def predict(self, tupla, prediction={}, w=1):
# Si es que es el nodo raiz
if len(prediction.keys()) == 0:
prediction = {c: 0.0 for c in self.data['class'].unique()}
if self.is_leaf:
aux = deepcopy(prediction)
aux[self.clase] += w
return aux
# Puede que falte chequear casos bordes, al igual que lo hago en get_menores y get_mayores
else:
feature_name = self.feat_name.replace('.mean', '')
mean = tupla[feature_name + '.mean']
std = tupla[feature_name + '.std']
l = tupla[feature_name + '.l']
r = tupla[feature_name + '.r']
pivote = self.feat_value
w_left = min(w * pyRF_prob.cdf(pivote, mean, std, l, r), 1)
w_right = min(w * (1 - pyRF_prob.cdf(pivote, mean, std, l, r)), 1)
a = self.right.predict(tupla, prediction, w_right)
b = self.left.predict(tupla, prediction, w_left)
# Tengo que retornar la suma elementwise de los diccionarios a y b
return {key: a[key] + b[key] for key in a}
def predict(self, tupla, prediction={}, w=1):
# Si es que es el nodo raiz
if len(prediction.keys()) == 0:
prediction = {c: 0.0 for c in self.data['class'].unique()}
if self.is_leaf:
aux = deepcopy(prediction)
aux[self.clase] += w
return aux
# Puede que falte chequear casos bordes, al igual que lo hago en get_menores y get_mayores
else:
mean = tupla[feature_name + '.mean']
std = tupla[feature_name + '.std']
l = tupla[feature_name + '.l']
r = tupla[feature_name + '.r']
pivote = self.feat_value
w_left = min(w * pyRF_prob.cdf(pivote, mean, std, l, r), 1)
w_right = min(w * (1 - pyRF_prob.cdf(pivote, mean, std, l, r)), 1)
a = self.right.predict(tupla, prediction, w_right)
b = self.left.predict(tupla, prediction, w_left)
# Tengo que retornar la suma elementwise de los diccionarios a y b
return {key: a[key] + b[key] for key in a}
def test_split_at_right_border(self):
feature_mass = pyRF_prob.cdf(8, 5, 1, 2, 8)
self.assertEqual(feature_mass, 1)
feature_mass = pyRF_prob.cdf(5, 5, 1, 2, 5)
self.assertEqual(feature_mass, 1)
feature_mass = pyRF_prob.cdf(4, 5, 1, 2, 4)
self.assertEqual(feature_mass, 1)
def test_split_at_right_border(self):
feature_mass = pyRF_prob.cdf(8, 5, 1, 2, 8)
self.assertEqual(feature_mass, 1)
feature_mass = pyRF_prob.cdf(5, 5, 1, 2, 5)
self.assertEqual(feature_mass, 1)
feature_mass = pyRF_prob.cdf(4, 5, 1, 2, 4)
self.assertEqual(feature_mass, 1)
def test_split_at_left_border(self):
# pyRF_prob.cdf(pivote, mean, std, left_bound, right_bound)
feature_mass = pyRF_prob.cdf(2, 5, 1, 2, 8)
self.assertEqual(feature_mass, 0)
feature_mass = pyRF_prob.cdf(5, 5, 1, 5, 8)
self.assertEqual(feature_mass, 0)
feature_mass = pyRF_prob.cdf(6, 5, 1, 6, 8)
self.assertEqual(feature_mass, 0)
def test_split_at_left_border(self):
# pyRF_prob.cdf(pivote, mean, std, left_bound, right_bound)
feature_mass = pyRF_prob.cdf(2, 5, 1, 2, 8)
self.assertEqual(feature_mass, 0)
feature_mass = pyRF_prob.cdf(5, 5, 1, 5, 8)
self.assertEqual(feature_mass, 0)
feature_mass = pyRF_prob.cdf(6, 5, 1, 6, 8)
self.assertEqual(feature_mass, 0)
def split_tuples_by_pivot(w_list, mean_list, std_list, left_bound_list, right_bound_list,
class_list, pivote):
"""divides a group of data according to a pivot
It operates along all the data. And then returns two dictionaries with the total sum
of the mass separated by class.
