class Regressor(BaseEstimator):
def __init__(self):
self.clf = AdaBoostRegressor(RandomForestRegressor(n_estimators=100, max_depth=40, max_features=25), n_estimators=100)
#self.clf_Boost = GradientBoostingRegressor( n_estimators = 500 , max_features = 20 )
#self.clf_Regression = LinearRegression()
def fit(self, X, y):
self.clf.fit(X,y)
def predict(self, X):
return self.clf.predict(X)
def fit(self, start_date, end_date):
for ticker in self.tickers:
self.stocks[ticker] = Stock(ticker)
params_ada = [{
'n_estimators': [25, 50, 100],
'learning_rate': [0.01, 0.1, 1, 10],
'loss': ['linear', 'square', 'exponential']
}]
params = ParameterGrid(params_ada)
# Find the split for training and CV
mid_date = train_test_split(start_date, end_date)
for ticker, stock in self.stocks.items():
X_train, y_train = stock.get_data(start_date, mid_date, fit=True)
# X_train = self.pca.fit_transform(X_train.values)
X_train = X_train.values
# pdb.set_trace()
X_cv, y_cv = stock.get_data(mid_date, end_date)
# X_cv = self.pca.transform(X_cv.values)
X_cv = X_cv.values
lowest_mse = np.inf
for i, param in enumerate(params):
ada = AdaBoostRegressor(**param)
ada.fit(X_train, y_train.values)
mse = mean_squared_error(
y_cv, ada.predict(X_cv))
if mse <= lowest_mse:
self.models[ticker] = ada
return self
def Round2(X, y):
# Set parameters
min_score = {}
for loss in ['linear', 'square', 'exponential']:
model = AdaBoostRegressor(loss=loss)
n = len(y)
# Perform 5-fold cross validation
scores = []
kf = KFold(n, n_folds=5, shuffle=True)
# Calculate mean absolute deviation for train/test for each fold
for train_idx, test_idx in kf:
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
model.fit(X_train, y_train)
prediction = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, prediction))
# score = model.score(X_test, y_test)
scores.append(rmse)
if len(min_score) == 0:
min_score['loss'] = loss
min_score['scores'] = scores
else:
if np.mean(scores) < np.mean(min_score['scores']):
min_score['loss'] = loss
min_score['scores'] = scores
print "Loss:", loss
print scores
print np.mean(scores)
return min_score
def round2(X_df, featurelist):
# Set parameters
model = AdaBoostRegressor()
y_df = X_df['target']
n = len(y_df)
# Perform 5-fold cross validation
scores = []
kf = KFold(n, n_folds=5, shuffle=True)
# Calculate mean absolute deviation for train/test for each fold
for train_idx, test_idx in kf:
X_train, X_test = X_df.iloc[train_idx, :], X_df.iloc[test_idx, :]
# y_train, y_test = y_df[train_idx], y_df[test_idx]
X_train, X_test = applyFeatures(X_train, X_test, featurelist)
Xtrain_array, ytrain_array, Xtest_array, ytest_array = dfToArray(X_train, X_test)
model.fit(Xtrain_array, ytrain_array)
prediction = model.predict(Xtest_array)
rmse = np.sqrt(mean_squared_error(ytest_array, prediction))
scores.append(rmse)
print rmse
print "Finish fold"
return scores
def ada_boost_regressor(train_x, train_y, pred_x, review_id, v_curve=False, l_curve=False, get_model=True):
"""
:param train_x: train
:param train_y: text
:param pred_x: test set to predict
:param review_id: takes in a review id
:param v_curve: run the model for validation curve
:param l_curve: run the model for learning curve
:param get_model: run the model
:return: the predicted values,learning curve, validation curve
"""
ada = AdaBoostRegressor(n_estimators=5)
if get_model:
print "Fitting Ada..."
ada.fit(train_x, np.log(train_y+1))
ada_pred = np.exp(ada.predict(pred_x))-1
Votes = ada_pred[:,np.newaxis]
Id = np.array(review_id)[:,np.newaxis]
# create submission csv for Kaggle
submission_ada= np.concatenate((Id,Votes),axis=1)
np.savetxt("submission_ada.csv", submission_ada,header="Id,Votes", delimiter=',',fmt="%s, %0.2f", comments='')
# plot validation and learning curves
if l_curve:
print "Working on Learning Curves"
plot_learning_curve(AdaBoostRegressor(), "Learning curve: Adaboost", train_x, np.log(train_y+1.0))
if v_curve:
print "Working on Validation Curves"
plot_validation_curve(AdaBoostRegressor(), "Validation Curve: Adaboost", train_x, np.log(train_y+1.0),
param_name="n_estimators", param_range=[2, 5, 10, 15, 20, 25, 30])
def train_learning_model_decision_tree_ada_boost(df):
#code taken from sklearn
X_all, y_all = preprocess_data(df)
X_train, X_test, y_train, y_test = split_data(X_all, y_all)
tree_regressor = DecisionTreeRegressor(max_depth = 6)
ada_regressor = AdaBoostRegressor(DecisionTreeRegressor(max_depth=6), n_estimators = 500, learning_rate = 0.01, random_state = 1)
tree_regressor.fit(X_train, y_train)
ada_regressor.fit(X_train, y_train)
y_pred_tree = tree_regressor.predict(X_test)
y_pred_ada = ada_regressor.predict(X_test)
mse_tree = mean_squared_error(y_test, y_pred_tree)
mse_ada = mean_squared_error(y_test, y_pred_ada)
mse_tree_train = mean_squared_error(y_train, tree_regressor.predict(X_train))
mse_ada_train = mean_squared_error(y_train, ada_regressor.predict(X_train))
print ("MSE tree: %.4f " %mse_tree)
print ("MSE ada: %.4f " %mse_ada)
print ("MSE tree train: %.4f " %mse_tree_train)
print ("MSE ada train: %.4f " %mse_ada_train)
def backTest(trainEndDate, code, testDate, predictDate):
conn = db.get_history_data_db('D')
df = None
# train more date
# model = pickle.load(open('%s/%s.pkl' % (config.model_dir, code), 'r'))
rng = np.random.RandomState(1)
model = AdaBoostRegressor(DecisionTreeRegressor(
max_depth=4), n_estimators=1000, random_state=rng, loss='square')
df = pd.read_sql_query(
"select * from history_data where date([date])<='%s' and code='%s' order by code, date([date]) asc" % (
trainEndDate, code), conn)
shift_1 = df['close'].shift(-2)
df['target'] = shift_1
data = df[df['target'] > -1000]
X_train = data.ix[:, 'code':'turnover']
y_train = data.ix[:, 'target']
if len(X_train) < 500:
return
print len(X_train)
# print data
# for i in range(0, 10):
# model.fit(X_train, y_train)
model.fit(X_train, y_train)
# predict tomorrow
try:
df = pd.read_sql_query(config.sql_history_data_by_code_date % (code, testDate), conn)
# print df
except Exception, e:
print e
def main():
ab = AdaBoostRegressor(base_estimator=None, n_estimators=50,
learning_rate=1.0, loss='exponential',
random_state=None)
ab.fit(X_train, y_train)
#Evaluation in train set
#Evaluation in train set
pred_proba_train = ab.predict(X_train)
mse_train = mean_squared_error(y_train, pred_proba_train)
rmse_train = np.sqrt(mse_train)
logloss_train = log_loss(y_train, pred_proba_train)
#Evaluation in validation set
pred_proba_val = ab.predict(X_val)
mse_val = mean_squared_error(y_val, pred_proba_val)
rmse_val = np.sqrt(mse_val)
logloss_val = log_loss(y_val, pred_proba_val)
rmse_train
rmse_val
logloss_train
logloss_val
def predict_volatility_1year_ahead(rows, day, num_days):
"""
SUMMARY: Predict volatility 1 year into the future
ALGORITHM:
a) The predictor will train on all data up to exactly 1 year (252 trading days) before `day`
b) The newest 10 days up to and including `day` will be used as the feature vector for the prediction
i.e. if day = 0, then the feature vector for prediction will consist of days (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
if day = 10, then the feature vector for predictor input will be days (10, 11, 12, 13, 14, 15, 16, 17, 19)
INPUT: minimum of (1 year + 10 days) of data before `day` (newest data is day=0)
"""
'''enforce that `day` is in the required range'''
assert len(rows) >= 252+num_days + day, 'You need to have AT LEAST 252+%d rows AFTER the day index. See predict_volatility_1year_ahead() for details.' % num_days
assert day >= 0
'''Compile features for fitting'''
feature_sets = []
value_sets = [];
for ii in range(day+num_days+252, len(rows) - num_days):
features = []
for jj in range(num_days):
day_index = ii + jj
features += [
float(rows[day_index][7]),
