feature
Binarize a column of continuous features given a threshold.
Binarize a column of continuous features given a threshold.
Bucketizer maps a column of continuous features to a column of feature buckets.
Bucketizer maps a column of continuous features to a column of feature buckets.
Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label.
Chi-Squared feature selection, which selects categorical features to use for predicting a categorical label.
Model fitted by ChiSqSelector.
Model fitted by ChiSqSelector.
Extracts a vocabulary from document collections and generates a CountVectorizerModel.
Extracts a vocabulary from document collections and generates a CountVectorizerModel.
Converts a text document to a sparse vector of token counts.
Converts a text document to a sparse vector of token counts.
A feature transformer that takes the 1D discrete cosine transform of a real vector.
A feature transformer that takes the 1D discrete cosine transform of a real vector. No zero padding is performed on the input vector. It returns a real vector of the same length representing the DCT. The return vector is scaled such that the transform matrix is unitary (aka scaled DCT-II).
More information on Wikipedia.
Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector.
Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a provided "weight" vector. In other words, it scales each column of the dataset by a scalar multiplier.
Maps a sequence of terms to their term frequencies using the hashing trick.
Maps a sequence of terms to their term frequencies using the hashing trick. Currently we use Austin Appleby's MurmurHash 3 algorithm (MurmurHash3_x86_32) to calculate the hash code value for the term object. Since a simple modulo is used to transform the hash function to a column index, it is advisable to use a power of two as the numFeatures parameter; otherwise the features will not be mapped evenly to the columns.
Compute the Inverse Document Frequency (IDF) given a collection of documents.
Compute the Inverse Document Frequency (IDF) given a collection of documents.
Model fitted by IDF.
Model fitted by IDF.
A Transformer that maps a column of indices back to a new column of corresponding string values.
A Transformer that maps a column of indices back to a new column of corresponding string values. The index-string mapping is either from the ML attributes of the input column, or from user-supplied labels (which take precedence over ML attributes).
StringIndexer for converting strings into indices
Implements the feature interaction transform.
Implements the feature interaction transform. This transformer takes in Double and Vector type columns and outputs a flattened vector of their feature interactions. To handle interaction, we first one-hot encode any nominal features. Then, a vector of the feature cross-products is produced.
For example, given the input feature values Double(2) and Vector(3, 4), the output would be
Vector(6, 8) if all input features were numeric. If the first feature was instead nominal
with four categories, the output would then be Vector(0, 0, 0, 0, 3, 4, 0, 0).
:: Experimental ::
:: Experimental ::
Class that represents the features and labels of a data point.
Label for this data point.
List of features for this data point.
:: Experimental :: Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature.
:: Experimental :: Rescale each feature individually to range [-1, 1] by dividing through the largest maximum absolute value in each feature. It does not shift/center the data, and thus does not destroy any sparsity.
:: Experimental :: Model fitted by MaxAbsScaler.
:: Experimental :: Model fitted by MaxAbsScaler.
Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling.
Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling. The rescaled value for feature E is calculated as,
Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
For the case E_{max} == E_{min}, Rescaled(e_i) = 0.5 * (max + min).
Note that since zero values will probably be transformed to non-zero values, output of the
transformer will be DenseVector even for sparse input.
Model fitted by MinMaxScaler.
Model fitted by MinMaxScaler.
A feature transformer that converts the input array of strings into an array of n-grams.
A feature transformer that converts the input array of strings into an array of n-grams. Null values in the input array are ignored. It returns an array of n-grams where each n-gram is represented by a space-separated string of words.
When the input is empty, an empty array is returned. When the input array length is less than n (number of elements per n-gram), no n-grams are returned.
Normalize a vector to have unit norm using the given p-norm.
Normalize a vector to have unit norm using the given p-norm.
A one-hot encoder that maps a column of category indices to a column of binary vectors, with at most a single one-value per row that indicates the input category index.
A one-hot encoder that maps a column of category indices to a column of binary vectors, with
at most a single one-value per row that indicates the input category index.
For example with 5 categories, an input value of 2.0 would map to an output vector of
[0.0, 0.0, 1.0, 0.0].
The last category is not included by default (configurable via OneHotEncoder!.dropLast
because it makes the vector entries sum up to one, and hence linearly dependent.
So an input value of 4.0 maps to [0.0, 0.0, 0.0, 0.0].
Note that this is different from scikit-learn's OneHotEncoder, which keeps all categories.
The output vectors are sparse.
StringIndexer for converting categorical values into category indices
PCA trains a model to project vectors to a lower dimensional space of the top PCA!.k principal components.
PCA trains a model to project vectors to a lower dimensional space of the top PCA!.k principal components.
Model fitted by PCA.
Model fitted by PCA. Transforms vectors to a lower dimensional space.
Perform feature expansion in a polynomial space.
Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
which is available at http://en.wikipedia.org/wiki/Polynomial_expansion, "In mathematics, an
expansion of a product of sums expresses it as a sum of products by using the fact that
multiplication distributes over addition". Take a 2-variable feature vector as an example:
(x, y), if we want to expand it with degree 2, then we get (x, x * x, y, x * y, y * y).
QuantileDiscretizer takes a column with continuous features and outputs a column with binned
categorical features.
QuantileDiscretizer takes a column with continuous features and outputs a column with binned
categorical features. The number of bins can be set using the numBuckets parameter.
The bin ranges are chosen using an approximate algorithm (see the documentation for
approxQuantile
for a detailed description). The precision of the approximation can be controlled with the
relativeError parameter. The lower and upper bin bounds will be -Infinity and +Infinity,
covering all real values.
:: Experimental :: Implements the transforms required for fitting a dataset against an R model formula.
