def _export_stories(tracker, export_file_path):
        # export current stories and quit


        exported = StoryExported(export_file_path)
        tracker.update(exported)
        logger.info("Stories got exported to '{}'.".format(
                os.path.abspath(exported.path)))
Exemplo n.º 2
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def test_json_parse_export():
    # DOCS MARKER StoryExported
    evt = \
        {
            'event': 'export',
        }
    # DOCS END
    assert Event.from_parameters(evt) == StoryExported()
Exemplo n.º 3
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 def _export_stories(self, tracker):
     # export current stories and quit
     export_file_path = utils.request_input(
             prompt="File to export to (if file exists, this "
                    "will append the stories) [stories.md]: ")
     exported = StoryExported(export_file_path)
     tracker.update(exported)
     logger.info("Stories got exported to '{}'.".format(
             os.path.abspath(exported.path)))
Exemplo n.º 4
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    def probabilities_using_best_policy(self, tracker, domain):
        # [feature vector, tracker, domain] -> int
        # given a state, predict next action via asking a human
        probabilities = self.base_ensemble.probabilities_using_best_policy(
            tracker, domain)
        pred_out = np.argmax(probabilities)
        latest_action_was_listen = self._print_history(tracker)

        action_name = domain.action_for_index(pred_out).name()
        colored_name = utils.wrap_with_color(action_name, utils.bcolors.OKBLUE)
        if latest_action_was_listen:
            print("The bot wants to [{}] due to the intent. "
                  "Is this correct?\n".format(colored_name))

            user_input = utils.request_input(
                ["1", "2", "3", "0"], "\t1.\tYes\n" +
                "\t2.\tNo, intent is right but the action is wrong\n" +
                "\t3.\tThe intent is wrong\n" +
                "\t0.\tExport current conversations as stories and quit\n")
        else:
            print("The bot wants to [{}]. "
                  "Is this correct?\n".format(colored_name))
            user_input = utils.request_input(
                ["1", "2", "0"],
                "\t1.\tYes.\n" + "\t2.\tNo, the action is wrong.\n" +
                "\t0.\tExport current conversations as stories and quit\n")

        feature_vector = domain.feature_vector_for_tracker(
            self.featurizer, tracker, self.max_history)
        X = np.expand_dims(np.array(feature_vector), 0)
        if user_input == "1":
            # max prob prediction was correct
            return probabilities
        elif user_input == "2":
            # max prob prediction was false, new action required
            # action wrong
            y = self._request_action(probabilities, domain, tracker)
            self._fit_example(X, y, domain)
            self.write_out_story(tracker)
            return utils.one_hot(y, domain.num_actions)
        elif user_input == "3":
            # intent wrong and maybe action wrong
            intent = self._request_intent(tracker, domain)
            latest_message = copy.copy(tracker.latest_message)
            latest_message.intent = intent
            tracker.update(UserUtteranceReverted())
            tracker.update(latest_message)
            return self.probabilities_using_best_policy(tracker, domain)
        elif user_input == "0":
            # export current stories and quit
            tracker.update(StoryExported())
            exit()
        else:
            raise Exception(
                "Incorrect user input received '{}'".format(user_input))
Exemplo n.º 5
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    def _export_stories(tracker):
        # export current stories and quit
        file_prompt = ("File to export to (if file exists, this "
                       "will append the stories) "
                       "[{}]: ").format(DEFAULT_FILE_EXPORT_PATH)
        export_file_path = utils.request_input(prompt=file_prompt)

        if not export_file_path:
            export_file_path = DEFAULT_FILE_EXPORT_PATH

        exported = StoryExported(export_file_path)
        tracker.update(exported)
        logger.info("Stories got exported to '{}'.".format(
            os.path.abspath(exported.path)))
Exemplo n.º 6
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@pytest.mark.parametrize("one_event,another_event", [
    (UserUttered("/greet", {
        "name": "greet",
        "confidence": 1.0
    }, []), UserUttered("/goodbye", {
        "name": "goodbye",
        "confidence": 1.0
    }, [])),
    (TopicSet("my_topic"), TopicSet("my_other_topic")),
    (SlotSet("my_slot", "value"), SlotSet("my__other_slot", "value")),
    (Restarted(), None),
    (AllSlotsReset(), None),
    (ConversationPaused(), None),
    (ConversationResumed(), None),
    (StoryExported(), None),
    (ActionReverted(), None),
    (ActionExecuted("my_action"), ActionExecuted("my_other_action")),
    (BotUttered("my_text",
                "my_data"), BotUttered("my_other_test", "my_other_data")),
    (ReminderScheduled("my_action",
                       "now"), ReminderScheduled("my_other_action", "now")),
])
def test_event_has_proper_implementation(one_event, another_event):
    # equals tests
    assert one_event != another_event, \
        "Same events with different values need to be different"
    assert one_event == deepcopy(one_event), \
        "Event copies need to be the same"
    assert one_event != 42, \
        "Events aren't equal to 42!"
Exemplo n.º 7
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    (SlotSet("my_slot", "value"),
     SlotSet("my__other_slot", "value")),

    (Restarted(),
     None),

    (AllSlotsReset(),
     None),

    (ConversationPaused(),
     None),

    (ConversationResumed(),
     None),

    (StoryExported(),
     None),

    (ActionReverted(),
     None),

    (ActionExecuted("my_action"),
     ActionExecuted("my_other_action")),

    (BotUttered("my_text", "my_data"),
     BotUttered("my_other_test", "my_other_data")),

    (ReminderScheduled("my_action", "now"),
     ReminderScheduled("my_other_action", "now")),
])
def test_event_has_proper_implementation(one_event, another_event):
Exemplo n.º 8
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 def run(self, dispatcher, tracker, domain):
     return [StoryExported("data.md"), Restarted()]