def dimension_reduction():
# save_model_file()
constants = GlobalConstants()
model = Model()
features = model.load_model('cm_np')
redn = DimensionReduction(dimension_reduction_model=constants.PCA,
extractor_model=constants.CM,
matrix=features,
conversion=True,
k_value=500)
redn.execute()
pass
def main():
"""Main function for the Task 8"""
k_value = get_input_k()
while k_value > 8:
print("Please enter a value of k within 8")
k_value = get_input_k()
folder = get_input_folder()
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print("k-value: {}\nFolder: {}".format(k_value, folder))
print(global_constants.LINE_SEPARATOR)
dim_red = DimensionReduction(None,
"NMF",
k_value,
image_metadata=True,
folder_metadata=folder)
w, h, model = dim_red.execute()
# printing the term weight
print_tw(w, h, image_metadata=True)
# save to csv
filename = "task8" + "_" + str(k_value)
CSVReader().save_to_csv(w, h, filename, image_metadata=True)
print("Please check the CSV file: output/{}.csv".format(filename))
def get_x_train(fea_ext_mod, dim_red_mod, k_value, train_set):
model_interact = Model()
dim_reduction = DimensionReduction(fea_ext_mod,
dim_red_mod,
k_value,
folder_metadata=train_set,
metadata_collection="labelled")
obj_lat, feat_lat, model = dim_reduction.execute()
filename = "{0}_{1}_{2}_{3}".format(fea_ext_mod, dim_red_mod, str(k_value),
os.path.basename(train_set))
model_interact.save_model(model=model, filename=filename)
return obj_lat
def main():
feature_extraction_model = task6.feature_extraction_model
dimension_reduction_model = task6.dimension_reduction_model
k_value_for_ss_similarity = 10
given_k_value = get_input_k()
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print("k-value: {}".format(given_k_value))
print(global_constants.LINE_SEPARATOR)
dim_reduction = DimensionReduction(feature_extraction_model, dimension_reduction_model, k_value_for_ss_similarity)
# original feature vectors
obj_feat_matrix = dim_reduction.get_object_feature_matrix()
# get the img IDs from the database for images in the fit model
img_set = pd.DataFrame({"imageId": obj_feat_matrix['imageId']})
# get the metadata for each image with given subject id
subject_data = dim_reduction.get_metadata("imageName", list(set(img_set["imageId"].tolist())))
# unique subject IDs in dataset
dataset_subject_ids = set((subject_data)["id"])
subject_subject_matrix = []
m_value = len(img_set)
starttime = time.time()
model = task6.load_model(dim_reduction, feature_extraction_model, dimension_reduction_model,
k_value_for_ss_similarity)
folder = path.basename(path.dirname(obj_feat_matrix['path'][0]))
for i, subjectid in enumerate(dataset_subject_ids):
given_subject_images = dim_reduction.get_metadata("id", list([subjectid]))["imageName"].tolist()
image_list_for_subject = list(set(given_subject_images).intersection(set(img_set["imageId"].tolist())))
similar_subjects = task6.find_similar_subjects(subjectid, image_list_for_subject, model,
img_set, dim_reduction, m_value, folder)
subject_subject_matrix.append(np.asarray(list(similar_subjects.values())))
print("\nTime taken to create subject subject matrix: {}\n".format(time.time() - starttime))
# perform nmf on subject_subject_matrix
# given_k_value = 1
matrix = pd.DataFrame(data={'imageId': list(dataset_subject_ids), 'featureVector': subject_subject_matrix})
dim_red = DimensionReduction(None, "NMF", given_k_value, subject_subject=True, matrix=matrix)
w, h, model = dim_red.execute()
# display latent semantics
# printing the term weight
print_tw(w, h, subject_subject=True)
# save to csv
filename = "task7" + '_' + str(given_k_value)
CSVReader().save_to_csv(w, None, filename, subject_subject=True)
print("Please check the CSV file: output/{}.csv".format(filename))
def run_task3(feature_extraction_model, dimension_reduction_model, label,
k_value):
"""Main function for the Task3"""
# Performs the dimensionality reduction
dim_reduction = DimensionReduction(feature_extraction_model,
dimension_reduction_model, k_value,
label)
obj_feature = dim_reduction.get_object_feature_matrix()
obj_lat, feat_lat, model = dim_reduction.execute()
# Saves the returned model
filename = "{0}_{1}_{2}_{3}".format(feature_extraction_model,
dimension_reduction_model,
label.replace(" ", ''), str(k_value))
model_interact.save_model(model=model, filename=filename)
def compute_latent_semantic_for_label(fea_ext_mod, dim_red_mod, label, k_value,
folder):
# p2task5.run_task3(fea_ext_mod, dim_red_mod, label, k_value)
dim_reduction = DimensionReduction(fea_ext_mod,
dim_red_mod,
k_value,
label,
folder_metadata=folder,
metadata_collection="labelled")
obj_lat, feat_lat, model = dim_reduction.execute()
# Saves the returned model
filename = "{0}_{1}_{2}_{3}_{4}".format(fea_ext_mod, dim_red_mod, label,
str(k_value),
os.path.basename(folder))
model_interact.save_model(model=model, filename=filename)
return obj_lat, feat_lat, model
def main():
"""Main function for the Task3"""
feature_extraction_model = get_input_feature_extractor_model()
dimension_reduction_model = get_input_dimensionality_reduction_model()
k_value = get_input_k()
label = get_input_image_label()
print(global_constants.LINE_SEPARATOR)
print("User Inputs summary")
print(global_constants.LINE_SEPARATOR)
print(
"Feature Extraction Model: {}\nDimensionality Reduction Model: {}\nk-value: {}"
.format(feature_extraction_model, dimension_reduction_model, k_value))
print(global_constants.LINE_SEPARATOR)
print(global_constants.LINE_SEPARATOR)
print("Saving the metadata to MongoDB")
print(global_constants.LINE_SEPARATOR)
csv_reader.save_hand_csv_mongo("HandInfo.csv")
print(global_constants.LINE_SEPARATOR)
# Performs the dimensionality reduction
dim_reduction = DimensionReduction(feature_extraction_model,
dimension_reduction_model, k_value,
label)
obj_lat, feat_lat, model = dim_reduction.execute()
# Saves the returned model
filename = "{0}_{1}_{2}_{3}".format(feature_extraction_model,
dimension_reduction_model,
label.replace(" ", ''), str(k_value))
model_interact.save_model(model=model, filename=filename)
# Printing the term weight pairs
data_tw = print_tw(obj_lat, feat_lat)
# save term weight pairs to csv
filename = "task3_{}_{}_{}_{}".format(feature_extraction_model,
dimension_reduction_model, label,
k_value)
csv_reader.save_to_csv(obj_lat, feat_lat, filename)
print("Please check the CSV file: output/{}.csv".format(filename))