Examples with Source com.google.cloud.securitycenter.v1.Source used on opensource projects

Example 21 with Source

use of com.google.cloud.securitycenter.v1.Source in project google-cloud-java by GoogleCloudPlatform.

the class SourceSnippets method createSource.

/**
 * Create a source under an organization.
 *
 * @param organizationName The organization for the source.
 */
// [START securitycenter_create_source]
static Source createSource(OrganizationName organizationName) {
    try (SecurityCenterClient client = SecurityCenterClient.create()) {
        // Start setting up a request to create a source in an organization.
        // OrganizationName organizationName = OrganizationName.of(/*organizationId=*/"123234324");
        Source source = Source.newBuilder().setDisplayName("Customized Display Name").setDescription("A new custom source that does X").build();
        CreateSourceRequest.Builder request = CreateSourceRequest.newBuilder().setParent(organizationName.toString()).setSource(source);
        // Call the API.
        Source response = client.createSource(request.build());
        System.out.println("Created Source: " + response);
        return response;
    } catch (IOException e) {
        throw new RuntimeException("Couldn't create client.", e);
    }
}

Example 22 with Source

use of com.google.cloud.securitycenter.v1.Source in project google-cloud-java by GoogleCloudPlatform.

the class FindingSnippets method groupActiveFindingsWithSourceAtTime.

// [END securitycenter_group_active_findings_with_source]
/**
 * Group active findings under an organization and a source by their specified properties (e.g.
 * category) at a specified time.
 *
 * @param sourceName The source to limit the findings to.
 */
// [START securitycenter_group_active_findings_with_source_at_time]
static ImmutableList<GroupResult> groupActiveFindingsWithSourceAtTime(SourceName sourceName) {
    try (SecurityCenterClient client = SecurityCenterClient.create()) {
        // SourceName sourceName = SourceName.of(/*organization=*/"123234324",/*source=*/
        // "423432321");
        // 1 day ago
        Instant oneDayAgo = Instant.now().minusSeconds(60 * 60 * 24);
        GroupFindingsRequest.Builder request = GroupFindingsRequest.newBuilder().setParent(sourceName.toString()).setGroupBy("category").setFilter("state=\"ACTIVE\"").setReadTime(Timestamp.newBuilder().setSeconds(oneDayAgo.getEpochSecond()).setNanos(oneDayAgo.getNano()));
        // Call the API.
        GroupFindingsPagedResponse response = client.groupFindings(request.build());
        // This creates one list for all findings.  If your organization has a large number of
        // findings
        // this can cause out of memory issues.  You can process them batches by returning
        // the Iterable returned response.iterateAll() directly.
        ImmutableList<GroupResult> results = ImmutableList.copyOf(response.iterateAll());
        System.out.println("Findings:");
        System.out.println(results);
        return results;
    } catch (IOException e) {
        throw new RuntimeException("Couldn't create client.", e);
    }
}

Example 23 with Source

use of com.google.cloud.securitycenter.v1.Source in project h2o-2 by h2oai.

the class GLMTest2 method testGammaRegression.

/**
   * Test Gamma regression on simple and small synthetic dataset.
   * Equation is: y = 1/(x+1);
   * @throws ExecutionException
   * @throws InterruptedException
   */
@Test
public void testGammaRegression() throws InterruptedException, ExecutionException {
    GLMModel model = null;
    Frame fr = null;
    try {
        // make data so that the expected coefficients is icept = col[0] = 1.0
        Key raw = Key.make("gamma_test_data_raw");
        Key parsed = Key.make("gamma_test_data_parsed");
        FVecTest.makeByteVec(raw, "x,y\n0,1\n1,0.5\n2,0.3333333\n3,0.25\n4,0.2\n5,0.1666667\n6,0.1428571\n7,0.125");
        fr = ParseDataset2.parse(parsed, new Key[] { raw });
        //      /public GLM2(String desc, Key dest, Frame src, Family family, Link link, double alpha, double lambda) {
        double[] vals = new double[] { 1.0, 1.0 };
        //public GLM2(String desc, Key dest, Frame src, Family family, Link link, double alpha, double lambda) {
        Key modelKey = Key.make("gamma_test");
        new GLM2("GLM test of gamma regression.", Key.make(), modelKey, new Source(fr, fr.lastVec(), false), Family.gamma).setRegularization(new double[] { 0 }, new double[] { 0 }).doInit().fork().get();
        model = DKV.get(modelKey).get();
        testHTML(model);
        for (double c : model.beta()) assertEquals(1.0, c, 1e-4);
    } finally {
        if (fr != null)
            fr.delete();
        if (model != null)
            model.delete();
    }
}

