How to use test_xen method in autotest

Best Python code snippet using autotest_python Github


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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(, shuffle=True, random_state=0)35 # [acc, x_entropy]36 scores = np.zeros(shape=(, 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, 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, train_prob = glcu.predict_b(train_feats, return_labels=False,71 train_pred = np.argmax(train_prob, axis=1)73 test_prob = glcu.predict_b(test_feats, return_labels=False,74 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.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 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__":...

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...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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