tutorial.py

import os

import pandas as pd
from sklearn import metrics

from passage.preprocessing import Tokenizer
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.model import RNN

def load_gender_data(ntrain=10000, ntest=10000):
    file_loc = os.path.dirname(os.path.realpath(__file__))
    relative_path = "data/blogger_data_2.csv"
    fullpath = os.path.join(file_loc, relative_path)
    data = pd.read_csv(fullpath, nrows=ntrain+ntest)
    X = data['text'].values
    X = [str(x) for x in X] #ugly nan cleaner
    Y = data['gender'].values
    trX = X[:-ntest]
    teX = X[-ntest:]
    trY = Y[:-ntest]
    teY = Y[-ntest:]
    return trX, teX, trY, teY

trX, teX, trY, teY = load_gender_data(ntrain=10000) #Can increase up to 250K or so

tokenizer = Tokenizer(min_df=10, max_features=50000)
print trX[1:2] #see a blog example
trX = tokenizer.fit_transform(trX)
teX = tokenizer.transform(teX)
print tokenizer.inverse_transform(trX[1:2]) #see what words are kept
print tokenizer.n_features

layers = [
    Embedding(size=128, n_features=tokenizer.n_features),
    GatedRecurrent(size=256, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False),
    Dense(size=1, activation='sigmoid', init='orthogonal') #sigmoid for binary classification
]

model = RNN(layers=layers, cost='bce') #bce is classification loss for binary classification and sigmoid output
for i in range(100):
    model.fit(trX, trY, n_epochs=1)
    tr_preds = model.predict(trX[:len(teY)])
    te_preds = model.predict(teX)

    tr_acc = metrics.accuracy_score(trY[:len(teY)], tr_preds > 0.5)
    te_acc = metrics.accuracy_score(teY, te_preds > 0.5)

    print i, tr_acc, te_acc