
-model_path = 'logistic_regression_model'
-loaded_model = LogisticRegressionModel.load(model_path)
-
-categorical_columns = ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']
-
-for column in categorical_columns:
- indexer = StringIndexer(inputCol=column, outputCol=column + "_index", handleInvalid="keep")
- data_drift_df = indexer.fit(data_drift_df).transform(data_drift_df)
-
-for column in categorical_columns:
- if data_drift_df.filter(isnull(col(column + "_index"))).count() > 0:
- print(f"Null values found in {column + '_index'}")
-
-feature_columns = [col + "_index" for col in categorical_columns] + ['tenure', 'MonthlyCharges', 'TotalCharges']
-assembler = VectorAssembler(inputCols=feature_columns, outputCol='features')
-
-data = assembler.transform(data_drift_df)
-
-predictions = loaded_model.transform(data)
-
-evaluator = MulticlassClassificationEvaluator(labelCol='Churn', predictionCol='prediction', metricName='accuracy')
-accuracy = evaluator.evaluate(predictions)
-
-print(f"Accuracy after data drift: {accuracy * 100:.2f}%")
-
-# Select only the original columns for merging
-data_drift_df = data_drift_df.select("customerID", "gender", "SeniorCitizen", "Partner", "Dependents", "tenure", "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", "MonthlyCharges", "TotalCharges", "Churn")
-
-# Merge old and new data
-merged_data_df = original_data_df.union(data_drift_df)
-
-output_path = "dataset/data_drifted_customers.csv"
-merged_data_df.write.csv(output_path, header=True, mode='overwrite')
-
-if accuracy < 0.70:
- print("Accuracy is below 70%. Triggering retraining of the model.")
- from airflow.models import DagBag
- dag_bag = DagBag()
- sparking_flow_dag = dag_bag.get_dag('sparking_flow')
-
- if sparking_flow_dag:
- sparking_flow_dag.clear()
- sparking_flow_dag.run()
- print("Successfully triggered sparking_flow DAG.")
- else:
- print("sparking_flow DAG not found.")
-else:
- print("Accuracy is acceptable.")
-
-spark.stop()
+from pyspark.sql import SparkSession
+from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
+from pyspark.sql.functions import when, col, isnull, monotonically_increasing_id, rand
+from pyspark.ml.classification import LogisticRegressionModel
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.ml.feature import VectorAssembler, StringIndexer
+from airflow import DAG
+from airflow.operators.python import PythonOperator
+import json
+import os
+import datetime
+
+spark = SparkSession.builder.master("local").appName("DataDriftSimulation").getOrCreate()
+
+schema = StructType([
+ StructField("customerID", StringType(), True),
+ StructField("gender", StringType(), True),
+ StructField("SeniorCitizen", StringType(), True),
+ StructField("Partner", StringType(), True),
+ StructField("Dependents", StringType(), True),
+ StructField("tenure", DoubleType(), True),
+ StructField("PhoneService", StringType(), True),
+ StructField("MultipleLines", StringType(), True),
+ StructField("InternetService", StringType(), True),
+ StructField("OnlineSecurity", StringType(), True),
+ StructField("OnlineBackup", StringType(), True),
+ StructField("DeviceProtection", StringType(), True),
+ StructField("TechSupport", StringType(), True),
+ StructField("StreamingTV", StringType(), True),
+ StructField("StreamingMovies", StringType(), True),
+ StructField("Contract", StringType(), True),
+ StructField("PaperlessBilling", StringType(), True),
+ StructField("PaymentMethod", StringType(), True),
+ StructField("MonthlyCharges", DoubleType(), True),
+ StructField("TotalCharges", DoubleType(), True),
+ StructField("Churn", StringType(), True)
+])
+
+original_data_path = "dataset/cleaned_data.csv"
+original_data_df = spark.read.csv(original_data_path, header=True, schema=schema)
+
+for column in ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']:
+ if original_data_df.filter(isnull(col(column))).count() > 0:
+ print(f"Null values found in {column}")
+
+original_data_df = original_data_df.withColumn("SeniorCitizen", col("SeniorCitizen").cast(IntegerType()))
+
+data_drift_df = original_data_df.sample(fraction=0.2, seed=42)
+
+if data_drift_df.count() == 0:
+ print("data_drift_df is empty after sampling.")
