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metalearners.py
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metalearners.py
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from __future__ import division
import functools
import util
__author__ = 'James Robert Lloyd, Emma Smith'
__description__ = 'Objects that model learner performance and make decisions'
# import time
import copy
# import os
# import cPickle as pickle
from collections import defaultdict
import psutil
# import multiprocessing
import logging
import random
import time as time_module
# set up logging for metalearner module
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
import numpy as np
from sklearn.cross_validation import KFold
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from agent import Agent
# import util
import freezethaw as ft
import constants
from stackcombiner import StackCombiner
import libscores
class ForgetfulGreedy(Agent):
"""Recommends the current best performing algorithm"""
def __init__(self, **kwargs):
super(ForgetfulGreedy, self).__init__(**kwargs)
self.learner_score_values = dict()
self.learner_score_times = dict()
self.learner_prediction_times = dict()
self.communication_sleep = 1
def read_messages(self):
while True:
try:
message = self.inbox.pop(0)
self.standard_responses(message)
if message['subject'] == 'scores':
# Update local copies of scores
self.learner_score_values = message['learner_score_values']
self.learner_score_times = message['learner_score_times']
# FIXME - assumes test data always available
self.learner_prediction_times = message['learner_test_prediction_times']
except (IndexError, AttributeError):
pass
def first_action(self):
# No reason to start paused at the moment
self.state = 'running'
p = psutil.Process()
current_cpus = p.cpu_affinity()
if len(current_cpus) > 1:
p.cpu_affinity([current_cpus[1]]) # meta learning only happens on the second CPU
def next_action(self):
# Check mail
self.read_messages()
# If running, form an opinion of the best learner and tell parent
if self.state == 'running':
names_scores = []
data = False
for name in self.learner_score_times.iterkeys():
if len(self.learner_score_values[name]) > 0:
score = self.learner_score_values[name][-1]
data = True
else:
score = -np.inf
names_scores.append((name, score))
names_scores.sort(key=lambda x: x[1])
sorted_names = [name for (name, score) in names_scores]
if data:
self.send_to_parent(dict(subject='preference', sender=self.name, preference=sorted_names))
class IndependentFreezeThaw(Agent):
"""Assumes exponential mixture decays of all learning curves modelled separately"""
def __init__(self, **kwargs):
super(IndependentFreezeThaw, self).__init__(**kwargs)
self.remaining_time = None
self.learner_score_values = dict()
self.learner_score_times = dict()
self.learner_prediction_times = dict()
self.communication_sleep = 1
# self.learner_names = []
self.scores = defaultdict(list)
self.times = defaultdict(list)
self.times_subset = defaultdict(list)
self.scores_subset = defaultdict(list)
self.t_star = defaultdict(list)
self.alpha = defaultdict(functools.partial(util.identity, 3))
self.beta = defaultdict(functools.partial(util.identity, 1))
self.scale = defaultdict(functools.partial(util.identity, 1))
self.log_noise = defaultdict(functools.partial(util.identity, np.log(0.1)))
self.x_scale = defaultdict(functools.partial(util.identity, 2))
self.x_ell = defaultdict(functools.partial(util.identity, 0.001)) # These are currently unused
self.a = defaultdict(functools.partial(util.identity, 1)) # These are currently unused
self.b = defaultdict(functools.partial(util.identity, 1)) # These are currently unused
self.y_mean = defaultdict(list)
self.y_covar = defaultdict(list)
self.predict_mean = defaultdict(list)
self.y_samples = defaultdict(list)
