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How to use get_fingerprint method in tempest

Best Python code snippet using tempest_python

expressionsApi.py

Source:expressionsApi.py

1#!/usr/bin/env python2"""3/*******************************************************************************4 * Copyright (c) cortical.io GmbH. All rights reserved.5 *  6 * This software is confidential and proprietary information.7 * You shall use it only in accordance with the terms of the8 * license agreement you entered into with cortical.io GmbH.9 ******************************************************************************/10"""11from models import *12class ExpressionsApi(object):13    def __init__(self, apiClient):14        self.apiClient = apiClient15    16    def resolveExpression(self, retina_name, body, sparsity=1.0):17        """Resolve an expression18        Args:19            body, ExpressionOperation: The JSON formatted encoded to be evaluated (required)20            retina_name, str: The retina name (required)21            sparsity, float: Sparsify the resulting expression to this percentage (optional)22            Returns: Fingerprint23        """24        resourcePath = '/expressions'25        method = 'POST'26        queryParams = {}27        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}28        postData = None29        queryParams['retina_name'] = retina_name30        queryParams['sparsity'] = sparsity31        postData = body32        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)33        return apiFingerprint.Fingerprint(**response.json())34        35    def getContextsForExpression(self, retina_name, body, get_fingerprint=None, start_index=0, max_results=5, sparsity=1.0):36        """Get semantic contexts for the input expression37        Args:38            body, ExpressionOperation: The JSON encoded expression to be evaluated (required)39            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)40            retina_name, str: The retina name (required)41            start_index, int: The start-index for pagination (optional) (optional)42            max_results, int: Max results per page (optional) (optional)43            sparsity, float: Sparsify the resulting expression to this percentage (optional)44            Returns: Array[Context]45        """46        resourcePath = '/expressions/contexts'47        method = 'POST'48        queryParams = {}49        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}50        postData = None51        queryParams['retina_name'] = retina_name52        queryParams['start_index'] = start_index53        queryParams['max_results'] = max_results54        queryParams['sparsity'] = sparsity55        queryParams['get_fingerprint'] = get_fingerprint56        postData = body57        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)58        return [apiContext.Context(**r) for r in response.json()]59        60    def getSimilarTermsForExpressionContext(self, retina_name, body, context_id=None, pos_type=None, get_fingerprint=None, start_index=0, max_results=10, sparsity=1.0):61        """Get similar terms for the contexts of an expression62        Args:63            body, ExpressionOperation: The JSON encoded expression to be evaluated (required)64            context_id, int: The identifier of a context (optional) (optional)65            pos_type, str: Part of speech (optional) (optional)66            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)67            retina_name, str: The retina name (required)68            start_index, int: The start-index for pagination (optional) (optional)69            max_results, int: Max results per page (optional) (optional)70            sparsity, float: Sparsify the resulting expression to this percentage (optional)71            Returns: Array[Term]72        """73        resourcePath = '/expressions/similar_terms'74        method = 'POST'75        queryParams = {}76        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}77        postData = None78        queryParams['retina_name'] = retina_name79        queryParams['context_id'] = context_id80        queryParams['start_index'] = start_index81        queryParams['max_results'] = max_results82        queryParams['pos_type'] = pos_type83        queryParams['sparsity'] = sparsity84        queryParams['get_fingerprint'] = get_fingerprint85        postData = body86        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)87        return [apiTerm.Term(**r) for r in response.json()]88        89    def resolveBulkExpression(self, retina_name, body, sparsity=1.0):90        """Bulk resolution of expressions91        Args:92            body, ExpressionOperation: The JSON encoded expression to be evaluated (required)93            retina_name, str: The retina name (required)94            sparsity, float: Sparsify the resulting expression to this percentage (optional)95            Returns: Array[Fingerprint]96        """97        resourcePath = '/expressions/bulk'98        method = 'POST'99        queryParams = {}100        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}101        postData = None102        queryParams['retina_name'] = retina_name103        queryParams['sparsity'] = sparsity104        postData = body105        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)106        return [apiFingerprint.Fingerprint(**r) for r in response.json()]107        108    def getContextsForBulkExpression(self, retina_name, body, get_fingerprint=None, start_index=0, max_results=5, sparsity=1.0):109        """Bulk get contexts for input expressions110        Args:111            body, ExpressionOperation: The JSON encoded expression to be evaluated (required)112            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)113            retina_name, str: The retina name (required)114            start_index, int: The start-index for pagination (optional) (optional)115            max_results, int: Max results per page (optional) (optional)116            sparsity, float: Sparsify the resulting expression to this percentage (optional)117            Returns: Array[Context]118        """119        resourcePath = '/expressions/contexts/bulk'120        method = 'POST'121        queryParams = {}122        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}123        postData = None124        queryParams['retina_name'] = retina_name125        queryParams['start_index'] = start_index126        queryParams['max_results'] = max_results127        queryParams['sparsity'] = sparsity128        queryParams['get_fingerprint'] = get_fingerprint129        postData = body130        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)131        return [[apiContext.Context(**c) for c in r] for r in response.json()]132        133    def getSimilarTermsForBulkExpressionContext(self, retina_name, body, context_id=None, pos_type=None, get_fingerprint=None, start_index=0, max_results=10, sparsity=1.0):134        """Bulk get similar terms for input expressions135        Args:136            body, ExpressionOperation: The JSON encoded expression to be evaluated (required)137            context_id, int: The identifier of a context (optional) (optional)138            pos_type, str: Part of speech (optional) (optional)139            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)140            retina_name, str: The retina name (required)141            start_index, int: The start-index for pagination (optional) (optional)142            max_results, int: Max results per page (optional) (optional)143            sparsity, float: Sparsify the resulting expression to this percentage (optional)144            Returns: Array[Term]145        """146        resourcePath = '/expressions/similar_terms/bulk'147        method = 'POST'148        queryParams = {}149        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}150        postData = None151        queryParams['retina_name'] = retina_name152        queryParams['context_id'] = context_id153        queryParams['start_index'] = start_index154        queryParams['max_results'] = max_results155        queryParams['pos_type'] = pos_type156        queryParams['sparsity'] = sparsity157        queryParams['get_fingerprint'] = get_fingerprint158        postData = body159        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)160        return [[apiTerm.Term(**t) for t in r] for r in response.json()]161        162    def getSimilarTermsForSinglePosition(self, retina_name, position, ):163        """Get similar terms for single bit position164        Args:165            position, int: A position in the retina space. (required)166            retina_name, str: The retina name (required)167            Returns: Array[Term]168        """169        resourcePath = '/expressions/similar_terms_bit'170        method = 'GET'171        queryParams = {}172        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}173        postData = None174        queryParams['retina_name'] = retina_name175        queryParams['position'] = position176        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)177        return [apiTerm.Term(**r) for r in response.json()]...