float(rows[day_index][8]),
float(rows[day_index][9]),
float(rows[day_index][10]),
float(rows[day_index][11]),
float(rows[day_index][12]),
float(rows[day_index][13]),
]
#print("issue here: " + str(rows[day_index][0]))
feature_sets += [features]
value_sets += [float(rows[ii-252][9])]
'''Create Regressor and fit'''
num_features = 16
rng = np.random.RandomState(1)
regr = AdaBoostRegressor(CustomClassifier(), n_estimators=3, random_state=rng)
regr.fit(feature_sets, value_sets)
'''Get prediction features'''
ii = day
features = []
for jj in range( num_days ):
day_index = ii + jj
features += [
float(rows[day_index][7]),
float(rows[day_index][8]),
float(rows[day_index][9]),
float(rows[day_index][10]),
float(rows[day_index][11]),
float(rows[day_index][12]),
float(rows[day_index][13]),
]
return float(regr.predict([features]))
class Regressor(BaseEstimator):
def __init__(self):
self.clf = AdaBoostRegressor(RandomForestRegressor(n_estimators=500, max_depth=78, max_features=10), n_estimators=40)
def fit(self, X, y):
self.clf.fit(X, y)
def predict(self, X):
return self.clf.predict(X)
def train_predict(train_id, test_id): # load libsvm files for training dataset Xs_train = [] ys_train = [] n_train = load_libsvm_files(train_id, Xs_train, ys_train) # load libsvm files for testing dataset Xs_test = [] ys_test = [] n_test = load_libsvm_files(test_id, Xs_test, ys_test) # models model = [] # ans ans_train = [] ans_test = [] # generate predictions for training dataset ps_train = [] for i in range(0, n_train): ps_train.append([0.0 for j in range(10)]) # generate predictions for testing dataset ps_test = [] for i in range(0, n_test): ps_test.append([0.0 for j in range(10)]) # fit models for i in range(10): l = np.array([ys_train[j][i] for j in range(n_train)]) clf = AdaBoostRegressor(DecisionTreeRegressor(max_depth=params['max_depth']), n_estimators=params['n_estimators'], learning_rate=params['learning_rate']) clf.fit(Xs_train[i].toarray(), l) print "[%s] [INFO] %d model training done" % (t_now(), i) preds_train = clf.staged_predict(Xs_train[i].toarray()) ans_train.append([item for item in preds_train]) # print "len(ans_train[%d]) = %d" % (i, len(ans_train[i])) print "[%s] [INFO] %d model predict for training data set done" % (t_now(), i) preds_test = clf.staged_predict(Xs_test[i].toarray()) ans_test.append([item for item in preds_test]) print "[%s] [INFO] %d model predict for testing data set done" % (t_now(), i) #print "len_ans_train=%d" % len(ans_train[0]) # predict for testing data set for i in range(params['n_estimators']): for j in range(10): tmp = min(i, len(ans_train[j]) - 1) for k in range(n_train): ps_train[k][j] = ans_train[j][tmp][k] tmp = min(i, len(ans_test[j]) - 1) for k in range(n_test): ps_test[k][j] = ans_test[j][tmp][k] print "%s,%d,%f,%f" % (t_now(), i + 1, mean_cos_similarity(ys_train, ps_train, n_train), mean_cos_similarity(ys_test, ps_test, n_test)) return 0
def AdaBoost(xTrain, yTrain, xTest, yTest, treeNum): rms = dict() for trees in treeNum: ab = AdaBoostRegressor(n_estimators = trees) ab.fit(xTrain, yTrain) yPred = ab.predict(xTest) rms[trees] = sqrt(mean_squared_error(yTest, yPred)) (bestRegressor, rmse) = sorted(rms.iteritems(), key = operator.itemgetter(1))[0] return bestRegressor, rmse
class Regressor(BaseEstimator):
def __init__(self):
cl = RandomForestRegressor(n_estimators=10, max_depth=10, max_features=10)
self.clf = AdaBoostRegressor(base_estimator = cl, n_estimators=100)
def fit(self, X, y):
self.clf.fit(X, y)
def predict(self, X):
return self.clf.predict(X)
#RandomForestClassifier
def ada_boost(data,classifier,sample):
from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.cluster import KMeans
from sklearn.naive_bayes import GaussianNB
func = GaussianNB()
func = DecisionTreeRegressor()
func = KMeans(n_clusters=2)
clf = AdaBoostRegressor(func,n_estimators=300,random_state=random.RandomState(1))
clf.fit(data,classifier)
print_result(clf,[sample])
def test_boston():
# Check consistency on dataset boston house prices.
reg = AdaBoostRegressor(random_state=0)
reg.fit(boston.data, boston.target)
score = reg.score(boston.data, boston.target)
assert score > 0.85
# Check we used multiple estimators
assert len(reg.estimators_) > 1
# Check for distinct random states (see issue #7408)
assert_equal(len(set(est.random_state for est in reg.estimators_)),
len(reg.estimators_))
def performAdaBoostReg(train, test, features, output):
"""
Ada Boost Regression
"""
clf = AdaBoostRegressor()
clf.fit(train[features], train[output])
Predicted = clf.predict(test[features])
plt.plot(test[output])
plt.plot(Predicted, color='red')
plt.show()
return mean_squared_error(test[output],Predicted), r2_score(test[output], Predicted)
def do_adaboost(filename):
df, Y = create_merged_dataset(filename)
# Ideas:
# Create a feature for accelerations e deacceleration.
# Leave default base regressor for AdaBoost(decision tree). Extra trees were tried with catastrophic results.
#ada = AdaBoostRegressor(n_estimators=350, learning_rate=0.05)
ada = AdaBoostRegressor(n_estimators=500, learning_rate=1)
#X = df.drop(['driver', 'trip', 'prob_points', 'prob_speed', 'prob_distance', 'prob_acceleration'], 1)
X = df.drop(['driver', 'trip'], 1)
ada.fit(X, Y)
probs = ada.predict(X[:200])
return pd.DataFrame({'driver': df['driver'][:200], 'trip': df['trip'][:200], 'probs': probs})
def predict_volatility_1year_ahead(rows, day):
"""
SUMMARY: Predict volatility 1 year into the future
ALGORITHM:
a) The predictor will train on all data up to exactly 1 year (252 trading days) before `day`
b) The newest 10 days up to and including `day` will be used as the feature vector for the prediction
i.e. if day = 0, then the feature vector for prediction will consist of days (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
if day = 10, then the feature vector for predictor input will be days (10, 11, 12, 13, 14, 15, 16, 17, 19)
INPUT: minimum of (1 year + 10 days) of data before `day` (newest data is day=0)
"""
#num_days = 10
num_days = 10
# enforce that `day` is in the required range
assert len(rows) >= 252+num_days + day, 'You need to have AT LEAST 252+%d rows AFTER the day index. See predict_volatility_1year_ahead() for details.' % num_days
assert day >= 0
# compile features (X) and values (Y)
feature_sets = []
value_sets = []; value_sets_index = []
for ii in range(day+252, len(rows) - num_days):
features = []
for jj in range(num_days):
day_index = ii + jj
features += [float(rows[day_index][1]), float(rows[day_index][2]), float(rows[day_index][3]), float(rows[day_index][5]), float(rows[day_index][7]), float(rows[day_index][8]), float(rows[day_index][9])]
feature_sets += [features]
value_sets += [float(rows[ii-252][9])]
value_sets_index.append([ii-252])
# fit
#regr = linear_model.Lasso(alpha=0.01,fit_intercept=False,normalize=False,max_iter=10000000) # they call lambda alpha
rng = np.random.RandomState(1)
regr = AdaBoostRegressor(CustomClassifier(), n_estimators=4, random_state=rng)
#regr = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), n_estimators=2, random_state=rng)
#regr = DecisionTreeRegressor(max_depth=4)
regr.fit(feature_sets, value_sets)
#print "Adaboost weights:", regr.estimator_weights_
ii = day
features = []
for jj in range( num_days ):
day_index = ii + jj +252
features += [float(rows[day_index][1]), float(rows[day_index][2]), float(rows[day_index][3]), float(rows[day_index][5]), float(rows[day_index][7]), float(rows[day_index][8]), float(rows[day_index][9])]
return float(regr.predict([features]))
def test_sample_weight_adaboost_regressor():
"""
AdaBoostRegressor should work without sample_weights in the base estimator
The random weighted sampling is done internally in the _boost method in
AdaBoostRegressor.
