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analysis_workbench_v8.py
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analysis_workbench_v8.py
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import numpy as np
import numpy.random as rnd
import itertools
import pandas as pd
import os
from time import time
import sys
from data_utils_v2 import *
import models_builder as mb
import data_handler as dh
# import derivatives_computation as dc
import json
import argparse
import scipy.stats as ss
import sklearn.preprocessing as skp
import hashlib
try:
import cPickle as pickle
except:
import pickle
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--start', help='flight to start from', type=int, default=95)
parser.add_argument('--end', help='flight to end on', type=int, default=127)
parser.add_argument('-v', help='if set the script would provide more detailed log', action='store_const', const=True, default=False)
parser.add_argument('-t', help='technique to build models with', type=str, default=None)
parser.add_argument('--model_options', help='options to build model with', type=str, default=None)
parser.add_argument('--run_options', help='options to run model with', type=str, default=None) # TODO
parser.add_argument('-ot', help='output transform to do', type=str, default='none')
parser.add_argument('-it', help='input transform to do', type=str, default='none')
parser.add_argument('--sample_name', help='sample to build model with', type=str, default='expert_v2')
parser.add_argument('--min_train', help='minimum train sample size to use for analysis', type=int, default=5)
parser.add_argument('--max_train', help='maximum train sample size to use for analysis', type=int, default=0)
parser.add_argument('--min_cenz', help='value of lower output cenz', type=int, default=None)
parser.add_argument('--max_cenz', help='value of upper output cenz', type=int, default=None)
parser.add_argument('--min_filter', help='value of lower output filter', type=int, default=None)
parser.add_argument('--max_filter', help='value of upper output filter', type=int, default=None)
parser.add_argument('--dom', help='domain name to use', type=str, default='dom2')
parser.add_argument('--join_domains', help='build separate model for each domain or not', action='store_const', const=True, default=False)
parser.add_argument('--train', help='which sample use to train', type=str, default='exclude_current') # 'current', 'exclude_current', 'all'
parser.add_argument('--test', help='which sample use to test', type=str, default='current')
parser.add_argument('--not_build_models', help='forbid to build models', action='store_const', const=False, default=True)
parser.add_argument('--rebuild_models', help='if models should be rebuilt', action='store_const', const=True, default=False)
parser.add_argument('--not_save_predictions', help='save predictions of each model on test', action='store_const', const=False, default=True)
parser.add_argument('--save_train', help='save train sample for each model', action='store_const', const=True, default=False)
parser.add_argument('--save_test', help='save test sample for each model', action='store_const', const=True, default=False)
parser.add_argument('--save_report', help='if report should be saved after script run', action='store_const', const=True, default=False)
parser.add_argument('--draw_errors', help='if error_plots_should_be_drawn', action='store_const', const=True, default=False)
parser.add_argument('--noise_statistics', help='computes noise statistics and draws noise plots', action='store_const', const=True, default=False)
parser.add_argument('--calc_distances', help='draw pairwise distances plot', action='store_const', const=True, default=False)
parser.add_argument('--neighbors_plot', help='if error_plots_should_be_drawn', action='store_const', const=True, default=False)
parser.add_argument('--rsm_report', help='write rsm coefficients to report', action='store_const', const=True, default=False)
parser.add_argument('--error_statistics', help='computes noise statistics and draws noise plots', action='store_const', const=True, default=False)
parser.add_argument('--scatter_all', help='computes noise statistics and draws noise plots', action='store_const', const=True, default=False)
return parser
def analysis_workbench(args=None):
global_options = {}
#########################################################################################
### OPTIONS ############################################################################
#######################################################################################
verbose = args['v']
START_FLIGHT = args['start']
END_FLIGHT = args['end']
DOMAINS_TO_CONSIDER = [
0,
1,
2,
3
]
BUILD_MODELS = args['not_build_models']
REBUILD_MODELS_IF_EXIST = args['rebuild_models']
DRAW_SCATTERS = False
DRAW_ERROR_PLOTS = args['draw_errors']
DRAW_RANGES = False
CALC_DISTANCES = args['calc_distances']
CHECK_DEPENDENCIES_WHOLE = False
CHECK_DEPENDENCIES_PER_FLIGHT = False
SAVE_REPORT = args['save_report']
RSM_REPORT = args['rsm_report']
SAVE_TRAIN = args['save_train']
