class AI():
"""AI managing agent for Penn AI.
Responsible for:
- checking for user requests for recommendations,
- checking for new results from experiments,
- calling the recommender system to generate experiment recommendations,
- posting the recommendations to the API.
- handling communication with the API.
:param rec_class: ai.BaseRecommender - recommender to use
:param api_path: string - path to the lab api server
:param extra_payload: dict - any additional payload that needs to be specified
:param user: string - test user
:param rec_score_file: file - pickled score file to keep persistent scores
between sessions
:param verbose: Boolean
:param warm_start: Boolean - if true, attempt to load the ai state from the file
provided by rec_score_file
:param n_recs: int - number of recommendations to make for each request
:param datasets: str or False - if not false, a comma seperated list of datasets
to turn the ai on for at startup
:param use_pmlb_knowledgebase: Boolean
"""
def __init__(self,
rec_class=None,
api_path=None,
extra_payload=dict(),
user='******',
rec_score_file='rec_state.obj',
verbose=True,
warm_start=False,
n_recs=1,
datasets=False,
use_knowledgebase=False,
term_condition='n_recs',
max_time=5):
"""Initializes AI managing agent."""
# default supervised learning recommender settings
self.DEFAULT_REC_CLASS = RandomRecommender
self.DEFAULT_REC_ARGS = {'metric': 'accuracy'}
# recommendation engines for different problem types
# will be expanded as more types of probles are supported
# (classification, regression, unsupervised, etc.)
self.rec_engines = {"classification": None}
# Request manager settings
self.n_recs = n_recs if n_recs > 0 else 1
self.continuous = n_recs < 1
# api parameters, will be removed from self once the recommenders no longer
# call the api directly.
# See #98 <https://github.com/EpistasisLab/pennai/issues/98>
if api_path == None:
api_path = ('http://' + os.environ['LAB_HOST'] + ':' +
os.environ['LAB_PORT'])
self.user = user
self.api_path = api_path
self.api_key = os.environ['APIKEY']
self.verbose = verbose #False: no printouts, True: printouts on updates
# file name of stored scores for the recommender
self.rec_score_file = rec_score_file
# timestamp of the last time new experiments were processed
self.last_update = 0
# api
self.labApi = api_utils.LabApi(api_path=self.api_path,
user=self.user,
api_key=self.api_key,
extra_payload=extra_payload,
verbose=self.verbose)
self.load_options() #loads algorithm parameters to self.ui_options
self.initilize_recommenders(rec_class) # set self.rec_engines
# build dictionary of ml ids to names conversion
self.ml_id_to_name = self.labApi.get_ml_id_dict()
# print('ml_id_to_name:',self.ml_id_to_name)
# dictionary of dataset threads, initilized and used by q_utils.
# Keys are datasetIds, values are q_utils.DatasetThread instances.
#WGL: this should get moved to the request manager
self.dataset_threads = {}
# local dataframe of datasets and their metafeatures
self.dataset_mf_cache = pd.DataFrame()
# store dataset_id to hash dictionary
self.dataset_mf_cache_id_hash_lookup = {}
if use_knowledgebase:
self.load_knowledgebase()
# set termination condition
self.term_condition = term_condition
if self.term_condition == 'n_recs':
self.term_value = self.n_recs
elif self.term_condition == 'time':
self.term_value = max_time
else:
self.term_value = None
# start the request manager
self.requestManager = RequestManager(
ai=self,
defaultTermConditionStr=self.term_condition,
defaultTermParam=self.term_value)
# if there is a pickle file, load it as the recommender scores
assert not (warm_start), "The `warm_start` option is not yet supported"
# for comma-separated list of datasets in datasets, turn AI request on
assert not (
datasets
), "The `datasets` option is not yet supported: " + str(datasets)
##-----------------
## Init methods
##-----------------
def initilize_recommenders(self, rec_class):
"""
Initilize classification recommender
"""
# Create supervised learning recommenders
if (rec_class):
self.rec_engines["classification"] = rec_class(
**self.DEFAULT_REC_ARGS)
else:
self.rec_engines["classification"] = self.DEFAULT_REC_CLASS(
**self.DEFAULT_REC_ARGS)
# set the registered ml parameters in the recommenders
ml_p = self.labApi.get_all_ml_p()
assert ml_p is not None
assert len(ml_p) > 0
self.rec_engines["classification"].ml_p = ml_p
# if hasattr(self.rec_engines["classification"],'mlp_combos'):
# self.rec_engines["classification"].mlp_combos = self.rec_engines["classification"].ml_p['algorithm']+'|'+self.rec_engines["classification"].ml_p['parameters']
# ml_p.to_csv('ml_p_options.csv')
logger.debug("recomendation engines initilized: ")
for prob_type, rec in self.rec_engines.items():
logger.debug(f'\tproblemType: {prob_type} - {rec}')
logger.debug('\trec.ml_p:\n' + str(rec.ml_p.head()))
def load_knowledgebase(self):
"""Bootstrap the recommenders with the knowledgebase."""
