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""" This example demonstrates how to specify pip requirements using `pip_requirements` and `extra_pip_requirements` when logging a model via `mlflow.*.log_model`. Training run, like the hyperparameters alpha and lambda, used to train the model and metrics, like This example uses the familiar glmnet package to create a simple machine learning Message("Elasticnet model (alpha=", alpha, ", lambda=", lambda, "):") R2 <- as.numeric(cor(predicted, test_y) ^ 2) Rmse <- sqrt(mean((predicted - test_y) ^ 2)) Predictor <- crate(~ glmnet::predict.glmnet(!!model, as.matrix(.x)), !!model) Model <- glmnet(train_x, train_y, alpha = alpha, lambda = lambda, family= "gaussian", standardize = FALSE) Lambda <- mlflow_param("lambda", 0.5, "numeric") Test_x <- as.matrix(test)Īlpha <- mlflow_param("alpha", 0.5, "numeric") # The predicted column is "quality" which is a scalar from Sampled <- sample(1:nrow(data), 0.75 * nrow(data)) # Split the data into training and test sets. # Modeling wine preferences by data mining from physicochemical properties. # The data set used in this example is from You can run the example with default hyperparameters as follows: Model in a format that MLflow knows how to deploy. The root mean square error, used to evaluate the model. Training run, like the hyperparameters alpha and l1_ratio, used to train the model and metrics, like The MLflow tracking APIs log information about each This example uses the familiar pandas, numpy, and sklearn APIs to create a simple machine learning log_model ( lr, "model", registered_model_name = "ElasticnetWineModel" ) else : mlflow.
scheme # Model registry does not work with file store if tracking_url_type_store != "file" : # Register the model # There are other ways to use the Model Registry, which depends on the use case, # please refer to the doc for more information: # mlflow. log_metric ( "mae", mae ) tracking_url_type_store = urlparse ( mlflow. log_param ( "l1_ratio", l1_ratio ) mlflow. predict ( test_x ) ( rmse, mae, r2 ) = eval_metrics ( test_y, predicted_qualities ) print ( "Elasticnet model (alpha= %f, l1_ratio= %f ):" % ( alpha, l1_ratio )) print ( " RMSE: %s " % rmse ) print ( " MAE: %s " % mae ) print ( " R2: %s " % r2 ) mlflow. fit ( train_x, train_y ) predicted_qualities = lr.
start_run (): lr = ElasticNet ( alpha = alpha, l1_ratio = l1_ratio, random_state = 42 ) lr. argv ) > 1 else 0.5 l1_ratio = float ( sys. drop (, axis = 1 ) train_y = train ] test_y = test ] alpha = float ( sys. train, test = train_test_split ( data ) # The predicted column is "quality" which is a scalar from train_x = train. Error: %s ", e ) # Split the data into training and test sets.
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exception ( "Unable to download training & test CSV, check your internet connection. read_csv ( csv_url, sep = " " ) except Exception as e : logger. seed ( 40 ) # Read the wine-quality csv file from the URL csv_url = ( "" ) try : data = pd. sqrt ( mean_squared_error ( actual, pred )) mae = mean_absolute_error ( actual, pred ) r2 = r2_score ( actual, pred ) return rmse, mae, r2 if _name_ = "_main_" : warnings. getLogger ( _name_ ) def eval_metrics ( actual, pred ): rmse = np. import os import warnings import sys import pandas as pd import numpy as np from trics import mean_squared_error, mean_absolute_error, r2_score from sklearn.model_selection import train_test_split from sklearn.linear_model import ElasticNet from urllib.parse import urlparse import mlflow import mlflow.sklearn import logging logging.
# The data set used in this example is from # P.