SAVE_TEST = args['save_test']
SAVE_PREDICTIONS = args['not_save_predictions']
NEIGHBORS_PLOT= args['neighbors_plot']
global_options['sample/min_considered_test_size'] = args['min_train']
global_options['sample/train_size_to_use'] = args['max_train']
global_options['sample/output_column'] = ['output']
global_options['sample/output_transform'] = args['ot'].lower()
global_options['sample/input_transform'] = args['it'].lower()
global_options['model/join_domains_for_train'] = args['join_domains']
global_options['sample/set_nans'] = global_options['model/join_domains_for_train']
# global_options['sample/set_nans'] = True
global_options['noise_statistics'] = args['noise_statistics']
global_options['error_statistics'] = args['error_statistics']
global_options['scatter_all'] = args['scatter_all']
global_options['sample/postfix'] = args['sample_name']
global_options['experiment_name'] = args['dom'] # 'dom2'
global_options['sample/train'] = args['train']
global_options['sample/test'] = args['test']
if not (args['min_filter'] is None and args['max_filter'] is None):
global_options['sample/train_output_filter'] = [args['min_filter'], args['max_filter']]
else:
global_options['sample/train_output_filter'] = None
if not (args['min_cenz'] is None and args['max_cenz'] is None):
global_options['sample/train_output_cenz'] = [args['min_cenz'], args['max_cenz']]
else:
global_options['sample/train_output_cenz'] = None
global_options['result/errors_filter'] = {
'all': [None, None],
'high_loads': [1200, None]
}
if args['dom'] == 'dom2':
domains = {
0: '(IAS <= 45) & (RA1_ALTI <= 100)',
1: '(IAS <= 45) & (RA1_ALTI >= 100)',
2: '(IAS >= 45) & (RA1_ALTI <= 100)',
3: '(IAS >= 45) & (RA1_ALTI >= 100)',
}
elif args['dom'] == 'base':
domains = {
0: '(IAS <= 30) & (RA1_ALTI <= 100)',
1: '(IAS <= 30) & (RA1_ALTI >= 100)',
2: '(IAS >= 30) & (RA1_ALTI <= 100)',
3: '(IAS >= 30) & (RA1_ALTI >= 100)',
}
inputs_to_ignore = {
-1: ['domain', 'flight_name', 'TEMPS'],
0: ['domain', 'flight_name', 'TEMPS', 'BETAI', 'IAS', 'Mu', 'MachTip'],
1: ['domain', 'flight_name', 'TEMPS', 'BETAI', 'IAS', 'Mu', 'MachTip', 'RA1_ALTI'],
2: ['domain', 'flight_name', 'TEMPS', 'Vy', 'Vx'],
3: ['domain', 'flight_name', 'TEMPS', 'Vy', 'Vx', 'RA1_ALTI'],
}
#########################################################################################
print args['model_options']
if not args['model_options'] is None:
model_options = json.loads(args['model_options'])
else:
model_options = {}
global_options['model/method'] = args['t']
global_options['model/options'] = model_options
global_options['model/default_options'] = mb.ModelsBuilder('./', technique=global_options['model/method']).get_options()
global_options['sample/inputs_to_ignore'] = inputs_to_ignore
global_options['general/domains'] = domains
## Generate full experiment name
pretty_global_options = dict_pretty_string(global_options)
pretty_model_options = dict_pretty_string(model_options, new_line=False)
string_hash = calc_hash(pretty_global_options)
folder_name = global_options['sample/postfix']+'_'+global_options['experiment_name']
if global_options['model/join_domains_for_train']:
folder_name += '_joinTrain'
if not global_options['sample/train'] == 'exclude_current':
folder_name += '_train='+global_options['sample/train']
if not global_options['sample/test'] == 'current':
folder_name += '_test='+global_options['sample/test']
if not global_options['sample/output_transform'] == 'none':
folder_name += '_ot='+global_options['sample/output_transform']
if not global_options['sample/input_transform'] == 'none':
folder_name += '_it='+global_options['sample/input_transform']
if not global_options['sample/train_size_to_use'] == 0:
folder_name += '_tSize='+str(global_options['sample/train_size_to_use'])
folder_name += '_'+global_options['model/method']
if not global_options['sample/train_output_cenz'] is None:
folder_name += '_tc'+str(global_options['sample/train_output_cenz'][0])+'-'+str(global_options['sample/train_output_cenz'][1])
if not global_options['sample/train_output_filter'] is None:
folder_name += '_tf'+str(global_options['sample/train_output_filter'][0])+'-'+str(global_options['sample/train_output_filter'][1])
# Create necessary working folders and options file
save_folder_path = './results/'+folder_name+'_{'+pretty_model_options+'}/'
report_name = save_folder_path+folder_name+'_{'+pretty_model_options+'}.xls'
pictures_folder = 'pictures/'
models_folder = 'models/'
samples_folder = 'samples/'
predictions_folder = samples_folder + 'predictions/'
train_folder = samples_folder + 'train/'
test_folder = samples_folder + 'test/'
cache_folder = 'cache/'
for folder in ['', pictures_folder, models_folder, samples_folder, predictions_folder, train_folder, test_folder, cache_folder]:
try:
if not os.path.exists(save_folder_path+folder):
os.makedirs(save_folder_path+folder)
print'Creating directory:', save_folder_path+folder, '\n'
except:
pass
try:
with open(save_folder_path+'!options.data', "wb") as options_file:
options_file.write(pretty_global_options)
except:
pass
# Load data
if verbose:
print 'Started load data...'
