def parse(self, result: Result) -> Result:
for qt in result.question_types:
# 查询语句翻译
if qt == 'year_status':
self.trans_year_status(result['year'])
elif qt == 'catalog_status':
self.trans_catalog_status(result['year'], result['catalog'])
elif qt == 'exist_catalog':
self.trans_exist_catalog(result['year'])
elif qt in ('index_value', 'indexes_m_compare',
'indexes_n_compare'):
self.trans_index_value(result['year'], result['index'])
elif qt == 'index_overall':
self.trans_index_overall(result['year'], result['index'])
elif qt in ('index_2_overall', 'indexes_overall_trend'):
self.trans_indexes_overall(result['year'], result['index'])
elif qt == 'index_compose':
self.trans_index_compose(result['year'], result['index'])
elif qt in ('indexes_2m_compare', 'indexes_2n_compare'):
self.trans_indexes_mn_compare(result['year'], result['index'])
elif qt == 'indexes_g_compare':
self.trans_indexes_g_compare(result['year'], result['index'])
elif qt in ('area_value', 'areas_m_compare', 'areas_n_compare'):
self.trans_area_value(result['year'], result['area'],
result['index'])
elif qt == 'area_overall':
self.trans_area_overall(result['year'], result['area'],
result['index'])
elif qt in ('area_2_overall', 'areas_overall_trend'):
self.trans_areas_overall(result['year'], result['area'],
result['index'])
elif qt == 'area_compose':
self.trans_area_compose(result['year'], result['index'])
elif qt in ('areas_2m_compare', 'areas_2n_compare'):
self.trans_areas_mn_compare(result['year'], result['area'],
result['index'])
elif qt == 'areas_g_compare':
self.trans_areas_g_compare(result['year'], result['area'],
result['index'])
elif qt in ('indexes_trend', 'indexes_max'):
self.trans_indexes_value(result['year'], result['index'])
elif qt in ('areas_trend', 'areas_max'):
self.trans_areas_value(result['year'], result['area'],
result['index'])
elif qt in ('index_change', 'indexes_change'):
self.trans_index_change(result['year'])
elif qt in ('catalog_change', 'catalogs_change'):
self.trans_catalog_change(result['year'])
elif qt == 'begin_stats':
self.trans_begin_stats(result['index'])
result.add_sql(qt, deepcopy(self.chain))
self.chain.reset()
return result
def extract_index(self,
result: Result,
len_threshold: int = 4,
ratio_threshold: float = 0.5):
""" 提取因错别字或说法而未识别到的指标 """
new_word, old_word = index_complement(result.filtered_question,
self.index_wds, len_threshold,
ratio_threshold)
if new_word:
debug('||REPLACE FOUND||', new_word, '<=', old_word)
result.add_word(new_word, self.word_type_dict.get(new_word))
result.replace_words(old_word, new_word)
def case(self):
#通过导入测试类来实现生成测试集
suite = unittest.TestLoader().loadTestsFromTestCase(AppDemo)
#实例化结果对象
#生成一个空的结果集
r = Result()
#运行case，并更新结果集，记录正确的case 失败的case
res = suite.run(r)
def question_filter(self, question: str) -> Result:
question = question.replace(' ', '')
# 过滤年份
filtered_question = year_complement(question)
# 过滤特征词
region_wds = []
for w in self.region_tree.iter(filtered_question):
region_wds.append(w[1][1])
region_dict = {w: self.word_type_dict.get(w) for w in region_wds}
return Result(region_dict, question, filtered_question)
def case(self):
suite = unittest.TestLoader().loadTestsFromTestCase(QQDemo)
local.result = Result()
res = suite.run(local.result)
logger.debug('当前线程的的名字：%s' % threading.current_thread().getName())
result = {threading.current_thread().getName(): res}
for deviceName, result in result.items():
html = HTMLTestAppRunner.HTMLTestRunner(stream=open(