self.compute_quantum = None
# TODO - think about the bounds on alpha and beta more critically! Transform them to make them scale free?
self.bounds = [[1, 5],
[0.1, 5],
[0, np.inf],
[np.log(0.0000001), np.inf],
[0.1, 10],
[0.1, 10],
[0.33, 3],
[0.33, 3]]
# self.t_star = dict()
self.waiting = True
def read_messages(self):
while True:
try:
message = self.inbox.pop(0)
except (IndexError, AttributeError):
break
else:
self.standard_responses(message)
if message['subject'] == 'scores':
self.waiting = False
# Update local copies of scores
self.learner_score_values = message['learner_score_values']
self.learner_score_times = message['learner_score_times']
# FIXME - assumes test data always available
self.learner_prediction_times = message['learner_test_prediction_times']
self.remaining_time = message['remaining_time']
self.compute_quantum = message['compute_quantum']
# print(self.remaining_time)
# FIXME - this is a bit of a hack to initialise everything here
# FIXME - and to use lists where dictionaries would be more appropriate
# if len(self.learner_names) == 0:
# # Initialise various things
# self.learner_names = sorted(list(self.learner_score_values.iterkeys()))
def first_action(self):
# No reason to start paused at the moment
self.state = 'running'
p = psutil.Process()
current_cpus = p.cpu_affinity()
if len(current_cpus) > 1:
p.cpu_affinity([current_cpus[1]]) # meta learning only happens on the second CPU
self.waiting = False
def next_action(self):
start_time = time_module.clock()
# Check mail
self.read_messages()
# If running, form an opinion of the best learner and tell parent
if self.state == 'running':
if not self.waiting:
# Check to see if we have sufficient data yet
# data = False
# for name in self.learner_score_times.iterkeys():
# if len(self.learner_score_values[name]) > 0:
# data = True
learning_curve_data = []
all_data = []
for name in self.learner_score_times.iterkeys():
if len(self.learner_score_values[name]) > 1:
learning_curve_data += self.learner_score_values[name]
all_data += self.learner_score_values[name]
if len(learning_curve_data) > 5:
names_scores = []
grand_mean = sum(all_data) / len(all_data)
grand_scale = max(all_data) - min(all_data)
if grand_scale == 0:
grand_mean = 1
grand_scale = 1
# Scale all the data
for name in self.learner_score_times.iterkeys():
self.learner_score_values[name] = [(value - grand_mean) / grand_scale
for value in self.learner_score_values[name]]
for name in self.learner_score_times.iterkeys():
# Need more than one data point to start predicting anything useful
if len(self.learner_score_values[name]) > 1:
# Set param defaults based on data
data_range = np.max(self.learner_score_values[name]) -\
np.min(self.learner_score_values[name])
if data_range <= 0 or len(self.learner_score_values[name]) == 0:
# Limited data for this child - get data from all other children
max_value = -np.inf
min_value = np.inf
for child in self.learner_score_times.iterkeys():
if len(self.learner_score_values[child]) > 0:
max_value = max(max_value, np.max(self.learner_score_values[child]))
min_value = min(min_value, np.min(self.learner_score_values[child]))
data_range = max_value - min_value
self.scale.default_factory = functools.partial(util.identity, data_range * data_range)
# Scale / 10 std deviation heuristic
self.log_noise.default_factory = functools.partial(util.identity,
np.log(data_range * data_range / 100))
self.bounds[2][-1] = data_range * data_range * 9
self.bounds[3][-1] = np.log(data_range * data_range)
self.bounds[3][0] = np.log(data_range * data_range / 40000)
# Also set the default factories
self.scale.default_factory = functools.partial(util.identity, data_range * data_range)
self.log_noise.default_factory = functools.partial(util.identity,
np.log(0.1 * data_range * data_range))
# print(data_range)
# print(np.log(data_range * data_range / 100))
# print(data_range / 10)
# Exponential mixture kernel inference
t_kernel = ft.ft_K_t_t_plus_noise
x_kernel = ft.cov_matern_5_2
m = [np.mean(self.learner_score_values[name])]
x = [0]
# Subsetting data
self.times_subset[name] = list(copy.deepcopy(self.learner_score_times[name]))
self.scores_subset[name] = list(copy.deepcopy(self.learner_score_values[name]))
# Add some jitter to make the GPs happier
# FIXME - should not need this really
for i in range(len(self.scores_subset[name])):
# self.scores_subset[name][i] += 0.001 * np.random.normal()
self.scores_subset[name][i] += (data_range / 200) * np.random.normal()
if len(self.times_subset[name]) > 50:
indices = [int(np.floor(k))
for k in np.linspace(0, len(self.times_subset[name]) - 1, 50)[1:]]
self.times_subset[name] = list(np.array(self.times_subset[name])[indices])
self.scores_subset[name] = list(np.array(self.scores_subset[name])[indices])
# self.t_star[name] = np.linspace(self.learner_score_times[name][-1],
# self.learner_score_times[name][-1] + self.remaining_time, 50)
self.t_star[name] = copy.deepcopy(self.learner_prediction_times[name])
if len(self.learner_prediction_times[name]) > 0:
current_time = self.learner_prediction_times[name][-1]
else:
current_time = 0
additional_time = self.compute_quantum
added_points = 0
while additional_time < self.remaining_time:
self.t_star[name].append(additional_time + current_time)
