""" Model Monitoring Repository This module contains the ModelMonitoringRepository class, which is responsible for handling the communication with the Model Monitoring API. It includes the methods that are used to answer ModelMonitoringService requests using the Model Monitoring API functions. By Monitoring we mean the evaluation of the performance of models, the generation of reports. """ from datetime import datetime import traceback import pandas as pd import mlflow from os import makedirs, path, remove from sientia.ModelServing import ModelServing from sientia_do.temporal.constants import DATETIME_FORMAT_WITH_TZ from sientia_do.observability.logger import Logger class MLFlowRepository(): def __init__(self, host, username, password, logger: Logger): self.model_serving = ModelServing(tracking_uri=host, username=username, password=password, logger=logger) self.logger = logger def detect_and_parse_datetime_index(self, data: pd.DataFrame, metadata: dict) -> pd.DataFrame: """ Detect and parse datetime index from data. index must be a timestamp like column. This function must detect the timestamp type (pandas Timestamp or datetime) and convert it to DATETIME_FORMAT_WITH_TZ. If the index is a string, must be in format DATETIME_FORMAT_WITH_TZ. If another type or format, must raise an error. """ index = data.index # Get type of first element of index index_type = type(index[0]) self.logger.custom_info(f"Index type: {index_type}", metadata) message = f"Index must be all timestamp like column. Valid formats are: pandas Timestamp, datetime, string in format {DATETIME_FORMAT_WITH_TZ}" # Check if all in index are of the same type if not all(isinstance(i, index_type) for i in index): raise ValueError( f"{message}") # Check type and converts to DATETIME_FORMAT_WITH_TZ if index_type == str: # Validate format of string and return error if not valid try: pd.to_datetime(data.index, format=DATETIME_FORMAT_WITH_TZ) except ValueError: raise ValueError( f"{message}") elif index_type == datetime or index_type == pd.Timestamp: data.index = data.index.strftime(DATETIME_FORMAT_WITH_TZ) else: raise ValueError( f"{message}") return data def transform(self, model_name: str, data: pd.DataFrame, model_config: dict, metadata: dict) -> dict: """ Transform data using a model. Parameters: - model_name (str): The name of the model to use for transformation. - data (pandas.DataFrame): The data to transform. - model_retention (int): The number of minutes to keep the model. Returns: - dict: A dictionary containing the transformed data. """ try: self.logger.custom_debug( f"Data received for model transformation: {data.to_csv()}", metadata) model_retention = model_config.get('retention_minutes', 0) flavor = model_config.get('transform_flavor', 'sklearn') compressed = model_config.get('is_compressed', False) retention_target = model_config.get('retention_target', 'model') transform_keyword = model_config.get( 'transform_function_keyword', 'predict') transformed_data = self.model_serving.get_cached_transform( model_name, data, model_retention, flavor, compressed, retention_target, transform_keyword ) self.logger.custom_debug( f"Data received from model transformation: {transformed_data.to_csv()}", metadata) transformed_data = self.detect_and_parse_datetime_index( transformed_data, metadata) return { 'success': True, 'content': transformed_data.to_dict() } except Exception as e: return { 'success': False, 'content': { 'message': str(e), 'traceback': traceback.format_exc() } } def predict(self, model_name: str, data: pd.DataFrame, model_config: dict, metadata: dict) -> dict: """ Predict data using a model. Parameters: - model_name (str): The name of the model to use for prediction. - data (pandas.DataFrame): The data to predict. - model_retention (int): The number of minutes to keep the model. Returns: - dict: A dictionary containing the predicted data. """ try: model_retention = model_config.get('retention_minutes', 0) flavor = model_config.get('predict_flavor', 'pyfunc') compressed = model_config.get('is_compressed', False) retention_target = model_config.get('retention_target', 'model') input_index = data.index start_time = datetime.now() self.logger.custom_debug( f"Data received for model prediction: {data.to_csv()}", metadata) data = self.model_serving.get_cached_predict( model_name, data, model_retention, flavor, compressed, retention_target ) end_time = datetime.now() data = pd.DataFrame(data, columns=['prediction']) self.logger.custom_debug( f"Data received from model prediction: {data.to_csv()}", metadata) data.index = input_index data['response_time'] = (end_time - start_time).total_seconds() return { 'success': True, 'content': data.to_dict() } except Exception as e: return { 'success': False, 'content': { 'message': str(e), 'traceback': traceback.format_exc() } } def get_experiment_by_run_id(self, run_id: str) -> dict: # Get the run information using the run_id run = mlflow.get_run(run_id) # Extract