def ingest():
seed = 1000
data = DataInitializer()
data.initialize("data/train.csv")
data = DataCleaning(data)
data.cleanup(DataCleaner())
data = Sentiments(data)
data.sentiment_analysis_by_text()
data = data.processed_data[['sentiment', 'text']]
print('dataset loaded with shape', data.shape)
print("Distribution of sentiments: ",
pd.Series(data["sentiment"]).value_counts())
# data["sentiment"] = data["sentiment"].map(codes)
return data
def preprocess(data_path,
is_testing,
min_occurrences=5,
cache_bow_output=None,
cache_word2vec_output=None,
duration=None):
if duration:
data = DataInitializer()
data.initialize(data_path, is_testing, duration=duration)
else:
data = DataInitializer()
data.initialize(data_path, is_testing)
if os.path.isfile("data/BTC.csv"):
prices_data = GetPricesData()
prices_data.main()
data = DataCleaning(data, is_testing)
data.cleanup(DataCleaner(is_testing))
if is_testing:
print("Testing data shape:", data.processed_data.shape)
else:
print("Training data shape:", data.processed_data.shape)
data = Sentiments(data)
data.sentiment_analysis_by_text()
print("First five rows with sentiment: ", data.processed_data.head())
if is_testing:
data.processed_data.to_csv("data/clean_test_with_sentiments.csv",
sep=',',
encoding='utf-8',
index=False)
# os.remove(data_path)
else:
data.processed_data.to_csv("data/clean_train_with_sentiments.csv",
sep=',',
encoding='utf-8',
index=False)
# os.remove(data_path)
data = DataTokenize(data)
data.tokenize()
data.stem()
data = WordList(data)
data.build_wordlist(min_occurrences=min_occurrences)
word2vec_data = data
data = BagOfWords(data.processed_data, data.wordlist, is_testing)
data.build_data_model()
print("data model head: ", data.data_model.head(5))
"""
Word 2 vec
"""
word2vec = Word2VecProvider()
# REPLACE PATH TO THE FILE
word2vec.load("../twitter/data/glove.twitter.27B.200d.txt")
word2vec_data = RedditData(word2vec_data)
word2vec_data.build_final_model(word2vec)
word2vec_data_model = word2vec_data.data_model
if "index" in word2vec_data_model.columns:
word2vec_data_model.drop("index", axis=1, inplace=True)
word2vec_data_model.dropna(axis=0, inplace=True)
word2vec_data_model.reset_index(inplace=True)
word2vec_data_model.index = word2vec_data_model['timestamp_ms']
print("final word2vec data model: \n", word2vec_data_model.head(), "\n")
"""
Tokenizing the data
"""
texts = []
sentiments = []
tokenized_data = pd.DataFrame()
for text in data.processed_data["summary"]:
texts.append(text)
for sentiment in data.processed_data["sentiment"]:
sentiments.append(sentiment)
print("texts: ", texts[0:5])
tokenizer = Tokenizer(num_words=20000)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences, maxlen=200)
print(
"\n\n##################################################\npadded sequence head: \n",
padded_sequences[0:5])
print(
"\n####################################################\n padded sequence length \n",
len(padded_sequences))
if not is_testing:
data = Plotting(data)
data.plot()
if cache_bow_output is not None:
data.data_model.to_csv(cache_bow_output,
index=False,
float_format="%.6f")
word2vec_data_model.to_csv(cache_word2vec_output,
index=False,
float_format="%.6f")
with open('sequences', 'wb') as fp:
pickle.dump(padded_sequences, fp)
with open('sentiments', 'wb') as fp:
pickle.dump(sentiments, fp)
return data.data_model, word2vec_data_model
def preprocess(data_path,
is_testing,
min_occurrences=5,
cache_bow_output=None,
cache_word2vec_output=None,
duration=None,
sentiment_method=None):
if duration and cache_bow_output and cache_word2vec_output:
data = DataInitializer()
data.initialize(data_path, is_testing, duration=duration)
elif cache_bow_output and cache_word2vec_output:
data = DataInitializer()
data.initialize(data_path,
is_testing,
cache_bow_output=cache_bow_output,
cache_word2vec_output=cache_word2vec_output)
else:
data = DataInitializer()
data.initialize(data_path, is_testing)
if not os.path.isfile("data/Train_BTC.csv"):
prices_data = GetPricesData()
prices_data.main()
if not os.path.isfile("data/Test_BTC.csv"):
prices_data = GetPricesData()
prices_data.main()
data = DataCleaning(data, is_testing)
data.cleanup(DataCleaner(is_testing))
if is_testing:
print("Testing data shape:", data.processed_data.shape)
else:
print("Training data shape:", data.processed_data.shape)
data = Sentiments(data, sentiment_method=sentiment_method)
data.sentiment_analysis_by_text()
print("First five rows with sentiment: ", data.processed_data.head())
if is_testing:
data.processed_data.to_csv(
"data/one_month_clean_test_data_with_prices.csv",
sep=',',
encoding='utf-8',
index=False)
# os.remove(data_path)
else:
data.processed_data.to_csv("data/one_month_clean_data_with_prices.csv",
sep=',',
encoding='utf-8',
index=False)
# os.remove(data_path)
if os.path.isfile(cache_word2vec_output):
print("cache_word2vec_output file name: ", cache_word2vec_output)
