Exemple #1
0
import arboretum
import numpy as np
from sklearn.datasets import load_boston
import xgboost


def rmse(y, y_hat):
    diff = np.power(y - y_hat, 2)
    return np.sqrt(np.sum(diff))

# load test data
boston = load_boston()
n = 10000

# create data matrix
data = arboretum.DMatrix(boston.data[0:n], y=boston.target[0:n])
y = boston.target[0:n]

# init model
model = arboretum.Garden('reg:linear', data, 6, 2, 1, 0.5)

# grow trees
for i in xrange(5):
    model.grow_tree()

# predict on train data set
pred = model.predict(data)

# print first n records
print pred[0:10]
import arboretum
import numpy as np
from sklearn.datasets import load_iris
import xgboost
import json

# load test data
iris = load_iris()
n = 10000

index = iris.target != 2

y = iris.target[index][0:n]
# create data matrix
data = arboretum.DMatrix(iris.data[index, 0:n], y=y)

config = json.dumps({
    'objective': 1,
    'verbose': {
        'gpu': True
    },
    'tree': {
        'eta': 0.2,
        'max_depth': 6,
        'gamma': 0.0,
        'min_child_weight': 2.0,
        'min_leaf_size': 0,
        'colsample_bytree': 1.0,
        'colsample_bylevel': 1.0,
        'lambda': 0.0,
        'alpha': 0.0
                         'tree':
                             {
                                 'eta': 0.01,
                                 'max_depth': 10,
                                 'gamma': 0.0,
                                 'min_child_weight': 20.0,
                                 'min_leaf_size': 0,
                                 'colsample_bytree': 0.6,
                                 'colsample_bylevel': 0.6,
                                 'lambda': 0.1,
                                 'gamma_relative': 0.0001
                             }})

    print(config)

    data = arboretum.DMatrix(data, data_category=data_categoties, y=labels)
    data_val = arboretum.DMatrix(data_val, data_category=data_categoties_val)

    model = arboretum.Garden(config, data)

    print('training...')

    # grow trees
    for i in range(7400):
        print('tree', i)
        model.grow_tree()
        model.append_last_tree(data_val)
        if i % 20 == 0:
            pred = model.get_y(data)
            print('train', sklearn.metrics.log_loss(labels, pred, eps=1e-6), roc_auc_score(labels, pred))
Exemple #4
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# load test data
boston = load_boston()
n = 4000

categoties = [3]
data_categories = []
for item in categoties:
    data_categories.append(convert2category(boston.data[:, item]))

data_categories = np.stack(data_categories, axis=-1)

data_source = boston.data[:, 4:5]

# create data matrix
data = arboretum.DMatrix(data_source[0:n],
                         data_category=data_categories,
                         y=boston.target[0:n])
y = boston.target[0:n]

config = json.dumps({
    'objective': 0,
    'internals': {
        'double_precision': True,
        'compute_overlap': 2
    },
    'verbose': {
        'gpu': True
    },
    'tree': {
        'eta': 0.5,
        'max_depth': 10,
Exemple #5
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import arboretum
import numpy as np
from sklearn.datasets import load_iris
import xgboost
import json

# load test data
iris = load_iris()
n = 10000

y = iris.target[0:n]
# create data matrix
data = arboretum.DMatrix(iris.data[:, 0:n], labels=y)

config = json.dumps({
    'objective': 3,
    'verbose': {
        'gpu': True,
        'booster': True
    },
    'tree': {
        'labels_count': 3,
        'eta': 0.2,
        'max_depth': 6,
        'gamma': 0.0,
        'min_child_weight': 2.0,
        'min_leaf_size': 0,
        'colsample_bytree': 1.0,
        'colsample_bylevel': 1.0,
        'lambda': 0.0,
        'alpha': 0.0,
Exemple #6
0
import arboretum
import numpy as np
from sklearn.datasets import load_iris
import xgboost
import json

# load test data
iris = load_iris()
n = 10000

index = iris.target != 2

y = iris.target[index][0:n]
# create data matrix
data = arboretum.DMatrix(iris.data[index, 0:n], y=y)

config = json.dumps({
    'objective': 1,
    'verbose': {
        'gpu': True
    },
    'tree': {
        'eta': 0.2,
        'max_depth': 6,
        'gamma': 0.0,
        'min_child_weight': 2.0,
        'min_leaf_size': 0,
        'colsample_bytree': 1.0,
        'colsample_bylevel': 1.0,
        'lambda': 0.0,
        'alpha': 0.0