Example 24 with Source

use of com.google.cloud.securitycenter.v1.Source in project h2o-2 by h2oai.

the class GLMTest2 method testNoInterceptAndOffset.

@Test
public void testNoInterceptAndOffset() {
    //    r <- glm(data=D,CAPSULE ~ . - ID - 1 - DCAPS,offset=as.numeric(DCAPS),family=binomial)
    //    > r
    //
    //    Call:  glm(formula = CAPSULE ~ . - ID - 1 - DCAPS, family = binomial,
    //      data = D, offset = as.numeric(DCAPS))
    //
    //    Coefficients:
    //    AGE      RACE     DPROS       PSA       VOL   GLEASON
    //    -0.07731  -1.34515   0.44422   0.03559  -0.01001   0.57414
    //
    //    Degrees of Freedom: 380 Total (i.e. Null);  374 Residual
    //    Null Deviance:	    696.8
    //    Residual Deviance:  402.9 	AIC: 414.9
    Key parsed = Key.make("prostate_parsed");
    Key modelKey = Key.make("prostate_model");
    GLMModel model = null;
    Frame fr = getFrameForFile(parsed, "smalldata/logreg/prostate.csv", new String[] { "ID" }, "CAPSULE");
    Key k = Key.make("rebalanced");
    H2O.submitTask(new RebalanceDataSet(fr, k, 64)).join();
    fr.delete();
    fr = DKV.get(k).get();
    Frame score = null;
    try {
        // H2O differs on intercept and race, same residual deviance though
        String[] cfs1 = new String[] { "RACE", "AGE", "DPROS", "PSA", "VOL", "GLEASON" };
        double[] vals = new double[] { -1.34515, -0.07731, 0.44422, 0.03559, -0.01001, 0.57414 };
        //.setHighAccuracy().doInit().fork().get();
        new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source(fr, fr.vec("CAPSULE"), false, false, fr.vec("DCAPS")), Family.binomial).setRegularization(new double[] { 0 }, new double[] { 0 }).doInit().fork().get();
        model = DKV.get(modelKey).get();
        Assert.assertTrue(model.get_params().state == Job.JobState.DONE);
        testHTML(model);
        HashMap<String, Double> coefs = model.coefficients();
        for (int i = 0; i < cfs1.length; ++i) assertEquals(vals[i], coefs.get(cfs1[i]), 1e-4);
        GLMValidation val = model.validation();
        assertEquals(696.8, model.null_validation.residualDeviance(), 1e-1);
        assertEquals(402.9, val.residualDeviance(), 1e-1);
        assertEquals(414.9, val.aic(), 1e-1);
        // test scoring
        score = model.score(fr);
        Vec mu = score.vec("1");
        final double[] exp_preds = new double[] { // R predictions using R model
        0.2714328, 0.6633844, 0.3332337, 0.261754, 0.1286857, 0.7700994, 0.7278019, 0.9234909, 0.2111474, 0.1685586, 0.4976614, 0.6792206, 0.9448146, 0.9433877, 0.07163949, 0.1055238, 0.01654711, 0.1167567, 0.6150695, 0.3851188, 0.5469755, 0.9775073, 0.3686457, 0.1734122, 0.4399385, 0.0647587, 0.3155403, 0.139005, 0.2258441, 0.5898738, 0.5922247, 0.2692957, 0.6137802, 0.8462912, 0.3058918, 0.2957983, 0.462281, 0.6524339, 0.6799241, 0.3226737, 0.4941844, 0.1330671, 0.2254533, 0.3066927, 0.9200088, 0.958889, 0.04513577, 0.5383411, 0.1038777, 0.2440525, 0.1092492, 0.9958668, 0.3193829, 0.9367929, 0.4897762, 0.663691, 0.4023, 0.5698951, 0.4341724, 0.4453501, 0.1106898, 0.1682721, 0.2460871, 0.4990961, 0.8788688, 0.4803311, 0.5465907, 0.1569607, 0.1633997, 0.1044413, 0.2212412, 0.1808158, 0.6181282, 0.2396049, 0.3213105, 0.7081065, 0.2098653, 0.3235215, 0.2801671, 0.6652517, 0.2194231, 0.5277527, 0.4905273, 0.2323389, 0.1850839, 0.2254083, 0.9757958, 0.9727239, 0.8319945, 0.482306, 0.1248545, 0.1360881, 0.5320567, 0.9574291, 0.2080324, 0.2144612, 0.4982939, 0.333785, 0.4088947, 0.4901476, 0.3223926, 0.3371016, 0.1693325, 0.1297989, 0.3793889, 0.6960325, 0.2683522, 0.7797531, 0.3924964, 0.5530082, 0.2158664, 0.1981704, 0.5450428, 0.4019126, 0.9884714, 