bayes2.py

@@ -1,45 +0,0 @@
-import pandas as pd
-import numpy as np
-import gzip
-from sklearn.pipeline import make_pipeline
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn import metrics
-
-# Read data
-dev = pd.read_table('dev-0/in.tsv', error_bad_lines=False, header=None)
-test = pd.read_table('test-A/in.tsv', error_bad_lines=False, header=None)
-test_expected = pd.read_table(
-    'dev-0/expected.tsv', error_bad_lines=False, header=None)
-
-X_train = []
-y_train = []
-
-with gzip.open('train/train.tsv.gz', 'r') as f:
-    for l in f:
-        line = l.decode('UTF-8').replace("\n", "").split("\t")
-        y_train.append(int(line[0]))
-        X_train.append(str(line[1:]))
-
-# Convert to unified types
-X_train = np.asarray(X_train)
-y_train = np.asarray(y_train)
-
-X_dev = dev[0].values
-X_test = test[0].values
-
-# Create model
-model = make_pipeline(TfidfVectorizer(), MultinomialNB())
-model.fit(X_train, y_train)
-
-# Predict
-dev_p = model.predict(X_dev)
-test_p = model.predict(X_test)
-
-# Accuracy
-score = metrics.accuracy_score(test_expected, dev_p)
-print("Accuracy:   %0.3f" % score)
-
-# Save to files
-dev_p.tofile('./dev-0/out.tsv', sep='\n')
-test_p.tofile('./test-A/out.tsv', sep='\n')