Skip to content
Browse files

reorder exercises

  • Loading branch information...
1 parent dc27354 commit d2006f47d6f6c4f203d16d4a53f0063a2dac8da3 @ogrisel ogrisel committed
View
0 ...etons/exercise_02_language_train_model.py → ...etons/exercise_01_language_train_model.py
File renamed without changes.
View
0 skeletons/exercise_01_sentiment.py → skeletons/exercise_02_sentiment.py
File renamed without changes.
View
0 ...tions/exercise_02_language_train_model.py → ...tions/exercise_01_language_train_model.py
File renamed without changes.
View
0 solutions/exercise_01_sentiment.py → solutions/exercise_02_sentiment.py
File renamed without changes.
View
37 tutorial/exercises.rst
@@ -2,7 +2,12 @@ Exercises
=========
To do the exercises, copy the content of the 'skeletons' folder as
-a new folder named 'workspace'.
+a new folder named 'workspace'::
+
+ % cp -r skeletons workspace
+
+You can then edit the content of the workspace without fear of loosing
+the original exercise instructions.
Then fire an ipython shell and run the work-in-progress script with::
@@ -13,33 +18,37 @@ mortem ipdb session.
Refine the implementation and iterate until the exercise is solved.
+**For each exercise, the skeleton file provides all the necessary import
+statements, boilerplate code to load the data and sample code to evaluate
+the predictive accurracy of the model.**
-Exercise 1: Sentiment Analysis on movie reviews
------------------------------------------------
-- Write a text classification pipeline to classify movie reviews as either
- positive or negative.
+Exercise 1: Language identification
+-----------------------------------
-- Find a good set of parameters using grid search.
+- Write a text classification pipeline using a custom preprocessor and
+ ``CharNGramAnalyzer`` using data from Wikipedia articles as training set.
-- Evaluate the performance on a held out test set.
+- Evaluate the performance on some held out test set.
ipython command line::
- %run workspace/exercise_01_sentiment.py data/movie_reviews/txt_sentoken/
+ %run workspace/exercise_01_language_train_model.py data/languages/paragraphs/
-Exercise 2: Language identification
------------------------------------
+Exercise 2: Sentiment Analysis on movie reviews
+-----------------------------------------------
-- Write a text classification pipeline using a custom preprocessor and
- ``CharNGramAnalyzer`` using data from Wikipedia articles as training set.
+- Write a text classification pipeline to classify movie reviews as either
+ positive or negative.
-- Evaluate the performance on some held out test set.
+- Find a good set of parameters using grid search.
+
+- Evaluate the performance on a held out test set.
ipython command line::
- %run workspace/exercise_02_language_train_model.py data/languages/paragraphs/
+ %run workspace/exercise_02_sentiment.py data/movie_reviews/txt_sentoken/
Exercise 3: CLI text classification utility

0 comments on commit d2006f4

Please sign in to comment.
Something went wrong with that request. Please try again.