How to use test_counts method in Slash

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test_similarity_deltas.py

Source:test_similarity_deltas.py

1import unittest2import numpy as np3from collections import Counter, defaultdict        4from compare_trajectories import *5from store_counts import store_counts6TEST_OUTPUT = 'test_output'7SIMILARITY_ORDER = [NOT, SOMEWHAT, MOSTLY, VERY]8DELTA_THRESHOLD = 109class TestCompareTrajectories(unittest.TestCase):10    golden_keywords = {11        '1-1': 'for if print',12        '1-2': 'for if print'13    }14    def similarity_deltas_multiple_thresholds(self) -> None:15        thresholds = [i/DELTA_THRESHOLD for i in range(DELTA_THRESHOLD)]16        for threshold in thresholds:17            if not os.path.exists(TEST_OUTPUT):18                os.mkdir(TEST_OUTPUT)19            storage = os.path.join(TEST_OUTPUT, str(threshold))20            store_counts(storage=storage, threshold=threshold)21            for pb, keywords in TestCompareTrajectories.golden_keywords.items():22                print(pb)23                counts = count_keywords(keywords)24                sim_strs = []25                for i in range(len(counts)):26                    test_count = list(counts)27                    if counts[i] >= 1:28                        test_count[i] -= 129                        sim_strs.append(get_similarity_string(get_similarity_percent(test_count, pb, storage=storage)))30                        test_count[i] += 231                    else:32                        test_count[i] += 133                    sim_strs.append(get_similarity_string(get_similarity_percent(test_count, pb, storage=storage)))34                counts = Counter(sim_strs)35                    36                output_path = os.path.join(storage, f'golden_deltas_{pb}.txt')37                if os.path.exists(output_path):38                    open(output_path, 'w').close()39                40                with open(output_path, 'a') as f:41                    f.write('keyword, count\n')42                    for similarity_keyword in SIMILARITY_ORDER:43                        f.write(similarity_keyword + ", ")44                        try:45                            count = counts[similarity_keyword]46                        except KeyError:47                            count = 048                        print(f"  {similarity_keyword}: {count}")49                        f.write(str(count) + '\n')50    def similarity_deltas_multiple_parameters(self) -> None:51        if not os.path.exists(TEST_OUTPUT):52            os.mkdir(TEST_OUTPUT)53        thresholds = np.arange(0.0, 1.0, 0.05)54        scales = np.arange(0.5, 1.5, 0.05)55        for threshold in thresholds:56            for scale in scales:57                store_counts(storage=TEST_OUTPUT, threshold=threshold, std_scale=scale)58                for pb, keywords in TestCompareTrajectories.golden_keywords.items():59                    counts = count_keywords(keywords)60                    sim_counts = {}61                    for n in range(1, 10):62                        all_test_counts = []63                        for i in range(len(counts)):64                            test_counts = list(counts)65                            test_counts[i] += n66                            all_test_counts.append(test_counts)67                            if counts[i] >= n:68                                test_counts[i] -= n*269                                all_test_counts.append(test_counts)70                        for z in range(1, n):71                            for i in range(len(counts) - 1):72                                test_counts = list(counts)73                                test_counts[i] += n-z74                                test_counts[i+1] += z75                                all_test_counts.append(test_counts)76                                if counts[i] >= n-z and counts[i+1] >= z:77                                    test_counts[i] -= (n-z)*278                                    test_counts[i+1] -= z*279                                    all_test_counts.append(test_counts)80                            