# additional_time *= 2
additional_time += self.compute_quantum
added_points += 1
if added_points >= 10:
break
self.t_star[name] = np.array(self.t_star[name])
# print(util.is_sorted(self.learner_score_times[name]))
# print(self.learner_score_times[name])
# print(self.t_star[name])
# print(self.remaining_time)
# Sample parameters
self.y_samples[name] = []
for _ in range(1):
# print('\nSampling\n')
xx = [self.alpha[name], self.beta[name], self.scale[name], self.log_noise[name],
self.x_scale[name], self.x_ell[name]]
logdist = lambda xx: ft.ft_ll(m, [self.times_subset[name]], [self.scores_subset[name]],
x, x_kernel,
dict(scale=xx[4], ell=xx[5]), t_kernel,
dict(scale=xx[2], alpha=xx[0], beta=xx[1], log_noise=xx[3]))
try:
xx = ft.slice_sample_bounded_max(1, 1, logdist, xx, 2, True, 10, self.bounds)[0]
except:
logger.error('Slice sampling failed - continuing')
# print('\nFinished sampling\n')
# xx = ft.slice_sample_bounded_max(1, 1, logdist, xx, 0.5, False, 10, self.bounds)[0]
# print xx
self.alpha[name] = xx[0]
self.beta[name] = xx[1]
self.scale[name] = xx[2]
self.log_noise[name] = xx[3]
self.x_scale[name] = xx[4]
self.x_ell[name] = xx[5]
# Setup params
x_kernel_params = dict(scale=self.x_scale[name], ell=self.x_ell[name])
t_kernel_params = dict(scale=self.scale[name], alpha=self.alpha[name], beta=self.beta[name],
log_noise=self.log_noise[name])
post_m, post_v = ft.ft_posterior(m, [self.times_subset[name]], [self.scores_subset[name]],
[self.t_star[name]], x, x_kernel, x_kernel_params,
t_kernel, t_kernel_params)
# Remove excess noise
for i in range(post_v[0].shape[0]):
post_v[0][i, i] -= np.exp(self.log_noise[name])
self.y_mean[name], self.y_covar[name] = post_m[0], post_v[0]
# Rescale
self.y_mean[name] = self.y_mean[name] * grand_scale + grand_mean
self.y_covar[name] = self.y_covar[name] * grand_scale
# self.y_samples[name] = []
for _ in range(5):
# print(post_m[0].shape)
# print(post_v[0].shape)
# print(np.sqrt(np.diag(post_v[0])).shape)
# sample = post_m[0].ravel() +\
# np.sqrt(np.diag(post_v[0])).ravel() * np.random.randn(*post_m[0].ravel().shape)
# print(sample.shape)
try:
# TODO - test this function when things getting close to singular
sample = np.random.multivariate_normal(post_m[0].ravel(),
post_v[0],
size=(1,))
except:
# Might have been singular
sample = post_m[0].ravel()
# Rescale
sample = sample * grand_scale + grand_mean
self.y_samples[name].append(sample)
# Send an update home
# self.send_to_parent(dict(subject='meta predictions', sender=self.name,
# t=self.times_subset, y=self.scores_subset,
# t_star=self.t_star,
# y_mean=self.y_mean,
# y_covar=self.y_covar))
# Remove old samples
while len(self.y_samples[name]) > 10:
self.y_samples[name].pop(0)
# Also compute posterior for already computed predictions
# FIXME - what if prediction times has empty lists
post_m, _ = ft.ft_posterior(m, [self.times_subset[name]], [self.scores_subset[name]],
[self.learner_prediction_times[name]], x,
x_kernel, x_kernel_params,
t_kernel, t_kernel_params)
self.predict_mean[name] = post_m[0]
# Rescale
self.predict_mean[name] = self.predict_mean[name] * grand_scale + grand_mean
# Rescale something else
self.scores_subset[name] = np.array(self.scores_subset[name]) * grand_scale + grand_mean
self.scores_subset[name] = list(self.scores_subset[name])
# Send an update home
self.send_to_parent(dict(subject='meta predictions', sender=self.name,
t=self.times_subset, y=self.scores_subset,
t_star=self.t_star,
y_mean=self.y_mean,
y_covar=self.y_covar,
y_samples=self.y_samples))
# Identify predictions thought to be the best currently
best_mean = -np.inf
best_learner = None
best_time_index = None
for name in self.learner_score_times.iterkeys():
if len(self.predict_mean[name]) > 0 and max(self.predict_mean[name]) >= best_mean:
best_mean = max(self.predict_mean[name])
best_learner = name
best_time_index = np.argmax(np.array(self.predict_mean[name]))
# print('Best learner : %s' % best_learner)
# print('Best time : %f' % self.learner_prediction_times[best_learner][best_time_index])
# print('Estimated performance : %f' %self. predict_mean[best_learner][best_time_index])
# Report home
self.send_to_parent(dict(subject='prediction selection', sender=self.name,
learner=best_learner,
time_index=best_time_index,
value=best_mean))
# Pick best candidate to run next
best_current_value = best_mean
best_learner = None
best_acq_fn = -np.inf
for name in self.learner_score_times.iterkeys():
if len(self.y_mean[name]) > 0:
mean = self.y_mean[name][-1]
# std = np.sqrt(self.y_covar[name][-1, -1] - np.exp(self.log_noise[name]))
std = np.sqrt(self.y_covar[name][-1, -1])
acq_fn = ft.trunc_norm_mean_upper_tail(a=best_current_value, mean=mean, std=std) -\
best_current_value
if acq_fn >= best_acq_fn:
best_acq_fn = acq_fn
best_learner = name
names_scores.append((name, acq_fn))
else:
names_scores.append((name, -np.inf))
# print('Selecting learner : %s' % best_learner)
# Collate result and send to parent
names_scores.sort(key=lambda x: x[1])
sorted_names = [name for (name, score) in names_scores]
self.send_to_parent(dict(subject='computation preference', sender=self.name, preference=sorted_names))
# Ask for more data
# if constants.DEBUG:
# print('\n\n\n\nAsking for scores\n\n\n\n')
self.send_to_parent(dict(subject='scores please'))