start_time = time()
analyzer = dh.DataHandler(load_time=True, domains=domains, inputs_to_ignore=inputs_to_ignore)
data = analyzer.get_data(postfix=global_options['sample/postfix'], mark_domains=True, verbose=verbose)
if verbose:
print 'Done. Elapsed time: ', time() - start_time
# Create model builder object
builder = mb.ModelsBuilder(save_folder_path+models_folder, technique=global_options['model/method'], approx_options=model_options, rebuild_if_exist=REBUILD_MODELS_IF_EXIST)
# TODO: Preprocess data
# data['RA1_ALTI'][data['RA1_ALTI']>=3500] = 3500
###
## Start experiment
domains = np.unique(data['domain'])
flight_names = np.unique(data['flight_name'])
if verbose:
print 'We consider'
print ' domains: ', ', '.join([str(name) for name in domains])
print
print ' flight_names: ', ', '.join([str(name) for name in flight_names])
print
all_predictions = {}
all_outputs = {}
if RSM_REPORT:
rsm_report = {}
# inputs transformation if needed
# if global_options['sample/input_transform'] == 'mapstd':
# parameters = data.columns.values
# for parameter in parameters:
# # if parameter in global_options['sample/output_column']:
# # continue
# if parameter in ['flight_name', 'domain', 'TEMPS']:
# continue
# mean = np.mean(data[parameter], axis=0)
# std = np.std(data[parameter], axis=0)
# data[parameter] = (data[parameter] - mean)/std
# for domain in domains:
for domain in DOMAINS_TO_CONSIDER:
current_domain_data = data[data['domain'] == domain]
# data_to_plot = current_domain_data.copy()
del current_domain_data['domain']
input_names = list(current_domain_data.columns.values)
input_names = remove_sublist_from_origin(input_names, analyzer.get_inputs_to_ignore(domain)+global_options['sample/output_column'])
# all_errors[domain] = {}
all_predictions[domain] = {}
all_outputs[domain] = {}
if RSM_REPORT:
rsm_report[domain] = {}
# for del_input in inputs_to_ignore[domain]:
# del data_to_plot[del_input]
# pd.scatter_matrix(data_to_plot, alpha=0.2)
# plt.show()
# continue
print 'Domain ', str(domain)
print 'Whole sample size:', len(current_domain_data)
print
if DRAW_RANGES:
draw_ranges_for_flights(current_domain_data, split_parameter='flight_name', title='Domain '+str(domain),
save_path='./pictures/ranges_per_flight/')
if CHECK_DEPENDENCIES_WHOLE:
inputs, output = dh.get_matrices(current_domain_data, input_columns_to_remove=analyzer.get_inputs_to_ignore(domain),
output_columns=global_options['sample/output_column'])
print 'Started partial pearson computation...'
