def compute_best_svm_radius(train_matrix, train_labels, val_matrix, val_labels,
radius_to_consider):
"""Compute the optimal SVM radius using the provided training and evaluation datasets.
You should only consider radius values within the radius_to_consider list.
You should use accuracy as a metric for comparing the different radius values.
Args:
train_matrix: The word counts for the training data
train_labels: The spma or not spam labels for the training data
val_matrix: The word counts for the validation data
val_labels: The spam or not spam labels for the validation data
radius_to_consider: The radius values to consider
Returns:
The best radius which maximizes SVM accuracy.
"""
# *** START CODE HERE ***
results = []
for r in radius_to_consider:
model = svm.svm_train(train_matrix, train_labels, r)
pred = svm.svm_predict(model, val_matrix, r)
accuracy = np.mean(pred == val_labels)
print("Radius: {}, Accuracy: {}".format(r, accuracy))
record = {"r": r, 'acc': accuracy}
results.append(record)
results.sort(key=lambda x: -x['acc'])
return results[0]['r']
def test_svm_predict(self):
w, max_p, max_acc = svm.svm(lambda ll: ll == 1,
self.train,
self.valid,
params=self.params)
predict = svm.svm_predict(self.test[1], [(1, w)])
self.assertGreaterEqual(sum(predict == self.test[0]), 460)
def predict(x_validation, config: EnsembleConfig, model_id=None):
from decision_tree import decision_tree_predict
from svm import svm_predict
if config.classifier_mode == 'DTREE':
return decision_tree_predict(x_validation, config, model_id)
elif config.classifier_mode == 'SVM':
return svm_predict(x_validation, config, model_id)
return []
def test_svm_multiclass(self):
svms = svm.svm_multiclass(self.train, self.valid, params=self.params)
preds = svm.svm_predict(self.test[1], svms)
acc = mean(preds == self.test[0])
print
print "Your current accuracy is:", acc
self.assertGreaterEqual(acc, .94)
#TODO: uncomment and dump the trained model.
data.dump_model(svms, "svms.p")
def test_svm_multiclass(self):
svms = svm.svm_multiclass(self.train, self.valid, params=self.params)
preds = svm.svm_predict(self.test[1], svms)
acc = mean(preds == self.test[0])
print
print "Your current accuracy is:", acc
self.assertGreaterEqual(acc, .94)
#TODO: uncomment and dump the trained model.
data.dump_model(svms, "svms.p")
def get_prediction(test_data, svm):
m = np.shape(test_data)[0]
prediction = []
for i in range(m):
# 对每一个样本得到预测值
predict = svm_predict(svm, test_data[i, :])
# 得到最终的预测类别
prediction.append(str(np.sign(predict)[0, 0]))
return prediction
def predict(chartID = 'chart_ID', chart_type = 'line', chart_height = 350):
if request.method == 'POST':
listtime = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
liststock = svm.svm_predict(15, 0, str(request.form['stockid']))
liststock1 = MLP.mlp_predict(15, 0, str(request.form['stockid']))
chart = {"renderTo": chartID, "type": chart_type, "height": chart_height,}
series = [{"name": 'SVM', "data": liststock}, {"name": 'MLP', "data": liststock1}]
# return '<h3>please log in firstly.</h3>'
title = {"text": 'Price in future 15 days'}
xAxis = {"categories": listtime}
yAxis = {"title": {"text": 'yAxis Label'}}
return render_template('predict.html', chartID=chartID, chart=chart, series=series, title=title, xAxis=xAxis,
yAxis=yAxis)
return render_template('predict.html')
def get_prediction(test_data, svm):
'''对样本进行预测
input: test_data(mat):测试数据
svm:SVM模型
output: prediction(list):预测所属的类别
'''
m = np.shape(test_data)[0]
prediction = []
for i in range(m):
# 对每一个样本得到预测值
predict = svm_predict(svm, test_data[i, :])
# 得到最终的预测类别
prediction.append(str(np.sign(predict)[0, 0]))
return prediction
def get_prediction(test_data, svm):
'''对样本进行预测
input: test_data(mat):测试数据
svm:SVM模型
output: prediction(list):预测所属的类别
'''
m = np.shape(test_data)[0]
prediction = []
for i in xrange(m):
# 对每一个样本得到预测值
predict = svm_predict(svm, test_data[i, :])
# 得到最终的预测类别
prediction.append(str(np.sign(predict)[0, 0]))
return prediction
def get_results(training_set, validation_set, options, tipo_modelo):
result_training = []
result_validation = []
if options.rl == True:
