Source code for dallinger.experiment

"""The base experiment class."""

from __future__ import print_function
from __future__ import unicode_literals

from cached_property import cached_property
from collections import Counter
from contextlib import contextmanager
from functools import wraps
import datetime
import inspect
from importlib import import_module
import logging
from operator import itemgetter
import os
import random
import requests
import sys
import time
import uuid

from sqlalchemy import and_
from sqlalchemy import create_engine
from sqlalchemy import func
from sqlalchemy.orm import sessionmaker, scoped_session

from dallinger import recruiters
from dallinger.config import get_config, LOCAL_CONFIG
from dallinger.config import initialize_experiment_package
from import Data
from import export
from import is_registered
from import load as data_load
from import find_experiment_export
from import ingest_zip
from dallinger.db import init_db, db_url
from dallinger.models import Network, Node, Info, Transformation, Participant
from import HerokuApp
from dallinger.information import Gene, Meme, State
from dallinger.nodes import Agent, Source, Environment
from dallinger.transformations import Compression, Response
from dallinger.transformations import Mutation, Replication
from dallinger.networks import Empty

logger = logging.getLogger(__file__)

def exp_class_working_dir(meth):
    def new_meth(self, *args, **kwargs):
            config = get_config()
            orig_path = os.getcwd()
            new_path = os.path.dirname(
            # Override configs
            return meth(self, *args, **kwargs)
    return new_meth

[docs]class Experiment(object): """Define the structure of an experiment.""" app_id = None # Optional Redis channel to create and subscribe to on launch. Note that if # you define a channel, you probably also want to override the send() # method, since this is where messages from Redis will be sent. channel = None exp_config = None replay_path = '/'
[docs] def __init__(self, session=None): """Create the experiment class. Sets the default value of attributes.""" #: Boolean, determines whether the experiment logs output when #: running. Default is True. self.verbose = True #: String, the name of the experiment. Default is "Experiment #: title". self.task = "Experiment title" #: session, the experiment's connection to the database. self.session = session #: int, the number of practice networks (see #: :attr:`~dallinger.models.Network.role`). Default is 0. self.practice_repeats = 0 #: int, the number of non practice networks (see #: :attr:`~dallinger.models.Network.role`). Default is 0. self.experiment_repeats = 0 #: int, the number of participants #: required to move from the waiting room to the experiment. #: Default is 0 (no waiting room). self.quorum = 0 #: int, the number of participants #: requested when the experiment first starts. Default is 1. self.initial_recruitment_size = 1 #: dictionary, the classes Dallinger can make in response #: to front-end requests. Experiments can add new classes to this #: dictionary. self.known_classes = { "Agent": Agent, "Compression": Compression, "Environment": Environment, "Gene": Gene, "Info": Info, "Meme": Meme, "Mutation": Mutation, "Node": Node, "Replication": Replication, "Response": Response, "Source": Source, "State": State, "Transformation": Transformation, } #: dictionary, the properties of this experiment that are exposed #: to the public over an AJAX call if not hasattr(self, 'public_properties'): # Guard against subclasses replacing this with a @property self.public_properties = {} if session: self.configure() try: location = type(self).__module__ parent, experiment_module = location.rsplit('.', 1) module = import_module(parent + '.jupyter') except (ImportError, ValueError): try: from .jupyter import ExperimentWidget self.widget = ExperimentWidget(self) except ImportError: self.widget = None else: self.widget = module.ExperimentWidget(self)
def configure(self): """Load experiment configuration here""" pass @property def background_tasks(self): """An experiment may define functions or methods to be started as background tasks upon experiment launch. """ return [] @cached_property def recruiter(self): """Reference to a Recruiter, the Dallinger class that recruits participants. """ return recruiters.from_config(get_config())