start_time = time()
result = gtsda.Analyzer().check(x=inputs, y=output,
options={
'GTSDA/Checker/DependenceType':'linear',
'GTSDA/Checker/Linear/ScoreType':"PearsonPartialCorrelation",
'GTSDA/Checker/PValuesCalculationType':'asymptotic'
})
print 'Scores (ppc): ',
print result.scores[0]
print 'P_values: ',
print result.p_values[0]
print 'Done. Elapsed time: ', time() - start_time
pd.scatter_matrix(current_domain_data)
plt.show()
all_inputs, all_output = dh.get_matrices(current_domain_data, input_columns_to_remove=analyzer.get_inputs_to_ignore(domain),
output_columns=global_options['sample/output_column'])
all_outputs_list = []
all_noise_list = []
if not BUILD_MODELS:
break
## Flights loop
for flight in flight_names:
if (int(flight) < START_FLIGHT) or (int(flight) > END_FLIGHT):
all_predictions[domain][flight] = np.ones((0,1))
all_outputs[domain][flight] = np.ones((0,1))
continue
print 'Current flight', str(int(flight)), '(d'+str(domain)+')'
print
# Prepare the train data
if global_options['model/join_domains_for_train']:
# Here we decide which flights would be considered in train sample
if global_options['sample/train'] == 'current':
train_data = data[(data['flight_name'] == flight)]
elif global_options['sample/train'] == 'exclude_current':
train_data = data[(data['flight_name'] != flight)]
elif global_options['sample/train'] == 'all':
train_data = data[(data['flight_name'] == data['flight_name'])]
all_train_inputs = []
all_train_output = []
all_train_time = []
for train_domain in domains:
temp_train_inputs, temp_train_output = dh.get_matrices(train_data[(train_data['domain'] == train_domain)],
input_columns_to_remove=analyzer.get_inputs_to_ignore(train_domain),
output_columns=global_options['sample/output_column'],
set_nan=global_options['sample/set_nans'])
all_train_inputs.append(temp_train_inputs)
all_train_output.append(temp_train_output)
all_train_time.append(train_data['TEMPS'][(train_data['domain'] == train_domain)][:, np.newaxis])
train_inputs = np.vstack(tuple(all_train_inputs))
train_output = np.vstack(tuple(all_train_output))
train_time = np.vstack(tuple(all_train_time))
if global_options['noise_statistics']:
smoother_options = {}
# smoother_options['kernelfunction'] = 'twohills' # 'simple', kernel function is usual RBF, alternative 'twohills' - turns to zero at zero and more suited fot noisy data
smoother_options['alpha'] = 1. # smoothing coefficient, the greater it is the smoother result is. Should be lower than 1 when data contains pikes.
# smoother_options['neighborhoodsbound'] = 40
temp_train_time = np.copy(train_time)
ind = np.argsort(train_time[:,0])
smoothed_train_output = dc.DerivativeCalculate().smooth_outputs(x=train_time[ind,0], y=train_output[ind,:], options=smoother_options)
noise = (train_output[ind,:] - smoothed_train_output)
# print train_output.shape, smoothed_train_output.shape, noise.shape
# print smoothed_train_output
# fig = plt.figure()
# plt.scatter(train_time, train_output)
# # # ind = np.argsort(train_time[:,0])
# # # plt.plot(train_time[ind,:], smoothed_train_output[ind,:], c='r')
# plt.plot(train_time[ind,0], smoothed_train_output, c='r')
# plt.show()
# plt.close()
fig = plt.figure()
plt.scatter(train_output[ind,:], noise)
q95p = np.percentile(noise, 95)
q05p = np.percentile(noise, 05)
maxp = np.percentile(noise, 100)
minp = np.percentile(noise, 0)
mm = [0., 1.1*np.max(train_output)]
plt.gca().set_xlim(mm)
plt.plot(mm, [maxp, maxp], c='m')
plt.plot(mm, [q95p, q95p], c='r')
plt.plot(mm, [q05p, q05p], c='r')
plt.plot(mm, [minp, minp], c='m')
plt.legend(['min-max', '90% confidence interval'])
# df = pd.DataFrame(np.hstack((train_output[ind,:], noise)), columns=['load', 'noise'] )
# fig = df.plot(kind='hexbin', x='load', y='noise', gridsize=200)
# plt.show()
# plt.title(title)
plt.ylabel('true load - smoothed load')
plt.xlabel('load')
save_path = save_folder_path+'pictures/noise_fn_'+str(int(flight))
fig.savefig(save_path)
plt.close()
all_outputs_list.append(train_output[ind,:])
all_noise_list.append(noise)
print 'q90', np.percentile(np.abs(noise), 90)
print 'max', np.percentile(np.abs(noise), 100)
continue
else:
