(qiime2-2018.11) qiime2@qiime2core2018-11:~/transfer$ qiime feature-classifier classify-sklearn --i-reads dada2_output/rep_seqs_filt.qza --i-classifier classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza --p-n-jobs 4 --output-dir taxa --verbose multiprocessing.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 350, in __call__ return self.func(*args, **kwargs) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__ return [func(*args, **kwargs) for func, args, kwargs in self.items] File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 131, in return [func(*args, **kwargs) for func, args, kwargs in self.items] File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py", line 52, in _predict_chunk return _predict_chunk_with_conf(pipeline, separator, confidence, chunk) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py", line 66, in _predict_chunk_with_conf prob_pos = pipeline.predict_proba(X) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/metaestimators.py", line 115, in out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/pipeline.py", line 357, in predict_proba return self.steps[-1][-1].predict_proba(Xt) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 104, in predict_proba return np.exp(self.predict_log_proba(X)) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 84, in predict_log_proba jll = self._joint_log_likelihood(X) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py", line 725, in _joint_log_likelihood return (safe_sparse_dot(X, self.feature_log_prob_.T) + File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/extmath.py", line 135, in safe_sparse_dot ret = a * b File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/base.py", line 407, in __mul__ result = self._mul_multivector(np.asarray(other)) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/compressed.py", line 511, in _mul_multivector fn(M, N, n_vecs, self.indptr, self.indices, self.data, other.ravel(), result.ravel()) MemoryError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/multiprocessing/pool.py", line 119, in worker result = (True, func(*args, **kwds)) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 359, in __call__ raise TransportableException(text, e_type) sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException ___________________________________________________________________________ MemoryError Sat Aug 3 13:24:31 2019 PID: 3521Python 3.5.5: /home/qiime2/miniconda/envs/qiime2-2018.11/bin/python ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] self.items = [(, (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]), {})] 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in (.0=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] func = args = (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) kwargs = {} 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: 50 return _predict_chunk_without_conf(pipeline, chunk) 51 else: ---> 52 return _predict_chunk_with_conf(pipeline, separator, confidence, chunk) pipeline = Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]) separator = ';' confidence = 0.7 chunk = [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...] 53 54 55 def _predict_chunk_without_conf(pipeline, chunk): 56 seq_ids, X = _extract_reads(chunk) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk_with_conf(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 61 def _predict_chunk_with_conf(pipeline, separator, confidence, chunk): 62 seq_ids, X = _extract_reads(chunk) 63 64 if not hasattr(pipeline, "predict_proba"): 65 raise ValueError('this classifier does not support confidence values') ---> 66 prob_pos = pipeline.predict_proba(X) prob_pos = undefined pipeline.predict_proba = X = (b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...) 67 if prob_pos.shape != (len(X), len(pipeline.classes_)): 68 raise ValueError('this classifier does not support confidence values') 69 70 y = pipeline.classes_[prob_pos.argmax(axis=1)] ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/metaestimators.py in (*args=((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),), **kwargs={}) 110 break 111 else: 112 attrgetter(self.delegate_names[-1])(obj) 113 114 # lambda, but not partial, allows help() to work with update_wrapper --> 115 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) args = ((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),) kwargs = {} 116 # update the docstring of the returned function 117 update_wrapper(out, self.fn) 118 return out 119 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/pipeline.py in predict_proba(self=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), X=(b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...)) 352 """ 353 Xt = X 354 for name, transform in self.steps[:-1]: 355 if transform is not None: 356 Xt = transform.transform(Xt) --> 357 return self.steps[-1][-1].predict_proba(Xt) self.steps.predict_proba = undefined Xt = <81x8192 sparse matrix of type ' 358 359 @if_delegate_has_method(delegate='_final_estimator') 360 def decision_function(self, X): 361 """Apply transforms, and decision_function of the final estimator ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 99 C : array-like, shape = [n_samples, n_classes] 100 Returns the probability of the samples for each class in 101 the model. The columns correspond to the classes in sorted 102 order, as they appear in the attribute `classes_`. 103 """ --> 104 return np.exp(self.predict_log_proba(X)) self.predict_log_proba = X = <81x8192 sparse matrix of type ' 105 106 107 class GaussianNB(BaseNB): 108 """ ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_log_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 79 C : array-like, shape = [n_samples, n_classes] 80 Returns the log-probability of the samples for each class in 81 the model. The columns correspond to the classes in sorted 82 order, as they appear in the attribute `classes_`. 83 """ ---> 84 jll = self._joint_log_likelihood(X) jll = undefined self._joint_log_likelihood = X = <81x8192 sparse matrix of type ' 85 # normalize by P(x) = P(f_1, ..., f_n) 86 log_prob_x = logsumexp(jll, axis=1) 87 return jll - np.atleast_2d(log_prob_x).T 88 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 720 def _joint_log_likelihood(self, X): 721 """Calculate the posterior log probability of the samples X""" 722 check_is_fitted(self, "classes_") 723 724 X = check_array(X, accept_sparse='csr') --> 725 return (safe_sparse_dot(X, self.feature_log_prob_.T) + X = <81x8192 sparse matrix of type ' self.feature_log_prob_.T = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) self.class_log_prior_ = array([-10.67876756, -10.67876756, -10.67876756,...