XGB_new = XGB_preds.apply(lambda x: np.power(np.e,x).astype('int64'))
GBR = GradientBoostingRegressor()
GBR.fit(X_train, Y_train)
GBR_preds = pd.DataFrame(GBR.predict(X_test),columns=['salePrice'],index=Y_test.index)
print(mean_absolute_error(Y_test,GBR_preds))
GBR_new = GBR_preds.apply(lambda x: np.power(np.e,x).astype('int64'))
sns.swarmplot(x=GBR_preds['salePrice'],y=Y_test)
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
kfold = KFold(n_splits=10, random_state=7)
results = cross_val_score(XGB, X, y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
results2 = cross_val_score(RFR, X, y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results2.mean()*100, results2.std()*100))
results3 = cross_val_score(GBR, X, y, cv=kfold)
print("Accuracy: %.2f%% (%.2f%%)" % (results3.mean()*100, results3.std()*100))
XGB.score(X_train, Y_train)
RFR.score(X_train, Y_train)
GBR.score(X_train, Y_train)
XGB.score(X_test, Y_test)
RFR.score(X_test, Y_test)
GBR.score(X_test, Y_test)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
train_size=0.8,
shuffle=True,
random_state=77)
#2. 모델
model = XGBRegressor(n_estimators=100, learning_rate=0.01, n_jobs=8)
#3. 훈련
model.fit(x_train,
y_train,
verbose=1,
eval_metric=['rmse', 'logloss', 'mae'],
eval_set=[(x_train, y_train), (x_test, y_test)])
aaa = model.score(x_test, y_test)
print('score : ', aaa)
y_pred = model.predict(x_test)
r2 = r2_score(y_test, y_pred)
print('r2 : ', r2)
#validation 0 - train
#validation 1 = teest
# print('==================================')
# results = model.evals_result()
# print(results)
from sklearn import cross_validation
train = pd.read_csv('../data/train_empty.csv')
features = ['store_nbr', 'item_nbr', # 'units', 'station_nbr',
'tmax', 'tmin', 'tavg', 'depart', 'dewpoint', 'wetbulb',
'heat', 'cool', 'snowfall', 'preciptotal', 'stnpressure',
'sealevel', 'resultspeed', 'resultdir', 'avgspeed',
'HZ', 'FU', 'UP', 'TSSN', 'VCTS', 'DZ', 'BR', 'FG',
'BCFG', 'DU', 'FZRA', 'TS', 'RA', 'PL', 'GS', 'GR',
'FZDZ', 'VCFG', 'PRFG', 'FG+', 'TSRA', 'FZFG', 'BLDU',
'MIFG', 'SQ', 'BLSN', 'SN', 'SG',
# 'month',
# 'day',
'day_length']
# 'sunset_hour',
# 'sunset_minute',
# 'sunrise_hour',
# 'sunrise_minute']
import xgboost
X = xgboost.DMatrix(train[features].values, missing=np.nan)
y = train["units"].values
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.3, random_state=0)
clf = XGBRegressor(silent=False)
print clf.score(X_test, y_test)
#concat two dataframes for better visualization
featureScores = pd.concat([dfcolumns, dfscores], axis=1)
featureScores.columns = ['Specs', 'Score'] #naming the dataframe columns
'''print(featureScores.nlargest(10,'Score')) #print 10 best features'''
#Using XGBoost as a model
no_survived = train.drop("Survived", axis=1)
survived = train["Survived"]
xgb = XGBRegressor()
xgb.fit(no_survived, survived, verbose=False)
#making the prediction
X_test = test.copy()
XGBPredict = np.round(xgb.predict(X_test), 0)
print("XGBoost")
print(round(xgb.score(no_survived, survived) * 100, 2)) #got a score of 60.82
#Random Forest 74.162%, 77.033% with titles
RF = RandomForestClassifier(n_estimators=100)
RF.fit(no_survived, survived)
RFPredict = RF.predict(X_test)
RF.score(no_survived, survived)
RFScore = round(RF.score(no_survived, survived) * 100, 2)
print("Random Forest")
print(RFScore)
#Decision Tree 75.598%, 75.119% with titles
decisionTree = DecisionTreeClassifier()