"""
class DummyEstimator(BaseEstimator):
def fit(self, X, y):
pass
def predict(self, X):
return np.zeros(X.shape[0])
boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
boost.fit(X, y_regr)
assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_))
def ada_learning(labels, train, test):
label_log=np.log1p(labels)
# try 50 / 1.0
#boost GradientBoostingRegressor(n_estimators=200, max_depth=8, learning_rate=0.1)
clf=AdaBoostRegressor(GradientBoostingRegressor(n_estimators=200, max_depth=8, learning_rate=0.1),n_estimators=50, learning_rate=1.0)
model=clf.fit(train, label_log)
preds1=model.predict(test)
preds=np.expm1(preds1)
return preds
def initGrid(X,y):
min_samples_split = [2,4,6,8]
max_depth = [2,4,6,8]
n_estimators=[50,100,150]
bootstrap=[False, True]
min_samples_leaf=[2,4,6,8]
grid = {
'min_samples_split':min_samples_split,
'max_depth': max_depth,
'min_samples_leaf':min_samples_leaf
}
model = DecisionTreeRegressor();
gs = GridSearchCV(estimator=model, param_grid=grid, verbose=10, n_jobs=-1)
gs.fit(X,y)
print(gs.best_params_)
search = AdaBoostRegressor(gs)
search.fit(X,y)
return search
def run_tree_regressor():
from sklearn.tree import DecisionTreeRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import train_test_split
import numpy as np
from sklearn.ensemble import AdaBoostRegressor
print "running me"
X = np.genfromtxt("/home/john/Downloads/kaggle.X1.train.txt",delimiter=",") # load the text file
Y = np.genfromtxt("/home/john/Downloads/kaggle.Y.train.txt",delimiter=",")
x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2)
rng = np.random.RandomState(1)
depth = 35 # current lowest
for estimators in [130,235,300,345,450]:
treeAdaBoost = AdaBoostRegressor(DecisionTreeRegressor(max_depth=depth),n_estimators=estimators, random_state=rng)
treeAdaBoost.fit(x_train, y_train)
print "adabost estimators @ " + str(estimators) + ":", treeAdaBoost.score(x_test, y_test)
def round1(X, y):
# Set parameters
model = AdaBoostRegressor()
n = len(y)
# Perform 5-fold cross validation
scores = []
kf = KFold(n, n_folds=5, shuffle=True)
# Calculate mean absolute deviation for train/test for each fold
for train_idx, test_idx in kf:
X_train, X_test = X[train_idx], X[test_idx]
y_train, y_test = y[train_idx], y[test_idx]
model.fit(X_train, y_train)
prediction = model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, prediction))
scores.append(rmse)
return scores
def svm_smooth(data, residual_imf, period):
train_data = []
lable = []
for i in range(period,len(residual_imf)-20):
tmp = data[i-period:i+1]
train_data.append(tmp)
lable.append(residual_imf[i])
rng = np.random.RandomState(1)
clf = AdaBoostRegressor(svm.SVR(),n_estimators=1, random_state=rng)
clf.fit(train_data, lable)
smooth_data = []
for i in range(len(data)):
if i<=period:
smooth_data.append(data[i])
else:
smooth_data.append(clf.predict([data[i-period:i+1]])[0])
return smooth_data
def train_model(training, testing, window=5, n=5):
X_train, y_train = prepare_data(training)
X_test, y_test = prepare_data(testing)
rf = RandomForestRegressor()
rf.fit(X_train, y_train)
predrf = rf.predict(X_test)
print "mse for random forest regressor: ", mean_squared_error(predrf, y_test)
gb = GradientBoostingRegressor(n_estimators=100, learning_rate=0.025)
gb.fit(X_train, y_train)
predgb = gb.predict(X_test)
print "mse for gradient boosting regressor: ", mean_squared_error(predgb, y_test)
## plot feature importance using GBR results
fx_imp = pd.Series(gb.feature_importances_, index=['bb', 'momentum', 'sma', 'volatility'])
fx_imp /= fx_imp.max() # normalize
fx_imp.sort()
ax = fx_imp.plot(kind='barh')
fig = ax.get_figure()
fig.savefig("output/feature_importance.png")
adb = AdaBoostRegressor(DecisionTreeRegressor())
adb.fit(X_train, y_train)
predadb = adb.predict(X_test)
print "mse for adaboosting decision tree regressor: ", mean_squared_error(predadb, y_test)
scale = StandardScaler()
scale.fit(X_train)
X_trainscale = scale.transform(X_train)
X_testscale = scale.transform(X_test)
knn = BaggingRegressor(KNeighborsRegressor(n_neighbors=10), max_samples=0.5, max_features=0.5)
knn.fit(X_trainscale, y_train)
predknn = knn.predict(X_testscale)
print "mse for bagging knn regressor: ", mean_squared_error(predknn, y_test)
pred_test = 0.1*predrf+0.2*predgb+0.1*predadb+0.6*predknn
print "mse for ensemble all the regressors: ", mean_squared_error(pred_test, y_test)
result = testing.copy()
result.ix[5:-5, 'trend'] = pred_test
result.ix[10:, 'pred'] = pred_test * result.ix[5:-5, 'IBM'].values
result.ix[:-5, 'pred_date'] = result.index[5:]
return result
def xgb_train(x_train, x_label, x_test):
model = 'xgb'
#model = 'adaboost'
#if model.count('xgb') >0:
params = {}
params["objective"] = "reg:linear"
params["eta"] = 0.005 # [0,1]
params["min_child_weight"] = 6
params["subsample"] = 0.7
params["colsample_bytree"] = 0.7
params["scale_pos_weight"] = 1.0
params["silent"] = 1
params["max_depth"] = 9
if config.nthread > 1:
params["nthread"] = 1
num_rounds = 10000
xgtrain = xgb.DMatrix(x_train, label=x_label)
xgval = xgb.DMatrix(x_test)
#train using early stopping and predict
watchlist = [(xgtrain, "train")]
#model = xgb.train(params, xgtrain, num_rounds, watchlist, early_stopping_rounds=120, feval=gini_metric)
model = xgb.train(params, xgtrain, num_rounds, watchlist, early_stopping_rounds=120)
pred1 = model.predict( xgval )
#clf = RandomForestRegressor()
#clf = LogisticRegression()
#clf = GradientBoostingRegressor()
clf = AdaBoostRegressor( ExtraTreesRegressor(max_depth=9), n_estimators=200 )
clf.fit(x_train, x_label)
pred2 = clf.predict(x_test)
#pred = pred1 * pred2 / (pred1 + pred2)
#pred = 0.7 * (pred1**0.01) + 0.3 * (pred2**0.01)
#pred = (pred1.argsort() + pred2.argsort()) / 2
pred = 0.6 * pred1 + 0.4 * pred2
return pred
def train_and_predict_adab_stacked_gbr (train, labels, test, feature_names = None) :
" Attmept with SVR ... "
print ("Training ADABoost with GBR as base model")
t0 = time.clock()
if (gridSearch) :
params_dict = {'adab__learning_rate' : [0.1, 0.3]}
#model = GridSearchCV(regr, params_dict, n_jobs = 3, cv = kfObject, verbose = 10, scoring = 'mean_squared_error')
else :
base = GradientBoostingRegressor(random_state = randomState, learning_rate = 0.1, n_estimators = 1500, max_depth = 6, subsample = 0.95, max_features = 1, verbose = 10)
model = AdaBoostRegressor(random_state = randomState, base_estimator = base, n_estimators = 3, learning_rate = 0.005)
model.fit(train, labels)
print ("Model fit completed in %.3f sec " %(time.clock() - t0))
if (gridSearch) :
print ("Best estimator: ", model.best_estimator_)
print ("Best MSLE scores: %.4f" %(model.best_score_))
print ("Best RMSLE score: %.4f" %(math.sqrt(-model.best_score_)))
else :
float_formatter = lambda x: "%.4f" %(x)
print ("Feature importances: ", sorted(zip([float_formatter(x) for x in model.feature_importances_], feature_names), reverse=True))
return model.predict(test)
ridge_model_full_data = ridge.fit(X_train, y_train)
#print('Fitting SVR...')