test_arr_estimators.py

Source:test_arr_estimators.py

...3import numpy as np4from pytest import approx5from math import log2 as log6def test_fingerprint():7    np.testing.assert_array_equal(get_fingerprint([0, 0, 0, 1]), [0, 1, 0, 1])8    np.testing.assert_array_equal(get_fingerprint([0, 1, 4, 3, 0, 0, 2, 4]), [0, 3, 1, 1])9    t_data0 = np.ceil(beta(1, 1, 500) * 20)10    t_data1 = np.ceil(beta(1, 3, 2000) * 500)11    fp0 = get_fingerprint(t_data0)12    assert sum([i * x for i, x in enumerate(fp0)]) == 50013    fp1 = get_fingerprint(t_data1)14    assert sum([i * x for i, x in enumerate(fp1)]) == 200015# Takes about 3 seconds16def test_unseen():17    np.random.seed(1)18    # Testdata was generated by Matlab implementation19    # data = makeFinger(round(rand([100 1])*10))20    # 0 added to account for different makeFinger python impl21    data = [0, 0, 0, 0, 1, 1, 2, 0, 1, 0, 1, 3, 0, 1, 0, 1]22    hist, p = unseen(data)23    prob_covered = sum([x * y for x, y in zip(hist, p)])24    assert abs(1 - prob_covered) < 1 * 10 ** 925    support_size = sum(hist)26    # value comes from matlab version27    assert abs(support_size - 8.8642) < 1 * 10 ** -328    # Data taken from KPT19 Fig629    N = 10 ** 530    data = np.round(beta(1, 2.5, N) * N)31    f = get_fingerprint(data)32    hist, _ = unseen(f)33    rerr = abs(N - sum(hist)) / N34    assert rerr < 0.1535    print(f"Test 1 relative error {rerr}")36    data = np.round(beta(1, 10, 10 * N) * N)37    f = get_fingerprint(data)38    hist, _ = unseen(f)39    rerr = abs(N - sum(hist)) / N40    assert rerr < 0.541    print(f"Test 2 relative error {rerr}")42# Takes about 30 minutes43def test_unseen_valiant():44    """45    Test based on testing data as described in the paper describing the unseen estimator. This differs from the other46    test, which only considers testdata from KPT19.47    """48    np.random.seed(1)49    def unif(k, n):50        return np.random.random_integers(k, size=n), - k * (1 / k) * log(1 / k)51    def mixunif(k, n):52        return (53        np.random.choice(np.arange(1, k + 1), n, p=[5 / (2 * k) if i < k / 5 else 5 / (8 * k) for i in range(k)]),54        - (k / 5) * (5 / (2 * k)) * log(5 / (2 * k)) - (4 * k / 5) * (5 / (8 * k)) * log(5 / (8 * k)))55    def zipf(k, n):56        s = sum([1 / i for i in range(1, k + 1)])57        p = [(1 / (i + 1)) / s for i in range(k)]58        ent = sum(map(lambda x: -log(x) * x, p))59        return np.random.choice(np.arange(1, k + 1), n, p=p), ent60    def zipf2(k, n):61        s = sum([1 / i ** 0.6 for i in range(1, k + 1)])62        p = [(1 / (i + 1) ** 0.6) / s for i in range(k)]63        ent = sum(map(lambda x: -log(x) * x, p))64        return np.random.choice(np.arange(1, k + 1), n, p=p), ent65    def geom(k, n):66        # Not sure if this is correct or contains one by off error67        p = 1 / k68        ent = (- (1 - p) * log(1 - p) - p * log(p)) / p69        return np.random.geometric(p, n), ent70    def mixgeomzipf(k, n):71        # Not sure if this is how the authors mixed the distributions72        pgeom = [(1 / k) * (1 - 1 / k) ** i for i in range(k // 2)]73        s = sum(range((k + 1) // 2))74        pzipf = [1 / (i + 1) / s for i in range((k + 1) // 2)]75        pgeom.extend(pzipf)76        tprob = sum(pgeom)77        p = list(map(lambda x: x / tprob, pgeom))78        ent = sum(map(lambda x: -log(x) * x, p))79        return np.random.choice(np.arange(1, k + 1), n, p=p), ent80    k = 100081    x0 = int(k ** 0.6)82    x1 = int(k ** 1.25)83    dist = [unif, mixunif, zipf, zipf2, geom, mixgeomzipf]84    for d in dist:85        print(f"Using distribution: {d.