-10.67876756, -10.67876756, -10.67876756]) 726 self.class_log_prior_) 727 728 729 class BernoulliNB(BaseDiscreteNB): ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a=<81x8192 sparse matrix of type ', b=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]), dense_output=False) 130 ------- 131 dot_product : array or sparse matrix 132 sparse if ``a`` or ``b`` is sparse and ``dense_output=False``. 133 """ 134 if issparse(a) or issparse(b): --> 135 ret = a * b ret = undefined a = <81x8192 sparse matrix of type ' b = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 136 if dense_output and hasattr(ret, "toarray"): 137 ret = ret.toarray() 138 return ret 139 else: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/base.py in __mul__(self=<81x8192 sparse matrix of type ', other=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]])) 402 # dense 2D array or matrix ("multivector") 403 404 if other.shape[0] != self.shape[1]: 405 raise ValueError('dimension mismatch') 406 --> 407 result = self._mul_multivector(np.asarray(other)) result = undefined self._mul_multivector = > other = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 408 409 if isinstance(other, np.matrix): 410 result = np.asmatrix(result) 411 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/compressed.py in _mul_multivector(self=<81x8192 sparse matrix of type ', other=array([[-10.30276523, -9.39982473, -7.46471481...9.34513933, -9.40504504, -7.19725709]])) 506 result = np.zeros((M,n_vecs), dtype=upcast_char(self.dtype.char, 507 other.dtype.char)) 508 509 # csr_matvecs or csc_matvecs 510 fn = getattr(_sparsetools,self.format + '_matvecs') --> 511 fn(M, N, n_vecs, self.indptr, self.indices, self.data, other.ravel(), result.ravel()) fn = M = 81 N = 8192 n_vecs = 43424 self.indptr = array([ 0, 388, 774, 1168, 1557, 1944,... 29859, 30241, 30631, 31019, 31406], dtype=int32) self.indices = array([ 9, 52, 60, ..., 8142, 8155, 8186], dtype=int32) self.data = array([ 0.04805693, 0.04805693, 0.09611387, ..., 0.04862166, 0.04862166, 0.04862166]) other.ravel = result.ravel = 512 513 return result 514 515 def _mul_sparse_matrix(self, other): MemoryError: ___________________________________________________________________________ """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 699, in retrieve self._output.extend(job.get(timeout=self.timeout)) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/multiprocessing/pool.py", line 644, in get raise self._value sklearn.externals.joblib.my_exceptions.TransportableException: TransportableException ___________________________________________________________________________ MemoryError Sat Aug 3 13:24:31 2019 PID: 3521Python 3.5.5: /home/qiime2/miniconda/envs/qiime2-2018.11/bin/python ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] self.items = [(, (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]), {})] 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in (.0=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] func = args = (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) kwargs = {} 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: 50 return _predict_chunk_without_conf(pipeline, chunk) 51 else: ---> 52 return _predict_chunk_with_conf(pipeline, separator, confidence, chunk) pipeline = Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]) separator = ';' confidence = 0.7 chunk = [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...] 53 54 55 def _predict_chunk_without_conf(pipeline, chunk): 56 seq_ids, X = _extract_reads(chunk) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk_with_conf(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 61 def _predict_chunk_with_conf(pipeline, separator, confidence, chunk): 62 seq_ids, X = _extract_reads(chunk) 63 64 if not hasattr(pipeline, "predict_proba"): 65 raise ValueError('this classifier does not support confidence values') ---> 66 prob_pos = pipeline.predict_proba(X) prob_pos = undefined pipeline.predict_proba = X = (b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...) 67 if prob_pos.shape != (len(X), len(pipeline.classes_)): 68 raise ValueError('this classifier does not support confidence values') 69 70 y = pipeline.classes_[prob_pos.argmax(axis=1)] ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/metaestimators.py in (*args=((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),), **kwargs={}) 110 break 111 else: 112 attrgetter(self.delegate_names[-1])(obj) 113 114 # lambda, but not partial, allows help() to work with update_wrapper --> 115 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) args = ((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),) kwargs = {} 116 # update the docstring of the returned function 117 update_wrapper(out, self.fn) 118 return out 119 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/pipeline.py in predict_proba(self=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), X=(b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...)) 352 """ 353 Xt = X 354 for name, transform in self.steps[:-1]: 355 if transform is not None: 356 Xt = transform.transform(Xt) --> 357 return self.steps[-1][-1].predict_proba(Xt) self.steps.predict_proba = undefined Xt = <81x8192 sparse matrix of type ' 358 359 @if_delegate_has_method(delegate='_final_estimator') 360 def decision_function(self, X): 361 """Apply transforms, and decision_function of the final estimator ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 99 C : array-like, shape = [n_samples, n_classes] 100 Returns the probability of the samples for each class in 101 the model. The columns correspond to the classes in sorted 102 order, as they appear in the attribute `classes_`. 