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3_prepare_datasets.py
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3_prepare_datasets.py
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import string
import os
import cPickle as pickle
from lib.data_io import Essays
from lib.utils import *
from lib.text_transform import *
from lib.math_helpers import *
from lib.pipeline import *
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.externals import joblib
from lib.porter import stemmer
def create_dataset_2():
essays = Essays("data_work/items_data/*.csv")
LABEL = essays.apply_cell_function("label",identity)
READ_1_SCORE = essays.apply_cell_function("read_1_score",identity)
READ_2_SCORE = essays.apply_cell_function("read_2_score",identity)
FINAL_SCORE = essays.apply_cell_function("final_score",identity)
# prepares text
print "Preparing text..."
RAW_TEXTS = essays.apply_cell_function("data_answer",identity)
RAW_TEXTS = func_over_dict(RAW_TEXTS, apply_map_func(string.upper),parallel=True)
TEXTS = essays.apply_cell_function("data_answer",identity)
print "Cleaning text..."
TEXTS = clean_text(TEXTS)
print "Spellchecking..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: spellcheck(x,exclude=["EQUATIONINCORRECT","EQUATIONINCORRECT"])))
for key in ["5_53299","7_46793","3_51802","7_46597"]:
TEXTS[key] = TEXTS[key].map(simplify_math)
print "Reducing vocabulary..."
TEXTS = reduce_vocabulary_dict(TEXTS)
print "Stemming..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: " ".join([stemmer(w) for w in x.split()])))
bow_args = {'min_df':2,'ngram_range':(1,1),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_1_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
bow_args = {'min_df':5,'ngram_range':(2,2),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_2_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
bow_args = {'min_df':5,'ngram_range':(3,3),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_3_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
META_LABEL = func_over_dict(LABEL, lambda x: pd.DataFrame({'META_LABEL':x}))
META_SCORE_1 = func_over_dict(READ_1_SCORE, lambda x: pd.DataFrame({'META_SCORE_1':x}))
META_SCORE_2 = func_over_dict(READ_2_SCORE, lambda x: pd.DataFrame({'META_SCORE_2':x}))
META_SCORE_FINAL = func_over_dict(FINAL_SCORE, lambda x: pd.DataFrame({'META_SCORE_FINAL':x}))
TEXT_STATISTICS = func_over_dict(RAW_TEXTS, text_statistics)
QUOTATIONS_NUM = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: len(re.findall('"(.*?)"',x)))), lambda x: pd.DataFrame({'QUOTATIONS_NUM':x}))
YES_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("YES"))), lambda x: pd.DataFrame({'YES_POSITION':x}))
NO_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("NO"))), lambda x: pd.DataFrame({'NO_POSITION':x}))
dataset = merge_dataframes([
META_LABEL
,META_SCORE_1
,META_SCORE_2
,META_SCORE_FINAL
,BOW_1_GRAM
,BOW_2_GRAM
,BOW_3_GRAM
,TEXT_STATISTICS
,QUOTATIONS_NUM
,YES_POSITION
,NO_POSITION
])
dataset = [dataset, dataset]
joblib.dump(dataset,"data_work/datasets/dataset_2_SE")
def create_dataset_2_SE():
essays = Essays("data_work/items_data_se/*.csv")
LABEL = essays.apply_cell_function("label",identity)
READ_1_SCORE = essays.apply_cell_function("read_1_score",identity)
READ_2_SCORE = essays.apply_cell_function("read_2_score",identity)
FINAL_SCORE = essays.apply_cell_function("final_score",identity)
# prepares text
print "Preparing text..."
RAW_TEXTS = essays.apply_cell_function("data_answer",identity)
RAW_TEXTS = func_over_dict(RAW_TEXTS, apply_map_func(string.upper))
TEXTS = essays.apply_cell_function("data_answer",identity)
print "Cleaning text..."
TEXTS = clean_text(TEXTS)
print "Spellchecking..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: spellcheck(x,exclude=["EQUATIONINCORRECT","EQUATIONINCORRECT"])))
#for key in ["5_53299","7_46793","3_51802","7_46597"]:
# TEXTS[key] = TEXTS[key].map(simplify_math)
print "Reducing vocabulary..."
