cancel
Showing results for 
Search instead for 
Did you mean: 
Machine Learning
Dive into the world of machine learning on the Databricks platform. Explore discussions on algorithms, model training, deployment, and more. Connect with ML enthusiasts and experts.
cancel
Showing results for 
Search instead for 
Did you mean: 

Model Lineage with Feature Engineering is missing tables and notebooks

MohsenJ
Contributor

I am trying to track the lineage of model and tables using the FeatureEngineeringClient. The table lineage shows the relevant tables and notebooks but the model lineage shows only the model. No notebook and tables. here is my code

 

 

fe = FeatureEngineeringClient() 
def split_data():
    spark = SparkSession.builder.getOrCreate()
    catalog_name = config["catalog_name"]
    gold_layer = config["gold_layer_name"]
    silver_layer = config["silver_layer_name"]
    user_item_table_name = config["user_item_table_name"]
    ft_user_item_name = config["ft_user_item_name"]
    
    SEED = 4
    df_ratings = spark.table(f"{catalog_name}.{silver_layer}.{user_item_table_name}")

    table_name = f"{catalog_name}.{gold_layer}.{ft_user_item_name}"
    lookup_key = config["ft_user_item_pk"]
    label = config["label_col"]
    model_feature_lookups = [FeatureLookup(table_name=table_name, lookup_key=lookup_key)]

    # fe.create_training_set looks up features in model_feature_lookups that match the primary key from df_ratings
    fe_data = fe.create_training_set(df=df_ratings, feature_lookups=model_feature_lookups, label=label, exclude_columns=["rating_date_dayofmonth","rating_date_month"])
    df_data = fe_data.load_df()
    df_data = df_data.na.drop()
    
    (df_train, df_test) = df_data.randomSplit([0.75,0.25],SEED)
    print(f'full dataset: {df_data.count()}' ,f'Training: {df_train.count()}', f'test: {df_test.count()}\n')
    return (fe_data, df_data, df_train, df_test) 


with mlflow.start_run(run_name="ALS_final_model") as run:
        fe_full_data, df_full_data, df_train, df_test = split_data()
        als = ALS()
        als.setMaxIter(MAX_ITER)\
        .setSeed(SEED)\
        .setRegParam(best_params["REG_PARAM"])\
        .setUserCol(COL_USER)\
        .setItemCol(COL_ITEM)\
        .setRatingCol(COL_LABEL)\
        .setRank(best_params["RANK"])

        mlflow.log_param("MAX_ITER", MAX_ITER)
        mlflow.log_param("RANK", best_params["RANK"])
        mlflow.log_param("REG_PARAM", best_params["REG_PARAM"])

        model = als.fit(df_full_data)
        model.setColdStartStrategy('drop') 
        predictions = model.transform(df_full_data)

        model_info = fe.log_model(model=model, 
                    artifact_path = model_name,
                    flavor=mlflow.spark,
                    training_set=fe_full_data,
                    conda_env=mlflow.spark.get_default_conda_env(),
                    registered_model_name= f"{catalog_name}.feature_store.{model_name}"
                    )

        evaluator = RegressionEvaluator(predictionCol=COL_PRED, labelCol=COL_LABEL)
        rmse = evaluator.setMetricName("rmse").evaluate(predictions)
        mlflow.log_metric('rmse', rmse) 

 

 

Attached you see the screenshot  of my lineage graphs for model and tables.

Any idea what could the problem? 

2 REPLIES 2

MohsenJ
Contributor

I just checked the feature_spec.yml file in the model registry and realized my feature tables are not tracked but only the final dataset. 

input_columns:
- user_id:
    data_type: int
    topological_ordering: 2
    source: training_data
- item_id:
    data_type: int
    topological_ordering: 0
    source: training_data
- timestamp:
    data_type: timestamp
    topological_ordering: 1
    source: training_data
workspace_id: '329425024234434367'
feature_store_client_version: 0.14.3
serialization_version:

MohsenJ
Contributor

ok I realized something else. That although I used FeatureEngineeringCient, MLflow model artifact suggest I used FeatureStoreClient. Please see attachment.  

 

Connect with Databricks Users in Your Area

Join a Regional User Group to connect with local Databricks users. Events will be happening in your city, and you won’t want to miss the chance to attend and share knowledge.

If there isn’t a group near you, start one and help create a community that brings people together.

Request a New Group