Merge pull request #184 from microsoft/master

merge master

src/nni_manager/training_service/remote_machine/sshClientUtility.ts

@@ -21,16 +21,16 @@
 
 import * as assert from 'assert';
 import * as cpp from 'child-process-promise';
-import * as path from 'path';
 import * as os from 'os';
+import * as path from 'path';
 import { Client, ClientChannel, SFTPWrapper } from 'ssh2';
 import * as stream from 'stream';
 import { Deferred } from 'ts-deferred';
 import { NNIError, NNIErrorNames } from '../../common/errors';
 import { getLogger, Logger } from '../../common/log';
-import { uniqueString, getRemoteTmpDir, unixPathJoin } from '../../common/utils';
-import { RemoteCommandResult } from './remoteMachineData';
+import { getRemoteTmpDir, uniqueString, unixPathJoin } from '../../common/utils';
 import { execRemove, tarAdd } from '../common/util';
+import { RemoteCommandResult } from './remoteMachineData';
 
 /**
  *
@@ -44,7 +44,8 @@ export namespace SSHClientUtility {
      * @param remoteDirectory remote directory
      * @param sshClient SSH client
      */
-    export async function copyDirectoryToRemote(localDirectory : string, remoteDirectory : string, sshClient : Client, remoteOS: string) : Promise<void> {
+    export async function copyDirectoryToRemote(localDirectory : string, remoteDirectory : string, sshClient : Client, remoteOS: string)
+     : Promise<void> {
         const deferred: Deferred<void> = new Deferred<void>();
         const tmpTarName: string = `${uniqueString(10)}.tar.gz`;
         const localTarPath: string = path.join(os.tmpdir(), tmpTarName);
@@ -75,7 +76,7 @@ export namespace SSHClientUtility {
         assert(sshClient !== undefined);
         const deferred: Deferred<boolean> = new Deferred<boolean>();
         sshClient.sftp((err : Error, sftp : SFTPWrapper) => {
-            if (err) {
+            if (err !== undefined && err !== null) {
                 log.error(`copyFileToRemote: ${err.message}, ${localFilePath}, ${remoteFilePath}`);
                 deferred.reject(err);
 
@@ -84,7 +85,7 @@ export namespace SSHClientUtility {
             assert(sftp !== undefined);
             sftp.fastPut(localFilePath, remoteFilePath, (fastPutErr : Error) => {
                 sftp.end();
-                if (fastPutErr) {
+                if (fastPutErr !== undefined && fastPutErr !== null) {
                     deferred.reject(fastPutErr);
                 } else {
                     deferred.resolve(true);
@@ -100,6 +101,7 @@ export namespace SSHClientUtility {
      * @param command the command to execute remotely
      * @param client SSH Client
      */
+    // tslint:disable:no-unsafe-any no-any
     export function remoteExeCommand(command : string, client : Client): Promise<RemoteCommandResult> {
         const log: Logger = getLogger();
         log.debug(`remoteExeCommand: command: [${command}]`);
@@ -109,7 +111,7 @@ export namespace SSHClientUtility {
         let exitCode : number;
 
         client.exec(command, (err : Error, channel : ClientChannel) => {
-            if (err) {
+            if (err !== undefined && err !== null) {
                 log.error(`remoteExeCommand: ${err.message}`);
                 deferred.reject(err);
 
@@ -117,13 +119,14 @@ export namespace SSHClientUtility {
             }
 
             channel.on('data', (data : any, dataStderr : any) => {
-                if (dataStderr) {
+                if (dataStderr !== undefined && dataStderr !== null) {
                     stderr += data.toString();
                 } else {
                     stdout += data.toString();
                 }
-            }).on('exit', (code, signal) => {
-                exitCode = code as number;
+            })
+              .on('exit', (code : any, signal : any) => {
+                exitCode = <number>code;
                 deferred.resolve({
                     stdout : stdout,
                     stderr : stderr,
@@ -138,8 +141,9 @@ export namespace SSHClientUtility {
     export function getRemoteFileContent(filePath: string, sshClient: Client): Promise<string> {
         const deferred: Deferred<string> = new Deferred<string>();
         sshClient.sftp((err: Error, sftp : SFTPWrapper) => {
-            if (err) {
-                getLogger().error(`getRemoteFileContent: ${err.message}`);
+            if (err !== undefined && err !== null) {
+                getLogger()
+                  .error(`getRemoteFileContent: ${err.message}`);
                 deferred.reject(new Error(`SFTP error: ${err.message}`));
 