# Here we decide which flights would be considered in train sample
if global_options['sample/train'] == 'current':
train_data = data[(data['flight_name'] == flight)&(data['domain'] == domain)]
elif global_options['sample/train'] == 'exclude_current':
train_data = data[(data['flight_name'] != flight)&(data['domain'] == domain)]
elif global_options['sample/train'] == 'all':
train_data = data[(data['domain'] == domain)]
train_inputs, train_output = dh.get_matrices(train_data,
input_columns_to_remove=analyzer.get_inputs_to_ignore(domain),
output_columns=global_options['sample/output_column'],
set_nan=global_options['sample/set_nans'])
if not global_options['sample/train_output_filter'] is None:
if not global_options['sample/train_output_filter'][0] is None:
idx = train_output[:, 0] >= global_options['sample/train_output_filter'][0]
train_inputs, train_output = train_inputs[idx, :], train_output[idx, :]
if not global_options['sample/train_output_filter'][1] is None:
idx = train_output[:, 0] <= global_options['sample/train_output_filter'][0]
train_inputs, train_output = train_inputs[idx, :], train_output[idx, :]
train_size = len(train_inputs)
if global_options['sample/input_transform'] == 'mapstd':
for input_dim in xrange(train_inputs.shape[1]):
mean = np.mean(train_inputs[:, input_dim], axis=0)
std = np.std(train_inputs[:, input_dim], axis=0)
train_inputs[:, input_dim] = (train_inputs[:, input_dim] - mean)/std
if global_options['sample/input_transform'] == 'mapminmax':
for input_dim in xrange(train_inputs.shape[1]):
min_value = np.min(train_inputs[:, input_dim], axis=0)
max_value = np.max(train_inputs[:, input_dim], axis=0)
if min_value == max_value:
train_inputs[:, input_dim] = 0.5
continue
train_inputs[:, input_dim] = (train_inputs[:, input_dim] - min_value)/(max_value - min_value)
if not global_options['sample/train_size_to_use'] == 0 and train_size > global_options['sample/train_size_to_use']:
idxs = np.array(range(train_size))
rnd.shuffle(idxs)
train_inputs = train_inputs[idxs[:global_options['sample/train_size_to_use']], :]
train_output = train_output[idxs[:global_options['sample/train_size_to_use']], :]
train_size = global_options['sample/train_size_to_use']
# Prepare the test data
# Here we decide which flights would be considered in train sample
if global_options['sample/test'] == 'current':
test_data = data[(data['flight_name'] == flight)&(data['domain'] == domain)]
elif global_options['sample/test'] == 'exclude_current':
test_data = data[(data['flight_name'] != flight)&(data['domain'] == domain)]
elif global_options['sample/test'] == 'all':
test_data = data[(data['domain'] == domain)]
test_inputs, test_output = dh.get_matrices(test_data, input_columns_to_remove=analyzer.get_inputs_to_ignore(domain),
output_columns=global_options['sample/output_column'], set_nan=global_options['sample/set_nans'])
test_time = test_data['TEMPS']
if not global_options['sample/train_output_cenz'] is None:
if not global_options['sample/train_output_cenz'][1] is None:
train_output = np.minimum(train_output, global_options['sample/train_output_cenz'][1])
test_output = np.minimum(test_output, global_options['sample/train_output_cenz'][1])
if not global_options['sample/train_output_cenz'][0] is None:
train_output = np.maximum(train_output, global_options['sample/train_output_cenz'][0])
test_output = np.maximum(test_output, global_options['sample/train_output_cenz'][0])
# Output transform
if 'mapstd' in global_options['sample/output_transform']:
mean = np.mean(train_output, axis=0)
std = np.std(train_output, axis=0)
train_output = (train_output - mean)/std
if 'log' in global_options['sample/output_transform']:
train_output = np.log(train_output)
test_size = len(test_data)
if verbose:
print 'train shape', train_inputs.shape, train_output.shape
print 'test shape', test_inputs.shape, test_output.shape
print
if test_size < global_options['sample/min_considered_test_size']:
all_predictions[domain][flight] = np.ones((0,1))
all_outputs[domain][flight] = np.ones((0,1))
print '[w] Test size is too small, so flight is skipped'
print
continue
if False:
if verbose:
print 'Started box...'
