def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression, debug):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.init_estimate = None
self.regression = regression
self.debug = debug
self.multipliers = []
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Square loss for regression
# Log loss for classification
self.loss = SquareLoss(grad_wrt_theta=False)
if not self.regression:
self.loss = LogisticLoss(grad_wrt_theta=False)
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = RegressionTree(
min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth)
self.trees.append(tree)
def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression, debug):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.init_estimate = None
self.regression = regression
self.debug = debug
self.multipliers = []
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Square loss for regression
# Log loss for classification
self.loss = SquareLoss(grad_wrt_theta=False)
if not self.regression:
self.loss = LogisticLoss(grad_wrt_theta=False)
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = RegressionTree(min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth)
self.trees.append(tree)
def __init__(self,
n_estimators=200,
learning_rate=0.001,
min_samples_split=2,
min_impurity=1e-7,
max_depth=2,
debug=False):
self.n_estimators = n_estimators # Number of trees
self.learning_rate = learning_rate
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
self.debug = debug
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Log loss for classification
self.loss = LogisticLoss(grad_wrt_theta=False)
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = XGBoostRegressionTree(
min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth,
loss=self.loss)
self.trees.append(tree)
class LogisticRegression():
"""The Logistic Regression classifier.
Parameters:
-----------
learning_rate: float
The step length that will be taken when following the negative gradient during
training.
gradient_descent: boolean
True or false depending if gradient descent should be used when training. If
false then we use batch optimization by least squares.
"""
def __init__(self, learning_rate=.1, gradient_descent=True):
self.param = None
self.learning_rate = learning_rate
self.gradient_descent = gradient_descent
self.sigmoid = Sigmoid()
self.log_loss = LogisticLoss()
def fit(self, X, y, n_iterations=4000):
# Add dummy ones for bias weights
X = np.insert(X, 0, 1, axis=1)
n_samples, n_features = np.shape(X)
# Initial parameters between [-1/sqrt(N), 1/sqrt(N)]
a = -1 / math.sqrt(n_features)
b = -a
self.param = (b - a) * np.random.random((n_features, )) + a
# Tune parameters for n iterations
for i in range(n_iterations):
# Make a new prediction
y_pred = self.sigmoid.function(X.dot(self.param))
if self.gradient_descent:
# Move against the gradient of the loss function with
# respect to the parameters to minimize the loss
self.param -= self.learning_rate * self.log_loss.gradient(
y, X, self.param)
else:
# Make a diagonal matrix of the sigmoid gradient column vector
diag_gradient = make_diagonal(
self.sigmoid.gradient(X.dot(self.param)))
# Batch opt:
self.param = np.linalg.pinv(X.T.dot(diag_gradient).dot(X)).dot(
X.T).dot(
diag_gradient.dot(X).dot(self.param) + y - y_pred)
def predict(self, X):
# Add dummy ones for bias weights
X = np.insert(X, 0, 1, axis=1)
# Print a final prediction
dot = X.dot(self.param)
y_pred = np.round(self.sigmoid.function(dot)).astype(int)
return y_pred
class LogisticRegression():
"""The Logistic Regression classifier.
Parameters:
-----------
learning_rate: float
The step length that will be taken when following the negative gradient during
training.
gradient_descent: boolean
True or false depending if gradient descent should be used when training. If
false then we use batch optimization by least squares.
"""
def __init__(self, learning_rate=.1, gradient_descent=True):
self.param = None
self.learning_rate = learning_rate
self.gradient_descent = gradient_descent
self.sigmoid = Sigmoid()
self.log_loss = LogisticLoss()
def fit(self, X, y, n_iterations=4000):
# Add dummy ones for bias weights
X = np.insert(X, 0, 1, axis=1)
n_samples, n_features = np.shape(X)
# Initial parameters between [-1/sqrt(N), 1/sqrt(N)]
a = -1 / math.sqrt(n_features)
b = -a
self.param = (b - a) * np.random.random((n_features,)) + a
# Tune parameters for n iterations
for i in range(n_iterations):
# Make a new prediction
y_pred = self.sigmoid.function(X.dot(self.param))
if self.gradient_descent:
# Move against the gradient of the loss function with
# respect to the parameters to minimize the loss
self.param -= self.learning_rate * self.log_loss.gradient(y, X, self.param)
else:
# Make a diagonal matrix of the sigmoid gradient column vector
diag_gradient = make_diagonal(self.sigmoid.gradient(X.dot(self.param)))
# Batch opt:
self.param = np.linalg.pinv(X.T.dot(diag_gradient).dot(X)).dot(X.T).dot(diag_gradient.dot(X).dot(self.param) + y - y_pred)
def predict(self, X):
# Add dummy ones for bias weights
X = np.insert(X, 0, 1, axis=1)
# Print a final prediction
dot = X.dot(self.param)
y_pred = np.round(self.sigmoid.function(dot)).astype(int)
return y_pred
class GradientBoosting(object):
"""Super class of GradientBoostingClassifier and GradientBoostinRegressor.