                 return;
@@ -150,16 +154,19 @@ export namespace SSHClientUtility {
                 let dataBuffer: string = '';
                 sftpStream.on('data', (data : Buffer | string) => {
                     dataBuffer += data;
-                }).on('error', (streamErr: Error) => {
+                })
+                  .on('error', (streamErr: Error) => {
                     sftp.end();
                     deferred.reject(new NNIError(NNIErrorNames.NOT_FOUND, streamErr.message));
-                }).on('end', () => {
+                })
+                  .on('end', () => {
                     // sftp connection need to be released manually once operation is done
                     sftp.end();
                     deferred.resolve(dataBuffer);
                 });
             } catch (error) {
-                getLogger().error(`getRemoteFileContent: ${error.message}`);
+                getLogger()
+                  .error(`getRemoteFileContent: ${error.message}`);
                 sftp.end();
                 deferred.reject(new Error(`SFTP error: ${error.message}`));
             }
@@ -167,4 +174,5 @@ export namespace SSHClientUtility {
 
         return deferred.promise;
     }
+    // tslint:enable:no-unsafe-any no-any
 }

src/nni_manager/training_service/test/hdfsClientUtility.test.ts

@@ -37,7 +37,7 @@ describe('WebHDFS', function () {
     {
         "user": "user1",
         "port": 50070,
-        "host": "10.0.0.0"        
+        "host": "10.0.0.0"
     }
     */
     let skip: boolean = false;
@@ -45,7 +45,7 @@ describe('WebHDFS', function () {
     let hdfsClient: any;
     try {
         testHDFSInfo = JSON.parse(fs.readFileSync('../../.vscode/hdfsInfo.json', 'utf8'));
-        console.log(testHDFSInfo);        
+        console.log(testHDFSInfo);
         hdfsClient = WebHDFS.createClient({
             user: testHDFSInfo.user,
             port: testHDFSInfo.port,
@@ -120,7 +120,7 @@ describe('WebHDFS', function () {
         chai.expect(actualFileData).to.be.equals(testFileData);
 
         const testHDFSDirPath : string = path.join('/nni_unittest_' + uniqueString(6) +  '_dir');
-        
+
         await HDFSClientUtility.copyDirectoryToHdfs(tmpLocalDirectoryPath, testHDFSDirPath, hdfsClient);
 
         const files : any[] = await HDFSClientUtility.readdir(testHDFSDirPath, hdfsClient);
@@ -133,7 +133,7 @@ describe('WebHDFS', function () {
 
         // Cleanup
         rmdir(tmpLocalDirectoryPath);
-        
+
         let deleteRestult : boolean = await HDFSClientUtility.deletePath(testHDFSFilePath, hdfsClient);
         chai.expect(deleteRestult).to.be.equals(true);
 

src/nni_manager/training_service/test/kubeflowTrainingService.test.ts

@@ -63,7 +63,7 @@ describe('Unit Test for KubeflowTrainingService', () => {
         if (skip) {
             return;
         }
-        kubeflowTrainingService = component.get(KubeflowTrainingService);        
+        kubeflowTrainingService = component.get(KubeflowTrainingService);
     });
 
     afterEach(() => {
@@ -78,6 +78,6 @@ describe('Unit Test for KubeflowTrainingService', () => {
             return;
         }
         await kubeflowTrainingService.setClusterMetadata(TrialConfigMetadataKey.KUBEFLOW_CLUSTER_CONFIG, testKubeflowConfig),
-        await kubeflowTrainingService.setClusterMetadata(TrialConfigMetadataKey.TRIAL_CONFIG, testKubeflowTrialConfig);                
+        await kubeflowTrainingService.setClusterMetadata(TrialConfigMetadataKey.TRIAL_CONFIG, testKubeflowTrialConfig);
     });
 });
\ No newline at end of file

