From 74107703412c6a6e2a2923f5a80dfb63eda52bfd Mon Sep 17 00:00:00 2001 From: Simone Balducci <93096843+sbaldu@users.noreply.github.com> Date: Tue, 6 Aug 2024 15:07:27 +0200 Subject: [PATCH] Remove automatic scaling (#57) * Remove default use of scaler * Update parameters in tests * Update version * Update test of clusterer equality * Update run_test.sh * Add sync on alpaka mempcy * Update test paramenters and truth files * Add backends to __init__.py --- CLUEstering/CLUEstering.py | 29 - CLUEstering/__init__.py | 1 + CLUEstering/alpaka/CLUE/CLUEAlgoAlpaka.h | 1 + tests/check_result.py | 2 +- tests/run_test.sh | 8 - tests/test_blob_dataset.py | 19 +- tests/test_clusterer_equality.py | 36 +- .../truth_files/moons_1000_truth.csv | 1998 ++++++++--------- .../truth_files/sissa_1000_truth.csv | 1998 ++++++++--------- .../truth_files/toy_det_1000_truth.csv | 1998 ++++++++--------- tests/test_moons_dataset.py | 8 +- tests/test_multiple_backends.py | 32 +- tests/test_sissa_dataset.py | 16 +- tests/test_toydetector_dataset.py | 16 +- 14 files changed, 3064 insertions(+), 3098 deletions(-) diff --git a/CLUEstering/CLUEstering.py b/CLUEstering/CLUEstering.py index ea11bda6..06351949 100644 --- a/CLUEstering/CLUEstering.py +++ b/CLUEstering/CLUEstering.py @@ -252,7 +252,6 @@ def __init__(self, dc_: float, rhoc_: float, outlier_: float, ppbin: int = 10): # Initialize attributes ## Data containers self.clust_data = None - self.scaler = StandardScaler() ## Kernel for calculation of local density self.kernel = clue_kernels.FlatKernel(0.5) @@ -393,25 +392,6 @@ def _handle_dataframe(self, df_: pd.DataFrame) -> None: n_dim, n_points) - def _rescale(self) -> None: - """ - Normalizes the input data using a standard scaler - - Modified attributes - ------------------- - clust_data.coords : np.ndarray - Array containing the coordinates and weight values of the data points - - Returns - ------- - None - """ - - for dim in range(self.clust_data.n_dim): - self.clust_data.coords.T[dim] = \ - self.scaler.fit_transform( - self.clust_data.coords.T[dim].reshape(-1, 1)).reshape(1, -1)[0] - def read_data(self, input_data: Union[pd.DataFrame,str,dict,list,np.ndarray]) -> None: """ @@ -467,9 +447,6 @@ def read_data(self, df = self._read_dict_df(input_data) self._handle_dataframe(df) - # Rescale the coordinates with a standard scaler - self._rescale() - def change_coordinates(self, **kwargs: types.FunctionType) -> None: """ Change the coordinate system @@ -494,12 +471,6 @@ def change_coordinates(self, **kwargs: types.FunctionType) -> None: for coord, func in kwargs.items(): self.clust_data.coords[int(coord[1])] = func(self.clust_data.original_coords) - # Normalize the coordinate with a standard scaler - self.clust_data.coords[int(coord[1])] = \ - self.scaler.fit_transform( - self.clust_data.coords[int(coord[1])].reshape(-1, 1) - ).reshape(1, -1)[0] - def choose_kernel(self, choice: str, parameters: Union[list,None] = None, diff --git a/CLUEstering/__init__.py b/CLUEstering/__init__.py index eb8e190c..20f72182 100644 --- a/CLUEstering/__init__.py +++ b/CLUEstering/__init__.py @@ -1,2 +1,3 @@ from CLUEstering.CLUEstering import clusterer from CLUEstering.CLUEstering import test_blobs +from CLUEstering.CLUEstering import backends diff --git a/CLUEstering/alpaka/CLUE/CLUEAlgoAlpaka.h b/CLUEstering/alpaka/CLUE/CLUEAlgoAlpaka.h index 420bcf44..6c8c3281 100644 --- a/CLUEstering/alpaka/CLUE/CLUEAlgoAlpaka.h +++ b/CLUEstering/alpaka/CLUE/CLUEAlgoAlpaka.h @@ -146,6 +146,7 @@ namespace ALPAKA_ACCELERATOR_NAMESPACE { queue_, cms::alpakatools::make_device_view(device, (*d_tiles)->tileSize(), Ndim), cms::alpakatools::make_host_view(tile_size, Ndim)); + alpaka::wait(queue_); const Idx tiles_grid_size = cms::alpakatools::divide_up_by(nTiles, block_size); const auto tiles_working_div = diff --git a/tests/check_result.py b/tests/check_result.py index 5eca4a78..ab016205 100644 --- a/tests/check_result.py +++ b/tests/check_result.py @@ -11,7 +11,7 @@ def check_result(output_file, truth_file): truth = pd.read_csv(truth_file) # Check if the number of clusters is the same - n_clusters_o = len(truth['cluster_ids'].unique()) + n_clusters_o = len(output['cluster_ids'].unique()) n_clusters_t = len(truth['cluster_ids'].unique()) if n_clusters_o != n_clusters_t: return False diff --git a/tests/run_test.sh b/tests/run_test.sh index 63703dfb..1ef3da88 100755 --- a/tests/run_test.sh +++ b/tests/run_test.sh @@ -27,14 +27,6 @@ python3 -m pytest test_test_blobs.py echo "## Test the equality operator for the results of the clustering" python3 -m pytest test_clusterer_equality.py -# Test the method of changind the domain extremes of the coordinates -echo "## Test the change_domains method" -python3 -m pytest test_change_domains.py - -# Test the clustering of points at the opposite extremes of a finite domain -echo "## Test the clustering of points at the opposite extremes of a finite domain" -python3 -m pytest test_domain_extremes.py - if [[ $1 == "-" || $1 == "--clean" ]] then rm -f ./*_output.csv diff --git a/tests/test_blob_dataset.py b/tests/test_blob_dataset.py index 7cb8af0d..d050a61a 100644 --- a/tests/test_blob_dataset.py +++ b/tests/test_blob_dataset.py @@ -1,17 +1,17 @@ ''' -Testing the algorithm on the blob dataset, a dataset where points are distributed to form -round clusters +Testing the algorithm on the blob dataset, a dataset where points are +distributed to form round clusters ''' +from check_result import check_result import os import sys import pandas as pd import pytest -sys.path.insert(1, '.') -from check_result import check_result sys.path.insert(1, '../CLUEstering/') import CLUEstering as clue + @pytest.fixture def blobs(): ''' @@ -22,24 +22,25 @@ def blobs(): def test_clustering(blobs): ''' - Checks that the output of the clustering is the one given by the truth dataset + Checks that the output of the clustering is the one given by the + truth dataset ''' # Check if the output file already exists and if it does, delete it if os.path.isfile('./blobs_output.csv'): os.remove('./blobs_output.csv') - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(1., 5, 2.) c.read_data(blobs) c.run_clue() c.to_csv('./', 'blobs_output.csv') - check_result('./blobs_output.csv', - './test_datasets/truth_files/blobs_truth.csv') + assert check_result('./blobs_output.csv', + './test_datasets/truth_files/blobs_truth.csv') if __name__ == "__main__": - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(1., 5, 2.) c.read_data("./test_datasets/blob.csv") c.run_clue() c.cluster_plotter() diff --git a/tests/test_clusterer_equality.py b/tests/test_clusterer_equality.py index 55b287b7..1fd56258 100644 --- a/tests/test_clusterer_equality.py +++ b/tests/test_clusterer_equality.py @@ -10,43 +10,43 @@ @pytest.fixture -def moons(): +def sissa(): ''' - Returns the dataframe containing the moon