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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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