self.waiting = True
time_taken = time_module.clock() - start_time
# # Do not ask for the scores too often!
# if not self.compute_quantum is None:
# self.communication_sleep = max(1, self.compute_quantum / 3 - time_taken)
class StackerV1(Agent):
"""First experiment at stacking"""
def __init__(self, data_info, **kwargs):
super(StackerV1, self).__init__(**kwargs)
self.data_info = data_info
self.remaining_time = None
self.learners = []
self.learner_score_values = dict()
self.learner_score_times = dict()
self.learner_held_out_pred_times = dict()
self.learner_held_out_pred_files = dict()
self.learner_valid_pred_times = dict()
self.learner_valid_pred_files = dict()
self.learner_test_pred_times = dict()
self.learner_test_pred_files = dict()
self.learner_order = list()
self.stack_times = list()
self.stack_scores = list()
self.improvement_amounts = defaultdict(list)
self.improvement_times = defaultdict(list)
self.stack_test_files = []
self.communication_sleep = 1
self.waiting = True
self.valid_data = None
self.test_data = None
# Meta learner predictions
self.meta_pred_times_past = defaultdict(list)
self.meta_pred_times = defaultdict(list)
self.meta_pred_means = defaultdict(list)
self.meta_pred_covar = defaultdict(list)
self.meta_pred_samples = defaultdict(list)
# Weights in stacking and blacklist
self.meta_data_set_order = []
self.stacking_weights = []
self.stacking_variances = []
self.stacking_importances = []
self.blacklist = [] # A list of algorithms not to include in stacking
# Meta data set
self.meta_X = None
self.meta_X_test = None
self.meta_X_valid = None
self.meta_y = None
self.targets = None
# Record data in different ways
self.time_ordered_held_out_files = dict() # Lists held out filenames in order of creation
self.time_ordered_valid_files = dict() # Sim for validation set
self.time_ordered_test_files = dict() # Sim for test set
self.predict_times = defaultdict(list) # The times of these file creations - relative to the learners
self.scores_at_predict_time = defaultdict(list) # Saving the scores at these times TODO - get from FT
self.best_scores = defaultdict(functools.partial(util.identity, -np.inf)) # The best scores for each learner TODO - get from FT
self.stacking_feature_data = defaultdict(list)
# Misc state
self.data_count = 0
self.total_time = 0
self.updated_child = None
self.saved_test_files = None
self.time_checkpoint = None
self.original_compute_quantum = None
def read_messages(self):
while True:
try:
message = self.inbox.pop(0)
except (IndexError, AttributeError):
break
else:
self.standard_responses(message)
if message['subject'] == 'scores':
self.waiting = False
# Update local copies of scores
self.learners = message['learners']
self.learner_score_values = message['learner_score_values']
self.learner_score_times = message['learner_score_times']
self.learner_held_out_pred_times = message['learner_held_out_prediction_times']
self.learner_held_out_pred_files = message['learner_held_out_prediction_files']
# FIXME - hax
self.meta_pred_times_past = copy.deepcopy(self.learner_held_out_pred_times)
self.learner_valid_pred_times = message['learner_valid_prediction_times']
self.learner_valid_pred_files = message['learner_valid_prediction_files']
self.learner_test_pred_times = message['learner_test_prediction_times']
self.learner_test_pred_files = message['learner_test_prediction_files']
self.learner_order = message['learner_order']
self.remaining_time = message['remaining_time']
if message['subject'] == 'predictions':
# print('Received predictions')
self.meta_pred_times = message['times']
self.meta_pred_means = message['means']
self.meta_pred_covar = message['covar']
self.meta_pred_samples = message['samples']
if message['subject'] == 'original compute quantum':
self.original_compute_quantum = message['compute_quantum']
def first_action(self):
self.state = 'running'
p = psutil.Process()
current_cpus = p.cpu_affinity()
if len(current_cpus) > 1:
p.cpu_affinity([current_cpus[1]]) # meta learning only happens on the second CPU
if constants.DEBUG:
with open(constants.STACK_DATA_FL, 'w') as stacking_data_file:
stacking_data_file.write('ID,current,imp,imp_over_best,imp_over_stack,corr,current_stack,stack_imp\n')
# Set a few flags
self.valid_data = 'X_valid' in self.data
self.test_data = 'X_test' in self.data
# Ask for data to get things started
self.send_to_parent(dict(subject='scores please'))
self.send_to_parent(dict(subject='predictions please'))
self.waiting = True
@property
def n_predictions(self):
return len(self.learner_order)
def perform_stacking(self):
"""Outer loop of stacking - data management and calling of routines"""
self.time_checkpoint = time_module.clock()
# FIXME - Dirty hax
self.saved_test_files = copy.deepcopy(self.learner_test_pred_files)