+
+data_drift_df = data_drift_df.withColumn('Churn', when(data_drift_df['Churn'] == 'Yes', 1).otherwise(0).cast(IntegerType()))
+data_drift_df = data_drift_df.withColumn("customerID", monotonically_increasing_id().cast(StringType()))
+data_drift_df = data_drift_df.withColumn("MonthlyCharges", col("MonthlyCharges") * (1 + rand() * 0.5))
+data_drift_df = data_drift_df.withColumn("TotalCharges", col("TotalCharges") * (1 + rand() * 0.5))
+data_drift_df = data_drift_df.withColumn("Churn", when(rand() > 0.5, 1).otherwise(0))
+
+model_base_path = 'logistic_regression_model'
+latest_model_path = sorted([os.path.join(model_base_path, d) for d in os.listdir(model_base_path)], key=os.path.getmtime)[-1]
+loaded_model = LogisticRegressionModel.load(latest_model_path)
+
+categorical_columns = ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']
+
+for column in categorical_columns:
+ indexer = StringIndexer(inputCol=column, outputCol=column + "_index", handleInvalid="keep")
+ data_drift_df = indexer.fit(data_drift_df).transform(data_drift_df)
+
+for column in categorical_columns:
+ if data_drift_df.filter(isnull(col(column + "_index"))).count() > 0:
+ print(f"Null values found in {column + '_index'}")
+
+feature_columns = [col + "_index" for col in categorical_columns] + ['tenure', 'MonthlyCharges', 'TotalCharges']
+assembler = VectorAssembler(inputCols=feature_columns, outputCol='features')
+data = assembler.transform(data_drift_df)
+
+predictions = loaded_model.transform(data)
+
+evaluator = MulticlassClassificationEvaluator(labelCol='Churn', predictionCol='prediction', metricName='accuracy')
+accuracy = evaluator.evaluate(predictions)
+
+print(f"Accuracy after data drift: {accuracy * 100:.2f}%")
+
+data_drift_df = data_drift_df.select("customerID", "gender", "SeniorCitizen", "Partner", "Dependents", "tenure", "PhoneService", "MultipleLines", "InternetService", "OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV", "StreamingMovies", "Contract", "PaperlessBilling", "PaymentMethod", "MonthlyCharges", "TotalCharges", "Churn")
+
+merged_data_df = original_data_df.union(data_drift_df)
+
+output_path = "dataset/data_drifted_customers.csv"
+merged_data_df.write.csv(output_path, header=True, mode='overwrite')
+
+print(f"Accuracy: {accuracy}")
+from airflow.models import Variable
+Variable.set("data_drift_accuracy", json.dumps({"accuracy": accuracy}))
+
+spark.stop()
diff --git a/jobs/python/logistic_regression.py b/jobs/python/logistic_regression.py
index 697c65bf36ee319a41267e8c77ad4031b96f9db3..f2861bedc24a55353d43de505d838b271165a146 100644
--- a/jobs/python/logistic_regression.py
+++ b/jobs/python/logistic_regression.py
@@ -5,6 +5,7 @@ from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.ml.classification import LogisticRegressionModel
import os
import shutil
+import datetime
spark = SparkSession.builder.master("local").appName("LogisticRegressionJob").getOrCreate()
@@ -30,14 +31,17 @@ train_data, test_data = data.randomSplit([0.80, 0.20], seed=135)
lr = LogisticRegression(featuresCol='features', labelCol='Churn', maxIter=2000)
model = lr.fit(train_data)
-model_path = 'logistic_regression_model'
+model_base_path = 'logistic_regression_model'
+timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+model_path = f"{model_base_path}/version_{timestamp}"
if os.path.exists(model_path):
shutil.rmtree(model_path)
model.save(model_path)
-loaded_model = LogisticRegressionModel.load(model_path)
+latest_model_path = sorted([os.path.join(model_base_path, d) for d in os.listdir(model_base_path)], key=os.path.getmtime)[-1]
+loaded_model = LogisticRegressionModel.load(latest_model_path)
predictions = loaded_model.transform(test_data)