:: Experimental :: Implements the transforms required for fitting a dataset against an R model formula. Currently we support a limited subset of the R operators, including '~', '.', ':', '+', and '-'. Also see the R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
The basic operators are:
~ separate target and terms+ concat terms, "+ 0" means removing intercept- remove a term, "- 1" means removing intercept: interaction (multiplication for numeric values, or binarized categorical values). all columns except targetSuppose a and b are double columns, we use the following simple examples
to illustrate the effect of RFormula:
y ~ a + b means model y ~ w0 + w1 * a + w2 * b where w0 is the intercept and w1, w2
are coefficients.y ~ a + b + a:b - 1 means model y ~ w1 * a + w2 * b + w3 * a * b where w1, w2, w3
are coefficients.RFormula produces a vector column of features and a double or string column of label.
Like when formulas are used in R for linear regression, string input columns will be one-hot
encoded, and numeric columns will be cast to doubles.
If the label column is of type string, it will be first transformed to double with
StringIndexer. If the label column does not exist in the DataFrame, the output label column
will be created from the specified response variable in the formula.
:: Experimental :: Model fitted by RFormula.
:: Experimental :: Model fitted by RFormula. Fitting is required to determine the factor levels of formula terms.
A regex based tokenizer that extracts tokens either by using the provided regex pattern to split
the text (default) or repeatedly matching the regex (if gaps is false).
A regex based tokenizer that extracts tokens either by using the provided regex pattern to split
the text (default) or repeatedly matching the regex (if gaps is false).
Optional parameters also allow filtering tokens using a minimal length.
It returns an array of strings that can be empty.
Implements the transformations which are defined by SQL statement.
Implements the transformations which are defined by SQL statement. Currently we only support SQL syntax like 'SELECT ... FROM THIS ...' where 'THIS' represents the underlying table of the input dataset. The select clause specifies the fields, constants, and expressions to display in the output, it can be any select clause that Spark SQL supports. Users can also use Spark SQL built-in function and UDFs to operate on these selected columns. For example, SQLTransformer supports statements like:
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
The "unit std" is computed using the corrected sample standard deviation, which is computed as the square root of the unbiased sample variance.
Model fitted by StandardScaler.
Model fitted by StandardScaler.
A feature transformer that filters out stop words from input.
A feature transformer that filters out stop words from input. Note: null values from input array are preserved unless adding null to stopWords explicitly.
A label indexer that maps a string column of labels to an ML column of label indices.
A label indexer that maps a string column of labels to an ML column of label indices. If the input column is numeric, we cast it to string and index the string values. The indices are in [0, numLabels), ordered by label frequencies. So the most frequent label gets index 0.
IndexToString for the inverse transformation
Model fitted by StringIndexer.
Model fitted by StringIndexer.
NOTE: During transformation, if the input column does not exist, StringIndexerModel.transform would return the input dataset unmodified. This is a temporary fix for the case when target labels do not exist during prediction.
A tokenizer that converts the input string to lowercase and then splits it by white spaces.
A tokenizer that converts the input string to lowercase and then splits it by white spaces.
A feature transformer that merges multiple columns into a vector column.
A feature transformer that merges multiple columns into a vector column.
Class for indexing categorical feature columns in a dataset of Vector.
Class for indexing categorical feature columns in a dataset of Vector.
This has 2 usage modes:
This returns a model which can transform categorical features to use 0-based indices.
Index stability:
TODO: Future extensions: The following functionality is planned for the future:
Model fitted by VectorIndexer.
Model fitted by VectorIndexer. Transform categorical features to use 0-based indices instead of their original values.
This maintains vector sparsity.
This class takes a feature vector and outputs a new feature vector with a subarray of the original features.
This class takes a feature vector and outputs a new feature vector with a subarray of the original features.
The subset of features can be specified with either indices (setIndices()) or names (setNames()). At least one feature must be selected. Duplicate features are not allowed, so there can be no overlap between selected indices and names.
The output vector will order features with the selected indices first (in the order given), followed by the selected names (in the order given).
Word2Vec trains a model of Map(String, Vector), i.e.
Word2Vec trains a model of Map(String, Vector), i.e. transforms a word into a code for further
natural language processing or machine learning process.
Model fitted by Word2Vec.
Model fitted by Word2Vec.
The expansion is done via recursion.
The expansion is done via recursion. Given n features and degree d, the size after expansion is (n + d choose d) (including 1 and first-order values). For example, let f([a, b, c], 3) be the function that expands [a, b, c] to their monomials of degree 3. We have the following recursion:
f([a, b, c], 3) = f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3]
To handle sparsity, if c is zero, we can skip all monomials that contain it. We remember the current index and increment it properly for sparse input.
Feature transformers
The
ml.featurepackage provides common feature transformers that help convert raw data or features into more suitable forms for model fitting. Most feature transformers are implemented as Transformers, which transform one DataFrame into another, e.g., HashingTF. Some feature transformers are implemented as Estimators, because the transformation requires some aggregated information of the dataset, e.g., document frequencies in IDF. For those feature transformers, calling Estimator!.fit is required to obtain the model first, e.g., IDFModel, in order to apply transformation. The transformation is usually done by appending new columns to the input DataFrame, so all input columns are carried over.We try to make each transformer minimal, so it becomes flexible to assemble feature transformation pipelines. Pipeline can be used to chain feature transformers, and VectorAssembler can be used to combine multiple feature transformations, for example:
Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn. The major difference is that most scikit-learn feature transformers operate eagerly on the entire input dataset, while MLlib's feature transformers operate lazily on individual columns, which is more efficient and flexible to handle large and complex datasets.
scikit-learn.preprocessing