start_time = time()
box = get_box(test_inputs, split_parameter='flight_name')
in_box = check_if_in_box(test_inputs, box)
save_path = save_folder_path+'classify_'+model_name
check_classify(train_data, test_data, model_type='logistic_regression', weighted=True, title='Classification results for flight '+str(flight), save_path=save_path)
if verbose:
print 'Done. Elapsed time: ', time() - start_time
print
# Check if predictions are already computed otherwise run model building
predictions_file = save_folder_path+predictions_folder+'d_'+str(domain)+'_fn_'+str(flight)+'.csv'
if not os.path.exists(predictions_file):
if global_options['model/join_domains_for_train']:
model_name = 'd_all'+'_fn_'+str(int(flight))
else:
model_name = 'd_'+str(domain)+'_fn_'+str(int(flight))
if global_options['sample/output_transform']:
model_name += '_'+global_options['sample/output_transform']
if verbose:
print 'Started build model...'
start_time = time()
model = builder.build_model(model_name, train_inputs, train_output)
if RSM_REPORT:
model_weights = list(model.details['models'][0]['weights'][0])
model_design = model.details['models'][0]['design_matrix']
model_bias = model.details['models'][0]['bias_weight'][0]
# print model_design
current_model = {}
current_model['bias'] = model_bias
for term_idx in xrange(len(model_weights)):
active_features = []
for feature_idx in xrange(model_design.shape[0]):
if int(model_design[feature_idx, term_idx]) == 1:
active_features.append(input_names[feature_idx])
if int(model_design[feature_idx, term_idx]) == 2:
active_features.append(input_names[feature_idx]+'^2')
current_model[' * '.join(active_features)] = model_weights[term_idx]
# print current_model
rsm_report[domain][flight] = current_model
# print details
if verbose:
print 'Done. Elapsed time: ', time() - start_time
print
if verbose:
print 'Computing predictions...'
start_time = time()
prediction = builder.compute_predictions(model_name, test_inputs)
# Output inverse transform
if 'log' in global_options['sample/output_transform']:
prediction = np.exp(prediction)
if SAVE_PREDICTIONS:
# df = pd.DataFrame(prediction, columns=['prediction'])
# df.to_csv(save_folder_path+predictions_folder+'d_'+str(domain)+'_fn_'+str(flight)+'.csv')
if verbose:
print 'Save predictions...',
np.savetxt(predictions_file, prediction, delimiter=',')
if verbose:
print 'Done!'
if verbose:
print 'Done. Elapsed time: ', time() - start_time
print
else:
if verbose:
print 'Load saved predictions...',
prediction = np.loadtxt(predictions_file, delimiter=',')[:, np.newaxis]
if verbose:
print 'Done!'
error_metrics = mb.compute_errors(test_output, prediction, error_filters=global_options['result/errors_filter'])
# print 'Test errors: '
# print dict_pretty_string(error_metrics)
# print
error_string0 = "%.2f" % error_metrics['all']['q95re']
error_string1 = "%.2f" % error_metrics['high_loads']['q95re']
title = 'domain '+str(domain)+' flight '+str(int(flight))+'\nq95re '+error_string0+' '+error_string1+'\ntrain: '+str(train_size)+' test: '+str(test_size)
if SAVE_REPORT or NEIGHBORS_PLOT:
all_predictions[domain][flight] = prediction
all_outputs[domain][flight] = test_output
if DRAW_SCATTERS:
save_path = save_folder_path+'pictures/'+'error_scatter_'+'d_'+str(domain)+'_fn_'+str(int(flight))
if verbose:
print 'Draw scatters...'
start_time = time()
draw_error_scatters(test_data, prediction, save_path=save_path, title=title)
if verbose:
print 'Done. Elapsed time: ', time() - start_time
print
if DRAW_ERROR_PLOTS:
save_path = save_folder_path+'pictures/'+'d_'+str(domain)+'_fn_'+str(int(flight))
if verbose:
print 'Draw plots...'
