def svr_rbf(self):
clf = svm.SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def linear(self):
clf = LinearRegression()
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def svr(self):
clf = svm.SVR()
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def pca(self):
train_img, test_img, train_lbl, test_lbl = self.X_train, self.X_test, self.y_train, self.y_test
from sklearn.preprocessing import StandardScaler
#scaler = StandardScaler()
# Fit on training set only.
#scaler.fit(train_img)
# Apply transform to both the training set and the test set.
#train_img = scaler.transform(train_img)
#test_img = scaler.transform(test_img)
from sklearn.decomposition import PCA
pca = PCA(.95)
pca.fit(train_img)
train_img = pca.transform(train_img)
test_img = pca.transform(test_img)
# from sklearn.linear_model import LogisticRegression
#logisticRegr = LogisticRegression(solver = 'lbfgs')
#logisticRegr.fit(train_img, train_lbl)
#y_pred = logisticRegr.predict(test_img)
from sklearn.tree import DecisionTreeRegressor
r = DecisionTreeRegressor(random_state=0)
r.fit(train_img, train_lbl)
y_pred = r.predict(test_img)
dict = {}
set_metrics(y_pred, test_lbl, dict)
return dict
def bayes(self):
clf = BayesianRidge(compute_score=True)
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def ard(self):
clf = linear_model.ARDRegression()
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def svr_linear(self):
clf = svm.SVR(kernel='linear', C=100, gamma='auto')
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def dec_tree_2(self):
from sklearn.tree import DecisionTreeRegressor
r = DecisionTreeRegressor(random_state=0)
r.fit(self.X_train, self.y_train)
y_pred = r.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def random_forest(self):
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
regr = RandomForestRegressor(max_depth=2, random_state=0)
regr.fit(self.X_train, self.y_train)
y_pred = regr.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def svr_poly(self):
clf = svm.SVR(kernel='poly',
C=100,
gamma='auto',
degree=3,
epsilon=.1,
coef0=1)
clf.fit(self.X_train, self.y_train)
y_pred = clf.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def boost(self):
import xgboost as xgb
xg_reg = xgb.XGBRegressor(objective='reg:linear',
colsample_bytree=0.3,
learning_rate=0.1,
max_depth=5,
alpha=10,
n_estimators=10)
xg_reg.fit(self.X_train, self.y_train)
y_pred = xg_reg.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
def lstm(self):
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
look_back = 1
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(self.X_train,
self.y_train,
epochs=100,
batch_size=1,
verbose=2)
y_pred = model.predict(self.X_test)
dict = {}
set_metrics(y_pred, self.y_test, dict)
return dict
