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How to use iam method in localstack

Best Python code snippet using localstack_python

test_iam.py

Source:test_iam.py

...611        json.loads(self.object_iam_string), IAM_OBJECT_READ_ROLE,612        self.public_object_read_binding)613    self.new_object_iam_path = self.CreateTempFile(614        contents=json.dumps(self.new_object_iam_policy))615  def test_seek_ahead_iam(self):616    """Ensures that the seek-ahead iterator is being used with iam commands."""617    gsutil_object = self.CreateObject(618        bucket_uri=self.bucket, contents='foobar')619    # This forces the seek-ahead iterator to be utilized.620    with SetBotoConfigForTest([('GSUtil', 'task_estimation_threshold', '1'),621                               ('GSUtil', 'task_estimation_force', 'True')]):622      stderr = self.RunGsUtil(623          ['-m', 'iam', 'set', self.new_object_iam_path, gsutil_object.uri],624          return_stderr=True)625      self.assertIn('Estimated work for this command: objects: 1\n', stderr)626  def test_set_invalid_iam_bucket(self):627    """Ensures invalid content returns error on input check."""628    inpath = self.CreateTempFile(contents='badIam')629    stderr = self.RunGsUtil(['iam', 'set', inpath, self.bucket.uri],...

writerid_estimator_training.py

Source:writerid_estimator_training.py

1# -*- coding: utf-8 -*-2"""3Writer identification based of shape context codebook features and 4a shared kernel GP classifier. This file covers the training and evaluation5of the estimators.6@author: Fredrik Wahlberg <fredrik.wahlberg@it.uu.se>7"""8from __future__ import print_function, division9import numpy as np10import os.path11from model import SharedKernelClassifier12import time13cache_path = os.path.expanduser("~/tmp/gp_multiclass")14def cache_filename_features(N, p, q):15    return os.path.join(cache_path, 16                        "features_N%i_p%i_q%i.npz" % (N, p, q))17def cache_filename_estimator(N, p, q):18    return os.path.join(cache_path, 19                        "estimator_N%i_p%i_q%i.npz" % (N, p, q))20def cache_filename_result_npz(N, p, q):21    return os.path.join(cache_path, 22                        "result_N%i_p%i_q%i.npz" % (N, p, q))23def cache_filename_result_csv(N, p, q):24    return os.path.join(cache_path, 25                        "result_N%i_p%i_q%i.csv" % (N, p, q))26parameter_distribution = {27        'p': list(range(2, 6+1)),28        'q': list(range(4, 9+1)),29        'N': list(range(7, 21+1, 2))}30from sklearn.model_selection import ParameterGrid31valid_parameters = [p for p in ParameterGrid(parameter_distribution) if p['N']>p['p']*2]32print("%i parameter configurations" % len(valid_parameters))33feature_files = [cache_filename_features(N=pa['N'], p=pa['p'], q=pa['q']) 34                 for pa in valid_parameters 35                 if os.path.exists(cache_filename_features(N=pa['N'], p=pa['p'], q=pa['q']))]36import random37random.shuffle(valid_parameters)38#%%39csv_categories = ['Dataset', 'Training authors', 'Training documents', 40                  'Testing authors', 'Testing documents', 'Kernel', 'ARD', 41                  'Codebook size', 'N', 'p', 'q', 'Hard 5-top', 'Hard 3-top', 42                  '1-top', 'Soft 3-top', 'Soft 5-top', 'Likelihood']43with open(os.path.join(cache_path, "00categories.csv"), 'w') as f:44    for i, cat in enumerate(csv_categories):45        f.write('"' + cat + '"')46        if i==len(csv_categories)-1:47            f.write("\n")48        else:49            f.write(";")50for feature_file in feature_files:51    # Read feature data file52    cache_file_data = np.load(feature_file)53    print(cache_file_data.keys())54    data = dict()55    for k in cache_file_data.keys():56        if k in ['cluster_centers', 'codebooks', 'N', 'p', 'q', 'rotationalinvariant', 'authors']:57            data[k] = cache_file_data[k].tolist()58        else:59            data[k] = cache_file_data[k]60    feature_keys = [k for k in data.keys() if k[:8]=='features']61    # Set up output and check for old results62    cvs_filename = cache_filename_result_csv(N=data['N'], p=data['p'], q=data['q'])63    if not os.path.exists(cvs_filename):64        csv_file = open(cvs_filename, 'w')65        # CVL authors are id>100066        authors = data['authors']67        cvl_mask = [a>1000 for a in authors]68        cvl_train_mask = [a>1000 and a<=1050 for a in authors]69        cvl_test_mask = [a>1000 and a>1050 for a in authors]70        cvl_y_train = np.vstack([a for a,b in zip(authors, cvl_train_mask) if b])71        assert np.sum(cvl_train_mask)==len(cvl_y_train)72        cvl_y_test = np.vstack([a for a,b in zip(authors, cvl_test_mask) if b])73        assert