from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
X, y = load_iris(return_X_y=True)
clf = LogisticRegression(random_state=0).fit(X, y)
print("X: ", type(X))
print("y: ", type(y))
print("X:", len(X), "x", len(X[0]))
print("y:", len(y))
clf.predict(X)
clf.predict_proba(X[:2, :])
corpus = [
'This is the first document.', 'This document is the second document.',
'And this is the third one.', 'Is this the first document?'
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transfrom(corpus)
matrix_X = X.toarray()
vectorizer = TfidfVectorizer()
vectorizer.fit(text)
aux = vectorizer.idf_
print(aux)
type(aux)
