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random_forest.py
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import numpy as np
import logging
from sklearn import tree, linear_model, ensemble
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from scipy.sparse import csr_matrix, lil_matrix
import operator
import code
from functools import reduce
from decision_tree_with_bagging import DecisionTreeWithBaggingRegressor
class WaveletsForestRegressor:
def __init__(self, regressor='random_forest', criterion='mse', bagging=0.8, depth=9, trees=5, features='auto', seed=None):
'''
Construct a new 'WaveletsForestRegressor' object.
:regressor: Regressor type. Either "rf" or "decision_tree_with_bagging". Default is "rf".
:criterion: Splitting criterion. Same options as sklearn\'s DecisionTreeRegressor. Default is "mse".
:bagging: Bagging. Only available when using the "decision_tree_with_bagging" regressor. Default is 0.8.
:depth: Maximum depth of each tree. Default is 9.
:trees: Number of trees in the forest. Default is 5.
:features: Features to consider in each split. Same options as sklearn\'s DecisionTreeRegressor.
:seed: Seed for random operations. Default is 2000.
'''
self.norms = None
self.vals = None
self.volumes = None
self.X = None
self.y = None
self.rf = None
self.regressor = regressor
self.criterion = criterion
self.bagging = bagging
self.depth = depth
self.trees = trees
self.seed = seed
def fit(self, X_raw, y):
'''
Fit non-normalized data to simplex labels.
:X_raw: Non-normalized features, given as a 2D array with each row representing a sample.
:y: Labels, each row is given as a vertex on the simplex.
'''
logging.info('Fitting %s samples' % np.shape(X_raw)[0])
X = (X_raw - np.min(X_raw, 0))/(np.max(X_raw, 0) - np.min(X_raw, 0))
X = np.nan_to_num(X)
self.X = X
self.y = y
regressor = None
if self.regressor == 'decision_tree_with_bagging':
regressor = DecisionTreeWithBaggingRegressor(
bagging=self.bagging,
criterion=self.criterion,
depth=self.depth,
trees=self.trees,
seed=self.seed,
)
else:
regressor = ensemble.RandomForestRegressor(
n_estimators=self.trees,
criterion=self.criterion,
max_depth=self.depth,
max_features='auto',
n_jobs=-1,
random_state=self.seed,
)
rf = regressor.fit(X,y)
self.rf = rf
self.norms = np.array([])
self.vals = np.zeros((np.shape(y)[1],0))
self.volumes = np.array([])
for i in range(len(rf.estimators_)):
logging.info('Working on tree %s' % i)
estimator = rf.estimators_[i]
num_nodes = len(estimator.tree_.value)
num_features = np.shape(X)[1]
node_box = np.zeros((num_nodes, num_features, 2))
node_box[:,:,1] = 1
norms = np.zeros(num_nodes)
vals = np.zeros((np.shape(y)[1], num_nodes))
self.__traverse_nodes(estimator, 0, node_box, norms, vals)
logging.info('Traversing nodes of tree %s to extract volumes and norms' % i)
volumes = np.product(node_box[:,:,1] - node_box[:,:,0], 1)
norms = np.multiply(norms, np.sqrt(volumes))
logging.info('Number of wavelets in tree %s: %s' % (i, np.shape(norms)[0]))
self.volumes = np.append(self.volumes, volumes)
self.norms = np.append(self.norms, norms)
self.vals = np.append(self.vals, vals, axis=1)
return self
def __compute_norm(self, avg, parent_avg, volume):
norm = np.sqrt(np.sum(np.square(avg-parent_avg))*volume)
return norm
def __traverse_nodes(self, estimator, base_node_id, node_box, norms, vals):
if base_node_id == 0:
vals[:, base_node_id] = estimator.tree_.value[base_node_id][:, 0]