decisionTree.fit(no_survived, survived)
decisionTreePredict = decisionTree.predict(X_test)
decisionTree.score(no_survived, survived)
def rest(F, X, Y, X_train, y_train, X_test, y_test):
best_ada_score = float('-inf')
best_ada_rmse = float('inf')
best_ada_score_f = -1
best_ada_rmse_f = -1
best_xg_score = float('-inf')
best_xg_rmse = float('inf')
best_xg_score_f = -1
best_xg_rmse_f = -1
best_svr_score = float('-inf')
best_svr_rmse = float('inf')
best_svr_score_f = -1
best_svr_rmse_f = -1
for f in F:
print("\npca %d" % f)
# # PCA Feature Selection
X_mu, X_Z = pca(f, X.values)
X_pca = pca_proj(X.values, X_mu, X_Z)
X_train_mu, X_train_Z = pca(f, X_train.values)
X_train_pca = pca_proj(X_train.values, X_train_mu, X_train_Z)
X_test_pca = pca_proj(X_test.values, X_train_mu, X_train_Z)
# # AdaBoost
print("\nAdaBoost")
from sklearn.ensemble import AdaBoostRegressor
adaBoost = AdaBoostRegressor()
k_z, k_mse, k_rmse, b_z, b_mse, b_rmse = evaluate_model(
adaBoost, f, X.values, Y.values.ravel(), k=5, B=5)
if k_rmse < best_ada_rmse:
best_ada_rmse = k_rmse
best_ada_rmse_f = f
adaBoost.fit(X_train_pca, y_train.values.ravel())
ada_score = adaBoost.score(X_test_pca, y_test.values.ravel())
print(ada_score)
if ada_score > best_ada_score:
best_ada_score = ada_score
best_ada_score_f = f
#View Predicted values
predicted = adaBoost.predict(X_test_pca)
ada_pred = y_test.copy()
ada_pred['predicted'] = predicted
ada_pred.head()
# # XGBoost Regressor
print("\nXGBoost")
from xgboost import XGBRegressor
xgb = XGBRegressor(max_depth=3,
learning_rate=0.2,
booster='gbtree',
n_estimators=70)
k_z, k_mse, k_rmse, b_z, b_mse, b_rmse = evaluate_model(
xgb, f, X.values, Y.values.ravel(), k=5, B=5)
if k_rmse < best_xg_rmse:
best_xg_rmse = k_rmse
best_xg_rmse_f = f
xgb.fit(X_train_pca, y_train)
xgb_score = xgb.score(X_test_pca, y_test.values.ravel())
print(xgb_score)
if xgb_score > best_xg_score:
best_xg_score = xgb_score
best_xg_score_f = f
predicted = xgb.predict(X_test_pca)
xgb_pred = y_test.copy()
xgb_pred['predicted'] = predicted
xgb_pred.head()
# # SVM (SVR)
print("\nSVR")
from sklearn import svm
svr_model = svm.SVR(kernel="poly", coef0=-3500, gamma='scale')
# coef0 only works with poly and sigmoid kernels
# it just puts that value instead of the column of 1's
# without it, this model breaks for some reason
k_z, k_mse, k_rmse, b_z, b_mse, b_rmse = evaluate_model(
svr_model, f, X.values, Y.values.ravel(), k=5, B=5)
if k_rmse < best_svr_rmse:
best_svr_rmse = k_rmse
best_svr_rmse_f = f
# epsilon, degree
svr_model.fit(X_train_pca, y_train.values.ravel())
svr_score = svr_model.score(X_test_pca, y_test.values.ravel())
print(svr_score)
if svr_score > best_svr_score:
best_svr_score = svr_score
best_svr_score_f = f
svr_predicted = svr_model.predict(X_test_pca)
svr_pred = y_test.copy()
svr_pred["predicted"] = svr_predicted
svr_pred.head()
return ((best_ada_score, best_ada_score_f),
(best_ada_rmse, best_ada_rmse_f), (best_xg_score, best_xg_score_f),
(best_xg_rmse, best_xg_rmse_f), (best_svr_score, best_svr_score_f),
(best_svr_rmse, best_svr_rmse_f))
#-----------------------------------------------------------------------------#
# XGBoost
from xgboost import XGBRegressor
# initialize model
xgb_clf = XGBRegressor()
# fit model
xgb_clf.fit(X_train, y_train)
#predictions: test data
y_pred = xgb_clf.predict(X_test)
print('\n\n\nXGBoost')
#Scores
print('Train score')
