Al-performance_plot

performance_plot1.py

def performance_plot(fully_supervised_accuracy, dic, models, selection_functions, Ks, repeats):  
    fig, ax = plt.subplots()
    ax.plot([0,500],[fully_supervised_accuracy, fully_supervised_accuracy],label = 'algorithm-upper-bound')
    for model_object in models:
      for selection_function in selection_functions:
        for idx, k in enumerate(Ks):
            x = np.arange(float(Ks[idx]), 500 + float(Ks[idx]), float(Ks[idx]))            
            Sum = np.array(dic[model_object][selection_function][k][0])
            for i in range(1, repeats):
                Sum = Sum + np.array(dic[model_object][selection_function][k][i])
            mean = Sum / repeats
            ax.plot(x, mean ,label = model_object + '-' + selection_function + '-' + str(k))
    ax.legend()
    ax.set_xlim([50,500])
    ax.set_ylim([40,100])
    ax.grid(True)
    plt.show()

models_str = ['SvmModel', 'RfModel', 'LogModel']
selection_functions_str = ['RandomSelection', 'MarginSamplingSelection', 'EntropySelection']
Ks_str = ['250','125','50','25','10'] 
repeats = 1
random_forest_upper_bound = 97.
svm_upper_bound = 94.
log_upper_bound = 92.47
total_experiments = len(models_str) * len(selection_functions_str) * len(Ks_str) * repeats

print('So which is the better model? under the stopping condition and hyper parameters - random forest is the winner!')
performance_plot(random_forest_upper_bound, d, ['RfModel'] , selection_functions_str    , Ks_str, 1)
performance_plot(svm_upper_bound, d, ['SvmModel'] , selection_functions_str    , Ks_str, 1)
performance_plot(log_upper_bound, d, ['LogModel'] , selection_functions_str    , Ks_str, 1)