Uses a collection of regression trees that trains on predicting the gradient
of the loss function.
Parameters:
-----------
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
training.
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.
regression: boolean
True or false depending on if we're doing regression or classification.
debug: boolean
True or false depending on if we wish to display the training progress.
"""
def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression, debug):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.init_estimate = None
self.regression = regression
self.debug = debug
self.multipliers = []
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Square loss for regression
# Log loss for classification
self.loss = SquareLoss(grad_wrt_theta=False)
if not self.regression:
self.loss = LogisticLoss(grad_wrt_theta=False)
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = RegressionTree(min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth)
self.trees.append(tree)
def fit(self, X, y):
y_pred = np.full(np.shape(y), np.mean(y, axis=0))
for i in self.bar(range(self.n_estimators)):
tree = self.trees[i]
gradient = self.loss.gradient(y, y_pred)
tree.fit(X, gradient)
update = tree.predict(X)
# Update y prediction
y_pred -= np.multiply(self.learning_rate, update)
def predict(self, X):
y_pred = np.array([])
# Make predictions
for i, tree in enumerate(self.trees):
update = tree.predict(X)
update = np.multiply(self.learning_rate, update)
# prediction = np.array(prediction).reshape(np.shape(y_pred))
y_pred = -update if not y_pred.any() else y_pred - update
if not self.regression:
# Turn into probability distribution
y_pred = np.exp(y_pred) / np.expand_dims(
np.sum(np.exp(y_pred), axis=1), axis=1)
# Set label to the value that maximizes probability
y_pred = np.argmax(y_pred, axis=1)
return y_pred
def __init__(self, learning_rate=.1, gradient_descent=True):
self.param = None
self.learning_rate = learning_rate
self.gradient_descent = gradient_descent
self.sigmoid = Sigmoid()
self.log_loss = LogisticLoss()
def __init__(self, learning_rate=.1, gradient_descent=True):
self.param = None
self.learning_rate = learning_rate
self.gradient_descent = gradient_descent
self.sigmoid = Sigmoid()
self.log_loss = LogisticLoss()
class GradientBoosting(object):
"""Super class of GradientBoostingClassifier and GradientBoostinRegressor.
Uses a collection of regression trees that trains on predicting the gradient
of the loss function.
Parameters:
-----------
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
training.
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.
regression: boolean
True or false depending on if we're doing regression or classification.
debug: boolean
True or false depending on if we wish to display the training progress.
"""
def __init__(self, n_estimators, learning_rate, min_samples_split,
min_impurity, max_depth, regression, debug):
self.n_estimators = n_estimators
self.learning_rate = learning_rate
self.min_samples_split = min_samples_split
self.min_impurity = min_impurity
self.max_depth = max_depth
self.init_estimate = None
self.regression = regression
self.debug = debug
self.multipliers = []
self.bar = progressbar.ProgressBar(widgets=bar_widgets)
# Square loss for regression
# Log loss for classification
self.loss = SquareLoss(grad_wrt_theta=False)
if not self.regression:
self.loss = LogisticLoss(grad_wrt_theta=False)
# Initialize regression trees
self.trees = []
for _ in range(n_estimators):
tree = RegressionTree(
min_samples_split=self.min_samples_split,
min_impurity=min_impurity,
max_depth=self.max_depth)
self.trees.append(tree)
def fit(self, X, y):
y_pred = np.full(np.shape(y), np.mean(y, axis=0))
for i in self.bar(range(self.n_estimators)):
tree = self.trees[i]
gradient = self.loss.gradient(y, y_pred)
tree.fit(X, gradient)
update = tree.predict(X)
# Update y prediction
y_pred -= np.multiply(self.learning_rate, update)
def predict(self, X):
y_pred = np.array([])
# Make predictions
for i, tree in enumerate(self.trees):
update = tree.predict(X)
update = np.multiply(self.learning_rate, update)
# prediction = np.array(prediction).reshape(np.shape(y_pred))
y_pred = -update if not y_pred.any() else y_pred - update
if not self.regression:
# Turn into probability distribution
y_pred = np.exp(y_pred) / np.expand_dims(np.sum(np.exp(y_pred), axis=1), axis=1)
# Set label to the value that maximizes probability
y_pred = np.argmax(y_pred, axis=1)
return y_pred