logger.info('loading pmlb knowledgebase')
kb = knowledgebase_loader.load_default_knowledgebases()
# replace algorithm names with their ids
self.ml_name_to_id = {v: k for k, v in self.ml_id_to_name.items()}
kb['resultsData']['algorithm'] = kb['resultsData']['algorithm'].apply(
lambda x: self.ml_name_to_id[x])
all_df_mf = kb['metafeaturesData'].set_index('_id')
# all_df_mf = pd.DataFrame.from_records(metafeatures).transpose()
# use _id to index the metafeatures, and
# keep only metafeatures with results
self.dataset_mf_cache = all_df_mf.loc[kb['resultsData']
['_id'].unique()]
# self.update_dataset_mf(kb['resultsData'])
self.rec_engines["classification"].update(kb['resultsData'],
self.dataset_mf_cache,
source='knowledgebase')
logger.info('pmlb knowledgebase loaded')
def load_options(self):
"""Loads algorithm UI parameters and sets them to self.ui_options."""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': loading options...')
responses = self.labApi.get_projects()
if len(responses) > 0:
self.ui_options = responses
else:
logger.warning("no algorithms found by load_options()")
##-----------------
## Utility methods
##-----------------
def get_results_metafeatures(self, results_data):
"""
Return a pandas dataframe of metafeatures associated with the datasets
in results_data.
Retireves metafeatures from self.dataset_mf_cache if they exist,
otherwise queries the api and updates the cache.
:param results_data: experiment results with associated datasets
"""
logger.debug('results_data:' + str(results_data.columns))
logger.debug('results_data:' + str(results_data.head()))
dataset_metafeatures = []
dataset_indicies = results_data['dataset_id'].unique()
# add dataset metafeatures to the cache
for d in dataset_indicies:
if len(
self.dataset_mf_cache
) == 0 or d not in self.dataset_mf_cache_id_hash_lookup.keys():
df = self.labApi.get_metafeatures(d)
df['dataset'] = d
dataset_metafeatures.append(df)
self.dataset_mf_cache_id_hash_lookup.update({d: df['_id']})
if dataset_metafeatures:
df_mf = pd.concat(dataset_metafeatures).set_index('dataset')
self.dataset_mf_cache = self.dataset_mf_cache.append(df_mf)
logger.info(f'mf count:\n {len(self.dataset_mf_cache.index.values)}')
#logger.info(f'mf:\n {list(self.dataset_mf_cache.index.values)}')
logger.info(f'indicies: \n\n {dataset_indicies}')
new_mf = self.dataset_mf_cache.loc[dataset_indicies, :]
assert len(new_mf) == len(dataset_indicies)
logger.info(f"new_mf: {new_mf}")
return new_mf
##-----------------
## Loop methods
##-----------------
def check_results(self):
"""Checks to see if new experiment results have been posted since the
previous time step. If so, set them to self.new_data and return True.
:returns: Boolean - True if new results were found
"""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': checking results...')
newResults = self.labApi.get_new_experiments_as_dataframe(
last_update=self.last_update)
if len(newResults) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) + ': ' +
str(len(newResults)) + ' new results!')
self.last_update = int(time.time()) * 1000 # update timestamp
self.new_data = newResults
return True
return False
def update_recommender(self):
"""Update recommender models based on new experiment results in
self.new_data, and then clear self.new_data.
