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Analyzer.py
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Analyzer.py
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import tensorflow as tf
import numpy as np
import os, io, gc, json
import _pickle as pickle
import argparse
from ProgressBar import progress
with open('Hyper_Parameters.json', 'r') as f:
hp_Dict = json.load(f)
if not hp_Dict['Device'] is None:
os.environ['CUDA_VISIBLE_DEVICES']= hp_Dict['Device']
class Analyzer:
def __init__(
self,
path,
absolute_Criterion= 0.7,
relative_Criterion= 0.05,
time_Dependency_Criterion= (10, 0.05),
step_Cut= True
):
self.result_Path = os.path.join(hp_Dict['Result_Path'], path).replace('\\', '/')
self.absolute_Criterion = absolute_Criterion
self.relative_Criterion = relative_Criterion
self.time_Dependency_Criterion = time_Dependency_Criterion
self.step_Cut = step_Cut
self.Pattern_Metadata_Load() # self.word_Index_Dict, self.step_Dict, self.max_Step, self.targets
self.Category_Dict_Generate() # self.category_Dict
self.Adjusted_Length_Dict_Generate() # self.adjusted_Length_Dict
self.Analysis()
def Analysis(self, batch_Steps= 200):
result_File_List = sorted([ #Result files sorting
os.path.join(self.result_Path, 'Test', x).replace('\\', '/')
for x in os.listdir(os.path.join(self.result_Path, 'Test').replace('\\', '/'))
if x.endswith('.pickle') and x != 'Metadata.pickle'
])
reaction_Times = [
'\t'.join(['{}'.format(x) for x in [
'Epoch',
'Word',
'Identifier',
'Pattern_Type',
'Pronunciation',
'Pronunciation_Length',
'Uniqueness_Point',
'Cohort_N',
'Rhyme_N',
'Neighborhood_N',
'Onset_Absolute_RT',
'Onset_Relative_RT',
'Onset_Time_Dependent_RT',
'Offset_Absolute_RT',
'Offset_Relative_RT',
'Offset_Time_Dependent_RT'
]])]
category_Flows = [
'\t'.join(['{}'.format(x) for x in [
'Epoch',
'Word',
'Identifier',
'Pattern_Type',
'Pronunciation',
'Pronunciation_Length',
'Uniqueness_Point',
'Cohort_N',
'Rhyme_N',
'Neighborhood_N',
'Category',
'Category_Count',
'Accuracy'
] + list(range(self.max_Step))])]
for result_File in result_File_List:
with open(result_File, 'rb') as f:
result_Dict = pickle.load(f)
epoch = result_Dict['Epoch']
infos = result_Dict['Info']
outputs = result_Dict['Result'] #[Batch, Steps, Dims]
for index, (output, (word, identifier, pattern_Type)) in enumerate(zip(outputs, infos)):
data = self.Data_Generate(output, word, identifier, batch_Steps) #[Num_Words, Steps]
rt_Dict = self.RT_Generate(word, identifier, data)
category_Flow_Dict = self.Category_Flow_Generate(word, data)
reaction_Times.append(
'\t'.join(['{}'.format(x) for x in [
epoch,
word,
identifier,
pattern_Type,
'.'.join(self.pattern_Metadata_Dict['Pronunciation_Dict'][word]),
len(self.pattern_Metadata_Dict['Pronunciation_Dict'][word]),
self.adjusted_Length_Dict[word],
len(self.category_Dict[word, 'Cohort']),
len(self.category_Dict[word, 'Rhyme']),
len(self.category_Dict[word, 'DAS_Neighborhood']),
rt_Dict['Onset', 'Absolute'],
