def rf_max_depth():
"""Run evaluation of different RF max depths"""
rf_10 = rf.RF(
label = 'RF with max depth 10',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100,
max_depth = 10
)
rf_100 = rf.RF(
label = 'RF with max depth 100',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100,
max_depth = 100
)
rf_250 = rf.RF(
label = 'RF with max depth 250',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100,
max_depth = 250
)
rf_1000 = rf.RF(
label = 'RF with max depth 1000',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100,
max_depth = 1000
)
rf_none = rf.RF(
label = 'RF with no max depth',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100,
max_depth = None
)
run_training_and_tests(
'system_selection_3_rf_max_depth',
'kaggle',
[
rf_10,
rf_100,
rf_250,
rf_1000,
rf_none
],
n_iterations = 5, n_images = 10000, training_split = 0.5
)
def rf_forest_size():
"""Run evaluation of different RF sizes"""
rf_1 = rf.RF(
label = 'RF with 1 tree',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 1
)
rf_10 = rf.RF(
label = 'RF with 10 trees',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 10
)
rf_50 = rf.RF(
label = 'RF with 50 trees',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 50
)
rf_100 = rf.RF(
label = 'RF with 100 trees',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 100
)
rf_250 = rf.RF(
label = 'RF with 250 trees',
preprocessing = hsv_saturation,
features = [greyscale_histogram(bins = 64)],
n_estimators = 250
)
run_training_and_tests(
'system_selection_3_rf_forest_size',
'kaggle',
[
rf_1,
rf_10,
rf_50,
rf_100,
rf_250
],
n_iterations = 5, n_images = 10000, training_split = 0.5
)
def run_experiment():
"""Run experiment one, testing performance on kaggle data"""
optimal_svm = svm.SVM(label='Optimised SVM model',
preprocessing=hsv_saturation_threshold,
features=[haralick],
kernel='poly',
degree=3)
optimal_rf = rf.RF(label='Optimised RF model',
preprocessing=hsv_saturation,
features=[greyscale_histogram(bins=64)],
n_estimators=100,
max_depth=100)
run_training_and_tests('experiment_1_NLM_performance',
'kaggle', [optimal_rf, optimal_svm],
n_iterations=10,
n_training_images=5000,
n_test_images=20000)
def initial_grey_histogram_bins_evaluation():
"""Run evaluation of grey histogram bin sizes"""
rf_model_grey2 = rf.RF(
label='grey 2 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=2)]
)
rf_model_grey4 = rf.RF(
label='grey 4 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=4)]
)
rf_model_grey8 = rf.RF(
label='grey 8 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=8)]
)
rf_model_grey16 = rf.RF(
label='grey 16 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=16)]
)
rf_model_grey32 = rf.RF(
label='grey 32 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=32)]
)
rf_model_grey64 = rf.RF(
label='grey 64 bin RF',
preprocessing=[],
features=[greyscale_histogram(bins=64)]
)
svm_model_grey2 = svm.SVM(
label='grey 2 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=2)]
)
svm_model_grey4 = svm.SVM(
label='grey 4 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=4)]
)
svm_model_grey8 = svm.SVM(
label='grey 8 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=8)]
)
svm_model_grey16 = svm.SVM(
label='grey 16 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=16)]
)
svm_model_grey32 = svm.SVM(
label='grey 32 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=32)]
)
svm_model_grey64 = svm.SVM(
label='grey 64 bin SVM',
preprocessing=[],
