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- <pre><code class="python">import numbers
- import numpy as np
- import pandas as pd
- from pandas.api.types import is_numeric_dtype
- from sklearn.base import BaseEstimator, TransformerMixin
- from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
- from sklearn.preprocessing import KBinsDiscretizer, OneHotEncoder
- from sklearn.utils.validation import check_is_fitted, check_array
- """
- The classes below (BasicDiscretizer and RFDiscretizer) provide
- additional functionalities and wrappers around KBinsDiscretizer
- from sklearn. In particular, the following AbstractDiscretizer classes
- - take a data frame as input and output a data frame
- - allow for discretization of a subset of columns in the data
- frame and returns the full data frame with both the
- discretized and non-discretized columns
- - allow quantile bins to be a single point if necessary
- """
- class AbstractDiscretizer(TransformerMixin, BaseEstimator):
- """
- Discretize numeric data into bins. Base class.
- Params
- ------
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {‘onehot’, ‘ordinal’}, default=’onehot’
- Method used to encode the transformed result.
- onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- strategy : {‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- """
- def __init__(self, n_bins=2, dcols=[],
- encode='onehot', strategy='quantile',
- onehot_drop='if_binary'):
- self.n_bins = n_bins
- self.encode = encode
- self.strategy = strategy
- self.dcols = dcols
- if encode == 'onehot':
- self.onehot_drop = onehot_drop
- def _validate_n_bins(self):
- """
- Check if n_bins argument is valid.
- """
- orig_bins = self.n_bins
- n_features = len(self.dcols_)
- if isinstance(orig_bins, numbers.Number):
- if not isinstance(orig_bins, numbers.Integral):
- raise ValueError(
- "{} received an invalid n_bins type. "
- "Received {}, expected int.".format(
- AbstractDiscretizer.__name__, type(orig_bins).__name__
- )
- )
- if orig_bins < 2:
- raise ValueError(
- "{} received an invalid number "
- "of bins. Received {}, expected at least 2.".format(
- AbstractDiscretizer.__name__, orig_bins
- )
- )
- self.n_bins = np.full(n_features, orig_bins, dtype=int)
- else:
- n_bins = check_array(orig_bins, dtype=int,
- copy=True, ensure_2d=False)
- if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
- raise ValueError(
- "n_bins must be a scalar or array of shape (n_features,).")
- bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
- violating_indices = np.where(bad_nbins_value)[0]
- if violating_indices.shape[0] > 0:
- indices = ", ".join(str(i) for i in violating_indices)
- raise ValueError(
- "{} received an invalid number "
- "of bins at indices {}. Number of bins "
- "must be at least 2, and must be an int.".format(
- AbstractDiscretizer.__name__, indices
- )
- )
- self.n_bins = n_bins
- def _validate_dcols(self, X):
- """
- Check if dcols argument is valid.
- """
- for col in self.dcols_:
- if col not in X.columns:
- raise ValueError("{} is not a column in X.".format(col))
- if not is_numeric_dtype(X[col].dtype):
- raise ValueError("Cannot discretize non-numeric columns.")
- def _validate_args(self):
- """
- Check if encode, strategy arguments are valid.
- """
- valid_encode = ('onehot', 'ordinal')
- if self.encode not in valid_encode:
- raise ValueError("Valid options for 'encode' are {}. Got encode={!r} instead."
- .format(valid_encode, self.encode))
- valid_strategy = ('uniform', 'quantile', 'kmeans')
- if (self.strategy not in valid_strategy):
- raise ValueError("Valid options for 'strategy' are {}. Got strategy={!r} instead."
- .format(valid_strategy, self.strategy))
- def _discretize_to_bins(self, x, bin_edges,
- keep_pointwise_bins=False):
- """
- Discretize data into bins of the form [a, b) given bin
- edges/boundaries
- Parameters
- ----------
- x : array-like of shape (n_samples,)
- Data vector to be discretized.
- bin_edges : array-like
- Values to serve as bin edges; should include min and
- max values for the range of x
- keep_pointwise_bins : boolean
- If True, treat duplicate bin_edges as a pointwise bin,
- i.e., [a, a]. If False, these bins are in effect ignored.
- Returns
- -------
- xd: array of shape (n_samples,) where x has been
- transformed to the binned space
- """
- # ignore min and max values in bin generation
- unique_edges = np.unique(bin_edges[1:-1])
- if keep_pointwise_bins:
- # note: min and max values are used to define pointwise bins
- pointwise_bins = np.unique(
- bin_edges[pd.Series(bin_edges).duplicated()])
- else:
- pointwise_bins = np.array([])
- xd = np.zeros_like(x)
- i = 1
- for idx, split in enumerate(unique_edges):
- if idx == (len(unique_edges) - 1): # uppermost bin
- if (idx == 0) & (split in pointwise_bins):
- # two bins total: (-inf, a], (a, inf)
- indicator = x > split
- else:
- indicator = x >= split # uppermost bin: [a, inf)
- else:
- if split in pointwise_bins:
- # create two bins: [a, a], (a, b)
- indicator = (x > split) & (x < unique_edges[idx + 1]) #
- if idx != 0:
- xd[x == split] = i
- i += 1
- else:
- # create bin: [a, b)
- indicator = (x >= split) & (x < unique_edges[idx + 1])
- xd[indicator] = i
- i += 1
- return xd.astype(int)
- def _fit_preprocessing(self, X):
- """
- Initial checks before fitting the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- Returns
- -------
- self
- """
- # by default, discretize all numeric columns
- if len(self.dcols) == 0:
- numeric_cols = [
- col for col in X.columns if is_numeric_dtype(X[col].dtype)]
- self.dcols_ = numeric_cols
- # error checking
- self._validate_n_bins()
- self._validate_args()
- self._validate_dcols(X)
- def _transform_postprocessing(self, discretized_df, X):
- """
- Final processing in transform method. Does one-hot encoding
- (if specified) and joins discretized columns to the
- un-transformed columns in X.
- Parameters
- ----------
- discretized_df : data frame of shape (n_sample, len(dcols))
- Discretized data in the transformed bin space.
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- Encoded either as ordinal or one-hot.
- """
- discretized_df = discretized_df[self.dcols_]
- # return onehot encoded X if specified
- if self.encode == "onehot":
- colnames = [str(col) for col in self.dcols_]
- try:
- onehot_col_names = self.onehot_.get_feature_names_out(colnames)
- except:
- onehot_col_names = self.onehot_.get_feature_names(
- colnames) # older versions of sklearn
- discretized_df = self.onehot_.transform(discretized_df.astype(str))
- discretized_df = pd.DataFrame(discretized_df,
- columns=onehot_col_names,
- index=X.index).astype(int)
- # join discretized columns with rest of X
- cols = [col for col in X.columns if col not in self.dcols_]
- X_discretized = pd.concat([discretized_df, X[cols]], axis=1)
- return X_discretized
- class ExtraBasicDiscretizer(TransformerMixin):
- """
- Discretize provided columns into bins and return in one-hot format.
- Generates meaningful column names based on bin edges.
- Wraps KBinsDiscretizer from sklearn.
- Params
- ------
- dcols : list of strings
- The names of the columns to be discretized.
- n_bins : int or array-like of shape (len(dcols),), default=4
- Number of bins to discretize each feature into.
- strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {'first', 'if_binary'} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- 'first'
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- 'if_binary'
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- discretizer_ : object of class KBinsDiscretizer()
- Primary discretization method used to bin numeric data
- Examples
- --------
- """
- def __init__(self,
- dcols,
- n_bins=4,
- strategy='quantile',
- onehot_drop='if_binary'):
- self.dcols = dcols
- self.n_bins = n_bins
- self.strategy = strategy
- self.onehot_drop = onehot_drop
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # Fit KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(
- n_bins=self.n_bins, strategy=self.strategy, encode='ordinal')
- discretizer.fit(X[self.dcols])
- self.discretizer_ = discretizer
- # Fit OneHotEncoder to the ordinal output of KBinsDiscretizer
- disc_ordinal_np = discretizer.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- encoder = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- encoder.fit(disc_ordinal_df_str)
- self.encoder_ = encoder
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- # Apply discretizer transform to get ordinally coded DF
- disc_ordinal_np = self.discretizer_.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- # One-hot encode the ordinal DF
- disc_onehot_np = self.encoder_.transform(disc_ordinal_df_str)
- disc_onehot = pd.DataFrame(
- disc_onehot_np, columns=self.encoder_.get_feature_names_out())
- # Name columns after the interval they represent (e.g. 0.1_to_0.5)
- for col, bin_edges in zip(self.dcols, self.discretizer_.bin_edges_):
- bin_edges = bin_edges.astype(str)
- for ordinal_value in disc_ordinal_df_str[col].unique():
- bin_lb = bin_edges[int(ordinal_value)]
- bin_ub = bin_edges[int(ordinal_value) + 1]
- interval_string = f'{bin_lb}_to_{bin_ub}'
- disc_onehot = disc_onehot.rename(
- columns={f'{col}_{ordinal_value}': f'{col}_' + interval_string})
- # Join discretized columns with rest of X
- non_dcols = [col for col in X.columns if col not in self.dcols]
- X_discretized = pd.concat([disc_onehot, X[non_dcols]], axis=1)
- return X_discretized
- class BasicDiscretizer(AbstractDiscretizer):
- """
- Discretize numeric data into bins. Provides a wrapper around
- KBinsDiscretizer from sklearn
- Params
- ------
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {'onehot', 'ordinal'}, default='onehot'
- Method used to encode the transformed result.
- onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- discretizer_ : object of class KBinsDiscretizer()
- Primary discretization method used to bin numeric data
- manual_discretizer_ : dictionary
- Provides bin_edges to feed into _quantile_discretization()
- and do quantile discretization manually for features where
- KBinsDiscretizer() failed. Ignored if strategy != 'quantile'
- or no errors in KBinsDiscretizer().
- onehot_ : object of class OneHotEncoder()
- One hot encoding fit. Ignored if encode != 'onehot'
- Examples
- --------
- """
- def __init__(self, n_bins=2, dcols=[],
- encode='onehot', strategy='quantile',
- onehot_drop='if_binary'):
- super().__init__(n_bins=n_bins, dcols=dcols,
- encode=encode, strategy=strategy,
- onehot_drop=onehot_drop)
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # apply KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(n_bins=self.n_bins,
- encode='ordinal',
- strategy=self.strategy)
- discretizer.fit(X[self.dcols_])
- self.discretizer_ = discretizer
- if (self.encode == 'onehot') | (self.strategy == 'quantile'):
- discretized_df = discretizer.transform(X[self.dcols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index).astype(int)
- # fix KBinsDiscretizer errors if any when strategy = "quantile"
- if self.strategy == "quantile":
- err_idx = np.where(discretized_df.nunique() != self.n_bins)[0]
- self.manual_discretizer_ = dict()
- for idx in err_idx:
- col = self.dcols_[idx]
- if X[col].nunique() > 1:
- q_values = np.linspace(0, 1, self.n_bins[idx] + 1)
- bin_edges = np.quantile(X[col], q_values)
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- self.manual_discretizer_[col] = bin_edges
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df.astype(str))
- self.onehot_ = onehot
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform using KBinsDiscretizer
- discretized_df = self.discretizer_.transform(
- X[self.dcols_]).astype(int)
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index)
- # fix KBinsDiscretizer errors (if any) when strategy = "quantile"
- if self.strategy == "quantile":
- for col in self.manual_discretizer_.keys():
- bin_edges = self.manual_discretizer_[col]
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized
- class RFDiscretizer(AbstractDiscretizer):
- """
- Discretize numeric data into bins using RF splits.
- Parameters
- ----------
- rf_model : RandomForestClassifer() or RandomForestRegressor()
- RF model from which to extract splits for discretization.
- Default is RandomForestClassifer(n_estimators = 500) or
- RandomForestRegressor(n_estimators = 500)
- classification : boolean; default=False
- Used only if rf_model=None. If True,
- rf_model=RandomForestClassifier(n_estimators = 500).
- Else, rf_model=RandomForestRegressor(n_estimators = 500)
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {‘onehot’, ‘ordinal’}, default=’onehot’
- Method used to encode the transformed result.
- onehot - Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal - Return the bin identifier encoded as an integer value.
- strategy : {‘uniform’, ‘quantile’}, default=’quantile’
- Strategy used to choose RF split points.
- uniform - RF split points chosen to be uniformly spaced out.
- quantile - RF split points chosen based on equally-spaced quantiles.
- backup_strategy : {‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
- Strategy used to define the widths of the bins if no rf splits exist for
- that feature. Used in KBinsDiscretizer.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- rf_splits : dictionary where
- key = feature name
- value = array of all RF split threshold values
- bin_edges_ : dictionary where
- key = feature name
- value = array of bin edges used for discretization, taken from
- RF split values
- missing_rf_cols_ : array-like
- List of features that were not used in RF
- backup_discretizer_ : object of class BasicDiscretizer()
- Discretization method used to bin numeric data for features
- in missing_rf_cols_
- onehot_ : object of class OneHotEncoder()
- One hot encoding fit. Ignored if encode != 'onehot'
- """
- def __init__(self, rf_model=None, classification=False,
- n_bins=2, dcols=[], encode='onehot',
- strategy='quantile', backup_strategy='quantile',
- onehot_drop='if_binary'):
- super().__init__(n_bins=n_bins, dcols=dcols,
- encode=encode, strategy=strategy,
- onehot_drop=onehot_drop)
- self.backup_strategy = backup_strategy
- self.rf_model = rf_model
- if rf_model is None:
- self.classification = classification
- def _validate_args(self):
- """
- Check if encode, strategy, backup_strategy arguments are valid.
- """
- super()._validate_args()
- valid_backup_strategy = ('uniform', 'quantile', 'kmeans')
- if (self.backup_strategy not in valid_backup_strategy):
- raise ValueError("Valid options for 'strategy' are {}. Got strategy={!r} instead."
- .format(valid_backup_strategy, self.backup_strategy))
- def _get_rf_splits(self, col_names):
- """
- Get all splits in random forest ensemble
- Parameters
- ----------
- col_names : array-like of shape (n_features,)
- Column names for X used to train rf_model
- Returns
- -------
- rule_dict : dictionary where
- key = feature name
- value = array of all RF split threshold values
- """
- rule_dict = {}
- for model in self.rf_model.estimators_:
- tree = model.tree_
- tree_it = enumerate(zip(tree.children_left,
- tree.children_right,
- tree.feature,
- tree.threshold))
- for node_idx, data in tree_it:
- left, right, feature, th = data
- if (left != -1) | (right != -1):
- feature = col_names[feature]
- if feature in rule_dict:
- rule_dict[feature].append(th)
- else:
- rule_dict[feature] = [th]
- return rule_dict
- def _fit_rf(self, X, y=None):
- """
- Fit random forest (if necessary) and obtain RF split thresholds
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Training data used to fit RF
- y : array-like of shape (n_samples,)
- Training response vector used to fit RF
- Returns
- -------
- rf_splits : dictionary where
- key = feature name
- value = array of all RF split threshold values
- """
- # If no rf_model given, train default random forest model
- if self.rf_model is None:
- if y is None:
- raise ValueError("Must provide y if rf_model is not given.")