"""
if (hasattr(self, 'new_data') and len(self.new_data) >= 1):
new_mf = self.get_results_metafeatures(self.new_data)
self.new_data['_id'] = self.new_data['dataset_id'].apply(
lambda x: self.dataset_mf_cache_id_hash_lookup[x])
self.rec_engines["classification"].update(self.new_data, new_mf)
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': recommender updated')
# reset new data
self.new_data = pd.DataFrame()
def check_requests(self):
"""Check to see if any new AI requests have been submitted.
If so, add them to self.request_queue.
:returns: Boolean - True if new AI requests have been submitted
"""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': checking requests...')
# get all dtasets that have an ai 'requested' status
# and initilize a new request
dsFilter = {'ai': [AI_STATUS.REQUESTED.value, 'dummy']}
aiOnRequests = self.labApi.get_filtered_datasets(dsFilter)
if len(aiOnRequests) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': new ai request for:' +
';'.join([r['name'] for r in aiOnRequests]))
# set AI flag to 'on' to acknowledge requests received
for r in aiOnRequests:
self.labApi.set_ai_status(datasetId=r['_id'], aiStatus='on')
self.requestManager.add_request(datasetId=r['_id'],
datasetName=r['name'])
time.sleep(.1)
# get all datasets that have a manual 'off' status
# and terminate their ai requests
dsFilter = {'ai': [AI_STATUS.OFF.value, 'dummy']}
aiOffRequests = self.labApi.get_filtered_datasets(dsFilter)
if len(aiOffRequests) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': ai termination request for:' +
';'.join([r['name'] for r in aiOffRequests]))
for r in aiOffRequests:
self.requestManager.terminate_request(datasetId=r['_id'])
return True
def process_rec(self):
self.requestManager.process_requests()
##-----------------
## Syncronous actions an AI request can take
##-----------------
def generate_recommendations(self,
datasetId,
numOfRecs,
predictionType="classification"):
"""Generate ml recommendation payloads for the given dataset.
:param datasetId
:param numOfRecs
:returns list of maps that represent request payload objects
"""
logger.info("generate_recommendations({},{})".format(
datasetId, numOfRecs))
recommendations = []
metafeatures = self.labApi.get_metafeatures(datasetId)
ml, p, ai_scores = self.rec_engines[predictionType].recommend(
dataset_id=metafeatures['_id'].values[0],
n_recs=numOfRecs,
dataset_mf=metafeatures)
for alg, params, score in zip(ml, p, ai_scores):
# TODO: just return dictionaries of parameters from rec
# modified_params = eval(params) # turn params into a dictionary
recommendations.append({
'dataset_id': datasetId,
'algorithm_id': alg,
'username': self.user,
'parameters': params,
'ai_score': score,
})
return recommendations
def transfer_rec(self, rec_payload):
"""Attempt to send a recommendation to the lab server.
If any error other then a no capacity error occurs, throw an exception.
:param rec_payload: dictionary - the payload describing the experiment
:return bool - true if successfully sent, false if no machine capacity available
"""
logger.info(f"transfer_rec({rec_payload})")
aiStatus = self.labApi.get_dataset_ai_status(rec_payload['dataset_id'])
if not (aiStatus == AI_STATUS.ON.value):
logger.debug("AI status is not on; not submitting experiment")
return False
submitstatus = self.labApi.launch_experiment(
algorithmId=rec_payload['algorithm_id'], payload=rec_payload)
logger.debug(f"transfer_rec() submitstatus: {submitstatus}")
if 'error' in submitstatus:
if ('No machine capacity available' in submitstatus['error']):
logger.debug(f"Waiting for capacity: {submitstatus['error']}")
return False
else:
msg = 'Unrecoverable error during transfer_rec : ' + str(
submitstatus)
logger.error(msg)
raise RuntimeError(msg)
return True
##-----------------
## Save/load ai state
##-----------------
def save_state(self):
"""Save ML+P scores in pickle or to DB
TODO: test that this still works
"""
raise RuntimeError("save_state is not currently supported")
out = open(self.rec_score_file, 'wb')
state = {}
if (hasattr(self.rec_engines["classification"], 'scores')):
#TODO: make this a more generic. Maybe just save the
# AI or rec object itself.
# state['trained_dataset_models'] = self.rec_engines["classification"].trained_dataset_models
state['scores'] = self.rec_engines["classification"].scores
state['last_update'] = self.last_update
pickle.dump(state, out)
def load_state(self):
"""Loads pickled score file and recommender model.