__name__}")86        for n in [x0, x1]:87            print(f"n = {n}")88            pred = []89            for _ in range(500):90                samples, entropy = d(k, n)91                fp = get_fingerprint(samples)92                hist, p = unseen(fp)93                e = sum(map(lambda x: - x[0] * x[1] * log(x[1]), zip(hist, p)))94                pred.append((e - entropy) ** 2)95            rmse = np.sqrt(np.mean(np.array(pred)))96            print(f"RMSE: {rmse}")97            if d == geom and n == int(k ** 0.6):98                assert rmse < 1.699            elif n == int(k ** 1.25):100                assert rmse < 0.1101            else:102                assert rmse < 1103def test_jackk_coeffs():104    f = [0, 1, 2, 3, 4, 0, 6, 7, 8, 9, 10, 300, 30298, 10000000]105    d = sum(f)106    m = len(f) - 1107    coeffs = get_jackk_coeffs(f, m)108    j1 = coeffs[0]109    j2 = coeffs[1]110    j3 = coeffs[2]111    j4 = coeffs[3]112    # formulas come from paper113    assert j1[0] == approx(d)114    assert j2[0] == approx(d)115    assert j3[0] == approx(d)116    assert j4[0] == approx(d)117    assert j1[1] == approx((m - 1) / m)118    assert j2[1] == approx((2 * m - 3) / m)119    assert j3[1] == approx((3 * m - 6) / m)120    assert j4[1] == approx((4 * m - 10) / m)121    assert j1[2] == approx(0)122    assert j2[2] == approx(-(m - 2) ** 2 / (m * (m - 1)))123    assert j3[2] == approx(-(3 * m ** 2 - 15 * m + 19) / ((m - 1) * m))124    assert j4[2] == approx(-(6 * m ** 2 - 36 * m + 55) / ((m - 1) * m))125    assert j1[3] == approx(0)126    assert j2[3] == approx(0)127    assert j3[3] == approx((m - 3) ** 3 / ((m - 2) * (m - 1) * m))128    assert j4[3] == approx((4 * m ** 3 - 42 * m ** 2 + 148 * m - 175) / (m * (m - 1) * (m - 2)))129    assert j1[4] == j2[4] == j3[4] == approx(0)130    assert j4[4] == approx(-(m - 4) ** 4 / ((m - 3) * (m - 2) * (m - 1) * m))131def test_jackk():132    np.random.seed(1)133    N = 10 ** 5134    data = np.round(beta(1, 2.5, 5 * 10 ** 4) * N)135    f = get_fingerprint(data)136    d = jackknife_selftune(f, N)137    r_err = abs((d - N) / N)138    assert r_err < 0.10139    print(f"Jack T1 RERR: {r_err}")140    data = np.round(beta(1, 17, 5 * 10 ** 4) * N)141    f = get_fingerprint(data)142    d = jackknife_selftune(f, N)143    r_err = abs((d - N) / N)144    assert r_err < 0.75145    print(f"Jack T2 RERR: {r_err}")146def test_jackknife_burnham():147    # Test uses data from burnham 1979148    fp = [0, 43, 16, 8, 6, 0, 2, 1]149    d = jackknife_selftune(fp, 18)150    assert abs(d - 158) < 1151def test_jackcrash():152    np.random.seed(1)153    data = [73050, 98048, 95043, 90051, 79044, 75051, 75052, 87053, 76050, 85050, 72050, 95043, 80045, 92053,154            95045, 76050, 75051, 84042, 83053, 77053, 88046, 77051, 95046, 95043, 74046, 75051, 83044, 78053,155            75049, 84046, 80043, 77050, 83042, 91042]156    f = get_fingerprint(data)157    d = jackknife_selftune(f, len(data))...

