I am trying to build a regression pipeline but there are two problems I cannot resolve:
I try the below code, but one thing I cannot understand is how to deal with "NOT NULL" values columns? What to do with them? See the first row of the pipeline, these columns don't contain any "NULL" values in the data frame. I just don't know what to do with these columns.
When I run this Pipeline with .fit it is showing an error.
ValueError: Some of the variables to transform contain NaN. Check and remove those before using this transformer.
regression_pipe = Pipeline([
("Imputer", mdi.ArbitraryNumberImputer(arbitrary_number=-1, variables=['longitude', 'latitude', 'number_of_reviews', 'minimum_nights', 'accommodates', 'availability_365'])),
('medianImputer', MeanMedianImputer(imputation_method='median', variables=['review_scores_rating', 'security_deposit', 'cleaning_fee', 'bathrooms', 'bedrooms', 'beds'])),
("Count_Frequency_Encoder", CountFrequencyEncoder(encoding_method="count", variables=cat_val)),
("One_Hot_Encoding", OneHotEncoder(variables=cat_val, drop_last=False)),
("MinMax_Scaling", MinMaxScaler()),
("Linear_Regression", LinearRegression())
])
I have read many posts on this that reference the get_feature_names() from sklearn which appears to be now deprecated and replaced by get_feature_names_out neither of which I can get to work. It also appears that there is no way to use the get_feature_names (or the get_feature_names_out) with the ColumnTransformer class. So I am trying to fit and transform my numeric columns with a SimpleImputer and then StandardScaler class then SimpleImpute ('most_frequent') and OneHotEncode the categorical variables. I run them all individually since I can't put them in a pipeline then I try to get_feature_names and this results:
ValueError: input_features should have length equal to number of features (5), got 11
I have also tried getting feature names for just the categorical features as well as just the numeric and each one give the following errors respectively:
ValueError: input_features should have length equal to number of features (5), got 121942
and
ValueError: input_features should have length equal to number of features (5), got 121942
I am completely lost and also open to an easier way to get the feature names so that I can make sure the prod data that I run this model on after training/testing has the exact same features as the ones the model is trained to expect (which is the root issue here).
If I'm "barking up the wrong tree" by trying to get the feature names for the reasoning outlined in the root issue I'm also more than willing to be corrected. Here is my code:
#ONE HOT
import sklearn
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
# !pip install -U scikit-learn
print('The scikit-learn version is {}.'.format(sklearn.__version__))
numeric_columns = X.select_dtypes(include=['int64','float64']).columns
cat_columns = X.select_dtypes(include=['object']).columns
si_num = SimpleImputer(strategy='median')
si_cat = SimpleImputer(strategy='most_frequent')
ss = StandardScaler()
ohe = OneHotEncoder()
si_num.fit_transform(X[numeric_columns])
si_cat.fit_transform(X[cat_columns])
ss.fit_transform(X[numeric_columns])
ohe.fit_transform(X[cat_columns])
ohe.get_feature_names(X[numeric_columns])
Thanks!
I think this should work as a single composite estimator that does all your transformations and provides get_feature_names_out:
num_pipe = Pipeline([
("imp", si_num),
("scale", ss),
])
cat_pipe = Pipeline([
("imp", si_cat),
("ohe", ohe),
])
preproc = ColumnTransformer([
("num", num_pipe, numeric_columns),
("cat", cat_pipe, cat_columns),
])
Ideally, you should save the fitted composite and use that to transform production data, rather than using the feature names to reconcile different categories.
You should also fit this composite only on the training set, transforming the test set separately.
I have a sklearn pipeline like the following:
features = Pipeline([
('feats_A', Function_transformer_A())
('feats_B', Function_transformer_B())
])
features.fit(X)
The input to feats_A is the fitted data X. And, the input to feats_B is the output from feats_A.
Instead, I want to be the input to feats_B the fitted data X and the output from feats_A, together. Given that, these two different data matrices could have different dimensions; Function_transformer_A applies aggregation to process the input data.
Is it possible?
You can try using FeatureUnion
def blank(df):
return df
subpipe = FeatureUnion(
[('prep_data', Function_transformer(blank)),
('feats_A', Function_transformer_A())])
features = Pipeline([
('subpipe', subpipe)
('feats_B', Function_transformer_B())
])
This is my first machine learning project and the first time that I use ColumnTransformer. My aim is to perform two steps of data preprocessing, and use ColumnTransformer for each of them.
In the first step, I want to replace the missing values in my dataframe with the string 'missing_value' for some features, and the most frequent value for the remaining features. Therefore, I combine these two operations using ColumnTransformer and passing to it the corresponding columns of my dataframe.