for k in range(1, z):81                                for i in range(len(counts) - 2):82                                    test_counts = list(counts)83                                    test_counts[i] += n-z-k84                                    test_counts[i+1] += z-k85                                    test_counts[i+2] += k86                                    all_test_counts.append(test_counts)87                                    if counts[i] >= n-z-k and counts[i+1] >= z-k and test_counts[i+2] >= k:88                                        test_counts[i] -= (n-z-k)*289                                        test_counts[i+1] -= (z-k)*290                                        test_counts[i+2] -= k*291                                        all_test_counts.append(test_counts)92                        unique_test_counts = [list(x) for x in set(tuple(x) for x in all_test_counts)]93                        sim_strs = [get_similarity_string(get_similarity_percent(c, pb, storage=TEST_OUTPUT)) for c in unique_test_counts]94                        current_counts = dict(Counter(sim_strs)) 95                        if n == 1 and 'very' not in current_counts:96                            break97                        elif n == 3 and len(current_counts) != 1:98                            break99                        sim_counts[n] = current_counts100                    if sim_counts:101                        print(pb, threshold, scale)102                        print("    " + str(sim_counts) + '\n')103    def similarity_deltas(self, threshold: float = 0.0, scale: float = 1.0) -> None:104        store_counts(storage=TEST_OUTPUT, threshold=threshold, std_scale=scale)105        for pb, keywords in TestCompareTrajectories.golden_keywords.items():106            counts = count_keywords(keywords)107            sim_counts = {}108            for n in range(1, 10):109                all_test_counts = []110                111                for i in range(len(counts)):112                    test_counts = list(counts)113                    test_counts[i] += n114                    all_test_counts.append(test_counts)115                    if counts[i] >= n:116                        test_counts[i] -= n*2117                        all_test_counts.append(test_counts)118                for z in range(1, n):119                    for i in range(len(counts) - 1):120                        test_counts = list(counts)121                        test_counts[i] += n-z122                        test_counts[i+1] += z123                        all_test_counts.append(test_counts)124                        125                        if counts[i] >= n-z and counts[i+1] >= z:126                            test_counts[i] -= (n-z)*2127                            test_counts[i+1] -= z*2128                            all_test_counts.append(test_counts)129                    for k in range(1, z):130                        for i in range(len(counts) - 2):131                            test_counts = list(counts)132                            test_counts[i] += n-z-k133                            test_counts[i+1] += z-k134                            test_counts[i+2] += k135                            all_test_counts.append(test_counts)136                            137                            if counts[i] >= n-z-k and counts[i+1] >= z-k and test_counts[i+2] >= k:138                                test_counts[i] -= (n-z-k)*2139                                test_counts[i+1] -= (z-k)*2140                                test_counts[i+2] -= k*2141                                all_test_counts.append(test_counts)142                unique_test_counts = [list(x) for x in set(tuple(x) for x in all_test_counts)]143                sim_strs = [get_similarity_string(get_similarity_percent(c, pb, storage=TEST_OUTPUT)) for c in unique_test_counts]144                current_counts = dict(Counter(sim_strs))145                sim_counts[n] = current_counts146            print(pb, sim_counts)147if __name__ == '__main__':148    tester = TestCompareTrajectories()149    tester.similarity_deltas_multiple_parameters()...