the experiment ID from the run experiment_id = run.info.experiment_id # Get the experiment details using the experiment ID experiment = mlflow.get_experiment(experiment_id) experiment_name = experiment.name return experiment_name def get_next_run_name(self, model_name: str) -> str: """ Generate the next run name for a specific MLFlow model. This method calculates the next sequential run number for a model by searching existing runs and incrementing the count. It ensures unique run names for model training and retraining operations. Args: model_name (str): The name of the MLFlow model Returns: str: The next run name in format 'model_name-run_number' """ runs = mlflow.search_runs( experiment_names=[model_name], order_by=["start_time desc"]) next_run_number = len(runs) + 1 return f"{model_name}-{next_run_number}" def create_model_experiment(self, model_name: str, data: pd.DataFrame) -> tuple: """ Create a new MLFlow experiment for model retraining. This method sets up the complete environment for model retraining by: 1. Loading the current production prediction model 2. Loading the current production transformation model 3. Fitting the transformation model with new data 4. Preparing data for prediction model retraining 5. Setting up the MLFlow experiment context Args: model_name (str): Name of the MLFlow model to retrain data (pd.DataFrame): Training data for model retraining Returns: tuple: (prediction_model, data_model, experiment) - prediction_model: Loaded prediction model for retraining - data_model: Fitted transformation model - experiment: MLFlow experiment name """ # load predictor model predictor_uri = f"models:/{model_name}/production" # load transform model latest_production_id = self.model_serving.get_model_run_id( model_name, stage="Production" ) transform_uri = self.model_serving.get_model_uri( latest_production_id, prediction=False ) # load data_model = mlflow.sklearn.load_model(transform_uri) prediction_model = mlflow.sklearn.load_model(predictor_uri) data_model = data_model.fit(data) treated_data = data_model.predict(data) target_name = data_model.target_variable y = data[target_name] treated_data = pd.merge( treated_data, y, left_index=True, right_index=True) prediction_model = prediction_model.fit(treated_data) experiment = self.get_experiment_by_run_id(latest_production_id) mlflow.set_experiment(experiment) return prediction_model, data_model, experiment def perform_model_retrain(self, prediction_model, data_model, experiment: str, model_name: str, data: pd.DataFrame): """ Execute the complete model retraining process in MLFlow. This method performs the actual model retraining by: 1. Starting a new MLFlow run with descriptive metadata 2. Logging model parameters and hyperparameters 3. Retraining both prediction and transformation models 4. Logging training data as artifacts 5. Saving retrained models to MLFlow registry Args: prediction_model: MLFlow prediction model to retrain data_model: MLFlow transformation model to retrain experiment (str): MLFlow experiment name for the retraining model_name (str): Name of the model being retrained data (pd.DataFrame): Training data used for retraining Returns: tuple: (status_message, experiment_name) - status_message (str): Success confirmation message - experiment_name (str): Name of the experiment """ pred_model_atributes = vars(prediction_model) # load class attributes data_model_atributes = vars(data_model) # load class attributes experiment_description = f"Retrain model {model_name} with new data" current_run_name = self.get_next_run_name(experiment) with mlflow.start_run( run_name=current_run_name, description=experiment_description ) as _run: # update transfomation model # fixed parameters for name_atribute, val_atribute in pred_model_atributes.items(): if name_atribute != "model": mlflow.log_param(name_atribute, val_atribute) # update prediction model for name_atribute, val_atribute in data_model_atributes.items(): if name_atribute != "model": mlflow.log_param(name_atribute, val_atribute) # dynamic parameters, including model itself mlflow.sklearn.log_model(data_model, "data_model") makedirs("temp", exist_ok=True) file_path = f"temp/raw_data_{model_name}.csv" data.to_csv(file_path, index=True) # log the data raw mlflow.log_artifact(file_path) # dynamic parameters, including model itself mlflow.sklearn.log_model(prediction_model, "prediction_model") mlflow.log_param("retrain", True) # clear temp file if path.exists(file_path): remove(file_path) return "Model retrained successfully", experiment def retrain_model(self, data: pd.DataFrame, model_name: str) -> tuple: """ Orchestrate the complete model retraining workflow. This method coordinates the entire model retraining process by: 1. Creating the MLFlow experiment environment 2. Loading existing production models 3. Executing the retraining process 4. Returning comprehensive retraining results Args: data (pd.DataFrame): Training data for model retraining