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from __future__ import division, print_function
import numpy as np
import progressbar
from mlfromscratch.utils import train_test_split, standardize, to_categorical, normalize
from mlfromscratch.utils import mean_squared_error, accuracy_score
from mlfromscratch.supervised_learning import XGBoostRegressionTree
from mlfromscratch.deep_learning.activation_functions import Sigmoid
from mlfromscratch.utils.misc import bar_widgets
from mlfromscratch.utils import Plot
class LogisticLoss():
def __init__(self):
sigmoid = Sigmoid()
self.log_func = sigmoid
self.log_grad = sigmoid.gradient
def loss(self, y, y_pred):
y_pred = np.clip(y_pred, 1e-15, 1 - 1e-15)
p = self.log_func(y_pred)
return y * np.log(p) + (1 - y) * np.log(1 - p)
# gradient w.r.t y_pred
def gradient(self, y, y_pred):
p = self.log_func(y_pred)
return -(y - p)
# w.r.t y_pred
def hess(self, y, y_pred):
p = self.log_func(y_pred)
return p * (1 - p)
class XGBoost(object):
"""The XGBoost classifier.
n_estimators: int
The number of classification trees that are used.
learning_rate: float
The step length that will be taken when following the negative gradient during
min_samples_split: int
The minimum number of samples needed to make a split when building a tree.
min_impurity: float
The minimum impurity required to split the tree further.
max_depth: int
The maximum depth of a tree.
def __init__(self, n_estimators=200, learning_rate=0.001, min_samples_split=2,
min_impurity=1e-7, max_depth=2):
self.n_estimators = n_estimators # Number of trees
self.learning_rate = learning_rate # Step size for weight update
self.min_samples_split = min_samples_split # The minimum n of sampels to justify split
self.min_impurity = min_impurity # Minimum variance reduction to continue
self.max_depth = max_depth # Maximum depth for tree = progressbar.ProgressBar(widgets=bar_widgets)
# Log loss for classification
self.loss = LogisticLoss()
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = XGBoostRegressionTree(
def fit(self, X, y):
y = to_categorical(y)
y_pred = np.zeros(np.shape(y))
for i in
tree = self.trees[i]
y_and_pred = np.concatenate((y, y_pred), axis=1), y_and_pred)
update_pred = tree.predict(X)
y_pred -= np.multiply(self.learning_rate, update_pred)
def predict(self, X):
y_pred = None
# Make predictions
for tree in self.trees:
# Estimate gradient and update prediction
update_pred = tree.predict(X)
if y_pred is None:
y_pred = np.zeros_like(update_pred)
y_pred -= np.multiply(self.learning_rate, update_pred)
# Turn into probability distribution (Softmax)
y_pred = np.exp(y_pred) / np.sum(np.exp(y_pred), axis=1, keepdims=True)
# Set label to the value that maximizes probability
y_pred = np.argmax(y_pred, axis=1)
return y_pred
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