features=[greyscale_histogram(bins=64)]
)
run_training_and_tests(
'system_selection_1_hist_grey',
'kaggle',
[
rf_model_grey2,
rf_model_grey4,
rf_model_grey8,
rf_model_grey16,
rf_model_grey32,
rf_model_grey64,
svm_model_grey2,
svm_model_grey4,
svm_model_grey8,
svm_model_grey16,
svm_model_grey32,
svm_model_grey64
],
n_iterations=5, n_images=10000, training_split=0.5
)
def hist_hu_moments_haralick_evaluation():
"""Run comparative evaluation of hu moments, haralick texture
attributes, colour histograms and greyscale histograms
"""
rf_model_haralick = rf.RF(
label='RF with haralick',
preprocessing=[],
features=[haralick]
)
rf_model_hu = rf.RF(
label='RF with hu moments',
preprocessing=[],
features=[hu_moments]
)
rf_model_grey_hist = rf.RF(
label='RF with grey hist',
preprocessing=[],
features=[greyscale_histogram(bins=64)]
)
rf_model_colour_hist = rf.RF(
label='RF with colour hist',
preprocessing=[],
features=[colour_histogram(bins=16)]
)
svm_model_haralick = svm.SVM(
label='SVM with haralick',
preprocessing=[],
features=[haralick]
)
svm_model_hu = svm.SVM(
label='SVM with hu moments',
preprocessing=[],
features=[hu_moments]
)
svm_model_grey_hist = svm.SVM(
label='SVM with grey hist',
preprocessing=[],
features=[greyscale_histogram(bins=64)]
)
svm_model_colour_hist = svm.SVM(
label='SVM with colour hist',
preprocessing=[],
features=[colour_histogram(bins=16)]
)
run_training_and_tests(
'system_selection_1_hist_hu_haralick',
'kaggle',
[
rf_model_haralick,
rf_model_hu,
rf_model_grey_hist,
rf_model_colour_hist,
svm_model_haralick,
svm_model_hu,
svm_model_grey_hist,
svm_model_colour_hist
],
n_iterations=5, n_images=10000, training_split=0.5)
def greyscale_histogram_with_filters():
"""Run evaluation of greyscale histograms with filters"""
rf_model_normal = rf.RF(label='RF with no preprocessing',
preprocessing=[],
features=[greyscale_histogram(bins=64)])
rf_model_hsv = rf.RF(label='RF with hsv',
preprocessing=[hsv_model],
features=[greyscale_histogram(bins=64)])
rf_model_hsv_is = rf.RF(label='RF with hsv and isolate saturation',
preprocessing=hsv_saturation,
features=[greyscale_histogram(bins=64)])
rf_model_hsv_is_thresh = rf.RF(
label='RF with hsv, isolate saturation, threshold',
preprocessing=hsv_saturation_threshold,
features=[greyscale_histogram(bins=64)])
rf_model_hsv_is_c = rf.RF(
label='RF with hsv, isolate saturation, contrast',
preprocessing=hsv_saturation_contrast,
features=[greyscale_histogram(bins=64)])
svm_model_normal = svm.SVM(label='SVM with no preprocessing',
preprocessing=[],
features=[greyscale_histogram(bins=64)])
svm_model_hsv = svm.SVM(label='SVM with hsv',
preprocessing=[hsv_model],
features=[greyscale_histogram(bins=64)])
svm_model_hsv_is = svm.SVM(label='SVM with hsv and isolate saturation',
preprocessing=hsv_saturation,
features=[greyscale_histogram(bins=64)])
svm_model_hsv_is_thresh = svm.SVM(
label='SVM with hsv, isolate saturation, threshold',
preprocessing=hsv_saturation_threshold,
features=[greyscale_histogram(bins=64)])
svm_model_hsv_is_c = svm.SVM(
label='SVM with hsv, isolate saturation, contrast',
preprocessing=hsv_saturation_contrast,
features=[greyscale_histogram(bins=64)])
run_training_and_tests('system_selection_2_grey_hist_preprocessing',
'kaggle', [
rf_model_normal,
rf_model_hsv,
rf_model_hsv_is,
rf_model_hsv_is_c,
rf_model_hsv_is_thresh,
svm_model_normal,
svm_model_hsv,
svm_model_hsv_is,
svm_model_hsv_is_c,
svm_model_hsv_is_thresh,
],
n_iterations=5,
n_images=10000,
training_split=0.5)