Merge pull request #163 from Microsoft/master

merge master

src/nni_manager/training_service/test/localTrainingService.test.ts

@@ -31,7 +31,7 @@ import { TrialConfigMetadataKey } from '../common/trialConfigMetadataKey';
 import { LocalTrainingService } from '../local/localTrainingService';
 
 // TODO: copy mockedTrail.py to local folder
-const localCodeDir: string = tmp.dirSync().name
+const localCodeDir: string = tmp.dirSync().name.split('\\').join('\\\\');
 const mockedTrialPath: string = './training_service/test/mockedTrial.py'
 fs.copyFileSync(mockedTrialPath, localCodeDir + '/mockedTrial.py')
 

src/sdk/pynni/nni/batch_tuner/batch_tuner.py

@@ -94,4 +94,7 @@ class BatchTuner(Tuner):
         return self.values[self.count]
 
     def receive_trial_result(self, parameter_id, parameters, value):
-        pass
\ No newline at end of file
+        pass
+
+    def import_data(self, data):
+        pass

src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py

@@ -573,3 +573,35 @@ class BOHB(MsgDispatcherBase):
 
     def handle_add_customized_trial(self, data):
         pass
+
+    def handle_import_data(self, data):
+        """Import additional data for tuning
+
+        Parameters
+        ----------
+        data:
+            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+
+        Raises
+        ------
+        AssertionError
+            data doesn't have required key 'parameter' and 'value'
+        """
+        _completed_num = 0
+        for trial_info in data:
+            logger.info("Importing data, current processing progress %s / %s" %(_completed_num), len(data))
+            _completed_num += 1
+            assert "parameter" in trial_info
+            _params = trial_info["parameter"]
+            assert "value" in trial_info
+            _value = trial_info['value']
+            if _KEY not in _params:
+                _params[_KEY] = self.max_budget
+                logger.info("Set \"TRIAL_BUDGET\" value to %s (max budget)" %self.max_budget)
+            if self.optimize_mode is OptimizeMode.Maximize:
+                reward = -_value
+            else:
+                reward = _value
+            _budget = _params[_KEY]
+            self.cg.new_result(loss=reward, budget=_budget, parameters=_params, update_model=True)
+        logger.info("Successfully import tuning data to BOHB advisor.")

src/sdk/pynni/nni/common.py

@@ -19,29 +19,13 @@
 # ==================================================================================================
 
 
-from collections import namedtuple
 from datetime import datetime
 from io import TextIOBase
 import logging
-import os
 import sys
 import time
 
-
-def _load_env_args():
-    args = {
-        'platform': os.environ.get('NNI_PLATFORM'),
-        'trial_job_id': os.environ.get('NNI_TRIAL_JOB_ID'),
-        'log_dir': os.environ.get('NNI_LOG_DIRECTORY'),
-        'role': os.environ.get('NNI_ROLE'),
-        'log_level': os.environ.get('NNI_LOG_LEVEL')
-    }
-    return namedtuple('EnvArgs', args.keys())(**args)
-
-env_args = _load_env_args()
-'''Arguments passed from environment'''
-
-logLevelMap = {
+log_level_map = {
     'fatal': logging.FATAL,
     'error': logging.ERROR,
     'warning': logging.WARNING,
@@ -49,7 +33,8 @@ logLevelMap = {
     'debug': logging.DEBUG
 }
 
-_time_format = '%m/%d/%Y, %I:%M:%S %P'
+_time_format = '%m/%d/%Y, %I:%M:%S %p'
+    
 class _LoggerFileWrapper(TextIOBase):
     def __init__(self, logger_file):
         self.file = logger_file
@@ -61,21 +46,12 @@ class _LoggerFileWrapper(TextIOBase):
             self.file.flush()
         return len(s)
 