0.9327079, 0.5102081, 0.495269, 0.112279, 0.6908168, 0.2224574, 0.08042856, 0.4025428, 0.6777602, 0.4874214, 0.3087576, 0.3105473, 0.2562402, 0.146729, 0.1274273, 0.06670393, 0.3613933, 0.1757637, 0.6709157, 0.2491046, 0.3167014, 0.2513937, 0.9851177, 0.9885838, 0.3577745, 0.1546274, 0.72168, 0.2868293, 0.9631664, 0.2343213, 0.06899739, 0.9769368, 0.3338782, 0.6347512, 0.3485507, 0.05771538, 0.175731, 0.1481765, 0.6632638, 0.4730665, 0.04656982, 0.6374328, 0.2778723, 0.4285074, 0.5850365, 0.2805779, 0.4880345, 0.6923684, 0.6431633, 0.2055755, 0.7452229, 0.2049388, 0.2320392, 0.5034929, 0.7341303, 0.2612126, 0.2810201, 0.7449264, 0.5941076, 0.1742119, 0.3320475, 0.7572676, 0.12218, 0.2932756, 0.6242029, 0.8294807, 0.7162603, 0.2853046, 0.5877545, 0.6005982, 0.8300135, 0.8581161, 0.04492492, 0.5175156, 0.5203193, 0.4543528, 0.8947635, 0.1580703, 0.149249, 0.2448207, 0.5182805, 0.1475552, 0.2579333, 0.2301171, 0.3908543, 0.3775577, 0.73236, 0.348705, 0.191458, 0.3810784, 0.6695089, 0.1496464, 0.3539768, 0.4774868, 0.3786629, 0.2268355, 0.5461673, 0.9277121, 0.4923828, 0.3681328, 0.2882241, 0.8115473, 0.9391989, 0.2793372, 0.1293679, 0.5765253, 0.6672549, 0.1085629, 0.6530963, 0.2099487, 0.4395374, 0.3355906, 0.5774188, 0.3320534, 0.553845, 0.2571366, 0.555651, 0.8082447, 0.9878579, 0.3889303, 0.2042754, 0.1462106, 0.2517127, 0.3565349, 0.05953082, 0.1621768, 0.2344363, 0.8734925, 0.1734602, 0.2671484, 0.2510726, 0.1139504, 0.4709787, 0.2081558, 0.8334942, 0.6389909, 0.8736708, 0.3707539, 0.1036151, 0.4302368, 0.9448559, 0.8462579, 0.2411054, 0.4935917, 0.13429, 0.241161, 0.3752208, 0.3194919, 0.3351406, 0.5469151, 0.3131954, 0.4897492, 0.1936143, 0.6055124, 0.324853, 0.2217794, 0.1513135, 0.889535, 0.3500166, 0.1428834, 0.1542479, 0.3720363, 0.1281707, 0.4973429, 0.3057025, 0.7167489, 0.009317823, 0.06013731, 0.07956679, 0.5774665, 0.3988433, 0.3558458, 0.7577223, 0.2729823, 0.08260389, 0.2412571, 0.1479702, 0.3244259, 0.7868388, 0.4718041, 0.7904578, 0.8405282, 0.1406044, 0.3987826, 0.1000184, 0.1835483, 0.1950392, 0.9977054, 0.4791991, 0.6242925, 0.2214897, 0.273065, 0.4966417, 0.04303004, 0.7397745, 0.04042993, 0.2130047, 0.7908355, 0.2323056, 0.3072782, 0.5756885, 0.2978859, 0.2074281, 0.8241888, 0.2913779, 0.5488127, 0.56141, 0.2361691, 0.6870129, 0.04781581, 0.1052127, 0.1451822, 0.482763, 0.3677431, 0.08324137, 0.7907079, 0.5541721, 0.05215705, 0.691283, 0.1813831, 0.5188524, 0.3090686, 0.9383484, 0.1454368, 0.8514089, 0.5071669, 0.2971994, 0.08048813, 0.3643893, 0.8936522, 0.09901629, 0.2188425, 0.07477413, 0.537254, 0.3051141, 0.4667297, 0.2651349, 0.7941239, 0.782783, 0.01870947, 0.2772949, 0.008545014, 0.1708219, 0.3550333, 0.239455, 0.3003783, 0.2502883, 0.7356557, 0.6204092, 0.1454039, 0.2910956, 0.09259536, 0.5196652, 0.2838468, 0.06632939, 0.1611899, 0.3349858, 0.1828278, 0.998065, 0.2499865, 0.3866357, 0.02725202, 0.252886, 0.03562421, 0.1845115 };
        assertEquals(exp_preds.length, mu.length());
        for (int i = 0; i < mu.length(); ++i) assertEquals(exp_preds[i], mu.at(i), 1e-4);
        // test that it throws with categoricals
        fr.delete();
        fr = getFrameForFile(parsed, "smalldata/glm_test/prostate_cat_replaced.csv", new String[] { "ID" }, "CAPSULE");
        try {
            new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source(fr, fr.vec("CAPSULE"), false, false), Family.binomial).doInit().fork().get();
            assertTrue("should've thrown", false);
        } catch (IllegalArgumentException iae) {
        }
    } finally {
        fr.delete();
        if (model != null)
            model.delete();
        if (score != null)
            score.delete();
    }
}

