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Commit 28999d44 authored by xuehui's avatar xuehui Committed by Yan Ni
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Update evaluate parameters from GMM (#1195) 

* update readme in ga_squad

* update readme

* fix typo

* Update README.md

* Update README.md

* Update README.md

* update readme

* update

* fix path

* update reference

* fix bug in config file

* update nni_arch_overview.png

* update

* update

* update

* update home page

* update default value of metis tuner

* fix broken link in CommunitySharings

* update docs about nested search space

* update docs

* rename cascding to nested

* fix broken link

* update

* update issue link

* fix typo

* update evaluate parameters from GMM

* refine code

* fix optimized mode bug

* update import warning

* update warning

* update optimized mode
parent 8329d30f
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Showing with 47 additions and 30 deletions
src/sdk/pynni/nni/bohb_advisor/bohb_advisor.py
View file @ 28999d44
......@@ -106,7 +106,7 @@ class Bracket():
self.s_max = s_max
self.eta = eta
self.max_budget = max_budget
self.optimize_mode = optimize_mode
self.optimize_mode = OptimizeMode(optimize_mode)
self.n = math.ceil((s_max + 1) * eta**s / (s + 1) - _epsilon)
self.r = max_budget / eta**s
......
src/sdk/pynni/nni/hyperband_advisor/hyperband_advisor.py
View file @ 28999d44
......@@ -144,7 +144,7 @@ class Bracket():
self.configs_perf = [] # [ {id: [seq, acc]}, {}, ... ]
self.num_configs_to_run = [] # [ n, n, n, ... ]
self.num_finished_configs = [] # [ n, n, n, ... ]
self.optimize_mode = optimize_mode
self.optimize_mode = OptimizeMode(optimize_mode)
self.no_more_trial = False
def is_completed(self):
......
src/sdk/pynni/nni/metis_tuner/Regression_GMM/Selection.py
View file @ 28999d44
......@@ -49,15 +49,16 @@ def selection_r(x_bounds,
num_starting_points=100,
minimize_constraints_fun=None):
'''
Call selection
Select using different types.
'''
minimize_starting_points = [lib_data.rand(x_bounds, x_types)\
for i in range(0, num_starting_points)]
minimize_starting_points = clusteringmodel_gmm_good.sample(n_samples=num_starting_points)
outputs = selection(x_bounds, x_types,
clusteringmodel_gmm_good,
clusteringmodel_gmm_bad,
minimize_starting_points,
minimize_starting_points[0],
minimize_constraints_fun)
return outputs
def selection(x_bounds,
......
src/sdk/pynni/nni/metis_tuner/metis_tuner.py
View file @ 28999d44
......@@ -20,15 +20,15 @@
import copy
import logging
import numpy as np
import os
import random
import statistics
import sys
import warnings
from enum import Enum, unique
from multiprocessing.dummy import Pool as ThreadPool
import numpy as np
import nni.metis_tuner.lib_constraint_summation as lib_constraint_summation
import nni.metis_tuner.lib_data as lib_data
import nni.metis_tuner.Regression_GMM.CreateModel as gmm_create_model
......@@ -42,8 +42,6 @@ from nni.utils import OptimizeMode, extract_scalar_reward
logger = logging.getLogger("Metis_Tuner_AutoML")
NONE_TYPE = ''
CONSTRAINT_LOWERBOUND = None
CONSTRAINT_UPPERBOUND = None
......@@ -93,7 +91,7 @@ class MetisTuner(Tuner):
self.space = None
self.no_resampling = no_resampling
self.no_candidates = no_candidates
self.optimize_mode = optimize_mode
self.optimize_mode = OptimizeMode(optimize_mode)
self.key_order = []
self.cold_start_num = cold_start_num
self.selection_num_starting_points = selection_num_starting_points
......@@ -254,6 +252,9 @@ class MetisTuner(Tuner):
threshold_samplessize_resampling=50, no_candidates=False,
minimize_starting_points=None, minimize_constraints_fun=None):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
next_candidate = None
candidates = []
samples_size_all = sum([len(i) for i in samples_y])
......@@ -271,13 +272,12 @@ class MetisTuner(Tuner):
minimize_constraints_fun=minimize_constraints_fun)