[docs] def is_overrecruited(self, waiting_count): """Returns True if the number of people waiting is in excess of the total number expected, indicating that this and subsequent users should skip the experiment. A quorum value of 0 means we don't limit recruitment, and always return False. """ if not self.quorum: return False return waiting_count > self.quorum
def send(self, raw_message): """socket interface implementation, and point of entry for incoming Redis messages. param raw_message is a string with a channel prefix, for example: 'shopping:{"type":"buy","color":"blue","quantity":"2"}' """ pass
[docs] def setup(self): """Create the networks if they don't already exist.""" if not self.networks(): for _ in range(self.practice_repeats): network = self.create_network() network.role = "practice" self.session.add(network) for _ in range(self.experiment_repeats): network = self.create_network() network.role = "experiment" self.session.add(network) self.session.commit()
[docs] def create_network(self): """Return a new network.""" return Empty()
[docs] def networks(self, role="all", full="all"): """All the networks in the experiment.""" if full not in ["all", True, False]: raise ValueError("full must be boolean or all, it cannot be {}" .format(full)) if full == "all": if role == "all": return Network.query.all() else: return Network\ .query\ .filter_by(role=role)\ .all() else: if role == "all": return Network.query.filter_by(full=full)\ .all() else: return Network\ .query\ .filter(and_(Network.role == role, Network.full == full))\ .all()
[docs] def get_network_for_participant(self, participant): """Find a network for a participant. If no networks are available, None will be returned. By default participants can participate only once in each network and participants first complete networks with `role="practice"` before doing all other networks in a random order. """ key = networks_with_space = Network.query.filter_by( full=False).order_by( networks_participated_in = [ node.network_id for node in Node.query.with_entities(Node.network_id) .filter_by( ] legal_networks = [ net for net in networks_with_space if not in networks_participated_in ] if not legal_networks: self.log("No networks available, returning None", key) return None self.log("{} networks out of {} available" .format(len(legal_networks), (self.practice_repeats + self.experiment_repeats)), key) legal_practice_networks = [net for net in legal_networks if net.role == "practice"] if legal_practice_networks: chosen_network = legal_practice_networks[0] self.log("Practice networks available." "Assigning participant to practice network {}." .format(, key) else: chosen_network = self.choose_network(legal_networks, participant) self.log("No practice networks available." "Assigning participant to experiment network {}" .format(, key) return chosen_network
def choose_network(self, networks, participant): return random.choice(networks)
[docs] def create_node(self, participant, network): """Create a node for a participant.""" return Node(network=network, participant=participant)
[docs] def add_node_to_network(self, node, network): """Add a node to a network. This passes `node` to :func:`~dallinger.models.Network.add_node()`. """ network.add_node(node)
[docs] def data_check(self, participant): """Check that the data are acceptable. Return a boolean value indicating whether the `participant`'s data is acceptable. This is meant to check for missing or invalid data. This check will be run once the `participant` completes the experiment. By default performs no checks and returns True. See also, :func:`~dallinger.experiments.Experiment.attention_check`. """ return True
[docs] def bonus(self, participant): """The bonus to be awarded to the given participant. Return the value of the bonus to be paid to `participant`. By default returns 0. """ return 0
[docs] def bonus_reason(self): """The reason offered to the participant for giving the bonus. Return a string that will be included in an email sent to the `participant` receiving a bonus. By default it is "Thank you for participating! Here is your bonus." """ return "Thank for participating! Here is your bonus."