TODO: test that this still works
"""
raise RuntimeError("load_state is not currently supported")
if os.stat(self.rec_score_file).st_size != 0:
filehandler = open(self.rec_score_file, 'rb')
state = pickle.load(filehandler)
if (hasattr(self.rec_engines["classification"], 'scores')):
self.rec_engines["classification"].scores = state['scores']
# self.rec_engines["classification"].trained_dataset_models = state['trained_dataset_models']
self.last_update = state['last_update']
logger.info('loaded previous state from ' + self.last_update)
class AI():
"""AI managing agent for Penn AI.
Responsible for:
- checking for user requests for recommendations,
- checking for new results from experiments,
- calling the recommender system to generate experiment recommendations,
- posting the recommendations to the API.
- handling communication with the API.
:param rec: ai.BaseRecommender - recommender to use
:param api_path: string - path to the lab api server
:param extra_payload: dict - any additional payload that needs to be specified
:param user: string - test user
:param rec_score_file: file - pickled score file to keep persistent scores
between sessions
:param verbose: Boolean
:param warm_start: Boolean - if true, attempt to load the ai state from the file
provided by rec_score_file
:param n_recs: int - number of recommendations to make for each request
:param datasets: str or False - if not false, a comma seperated list of datasets
to turn the ai on for at startup
:param use_pmlb_knowledgebase: Boolean
"""
def __init__(self,
rec=None,
api_path=None,
extra_payload=dict(),
user='******',
rec_score_file='rec_state.obj',
verbose=True,
warm_start=False,
n_recs=1,
datasets=False,
use_knowledgebase=False,
term_condition='n_recs',
max_time=5):
"""initializes AI managing agent."""
# recommender settings
if api_path == None:
api_path = ('http://' + os.environ['LAB_HOST'] + ':' +
os.environ['LAB_PORT'])
self.rec = rec
self.n_recs = n_recs if n_recs > 0 else 1
self.continous = n_recs < 1
# api parameters, will be removed from self once the recommenders no longer
# call the api directly.
# See #98 <https://github.com/EpistasisLab/pennai/issues/98>
self.user = user
self.api_path = api_path
self.api_key = os.environ['APIKEY']
self.verbose = verbose #False: no printouts, True: printouts on updates
# file name of stored scores for the recommender
self.rec_score_file = rec_score_file
# timestamp of the last time new experiments were processed
self.last_update = 0
# api
self.labApi = api_utils.LabApi(api_path=self.api_path,
user=self.user,
api_key=self.api_key,
extra_payload=extra_payload,
verbose=self.verbose)
self.load_options() #loads algorithm parameters to self.ui_options
# create a default recommender if not set
if (rec):
self.rec = rec
else:
self.rec = RandomRecommender(ml_p=self.labApi.get_all_ml_p())
# set the registered ml parameters in the recommender
ml_p = self.labApi.get_all_ml_p()
assert ml_p is not None
assert len(ml_p) > 0
self.rec.ml_p = ml_p
# if hasattr(self.rec,'mlp_combos'):
# self.rec.mlp_combos = self.rec.ml_p['algorithm']+'|'+self.rec.ml_p['parameters']
logger.debug('self.rec.ml_p:\n' + str(self.rec.ml_p.head()))
# tmp = self.labApi.get_all_ml_p()
# tmp.to_csv('ml_p_options.csv')
# build dictionary of ml ids to names conversion
self.ml_id_to_name = self.labApi.get_ml_id_dict()
