Running a Python function on Spark Dataframe
<p>I have a python function which basically does some sampling from the original dataset and converts it into training_test. </p>

<p>I have written that code to work on pandas data frame. </p>

<p>I was wondering if anyone knows how to implement the same on Spark DAtaframe in pyspark?. Instead of Pandas data frame or numpy array mentioned should I use Spark Dataframe and that's it?</p>

<p>Please let me know</p>

<pre><code>def train_test_split(recommender,pct_test=0.20,alpha=40):
    """ This function takes a ratings data and splits it into 
    train, validation and test datasets

    This function will take in the original user-item matrix and "mask" a percentage of the original ratings where a
    user-item interaction has taken place for use as a test set. The test set will contain all of the original ratings, 
    while the training set replaces the specified percentage of them with a zero in the original ratings matrix. 

    parameters: 

    ratings - the original ratings matrix from which you want to generate a train/test set. Test is just a complete
    copy of the original set. This is in the form of a sparse csr_matrix. 

    pct_test - The percentage of user-item interactions where an interaction took place that you want to mask in the 
    training set for later comparison to the test set, which contains all of the original ratings. 

    returns:

    training_set - The altered version of the original data with a certain percentage of the user-item pairs 
    that originally had interaction set back to zero.

    test_set - A copy of the original ratings matrix, unaltered, so it can be used to see how the rank order 
    compares with the actual interactions.

    user_inds - From the randomly selected user-item indices, which user rows were altered in the training data.
    This will be necessary later when evaluating the performance via AUC.

    """

    test_set = recommender.copy() # Make a copy of the original set to be the test set. 

    test_set=(test_set&gt;0).astype(np.int8)
    training_set = recommender.copy() # Make a copy of the original data we can alter as our training set. 
    nonzero_inds = training_set.nonzero() # Find the indices in the ratings data where an interaction exists
    nonzero_pairs = list(zip(nonzero_inds[0], nonzero_inds[1])) # Zip these pairs together of user,item index into list
    random.seed(0) # Set the random seed to zero for reproducibility
    num_samples = int(np.ceil(pct_test*len(nonzero_pairs))) # Round the number of samples needed to the nearest integer
    samples = random.sample(nonzero_pairs, num_samples) # Sample a random number of user-item pairs without replacement
    user_inds = [index[0] for index in samples] # Get the user row indices
    item_inds = [index[1] for index in samples] # Get the item column indices
    training_set[user_inds, item_inds] = 0 # Assign all of the randomly chosen user-item pairs to zero

    conf_set=1+(alpha*training_set)
    return training_set, test_set, conf_set, list(set(user_inds)) 
</code></pre>

<p>You can use randomSplit function on Spark dataframe. </p>

python pandas apache-spark