# Setup before constructing data
# TODO - no need to repeat this all the time - can save the information!
self.time_ordered_held_out_files = dict() # Lists held out filenames in order of creation
self.time_ordered_valid_files = dict() # Sim for validation set
self.time_ordered_test_files = dict() # Sim for test set
self.predict_times = dict() # The times of these file creations - relative to the learners
self.scores_at_predict_time = defaultdict(list) # Saving the scores at these times
self.best_scores = defaultdict(functools.partial(util.identity, -np.inf)) # The best scores for each learner
# Count through the data
for n in range(self.n_predictions):
# Which child last made predictions?
self.updated_child = self.learner_order[n]
# Update time
self.predict_times[self.updated_child] = self.learner_held_out_pred_times[self.updated_child].pop(0)
# Determine most recent score
child_score = None
# Do we have access to a smoothed score?
for time, score in zip(self.meta_pred_times[self.updated_child],
self.meta_pred_means[self.updated_child]):
if np.allclose([time], [self.predict_times[self.updated_child]]):
child_score = score
# print('Used smoothed score %f' % score)
# If not - take from data
if child_score is None:
for time, score in zip(self.learner_score_times[self.updated_child],
self.learner_score_values[self.updated_child]):
if time <= self.predict_times[self.updated_child]:
child_score = score
self.scores_at_predict_time[self.updated_child].append(child_score)
if child_score > self.best_scores[self.updated_child]:
self.best_scores[self.updated_child] = child_score
update_files = True
else:
update_files = False
# Update files
if update_files:
self.time_ordered_held_out_files[self.updated_child] = self.learner_held_out_pred_files[self.updated_child].pop(0)
if self.valid_data:
self.time_ordered_valid_files[self.updated_child] = self.learner_valid_pred_files[self.updated_child].pop(0)
if self.test_data:
self.time_ordered_test_files[self.updated_child] = self.learner_test_pred_files[self.updated_child].pop(0)
else:
# Individual score did not improve - do not update file
self.learner_held_out_pred_files[self.updated_child].pop(0)
if self.valid_data:
self.learner_valid_pred_files[self.updated_child].pop(0)
if self.test_data:
self.learner_test_pred_files[self.updated_child].pop(0)
# Should we update the stacking performance and blame?
if n + 1 > len(self.stack_scores):
stacking_time_checkpoint = time_module.clock()
# Construct meta data set
self.construct_meta_data_set()
# Learn meta model
self.learn_stack()
# Has this taken a while
stacking_time_taken = time_module.clock() - stacking_time_checkpoint
# print('Original CQ = %f' % self.original_compute_quantum)
# print('Stacking learning time = %f' % stacking_time_taken)
if ((not self.original_compute_quantum is None) and
(stacking_time_taken > self.original_compute_quantum)):
self.blacklist.append(self.stacking_importances[-1][0][0]) # Most recent, least important, name
# print(self.blacklist)
# print(self.stacking_importances[-1])
self.send_to_parent(dict(subject='time taken', sender=self.name, time=stacking_time_taken))
# Record data about algorithm performance and stacking performance
self.record_stacking_data()
# Decide preferences for learners if enough data
if n >= 1:
self.recommend_learners()
# Recommend which predictions to use
self.select_predictions()
time_taken = time_module.clock() - self.time_checkpoint
# print('Total stacking time = %f' % time_taken)
# self.send_to_parent(dict(subject='time taken', sender=self.name, time=time_taken))
self.send_to_parent(dict(subject='finished stacking', send=self.name))
def construct_meta_data_set(self):
"""Assemble latest predictions into a meta data set for the purposes of stacking"""
if self.data_info['task'] == 'binary.classification':
targets = 1
elif self.data_info['task'] == 'multiclass.classification':
targets = self.data_info['target_num']
else:
raise Exception('I do not know how to set the number of targets for %s' % self.data_info['task'])
# Set number of base learners
count = 0
learners_on_the_guestlist = []
for name in self.time_ordered_held_out_files.iterkeys():
if not name in self.blacklist:
learners_on_the_guestlist.append(name)
if len(learners_on_the_guestlist) == 0:
learners_on_the_guestlist.append(self.learner_order[-1])
meta_X = np.zeros((self.data['Y_train'].shape[0], len(learners_on_the_guestlist) * targets))
if self.valid_data:
meta_X_valid = np.zeros((self.data['X_valid'].shape[0],
len(learners_on_the_guestlist) * targets))
else:
meta_X_valid = None
if self.test_data:
meta_X_test = np.zeros((self.data['X_test'].shape[0],
len(learners_on_the_guestlist) * targets))