- if self.classification:
- self.rf_model = RandomForestClassifier(n_estimators=500)
- else:
- self.rf_model = RandomForestRegressor(n_estimators=500)
- self.rf_model.fit(X, y)
- else:
- # provided rf model has not yet been trained
- if not check_is_fitted(self.rf_model):
- if y is None:
- raise ValueError(
- "Must provide y if rf_model has not been trained.")
- self.rf_model.fit(X, y)
- # get all random forest split points
- self.rf_splits = self._get_rf_splits(list(X.columns))
- def reweight_n_bins(self, X, y=None, by="nsplits"):
- """
- Reallocate number of bins per feature.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- by : {'nsplits'}, default='nsplits'
- Specifies how to reallocate number of bins per feature.
- nsplits
- Reallocate number of bins so that each feature
- in dcols get at a minimum of 2 bins with the
- remaining bins distributed proportionally to the
- number of RF splits using that feature
- Returns
- -------
- self.n_bins : array of shape (len(dcols),)
- number of bins per feature reallocated according to
- 'by' argument
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # get total number of bins to reallocate
- total_bins = self.n_bins.sum()
- # reweight n_bins
- if by == "nsplits":
- # each col gets at least 2 bins; remaining bins get
- # reallocated based on number of RF splits using that feature
- n_rules = np.array([len(self.rf_splits[col])
- for col in self.dcols_])
- self.n_bins = np.round(n_rules / n_rules.sum() *
- (total_bins - 2 * len(self.dcols_))) + 2
- else:
- valid_by = ('nsplits')
- raise ValueError("Valid options for 'by' are {}. Got by={!r} instead."
- .format(valid_by, by))
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # features that were not used in the rf but need to be discretized
- self.missing_rf_cols_ = list(set(self.dcols_) -
- set(self.rf_splits.keys()))
- if len(self.missing_rf_cols_) > 0:
- print("{} did not appear in random forest so were discretized via {} discretization"
- .format(self.missing_rf_cols_, self.strategy))
- missing_n_bins = np.array([self.n_bins[np.array(self.dcols_) == col][0]
- for col in self.missing_rf_cols_])
- backup_discretizer = BasicDiscretizer(n_bins=missing_n_bins,
- dcols=self.missing_rf_cols_,
- encode='ordinal',
- strategy=self.backup_strategy)
- backup_discretizer.fit(X[self.missing_rf_cols_])
- self.backup_discretizer_ = backup_discretizer
- else:
- self.backup_discretizer_ = None
- if self.encode == 'onehot':
- if len(self.missing_rf_cols_) > 0:
- discretized_df = backup_discretizer.transform(
- X[self.missing_rf_cols_])
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- self.bin_edges_ = dict()
- for col in self.dcols_:
- if col in self.rf_splits.keys():
- b = self.n_bins[np.array(self.dcols_) == col]
- if self.strategy == "quantile":
- q_values = np.linspace(0, 1, int(b) + 1)
- bin_edges = np.quantile(self.rf_splits[col], q_values)
- elif self.strategy == "uniform":
- width = (max(self.rf_splits[col]) -
- min(self.rf_splits[col])) / b
- bin_edges = width * \
- np.arange(0, b + 1) + min(self.rf_splits[col])
- self.bin_edges_[col] = bin_edges
- if self.encode == 'onehot':
- discretized_df[col] = self._discretize_to_bins(
- X[col], bin_edges)
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df[self.dcols_].astype(str))
- self.onehot_ = onehot
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform features that did not appear in RF
- if len(self.missing_rf_cols_) > 0:
- discretized_df = self.backup_discretizer_.transform(
- X[self.missing_rf_cols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.missing_rf_cols_,
- index=X.index)
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- for col in self.bin_edges_.keys():
- discretized_df[col] = self._discretize_to_bins(
- X[col], self.bin_edges_[col])
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-classes">Classes</h2>
- <dl>
- <dt id="imodels.discretization.discretizer.AbstractDiscretizer"><code class="flex name class">
- <span>class <span class="ident">AbstractDiscretizer</span></span>
- <span>(</span><span>n_bins=2, dcols=[], encode='onehot', strategy='quantile', onehot_drop='if_binary')</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize numeric data into bins. Base class.</p>
- <h2 id="params">Params</h2>
- <p>n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.</p>
- <p>dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.</p>
- <p>encode : {‘onehot’, ‘ordinal’}, default=’onehot’
- Method used to encode the transformed result.</p>
- <pre><code>onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- </code></pre>
- <p>strategy : {‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
- Strategy used to define the widths of the bins.</p>
- <pre><code>uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- </code></pre>
- <p>onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".</p>
- <pre><code>None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- </code></pre></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class AbstractDiscretizer(TransformerMixin, BaseEstimator):
- """
- Discretize numeric data into bins. Base class.
- Params
- ------
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {‘onehot’, ‘ordinal’}, default=’onehot’
- Method used to encode the transformed result.
- onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- strategy : {‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- """
- def __init__(self, n_bins=2, dcols=[],
- encode='onehot', strategy='quantile',
- onehot_drop='if_binary'):
- self.n_bins = n_bins
- self.encode = encode
- self.strategy = strategy
- self.dcols = dcols
- if encode == 'onehot':
- self.onehot_drop = onehot_drop
- def _validate_n_bins(self):
- """
- Check if n_bins argument is valid.
- """
- orig_bins = self.n_bins
- n_features = len(self.dcols_)
- if isinstance(orig_bins, numbers.Number):
- if not isinstance(orig_bins, numbers.Integral):
- raise ValueError(
- "{} received an invalid n_bins type. "
- "Received {}, expected int.".format(
- AbstractDiscretizer.__name__, type(orig_bins).__name__
- )
- )
- if orig_bins < 2:
- raise ValueError(
- "{} received an invalid number "
- "of bins. Received {}, expected at least 2.".format(
- AbstractDiscretizer.__name__, orig_bins
- )
- )
- self.n_bins = np.full(n_features, orig_bins, dtype=int)
- else:
- n_bins = check_array(orig_bins, dtype=int,
- copy=True, ensure_2d=False)
- if n_bins.ndim > 1 or n_bins.shape[0] != n_features:
- raise ValueError(
- "n_bins must be a scalar or array of shape (n_features,).")
- bad_nbins_value = (n_bins < 2) | (n_bins != orig_bins)
- violating_indices = np.where(bad_nbins_value)[0]
- if violating_indices.shape[0] > 0:
- indices = ", ".join(str(i) for i in violating_indices)
- raise ValueError(
- "{} received an invalid number "
- "of bins at indices {}. Number of bins "
- "must be at least 2, and must be an int.".format(
- AbstractDiscretizer.__name__, indices
- )
- )
- self.n_bins = n_bins
- def _validate_dcols(self, X):
- """
- Check if dcols argument is valid.
