def train_nlu(data_path, configs, model_path): logging.basicConfig(filename=logfile, level=logging.DEBUG) training_data = load_data(data_path) trainer = Trainer(config.load(configs)) trainer.train(training_data) model_directory = trainer.persist(model_path, project_name='current', fixed_model_name='nlu') run_evaluation(data_path, model_directory)
def evaluate_model(): # evaluates a model and times it model_name = os.listdir('./tmp/models/default')[0] # get first (and only) model t = time() evaluate.run_evaluation('data/demo_test.md', './tmp/models/default/' + model_name) eval_time = time() - t return eval_time
def _get_nlu_evaluation_loss(model_path, metric, data_path): logger.info("Calculating '{}' loss.".format(metric)) evaluation_result = run_evaluation(data_path, model_path, confmat_filename=None) metric_result = evaluation_result['intent_evaluation'][metric] logger.info("{}: {}".format(metric, metric_result)) return 1 - metric_result
def evaluate_model(td_file, model_loc): # evaluates the model on the training data # wrapper for rasa_nlu.evaluate.run_evaluation evaluate.run_evaluation(td_file, model_loc)
def evaluate_model(td_file, model_loc): # evaluates the model on the training data # wrapper for rasa_nlu.evaluate.run_evaluation evaluate.run_evaluation(td_file, model_loc, intent_hist_filename='intent-hist.png')
training_data = load_data("nlu.md") # trainer to educate our pipeline trainer = Trainer(config.load("config.yml")) # train the model! interpreter = trainer.train(training_data) # store it for future use model_directory = trainer.persist("./models/nlu", fixed_model_name="current") pprint(interpreter.parse("i'm sad")) from rasa_nlu.evaluate import run_evaluation run_evaluation("nlu.md", model_directory) #from IPython.display import Image #from rasa_core.agent import Agent # #agent = Agent('domain.yml') #agent.visualize("stories.md", "story_graph.png", max_history=2) #Image(filename="story_graph.png") from rasa_core.policies import FallbackPolicy, KerasPolicy, MemoizationPolicy from rasa_core.agent import Agent
def evaluate_model(td_file, model_loc): """evaluates the model on the training data.""" evaluate.run_evaluation(td_file, model_loc)