448 coda transformations data closure

eis_toolkit/transformations/coda/alr.py

@@ -1,88 +1,88 @@
-from numbers import Number
-
-import numpy as np
-import pandas as pd
-from beartype import beartype
-from beartype.typing import Optional, Sequence
-
-from eis_toolkit.exceptions import InvalidColumnException, NumericValueSignException
-from eis_toolkit.utilities.aitchison_geometry import _closure
-from eis_toolkit.utilities.checks.compositional import check_compositional_data
-from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern
-
-
-@beartype
-def _alr_transform(df: pd.DataFrame, columns: Sequence[str], denominator_column: str) -> pd.DataFrame:
-
-    ratios = df[columns].div(df[denominator_column], axis=0)
-    return np.log(ratios)
-
-
-@beartype
-def alr_transform(
-    df: pd.DataFrame, column: Optional[str] = None, keep_denominator_column: bool = False
-) -> pd.DataFrame:
-    """
-    Perform an additive logratio transformation on the data.
-
-    Args:
-        df: A dataframe of compositional data.
-        column: The name of the column to be used as the denominator column.
-        keep_denominator_column: Whether to include the denominator column in the result. If True, the returned
-            dataframe retains its original shape.
-
-    Returns:
-        A new dataframe containing the ALR transformed data.
-
-    Raises:
-        InvalidColumnException: The input column isn't found in the dataframe.
-        InvalidCompositionException: Data is not normalized to the expected value.
-        NumericValueSignException: Data contains zeros or negative values.
-    """
-    check_compositional_data(df)
-
-    if column is not None and column not in df.columns:
-        raise InvalidColumnException(f"The column {column} was not found in the dataframe.")
-
-    column = column if column is not None else df.columns[-1]
-
-    columns = [col for col in df.columns]
-
-    if not keep_denominator_column and column in columns:
-        columns.remove(column)
-
-    return rename_columns_by_pattern(_alr_transform(df, columns, column))
-
-
-@beartype
-def _inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame:
-    dfc = df.copy()
-
-    if denominator_column not in dfc.columns.values:
-        # Add the denominator column
-        dfc[denominator_column] = 0.0
-
-    return _closure(np.exp(dfc), scale)
-
-
-@beartype
-def inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame:
-    """
-    Perform the inverse transformation for a set of ALR transformed data.
-
-    Args:
-        df: A dataframe of ALR transformed compositional data.
-        denominator_column: The name of the denominator column.
-        scale: The value to which each composition should be normalized. Eg., if the composition is expressed
-            as percentages, scale=100.
-
-    Returns:
-        A dataframe containing the inverse transformed data.
-
-    Raises:
-        NumericValueSignException: The input scale value is zero or less.
-    """
-    if scale <= 0:
-        raise NumericValueSignException("The scale value should be positive.")
-
-    return _inverse_alr(df, denominator_column, scale)
+from numbers import Number
+
+import numpy as np
+import pandas as pd
+from beartype import beartype
+from beartype.typing import Optional, Sequence
+
+from eis_toolkit.exceptions import InvalidColumnException, NumericValueSignException
+from eis_toolkit.utilities.aitchison_geometry import _closure
+from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space
+from eis_toolkit.utilities.miscellaneous import rename_columns_by_pattern
+
+
+@beartype
+def _alr_transform(df: pd.DataFrame, columns: Sequence[str], denominator_column: str) -> pd.DataFrame:
+
+    ratios = df[columns].div(df[denominator_column], axis=0)
+    return np.log(ratios)
+
+
+@beartype
+def alr_transform(
+    df: pd.DataFrame, column: Optional[str] = None, keep_denominator_column: bool = False
+) -> pd.DataFrame:
+    """
+    Perform an additive logratio transformation on the data.
+
+    Args:
+        df: A dataframe of compositional data.
+        column: The name of the column to be used as the denominator column.
+        keep_denominator_column: Whether to include the denominator column in the result. If True, the returned
+            dataframe retains its original shape.
+
+    Returns:
+        A new dataframe containing the ALR transformed data.
+
+    Raises:
+        InvalidColumnException: The input column isn't found in the dataframe.
+        InvalidCompositionException: Data is not normalized to the expected value.
+        NumericValueSignException: Data contains zeros or negative values.
+    """
+    check_in_simplex_sample_space(df)
+
+    if column is not None and column not in df.columns:
+        raise InvalidColumnException(f"The column {column} was not found in the dataframe.")
+
+    column = column if column is not None else df.columns[-1]
+
+    columns = [col for col in df.columns]
+
+    if not keep_denominator_column and column in columns:
+        columns.remove(column)
+
+    return rename_columns_by_pattern(_alr_transform(df, columns, column))
+
+
+@beartype
+def _inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame:
+    dfc = df.copy()
+
+    if denominator_column not in dfc.columns.values:
+        # Add the denominator column
+        dfc[denominator_column] = 0.0
+
+    return _closure(np.exp(dfc), scale)
+
+
+@beartype
+def inverse_alr(df: pd.DataFrame, denominator_column: str, scale: Number = 1.0) -> pd.DataFrame:
+    """
+    Perform the inverse transformation for a set of ALR transformed data.
+
+    Args:
+        df: A dataframe of ALR transformed compositional data.
+        denominator_column: The name of the denominator column.
+        scale: The value to which each composition should be normalized. Eg., if the composition is expressed
+            as percentages, scale=100.
+
+    Returns:
+        A dataframe containing the inverse transformed data.
+
+    Raises:
+        NumericValueSignException: The input scale value is zero or less.
+    """
+    if scale <= 0:
+        raise NumericValueSignException("The scale value should be positive.")
+
+    return _inverse_alr(df, denominator_column, scale)