APPREPORT.format('{}.html'.format(deviceName)), "wb"),
verbosity=2,
title='测试')
html.generateReport('', result)
def question_filter(self, question: str) -> Result:
question = question.replace(' ', '')
# 过滤年份
filtered_question = year_complement(question)
# 过滤特征词
region_wds = []
for w in self.region_tree.iter(filtered_question):
region_wds.append(w[1][1])
region_dict = {w: self.word_type_dict.get(w) for w in region_wds}
# 过滤指标(在提取失败或问题中无指标值时)
if not region_dict or 'index' not in region_dict.values():
new_word, old_word = index_complement(filtered_question, self.index_wds)
if new_word:
filtered_question = filtered_question.replace(old_word, new_word)
region_dict[new_word] = self.word_type_dict.get(new_word)
return Result(region_dict, question, filtered_question)
def case(self):
#通过导入测试类来实现生成测试集
suite = unittest.TestLoader().loadTestsFromTestCase(DingDang_Login)
#生成一个空的结果集
local.result = Result()
#运行case,并更新结果，记录通过与失败的case
res = suite.run(local.result)
#将结果通过测试手机名称进行区分
logger.debug('当前线程的的名字：%s' % threading.current_thread().getName())
# 当前线程的名字 就是当前运行手机的名字
result = {threading.current_thread().getName(): res}
for deviceName, result in result.items():
html = HTMLTestAppRunner.HTMLTestRunner(stream=open(
APPREPORT_PATH.format('{}.html'.format(deviceName)), 'wb'),
verbosity=2,
title='测试报告')
#这个方法就是生成报告的主要函数
html.generateReport('', result)
def case(self):
# 通过导入测试类来实现生成测试集
local.suite = unittest.TestLoader().loadTestsFromTestCase(ThreadDemo)
# 生成空的结果集，用来存执行结果
local.result = Result()
# 运行case，并更新结果集，如执行状态
local.res = local.suite.run(local.result)
# 将结果通过测试手机名称进行区分
logger.debug('当前线程的的名字：%s' % threading.current_thread().getName())
# 当前线程的名称就是当前手机的名字
result = {threading.current_thread().getName(): local.res}
for deviceName, result in result.items():
report_name = deviceName + '-' + time.strftime('%Y%m%d%H%M%S')
html = HTMLTestAppRunner.HTMLTestRunner(stream=open(
APP_REPORT.format('{}.html'.format(report_name)), 'wb'),
verbosity=2,
title='Test')
# 这个方法就是生成报告的主要函数
html.generateReport('', result)
def main(locality_file):
if args.l_shape:
localities = ShapeLocalities(args, locality_file)
if args.localities:
localities = TextLocalities(args, locality_file)
from lib.result import Result
# This next part is probably redundant, sinch assigning the 'polygons' variable is now done in the main() function.
if args.p_shape:
polygons = ShapePolygons(args)
if args.polygons:
polygons = TextPolygons(args)
result = Result(polygons, args)
done = 0
# Index the geotiff files if available.
if args.tif:
from lib.readGeoTiff import indexTiffs
try:
index = indexTiffs(args.tif)
except AttributeError:
sys.exit("[ Error ] No such file \'%s\'" % args.tif[0])
# Read the locality data and test if the coordinates
# are located in any of the polygons.
# For each locality record ...
if args.localities or args.l_shape:
# localities = TextLocalities(args, locality_file)
numLoc = localities.getNrLocalities()
result.setSpeciesNames(localities)
for locality in localities.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
# locality[0] = species name, locality[1] = latitude, locality[2] = longitude
# if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
if pointInPolygon(polygon[1], locality) == True:
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon, index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
if args.gbif:
gbifData = GbifLocalities(args)
result.setSpeciesNames(gbifData)
numLoc = gbifData.getNrLocalities()