-def init_logger(logger_file_path):
+def init_logger(logger_file_path, log_level_name='info'):
     """Initialize root logger.
     This will redirect anything from logging.getLogger() as well as stdout to specified file.
     logger_file_path: path of logger file (path-like object).
     """
-    if env_args.platform == 'unittest':
-        logger_file_path = 'unittest.log'
-    elif env_args.log_dir is not None:
-        logger_file_path = os.path.join(env_args.log_dir, logger_file_path)
-    if env_args.log_level and logLevelMap.get(env_args.log_level):
-        log_level = logLevelMap[env_args.log_level]
-    else:
-        log_level = logging.INFO #default log level is INFO
-
-    
+    log_level = log_level_map.get(log_level_name, logging.INFO)
     logger_file = open(logger_file_path, 'w')
     fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
     logging.Formatter.converter = time.localtime

src/sdk/pynni/nni/env_vars.py

+# Copyright (c) Microsoft Corporation. All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+# associated documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish, distribute,
+# sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all copies or
+# substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+# NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
+# OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+# ==================================================================================================
+
+import os
+from collections import namedtuple
+
+
+_trial_env_var_names = [
+    'NNI_PLATFORM',
+    'NNI_TRIAL_JOB_ID',
+    'NNI_SYS_DIR',
+    'NNI_OUTPUT_DIR',
+    'NNI_TRIAL_SEQ_ID',
+    'MULTI_PHASE'
+]
+
+_dispatcher_env_var_names = [
+    'NNI_MODE',
+    'NNI_CHECKPOINT_DIRECTORY',
+    'NNI_LOG_DIRECTORY',
+    'NNI_LOG_LEVEL',
+    'NNI_INCLUDE_INTERMEDIATE_RESULTS'
+]
+
+def _load_env_vars(env_var_names):
+    env_var_dict = {k: os.environ.get(k) for k in env_var_names}
+    return namedtuple('EnvVars', env_var_names)(**env_var_dict)
+
+trial_env_vars = _load_env_vars(_trial_env_var_names)
+
+dispatcher_env_vars = _load_env_vars(_dispatcher_env_var_names)

src/sdk/pynni/nni/evolution_tuner/evolution_tuner.py

@@ -34,7 +34,6 @@ from nni.tuner import Tuner
 from nni.utils import extract_scalar_reward
 from .. import parameter_expressions
 
-
 @unique
 class OptimizeMode(Enum):
     """Optimize Mode class
@@ -299,3 +298,6 @@ class EvolutionTuner(Tuner):
 
         indiv = Individual(config=params, result=reward)
         self.population.append(indiv)
+
+    def import_data(self, data):
+        pass

src/sdk/pynni/nni/gridsearch_tuner/gridsearch_tuner.py

@@ -24,14 +24,17 @@ gridsearch_tuner.py including:
 
 import copy
 import numpy as np
+import logging
 
 import nni
 from nni.tuner import Tuner
+from nni.utils import convert_dict2tuple
 
 TYPE = '_type'
 CHOICE = 'choice'
 VALUE = '_value'
 
+logger = logging.getLogger('grid_search_AutoML')
 
 class GridSearchTuner(Tuner):
     '''
@@ -51,6 +54,7 @@ class GridSearchTuner(Tuner):
     def __init__(self):
         self.count = -1
         self.expanded_search_space = []
+        self.supplement_data = dict()
 
     def json2paramater(self, ss_spec):
         '''
@@ -135,9 +139,31 @@ class GridSearchTuner(Tuner):
 
     def generate_parameters(self, parameter_id):
         self.count += 1
-        if self.count > len(self.expanded_search_space)-1:
-            raise nni.NoMoreTrialError('no more parameters now.')
-        return self.expanded_search_space[self.count]
+        while (self.count <= len(self.expanded_search_space)-1):
+            _params_tuple = convert_dict2tuple(self.expanded_search_space[self.count])
+            if _params_tuple in self.supplement_data:
+                self.count += 1
+            else:
+                return self.expanded_search_space[self.count]
+        raise nni.NoMoreTrialError('no more parameters now.')
 
