Python create_grid_search Examples

Example #1

def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name):
    X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath)
    y = np.loadtxt(classes_fpath)

    clf = create_grid_search(clf_name)
    class_matrices, conf_matrices = run_classifier(clf, X, y)

    metric_means = np.mean(class_matrices, axis=0)
    metric_ci = hci(class_matrices, .95, axis=0)
    print(clf_summary(metric_means, metric_ci))
    print()

    conf_means = np.mean(conf_matrices, axis=0)
    conf_ci = hci(conf_matrices, .95, axis=0)
    print("Average confusion matrix with .95 confidence interval")
    print(" \ttrue ")
    print("predic")
    for i in range(conf_means.shape[0]):
        print(i, end="\t \t")
        for j in range(conf_means.shape[1]):
            print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t")
        print()

Example #2

File: class_predict.py Project: antoine-tran/pyksc

def main(features_fpath, tseries_fpath, tags_fpath, classes_fpath, clf_name):
    X, params = create_input_table(features_fpath, tseries_fpath, tags_fpath)
    y = np.loadtxt(classes_fpath)
    
    clf = create_grid_search(clf_name)
    class_matrices, conf_matrices = run_classifier(clf, X, y)
    
    metric_means = np.mean(class_matrices, axis=0)
    metric_ci = hci(class_matrices, .95, axis=0)
    print(clf_summary(metric_means, metric_ci))
    print()
    
    conf_means = np.mean(conf_matrices, axis=0)
    conf_ci = hci(conf_matrices, .95, axis=0)
    print("Average confusion matrix with .95 confidence interval")
    print(" \ttrue ")
    print("predic")
    for i in xrange(conf_means.shape[0]):
        print(i, end="\t \t")
        for j in xrange(conf_means.shape[1]):
            print('%.3f +- %.3f' % (conf_means[i, j], conf_ci[i, j]), end="\t")
        print()

Example #3

def create_learners(learner_name='extra_trees'):
    clf = create_grid_search(learner_name, n_jobs=-1)
    rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1)

    return clf, rgr

Example #4

File: pop_predict.py Project: regstrtn/pyksc

def create_learners(learner_name='rbf_svm'):
    clf = create_grid_search(learner_name, n_jobs=-1)
    rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1)

    return clf, rgr

Example #5

File: leave_k.py Project: antoine-tran/pyksc

def create_learners(learner_name='extra_trees'):
    clf = create_grid_search(learner_name, n_jobs=-1)
    rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1)

    return clf, rgr

Example #6

File: pop_predict.py Project: antoine-tran/pyksc

def create_learners(learner_name='rbf_svm'):
    clf = create_grid_search(learner_name, n_jobs=-1)
    rgr = create_grid_search(learner_name, regressor=True, n_jobs=-1)

    return clf, rgr