def load_predict(directory):
clf = joblib.load('svm_trained.joblib')
documents = utils.load_dirs_custom(directory)
documents = utils.n_gram_documents_range(documents, 5, 6)
documents = np.array(documents)
doc_test = utils.convert_docs_to_lines(documents)
predicted_lines = []
target_lines = []
for doc in doc_test:
lines = clf.predict(doc.data)
predicted_lines += list(lines)
return predicted_lines
from keras.regularizers import l1
from keras.callbacks import EarlyStopping
import utils
from keras import backend as K
import numpy as np
import pickle
from keras.utils import np_utils
#utils.personal_categories_dict (.inv)
documents = utils.load_dirs_custom([
'../../TAGGED_DATA_NEW_NEW/SENSITIVE_DATA/html-tagged',
'../../TAGGED_DATA_NEW_NEW/PERSONAL_DATA/html-tagged',
'../../TAGGED_DATA_NEW_NEW/NON_PERSONAL_DATA'
],
individual=True)
x = []
y = []
for document in documents:
lines = document.lines
categories = []
for line in lines:
for category in line.categories:
if category not in categories:
categories.append(category)
x += ['\n'.join(document.data)]
y += [categories]
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.metrics import fbeta_score
from sklearn.model_selection import GridSearchCV
import utils
from sklearn.pipeline import Pipeline
from sklearn.linear_model import SGDClassifier
from sklearn import metrics
from sklearn.externals import joblib
documents = utils.load_dirs_custom([
'./SENSITIVE_DATA/html-tagged', './PERSONAL_DATA/html-tagged',
'./NON_PERSONAL_DATA'
])
documents = utils.n_gram_documents_range(documents, 5, 6)
documents = np.array(documents)
doc_train, doc_test, = utils.document_test_train_split(documents, 0.01)
print("Doc train: ", len(doc_train))
print("Doc test: ", len(doc_test))
X_train, y_train = utils.convert_docs_to_lines(doc_train)
X_test, y_test = utils.convert_docs_to_lines(doc_test)
text_clf = Pipeline([('vect', CountVectorizer(ngram_range=(1, 2))),
('tfidf', TfidfTransformer()),
('clf',
SGDClassifier(loss='hinge',
new_arr = []
for value in arr:
new_arr.append((value - minimum) / (maximum - minimum))
return new_arr
def show_overfit_plot():
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['train', 'test'], loc='upper left')
plt.show()
documents = utils.load_dirs_custom([
'../../anondata_lines/sensitive', '../../anondata_lines/personal',
'../../anondata_lines/nonpersonal'
],
individual=True)
documents = utils.n_gram_documents_range(documents, 8, 8)
doc_train, doc_test, = utils.document_test_train_split(documents, 0.05)
print("Doc train: ", len(doc_train))
print("Doc test: ", len(doc_test))
x_train, y_train = utils.convert_docs_to_lines(doc_train)
x_test, y_test = utils.convert_docs_to_lines(doc_test)
y_train = np.where((y_train == 2) | (y_train == 1), 1, 0)
y_test = np.where((y_test == 2) | (y_test == 1), 1, 0)