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How to use test_xen method in autotest

Best Python code snippet using autotest_python

train_and_clf_cv.py

Source:train_and_clf_cv.py

1#!/usr/bin/env python32# -*- coding: utf-8 -*-3# author : Santosh Kesiraju4# e-mail : kcraj2[AT]gmail[DOT]com5# Date created : 30 Nov 20176# Last modified : 22 Aug 20197"""8Gaussian linear classifier or multi-class logistic regression on i-vectors.9Gaussian linear classifier with uncertainty on i-vector posterior dists.10"""11import os12import sys13import argparse14from time import time15import h5py16import numpy as np17import scipy18import sklearn19from sklearn.metrics import log_loss20from sklearn.model_selection import StratifiedKFold as SKFold21from sklearn.linear_model import LogisticRegressionCV22from glcu import GLCU23from glc import GLC24def kfold_cv_train_set(train_feats, train_labels, args):25    """ Run k-fold cross validation on train set26    Args:27        train_feats (np.ndarray): training features (n_samples x dim)28        train_labels (np.ndarray): corresponding labels29        args: args30    Returns:31        np.float64: average classification accuracy over k-folds32        np.float64: average cross-entropy loss over k-folds33    """34    skf = SKFold(n_splits=args.nf, shuffle=True, random_state=0)35    # [acc, x_entropy]36    scores = np.zeros(shape=(args.nf, 2))37    i = 038    for trn_ixs, dev_ixs in skf.split(train_feats, train_labels):39        (_, _, acc, xen), _, _ = run_clf(train_feats[trn_ixs],40                                         train_labels[trn_ixs],41                                         train_feats[dev_ixs],42                                         train_labels[dev_ixs], args)43        scores[i, :2] = acc, xen44        i += 145    return np.mean(scores[:, 0]), np.mean(scores[:, 1])46def run_clf(train_feats, train_labels, test_feats, test_labels, args):47    """ Train and classify using Gaussian linear classifier or48    multi-class logistic regression """49    if args.clf == 'glc':50        glc = GLC(est_prior=True)51        glc.train(train_feats, train_labels)52        train_pred = glc.predict(train_feats)53        train_prob = glc.predict(train_feats, return_probs=True)54        test_pred = glc.predict(test_feats)55        test_prob = glc.predict(test_feats, return_probs=True)56    elif args.clf == "lr":57        mclr = LogisticRegressionCV(Cs=[0.01, 0.1, 0.2, 0.5, 1.0, 10.0],58                                    multi_class='multinomial', cv=5,59                                    random_state=0, n_jobs=1,60                                    class_weight='balanced',61                                    max_iter=3000)62        mclr.fit(train_feats, train_labels)63        train_pred = mclr.predict(train_feats)64        train_prob = mclr.predict_proba(train_feats)65        test_pred = mclr.predict(test_feats)66        test_prob = mclr.predict_proba(test_feats)67    else:68        glcu = GLCU(args.trn, cov_type='diag', est_prior=True)69        glcu.train_b(train_feats, train_labels, args.bs)70        train_prob = glcu.predict_b(train_feats, return_labels=False,71                                    bsize=args.bs)72        train_pred = np.argmax(train_prob, axis=1)73        test_prob = glcu.predict_b(test_feats, return_labels=False,74                                   bsize=args.bs)75        test_pred = np.argmax(test_prob, axis=1)76    train_acc = np.mean(train_labels == train_pred) * 100.77    train_xen = log_loss(train_labels, train_prob)78    test_acc = np.mean(test_labels == test_pred) * 100.79    test_xen = log_loss(test_labels, test_prob)80    return (train_acc, train_xen, test_acc, test_xen), test_pred, test_prob81def print_and_save_scores(scores, res_f, ovr):82    """ Print and save scores """83    # print('scores:', scores.shape)84    dev_acc_ix = np.argmax(scores[:, 0])85    dev_xen_ix = np.argmin(scores[:, 1])86    print("      dev_A dev_X   trn_A  trn_X  test_A  test_X  xtr_iter")87    print("acc {:.4f} {:.4f} {:.4f} {:.4f} {:.4f} \88{:.4f} {:.0f}".format(*scores[dev_acc_ix]))89    print("xen {:.4f} {:.4f} {:.4f} {:.4f} {:.4f} \90{:.4f} {:.0f}".format(*scores[dev_xen_ix]))91    header = "\ndev_acc,dev_xen,train_acc,train_xen,test_acc,test_xen,xtr_iter"92    if ovr:93        mode = 'wb'94    else:95        mode = 'ab'96    with open(res_f, mode) as fpw:97        np.savetxt(fpw, scores, fmt='%.4f', header=header)98    print("Saved to", res_f)99    with open(res_f.replace('results_', 'best_score_'), 'w') as fpw:100        np.savetxt(fpw, scores[dev_acc_ix].reshape(1, -1),101                   fmt='%.4f', header=header[1:])102def run(train_h5, test_h5, max_iters, args):103    """ Train and classify for every iteration of extracted i-vector """104    # columns in each row:105    # dev_acc, dev_xen, train_acc, train_xen, test_acc, test_xen, xtr_iter106    scores = []107    train_labels = np.loadtxt(args.train_label_f, dtype=int)108    test_labels = np.loadtxt(args.train_label_f.replace("train", "test"),109                             dtype=int)110    if min(train_labels) == 1:111        train_labels -= 1112    if min(test_labels) == 1:113        test_labels -= 1114    if args.final:115        args.start = max_iters116    for i in range(args.start, max_iters+1):117        if str(i) not in train_h5:118            continue119        score = [0, 0, 0, 0, 0, 0, i]120        train_feats = train_h5.get(str(i))[()]121        test_feats = test_h5.get(str(i))[()]122        if train_feats.shape[0] != train_labels.shape[0]:123            train_feats = train_feats.T124        if test_feats.shape[0] != test_labels.shape[0]:125            test_feats = test_feats.T126        if args.clf in ("glc", "lr"):127            # Get only the mean parameter from the post. dist128            dim = train_feats.shape[1] // 2129            train_feats = train_feats[:, :dim]130            test_feats = test_feats[:, :dim]131        score[:2] = kfold_cv_train_set(train_feats, train_labels, args)132        score[2:6], test_pred, test_prob = run_clf(train_feats, train_labels,133                                                   test_feats, test_labels,134                                                   args)135        scores.append(score)136        if args.verbose:137            print("i-vec xtr no: {:4d}/{:4d}".format(i, max_iters), end=" ")138            print("{:.2f} {:.4f} {:.2f} {:.4f} {:.2f} \139{:.4f} {:.0f}".format(*score))140    return np.asarray(scores), np.asarray(test_pred, dtype=int), test_prob141def main():142    """ main method """143    stime = time()144    args = parse_arguments()145    train_ivecs_h5f = args.train_ivecs_h5146    if not os.path.exists(train_ivecs_h5f):147        print(train_ivecs_h5f, "not found.")148        sys.exit()149    test_ivecs_h5f = train_ivecs_h5f.replace("train_", "test_")150    if not os.path.exists(test_ivecs_h5f):151        print(test_ivecs_h5f, "not found.")152        sys.exit()153    max_iters = int(os.path.splitext(os.path.basename(154        train_ivecs_h5f))[0].split("_")[-1][1:])155    mbase = os.path.splitext(156        os.path.basename(train_ivecs_h5f))[0].split("_")[-2]157    print('max_iters :', max_iters, 'model_base:', mbase)158    try:159        train_h5f = h5py.File(train_ivecs_h5f, 'r')160        train_h5 = train_h5f.get('ivecs')161        test_h5f = h5py.File(test_ivecs_h5f, 'r')162        test_h5 = test_h5f.get('ivecs')163        # results file164        res_f = os.path.realpath(os.path.dirname(train_ivecs_h5f) + "/../")165        sfx = "_" + mbase + "_" + str(max_iters) + "_cv"166        if args.final:167            sfx += "_final"168        res_f += "/results/results_" + args.clf + sfx + ".txt"169        if os.path.exists(res_f) and args.ovr is False:170            print(res_f, 'already EXISTS.')171            sys.exit()172        scores, test_pred, test_prob = run(train_h5, test_h5, max_iters, args)173        print_and_save_scores(scores, res_f, args)174        np.savetxt(res_f.replace("results_", "test_pred_"),175                   test_pred.reshape(-1, 1), fmt="%d")176        np.savetxt(res_f.replace("results_", "test_prob_"),177                   test_prob, fmt="%f")178    except IOError as err:179        print(err)180    finally:181        train_h5f.close()182        test_h5f.close()183    print("== Done: {:.2f} sec ==".format(time() - stime))184def parse_arguments():185    """ parse command line args """186    parser = argparse.ArgumentParser(description=__doc__)187    parser.add_argument("train_ivecs_h5", help="path to train_ivecs.h5 file")188    parser.add_argument("train_label_f", help="path to train label file")189    parser.add_argument("clf", default="glc", type=str,190                        choices=["glcu", "glc", "lr"],191                        help="Choice of classifier: glcu or glc or lr")192    parser.add_argument("-trn", type=int, default=10,193                        help='GLCU training iters')194    parser.add_argument("-bs", type=int, default=1500,195                        help="batch size for training GLCU")196    parser.add_argument("-nf", type=int, default=5,197                        help="Number of folds for k-fold CV")198    parser.add_argument("-start", type=int, default=1, help="start iter num")199    parser.add_argument("-mkl", default="1", help="MKL threads")200    parser.add_argument("--final", action="store_true",201                        help="use only final iteration of emebddings")202    parser.add_argument("--ovr", action="store_true",203                        help="over-write results file")204    parser.add_argument("--verbose", action="store_true", help="verbose")205    parser.add_argument("--versions", action="store_true", help="verbose")206    args = parser.parse_args()207    os.environ['OMP_NUM_THREADS'] = args.mkl208    os.environ['MKL_NUM_THREADS'] = args.mkl209    if args.versions:210        versions()211    return args212def versions():213    """ Print versions of packages """214    print("python :", sys.version)215    print("numpy  :", np.__version__)216    print("scipy  :", scipy.__version__)217    print("h5py   :", h5py.__version__)218    print("sklearn:", sklearn.__version__)219if __name__ == "__main__":...

test_module.py

Source:test_module.py

...4run with `--ansible-host-pattern=localhost`.5"""6import pytest7@pytest.mark.parametrize("execute", [True, False])8def test_xen(ansible_module, execute):9    """ Test the xen module.10    11    """12    params = {13        "execute": execute,14    }15    result = ansible_module.xen(**params)16    host = result["localhost"]17    assert not host.get("failed", False)18    assert host["changed"] == execute19    assert host["execute"] == execute20    return21# Make the module executable.22if __name__ == "__main__":...

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