def predict_function():
x_list = []
predict_doc = joblib.load('logreg.pkl')
y_train, x_train = get_feature()
for line in x_train:
x_list.append(dict(Counter(line)))
X = DictVectorizer().fit_transform(x_list)
pred = predict_doc.predict(X)
prob = predict_doc.predict_proba(X)
return pred, y_train, prob
def log_regression():
x_list = []
logreg = LogisticRegression()
y_train, x_train = get_feature()
for line in x_train:
x_list.append(dict(Counter(line)))
word_vec = DictVectorizer()
X = word_vec.fit_transform(x_list)
logreg.fit(X, y_train)
joblib.dump(logreg, 'logreg.pkl')
joblib.dump(word_vec, "word_vec.pkl")
def log_regression():
x_list = []
logreg = LogisticRegression()
y_train, x_train = get_feature()
for line in x_train:
x_list.append(dict(Counter(line)))
word_vec = DictVectorizer()
X = word_vec.fit_transform(x_list)
logreg.fit(X, y_train)
joblib.dump(logreg, 'logreg.pkl')
joblib.dump(word_vec,"word_vec.pkl")
def predict_function():
x_list = []
line_list = []
line_dict = {}
predict_doc = joblib.load('logreg.pkl')
feature_doc = joblib.load("word_vec.pkl")
y_train, x_train = get_feature()
line = "bad bad good good"
line_list = line.split()
for line in x_train:
for key in line:
line_dict[key] = 0
line_dict.update(dict(Counter(line_list)))
for a in sorted(line_dict.items(), key=lambda x: x[1]):
print(a)
x_list.append(line_dict)
print(x_list)
exit()
X = DictVectorizer().fit_transform(x_list)
pred = predict_doc.predict(X)
prob = predict_doc.predict_proba(X)
for pred, prob in zip(pred, prob):
print(pred, prob)
def predict_function():
x_list = []
line_list = []
line_dict = {}
predict_doc = joblib.load('logreg.pkl')
feature_doc = joblib.load("word_vec.pkl")
y_train, x_train = get_feature()
line = "bad bad good good"
line_list = line.split()
for line in x_train:
for key in line:
line_dict[key] = 0
line_dict.update(dict(Counter(line_list)))
for a in sorted(line_dict.items(), key = lambda x:x[1]):
print(a)
x_list.append(line_dict)
print(x_list)
exit()
X = DictVectorizer().fit_transform(x_list)
pred = predict_doc.predict(X)
prob = predict_doc.predict_proba(X)
for pred, prob in zip(pred,prob):
print(pred, prob)
def main():
# モデルのロード
model = joblib.load('model')
vocab = joblib.load('vocab')
# sentence = input('Sentence -> ').strip()[3:]
f = open('sentiment.txt', encoding='utf8')
for _ in range(124):
next(f)
sentence = f.readline()
print('input\n', sentence)
sentence = sentence.strip()[3:]
feature = get_feature(sentence)
vectorizer = TfidfVectorizer(vocabulary=vocab)
feature_vec = vectorizer.fit_transform([feature]).toarray()
predict = model.predict(feature_vec)[0]
probability = model.predict_proba(feature_vec)[0]
if predict == -1:
print(f'label : negative ({probability[1] * 100:.3f}%)')
else:
print(f'label : positive ({probability[1] * 100:.3f}%)')
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction import DictVectorizer
from sklearn.externals import joblib
from knock72 import get_feature
clf = LogisticRegression()
dic2vec = DictVectorizer()
features = list()
y = list()
for line in open('sentiment.txt'):
spl = line.rstrip('\n').split()
label, sent = spl[0], ' '.join(spl[1:])
features.append(get_feature(sent))
y.append(int(label))
x = dic2vec.fit_transform(features)
clf.fit(x, y)
joblib.dump(clf, 'knock73.model')
for key, value in dict(Counter(feature[test])).items():
if key in feature_dict:
feature_dict[key] = value
pred_list.append(feature_dict)
preds.append(logreg.predict(word_vec.fit_transform(pred_list)))
return preds, test_numbers
def get_score(preds, target, test_numbers):
all_accuracy = []
all_precision = []
all_recall = []
all_f_values = []