print("Realizando regresion logistica")
elif options.rn == True:
print("Realizando redes neuronales")
##no hice esta parte porque hay que cambiar cosas desde atras y no queria cambiarle su codigo y luego cagar algo
elif options.a == True:
print("Realizando arbol de decision")
#Obtenemos los atributos y el target, q van a variar dependiendo del tipo de corrida
attributes = ["CANTON", "GENERO","EDAD","ZONA","DEPENDIENTE","CASA_ESTADO","CASA_HACINADA","ALFABETA", "ESCOLARIDAD", "EDUACION", "TRABAJADO", "ASEGURADO","EXTRANJERO", "DISCAPACITADO", "JEFE_HOGAR", "POBLACION","SUPERFICIE","DENSIDAD","V_OCUPADAS","OCUPANTES","VOTO1", "VOTO2"]
if tipo_modelo == "1r":
del attributes[-1]
elif tipo_modelo == "2r":
del attributes[-2]
target = attributes[-1]
#Generamos el arbol
tree = decisionTree.crearArbol(training_set, attributes, target)
#Realizamos la poda
decisionTree.pruneTree(tree, float(options.up))
#Realizamos las predicciones con el training set
for example in training_set:
example_dic = {}
for i in range(len(example)):
example_dic[attributes[i]] = example[i]
newResult = decisionTree.decisionTreePredict(tree, example_dic)
result_training.append(newResult)
#Realizamos las predicciones con el validation set
for example in validation_set:
example_dic = {}
for i in range(len(example)):
example_dic[attributes[i]] = example[i]
newResult = decisionTree.decisionTreePredict(tree, example_dic)
result_validation.append(newResult)
elif options.knn == True:
print("Realizando k nearest neighbors")
training_set_copia = copy.deepcopy(training_set)
#Se agrega un identificador unico a cada ejemplo
for i in range(len(training_set_copia)):
training_set_copia[i].append(i)
kd_tree = kd_trees.construir_kd_tree(training_set_copia,0,len(training_set_copia[0]) - 2) #Se le resta 2, ya que el target y el identificador no deben ser tomados como dimensiones
for example in training_set:
example_copia = example[:]
del example_copia[-1]
newResult = kd_trees.kd_predict(kd_tree, example_copia, 0, len(example), int(options.k))
result_training.append(newResult)
#Realizamos las predicciones con el validation set
for example in validation_set:
example_copia = example[:]
del example_copia[-1]
newResult = kd_trees.kd_predict(kd_tree, example_copia, 0, len(example), int(options.k))
result_validation.append(newResult)
elif options.svm == True:
print("Realizando SVM")
#Obtenemos las respuestas del training set
respuestas = get_real_results(training_set)
training_set_x = separarXY(copy.deepcopy(training_set))
if tipo_modelo == "1r":
modelo = svm.generate_svm_model(training_set_x, respuestas, 'ovo', options.kernel)
else:
modelo = svm.generate_svm_model(training_set_x, respuestas, 'ovr', options.kernel)
for example in training_set_x:
newResult = svm.svm_predict(example, modelo)
result_training.append(newResult)
#Realizamos las predicciones con el validation set
for example in validation_set:
example_copy = example[:]
del example_copy[-1]
newResult = svm.svm_predict(example_copy, modelo)
result_validation.append(newResult)
return result_training, result_validation
from datetime import datetime
import json
import svm
import pandas as pd
import datetime
import calendar
from flask import jsonify
data = []
predicted_data = svm.svm_predict(30, 0, 'AAPL')
for x in predicted_data:
data.append(round(x,3))
utc = []
today = datetime.date.today()
thirtyday = datetime.timedelta(days=29)
daterange = pd.date_range(today, today + thirtyday)
for single_date in daterange:
utc.append(calendar.timegm(single_date.timetuple()))
json_data=[]
for x in range(1,31):
json_data.append([utc[x-1],data[x-1]])
# print json.dumps(json_data, separators=(',',','))
b = '''(
[1461110400000,107.13],
[1461196800000,105.97],
[1461283200000,105.68],
def test_svm_predict(self):
w, max_p, max_acc = svm.svm(lambda ll : ll == 1, self.train, self.valid, params=self.params)
predict = svm.svm_predict(self.test[1], [(1, w)])
self.assertGreaterEqual(sum(predict == self.test[0]), 460)
def predict(self,x): data = _convert_to_svm_node_array(x) ret = svmc.svm_predict(self.model,data) svmc.svm_node_array_destroy(data) return ret