In the second step, I want to use the just preprocessed data and apply OrdinalEncoder or OneHotEncoder depending on the features. For that I use again ColumnTransformer.
I then combine the two steps into a single pipeline.
I am using the Kaggle Houses Price dataset, I have scikit-learn version 0.20 and this is a simplified version of my code:
cat_columns_fill_miss = ['PoolQC', 'Alley']
cat_columns_fill_freq = ['Street', 'MSZoning', 'LandContour']
cat_columns_ord = ['Street', 'Alley', 'PoolQC']
ord_mapping = [['Pave', 'Grvl'], # Street
['missing_value', 'Pave', 'Grvl'], # Alley
['missing_value', 'Fa', 'TA', 'Gd', 'Ex'] # PoolQC
]
cat_columns_onehot = ['MSZoning', 'LandContour']
imputer_cat_pipeline = ColumnTransformer([
('imp_miss', SimpleImputer(strategy='constant'), cat_columns_fill_miss), # fill_value='missing_value' by default
('imp_freq', SimpleImputer(strategy='most_frequent'), cat_columns_fill_freq),
])
encoder_cat_pipeline = ColumnTransformer([
('ordinal', OrdinalEncoder(categories=ord_mapping), cat_columns_ord),
('pass_ord', OneHotEncoder(), cat_columns_onehot),
])
cat_pipeline = Pipeline([
('imp_cat', imputer_cat_pipeline),
('cat_encoder', encoder_cat_pipeline),
])
Unfortunately, when I apply it to housing_cat, the subset of my dataframe including only categorical features,
cat_pipeline.fit_transform(housing_cat)
I get the error:
AttributeError: 'numpy.ndarray' object has no attribute 'columns'
During handling of the above exception, another exception occurred:
...
ValueError: Specifying the columns using strings is only supported for pandas DataFrames
I have tried this simplified pipeline and it works properly:
new_cat_pipeline = Pipeline([
('imp_cat', imputer_cat_pipeline),
('onehot', OneHotEncoder()),
])
However, if I try:
enc_one = ColumnTransformer([
('onehot', OneHotEncoder(), cat_columns_onehot),
('pass_ord', 'passthrough', cat_columns_ord)
])
new_cat_pipeline = Pipeline([
('imp_cat', imputer_cat_pipeline),
('onehot_encoder', enc_one),
])
I start to get the same error.
I suspect then that this error is related to the use of ColumnTransformer in the second step, but I do not actually understand where it comes from. The way I identify the columns in the second step is the same as in the first step, so it remains unclear to me why only in the second step I get the Attribute Error...
ColumnTransformer returns numpy.array, so it can't have column attribute (as indicated by your error).
If I may suggest a different solution, use pandas for both of your tasks, it will be easier.
Step 1 - replacing missing values
To replace missing value in a subset of columns with missing_value string use this:
dataframe[["PoolQC", "Alley"]].fillna("missing_value", inplace=True)
For the rest (imputing with mean of each column), this will work perfectly:
dataframe[["Street", "MSZoning", "LandContour"]].fillna(
dataframe[["Street", "MSZoning", "LandContour"]].mean(), inplace=True
)
Step 2 - one hot encoding and categorical variables
pandas provides get_dummies, which returns pandas Dataframe, unlike ColumnTransfomer, code for this would be:
encoded = pd.get_dummies(dataframe[['MSZoning', 'LandContour']], drop_first=True)
pd.dropna(['MSZoning', 'LandContour'], axis=columns, inplace=True)
dataframe = dataframe.join(encoded)
For ordinal variables and their encoding I would suggest you to look at this SO answer (unluckily some manual mapping would be needed in this case).
If you want to use transformer anyway
Get np.array from the dataframe using values attribute, pass it through the pipeline and recreate columns and indices from the array like this:
pd.DataFrame(data=your_array, index=np.arange(len(your_array)), columns=["A", "B"])
There is one caveat of this aprroach though; you will not know the names of custom created one-hot-encoded columns (the pipeline will not do this for you).
Additionally, you could get the names of columns from sklearn's transforming objects (e.g. using categories_ attribute), but I think it would break the pipeline (someone correct me if I'm wrong).