103 """ --> 104 return np.exp(self.predict_log_proba(X)) self.predict_log_proba = X = <81x8192 sparse matrix of type ' 105 106 107 class GaussianNB(BaseNB): 108 """ ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_log_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 79 C : array-like, shape = [n_samples, n_classes] 80 Returns the log-probability of the samples for each class in 81 the model. The columns correspond to the classes in sorted 82 order, as they appear in the attribute `classes_`. 83 """ ---> 84 jll = self._joint_log_likelihood(X) jll = undefined self._joint_log_likelihood = X = <81x8192 sparse matrix of type ' 85 # normalize by P(x) = P(f_1, ..., f_n) 86 log_prob_x = logsumexp(jll, axis=1) 87 return jll - np.atleast_2d(log_prob_x).T 88 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 720 def _joint_log_likelihood(self, X): 721 """Calculate the posterior log probability of the samples X""" 722 check_is_fitted(self, "classes_") 723 724 X = check_array(X, accept_sparse='csr') --> 725 return (safe_sparse_dot(X, self.feature_log_prob_.T) + X = <81x8192 sparse matrix of type ' self.feature_log_prob_.T = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) self.class_log_prior_ = array([-10.67876756, -10.67876756, -10.67876756,...-10.67876756, -10.67876756, -10.67876756]) 726 self.class_log_prior_) 727 728 729 class BernoulliNB(BaseDiscreteNB): ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a=<81x8192 sparse matrix of type ', b=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]), dense_output=False) 130 ------- 131 dot_product : array or sparse matrix 132 sparse if ``a`` or ``b`` is sparse and ``dense_output=False``. 133 """ 134 if issparse(a) or issparse(b): --> 135 ret = a * b ret = undefined a = <81x8192 sparse matrix of type ' b = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 136 if dense_output and hasattr(ret, "toarray"): 137 ret = ret.toarray() 138 return ret 139 else: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/base.py in __mul__(self=<81x8192 sparse matrix of type ', other=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]])) 402 # dense 2D array or matrix ("multivector") 403 404 if other.shape[0] != self.shape[1]: 405 raise ValueError('dimension mismatch') 406 --> 407 result = self._mul_multivector(np.asarray(other)) result = undefined self._mul_multivector = > other = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 408 409 if isinstance(other, np.matrix): 410 result = np.asmatrix(result) 411 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/compressed.py in _mul_multivector(self=<81x8192 sparse matrix of type ', other=array([[-10.30276523, -9.39982473, -7.46471481...9.34513933, -9.40504504, -7.19725709]])) 506 result = np.zeros((M,n_vecs), dtype=upcast_char(self.dtype.char, 507 other.dtype.char)) 508 509 # csr_matvecs or csc_matvecs 510 fn = getattr(_sparsetools,self.format + '_matvecs') --> 511 fn(M, N, n_vecs, self.indptr, self.indices, self.data, other.ravel(), result.ravel()) fn = M = 81 N = 8192 n_vecs = 43424 self.indptr = array([ 0, 388, 774, 1168, 1557, 1944,... 29859, 30241, 30631, 31019, 31406], dtype=int32) self.indices = array([ 9, 52, 60, ..., 8142, 8155, 8186], dtype=int32) self.data = array([ 0.04805693, 0.04805693, 0.09611387, ..., 0.04862166, 0.04862166, 0.04862166]) other.ravel = result.ravel = 512 513 return result 514 515 def _mul_sparse_matrix(self, other): MemoryError: ___________________________________________________________________________ During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2cli/commands.py", line 274, in __call__ results = action(**arguments) File "", line 2, in classify_sklearn File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py", line 231, in bound_callable output_types, provenance) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py", line 362, in _callable_executor_ output_views = self._callable(**view_args) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/classifier.py", line 215, in classify_sklearn confidence=confidence) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py", line 45, in predict for chunk in _chunks(reads, chunk_size)) for m in c) File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 789, in __call__ self.retrieve() File "/home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 740, in retrieve raise exception sklearn.externals.joblib.my_exceptions.JoblibMemoryError: JoblibMemoryError ___________________________________________________________________________ Multiprocessing exception: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/bin/qiime in () 6 7 from q2cli.__main__ import qiime 8 9 if __name__ == '__main__': 10 sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) ---> 11 sys.exit(qiime()) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in __call__(self=, *args=(), **kwargs={}) 759 echo('Aborted!', file=sys.stderr) 760 sys.exit(1) 761 762 def __call__(self, *args, **kwargs): 763 """Alias for :meth:`main`.""" --> 764 return self.main(*args, **kwargs) self.main = > args = () kwargs = {} 765 766 767 class Command(BaseCommand): 768 """Commands are the basic building block of command line interfaces in ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in main(self=, args=['feature-classifier', 'classify-sklearn', '--i-reads', 'dada2_output/rep_seqs_filt.qza', '--i-classifier', 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', '--p-n-jobs', '4', '--output-dir', 'taxa', '--verbose'], prog_name='qiime', complete_var=None, standalone_mode=True, **extra={}) 712 _bashcomplete(self, prog_name, complete_var) 713 714 try: 715 try: 716 with self.make_context(prog_name, args, **extra) as ctx: --> 717 rv = self.invoke(ctx) rv = undefined self.invoke = > ctx = 718 if not standalone_mode: 719 return rv 720 # it's not safe to `ctx.exit(rv)` here! 721 # note that `rv` may actually contain data like "1" which ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 1132 cmd_name, cmd, args = self.resolve_command(ctx, args) 1133 ctx.invoked_subcommand = cmd_name 1134 Command.invoke(self, ctx) 1135 sub_ctx = cmd.make_context(cmd_name, args, parent=ctx) 1136 with sub_ctx: -> 1137 return _process_result(sub_ctx.command.invoke(sub_ctx)) _process_result = ._process_result> sub_ctx.command.invoke = > sub_ctx = 1138 1139 # In chain mode we create the contexts step by step, but after the 1140 # base command has been invoked. Because at that point we do not 1141 # know the subcommands yet, the invoked subcommand attribute is ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 1132 cmd_name, cmd, args = self.resolve_command(ctx, args) 1133 ctx.invoked_subcommand = cmd_name 1134 Command.invoke(self, ctx) 1135 sub_ctx = cmd.make_context(cmd_name, args, parent=ctx) 1136 with sub_ctx: -> 1137 return _process_result(sub_ctx.command.invoke(sub_ctx)) _process_result = ._process_result> sub_ctx.command.invoke = > sub_ctx = 1138 1139 # In chain mode we create the contexts step by step, but after the 1140 # base command has been invoked. Because at that point we do not 1141 # know the subcommands yet, the invoked subcommand attribute is ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 951 """Given a context, this invokes the attached callback (if it exists) 952 in the right way. 953 """ 954 _maybe_show_deprecated_notice(self) 955 if self.callback is not None: --> 956 return ctx.invoke(self.callback, **ctx.params) ctx.invoke = > self.callback = ctx.params = {'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...