U mdM @sdZddlmZmZmZddlZddlmZddl m Z ddl Z d ddeej e jefeej e jefeejeejdd d ZdS) z3 Metrics which don't quite deserve their own file. )OptionalSequenceUnionN)is_categorical_dtype) natsortedT) normalize)orignewdatarreturnc Cstddlm}|dk r8t|tr&||}t|tr8||}t|t|}}t|t|ksbttt |j |j f}||||d}|r|j ddddt j f}t j|||dkd}|jdkrdn|j}|jdkrd n|j} tj|tj||d tj|| d d } t|r t|jj} ntt|} t|rHt|jj} ntt|} | jt | t | f} | S) a Given an original and new set of labels, create a labelled confusion matrix. Parameters `orig` and `new` can either be entries in data or categorical arrays of the same size. Params ------ orig Original labels. new New labels. data Optional dataframe to fill entries from. normalize Should the confusion matrix be normalized? Examples -------- .. plot:: import scanpy as sc; import seaborn as sns pbmc = sc.datasets.pbmc68k_reduced() cmtx = sc.metrics.confusion_matrix("bulk_labels", "louvain", pbmc.obs) sns.heatmap(cmtx) r)confusion_matrixN)labels)Zaxis)wherezOriginal labelsz New Labels)name)indexcolumns)Zsklearn.metricsr isinstancestrpdSerieslenAssertionErroruniquenpZ concatenatevaluessumZnewaxisdivider DataFrameIndexrcat categoriesrlocarray) rr r rZ_confusion_matrixZ unique_labelsZmtxZsums orig_namenew_nameZdfZorig_idxZnew_idxr&P/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/metrics/_metrics.pyr s8$       r )N)__doc__typingrrrZpandasrZpandas.api.typesrZnatsortrnumpyrrZndarrayrboolr r&r&r&r's