0.1.1

Makefile

@@ -20,7 +20,6 @@ lint-notebooks:
 	@echo "Linting notebooks..."
 	@nbqa black notebooks/*.ipynb
 	@nbqa isort notebooks/*.ipynb
-	nbstripout notebooks/*.ipynb
 
 
 test:

denseclus/DenseClus.py

@@ -127,6 +127,7 @@ def __init__(
         verbose: bool = False,
         umap_params=None,
         hdbscan_params=None,
+        **kwargs,
     ):
         if umap_combine_method not in [
             "intersection",
@@ -168,26 +169,19 @@ def __init__(
             "metric": "euclidean",
         }
 
-        # self.umap_params = dict()
-        # if umap_params is None:
-        #     self.umap_params = default_umap_params
-        # else:
-        #     for key, new_params in umap_params.items():
-        #         self.umap_params[key] = default_umap_params | new_params
-        if umap_params is None:
-            self.umap_params = default_umap_params
-        else:
+        if umap_params:
             for key in umap_params:
                 if key in default_umap_params:
                     default_umap_params[key].update(umap_params[key])  # type: ignore # noqa
                 else:
                     raise ValueError(f"Invalid key '{key}' in umap_params")
             self.umap_params = default_umap_params
+        else:
+            self.umap_params = default_umap_params
 
-        if hdbscan_params is None:
-            self.hdbscan_params = default_hdbscan_params
+        if hdbscan_params:
+            self.hdbscan_params = hdbscan_params
         else:
-            default_hdbscan_params.update(hdbscan_params)
             self.hdbscan_params = default_hdbscan_params
 
         if verbose:
@@ -210,6 +204,8 @@ def noop(*args, **kargs):
         else:
             logger.info("No random seed passed, running UMAP in Numba, parallel")
 
+        self.kwargs = kwargs
+
     def __repr__(self):
         return str(self.__dict__)
 
@@ -231,10 +227,10 @@ def fit(self, df: pd.DataFrame) -> None:
             raise TypeError("Requires DataFrame as input")
 
         logger.info("Extracting categorical features")
-        self.categorical_ = extract_categorical(df)
+        self.categorical_ = extract_categorical(df, **self.kwargs)
 
         logger.info("Extracting numerical features")
-        self.numerical_ = extract_numerical(df)
+        self.numerical_ = extract_numerical(df, **self.kwargs)
 
         logger.info("Fitting categorical UMAP")
         self._fit_categorical()

denseclus/__init__.py

@@ -8,7 +8,7 @@
 from .DenseClus import DenseClus
 from .utils import extract_categorical, extract_numerical
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 
 if __name__ == "__main__":
     print(type(DenseClus), type(extract_categorical), type(extract_numerical))

denseclus/utils.py

@@ -4,49 +4,164 @@
 Utility functions for making fits to UMAP
 """
 import warnings
+from typing import Callable, Optional
 
 import numpy as np
 import pandas as pd
 from sklearn.datasets import make_blobs
+from sklearn.feature_extraction import FeatureHasher
+from sklearn.impute import SimpleImputer
 from sklearn.preprocessing import KBinsDiscretizer, PowerTransformer, StandardScaler
 
 
-def extract_categorical(df: pd.DataFrame) -> pd.DataFrame:
+def extract_categorical(
+    df: pd.DataFrame,
+    cardinality_threshold: int = 25,
+    strategy: str = "constant",
+    fill_value="Missing",
+    **kwargs,
+) -> pd.DataFrame:
     """Extracts categorical features into binary dummy dataframe
 
     Parameters:
         df (pd.DataFrame): DataFrame with numerical and categorical features
+        cardinality_threshold: (int): Threshold to revert to using hashing when the number of
+        categorical features are high. Default: 25
+        **kwargs : Additional arguments to pass to imputation, allows to customize
+            Note: Imputation defaults to filling with 'Missing'
 
     Returns:
         pd.DataFrame: binary dummy DataFrame of categorical features
     """
+    if not isinstance(df, pd.DataFrame):
+        raise TypeError("Input should be a pandas DataFrame")
+    if df.empty:
+        raise ValueError("Input DataFrame should not be empty.")
+    if df.shape[1] < 1:
+        raise ValueError("Input DataFrame should have at least one column.")
 
     categorical = df.select_dtypes(exclude=["float", "int"])
-    if categorical.shape[1] == 0:
-        raise ValueError("No Categories found, check that objects are in dataframe")
 
-    categorical_dummies = pd.get_dummies(categorical)
+    if categorical.empty:
+        raise ValueError("No categorical data found in the input DataFrame.")
 