test_chunking.py

Source:test_chunking.py

1from htrc_features.transformations import chunk_ends, chunk_even, chunk_last2from htrc_features.resolvers import LocalResolver3from htrc_features import Volume4from pathlib import Path5import random6import numpy as np7from collections import Counter8import pandas as pd9class TestChunking():10    def test_write_to_chunked_parquet_sectionless(self, tmpdir):11        dir = "tests/data"12        vol_in = Volume(id='aeu.ark:/13960/t1rf63t52', dir = str(dir), id_resolver = 'local')13        output = Volume(id='foo.123', format = 'parquet', mode = 'wb', id_resolver='local', dir = tmpdir )14        output.write(vol_in, token_kwargs = {"chunk": True,"drop_section": True, "pos":False})15        read = pd.read_parquet(Path(tmpdir, "foo.123.tokens.parquet")).reset_index()16        print(read.columns)17        assert("chunk" in read.columns)18    19    def test_write_to_chunked_parquet(self, tmpdir):20        dir = "tests/data"21        vol_in = Volume(id='aeu.ark:/13960/t1rf63t52', dir = str(dir), id_resolver = 'local')22        output = Volume(id='foo.123', dir = tmpdir, format = 'parquet', mode = 'wb')23        output.write(vol_in, token_kwargs = {"chunk": True})24        read = pd.read_parquet(Path(tmpdir, "foo.123.tokens.parquet")).reset_index()25        assert("chunk" in read.columns)26            27    def test_even_chunking(self):28        # All methods should solve it when pages are only one thing long.29        for method in chunk_ends, chunk_even, chunk_last:30            test_counts = np.ones(1000)31            target = 10032            c = Counter()33            for chunk, count in zip(method(test_counts, target), test_counts):34                c[chunk] += count35            assert(Counter(c.values()) == Counter({100:10}))36    def test_assymetric_chunking_end(self):37        # Previously this caused an infinite loop.38        for method in chunk_ends, chunk_even, chunk_last:39            test_counts = np.ones(1000)40            test_counts[-1] = 50041            target = 10042            c = Counter()43            for chunk, count in zip(method(test_counts, target), test_counts):44                c[chunk] += count45            assert(np.max([*c.values()]) == 500)46            assert(np.min([*c.values()]) == 99)47    def test_assymetric_chunking_middle(self):48        # in cases with page lengths like [1, 500, 2] the49        # outer chunks may try to eat the inner chunk at the same50        # time. The test assertion here is unimportant: the51        # goal is really not to raise an error.52        for method in chunk_ends, chunk_even, chunk_last:53            test_counts = np.ones(1000)54            test_counts[500] = 50055            target = 10056            c = Counter()57            for chunk, count in zip(method(test_counts, target), test_counts):58                c[chunk] += count59            assert(np.max([*c.values()]) <= 501)60    def test_tiny_chunk_size(self):61        # What if the chunk size is much smaller than any page?62        # The only reasonable response is 63        for method in chunk_ends, chunk_even, chunk_last:64            test_counts = np.array([500] * 10)65            target = 10066            c = Counter()67            for chunk, count in zip(method(test_counts, target), test_counts):68                c[chunk] += count69            assert(np.max([*c.values()]) == 500)...

memory_leak_check.py

Source:memory_leak_check.py

1"""Test for memory leaks"""2from minpower import powersystems, solve3from .test_utils import solve_problem, make_loads_times4import objgraph5import inspect6def show_memory_backrefs(name):7    objgraph.show_backrefs(8        objgraph.by_type(name), filename="backrefs-{}.png".format(name)9    )10def show_memory_refs(name):11    try:12        obj = objgraph.by_type(name)[0]13    except IndexError:14        print(("no object of type", name))15        return16    objgraph.show_chain(17        objgraph.find_backref_chain(obj, inspect.ismodule),18        filename="chain-{}.png".format(name),19    )20def show_memory_growth():21    objgraph.show_growth()22def get_counts(prefix=""):23    test_types = [24        "Var",25        "Piecewise",26        "ScenarioTree",27        "ScenarioTreeNode",28        "Scenario",29        "_SetContainer",30        "_ConstraintArray",31    ]32    # use objgraph to check if these pyomo objects still exist33    objects = {}34    test_counts = {}35    for name in test_types:36        objects[name] = objgraph.by_type(name)37        test_counts[name] = len(objects[name])38    if True:39        for name in test_types:40            if test_counts[name] == 0:41                continue42            else:43                obj = objects[name][0]44            fname = prefix + "-" + "objgraph-{}-".format(name)45            objgraph.show_refs([obj], filename=fname + "refs.png")  # too_many=50,46            objgraph.show_backrefs([obj], filename=fname + "backref.png")47            objgraph.show_chain(48                objgraph.find_backref_chain(obj, inspect.ismodule),49                filename=fname + "chain.png",50            )51    return test_counts52def leak_on_reset():53    # create a problem54    loads_times = make_loads_times(Pdt=[20, 30, 40, 50])55    generators = [56        powersystems.Generator(name="expensive", costcurveequation="30P+0.01P^2"),57        powersystems.Generator(name="cheap", costcurveequation="20P+0.005P^2"),58    ]59    # solve it60    power_system, times = solve_problem(generators, **loads_times)61    # reset the model - no pyomo components should persist62    power_system.reset_model()63    test_counts = get_counts("uc")64    assert sum(test_counts.values()) == 065def leak_on_stochastic_reset():66    solve.solve_problem(67        datadir="./uc-stochastic",68        shell=False,69        csv=False,70        hours_commitment=24,71        hours_commitment_overlap=12,72        get_duals=False,73        Nscenarios=None,74    )75    test_counts = get_counts("uc-stochastic")76    assert sum(test_counts.values()) == 077if __name__ == "__main__":...

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