TEXTS = reduce_vocabulary_dict(TEXTS)
print "Stemming..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: " ".join([stemmer(w) for w in x.split()])))
bow_args = {'min_df':2,'ngram_range':(1,1),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_1_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
#bow_args = {'min_df':5,'ngram_range':(2,2),'stop_words':'english','tokenizer':lambda x: x.split()}
#BOW_2_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
#bow_args = {'min_df':5,'ngram_range':(3,3),'stop_words':'english','tokenizer':lambda x: x.split()}
#BOW_3_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
META_LABEL = func_over_dict(LABEL, lambda x: pd.DataFrame({'META_LABEL':x}))
META_SCORE_1 = func_over_dict(READ_1_SCORE, lambda x: pd.DataFrame({'META_SCORE_1':x}))
META_SCORE_2 = func_over_dict(READ_2_SCORE, lambda x: pd.DataFrame({'META_SCORE_2':x}))
META_SCORE_FINAL = func_over_dict(FINAL_SCORE, lambda x: pd.DataFrame({'META_SCORE_FINAL':x}))
TEXT_STATISTICS = func_over_dict(RAW_TEXTS, text_statistics)
QUOTATIONS_NUM = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: len(re.findall('"(.*?)"',x)))), lambda x: pd.DataFrame({'QUOTATIONS_NUM':x}))
YES_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("YES"))), lambda x: pd.DataFrame({'YES_POSITION':x}))
NO_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("NO"))), lambda x: pd.DataFrame({'NO_POSITION':x}))
dataset = merge_dataframes([
META_LABEL
,META_SCORE_1
,META_SCORE_2
,META_SCORE_FINAL
,BOW_1_GRAM
#,BOW_2_GRAM
#,BOW_3_GRAM
,TEXT_STATISTICS
,QUOTATIONS_NUM
,YES_POSITION
,NO_POSITION
])
dataset = [dataset, dataset]
joblib.dump(dataset,"data_work/datasets/dataset_2_SE")
def create_dataset_2_gaming():
essays = Essays("data_work/items_data_gaming/*.csv")
LABEL = essays.apply_cell_function("label",identity)
READ_1_SCORE = essays.apply_cell_function("read_1_score",identity)
READ_2_SCORE = essays.apply_cell_function("read_2_score",identity)
FINAL_SCORE = essays.apply_cell_function("final_score",identity)
# prepares text
print "Preparing text..."
RAW_TEXTS = essays.apply_cell_function("data_answer",identity)
RAW_TEXTS = func_over_dict(RAW_TEXTS, apply_map_func(string.upper))
TEXTS = essays.apply_cell_function("data_answer",identity)
print "Cleaning text..."
TEXTS = clean_text(TEXTS)
print "Spellchecking..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: spellcheck(x,exclude=["EQUATIONINCORRECT","EQUATIONINCORRECT"])))
#for key in ["5_53299","7_46793","3_51802","7_46597"]:
# TEXTS[key] = TEXTS[key].map(simplify_math)
print "Reducing vocabulary..."
TEXTS = reduce_vocabulary_dict(TEXTS)
print "Stemming..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: " ".join([stemmer(w) for w in x.split()])))
bow_args = {'min_df':2,'ngram_range':(1,1),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_1_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
#bow_args = {'min_df':5,'ngram_range':(2,2),'stop_words':'english','tokenizer':lambda x: x.split()}
#BOW_2_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
#bow_args = {'min_df':5,'ngram_range':(3,3),'stop_words':'english','tokenizer':lambda x: x.split()}
#BOW_3_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args))
META_LABEL = func_over_dict(LABEL, lambda x: pd.DataFrame({'META_LABEL':x}))
META_SCORE_1 = func_over_dict(READ_1_SCORE, lambda x: pd.DataFrame({'META_SCORE_1':x}))
META_SCORE_2 = func_over_dict(READ_2_SCORE, lambda x: pd.DataFrame({'META_SCORE_2':x}))
META_SCORE_FINAL = func_over_dict(FINAL_SCORE, lambda x: pd.DataFrame({'META_SCORE_FINAL':x}))
TEXT_STATISTICS = func_over_dict(RAW_TEXTS, text_statistics)
QUOTATIONS_NUM = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: len(re.findall('"(.*?)"',x)))), lambda x: pd.DataFrame({'QUOTATIONS_NUM':x}))
YES_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("YES"))), lambda x: pd.DataFrame({'YES_POSITION':x}))
NO_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("NO"))), lambda x: pd.DataFrame({'NO_POSITION':x}))
dataset = merge_dataframes([
META_LABEL
,META_SCORE_1
,META_SCORE_2
,META_SCORE_FINAL
,BOW_1_GRAM
#,BOW_2_GRAM
#,BOW_3_GRAM
,TEXT_STATISTICS
,QUOTATIONS_NUM
,YES_POSITION
,NO_POSITION
])
dataset = [dataset, dataset]
joblib.dump(dataset,"data_work/datasets/dataset_2_gaming")
def create_dataset_2_sample_size(input_path, output_path):
essays = Essays(input_path)
LABEL = essays.apply_cell_function("label",identity)
READ_1_SCORE = essays.apply_cell_function("read_1_score",identity)
READ_2_SCORE = essays.apply_cell_function("read_2_score",identity)
FINAL_SCORE = essays.apply_cell_function("final_score",identity)
# prepares text
print "Preparing text..."
