|
| 1 | + |
| 2 | + |
| 3 | + |
| 4 | +import os |
| 5 | +import sys |
| 6 | +import timeit |
| 7 | +import typing as tp |
| 8 | + |
| 9 | +from arraykit import nonzero_1d |
| 10 | +import arraykit as ak |
| 11 | + |
| 12 | +import matplotlib.pyplot as plt |
| 13 | +import numpy as np |
| 14 | +import pandas as pd |
| 15 | + |
| 16 | +sys.path.append(os.getcwd()) |
| 17 | + |
| 18 | + |
| 19 | + |
| 20 | +class ArrayProcessor: |
| 21 | + NAME = '' |
| 22 | + SORT = -1 |
| 23 | + |
| 24 | + def __init__(self, array: np.ndarray): |
| 25 | + self.array = array |
| 26 | + |
| 27 | +#------------------------------------------------------------------------------- |
| 28 | +class AKNonZero(ArrayProcessor): |
| 29 | + NAME = 'ak.nonzero_1d()' |
| 30 | + SORT = 0 |
| 31 | + |
| 32 | + def __call__(self): |
| 33 | + _ = nonzero_1d(self.array) |
| 34 | + |
| 35 | +class NPNonZero(ArrayProcessor): |
| 36 | + NAME = 'np.nonzero()' |
| 37 | + SORT = 1 |
| 38 | + |
| 39 | + def __call__(self): |
| 40 | + _ = np.nonzero(self.array)[0] |
| 41 | + |
| 42 | +class NPNonZeroInt64Convert(ArrayProcessor): |
| 43 | + NAME = 'np.nonzero().astype(np.int64)' |
| 44 | + SORT = 3 |
| 45 | + |
| 46 | + def __call__(self): |
| 47 | + _ = np.nonzero(self.array)[0].astype(np.int64) |
| 48 | + |
| 49 | + |
| 50 | +#------------------------------------------------------------------------------- |
| 51 | +NUMBER = 400 |
| 52 | + |
| 53 | +def seconds_to_display(seconds: float) -> str: |
| 54 | + seconds /= NUMBER |
| 55 | + if seconds < 1e-4: |
| 56 | + return f'{seconds * 1e6: .1f} (µs)' |
| 57 | + if seconds < 1e-1: |
| 58 | + return f'{seconds * 1e3: .1f} (ms)' |
| 59 | + return f'{seconds: .1f} (s)' |
| 60 | + |
| 61 | + |
| 62 | +def plot_performance(frame): |
| 63 | + fixture_total = len(frame['fixture'].unique()) |
| 64 | + cat_total = len(frame['size'].unique()) |
| 65 | + processor_total = len(frame['cls_processor'].unique()) |
| 66 | + fig, axes = plt.subplots(cat_total, fixture_total) |
| 67 | + |
| 68 | + # cmap = plt.get_cmap('terrain') |
| 69 | + cmap = plt.get_cmap('plasma') |
| 70 | + |
| 71 | + color = cmap(np.arange(processor_total) / max(processor_total, 3)) |
| 72 | + |
| 73 | + # category is the size of the array |
| 74 | + for cat_count, (cat_label, cat) in enumerate(frame.groupby('size')): |
| 75 | + # each fixture is a collection of tests for one display |
| 76 | + fixtures = {fixture_label: fixture for fixture_label, fixture in cat.groupby('fixture')} |
| 77 | + for fixture_count, (fixture_label, fixture) in enumerate(fixtures.items()): |
| 78 | + ax = axes[cat_count][fixture_count] |
| 79 | + |
| 80 | + # set order |
| 81 | + fixture['sort'] = [f.SORT for f in fixture['cls_processor']] |
| 82 | + fixture = fixture.sort_values('sort') |
| 83 | + |
| 84 | + results = fixture['time'].values.tolist() |
| 85 | + names = [cls.NAME for cls in fixture['cls_processor']] |
| 86 | + # x = np.arange(len(results)) |
| 87 | + names_display = names |
| 88 | + post = ax.bar(names_display, results, color=color) |
| 89 | + |
| 90 | + # density, position = fixture_label.split('-') |
| 91 | + # cat_label is the size of the array |
| 92 | + title = f'{cat_label:.0e}\n{FixtureFactory.DENSITY_TO_DISPLAY[fixture_label]}' |
| 93 | + |
| 94 | + ax.set_title(title, fontsize=6) |
| 95 | + ax.set_box_aspect(0.75) # makes taller tan wide |
| 96 | + time_max = fixture['time'].max() |
| 97 | + ax.set_yticks([0, time_max * 0.5, time_max]) |
| 98 | + ax.set_yticklabels(['', |
| 99 | + seconds_to_display(time_max * .5), |
| 100 | + seconds_to_display(time_max), |
| 101 | + ], fontsize=6) |
| 102 | + # ax.set_xticks(x, names_display, rotation='vertical') |
| 103 | + ax.tick_params( |
| 104 | + axis='x', |
| 105 | + which='both', |
| 106 | + bottom=False, |
| 107 | + top=False, |
| 108 | + labelbottom=False, |
| 109 | + ) |
| 110 | + |
| 111 | + fig.set_size_inches(9, 4) # width, height |
| 112 | + fig.legend(post, names_display, loc='center right', fontsize=6) |
| 113 | + # horizontal, vertical |
| 114 | + fig.text(.05, .96, f'nonzero_1d() Performance: {NUMBER} Iterations', fontsize=10) |
| 115 | + fig.text(.05, .90, get_versions(), fontsize=6) |
| 116 | + |
| 117 | + fp = '/tmp/nonzero.png' |
| 118 | + plt.subplots_adjust( |
| 119 | + left=0.075, |
| 120 | + bottom=0.05, |
