import jsonĬlass MyLogReg( LogisticRegression): # Override the class constructor def _init_( self, C= 1.0, solver= 'liblinear', max_iter= 100, X_train= None, Y_train= None):
Some additional data we could store with this approach is, for example, a cross-validation score on the training set, test data, accuracy score on the test data, etc. The new class, called MyLogReg, then implements the methods save_json and load_json for saving and restoring to/from a JSON file, respectively.įor simplicity, we'll save only three model parameters and the training data. Since we want to save all of this data in a single object, one possible way to do it is to create a new class which inherits from the model class, which in our example is LogisticRegression. This approach allows us to select the data which needs to be saved, such as the model parameters, coefficients, training data, and anything else we need. The following shows an example of manually saving and restoring objects using JSON.
#The forest save file full#
Anyway, whenever you want to have full control over the save and restore process, the best way is to build your own functions manually. Some of these reasons are discussed later in the Compatibility Issues section.
#The forest save file manual#
Manual Save and Restore to JSONĭepending on your project, many times you would find Pickle and Joblib as unsuitable solutions. Joblib also allows different compression methods, such as 'zlib', 'gzip', 'bz2', and different levels of compression. In case your model contains large arrays of data, each array will be stored in a separate file, but the save and restore procedure will remain the same. While Pickle requires a file object to be passed as an argument, Joblib works with both file objects and string filenames. format( 100 * score))Īs seen from the example, the Joblib library offers a bit simpler workflow compared to Pickle. # Calculate the accuracy score and predict target values # Load from file with open(pkl_filename, 'rb') as file: Pkl_filename = "pickle_model.pkl" with open(pkl_filename, 'wb') as file: # Create your model here (same as above) # Save to file in the current working directory The loaded model is then used to calculate the accuracy score and predict outcomes on new unseen (test) data. In the following few lines of code, the model which we created in the previous step is saved to file, and then loaded as a new object called pickled_model.
The goal is to save the model's parameters and coefficients to file, so you don't need to repeat the model training and parameter optimization steps again on new data. Using the fit method, the model has learned its coefficients which are stored in ef_. Penalty= 'l2', random_state= None, solver= 'liblinear', tol= 0.0001, Intercept_scaling= 1, max_iter= 20, multi_class= 'ovr', n_jobs= 3, the ones which produce highest estimated accuracy.Īnd our resulting model: LogisticRegression(C= 0.1, class_weight= None, dual= False, fit_intercept= True, We assume that you have previously found the optimal parameters of the model, i.e. Now let's create the model with some non-default parameters and fit it to the training data. Xtrain, Xtest, Ytrain, Ytest = train_test_split(data.data, data.target, test_size= 0.3, random_state= 4) from sklearn.linear_model import LogisticRegressionįrom sklearn.model_selection import train_test_split Let's import the needed libraries, load the data, and split it in training and test sets. In our example we'll use a Logistic Regression model and the Iris dataset. Initially, let's create one scikit-learn model. None of these approaches represents an optimal solution, but the right fit should be chosen according to the needs of your project. Afterwards, we look at the Joblib library which offers easy (de)serialization of objects containing large data arrays, and finally we present a manual approach for saving and restoring objects to/from JSON (JavaScript Object Notation). The first tool we describe is Pickle, the standard Python tool for object (de)serialization. In this article, we look at three possible ways to do this in Python and scikit-learn, each presented with its pros and cons. This saving procedure is also known as object serialization - representing an object with a stream of bytes, in order to store it on disk, send it over a network or save to a database, while the restoring procedure is known as deserialization. On many occasions, while working with the scikit-learn library, you'll need to save your prediction models to file, and then restore them in order to reuse your previous work to: test your model on new data, compare multiple models, or anything else.