start_time = time.time()
decision_tree.fit(examples=examples, tree_builder=tree_builder)
end_time = time.time()
run_time_sec = end_time - start_time
run_time_ms = 1000.0 * run_time_sec
print("run time (ms):", run_time_ms)
print(decision_tree)
test_examples = []
for ex_wr_sp in training_examples_collection.get_example_wrappers_sp():
example_clause = build_clause(ex_wr_sp, training=False)
example = Example(data=example_clause, label=ex_wr_sp.label)
example.classification_term = ex_wr_sp.classification_term
test_examples.append(example)
statistics_handler = verify(decision_tree, test_examples)
accuracy = statistics_handler.get_accuracy()
print("accuracy:", accuracy)
first_test_example = test_examples[0]
prediction = decision_tree.predict(first_test_example)
print("prediction:", prediction)
print('=== END tree building ===\n')
print(decision_tree)
def run_experiment(file_name_data: FileNameData, fold_info_controller: FoldInfoController,
default_handler: DefaultHandler,
hide_printouts: bool = False,
filter_out_unlabeled_examples=False,
debug_printing_options=DebugPrintingOptions()):
# -- create output directory
if not os.path.exists(file_name_data.output_dir):
os.makedirs(file_name_data.output_dir)
print("start", file_name_data.test_name)
save_stdout = sys.stdout
if hide_printouts:
sys.stdout = open(os.devnull, "w")
experiment = Experiment()
experiment.preprocess_examples_and_background_knowledge(file_name_data, filter_out_unlabeled_examples,
debug_printing_options)
fold_example_splitter = FoldExampleSplitter(fold_info_controller)
for fold_info, training_examples_collection, test_examples in fold_example_splitter.fold_split_generator(
experiment): # type: FoldInfo, ExampleCollection, List[ExampleWrapper]
print("fold: ", fold_info.index)
training_examples = default_handler.get_transformed_example_list(training_examples_collection)
tree_builder = default_handler.get_default_decision_tree_builder(experiment.language,
experiment.prediction_goal) # type: TreeBuilder
decision_tree = DecisionTree()
start_build_time = time.time()
decision_tree.fit(examples=training_examples, tree_builder=tree_builder)
if debug_printing_options.tree_building:
print("unpruned:")
print(decision_tree)
decision_tree.prune(prune_leaf_nodes_with_same_label)
end_build_time = time.time()
# run_time_sec = end_time - start_time
build_time_sec = end_build_time - start_build_time
build_time_ms = 1000.0 * build_time_sec
fold_info.dt_build_time_ms = build_time_ms
if debug_printing_options.tree_building or debug_printing_options.tree_pruning:
print("build time (ms):", build_time_ms)
print("pruned")
print(decision_tree)
# write out tree
tree_fname = os.path.join(file_name_data.output_dir,
default_handler.back_end_name + "_" + fold_info_controller.fname_prefix_fold + '_fold' + str(
fold_info.index) + ".tree")
write_out_tree(tree_fname, decision_tree)
start_test_example_transformation = time.time()
test_examples_reformed = default_handler.get_transformed_test_example_list(test_examples)
# for ex_wr_sp in test_examples:
# example_clause = build_clause(ex_wr_sp, training=False)
# example = Example(data=example_clause, label=ex_wr_sp.label)
# example.classification_term = ex_wr_sp.classification_term
# test_examples_reformed.append(example)
end_test_example_transformation = time.time()
statistics_handler = verify(decision_tree, test_examples_reformed)
accuracy = statistics_handler.get_accuracy()
if debug_printing_options.get_classifier:
print("accuracy:", accuracy)
# ===================
end_time = time.time()
# time in seconds: # time in seconds
elapsed_time = end_time - start_build_time - (end_test_example_transformation - start_test_example_transformation)
elapsed_time_ms = 1000.0 * elapsed_time
fold_info.execution_time_ms = elapsed_time_ms
accuracy, _ = statistics_handler.get_accuracy()
fold_info.accuracy = accuracy
statistics_fname = os.path.join(file_name_data.output_dir,
default_handler.back_end_name + "_" + fold_info_controller.fname_prefix_fold + '_fold'
+ str(fold_info.index) + ".statistics")
# statistics_fname = file_name_data.output_dir + .fname_prefix_fold + '_fold' + str(fold_index) + ".statistics"
statistics_handler.write_out_statistics_to_file(statistics_fname)
with open(statistics_fname, 'a') as f:
f.write('\n\nnb of TRAINING ex: ' + str(len(training_examples)) + "\n")
f.write('nb of TEST ex: ' + str(len(test_examples)) + "\n\n")
nb_nodes = decision_tree.get_nb_of_nodes()
fold_info.n_nodes = nb_nodes
f.write("total nb of nodes: " + str(nb_nodes) + "\n")
nb_inner_nodes = decision_tree.get_nb_of_inner_nodes()
fold_info.n_inner_nodes = nb_inner_nodes
f.write("nb of internal nodes: " + str(nb_inner_nodes) + "\n\n")
f.write("execution time of fold (ms): " + str(elapsed_time_ms) + "\n")
# verify(decision_tree, )
# ------------------------------------------
# --- DESTRUCTION (necessary for Django) ---
decision_tree.destruct()
for ex in training_examples:
ex.destruct()
mean_accuracy_of_folds = statistics.mean(
[fold_info.accuracy for (_index, fold_info) in fold_info_controller.fold_infos.items()])
dt_build_times = [fold_info.dt_build_time_ms for (_index, fold_info) in fold_info_controller.fold_infos.items()]
mean_decision_tree_build_time = statistics.mean(dt_build_times)
fold_execution_times_ms = [
fold_info.execution_time_ms for (_index, fold_info) in fold_info_controller.fold_infos.items()
]
total_execution_time_ms_of_cross_validation = sum(fold_execution_times_ms)
mean_execution_time_ms_of_folds = statistics.mean(fold_execution_times_ms)
folds_total_nb_of_nodes = [
fold_info.n_nodes for (_index, fold_info) in fold_info_controller.fold_infos.items()
]
mean_total_nb_of_nodes = statistics.mean(folds_total_nb_of_nodes)
fold_nb_of_inner_nodes = [
fold_info.n_inner_nodes for (_index, fold_info) in fold_info_controller.fold_infos.items()
]
mean_nb_of_inner_nodes = statistics.mean(fold_nb_of_inner_nodes)
if debug_printing_options.debug_printing_classification:
print("mean decision tree build time (ms):", mean_decision_tree_build_time)
print("total time cross (sum folds): " + str(total_execution_time_ms_of_cross_validation) + "\n")
statistics_fname = os.path.join(file_name_data.output_dir,
default_handler.back_end_name + "_" + fold_info_controller.fname_prefix_fold + ".statistics")
# statistics_fname = fd.dir_output_files + fd.fname_prefix_fold + ".statistics"
with open(statistics_fname, 'w') as f:
f.write("mean accuracy: " + str(mean_accuracy_of_folds) + "\n")
f.write("mean decision tree build time (ms):" + str(mean_decision_tree_build_time) + "\n")
f.write("mean fold execution time (ms):" + str(mean_execution_time_ms_of_folds) + "\n")
f.write("mean total nb of nodes:" + str(mean_total_nb_of_nodes) + "\n")
f.write("mean nb of inner nodes:" + str(mean_nb_of_inner_nodes) + "\n")
f.write("total time cross (sum folds) (ms): " + str(total_execution_time_ms_of_cross_validation) + "\n")
if hide_printouts:
sys.stdout = save_stdout
print("finished", file_name_data.test_name)