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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from visualization_functions import plt_vis, sns_vis
from parameters import data_colors, data_shapes, methods_colors, methods_shapes, augmentation_colors, augmentation_shapes, augmentation_shapes_same, models_colors, models_shapes
MODEL_COMPARISON = False
AUGMENTATION_COMPARISON = False
DATASET_COMPARISON = False
METHOD_COMPARISON = False
SM_GI_COMPARISION = False
INPUT_COMPARISION = True
ENSEMBLE_COMPARISION = False
BEST_MODELS_COMPARISION = False
model_name = "_resnet18_out2"
folder_models = "comparision_models_graphs/"
folder_all_attribrutes = "comparision_all_attributes_graphs/"
folder_N1_N2_N3 = "comparision_N1_N2_N3_graphs/"
folder_inputs_comp = "comparision_inputs_graphs/"
folder_ensembles = "comparision_ensembles_graphs/"
if MODEL_COMPARISON: # visualize resnet18 vs resnet18.a2_in1k
table1 = 'models.xlsx'
data_real = pd.read_excel(table1)
resnet18_var_subtable = data_real[['Model name', 'Test accuracy', 'Balanced test accuracy', 'Dataset name', 'Method']].dropna()
sns_vis(resnet18_var_subtable, 'Method', 'Test accuracy', 'Model name', 'Dataset name', models_colors, data_shapes, "Model comparison")
plt.savefig(folder_models + "model_comparison_TA_method.png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Method', 'Balanced test accuracy', 'Model name', 'Dataset name', models_colors, data_shapes, "Model comparison")
plt.savefig(folder_models + "model_comparison_BTA_method.png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Dataset name', 'Test accuracy', 'Model name', 'Method', models_colors, methods_shapes, "Model comparison")
plt.savefig(folder_models + "model_comparison_TA_dataset.png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Dataset name', 'Balanced test accuracy', 'Model name', 'Method', models_colors, methods_shapes, "Model comparison")
plt.savefig(folder_models + "model_comparison_BTA_dataset.png", dpi=300, bbox_inches='tight')
table2 = 'resnet18.xlsx'
resnet18_var_data = pd.read_excel(table2)
resnet18_var_subtable = resnet18_var_data[['Augmentation', 'Test accuracy', 'Balanced test accuracy', 'Dataset name', 'Method']].dropna()
if AUGMENTATION_COMPARISON:
#sns_vis(resnet18_var_subtable, 'Augmentation', 'Test accuracy', 'Method', 'Dataset name', methods_colors, data_shapes, "Augmentation comparison")
#sns_vis(resnet18_var_subtable, 'Augmentation', 'Balanced test accuracy', 'Method', 'Dataset name', methods_colors, data_shapes, "Augmentation comparison")
sns_vis(resnet18_var_subtable, 'Augmentation', 'Test accuracy', 'Dataset name', 'Method', data_colors, methods_shapes, "Augmentation comparison", "out")
plt.savefig(folder_all_attribrutes + "augmentation_comparison_TA"+ model_name +".png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Augmentation', 'Balanced test accuracy', 'Dataset name', 'Method', data_colors, methods_shapes, "Augmentation comparison", "out")
plt.savefig(folder_all_attribrutes + "augmentation_comparison_BTA"+ model_name +".png", dpi=300, bbox_inches='tight')
if DATASET_COMPARISON:
#sns_vis(resnet18_var_subtable, 'Dataset name', 'Test accuracy', 'Method', 'Augmentation', methods_colors, augmentation_shapes, "Dataset comparison")
#sns_vis(resnet18_var_subtable, 'Dataset name', 'Balanced test accuracy', 'Method', 'Augmentation', methods_colors, augmentation_shapes, "Dataset comparison")
sns_vis(resnet18_var_subtable, 'Dataset name', 'Test accuracy', 'Augmentation', 'Method', augmentation_colors, methods_shapes, "Dataset comparison", "out")
plt.savefig(folder_all_attribrutes + "dataset_comparison_TA"+ model_name +".png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Dataset name', 'Balanced test accuracy', 'Augmentation', 'Method', augmentation_colors, methods_shapes, "Dataset comparison", "out")
plt.savefig(folder_all_attribrutes + "dataset_comparison_BTA"+ model_name +".png", dpi=300, bbox_inches='tight')
if METHOD_COMPARISON:
#sns_vis(resnet18_var_subtable, 'Method', 'Test accuracy', 'Dataset name', 'Augmentation', data_colors, augmentation_shapes, "Method comparison")
#sns_vis(resnet18_var_subtable, 'Method', 'Balanced test accuracy', 'Dataset name', 'Augmentation', data_colors, augmentation_shapes, "Dataset comparison")
sns_vis(resnet18_var_subtable, 'Method', 'Test accuracy', 'Augmentation', 'Dataset name', augmentation_colors, data_shapes, "Method comparison", "out")
plt.savefig(folder_all_attribrutes + "method_comparison_TA"+ model_name +".png", dpi=300, bbox_inches='tight')
sns_vis(resnet18_var_subtable, 'Method', 'Balanced test accuracy', 'Augmentation', 'Dataset name', augmentation_colors, data_shapes, "Method comparison", "out")
