# -*- coding: utf-8 -*-
"""
@author: Chenglong Chen <c.chenglong@gmail.com>
@brief: distance features
"""
import re
import sys
import string
import numpy as np
import pandas as pd
import config
from utils import dist_utils, ngram_utils, nlp_utils
from utils import logging_utils, time_utils, pkl_utils
from feature_base import BaseEstimator, PairwiseFeatureWrapper
# tune the token pattern to get a better correlation with y_train
# token_pattern = r"(?u)\b\w\w+\b"
# token_pattern = r"\w{1,}"
# token_pattern = r"\w+"
# token_pattern = r"[\w']+"
token_pattern = " " # just split the text into tokens
# ------------------- Jaccard & Dice --------------------------------------
class JaccardCoef_Ngram(BaseEstimator):
def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode)
self.ngram = ngram
self.ngram_str = ngram_utils._ngram_str_map[self.ngram]
def __name__(self):
return "JaccardCoef_%s"%self.ngram_str
def transform_one(self, obs, target, id):
obs_tokens = nlp_utils._tokenize(obs, token_pattern)
target_tokens = nlp_utils._tokenize(target, token_pattern)
obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
return dist_utils._jaccard_coef(obs_ngrams, target_ngrams)
class DiceDistance_Ngram(BaseEstimator):
def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode)
self.ngram = ngram
self.ngram_str = ngram_utils._ngram_str_map[self.ngram]
def __name__(self):
return "DiceDistance_%s"%self.ngram_str
def transform_one(self, obs, target, id):
obs_tokens = nlp_utils._tokenize(obs, token_pattern)
target_tokens = nlp_utils._tokenize(target, token_pattern)
obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
return dist_utils._dice_dist(obs_ngrams, target_ngrams)
# ------------------ Edit Distance --------------------------------
class EditDistance(BaseEstimator):
def __init__(self, obs_corpus, target_corpus, aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode)
def __name__(self):
return "EditDistance"
def transform_one(self, obs, target, id):
return dist_utils._edit_dist(obs, target)
class EditDistance_Ngram(BaseEstimator):
"""Double aggregation features"""
def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode_prev="", aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode, None, aggregation_mode_prev)
self.ngram = ngram
self.ngram_str = ngram_utils._ngram_str_map[self.ngram]
def __name__(self):
feat_name = []
for m1 in self.aggregation_mode_prev:
for m in self.aggregation_mode:
n = "EditDistance_%s_%s_%s"%(self.ngram_str, string.capwords(m1), string.capwords(m))
feat_name.append(n)
return feat_name
def transform_one(self, obs, target, id):
obs_tokens = nlp_utils._tokenize(obs, token_pattern)
target_tokens = nlp_utils._tokenize(target, token_pattern)
obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
val_list = []
for w1 in obs_ngrams:
_val_list = []
for w2 in target_ngrams:
s = dist_utils._edit_dist(w1, w2)
_val_list.append(s)
if len(_val_list) == 0:
_val_list = [ config.MISSING_VALUE_NUMERIC ]
val_list.append( _val_list )
if len(val_list) == 0:
val_list = [ [config.MISSING_VALUE_NUMERIC] ]
return val_list
# ------------------ Compression Distance --------------------------------
class CompressionDistance(BaseEstimator):
"""Very time consuming"""
def __init__(self, obs_corpus, target_corpus, aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode)
def __name__(self):
return "CompressionDistance"
def transform_one(self, obs, target, id):
return dist_utils._compression_dist(obs, target)
class CompressionDistance_Ngram(BaseEstimator):
"""Double aggregation features"""
def __init__(self, obs_corpus, target_corpus, ngram, aggregation_mode_prev="", aggregation_mode=""):
super().__init__(obs_corpus, target_corpus, aggregation_mode, None, aggregation_mode_prev)
self.ngram = ngram
self.ngram_str = ngram_utils._ngram_str_map[self.ngram]
def __name__(self):
feat_name = []
for m1 in self.aggregation_mode_prev:
for m in self.aggregation_mode:
n = "CompressionDistance_%s_%s_%s"%(self.ngram_str, string.capwords(m1), string.capwords(m))
feat_name.append(n)
return feat_name
def transform_one(self, obs, target, id):
obs_tokens = nlp_utils._tokenize(obs, token_pattern)
target_tokens = nlp_utils._tokenize(target, token_pattern)
obs_ngrams = ngram_utils._ngrams(obs_tokens, self.ngram)
target_ngrams = ngram_utils._ngrams(target_tokens, self.ngram)
val_list = []
for w1 in obs_ngrams:
_val_list = []
for w2 in target_ngrams:
s = dist_utils._compression_dist(w1, w2)
_val_list.append(s)
if len(_val_list) == 0:
_val_list = [ config.MISSING_VALUE_NUMERIC ]
val_list.append( _val_list )
if len(val_list) == 0:
val_list = [ [config.MISSING_VALUE_NUMERIC] ]
return val_list
# ---------------------------- Main --------------------------------------
def run_ngram_jaccard():
logname = "generate_feature_ngram_jaccard_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
generators = [JaccardCoef_Ngram, DiceDistance_Ngram]
obs_fields_list = []
target_fields_list = []
obs_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] )
target_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] )
ngrams = [1,2,3,12,123][:3]
for obs_fields, target_fields in zip(obs_fields_list, target_fields_list):
for generator in generators:
for ngram in ngrams:
param_list = [ngram]
pf = PairwiseFeatureWrapper(generator, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger)
pf.go()
def run_edit_distance():
logname = "generate_feature_edit_distance_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
obs_fields_list = []
target_fields_list = []
obs_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] )
target_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] )
ngrams = [1,2,3,12,123][:3]
aggregation_mode_prev = ["mean", "max", "min", "median"]
aggregation_mode = ["mean", "std", "max", "min", "median"]
for obs_fields, target_fields in zip(obs_fields_list, target_fields_list):
param_list = []
pf = PairwiseFeatureWrapper(EditDistance, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger)
pf.go()
for ngram in ngrams:
param_list = [ngram, aggregation_mode_prev, aggregation_mode]
pf = PairwiseFeatureWrapper(EditDistance_Ngram, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger)
pf.go()
def run_compression_distance():
logname = "generate_feature_compression_distance_%s.log"%time_utils._timestamp()
logger = logging_utils._get_logger(config.LOG_DIR, logname)
dfAll = pkl_utils._load(config.ALL_DATA_LEMMATIZED_STEMMED)
obs_fields_list = []
target_fields_list = []
obs_fields_list.append( ["search_term", "search_term_product_name", "search_term_alt", "search_term_auto_corrected"][:2] )
target_fields_list.append( ["product_title", "product_title_product_name", "product_description", "product_attribute", "product_brand", "product_color"] )
for obs_fields, target_fields in zip(obs_fields_list, target_fields_list):
param_list = []
pf = PairwiseFeatureWrapper(CompressionDistance, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger)
pf.go()
for ngram in ngrams:
param_list = [ngram, aggregation_mode_prev, aggregation_mode]
pf = PairwiseFeatureWrapper(CompressionDistance_Ngram, dfAll, obs_fields, target_fields, param_list, config.FEAT_DIR, logger)
pf.go()
def main(which):
if which == "jaccard":
run_ngram_jaccard()
elif which == "edit":
run_edit_distance()
elif which == "compression":
run_compression_distance()
if __name__ == "__main__":
main(sys.argv[1])
