def createHistogram(self):
graphic = createCharts.graphicsCreator()
for key in self.performance:
print "Create histogram for ", key
namePicture = self.pathResponse+key+".svg"
title = "Histogram for "+key
print self.dataFrame
graphic.generateHistogram(self.dataFrame, key, namePicture, title)
def createHistogram(self):
keys = ['calinski_harabaz_score', 'silhouette_score']
graphic = createCharts.graphicsCreator()
for key in keys:
namePicture = self.pathResponse + key + ".svg"
title = "Histogram for " + key
graphic.generateHistogram(self.dataFrame, key, namePicture, title)
def createConfusionMatrix(self, dictTransform):
self.predictions = cross_val_predict(self.modelData,
self.dataSet,
self.target,
cv=self.cv_values)
matrix = confusion_matrix(self.target, self.predictions)
dictResponse = self.exportConfusionMatrix(matrix, dictTransform)
graph = createCharts.graphicsCreator()
graph.createConfusionMatrixPictures(matrix, self.target,
self.path + "confusionMatrix.svg")
return dictResponse
def exportConfusionMatrix(self, matrix, dictTransform):
#calculamos la sensitividad del modelo (en base a los valores de la primera columna)
bakanosidad = []
for i in range(len(matrix)):
sumRow = sum(matrix[i])
value = (matrix[i][i] / float(sumRow)) * 100
bakanosidad.append(value)
#calculamos la especificidad del modelo...
transpose = matrix.transpose()
fiabilidad = []
for i in range(len(transpose)):
sumRow = sum(transpose[i])
value = (transpose[i][i] / float(sumRow)) * 100
fiabilidad.append(value)
header = []
for element in self.classList:
header.append(self.getKeyToDict(dictTransform, element))
matrixData = []
for element in matrix: #obtenemos las columnas
rowSum = sum(element)
row = []
for value in element:
dataInValue = (value / float(rowSum)) * 100
row.append(dataInValue)
matrixData.append(row)
dictResponse = {
"Specificity": fiabilidad,
"Sensitivity": bakanosidad,
"matrix": matrixData,
"header": header
}
#generamos el grafico de barras comparativas entre estas medidas
graph = createCharts.graphicsCreator()
graph.createBarChartCompare(fiabilidad, bakanosidad, 'Specificity',
'Sensitivity', 'Class Response',
'Percentage', "Quality of the model",
self.target,
self.path + "barchartCompare.png")
return dictResponse
def execAlgorithmByOptions(self):
nameDoc = ""
if self.algorithm == 1: #kmeans
responseExec = self.applyClustering.aplicateKMeans(
int(self.params[0]))
self.response.update({"algorithm": "K Means"})
paramsData = {}
paramsData.update({"Number K": self.params[0]})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
elif self.algorithm == 2: #Birch
responseExec = self.applyClustering.aplicateBirch(
int(self.params[0]))
self.response.update({"algorithm": "Birch"})
paramsData = {}
paramsData.update({"Number K": self.params[0]})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
elif self.algorithm == 3: #Agglomerative
responseExec = self.applyClustering.aplicateAlgomerativeClustering(
self.params[0], self.params[1], int(self.params[2]))
self.response.update({"algorithm": "Agglomerative Clustering"})
paramsData = {}
paramsData.update({"Linkage": self.params[0]})
paramsData.update({"Affinity": self.params[1]})
paramsData.update({"Number K": self.params[2]})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
elif self.algorithm == 4: #DBSCAN
responseExec = self.applyClustering.aplicateDBSCAN()
self.response.update({"algorithm": "DBSCAN"})
paramsData = {}
paramsData.update({"Default": "Default"})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
elif self.algorithm == 5: #MeanShift
responseExec = self.applyClustering.aplicateMeanShift()
self.response.update({"algorithm": "Mean Shift"})
paramsData = {}
paramsData.update({"Default": "Default"})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
else:
responseExec = self.applyClustering.aplicateAffinityPropagation()
self.response.update({"algorithm": "Affinity Propagation"})
paramsData = {}
paramsData.update({"Default": "Default"})
self.response.update({"Params": paramsData})
if responseExec == 0:
self.response.update({"responseExec": "OK"})
else:
self.response.update({"responseExec": "ERROR"})
#solo si la ejecucion fue correcta!