plt.savefig(folder_all_attribrutes + "method_comparison_BTA"+ model_name +".png", dpi=300, bbox_inches='tight')
if SM_GI_COMPARISION:
table3 = "comparision_N1_N2_N3.xlsx"
comparision_n1_n2_n3 = pd.read_excel(table3)
comparision_n1_n2_n3_subtable = comparision_n1_n2_n3[['Augmentation', 'Test accuracy', 'Balanced test accuracy', 'Train set', 'Test set', 'Method']].dropna()
augmentation_types = comparision_n1_n2_n3_subtable['Augmentation'].unique()
subtables = {}
for augm in augmentation_types:
subtables[augm] = comparision_n1_n2_n3_subtable[(comparision_n1_n2_n3_subtable['Augmentation'] == augm) & (comparision_n1_n2_n3_subtable['Method'] == 'SM-GI')]
sns_vis(subtables[augm], 'Train set', 'Balanced test accuracy', 'Test set', 'Method', data_colors, methods_shapes, "Dataset comparison", "out")
plt.savefig(folder_N1_N2_N3 + augm + "_ SM-GI_" + "_data_comparison_BTA.png", dpi=300, bbox_inches='tight')
subtables[augm] = comparision_n1_n2_n3_subtable[(comparision_n1_n2_n3_subtable['Augmentation'] == augm) & (comparision_n1_n2_n3_subtable['Method'] == 'SM-GI-Avg')]
sns_vis(subtables[augm], 'Train set', 'Balanced test accuracy', 'Test set', 'Method', data_colors, methods_shapes, "Dataset comparison", "out")
plt.savefig(folder_N1_N2_N3 + augm + "_ SM-GI-AVG_" + "_data_comparison_BTA.png", dpi=300, bbox_inches='tight')
subtables[augm] = comparision_n1_n2_n3_subtable[comparision_n1_n2_n3_subtable['Augmentation'] == augm]
sns_vis(subtables[augm], 'Train set', 'Balanced test accuracy', 'Test set', 'Method', data_colors, methods_shapes, "Dataset comparison", "out")
plt.savefig(folder_N1_N2_N3 + augm + "_data_comparison_BTA.png", dpi=300, bbox_inches='tight')
if INPUT_COMPARISION:
table4 = "comparision_inputs.xlsx"
comparision_inputs = pd.read_excel(table4)
comparision_inputs_subtable = comparision_inputs[['Augmentation', 'Balanced test accuracy', 'Method', 'Dataset name']].dropna()
augmentation_types = comparision_inputs_subtable['Augmentation'].unique()
subtables = {}
for augm in augmentation_types:
subtables[augm] = comparision_inputs_subtable[comparision_inputs_subtable['Augmentation'] == augm]
sns_vis(subtables[augm], 'Method', 'Balanced test accuracy', 'Dataset name', None, data_colors, None, legend = "out")
plt.savefig(folder_inputs_comp + augm + "_method_comparison_BTA_same_axis.png", dpi=300, bbox_inches='tight')
sns_vis(comparision_inputs_subtable, 'Method', 'Balanced test accuracy', 'Dataset name', 'Augmentation', data_colors, augmentation_shapes, "Method comparison", "out")
plt.savefig(folder_inputs_comp + "all_aug_method_comparison_BTA_same_axis.png", dpi=300, bbox_inches='tight')
if ENSEMBLE_COMPARISION:
table5 = "comparision_ensembles.xlsx"
comparision_ensembles = pd.read_excel(table5)
comparision_ensembles_subtable = comparision_ensembles[['Augmentation', 'Balanced test accuracy', 'Method', 'Dataset name']].dropna()
augmentation_types = comparision_ensembles_subtable['Augmentation'].unique()
subtables = {}
"""for augm in augmentation_types:
subtables[augm] = comparision_ensembles_subtable[comparision_ensembles_subtable['Augmentation'] == augm]
sns_vis(subtables[augm], 'Method', 'Balanced test accuracy', 'Dataset name', None, data_colors, None, legend = "out")
plt.savefig(folder_ensembles + augm + "_ensemble_comparison_BTA.png", dpi=300, bbox_inches='tight')"""
sns_vis(comparision_ensembles_subtable, 'Augmentation', 'Balanced test accuracy', 'Dataset name', 'Method', data_colors, methods_shapes, legend = "out")
plt.savefig(folder_ensembles + "all_aug_ensemble_comparison_BTA_4.png", dpi=300, bbox_inches='tight')
if BEST_MODELS_COMPARISION:
table6 = "comparision_best_models.xlsx"
comparision_best_models = pd.read_excel(table6)
comparision_best_models_subtable = comparision_best_models[['Augmentation', 'Balanced test accuracy', 'Method', 'Dataset name']].dropna()
sns_vis(comparision_best_models_subtable, 'Augmentation', 'Balanced test accuracy', 'Dataset name', 'Method', data_colors, methods_shapes, legend = "out")
plt.savefig("best_models_comparison_BTA.png", dpi=300, bbox_inches='tight')
table7 = "comparision_best_models_aug_test_set.xlsx"
comparision_best_models_aug_test_set = pd.read_excel(table7)
comparision_best_models_aug_test_set_subtable = comparision_best_models_aug_test_set[['Augmentation', 'Balanced test accuracy', 'Method', 'Dataset name']].dropna()
sns_vis(comparision_best_models_aug_test_set_subtable, 'Augmentation', 'Balanced test accuracy', 'Dataset name', 'Method', data_colors, methods_shapes, legend = "out")
plt.savefig("best_models_comparison_aug_test_set_BTA.png", dpi=300, bbox_inches='tight')
#plt.show()