3 examples of 'confusion matrix python' in Python

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moskomule/homura

16 @property
17 def numpy(self):
18     return self._matrix.numpy()

rguthrie3/BiLSTM-CRF

25 def __init__(self, classes):
26     self.cm = None
27     self.classes = classes

cgnorthcutt/cleanlab

273 def confusion_matrix(true, pred):
274     '''Implements a confusion matrix for true labels
275     and predicted labels. true and pred MUST BE the same length
276     and have the same distinct set of class labels represtented.
277 
278     Results are identical (and similar computation time) to: 
279         "sklearn.metrics.confusion_matrix"
280 
281     However, this function avoids the dependency on sklearn.
282     
283     Parameters
284     ----------
285     y : np.array 1d
286       Contains labels.
287       Assumes s and y contains the same distinct set of labels.
288       
289     s : np.array 1d
290       Contains labels.
291       Assumes s and y contains the same distinct set of labels.
292   
293     Returns
294     -------
295     confusion_matrix : np.array (2D)
296       matrix of confusion counts with true on rows and pred on columns.'''
297 
298     assert(len(true) == len(pred))
299     true_classes = np.unique(true)
300     pred_classes = np.unique(pred)
301     K_true = len(true_classes) # Number of classes in true
302     K_pred = len(pred_classes) # Number of classes in pred    
303     map_true = dict(zip(true_classes, range(K_true)))    
304     map_pred = dict(zip(pred_classes, range(K_pred)))
305     
306     result = np.zeros((K_true, K_pred))
307     for i in range(len(true)):
308         result[map_true[true[i]]][map_pred[pred[i]]] += 1
309 
310     return result

25	def __init__(self, classes):
26	self.cm = None
27	self.classes = classes

273	def confusion_matrix(true, pred):
274	'''Implements a confusion matrix for true labels
275	and predicted labels. true and pred MUST BE the same length
276	and have the same distinct set of class labels represtented.
277
278	Results are identical (and similar computation time) to:
279	"sklearn.metrics.confusion_matrix"
280
281	However, this function avoids the dependency on sklearn.
282
283	Parameters
284	----------
285	y : np.array 1d
286	Contains labels.
287	Assumes s and y contains the same distinct set of labels.
288
289	s : np.array 1d
290	Contains labels.
291	Assumes s and y contains the same distinct set of labels.
292
293	Returns
294	-------
295	confusion_matrix : np.array (2D)
296	matrix of confusion counts with true on rows and pred on columns.'''
297
298	assert(len(true) == len(pred))
299	true_classes = np.unique(true)
300	pred_classes = np.unique(pred)
301	K_true = len(true_classes) # Number of classes in true
302	K_pred = len(pred_classes) # Number of classes in pred
303	map_true = dict(zip(true_classes, range(K_true)))
304	map_pred = dict(zip(pred_classes, range(K_pred)))
305
306	result = np.zeros((K_true, K_pred))
307	for i in range(len(true)):
308	result[map_true[true[i]]][map_pred[pred[i]]] += 1
309
310	return result

3 examples of 'confusion matrix python' in Python

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