It achieves this by pooling compute resources and leveraging them flexibly with elastic GPU clusters. The Run:ai platform takes the complexity out of distributed computing and provides unlimited compute power. Optimize Machine Learning Compute with Run:ai We will use a Gaussian process as the probabilistic model, and the expected improvement acquisition function as the acquisition function. Now, we can define the Bayesian optimization procedure using the BayesSearchCV class from scikit-optimize. Num_hidden_units_space = hp.quniform('num_hidden_units', 10, 100, 1) Learning_rate_space = hp.uniform('learning_rate', 0.01, 1) For example, if we have a hyperparameter learning_rate that can take on values between 0.01 and 1, and a hyperparameter num_hidden_units that can take on values between 10 and 100, we can define the search space like this: This can be done using the hp module from scikit-optimize. Now, we can define the search space for the hyperparameters. Step 3: Define Search Space and Optimization Procedure Optimizer = BayesianOptimization(f = internal_method, # Create a BayesianOptimization optimizer and optimize the function # bayes_opt requires this to be a dictionary. X_train_scaled = min_max_sclr.fit_transform(X_trn) X_trn, X_tst, y_train, y_test = train_test_split(X, y, # Create training sets using random distribution of 48 # Retrieve dataset components data and target Test_dataset = load_data_set("car_speeding_tickets.csv") # Load the wine data set using load_wine() # C: SVC hyper parameter to optimize for. # Define the Internal method for optimization # - date, latitude, longitude, car, speed, ticketed, expected result # We will use a custom CSV file to get the test data for this test This function will be used to evaluate the performance of different sets of hyperparameters.įrom bayes_opt import BayesianOptimization, UtilityFunctionįrom sklearn.model_selection import cross_val_scoreįrom sklearn.preprocessing import MinMaxScalerįrom sklearn.model_selection import train_test_splitįrom trics import roc_auc_score Next, we will define a function that takes in a set of hyperparameters and returns the cross-validated mean performance of a model trained with those hyperparameters.
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