def AllModels (file, in_columns, out_columns):
data = numpy.genfromtxt(file ,delimiter="," , autostrip = True )
data = data[2:]
# numpy.asarray(numpy.random.shuffle(data[:2400]))
array = data
X = array[50:-50,in_columns]
# print X
X = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0).fit_transform(X)
Y = array[50:-50,out_columns]
#print X
Y = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0).fit_transform(Y)
# print Y
validation_size = 0.2
#scoring = 'accuracy'
# X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state = 0)
X_train, X_validation, Y_train, Y_validation = X[0:2400], X[2400:], Y[0:2400], Y[2400:]
# print X_train.pvalues_()
lr = LogisticRegression()
lr.fit(X_train, Y_train)
predictions = lr.predict (X_validation)
print 'LR : ' + str(accuracy_score(Y_validation, predictions))
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, Y_train)
predictions = lda.predict (X_validation)
print 'LDA: ' +str(accuracy_score(Y_validation, predictions))
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
predictions = knn.predict (X_validation)
print 'KNN: '+str(accuracy_score(Y_validation, predictions))
rf = DecisionTreeClassifier()
rf.fit(X_train, Y_train)
predictions = rf.predict (X_validation)
print 'DT : ' +str(accuracy_score(Y_validation, predictions))
nb = GaussianNB()
nb.fit(X_train, Y_train)
predictions = nb.predict (X_validation)
print 'NB : '+str(accuracy_score(Y_validation, predictions))
svm = SVC()
svm.fit(X_train, Y_train)
predictions = svm.predict (X_validation)
print 'SVM: '+str(accuracy_score(Y_validation, predictions))
print '--------------------'
rf=RandomForestClassifier(n_estimators=300, criterion='gini', max_depth=None,
min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1,
random_state=None, verbose=0, warm_start=False, class_weight=None)
rf.fit(X_train, Y_train)
print 'rf: '+str(rf.score(X_validation,Y_validation))
et=ExtraTreesClassifier(n_estimators=300, criterion='gini', max_depth=None, min_samples_split=2,
min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto',
max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None,
bootstrap=False, oob_score=False, n_jobs=1, random_state=None, verbose=0,
warm_start=False, class_weight=None)
et.fit(X_train, Y_train)
print 'et: '+ str(et.score(X_validation,Y_validation))
#cnf_matrix = confusion_matrix(Y_validation, y_pred)
#print cnf_matrix
rf = []
for i in range(1,5):
rf.append(ExtraTreesClassifier(n_estimators=300, criterion='gini', max_depth=None,
min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
max_features=i*6, max_leaf_nodes=None, min_impurity_decrease=0.0,
min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1,
random_state=None, verbose=0, warm_start=False, class_weight=None))
#cnf_matrix = confusion_matrix(Y_validation, y_pred)
#print cnf_matrix
l = []
for i in range(len(rf)):
l.append((str(i),rf[i]))
lda = LinearDiscriminantAnalysis()
# l.append(('a',lda))
# l.append(('b',lda))
l.append(('c',lda))
l.append(('d',lda))
ecl = VotingClassifier(estimators = l, voting = 'hard')
# ecl = AdaBoostClassifier(base_estimator = rf[0])
ecl.fit(X_train, Y_train)
y_pred = ecl.predict(X_validation)
ret = accuracy_score(Y_validation, y_pred)
print ret
cnf_matrix = confusion_matrix(Y_validation, y_pred,labels=[-3,-2,-1,0,1,2,3])
#print cnf_matrix
s1 = 0.0
for i in cnf_matrix:
s1 = s1 + sum(i)
print '---------------'
s = 0.0
for i in cnf_matrix[0:3,0:3]:
s = s+sum(i)
for i in cnf_matrix[4:7,4:7]:
s = s+sum(i)
print s/s1
return ret
for j in i:
if j not in best:
best.append(j)
X2=X[best[0:20]]
X_test2=X_test[best[0:20]]
#Building a loop to find best model and feature selection (results are lda with the 23 best features)
model=[]
score=[]
for i in range(10,len(best)):
X2=X[best[0:i]]
X_test2=X_test[best[0:i]]
#running the train and test data in LDA (this typically gives the best model)
model.append(['lda',i])
lda= LDA(n_components=2)
lda_x_axis = lda.fit(X2, y).transform(X2)
score.append(lda.score(X_test2, y_test, sample_weight=None))
#Look at Decision Tree Accuracy
model.append(['dt',i])
dt = DecisionTreeClassifier(class_weight='balanced')
dt.fit(X2,y)
score.append(dt.score(X_test2,y_test))
#Look at Random Forest Accuracy
model.append(['rf',i])
rf = RandomForestClassifier(class_weight='balanced')
rf.fit(X2,y)
score.append(rf.score(X_test2,y_test))