[docs] def attention_check(self, participant): """Check if participant performed adequately. Return a boolean value indicating whether the `participant`'s data is acceptable. This is mean to check the participant's data to determine that they paid attention. This check will run once the *participant* completes the experiment. By default performs no checks and returns True. See also :func:`~dallinger.experiments.Experiment.data_check`. """ return True
[docs] def submission_successful(self, participant): """Run when a participant submits successfully.""" pass
[docs] def recruit(self): """Recruit participants to the experiment as needed. This method runs whenever a participant successfully completes the experiment (participants who fail to finish successfully are automatically replaced). By default it recruits 1 participant at a time until all networks are full. """ if not self.networks(full=False): self.log("All networks full: closing recruitment", "-----") self.recruiter.close_recruitment()
[docs] def log(self, text, key="?????", force=False): """Print a string to the logs.""" if force or self.verbose: print(">>>> {} {}".format(key, text)) sys.stdout.flush()
[docs] def log_summary(self): """Log a summary of all the participants' status codes.""" participants = Participant.query\ .with_entities(Participant.status).all() counts = Counter([p.status for p in participants]) sorted_counts = sorted(counts.items(), key=itemgetter(0)) self.log("Status summary: {}".format(str(sorted_counts))) return sorted_counts
[docs] def save(self, *objects): """Add all the objects to the session and commit them. This only needs to be done for networks and participants. """ if len(objects) > 0: self.session.add_all(objects) self.session.commit()
[docs] def node_post_request(self, participant, node): """Run when a request to make a node is complete.""" pass
[docs] def node_get_request(self, node=None, nodes=None): """Run when a request to get nodes is complete.""" pass
[docs] def vector_post_request(self, node, vectors): """Run when a request to connect is complete.""" pass
[docs] def vector_get_request(self, node, vectors): """Run when a request to get vectors is complete.""" pass
[docs] def info_post_request(self, node, info): """Run when a request to create an info is complete.""" pass
[docs] def info_get_request(self, node, infos): """Run when a request to get infos is complete.""" pass
[docs] def transmission_post_request(self, node, transmissions): """Run when a request to transmit is complete.""" pass
[docs] def transmission_get_request(self, node, transmissions): """Run when a request to get transmissions is complete.""" pass
[docs] def transformation_post_request(self, node, transformation): """Run when a request to transform an info is complete.""" pass
[docs] def transformation_get_request(self, node, transformations): """Run when a request to get transformations is complete.""" pass
[docs] def fail_participant(self, participant): """Fail all the nodes of a participant.""" participant_nodes = Node.query\ .filter_by(, failed=False)\ .all() for node in participant_nodes:
[docs] def data_check_failed(self, participant): """What to do if a participant fails the data check. Runs when `participant` has failed :func:`~dallinger.experiments.Experiment.data_check`. By default calls :func:`~dallinger.experiments.Experiment.fail_participant`. """ self.fail_participant(participant)
[docs] def attention_check_failed(self, participant): """What to do if a participant fails the attention check. Runs when `participant` has failed the :func:`~dallinger.experiments.Experiment.attention_check`. By default calls :func:`~dallinger.experiments.Experiment.fail_participant`. """ self.fail_participant(participant)
[docs] def assignment_abandoned(self, participant): """What to do if a participant abandons the hit. This runs when a notification from AWS is received indicating that `participant` has run out of time. Calls :func:`~dallinger.experiments.Experiment.fail_participant`. """ self.fail_participant(participant)
[docs] def assignment_returned(self, participant): """What to do if a participant returns the hit. This runs when a notification from AWS is received indicating that `participant` has returned the experiment assignment. Calls :func:`~dallinger.experiments.Experiment.fail_participant`. """ self.fail_participant(participant)
