def RNN(data_train, labels_train, data_test, labels_test, n_features):
"""
Adapted from Passage's sentiment.py at
https://github.com/IndicoDataSolutions/Passage/blob/master/examples/sentiment.py
License: MIT
"""
import numpy as np
import pandas as pd
from passage.models import RNN
from passage.updates import Adadelta
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.preprocessing import Tokenizer
layers = [
Embedding(size=128, n_features=n_features),
GatedRecurrent(size=128, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False, p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
tokenizer = Tokenizer(min_df=10)
X = tokenizer.fit_transform(data)
model.fit(X, labels, n_epochs=10)
predi = model.predit(data_test).flatten
labels_predicted = np.ones(len(data_test))
labels_predicted[predi<0.5] = 0