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

@@ -63,7 +63,7 @@ describe('Unit Test for LocalTrainingService', () => {
         //trial jobs should be empty, since there are no submitted jobs
         chai.expect(await localTrainingService.listTrialJobs()).to.be.empty;
     });
-    
+
     it('setClusterMetadata and getClusterMetadata', async () => {
         await localTrainingService.setClusterMetadata(TrialConfigMetadataKey.TRIAL_CONFIG, trialConfig);
         localTrainingService.getClusterMetadata(TrialConfigMetadataKey.TRIAL_CONFIG).then((data)=>{
@@ -87,7 +87,7 @@ describe('Unit Test for LocalTrainingService', () => {
         await localTrainingService.cancelTrialJob(jobDetail.id);
         chai.expect(jobDetail.status).to.be.equals('USER_CANCELED');
     }).timeout(20000);
-    
+
     it('Read metrics, Add listener, and remove listener', async () => {
         // set meta data
         const trialConfig: string = `{\"command\":\"python3 mockedTrial.py\", \"codeDir\":\"${localCodeDir}\",\"gpuNum\":0}`

src/nni_manager/training_service/test/paiTrainingService.test.ts

@@ -89,7 +89,7 @@ describe('Unit Test for PAITrainingService', () => {
             chai.expect(trialDetail.status).to.be.equals('WAITING');
         } catch(error) {
             console.log('Submit job failed:' + error);
-            chai.assert(error)            
+            chai.assert(error)
         }
     });
 });
\ No newline at end of file

src/nni_manager/tslint.json

@@ -9,7 +9,10 @@
         "no-increment-decrement": false,
         "promise-function-async": false,
         "no-console": [true, "log"],
-        "no-multiline-string": false
+        "no-multiline-string": false,
+        "no-suspicious-comment": false,
+        "no-backbone-get-set-outside-model": false,
+        "max-classes-per-file": false
     },
     "rulesDirectory": [],
     "linterOptions": {

src/nni_manager/types/child-process-promise/index.d.ts

@@ -7,5 +7,5 @@ declare module 'child-process-promise' {
             stderr: string,
             message: string
         }
-    }    
+    }
 }
\ No newline at end of file

src/nni_manager/types/webhdfs/index.d.ts

+declare module 'webhdfs' {
+    export function createClient(arg: any): any;
+}
\ No newline at end of file

src/sdk/pynni/nni/__main__.py

@@ -154,7 +154,7 @@ def main():
         assessor = None
         if args.tuner_class_name in ModuleName:
             tuner = create_builtin_class_instance(
-                args.tuner_class_name, 
+                args.tuner_class_name,
                 args.tuner_args)
         else:
             tuner = create_customized_class_instance(

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

@@ -81,7 +81,7 @@ def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-
 
 class Bracket():
     """
-    A bracket in BOHB, all the information of a bracket is managed by 
+    A bracket in BOHB, all the information of a bracket is managed by
     an instance of this class.
 
     Parameters
@@ -251,7 +251,7 @@ class BOHB(MsgDispatcherBase):
     BOHB performs robust and efficient hyperparameter optimization
     at scale by combining the speed of Hyperband searches with the
     guidance and guarantees of convergence of Bayesian Optimization.
-    Instead of sampling new configurations at random, BOHB uses 
+    Instead of sampling new configurations at random, BOHB uses
     kernel density estimators to select promising candidates.
 
     Parameters
@@ -335,7 +335,7 @@ class BOHB(MsgDispatcherBase):
         pass
 
     def handle_initialize(self, data):
-        """Initialize Tuner, including creating Bayesian optimization-based parametric models 
+        """Initialize Tuner, including creating Bayesian optimization-based parametric models
         and search space formations
 
         Parameters
@@ -403,7 +403,7 @@ class BOHB(MsgDispatcherBase):
 