Example 25 with Source

use of com.google.cloud.securitycenter.v1.Source in project h2o-2 by h2oai.

the class GLMTest2 method testNoIntercept.

@Test
public void testNoIntercept() {
    //    Call:  glm(formula = CAPSULE ~ . - ID - 1, family = binomial, data = D)
    //
    //    Coefficients:
    //    AGE      RACE     DPROS     DCAPS       PSA       VOL   GLEASON
    //      -0.07205  -1.23262   0.47899   0.13934   0.03626  -0.01155   0.63645
    //
    //    Degrees of Freedom: 380 Total (i.e. Null);  373 Residual
    //    Null Deviance:	    526.8
    //    Residual Deviance: 399 	AIC: 413
    Key parsed = Key.make("prostate_parsed");
    Key modelKey = Key.make("prostate_model");
    GLMModel model = null;
    Frame fr = getFrameForFile(parsed, "smalldata/logreg/prostate.csv", new String[] { "ID" }, "CAPSULE");
    Key k = Key.make("rebalanced");
    H2O.submitTask(new RebalanceDataSet(fr, k, 64)).join();
    fr.delete();
    fr = DKV.get(k).get();
    Frame score = null;
    try {
        // H2O differs on intercept and race, same residual deviance though
        // VOL=0.07800102000216368, AGE=0.4763358013317742, Intercept=-33.31091128704016, DCAPS=1.3862187988985282, PSA=-0.14941426719071213, DPROS=-0.47448094905875854, RACE=0.7167868302661414, GLEASON=0.013676100109077757}
        String[] cfs1 = new String[] { "RACE", "AGE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON" };
        double[] vals = new double[] { -1.23262, -0.07205, 0.47899, 0.13934, 0.03626, -0.01155, 0.63645 };
        //.setHighAccuracy().doInit().fork().get();
        new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source((Frame) fr.clone(), fr.vec("CAPSULE"), false, false), Family.binomial).setRegularization(new double[] { 0 }, new double[] { 0 }).doInit().fork().get();
        model = DKV.get(modelKey).get();
        Assert.assertTrue(model.get_params().state == Job.JobState.DONE);
        testHTML(model);
        HashMap<String, Double> coefs = model.coefficients();
        for (int i = 0; i < cfs1.length; ++i) assertEquals(vals[i], coefs.get(cfs1[i]), 1e-4);
        GLMValidation val = model.validation();
        assertEquals(526.8, model.null_validation.residualDeviance(), 1e-1);
        assertEquals(399, val.residualDeviance(), 1e-1);
        assertEquals(413, val.aic(), 1e-1);
        // test scoring
        score = model.score((Frame) fr.clone());
        Vec mu = score.vec("1");
        final double[] exp_preds = new double[] { // R predictions using R model