| 121 | + right=0.80, |
| 122 | + top=0.85, |
| 123 | + wspace=0.9, # width |
| 124 | + hspace=0.2, |
| 125 | + ) |
| 126 | + # plt.rcParams.update({'font.size': 22}) |
| 127 | + plt.savefig(fp, dpi=300) |
| 128 | + |
| 129 | + if sys.platform.startswith('linux'): |
| 130 | + os.system(f'eog {fp}&') |
| 131 | + else: |
| 132 | + os.system(f'open {fp}') |
| 133 | + |
| 134 | + |
| 135 | +#------------------------------------------------------------------------------- |
| 136 | + |
| 137 | +class FixtureFactory: |
| 138 | + NAME = '' |
| 139 | + |
| 140 | + @staticmethod |
| 141 | + def get_array(size: int) -> np.ndarray: |
| 142 | + return np.full(size, False, dtype=bool) |
| 143 | + |
| 144 | + def _get_array_filled( |
| 145 | + size: int, |
| 146 | + start_third: int, #0, 1 or 2 |
| 147 | + density: float, # less than 1 |
| 148 | + ) -> np.ndarray: |
| 149 | + a = FixtureFactory.get_array(size) |
| 150 | + count = size * density |
| 151 | + start = int(len(a) * (start_third/3)) |
| 152 | + length = len(a) - start |
| 153 | + step = max(int(length / count), 1) |
| 154 | + fill = np.arange(start, len(a), step) |
| 155 | + a[fill] = True |
| 156 | + return a |
| 157 | + |
| 158 | + @classmethod |
| 159 | + def get_label_array(cls, size: int) -> tp.Tuple[str, np.ndarray]: |
| 160 | + array = cls.get_array(size) |
| 161 | + return cls.NAME, array |
| 162 | + |
| 163 | + DENSITY_TO_DISPLAY = { |
| 164 | + 'single': '1 True', |
| 165 | + 'quarter': '25% True', |
| 166 | + 'half': '50% True', |
| 167 | + 'full': '100% True', |
| 168 | + } |
| 169 | + |
| 170 | + # POSITION_TO_DISPLAY = { |
| 171 | + # 'first_third': 'Fill 1/3 to End', |
| 172 | + # 'second_third': 'Fill 2/3 to End', |
| 173 | + # } |
| 174 | + |
| 175 | + |
| 176 | +class FFSingle(FixtureFactory): |
| 177 | + NAME = 'single' |
| 178 | + |
| 179 | + @staticmethod |
| 180 | + def get_array(size: int) -> np.ndarray: |
| 181 | + a = FixtureFactory.get_array(size) |
| 182 | + a[len(a) // 2] = True |
| 183 | + return a |
| 184 | + |
| 185 | +class FFQuarter(FixtureFactory): |
| 186 | + NAME = 'quarter' |
| 187 | + |
| 188 | + @classmethod |
| 189 | + def get_array(cls, size: int) -> np.ndarray: |
| 190 | + return cls._get_array_filled(size, start_third=0, density=0.25) |
| 191 | + |
| 192 | +class FFHalf(FixtureFactory): |
| 193 | + NAME = 'half' |
| 194 | + |
| 195 | + @classmethod |
| 196 | + def get_array(cls, size: int) -> np.ndarray: |
| 197 | + return cls._get_array_filled(size, start_third=0, density=0.5) |
| 198 | + |
| 199 | + |
| 200 | +class FFFull(FixtureFactory): |
| 201 | + NAME = 'full' |
| 202 | + |
| 203 | + @classmethod |
| 204 | + def get_array(cls, size: int) -> np.ndarray: |
| 205 | + return cls._get_array_filled(size, start_third=0, density=1) |
| 206 | + |
| 207 | + |
| 208 | +def get_versions() -> str: |
| 209 | + import platform |
| 210 | + return f'OS: {platform.system()} / ArrayKit: {ak.__version__} / NumPy: {np.__version__}\n' |
| 211 | + |
| 212 | + |
| 213 | +CLS_PROCESSOR = ( |
| 214 | + AKNonZero, |
| 215 | + NPNonZero, |
| 216 | + NPNonZeroInt64Convert, |
| 217 | + ) |
| 218 | + |
| 219 | +CLS_FF = ( |
| 220 | + FFSingle, |
| 221 | + FFQuarter, |
| 222 | + FFHalf, |
| 223 | + FFFull, |
| 224 | +) |
| 225 | + |
| 226 | + |
| 227 | +def run_test(): |
| 228 | + records = [] |
| 229 | + for size in (100_000, 1_000_000, 10_000_000): |
| 230 | + for ff in CLS_FF: |
| 231 | + fixture_label, fixture = ff.get_label_array(size) |
| 232 | + for cls in CLS_PROCESSOR: |
| 233 | + runner = cls(fixture) |
| 234 | + |
| 235 | + record = [cls, NUMBER, fixture_label, size] |
| 236 | + print(record) |
| 237 | + try: |
| 238 | + result = timeit.timeit( |
| 239 | + f'runner()', |
| 240 | + globals=locals(), |
| 241 | + number=NUMBER) |
| 242 | + except OSError: |
| 243 | + result = np.nan |
| 244 | + finally: |
| 245 | + pass |
| 246 | + record.append(result) |
| 247 | + records.append(record) |
| 248 | + |
| 249 | + f = pd.DataFrame.from_records(records, |
| 250 | + columns=('cls_processor', 'number', 'fixture', 'size', 'time') |
| 251 | + ) |
| 252 | + print(f) |
| 253 | + plot_performance(f) |
| 254 | + |
| 255 | +if __name__ == '__main__': |
| 256 | + |
| 257 | + run_test() |
| 258 | + |
| 259 | + |
| 260 | + |
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