# print('ml_id_to_name:',self.ml_id_to_name)
# dictionary of dataset threads, initilized and used by q_utils.
# Keys are datasetIds, values are q_utils.DatasetThread instances.
#WGL: this should get moved to the request manager
self.dataset_threads = {}
# local dataframe of datasets and their metafeatures
self.dataset_mf = pd.DataFrame()
if use_knowledgebase:
self.load_knowledgebase()
# set termination condition
self.term_condition = term_condition
if self.term_condition == 'n_recs':
self.term_value = self.n_recs
elif self.term_condition == 'time':
self.term_value = max_time
else:
self.term_value = None
# start the request manager
self.requestManager = RequestManager(
ai=self,
defaultTermConditionStr=self.term_condition,
defaultTermParam=self.term_value)
# if there is a pickle file, load it as the recommender scores
assert not (warm_start), "The `warm_start` option is not yet supported"
# for comma-separated list of datasets in datasets, turn AI request on
assert not (
datasets
), "The `datasets` option is not yet supported: " + str(datasets)
##-----------------
## Init methods
##-----------------
def load_knowledgebase(self):
""" Bootstrap the recommenders with the knowledgebase
"""
logger.info('loading pmlb knowledgebase')
kb = knowledgebase_loader.load_pmlb_knowledgebase()
# replace algorithm names with their ids
self.ml_name_to_id = {v: k for k, v in self.ml_id_to_name.items()}
kb['resultsData']['algorithm'] = kb['resultsData']['algorithm'].apply(
lambda x: self.ml_name_to_id[x])
#TODO: Verify that conversion from name to id is needed....
# WGL: yes at the moment we need this until hash is implemented.
# we can add a check at dataset upload to prevent repeat dataset names in
# the mean time.
self.user_datasets = self.labApi.get_user_datasets(self.user)
self.dataset_name_to_id = {v: k for k, v in self.user_datasets.items()}
kb['resultsData']['dataset'] = kb['resultsData']['dataset'].apply(
lambda x: self.dataset_name_to_id[x]
if x in self.dataset_name_to_id.keys() else x)
metafeatures = {}
for k, v in kb['metafeaturesData'].items():
if k in self.dataset_name_to_id.keys():
metafeatures[self.dataset_name_to_id[k]] = v
else:
metafeatures[k] = v
# all_df_mf = pd.DataFrame.from_records(kb['metafeaturesData']).transpose()
all_df_mf = pd.DataFrame.from_records(metafeatures).transpose()
# keep only metafeatures with results
self.dataset_mf = all_df_mf.reindex(kb['resultsData'].dataset.unique())
# self.update_dataset_mf(kb['resultsData'])
self.rec.update(kb['resultsData'],
self.dataset_mf,
source='knowledgebase')
logger.info('pmlb knowledgebase loaded')
def load_options(self):
"""Loads algorithm UI parameters and sets them to self.ui_options."""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': loading options...')
responses = self.labApi.get_projects()
if len(responses) > 0:
self.ui_options = responses
else:
logger.warning("no algorithms found by load_options()")
##-----------------
## Utility methods
##-----------------
def update_dataset_mf(self, results_data):
"""Grabs metafeatures of datasets in results_data
and concatinates them to result_data
:param results_data: experiment results with associated datasets
"""
logger.debug('results_data:' + str(results_data.columns))
logger.debug('results_data:' + str(results_data.head()))
dataset_metafeatures = []
for d in results_data['dataset'].unique():
if len(self.dataset_mf) == 0 or d not in self.dataset_mf.index:
# fetch metafeatures from server for dataset and append
df = self.labApi.get_metafeatures(d)
# df['dataset'] = d
# print('metafeatures:',df)
dataset_metafeatures.append(df)
if dataset_metafeatures:
df_mf = pd.concat(dataset_metafeatures).set_index('dataset')
# print('df_mf:',df_mf['dataset'], df_mf)
self.dataset_mf = self.dataset_mf.append(df_mf)
# print('self.dataset_mf:\n',self.dataset_mf)
##-----------------
## Loop methods
##-----------------
def check_results(self):
"""Checks to see if new experiment results have been posted since the
previous time step. If so, set them to self.new_data and return True.
:returns: Boolean - True if new results were found
"""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': checking results...')
newResults = self.labApi.get_new_experiments_as_dataframe(
last_update=self.last_update)
if len(newResults) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) + ': ' +
str(len(newResults)) + ' new results!')
self.last_update = int(time.time()) * 1000 # update timestamp
self.new_data = newResults
return True
return False
def update_recommender(self):
"""Update recommender models based on new experiment results in
self.new_data, and then clear self.new_data.
"""
if (hasattr(self, 'new_data') and len(self.new_data) >= 1):
self.update_dataset_mf(self.new_data)
self.rec.update(self.new_data, self.dataset_mf)
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': recommender updated')
# reset new data
self.new_data = pd.DataFrame()
def check_requests(self):
"""Check to see if any new AI requests have been submitted.
If so, add them to self.request_queue.
:returns: Boolean - True if new AI requests have been submitted
"""
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': checking requests...')
