__author__ = 'Adam Kosiorek'
from sklearn import tree
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score
from ex1.data import getData, plot_feature_importance, plot_tree
trainData, testData = getData(fraction=1, deskew=True, subtractMean=True)
trainSet, trainLabels = trainData
testSet, testLabels = testData
# Parameter Grid Search with Cross-Validation
# tuned_parameters = {
# 'criterion' : ['gini', 'entropy'],
# 'max_depth' : [3, 5, 10, 30, 50, 70, 100],
# 'max_features' : [3, 5, 50, 70, 100, 350, 500, 650, 784]
# }
#
# classifier = GridSearchCV(tree.DecisionTreeClassifier(), tuned_parameters, cv=5, n_jobs=-1)
# print classifier.fit(trainSet, trainLabels)
# print classifier.best_params_
# predictions = classifier.predict(testSet)
# print accuracy_score(predictions, testLabels)
# #Best Parameters from CrossValidation
best_params = {'max_features': 500, 'criterion': 'entropy', 'max_depth': 100}
classifier = tree.DecisionTreeClassifier(**best_params)
__author__ = 'Adam Kosiorek'
from sklearn import svm
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import accuracy_score
from ex1.data import getData
from sklearn import tree
trainData, testData = getData(deskew=True, subtractMean=True)
trainSet, trainLabels = trainData
testSet, testLabels = testData
print 'fitting SVM'
## Parameter Grid Search with Cross-Validation
# c_range= [1e-2, 0.1, 1, 10, 100, 1000]
# tuned_parameters = [{'kernel': ['rbf'],
# 'gamma': [1e-3, 1e-4],
# 'C': c_range},
# {'kernel': ['linear'],
# 'C': c_range},
# {'kernel':['poly'],
# 'C' : c_range,
# 'degree' : [2, 3, 5, 7, 9]}
# ]
#
# classifier = GridSearchCV(svm.SVC(cache_size=2000), tuned_parameters, cv=5, n_jobs=-1)
# print classifier.fit(trainSet, trainLabels)
# print classifier.best_params_
classifier = svm.SVC(kernel='poly', degree=2, C=1000)
print classifier.fit(trainSet, trainLabels)