# For each GBIF locality record ...
for locality in gbifData.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
# if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
if pointInPolygon(polygon[1], locality) == True:
result.setResult(locality, polygon[0])
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon, index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
# Clean up
if args.np > 1:
try:
os.remove(locality_file)
except:
pass
sys.stderr.write("\n")
return result
# Multiprocessing if args.np > 1: from lib.splitLocalityFile import split_file from multiprocessing import Pool from lib.result import Result from lib.joinResults import joinResults if args.localities: tmp_input_files = split_file(args.localities, args.np) if args.gbif: tmp_input_files = split_file(args.gbif, args.np) pool = Pool(processes = args.np) result_objects = pool.map(main, tmp_input_files) # Instantiate a Result object to join the results from the parallel processes. finalResult = Result(polygons, args) Result.joinResults(finalResult, result_objects) plottResult(finalResult) else: if args.test == True: if args.localities: from lib.testData import testLocality localities = TextLocalities(args) testLocality(localities, args.localities) if args.polygons: from lib.testData import testPolygons testPolygons(polygons, args.polygons) else:
def main():
from lib.result import Result
# Create list to store the geotif objects in.
polygons = Polygons()
result = Result(polygons, args)
done = 0
# Index the geotiff files if available.
if args.tif:
from lib.readGeoTiff import indexTiffs
try:
index = indexTiffs(args.tif)
except AttributeError:
sys.exit("[ Error ] No such file \'%s\'" % args.tif[0])
# Read the locality data and test if the coordinates
# are located in any of the polygons.
# For each locality record ...
if args.localities:
localities = MyLocalities()
numLoc = localities.getQuant()
result.setSpeciesNames(localities)
for locality in localities.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
if localities.getCoOrder() == "lat-long":
# locality[0] = species name, locality[1] = latitude, locality[2] = longitude
if pointInPolygon(polygon[1], locality[2],
locality[1]) == True:
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon,
index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
else:
# locality[0] = species name, locality[1] = longitude, locality[2] = latitude
if pointInPolygon(polygon[1], locality[1],
locality[2]) == True:
if args.tif:
if elevationTest(locality[2], locality[1], polygon,
index) == True:
result.setResult(locality[0], polygon[0])
if args.gbif:
gbifData = GbifLocalities()
result.setSpeciesNames(gbifData)
numLoc = gbifData.getQuant()
# For each GBIF locality record ...
for locality in gbifData.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
if pointInPolygon(polygon[1], locality[2],
locality[1]) == True:
result.setResult(locality, polygon[0])
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon,
index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
sys.stderr.write("\n")
result.printNexus(args.out)
if args.plot == True:
import os
from lib.plot import prepare_plots
prepare_plots(result, polygons)
#__ GUI STUFF
dir_output = args.dir_output # Working directory
path_script = args.path_script
cmd="Rscript %s/R/graphical_output.R %s %s %s %s %s %s" \
% (path_script,path_script, "coordinates.sgc.txt", "polygons.sgc.txt", "sampletable.sgc.txt", "speciestable.sgc.txt",dir_output)
os.system(cmd)
if args.stochastic_mapping == True:
import os
import lib.stochasticMapping as stochasticMapping
# Run the stochastic mapping analysis
stochasticMapping.main(args, result)
def main():
from lib.result import Result
# Create list to store the geotif objects in.
polygons = Polygons()
result = Result(polygons, args)
done = 0
# Index the geotiff files if available.
if args.tif:
from lib.readGeoTiff import indexTiffs
try:
index = indexTiffs(args.tif)
except AttributeError:
sys.exit("[ Error ] No such file \'%s\'" % args.tif[0])
# Read the locality data and test if the coordinates
# are located in any of the polygons.
# For each locality record ...
if args.localities:
localities = MyLocalities()
numLoc = localities.getQuant()
result.setSpeciesNames(localities)
for locality in localities.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
if localities.getCoOrder() == "lat-long":
# locality[0] = species name, locality[1] = latitude, locality[2] = longitude
if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon, index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
else:
# locality[0] = species name, locality[1] = longitude, locality[2] = latitude
if pointInPolygon(polygon[1], locality[1], locality[2]) == True:
if args.tif:
if elevationTest(locality[2], locality[1], polygon, index) == True:
result.setResult(locality[0], polygon[0])
if args.gbif:
gbifData = GbifLocalities()
result.setSpeciesNames(gbifData)
numLoc = gbifData.getQuant()