[docs] def assignment_reassigned(self, participant): """What to do if the assignment assigned to a participant is reassigned to another participant while the first participant is still working. This runs when a participant is created with the same assignment_id as another participant if the earlier participant still has the status "working". Calls :func:`~dallinger.experiments.Experiment.fail_participant`. """ self.fail_participant(participant)
[docs] @exp_class_working_dir def run(self, exp_config=None, app_id=None, bot=False, **kwargs): """Deploy and run an experiment. The exp_config object is either a dictionary or a ``localconfig.LocalConfig`` object with parameters specific to the experiment run grouped by section. """ import dallinger as dlgr app_id = self.make_uuid(app_id) if bot: kwargs['recruiter'] = 'bots' self.app_id = app_id self.exp_config = exp_config or kwargs self.update_status('Starting') try: if self.exp_config.get("mode") == "debug": dlgr.command_line.debug.callback( verbose=True, bot=bot, proxy=None, exp_config=self.exp_config ) else: dlgr.deployment.deploy_sandbox_shared_setup( dlgr.command_line.log, app=app_id, verbose=self.verbose, exp_config=self.exp_config ) except Exception: self.update_status('Errored') raise else: self.update_status('Running') self._await_completion() self.update_status('Retrieving data') data = self.retrieve_data() self.update_status('Completed') return data
[docs] def collect(self, app_id, exp_config=None, bot=False, **kwargs): """Collect data for the provided experiment id. The ``app_id`` parameter must be a valid UUID. If an existing data file is found for the UUID it will be returned, otherwise - if the UUID is not already registered - the experiment will be run and data collected. See :meth:`` method for other parameters. """ try: results = data_load(app_id) self.log('Data found for experiment {}, retrieving.'.format(app_id), key="Retrieve:") return results except IOError: self.log( 'Could not fetch data for id: {}, checking registry'.format(app_id), key="Retrieve:" ) exp_config = exp_config or {} if is_registered(app_id): raise RuntimeError('The id {} is registered, '.format(app_id) + 'but you do not have permission to access to the data') elif kwargs.get('mode') == 'debug' or exp_config.get('mode') == 'debug': raise RuntimeError('No remote or local data found for id {}'.format(app_id)) try: assert isinstance(uuid.UUID(app_id, version=4), uuid.UUID) except (ValueError, AssertionError): raise ValueError('Invalid UUID supplied {}'.format(app_id)) self.log('{} appears to be a new experiment id, running experiment.'.format(app_id), key="Retrieve:") return, app_id, bot, **kwargs)
[docs] @classmethod def make_uuid(cls, app_id=None): """Generates a new UUID. This is a class method and can be called as `Experiment.make_uuid()`. Takes an optional `app_id` which is converted to a string and, if it is a valid UUID, returned. """ try: if app_id and isinstance(uuid.UUID(str(app_id), version=4), uuid.UUID): return str(app_id) except (ValueError, AssertionError): pass return str(uuid.UUID(int=random.getrandbits(128)))
def experiment_completed(self): """Checks the current state of the experiment to see whether it has completed. This makes use of the experiment server `/summary` route, which in turn uses :meth:`~Experiment.is_complete`. """ heroku_app = HerokuApp(self.app_id) status_url = '{}/summary'.format(heroku_app.url) data = {} try: resp = requests.get(status_url) data = resp.json() except (ValueError, requests.exceptions.RequestException): logger.exception('Error fetching experiment status.') logger.debug('Current application state: {}'.format(data)) return data.get('completed', False) def _await_completion(self): # Debug runs synchronously, but in live mode we need to loop and check # experiment status if self.exp_config.get('mode') != 'debug': self.log("Waiting for experiment to complete.", "") while not self.experiment_completed(): time.sleep(30) return True def retrieve_data(self): """Retrieves and saves data from a running experiment""" local = False if self.exp_config.get('mode') == 'debug': local = True filename = export(self.app_id, local=local) logger.debug('Data exported to %s' % filename) return Data(filename) def end_experiment(self): """Terminates a running experiment""" if self.exp_config.get('mode') != 'debug': HerokuApp(self.app_id).destroy() return True
[docs] def events_for_replay(self, session=None, target=None): """Returns an ordered list of "events" for replaying. Experiments may override this method to provide custom replay logic. The "events" returned by this method will be passed to :meth:`~Experiment.replay_event`. The default implementation simply returns all :class:`~dallinger.models.Info` objects in the order they were created. """ if session is None: session = self.session return session.query(Info).order_by(Info.creation_time)