norms[base_node_id] = self.__compute_norm(vals[:, base_node_id], 0, 1)
left_id = estimator.tree_.children_left[base_node_id]
right_id = estimator.tree_.children_right[base_node_id]
if left_id >= 0:
node_box[left_id, :, :] = node_box[base_node_id, :, :]
node_box[left_id, estimator.tree_.feature[base_node_id], 1] = np.min([estimator.tree_.threshold[base_node_id], node_box[left_id, estimator.tree_.feature[base_node_id], 1]])
self.__traverse_nodes(estimator, left_id, node_box, norms, vals)
vals[:, left_id] = estimator.tree_.value[left_id][:, 0] - estimator.tree_.value[base_node_id][:, 0]
norms[left_id] = self.__compute_norm(vals[:, left_id], vals[:, base_node_id], 1)
if right_id >= 0:
node_box[right_id, :, :] = node_box[base_node_id, :, :]
node_box[right_id, estimator.tree_.feature[base_node_id], 0] = np.max([estimator.tree_.threshold[base_node_id], node_box[right_id, estimator.tree_.feature[base_node_id], 0]])
self.__traverse_nodes(estimator, right_id, node_box, norms, vals)
vals[:, right_id] = estimator.tree_.value[right_id][:, 0] - estimator.tree_.value[base_node_id][:, 0]
norms[right_id] = self.__compute_norm(vals[:, right_id], vals[:, base_node_id], 1)
def predict(self, X, m=1000, start_m=0, paths=None):
'''
Predict using a maximum of M-terms
:X: Data samples.
:m: Maximum of M-terms.
:start_m: The index of the starting term. Can be used to evaluate predictions incrementally over terms.
:paths: Instead of computing decision paths for each sample, the method can receive the indicator matrix. Can be used to evaluate predictions incrementally over terms.
:return: Predictions.
'''
sorted_norms = np.argsort(-self.norms)[start_m:m]
if paths == None:
paths, n_nodes_ptr = self.rf.decision_path(X)
pruned = lil_matrix(paths.shape, dtype=np.float32)
pruned[:, sorted_norms] = paths[:, sorted_norms]
predictions = pruned*self.vals.T/len(self.rf.estimators_)
return predictions
def evaluate_smoothness(self, m=1000):
'''
Evaluates smoothness for a maximum of M-terms
:m: Maximum terms to use. Default is 1000.
:return: Smothness index, n_wavelets, errors.
'''
n_wavelets = []
errors = []
step = 10
power = 2
paths, n_nodes_ptr = self.rf.decision_path(self.X)
predictions = np.zeros(np.shape(self.y))
for m_step in range(2, m, step):
if m_step > len(self.norms):
break
predictions += self.predict(self.X, m_step, max(m_step-step, 0), paths=paths)
error_norms = np.power(np.sum(np.power(self.y - predictions, power), 1), 1./power)
total_error = np.sum(np.square(error_norms), 0)/len(self.X)
if m_step > 0 and total_error > 0:
logging.info('Error for m=%s: %s' % (m_step-1, total_error) )
n_wavelets.append(m_step-1)
errors.append(total_error)
n_wavelets_log = np.log(np.reshape(n_wavelets, (-1, 1)))
errors_log = np.log(np.reshape(errors, (-1, 1)))
regr = linear_model.LinearRegression()
regr.fit(n_wavelets_log, errors_log)
alpha = np.abs(regr.coef_[0][0])
logging.info('Smoothness index: %s' % alpha)
return alpha, n_wavelets, errors
def accuracy(self, y_pred, y):
'''
Evaluates accuracy given predictions and actual labels.
:y_pred: Predictions as vertices on the simplex (preprocessed by 'pred_to_one_hot').
:y: Actual labels.
:return: Accuracy.
'''
return accuracy_score(y, y_pred)
def pred_to_one_hot(self, y_pred):
'''
Converts regression predictions to their closest vertices on the simplex
'''
argmax = np.argmax(y_pred, 1)
ret = np.zeros((len(argmax), np.shape(y_pred)[1]))
ret[np.arange(len(argmax)), argmax] = 1
return ret