else:
meta_X_test = None
self.data_count = 0
self.total_time = 0
self.meta_data_set_order = []
self.stacking_variances = []
for name in learners_on_the_guestlist:
self.meta_data_set_order.append(name)
# TODO - these temporary files could be destroyed here
filename = self.time_ordered_held_out_files[name]
predictions = np.load(filename)
predictions = util.ensure_2d(predictions)
if targets == 1:
self.stacking_variances.append(np.var(predictions.ravel()))
else:
var = 0
for i in range(targets):
var += np.var(predictions[:,i].ravel())
var = var / targets
self.stacking_variances.append(var)
meta_X[:, (self.data_count * targets):((self.data_count + 1) * targets)] = predictions
if self.valid_data:
filename = self.time_ordered_valid_files[name]
predictions = np.load(filename)
predictions = util.ensure_2d(predictions)
meta_X_valid[:, (self.data_count * targets):((self.data_count + 1) * targets)] = predictions
if self.test_data:
filename = self.time_ordered_test_files[name]
predictions = np.load(filename)
predictions = util.ensure_2d(predictions)
meta_X_test[:, (self.data_count * targets):((self.data_count + 1) * targets)] = predictions
self.data_count += 1
self.total_time += self.predict_times[name]
if self.data_info['task'] == 'multiclass.classification':
meta_y = self.data['Y_train_1_of_k']
else:
meta_y = self.data['Y_train']
self.meta_X = meta_X
self.meta_X_test = meta_X_test
self.meta_X_valid = meta_X_valid
self.meta_y = meta_y
self.targets = targets
# if constants.DEBUG:
# np.savetxt('../stacking-data/meta_X.csv', meta_X, delimiter=',')
# np.savetxt('../stacking-data/meta_y.csv', meta_y, delimiter=',')
# if np.all(meta_X_test == 0):
# raise Exception('Meta X test is just zeros')
# if np.all(meta_X == 0):
# raise Exception('Meta X is just zeros')
def learn_stack(self):
"""Form meta model, make predictions and update manager"""
folds = KFold(n=self.meta_X.shape[0], n_folds=5)
# Cs = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000]
# Cs = [0.01, 1, 100]
# Cs = [0.01, 100]
Cs = [100]
scores = []
best_score = -np.inf
best_C = None
for C in Cs:
sum_score = 0
n_score = 0
sum_score_test = 0
n_score_test = 0
for train, test in folds:
if self.data_info['task'] == 'multiclass.classification':
meta_model = StackCombiner(num_classes=self.targets, C=C, combine_method="tied_ovr")
else:
meta_model = LogisticRegression(C=C, penalty='l2')
meta_model.fit(self.meta_X[train], self.meta_y[train])
# FIXME - get rid of this if / else
if self.data_info['task'] == 'multiclass.classification':
pred = meta_model.predict_proba(self.meta_X[test])
else:
pred = meta_model.predict_proba(self.meta_X[test])[:, 1]
score = libscores.eval_metric(metric=self.data_info['eval_metric'],
truth=self.meta_y[test],
predictions=pred,
task=self.data_info['task'])
if not np.isnan(score):
sum_score += score
n_score += 1
score = sum_score / n_score
scores.append(score)
if score > best_score:
best_score = score
best_C = C
# Make predictions with best model
if self.data_info['task'] == 'multiclass.classification':
meta_model = StackCombiner(num_classes=self.targets, C=best_C, combine_method="tied_ovr")
else:
meta_model = LogisticRegression(C=best_C, penalty='l2')
meta_model.fit(self.meta_X, self.meta_y)
if self.valid_data:
if self.data_info['task'] == 'multiclass.classification':
pred_valid = meta_model.predict_proba(self.meta_X_valid)
else:
pred_valid = meta_model.predict_proba(self.meta_X_valid)[:, 1]
tmp_valid = util.random_temp_file_name('.npy')
np.save(tmp_valid, pred_valid)
else:
pred_valid = None
tmp_valid = None
if self.test_data:
if self.data_info['task'] == 'multiclass.classification':
pred_test = meta_model.predict_proba(self.meta_X_test)
else:
pred_test = meta_model.predict_proba(self.meta_X_test)[:, 1]
tmp_test = util.random_temp_file_name('.npy')
np.save(tmp_test, pred_test)
else:
pred_test = None
tmp_test = None
if 'Y_test' in self.data:
if self.data_info['task'] == 'multiclass.classification':
meta_y_test = self.data['Y_test_1_of_k']
else:
meta_y_test = self.data['Y_test']
test_score = libscores.eval_metric(metric=self.data_info['eval_metric'],
truth=meta_y_test,
predictions=pred_test,
task=self.data_info['task'])
else:
test_score = None
# Record weights
coefs = list(np.array(meta_model.coef_).ravel())
# print(coefs)
self.stacking_weights.append(list(zip(self.meta_data_set_order, coefs)))
self.stacking_importances.append([(learner, abs(coef) * np.sqrt(var))
for (learner, coef, var) in zip(self.meta_data_set_order,
coefs,
self.stacking_variances)])
self.stacking_weights[-1].sort(key=lambda x: abs(x[-1]))
self.stacking_importances[-1].sort(key=lambda x: abs(x[-1]))