RAW_TEXTS = essays.apply_cell_function("data_answer",identity)
RAW_TEXTS = func_over_dict(RAW_TEXTS, apply_map_func(string.upper), parallel=True)
TEXTS = essays.apply_cell_function("data_answer",identity)
print "Cleaning text..."
TEXTS = clean_text(TEXTS)
print "Simplifying math expressions..."
math_essays = [key for key in TEXTS.keys() if key.startswith("53299")]
for key in math_essays:
TEXTS[key] = TEXTS[key].map(simplify_math)
print "Spellchecking..."
TEXTS = func_over_dict(TEXTS, apply_map_func(spellcheck), parallel=False)
print "Reducing vocabulary..."
TEXTS = func_over_dict(TEXTS, reduce_vocabulary_func, parallel=True)
print "Stemming..."
TEXTS = func_over_dict(TEXTS, apply_map_func(lambda x: " ".join([stemmer(w) for w in x.split()])), parallel=True)
bow_args = {'min_df':2,'ngram_range':(1,1),'stop_words':'english','tokenizer':lambda x: x.split()}
BOW_1_GRAM = func_over_dict(TEXTS, lambda x: bag_of_words(x,**bow_args), parallel=True)
META_LABEL = func_over_dict(LABEL, lambda x: pd.DataFrame({'META_LABEL':x}), parallel=True)
META_SCORE_1 = func_over_dict(READ_1_SCORE, lambda x: pd.DataFrame({'META_SCORE_1':x}), parallel=True)
META_SCORE_2 = func_over_dict(READ_2_SCORE, lambda x: pd.DataFrame({'META_SCORE_2':x}), parallel=True)
META_SCORE_FINAL = func_over_dict(FINAL_SCORE, lambda x: pd.DataFrame({'META_SCORE_FINAL':x}), parallel=True)
TEXT_STATISTICS = func_over_dict(RAW_TEXTS, text_statistics, parallel=True)
QUOTATIONS_NUM = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: len(re.findall('"(.*?)"',x)))), lambda x: pd.DataFrame({'QUOTATIONS_NUM':x}), parallel=True)
YES_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("YES"))), lambda x: pd.DataFrame({'YES_POSITION':x}), parallel=True)
NO_POSITION = func_over_dict(func_over_dict(RAW_TEXTS, apply_map_func(lambda x: x.find("NO"))), lambda x: pd.DataFrame({'NO_POSITION':x}), parallel=True)
dataset = merge_dataframes([
META_LABEL
,META_SCORE_1
,META_SCORE_2
,META_SCORE_FINAL
,BOW_1_GRAM
,TEXT_STATISTICS
,QUOTATIONS_NUM
,YES_POSITION
,NO_POSITION
])
dataset = [dataset, dataset]
joblib.dump(dataset,output_path)
if __name__=="__main__":
create_dataset_2()
create_dataset_2_SE()
create_dataset_2_gaming()
create_dataset_2_sample_size("data_work/items_datasample_size/*.csv" , "data_work/datasets/dataset_2_sample_size")