- """
- for col in self.dcols_:
- if col not in X.columns:
- raise ValueError("{} is not a column in X.".format(col))
- if not is_numeric_dtype(X[col].dtype):
- raise ValueError("Cannot discretize non-numeric columns.")
- def _validate_args(self):
- """
- Check if encode, strategy arguments are valid.
- """
- valid_encode = ('onehot', 'ordinal')
- if self.encode not in valid_encode:
- raise ValueError("Valid options for 'encode' are {}. Got encode={!r} instead."
- .format(valid_encode, self.encode))
- valid_strategy = ('uniform', 'quantile', 'kmeans')
- if (self.strategy not in valid_strategy):
- raise ValueError("Valid options for 'strategy' are {}. Got strategy={!r} instead."
- .format(valid_strategy, self.strategy))
- def _discretize_to_bins(self, x, bin_edges,
- keep_pointwise_bins=False):
- """
- Discretize data into bins of the form [a, b) given bin
- edges/boundaries
- Parameters
- ----------
- x : array-like of shape (n_samples,)
- Data vector to be discretized.
- bin_edges : array-like
- Values to serve as bin edges; should include min and
- max values for the range of x
- keep_pointwise_bins : boolean
- If True, treat duplicate bin_edges as a pointwise bin,
- i.e., [a, a]. If False, these bins are in effect ignored.
- Returns
- -------
- xd: array of shape (n_samples,) where x has been
- transformed to the binned space
- """
- # ignore min and max values in bin generation
- unique_edges = np.unique(bin_edges[1:-1])
- if keep_pointwise_bins:
- # note: min and max values are used to define pointwise bins
- pointwise_bins = np.unique(
- bin_edges[pd.Series(bin_edges).duplicated()])
- else:
- pointwise_bins = np.array([])
- xd = np.zeros_like(x)
- i = 1
- for idx, split in enumerate(unique_edges):
- if idx == (len(unique_edges) - 1): # uppermost bin
- if (idx == 0) & (split in pointwise_bins):
- # two bins total: (-inf, a], (a, inf)
- indicator = x > split
- else:
- indicator = x >= split # uppermost bin: [a, inf)
- else:
- if split in pointwise_bins:
- # create two bins: [a, a], (a, b)
- indicator = (x > split) & (x < unique_edges[idx + 1]) #
- if idx != 0:
- xd[x == split] = i
- i += 1
- else:
- # create bin: [a, b)
- indicator = (x >= split) & (x < unique_edges[idx + 1])
- xd[indicator] = i
- i += 1
- return xd.astype(int)
- def _fit_preprocessing(self, X):
- """
- Initial checks before fitting the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- Returns
- -------
- self
- """
- # by default, discretize all numeric columns
- if len(self.dcols) == 0:
- numeric_cols = [
- col for col in X.columns if is_numeric_dtype(X[col].dtype)]
- self.dcols_ = numeric_cols
- # error checking
- self._validate_n_bins()
- self._validate_args()
- self._validate_dcols(X)
- def _transform_postprocessing(self, discretized_df, X):
- """
- Final processing in transform method. Does one-hot encoding
- (if specified) and joins discretized columns to the
- un-transformed columns in X.
- Parameters
- ----------
- discretized_df : data frame of shape (n_sample, len(dcols))
- Discretized data in the transformed bin space.
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- Encoded either as ordinal or one-hot.
- """
- discretized_df = discretized_df[self.dcols_]
- # return onehot encoded X if specified
- if self.encode == "onehot":
- colnames = [str(col) for col in self.dcols_]
- try:
- onehot_col_names = self.onehot_.get_feature_names_out(colnames)
- except:
- onehot_col_names = self.onehot_.get_feature_names(
- colnames) # older versions of sklearn
- discretized_df = self.onehot_.transform(discretized_df.astype(str))
- discretized_df = pd.DataFrame(discretized_df,
- columns=onehot_col_names,
- index=X.index).astype(int)
- # join discretized columns with rest of X
- cols = [col for col in X.columns if col not in self.dcols_]
- X_discretized = pd.concat([discretized_df, X[cols]], axis=1)
- return X_discretized</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>sklearn.base.TransformerMixin</li>
- <li>sklearn.utils._set_output._SetOutputMixin</li>
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- </ul>
- <h3>Subclasses</h3>
- <ul class="hlist">
- <li><a title="imodels.discretization.discretizer.BasicDiscretizer" href="#imodels.discretization.discretizer.BasicDiscretizer">BasicDiscretizer</a></li>
- <li><a title="imodels.discretization.discretizer.RFDiscretizer" href="#imodels.discretization.discretizer.RFDiscretizer">RFDiscretizer</a></li>
- </ul>
- </dd>
- <dt id="imodels.discretization.discretizer.BasicDiscretizer"><code class="flex name class">
- <span>class <span class="ident">BasicDiscretizer</span></span>
- <span>(</span><span>n_bins=2, dcols=[], encode='onehot', strategy='quantile', onehot_drop='if_binary')</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize numeric data into bins. Provides a wrapper around
- KBinsDiscretizer from sklearn</p>
- <h2 id="params">Params</h2>
- <p>n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.</p>
- <p>dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.</p>
- <p>encode : {'onehot', 'ordinal'}, default='onehot'
- Method used to encode the transformed result.</p>
- <pre><code>onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- </code></pre>
- <p>strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.</p>
- <pre><code>uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- </code></pre>
- <p>onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape
- (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".</p>
- <pre><code>None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- </code></pre>
- <h2 id="attributes">Attributes</h2>
- <dl>
- <dt><strong><code>discretizer_</code></strong> : <code>object</code> of <code>class KBinsDiscretizer()</code></dt>
- <dd>Primary discretization method used to bin numeric data</dd>
- <dt><strong><code>manual_discretizer_</code></strong> : <code>dictionary</code></dt>
- <dd>Provides bin_edges to feed into _quantile_discretization()
- and do quantile discretization manually for features where
- KBinsDiscretizer() failed. Ignored if strategy != 'quantile'
- or no errors in KBinsDiscretizer().</dd>
- <dt><strong><code>onehot_</code></strong> : <code>object</code> of <code>class OneHotEncoder()</code></dt>
- <dd>One hot encoding fit. Ignored if encode != 'onehot'</dd>
- </dl>
- <h2 id="examples">Examples</h2></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class BasicDiscretizer(AbstractDiscretizer):
- """
- Discretize numeric data into bins. Provides a wrapper around
- KBinsDiscretizer from sklearn
- Params
- ------
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {'onehot', 'ordinal'}, default='onehot'
- Method used to encode the transformed result.
- onehot
- Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal
- Return the bin identifier encoded as an integer value.
- strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- discretizer_ : object of class KBinsDiscretizer()
- Primary discretization method used to bin numeric data
- manual_discretizer_ : dictionary
- Provides bin_edges to feed into _quantile_discretization()
- and do quantile discretization manually for features where
- KBinsDiscretizer() failed. Ignored if strategy != 'quantile'
- or no errors in KBinsDiscretizer().
