fire_prediction_training_loop.py

models = {}
import time
for window_size in range(1,11):
    time_start = time.time()
    key = f'{window_size}year_window'
    print(f'Training linear model for {key}')
    models[f'linear_{key}'] = LogisticRegression(random_state=321, max_iter=1000, solver='saga').fit(data_train_x[key], data_train_y[key]>0.01)

    n_est = 20
    print(f'Training gb{n_est} model for {key}')
    models[f'gb_{n_est}_{key}'] = GradientBoostingClassifier(random_state=321, n_estimators=n_est).fit(data_train_x[key], data_train_y[key]>0.01)

    n_est = 200
    print(f'Training gb{n_est} model for {key}')
    models[f'gb_{n_est}_{key}'] = GradientBoostingClassifier(random_state=321, n_estimators=n_est).fit(data_train_x[key], data_train_y[key]>0.01)
    
    c_weight = 30
    print(f'Training SVC{c_weight} mdoel for {key}')
    models[f'SVC{c_weight}_{key}'] = SVC(random_state=321, probability=True, class_weight = {0:1.0, 1:c_weight}).fit(data_train_x[key], data_train_y[key]>0.01)

    c_weight = 300
    print(f'Training SVC{c_weight} model for {key}')
    models[f'SVC{c_weight}_{key}'] = SVC(random_state=321, probability=True, class_weight = {0:1.0, 1:c_weight}).fit(data_train_x[key], data_train_y[key]>0.01)
    print(f'elapsed: {time.time() - time_start}')