def test_over_column(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/Over column.csv')
controller.popup_format_error = Mock(return_value='')
controller.model_accuracy = Mock(return_value='')
controller.train_model()
self.assertTrue(controller.popup_format_error.called)
def test_1000_record_valid_csv(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/1000 rows.csv')
controller.model_accuracy = Mock(return_value='')
controller.bar = Mock(return_value='')
controller.train_model()
self.assertTrue(controller.model_accuracy.called)
def test_call_pdf_file(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/pdf.pdf')
controller.popup_msg = Mock(return_value='')
controller.bar = Mock(return_value='')
controller.train_model()
self.assertTrue(controller.popup_msg.called)
def test_one_row_valid_csv(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/Data.csv')
controller.model_accuracy = Mock(return_value='')
controller.bar = Mock(return_value='')
controller.train_model()
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/One row classifier.csv')
controller.model_accuracy = Mock(return_value='')
controller.popup_output = Mock(return_value='')
controller.classifier_function()
self.assertTrue(controller.popup_output.called)
def test_classifier_with_module(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/Data.csv')
controller.model_accuracy = Mock(return_value='')
controller.popup_output = Mock(return_value='')
controller.bar = Mock(return_value='')
# popup that called after the classifier finished successfully
controller.popup_output = Mock(return_value='')
# First train the module
controller.train_model()
# Then run the classifier
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/New dogs.csv')
controller.classifier_function()
self.assertTrue(controller.popup_output.called)
def test_empty_path(self):
controller.askopenfilename = Mock(return_value='')
controller.popup_msg = Mock(return_value='')
controller.bar = Mock(return_value='')
controller.train_model()
self.assertTrue(controller.popup_msg.called)
def test_one_row_valid_csv(self):
controller.askopenfilename = Mock(
return_value='C:/school/guide_dog/resource/One row.csv')
controller.popup_msg = Mock(return_value='')
controller.train_model()
self.assertTrue(controller.popup_msg.called)
def main():
global show_hist
global show_pdfs
global show_post_plots
global threshold
x_seabass = ctrl.create_sample_population(30, 3, 400)
x_salmon = ctrl.create_sample_population(32, 2, 600)
# Create the dataset objects
sea_bass = DataObject(x_seabass)
salmon = DataObject(x_salmon)
total = DataObject(ctrl.concatenate_data_sets(x_seabass, x_salmon))
# Check p-sums
print(
"----------------------------------------------------------------------------------------------------------"
)
print("Sums probabilities")
sum_sea_bass = np.sum(sea_bass.get_probabilities()[0:] *
sea_bass.get_bin_width())
sum_salmon = np.sum(salmon.get_probabilities()[0:] *
salmon.get_bin_width())
print("Seabass: %f" % sum_sea_bass)
print("Salmon: %f" % sum_salmon)
print(
"----------------------------------------------------------------------------------------------------------"
)
# ToDo: calculate decision vector
# ToDo: calculate Accuracy
# ToDo: Test samples to trained Model
print(
"----------------------------------------------------------------------------------------------------------"
)
print("Probabilities")
p_sea_bass = sea_bass.get_probabilities()
p_salmon = salmon.get_probabilities()
print("Seabass: %s" % str(p_sea_bass))
print("Salmon: %s" % str(p_salmon))
print(
"----------------------------------------------------------------------------------------------------------"
)
# Posteriors informative
print(
"----------------------------------------------------------------------------------------------------------"
)
print("Informative posteriors")
p_sea_bass = sea_bass.get_probabilities()
p_salmon = salmon.get_probabilities()
post_sea_bass = ctrl.calc_posterior(p_sea_bass, p_salmon,
sea_bass.get_data_set(),
salmon.get_data_set())
post_salmon = ctrl.calc_posterior(p_salmon, p_sea_bass,
salmon.get_data_set(),
sea_bass.get_data_set())
print("Seabass: %s" % str(post_sea_bass))
print("Salmon: %s" % str(post_salmon))
print(
"----------------------------------------------------------------------------------------------------------"
)
# Non informative posteriors
print(
"----------------------------------------------------------------------------------------------------------"
)
print("Non-Informative posteriors")
post_sea_bass_non_inf = ctrl.calc_posterior_non_inf(p_sea_bass, p_salmon)
post_salmon_non_inf = ctrl.calc_posterior_non_inf(p_salmon, p_sea_bass)
print("Seabass: %s" % str(post_sea_bass_non_inf))
print("Salmon: %s" % str(post_salmon_non_inf))
print(
"----------------------------------------------------------------------------------------------------------"
)
# Create Groundtrough Vector
ground_truth_vec = ctrl.create_ground_truth_vector(x_salmon, x_seabass)
# Train Model
print(
"----------------------------------------------------------------------------------------------------------"
)
print("Training of Model")
result_training = ctrl.train_model(post_salmon_non_inf,
total.get_data_set(),
salmon.get_bin_width(),
salmon.get_bin_center(),
ground_truth_vec)
print("Accuracy: %s" % str(result_training))
print(
"----------------------------------------------------------------------------------------------------------"
)
# Plots
if show_post_plots:
bin_center_total = total.get_bin_center()
plots.plot_posteriors(post_sea_bass, post_salmon, bin_center_total,
"Seabass", "Salmon")
plots.plot_posteriors_non_inf(post_sea_bass_non_inf,
post_salmon_non_inf, bin_center_total,
"Seabass", "Salmon")
if show_pdfs:
plots.plot_pdf(total.get_bin_center(), sea_bass.get_probabilities(),
salmon.get_probabilities(), "Seabass", "Salmon")
if show_hist:
plots.plot_histogram(sea_bass.get_frequencies(), sea_bass.get_bins())
plots.plot_histogram(salmon.get_frequencies(), salmon.get_bins())
plots.plot_histogram(total.get_frequencies(), total.get_bins())