start_time = time()
draw_errors_plot_v2(test_output, prediction, save_path=save_path, title=title)
if verbose:
print 'Done. Elapsed time: ', time() - start_time
print
if global_options['sample/postfix'] == 'expert_v2':
load_name = 'dynamic MR pitch rod load'
elif global_options['sample/postfix'] == 'expert_v4':
load_name = 'static MR pitch rod load'
elif global_options['sample/postfix'] == 'expert_v5':
load_name = 'mass bending moment'
else:
load_name = 'load'
if global_options['noise_statistics']:
all_outputs = np.vstack(tuple(all_outputs_list))
all_noise = np.vstack(tuple(all_noise_list))
plt.hist(all_noise/all_outputs, bins=100, color='blue')
plt.show()
plt.hist(all_noise[all_outputs[:,0]>1200,:]/all_outputs[all_outputs[:,0]>1200,:], bins=100, color='blue')
plt.show()
df = pd.DataFrame(np.hstack((train_output[ind,:], noise)), columns=['load', 'noise'] )
fig = df.plot(kind='hexbin', x='load', y='noise', gridsize=200)
plt.show()
fig = plt.figure()
plt.scatter(train_output[ind,:], noise)
q95p = np.percentile(noise, 95)
q05p = np.percentile(noise, 05)
maxp = np.percentile(noise, 100)
minp = np.percentile(noise, 0)
mm = [0., 1.1*np.max(train_output)]
plt.gca().set_xlim(mm)
plt.plot(mm, [maxp, maxp], c='m')
plt.plot(mm, [q95p, q95p], c='r')
plt.plot(mm, [q05p, q05p], c='r')
plt.plot(mm, [minp, minp], c='m')
plt.legend(['min-max', '90% confidence interval'])
plt.xlabel('load value')
plt.ylabel('true load - smoothed load')
save_path = save_folder_path+'pictures/noise_all'
fig.savefig(save_path)
plt.close()
print 'q90', np.percentile(np.abs(all_noise), 90)
print 'max', np.percentile(np.abs(all_noise), 100)
if global_options['scatter_all']:
domains = all_outputs.keys()
all_outputs_joint = []
all_predictions_joint = []
for domain in domains:
flights = all_outputs[domain].keys()
for flight in flights:
all_outputs_joint.append(all_outputs[domain][flight])
all_predictions_joint.append(all_predictions[domain][flight])
all_outputs_joint = np.vstack(all_outputs_joint)
all_predictions_joint = np.vstack(all_predictions_joint)
all_errors_joint = np.abs(all_predictions_joint - all_outputs_joint)
fig = plt.figure()
plt.scatter(np.abs(all_outputs_joint), np.abs(all_predictions_joint), s=0.01)
q95p = np.percentile(all_errors_joint, 95)
q05p = np.percentile(all_errors_joint, 05)
maxp = np.percentile(all_errors_joint, 100)
minp = np.percentile(all_errors_joint, 0)
mm0 = [np.min((np.min(all_predictions_joint),np.min(all_outputs_joint))), np.max((np.max(all_predictions_joint),np.max(all_outputs_joint)))]
mm = np.array([0., np.max(np.abs(all_outputs_joint)+700)])
# mm = [0., 1.1*np.max(all_outputs_joint)]
# plt.gca().set_xlim(mm)
# plt.plot(mm, [maxp, maxp], c='m')
# plt.plot(mm, [q95p, q95p], c='r')
# plt.plot(mm, [q05p, q05p], c='r')
# plt.plot(mm, [minp, minp], c='m')
plt.plot(mm, mm, c='black')
plt.plot(mm, 1.1*mm, c='g')
plt.plot(mm, 1.2*mm, c='r')
plt.plot(mm, 1.5*mm, c='c')
plt.plot(mm, 2.*mm, c='m')
plt.plot(mm, mm/1.1, c='g')
plt.plot(mm, mm/1.2, c='r')
plt.plot(mm, mm/1.5, c='c')
plt.plot(mm, mm/2., c='m')
plt.xlim(mm)
plt.ylim(mm)
plt.legend(['0%', '10%', '20%', '50%', '100%'])
plt.xlabel('prediction')
plt.ylabel('true')
# plt.legend(['min-max', '90% confidence interval'])
plt.xlabel('true '+load_name)
plt.ylabel('predicted '+load_name)
save_path = save_folder_path+'pictures/scatters_all'
fig.savefig(save_path)
plt.close()