     def receive_trial_result(self, parameter_id, parameters, value):
         pass
+
+    def import_data(self, data):
+        """Import additional data for tuning
+
+        Parameters
+        ----------
+        data:
+            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        _completed_num = 0
+        for trial_info in data:
+            logger.info("Importing data, current processing progress %s / %s" %(_completed_num), len(data))
+            _completed_num += 1
+            assert "parameter" in trial_info
+            _params = trial_info["parameter"]
+            _params_tuple = convert_dict2tuple(_params)
+            self.supplement_data[_params_tuple] = True
+        logger.info("Successfully import data to grid search tuner.")

src/sdk/pynni/nni/hyperband_advisor/hyperband_advisor.py

@@ -31,7 +31,6 @@ import json_tricks
 
 from nni.protocol import CommandType, send
 from nni.msg_dispatcher_base import MsgDispatcherBase
-from nni.common import init_logger
 from nni.utils import extract_scalar_reward
 from .. import parameter_expressions
 
@@ -420,3 +419,6 @@ class Hyperband(MsgDispatcherBase):
 
     def handle_add_customized_trial(self, data):
         pass
+
+    def handle_import_data(self, data):
+        pass

src/sdk/pynni/nni/hyperopt_tuner/hyperopt_tuner.py

@@ -172,6 +172,7 @@ class HyperoptTuner(Tuner):
         self.json = None
         self.total_data = {}
         self.rval = None
+        self.supplement_data_num = 0
 
     def _choose_tuner(self, algorithm_name):
         """
@@ -353,3 +354,27 @@ class HyperoptTuner(Tuner):
         # remove '_index' from json2parameter and save params-id
         total_params = json2parameter(self.json, parameter)
         return total_params
+
+    def import_data(self, data):
+        """Import additional data for tuning
+
+        Parameters
+        ----------
+        data:
+            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        _completed_num = 0
+        for trial_info in data:
+            logger.info("Importing data, current processing progress %s / %s" %(_completed_num), len(data))
+            _completed_num += 1
+            if self.algorithm_name == 'random_search':
+                return
+            assert "parameter" in trial_info
+            _params = trial_info["parameter"]
+            assert "value" in trial_info
+            _value = trial_info['value']
+            self.supplement_data_num += 1
+            _parameter_id = '_'.join(["ImportData", str(self.supplement_data_num)])
+            self.total_data[_parameter_id] = _params
+            self.receive_trial_result(parameter_id=_parameter_id, parameters=_params, value=_value)
+        logger.info("Successfully import data to TPE/Anneal tuner.")

src/sdk/pynni/nni/metis_tuner/metis_tuner.py

@@ -96,7 +96,7 @@ class MetisTuner(Tuner):
         self.samples_x = []
         self.samples_y = []
         self.samples_y_aggregation = []
-        self.history_parameters = []
+        self.total_data = []
         self.space = None
         self.no_resampling = no_resampling
         self.no_candidates = no_candidates
@@ -107,6 +107,7 @@ class MetisTuner(Tuner):
         self.exploration_probability = exploration_probability
         self.minimize_constraints_fun = None
         self.minimize_starting_points = None
+        self.supplement_data_num = 0
 
 
     def update_search_space(self, search_space):
@@ -392,15 +393,35 @@ class MetisTuner(Tuner):
         # ===== STEP 7: If current optimal hyperparameter occurs in the history or exploration probability is less than the threshold, take next config as exploration step  =====
         outputs = self._pack_output(lm_current['hyperparameter'])
         ap = random.uniform(0, 1)
-        if outputs in self.history_parameters or ap<=self.exploration_probability:
+        if outputs in self.total_data or ap<=self.exploration_probability:
             if next_candidate is not None:
                 outputs = self._pack_output(next_candidate['hyperparameter'])
             else:
                 random_parameter = _rand_init(x_bounds, x_types, 1)[0]
                 outputs = self._pack_output(random_parameter)
-        self.history_parameters.append(outputs)
+        self.total_data.append(outputs)
         return outputs
 