} 957 958 959 class MultiCommand(Command): 960 """A multi command is the basic implementation of a command that ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(*args=(), **kwargs={'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...}) 550 kwargs[param.name] = param.get_default(ctx) 551 552 args = args[2:] 553 with augment_usage_errors(self): 554 with ctx: --> 555 return callback(*args, **kwargs) callback = args = () kwargs = {'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...} 556 557 def forward(*args, **kwargs): 558 """Similar to :meth:`invoke` but fills in default keyword 559 arguments from the current context if the other command expects ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2cli/commands.py in __call__(self=, **kwargs={'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...}) 269 delete=False, mode='w') 270 271 cleanup_logfile = False 272 try: 273 with qiime2.util.redirected_stdio(stdout=log, stderr=log): --> 274 results = action(**arguments) results = undefined action = arguments = {'classifier': , 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0} 275 except Exception as e: 276 header = ('Plugin error from %s:' 277 % q2cli.util.to_cli_name(self.plugin['name'])) 278 if verbose: ........................................................................... /home/qiime2/transfer/ in classify_sklearn(reads=, classifier=, reads_per_batch=0, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7, read_orientation=None) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py in bound_callable(*args=(, , 0, 4, '2*n_jobs', 0.7, None), **kwargs={}) 226 else: 227 callable_args[name] = artifact 228 229 # Execute 230 outputs = self._callable_executor_(scope, callable_args, --> 231 output_types, provenance) output_types = OrderedDict([('classification', ParameterSpec(qi...Frame'>, default=NOVALUE, description=NOVALUE))]) provenance = 232 233 if len(outputs) != len(self.signature.outputs): 234 raise ValueError( 235 "Number of callable outputs must match number of " ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py in _callable_executor_(self=, scope=, view_args={'classifier': Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0}, output_types=OrderedDict([('classification', ParameterSpec(qi...Frame'>, default=NOVALUE, description=NOVALUE))]), provenance=) 357 def _callable_sig_converter_(self, callable): 358 # No conversion necessary. 359 return callable 360 361 def _callable_executor_(self, scope, view_args, output_types, provenance): --> 362 output_views = self._callable(**view_args) output_views = undefined self._callable = view_args = {'classifier': Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0} 363 output_views = tuplize(output_views) 364 365 # TODO this won't work if the user has annotated their "view API" to 366 # return a `typing.Tuple` with some number of components. Python will ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/classifier.py in classify_sklearn(reads=, classifier=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), reads_per_batch=81, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7, read_orientation=None) 210 211 reads = _autodetect_orientation( 212 reads, classifier, read_orientation=read_orientation) 213 predictions = predict(reads, classifier, chunk_size=reads_per_batch, 214 n_jobs=n_jobs, pre_dispatch=pre_dispatch, --> 215 confidence=confidence) confidence = 0.7 216 seq_ids, taxonomy, confidence = list(zip(*predictions)) 217 result = pd.DataFrame({'Taxon': taxonomy, 'Confidence': confidence}, 218 index=seq_ids, columns=['Taxon', 'Confidence']) 219 result.index.name = 'Feature ID' ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in predict(reads=, pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', chunk_size=81, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7) 40 def predict(reads, pipeline, separator=';', chunk_size=262144, n_jobs=1, 41 pre_dispatch='2*n_jobs', confidence=-1.): 42 return (m for c in Parallel(n_jobs=n_jobs, batch_size=1, 43 pre_dispatch=pre_dispatch) 44 (delayed(_predict_chunk)(pipeline, separator, confidence, chunk) ---> 45 for chunk in _chunks(reads, chunk_size)) for m in c) reads = chunk_size = 81 46 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=4), iterable=.>) 784 if pre_dispatch == "all" or n_jobs == 1: 785 # The iterable was consumed all at once by the above for loop. 786 # No need to wait for async callbacks to trigger to 787 # consumption. 788 self._iterating = False --> 789 self.retrieve() self.retrieve = 790 # Make sure that we get a last message telling us we are done 791 elapsed_time = time.time() - self._start_time 792 self._print('Done %3i out of %3i | elapsed: %s finished', 793 (len(self._output), len(self._output), --------------------------------------------------------------------------- Sub-process traceback: --------------------------------------------------------------------------- MemoryError Sat Aug 3 13:24:31 2019 PID: 3521Python 3.5.5: /home/qiime2/miniconda/envs/qiime2-2018.11/bin/python ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] self.items = [(, (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]), {})] 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in (.0=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] func = args = (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) kwargs = {} 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: 50 return _predict_chunk_without_conf(pipeline, chunk) 51 else: ---> 52 return _predict_chunk_with_conf(pipeline, separator, confidence, chunk) pipeline = Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]) separator = ';' confidence = 0.7 chunk = [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...] 