[docs] def replay_event(self, event): """Stub method to replay an event returned by :meth:`~Experiment.events_for_replay`. Experiments must override this method to provide replay support. """ pass
[docs] def replay_start(self): """Stub method for starting an experiment replay. Experiments must override this method to provide replay support. """ pass
[docs] def replay_finish(self): """Stub method for ending an experiment replay. Experiments must override this method to provide replay support. """ pass
[docs] def replay_started(self): """Returns `True` if an experiment replay has started.""" return True
[docs] def is_complete(self): """Method for custom determination of experiment completion. Experiments should override this to provide custom experiment completion logic. Returns `None` to use the experiment server default logic, otherwise should return `True` or `False`. """ return None
@property def usable_replay_range(self): """The range of times that represent the active part of the experiment""" return self._replay_range @contextmanager def restore_state_from_replay(self, app_id, session, zip_path=None, **configuration_options): # We need to fake dallinger_experiment to point at the current experiment module = sys.modules[type(self).__module__] if sys.modules.get('dallinger_experiment', module) != module: logger.warning('dallinger_experiment is already set, updating') sys.modules['dallinger_experiment'] = module # Load the configuration system and globals config = get_config() # Manually load extra parameters and ignore errors try: from dallinger_experiment.experiment import extra_parameters try: extra_parameters() extra_parameters.loaded = True except KeyError: pass except ImportError: pass config.load() self.app_id = self.original_app_id = app_id self.session = session self.exp_config = config # The replay index is initialised to 1970 as that is guaranteed # to be before any experiment Info objects self._replay_time_index = datetime.datetime(1970, 1, 1, 1, 1, 1) # Create a second database session so we can load the full history # of the experiment to be replayed and selectively import events # into the main database specific_db_url = db_url + '-import-' + app_id import_engine = create_engine( specific_db_url ) try: # Clear the temporary storage and import it init_db(drop_all=True, bind=import_engine) except Exception: create_db_engine = create_engine(db_url) conn = create_db_engine.connect() conn.execute('COMMIT;') conn.execute('CREATE DATABASE "{}"'.format(specific_db_url.rsplit('/', 1)[1])) conn.close() import_engine = create_engine( specific_db_url ) init_db(drop_all=True, bind=import_engine) self.import_session = scoped_session( sessionmaker(autocommit=False, autoflush=True, bind=import_engine) ) # Find the real data for this experiment if zip_path is None: zip_path = find_experiment_export(app_id) if zip_path is None: msg = 'Dataset export for app id "{}" could not be found.' raise IOError(msg.format(app_id)) print("Ingesting dataset from {}...".format(os.path.basename(zip_path))) ingest_zip(zip_path, engine=import_engine) self._replay_range = tuple( self.import_session.query( func.min(Info.creation_time), func.max(Info.creation_time) ) )[0] # We apply the configuration options we were given and yield # the scrubber function into the context manager, so within the # with experiment.restore_state_from_replay(...): block the configuration # options are correctly set with config.override(configuration_options, strict=True): self.replay_start() yield Scrubber(self, session=self.import_session) self.replay_finish() # Clear up global state self.import_session.rollback() self.import_session.close() session.rollback() session.close() # Remove marker preventing experiment config variables being reloaded try: del module.extra_parameters.loaded except AttributeError: pass config._reset(register_defaults=True) del sys.modules['dallinger_experiment'] def revert_to_time(self, session, target): # We do not support going back in time raise NotImplementedError def _ipython_display_(self): """Display Jupyter Notebook widget""" from IPython.display import display display(self.widget) def update_status(self, status): if self.widget is not None: self.widget.status = status def jupyter_replay(self, *args, **kwargs): from ipywidgets import widgets from IPython.display import display try: sys.modules['dallinger_experiment']._jupyter_cleanup() except (KeyError, AttributeError): pass replay = self.restore_state_from_replay(*args, **kwargs) scrubber = replay.__enter__() scrubber.build_widget() replay_widget = widgets.VBox([ self.widget, scrubber.widget, ]) # Scrub to start of experiment and re-render the main widget scrubber(self.usable_replay_range[0]) self.widget.render() display(replay_widget) # Defer the cleanup until this function is re-called by # keeping a copy of the function on the experiment module # This allows us to effectively detect the cell being # re-run as there doesn't seem to be a cleanup hook for widgets # displayed as part of a cell that is being re-rendered def _jupyter_cleanup(): replay.__exit__(None, None, None) sys.modules['dallinger_experiment']._jupyter_cleanup = _jupyter_cleanup