+    def import_data(self, data):
+        """Import additional data for tuning
+        Parameters
+        ----------
+        data:
+            a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        _completed_num = 0
+        for trial_info in data:
+            logger.info("Importing data, current processing progress %s / %s" %(_completed_num), len(data))
+            _completed_num += 1
+            assert "parameter" in trial_info
+            _params = trial_info["parameter"]
+            assert "value" in trial_info
+            _value = trial_info['value']
+            self.supplement_data_num += 1
+            _parameter_id = '_'.join(["ImportData", str(self.supplement_data_num)])
+            self.total_data.append(_params)
+            self.receive_trial_result(parameter_id=_parameter_id, parameters=_params, value=_value)
+        logger.info("Successfully import data to metis tuner.")
 
 def _rand_with_constraints(x_bounds, x_types):
     outputs = None

src/sdk/pynni/nni/msg_dispatcher.py

@@ -27,6 +27,7 @@ from .protocol import CommandType, send
 from .msg_dispatcher_base import MsgDispatcherBase
 from .assessor import AssessResult
 from .common import multi_thread_enabled
+from .env_vars import dispatcher_env_vars
 
 _logger = logging.getLogger(__name__)
 
@@ -108,18 +109,24 @@ class MsgDispatcher(MsgDispatcherBase):
     def handle_update_search_space(self, data):
         self.tuner.update_search_space(data)
 
+    def handle_import_data(self, data):
+        """Import additional data for tuning
+        data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        self.tuner.import_data(data)
+
     def handle_add_customized_trial(self, data):
-         # data: parameters
+        # data: parameters
         id_ = _create_parameter_id()
         _customized_parameter_ids.add(id_)
         send(CommandType.NewTrialJob, _pack_parameter(id_, data, customized=True))
 
     def handle_report_metric_data(self, data):
         """
-        :param data: a dict received from nni_manager, which contains:
-                    - 'parameter_id': id of the trial
-                    - 'value': metric value reported by nni.report_final_result()
-                    - 'type': report type, support {'FINAL', 'PERIODICAL'}
+        data: a dict received from nni_manager, which contains:
+              - 'parameter_id': id of the trial
+              - 'value': metric value reported by nni.report_final_result()
+              - 'type': report type, support {'FINAL', 'PERIODICAL'}
         """
         if data['type'] == 'FINAL':
             self._handle_final_metric_data(data)
@@ -134,9 +141,9 @@ class MsgDispatcher(MsgDispatcherBase):
     def handle_trial_end(self, data):
         """
         data: it has three keys: trial_job_id, event, hyper_params
-            trial_job_id: the id generated by training service
-            event: the job's state
-            hyper_params: the hyperparameters generated and returned by tuner
+             - trial_job_id: the id generated by training service
+             - event: the job's state
+             - hyper_params: the hyperparameters generated and returned by tuner
         """
         trial_job_id = data['trial_job_id']
         _ended_trials.add(trial_job_id)
@@ -190,8 +197,8 @@ class MsgDispatcher(MsgDispatcherBase):
             _logger.debug('BAD, kill %s', trial_job_id)
             send(CommandType.KillTrialJob, json_tricks.dumps(trial_job_id))
             # notify tuner
-            _logger.debug('env var: NNI_INCLUDE_INTERMEDIATE_RESULTS: [%s]', os.environ.get('NNI_INCLUDE_INTERMEDIATE_RESULTS'))
-            if os.environ.get('NNI_INCLUDE_INTERMEDIATE_RESULTS') == 'true':
+            _logger.debug('env var: NNI_INCLUDE_INTERMEDIATE_RESULTS: [%s]', dispatcher_env_vars.NNI_INCLUDE_INTERMEDIATE_RESULTS)
+            if dispatcher_env_vars.NNI_INCLUDE_INTERMEDIATE_RESULTS == 'true':
                 self._earlystop_notify_tuner(data)
         else:
             _logger.debug('GOOD')

src/sdk/pynni/nni/msg_dispatcher_base.py

@@ -26,11 +26,14 @@ from multiprocessing.dummy import Pool as ThreadPool
 from queue import Queue, Empty
 import json_tricks
 