# For each GBIF locality record ...
for locality in gbifData.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
result.setResult(locality, polygon[0])
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon, index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
sys.stderr.write("\n")
result.printNexus(args.out)
if args.plot == True:
import os
from lib.plot import prepare_plots
prepare_plots(result, polygons)
#__ GUI STUFF
dir_output = args.dir_output # Working directory
path_script = args.path_script
cmd="Rscript %s/R/graphical_output.R %s %s %s %s %s %s" \
% (path_script,path_script, "coordinates.sgc.txt", "polygons.sgc.txt", "sampletable.sgc.txt", "speciestable.sgc.txt",dir_output)
os.system(cmd)
if args.stochastic_mapping == True:
import os
import lib.stochasticMapping as stochasticMapping
# Run the stochastic mapping analysis
stochasticMapping.main(args, result)
# Multiprocessing
if args.np > 1:
from lib.splitLocalityFile import split_file
from multiprocessing import Pool
from lib.result import Result
from lib.joinResults import joinResults
if args.localities:
tmp_input_files = split_file(args.localities, args.np)
if args.gbif:
tmp_input_files = split_file(args.gbif, args.np)
pool = Pool(processes=args.np)
result_objects = pool.map(main, tmp_input_files)
# Instantiate a Result object to join the results from the parallel processes.
finalResult = Result(polygons, args)
Result.joinResults(finalResult, result_objects)
plottResult(finalResult)
else:
if args.test == True:
if args.localities:
from lib.testData import testLocality
localities = TextLocalities(args)
testLocality(localities, args.localities)
if args.polygons:
from lib.testData import testPolygons
testPolygons(polygons, args.polygons)
def run_experiment():
args = get_args()
args.experiment = "results_physionet"
N_exp = 10
args.ckpt_dir = "paper"
args.data_dir = "../data"
args.dataset = "physionet_burst"
args.n_epochs = 100
args.z_dim = 35
args.K = 10
args.plot_every = 20
args.kl_annealing_epochs = 20
for n_exp in range(N_exp):
# Shi-VAE
args.model_name = '{}_{}_{}z_{}h_{}s_{}'.format(args.model, args.dataset, args.z_dim, args.h_dim, args.K, n_exp)
args.result_dir = os.path.join(args.ckpt_dir, args.experiment, args.model_name)
args.ckpt_file = os.path.join(args.result_dir, args.model_name + ".pth")
args.best_ckpt_file = os.path.join(args.result_dir, args.model_name + "_best.pth")
# Restore training
if (args.restore == 1):
if (not os.path.isfile(args.ckpt_file)):
print('Model not found at {}'.format(args.ckpt_file))
sys.exit()
model_dict = torch.load(args.ckpt_file)
n = args.n_epochs
# Restore args from training args.
args = model_dict['params']
args.n_epochs = n
args.restore = 1
# Print Arguments
print('ARGUMENTS')
for arg in vars(args):
print('{} = {}'.format(arg, getattr(args, arg)))
# Create checkpoint directory
if (not os.path.exists(args.ckpt_dir)):
os.makedirs(args.ckpt_dir)
# Create results directory
if (not os.path.exists(args.result_dir)):
os.makedirs(args.result_dir)
# ============= LOAD DATA ============= #
# Load data
data = np.load(os.path.join(args.data_dir, args.dataset, args.dataset + ".npz"))
types_csv = os.path.join(args.data_dir, args.dataset, "data_types_real.csv")
types_list = utils.read_csv_types(types_csv)
# Train
x_train = data["x_train_miss"].astype(np.float32)
x_train_full = data["x_train_full"].astype(np.float32)
m_train = data["m_train_miss"].astype(bool)
m_train_artificial = data["m_train_artificial"].astype(bool)
y_train = data["y_train"]
# Val
x_val = data["x_val_miss"].astype(np.float32)
x_val_full = data["x_val_full"].astype(np.float32)
m_val = data["m_val_miss"].astype(bool)
m_val_artificial = data["m_val_artificial"].astype(bool)
y_val = data["y_val"]
# Test
x_test = data["x_test_miss"].astype(np.float32)
x_test_full = data["x_test_full"].astype(np.float32)
m_test = data["m_test_miss"].astype(bool)
m_test_artificial = data["m_test_artificial"].astype(bool)
y_test = data["y_test"]
# ===== Scaler ===== #
scaler = HeterogeneousScaler(types_list)