- onehot_ : object of class OneHotEncoder()
- One hot encoding fit. Ignored if encode != 'onehot'
- Examples
- --------
- """
- def __init__(self, n_bins=2, dcols=[],
- encode='onehot', strategy='quantile',
- onehot_drop='if_binary'):
- super().__init__(n_bins=n_bins, dcols=dcols,
- encode=encode, strategy=strategy,
- onehot_drop=onehot_drop)
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # apply KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(n_bins=self.n_bins,
- encode='ordinal',
- strategy=self.strategy)
- discretizer.fit(X[self.dcols_])
- self.discretizer_ = discretizer
- if (self.encode == 'onehot') | (self.strategy == 'quantile'):
- discretized_df = discretizer.transform(X[self.dcols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index).astype(int)
- # fix KBinsDiscretizer errors if any when strategy = "quantile"
- if self.strategy == "quantile":
- err_idx = np.where(discretized_df.nunique() != self.n_bins)[0]
- self.manual_discretizer_ = dict()
- for idx in err_idx:
- col = self.dcols_[idx]
- if X[col].nunique() > 1:
- q_values = np.linspace(0, 1, self.n_bins[idx] + 1)
- bin_edges = np.quantile(X[col], q_values)
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- self.manual_discretizer_[col] = bin_edges
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df.astype(str))
- self.onehot_ = onehot
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform using KBinsDiscretizer
- discretized_df = self.discretizer_.transform(
- X[self.dcols_]).astype(int)
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index)
- # fix KBinsDiscretizer errors (if any) when strategy = "quantile"
- if self.strategy == "quantile":
- for col in self.manual_discretizer_.keys():
- bin_edges = self.manual_discretizer_[col]
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.discretization.discretizer.AbstractDiscretizer" href="#imodels.discretization.discretizer.AbstractDiscretizer">AbstractDiscretizer</a></li>
- <li>sklearn.base.TransformerMixin</li>
- <li>sklearn.utils._set_output._SetOutputMixin</li>
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.discretization.discretizer.BasicDiscretizer.fit"><code class="name flex">
- <span>def <span class="ident">fit</span></span>(<span>self, X, y=None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Fit the estimator.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>(Training) data to be discretized.</dd>
- <dt><strong><code>y</code></strong> : <code>Ignored. This parameter exists only for compatibility with</code></dt>
- <dd>:class:<code>~sklearn.pipeline.Pipeline</code> and fit_transform method</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><code>self</code></dt>
- <dd> </dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # apply KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(n_bins=self.n_bins,
- encode='ordinal',
- strategy=self.strategy)
- discretizer.fit(X[self.dcols_])
- self.discretizer_ = discretizer
- if (self.encode == 'onehot') | (self.strategy == 'quantile'):
- discretized_df = discretizer.transform(X[self.dcols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index).astype(int)
- # fix KBinsDiscretizer errors if any when strategy = "quantile"
- if self.strategy == "quantile":
- err_idx = np.where(discretized_df.nunique() != self.n_bins)[0]
- self.manual_discretizer_ = dict()
- for idx in err_idx:
- col = self.dcols_[idx]
- if X[col].nunique() > 1:
- q_values = np.linspace(0, 1, self.n_bins[idx] + 1)
- bin_edges = np.quantile(X[col], q_values)
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- self.manual_discretizer_[col] = bin_edges
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df.astype(str))
- self.onehot_ = onehot
- return self</code></pre>
- </details>
- </dd>
- <dt id="imodels.discretization.discretizer.BasicDiscretizer.transform"><code class="name flex">
- <span>def <span class="ident">transform</span></span>(<span>self, X)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize the data.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>Data to be discretized.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>X_discretized</code></strong> : <code>data frame</code></dt>
- <dd>Data with features in dcols transformed to the
- binned space. All other features remain unchanged.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform using KBinsDiscretizer
- discretized_df = self.discretizer_.transform(
- X[self.dcols_]).astype(int)
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.dcols_,
- index=X.index)
- # fix KBinsDiscretizer errors (if any) when strategy = "quantile"
- if self.strategy == "quantile":
- for col in self.manual_discretizer_.keys():
- bin_edges = self.manual_discretizer_[col]
- discretized_df[col] = self._discretize_to_bins(X[col], bin_edges,
- keep_pointwise_bins=True)
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="imodels.discretization.discretizer.ExtraBasicDiscretizer"><code class="flex name class">
- <span>class <span class="ident">ExtraBasicDiscretizer</span></span>
- <span>(</span><span>dcols, n_bins=4, strategy='quantile', onehot_drop='if_binary')</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize provided columns into bins and return in one-hot format.
- Generates meaningful column names based on bin edges.
- Wraps KBinsDiscretizer from sklearn.</p>
- <h2 id="params">Params</h2>
- <p>dcols : list of strings
- The names of the columns to be discretized.</p>
- <p>n_bins : int or array-like of shape (len(dcols),), default=4
- Number of bins to discretize each feature into.</p>
- <p>strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.</p>
- <pre><code>uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- </code></pre>
- <p>onehot_drop : {'first', 'if_binary'} or a array-like of shape
- (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".</p>
- <pre><code>None
- Retain all features (the default).
- 'first'
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- 'if_binary'
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- </code></pre>
- <h2 id="attributes">Attributes</h2>
- <dl>
- <dt><strong><code>discretizer_</code></strong> : <code>object</code> of <code>class KBinsDiscretizer()</code></dt>
- <dd>Primary discretization method used to bin numeric data</dd>
- </dl>
- <h2 id="examples">Examples</h2></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class ExtraBasicDiscretizer(TransformerMixin):
- """
- Discretize provided columns into bins and return in one-hot format.
- Generates meaningful column names based on bin edges.
- Wraps KBinsDiscretizer from sklearn.
- Params
- ------
- dcols : list of strings
- The names of the columns to be discretized.
- n_bins : int or array-like of shape (len(dcols),), default=4
- Number of bins to discretize each feature into.