#svr_model_full_data = svr.fit(X_train,y_train)
print('Fitting GradientBoosting...')
gbr_model_full_data = gbr.fit(X_train, y_train)
print('Fitting XGBoost...')
xgb_model_full_data = xgboost.fit(X_train, y_train)
print('Fitting LightGBM...')
lgb_model_full_data = lightgbm.fit(X_train, y_train)
print('Fitting AdaBoost...')
adaboost_model_full_data = adaboost.fit(X_train, y_train)
#print('Fitting extratrees...')
#extratrees_model_full_data = extratrees.fit(X_train,y_train)
#print('Fitting Bagging...')
#bagging_model_full_data = bagging.fit(X_train,y_train)
print('Done fitting all models')
# In[57]:
#Blending model predictions
print('Blending model predictions...')
#def blend_models_predict(X):
# return ((0.045 * elastic_model_full_data.predict(X))+(0.5 * lasso_model_full_data.predict(X))
#Store the accuracy results for each model in a dataframe for final comparison
tempResultsDf = pd.DataFrame({'Model':['Bagging'],'Training_Score': acc_BG_train,
'Test_Score': acc_BG, 'K_Fold_Mean': kf_res_mean, 'K_Fold_Std': kf_res_std})
resultsDf = pd.concat([resultsDf, tempResultsDf])
resultsDf = resultsDf[['Model', 'Training_Score','Test_Score','K_Fold_Mean','K_Fold_Std']]
resultsDf
# # Ensemble Technique - AdaBoosting
# In[201]:
from sklearn.ensemble import AdaBoostRegressor
abcl = AdaBoostRegressor(n_estimators=50,random_state=1)
abcl = abcl.fit(X_train, y_train)
# In[204]:
y_predict = abcl.predict(X_test)
abcl_train=abcl.score(X_train , y_train)
print("Ada Boosting - Train Accuracy:",abcl_train)
abcl_test = abcl.score(X_test , y_test)
print("Ada Boosting - Test Accuracy:",abcl_test)
results = cross_val_score(abcl, X, y, cv=kfold, scoring='r2')
print(results)
kf_res_mean=results.mean()*100.0
kf_res_std=results.std()*100.0
'linear',
'square',
'exponential',
]
min_mean = 999999
minloss = ''
min_n = 0
data_list = []
for loss in losstype:
for n in range(100, 5000, 100):
ada_1 = AdaBoostRegressor(n_estimators=n, loss=loss)
ada_1.fit(X, Y)
ysame = ada_1.predict(X)
mean_diff = abs(Y - ysame).mean()
print loss + ' ' + str(n) + ' ' + str(mean_diff)
data_list.append([loss, n, mean_diff])
if (mean_diff < min_mean):
min_mean = mean_diff
minloss = loss
min_n = n
with open("ada_data.csv", "w") as f:
writer = csv.writer(f)
writer.writerows(data_list)
def price_predictions(ticker, start, end, forecast_out):
file_path = symbol_to_path(ticker)
df = pd.read_csv(file_path,
index_col="<DTYYYYMMDD>",
parse_dates=True,
usecols=[
"<DTYYYYMMDD>", "<OpenFixed>", "<HighFixed>",
"<LowFixed>", "<CloseFixed>", "<Volume>"
],
na_values="nan")
df = df.rename(
columns={
'<DTYYYYMMDD>': 'Date',
"<OpenFixed>": 'Open',
'<HighFixed>': 'High',
'<LowFixed>': 'Low',
'<CloseFixed>': 'Close',
'<Volume>': 'Volume'
})
# columns order for backtrader type
columnsOrder = ["Open", "High", "Low", "Close", "Volume", "OpenInterest"]
# change the index by new index
df = df.reindex(columns=columnsOrder)
# change date index to increasing order
df = df.sort_index()
# take a part of dataframe
df = df.loc[start:end]
df['HL_PCT'] = (df['High'] - df['Low']) / df['Close'] * 100.0
df['PCT_change'] = (df['Close'] - df['Open']) / df['Open'] * 100.0
bbwindow = 25
vlwindow = 10
mmtum = 10
df['BB_Value'] = compute_indicator_bb(df, window=bbwindow)
df['Volatility'] = compute_indicator_volatility(df, timeperiod=vlwindow)
df['Momentum'] = talib.MOM(df['Close'].values, timeperiod=mmtum)
df['OBV'] = talib.OBV(df['Close'].values,
df['Volume'].values.astype(np.float64))
df['MACD'], _, _ = talib.MACD(df['Close'].values,
fastperiod=12,
slowperiod=26,
signalperiod=9)
_, df['STOCH'] = talib.STOCH(df['High'].values,
df['Low'].values,
df['Close'].values,
fastk_period=14,
slowk_period=1,
slowd_period=5)
df['MFI'] = talib.MFI(df['High'].values,
df['Low'].values,
df['Close'].values,
df['Volume'].values.astype(np.float64),
timeperiod=14)
# df['EMA3'] = pd.Series(pd.Series.ewm(df['Close'], span = 3, min_periods = 3-1).mean())
# df['EMA6'] = pd.Series(pd.Series.ewm(df['Close'], span = 6, min_periods = 6-1).mean())
# df['EMA18'] = pd.Series(pd.Series.ewm(df['Close'], span = 18, min_periods = 18-1).mean())
df['PDI'] = talib.PLUS_DI(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=14)
df['NDI'] = talib.MINUS_DI(df['High'].values,
df['Low'].values,
df['Close'].values,
timeperiod=14)
# df = df[['Close', 'HL_PCT', 'PCT_change', 'Volume','BB_Value',
# 'Volatility', 'Momentum', 'MACD', 'STOCH', 'MFI', 'OBV']]
#
df = df[['Close', 'HL_PCT', 'PCT_change', 'Volume', 'BB_Value']]
df.fillna(method="ffill", inplace=True)
df.fillna(method="backfill", inplace=True)
forecast_col = 'Close'
#inplace : boolean, default False
# If True, fill in place. Note: this will modify any other views on this object,
# (e.g. a no-copy slice for a column in a DataFrame).
# Du bao 1% cua du lieu
# Copy du lieu tu cot Adj. Close vao cot moi
# Lenh Shift
df['Target'] = df[forecast_col].shift(-forecast_out)