eis_toolkit/transformations/coda/clr.py

@@ -1,79 +1,79 @@
-from numbers import Number
-
-import numpy as np
-import pandas as pd
-from beartype import beartype
-from beartype.typing import Optional, Sequence
-from scipy.stats import gmean
-
-from eis_toolkit.exceptions import NumericValueSignException
-from eis_toolkit.utilities.aitchison_geometry import _closure
-from eis_toolkit.utilities.checks.compositional import check_compositional_data
-from eis_toolkit.utilities.miscellaneous import rename_columns, rename_columns_by_pattern
-
-
-@beartype
-def _centered_ratio(row: pd.Series) -> pd.Series:
-
-    return row / gmean(row)
-
-
-@beartype
-def _clr_transform(df: pd.DataFrame) -> pd.DataFrame:
-
-    dfc = df.copy()
-    dfc = dfc.apply(_centered_ratio, axis=1)
-
-    return np.log(dfc)
-
-
-@beartype
-def clr_transform(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    Perform a centered logratio transformation on the data.
-
-    Args:
-        df: A dataframe of compositional data.
-
-    Returns:
-        A new dataframe containing the CLR transformed data.
-
-    Raises:
-        InvalidCompositionException: Data is not normalized to the expected value.
-        NumericValueSignException: Data contains zeros or negative values.
-    """
-    check_compositional_data(df)
-    return rename_columns_by_pattern(_clr_transform(df))
-
-
-@beartype
-def _inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame:
-    inverse = _closure(np.exp(df), scale)
-
-    if colnames is not None:
-        return rename_columns(inverse, colnames)
-
-    return inverse
-
-
-@beartype
-def inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame:
-    """
-    Perform the inverse transformation for a set of CLR transformed data.
-
-    Args:
-        df: A dataframe of CLR transformed compositional data.
-        colnames: List of column names to rename the columns to.
-        scale: The value to which each composition should be normalized. Eg., if the composition is expressed
-            as percentages, scale=100.
-
-    Returns:
-        A dataframe containing the inverse transformed data.
-
-    Raises:
-        NumericValueSignException: The input scale value is zero or less.
-    """
-    if scale <= 0:
-        raise NumericValueSignException("The scale value should be positive.")
-
-    return _inverse_clr(df, colnames, scale)
+from numbers import Number
+
+import numpy as np
+import pandas as pd
+from beartype import beartype
+from beartype.typing import Optional, Sequence
+from scipy.stats import gmean
+
+from eis_toolkit.exceptions import NumericValueSignException
+from eis_toolkit.utilities.aitchison_geometry import _closure
+from eis_toolkit.utilities.checks.compositional import check_in_simplex_sample_space
+from eis_toolkit.utilities.miscellaneous import rename_columns, rename_columns_by_pattern
+
+
+@beartype
+def _centered_ratio(row: pd.Series) -> pd.Series:
+
+    return row / gmean(row)
+
+
+@beartype
+def _clr_transform(df: pd.DataFrame) -> pd.DataFrame:
+
+    dfc = df.copy()
+    dfc = dfc.apply(_centered_ratio, axis=1)
+
+    return np.log(dfc)
+
+
+@beartype
+def clr_transform(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Perform a centered logratio transformation on the data.
+
+    Args:
+        df: A dataframe of compositional data.
+
+    Returns:
+        A new dataframe containing the CLR transformed data.
+
+    Raises:
+        InvalidCompositionException: Data is not normalized to the expected value.
+        NumericValueSignException: Data contains zeros or negative values.
+    """
+    check_in_simplex_sample_space(df)
+    return rename_columns_by_pattern(_clr_transform(df))
+
+
+@beartype
+def _inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame:
+    inverse = _closure(np.exp(df), scale)
+
+    if colnames is not None:
+        return rename_columns(inverse, colnames)
+
+    return inverse
+
+
+@beartype
+def inverse_clr(df: pd.DataFrame, colnames: Optional[Sequence[str]] = None, scale: Number = 1.0) -> pd.DataFrame:
+    """
+    Perform the inverse transformation for a set of CLR transformed data.
+
+    Args:
+        df: A dataframe of CLR transformed compositional data.
+        colnames: List of column names to rename the columns to.
+        scale: The value to which each composition should be normalized. Eg., if the composition is expressed
+            as percentages, scale=100.
+
+    Returns:
+        A dataframe containing the inverse transformed data.
+
+    Raises:
+        NumericValueSignException: The input scale value is zero or less.
+    """
+    if scale <= 0:
+        raise NumericValueSignException("The scale value should be positive.")
+
+    return _inverse_clr(df, colnames, scale)