my brilliant solution

foo.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[3]:
+
+
+import pathlib
+from collections import Counter
+from sklearn.metrics import *
+import pandas as pd
+
+
+# In[1]:
+
+
+import numpy as np, pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.datasets import fetch_20newsgroups
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.metrics import confusion_matrix, accuracy_score
+sns.set() # use seaborn plotting style
+
+
+# In[5]:
+
+
+train_x = pd.read_csv('train/in.tsv', header=None, sep='\t')
+train_y = pd.read_csv('train/expected.tsv', header=None, sep='\t')
+dev_x = pd.read_csv('dev-0/in.tsv', header=None, sep='\t')
+dev_y = pd.read_csv('dev-0/expected.tsv', header=None, sep='\t')
+test_x = pd.read_csv('test-A/in.tsv', header=None, sep='\t')
+
+
+# In[61]:
+
+
+print(dev_y.shape)
+print(dev_x.shape)
+
+
+# In[11]:
+
+
+print(train_x[:15])
+
+
+# In[27]:
+
+
+print(train_x.shape)
+
+
+# In[49]:
+
+
+print(train_y.shape)
+
+
+# In[8]:
+
+
+print(train_y[:15])
+
+
+# In[53]:
+
+
+print(dev_x[:4])
+
+
+# In[119]:
+
+
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.feature_extraction.text import TfidfTransformer
+
+vec = CountVectorizer(stop_words='english')
+x1 = vec.fit_transform(train_x[:20000][0])
+tfidf_transformer = TfidfTransformer()
+
+x1_tf = tfidf_transformer.fit_transform(x1)
+
+
+# In[120]:
+
+
+# Build the model
+#model = make_pipeline(TfidfVectorizer(), MultinomialNB())
+clf = MultinomialNB().fit(x1_tf, train_y[:20000][0])
+
+
+# In[121]:
+
+
+# Train the model using the training data
+#model.fit(x1[:][0], train_y[:289541][0])
+# Predict the categories of the test data
+X_new_counts = vec.transform(dev_x[:][0])
+# We call transform instead of fit_transform because it's already been fit
+X_new_tfidf = tfidf_transformer.transform(X_new_counts)
+#predicted_categories = model.predict(dev_x[:][0])
+
+
+# In[122]:
+
+
+predicted = clf.predict(X_new_tfidf)
+
+
+# In[125]:
+
+
+print(predicted[:10])
+
+
+# In[126]:
+
+
+print(predicted.shape)
+
+
+# In[123]:
+
+
+#mat = confusion_matrix(dev_y[:][0],predicted_categories)
+
+print("The accuracy is {}".format(accuracy_score( dev_y[:][0],predicted_categories)))
+ 
+
+
+# In[124]:
+
+
+print('We got an accuracy of',np.mean(predicted == dev_y[:][0])*100, '% over the test data.')
+
+
+# In[130]:
+
+
+np.savetxt("out.tsv",predicted, delimiter="\t", fmt='%d')
+
+
+# In[131]:
+
+
+X_test = vec.transform(test_x[:][0])
+# We call transform instead of fit_transform because it's already been fit
+X_tfidf_test = tfidf_transformer.transform(X_test)
+predicted_test = clf.predict(X_tfidf_test)
+np.savetxt("out.tsv",predicted_test, delimiter="\t", fmt='%d')
+
+
+# In[ ]:
+
+
+
+