class Scrubber(object): def __init__(self, experiment, session): self.experiment = experiment self.session = session self.realtime = False def __call__(self, time): """Scrub to a point in the experiment replay, given by time which is a datetime object.""" if self.experiment._replay_time_index > time: self.experiment.revert_to_time(session=self.session, target=time) events = self.experiment.events_for_replay(session=self.session, target=time).all() for event in events: if event.creation_time <= self.experiment._replay_time_index: # Skip events we've already handled continue if event.creation_time > time: # Stop once we get future events break self.experiment.replay_event(event) self.experiment._replay_time_index = event.creation_time # Override app_id to allow exports to be created that don't # overwrite the original dataset self.experiment.app_id = "{}_{}".format(self.experiment.original_app_id, time.isoformat()) def in_realtime(self, callback=None): exp_start, exp_end = self.experiment.usable_replay_range replay_offset = time.time() current = self.experiment._replay_time_index if current < exp_start: current = exp_start self.realtime = True # Disable the scrubbing slider self.widget.children[0].disabled = True try: while current < exp_end: now = time.time() seconds = now - replay_offset current = current + datetime.timedelta(seconds=seconds) self(current) if callable(callback): try: callback() except StopIteration: return replay_offset = now finally: self.realtime = False self.widget.children[0].disabled = False def build_widget(self): from ipywidgets import widgets start, end = self.experiment.usable_replay_range options = [] current = start while current <= end: # Never display microseconds options.append((current.replace(microsecond=0).time().isoformat(), current)) current += datetime.timedelta(seconds=1) # But we need to keep microseconds in the first value, so we don't go before # the experiment start when scrubbing backwards current = current.replace(microsecond=0) scrubber = widgets.SelectionSlider( description='Current time', options=options, disabled=False, continuous_update=False, ) def advance(change): if self.realtime: # We're being driven in realtime, the advancement # here is just to keep the UI in sync return old_status = self.experiment.widget.status self.experiment.widget.status = 'Updating' self.experiment.widget.render() self(change['new']) self.experiment.widget.status = old_status self.experiment.widget.render() scrubber.observe(advance, 'value') def realtime_callback(): self.experiment.widget.render() try: scrubber.value = self.experiment._replay_time_index.replace(microsecond=0) except Exception: # The scrubber is an approximation of the current time, we shouldn't # bail out if it can't be updated (for example at experiment bounds) pass if not self.realtime: raise StopIteration() play_button = widgets.ToggleButton( value=False, description='', disabled=False, tooltip='Play back in realtime', icon='play' ) def playback(change): import threading if change['new']: thread = threading.Thread( target=self.in_realtime, kwargs={ 'callback': realtime_callback } ) thread.start() else: self.realtime = False play_button.observe(playback, 'value') self.widget = widgets.HBox(children=[scrubber, play_button]) return self.widget def _ipython_display_(self): """Display Jupyter Notebook widget""" from IPython.display import display self.build_widget() display(self.widget()) def load(): """Load the active experiment.""" initialize_experiment_package(os.getcwd()) try: try: from dallinger_experiment import experiment except ImportError: from dallinger_experiment import dallinger_experiment as experiment classes = inspect.getmembers(experiment, inspect.isclass) for name, c in classes: if 'Experiment' in c.__bases__[0].__name__: return c else: raise ImportError except ImportError: logger.error('Could not import experiment.') raise