Version 1.1.0

# Mean encodings

In this programming assignment you will be working with 1C dataset from the final competition. You are asked to encode item_id in 4 different ways:

1) Via KFold scheme;
2) Via Leave-one-out scheme;
3) Via smoothing scheme;
4) Via expanding mean scheme.


You will need to submit the correlation coefficient between resulting encoding and target variable up to 4 decimal places.

### General tips

• Fill NANs in the encoding with 0.3343.
• Some encoding schemes depend on sorting order, so in order to avoid confusion, please use the following code snippet to construct the data frame. This snippet also implements mean encoding without regularization.
[('A', 'x'),
('A', 'y'),
('B', 'x'),
('B', 'y'),
('C', 'x'),
('C', 'y'),
('D', 'x'),
('D', 'y')]


date date_block_num shop_id item_id item_price item_cnt_day
0 02.01.2013 0 59 22154 999.00 1.0
1 03.01.2013 0 25 2552 899.00 1.0
2 05.01.2013 0 25 2552 899.00 -1.0
3 06.01.2013 0 25 2554 1709.05 1.0
4 15.01.2013 0 25 2555 1099.00 1.0
5 10.01.2013 0 25 2564 349.00 1.0
6 02.01.2013 0 25 2565 549.00 1.0
7 04.01.2013 0 25 2572 239.00 1.0
8 11.01.2013 0 25 2572 299.00 1.0
9 03.01.2013 0 25 2573 299.00 3.0
date               1034
date_block_num       34
shop_id              60
item_id           21807
item_price        19993
item_cnt_day        198
dtype: int64

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33])

date date_block_num shop_id item_id item_price item_cnt_day
115690 21.02.2013 1 50 3880 1499.0 1.0
115691 14.02.2013 1 50 3880 1499.0 1.0
115692 21.02.2013 1 50 4128 899.0 1.0
115693 13.02.2013 1 50 4124 249.0 1.0
115694 24.02.2013 1 50 3880 1499.0 1.0

# Aggregate data

Since the competition task is to make a monthly prediction, we need to aggregate the data to montly level before doing any encodings. The following code-cell serves just that purpose.

/opt/conda/lib/python3.6/site-packages/pandas/core/groupby.py:4036: FutureWarning: using a dict with renaming is deprecated and will be removed in a future version
return super(DataFrameGroupBy, self).aggregate(arg, *args, **kwargs)

shop_id item_id date_block_num target
139255 0 19 0 0.0
141495 0 27 0 0.0
144968 0 28 0 0.0
142661 0 29 0 0.0
138947 0 32 0 6.0
138948 0 33 0 3.0
138949 0 34 0 0.0
139247 0 35 0 1.0
142672 0 40 0 0.0
142065 0 41 0 0.0

# Mean encodings without regularization

After we did the techinical work, we are ready to actually mean encode the desired item_id variable.

Here are two ways to implement mean encoding features without any regularization. You can use this code as a starting point to implement regularized techniques.

#### Method 1

0.483038698862

shop_id item_id date_block_num target item_target_enc
139255 0 19 0 0.0 0.022222
147370 1 19 0 0.0 0.022222
114910 2 19 0 0.0 0.022222
123025 3 19 0 0.0 0.022222
98680 4 19 0 0.0 0.022222

#### Method 2

0.483038698862


See the printed value? It is the correlation coefficient between the target variable and your new encoded feature. You need to compute correlation coefficient between the encodings, that you will implement and submit those to coursera.

# 1. KFold scheme

Explained starting at 41 sec of Regularization video.

Now it’s your turn to write the code!

You may use ‘Regularization’ video as a reference for all further tasks.

First, implement KFold scheme with five folds. Use KFold(5) from sklearn.model_selection.

1. Split your data in 5 folds with sklearn.model_selection.KFold with shuffle=False argument.
2. Iterate through folds: use all but the current fold to calculate mean target for each level item_id, and fill the current fold.

• See the Method 1 from the example implementation. In particular learn what map and pd.Series.map functions do. They are pretty handy in many situations.
0.41645907128


# 2. Leave-one-out scheme

Now, implement leave-one-out scheme. Note that if you just simply set the number of folds to the number of samples and run the code from the KFold scheme, you will probably wait for a very long time.

To implement a faster version, note, that to calculate mean target value using all the objects but one given object, you can:

1. Calculate sum of the target values using all the objects.
2. Then subtract the target of the given object and divide the resulting value by n_objects - 1.

Note that you do not need to perform 1. for every object. And 2. can be implemented without any for loop.

It is the most convenient to use .transform function as in Method 2.

0.480384831129


# 3. Smoothing

Explained starting at 4:03 of Regularization video.

Next, implement smoothing scheme with $\alpha = 100$. Use the formula from the first slide in the video and $0.3343$ as globalmean. Note that nrows is the number of objects that belong to a certain category (not the number of rows in the dataset).

0.48181987971


# 4. Expanding mean scheme

Explained starting at 5:50 of Regularization video.

Finally, implement the expanding mean scheme. It is basically already implemented for you in the video, but you can challenge yourself and try to implement it yourself. You will need cumsum and cumcount functions from pandas.

0.502524521108


## Authorization & Submission

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