def repeatTheLearningProcess(self, bestGridSearched, set):
_, bestParams = self.getBestGridSearchedModel(bestGridSearched, set)
if bestGridSearched.learnerType == 'ANN':
if 'Income' in set.name:
set.datasetNo = 1
elif 'Wine' in set.name:
set.datasetNo = 2
bestGridSearched = ANN.ANNLearner(**bestParams,
datasetNo=set.datasetNo)
self.getLearningCurve(bestGridSearched, set)
self.getComplexityCurve(bestGridSearched, set)
return bestGridSearched
def repeatTheLearningProcess(self, bestGridSearched, set):
_, bestParams = self.getBestGridSearchedModel(bestGridSearched, set)
if bestGridSearched.learnerType == 'KNN':
bestGridSearched = KNN.KNNLearner(**bestParams,
datasetNo=set.datasetNo)
elif bestGridSearched.learnerType == 'DT':
bestGridSearched = DT.DTLearner(**bestParams,
datasetNo=set.datasetNo)
elif bestGridSearched.learnerType == 'SVM':
bestGridSearched = SVM.SVMLearner(**bestParams,
datasetNo=set.datasetNo)
elif bestGridSearched.learnerType == 'Boosting':
bestGridSearched = Boosting.BoostingLearner(
**bestParams, datasetNo=set.datasetNo)
elif bestGridSearched.learnerType == 'ANN':
bestGridSearched = ANN.ANNLearner(**bestParams,
datasetNo=set.datasetNo)
self.getLearningCurve(bestGridSearched, set)
self.getComplexityCurve(bestGridSearched, set)
return bestGridSearched
def getLearningCurveAll(self, Datasets, isWithClusters=False):
color = ['red', 'blue', 'green', 'orange', 'purple']
cntr = 0
for set in Datasets:
# start the third learner ANN
if 'Income' in set.name:
set.datasetNo = 1
if isWithClusters == False:
thisSetName = 'Income'
else:
thisSetName = 'Income, 2'
annBestGridSearched = ANN.ANNLearner(datasetNo=set.datasetNo,
hidden_layer_sizes=(11, ))
elif 'Wine' in set.name:
set.datasetNo = 2
if isWithClusters == False:
thisSetName = 'Wine'
else:
thisSetName = 'Wine, 2'
annBestGridSearched = ANN.ANNLearner(datasetNo=set.datasetNo,
hidden_layer_sizes=(15, ))
x, train_mean, train_std, test_mean, test_std = self.getLearningCurve(
annBestGridSearched, set)
score = self.scoreTestingSet(annBestGridSearched, set)
plt.plot(x,
train_mean,
label=set.name,
marker='o',
color=color[cntr % 5])
plt.fill_between(x,
train_mean - train_std,
train_mean + train_std,
alpha=0.25)
plt.plot(x, test_mean, marker='o', color=color[cntr % 5])
plt.fill_between(x,
test_mean - test_std,
test_mean + test_std,
alpha=0.25)
cntr += 1
plt.style.use('seaborn-whitegrid')
plt.ylabel('Score', fontsize=12)
if thisSetName == 'Income':
plt.ylim(0.5, 1.05)
else:
plt.ylim(0.0, 1.05)
plt.xlabel('Training set size', fontsize=12)
plt.title('Learning curves for all ' + thisSetName.split(',')[0] +
' Datasets',
fontsize=12,
y=1.03)
plt.legend()
plt.savefig('Figures/ANN/All-Learning-Curve, Dataset ' + thisSetName +
'.png')
plt.close()
cntr = 0
for set in Datasets:
# start the third learner ANN
if 'Income' in set.name:
set.datasetNo = 1
elif 'Wine' in set.name:
set.datasetNo = 2
annBestGridSearched = ANN.ANNLearner(datasetNo=set.datasetNo)
time, train_time = self.getTrainingTimeCurve(
annBestGridSearched, set)
score = self.scoreTestingSet(annBestGridSearched, set)
print("Testing Score for " + set.name + " is: ", score)
plt.plot(time,
train_time,
label=set.name,
marker='o',
color=color[cntr % 5])
cntr += 1
plt.style.use('seaborn-whitegrid')
plt.ylabel('Score', fontsize=12)
plt.xlabel('Number of Training Samples', fontsize=12)
plt.title('Training Time for all ' + thisSetName.split(',')[0] +
' Datasets',
fontsize=12,
y=1.03)
plt.legend()
plt.savefig('Figures/ANN/All-Training-Time-Curve, Dataset ' +
thisSetName + '.png')
plt.close()