dataset + Returns the dataframe containing the sissa ataset ''' - return pd.read_csv("./test_datasets/moons.csv") + return pd.read_csv("./test_datasets/sissa.csv") @pytest.fixture -def circles(): +def toyDet(): ''' - Returns the dataframe containing the circle dataset + Returns the dataframe containing the toy detector dataset ''' - return pd.read_csv("./test_datasets/circles.csv") + return pd.read_csv("./test_datasets/toyDetector.csv") -def test_clusterer_equality(moons, circles): +def test_clusterer_equality(sissa, toyDet): ''' Test the equality operator for clusterer objects ''' - # Moons dataset - clust1 = clue.clusterer(0.5, 5, 1.) - clust1.read_data(moons) + # Sissa dataset + clust1 = clue.clusterer(20., 10., 1.) + clust1.read_data(sissa) clust1.run_clue() - # Create a copy of the moons clusterer to check the equality of clusterers - clust1_copy = clue.clusterer(0.5, 5, 1.) - clust1_copy.read_data(moons) + # Create a copy of the sissa lusterer to check the equality of clusterers + clust1_copy = clue.clusterer(20., 10., 1.) + clust1_copy.read_data(sissa) clust1_copy.run_clue() - # Circles dataset - clust2 = clue.clusterer(0.9, 5, 1.5) - clust2.read_data(circles) + # toyDet dataset + clust2 = clue.clusterer(5., 2.5, 1.) + clust2.read_data(toyDet) clust2.run_clue() # Create a copy to check the equality of clusterers - clust2_copy = clue.clusterer(0.9, 5, 1.5) - clust2_copy.read_data(circles) + clust2_copy = clue.clusterer(5., 2.5, 1.) + clust2_copy.read_data(toyDet) clust2_copy.run_clue() # Check equality diff --git a/tests/test_datasets/truth_files/moons_1000_truth.csv b/tests/test_datasets/truth_files/moons_1000_truth.csv index df18b57b..c0c4dbac 100644 --- a/tests/test_datasets/truth_files/moons_1000_truth.csv +++ b/tests/test_datasets/truth_files/moons_1000_truth.csv @@ -1,1000 +1,1000 @@ x0,x1,cluster_ids,is_seed -1.6937740663127772,0.44067789444079564,0,0 --0.7270773009434615,1.3810221634509465,1,0 -0.11632978475288924,1.2416590902225468,1,0 -0.23341064523578597,0.9974547605051458,1,0 --1.0816245840993304,1.334642853135841,1,0 -0.48535480872115916,0.21583580106853612,1,0 -1.581571575016668,-0.4181648680932959,0,0 -1.67465400643553,0.17395933641509123,0,0 --0.2601701597435226,-0.626196337662022,0,0 -0.27912568551842243,-1.5487543663086132,0,0 --1.2122389849068629,1.3742678949584555,1,0 --0.1351422784886013,-0.9785440106869748,0,0 --1.6393323388726986,0.29756244982767854,1,0 -0.48677110945280705,-0.022064544722539586,1,0 --0.3614356620563506,-0.6986171053870656,0,0 -0.9635136724003012,-1.4159204192896213,0,0 -0.5545961778239475,-1.5089041822029157,0,0 -0.0833614510550843,-1.3000471909295517,0,0 -0.3295617282398853,0.7931756623211371,1,0 --0.28637172327900956,-0.6306991833236827,0,0 -0.07643731414480547,-1.3085275502590126,0,0 -1.6825223438335741,-0.5249573577023497,0,0 -1.6054126373327415,-0.32142873379528436,0,0 -0.4031306829115979,0.6339250207537357,1,0 --0.8548591002876982,1.6229000229098225,1,0 --0.22712314267173722,-0.7663098851673653,0,0 -0.14630815023943738,-1.321810944960912,0,0 -0.2171231868218346,0.7970030811335488,1,0 --0.22082847275330195,1.3265377309448514,1,0 --0.09564322475041975,-0.962483861160385,0,0 --1.585355544322116,0.33973910419190073,1,0 -0.08029279946984709,0.861543868950686,1,0 -0.3131955864519535,-1.626653596255344,0,0 -0.19965547779817663,0.8597427306860218,1,0 --0.6031509869242664,0.47610028031252677,0,0 -1.385492607057408,-0.9447726682245192,0,0 -0.22310312324434814,0.8220689219834603,1,0 --1.771677773907801,-0.26664411157841206,1,0 --1.58968312989104,0.042176053383820286,1,0 -0.181007518164812,-1.3690908244083497,0,0 -1.3688904151475347,-0.8092370138085311,0,0 --0.29880369636791926,1.3545304214748426,1,0 -1.2986261621830006,-1.0577190469045095,0,0 -0.2699984141366912,-1.4503671886013259,0,0 -1.3182183223041306,-1.0610211337230608,0,0 -0.6620776666812304,-1.4319805688162113,0,0 -1.6520718781031434,-0.1368871090948884,0,0 -1.5014719003045784,-0.9465738064891835,0,0 --0.17464133222678282,-1.036330530011621,0,0 --0.55208547721096,0.5606787313240544,0,0 -0.27102129799843694,-1.4797107328298151,0,0 -0.41894604108166666,0.8733263150986983,1,0 --1.7470498778519228,-0.32713233830005467,1,0 -1.514926757255234,-0.7576043835548213,0,0 --0.6638158682631866,1.6478157689043451,1,0 --1.3446631033159457,1.187474847427229,1,0 --0.13577174548044482,1.161883674583457,1,0 --1.7541313815101625,-0.4279960811212552,1,0 -1.514769390507273,-0.6188416897479762,0,0 -1.6033668696092502,-0.31857693154289923,0,0 -0.577493039652256,-0.01335904310999549,1,0 --0.19761687742907172,1.4521671249051862,1,0 --0.9949155059728839,1.773670305147763,1,0 -0.20296017950535514,0.9244336133585477,1,0 --1.3580392768926208,0.954902869002452,1,0 --0.8060754084198247,1.4899910284631364,1,0 --1.6782019256190366,0.2873559996612475,1,0 --1.5831524098506635,0.8430071543101827,1,0 --0.8412468765890819,1.2574190500383593,1,0 --0.45380994310938877,-0.44233013981087516,0,0 -1.558989446684281,-0.7727639639490791,0,0 -0.421149175553119,-1.6421884137880733,0,0 --0.436735650955633,1.5833500285149023,1,0 --0.7481644451702198,1.5268393154610602,1,0 -1.482509207175292,-0.7028197613379489,0,0 --1.0516462186127822,1.37261685154918,1,0 --1.3217662414876374,0.8971913971055002,1,0 --0.19061405714481244,-1.0744546232803487,0,0 --0.15410497161788764,-1.0160677245341476,0,0 --1.5235890957474694,0.6258949459904407,1,0 --0.5954400162741831,0.17546028496897814,0,0 -0.26653634568155177,0.5795906831030295,1,0 --1.4077671692482596,0.936441201789643,1,0 -1.4241261436818047,-0.5620307869833565,0,0 -0.25205860486915055,-1.6785113687921367,0,0 --1.6065213719228546,0.44773235264406414,1,0 -1.4400201852258538,-0.9209826336454117,0,0 -1.553088193635748,-0.6205677805849461,0,0 --0.2376667147851164,-0.7128761166489912,0,0 -0.1358432615000387,-1.3842504048026074,0,0 -1.3013013968983358,-0.899744211607912,0,0 -0.6023569858300754,0.08795498427737111,1,0 -0.658064814608228,-0.3509223728791622,1,0 -0.06612979215336766,-1.1986581161144907,0,0 -0.014749548944139457,-1.3683403501314062,0,0 --0.6254183817607313,0.35234707204455085,0,0 -0.021280268984516088,-1.0879631602653308,0,0 -1.593216714365773,-0.3335864170817684,0,0 -1.270142780802081,-1.2311536523061422,0,0 -0.22491284084589824,-1.4238003991975277,0,0 --0.39157139429085963,-0.5969278408612272,0,0 -0.5104548050209199,-1.425151252896026,0,0 -0.6279290823737188,-1.4310799996838792,0,0 --1.4390831520924754,0.8659716671846525,1,0 --0.6343096030205211,0.2848043871196398,0,0 --0.4615995971334524,-0.2921602369944896,0,0 --0.02490687154200298,-1.2420355293218226,0,0 -1.3286045276695488,-0.8153158554517731,0,0 --0.4404337695327137,-0.38844608672633507,0,0 -1.465592281769497,-0.8681492445485923,0,0 -0.6233654466828532,-1.3780965157316711,0,1 --0.5990594514772835,0.29756244982767854,0,0 --0.6604324831820277,1.3816225428725013,1,0 --0.41887452506207273,0.07609749070166452,0,0 -0.34718680401150415,-1.6051149845070667,0,0 --0.41061277079412645,1.5397474730244876,1,0 --1.330106679129564,0.9904003023018774,1,0 --0.6045672876559144,0.2643164393590834,0,0 -1.5654414833506773,-0.39647616148963,0,0 --1.4129602719309686,0.7678846791881427,1,0 --1.6150978596867227,0.6939629629092122,1,0 -1.483217357541116,-0.5729877114267309,0,0 -1.0273258886984389,-1.2548686394575557,0,0 --0.3262641938870933,-0.8359538980677181,0,0 --0.6581506653365948,1.4907415027400799,1,0 -0.5085664040453894,-0.36187929732253665,1,0 -0.5001472830294821,0.3662308461680048,1,0 -1.6359417864371528,0.036322354023661324,0,0 --0.6345456531424625,1.5838753605087628,1,0 --1.1817098358024518,1.0120890089055434,1,0 -0.4682805165674034,-1.5063525696613078,0,0 -0.4623005801448898,-1.573520017447747,0,0 -0.044963964552628924,1.1510768449954714,1,0 -0.523673611849634,-0.368258328676556,1,0 -0.1493768018246746,1.0831589229320886,1,0 -0.9321976895560855,-1.3753197609069803,0,0 -1.6187888109094164,0.10168866354543639,0,0 --0.475605237701971,-0.30506839455791707,0,0 -1.0548650695915935,-1.253893022897529,0,0 -0.16464137637688023,-1.3666893067221304,0,0 -0.7641300027338628,-1.5684167923645318,0,0 --1.5180812595688384,0.48848310588209387,1,0 -0.4971573148182254,0.0834521386157104,1,0 -1.4761358538828764,-0.7421446134497861,0,0 -0.03922007825205671,1.5492034489139752,1,0 --0.33617829900862894,-0.49396277006458494,0,0 -0.517064208435277,-1.4723560849157693,0,0 --0.4906337621322353,-0.431223120512112,0,0 -0.6296601166012886,-1.5738202071585246,0,0 -0.4236670435204931,0.20668001488982593,1,0 -0.06266772369822823,-1.1434982567591467,0,0 --1.725726683503223,0.042176053383820286,1,0 -0.4306698638047524,-1.5134070278645764,0,0 --0.43059834778515854,-0.30416782542558485,0,0 --0.06574354263785205,1.372016472127625,1,0 --0.4229660605090557,1.4259755726398595,1,0 -0.1834467027582057,-1.2731051643872815,0,0 -1.4434822536809935,-0.7023694767717829,0,0 -0.04905549999961189,1.121433111056205,1,0 -0.5948820653019335,0.12052556789671716,1,0 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a/tests/test_datasets/truth_files/sissa_1000_truth.csv b/tests/test_datasets/truth_files/sissa_1000_truth.csv index 04632e5e..b4c20464 100644 --- a/tests/test_datasets/truth_files/sissa_1000_truth.csv +++ b/tests/test_datasets/truth_files/sissa_1000_truth.csv @@ -1,1000 +1,1000 @@ x0,x1,cluster_ids,is_seed -0.388217893157776,-0.2799316422653074,0,0 -0.4142971116502898,-0.26687139106054597,0,0 -0.3439835264070121,-0.2718710184748687,0,0 -0.3855096666220149,-0.24248545326415538,0,0 -0.4546195956271767,-0.40308572979770657,0,0 -0.48240399379035487,-0.39380070745682144,0,0 -0.36103532311365577,-0.29278782704499445,0,0 -0.40998401013037405,-0.29288986025753166,0,0 -0.3998532367928975,-0.22228287718179002,0,0 -0.45231258783745426,-0.4231862726675348,0,0 -0.3777862057607704,-0.2079982274265821,0,0 -0.4079779164001807,-0.3910458107183171,0,0 -0.47598449385373615,-0.3465593300520984,0,0 -0.31589821418430486,-0.28717600035544855,0,0 -0.4447897363492291,-0.28625770144261375,0,0 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+-197.28,117.36,94,1 +-14.81,28.18,-1,0 +28.97,89.67,-1,0 +-146.53,-117.83,-1,0 +-6.62,-92.72,-1,0 +192.11,32.0,-1,0 +-80.31,123.75,-1,0 +-90.77,144.87,-1,0 +-22.33,30.93,-1,0 +104.13,-176.38,-1,0 +133.83,139.75,-1,0 +-75.05,-143.18,-1,0 +-144.71,21.3,-1,0 +74.21,180.97,-1,0 +114.35,218.23,-1,0 +35.78,65.42,-1,0 +118.93,-222.89,-1,0 diff --git a/tests/test_moons_dataset.py b/tests/test_moons_dataset.py index 989b8d38..85a84f95 100644 --- a/tests/test_moons_dataset.py +++ b/tests/test_moons_dataset.py @@ -30,17 +30,17 @@ def test_clustering(moons): if os.path.isfile('./moons_output.csv'): os.remove('./moons_output.csv') - c = clue.clusterer(0.5, 5, 1.) + c = clue.clusterer(70., 5., 2.5) c.read_data(moons) c.run_clue() c.to_csv('./', 'moons_output.csv') - check_result('./moons_output.csv', - './test_datasets/truth_files/moons_1000_truth.csv') + assert check_result('./moons_output.csv', + './test_datasets/truth_files/moons_1000_truth.csv') if __name__ == "__main__": - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(70., 5., 2.5) c.read_data("./test_datasets/moons.csv") c.run_clue() c.cluster_plotter() diff --git a/tests/test_multiple_backends.py b/tests/test_multiple_backends.py index c45fc174..3282a038 100644 --- a/tests/test_multiple_backends.py +++ b/tests/test_multiple_backends.py @@ -63,13 +63,13 @@ def test_blobs_clustering(blobs): if os.path.isfile(f'./blobs_output_{_fill_space(backend)}.csv'): os.remove(f'./blobs_output_{_fill_space(backend)}.csv') - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(1., 5, 2.) c.read_data(blobs) c.run_clue(backend=backend) c.to_csv('./', f'blobs_output_{_fill_space(backend)}.csv') - check_result(f'./blobs_output_{_fill_space(backend)}.csv', - './test_datasets/truth_files/blobs_truth.csv') + assert check_result(f'./blobs_output_{_fill_space(backend)}.csv', + './test_datasets/truth_files/blobs_truth.csv') def test_moons_clustering(moons): @@ -83,13 +83,13 @@ def test_moons_clustering(moons): if os.path.isfile(f'./moons_output_{_fill_space(backend)}.csv'): os.remove(f'./moons_output_{_fill_space(backend)}.csv') - c = clue.clusterer(0.5, 5, 1.) + c = clue.clusterer(70., 5., 2.5) c.read_data(moons) c.run_clue(backend=backend) c.to_csv('./', f'moons_output_{_fill_space(backend)}.csv') - check_result(f'./moons_output_{_fill_space(backend)}.csv', - './test_datasets/truth_files/moons_1000_truth.csv') + assert check_result(f'./moons_output_{_fill_space(backend)}.csv', + './test_datasets/truth_files/moons_1000_truth.csv') def test_sissa_clustering(sissa): @@ -103,13 +103,13 @@ def test_sissa_clustering(sissa): if os.path.isfile(f'./sissa_output_{_fill_space(backend)}.csv'): os.remove(f'./sissa_output_{_fill_space(backend)}.csv') - c = clue.clusterer(0.4, 5, 1.) + c = clue.clusterer(20., 10., 1.) c.read_data(sissa) c.run_clue(backend=backend) c.to_csv('./', f'sissa_output_{_fill_space(backend)}.csv') - check_result(f'./sissa_output_{_fill_space(backend)}.csv', - './test_datasets/truth_files/sissa_1000_truth.csv') + assert check_result(f'./sissa_output_{_fill_space(backend)}.csv', + './test_datasets/truth_files/sissa_1000_truth.csv') def test_toydet_clustering(toy_det): @@ -123,32 +123,32 @@ def test_toydet_clustering(toy_det): if os.path.isfile(f'./toy_det_output_{_fill_space(backend)}.csv'): os.remove(f'./toy_det_output_{_fill_space(backend)}.csv') - c = clue.clusterer(0.06, 5, 1.) + c = clue.clusterer(4.5, 2.5, 1.) c.read_data(toy_det) c.run_clue(backend=backend) c.to_csv('./', f'toy_det_output_{_fill_space(backend)}.csv') - check_result(f'./toy_det_output_{_fill_space(backend)}.csv', - './test_datasets/truth_files/toy_det_1000_truth.csv') + assert