scaler.fit(x_train, m_train)
data_train = dset.HeterDataset(x_train, m_train, x_train_full, m_train_artificial, types_list=types_list)
data_valid = dset.HeterDataset(x_val, m_val, x_val_full, m_val_artificial, types_list=types_list)
data_test = dset.HeterDataset(x_test, m_test, x_test_full, m_test_artificial, types_list=types_list)
train_loader = torch.utils.data.DataLoader(data_train, batch_size=64, shuffle=True,
collate_fn=dset.standard_collate)
valid_loader = torch.utils.data.DataLoader(data_valid, batch_size=64, shuffle=False,
collate_fn=dset.standard_collate)
test_loader = torch.utils.data.DataLoader(data_test, batch_size=64, shuffle=False,
collate_fn=dset.standard_collate)
# ============= MODEL ============= #
# Shi-VAE
from models.shivae import ShiVAE
model = ShiVAE(h_dim=args.h_dim, z_dim=args.z_dim, s_dim=args.K, types_list=types_list,
n_layers=1,
learn_std=False)
optimizer = torch.optim.Adam(model.parameters(), lr=args.l_rate)
total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('Trainable params: {}'.format(total_params))
# ============= TRAIN ============= #
# Train model
from models.trainers import Trainer
if args.train == 1 or args.train == -1:
trainer = Trainer(model, optimizer, args, scaler=scaler)
# Train from pretrained model
if (args.restore == 1 and os.path.isfile(args.ckpt_file)):
print('Model loaded at {}'.format(args.ckpt_file))
trainer.load_checkpoint(model_dict)
print('Training points: {}'.format(len(train_loader.dataset)))
trainer.train(train_loader, test_loader)
# ============= RESULTS ============= #
if args.train == 0 or args.train == -1:
from lib.result import Result
result_dir = os.path.dirname(args.ckpt_file)
print('Save images in: {}'.format(result_dir))
# Load pretrained model
model_dict = torch.load(args.best_ckpt_file)
model.load_state_dict(model_dict['state_dict'])
# Create test loader
test_loader = torch.utils.data.DataLoader(data_test, batch_size=64, shuffle=False,
collate_fn=dset.standard_collate)
# Reconstruction and generation
result = Result(test_loader, scaler, model, result_dir, args)
model_name = "ShiVAE"
result.avg_error(model_name=model_name)
result.reconstruction(types_list=types_list)
result.generation(args.result_imgs, types_list=types_list)
# ===== Save args ===== #
args_path = os.path.join(args.result_dir, args.model_name) + args.model_name + '.json'
save_args(args, args_path)
def main(locality_file):
if args.l_shape:
localities = ShapeLocalities(args, locality_file)
if args.localities:
localities = TextLocalities(args, locality_file)
from lib.result import Result
# This next part is probably redundant, sinch assigning the 'polygons' variable is now done in the main() function.
if args.p_shape:
polygons = ShapePolygons(args)
if args.polygons:
polygons = TextPolygons(args)
result = Result(polygons, args)
done = 0
# Index the geotiff files if available.
if args.tif:
from lib.readGeoTiff import indexTiffs
try:
index = indexTiffs(args.tif)
except AttributeError:
sys.exit("[ Error ] No such file \'%s\'" % args.tif[0])
# Read the locality data and test if the coordinates
# are located in any of the polygons.
# For each locality record ...
if args.localities or args.l_shape:
# localities = TextLocalities(args, locality_file)
numLoc = localities.getNrLocalities()
result.setSpeciesNames(localities)
for locality in localities.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
# locality[0] = species name, locality[1] = latitude, locality[2] = longitude
# if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
if pointInPolygon(polygon[1], locality) == True:
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon,
index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
if args.gbif:
gbifData = GbifLocalities(args)
result.setSpeciesNames(gbifData)
numLoc = gbifData.getNrLocalities()
# For each GBIF locality record ...
for locality in gbifData.getLocalities():
done = print_progress(done, numLoc)
# ... and for each polygon ...
for polygon in polygons.getPolygons():
