Create a custom sklearn TransformerMixin that transforms categorical variables consistently
I made a blog post to address this. Below is the transformer I built.
class CategoryGrouper(BaseEstimator, TransformerMixin):
"""A tranformer for combining low count observations for categorical features.
This transformer will preserve category values that are above a certain
threshold, while bucketing together all the other values. This will fix issues
where new data may have an unobserved category value that the training data
did not have.
"""
def __init__(self, threshold=0.05):
"""Initialize method.
Args:
threshold (float): The threshold to apply the bucketing when
categorical values drop below that threshold.
"""
self.d = defaultdict(list)
self.threshold = threshold
def transform(self, X, **transform_params):
"""Transforms X with new buckets.
Args:
X (obj): The dataset to pass to the transformer.
Returns:
The transformed X with grouped buckets.
"""
X_copy = X.copy()
for col in X_copy.columns:
X_copy[col] = X_copy[col].apply(lambda x: x if x in self.d[col] else 'CategoryGrouperOther')
return X_copy
def fit(self, X, y=None, **fit_params):
"""Fits transformer over X.
Builds a dictionary of lists where the lists are category values of the
column key for preserving, since they meet the threshold.
"""
df_rows = len(X.index)
for col in X.columns:
calc_col = X.groupby(col)[col].agg(lambda x: (len(x) * 1.0) / df_rows)
self.d[col] = calc_col[calc_col >= self.threshold].index.tolist()
return self
Basically, the motivation originally came from me having to handle sparse category values, but then I realized this could be applied to unknown values. The transformer essentially groups sparse category values together, given a threshold, so since unknown values would inherit 0% of the value space, they would get bucketed into a CategoryGrouperOther group.
Here's just a demonstration of the transformer:
# dfs with 100 elements in cat1 and cat2
# note how df_test has elements 'g' and 't' in the respective categories (unknown values)
df_train = pd.DataFrame({'cat1': ['a'] * 20 + ['b'] * 30 + ['c'] * 40 + ['d'] * 3 + ['e'] * 4 + ['f'] * 3,
'cat2': ['z'] * 25 + ['y'] * 25 + ['x'] * 25 + ['w'] * 20 +['v'] * 5})
df_test = pd.DataFrame({'cat1': ['a'] * 10 + ['b'] * 20 + ['c'] * 5 + ['d'] * 50 + ['e'] * 10 + ['g'] * 5,
'cat2': ['z'] * 25 + ['y'] * 55 + ['x'] * 5 + ['w'] * 5 + ['t'] * 10})
catgrouper = CategoryGrouper()
catgrouper.fit(df_train)
df_test_transformed = catgrouper.transform(df_test)
df_test_transformed
cat1 cat2
0 a z
1 a z
2 a z
3 a z
4 a z
5 a z
6 a z
7 a z
8 a z
9 a z
10 b z
11 b z
12 b z
13 b z
14 b z
15 b z
16 b z
17 b z
18 b z
19 b z
20 b z
21 b z
22 b z
23 b z
24 b z
25 b y
26 b y
27 b y
28 b y
29 b y
... ... ...
70 CategoryGrouperOther y
71 CategoryGrouperOther y
72 CategoryGrouperOther y
73 CategoryGrouperOther y
74 CategoryGrouperOther y
75 CategoryGrouperOther y
76 CategoryGrouperOther y
77 CategoryGrouperOther y
78 CategoryGrouperOther y
79 CategoryGrouperOther y
80 CategoryGrouperOther x
81 CategoryGrouperOther x
82 CategoryGrouperOther x
83 CategoryGrouperOther x
84 CategoryGrouperOther x
85 CategoryGrouperOther w
86 CategoryGrouperOther w
87 CategoryGrouperOther w
88 CategoryGrouperOther w
89 CategoryGrouperOther w
90 CategoryGrouperOther CategoryGrouperOther
91 CategoryGrouperOther CategoryGrouperOther
92 CategoryGrouperOther CategoryGrouperOther
93 CategoryGrouperOther CategoryGrouperOther
94 CategoryGrouperOther CategoryGrouperOther
95 CategoryGrouperOther CategoryGrouperOther
96 CategoryGrouperOther CategoryGrouperOther
97 CategoryGrouperOther CategoryGrouperOther
98 CategoryGrouperOther CategoryGrouperOther
99 CategoryGrouperOther CategoryGrouperOther
Even works when I set threshold to 0 (this will exclusively set unknown values to the 'other' group while preserving all the other category values). I would caution against setting threshold to 0 though, because your training dataset would not have the 'other' category so tweak the threshold to flag at least one value to be the 'other' group:
catgrouper = CategoryGrouper(threshold=0)
catgrouper.fit(df_train)
df_test_transformed = catgrouper.transform(df_test)
df_test_transformed
cat1 cat2
0 a z
1 a z
2 a z
3 a z
4 a z
5 a z
6 a z
7 a z
8 a z
9 a z
10 b z
11 b z
12 b z
13 b z
14 b z
15 b z
16 b z
17 b z
18 b z
19 b z
20 b z
21 b z
22 b z
23 b z
24 b z
25 b y
26 b y
27 b y
28 b y
29 b y
... ... ...
70 d y
71 d y
72 d y
73 d y
74 d y
75 d y
76 d y
77 d y
78 d y
79 d y
80 d x
81 d x
82 d x
83 d x
84 d x
85 e w
86 e w
87 e w
88 e w
89 e w
90 e CategoryGrouperOther
91 e CategoryGrouperOther
92 e CategoryGrouperOther
93 e CategoryGrouperOther
94 e CategoryGrouperOther
95 CategoryGrouperOther CategoryGrouperOther
96 CategoryGrouperOther CategoryGrouperOther
97 CategoryGrouperOther CategoryGrouperOther
98 CategoryGrouperOther CategoryGrouperOther
99 CategoryGrouperOther CategoryGrouperOther
And like I said, answering my own question. Here's the solution I'm going with for now.
def get_datasets(df):
trans1= DFTransformer()
trans2= DFTransformer()
train = trans1.fit_transform(df.iloc[:, :-1])
test = trans2.fit_transform(pd.read_pickle(TEST_PICKLE_PATH))
columns = train.columns.intersection(test.columns).tolist()
X_train = train[columns]
y_train = df.iloc[:, -1]
X_test = test[columns]
return X_train, y_train, X_test