check_result(f'./toy_det_output_{_fill_space(backend)}.csv', + './test_datasets/truth_files/toy_det_1000_truth.csv') if __name__ == "__main__": - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(1., 5, 2.) c.read_data("./test_datasets/blob.csv") c.run_clue() c.cluster_plotter() - c = clue.clusterer(0.5, 5., 1.) + c = clue.clusterer(70., 5., 2.5) c.read_data("./test_datasets/moons.csv") c.run_clue() c.cluster_plotter() - c = clue.clusterer(0.4, 5., 1.) + c = clue.clusterer(20., 10., 1.) c.read_data("./test_datasets/sissa.csv") c.run_clue() c.cluster_plotter() - c = clue.clusterer(0.06, 5., 1.) + c = clue.clusterer(4.5, 2.5, 1.) c.read_data("./test_datasets/toyDetector.csv") c.run_clue() c.cluster_plotter() diff --git a/tests/test_sissa_dataset.py b/tests/test_sissa_dataset.py index 99f45a8e..97fe533f 100644 --- a/tests/test_sissa_dataset.py +++ b/tests/test_sissa_dataset.py @@ -1,6 +1,6 @@ ''' -Testing the algorithm on the circle dataset, a dataset where points are distributed to form -many small clusters +Testing the algorithm on the circle dataset, a dataset where points are +distributed to form many small clusters ''' import os @@ -22,25 +22,25 @@ def sissa(): def test_clustering(sissa): ''' - Checks that the output of the clustering is the one given by the truth - dataset + Checks that the output of the clustering is the one given by the + truth dataset. ''' # Check if the output file already exists and if it does, delete it if os.path.isfile('./sissa_output.csv'): os.remove('./sissa_output.csv') - c = clue.clusterer(0.4, 5, 1.) + c = clue.clusterer(20., 10., 1.) c.read_data(sissa) c.run_clue() c.to_csv('./', 'sissa_output.csv') - check_result('./sissa_output.csv', - './test_datasets/truth_files/sissa_1000_truth.csv') + assert check_result('./sissa_output.csv', + './test_datasets/truth_files/sissa_1000_truth.csv') if __name__ == "__main__": - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(20., 10., 1.) c.read_data("./test_datasets/sissa.csv") c.run_clue() c.cluster_plotter() diff --git a/tests/test_toydetector_dataset.py b/tests/test_toydetector_dataset.py index c71da489..43a7f312 100644 --- a/tests/test_toydetector_dataset.py +++ b/tests/test_toydetector_dataset.py @@ -1,6 +1,6 @@ ''' -Testing the algorithm on the circle dataset, a dataset where points are distributed to -simulate the hits of a small set of particles in a detector +Testing the algorithm on the circle dataset, a dataset where points are +distributed to simulate the hits of a small set of particles in a detector. ''' import os @@ -22,25 +22,25 @@ def toy_det(): def test_clustering(toy_det): ''' - Checks that the output of the clustering is the one given by the truth - dataset + Checks that the output of the clustering is the one given by the + truth dataset. ''' # Check if the output file already exists and if it does, delete it if os.path.isfile('./toy_det_output.csv'): os.remove('./toy_det_output.csv') - c = clue.clusterer(0.06, 5, 1.) + c = clue.clusterer(4.5, 2.5, 1.) c.read_data(toy_det) c.run_clue() c.to_csv('./', 'toy_det_output.csv') - check_result('./toy_det_output.csv', - './test_datasets/truth_files/toy_det_1000_truth.csv') + assert check_result('./toy_det_output.csv', + './test_datasets/truth_files/toy_det_1000_truth.csv') if __name__ == "__main__": - c = clue.clusterer(0.8, 5, 1.5) + c = clue.clusterer(4.5, 2.5, 1.) c.read_data("./test_datasets/toyDetector.csv") c.run_clue() c.cluster_plotter()