print(xgb_clf.score(X_train, y_train))
print('Test score')
print(xgb_clf.score(X_test, y_test))
print('-------------------------------------------------------')
# MAE
print('Mean absolute error')
print(mean_absolute_error(y_test, y_pred))
print('-------------------------------------------------------')
# MSE
print('Mean squared error')
print(mean_squared_error(y_test, y_pred))
print('-------------------------------------------------------')
# R-squared
y,
train_size=0.8,
random_state=66,
shuffle=True)
#2. 모델
# model = DecisionTreeRegressor(max_depth=4)
# model = RandomForestRegressor(max_depth=4)
#model = GradientBoostingRegressor(max_depth=4)
model = XGBRegressor(n_job=-1)
#3. 훈련
model.fit(x_train, y_train)
#4. 평가, 예측
acc = model.score(x_test, y_test)
print(model.feature_importances_)
print("acc : ", acc)
#5. 시각화
import matplotlib.pyplot as plt
import numpy as np
def plot_feature_importances_dataset(model):
n_features = dataset.data.shape[1]
plt.barh(np.arange(n_features), model.feature_importances_, align='center')
plt.yticks(np.arange(n_features), dataset.feature_names)
plt.xlabel("Feature Improtances")
plt.ylabel("Features")
plt.ylim(-1, n_features)
model = XGBRegressor(n_estimators=550,
learning_rate=0.05,
max_depth=8,
colsample_bytree=0.7,
reg_alpha=1,
scale_pos_weight=1,
reg_lambda=1.1,
n_jobs=6)
model.fit(x_train,
y_train1,
verbose=False,
eval_metric=['logloss', 'mae'],
eval_set=[(x_train, y_train1), (x_test, y_test1)],
early_stopping_rounds=20)
score1 = model.score(x_test, y_test1)
print("score1 : %.4f" % (score1 * 100.0))
# print(model.feature_importances_)
y_pred_1 = model.predict(x_test)
mae1 = mean_absolute_error(y_test1, y_pred_1)
print('mae1 : %.4f' % (mae1))
y_pred1 = model.predict(x_pred)
model.fit(x_train,
y_train2,
verbose=False,
eval_metric=['logloss', 'mae'],
eval_set=[(x_train, y_train2), (x_test, y_test2)],
early_stopping_rounds=20)
score2 = model.score(x_test, y_test2)
print("score2 : %.4f" % (score2 * 100.0))
# Shape of train and test data print(X_train_temp.shape, X_val_temp.shape) # -------------- from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error # Code starts here dt = DecisionTreeRegressor(random_state=5) dt.fit(X_train, y_train) accuracy = dt.score(X_val, y_val) y_pred = dt.predict(X_val) rmse = np.sqrt(mean_squared_error(y_val, y_pred)) print(accuracy) print(y_pred) print(rmse) # -------------- from xgboost import XGBRegressor xgb = XGBRegressor(max_depth=50, learning_rate=0.83, n_estimators=100) xgb.fit(X_train, y_train) accuracy = xgb.score(X_val, y_val) y_pred = xgb.predict(X_val) rmse = np.sqrt(mean_squared_error(y_val, y_pred)) print(accuracy) print(y_pred) print(rmse) # Code ends here
model = forest.RandomForestRegressor(max_depth=6, n_estimators=i)
model.fit(x_train, y_train)
ploter.append((i, mean_squared_error(y_test, model.predict(x_test))))
print(i, '/', 99)
plt.plot([x for x, y in ploter], [y for x, y in ploter], c='b', linewidth=1.5)
plt.show()
pre_data = model_2.predict(test_data)
pre_data = pd.DataFrame(pre_data)
#
# simple = pd.read_csv('SampleSubmission.csv')
# simple['Item_Outlet_Sales'] = pre_data
# simple.to_csv('generated_SampleSubmission.csv')
model_3 = XGBRegressor(max_depth=4, n_estimators=12, learning_rate=.2)
model_3.fit(x_train, y_train)
print('model 3:', mean_squared_error(y_test, model_3.predict(x_test)))
print('score :', model_3.score(x_test, y_test))
