Solution : what worked for me python - sklearn - AttributeError: 'CustomScaler' object has no attribute 'copy' - Stack Overflow Ben reiniger No attribute copy errorThis is only being thrown when trying to display the html representation of the transformer, after fitting has already succeeded, which is why you're able to continue and transform successfully. But the issue is more serious than that if you want to make use of the transformer in pipelines, grid searches, etc. In order to clone properly, you need to follow the specific guidance of the sklearn API, or else provide your own get_params and set_params. The __init__ method should set an attribute for every parameter, and only those attributes. So here it should be def __init__(self, columns, copy=True, with_mean=True, with_std=True): self.columns = columns self.copy = copy self.with_mean = with_mean self.with_std = with_std And then make the contained StandardScaler at fit time: def fit(self, X, y=None): self.scaler = StandardScaler(copy=self.copy, with_mean=self.with_mean, with_std=self.with_std) self.scaler.fit(X[self.columns], y) self.mean_ = np.mean(X[self.columns]) self.var_ = np.var(X[self.columns]) return self Hope this can help you. Full solution class CustomScaler(BaseEstimator, TransformerMixin): def __init__(self, columns, copy=True, with_mean=True, with_std=True): self.columns = columns self.copy = copy self.with_mean = with_mean self.with_std = with_std def fit(self, X, y=None): self.scaler = StandardScaler(copy= self.copy, with_mean=self.with_mean, with_std=self.with_std) self.scaler.fit(X[self.columns], y) self.mean_ = np.mean(X[self.columns]) self.var_ = np.var(X[self.columns]) return self def transform(self, X, y=None, copy=None): init_col_order = X.columns X_scaled = pd.DataFrame(self.scaler.transform(X[self.columns]), columns=self.columns) X_not_scaled = X.loc[:, ~X.columns.isin(self.columns)] return pd.concat([X_not_scaled, X_scaled], axis=1)[init_col_order]

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Posted on:

04 Feb 2023

Solution Sci kit learn

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