Predict if a tweet belongs to a category(based on its text) using a pre-built[Supoort Vector Machine](http://en.wikipedia.org/wiki/Support_vector_machine)

binary_classifier.hpy

#!/bin/sh
"exec" "twxec" "-e" "predict_tweet" "$0" "$@"

{{docstring "Predict if a tweet belongs to a category(based on its text) using a pre-built[Supoort Vector Machine](http://en.wikipedia.org/wiki/Support_vector_machine)"}}

from trickle.nner.en.nner import tokens as tokenize
import numpy as np
import csv
import importlib
import pickle

with open({{string Model_File}}) as f:
    model_data = pickle.load(f)

vocab_coordspace = model_data["vocab_coordspace"]
vocab_set = set(vocab_coordspace.keys())
split_modules = model_data["feature_generator"].split(".")
tokenization_module = importlib.import_module(".".join(split_modules[:-1]))
tokenize = getattr(tokenization_module, split_modules[-1])

class_label = {{string Classification_Label}}
classifier = model_data["classifier"]


def token_vector(doc):
    tokens = set(tokenize(doc)) & vocab_set
    t_vec = np.zeros([len(vocab_set)])
    t_vec[[vocab_coordspace[token] for token in tokens]] = 1
    return(t_vec)


def predict_tweet(msg):
    try:
        tvec = token_vector(msg["text"])
        classifications = msg.get("classifications", {})
        classifications[class_label] = np.asscalar(
            classifier.predict(tvec)) == 1
        msg["classifications"] = classifications
    except:
        msg["classifications"] = msg.get("classifications", {})
    return msg