# Load the "autoreload" extension
%load_ext autoreload
# always reload modules marked with "%aimport"
%autoreload 1
import os
import sys
# add the 'src' directory as one where we can import modules
src_dir = os.path.join(os.getcwd(), os.pardir, 'src')
sys.path.append(src_dir)
# import my method from the source code
%aimport data.read_data
%aimport models.train_model
%aimport features.build_features
%aimport visualization.visualize
from data.read_data import read_data, get_stopwords
from models.train_model import split_train, score_function, model_ridge, model_xgb, model_lightgbm
from features.build_features import get_vec, get_fasttext
from visualization.visualize import plot_roc, plot_scatter
The autoreload extension is already loaded. To reload it, use: %reload_ext autoreload
model_fasttext = get_fasttext()
[14:16:04] INFO loading projection weights from ../data/external/wiki.fr.bin [14:16:04] DEBUG {'kw': {}, 'mode': 'rb', 'uri': '../data/external/wiki.fr.bin'} [14:16:04] DEBUG encoding_wrapper: {'errors': 'strict', 'encoding': None, 'mode': 'rb', 'fileobj': <_io.BufferedReader name='../data/external/wiki.fr.bin'>} [14:16:33] INFO loaded (1152449, 300) matrix from ../data/external/wiki.fr.bin
result = model_fasttext.most_similar(positive=['femme', 'roi'], negative=['homme'], topn=1)
print(result)
[14:16:33] INFO precomputing L2-norms of word weight vectors [('reine', 0.671850860118866)]
stopwords = get_stopwords()
train = read_data()
y = train['Target']
train.head()
ID | review_content | review_title | review_stars | product | Target | |
---|---|---|---|---|---|---|
0 | 0 | En appelant un acheteur pour demander si l'écr... | La Police s'inscrit en acheteur privé sur Pric... | 5 | 2fbb619e3606f9b7c213e858a109cda771aa2c47ce50d5... | 0 |
1 | 1 | Alors, là, on a affaire au plus grand Navet ja... | Chef D'Oeuvre Absolu en vue... | 5 | 7b56d9d378d9e999d293f301ac43d044cd7b4786d09afb... | 1 |
2 | 2 | Effet garanti sur la terrase. Ils donnent immé... | Effet garanti sur la terrase. Ils donnent immé... | 3 | 7b37bf5dcb2fafd9229897910318a7dfa11a04ca36893c... | 0 |
3 | 3 | tres bon rapport qualite prix tre pratique en ... | bon produit | 4 | 77d2dbd504b933ab3aaf7cb0cd81c22f7c3549012f4f88... | 1 |
4 | 4 | Ordinateur de bureau trés bien pour quelqu'un ... | Apple Power MAC G4 | 3 | f574512e7d2dd1dd73c7f8f804bf16f14c932c5651a01b... | 1 |
xtrain = get_vec(train['review_content'].values, model_fasttext, stopwords)
X_train, X_test, y_train, y_test = split_train(xtrain, y, 0.2)
model_rdg = model_ridge(X_train, y_train)
preds = model_rdg.predict(X=X_test)
score_function(y_test, preds)
0.67527133206010426
model_xgboost = model_xgb(X_train, y_train)
preds = model_xgboost.predict_proba(X_test)
preds1 = preds[:,1]
score_function(y_test, preds1)
0.71189840682603589
model_lgb = model_lightgbm(X_train, y_train)
preds = model_lgb.predict_proba(X_test)
preds1 = preds[:,1]
score_function(y_test, preds1)
0.70444712049985525
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
result = pca.fit_transform(xtrain)
plot_scatter(values=result, colors=train['Target'].values,
ticks=[0,1], ticks_labels=['Negative', 'Positive'])
plot_scatter(values=result, colors=train['review_stars'].values,
ticks=[1, 2, 3, 4, 5], ticks_labels=['1', '2', '3', '4', '5'])