# Record times and scores
self.stack_scores.append(best_score)
self.stack_times.append(self.total_time)
self.stack_test_files.append(tmp_test)
if len(self.stack_scores) >= 2:
self.improvement_times[self.updated_child].append(self.total_time)
self.improvement_amounts[self.updated_child].append(self.stack_scores[-1] -
self.stack_scores[-2])
# Tell parent about the estimated performance of the stacking
self.send_to_parent(dict(subject='stacking performance',
time=self.total_time,
held_out_score=best_score,
test_score=test_score,
valid_pred_file=tmp_valid,
test_pred_file=tmp_test))
# Tell parent who to blame / praise
self.send_to_parent(dict(subject='stacking blame',
times=self.improvement_times,
amounts=self.improvement_amounts))
def record_stacking_data(self):
"""Save data about stacking to file"""
# FIXME - need more object properties
if len(self.stack_scores) > 1:
stack_performance_change = self.stack_scores[-1] - self.stack_scores[-2]
previous_stack_performance = self.stack_scores[-2]
else:
stack_performance_change = np.nan
previous_stack_performance = np.nan
if len(self.scores_at_predict_time[self.updated_child]) > 1:
# individual_improvement = self.scores_at_predict_time[self.updated_child][-1] - \
# self.scores_at_predict_time[self.updated_child][-2]
individual_improvement = self.scores_at_predict_time[self.updated_child][-1] - \
max(self.scores_at_predict_time[self.updated_child][:-1])
# TODO - is this misleading? Negative improvements are not improvements
individual_improvement = max(0, individual_improvement)
previous_learner_performance = self.scores_at_predict_time[self.updated_child][-2]
else:
individual_improvement = np.nan
previous_learner_performance = np.nan
child_identity = self.updated_child
# Compute best algorithm
best_score = -np.inf
for learner, scores in self.scores_at_predict_time.iteritems():
if learner == self.updated_child:
# Do not include most recent score
best_score = max([best_score] + scores[:-1])
else:
best_score = max([best_score] + scores)
improvement_over_best = self.scores_at_predict_time[self.updated_child][-1] - best_score
if len(self.stack_scores) > 1:
improvement_over_stack = self.scores_at_predict_time[self.updated_child][-1] - \
self.stack_scores[-2]
else:
improvement_over_stack = np.nan
# Correlation
if self.data_info['task'] == 'binary.classification' and \
len(self.saved_test_files[self.updated_child]) > 1 and \
len(self.stack_scores) > 1:
learner_test_predictions = np.load(self.saved_test_files[self.updated_child][-2])
stack_predictions = np.load(self.stack_test_files[-2])
correlation = np.corrcoef(learner_test_predictions, stack_predictions)[0, 1]
else:
correlation = np.nan
# print(stack_performance_change, individual_improvement, child_identity, improvement_over_best,
# improvement_over_stack, correlation)
# Save to local structure
self.stacking_feature_data['learners'].append(child_identity)
self.stacking_feature_data['previous_performances'].append(previous_learner_performance)
self.stacking_feature_data['imps'].append(individual_improvement)
self.stacking_feature_data['imps_over_best'].append(improvement_over_best)
self.stacking_feature_data['imps_over_stack'].append(improvement_over_stack)
self.stacking_feature_data['correlations'].append(correlation)
self.stacking_feature_data['previous_stack_performances'].append(previous_stack_performance)
self.stacking_feature_data['stack_imps'].append(stack_performance_change)
# Send to parent
self.send_to_parent(dict(subject='stacking stats', sender=self.name,
data=[]))
# Save to file
if constants.DEBUG:
with open(constants.STACK_DATA_FL, 'a') as stacking_data_file:
stacking_data_file.write('%s,%f,%f,%f,%f,%f,%f,%f\n' % (child_identity,
previous_learner_performance,
individual_improvement,
improvement_over_best,
improvement_over_stack,
correlation,
previous_stack_performance,
stack_performance_change))
def recommend_learners(self):
self.recommend_ft_regression_tree()
# self.recommend_past_performance()
def recommend_past_performance(self):
"""Make recommendations based on a really simple model that tracks performance of individual algorithms"""
# First set empirical mean and variance
i = 0
temp_imp_amounts = copy.deepcopy(self.improvement_amounts)
all_recent_imps = defaultdict(list)
sum_imps = 0
sum_sqr_imps = 0
# prior_window_length = max(10, len(self.learners) * 2)
prior_window_length = 20
# FIXME - hax
n = len(self.predict_times) - 1
while i < prior_window_length and n - i >= 1:
imp = temp_imp_amounts[self.learner_order[n-i]].pop(-1)
all_recent_imps[self.learner_order[n-i]].append(imp)
sum_imps += imp
sum_sqr_imps += imp * imp
i += 1
if i > 0: # TODO - work out why this is necessary!