53 54 55 def _predict_chunk_without_conf(pipeline, chunk): 56 seq_ids, X = _extract_reads(chunk) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk_with_conf(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 61 def _predict_chunk_with_conf(pipeline, separator, confidence, chunk): 62 seq_ids, X = _extract_reads(chunk) 63 64 if not hasattr(pipeline, "predict_proba"): 65 raise ValueError('this classifier does not support confidence values') ---> 66 prob_pos = pipeline.predict_proba(X) prob_pos = undefined pipeline.predict_proba = X = (b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...) 67 if prob_pos.shape != (len(X), len(pipeline.classes_)): 68 raise ValueError('this classifier does not support confidence values') 69 70 y = pipeline.classes_[prob_pos.argmax(axis=1)] ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/metaestimators.py in (*args=((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),), **kwargs={}) 110 break 111 else: 112 attrgetter(self.delegate_names[-1])(obj) 113 114 # lambda, but not partial, allows help() to work with update_wrapper --> 115 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) args = ((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),) kwargs = {} 116 # update the docstring of the returned function 117 update_wrapper(out, self.fn) 118 return out 119 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/pipeline.py in predict_proba(self=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), X=(b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...)) 352 """ 353 Xt = X 354 for name, transform in self.steps[:-1]: 355 if transform is not None: 356 Xt = transform.transform(Xt) --> 357 return self.steps[-1][-1].predict_proba(Xt) self.steps.predict_proba = undefined Xt = <81x8192 sparse matrix of type ' 358 359 @if_delegate_has_method(delegate='_final_estimator') 360 def decision_function(self, X): 361 """Apply transforms, and decision_function of the final estimator ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 99 C : array-like, shape = [n_samples, n_classes] 100 Returns the probability of the samples for each class in 101 the model. The columns correspond to the classes in sorted 102 order, as they appear in the attribute `classes_`. 103 """ --> 104 return np.exp(self.predict_log_proba(X)) self.predict_log_proba = X = <81x8192 sparse matrix of type ' 105 106 107 class GaussianNB(BaseNB): 108 """ ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_log_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 79 C : array-like, shape = [n_samples, n_classes] 80 Returns the log-probability of the samples for each class in 81 the model. The columns correspond to the classes in sorted 82 order, as they appear in the attribute `classes_`. 83 """ ---> 84 jll = self._joint_log_likelihood(X) jll = undefined self._joint_log_likelihood = X = <81x8192 sparse matrix of type ' 85 # normalize by P(x) = P(f_1, ..., f_n) 86 log_prob_x = logsumexp(jll, axis=1) 87 return jll - np.atleast_2d(log_prob_x).T 88 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 720 def _joint_log_likelihood(self, X): 721 """Calculate the posterior log probability of the samples X""" 722 check_is_fitted(self, "classes_") 723 724 X = check_array(X, accept_sparse='csr') --> 725 return (safe_sparse_dot(X, self.feature_log_prob_.T) + X = <81x8192 sparse matrix of type ' self.feature_log_prob_.T = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) self.class_log_prior_ = array([-10.67876756, -10.67876756, -10.67876756,...-10.67876756, -10.67876756, -10.67876756]) 726 self.class_log_prior_) 727 728 729 class BernoulliNB(BaseDiscreteNB): ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a=<81x8192 sparse matrix of type ', b=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]), dense_output=False) 130 ------- 131 dot_product : array or sparse matrix 132 sparse if ``a`` or ``b`` is sparse and ``dense_output=False``. 133 """ 134 if issparse(a) or issparse(b): --> 135 ret = a * b ret = undefined a = <81x8192 sparse matrix of type ' b = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 136 if dense_output and hasattr(ret, "toarray"): 137 ret = ret.toarray() 138 return ret 139 else: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/base.py in __mul__(self=<81x8192 sparse matrix of type ', other=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]])) 402 # dense 2D array or matrix ("multivector") 403 404 if other.shape[0] != self.shape[1]: 405 raise ValueError('dimension mismatch') 406 --> 407 result = self._mul_multivector(np.asarray(other)) result = undefined self._mul_multivector = > other = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 408 409 if isinstance(other, np.matrix): 410 result = np.asmatrix(result) 411 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/compressed.py in _mul_multivector(self=<81x8192 sparse matrix of type ', other=array([[-10.30276523, -9.39982473, -7.46471481...9.34513933, -9.40504504, -7.19725709]])) 506 result = np.zeros((M,n_vecs), dtype=upcast_char(self.dtype.char, 507 other.dtype.char)) 508 509 # csr_matvecs or csc_matvecs 510 fn = getattr(_sparsetools,self.format + '_matvecs') --> 511 fn(M, N, n_vecs, self.indptr, self.indices, self.data, other.ravel(), result.ravel()) fn = M = 81 N = 8192 n_vecs = 43424 self.indptr = array([ 0, 388, 774, 1168, 1557, 1944,... 29859, 30241, 30631, 31019, 31406], dtype=int32) self.indices = array([ 9, 52, 60, ..., 8142, 8155, 8186], dtype=int32) self.data = array([ 0.04805693, 0.04805693, 0.09611387, ..., 0.04862166, 0.04862166, 0.04862166]) other.ravel = result.ravel = 512 513 return result 514 515 def _mul_sparse_matrix(self, other): MemoryError: ___________________________________________________________________________ Plugin error from feature-classifier: JoblibMemoryError ___________________________________________________________________________ Multiprocessing exception: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/bin/qiime in () 6 7 from q2cli.