rt_Dict['Onset', 'Relative'],
rt_Dict['Onset', 'Time_Dependent'],
rt_Dict['Offset', 'Absolute'],
rt_Dict['Offset', 'Relative'],
rt_Dict['Offset', 'Time_Dependent']
]])
)
for category in ["Target", "Cohort", "Rhyme", "Unrelated", "Other_Max"]:
if category == "Other_Max":
category_Count = np.nan
else:
category_Count = len(self.category_Dict[word, category])
category_Flows.append(
'\t'.join(['{}'.format(x) for x in [
epoch,
word,
identifier,
pattern_Type,
'.'.join(self.pattern_Metadata_Dict['Pronunciation_Dict'][word]),
len(self.pattern_Metadata_Dict['Pronunciation_Dict'][word]),
self.adjusted_Length_Dict[word],
len(self.category_Dict[word, 'Cohort']),
len(self.category_Dict[word, 'Rhyme']),
len(self.category_Dict[word, 'DAS_Neighborhood']),
category,
category_Count,
not np.isnan(rt_Dict["Onset", "Time_Dependent"])
] + ['{:.5f}'.format(x) for x in category_Flow_Dict[category]]])
)
progress(
index + 1,
outputs.shape[0],
status= result_File
)
print()
with open(os.path.join(self.result_Path, 'Test', 'RTs.txt').replace('\\', '/'), 'w') as f:
f.write('\n'.join(reaction_Times))
with open(os.path.join(self.result_Path, 'Test', 'Category_Flows.txt').replace('\\', '/'), 'w') as f:
f.write('\n'.join(category_Flows))
def Data_Generate(self, output, word, identifier, batch_Steps= 200):
'''
Data generation is progressed pattern by pattern, not multiple pattern because GPU consuming.
output: [Steps, Dims]
'''
cs_List = []
for batch_Index in range(0, output.shape[1], batch_Steps):
cs_List.append(self.Data_Calc(output= output[batch_Index:batch_Index + batch_Steps]))
cosine_Similarity = np.hstack(cs_List)
if self.step_Cut:
cosine_Similarity[:, self.step_Dict[word, identifier]:] = cosine_Similarity[:, [self.step_Dict[word, identifier] - 1]]
return cosine_Similarity
@tf.function
def Data_Calc(self, output):
'''
output: [Steps, Dims]
self.targets: [Num_Words, Dims]
'''
output = tf.convert_to_tensor(output, dtype= tf.float32)
targets = tf.convert_to_tensor(self.targets, dtype= tf.float32)
tiled_Output = tf.tile(
tf.expand_dims(output, [0]),
multiples = [tf.shape(targets)[0], 1, 1]
) #[Num_Words, Steps, Dims], increase dimension and tiled for 2D comparing.
tiled_Targets = tf.tile(
tf.expand_dims(targets, [1]),
multiples = [1, tf.shape(output)[0], 1]
) #[Num_Words, Steps, Dims], increase dimension and tiled for 2D comparing.
cosine_Similarity = \
tf.reduce_sum(tiled_Targets * tiled_Output, axis = 2) / \
(
tf.sqrt(tf.reduce_sum(tf.pow(tiled_Targets, 2), axis = 2)) * \
tf.sqrt(tf.reduce_sum(tf.pow(tiled_Output, 2), axis = 2)) + \
1e-7
) #[Num_Words, Steps]
return cosine_Similarity
def RT_Generate(self, word, identifier, data):
rt_Dict = {
('Onset', 'Absolute'): np.nan,
('Onset', 'Relative'): np.nan,
('Onset', 'Time_Dependent'): np.nan
}
target_Index = self.word_Index_Dict[word]
target_Array = data[target_Index]
other_Max_Array = np.max(np.delete(data, target_Index, 0), axis=0) #Target is removed, and using the max value of each time step.
