import numpy
from sklearn.ensemble import RandomForestClassifier
from sklearn import neighbors
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
def buildModel(dataset, method, parameters):
"""
Build final model for predicting real testing data
"""
features = dataset.columns[0:-1]
if method == 'RNN':
clf = performRNNlass(dataset[features], dataset['UpDown'])
return clf
elif method == 'RF':
clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
elif method == 'KNN':
clf = neighbors.KNeighborsClassifier()
elif method == 'SVM':
c = parameters[0]
g = parameters[1]
clf = SVC(C=c, gamma=g)
elif method == 'ADA':
clf = AdaBoostClassifier()
return clf.fit(dataset[features], dataset['UpDown'])
def prepareDataForClassification(dataset, start_test):
"""
generates categorical output column, attach to dataframe
label the categories and split into train and test
"""
features = dataset.columns[0:-1]
X = dataset[features]
y = dataset.UpDown
X_train = X[X.index < start_test]
y_train = y[y.index < start_test]
X_test = X[X.index >= start_test]
y_test = y[y.index >= start_test]
return X_train, y_train, X_test, y_test
def performClassification(X_train, y_train, X_test, y_test, method, parameters, savemodel):
"""
performs classification on returns using serveral algorithms
"""
if method == 'RF':
return performRFClass(X_train, y_train, X_test, y_test, parameters, savemodel)
elif method == 'KNN':
return performKNNClass(X_train, y_train, X_test, y_test, parameters, savemodel)
elif method == 'SVM':
return performSVMClass(X_train, y_train, X_test, y_test, parameters, savemodel)
elif method == 'ADA':
return performAdaBoostClass(X_train, y_train, X_test, y_test, parameters, savemodel)
elif method == 'RNN':
X_test = numpy.reshape(numpy.array(X_test), (X_test.shape[0], 1, X_test.shape[1]))
model = performRNNlass(X_train, y_train)
return model.evaluate(X_test, y_test)[1]
####### Classifier Arsenal ####################################################
def performRFClass(X_train, y_train, X_test, y_test, parameters, savemodel):
"""
Random Forest Binary Classification
"""
clf = RandomForestClassifier(n_estimators=1000, n_jobs=-1)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
return accuracy
def performKNNClass(X_train, y_train, X_test, y_test, parameters, savemodel):
"""
KNN binary Classification
"""
clf = neighbors.KNeighborsClassifier(parameters[0])
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
return accuracy
def performSVMClass(X_train, y_train, X_test, y_test, parameters, savemodel):
"""
SVM binary Classification
"""
c = parameters[0]
g = parameters[1]
clf = SVC(C=c, gamma=g)
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
return accuracy
def performAdaBoostClass(X_train, y_train, X_test, y_test, parameters, savemodel):
"""
Ada Boosting binary Classification
"""
# n = parameters[0]
# l = parameters[1]
clf = AdaBoostClassifier()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
return accuracy
def performRNNlass(X_train, y_train):
X_train = numpy.reshape(numpy.array(X_train), (X_train.shape[0], 1, X_train.shape[1]))
model = Sequential()
model.add(LSTM(
128,
input_shape=(None, X_train.shape[2]),
return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(
100,
return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(
units=1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(
X_train,
y_train,
batch_size=64,
epochs=64,
validation_split=0.1)
return model