np.sum(cvl_test_mask)==len(cvl_y_test)74        75        iam_mask = [a<1000 for a in authors]76        iam_train_mask = [a<1000 and a<=26 for a in authors]77        iam_test_mask = [a<1000 and a>26 for a in authors]78        iam_y_train = np.vstack([a for a,b in zip(authors, iam_train_mask) if b])79        assert np.sum(iam_train_mask)==len(iam_y_train)80        iam_y_test = np.vstack([a for a,b in zip(authors, iam_test_mask) if b])81        assert np.sum(iam_test_mask)==len(iam_y_test)82        cvl_iam_train_mask = [a or b for a, b in zip(cvl_train_mask, iam_train_mask)]83        cvl_iam_test_mask = [a or b for a, b in zip(cvl_test_mask, iam_test_mask)]84        assert np.sum(cvl_iam_train_mask) == np.sum(cvl_train_mask) + np.sum(iam_train_mask)85        assert np.sum(cvl_iam_test_mask) == np.sum(cvl_test_mask) + np.sum(iam_test_mask)86        cvl_iam_y_train = np.vstack([a for a,b in zip(authors, cvl_iam_train_mask) if b])87        assert np.sum(cvl_iam_train_mask)==len(cvl_iam_y_train)88        cvl_iam_y_test = np.vstack([a for a,b in zip(authors, cvl_iam_test_mask) if b])89        assert np.sum(cvl_iam_test_mask)==len(cvl_iam_y_test)90                91        # Model standard parameters92#        kernel='rbf'93        ard=False94        n_iter = 1595        n_restarts = 096        97#        for feature_key in feature_keys[:1]:98        for feature_key in feature_keys:99    100            # Separate feature data sets101            cvl_X_train = data[feature_key][cvl_train_mask, :]102            cvl_X_test = data[feature_key][cvl_test_mask, :]103            assert cvl_X_train.shape[0] == np.sum(cvl_train_mask)104            assert cvl_X_test.shape[0] == np.sum(cvl_test_mask)105            iam_X_train = data[feature_key][iam_train_mask, :]106            iam_X_test = data[feature_key][iam_test_mask, :]107            assert iam_X_train.shape[0] == np.sum(iam_train_mask)108            assert iam_X_test.shape[0] == np.sum(iam_test_mask)109            cvl_iam_X_train = data[feature_key][cvl_iam_train_mask, :]110            cvl_iam_X_test = data[feature_key][cvl_iam_test_mask, :]111            assert cvl_iam_X_train.shape[0] == np.sum(cvl_iam_train_mask)112            assert cvl_iam_X_test.shape[0] == np.sum(cvl_iam_test_mask)113            #%% CVL Estimator114            for kernel in ['matern52', 'rbf']:115#                for ard in [True, False]:116                print("Kernel %s " % kernel, end="")117                if ard:118                    print("with ard")119                else:120                    print("without ard")121                cvl_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 122                                                   n_restarts=n_restarts, verbose=True)123                print("Training CVL estimator...", end="")124                t0 = time.time()125                cvl_estimator.fit(cvl_X_train, cvl_y_train)126                print("done (%.1fs)" % (time.time()-t0))127                128                print("CVL 1-top training set: %.1f%%" % (cvl_estimator.score_covar_ntop(cvl_X_train, cvl_y_train)[4]*100))129                ntop = cvl_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)130                print("CVL 1-top test set: %.1f%%" % (ntop[4]*100))131                print("Likelihood %.2f" % cvl_estimator.log_likelihood_)132                ntop2 = cvl_estimator.score_covar_ntop(iam_X_test, iam_y_test)133                print("CVL on IAM 1-top test set: %.1f%%" % (ntop2[4]*100))134        135                dataset = 'CVL'136                d = (dataset, len(np.unique(cvl_y_train)), len(cvl_y_train), 137                     len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 138                     cvl_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 139                     ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, cvl_estimator.log_likelihood_)140                csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)141    142                dataset = 'CVL on IAM'143                d = (dataset, len(np.unique(cvl_y_train)), len(cvl_y_train), 144                     len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 145                     cvl_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 146                     ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, cvl_estimator.log_likelihood_)147                csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)148            #%% IAM Estimator149            iam_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 150                                               n_restarts=n_restarts, verbose=True)151            print("Training IAM estimator...", end="")152            t0 = time.time()153            