#Absolute threshold RT
if not (other_Max_Array > self.absolute_Criterion).any():
absolute_Check_Array = target_Array > self.absolute_Criterion
for step in range(self.max_Step):
if absolute_Check_Array[step]:
rt_Dict['Onset', 'Absolute'] = step
break
#Relative threshold RT
relative_Check_Array = target_Array > (other_Max_Array + self.relative_Criterion)
for step in range(self.max_Step):
if relative_Check_Array[step]:
rt_Dict['Onset', 'Relative'] = step
break
#Time dependent RT
time_Dependency_Check_Array_with_Criterion = target_Array > other_Max_Array + self.time_Dependency_Criterion[1]
time_Dependency_Check_Array_Sustainment = target_Array > other_Max_Array
for step in range(self.max_Step - self.time_Dependency_Criterion[0]):
if all(np.hstack([
time_Dependency_Check_Array_with_Criterion[step:step + self.time_Dependency_Criterion[0]],
time_Dependency_Check_Array_Sustainment[step + self.time_Dependency_Criterion[0]:]
])):
rt_Dict['Onset', 'Time_Dependent'] = step
break
#Offset_RT = Onset_RT - length
if not np.isnan(rt_Dict['Onset', 'Absolute']):
rt_Dict['Offset', 'Absolute'] = rt_Dict['Onset', 'Absolute'] - self.step_Dict[word, identifier]
else:
rt_Dict['Offset', 'Absolute'] = rt_Dict['Onset', 'Absolute'] #np.nan
if not np.isnan(rt_Dict['Onset', 'Relative']):
rt_Dict['Offset', 'Relative'] = rt_Dict['Onset', 'Relative'] - self.step_Dict[word, identifier]
else:
rt_Dict['Offset', 'Relative'] = rt_Dict['Onset', 'Relative'] #np.nan
if not np.isnan(rt_Dict['Onset', 'Time_Dependent']):
rt_Dict['Offset', 'Time_Dependent'] = rt_Dict['Onset', 'Time_Dependent'] - self.step_Dict[word, identifier]
else:
rt_Dict['Offset', 'Time_Dependent'] = rt_Dict['Onset', 'Time_Dependent'] #np.nan
return rt_Dict
def Category_Flow_Generate(self, word, data): #For categorized flow
category_Flow_Dict = {}
for category in ['Target', 'Cohort', 'Rhyme', 'Unrelated']:
if len(self.category_Dict[word, category]) > 0:
category_Flow_Dict[category] = np.mean(data[self.category_Dict[word, category],:], axis=0) #Calculation mean of several same category flows.
else:
category_Flow_Dict[category] = np.zeros((data.shape[1])) * np.nan # If there is no word which is belonged a specific category, nan value.
category_Flow_Dict['All'] = np.mean(data, axis=0)
category_Flow_Dict['Other_Max'] = np.max(np.delete(data, self.word_Index_Dict[word], 0), axis=0) #Target is removed, and using the max value of each time step.
return category_Flow_Dict
def Pattern_Metadata_Load(self):
with open(os.path.join(hp_Dict['Pattern']['Pattern_Path'], hp_Dict['Pattern']['Metadata_File']).replace('\\', '/'), 'rb') as f:
self.pattern_Metadata_Dict = pickle.load(f)
self.word_Index_Dict = {
word: index
for index, (word, _) in enumerate(self.pattern_Metadata_Dict['Target_Dict'].items())
}
self.step_Dict = {
self.pattern_Metadata_Dict['Word_and_Identifier_Dict'][path]: step
for path, step in self.pattern_Metadata_Dict['Step_Dict'].items()
}
self.max_Step = max([step for step in self.step_Dict.values()])
self.targets = np.array([
self.pattern_Metadata_Dict['Target_Dict'][word]
for word, _ in sorted(list(self.word_Index_Dict.items()), key= lambda x: x[1])
]).astype(np.float32)
def Category_Dict_Generate(self):
self.category_Dict = {}
for target_Word, target_Pronunciation in self.pattern_Metadata_Dict['Pronunciation_Dict'].items():
self.category_Dict[target_Word, 'Target'] = []
self.category_Dict[target_Word, 'Cohort'] = []
self.category_Dict[target_Word, 'Rhyme'] = []
self.category_Dict[target_Word, 'DAS_Neighborhood'] = []
self.category_Dict[target_Word, 'Unrelated'] = []