word2vec_data_model = pd.read_csv(cache_word2vec_output)
data.data_model = pd.read_csv(cache_bow_output)
print("data model head: ", data.data_model.head(5))
else:
data = DataTokenize(data)
data.tokenize()
data.stem()
data = WordList(data)
data.build_wordlist(min_occurrences=min_occurrences)
word2vec_data = data
data = BagOfWords(data.processed_data, data.wordlist, is_testing)
data.build_data_model()
print("data model head: ", data.data_model.head(5))
"""
Word 2 vec
"""
word2vec = Word2VecProvider()
# REPLACE PATH TO THE FILE
word2vec.load("data/glove.twitter.27B.200d-with2num.txt")
word2vec_data = TwitterData(word2vec_data)
word2vec_data.build_final_model(word2vec)
word2vec_data_model = word2vec_data.data_model
if "original_id" in word2vec_data_model.columns:
word2vec_data_model.drop("original_id", axis=1, inplace=True)
word2vec_data_model.dropna(axis=0, inplace=True)
word2vec_data_model.reset_index(inplace=True, drop=True)
word2vec_data_model.index = word2vec_data_model['timestamp']
print("final word2vec data model: \n", word2vec_data_model.head(), "\n")
# if not is_testing:
# data = Plotting(data)
# data.plot()
if not is_testing:
if not os.path.isfile("train_sequences"):
print("\n##########################\n"
"Tokenizing the tweets\n"
"############################\n")
texts = []
sentiments = []
tokenized_data = pd.DataFrame()
for text in data.processed_data["text"]:
texts.append(text)
for sentiment in data.processed_data['sentiment']:
sentiments.append(sentiment)
print("texts: ", texts[0:5])
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences,
maxlen=20,
padding='post')
padded_sequences = pd.DataFrame(data=padded_sequences)
merged_train_data = pd.concat([
padded_sequences, data.processed_data[[
"high", "low", "open", "quoteVolume", "volume",
"weightedAverage"
]]
],
axis=1)
train_targets = data.processed_data[["close"]]
print("shape of merged train data: ", merged_train_data.shape)
with open('data/train_sequences', 'wb') as fp:
pickle.dump(merged_train_data, fp)
with open('data/train_prices', 'wb') as fp:
pickle.dump(train_targets, fp)
# load the whole embedding into memory
embeddings_index = dict()
with open("data/glove.twitter.27B.200d-with2num.txt",
"r",
encoding="utf-8") as my_file:
for line in my_file:
values = line.split()
word = values[0]
coefs = numpy.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
# f.close()
print("*" * 80, "\n" * 10)
print('Loaded %s train word vectors.' % len(embeddings_index))
print('Total %s of word indexes.' % len(tokenizer.word_index))
with open('data/embeddings_index', 'wb') as fp:
pickle.dump(embeddings_index, fp)
with open('data/train_word_indexes', 'wb') as fp:
pickle.dump(tokenizer.word_index, fp)
# encode class values as integers
# encoder = LabelEncoder()
# encoder.fit(sentiments)
# encoded_sentiments = encoder.transform(sentiments)
# convert integers to dummy variables (i.e. one hot encoded)
# dummy_sentiments = np_utils.to_categorical(encoded_sentiments)
# for text in data.processed_data.loc[data.processed_data['sentiment'] != 0, "text"]:
# texts.append(text)
#
# for sentiment in data.processed_data.loc[data.processed_data['sentiment'] != 0, "sentiment"]:
# sentiments.append(sentiment)
else:
if not os.path.isfile("test_sequences"):
print("\n##########################\n"
"Tokenizing the tweets\n"
"############################\n")
texts = []
sentiments = []
tokenized_data = pd.DataFrame()
for text in data.processed_data["text"]:
texts.append(text)
for sentiment in data.processed_data['sentiment']:
sentiments.append(sentiment)
print("texts: ", texts[0:5])
tokenizer = Tokenizer()
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
padded_sequences = pad_sequences(sequences,
maxlen=20,
padding='post')
padded_sequences = pd.DataFrame(data=padded_sequences)
merged_test_data = pd.concat([
padded_sequences, data.processed_data[[
"high", "low", "open", "quoteVolume", "volume",
"weightedAverage"
]]
],
axis=1)
test_targets = data.processed_data[["close"]]
print("shape of merged test data: ", merged_test_data.shape)
with open('data/test_sequences', 'wb') as fp:
pickle.dump(merged_test_data, fp)
with open('data/test_prices', 'wb') as fp:
pickle.dump(test_targets, fp)
with open('data/test_word_indexes', 'wb') as fp:
pickle.dump(tokenizer.word_index, fp)
# padded_sequences = pd.DataFrame(data=padded_sequences)
print(
"\n\n##################################################\npadded sequence head: \n",
padded_sequences[0:5])
print(
"\n####################################################\n padded sequence length \n",
len(padded_sequences))
if not os.path.isfile(train_data_word2vec_file_name) or not os.path.isfile(
test_data_word2vec_file_name):
if cache_bow_output is not None:
data.data_model.to_csv(cache_bow_output,
index=False,
float_format="%.6f")
word2vec_data_model.to_csv(cache_word2vec_output,
index=False,
float_format="%.6f")
return data.data_model, word2vec_data_model