Learning Data Mining with Python

Chapter 6
Next, we create a simple function that will return the next tweet that needs to be
labeled. We can work out which is the next tweet by finding the first one that hasn't
yet been labeled. The code is as follows:
def get_next_tweet():
return tweet_sample[len(labels)]['text']
The next step in our experiment is to collect information from
the user (you!) on which tweets are referring to Python (the
programming language) and which are not. As of yet, there is
not a good, straightforward way to get interactive feedback with
pure Python in IPython Notebooks. For this reason, we will use
some JavaScript and HTML to get this input from the user.
Next we create some JavaScript in the IPython Notebook to run our input.
Notebooks allow us to use magic functions to embed HTML and JavaScript
(among other things) directly into the Notebook itself. Start a new cell with the
following line at the top:
%%javascript
The code in here will be in JavaScript, hence the curly braces that are coming up.
Don't worry, we will get back to Python soon. Keep in mind here that the following
code must be in the same cell as the %%javascript magic function.
The first function we will define in JavaScript shows how easy it is to talk to
your Python code from JavaScript in IPython Notebooks. This function, if called,
will add a label to the labels array (which is in python code). To do this, we load
the IPython kernel as a JavaScript object and give it a Python command to execute.
The code is as follows:
function set_label(label){
var kernel = IPython.notebook.kernel;
kernel.execute("labels.append(" + label + ")");
load_next_tweet();
}
At the end of that function, we call the load_next_tweet function. This function
loads the next tweet to be labeled. It runs on the same principle; we load the IPython
kernel and give it a command to execute (calling the get_next_tweet function we
defined earlier).
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