        0.2790619, 0.4983728, 0.1791504, 0.3179892, 0.1227505, 0.6407688, 0.6086971, 0.8692052, 0.2198773, 0.08973103, 0.3612737, 0.5100686, 0.9109716, 0.8954879, 0.07149991, 0.1073158, 0.01838251, 0.1152114, 0.3904417, 0.2489027, 0.5629947, 0.9801603, 0.4037248, 0.179598, 0.459759, 0.06532427, 0.3314463, 0.1428987, 0.2182262, 0.5992186, 0.5902301, 0.2907103, 0.6824957, 0.723047, 0.3096409, 0.3182108, 0.4573366, 0.4957492, 0.6979306, 0.3537596, 0.5224244, 0.1372099, 0.2386254, 0.3372425, 0.9438167, 0.9186791, 0.04887559, 0.5780143, 0.101601, 0.2495288, 0.1152114, 0.992729, 0.3241788, 0.9455764, 0.527273, 0.6789504, 0.4396949, 0.6118608, 0.4396932, 0.4433259, 0.09217928, 0.1718421, 0.2733261, 0.534392, 0.8947366, 0.5070448, 0.543244, 0.1760429, 0.1587279, 0.120139, 0.230559, 0.1838054, 0.6437882, 0.2357325, 0.3408042, 0.7405974, 0.225001, 0.3285307, 0.2709872, 0.698206, 0.2430985, 0.54366, 0.5325359, 0.2517555, 0.20072, 0.2483879, 0.957223, 0.9493145, 0.866129, 0.5205794, 0.1206937, 0.1304155, 0.5742516, 0.9235101, 0.2142854, 0.2317031, 0.5402695, 0.3272389, 0.4129856, 0.5158623, 0.3303411, 0.3651679, 0.1585129, 0.1237278, 0.4078402, 0.4843822, 0.2863726, 0.8078961, 0.4044774, 0.5935165, 0.2365318, 0.2232613, 0.5775281, 0.4272229, 0.97787, 0.9394984, 0.5734764, 0.5001313, 0.1140847, 0.7091469, 0.2474317, 0.07108103, 0.4702847, 0.7315436, 0.5285277, 0.3130729, 0.3107732, 0.2458944, 0.1584744, 0.1261198, 0.06565271, 0.3980803, 0.1742766, 0.6937854, 0.2508427, 0.3177764, 0.2621678, 0.9889184, 0.9792494, 0.3773912, 0.1606691, 0.7699755, 0.3038182, 0.9349492, 0.222803, 0.07258553, 0.9597009, 0.3351248, 0.6378875, 0.3786587, 0.06284628, 0.1737639, 0.1482272, 0.6689168, 0.4699873, 0.04251894, 0.6456895, 0.3105649, 0.4429625, 0.595572, 0.3196979, 0.5035891, 0.7084547, 0.6600298, 0.2110469, 0.5676662, 0.2077393, 0.2516736, 0.5292617, 0.777053, 0.2858721, 0.3028988, 0.7719771, 0.6168979, 0.1803735, 0.3461169, 0.7885772, 0.1189895, 0.2998581, 0.6705114, 0.7083223, 0.7471706, 0.2958453, 0.5998061, 0.6174054, 0.8464897, 0.8724295, 0.0529646, 0.323008, 0.5425115, 0.4691805, 0.9033616, 0.1397801, 0.1515056, 0.2604321, 0.5680744, 0.1702089, 0.2599474, 0.2410981, 0.4224218, 0.3699072, 0.7741795, 0.352852, 0.202532, 0.3876063, 0.5091125, 0.1403465, 0.3263904, 0.4990924, 0.3713234, 0.2126325, 0.5911457, 0.9437311, 0.4720828, 0.387815, 0.2707227, 0.8353962, 0.896327, 0.2910632, 0.1353718, 0.5688478, 0.6956094, 0.09815098, 0.675314, 0.2265392, 