-    return categorical_dummies
+    categorical = impute_categorical(
+        categorical,
+        strategy=strategy,
+        fill_value=fill_value,
+        **kwargs,
+    )
 
+    max_cardinality = max(categorical.nunique())
 
-def extract_numerical(df: pd.DataFrame) -> pd.DataFrame:
+    if max_cardinality > cardinality_threshold:
+        print(f"Max of {max_cardinality} is greater than threshold {cardinality_threshold}")
+        print("Hashing categorical features")
+        hasher = FeatureHasher(n_features=cardinality_threshold, input_type="string")
+        hashed_df = pd.DataFrame()
+        for col in categorical.columns:
+            hashed_features = hasher.transform(categorical[col].apply(lambda x: [x]))
+            hashed_features = pd.DataFrame(hashed_features.toarray())
+            hashed_df = pd.concat([hashed_df, hashed_features], axis=1)
+
+        categorical = hashed_df
+    else:
+        categorical = pd.get_dummies(categorical, drop_first=True)
+
+    return categorical
+
+
+def impute_categorical(
+    categorical: pd.DataFrame,
+    strategy: str,
+    fill_value: str,
+    custom_strategy: Optional[Callable[[pd.Series], object]] = None,
+    **kwargs,
+) -> pd.DataFrame:
+    """Imputes missing values in categorical features.
+
+    Parameters:
+        df (pd.DataFrame): DataFrame with categorical features
+        strategy (str, optional): The imputation strategy. Default is 'constant'.
+        fill_value (str, optional): The value to use for imputation when strategy is 'constant'. Default is 'Missing'.
+        custom_strategy (callable, optional): A custom function to compute the imputation value. Should take a Series and return an object.
+
+    Returns:
+        pd.DataFrame: DataFrame with imputed categorical features
+
+    Example:
+        To use a custom strategy that imputes missing values with the second most frequent value, you can do:
+
+        def second_most_frequent(s):
+            return s.value_counts().index[1] if len(s.value_counts()) > 1 else s.value_counts().index[0]
+
+        impute_categorical(df, custom_strategy=second_most_frequent)
+    """
+    if strategy not in ["constant", "most_frequent"]:
+        raise ValueError(f"Invalid strategy for categorical: {strategy}")
+
+    if categorical.isnull().sum().sum() == 0:
+        return categorical
+
+    for col in categorical.columns:
+        if custom_strategy:
+            fill_value = custom_strategy(categorical[col])  # type: ignore
+            categorical[col].fillna(fill_value, inplace=True)
+        else:
+            imputer = SimpleImputer(strategy=strategy, fill_value=fill_value, **kwargs)
+            categorical[col] = imputer.fit_transform(categorical[[col]])[:, 0]
+
+    return categorical
+
+
+def extract_numerical(df: pd.DataFrame, impute_strategy: str = "median", **kwargs) -> pd.DataFrame:
     """Extracts numerical features into normalized numeric only dataframe
 
     Parameters:
         df (pd.DataFrame): DataFrame with numerical and categorical features
+        impute_strategy (str): The imputation strategy to use if null values are found. Default is 'median'
 
     Returns:
         pd.DataFrame: normalized numerical DataFrame of numerical features
     """
+    if not isinstance(df, pd.DataFrame):
+        raise TypeError("Input should be a pandas DataFrame")
+    if df.empty:
+        raise ValueError("Input DataFrame should not be empty.")
+    if df.shape[1] < 1:
+        raise ValueError("Input DataFrame should have at least one column.")
 
     numerical = df.select_dtypes(include=["float", "int"])
     if numerical.shape[1] == 0:
         raise ValueError("No numerics found, check that numerics are in dataframe")
 
+    numerical = impute_numerical(numerical, strategy=impute_strategy, **kwargs)
+
     return transform_numerics(numerical)
 
 
+def impute_numerical(numerical: pd.DataFrame, strategy: str = "median", **kwargs) -> pd.DataFrame:
+    """Imputes numerical features with the given strategy
+
+    Parameters:
+        numerical (pd.DataFrame): DataFrame with numerical features
+        strategy (str): The imputation strategy. Default is 'median'
+
+    Returns:
+        pd.DataFrame: DataFrame with imputed numerical features
+    """
+    if strategy not in ["median", "mean"]:
+        raise ValueError(f"Invalid strategy for numerical: {strategy}")
+
+    if numerical.isnull().sum().sum() == 0:
+        return numerical
+
+    imputer = SimpleImputer(strategy=strategy, **kwargs)
+    numerical_imputed = pd.DataFrame(imputer.fit_transform(numerical), columns=numerical.columns)
+
+    return numerical_imputed
+
+
 def transform_numerics(numerical: pd.DataFrame) -> pd.DataFrame:
     """Power transforms numerical DataFrame