demo_model_save_load.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jan 14 20:24:30 2021

@author: yuezh
"""
import os
import sys

import scipy as sp

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from joblib import dump, load

from pyod.models.iforest import IForest
from pyod.models.lof import LOF
from pyod.utils.data import evaluate_print

from combo.models.score_comb import majority_vote, maximization, average

# suppress warnings
import warnings

warnings.filterwarnings("ignore")

# temporary solution for relative imports in case combo is not installed
# if combo is installed, no need to use the following line
sys.path.append(
    os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))

from suod.models.base import SUOD
from suod.utils.utility import get_estimators_small

if __name__ == "__main__":
    # load files
    mat_file_list = [
        'cardio.mat',
        # 'satellite.mat',
        # 'satimage-2.mat',
        # 'mnist.mat',
    ]

    mat_file = mat_file_list[0]
    mat_file_name = mat_file.replace('.mat', '')
    print("\n... Processing", mat_file_name, '...')
    mat = sp.io.loadmat(os.path.join('', 'datasets', mat_file))

    X = mat['X']
    y = mat['y']

    # standardize data to be digestible for most algorithms
    X = StandardScaler().fit_transform(X)

    X_train, X_test, y_train, y_test = \
        train_test_split(X, y, test_size=0.4, random_state=42)

    contamination = y.sum() / len(y)
    base_estimators = get_estimators_small(contamination)

    model = SUOD(base_estimators=base_estimators, n_jobs=6, bps_flag=True,
                 contamination=contamination, approx_flag_global=True)

    model.fit(X_train)  # fit all models with X
    model.approximate(X_train)  # conduct model approximation if it is enabled
    
    # save the model
    dump(model, 'model.joblib')
    # load the model
    model = load('model.joblib')

    predicted_labels = model.predict(X_test)  # predict labels
    predicted_scores = model.decision_function(X_test)  # predict scores
    predicted_probs = model.predict_proba(X_test)  # predict scores

    ###########################################################################
    # model evaluation with the loaded model
    evaluate_print('majority vote', y_test, majority_vote(predicted_labels))
    evaluate_print('average', y_test, average(predicted_scores))
    evaluate_print('maximization', y_test, maximization(predicted_scores))