if self.response['responseExec'] == "OK":
print "Eval clustering"
#evaluamos el clustering y obtenemos los resultados...
result = evaluationClustering.evaluationClustering(
self.dataSet, self.applyClustering.labels) #evaluamos...
self.response.update({"calinski_harabaz_score": result.calinski})
self.response.update({"silhouette_score": result.siluetas})
#print self.response
#finalmente, agregamos los labels al set de datos y generamos el resultado en el path entregado...
self.dataSet["Labels"] = pd.Series(self.applyClustering.labels,
index=self.dataSet.index)
self.dataSet.to_csv(self.pathResponse + "responseClustering.csv")
print "Create file responseClustering.csv"
#hacemos el conteo de los elementos por grupo para la generacion del grafico de torta asociada a la cantidad de grupos...
countGroup, keys, values = self.countMemberGroup()
self.response.update({"membersGroup": countGroup})
#hacemos la instancia para generar el grafico
namePic = self.pathResponse + "distributionGroup.png"
createChartsObject = createCharts.graphicsCreator()
createChartsObject.createPieChart(keys, values, namePic)
print self.response
#exportamos tambien el resultado del json
with open(self.pathResponse + "responseClustering.json", 'w') as fp:
json.dump(self.response, fp)
print "Create file responseClustering.json"
def execAlgorithmByOptions(self):
if self.algorithm == 1: #AdaBoost
errorData = {}
self.responseExec.update({"algorithm": "AdaBoostRegressor"})
paramsData = {}
paramsData.update({"n_estimators": self.params[0]})
paramsData.update({"loss": self.params[1]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
AdaBoostObject = AdaBoost.AdaBoost(self.data, self.response,
int(self.params[0]),
self.params[1])
AdaBoostObject.trainingMethod()
performance = {}
performance.update({"r_score": AdaBoostObject.r_score})
performance.update(
{"predict_values": AdaBoostObject.predicctions.tolist()})
performance.update(
{"real_values": AdaBoostObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, AdaBoostObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
AdaBoostObject.response.tolist(),
AdaBoostObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 2: #Bagging
errorData = {}
self.responseExec.update({"algorithm": "BaggingRegressor"})
paramsData = {}
paramsData.update({"n_estimators": self.params[0]})
paramsData.update({"bootstrap": self.params[1]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
baggingObject = Baggin.Baggin(self.data, self.response,
int(self.params[0]),
self.params[1])
baggingObject.trainingMethod()
performance = {}
performance.update({"r_score": baggingObject.r_score})
performance.update(
{"predict_values": baggingObject.predicctions.tolist()})
performance.update(
{"real_values": baggingObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, baggingObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
baggingObject.response.tolist(),
baggingObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 3: #DecisionTree
errorData = {}
self.responseExec.update({"algorithm": "DecisionTreeRegressor"})
paramsData = {}
paramsData.update({"criterion": self.params[0]})
paramsData.update({"splitter": self.params[1]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
decisionObject = DecisionTree.DecisionTree(
self.data, self.response, self.params[0], self.params[1])
decisionObject.trainingMethod()
performance = {}
performance.update({"r_score": decisionObject.r_score})
performance.update(
{"predict_values": decisionObject.predicctions.tolist()})
performance.update(
{"real_values": decisionObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, decisionObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
decisionObject.response.tolist(),
decisionObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 4: #Gradient
errorData = {}
self.responseExec.update(
{"algorithm": "GradientBoostingRegressor"})
paramsData = {}
paramsData.update({"n_estimators": self.params[0]})
paramsData.update({"loss": self.params[1]})
paramsData.update({"criterion": self.params[2]})
paramsData.update({"min_samples_leaf": self.params[3]})
paramsData.update({"min_samples_split": self.params[4]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
gradientObject = Gradient.Gradient(self.data, self.response,
int(self.params[0]),
self.params[1],
self.params[2],
int(self.params[4]),
int(self.params[3]))
gradientObject.trainingMethod()
performance = {}
performance.update({"r_score": gradientObject.r_score})
performance.update(
{"predict_values": gradientObject.predicctions.tolist()})
performance.update(
{"real_values": gradientObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, gradientObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
gradientObject.response.tolist(),
gradientObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 5: #KNN