         If this function is called, Command will be sent by BOHB:
         a. If there is a parameter need to run, will return "NewTrialJob" with a dict:
-        { 
+        {
             'parameter_id': id of new hyperparameter
             'parameter_source': 'algorithm'
             'parameters': value of new hyperparameter
@@ -458,30 +458,30 @@ class BOHB(MsgDispatcherBase):
                     var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1]))
             elif _type == 'quniform':
                 cs.add_hyperparameter(CSH.UniformFloatHyperparameter(
-                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1], 
+                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1],
                     q=search_space[var]["_value"][2]))
             elif _type == 'loguniform':
                 cs.add_hyperparameter(CSH.UniformFloatHyperparameter(
-                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1], 
+                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1],
                     log=True))
             elif _type == 'qloguniform':
                 cs.add_hyperparameter(CSH.UniformFloatHyperparameter(
-                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1], 
+                    var, lower=search_space[var]["_value"][0], upper=search_space[var]["_value"][1],
                     q=search_space[var]["_value"][2], log=True))
             elif _type == 'normal':
                 cs.add_hyperparameter(CSH.NormalFloatHyperparameter(
                     var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2]))
             elif _type == 'qnormal':
                 cs.add_hyperparameter(CSH.NormalFloatHyperparameter(
-                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2], 
+                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2],
                     q=search_space[var]["_value"][3]))
             elif _type == 'lognormal':
                 cs.add_hyperparameter(CSH.NormalFloatHyperparameter(
-                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2], 
+                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2],
                     log=True))
             elif _type == 'qlognormal':
                 cs.add_hyperparameter(CSH.NormalFloatHyperparameter(
-                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2], 
+                    var, mu=search_space[var]["_value"][1], sigma=search_space[var]["_value"][2],
                     q=search_space[var]["_value"][3], log=True))
             else:
                 raise ValueError(
@@ -553,7 +553,7 @@ class BOHB(MsgDispatcherBase):
             self.brackets[s].set_config_perf(
                 int(i), data['parameter_id'], sys.maxsize, value)
             self.completed_hyper_configs.append(data)
-       
+
             _parameters = self.parameters[data['parameter_id']]
             _parameters.pop(_KEY)
             # update BO with loss, max_s budget, hyperparameters

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

@@ -117,7 +117,7 @@ class CG_BOHB(object):
         seperated by budget. This function sample a configuration from
         largest budget. Firstly we sample "num_samples" configurations,
         then prefer one with the largest l(x)/g(x).
-        
+
         Parameters:
         -----------
         info_dict: dict

src/sdk/pynni/nni/common.py

@@ -34,7 +34,7 @@ log_level_map = {
 }
 
 _time_format = '%m/%d/%Y, %I:%M:%S %p'
-    
+
 class _LoggerFileWrapper(TextIOBase):
     def __init__(self, logger_file):
         self.file = logger_file

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -67,7 +67,7 @@ class CurvefittingAssessor(Assessor):
 
     def trial_end(self, trial_job_id, success):
         """update the best performance of completed trial job
-        
+
         Parameters
         ----------
         trial_job_id: int
@@ -112,7 +112,7 @@ class CurvefittingAssessor(Assessor):
         curr_step = len(trial_history)
         if curr_step < self.start_step:
             return AssessResult.Good
-        
+
         if trial_job_id in self.last_judgment_num.keys() and curr_step - self.last_judgment_num[trial_job_id] < self.gap:
             return AssessResult.Good
         self.last_judgment_num[trial_job_id] = curr_step

src/sdk/pynni/nni/curvefitting_assessor/curvefunctions.py

@@ -26,7 +26,7 @@ curve_combination_models = ['vap', 'pow3', 'linear', 'logx_linear', 'dr_hill_zer
 
 def vap(x, a, b, c):
     """Vapor pressure model
-    
+
     Parameters
     ----------
     x: int
@@ -109,7 +109,7 @@ model_para_num['logx_linear'] = 2
 
 def dr_hill_zero_background(x, theta, eta, kappa):
     """dr hill zero background
- 
+
     Parameters
     ----------
     x: int
@@ -261,7 +261,7 @@ model_para_num['weibull'] = 4
 
 def janoschek(x, a, beta, k, delta):
     """http://www.pisces-conservation.com/growthhelp/janoschek.htm
-    
+
     Parameters
     ----------
     x: int

src/sdk/pynni/nni/curvefitting_assessor/model_factory.py

@@ -35,7 +35,7 @@ logger = logging.getLogger('curvefitting_Assessor')
 
 class CurveModel(object):
     """Build a Curve Model to predict the performance
-    
+
     Algorithm: https://github.com/Microsoft/nni/blob/master/src/sdk/pynni/nni/curvefitting_assessor/README.md
 