-from .common import init_logger, multi_thread_enabled
+from .common import multi_thread_enabled
+from .env_vars import dispatcher_env_vars
+from .utils import init_dispatcher_logger
 from .recoverable import Recoverable
 from .protocol import CommandType, receive
 
-init_logger('dispatcher.log')
+init_dispatcher_logger()
+
 _logger = logging.getLogger(__name__)
 
 QUEUE_LEN_WARNING_MARK = 20
@@ -56,8 +59,7 @@ class MsgDispatcherBase(Recoverable):
         This function will never return unless raise.
         """
         _logger.info('Start dispatcher')
-        mode = os.getenv('NNI_MODE')
-        if mode == 'resume':
+        if dispatcher_env_vars.NNI_MODE == 'resume':
             self.load_checkpoint()
 
         while True:
@@ -142,6 +144,7 @@ class MsgDispatcherBase(Recoverable):
             CommandType.Initialize: self.handle_initialize,
             CommandType.RequestTrialJobs: self.handle_request_trial_jobs,
             CommandType.UpdateSearchSpace: self.handle_update_search_space,
+            CommandType.ImportData: self.handle_import_data,
             CommandType.AddCustomizedTrialJob: self.handle_add_customized_trial,
 
             # Tunner/Assessor commands:
@@ -166,6 +169,9 @@ class MsgDispatcherBase(Recoverable):
     def handle_update_search_space(self, data):
        raise NotImplementedError('handle_update_search_space not implemented')
 
+    def handle_import_data(self, data):
+        raise NotImplementedError('handle_import_data not implemented')
+
     def handle_add_customized_trial(self, data):
         raise NotImplementedError('handle_add_customized_trial not implemented')
 

src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py

@@ -112,6 +112,13 @@ class MultiPhaseMsgDispatcher(MsgDispatcherBase):
         self.tuner.update_search_space(data)
         return True
 
+    def handle_import_data(self, data):
+        """import additional data for tuning
+        data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        self.tuner.import_data(data)
+        return True
+
     def handle_add_customized_trial(self, data):
          # data: parameters
         id_ = _create_parameter_id()
@@ -154,6 +161,9 @@ class MultiPhaseMsgDispatcher(MsgDispatcherBase):
             self.tuner.trial_end(json_tricks.loads(data['hyper_params'])['parameter_id'], data['event'] == 'SUCCEEDED', trial_job_id)
         return True
 
+    def handle_import_data(self, data):
+        pass
+
     def _handle_intermediate_metric_data(self, data):
         if data['type'] != 'PERIODICAL':
             return True

src/sdk/pynni/nni/multi_phase/multi_phase_tuner.py

@@ -76,6 +76,12 @@ class MultiPhaseTuner(Recoverable):
         """
         raise NotImplementedError('Tuner: update_search_space not implemented')
 
+    def import_data(self, data):
+        """Import additional data for tuning
+        data: a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
+        """
+        pass
+
     def load_checkpoint(self):
         """Load the checkpoint of tuner.
         path: checkpoint directory for tuner
@@ -95,3 +101,6 @@ class MultiPhaseTuner(Recoverable):
 
     def _on_error(self):
         pass
+
+    def import_data(self, data):
+        pass

src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py

@@ -307,3 +307,7 @@ class NetworkMorphismTuner(Tuner):
             if item["model_id"] == model_id:
                 return item["metric_value"]
         return None
+
+    def import_data(self, data):
+        pass
+