# ... test if the locality record is found in the polygon.
# if pointInPolygon(polygon[1], locality[2], locality[1]) == True:
if pointInPolygon(polygon[1], locality) == True:
result.setResult(locality, polygon[0])
# Test if elevation files are available.
if args.tif:
if elevationTest(locality[1], locality[2], polygon,
index) == True:
# Store the result
result.setResult(locality, polygon[0])
else:
# Store the result
result.setResult(locality, polygon[0])
# Clean up
if args.np > 1:
try:
os.remove(locality_file)
except:
pass
sys.stderr.write("\n")
return result
print('Training points: {}'.format(len(train_loader.dataset)))
trainer.train(train_loader, valid_loader)
# ============= RESULTS ============= #
if args.train == 0 or args.train == -1:
from lib.result import Result
result_dir = os.path.dirname(args.ckpt_file)
print('Save images in: {}'.format(result_dir))
# Load pretrained model
model_dict = torch.load(args.ckpt_file)
model.load_state_dict(model_dict['state_dict'])
# Create test loader
test_loader = torch.utils.data.DataLoader(data_test,
batch_size=64,
shuffle=False,
collate_fn=dset.standard_collate)
# Reconstruction and generation
result = Result(test_loader, scaler, model, result_dir, args)
model_name = "ShiVAE"
result.avg_error(model_name=model_name)
result.reconstruction(types_list=types_list)
result.generation(args.result_imgs, types_list=types_list)
# ===== Save args ===== #
args_path = os.path.join(args.result_dir,
args.model_name) + args.model_name + '.json'
save_args(args, args_path)
def _classify_tree(self, result: Result):
# 收集实体类型
question = result.filtered_question
year_count = result.count('year')
# 问题与单个年份相关
if year_count == 1:
# 全年总体情况
if check_contain(self.status_rwds, question) and 'year' in result and len(result) == 1:
result.add_qtype('year_status')
# 全年含有目录
if check_contain(self.exist_qwds, question) and check_contain(self.catalog_rwds, question):
result.add_qtype('exist_catalog')
# 目录
if 'catalog' in result:
# 总体情况
if check_contain(self.status_rwds, question):
result.add_qtype('catalog_status')
# 指标
if 'index' in result:
# 值
if check_contain(self.value_qwds, question) or check_endswith(self.is_twds, question):
if not check_contain(self.child_index_rwds, question):
# 涉及地区
if 'area' in result:
result.add_qtype('area_value')
else:
result.add_qtype('index_value')
# 值比较(上级)
if check_regexp(question, MultipleCmp1,
functions=[lambda x: check_contain(self.parent_index_rwds, x[0][-1])],
callback=lambda x: QuestionOrderError.check(x, self.parent_index_rwds)
):
# 涉及地区
if 'area' in result:
result.add_qtype('area_overall')
else:
result.add_qtype('index_overall')
# 值比较(同类同单位)
if result.count('index') == 2 and 'area' not in result:
if check_regexp(question, MultipleCmp1, functions=[
lambda x: check_list_contain(result['index'], x[0], 0, -1)
]):
result.add_qtype('indexes_m_compare') # 比较倍数关系
if check_regexp(question, NumberCmp1, functions=[
lambda x: (check_list_contain(result['index'], x[0], 0, -1) or
check_all_contain(result['index'], x[0][0]))
]):
result.add_qtype('indexes_n_compare') # 比较数量关系
# 地区值比较(相同指标不同地区)
if result.count('index') == 1 and result.count('area') == 2:
if check_regexp(question, MultipleCmp1, functions=[
lambda x: (check_list_contain(result['area'], x[0], 0, -1) and
check_list_contain(result['index'], x[0], 0))
]):
result.add_qtype('areas_m_compare') # 比较倍数关系
if check_regexp(question, NumberCmp1, functions=[
lambda x: ((check_list_contain(result['area'], x[0], 0, -1) and