Returns:
menores: Dictionary for the data thats inferior than the pivot
mayores: Dictionary for the data thats superior to the pivot
"""
clip = lambda x, l, r: l if x < l else r if x > r else x
clases = set(class_list)
menores = {c: 0.0 for c in clases}
mayores = {c: 0.0 for c in clases}
for i in xrange(len(class_list)):
cum_prob = pyRF_prob.cdf(pivote, mean_list[i], std_list[i], left_bound_list[i],
right_bound_list[i])
cum_prob = clip(cum_prob, 0, 1)
menores[class_list[i]] += w_list[i] * cum_prob
mayores[class_list[i]] += w_list[i] * (1 - cum_prob)
return menores, mayores
def split_tuples_by_pivot(w_list, mean_list, std_list, left_bound_list,
right_bound_list, class_list, pivote):
"""divides a group of data according to a pivot
It operates along all the data. And then returns two dictionaries with the total sum
of the mass separated by class.
Returns:
menores: Dictionary for the data thats inferior than the pivot
mayores: Dictionary for the data thats superior to the pivot
"""
clip = lambda x, l, r: l if x < l else r if x > r else x
clases = set(class_list)
menores = {c: 0.0 for c in clases}
mayores = {c: 0.0 for c in clases}
for i in xrange(len(class_list)):
cum_prob = pyRF_prob.cdf(pivote, mean_list[i], std_list[i],
left_bound_list[i], right_bound_list[i])
cum_prob = clip(cum_prob, 0, 1)
menores[class_list[i]] += w_list[i] * cum_prob
mayores[class_list[i]] += w_list[i] * (1 - cum_prob)
return menores, mayores
def get_weight(self, tupla, pivote, feature_name, how):
""" Determina la distribucion de probabilidad gaussiana acumulada entre dos bordes.
pivote: valor de corte
how: determina si la probabilidad se calcula desde l hasta pivote o desde pivote hasta r
-> mayor: probabilidad de caer entre pivote y limite right_bound
-> menor: probabilidad de caer entre left_bound y pivote
"""
left_bound = tupla[feature_name + '.l']
right_bound = tupla[feature_name + '.r']
if left_bound >= pivote and how == 'mayor' or right_bound <= pivote and how == 'menor':
return tupla
else:
w = tupla['weight']
mean = tupla[feature_name + '.mean']
std = tupla[feature_name + '.std']
feature_mass = pyRF_prob.cdf(pivote, mean, std, left_bound,
right_bound)
if math.isnan(feature_mass):
if pivote > right_bound:
if how == 'menor':
feature_mass = 1.0
else:
feature_mass = 0.0
else:
if how == 'menor':
feature_mass = 0.0
else:
feature_mass = 1.0
if how == 'menor':
if (feature_mass >= self.min_mass_threshold):
tupla['weight'] = min(w * feature_mass, 1)
else:
tupla['weight'] = 0
# tupla[feature_name+'.r'] = min(pivote, tupla[feature_name + '.r'])
tupla[feature_name + '.r'] = pivote
return tupla
elif how == 'mayor':
feature_mass = 1 - feature_mass
if (feature_mass >= self.min_mass_threshold):
tupla['weight'] = min(w * feature_mass, 1)
else:
tupla['weight'] = 0
# tupla[feature_name+'.l'] = max(pivote, tupla[feature_name + '.l'])
tupla[feature_name + '.l'] = pivote
return tupla
def predict(self, tupla, prediction={}, w=1.0):
# Si es que es el nodo raiz
if len(prediction.keys()) == 0:
prediction = {c: 0.0 for c in self.classes}
if self.is_leaf:
aux = deepcopy(prediction)
aux[self.clase] += w
return aux
# Puede que falte chequear casos bordes, al igual que lo hago en get_menores y get_mayores
else:
feature_name = self.feat_name.replace('.mean', '')
mean = tupla[feature_name + '.mean']
std = tupla[feature_name + '.std']
l = tupla[feature_name + '.l']
r = tupla[feature_name + '.r']
pivote = self.feat_value
aux_mass = pyRF_prob.cdf(pivote, mean, std, l, r)
# MAL FIX
if math.isnan(aux_mass):
if pivote > r:
aux_mass = 1.0
else:
aux_mass = 0.0
clip = lambda hi, lo, x: lo if x <= lo else hi if x >= hi else x
aux_mass = clip(1, 0, aux_mass)
w_left = w * aux_mass
w_right = w * (1.0 - aux_mass)
print str(aux_mass) + ' ' + str(1.0 - aux_mass)
a = self.right.predict(tupla, prediction, w_right)
b = self.left.predict(tupla, prediction, w_left)
# Tengo que retornar la suma elementwise de los diccionarios a y b
return {key: a[key] + b[key] for key in a}
def get_weight(self, tupla, pivote, feature_name, how):
""" Determina la distribucion de probabilidad gaussiana acumulada entre dos bordes.
