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FEA/MAINT Add column-wise transforms & refactor TableVectorizer (#902)
Co-authored-by: Théo Jolivet <57430673+TheooJ@users.noreply.github.com>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,184 @@ | ||
| import warnings | ||
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| import numpy as np | ||
| from sklearn.base import BaseEstimator, TransformerMixin | ||
| from sklearn.utils.validation import check_is_fitted | ||
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| from . import _dataframe as sbd | ||
| from . import _join_utils, _utils | ||
| from ._dispatch import dispatch | ||
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| __all__ = ["CheckInputDataFrame"] | ||
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| def _column_names_to_strings(column_names): | ||
| non_string = [c for c in column_names if not isinstance(c, str)] | ||
| if not non_string: | ||
| return column_names | ||
| warnings.warn( | ||
| f"Some column names are not strings: {non_string}. All column names" | ||
| " must be strings; converting to strings." | ||
| ) | ||
| return list(map(str, column_names)) | ||
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| def _deduplicated_column_names(column_names): | ||
| duplicates = _utils.get_duplicates(column_names) | ||
| if not duplicates: | ||
| return column_names | ||
| warnings.warn( | ||
| f"Found duplicated column names: {duplicates}. Please make sure column names" | ||
| " are unique. Renaming columns that have duplicated names." | ||
| ) | ||
| return _join_utils.pick_column_names(column_names) | ||
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| def _cleaned_column_names(column_names): | ||
| return _deduplicated_column_names(_column_names_to_strings(column_names)) | ||
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| @dispatch | ||
| def _check_not_pandas_sparse(df): | ||
| pass | ||
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| @_check_not_pandas_sparse.specialize("pandas") | ||
| def _check_not_pandas_sparse_pandas(df): | ||
| import pandas as pd | ||
|
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| sparse_cols = [ | ||
| col for col in df.columns if isinstance(df[col].dtype, pd.SparseDtype) | ||
| ] | ||
| if sparse_cols: | ||
| raise TypeError( | ||
| f"Columns {sparse_cols} are sparse Pandas series, but dense " | ||
| "data is required. Use ``df[col].sparse.to_dense()`` to convert " | ||
| "a series from sparse to dense." | ||
| ) | ||
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| def _check_is_dataframe(df): | ||
| if not sbd.is_dataframe(df): | ||
| raise TypeError( | ||
| "Only pandas and polars DataFrames are supported. Cannot handle X of" | ||
| f" type: {type(df)}." | ||
| ) | ||
|
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| def _collect_lazyframe(df): | ||
| if not sbd.is_lazyframe(df): | ||
| return df | ||
| warnings.warn( | ||
| "At the moment, skrub only works on eager DataFrames, calling collect()." | ||
| ) | ||
| return sbd.collect(df) | ||
|
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|
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| class CheckInputDataFrame(TransformerMixin, BaseEstimator): | ||
| """Check the dataframe entering a skrub pipeline. | ||
| This transformer ensures that: | ||
| - The input is a dataframe. | ||
| - Numpy arrays are converted to pandas dataframes with a warning. | ||
| - The dataframe library is the same during ``fit`` and ``transform``, e.g. | ||
| fitting on a polars dataframe and then transforming a pandas dataframe is | ||
| not allowed. | ||
| - A TypeError is raised otherwise. | ||
| - Column names are unique strings. | ||
| - Non-strings are cast to strings. | ||
| - A random suffix is added to duplicated names. | ||
| - If either of these operations is needed, a warning is emitted. | ||
| - Only applies to pandas; polars column names are always unique strings. | ||
| - The input is not sparse. | ||
| - A TypeError is raised otherwise. | ||
| - The input is not a ``LazyFrame``. | ||
| - A ``LazyFrame`` is ``collect``ed with a warning. | ||
| - The column names are the same during ``fit`` and ``transform``. | ||
| - A ValueError is raised otherwise. | ||
| Attributes | ||
| ---------- | ||
| module_name_ : str | ||
| The name of the dataframe module, 'polars' or 'pandas'. | ||
| feature_names_in_ : list | ||
| The column names of the input (before cleaning). | ||
| n_features_in_ : int | ||
| The number of input columns. | ||
| feature_names_out_ : list of str | ||
| The column names after converting to string and deduplication. | ||
| """ | ||
|
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| def fit(self, X, y=None): | ||
| self.fit_transform(X, y) | ||
| return self | ||
|
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| def fit_transform(self, X, y=None): | ||
| del y | ||
| X = self._handle_array(X) | ||
| _check_is_dataframe(X) | ||
| self.module_name_ = sbd.dataframe_module_name(X) | ||
| # TODO check schema (including dtypes) not just names. | ||
| # Need to decide how strict we should be about types | ||
| column_names = sbd.column_names(X) | ||
| self.feature_names_in_ = column_names | ||
| self.n_features_in_ = len(column_names) | ||
| self.feature_names_out_ = _cleaned_column_names(column_names) | ||
| if sbd.column_names(X) != self.feature_names_out_: | ||
| X = sbd.set_column_names(X, self.feature_names_out_) | ||
| _check_not_pandas_sparse(X) | ||
| X = _collect_lazyframe(X) | ||
| return X | ||
|
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| def transform(self, X): | ||
| check_is_fitted(self, "module_name_") | ||
| X = self._handle_array(X) | ||
| _check_is_dataframe(X) | ||
| module_name = sbd.dataframe_module_name(X) | ||
| if module_name != self.module_name_: | ||
| raise TypeError( | ||
| f"Pipeline was fitted to a {self.module_name_} dataframe " | ||
| f"but is being applied to a {module_name} dataframe. " | ||
| "This is likely to produce errors and is not supported." | ||
| ) | ||
| column_names = sbd.column_names(X) | ||
| if column_names != self.feature_names_in_: | ||
| import difflib | ||
|
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||
| diff = "\n".join( | ||
| difflib.Differ().compare(self.feature_names_in_, column_names) | ||
| ) | ||
| message = ( | ||
| f"Columns of dataframes passed to fit() and transform() differ:\n{diff}" | ||
| ) | ||
| raise ValueError(message) | ||
| if sbd.column_names(X) != self.feature_names_out_: | ||
| X = sbd.set_column_names(X, self.feature_names_out_) | ||
| _check_not_pandas_sparse(X) | ||
| X = _collect_lazyframe(X) | ||
| return X | ||
|
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| def _handle_array(self, X): | ||
| if not isinstance(X, np.ndarray): | ||
| return X | ||
| if X.ndim != 2: | ||
| raise ValueError( | ||
| "Input should be a DataFrame. Found an array with incompatible shape:" | ||
| f" {X.shape}." | ||
| ) | ||
| warnings.warn( | ||
| "Only pandas and polars DataFrames are supported, but input is a Numpy" | ||
| " array. Please convert Numpy arrays to DataFrames before passing them to" | ||
| " skrub transformers. Converting to pandas DataFrame with columns" | ||
| " ['0', '1', …]." | ||
| ) | ||
| import pandas as pd | ||
|
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| columns = list(map(str, range(X.shape[1]))) | ||
| X = pd.DataFrame(X, columns=columns) | ||
| return X | ||
|
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| # set_output api compatibility | ||
|
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| def get_feature_names_out(self): | ||
| return self.feature_names_out_ |
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