errorData = {}
self.responseExec.update({"algorithm": "KNeighborsRegressor"})
paramsData = {}
paramsData.update({"n_neighbors": self.params[0]})
paramsData.update({"algorithm": self.params[1]})
paramsData.update({"metric": self.params[2]})
paramsData.update({"weights": self.params[3]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
knnObject = knn_regression.KNN_Model(self.data, self.response,
int(self.params[0]),
self.params[1],
self.params[2],
self.params[3])
knnObject.trainingMethod()
performance = {}
performance.update({"r_score": knnObject.r_score})
performance.update(
{"predict_values": knnObject.predicctions.tolist()})
performance.update(
{"real_values": knnObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, knnObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
knnObject.response.tolist(),
knnObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 6: #MLP
errorData = {}
self.responseExec.update({"algorithm": "MLPRegressor"})
paramsData = {}
paramsData.update({"activation": self.params[0]})
paramsData.update({"solver": self.params[1]})
paramsData.update({"learning_rate": self.params[2]})
paramsData.update({"hidden_layer_sizes_a": self.params[3]})
paramsData.update({"hidden_layer_sizes_b": self.params[4]})
paramsData.update({"hidden_layer_sizes_c": self.params[5]})
paramsData.update({"alpha": self.params[6]})
paramsData.update({"max_iter": self.params[7]})
paramsData.update({"shuffle": self.params[8]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
MLPObject = MLP.MLP(self.data, self.response, self.params[0],
self.params[1], self.params[2],
int(self.params[3]), int(self.params[4]),
int(self.params[5]), float(self.params[6]),
int(self.params[7]), self.params[8])
MLPObject.trainingMethod()
performance = {}
performance.update({"r_score": MLPObject.r_score})
performance.update(
{"predict_values": MLPObject.predicctions.tolist()})
performance.update(
{"real_values": MLPObject.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, MLPObject.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
MLPObject.response.tolist(),
MLPObject.predicctions.tolist(), namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 7: #NuSVR
errorData = {}
self.responseExec.update({"algorithm": "NuSVR"})
paramsData = {}
paramsData.update({"kernel": self.params[0]})
paramsData.update({"nu": self.params[1]})
paramsData.update({"degree": self.params[2]})
paramsData.update({"gamma": self.params[3]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
nuSVM = NuSVR.NuSVRModel(self.data, self.response,
self.params[0], int(self.params[2]),
float(self.params[3]),
float(self.params[1]))
nuSVM.trainingMethod()
performance = {}
performance.update({"r_score": nuSVM.r_score})
performance.update(
{"predict_values": nuSVM.predicctions.tolist()})
performance.update({"real_values": nuSVM.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, nuSVM.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
nuSVM.response.tolist(), nuSVM.predicctions.tolist(),
namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
elif self.algorithm == 8: #RandomForest
errorData = {}
self.responseExec.update({"algorithm": "RandomForestRegressor"})
paramsData = {}
paramsData.update({"n_estimators": self.params[0]})
paramsData.update({"criterion": self.params[1]})
paramsData.update({"min_samples_split": self.params[2]})
paramsData.update({"min_samples_leaf": self.params[3]})
paramsData.update({"bootstrap": self.params[4]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
rf = RandomForest.RandomForest(self.data, self.response,
int(self.params[0]),
self.params[1],
int(self.params[2]),
int(self.params[3]),
self.params[4])
rf.trainingMethod()
performance = {}
performance.update({"r_score": rf.r_score})
performance.update(
{"predict_values": rf.predicctions.tolist()})
performance.update({"real_values": rf.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, rf.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
rf.response.tolist(), rf.predicctions.tolist(),
namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
else: #SVR
errorData = {}
self.responseExec.update({"algorithm": "SVR"})
paramsData = {}
paramsData.update({"kernel": self.params[0]})
paramsData.update({"degree": self.params[1]})
paramsData.update({"gamma": self.params[2]})
self.responseExec.update({"Params": paramsData})
try:
#instancia al objeto...
svm = SVR.SVRModel(self.data, self.response, self.params[0],
int(self.params[1]), float(self.params[2]))
svm.trainingMethod()
performance = {}
performance.update({"r_score": svm.r_score})
performance.update(
{"predict_values": svm.predicctions.tolist()})
performance.update({"real_values": svm.response.tolist()})
#calculamos las medidas asociadas a la data de interes...