- strategy : {'uniform', 'quantile', 'kmeans'}, default='quantile'
- Strategy used to define the widths of the bins.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {'first', 'if_binary'} or a array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- 'first'
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- 'if_binary'
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- discretizer_ : object of class KBinsDiscretizer()
- Primary discretization method used to bin numeric data
- Examples
- --------
- """
- def __init__(self,
- dcols,
- n_bins=4,
- strategy='quantile',
- onehot_drop='if_binary'):
- self.dcols = dcols
- self.n_bins = n_bins
- self.strategy = strategy
- self.onehot_drop = onehot_drop
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # Fit KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(
- n_bins=self.n_bins, strategy=self.strategy, encode='ordinal')
- discretizer.fit(X[self.dcols])
- self.discretizer_ = discretizer
- # Fit OneHotEncoder to the ordinal output of KBinsDiscretizer
- disc_ordinal_np = discretizer.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- encoder = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- encoder.fit(disc_ordinal_df_str)
- self.encoder_ = encoder
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- # Apply discretizer transform to get ordinally coded DF
- disc_ordinal_np = self.discretizer_.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- # One-hot encode the ordinal DF
- disc_onehot_np = self.encoder_.transform(disc_ordinal_df_str)
- disc_onehot = pd.DataFrame(
- disc_onehot_np, columns=self.encoder_.get_feature_names_out())
- # Name columns after the interval they represent (e.g. 0.1_to_0.5)
- for col, bin_edges in zip(self.dcols, self.discretizer_.bin_edges_):
- bin_edges = bin_edges.astype(str)
- for ordinal_value in disc_ordinal_df_str[col].unique():
- bin_lb = bin_edges[int(ordinal_value)]
- bin_ub = bin_edges[int(ordinal_value) + 1]
- interval_string = f'{bin_lb}_to_{bin_ub}'
- disc_onehot = disc_onehot.rename(
- columns={f'{col}_{ordinal_value}': f'{col}_' + interval_string})
- # Join discretized columns with rest of X
- non_dcols = [col for col in X.columns if col not in self.dcols]
- X_discretized = pd.concat([disc_onehot, X[non_dcols]], axis=1)
- return X_discretized</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>sklearn.base.TransformerMixin</li>
- <li>sklearn.utils._set_output._SetOutputMixin</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.discretization.discretizer.ExtraBasicDiscretizer.fit"><code class="name flex">
- <span>def <span class="ident">fit</span></span>(<span>self, X, y=None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Fit the estimator.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>(Training) data to be discretized.</dd>
- <dt><strong><code>y</code></strong> : <code>Ignored. This parameter exists only for compatibility with</code></dt>
- <dd>:class:<code>~sklearn.pipeline.Pipeline</code> and fit_transform method</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><code>self</code></dt>
- <dd> </dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : Ignored. This parameter exists only for compatibility with
- :class:`~sklearn.pipeline.Pipeline` and fit_transform method
- Returns
- -------
- self
- """
- # Fit KBinsDiscretizer to the selected columns
- discretizer = KBinsDiscretizer(
- n_bins=self.n_bins, strategy=self.strategy, encode='ordinal')
- discretizer.fit(X[self.dcols])
- self.discretizer_ = discretizer
- # Fit OneHotEncoder to the ordinal output of KBinsDiscretizer
- disc_ordinal_np = discretizer.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- encoder = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- encoder.fit(disc_ordinal_df_str)
- self.encoder_ = encoder
- return self</code></pre>
- </details>
- </dd>
- <dt id="imodels.discretization.discretizer.ExtraBasicDiscretizer.transform"><code class="name flex">
- <span>def <span class="ident">transform</span></span>(<span>self, X)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize the data.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>Data to be discretized.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>X_discretized</code></strong> : <code>data frame</code></dt>
- <dd>Data with features in dcols transformed to the
- binned space. All other features remain unchanged.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- # Apply discretizer transform to get ordinally coded DF
- disc_ordinal_np = self.discretizer_.transform(X[self.dcols])
- disc_ordinal_df = pd.DataFrame(disc_ordinal_np, columns=self.dcols)
- disc_ordinal_df_str = disc_ordinal_df.astype(int).astype(str)
- # One-hot encode the ordinal DF
- disc_onehot_np = self.encoder_.transform(disc_ordinal_df_str)
- disc_onehot = pd.DataFrame(
- disc_onehot_np, columns=self.encoder_.get_feature_names_out())
- # Name columns after the interval they represent (e.g. 0.1_to_0.5)
- for col, bin_edges in zip(self.dcols, self.discretizer_.bin_edges_):
- bin_edges = bin_edges.astype(str)
- for ordinal_value in disc_ordinal_df_str[col].unique():
- bin_lb = bin_edges[int(ordinal_value)]
- bin_ub = bin_edges[int(ordinal_value) + 1]
- interval_string = f'{bin_lb}_to_{bin_ub}'
- disc_onehot = disc_onehot.rename(
- columns={f'{col}_{ordinal_value}': f'{col}_' + interval_string})
- # Join discretized columns with rest of X
- non_dcols = [col for col in X.columns if col not in self.dcols]
- X_discretized = pd.concat([disc_onehot, X[non_dcols]], axis=1)
- return X_discretized</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="imodels.discretization.discretizer.RFDiscretizer"><code class="flex name class">
- <span>class <span class="ident">RFDiscretizer</span></span>
- <span>(</span><span>rf_model=None, classification=False, n_bins=2, dcols=[], encode='onehot', strategy='quantile', backup_strategy='quantile', onehot_drop='if_binary')</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize numeric data into bins using RF splits.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>rf_model</code></strong> : <code>RandomForestClassifer()</code> or <code>RandomForestRegressor()</code></dt>
- <dd>RF model from which to extract splits for discretization.
- Default is RandomForestClassifer(n_estimators = 500) or
- RandomForestRegressor(n_estimators = 500)</dd>
- <dt><strong><code>classification</code></strong> : <code>boolean; default=False</code></dt>
- <dd>Used only if rf_model=None. If True,
- rf_model=RandomForestClassifier(n_estimators = 500).
- Else, rf_model=RandomForestRegressor(n_estimators = 500)</dd>
- <dt><strong><code>n_bins</code></strong> : <code>int</code> or <code>array-like</code> of <code>shape (len(dcols),)</code>, default=<code>2</code></dt>
- <dd>Number of bins to discretize each feature into.</dd>
- <dt><strong><code>dcols</code></strong> : <code>list</code> of <code>strings</code></dt>
- <dd>The names of the columns to be discretized; by default,
- discretize all float and int columns in X.</dd>
- <dt><strong><code>encode</code></strong> : <code>{‘onehot’, ‘ordinal’}</code>, default=<code>’onehot’</code></dt>
- <dd>
- <p>Method used to encode the transformed result.</p>
- <p>onehot - Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal - Return the bin identifier encoded as an integer value.</p>
- </dd>
- <dt><strong><code>strategy</code></strong> : <code>{‘uniform’, ‘quantile’}</code>, default=<code>’quantile’</code></dt>
- <dd>Strategy used to choose RF split points.
- uniform - RF split points chosen to be uniformly spaced out.
- quantile - RF split points chosen based on equally-spaced quantiles.</dd>
- <dt><strong><code>backup_strategy</code></strong> : <code>{‘uniform’, ‘quantile’, ‘kmeans’}</code>, default=<code>’quantile’</code></dt>
- <dd>Strategy used to define the widths of the bins if no rf splits exist for
- that feature. Used in KBinsDiscretizer.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.</dd>
- <dt><strong><code>onehot_drop</code></strong> : <code>{‘first’, ‘if_binary’}</code> or <code>array-like</code> of <code>shape
- (len(dcols),)</code>, default=<code>'if_binary'</code></dt>
- <dd>Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.</dd>
- </dl>
- <h2 id="attributes">Attributes</h2>
- <dl>
- <dt><strong><code>rf_splits</code></strong> : <code>dictionary where</code></dt>
- <dd>key = feature name
- value = array of all RF split threshold values</dd>
- <dt><strong><code>bin_edges_</code></strong> : <code>dictionary where</code></dt>
- <dd>key = feature name
- value = array of bin edges used for discretization, taken from
- RF split values</dd>
- <dt><strong><code>missing_rf_cols_</code></strong> : <code>array-like</code></dt>
- <dd>List of features that were not used in RF</dd>
- <dt><strong><code>backup_discretizer_</code></strong> : <code>object</code> of <code>class <a title="imodels.discretization.discretizer.BasicDiscretizer" href="#imodels.discretization.discretizer.BasicDiscretizer">BasicDiscretizer</a></code></dt>
- <dd>Discretization method used to bin numeric data for features
- in missing_rf_cols_</dd>
- <dt><strong><code>onehot_</code></strong> : <code>object</code> of <code>class OneHotEncoder()</code></dt>
- <dd>One hot encoding fit. Ignored if encode != 'onehot'</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class RFDiscretizer(AbstractDiscretizer):
- """
- Discretize numeric data into bins using RF splits.