iam_estimator.fit(iam_X_train, iam_y_train)154            print("done (%.1fs)" % (time.time()-t0))155            156            print("IAM 1-top training set: %.1f%%" % (iam_estimator.score_covar_ntop(iam_X_train, iam_y_train)[4]*100))157            ntop = iam_estimator.score_covar_ntop(iam_X_test, iam_y_test)158            print("IAM 1-top test set: %.1f%%" % (ntop[4]*100))159            print("Likelihood %.2f" % iam_estimator.log_likelihood_)160            ntop2 = iam_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)161            print("IAM on CVL 1-top test set: %.1f%%" % (ntop2[4]*100))162    163            dataset = 'IAM'164            d = (dataset, len(np.unique(iam_y_train)), len(iam_y_train), 165                 len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 166                 iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 167                 ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, iam_estimator.log_likelihood_)168            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)169            dataset = 'IAM on CVL'170            d = (dataset, len(np.unique(iam_y_train)), len(iam_y_train), 171                 len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 172                 iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 173                 ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, iam_estimator.log_likelihood_)174            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)175    #        b = estimator.score_unseen_proba_metric(cvl_X_test, cvl_y_test)176            #%% CVL+IAM Estimator177            cvl_iam_estimator = SharedKernelClassifier(kernel=kernel, ard=ard, n_iter=n_iter, 178                                               n_restarts=n_restarts, verbose=True)179            print("Training CVL+IAM estimator...", end="")180            t0 = time.time()181            cvl_iam_estimator.fit(cvl_iam_X_train, cvl_iam_y_train)182            print("done (%.1fs)" % (time.time()-t0))183            184            print("CVL+IAM 1-top training set: %.1f%%" % (cvl_iam_estimator.score_covar_ntop(cvl_iam_X_train, cvl_iam_y_train)[4]*100))185            ntop = cvl_iam_estimator.score_covar_ntop(cvl_iam_X_test, cvl_iam_y_test)186            ntop2 = cvl_iam_estimator.score_covar_ntop(cvl_X_test, cvl_y_test)187            ntop3 = cvl_iam_estimator.score_covar_ntop(iam_X_test, iam_y_test)188            print("CVL+IAM 1-top test set: %.1f%%" % (ntop[4]*100))189            print("CVL+IAM on CVL 1-top test set: %.1f%%" % (ntop2[4]*100))190            print("CVL+IAM on IAM 1-top test set: %.1f%%" % (ntop3[4]*100))191            print("Likelihood %.2f" % cvl_iam_estimator.log_likelihood_)192            dataset = 'CVL+IAM'193            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 194                 len(np.unique(cvl_iam_y_test)), len(cvl_iam_y_test), kernel, ard, 195                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop[0]*100, 196                 ntop[2]*100, ntop[4]*100, ntop[6]*100, ntop[8]*100, cvl_iam_estimator.log_likelihood_)197            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))198            dataset = 'CVL+IAM on CVL'199            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 200                 len(np.unique(cvl_y_test)), len(cvl_y_test), kernel, ard, 201                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop2[0]*100, 202                 ntop2[2]*100, ntop2[4]*100, ntop2[6]*100, ntop2[8]*100, cvl_iam_estimator.log_likelihood_)203            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))204            dataset = 'CVL+IAM on IAM'205            d = (dataset, len(np.unique(cvl_iam_y_train)), len(cvl_iam_y_train), 206                 len(np.unique(iam_y_test)), len(iam_y_test), kernel, ard, 207                 cvl_iam_X_train.shape[1], data['N'], data['p'], data['q'], ntop3[0]*100, 208                 ntop3[2]*100, ntop3[4]*100, ntop3[6]*100, ntop3[8]*100, cvl_iam_estimator.log_likelihood_)209            csv_file.write(("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d).replace('.', ','))210#            csv_file.write("\"%s\";%i;%i;%i;%i;\"%s\";\"%s\";%i;%i;%i;%i;%f;%f;%f;%f;%f;%f\n" % d)211            if feature_key==feature_keys[-1]:212                np.savez_compressed(cache_filename_result_npz(N=data['N'], p=data['p'], q=data['q']), 213                                covariance_matrix = np.asarray(cvl_iam_estimator.get_kernel()(cvl_iam_X_test), dtype=np.float16),214                                labels = cvl_iam_y_test.ravel(),215                                likelihood = cvl_iam_estimator.log_likelihood_)216            217        csv_file.close()218    else:...