# Lenh Drop loai bo label
#axis : int or axis name: column
# Whether to drop labels from the index (0 / ‘index’) or columns (1 / ‘columns’).
X = np.array(df.drop(['Target'], 1))
y_true = df[forecast_col][-forecast_out:]
# Preprocessing Input Data
X = preprocessing.scale(X)
#from sklearn.preprocessing import MinMaxScaler
#scaler = MinMaxScaler()
#X = scaler.fit_transform(X)
# Tach gia tri X va X_lately ra khoi chuoi
X_lately = X[-forecast_out:]
X = X[:-forecast_out]
# Loai bo cac gia tri NA
# df.dropna(inplace=True)
# Target la vector y lay tu cot label
y = np.array(df['Target'].dropna())
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
#X_train, X_test, y_train, y_test = train_test_split(X, y)
#from sklearn.preprocessing import MinMaxScaler
#from sklearn.preprocessing import StandardScaler
#scaler = MinMaxScaler()
#scaler = StandardScaler()
#X_train = scaler.fit_transform(X_train)
#X_test = scaler.transform(X_test)
#X_lately = scaler.transform(X_lately)
n_neighbors = 5
knn = neighbors.KNeighborsRegressor(n_neighbors, weights='uniform')
knn.fit(X_train, y_train)
print('Train score KNN: ', knn.score(X_train, y_train),
'Test score KNN : ', knn.score(X_test, y_test))
forecast_set = knn.predict(X_lately)
print('Price for next {} days'.format(forecast_out), forecast_set)
bagging = BaggingRegressor(DecisionTreeRegressor(),
n_estimators=50,
random_state=50)
bagging.fit(X_train, y_train)
print('Train score BAG: ', bagging.score(X_train, y_train),
'Test score BAG : ', bagging.score(X_test, y_test))
forecast_set = bagging.predict(X_lately)
print('Price for next {} days'.format(forecast_out), forecast_set)
rf = RandomForestRegressor(n_estimators=50, random_state=50)
rf.fit(X_train, y_train)
print('Train score RF: ', rf.score(X_train, y_train), 'Test score RF : ',
rf.score(X_test, y_test))
forecast_set = rf.predict(X_lately)
print('Price for next {} days'.format(forecast_out), forecast_set)
adaboost = AdaBoostRegressor(neighbors.KNeighborsRegressor(n_neighbors=5),
n_estimators=30,
random_state=0)
#adaboost = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
# n_estimators=30, random_state=0)
adaboost.fit(X_train, y_train)
print('Train score Ada: ', adaboost.score(X_train, y_train),
'Test score Ada : ', adaboost.score(X_test, y_test))
forecast_set = adaboost.predict(X_lately)
print('Price for next {} days'.format(forecast_out), forecast_set)
X, y = shuffle(housing_data.data, housing_data.target, random_state=7)
# Split the data 80/20 (80% for training, 20% for testing)
num_training = int(0.8 * len(X))
X_train, y_train = X[:num_training], y[:num_training]
X_test, y_test = X[num_training:], y[num_training:]
# Fit decision tree regression model
dt_regressor = DecisionTreeRegressor(max_depth=4)
dt_regressor.fit(X_train, y_train)
# Fit decision tree regression model with AdaBoost
ab_regressor = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
n_estimators=400,
random_state=7)
ab_regressor.fit(X_train, y_train)
# Evaluate performance of Decision Tree regressor
y_pred_dt = dt_regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred_dt)
evs = explained_variance_score(y_test, y_pred_dt)
print("\n#### Decision Tree performance ####")
print("Mean squared error =", round(mse, 2))
# Evaluate performance of AdaBoost
y_pred_ab = ab_regressor.predict(X_test)
mse = mean_squared_error(y_test, y_pred_ab)
evs = explained_variance_score(y_test, y_pred_ab)
print("\n#### AdaBoost performance ####")
print("Mean squared error =", round(mse, 2))
print("Explained variance score =", round(evs, 2))
0:
-1] #names will be replaced by features directly taken from user selection
X = data[feature_cols]
y = data[names[-1]] #names replaced by target taken from user selection
#print(X.shape)
#print(y.shape)
'''#preprocessing output in integers
le = preprocessing.LabelEncoder()
le.fit(y)
Encoded_classes = list(le.classes_)
y = list(map(int, le.transform(y)))'''
validation_size = 0.20
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=validation_size,
random_state=10)
# Instantiate
abc = AdaBoostRegressor()
# Fit
abc.fit(X_train, y_train)
# Predict
y_pred = abc.predict(X_test)
accuracy = np.sqrt(metrics.mean_squared_error(y_test, y_pred))
print(accuracy)
import pickle
df = pd.read_csv('hdf_denorm.csv')
## preprocess
#df.rename(columns={'instant':'rec_id',
#'dteday':'datetime',
#'holiday':'is_holiday',
#'workingday':'is_workingday',
#'weathersit':'weather_condition',
#'hum':'humidity',
#'atemp':'felt_temperature',
#'mnth':'month',
#'cnt':'total_count',
#'hr':'hour',
#'yr':'year'},inplace=True)
df = df[['hour','is_holiday', 'weekday','felt_temperature_actual','humidity_actual','users_total']]
#df.is_holiday = df.is_holiday.astype('category')
#df.weekday = df.weekday.astype('category')
df = pd.get_dummies(df)
## modelling
x = df.drop(columns = ['users_total'])
y = df['users_total']
ada = AdaBoostRegressor()
ada.fit(x,y)
pickle.dump(ada, open('my_model.pkl','wb'))
max_depth=8,
min_samples_leaf=4,
random_state=2)
#scoring(gbr)
gbr = gbr.fit(X_train, y_train)
gbr_accuracy = evaluate(gbr, X_test, y_test)
dtr = DecisionTreeRegressor(min_samples_leaf=3, max_depth=8, random_state=2)
dtr = dtr.fit(X_train, y_train)
dtr_accuracy = evaluate(dtr, X_test, y_test)
abr = AdaBoostRegressor(n_estimators=100,
learning_rate=0.1,
loss='linear',
random_state=2)
abr = abr.fit(X_train, y_train)
abr_accuracy = evaluate(abr, X_test, y_test)
def plot_importances(model, model_name):
importances = model.feature_importances_
std = np.std([model.feature_importances_ for feature in model.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]
# Plot the feature importances of the forest
plt.figure(figsize=(8, 5))
plt.title("Feature importances of " + model_name)
plt.bar(range(X_train.shape[1]),
importances[indices],
color="r",
n_estimators=n_estimators,
sample_size=sample_size,
steps=steps,
fold=fold,
random_state=random_state)
print(X)
print(type(X))
#regr_1.fit(X, y)
y_pred1 = regr_1.predict(x_target_test)
# 4.3 As comparision, use AdaBoostR2 without transfer learning
#==============================================================================
regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=6),
n_estimators=n_estimators)
#==============================================================================
regr_2.fit(x_target_train, y_target_train)
y_pred2 = regr_2.predict(x_target_test)
# 4.4 Plot the results
plt.figure()
plt.scatter(x_target_train, y_target_train, c="k", label="target_train")
plt.plot(x_target_test,
y_target_test,
c="b",
label="target_test",
linewidth=0.5)
plt.plot(x_target_test,
y_pred1,
c="r",
label="TwoStageTrAdaBoostR2",
linewidth=2)
def test_regression_toy():