- Parameters
- ----------
- rf_model : RandomForestClassifer() or RandomForestRegressor()
- RF model from which to extract splits for discretization.
- Default is RandomForestClassifer(n_estimators = 500) or
- RandomForestRegressor(n_estimators = 500)
- classification : boolean; default=False
- Used only if rf_model=None. If True,
- rf_model=RandomForestClassifier(n_estimators = 500).
- Else, rf_model=RandomForestRegressor(n_estimators = 500)
- n_bins : int or array-like of shape (len(dcols),), default=2
- Number of bins to discretize each feature into.
- dcols : list of strings
- The names of the columns to be discretized; by default,
- discretize all float and int columns in X.
- encode : {‘onehot’, ‘ordinal’}, default=’onehot’
- Method used to encode the transformed result.
- onehot - Encode the transformed result with one-hot encoding and
- return a dense array.
- ordinal - Return the bin identifier encoded as an integer value.
- strategy : {‘uniform’, ‘quantile’}, default=’quantile’
- Strategy used to choose RF split points.
- uniform - RF split points chosen to be uniformly spaced out.
- quantile - RF split points chosen based on equally-spaced quantiles.
- backup_strategy : {‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’
- Strategy used to define the widths of the bins if no rf splits exist for
- that feature. Used in KBinsDiscretizer.
- uniform
- All bins in each feature have identical widths.
- quantile
- All bins in each feature have the same number of points.
- kmeans
- Values in each bin have the same nearest center of a 1D
- k-means cluster.
- onehot_drop : {‘first’, ‘if_binary’} or array-like of shape (len(dcols),), default='if_binary'
- Specifies a methodology to use to drop one of the categories
- per feature when encode = "onehot".
- None
- Retain all features (the default).
- ‘first’
- Drop the first y_str in each feature. If only one y_str
- is present, the feature will be dropped entirely.
- ‘if_binary’
- Drop the first y_str in each feature with two categories.
- Features with 1 or more than 2 categories are left intact.
- Attributes
- ----------
- rf_splits : dictionary where
- key = feature name
- value = array of all RF split threshold values
- bin_edges_ : dictionary where
- key = feature name
- value = array of bin edges used for discretization, taken from
- RF split values
- missing_rf_cols_ : array-like
- List of features that were not used in RF
- backup_discretizer_ : object of class BasicDiscretizer()
- Discretization method used to bin numeric data for features
- in missing_rf_cols_
- onehot_ : object of class OneHotEncoder()
- One hot encoding fit. Ignored if encode != 'onehot'
- """
- def __init__(self, rf_model=None, classification=False,
- n_bins=2, dcols=[], encode='onehot',
- strategy='quantile', backup_strategy='quantile',
- onehot_drop='if_binary'):
- super().__init__(n_bins=n_bins, dcols=dcols,
- encode=encode, strategy=strategy,
- onehot_drop=onehot_drop)
- self.backup_strategy = backup_strategy
- self.rf_model = rf_model
- if rf_model is None:
- self.classification = classification
- def _validate_args(self):
- """
- Check if encode, strategy, backup_strategy arguments are valid.
- """
- super()._validate_args()
- valid_backup_strategy = ('uniform', 'quantile', 'kmeans')
- if (self.backup_strategy not in valid_backup_strategy):
- raise ValueError("Valid options for 'strategy' are {}. Got strategy={!r} instead."
- .format(valid_backup_strategy, self.backup_strategy))
- def _get_rf_splits(self, col_names):
- """
- Get all splits in random forest ensemble
- Parameters
- ----------
- col_names : array-like of shape (n_features,)
- Column names for X used to train rf_model
- Returns
- -------
- rule_dict : dictionary where
- key = feature name
- value = array of all RF split threshold values
- """
- rule_dict = {}
- for model in self.rf_model.estimators_:
- tree = model.tree_
- tree_it = enumerate(zip(tree.children_left,
- tree.children_right,
- tree.feature,
- tree.threshold))
- for node_idx, data in tree_it:
- left, right, feature, th = data
- if (left != -1) | (right != -1):
- feature = col_names[feature]
- if feature in rule_dict:
- rule_dict[feature].append(th)
- else:
- rule_dict[feature] = [th]
- return rule_dict
- def _fit_rf(self, X, y=None):
- """
- Fit random forest (if necessary) and obtain RF split thresholds
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Training data used to fit RF
- y : array-like of shape (n_samples,)
- Training response vector used to fit RF
- Returns
- -------
- rf_splits : dictionary where
- key = feature name
- value = array of all RF split threshold values
- """
- # If no rf_model given, train default random forest model
- if self.rf_model is None:
- if y is None:
- raise ValueError("Must provide y if rf_model is not given.")
- if self.classification:
- self.rf_model = RandomForestClassifier(n_estimators=500)
- else:
- self.rf_model = RandomForestRegressor(n_estimators=500)
- self.rf_model.fit(X, y)
- else:
- # provided rf model has not yet been trained
- if not check_is_fitted(self.rf_model):
- if y is None:
- raise ValueError(
- "Must provide y if rf_model has not been trained.")
- self.rf_model.fit(X, y)
- # get all random forest split points
- self.rf_splits = self._get_rf_splits(list(X.columns))
- def reweight_n_bins(self, X, y=None, by="nsplits"):
- """
- Reallocate number of bins per feature.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- by : {'nsplits'}, default='nsplits'
- Specifies how to reallocate number of bins per feature.
- nsplits
- Reallocate number of bins so that each feature
- in dcols get at a minimum of 2 bins with the
- remaining bins distributed proportionally to the
- number of RF splits using that feature
- Returns
- -------
- self.n_bins : array of shape (len(dcols),)
- number of bins per feature reallocated according to
- 'by' argument
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # get total number of bins to reallocate
- total_bins = self.n_bins.sum()
- # reweight n_bins
- if by == "nsplits":
- # each col gets at least 2 bins; remaining bins get
- # reallocated based on number of RF splits using that feature
- n_rules = np.array([len(self.rf_splits[col])
- for col in self.dcols_])
- self.n_bins = np.round(n_rules / n_rules.sum() *
- (total_bins - 2 * len(self.dcols_))) + 2
- else:
- valid_by = ('nsplits')
- raise ValueError("Valid options for 'by' are {}. Got by={!r} instead."