Option #2
use the make_pipeline function
(Had the same Error, found this answer, than found this: Introducing the ColumnTransformer)
from sklearn.compose import make_column_transformer
from sklearn.pipeline import make_pipeline
cat_columns_fill_miss = ['PoolQC', 'Alley']
cat_columns_fill_freq = ['Street', 'MSZoning', 'LandContour']
cat_columns_ord = ['Street', 'Alley', 'PoolQC']
ord_mapping = [['Pave', 'Grvl'], # Street
['missing_value', 'Pave', 'Grvl'], # Alley
['missing_value', 'Fa', 'TA', 'Gd', 'Ex'] # PoolQC
]
cat_columns_onehot = ['MSZoning', 'LandContour']
imputer_cat_pipeline = make_column_transformer(
(make_pipeline(SimpleImputer(strategy='constant'), cat_columns_fill_miss),
(make_pipeline(SimpleImputer(strategy='most_frequent'), cat_columns_fill_freq),
)
encoder_cat_pipeline = make_column_transformer(
(OrdinalEncoder(categories=ord_mapping), cat_columns_ord),
(OneHotEncoder(), cat_columns_onehot),
)
cat_pipeline = Pipeline([
('imp_cat', imputer_cat_pipeline),
('cat_encoder', encoder_cat_pipeline),
])
In my own pipelines i do not have overlapping preprocessing in the column space. So i am not sure, how the transformation and than the "outer pipelining" works.
However, the important part is to use make_pipeline around the SimpleImputer to use it in a pipeline properly:
imputer_cat_pipeline = make_column_transformer(
(make_pipeline(SimpleImputer(strategy='constant'), cat_columns_fill_miss),
)
Just to add to the other answers here. I'm no Python or data science expert but you can pass another pipeline to ColumnTransformer in order to do what you need an add more than one transformer to a column. I came here looking for an answer to the same question and found this solution.
Doing it all via pipelines enables you to control the test/train data a lot easier to avoid leakage, and opens up more Grid Search possibilities too. I'm personally not a fan of the pandas approach in another answer for these reasons, but it would work ok still.
encoder_cat_pipeline = Pipeline([
('ordinal', OrdinalEncoder(categories=ord_mapping)),
('pass_ord', OneHotEncoder()),
])
imputer_cat_pipeline = ColumnTransformer([
('imp_miss', SimpleImputer(strategy='constant'), cat_columns_fill_miss),
('new_pipeline', encoder_cat_pipeline, cat_columns_fill_freq)
])
cat_pipeline = Pipeline([
('imp_cat', imputer_cat_pipeline),
])
I like to use the FunctionTransformer sklearn offers instead of doing transformations directly in pandas whenever I am doing any transformations. The reason for this is now my feature transformations are more generalizable on new incoming data (e.g. suppose you win, and you need to use the same code to predict on next years data). This way you won't have to re-run your code, you can save your preprocessor and call transform. I use something like this
FE_pipeline = {
'numeric_pipe': make_pipeline(
FunctionTransformer(lambda x: x.replace([np.inf, -np.inf], np.nan)),
MinMaxScaler(),
SimpleImputer(strategy='median', add_indicator=True),
),
'oh_pipe': make_pipeline(
FunctionTransformer(lambda x: x.astype(str)),
SimpleImputer(strategy='constant'),
OneHotEncoder(handle_unknown='ignore')
)
}
I'm trying to use FeatureUnion to extract different features from a datastructure, but it fails due to different dimensions: ValueError: blocks[0,:] has incompatible row dimensions
Implementaion
My FeatureUnion is built the following way:
features = FeatureUnion([
('f1', Pipeline([
('get', GetItemTransformer('f1')),
('transform', vectorizer_f1)
])),
('f2', Pipeline([
('get', GetItemTransformer('f2')),
('transform', vectorizer_f1)
]))
])
GetItemTransformer is used to get different parts of data out of the same structure. The Idea is described here in the scikit-learn issue-tracker.
The Structure itself is stored as {'f1': data_f1, 'f2': data_f2} where data_f1 are different lists with different lengths.
Question
Since the Y-Vector is different to the Data-Fields I assume that the error occurs, but how can I scale the vector to fit in both cases?
Here's what worked for me:
class ArrayCaster(BaseEstimator, TransformerMixin):
def fit(self, x, y=None):
return self
def transform(self, data):
print data.shape
print np.transpose(np.matrix(data)).shape
return np.transpose(np.matrix(data))
FeatureUnion([('text', Pipeline([
('selector', ItemSelector(key='text')),
('vect', CountVectorizer(ngram_range=(1,1), binary=True, min_df=3)),
('tfidf', TfidfTransformer())
])
),
('other data', Pipeline([
('selector', ItemSelector(key='has_foriegn_char')),
('caster', ArrayCaster())
])
)])
I don't know if this applies to your question, but we ran into the same error in a slightly different situation and just solved it.
Our f1 entries were each lists of 15 numeric values and we needed to do tf-idf on f2. This generated the same error about incompatible row dimensions.
After running it through the debugger, we found that the shapes of our matrices were subtly different going into the hstack() call in FeatureUnion: (2569,) and (2659, 706).
If we cast f1 to a 2D numpy array, the shape changed to (2659, 15) and the hstack call works.
The cast was something like this: f1 = np.array(list(f1)).