def setup_module():
global x_tr, y_tr, x_dv, y_dv, counts_tr, x_dv_pruned, x_tr_pruned
global labels
global vocab
global X_tr, X_tr_var, X_dv_var, Y_tr, Y_dv, Y_tr_var, Y_dv_var
y_tr, x_tr = preproc.read_data('lyrics-train.csv',
preprocessor=preproc.bag_of_words)
labels = set(y_tr)
counts_tr = preproc.aggregate_counts(x_tr)
y_dv, x_dv = preproc.read_data('lyrics-dev.csv',
preprocessor=preproc.bag_of_words)
x_tr_pruned, vocab = preproc.prune_vocabulary(counts_tr, x_tr, 10)
x_dv_pruned, _ = preproc.prune_vocabulary(counts_tr, x_dv, 10)
## remove this, so people can run earlier tests
X_tr = preproc.make_numpy(x_tr_pruned, vocab)
X_dv = preproc.make_numpy(x_dv_pruned, vocab)
label_set = sorted(list(set(y_tr)))
Y_tr = np.array([label_set.index(y_i) for y_i in y_tr])
Y_dv = np.array([label_set.index(y_i) for y_i in y_dv])
X_tr_var = Variable(torch.from_numpy(X_tr.astype(np.float32)))
X_dv_var = Variable(torch.from_numpy(X_dv.astype(np.float32)))
Y_tr_var = Variable(torch.from_numpy(Y_tr))
Y_dv_var = Variable(torch.from_numpy(Y_dv))
def test_d1_4_prune():
global x_dv, counts_tr
x_tr_pruned, vocab = preproc.prune_vocabulary(counts_tr, x_tr, 3)
x_dv_pruned, vocab2 = preproc.prune_vocabulary(counts_tr, x_dv, 3)
eq_(len(vocab), len(vocab2))
eq_(len(vocab), 11824)
eq_(len(x_dv[95].keys()) - len(x_dv_pruned[95].keys()), 8)
def setup_module():
#global y_tr, x_tr, corpus_counts, labels, vocab
#corpus_counts = get_corpus_counts(x_tr)
global x_tr, y_tr, x_dv, y_dv, counts_tr, x_dv_pruned, x_tr_pruned, x_bl_pruned
global labels
global vocab
y_tr, x_tr = preproc.read_data('lyrics-train.csv',
preprocessor=preproc.bag_of_words)
labels = set(y_tr)
counts_tr = preproc.aggregate_counts(x_tr)
y_dv, x_dv = preproc.read_data('lyrics-dev.csv',
preprocessor=preproc.bag_of_words)
x_tr_pruned, vocab = preproc.prune_vocabulary(counts_tr, x_tr, 10)
x_dv_pruned, _ = preproc.prune_vocabulary(counts_tr, x_dv, 10)
def test_d5_1_numpy():
global x_dv, counts_tr
x_dv_pruned, vocab = preproc.prune_vocabulary(counts_tr, x_dv, 10)
X_dv = preproc.make_numpy(x_dv_pruned, vocab)
eq_(X_dv.sum(), 137687)
eq_(X_dv.sum(axis=1)[4], 417)
eq_(X_dv.sum(axis=1)[144], 175)
eq_(X_dv.sum(axis=0)[10], 3)
eq_(X_dv.sum(axis=0)[100], 0)