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How to use test_monotonic method in pytest-benchmark

Best Python code snippet using pytest-benchmark

reweight_groundtruth_plotter.py

Source:reweight_groundtruth_plotter.py

...73    y_hat2_3 = lowess(y, x) # note, default frac=2/374    y_hat1_5 = lowess(y, x, frac=1/5)75    y_hat1_4 = lowess(y, x, frac=1/4)76    y_hat1_7 = lowess(y, x, frac=1/7)77    print(test_monotonic(y_hat1_4))78    print(get_LOWESS_degree_by_ratio(y_hat1_4, -3))79    80    if(input('DO YOU WANT TO PLOT THE FILTERED RESULT? (y/n)')=='y'):81        fig = px.scatter(df, x=df['Phi [deg]'], y=df['SUM-DIFF'], opacity=0.8, color_discrete_sequence=['black'])82        fig.add_traces(go.Scatter(x=y_hat2_3[:,0], y=y_hat2_3[:,1], name='LOWESS, frac=2/3', line=dict(color='red')))83        fig.add_traces(go.Scatter(x=y_hat2_3[:,0], y=y_hat1_4[:,1], name='LOWESS, frac=1/4', line=dict(color='orange')))84        fig.add_traces(go.Scatter(x=y_hat2_3[:,0], y=y_hat1_7[:,1], name='LOWESS, frac=1/7', line=dict(color='yellowgreen')))85        fig.update_layout(dict(plot_bgcolor = 'white'))86        fig.update_traces(marker=dict(size=3))87        fig.show() 88def get_LOWESS_degree_by_ratio(filtered_data, ratio):89    if(test_monotonic(filtered_data)==False):90        print("!! the data is not monotonic !!")91        return92    min_data=1093    for i in range(len(filtered_data)-1):94        if filtered_data[i][1] < min_data:95            min_data = filtered_data[i][1]96        if min(filtered_data[i][1], filtered_data[i+1][1])<ratio and ratio<max(filtered_data[i][1], filtered_data[i+1][1]):97            return interpolator(filtered_data, i, i+1, ratio)98    print("!! no data found !!")99    if ratio <= min_data:100        return 0101def interpolator(data, index1, index2, ratio): # returns degree (float)102    return data[index1][0] + (ratio-data[index1][1])/(data[index2][1]-data[index1][1])103def test_monotonic(input):  # for lowess filter104    peak_num=0105    for i in range(1,len(input)-1):106        if (input[i+1][1]<input[i][1] and input[i-1][1]<input[i][1]) or (input[i+1][1]>input[i][1] and input[i-1][1]>input[i][1]):  # if there is a peak107            peak_num+=1108    if peak_num!=1:109        return False110    else:111        return True112###########################################################################113################ Savitzky-Golay filter  ###################################114###########################################################################115def SAVGOL_filter():116    df = pd.read_csv('ground_truth_reweight.csv', encoding='utf-8')117    x=df['Phi [deg]'].values ...

tests.py

Source:tests.py

...21        assert core.base(13, "0123456789ABCDEF", 2) == "0D"22        assert core.base(16, "0123456789ABCDEF", 2) == "10"23        assert core.base(256, "0123456789ABCDEF", 2) == "100"24        pass25    def test_monotonic(self):26        id = core.monotonic(27            resolution=core.Resolution.days,28            now=datetime.datetime(2018, 12, 31, 23, 59, 59),29            alphabet="0123456789",30            start=datetime.datetime(2018, 1, 1),31            overflow_years=1,32        )33        assert id == "999"34        pass35    def test_equivalence(self):36        hs = core.HagelSource()37        now = datetime.datetime.now()38        assert hs.monotonic(now) == core.monotonic(now=now)39        pass40    def test_random(self):41        ids = [core.random() for i in range(100)]42        assert len(set(ids)) == 10043        pass44class TestHagelSource(unittest.TestCase):45    def test_init(self):46        hs = core.HagelSource(overflow_years=42)47        assert (hs.end - hs.start).total_seconds() == hs.total_seconds48        assert hs.total_seconds == 42 * 3153600049        assert hs.B == len(hs.alphabet)50        assert hs.B ** hs.digits == hs.combinations51        assert hs.total_seconds / hs.combinations == hs.resolution52        pass53    def test_monotonic(self):54        hs = core.HagelSource(55            resolution=core.Resolution.days,56            alphabet="0123456789",57            start=datetime.datetime(2018, 1, 1),58            overflow_years=1,59        )60        id = hs.monotonic(now=datetime.datetime(2018, 12, 31, 23, 59, 59))61        assert len(id) == hs.digits62        assert id == "999"63        pass64    def test_t_0(self):65        hs = core.HagelSource()66        first = hs.monotonic(now=hs.start)67        assert set(first) == set(hs.alphabet[0]), (...

post_histogram_monotonic.py

Source:post_histogram_monotonic.py

...12    Error increases linearly as the range increases.13    See Section 3.1: https://arxiv.org/pdf/0904.0942.pdf14    """15    return np.diff(isotonic_regression(counts.cumsum()), prepend=0)16def test_monotonic():17    epsilon = .0518    sensitivity = 119    # setup20    data = np.sqrt(np.random.uniform(size=1000))21    edges = np.sort(np.sqrt(np.random.uniform(size=100)))22    edge_indexes = list(range(len(edges)))23    # make measurement24    from opendp.trans import make_find_bin, make_count_by_categories25    from opendp.meas import make_base_geometric26    trans = make_find_bin(edges) >> make_count_by_categories(edge_indexes, TIA="usize")27    meas = trans >> make_base_geometric(sensitivity / epsilon, D="VectorDomain<AllDomain<i32>>")28    # release29    # the first bin is anything below the first edge and last bin is anything after the last edge30    exact_counts = np.array(trans(data)[1:-1])31    noisy_counts = np.array(meas(data)[1:-1])32    monot_counts = postprocess_histogram_monotonic_cumsum(noisy_counts)33    import matplotlib.pyplot as plt34    midpoints = get_midpoints(edges)35    plt.plot(midpoints, exact_counts.cumsum(), label="exact")36    plt.plot(midpoints, noisy_counts.cumsum(), label="noisy")37    plt.plot(midpoints, monot_counts.cumsum(), label="consistent")38    plt.legend()39    plt.show()...

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