for tests, pred in zip(test_numbers, preds):
answers = []
for test in tests:
answers.append(target[test])
all_accuracy.append(accuracy_score(answers, pred))
all_precision.append(precision_score(answers, pred))
all_recall.append(recall_score(answers, pred))
all_f_values.append(f1_score(answers, pred))
return np.array(all_accuracy).mean(), np.array(all_precision).mean(
), np.array(all_recall).mean(), np.array(all_f_values).mean()
if __name__ == '__main__':
target, feature = get_feature()
preds, test_numbers = make_kfold(target, feature)
print("正解率:{}\t適合率:{}\t再現率:{}\tF1スコア:{}".format(
*get_score(preds, target, test_numbers)))
from sklearn.feature_extraction import DictVectorizer
from sklearn.externals import joblib
from knock72 import get_feature
clf = joblib.load('knock73.model')
dic2vec = DictVectorizer()
features = list()
y = list()
sents = list()
for line in open('sentiment.txt'):
spl = line.rstrip('\n').split()
label, sent = spl[0], ' '.join(spl[1:])
sents.append(sent)
features.append(get_feature(sent))
y.append(int(label))
x = dic2vec.fit_transform(features)
pred = clf.predict(x)
pred_proba = clf.predict_proba(x)
for pre, t, (prob_n, prob_p) in zip(pred, y, pred_proba):
prob = prob_n if pre == -1 else prob_p
print('{}\t{}\t{}'.format(t, pre, prob))
for test in tests:
feature_dict = defaultdict(int)
for f in word_vec.get_feature_names():
feature_dict[f] = 0
for key, value in dict(Counter(feature[test])).items():
if key in feature_dict:
feature_dict[key] = value
pred_list.append(feature_dict)
preds.append(logreg.predict(word_vec.fit_transform(pred_list)))
return preds, test_numbers
def get_score(preds, target, test_numbers):
all_accuracy = []
all_precision = []
all_recall = []
all_f_values = []
for tests, pred in zip(test_numbers, preds):
answers = []
for test in tests:
answers.append(target[test])
all_accuracy.append(accuracy_score(answers, pred))
all_precision.append(precision_score(answers, pred))
all_recall.append(recall_score(answers, pred))
all_f_values.append(f1_score(answers, pred))
return np.array(all_accuracy).mean(), np.array(all_precision).mean(), np.array(all_recall).mean(), np.array(all_f_values).mean()
if __name__ == '__main__':
target, feature = get_feature()
preds, test_numbers = make_kfold(target, feature)
print("正解率:{}\t適合率:{}\t再現率:{}\tF1スコア:{}".format(*get_score(preds, target, test_numbers)))
if __name__ == '__main__':
# モデルのロード
with open('model', 'rb') as f:
model = pickle.load(f)
# stopwordのリストをロード
with open('stopwords', 'rb') as f:
stopwords = pickle.load(f)
# vocabサイズをロード
with open('vocab_size', 'rb') as f:
vocab = pickle.load(f)
# 入力を受け取り素性抽出
sentence = input('Please input sentence -> ').strip()[3:]
sent_feature = get_feature(sentence, stopwords)
# 素性をベクトルに変換
voctorizer = CountVectorizer(vocabulary=vocab)
feature_vec = voctorizer.fit_transform([sent_feature]).toarray()
# 予測
label_predict = model.predict(feature_vec)
prob = model.predict_proba(feature_vec)
print(f'label : {label_predict[0]}')
print(f'prob(+1): {round(prob[0][0] * 100, 3)}%')
print(f'prob(-1): {round(prob[0][1] * 100, 3)}%')
from knock72 import get_feature
from sklearn.feature_extraction.text import CountVectorizer
import pickle
with open("./chapter08/model", "rb") as f:
model = pickle.load(f)
with open("./chapter08/vocab_size", "rb") as f:
vocab_size = pickle.load(f)
sentence = "This is a very good wonderful fantastic movie."
sentence_feature = get_feature(sentence)
vectorizer = CountVectorizer(vocabulary=vocab_size)
feature_vec = vectorizer.fit_transform([sentence_feature]).toarray()
label_predict = model.predict(feature_vec)
prob = model.predict_proba(feature_vec)
print(label_predict, prob)