# Check classification on a toy dataset.
clf = AdaBoostRegressor(random_state=0)
clf.fit(X, y_regr)
assert_array_equal(clf.predict(T), y_t_regr)
print('SVR')
mod_SVR = model_validation(dat, SVR(), param_RF, t_s=test_size)
#%%
''' Standardize '''
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
''' Regression '''
# Use numpy array from now on
data = df.values[:100]
new_data = df.values[100:]
X = data[:, 1:]
y = data[:, 0]
X_new = new_data[:, 1:]
model = AdaBoostRegressor(n_estimators=50, learning_rate=0.1)
fitter = model.fit(X_train, y_train) # Use all the data from case1Data
predict = fitter.predict(X_new) # Use data from Case1Data.txt
def cal_MSE_firm_onedate_svr(start_date_dt,firm_macro_2,firstdate):
h=5
c = 1
beforeYears=3
n_steps = 5 # the length of X data
n_inputs_1 = 10 # the number of variables
n_inputs_2 = 7
num_layers_0=30
keep_prob=0.5
#n_neurons = 10
n_outputs = 1
learning_rate = 0.0001
n_epochs = 400
# global numpy_array_1_1
# global numpy_array_bias_1_1
# global numpy_array_1_2
# global numpy_array_bias_1_2
# global numpy_array_2_1
# global numpy_array_bias_2_1
# global numpy_array_2_2
# global numpy_array_bias_2_2
back_date = (start_date_dt-relativedelta(years=beforeYears,months=0,days=0)).strftime('%Y-%m-%d')
start_date=start_date_dt.strftime('%Y-%m-%d')
firm_hist_t = firm_macro_2[back_date:start_date]
xydata=firm_hist_t
#xydata_train =xydata.dropna()
xydata=xydata.dropna(subset=filter(lambda x: x not in ['VaR_sp500','VaR_sse','VaR_hsi',
'VaR_sti'],xydata.columns))
xydata_train =xydata
# xdata = xydata_train[['VOL', 'RET', 'X3T_change', 'change_slope', 'ted', 'cre_spread',
# 'STI','re_excess','equ_vol', 'VOL_2','RET_2','X3T_change_2','change_slope_2','ted_2','cre_spread_2','STI_2',
# 're_excess_2','equ_vol_2','Rsysh']].iloc[:-5,:]
xdata = xydata_train[['VOL', 'RET', 'HK_equ_vol', 'HIBOR', 'VOL_2',
'HIBOR_2','HK_equ_vol_2','Rhsih','Rhsi_t']].iloc[:-5,:]
std_scale_x = preprocessing.StandardScaler().fit(xdata)
xdata = std_scale_x.transform(xdata)
ydata = xydata_train[['Rjh']].iloc[:-5,:]
std_scale_y = preprocessing.StandardScaler().fit(ydata)
ydata = std_scale_y.transform(ydata)
# xdata_predict = xydata[['VOL', 'RET', 'X3T_change', 'change_slope', 'ted', 'cre_spread',
# 'STI','re_excess','equ_vol', 'VOL_2','RET_2','X3T_change_2','change_slope_2','ted_2','cre_spread_2','STI_2',
# 're_excess_2','equ_vol_2','Rsysh','Varh']].iloc[xydata.shape[0]-1:xydata.shape[0],:]
xdata_predict = xydata[['VOL', 'RET', 'HK_equ_vol', 'HIBOR', 'VOL_2',
'HIBOR_2','HK_equ_vol_2','Rhsih','Rhsi_t',
'VaR_hsi']].iloc[xydata.shape[0]-1:xydata.shape[0],:]
xdata_predict2 = xydata[['VOL', 'RET', 'HK_equ_vol', 'HIBOR', 'VOL_2',
'HIBOR_2','HK_equ_vol_2','Rhsih',
'Rhsi_t']].iloc[xydata.shape[0]-1:xydata.shape[0],:]
y2 = xydata[['Rjh']].iloc[xydata.shape[0]-1:xydata.shape[0],:]
if not math.isnan(xdata_predict.VaR_hsi):
xdata_predict.Rhsih=xdata_predict.VaR_hsi
# if not math.isnan(xdata_predict.VaR_hsi):
# xdata_predict.Rhsih=xdata_predict.VaR_hsi
#drop column Varh
xdata_predict=xdata_predict.drop(labels=['VaR_hsi'], axis=1)
xdata_predict = std_scale_x.transform(xdata_predict)
xdata_predict2 = std_scale_x.transform(xdata_predict2)
#xdata_predict = xdata_predict.reshape(1,n_steps,xdata.shape[2])
xdata_startdate = xdata_predict
np.random.seed(0)
regr = AdaBoostRegressor(random_state=0, n_estimators=100)
regr.fit(xdata,ydata.ravel())
MES_startdate=regr.predict(xdata_startdate)
y_predict=regr.predict(xdata_predict2)
MES_startdate = std_scale_y.inverse_transform(MES_startdate)
print(MES_startdate)
y_predict = std_scale_y.inverse_transform(y_predict)
loss_y = pow((y2.iat[0,0]-y_predict[0]),2)
print(loss_y)
if MES_startdate[0]<-1:
MES_startdate[0]=-1
#if MES_startdate[0,0]>0:
# MES_startdate[0,0]=0
return (-MES_startdate[0],loss_y)
def test_regression_toy():
# Check classification on a toy dataset.
clf = AdaBoostRegressor(random_state=0)
clf.fit(X, y_regr)
assert_array_equal(clf.predict(T), y_t_regr)
def train_adboost_cart(data, avg={}):
test_X, test_Y = load_data(data, avg)
adaboost = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4), loss="square", learning_rate=0.01, n_estimators=500)
adaboost.fit(test_X, test_Y)
return adaboost
# 학습모델 구축을 위해 data형식을 Vector로 변환
AB_X1 = AB_m_Inputdata.values
AB_Y1 = AB_m_Outputdata.values
# Training Data, Test Data 분리
AB_X1_train, AB_X1_test, AB_Y1_train, AB_Y1_test = train_test_split(
AB_X1, AB_Y1, test_size=0.33, random_state=42)
########################################################################################################################
# AdaBoost 학습 모델 구축
making_adaboost_model = AdaBoostRegressor(DecisionTreeRegressor(max_depth=10),
n_estimators=100,
learning_rate=0.5,
random_state=42)
making_adaboost_model.fit(AB_X1_train, AB_Y1_train)
AB_m_predicted = making_adaboost_model.predict(AB_X1_test)
# [1,n]에서 [n,1]로 배열을 바꿔주는 과정을 추가
AB_length_x1test = len(AB_X1_test)
AB_m_predicted = AB_m_predicted.reshape(AB_length_x1test, 1)
# 학습 모델 성능 확인
AB_m_mae = abs(AB_m_predicted - AB_Y1_test).mean(axis=0)
AB_m_mape = (np.abs((AB_m_predicted - AB_Y1_test) / AB_Y1_test).mean(axis=0))
AB_m_rmse = np.sqrt(((AB_m_predicted - AB_Y1_test)**2).mean(axis=0))
AB_m_rmsle = np.sqrt(
(((np.log(AB_m_predicted + 1) - np.log(AB_Y1_test + 1))**2).mean(axis=0)))
print(AB_m_mae)
def AdaBoost(train_features, test_feat, train_labels):
clf = AdaBoostRegressor()
clf.fit(train_features, train_labels)
pred_test_labels = clf.predict(test_feat)
return [pred_test_labels, clf]
print(u'score 准确率为 %.4lf' % acc_decision_tree)
# K 折交叉验证统计决策树准确率
print(u'cross_val_score 准确率为 %.4lf' %
np.mean(cross_val_score(clf, train_features, train_labels, cv=10)))
# 房价预测
# 加载数据
data = load_boston()
# 分割数据
train_x, test_x, train_y, test_y = train_test_split(data.data,
data.target,
test_size=0.25,
random_state=33)
# 使用 AdaBoost 回归模型
regressor = AdaBoostRegressor()
regressor.fit(train_x, train_y)
pred_y = regressor.predict(test_x)
mse = mean_squared_error(test_y, pred_y)
print(" 房价预测结果 ", pred_y)
print(" 均方误差 = ", round(mse, 2))
# 使用决策树回归模型
dec_regressor = DecisionTreeRegressor()
dec_regressor.fit(train_x, train_y)
pred_y = dec_regressor.predict(test_x)
mse = mean_squared_error(test_y, pred_y)
print(" 决策树均方误差 = ", round(mse, 2))
# 使用 KNN 回归模型
knn_regressor = KNeighborsRegressor()
knn_regressor.fit(train_x, train_y)
pred_y = knn_regressor.predict(test_x)
from sklearn.tree import DecisionTreeRegressor
# Create the dataset
rng = np.random.RandomState(1)
X = np.linspace(0, 6, 100)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
# dataArr, labelArr = loadDataSet("7. AdaBoost/horseColicTraining2.txt")
# Fit regression model
regr_1 = DecisionTreeRegressor(max_depth=4)
regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
n_estimators=300,
random_state=rng)
regr_1.fit(X, y)
regr_2.fit(X, y)
# Predict
y_1 = regr_1.predict(X)
y_2 = regr_2.predict(X)
# Plot the results
plt.figure()
plt.scatter(X, y, c="k", label="training samples")
plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Boosted Decision Tree Regression")
plt.legend()
plt.show()
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
ax.legend(["XG Boost Regression"])
plt.show()
#Fitting AdaBoostRegressor
min = 1000
for dep in [10, 15, 18, 25]:
for esti in [550, 575, 600]:
for lr in [0.01, 0.3, 1.25, 1.5]:
regr_ada = AdaBoostRegressor(DecisionTreeRegressor(max_depth=dep),
n_estimators=esti,
random_state=0,
learning_rate=lr,
loss="exponential")
regr_ada.fit(X_train, Y_train)
ada_pred = regr_ada.predict(X_CV)
RMLSE = np.sqrt(mean_squared_log_error(Y_CV, ada_pred))
#print ("Error (AdaBoostRegressor)=",RMLSE," for depth=",dep," for estimators=",esti," and learning rate=",lr)
if (min > RMLSE):
min = RMLSE
lr_f = lr
esti_f = esti
dep_f = dep
print(
"Root Mean Square Logarithmic Cross Validation Error (AdaBoostRegressor)=",
RMLSE, " for depth=", dep, " for estimators=", esti, " and learning rate=",
lr)
regr_ada = AdaBoostRegressor(DecisionTreeRegressor(max_depth=dep_f),
n_estimators=esti_f,
wderrn = np.array(errn)[np.array(errn) <= 20]
wderrn = wderrn[wderrn >= -20]
wderrn.size / len(errn)
np.median(wderrn)
np.mean(wderrn)
plt.figure()
plt.plot(wderrn)
x = np.linspace(0, 8760, num=8760)[:, np.newaxis]
y = nord['FABBISOGNO REALE'].ix[nord.index.year == 2015].values.ravel()
regr = AdaBoostRegressor(DecisionTreeRegressor(max_depth=24),
n_estimators=3000)
regr.fit(x, y)
yhat = regr.predict(x)
plt.figure()
plt.plot(yhat, color='blue', marker='o')
plt.plot(y, color='red')
plt.figure()
plt.plot(y - yhat)
#### fabbisogno 2009
sbil2009 = pd.read_excel(
'C:/Users/utente/Documents/misure/aggregato_sbilanciamento2009.xlsx')
nord2009 = sbil2009.ix[sbil2009['CODICE RUC'] == 'UC_DP1608_NORD']
nord2009.index = pd.date_range('2009-01-01', '2010-01-02',
freq='H')[:nord2009.shape[0]]
dt.predict(X[:10])
print("profiling...")
