def get_TP_TN_FN_FP(true_labels, pred_labels):
li = [None, None, None, None]
var = R.equal(true_labels, pred_labels)
TP = R.logical_and(true_labels, pred_labels)
TN = R.logical_not(R.logical_or(true_labels, pred_labels))
FN = R.logical_not(R.logical_or(pred_labels, var))
FP = R.logical_and(pred_labels, R.logical_not(true_labels))
return [R.sum(TP), R.sum(TN), R.sum(FN), R.sum(FP)]
def r2_score(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
scalar1 = R.Scalar(1)
SS_res = R.sum(R.square(R.sub(y_true, y_pred)))
SS_tot = R.sum(R.square(R.sub(y_true, R.mean(y_true))))
return R.sub(scalar1, R.div(SS_res, R.add(SS_tot, R.epsilon())))
def grow_tree(self, X, y, depth=0):
pop_per_class = R.Tensor([])
for c in range(self.num_classes):
pop_per_class = pop_per_class.concat(
R.sum(R.equal(y, R.Scalar(c))).expand_dims())
predicted_class = R.argmax(pop_per_class)
node = Node(predicted_class=predicted_class, depth=depth)
node.samples = R.shape(y).gather(R.Scalar(0))
if depth < self.max_depth:
#col, threshold = self.find_split(X, y)
col, threshold = 0, R.Tensor([12.895])
'''
'''
decision = R.Scalar(col).logical_and(threshold)
while decision.status != "computed":
pass
if decision.output == 1:
indices_left = X.transpose().gather(
R.Scalar(col)).less(threshold)
X_left, y_left = X.gather(indices_left), y.gather(indices_left)
indices_right = X.transpose().gather(
R.Scalar(col)).greater_equal(threshold)
X_right, y_right = X.gather(indices_right), y.gather(
indices_right)
node.feature_index = col
node.threshold = threshold
node.left = self.grow_tree(X_left, y_left, depth + 1)
node.left.leftbranch = True
node.right = self.grow_tree(X_right, y_right, depth + 1)
node.right.rightbranch = True
return node
def grow_tree(self, X, y, depth=0):
pop_per_class = R.Tensor([])
for c in range(self.num_classes):
pop_per_class = pop_per_class.concat(
R.sum(R.equal(y, R.Scalar(c))).expand_dims())
predicted_class = R.argmax(pop_per_class)
while predicted_class.status != "computed":
pass
node = Node(predicted_class=predicted_class.output, depth=depth)
node.samples = R.shape(y).gather(R.Scalar(0))
if depth < self.max_depth:
col, threshold = self.find_split(X, y)
while threshold.status != "computed":
pass
z = X.shape_()
z1 = y.shape_()
while z1.status != "computed":
pass
if col is not None and threshold.output is not [None]:
indices_left = X.transpose().gather(
R.Scalar(col)).less(threshold)
X_left = X.gather(
R.find_indices(indices_left, R.Tensor(
[1])).reshape(shape=R.sum(indices_left).expand_dims()))
y_left = y.gather(
R.find_indices(indices_left, R.Tensor(
[1])).reshape(shape=R.sum(indices_left).expand_dims()))
indices_right = X.transpose().gather(
R.Scalar(col)).greater_equal(threshold)
X_right = X.gather(
R.find_indices(indices_right, R.Tensor([
1
])).reshape(shape=R.sum(indices_right).expand_dims()))
y_right = y.gather(
R.find_indices(indices_right, R.Tensor([
1
])).reshape(shape=R.sum(indices_right).expand_dims()))
node.feature_index = col
node.threshold = threshold
node.left = self.grow_tree(X_left, y_left, depth + 1)
node.left.leftbranch = True
node.right = self.grow_tree(X_right, y_right, depth + 1)
node.right.rightbranch = True
return node
def accuracy(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
accuracy = R.div(R.sum((y_pred == y_val)),y_pred.shape[0])
