import pickle
import numpy as np
from sklearn.decomposition import PCA
from linear_regression import create_dictionary
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD

def predict():
    input_file = open("l_regression.pkl",'rb')
    l_regression = pickle.load(input_file)
    input_file = open("tfidf_model.pkl",'rb')
    tfidf = pickle.load(input_file)

    dev0 = create_dictionary("dev-0/in.tsv")
    testA = create_dictionary("test-A/in.tsv")
    dev0_vector = tfidf.fit_transform(dev0)
    testA_vector = tfidf.fit_transform(testA)

    #print(testA_vector)
    pca = TruncatedSVD(n_components=100)

    dev0_pca = pca.fit_transform(dev0_vector)
    testA_pca = pca.fit_transform(testA_vector)
    output= open("dev-0/out.tsv","w+",encoding="UTF-8")
    y_dev = l_regression.predict(dev0_pca)
    print(y_dev)
    foo = np.array(y_dev)
    print(foo)
    np.savetxt(output,foo)
    output = open("test-A/out.tsv", "w+", encoding="UTF-8")
    y_test = l_regression.predict(testA_pca)
    foo = np.array(y_test)
    np.savetxt(output,foo)

    #print(y_test)

   # dev0_vectorizer =


predict()