iam-groups-cf-template.py

Source:iam-groups-cf-template.py

1"""Generating CloudFormation template."""2from troposphere import (3    Template,4    Join,5    Ref,6)7from troposphere.iam import (8    Group,9    ManagedPolicy,10)11from awacs.aws import (12    Action,13    Allow,14    Condition,15    NumericGreaterThan,16    Deny,17    Null,18    Policy,19    Statement,20)21t = Template()22t.add_description("Effective DevOps in AWS: User Groups")23t.add_resource(Group(24    "Admins",25    GroupName="Admins",26    ManagedPolicyArns=[27        "arn:aws:iam::aws:policy/AdministratorAccess"28    ],29))30t.add_resource(ManagedPolicy(31    "CommonIamPolicy",32    Description="Common policy to manage IAM resources",33    PolicyDocument=Policy(34        Version="2012-10-17",35        Statement=[36            Statement(37                Effect=Allow,38                Action=[39                    Action("iam", "GetAccountPasswordPolicy"),40                    Action("iam", "ListUsers"),41                    Action("iam", "ListMFADevices"),42                    Action("iam", "ListVirtualMFADEvices")43                ],44                Resource=["*"]45            ),46            Statement(47                Effect=Allow,48                Action=[49                    Action("iam", "CreateVirtualMFADevice")50                ],51                Resource=[52                    Join(53                        "",54                        [55                            "arn:aws:iam::",56                            Ref("AWS::AccountId"),57                            ":mfa/${aws:username}",58                        ]59                    )60                ]61            ),62            Statement(63                Effect=Allow,64                Action=[65                    Action("iam", "ChangePassword"),66                    Action("iam", "CreateAccessKey"),67                    Action("iam", "CreateLoginProfile"),68                    Action("iam", "DeleteAccessKey"),69                    Action("iam", "DeleteLoginProfile"),70                    Action("iam", "EnableMFADevice"),71                    Action("iam", "GetAccessKeyLastUsed"),72                    Action("iam", "GetLoginProfile"),73                    Action("iam", "GetUser"),74                    Action("iam", "ListAccessKeys"),75                    Action("iam", "UpdateAccessKey"),76                    Action("iam", "UpdateLoginProfile")77                ],78                Resource=[79                    Join(80                        "",81                        [82                            "arn:aws:iam::",83                            Ref("AWS::AccountId"),84                            ":user/${aws:username}",85                        ]86                    )87                ]88            ),89            Statement(90                Effect=Deny,91                NotAction=[92                    Action("iam", "ChangePassword"),93                    Action("iam", "CreateVirtualMFADevice"),94                    Action("iam", "EnableMFADevice"),95                    Action("iam", "GetUser"),96                    Action("iam", "ListMFADevices"),97                    Action("iam", "ListUsers"),98                    Action("iam", "ListVirtualMFADEvices")99                ],100                Resource=["*"],101                Condition=Condition(102                    Null("aws:MultiFactorAuthAge", "true"),103                ),104            ),105            Statement(106                Effect=Deny,107                NotAction=[108                    Action("iam", "ChangePassword"),109                    Action("iam", "CreateVirtualMFADevice"),110                    Action("iam", "EnableMFADevice"),111                    Action("iam", "GetUser"),112                    Action("iam", "ListMFADevices"),113                    Action("iam", "ListUsers"),114                    Action("iam", "ListVirtualMFADEvices")115                ],116                Resource=["*"],117                Condition=Condition(118                    NumericGreaterThan("aws:MultiFactorAuthAge", "43200")119                ),120            ),121        ]122    )123))124t.add_resource(Group(125    "AllUsers",126    GroupName="AllUsers",127    ManagedPolicyArns=[128        Ref("CommonIamPolicy")129    ]130))...

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