__main__ import qiime 8 9 if __name__ == '__main__': 10 sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) ---> 11 sys.exit(qiime()) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in __call__(self=, *args=(), **kwargs={}) 759 echo('Aborted!', file=sys.stderr) 760 sys.exit(1) 761 762 def __call__(self, *args, **kwargs): 763 """Alias for :meth:`main`.""" --> 764 return self.main(*args, **kwargs) self.main = > args = () kwargs = {} 765 766 767 class Command(BaseCommand): 768 """Commands are the basic building block of command line interfaces in ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in main(self=, args=['feature-classifier', 'classify-sklearn', '--i-reads', 'dada2_output/rep_seqs_filt.qza', '--i-classifier', 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', '--p-n-jobs', '4', '--output-dir', 'taxa', '--verbose'], prog_name='qiime', complete_var=None, standalone_mode=True, **extra={}) 712 _bashcomplete(self, prog_name, complete_var) 713 714 try: 715 try: 716 with self.make_context(prog_name, args, **extra) as ctx: --> 717 rv = self.invoke(ctx) rv = undefined self.invoke = > ctx = 718 if not standalone_mode: 719 return rv 720 # it's not safe to `ctx.exit(rv)` here! 721 # note that `rv` may actually contain data like "1" which ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 1132 cmd_name, cmd, args = self.resolve_command(ctx, args) 1133 ctx.invoked_subcommand = cmd_name 1134 Command.invoke(self, ctx) 1135 sub_ctx = cmd.make_context(cmd_name, args, parent=ctx) 1136 with sub_ctx: -> 1137 return _process_result(sub_ctx.command.invoke(sub_ctx)) _process_result = ._process_result> sub_ctx.command.invoke = > sub_ctx = 1138 1139 # In chain mode we create the contexts step by step, but after the 1140 # base command has been invoked. Because at that point we do not 1141 # know the subcommands yet, the invoked subcommand attribute is ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 1132 cmd_name, cmd, args = self.resolve_command(ctx, args) 1133 ctx.invoked_subcommand = cmd_name 1134 Command.invoke(self, ctx) 1135 sub_ctx = cmd.make_context(cmd_name, args, parent=ctx) 1136 with sub_ctx: -> 1137 return _process_result(sub_ctx.command.invoke(sub_ctx)) _process_result = ._process_result> sub_ctx.command.invoke = > sub_ctx = 1138 1139 # In chain mode we create the contexts step by step, but after the 1140 # base command has been invoked. Because at that point we do not 1141 # know the subcommands yet, the invoked subcommand attribute is ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(self=, ctx=) 951 """Given a context, this invokes the attached callback (if it exists) 952 in the right way. 953 """ 954 _maybe_show_deprecated_notice(self) 955 if self.callback is not None: --> 956 return ctx.invoke(self.callback, **ctx.params) ctx.invoke = > self.callback = ctx.params = {'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...} 957 958 959 class MultiCommand(Command): 960 """A multi command is the basic implementation of a command that ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/click/core.py in invoke(*args=(), **kwargs={'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...}) 550 kwargs[param.name] = param.get_default(ctx) 551 552 args = args[2:] 553 with augment_usage_errors(self): 554 with ctx: --> 555 return callback(*args, **kwargs) callback = args = () kwargs = {'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...} 556 557 def forward(*args, **kwargs): 558 """Similar to :meth:`invoke` but fills in default keyword 559 arguments from the current context if the other command expects ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2cli/commands.py in __call__(self=, **kwargs={'cmd_config': None, 'i_classifier': 'classifier_silva_132_99_16S_V6.V8_A956F_A1401R.qza', 'i_reads': 'dada2_output/rep_seqs_filt.qza', 'o_classification': None, 'output_dir': 'taxa', 'p_confidence': None, 'p_n_jobs': 4, 'p_pre_dispatch': None, 'p_read_orientation': None, 'p_reads_per_batch': None, ...}) 269 delete=False, mode='w') 270 271 cleanup_logfile = False 272 try: 273 with qiime2.util.redirected_stdio(stdout=log, stderr=log): --> 274 results = action(**arguments) results = undefined action = arguments = {'classifier': , 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0} 275 except Exception as e: 276 header = ('Plugin error from %s:' 277 % q2cli.util.to_cli_name(self.plugin['name'])) 278 if verbose: ........................................................................... /home/qiime2/transfer/ in classify_sklearn(reads=, classifier=, reads_per_batch=0, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7, read_orientation=None) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py in bound_callable(*args=(, , 0, 4, '2*n_jobs', 0.7, None), **kwargs={}) 226 else: 227 callable_args[name] = artifact 228 229 # Execute 230 outputs = self._callable_executor_(scope, callable_args, --> 231 output_types, provenance) output_types = OrderedDict([('classification', ParameterSpec(qi...Frame'>, default=NOVALUE, description=NOVALUE))]) provenance = 232 233 if len(outputs) != len(self.signature.outputs): 234 raise ValueError( 235 "Number of callable outputs must match number of " ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/qiime2/sdk/action.py in _callable_executor_(self=, scope=, view_args={'classifier': Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0}, output_types=OrderedDict([('classification', ParameterSpec(qi...Frame'>, default=NOVALUE, description=NOVALUE))]), provenance=) 357 def _callable_sig_converter_(self, callable): 358 # No conversion necessary. 