src/sdk/pynni/nni/platform/__init__.py

@@ -21,13 +21,13 @@
 
 # pylint: disable=wildcard-import
 
-from ..common import env_args
+from ..env_vars import trial_env_vars
 
-if env_args.platform is None:
+if trial_env_vars.NNI_PLATFORM is None:
     from .standalone import *
-elif env_args.platform == 'unittest':
+elif trial_env_vars.NNI_PLATFORM == 'unittest':
     from .test import *
-elif env_args.platform in ('local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller'):
+elif trial_env_vars.NNI_PLATFORM in ('local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller'):
     from .local import *
 else:
-    raise RuntimeError('Unknown platform %s' % env_args.platform)
+    raise RuntimeError('Unknown platform %s' % trial_env_vars.NNI_PLATFORM)

src/sdk/pynni/nni/platform/local.py

@@ -19,34 +19,36 @@
 # ==================================================================================================
 
 import os
+import sys
 import json
 import time
-import json_tricks
 import subprocess
+import json_tricks
 
-from ..common import init_logger, env_args
+from ..common import init_logger
+from ..env_vars import trial_env_vars
 
-_sysdir = os.environ['NNI_SYS_DIR']
+_sysdir = trial_env_vars.NNI_SYS_DIR
 if not os.path.exists(os.path.join(_sysdir, '.nni')):
     os.makedirs(os.path.join(_sysdir, '.nni'))
 _metric_file = open(os.path.join(_sysdir, '.nni', 'metrics'), 'wb')
 
-_outputdir = os.environ['NNI_OUTPUT_DIR']
+_outputdir = trial_env_vars.NNI_OUTPUT_DIR
 if not os.path.exists(_outputdir):
     os.makedirs(_outputdir)
 
-_nni_platform = os.environ['NNI_PLATFORM']
+_nni_platform = trial_env_vars.NNI_PLATFORM
 if _nni_platform == 'local':
    _log_file_path = os.path.join(_outputdir, 'trial.log')
    init_logger(_log_file_path)
 
-_multiphase = os.environ.get('MULTI_PHASE')
+_multiphase = trial_env_vars.MULTI_PHASE
 
 _param_index = 0
 
 def request_next_parameter():
     metric = json_tricks.dumps({
-        'trial_job_id': env_args.trial_job_id,
+        'trial_job_id': trial_env_vars.NNI_TRIAL_JOB_ID,
         'type': 'REQUEST_PARAMETER',
         'sequence': 0,
         'parameter_index': _param_index
@@ -86,7 +88,11 @@ def send_metric(string):
         assert len(data) < 1000000, 'Metric too long'    
         _metric_file.write(b'ME%06d%b' % (len(data), data))
         _metric_file.flush()
-        subprocess.run(['touch', _metric_file.name], check = True)
+        if sys.platform == "win32":
+            file = open(_metric_file.name)
+            file.close()
+        else:
+            subprocess.run(['touch', _metric_file.name], check = True)
 
 def get_sequence_id():
-    return os.environ['NNI_TRIAL_SEQ_ID']
\ No newline at end of file
+    return trial_env_vars.NNI_TRIAL_SEQ_ID

src/sdk/pynni/nni/protocol.py

@@ -30,6 +30,7 @@ class CommandType(Enum):
     RequestTrialJobs = b'GE'
     ReportMetricData = b'ME'
     UpdateSearchSpace = b'SS'
+    ImportData = b'FD'
     AddCustomizedTrialJob = b'AD'
     TrialEnd = b'EN'
     Terminate = b'TE'

src/sdk/pynni/nni/recoverable.py

@@ -24,7 +24,7 @@ class Recoverable:
     def load_checkpoint(self):
         pass
 
-    def save_checkpont(self):
+    def save_checkpoint(self):
         pass
 
     def get_checkpoint_path(self):

src/sdk/pynni/nni/smac_tuner/smac_tuner.py

@@ -261,3 +261,6 @@ class SMACTuner(Tuner):
                 params.append(self.convert_loguniform_categorical(challenger.get_dictionary()))
                 cnt += 1
         return params
+
+    def import_data(self, data):
+        pass