0.4702665, 0.321468, 0.5911756, 0.350539, 0.5475017, 0.3069707, 0.5467453, 0.6713496, 0.9915501, 0.421299, 0.2042643, 0.1522847, 0.2505383, 0.3841292, 0.0665612, 0.1617935, 0.251719, 0.8010179, 0.1755443, 0.2864689, 0.3067574, 0.1087108, 0.4872522, 0.1974353, 0.8422357, 0.4334588, 0.8472403, 0.4085235, 0.1092982, 0.4357049, 0.8977747, 0.7387849, 0.2449383, 0.4908928, 0.1334274, 0.2282918, 0.3815987, 0.3493979, 0.3307988, 0.5747723, 0.3146818, 0.5184166, 0.1786566, 0.6330598, 0.3373586, 0.2120764, 0.134929, 0.9091373, 0.3451438, 0.142635, 0.1559291, 0.3735968, 0.1252362, 0.4867681, 0.305977, 0.7427962, 0.006477887, 0.06593239, 0.07762176, 0.5986354, 0.3879587, 0.4083299, 0.7713339, 0.2778816, 0.07709849, 0.2372032, 0.1341624, 0.3215959, 0.814327, 0.4853451, 0.8217658, 0.7465689, 0.1396363, 0.3774837, 0.09754716, 0.1782466, 0.2008813, 0.9958686, 0.5042077, 0.6177981, 0.2189784, 0.2797684, 0.5289506, 0.03569642, 0.7797529, 0.03918494, 0.2265129, 0.6268007, 0.2234737, 0.3341935, 0.6285033, 0.3302472, 0.2205676, 0.8441454, 0.2983196, 0.5755281, 0.5844469, 0.2310026, 0.7117795, 0.04170531, 0.1020103, 0.1554328, 0.4709666, 0.3739278, 0.07840264, 0.634026, 0.592427, 0.06120752, 0.692224, 0.1963099, 0.5465022, 0.3068802, 0.868874, 0.1502109, 0.8650777, 0.5293211, 0.3454249, 0.07389645, 0.3731161, 0.9075499, 0.0944298, 0.2188017, 0.06919131, 0.5516276, 0.3083056, 0.4818407, 0.2932327, 0.8026013, 0.6212048, 0.01829989, 0.2865116, 0.005850647, 0.1678272, 0.3456439, 0.260818, 0.2883414, 0.2521343, 0.5790858, 0.6529569, 0.1452642, 0.2745046, 0.1087368, 0.546329, 0.2560442, 0.06902664, 0.1696336, 0.3607475, 0.1879519, 0.9986248, 0.2345369, 0.4297052, 0.028796, 0.2803801, 0.03908738, 0.1887357 };
        assertEquals(exp_preds.length, mu.length());
        for (int i = 0; i < mu.length(); ++i) assertEquals(exp_preds[i], mu.at(i), 1e-4);
        // test that it throws with categoricals
        fr.delete();
        fr = getFrameForFile(parsed, "smalldata/glm_test/prostate_cat_replaced.csv", new String[] { "ID" }, "CAPSULE");
        try {
            new GLM2("GLM offset test on prostate.", Key.make(), modelKey, new GLM2.Source(fr, fr.vec("CAPSULE"), false, false), Family.binomial).doInit().fork().get();
            assertTrue("should've thrown", false);
        } catch (IllegalArgumentException iae) {
        }
    } finally {
        if (score != null)
            score.delete();
        fr.delete();
        if (model != null)
            model.delete();
    }
}