359 return callable 360 361 def _callable_executor_(self, scope, view_args, output_types, provenance): --> 362 output_views = self._callable(**view_args) output_views = undefined self._callable = view_args = {'classifier': Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), 'confidence': 0.7, 'n_jobs': 4, 'pre_dispatch': '2*n_jobs', 'read_orientation': None, 'reads': , 'reads_per_batch': 0} 363 output_views = tuplize(output_views) 364 365 # TODO this won't work if the user has annotated their "view API" to 366 # return a `typing.Tuple` with some number of components. Python will ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/classifier.py in classify_sklearn(reads=, classifier=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), reads_per_batch=81, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7, read_orientation=None) 210 211 reads = _autodetect_orientation( 212 reads, classifier, read_orientation=read_orientation) 213 predictions = predict(reads, classifier, chunk_size=reads_per_batch, 214 n_jobs=n_jobs, pre_dispatch=pre_dispatch, --> 215 confidence=confidence) confidence = 0.7 216 seq_ids, taxonomy, confidence = list(zip(*predictions)) 217 result = pd.DataFrame({'Taxon': taxonomy, 'Confidence': confidence}, 218 index=seq_ids, columns=['Taxon', 'Confidence']) 219 result.index.name = 'Feature ID' ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in predict(reads=, pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', chunk_size=81, n_jobs=4, pre_dispatch='2*n_jobs', confidence=0.7) 40 def predict(reads, pipeline, separator=';', chunk_size=262144, n_jobs=1, 41 pre_dispatch='2*n_jobs', confidence=-1.): 42 return (m for c in Parallel(n_jobs=n_jobs, batch_size=1, 43 pre_dispatch=pre_dispatch) 44 (delayed(_predict_chunk)(pipeline, separator, confidence, chunk) ---> 45 for chunk in _chunks(reads, chunk_size)) for m in c) reads = chunk_size = 81 46 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=Parallel(n_jobs=4), iterable=.>) 784 if pre_dispatch == "all" or n_jobs == 1: 785 # The iterable was consumed all at once by the above for loop. 786 # No need to wait for async callbacks to trigger to 787 # consumption. 788 self._iterating = False --> 789 self.retrieve() self.retrieve = 790 # Make sure that we get a last message telling us we are done 791 elapsed_time = time.time() - self._start_time 792 self._print('Done %3i out of %3i | elapsed: %s finished', 793 (len(self._output), len(self._output), --------------------------------------------------------------------------- Sub-process traceback: --------------------------------------------------------------------------- MemoryError Sat Aug 3 13:24:31 2019 PID: 3521Python 3.5.5: /home/qiime2/miniconda/envs/qiime2-2018.11/bin/python ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in __call__(self=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] self.items = [(, (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]), {})] 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py in (.0=) 126 def __init__(self, iterator_slice): 127 self.items = list(iterator_slice) 128 self._size = len(self.items) 129 130 def __call__(self): --> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items] func = args = (Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), ';', 0.7, [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) kwargs = {} 132 133 def __len__(self): 134 return self._size 135 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 47 48 def _predict_chunk(pipeline, separator, confidence, chunk): 49 if confidence < 0.: 50 return _predict_chunk_without_conf(pipeline, chunk) 51 else: ---> 52 return _predict_chunk_with_conf(pipeline, separator, confidence, chunk) pipeline = Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]) separator = ';' confidence = 0.7 chunk = [DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...] 53 54 55 def _predict_chunk_without_conf(pipeline, chunk): 56 seq_ids, X = _extract_reads(chunk) ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/q2_feature_classifier/_skl.py in _predict_chunk_with_conf(pipeline=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), separator=';', confidence=0.7, chunk=[DNA --------------------------------------------...TAGTAAT CGTAGATCAG CGTGCTACGG TGAATACGTT CCCGGGTC, DNA --------------------------------------------... GTAATCGCGC ATCAGCCATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------... AGTAATCGCG GATCAGCATG CCGCGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------... GTAATCGTAG ATCAGCATTG CTACGGTGAA TACGTTCCCG GGTC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCGCG CATCAGCCAC GGCGCGGTGA ATACGTTCCC GGACC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGG C, DNA --------------------------------------------...60 TAATCGCGGA TCAGCATGCC GCGGTGAATA CGTTCCCGGG CC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------... GTAATCGCGC ATCAGCAATG GCGCGGTGAA TACGTTCCCG GACC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...AGTAATCG TATATCAGCA ATGATACGGT GAATACGTTC CCGGACC, DNA --------------------------------------------...A 360 ATCGCGGATC AGCTTGCCGC GGTGAATACG TTCCCGGGTC, DNA --------------------------------------------...TAGTAATGGC GCATCAGCAT GGCGCCGTGA ATACGTTCCC GGGCC, DNA --------------------------------------------... AGTAATCGTG GATCAGAACG CCACGGTGAA TACGTTCCCG GGCC, DNA --------------------------------------------...TAGTAATCG CGGATCAGCA TGCCGCGGTG AATACGTTCC CAGGCC, DNA --------------------------------------------...360 ATCGTAGATC AGCAACGCTA CGGTGAATAC GTTCCCGGGT C, DNA --------------------------------------------...0 TAATCGCAGA TCAGCAACGC TGCGGTGAAT ACGTTCCCGG GCC, DNA --------------------------------------------...TAGTAATCGT AGATCAGAAC GCTACGGTGA ATACGTTCCC GGGCC, ...]) 61 def _predict_chunk_with_conf(pipeline, separator, confidence, chunk): 62 seq_ids, X = _extract_reads(chunk) 63 64 if not hasattr(pipeline, "predict_proba"): 65 raise ValueError('this classifier does not support confidence values') ---> 66 prob_pos = pipeline.predict_proba(X) prob_pos = undefined pipeline.predict_proba = X = (b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...) 67 if prob_pos.shape != (len(X), len(pipeline.classes_)): 68 raise ValueError('this classifier does not support confidence values') 69 70 y = pipeline.classes_[prob_pos.argmax(axis=1)] ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/metaestimators.py in (*args=((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),), **kwargs={}) 110 break 111 else: 112 attrgetter(self.delegate_names[-1])(obj) 113 114 # lambda, but not partial, allows help() to work with update_wrapper --> 115 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) args = ((b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...),) kwargs = {} 116 # update the docstring of the returned function 117 update_wrapper(out, self.fn) 118 return out 119 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/pipeline.py in predict_proba(self=Pipeline(memory=None, steps=[('feat_ext', H...class_prior=None, fit_prior=False)]]), X=(b'AACCCTTGACATGCCTGTCGCGAGTTTAGGAAACTAGACTCTTCGG...CGCTAGTAATCGTAGATCAGCGTGCTACGGTGAATACGTTCCCGGGTC', b'CGGGCTAGAATGTGCGCGCCGTTTATTGAAAGATAGATTTCCCGCA...GCTAGTAATCGCGCATCAGCCATGGCGCGGTGAATACGTTCCCGGACC', b'AGGACTTGACATGGAGGGGCCGCGTGCAGAGATGCACGTTTCCGCA...