     Parameters
@@ -53,7 +53,7 @@ class CurveModel(object):
 
     def fit_theta(self):
         """use least squares to fit all default curves parameter seperately
-        
+
         Returns
         -------
         None
@@ -87,7 +87,7 @@ class CurveModel(object):
 
     def filter_curve(self):
         """filter the poor performing curve
-        
+
         Returns
         -------
         None
@@ -117,7 +117,7 @@ class CurveModel(object):
 
     def predict_y(self, model, pos):
         """return the predict y of 'model' when epoch = pos
-        
+
         Parameters
         ----------
         model: string
@@ -162,7 +162,7 @@ class CurveModel(object):
 
     def normalize_weight(self, samples):
         """normalize weight
-        
+
         Parameters
         ----------
         samples: list
@@ -184,7 +184,7 @@ class CurveModel(object):
 
     def sigma_sq(self, sample):
         """returns the value of sigma square, given the weight's sample
-        
+
         Parameters
         ----------
         sample: list
@@ -203,7 +203,7 @@ class CurveModel(object):
 
     def normal_distribution(self, pos, sample):
         """returns the value of normal distribution, given the weight's sample and target position
-        
+
         Parameters
         ----------
         pos: int
@@ -227,7 +227,7 @@ class CurveModel(object):
         ----------
         sample: list
             sample is a (1 * NUM_OF_FUNCTIONS) matrix, representing{w1, w2, ... wk}
-        
+
         Returns
         -------
         float
@@ -241,13 +241,13 @@ class CurveModel(object):
 
     def prior(self, samples):
         """priori distribution
- 
+
         Parameters
         ----------
         samples: list
             a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
             representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
-        
+
         Returns
         -------
         float
@@ -264,13 +264,13 @@ class CurveModel(object):
 
     def target_distribution(self, samples):
         """posterior probability
-        
+
         Parameters
         ----------
         samples: list
             a collection of sample, it's a (NUM_OF_INSTANCE * NUM_OF_FUNCTIONS) matrix,
             representing{{w11, w12, ..., w1k}, {w21, w22, ... w2k}, ...{wk1, wk2,..., wkk}}
-        
+
         Returns
         -------
         float
@@ -319,7 +319,7 @@ class CurveModel(object):
 
     def predict(self, trial_history):
         """predict the value of target position
-        
+
         Parameters
         ----------
         trial_history: list

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

@@ -167,7 +167,7 @@ class EvolutionTuner(Tuner):
         self.space = None
 
     def update_search_space(self, search_space):
-        """Update search space. 
+        """Update search space.
         Search_space contains the information that user pre-defined.
 
         Parameters
@@ -194,7 +194,7 @@ class EvolutionTuner(Tuner):
         Parameters
         ----------
         parameter_id : int
-    
+
         Returns
         -------
         config : dict

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

@@ -43,7 +43,7 @@ _epsilon = 1e-6
 
 def create_parameter_id():
     """Create an id
-    
+
     Returns
     -------
     int
@@ -55,7 +55,7 @@ def create_parameter_id():
 
 def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-1):
     """Create a full id for a specific bracket's hyperparameter configuration
-    
+
     Parameters
     ----------
     brackets_id: int
@@ -79,7 +79,7 @@ def create_bracket_parameter_id(brackets_id, brackets_curr_decay, increased_id=-
 
 def json2parameter(ss_spec, random_state):
     """Randomly generate values for hyperparameters from hyperparameter space i.e., x.
-    
+
     Parameters
     ----------
     ss_spec:
@@ -116,7 +116,7 @@ def json2parameter(ss_spec, random_state):
 