# fig = plt.figure()
# plt.xlabel('prediction')
# plt.ylabel('true')
# # heatmap, xedges, yedges = np.histogram2d(all_outputs_joint[:,0], all_predictions_joint[:,0], bins=100)
# # extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
# # plt.clf()
# plt.hexbin(all_outputs_joint[:,0], all_predictions_joint[:,0])
# plt.axis([mm[0], mm[1], mm[0], mm[1]])
# # plt.imshow(heatmap, extent=extent)
# fig.savefig(save_path+'_heat')
# plt.close()
print 'q90', q05p
print 'max', maxp
if global_options['error_statistics']:
domains = all_outputs.keys()
all_outputs_joint = []
all_predictions_joint = []
for domain in domains:
flights = all_outputs[domain].keys()
for flight in flights:
all_outputs_joint.append(all_outputs[domain][flight])
all_predictions_joint.append(all_predictions[domain][flight])
all_outputs_joint = np.vstack(all_outputs_joint)
all_predictions_joint = np.vstack(all_predictions_joint)
idx = all_outputs_joint[:,0] > 30
all_errors_joint = np.abs(all_predictions_joint[idx, :] - all_outputs_joint[idx, :])/np.abs(all_outputs_joint[idx, :])
fig = plt.figure()
plt.scatter(np.abs(all_outputs_joint[idx, :]), all_errors_joint, s=0.01)
q95p = np.percentile(all_errors_joint, 95)
q05p = np.percentile(all_errors_joint, 05)
maxp = np.percentile(all_errors_joint, 100)
minp = np.percentile(all_errors_joint, 0)
mm = [0., np.max(np.abs(all_outputs_joint)+700)]
# plt.gca().set_xlim(mm)
plt.plot(mm, [maxp, maxp], c='m')
plt.plot(mm, [q95p, q95p], c='r')
plt.plot(mm, [q05p, q05p], c='r')
plt.plot(mm, [minp, minp], c='m')
plt.xlim(mm)
# plt.ylim(mm)
# plt.legend(['data', '0%', '10%', '20%', '50%', '100%'])
plt.legend(['min-max', '90% confidence interval'])
plt.xlabel('true '+load_name)
plt.ylabel('relative error')
save_path = save_folder_path+'pictures/errors_all'
fig.savefig(save_path)
plt.close()
# fig = plt.figure()
# plt.xlabel('true')
# plt.ylabel('absolute error')
# # heatmap, xedges, yedges = np.histogram2d(all_outputs_joint[:,0], all_errors_joint[:,0], bins=100)
# # extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
# plt.hexbin(all_outputs_joint[:,0], all_errors_joint[:,0])
# plt.axis([mm[0], mm[1], all_errors_joint[:,0].min(), all_errors_joint[:,0].max()])
# # plt.clf()
# # plt.imshow(heatmap, extent=extent)
# fig.savefig(save_path+'_heat')
# plt.close()
print 'q90', q05p
print 'max', maxp
if RSM_REPORT:
# print rsm_report
writer = pd.ExcelWriter(save_folder_path+'rsm_report.xls')
for domain, rsm_models in rsm_report.items():
all_keys = []
for flight, rsm_model in rsm_models.items():
for coefficient in rsm_model.keys():
if not coefficient in all_keys:
all_keys.append(coefficient)
# print all_keys
all_keys.sort()
all_keys = ['flight']+all_keys
df = pd.DataFrame(columns=all_keys)
for flight, rsm_model in rsm_models.items():
row_to_add = []
for coefficient in all_keys:
if coefficient == 'flight':
row_to_add.append(int(flight))
elif coefficient in rsm_model.keys():
row_to_add.append(float(rsm_model[coefficient]))
else:
row_to_add.append(None)
# print row_to_add
df.loc[len(df)] = row_to_add
df.to_excel(writer, 'Domain '+str(domain), float_format='%.3f', startrow=0, startcol=0, index=False)# , float_format='%.3f'
writer.save()
if SAVE_REPORT:
mb.save_errors_tables(report_name, all_outputs, all_predictions, global_options['result/errors_filter'])
if NEIGHBORS_PLOT:
all_inputs, all_output = dh.get_matrices(data[data['domain']==2], input_columns_to_remove=analyzer.get_inputs_to_ignore(2),
output_columns=global_options['sample/output_column'])
NORMALIZE = True
if NORMALIZE:
all_inputs = skp.normalize(all_inputs)
occuring_flight_names = np.unique(current_domain_data['flight_name'])
flight_ranks = ss.rankdata(occuring_flight_names)
print occuring_flight_names
print flight_ranks
import itertools