if not lm_current:
return None
if no_candidates is False:
candidates.append({'hyperparameter': lm_current['hyperparameter'],
logger.info({'hyperparameter': lm_current['hyperparameter'],
'expected_mu': lm_current['expected_mu'],
'expected_sigma': lm_current['expected_sigma'],
'reason': "exploitation_gp"})
if no_candidates is False:
# ===== STEP 2: Get recommended configurations for exploration =====
results_exploration = gp_selection.selection(
"lc",
......@@ -290,34 +290,48 @@ class MetisTuner(Tuner):
if results_exploration is not None:
if _num_past_samples(results_exploration['hyperparameter'], samples_x, samples_y) == 0:
candidates.append({'hyperparameter': results_exploration['hyperparameter'],
temp_candidate = {'hyperparameter': results_exploration['hyperparameter'],
'expected_mu': results_exploration['expected_mu'],
'expected_sigma': results_exploration['expected_sigma'],
'reason': "exploration"})
'reason': "exploration"}
candidates.append(temp_candidate)
logger.info("DEBUG: 1 exploration candidate selected\n")
logger.info(temp_candidate)
else:
logger.info("DEBUG: No suitable exploration candidates were")
# ===== STEP 3: Get recommended configurations for exploitation =====
if samples_size_all >= threshold_samplessize_exploitation:
print("Getting candidates for exploitation...\n")
logger.info("Getting candidates for exploitation...\n")
try:
gmm = gmm_create_model.create_model(samples_x, samples_y_aggregation)
results_exploitation = gmm_selection.selection(
x_bounds,
x_types,
gmm['clusteringmodel_good'],
gmm['clusteringmodel_bad'],
minimize_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
if ("discrete_int" in x_types) or ("range_int" in x_types):
results_exploitation = gmm_selection.selection(x_bounds, x_types,
gmm['clusteringmodel_good'],
gmm['clusteringmodel_bad'],
minimize_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
else:
# If all parameters are of "range_continuous", let's use GMM to generate random starting points
results_exploitation = gmm_selection.selection_r(x_bounds, x_types,
gmm['clusteringmodel_good'],
gmm['clusteringmodel_bad'],
num_starting_points=self.selection_num_starting_points,
minimize_constraints_fun=minimize_constraints_fun)
if results_exploitation is not None:
if _num_past_samples(results_exploitation['hyperparameter'], samples_x, samples_y) == 0:
candidates.append({'hyperparameter': results_exploitation['hyperparameter'],\
'expected_mu': results_exploitation['expected_mu'],\
'expected_sigma': results_exploitation['expected_sigma'],\
'reason': "exploitation_gmm"})
temp_expected_mu, temp_expected_sigma = gp_prediction.predict(results_exploitation['hyperparameter'], gp_model['model'])
temp_candidate = {'hyperparameter': results_exploitation['hyperparameter'],
'expected_mu': temp_expected_mu,
'expected_sigma': temp_expected_sigma,
'reason': "exploitation_gmm"}
candidates.append(temp_candidate)
logger.info("DEBUG: 1 exploitation_gmm candidate selected\n")
logger.info(temp_candidate)
else:
logger.info("DEBUG: No suitable exploitation_gmm candidates were found\n")
......@@ -338,11 +352,13 @@ class MetisTuner(Tuner):
if results_outliers is not None:
for results_outlier in results_outliers:
if _num_past_samples(samples_x[results_outlier['samples_idx']], samples_x, samples_y) < max_resampling_per_x:
candidates.append({'hyperparameter': samples_x[results_outlier['samples_idx']],\
temp_candidate = {'hyperparameter': samples_x[results_outlier['samples_idx']],\
'expected_mu': results_outlier['expected_mu'],\
'expected_sigma': results_outlier['expected_sigma'],\
'reason': "resampling"})
'reason': "resampling"}
candidates.append(temp_candidate)
logger.info("DEBUG: %d re-sampling candidates selected\n")
logger.info(temp_candidate)
else:
logger.info("DEBUG: No suitable resampling candidates were found\n")
......
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