mean_imp = sum_imps / i
var_imp = sum_sqr_imps / i - mean_imp * mean_imp
# Estimate child means and variances
learner_values = list()
for name in self.learners:
# recent_learner_imps = self.improvement_amounts[name][-3:]
recent_learner_imps = all_recent_imps[name][-3:]
N = len(recent_learner_imps)
learner_imp_sum = sum(recent_learner_imps)
value = (mean_imp + learner_imp_sum) / ( 1 + N) +\
np.sqrt((var_imp / (1 + N))) * np.random.normal()
learner_values.append((name, value))
learner_values.sort(key=lambda x: x[-1])
sorted_names = [name for (name, score) in learner_values]
# Tell parent
self.send_to_parent(dict(subject='computation preference', sender=self.name,
preference=sorted_names))
# Remove good algorithms from the blacklist
best = sorted_names[-1]
if best in self.blacklist:
self.blacklist.remove(best)
def recommend_ft_regression_tree(self):
"""Pass freeze thaw predictions through a regression tree to produce utility estimates"""
# Collect up data to learn model
imps = self.stacking_feature_data['imps']
imps_over_best = self.stacking_feature_data['imps_over_best']
stack_imps = self.stacking_feature_data['stack_imps']
features = []
for imp, imp_over_best, stack_imp in zip(imps, imps_over_best, stack_imps):
if not np.isnan(imp) and not np.isnan(imp_over_best) and not np.isnan(stack_imp) and not imp <= 0:
features.append((imp_over_best, stack_imp / imp))
# Learn model
# TODO - learn a real model
features.sort(key=lambda x: x[0])
# print(features)
cut_offs = []
ratios = []
sum_ratios = 0
n = 0
ratio_list = []
while len(features) > 0:
imp_over_best, ratio = features.pop()
# Clip data to avoid outliers to some extent
if ratio < 0:
ratio = 0
if ratio > 2:
ratio = 2
# Count
n += 1
sum_ratios += ratio
ratio_list.append(ratio)
if n >= 10:
cut_offs.append(imp_over_best)
# ratios.append(sum_ratios / n)
ratios.append(copy.deepcopy(ratio_list))
n = 0
sum_ratios = 0
ratio_list = []
if len(cut_offs) > 0:
cut_offs[-1] = -np.inf
ratios[-1] += ratio_list
# print(cut_offs)
# print(ratios)
# Featurise potential actions
# First find best scores
best_learner_scores = defaultdict(list)
for learner in self.meta_pred_times_past.iterkeys():
if len(self.meta_pred_times_past[learner]) > 0:
best_learner_score = -np.inf
for t, m, v in zip(self.meta_pred_times[learner], self.meta_pred_means[learner],
self.meta_pred_covar[learner]):
if t <= self.meta_pred_times_past[learner][-1]:
if m > best_learner_score:
best_learner_score = m
else:
break
best_learner_scores[learner] = best_learner_score
best_score = -np.inf
for score in best_learner_scores.itervalues():
if score > best_score:
best_score = score
# Now compute featurers
# TODO - sample features - should we take a joint sample?
action_features = defaultdict(lambda: defaultdict(list))
for learner in self.meta_pred_times.iterkeys():
if len(self.meta_pred_times_past[learner]) > 0:
for sample in self.meta_pred_samples[learner]:
sample_learner_best = best_learner_scores[learner]
sample_global_best = best_score
imp_sequence = []
imp_over_best_sequence = []
for t, y in zip(self.meta_pred_times[learner], sample.ravel()):
if t <= self.meta_pred_times_past[learner][-1]:
last_time = t
else:
imp = max(0, y - sample_learner_best)
imp_over_best = y - sample_global_best
imp_sequence.append(imp)
imp_over_best_sequence.append(imp_over_best)
sample_learner_best = max(sample_learner_best, y)
sample_global_best = max(sample_global_best, y)
action_features[learner]['imps'].append(imp_sequence)
action_features[learner]['imps_over_best'].append(imp_over_best_sequence)
# Compute utilities of actions by passing through model