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'TGGTCTTGACATCCTAGGAATCCTGCAGAGATGCGGGAGTGCCTTC...GCTAGTAATCGTAGATCAGCATTGCTACGGTGAATACGTTCCCGGGTC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'CGGTCTAGAATGCTTATGGAAATGATCTGAAAGGTGATTGGCCCGC...GCTAGTAATCGCGCATCAGCCACGGCGCGGTGAATACGTTCCCGGACC', b'AAGGCTTGACATATACCGAAAAGCAGCAGAGATGTTGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGGC', b'ACCTTTTGACATGCCCTGATCGCTGGAGAGATCCAGTTTTCCCTTC...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCCCTTGACATTCCCAGAGATGGGAAGTTCCGCAA...GCTAGTAATCGCGCATCAGCAATGGCGCGGTGAATACGTTCCCGGACC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'TGGGCTAGAATGCTGGGAGACCGTGGGTGAAAGCTCACTTTGTAGC...GCTAGTAATCGTATATCAGCAATGATACGGTGAATACGTTCCCGGACC', b'TACCCTTGACATGTCAAGAATCTTGCAGAGATGTGGGAGTGCTCGA...CGCTAGTAATCGCGGATCAGCTTGCCGCGGTGAATACGTTCCCGGGTC', b'TGGGCTCGAACGGCATTCGAACGTTCGTAGAAATACGGATATCCCG...CGCTAGTAATGGCGCATCAGCATGGCGCCGTGAATACGTTCCCGGGCC', b'AGCTCTTGACATTTACTGATCGTTTCCAGAGATGGATTCATCCCAG...CGCTAGTAATCGTGGATCAGAACGCCACGGTGAATACGTTCCCGGGCC', b'AGCGTTTGACATGGCTAGTATGTTTTCCAGAGATGGATTACTTCAG...CGCTAGTAATCGCGGATCAGCATGCCGCGGTGAATACGTTCCCAGGCC', b'AAGGCTTGACATATAGCGAAAAGCGGCAGAGATGTCGTGTCCGCAA...GCTAGTAATCGTAGATCAGCAACGCTACGGTGAATACGTTCCCGGGTC', b'AAGGCTTGACATATACCGGAAATGGCTGGAAACAGTCCCCCCGCAA...GCTAGTAATCGCAGATCAGCAACGCTGCGGTGAATACGTTCCCGGGCC', b'AGCTTTTGACATGTCTCGTTTGGTCGCCAGAGATGGCTTCCTTCAT...CGCTAGTAATCGTAGATCAGAACGCTACGGTGAATACGTTCCCGGGCC', ...)) 352 """ 353 Xt = X 354 for name, transform in self.steps[:-1]: 355 if transform is not None: 356 Xt = transform.transform(Xt) --> 357 return self.steps[-1][-1].predict_proba(Xt) self.steps.predict_proba = undefined Xt = <81x8192 sparse matrix of type ' 358 359 @if_delegate_has_method(delegate='_final_estimator') 360 def decision_function(self, X): 361 """Apply transforms, and decision_function of the final estimator ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 99 C : array-like, shape = [n_samples, n_classes] 100 Returns the probability of the samples for each class in 101 the model. The columns correspond to the classes in sorted 102 order, as they appear in the attribute `classes_`. 103 """ --> 104 return np.exp(self.predict_log_proba(X)) self.predict_log_proba = X = <81x8192 sparse matrix of type ' 105 106 107 class GaussianNB(BaseNB): 108 """ ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in predict_log_proba(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 79 C : array-like, shape = [n_samples, n_classes] 80 Returns the log-probability of the samples for each class in 81 the model. The columns correspond to the classes in sorted 82 order, as they appear in the attribute `classes_`. 83 """ ---> 84 jll = self._joint_log_likelihood(X) jll = undefined self._joint_log_likelihood = X = <81x8192 sparse matrix of type ' 85 # normalize by P(x) = P(f_1, ..., f_n) 86 log_prob_x = logsumexp(jll, axis=1) 87 return jll - np.atleast_2d(log_prob_x).T 88 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self=LowMemoryMultinomialNB(alpha=0.01, chunk_size=20000, class_prior=None, fit_prior=False), X=<81x8192 sparse matrix of type ') 720 def _joint_log_likelihood(self, X): 721 """Calculate the posterior log probability of the samples X""" 722 check_is_fitted(self, "classes_") 723 724 X = check_array(X, accept_sparse='csr') --> 725 return (safe_sparse_dot(X, self.feature_log_prob_.T) + X = <81x8192 sparse matrix of type ' self.feature_log_prob_.T = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) self.class_log_prior_ = array([-10.67876756, -10.67876756, -10.67876756,...-10.67876756, -10.67876756, -10.67876756]) 726 self.class_log_prior_) 727 728 729 class BernoulliNB(BaseDiscreteNB): ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a=<81x8192 sparse matrix of type ', b=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]), dense_output=False) 130 ------- 131 dot_product : array or sparse matrix 132 sparse if ``a`` or ``b`` is sparse and ``dense_output=False``. 133 """ 134 if issparse(a) or issparse(b): --> 135 ret = a * b ret = undefined a = <81x8192 sparse matrix of type ' b = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 136 if dense_output and hasattr(ret, "toarray"): 137 ret = ret.toarray() 138 return ret 139 else: ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/base.py in __mul__(self=<81x8192 sparse matrix of type ', other=memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]])) 402 # dense 2D array or matrix ("multivector") 403 404 if other.shape[0] != self.shape[1]: 405 raise ValueError('dimension mismatch') 406 --> 407 result = self._mul_multivector(np.asarray(other)) result = undefined self._mul_multivector = > other = memmap([[-10.30276523, -9.39982473, -7.4647148...9.34513933, -9.40504504, -7.19725709]]) 408 409 if isinstance(other, np.matrix): 410 result = np.asmatrix(result) 411 ........................................................................... /home/qiime2/miniconda/envs/qiime2-2018.11/lib/python3.5/site-packages/scipy/sparse/compressed.py in _mul_multivector(self=<81x8192 sparse matrix of type ', other=array([[-10.30276523, -9.39982473, -7.46471481...9.34513933, -9.40504504, -7.19725709]])) 506 result = np.zeros((M,n_vecs), dtype=upcast_char(self.dtype.char, 507 other.dtype.char)) 508 509 # csr_matvecs or csc_matvecs 510 fn = getattr(_sparsetools,self.format + '_matvecs') --> 511 fn(M, N, n_vecs, self.indptr, self.indices, self.data, other.ravel(), result.ravel()) fn = M = 81 N = 8192 n_vecs = 43424 self.indptr = array([ 0, 388, 774, 1168, 1557, 1944,... 29859, 30241, 30631, 31019, 31406], dtype=int32) self.indices = array([ 9, 52, 60, ..., 8142, 8155, 8186], dtype=int32) self.data = array([ 0.04805693, 0.04805693, 0.09611387, ..., 0.04862166, 0.04862166, 0.04862166]) other.ravel = result.ravel = 512 513 return result 514 515 def _mul_sparse_matrix(self, other): MemoryError: ___________________________________________________________________________ See above for debug info.