U _{jF @sdZddlZddlZddlmZmZddlmZddl m Z ddl m Z m Z mZddlmZddlmZdd lmZdd lmZmZdd lmZmZdd lmZdd lmZddlm Z m!Z!dZ"eddd\Z#Z$ee#e$dd\Z#Z$e%e#Z#ddddgZ&dhdde'DBZ(ej)*deddZ+dd Z,ej)*d!ej-ej.gd"d#Z/d$d%Z0ej)*d&e&ej)*d'ed(d)Z1d*d+Z2ej)*d,d-d.d/Z3d0d1Z4d2d3Z5d4d5Z6d6d7Z7d8d9Z8ej)*d:d;dZ9d?d@Z:ej)*dAe&dBdCZ;dDdEZdJej?gej?dJggfd'dIidJdKgdKdJggfidJdKgddLggfgdMdNZ@dOdPZAdQdRZBdSdTZCdUdVZDej)*dWdXdYgej)*dZdd[gd\d]ZEd^d_ZFdS)`zF Tests for HDBSCAN clustering algorithm Based on the DBSCAN test code N)sparsestats)distance)HDBSCAN)CONDENSED_dtype_condense_tree _do_labelling)_OUTLIER_ENCODING) make_blobs)fowlkes_mallows_score)_VALID_METRICSeuclidean_distances)BallTreeKDTree)StandardScaler)shuffle)assert_allcloseassert_array_equal ) n_samples random_state)rkdtreeballtreebruteautocCsh|]\}}|dqS)label).0_outr r a/var/www/html/bwave/python/venv/lib/python3.8/site-packages/sklearn/cluster/tests/test_hdbscan.py %sr% outlier_typec Cstjtjd|}ddddd|}t|d}t|d}t}|dg|d<||g|d <t|}|j|k \}t |dd g||j | \}t |dd gt t dd t t d d } t|| } t | j|j| d S) O Tests if np.inf and np.nan data are each treated as special outliers. )infinitemissingcSs||kSNr xyr r r$2z#test_outlier_data..cSs t|Sr*)npisnanr+r r r$r.3r/rprobrrN)r0infnanr Xcopyrfitlabels_ZnonzerorZprobabilities_listrange) r&ZoutlierZ prob_checkrr2 X_outliermodelmissing_labels_idxZmissing_probs_idxZ clean_indices clean_modelr r r$test_outlier_data(s.     rBc Cstt}|}tddd|}t||tt|t}|t ksHt t t |}|dks^t d}t jt|dtdddtW5QRXd}d|d <d |d <t jt|dtdd |W5QRXd S)zy Tests that HDBSCAN works with precomputed distance matrices, and throws the appropriate errors when needed. precomputedT)metricr9\(\?z*The precomputed distance matrix.*has shapematchz'The precomputed distance matrix.*valuesr)rr3r3)r3rrDN)r r8r9r fit_predictrlenset OUTLIER_SETn_clusters_trueAssertionErrorr r-pytestraises ValueError)DZ D_originallabels n_clustersscoremsgr r r$test_hdbscan_distance_matrixHs     rWsparse_constructorcCszttt}|t|}t|d}d|||k<||}| t dd |}t t |t}|tksvtdS)zA Tests that HDBSCAN works with sparse distance matrices. 2rCrHN)rZ squareformZpdistr8r0maxrZscoreatpercentileflattenZeliminate_zerosrrIrJrKrLrMrN)rXrR thresholdrSrTr r r$#test_hdbscan_sparse_distance_matrixfs r^cCsBtt}tt|t}|tks(ttt |}|dks>tdS)z Tests that HDBSCAN works with feature array, including an arbitrary goodness of fit check. Note that the check is a simple heuristic. rEN) rrIr8rJrKrLrMrNr r-)rSrTrUr r r$test_hdbscan_feature_arrayys    r_algorDc Cst|dt}tt|t}|tks,t|dkr8dStt d}dt tj didt tj diddidt tj dd d |d}t|||d }|||jkrtt|tW5QRXn4|d krtt|tW5QRXn |tdS) z Tests that HDBSCAN works with the expected combinations of algorithms and metrics, or raises the expected errors. ) algorithm)rrN)rrVr3p)rcw)Z mahalanobis seuclideanZ minkowski wminkowskirarD metric_paramsrg)rrIr8rJrKrLrMrNrrr0eyeshapeonesget valid_metricsrOrPrQr:Zwarns FutureWarning)r`rDrSrTZ ALGOS_TREESrihdbr r r$test_hdbscan_algorithmss8   rqcCs6tt}|d}tt|t}|tks2tdS)z Tests that HDBSCAN can generate a sufficiently accurate dbscan clustering. This test is more of a sanity check than a rigorous evaluation. 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Evaluates correctness by comparing against the same data passed as a dense array. rr(r)rrrrz4Sparse data matrices only support algorithm `brute`.rFrrrN)rr:r8r;rJrKrLrNr csr_matrixr9rr0r6r7r rOrPrQ) Z dense_labelsrTZ _X_sparseZX_sparseZ sparse_labelsZ outlier_valr&ZX_denserVr r r$test_hdbscan_sparses*     rracCsddg}tdd|dd\}}tdd|}t||j|jD]*\}}}t||d d d t||d d d q.zV.* is not a valid metric for a .*-based algorithm\. 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