pivote: valor de corte
how: determina si la probabilidad se calcula desde l hasta pivote o desde pivote hasta r
"""
left_bound = tupla[feature_name + '.l']
right_bound = tupla[feature_name + '.r']
if left_bound >= pivote and how == 'mayor' or right_bound <= pivote and how == 'menor':
return tupla
else:
w = tupla['weight']
mean = tupla[feature_name + '.mean']
std = tupla[feature_name + '.std']
feature_mass = pyRF_prob.cdf(pivote, mean, std, left_bound, right_bound)
if how == 'menor':
if (feature_mass >= self.min_mass_threshold):
tupla['weight'] = min(w * feature_mass, 1)
else:
tupla['weight'] = 0
# tupla[feature_name+'.r'] = min(pivote, tupla[feature_name + '.r'])
tupla[feature_name + '.r'] = pivote
return tupla
elif how == 'mayor':
feature_mass = 1 - feature_mass
if (feature_mass >= self.min_mass_threshold):
tupla['weight'] = min(w * feature_mass, 1)
else:
tupla['weight'] = 0
# tupla[feature_name+'.l'] = max(pivote, tupla[feature_name + '.l'])
tupla[feature_name + '.l'] = pivote
return tupla
def split_tuples_by_pivot(self, w_list, mean_list, std_list, left_bound_list, right_bound_list,
class_list, pivote, menores, mayores):
"""divides a group of data according to a pivot
It operates along all the data. And then returns two dictionaries with the total sum
of the mass separated by class.
Returns:
menores: Dictionary for the data thats inferior than the pivot
mayores: Dictionary for the data thats superior to the pivot
"""
clip = lambda x, l, r: l if x < l else r if x > r else x
# Este loop es fundamental paralelizarlo
for i in xrange(len(class_list)):
cum_prob = pyRF_prob.cdf(pivote, mean_list[i], std_list[i], left_bound_list[i],
right_bound_list[i])
cum_prob = clip(cum_prob, 0, 1)
# En vez de agregar estas cantidades hago un submetodo que las retorne
# Hago un map y dsp las unzipeo y las sumo según su clase
menores[class_list[i]] += w_list[i] * cum_prob
mayores[class_list[i]] += w_list[i] * (1 - cum_prob)
return menores, mayores
def split_tuples_by_pivot(self, w_list, mean_list, std_list, left_bound_list, right_bound_list,
class_list, pivote, menores, mayores):
"""divides a group of data according to a pivot
It operates along all the data. And then returns two dictionaries with the total sum
of the mass separated by class.
Returns:
menores: Dictionary for the data thats inferior than the pivot
mayores: Dictionary for the data thats superior to the pivot
"""
clip = lambda x, l, r: l if x < l else r if x > r else x
# Este loop es fundamental paralelizarlo
for i in xrange(len(class_list)):
cum_prob = pyRF_prob.cdf(pivote, mean_list[i], std_list[i], left_bound_list[i],
right_bound_list[i])
cum_prob = clip(cum_prob, 0, 1)
# En vez de agregar estas cantidades hago un submetodo que las retorne
# Hago un map y dsp las unzipeo y las sumo según su clase
menores[class_list[i]] += w_list[i] * cum_prob
mayores[class_list[i]] += w_list[i] * (1 - cum_prob)
return menores, mayores
