example2 = example.lower() print(example2) # In[ ]: example3 = CountVectorizer().build_tokenizer()(example2) print(example3) # In[ ]: pd.DataFrame([[x,example3.count(x)] for x in set(example3)], columns = ['Word', 'Count']) # Were you able to see everything that changed? # The process involved: # - Converting the headline to lowercase letters # - Splitting the sentence into a list of words # - Removing punctuation and meaningless words # - Transforming that list into a table of counts # What started as a relatively "messy" sentence has now become an neatly organized table! # And while this may not be exactly what goes on behind the scenes with scikit-learn, this example should give you a pretty good idea about how it works. # So now that you've seen what the text processing looks like, let's get started on the fun part, modeling! # ----------
# encoding=utf-8
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
# pd.set_option("display.max_columns",None)
data = pd.read_csv("G:\\datas\\ai\\Combined_News_DJIA.csv")
# print(data.head())
print(type(data))
train = data[data["Date"] < "2015-01-01"]
test = data[data["Date"] < "2014-12-31"]
example = train.iloc[3, 10]
print(example)
# 全部转换为小写
example2 = example.lower()
print(example2)
example3 = CountVectorizer().build_tokenizer()(example2)
print(example3)
df = pd.DataFrame([[x,example3.count(x)] for x in set(example3)],columns=["Word","Count"])
print(df)
df.index = pd.DatetimeIndex(df.index)
# Set X and y
X = df.drop('Label', axis=1)
y = df['Label']
# Split data into train and test data
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
# Show tokenized words for the first row
X_combined = combine_text_columns(X)
tokenizer = CountVectorizer().build_tokenizer()(X_combined.iloc[0])
df = pd.DataFrame([[x, tokenizer.count(x)] for x in set(tokenizer)],
columns=['Word', 'Count'])
df.sort_values('Count', inplace=True, ascending=False)
print(X.iloc[0].name, '\n')
print(X_combined.iloc[0], '\n')
print(df.head(15), '\n')
# Create a FunctionTransfomer to combine text columns in a row
combine_text_ft = FunctionTransformer(combine_text_columns, validate=False)
# Create pipeline
pl = Pipeline([('cmb', combine_text_ft),
('vct', CountVectorizer(ngram_range=(2, 2))),
('int', SparseInteractions(degree=2)),
('clf', LogisticRegression(C=.027, solver='sag'))])
print(data.head())
train = data[data['Date'] < '2015-01-01']
test = data[data['Date'] > '2014-12-31']
example = train.iloc[3, 10]
print(example)
example2 = example.lower()
print(example2)
example3 = CountVectorizer().build_tokenizer()(example2)
print(example3)
print(
pd.DataFrame([[x, example3.count(x)] for x in set(example3)],
columns=['Word', 'Count']))
trainheadlines = []
for row in range(0, len(train.index)):
trainheadlines.append(' '.join(str(x) for x in train.iloc[row, 2:27]))
print(trainheadlines)
basicvectorizer = CountVectorizer(stop_words='english')
basictrain = basicvectorizer.fit_transform(trainheadlines)
print(basictrain.shape)
basicmodel = LogisticRegression()
basicmodel = basicmodel.fit(basictrain, train["Label"])
