Python scipy.subtract() Examples

The following are 11 code examples of scipy.subtract(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy , or try the search function .
Example #1
Source File: math_util.py    From smappPy with GNU General Public License v2.0 6 votes vote down vote up 
def log_loss(actual, predicted, epsilon=1e-15):
    """
    Calculates and returns the log loss (error) of a set of predicted probabilities
    (hint: see sklearn classifier's predict_proba methods).

    Source: https://www.kaggle.com/wiki/LogarithmicLoss
    
    In plain English, this error metric is typically used where you have to predict 
    that something is true or false with a probability (likelihood) ranging from 
    definitely true (1) to equally true (0.5) to definitely false(0).

    Note: also see (and use) scikitlearn: 
    http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html#sklearn.metrics.log_loss
    """
    predicted = sp.maximum(epsilon, predicted)
    predicted = sp.minimum(1-epsilon, predicted)
    ll = sum(actual*sp.log(predicted) + sp.subtract(1,actual)*sp.log(sp.subtract(1,predicted)))
    ll = ll * -1.0/len(actual)
    return ll
Example #2
Source File: np_utils.py    From CAPTCHA-breaking with MIT License 5 votes vote down vote up 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
Example #3
Source File: np_utils.py    From seq2seq-keyphrase with MIT License 5 votes vote down vote up 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
Example #4
Source File: py_lh_20Sep2014.py    From Predict-click-through-rates-on-display-ads with MIT License 5 votes vote down vote up 
def logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    ll = sum(y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p)))
    ll = ll * -1.0/len(y)
    return ll

# B. Apply hash trick of the original csv row
# for simplicity, we treat both integer and categorical features as categorical
# INPUT:
#     csv_row: a csv dictionary, ex: {'Lable': '1', 'I1': '357', 'I2': '', ...}
#     D: the max index that we can hash to
# OUTPUT:
#     x: a list of indices that its value is 1
Example #5
Source File: py_lh_20Sep2014.py    From Predict-click-through-rates-on-display-ads with MIT License 5 votes vote down vote up 
def logloss(p, y):
    epsilon = 1e-15
    p = max(min(p, 1. - epsilon), epsilon)
    ll = y*sp.log(p) + sp.subtract(1,y)*sp.log(sp.subtract(1,p))
    ll = ll * -1.0/1
    return ll

# B. Apply hash trick of the original csv row
# for simplicity, we treat both integer and categorical features as categorical
# INPUT:
#     csv_row: a csv dictionary, ex: {'Lable': '1', 'I1': '357', 'I2': '', ...}
#     D: the max index that we can hash to
# OUTPUT:
#     x: a list of indices that its value is 1
Example #6
Source File: utils.py    From pCVR with Apache License 2.0 5 votes vote down vote up 
def logloss(act, pred):
    '''
    官方给的损失函数
    :param act: 
    :param pred: 
    :return: 
    '''
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1 - epsilon, pred)
    ll = sum(act * sp.log(pred) + sp.subtract(1, act) * sp.log(sp.subtract(1, pred)))
    ll = ll * -1.0 / len(act)
    return ll
Example #7
Source File: log_loss.py    From classifier-calibration with MIT License 5 votes vote down vote up 
def log_loss( act, pred ):
	epsilon = 1e-15
	pred = sp.maximum(epsilon, pred)
	pred = sp.minimum(1-epsilon, pred)
	ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
	ll = ll * -1.0/len(act)
	return ll
Example #8
Source File: np_utils.py    From KerasNeuralFingerprint with MIT License 5 votes vote down vote up 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res
Example #9
Source File: ml.py    From kaggle_avazu_benchmark with Apache License 2.0 5 votes vote down vote up 
def llfun(act, pred):
    p_true = pred[:, 1]
    epsilon = 1e-15
    p_true = sp.maximum(epsilon, p_true)
    p_true = sp.minimum(1 - epsilon, p_true)
    ll = sum(act * sp.log(p_true) + sp.subtract(1, act) * sp.log(sp.subtract(1, p_true)))
    ll = ll * -1.0 / len(act)
    return ll
Example #10
Source File: classify_nodes.py    From PyTorch-Luna16 with Apache License 2.0 5 votes vote down vote up 
def logloss(act, pred):
    epsilon = 1e-15
    pred = sp.maximum(epsilon, pred)
    pred = sp.minimum(1-epsilon, pred)
    ll = sum(act*sp.log(pred) + sp.subtract(1,act)*sp.log(sp.subtract(1,pred)))
    ll = ll * -1.0/len(act)
    return ll
Example #11
Source File: np_utils.py    From CopyNet with MIT License 5 votes vote down vote up 
def binary_logloss(p, y):
    epsilon = 1e-15
    p = sp.maximum(epsilon, p)
    p = sp.minimum(1-epsilon, p)
    res = sum(y * sp.log(p) + sp.subtract(1, y) * sp.log(sp.subtract(1, p)))
    res *= -1.0/len(y)
    return res