return accuracy
def accuracy(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
if not isinstance(y_true, R.Op):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
if not isinstance(y_pred, R.Op):
y_pred = R.Tensor(y_pred)
return R.div(R.sum(R.equal(y_pred, y_true)), y_pred.shape_())
def r2_score(y_true, y_pred):
if isinstance(y_true, R.Tensor) or isinstance(y_true, R.Op):
pass
else:
y_true = R.Tensor(y_true, name="y_true")
if isinstance(y_pred, R.Tensor) or isinstance(y_pred, R.Op):
pass
else:
y_pred = R.Tensor(y_pred, name="y_pred")
print(type(y_true), type(y_pred))
scalar1 = R.Scalar(1)
SS_res = R.sum(R.square(R.sub(y_pred, y_true)), name="ss_res")
SS_tot = R.sum(R.square(R.sub(y_true, R.mean(y_true))), name="ss_tot")
return R.sub(scalar1, R.div(SS_res, SS_tot), name="r2_score")
def update_weights(self):
y_pred = self.predict(self.X)
dW = Scalar(-1).multiply(Scalar(2).multiply(self.X.transpose().dot(self.y.sub(y_pred))).div(self.no_samples))
db = Scalar(-2).multiply(R.sum(self.y.sub(y_pred))).div(self.no_samples)
self.W = self.W.sub(self.learning_rate.multiply(dW), name="W")
self.b = self.b.sub(self.learning_rate.multiply(db), name="b")
return self
def categorical_hinge(y_true, y_pred):
if not isinstance(y_true, R.Tensor):
y_true = R.Tensor(y_true)
if not isinstance(y_pred, R.Tensor):
y_pred = R.Tensor(y_pred)
neg = R.max(R.elemul(R.sub(R.Scalar(-1), y_true), y_pred))
pos = R.sum(R.elemul(y_true, y_pred))
loss = R.max((R.sub(neg, pos), R.Scalar(1)), R.Scalar(0))
return loss
def sparse_cross_entropy(y_true, y_pred, with_logit=True):
if with_logit:
y_pred = softmax(y_pred)
else:
pass
y_pred = R.clip(y_pred, R.epsilon(), R.div(R.Scalar(1), R.epsilon()))
N = y_pred.shape[0]
loss = R.elemul(R.Scalar(-1), R.div(R.sum(R.natlog(y_pred[R.len(y_pred), y_true])), R.Scalar(N)))
return loss
def one_hot_cross_entropy(y_true, y_pred, with_logit=True):
if with_logit:
y_pred = softmax(y_pred)
else:
pass
y_pred = R.clip(y_pred, R.epsilon(), R.div(R.Scalar(1), R.epsilon()))
N = y_pred.shape[0]
loss = R.div(R.elemul(R.Scalar(-1), R.mul(R.sum(y_true, R.natlog(R.add(y_pred, 1e-9))))), R.Scalar(N))
return loss
def pearson_correlation(x, y):
"""
Calculate linear correlation(pearson correlation)
"""
if not isinstance(x, R.Tensor):
x = R.Tensor(x)
if not isinstance(y, R.Tensor):
y = R.Tensor(y)
a = R.sum(R.square(x))
b = R.sum(R.square(y))
n = a.output.shape[0]
return R.div(
R.sub(R.multiply(R.Scalar(n), R.sum(R.multiply(x, y))),
R.multiply(R.sum(x), R.sum(y))),
R.multiply(
R.square_root(R.sub(R.multiply(R.Scalar(n), a), R.square(b))),
R.square_root(R.sub(R.multiply(R.Scalar(n), b), R.square(b)))))
def __compute_cost(self, y, y_pred, no_samples, name="cost"):
"""Cost function"""
return R.multiply(R.Scalar(1.0 / (2.0 * no_samples.output)),
R.sum(R.square(R.sub(y_pred, y))),
name=name)
confusion = R.Tensor(confusion)
if average=='macro':
for i in confusion:
TP ,TN ,FP ,FN = i[0] ,i[1] ,i[2] ,i[3]
Recall = R.div(TP, R.add(TP, FN))
Precision = R.div(TP, R.add(TP, FP))
if Precision == 0 or Recall == 0
or Recall == np.nan or Precision == np.nan:
final.append(0)
else:
F1 = R.div(R.elemul(R.Scalar(2), R.elemul(Recall, Precision)),R.sum(Recall ,Precision))
final.append(F1)
return R.mean(final)
if average=='micro':
confusion = R.Tensor(confusion)
TP = R.sum(confusion ,axis=0)[0]