termsApi.py

Source:termsApi.py

1#!/usr/bin/env python2"""3/*******************************************************************************4 * Copyright (c) cortical.io GmbH. All rights reserved.5 *  6 * This software is confidential and proprietary information.7 * You shall use it only in accordance with the terms of the8 * license agreement you entered into with cortical.io GmbH.9 ******************************************************************************/10"""11from models import *12class TermsApi(object):13    def __init__(self, apiClient):14        self.apiClient = apiClient15    16    def getTerm(self, retina_name, term=None, get_fingerprint=None, start_index=0, max_results=10):17        """Get term objects18        Args:19            term, str: A term in the retina (optional) (optional)20            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)21            retina_name, str: The retina name (required)22            start_index, int: The start-index for pagination (optional) (optional)23            max_results, int: Max results per page (optional) (optional)24            Returns: Array[Term]25        """26        resourcePath = '/terms'27        method = 'GET'28        queryParams = {}29        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}30        postData = None31        queryParams['retina_name'] = retina_name32        queryParams['term'] = term33        queryParams['start_index'] = start_index34        queryParams['max_results'] = max_results35        queryParams['get_fingerprint'] = get_fingerprint36        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)37        return [apiTerm.Term(**r) for r in response.json()]38        39    def getContextsForTerm(self, retina_name, term, get_fingerprint=None, start_index=0, max_results=5):40        """Get the contexts for a given term41        Args:42            term, str: A term in the retina (required)43            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)44            retina_name, str: The retina name (required)45            start_index, int: The start-index for pagination (optional) (optional)46            max_results, int: Max results per page (optional) (optional)47            Returns: Array[Context]48        """49        resourcePath = '/terms/contexts'50        method = 'GET'51        queryParams = {}52        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}53        postData = None54        queryParams['retina_name'] = retina_name55        queryParams['term'] = term56        queryParams['start_index'] = start_index57        queryParams['max_results'] = max_results58        queryParams['get_fingerprint'] = get_fingerprint59        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)60        return [apiContext.Context(**r) for r in response.json()]61        62    def getSimilarTerms(self, retina_name, term, context_id=None, pos_type=None, get_fingerprint=None, start_index=0, max_results=10):63        """Get the similar terms of a given term64        Args:65            term, str: A term in the retina (required)66            context_id, int: The identifier of a context (optional) (optional)67            pos_type, str: Part of speech (optional) (optional)68            get_fingerprint, bool: Configure if the fingerprint should be returned as part of the results (optional)69            retina_name, str: The retina name (required)70            start_index, int: The start-index for pagination (optional) (optional)71            max_results, int: Max results per page (optional) (optional)72            Returns: Array[Term]73        """74        resourcePath = '/terms/similar_terms'75        method = 'GET'76        queryParams = {}77        headerParams = {'Accept': 'Application/json', 'Content-Type': 'application/json'}78        postData = None79        queryParams['retina_name'] = retina_name80        queryParams['term'] = term81        queryParams['context_id'] = context_id82        queryParams['start_index'] = start_index83        queryParams['max_results'] = max_results84        queryParams['pos_type'] = pos_type85        queryParams['get_fingerprint'] = get_fingerprint86        response = self.apiClient._callAPI(resourcePath, method, queryParams, postData, headerParams)...

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