# get all dtasets that have an ai 'requested' status
# and initilize a new request
dsFilter = {'ai': ['requested', 'dummy']}
aiOnRequests = self.labApi.get_filtered_datasets(dsFilter)
if len(aiOnRequests) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': new ai request for:' +
';'.join([r['name'] for r in aiOnRequests]))
# set AI flag to 'on' to acknowledge requests received
for r in aiOnRequests:
self.labApi.set_ai_status(datasetId=r['_id'], aiStatus='on')
self.requestManager.add_request(datasetId=r['_id'],
datasetName=r['name'])
time.sleep(.1)
# get all datasets that have a manual 'off' status
# and terminate their ai requests
dsFilter = {'ai': ['off', 'dummy']}
aiOffRequests = self.labApi.get_filtered_datasets(dsFilter)
if len(aiOffRequests) > 0:
logger.info(
time.strftime("%Y %I:%M:%S %p %Z", time.localtime()) +
': ai termination request for:' +
';'.join([r['name'] for r in aiOffRequests]))
for r in aiOffRequests:
self.requestManager.terminate_request(datasetId=r['_id'])
return True
def process_rec(self):
self.requestManager.process_requests()
##-----------------
## Syncronous actions an AI request can take
##-----------------
def generate_recommendations(self, datasetId, numOfRecs):
"""Generate ml recommendation payloads for the given dataset.
:param datasetId
:param numOfRecs
:returns list of maps that represent request payload objects
"""
logger.info("generate_recommendations({},{})".format(
datasetId, numOfRecs))
recommendations = []
metafeatures = self.labApi.get_metafeatures(datasetId)
ml, p, ai_scores = self.rec.recommend(dataset_id=datasetId,
n_recs=numOfRecs,
dataset_mf=metafeatures)
for alg, params, score in zip(ml, p, ai_scores):
# TODO: just return dictionaries of parameters from rec
# modified_params = eval(params) # turn params into a dictionary
recommendations.append({
'dataset_id': datasetId,
'algorithm_id': alg,
'username': self.user,
'parameters': params,
'ai_score': score,
})
return recommendations
def transfer_rec(self, rec_payload):
"""Attempt to send a recommendation to the lab server.
Continues until recommendation is successfully submitted or an
unexpected error occurs.
:param rec_payload: dictionary - the payload describing the experiment
"""
logger.info("transfer_rec(" + str(rec_payload) + ")")
submitstatus = self.labApi.launch_experiment(
algorithmId=rec_payload['algorithm_id'], payload=rec_payload)
logger.debug("transfer_rec() starting loop, submitstatus: " +
str(submitstatus))
while ('error' in submitstatus
and submitstatus['error'] == 'No machine capacity available'):
logger.debug("Waiting for server capacity: {}".format(
submitstatus['error']))
sleep(3)
submitstatus = self.labApi.launch_experiment(
rec_payload['algorithm_id'], rec_payload)
logger.debug("transfer_rec() exiting loop, submitstatus: " +
str(submitstatus))
if 'error' in submitstatus:
msg = 'Unrecoverable error during transfer_rec : ' + str(
submitstatus)
logger.error(msg)
raise RuntimeError(msg)
#pdb.set_trace()
##-----------------
## Save/load ai state
##-----------------
def save_state(self):
"""Save ML+P scores in pickle or to DB
TODO: test that this still works
"""
raise RuntimeError("save_state is not currently supported")
out = open(self.rec_score_file, 'wb')
state = {}
if (hasattr(self.rec, 'scores')):
#TODO: make this a more generic. Maybe just save the
# AI or rec object itself.
# state['trained_dataset_models'] = self.rec.trained_dataset_models
state['scores'] = self.rec.scores
state['last_update'] = self.last_update
pickle.dump(state, out)
def load_state(self):
"""Loads pickled score file and recommender model.
TODO: test that this still works
"""
raise RuntimeError("load_state is not currently supported")
if os.stat(self.rec_score_file).st_size != 0:
filehandler = open(self.rec_score_file, 'rb')
state = pickle.load(filehandler)
if (hasattr(self.rec, 'scores')):
self.rec.scores = state['scores']
# self.rec.trained_dataset_models = state['trained_dataset_models']
self.last_update = state['last_update']
logger.info('loaded previous state from ' + self.last_update)