for compare_Word, compare_Pronunciation in self.pattern_Metadata_Dict['Pronunciation_Dict'].items():
compare_Word_Index = self.word_Index_Dict[compare_Word]
unrelated = True
if target_Word == compare_Word:
self.category_Dict[target_Word, 'Target'].append(compare_Word_Index)
unrelated = False
if target_Pronunciation[0:2] == compare_Pronunciation[0:2] and target_Word != compare_Word: #Cohort
self.category_Dict[target_Word, 'Cohort'].append(compare_Word_Index)
unrelated = False
if target_Pronunciation[1:] == compare_Pronunciation[1:] and target_Pronunciation[0] != compare_Pronunciation[0] and target_Word != compare_Word: #Rhyme
self.category_Dict[target_Word, 'Rhyme'].append(compare_Word_Index)
unrelated = False
if unrelated:
self.category_Dict[target_Word, 'Unrelated'].append(compare_Word_Index) #Unrelated
#For test
if self.DAS_Neighborhood_Checker(target_Pronunciation, compare_Pronunciation): #Neighborhood
self.category_Dict[target_Word, 'DAS_Neighborhood'].append(compare_Word_Index)
def DAS_Neighborhood_Checker(self, pronunciation1, pronunciation2): #Delete, Addition, Substitution neighborhood checking
#Same pronunciation
if pronunciation1 == pronunciation2:
return False
#Exceed range
elif abs(len(pronunciation1) - len(pronunciation2)) > 1: #The length difference is bigger than 1, two pronunciations are not related.
return False
#Deletion
elif len(pronunciation1) == len(pronunciation2) + 1:
for index in range(len(pronunciation1)):
deletion = pronunciation1[:index] + pronunciation1[index + 1:]
if deletion == pronunciation2:
return True
#Addition
elif len(pronunciation1) == len(pronunciation2) - 1:
for index in range(len(pronunciation2)):
deletion = pronunciation2[:index] + pronunciation2[index + 1:]
if deletion == pronunciation1:
return True
#Substitution
elif len(pronunciation1) == len(pronunciation2):
for index in range(len(pronunciation1)):
pronunciation1_Substitution = pronunciation1[:index] + pronunciation1[index + 1:]
pronunciation2_Substitution = pronunciation2[:index] + pronunciation2[index + 1:]
if pronunciation1_Substitution == pronunciation2_Substitution:
return True
return False
def Adjusted_Length_Dict_Generate(self): #For uniqueness point.
self.adjusted_Length_Dict = {}
for word, pronunciation in self.pattern_Metadata_Dict['Pronunciation_Dict'].items():
for cut_Length in range(1, len(pronunciation) + 1):
cut_Pronunciation = pronunciation[:cut_Length]
cut_Comparer_List = [comparer[:cut_Length] for comparer in self.pattern_Metadata_Dict['Pronunciation_Dict'].values() if pronunciation != comparer]
if not cut_Pronunciation in cut_Comparer_List: #When you see a part of target phoneme string, if there is no other competitor.
self.adjusted_Length_Dict[word] = cut_Length - len(pronunciation) - 1
break
if not word in self.adjusted_Length_Dict.keys():
self.adjusted_Length_Dict[word] = 0
if __name__ == '__main__':
argParser = argparse.ArgumentParser()
argParser.add_argument('-d', '--directory', default= '', type= str)
argParser.add_argument('-a', '--absolute', default= 0.7, type= float)
argParser.add_argument('-r', '--relative', default= 0.05, type= float)
argParser.add_argument('-tw', '--time_dependency_width', default= 10, type= float)
argParser.add_argument('-th', '--time_dependency_height', default= 0.05, type= float)
argument_Dict = vars(argParser.parse_args())
new_Analyzer = Analyzer(
path= argument_Dict['directory'],
absolute_Criterion= argument_Dict['absolute'],
relative_Criterion= argument_Dict['relative'],
time_Dependency_Criterion= (
argument_Dict['time_dependency_width'],
argument_Dict['time_dependency_height']
),
step_Cut= True
)