performanceValues = performanceData.performancePrediction(
self.response, svm.predicctions.tolist())
pearsonValue = performanceValues.calculatedPearson()
spearmanValue = performanceValues.calculatedSpearman()
kendalltauValue = performanceValues.calculatekendalltau()
#los agregamos al diccionario
performance.update({"pearson": pearsonValue})
performance.update({"spearman": spearmanValue})
performance.update({"kendalltau": kendalltauValue})
self.responseExec.update({"Performance": performance})
errorData.update({"Process": "OK"})
#instancia a graphic para crear scatter plot
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "scatter.png"
graphic.createScatterPlotErrorPrediction(
svm.response.tolist(), svm.predicctions.tolist(),
namePicture)
except:
errorData.update({"Process": "ERROR"})
pass
self.responseExec.update({"errorExec": errorData})
#exportamos tambien el resultado del json
nameFile = self.pathResponse + "responseTraining.json"
with open(self.pathResponse + "responseTraining.json", 'w') as fp:
json.dump(self.responseExec, fp)
def checkExec(self):
if self.optionProcess == 1: #show data continue
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "viewContinueValuesFor_" + self.keyFeature + ".png"
graphic.createScatterContinueData(
self.dataSet[self.keyFeature], namePicture,
self.keyFeature)
print "Create graphic OK"
except:
print "Error during create graphic"
pass
elif self.optionProcess == 2: #boxplot and violinplot
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "boxplot.svg"
graphic.createBoxPlot(self.dataSet, namePicture)
print "Box plot graphic OK"
except:
print "Error during create BoxPlot"
pass
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "violinplot.svg"
graphic.createViolinPlot(self.dataSet, namePicture)
print "Violin plot graphic OK"
except:
print "Error during create Violin"
pass
elif self.optionProcess == 3: #histograma
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "histogram_" + self.keyFeature + ".svg"
title = "Histogram for feature " + self.keyFeature
graphic.generateHistogram(self.dataSet, self.keyFeature,
namePicture, title)
print "create histogram for feature: ", self.keyFeature
except:
print "Error during create Histogram"
pass
elif self.optionProcess == 4: #frequence
try:
keys = list(set(self.dataSet[self.keyFeature]))
values = []
for key in keys:
cont = 0
for i in range(len(self.dataSet[self.keyFeature])):
if self.dataSet[self.keyFeature][i] == key:
cont += 1
values.append(cont)
namePicture = self.pathResponse + "piechartFor_" + self.keyFeature + ".svg"
graphic = createCharts.graphicsCreator()
graphic.createPieChart(keys, values, namePicture)
print "Create pie chart for " + self.keyFeature
except:
print "Error during create a pie chart"
pass
elif self.optionProcess == 5: #parallel
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "parallel_coordinates_" + self.keyFeature + ".svg"
title = "parallel_coordinates for " + self.keyFeature
graphic.createParallelCoordinates(self.dataSet,
self.keyFeature, namePicture,
title)
print "Create parallel_coordinates graphic"
except:
print "Error during create a parallel_coordinates"
pass
elif self.optionProcess == 6: #SPLOM
try:
graphic = createCharts.graphicsCreator()
namePicture = self.pathResponse + "splom.svg"
graphic.createScatterPlotMatrix(self.dataSet, namePicture,
self.keyFeature)
print "Create SPLOM for feature ", self.keyFeature
except:
print "Error during create SPLOM"
pass
else:
matrixResponse = []
header = ["Feature", "Mean", "STD", "Variance", "Min", "Max"]
#trabajamos con las estadisticas...
for key in self.dataSet:
try:
print "Process ", key
row = []
row.append(key)
row.append(np.mean(self.dataSet[key]))
row.append(np.std(self.dataSet[key]))
row.append(np.var(self.dataSet[key]))
row.append(min(self.dataSet[key]))
row.append(max(self.dataSet[key]))
matrixResponse.append(row)
except:
pass
df = pd.DataFrame(matrixResponse, columns=header)
df.to_csv(self.pathResponse + "summaryStatistical.csv",
index=False)
print "Create summaryStatistical.csv file"
