Feature Transformation – PCA (Estimator)
PCA trains a model to project vectors to a lower dimensional space of the top k principal components.
ft_pca(
x,
input_col = NULL,
output_col = NULL,
k = NULL,
uid = random_string("pca_"),
...
)
ml_pca(x, features = tbl_vars(x), k = length(features), pc_prefix = "PC", ...)x |
A |
input_col |
The name of the input column. |
output_col |
The name of the output column. |
k |
The number of principal components |
uid |
A character string used to uniquely identify the feature transformer. |
... |
Optional arguments; currently unused. |
features |
The columns to use in the principal components
analysis. Defaults to all columns in |
pc_prefix |
Length-one character vector used to prepend names of components. |
In the case where x is a tbl_spark, the estimator fits against x
to obtain a transformer, which is then immediately used to transform x, returning a tbl_spark.
ml_pca() is a wrapper around ft_pca() that returns a
ml_model.
The object returned depends on the class of x.
spark_connection: When x is a spark_connection, the function returns a ml_transformer,
a ml_estimator, or one of their subclasses. The object contains a pointer to
a Spark Transformer or Estimator object and can be used to compose
Pipeline objects.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with
the transformer or estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a transformer is constructed then
immediately applied to the input tbl_spark, returning a tbl_spark
See http://spark.apache.org/docs/latest/ml-features.html for more information on the set of transformations available for DataFrame columns in Spark.
Other feature transformers:
ft_binarizer(),
ft_bucketizer(),
ft_chisq_selector(),
ft_count_vectorizer(),
ft_dct(),
ft_elementwise_product(),
ft_feature_hasher(),
ft_hashing_tf(),
ft_idf(),
ft_imputer(),
ft_index_to_string(),
ft_interaction(),
ft_lsh,
ft_max_abs_scaler(),
ft_min_max_scaler(),
ft_ngram(),
ft_normalizer(),
ft_one_hot_encoder_estimator(),
ft_one_hot_encoder(),
ft_polynomial_expansion(),
ft_quantile_discretizer(),
ft_r_formula(),
ft_regex_tokenizer(),
ft_robust_scaler(),
ft_sql_transformer(),
ft_standard_scaler(),
ft_stop_words_remover(),
ft_string_indexer(),
ft_tokenizer(),
ft_vector_assembler(),
ft_vector_indexer(),
ft_vector_slicer(),
ft_word2vec()
## Not run: library(dplyr) sc <- spark_connect(master = "local") iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE) iris_tbl %>% select(-Species) %>% ml_pca(k = 2) ## End(Not run)
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