# 8,40 = 1324449
# 4, 34 = 1150544
# ploter=[]
# for i in range(1, 80):
# model = XGBRegressor(max_depth=4, n_estimators=i, learning_rate=.2)
# model.fit(x_train, y_train)
# ploter.append((i, mean_squared_error(y_test, model.predict(x_test))))
# print(i,'/',99)
# plt.plot([x for x, y in ploter], [y for x, y in ploter], c='r', marker='.')
# plt.show()
#
y,
train_size=0.8,
random_state=66,
shuffle=True)
#2. 모델
# model = DecisionTreeRegressor(max_depth=4)
# model = RandomForestRegressor(max_depth=4)
# model = GradientBoostingRegressor(max_depth=4)
model = XGBRegressor(n_job=-1)
#3. 훈련
model.fit(x_train, y_train)
#4. 평가, 예측
acc = model.score(x_test, y_test)
print(model.feature_importances_)
print("acc : ", acc)
#5. 시각화
import matplotlib.pyplot as plt
import numpy as np
'''
def plot_feature_importances_dataset(model):
n_features = dataset.data.shape[1]
plt.barh(np.arange(n_features), model.feature_importances_,
align='center')
plt.yticks(np.arange(n_features), dataset.feature_names)
plt.xlabel("Feature Improtances")
plt.ylabel("Features")
plt.ylim(-1, n_features)
rf = RandomForestRegressor(n_estimators=100, random_state=0)
rf.fit(x_train, y_train)
y_pred = rf.predict(x_test)
plt.scatter(y_test, y_pred)
rf.score(x_test, y_test)
print('MAE :', " ", metrics.mean_absolute_error(y_test, y_pred))
print('MSE :', " ", metrics.mean_squared_error(y_test, y_pred))
print('RMSE :', " ", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
# XGBoost
xgb = XGBRegressor(n_estimators=500,
max_depth=4,
learning_rate=0.1,
early_stopping_rounds=10)
xgb.fit(x_train, y_train, eval_set=[(x_test, y_test)], verbose=False)
y_pred = xgb.predict(x_test)
plt.scatter(y_test, y_pred)
xgb.score(x_test, y_test)
plot_importance(xgb)
print('MAE :', " ", metrics.mean_absolute_error(y_test, y_pred))
print('MSE :', " ", metrics.mean_squared_error(y_test, y_pred))
print('RMSE :', " ", np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
X = data.data
y = data.target
Xtrain, Xtest, Ytrain, Ytest = TTS(X, y, test_size=0.3, random_state=420)
# In[]:
# B、弱评估器超参数：
# 4、booster（选择弱评估器）
for booster in ["gbtree", "gblinear", "dart"]:
reg = XGBR(n_estimators=260,
learning_rate=0.25,
random_state=420,
booster=booster,
silent=True).fit(Xtrain, Ytrain)
print(booster)
print(reg.score(Xtest, Ytest))
# gblinear线性弱评估器表现最差： 说明 波斯顿数据集 不是线性数据集（特征X 与 因变量Y 不是线性联系）
# In[]:
# 5、objective（损失函数）
# Sklearn的XGB： objective：默认reg:linear
reg = XGBR(n_estimators=270,
subsample=0.75,
learning_rate=0.13,
random_state=420).fit(Xtrain, Ytrain)
print(reg.score(Xtest, Ytest))
print(MSE(Ytest, reg.predict(Xtest)))
# In[]:
# xgb原生库： obj：默认binary:logistic
# 使用类Dmatrix读取数据
ada = AdaBoostClassifier()
ada.fit(X_train, y_train)
print(ada.score(X_test, y_test))
print('Parameters currently in use:\n')
pprint(ada.get_params())
# XGBoost
# XGBoost and Adaboost are bad in multiclassification. The reason can be c=found in the lightgbm literature review.
from xgboost import XGBRegressor
from sklearn.metrics import accuracy_score
xgb = XGBRegressor()
xgb.fit(X_train, y_train)
xgb.score(X_test, y_test)
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from keras import optimizers
def baseline_model():
# create model
model = Sequential()
model.add(
Dense(64, input_dim=25, kernel_initializer='normal',
activation='relu'))
