Source code for evalml.pipelines.components.transformers.preprocessing.drop_nan_rows_transformer

"""Transformer to drop rows specified by row indices."""
import pandas as pd
from woodwork import init_series

from evalml.pipelines.components.transformers import Transformer
from evalml.utils import drop_rows_with_nans, infer_feature_types

[docs]class DropNaNRowsTransformer(Transformer): """Transformer to drop rows with NaN values. Args: random_seed (int): Seed for the random number generator. Is not used by this component. Defaults to 0. """ name = "Drop NaN Rows Transformer" modifies_target = True hyperparameter_ranges = {} """{}"""
[docs] def fit(self, X, y=None): """Fits component to data. Args: X (pd.DataFrame): The input training data of shape [n_samples, n_features]. y (pd.Series, optional): The target training data of length [n_samples]. Returns: self """ return self
[docs] def transform(self, X, y=None): """Transforms data using fitted component. Args: X (pd.DataFrame): Features. y (pd.Series, optional): Target data. Returns: (pd.DataFrame, pd.Series): Data with NaN rows dropped. """ X_t = infer_feature_types(X) y_t = infer_feature_types(y) if y is not None else None X_t_schema = X_t.ww.schema y_t_logical = None y_t_semantic = None if y_t is not None: if isinstance(y_t, pd.DataFrame): y_t_logical = y_t.ww.logical_types else: y_t_logical = y_t.ww.logical_type y_t_semantic = y_t.ww.semantic_tags X_t, y_t = drop_rows_with_nans(X_t, y_t) X_t.ww.init_with_full_schema(X_t_schema) if y_t is not None: if isinstance(y_t, pd.DataFrame): y_t.ww.init(logical_types=y_t_logical, semantic_tags=y_t_semantic) else: y_t = init_series( y_t, logical_type=y_t_logical, semantic_tags=y_t_semantic, ) return X_t, y_t