txt = profile(runlocaldt, pyinst_format='text')
print(txt[1])
###########################################
# Profiling for AdaBoostRegressor
# +++++++++++++++++++++++++++++++
#
# The next example shows how long the python runtime
# spends in each operator.
ada = AdaBoostRegressor()
ada.fit(X, y)
onx = to_onnx(ada, X[:1].astype(numpy.float32), target_opset=11)
oinf = OnnxInference(onx, runtime='python_compiled')
print(oinf)
########################################
# The profiling.
def runlocal():
for i in range(0, 500):
oinf.run({'X': X32})
print("profiling...")
txt = profile(runlocal, pyinst_format='text')
model_parameter = "adaboost_lr0p1_lossSquare_nest1000_minsamplesplit2_maxdepth5_sqrt_random_skipdatacolumn012"
# In[ ]:
#estimator for adaboost
ada_tree_estimator = DecisionTreeRegressor(min_samples_split=2,
max_depth=5,
max_features='sqrt',
splitter='random')
#adaboost regressor
ab = AdaBoostRegressor(ada_tree_estimator,
learning_rate=0.1,
loss='square',
n_estimators=1000)
#fit
ab.fit(X_train, Y_train)
# ## Validation
# In[ ]:
def visualize_loss_curves(model, X_train, Y_train, X_test, Y_test, cut):
plt.close('all')
Y_train_pred = np.zeros_like(Y_train)
Y_test_pred = np.zeros_like(Y_test)
losses_train = []
losses_test = []
# For each added classifier, store the new training and test losses.
clf_1 = ensemble.GradientBoostingClassifier()
clf_2 = AdaBoostRegressor(ensemble.GradientBoostingClassifier(),
n_estimators=50,
random_state=None)
# CV Loop
for train_index, test_index in kf:
# for each iteration of the for loop we'll do a test train split
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
t = StandardScaler()
X_train = t.fit_transform(X_train)
clf_1.fit(X_train, y_train) # Train clf_1 on the training data
clf_2.fit(X_train, y_train) # Train clf_2 on the training data
X_test = t.transform(X_test)
y_pred1[test_index] = clf_1.predict(
X_test) # Predict clf_1 using the test and store in y_pred
y_pred2[test_index] = clf_2.predict(
X_test) # Predict clf_2 using the test and store in y_pred
plot_r2(y, y_pred1, "Performance of CV DecisionTreeRegressor")
plt.show()
r2_score(y, y_pred1)
rmse = sqrt(mean_squared_error(y, y_pred1))
print("GradientBoostingClassifier CV 1 rmse: ", rmse)
plot_r2(y, y_pred2, "Performance of CV AdaBoost")
# --> 0.37737
r2_score(y_test, y_pred)
# --> 0.62263
############################################################
## Decision tree regression with AdaBoost
from sklearn.ensemble import AdaBoostRegressor
regressor2 = AdaBoostRegressor(DecisionTreeRegressor(
criterion='mse',
max_depth=5,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
splitter='best'),
n_estimators=500,
random_state=42)
regressor2.fit(X_train, y_train)
# predict
y_pred = regressor2.predict(X_test)
MSE = mean_squared_error(y_test, y_pred)
# --> 0.37569
r2_score(y_test, y_pred)
# --> 0.6243
#mse in $
mse = mean_absolute_error(y_test, y_pred)
print("The mean absolute error is:$", mse)
#chceking r^2
from sklearn.metrics import r2_score
print("r_Score:", r2_score(y_test, y_pred))
bg = BaggingRegressor(RandomForestRegressor(), n_estimators=10)
bg.fit(X_train, y_train)
bg.score(X_train, y_train)
bg.score(X_test, y_test)
#Adaboosting
regr = AdaBoostRegressor()
regr.fit(X_train, y_train)
regr.score(X_test, y_test)
#Decision
from sklearn.tree import DecisionTreeRegressor
dt = DecisionTreeRegressor()
dt.fit(X_train, y_train)
dt.score(X_test, y_test)
#gradientBoost
from sklearn.ensemble import GradientBoostingRegressor
gb = GradientBoostingRegressor()
gb.fit(X_train, y_train)
gb.score(X_train, y_train)
gb.score(X_test, y_test)
svr = SVR(kernel='rbf')
svr_fit = svr.fit(Rtrain_X, Rtrain_y)
# 預測
#svr = svr.predict(train_X)
svr_test_y_predicted = svr_fit.predict(Rtest_X)
# 績效
Train_r2 = r2_score(Rtrain_y, svr_fit.predict(Rtrain_X))
print('Train R2: ', Train_r2)
PCCs = np.corrcoef(svr_test_y_predicted, Rtest_y)
RMSE = (mean_squared_error(Rtest_y, svr_test_y_predicted))**(1 / 2)
R_squared = r2_score(Rtest_y, svr_test_y_predicted)
print('r2:', R_squared)
print(PCCs)
print(RMSE)
#__________________________________________________________________
'''Adaboost Regression'''
from sklearn.ensemble import AdaBoostRegressor
abtR = AdaBoostRegressor() #n_estimators=1000)
abtR.fit(Rtrain_X, Rtrain_y)
abtR_predicted = abtR.predict(Rtest_X)
abtR_PCCs = np.corrcoef(abtR_predicted, Rtest_y)
abtR_RMSE = (mean_squared_error(Rtest_y, abtR_predicted))**(1 / 2)
abtR_R_squared = r2_score(Rtest_y, abtR_predicted)
print('TRAIN SCORE: ', abtR.score(Rtrain_X, Rtrain_y), ' TEST SCORE: ',
abtR.score(Rtest_X, Rtest_y), '\n')
print('#_____________________________________________________', '\n')
#__________________________________________________________________
h_GbrModel = GradientBoostingRegressor() h_rdModel = RandomForestRegressor() h_sgdModel = SGDRegressor() h_elnModel = ElasticNet() x_train, x_test, y_train, y_test = model_selection.train_test_split(h_data.data,h_data.target, test_size = 0.3) #h_normalizer = Normalizer() h_scaler = MinMaxScaler() #h_data.data = h_normalizer.fit_transform(h_data.data) h_scaler.fit(x_train) h_scaler.transform(x_test) h_LnModel.fit(x_train,y_train) h_SVRModel.fit(x_train,y_train) h_nnModel.fit(x_train,y_train) h_adaModel.fit(x_train,y_train) h_GbrModel.fit(x_train,y_train) h_rdModel.fit(x_train,y_train) h_sgdModel.fit(x_train,y_train) h_elnModel.fit(x_train,y_train) print(metrics.r2_score(h_LnModel.predict(x_test),y_test)) print(metrics.r2_score(h_SVRModel.predict(x_test),y_test)) print(metrics.r2_score(h_nnModel.predict(x_test),y_test)) print(metrics.r2_score(h_adaModel.predict(x_test),y_test)) print(metrics.r2_score(h_GbrModel.predict(x_test),y_test)) print(metrics.r2_score(h_rdModel.predict(x_test),y_test)) print(metrics.r2_score(h_sgdModel.predict(x_test),y_test)) print(metrics.r2_score(h_elnModel.predict(x_test),y_test))