 class Bracket():
     """A bracket in Hyperband, all the information of a bracket is managed by an instance of this class
-    
+
     Parameters
     ----------
     s: int
@@ -132,7 +132,7 @@ class Bracket():
     optimize_mode: str
         optimize mode, 'maximize' or 'minimize'
     """
-    
+
     def __init__(self, s, s_max, eta, R, optimize_mode):
         self.bracket_id = s
         self.s_max = s_max
@@ -163,7 +163,7 @@ class Bracket():
 
     def set_config_perf(self, i, parameter_id, seq, value):
         """update trial's latest result with its sequence number, e.g., epoch number or batch number
-        
+
         Parameters
         ----------
         i: int
@@ -184,7 +184,7 @@ class Bracket():
                 self.configs_perf[i][parameter_id] = [seq, value]
         else:
             self.configs_perf[i][parameter_id] = [seq, value]
-            
+
 
     def inform_trial_end(self, i):
         """If the trial is finished and the corresponding round (i.e., i) has all its trials finished,
@@ -230,7 +230,7 @@ class Bracket():
         ----------
         num: int
             the number of hyperparameter configurations
-        
+
         Returns
         -------
         list
@@ -350,7 +350,7 @@ class Hyperband(MsgDispatcherBase):
 
     def handle_update_search_space(self, data):
         """data: JSON object, which is search space
-        
+
         Parameters
         ----------
         data: int
@@ -392,9 +392,9 @@ class Hyperband(MsgDispatcherBase):
         """
         Parameters
         ----------
-        data: 
+        data:
             it is an object which has keys 'parameter_id', 'value', 'trial_job_id', 'type', 'sequence'.
-        
+
         Raises
         ------
         ValueError

src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py

@@ -21,10 +21,10 @@ from nni.assessor import Assessor, AssessResult
 logger = logging.getLogger('medianstop_Assessor')
 
 class MedianstopAssessor(Assessor):
-    """MedianstopAssessor is The median stopping rule stops a pending trial X at step S 
-    if the trial’s best objective value by step S is strictly worse than the median value 
+    """MedianstopAssessor is The median stopping rule stops a pending trial X at step S
+    if the trial’s best objective value by step S is strictly worse than the median value
     of the running averages of all completed trials’ objectives reported up to step S
-    
+
     Parameters
     ----------
     optimize_mode: str
@@ -60,7 +60,7 @@ class MedianstopAssessor(Assessor):
 
     def trial_end(self, trial_job_id, success):
         """trial_end
-        
+
         Parameters
         ----------
         trial_job_id: int
@@ -83,7 +83,7 @@ class MedianstopAssessor(Assessor):
 
     def assess_trial(self, trial_job_id, trial_history):
         """assess_trial
-        
+
         Parameters
         ----------
         trial_job_id: int

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

@@ -27,7 +27,7 @@ from scipy.optimize import minimize
 import nni.metis_tuner.lib_data as lib_data
 
 
-def next_hyperparameter_expected_improvement(fun_prediction,   
+def next_hyperparameter_expected_improvement(fun_prediction,
                                              fun_prediction_args,
                                              x_bounds, x_types,
                                              samples_y_aggregation,

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

@@ -69,7 +69,7 @@ class NetworkMorphismTuner(Tuner):
         optimize_mode : str
             optimize mode "minimize" or "maximize" (default: {"minimize"})
         path : str
-            default mode path to save the model file (default: {"model_path"})  
+            default mode path to save the model file (default: {"model_path"})
         verbose : bool
             verbose to print the log (default: {True})
         beta : float
@@ -154,7 +154,7 @@ class NetworkMorphismTuner(Tuner):
 
     def receive_trial_result(self, parameter_id, parameters, value):
         """ Record an observation of the objective function.
-    
+
         Parameters
         ----------
         parameter_id : int
@@ -267,7 +267,7 @@ class NetworkMorphismTuner(Tuner):
         ----------
         model_id : int
             model index
-        
+
         Returns
         -------
         load_model : Graph
@@ -297,7 +297,7 @@ class NetworkMorphismTuner(Tuner):
         ----------
         model_id : int
             model index
-        
+
         Returns
         -------
         float