TN = R.sum(confusion ,axis=0)[1]
FP = R.sum(confusion ,axis=0)[2]
FN = R.sum(confusion ,axis=0)[3]
Recall = R.div(TP, R.add(TP, FN))
Precision = R.div(TP, R.add(TP, FP))
F1 = R.div(R.elemul(R.Scalar(2), R.elemul(Recall, Precision)),R.sum(Recall ,Precision))
def softmax(x):
"""
Softmax Activation Function
"""
exp = R.exp(x)
return R.div(exp, R.sum(exp))
def find_split(self, X, y):
ideal_col = None
ideal_threshold = None
num_observations = y.shape_().gather(R.Scalar(0))
while num_observations.status != 'computed':
pass
num_observations = int(num_observations.output)
if num_observations <= 1:
return ideal_col, ideal_threshold
y = y.reshape(shape=[num_observations])
count_in_parent = R.Tensor([])
for c in range(self.num_classes):
count_in_parent = count_in_parent.concat(
R.sum(R.equal(y, R.Scalar(c))).expand_dims())
gini = R.square(
count_in_parent.foreach(operation='div', params=num_observations))
best_gini = R.sub(R.Scalar(1.0), R.sum(gini))
temp_y = y.reshape(shape=[num_observations, 1])
for col in range(self.num_features):
temp_X = R.gather(
R.transpose(X),
R.Scalar(col)).reshape(shape=[num_observations, 1])
all_data = R.concat(temp_X, temp_y, axis=1)
column = R.gather(R.transpose(X), R.Scalar(col))
ind = column.find_indices(R.sort(R.unique(column)))
while ind.status != "computed":
pass
inform_server()
sorted_data = R.Tensor([])
for i in ind.output:
sorted_data = sorted_data.concat(all_data.gather(
R.Tensor(i))) # need to find another way to sort
sorted_data_tpose = sorted_data.transpose()
thresholds = sorted_data_tpose.gather(R.Scalar(0)).gather(
R.Scalar(0))
obs_classes = sorted_data_tpose.gather(R.Scalar(1)).gather(
R.Scalar(0))
num_left = R.Tensor([0] * self.num_classes) # need ops
num_right = count_in_parent
for i in range(1, num_observations):
class_ = R.gather(obs_classes, R.Tensor([i - 1]))
classencoding = R.one_hot_encoding(
class_, depth=self.num_classes).gather(R.Scalar(0))
num_left = num_left.add(classencoding)
num_right = num_right.sub(classencoding)
gini_left = R.sub(
R.Scalar(1),
R.sum(
R.square(R.foreach(num_left, operation='div',
params=i))))
gini_right = R.sub(
R.Scalar(1),
R.sum(
R.square(
R.foreach(num_right,
operation='div',
params=num_observations - i))))
gini = R.div(
R.add(
R.multiply(R.Scalar(i), gini_left),
R.multiply(R.Scalar(num_observations - i),
gini_right)), R.Scalar(num_observations))
decision1 = R.logical_and(thresholds.gather(R.Tensor([i])),
thresholds.gather(R.Tensor([i - 1])))
decision2 = gini.less(best_gini)
while decision2.status != "computed":
pass
print(decision2.output == 1)
if decision2.output == 1 and decision1 != 1:
best_gini = gini
ideal_col = col
ideal_threshold = R.div(
R.add(thresholds.gather(R.Tensor([i])),
thresholds.gather(R.Tensor([i - 1]))),
R.Scalar(2))
print(ideal_col, ideal_threshold)
return ideal_col, ideal_threshold
def closest_centroids(self, points, centroids):
centroids = R.expand_dims(centroids, axis=1)
return R.argmin(
R.square_root(R.sum(R.square(R.sub(points, centroids)), axis=2)))
def precision(true_labels, pred_labels):
var = R.sum(R.equal(true_labels, pred_labels))
[TP, TN, FN, FP] = get_TP_TN_FN_FP(true_labels, pred_labels)
return R.div(TP, R.add(TP, FP))
def loss(out, Y):
# out and Y are both R.Tensor objects
s = R.square(out.sub(Y))
s = R.sum(s).div(R.Scalar(leny))
return (s)