from sklearn.model_selection import train_test_split
X_dev,X_eval, y_dev,y_eval = train_test_split(X, y,
test_size=0.33, random_state=42)
X_train,X_test, y_train,y_test = train_test_split(X_dev, y_dev,
test_size=0.33, random_state=492)
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import GradientBoostingRegressor
rng = np.random.RandomState(1)
bdt = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
n_estimators=300, random_state=rng)
bdt.fit(X_train, y_train)
print('BDT fitted')
import pickle
pickle.dump(bdt,open("bdt.joblib","wb"))
bdt = pickle.load(open("bdt.joblib","rb"))
y_predicted = bdt.predict(X)
y_predicted.dtype = [('Trigger_correction', 'float64')]
print(type(y_predicted))
print(len(y_predicted))
from root_numpy import array2root
from sklearn.ensemble import AdaBoostRegressor
train = pd.read_csv('parkinsons_train.csv')
test = pd.read_csv('parkinsons_test.csv')
features = ["MDVP:Fo(Hz)", "MDVP:Fhi(Hz)", "MDVP:Flo(Hz)", "MDVP:Jitter(%)", "MDVP:Jitter(Abs)", "MDVP:RAP", "MDVP:PPQ", "Jitter:DDP", "MDVP:Shimmer", "MDVP:Shimmer(dB)", "Shimmer:APQ3", "Shimmer:APQ5", "MDVP:APQ", "Shimmer:DDA", "NHR", "HNR", "RPDE", "DFA", "spread1", "spread2", "D2", "PPE"]
# 64%
X = train[features]
y = train['status']
temp = test['status']
i = 1
j = 1
results = []
regr = AdaBoostRegressor(DecisionTreeRegressor(max_depth=100), n_estimators=100, random_state=)
regr.fit(X,y)
'''
for i,j in range(1000):
acc = accuracy_score(regr.predict(test[jitter], y))
if results.length() == 0 or acc > results[2]:
results = [i, j, acc]
print('Use max_depth: ' + results[0] + ' , n_estimators: ' + results[1] + ' to get a maximum accuracy score of ' + results[2])
i = results[0]
j = results[1]
'''
pred5 = regr.predict(test[jitter])
print(str(accuracy_score(pred5, temp)))
X, y = shuffle(boston.data, boston.target)
offset = int(0.7*len(X))
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
# We will vary the number of base learners from 2 to 300
max_learners = arange(2, 300)
train_err = zeros(len(max_learners))
test_err = zeros(len(max_learners))
for i, l in enumerate(max_learners):
# Set up a Adaboost Regression Learner with l base learners
regressor = AdaBoostRegressor(n_estimators=l)
# Fit the learner to the training data
regressor.fit(X_train, y_train)
# Find the MSE on the training set
train_err[i] = mean_squared_error(y_train, regressor.predict(X_train))
# Find the MSE on the testing set
test_err[i] = mean_squared_error(y_test, regressor.predict(X_test))
# Plot training and test error as a function of the number of base learners
pl.figure()
pl.title('Boosting: Performance vs Number of Learners')
pl.plot(max_learners, test_err, lw=2, label = 'test error')
pl.plot(max_learners, train_err, lw=2, label = 'training error')
pl.legend()
pl.xlabel('Number of Learners')
pl.ylabel('RMS Error')
pl.show()
def def_AdaBoostRegressor(self, estimators): abr_ = AdaBoostRegressor(n_estimators=estimators) abr_.fit(self.Xtrain, self.Ytrain) pred = abr_.predict(self.Xtest) return pred
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, spredictions)))
coeffecients = pd.DataFrame(sgd.coef_,X.columns)
coeffecients.columns = ['Coeffecient']
#print(coeffecients)
#ADABOOST
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
from sklearn.ensemble import AdaBoostRegressor
abreg = AdaBoostRegressor(random_state=0, n_estimators=100)
abreg.fit(X_train,y_train)
abpredictions = abreg.predict( X_test)
#print(abpredictions)
plt.scatter(y_test,abpredictions)
plt.title('ADABOOST')
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')
plt.show()
from sklearn import metrics
print('MAE:', metrics.mean_absolute_error(y_test, abpredictions))
print('MSE:', metrics.mean_squared_error(y_test, abpredictions))
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, abpredictions)))
def decision_tree(X, y1, y2, y3):
n, _ = X.shape
nTrain = int(0.5 * n) #training on 50% of the data
Xtrain = X[:nTrain, :]
ytrain = y1[:nTrain]
ytrain_registered = y2[:nTrain]
ytest_registered = y2[nTrain:]
ytrain_casual = y3[:nTrain]
ytest_casual = y3[nTrain:]
Xtest = X[nTrain:, :]
ytest = y1[nTrain:]
#regular
clf_1 = DecisionTreeRegressor(max_depth=None)
clf_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=None),
n_estimators=500)
clf_4 = RandomForestRegressor(n_estimators=500,
max_depth=None,
min_samples_split=1,
random_state=0)
clf_5 = ExtraTreesRegressor(n_estimators=500,
max_depth=None,
min_samples_split=1,
random_state=0)
clf_3 = GradientBoostingRegressor(n_estimators=500,
max_depth=None,
random_state=0)
print "finished generating tree"
clf_1.fit(Xtrain, ytrain_registered)
clf_2.fit(Xtrain, ytrain_registered)
clf_3.fit(Xtrain, ytrain_registered)
clf_4.fit(Xtrain, ytrain_registered)
clf_5.fit(Xtrain, ytrain_registered)
print 'Finished fitting'
dt_regular = clf_1.predict(Xtest)
ada_regular = clf_2.predict(Xtest)
grad_regular = clf_3.predict(Xtest)
rf_regular = clf_4.predict(Xtest)
et_regular = clf_5.predict(Xtest)
#casual
print "finished generating tree"
clf_1.fit(Xtrain, ytrain_casual)
clf_2.fit(Xtrain, ytrain_casual)
clf_3.fit(Xtrain, ytrain_casual)
clf_4.fit(Xtrain, ytrain_casual)
clf_5.fit(Xtrain, ytrain_casual)
print 'Finished fitting'
dt_casual = clf_1.predict(Xtest)
ada_casual = clf_2.predict(Xtest)
grad_casual = clf_3.predict(Xtest)
rf_casual = clf_4.predict(Xtest)
et_casual = clf_5.predict(Xtest)
feature_imps = clf_4.feature_importances_
print "regular decision tree"
print rmsle(ytest, dt_regular + dt_casual)
print "boosted decision tree"
print rmsle(ytest, ada_regular + ada_casual)
print "gradient tree boosting"
print rmsle(ytest, grad_regular + grad_casual)
print "random forest classifier"
print rmsle(ytest, rf_regular + rf_casual)
print "extra trees classifier"
print rmsle(ytest, et_casual + et_regular)
print "feature importances"
print feature_imps