model_name (str): Name of the MLFlow model to retrain Returns: tuple: (status_message, experiment_name) - status_message (str): Retraining operation status - experiment_name (str): MLFlow experiment identifier """ prediction_model, data_model, experiment = self.create_model_experiment( model_name, data) retrain_result = self.perform_model_retrain( prediction_model, data_model, experiment, model_name, data) return retrain_result def get_experiment(self, experiment_name: str) -> int: """ Retrieve MLFlow experiment ID by experiment name. This method searches for an MLFlow experiment by name and returns its unique identifier. It provides error handling for non-existent experiments. Args: experiment_name (str): Name of the MLFlow experiment Returns: int: MLFlow experiment ID Raises: ValueError: If the experiment name is not found """ experiment = mlflow.get_experiment_by_name(experiment_name) if experiment is None: raise ValueError(f'Experiment {experiment_name} not found') return int(experiment.experiment_id) def get_experiment_last_run(self, experiment_id: int) -> str: """ Retrieve the most recent retraining run ID for an experiment. This method searches for the latest run in an MLFlow experiment that has been marked as a retraining run. It filters runs by the 'retrain' parameter and orders them by completion time. Args: experiment_id (int): MLFlow experiment ID Returns: str: MLFlow run ID of the most recent retraining run Raises: ValueError: If runs data is not in expected DataFrame format """ runs = mlflow.search_runs( experiment_ids=[experiment_id], filter_string="", # Sem filtro no MLflow ainda output_format="pandas" ) if not isinstance(runs, pd.DataFrame): raise ValueError('Runs is not a pandas DataFrame') # Filtrar apenas as runs onde params.retrain == True filtered_runs = runs[runs["params.retrain"] == 'True'] # Converter a coluna 'end_time' para datetime filtered_runs['end_time'] = pd.to_datetime(filtered_runs['end_time']) # Ordenar o DataFrame de forma descendente pela coluna 'end_time' filtered_runs = filtered_runs.sort_values( by='end_time', ascending=False) # Pegar a última run_id do DataFrame filtrado e ordenado latest_run_id = filtered_runs.iloc[0]['run_id'] return latest_run_id def update_production_model_by_run_id(self, run_id: str, model_name: str) -> dict: """ Update production model with a specific MLFlow run. This method promotes a model from a specific MLFlow run to production stage. It handles model registration, versioning, and stage transitions with proper error handling. Args: run_id (str): MLFlow run ID containing the model to promote model_name (str): Name of the MLFlow model Returns: dict: Model update metadata containing: - model_name (str): Name of the updated model - version (str): New model version number - mlflow_run_id (str): Source run ID Update Process: 1. Registers the model from the specified run 2. Retrieves the latest model version 3. Transitions the model to 'Production' stage 4. Archives existing production versions """ # Registrar o modelo # Aqui estamos assumindo que você já tem um modelo salvo, caso contrário você precisará treiná-lo e salvá-lo primeiro. # Se o modelo já está registrado, você pode usar o método register_model() ou pyfunc.load_model() para isso. mlflow.register_model( f"runs:/{run_id}/prediction_model", model_name) # Colocar a versão do modelo em produção # Depois de registrar o modelo, precisamos pegar a versão mais recente do modelo e movê-lo para o estágio 'Production' client = mlflow.tracking.MlflowClient() # Obter a versão mais recente registrada do modelo model_versions = client.get_registered_model( model_name).latest_versions if not isinstance(model_versions, list): raise ValueError('Model versions is not a list') max_version = max(model_versions, key=lambda x: int(x.version)).version # Mover a versão mais recente do modelo para o estágio de 'Production' client.transition_model_version_stage( name=model_name, version=max_version, stage="Production", archive_existing_versions=True ) return { 'model_name': model_name, 'version': max_version, 'mlflow_run_id': run_id } def update_production_model(self, experiment: str, model_name: str) -> dict: """ Update production model using the latest retraining run. This method orchestrates the complete production model update process by identifying the most recent retraining run and promoting it to production stage. Args: experiment (str): MLFlow experiment name model_name (str): Name of the MLFlow model Returns: dict: Complete model update metadata containing: - model_name (str): Name of the updated model - version (str): New model version number - mlflow_run_id (str): Source run ID - mlflow_experiment_id (int): Experiment ID """ experiment_id = self.get_experiment(experiment) run_id = self.get_experiment_last_run(experiment_id) metadata = self.update_production_model_by_run_id(run_id, model_name) metadata['mlflow_experiment_id'] = experiment_id return metadata