check_contain(result['index'], x[0][0]))
or
(check_all_contain(result['area'], x[0][0]) and
check_list_any_contain(result['index'], x[0], 0, -1)))
]):
result.add_qtype('areas_n_compare') # 比较数量关系
# 同比值比较
if check_regexp(question, GrowthCmp, functions=[
lambda x: check_all_contain(result['index'], x[0])
]):
if 'area' in result:
# 单地区多指标
if result.count('area') == 1:
result.add_qtype('areas_g_compare')
else:
result.add_qtype('indexes_g_compare')
# 指标下不同地区组成情况
if check_contain(self.location_rwds, question):
if check_contain(self.status_rwds, question):
result.add_qtype('area_compose')
# 指标的子组成
else:
if check_contain(self.child_index_rwds, question):
result.add_qtype('index_compose')
# 问题与两个年份相关
elif year_count == 2:
# 目录与指标的变化情况
if result.count('year') == len(result):
if check_contain(self.catalog_rwds, question):
result.add_qtype('catalog_change')
elif check_contain(self.index_rwds, question):
result.add_qtype('index_change')
# 指标
if 'index' in result:
if check_contain(self.parent_index_rwds, question):
# 上级占比变化
if check_regexp(question, NumberCmp2[0], NumberCmp2[1], functions=[
lambda x: check_contain(self.parent_index_rwds, x[0])
]*2):
if 'area' not in result:
result.add_qtype('index_2_overall')
elif check_regexp(question, NumberCmp2[0], NumberCmp2[1], functions=[
lambda x: check_contain(result['area'], x[0])
]*2):
result.add_qtype('area_2_overall')
else:
# 比较数值
if check_regexp(question, *NumberCmp2, functions=[
lambda x: check_contain(result['index'], x[0]),
lambda x: check_contain(result['index'], x[0]),
lambda x: check_contain(result['index'], x[0][-1])
]):
if 'area' not in result: # 不涉及地区
result.add_qtype('indexes_2n_compare')
else: # 涉及地区
if result.count('index') == 1: # 单指标下不同地区比较
if check_regexp(question, *NumberCmp2, functions=[
lambda x: check_contain(result['area'], x[0]),
lambda x: check_contain(result['area'], x[0]),
lambda x: check_contain(result['area'], x[0][0]),
]):
result.add_qtype('areas_2n_compare')
# 比较倍数
if check_regexp(question, MultipleCmp2, functions=[
lambda x: check_list_any_contain(result['index'], x[0], 0, -1)
]):
if 'area' not in result: # 不涉及地区
result.add_qtype('indexes_2m_compare')
else: # 涉及地区
if check_regexp(question, MultipleCmp2, functions=[
lambda x: check_list_any_contain(result['area'], x[0], 0, -1)
]):
result.add_qtype('areas_2m_compare')
# 问题与多个年份相关
elif year_count > 2:
# 指标/目录变化趋势
if result.count('year') == len(result) and check_contain(self.status_rwds, question):
if check_contain(self.catalog_rwds, question):
result.add_qtype('catalogs_change')
elif check_contain(self.index_rwds, question):
result.add_qtype('indexes_change')
# 关于指标的变化趋势
if 'index' in result:
# 占上级的
if check_regexp(question, MultipleCmp1, functions=[
lambda x: (check_contain(result['index'], x[0][0]) and
check_contain(self.status_rwds, x[0][-1]) and
check_contain(self.parent_index_rwds, x[0][-1]))
]):
if 'area' in result:
result.add_qtype('areas_overall_trend')
else:
result.add_qtype('indexes_overall_trend')
# 值的
if check_contain(self.status_rwds, question) and not check_contain(self.parent_index_rwds, question):
if 'area' in result:
result.add_qtype('areas_trend')
else:
result.add_qtype('indexes_trend')
# 最值
if check_contain(self.max_rwds, question):
if 'area' in result:
result.add_qtype('areas_max')
else:
result.add_qtype('indexes_max')
# 问题与年份无关
else:
if 'index' in result and check_contain(self.when_qwds, question):
result.add_qtype('begin_stats')