staticconvertMatrixColumnsToML(dataset, *cols) 將輸入 DataFrame 中的矩陣列從pyspark.mllib.linalg.Matrix類型轉換為spark.ml包下的新pyspark.ml.linalg.Matrix類型。 2.0.0 版中的新函數。 參數: dataset:DataFrame 輸入數據集 *cols:str 要轉換的矩陣列。
"sparse"/"sparse_csr" "array" "matrix" "sparse_csc" "array" "matrix" "csc" "sparse_csr_array" "array" "array" "sparse_csc_array" "array" "array" "csc" "dataframe"/"pandas" "dataframe" "polars" "dataframe" "polars" "pyarrow" "dataframe" "pyarrow" "series" "series" "...
The second and third dataframes are derived from a sparse matrix generated by a TF-IDF Vectorizer. The dtypes of X_train_final are as follows. When attempting to fit the data, I encountered an error while using XGBoost on this final dataframe. I believe the error is due...
to trainWithRDD, rather than @@ -188,39 +172,34 @@ Parameters --- - trainingData : pyspark.sql.DataFrame + fold: dict + map from string name to list of data files for the split + train_matrix: str + name of split in fold to train against params : dict + XGBoost training parame...