class Model():
def __init__(self):
self.knn = KNeighborsTransformer(n_neighbors=5,n_jobs=-1)
def fit(self, dtm):
self.knn.fit(dtm, dtm.index.tolist())
def predict(self):
pass
def weighted_knn(train_adata,
valid_adata,
label_key,
n_neighbors=50,
threshold=0.5,
pred_unknown=True):
"""Annotates ``valid_adata`` cells with a trained weighted KNN classifier on ``train_adata``.
Parameters
----------
train_adata: :class:`~anndata.AnnData`
Annotated dataset to be used to train KNN classifier with ``label_key`` as the target variable.
valid_adata: :class:`~anndata.AnnData`
Annotated dataset to be used to validate KNN classifier.
label_key: str
Name of the column to be used as target variable (e.g. cell_type) in ``train_adata`` and ``valid_adata``.
n_neighbors: int
Number of nearest neighbors in KNN classifier.
threshold: float
Threshold of uncertainty used to annotating cells as "Unknown". cells with uncertainties upper than this
value will be annotated as "Unknown".
pred_unknown: bool
``True`` by default. Whether to annotate any cell as "unknown" or not. If `False`, will not use
``threshold`` and annotate each cell with the label which is the most common in its
``n_neighbors`` nearest cells.
"""
print(
f'Weighted KNN with n_neighbors = {n_neighbors} and threshold = {threshold} ... ',
end='')
k_neighbors_transformer = KNeighborsTransformer(n_neighbors=n_neighbors,
mode='distance',
algorithm='brute',
metric='euclidean',
n_jobs=-1)
k_neighbors_transformer.fit(train_adata.X)
y_train_labels = train_adata.obs[label_key].values
y_valid_labels = valid_adata.obs[label_key].values
top_k_distances, top_k_indices = k_neighbors_transformer.kneighbors(
X=valid_adata.X)
stds = np.std(top_k_distances, axis=1)
stds = (2. / stds)**2
stds = stds.reshape(-1, 1)
top_k_distances_tilda = np.exp(-np.true_divide(top_k_distances, stds))
weights = top_k_distances_tilda / np.sum(
top_k_distances_tilda, axis=1, keepdims=True)
uncertainties = []
pred_labels = []
for i in range(len(weights)):
unique_labels = np.unique(y_train_labels[top_k_indices[i]])
best_label, best_prob = None, 0.0
for candidate_label in unique_labels:
candidate_prob = weights[i, y_train_labels[top_k_indices[i]] ==
candidate_label].sum()
if best_prob < candidate_prob:
best_prob = candidate_prob
best_label = candidate_label
if pred_unknown:
if best_prob >= threshold:
pred_label = best_label
else:
pred_label = 'Unknown'
else:
pred_label = best_label
if pred_label == y_valid_labels[i]:
uncertainties.append(max(1 - best_prob, 0))
else:
true_prob = weights[i, y_train_labels[top_k_indices[i]] ==
y_valid_labels[i]].sum()
if true_prob > 0.5:
pass
uncertainties.append(max(1 - true_prob, 0))
pred_labels.append(pred_label)
pred_labels = np.array(pred_labels).reshape(-1, )
uncertainties = np.array(uncertainties).reshape(-1, )
labels_eval = pred_labels == y_valid_labels
labels_eval = labels_eval.astype(object)
n_correct = len(labels_eval[labels_eval == True])
n_incorrect = len(labels_eval[labels_eval == False]) - len(
labels_eval[pred_labels == 'Unknown'])
n_unknown = len(labels_eval[pred_labels == 'Unknown'])
labels_eval[labels_eval == True] = f'Correct'
labels_eval[labels_eval == False] = f'InCorrect'
labels_eval[pred_labels == 'Unknown'] = f'Unknown'
valid_adata.obs['uncertainty'] = uncertainties
valid_adata.obs[f'pred_{label_key}'] = pred_labels
valid_adata.obs['evaluation'] = labels_eval
print('finished!')
print(f"Number of correctly classified samples: {n_correct}")
print(f"Number of misclassified samples: {n_incorrect}")
print(f"Number of samples classified as unknown: {n_unknown}")
"""A function to take a feature and tokenize then return a tfidf df of that input
"""
self.tokenizer.fit_on_texts(feature)
a = self.tokenizer.texts_to_matrix(feature, mode='tfidf')
config = self.tokenizer.get_config()
feature_names = json_normalize(loads(
config['word_index'])).columns.tolist()
dtm = pd.DataFrame(a)
return dtm
if __name__ == "__main__":
tr = Transformer()
negative = ['negative']
ignore = []
user_transformed, y = tr.transform(
pd.DataFrame({
'name':
"blue berry kush",
'race':
'sativa',
'flavors': ['blueberry', 'sweet'],
'negative': ['dry mouth', 'dry eyes'],
'positive': ['creativity', 'stress'],
'medical': ['ptsd', 'stress'],
'description':
"blueberry kush my dude blueberry_kush:10, whitewhidow:10 ",
}), negative, ignore)
model = KNeighborsTransformer()
model.fit()
def weighted_knn(train_adata,
valid_adata,
label_key,
n_neighbors=50,
threshold=0.5,
pred_unknown=True,
return_uncertainty=True):
"""
Taken from scnet:
https://github.com/theislab/scarches/blob/e84cfa5cf361bb22fd70865cb1f398af72248684/scnet/utils.py
"""
print(
f'Weighted KNN with n_neighbors = {n_neighbors} and threshold = {threshold} ... ',
end='')
k_neighbors_transformer = KNeighborsTransformer(n_neighbors=n_neighbors,
mode='distance',
algorithm='brute',
metric='euclidean',
n_jobs=-1)
train_adata = remove_sparsity(train_adata)
valid_adata = remove_sparsity(valid_adata)
k_neighbors_transformer.fit(train_adata.X)
y_train_labels = train_adata.obs[label_key].values
y_valid_labels = valid_adata.obs[label_key].values
top_k_distances, top_k_indices = k_neighbors_transformer.kneighbors(
X=valid_adata.X)
stds = np.std(top_k_distances, axis=1)
stds = (2. / stds)**2
stds = stds.reshape(-1, 1)
top_k_distances_tilda = np.exp(-np.true_divide(top_k_distances, stds))
weights = top_k_distances_tilda / np.sum(
top_k_distances_tilda, axis=1, keepdims=True)
uncertainties = []
pred_labels = []
for i in range(len(weights)):
# labels = y_train_labels[top_k_indices[i]]
most_common_label, _ = Counter(
y_train_labels[top_k_indices[i]]).most_common(n=1)[0]
most_prob = weights[i, y_train_labels[top_k_indices[i]] ==
most_common_label].sum()
if pred_unknown:
if most_prob >= threshold:
pred_label = most_common_label
else:
pred_label = 'Unknown'
else:
pred_label = most_common_label
if pred_label == y_valid_labels[i]:
uncertainties.append(1 - most_prob)
else:
true_prob = weights[i, y_train_labels[top_k_indices[i]] ==
y_valid_labels[i]].sum()
uncertainties.append(1 - true_prob)
pred_labels.append(pred_label)
pred_labels = np.array(pred_labels).reshape(-1, 1)
uncertainties = np.array(uncertainties).reshape(-1, 1)
print('finished!')
if return_uncertainty:
return pred_labels, uncertainties
else:
return pred_labels