- .format(valid_by, by))
- def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # features that were not used in the rf but need to be discretized
- self.missing_rf_cols_ = list(set(self.dcols_) -
- set(self.rf_splits.keys()))
- if len(self.missing_rf_cols_) > 0:
- print("{} did not appear in random forest so were discretized via {} discretization"
- .format(self.missing_rf_cols_, self.strategy))
- missing_n_bins = np.array([self.n_bins[np.array(self.dcols_) == col][0]
- for col in self.missing_rf_cols_])
- backup_discretizer = BasicDiscretizer(n_bins=missing_n_bins,
- dcols=self.missing_rf_cols_,
- encode='ordinal',
- strategy=self.backup_strategy)
- backup_discretizer.fit(X[self.missing_rf_cols_])
- self.backup_discretizer_ = backup_discretizer
- else:
- self.backup_discretizer_ = None
- if self.encode == 'onehot':
- if len(self.missing_rf_cols_) > 0:
- discretized_df = backup_discretizer.transform(
- X[self.missing_rf_cols_])
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- self.bin_edges_ = dict()
- for col in self.dcols_:
- if col in self.rf_splits.keys():
- b = self.n_bins[np.array(self.dcols_) == col]
- if self.strategy == "quantile":
- q_values = np.linspace(0, 1, int(b) + 1)
- bin_edges = np.quantile(self.rf_splits[col], q_values)
- elif self.strategy == "uniform":
- width = (max(self.rf_splits[col]) -
- min(self.rf_splits[col])) / b
- bin_edges = width * \
- np.arange(0, b + 1) + min(self.rf_splits[col])
- self.bin_edges_[col] = bin_edges
- if self.encode == 'onehot':
- discretized_df[col] = self._discretize_to_bins(
- X[col], bin_edges)
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df[self.dcols_].astype(str))
- self.onehot_ = onehot
- return self
- def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform features that did not appear in RF
- if len(self.missing_rf_cols_) > 0:
- discretized_df = self.backup_discretizer_.transform(
- X[self.missing_rf_cols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.missing_rf_cols_,
- index=X.index)
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- for col in self.bin_edges_.keys():
- discretized_df[col] = self._discretize_to_bins(
- X[col], self.bin_edges_[col])
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.discretization.discretizer.AbstractDiscretizer" href="#imodels.discretization.discretizer.AbstractDiscretizer">AbstractDiscretizer</a></li>
- <li>sklearn.base.TransformerMixin</li>
- <li>sklearn.utils._set_output._SetOutputMixin</li>
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.discretization.discretizer.RFDiscretizer.fit"><code class="name flex">
- <span>def <span class="ident">fit</span></span>(<span>self, X, y=None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Fit the estimator.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>(Training) data to be discretized.</dd>
- <dt><strong><code>y</code></strong> : <code>array-like</code> of <code>shape (n_samples,)</code></dt>
- <dd>(Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><code>self</code></dt>
- <dd> </dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def fit(self, X, y=None):
- """
- Fit the estimator.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- Returns
- -------
- self
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # features that were not used in the rf but need to be discretized
- self.missing_rf_cols_ = list(set(self.dcols_) -
- set(self.rf_splits.keys()))
- if len(self.missing_rf_cols_) > 0:
- print("{} did not appear in random forest so were discretized via {} discretization"
- .format(self.missing_rf_cols_, self.strategy))
- missing_n_bins = np.array([self.n_bins[np.array(self.dcols_) == col][0]
- for col in self.missing_rf_cols_])
- backup_discretizer = BasicDiscretizer(n_bins=missing_n_bins,
- dcols=self.missing_rf_cols_,
- encode='ordinal',
- strategy=self.backup_strategy)
- backup_discretizer.fit(X[self.missing_rf_cols_])
- self.backup_discretizer_ = backup_discretizer
- else:
- self.backup_discretizer_ = None
- if self.encode == 'onehot':
- if len(self.missing_rf_cols_) > 0:
- discretized_df = backup_discretizer.transform(
- X[self.missing_rf_cols_])
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- self.bin_edges_ = dict()
- for col in self.dcols_:
- if col in self.rf_splits.keys():
- b = self.n_bins[np.array(self.dcols_) == col]
- if self.strategy == "quantile":
- q_values = np.linspace(0, 1, int(b) + 1)
- bin_edges = np.quantile(self.rf_splits[col], q_values)
- elif self.strategy == "uniform":
- width = (max(self.rf_splits[col]) -
- min(self.rf_splits[col])) / b
- bin_edges = width * \
- np.arange(0, b + 1) + min(self.rf_splits[col])
- self.bin_edges_[col] = bin_edges
- if self.encode == 'onehot':
- discretized_df[col] = self._discretize_to_bins(
- X[col], bin_edges)
- # fit onehot encoded X if specified
- if self.encode == "onehot":
- onehot = OneHotEncoder(drop=self.onehot_drop) # , sparse=False)
- onehot.fit(discretized_df[self.dcols_].astype(str))
- self.onehot_ = onehot
- return self</code></pre>
- </details>
- </dd>
- <dt id="imodels.discretization.discretizer.RFDiscretizer.reweight_n_bins"><code class="name flex">
- <span>def <span class="ident">reweight_n_bins</span></span>(<span>self, X, y=None, by='nsplits')</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Reallocate number of bins per feature.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>(Training) data to be discretized.</dd>
- <dt><strong><code>y</code></strong> : <code>array-like</code> of <code>shape (n_samples,)</code></dt>
- <dd>(Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted</dd>
- <dt><strong><code>by</code></strong> : <code>{'nsplits'}</code>, default=<code>'nsplits'</code></dt>
- <dd>
- <p>Specifies how to reallocate number of bins per feature.</p>
- <p>nsplits
- Reallocate number of bins so that each feature
- in dcols get at a minimum of 2 bins with the
- remaining bins distributed proportionally to the
- number of RF splits using that feature</p>
- </dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><code>self.n_bins : array</code> of <code>shape (len(dcols),)</code></dt>
- <dd>number of bins per feature reallocated according to
- 'by' argument</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def reweight_n_bins(self, X, y=None, by="nsplits"):
- """
- Reallocate number of bins per feature.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- (Training) data to be discretized.
- y : array-like of shape (n_samples,)
- (Training) response vector. Required only if
- rf_model = None or rf_model has not yet been fitted
- by : {'nsplits'}, default='nsplits'
- Specifies how to reallocate number of bins per feature.
- nsplits
- Reallocate number of bins so that each feature
- in dcols get at a minimum of 2 bins with the
- remaining bins distributed proportionally to the
- number of RF splits using that feature
- Returns
- -------
- self.n_bins : array of shape (len(dcols),)
- number of bins per feature reallocated according to
- 'by' argument
- """
- # initialization and error checking
- self._fit_preprocessing(X)
- # get all random forest split points
- self._fit_rf(X=X, y=y)
- # get total number of bins to reallocate
- total_bins = self.n_bins.sum()
- # reweight n_bins
- if by == "nsplits":
- # each col gets at least 2 bins; remaining bins get
- # reallocated based on number of RF splits using that feature
- n_rules = np.array([len(self.rf_splits[col])
- for col in self.dcols_])
- self.n_bins = np.round(n_rules / n_rules.sum() *
- (total_bins - 2 * len(self.dcols_))) + 2
- else:
- valid_by = ('nsplits')
- raise ValueError("Valid options for 'by' are {}. Got by={!r} instead."
- .format(valid_by, by))</code></pre>
- </details>
- </dd>
- <dt id="imodels.discretization.discretizer.RFDiscretizer.transform"><code class="name flex">
- <span>def <span class="ident">transform</span></span>(<span>self, X)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Discretize the data.</p>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>X</code></strong> : <code>data frame</code> of <code>shape (n_samples, n_features)</code></dt>
- <dd>Data to be discretized.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>X_discretized</code></strong> : <code>data frame</code></dt>
- <dd>Data with features in dcols transformed to the
- binned space. All other features remain unchanged.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def transform(self, X):
- """
- Discretize the data.
- Parameters
- ----------
- X : data frame of shape (n_samples, n_features)
- Data to be discretized.
- Returns
- -------
- X_discretized : data frame
- Data with features in dcols transformed to the
- binned space. All other features remain unchanged.
- """
- check_is_fitted(self)
- # transform features that did not appear in RF
- if len(self.missing_rf_cols_) > 0:
- discretized_df = self.backup_discretizer_.transform(
- X[self.missing_rf_cols_])
- discretized_df = pd.DataFrame(discretized_df,
- columns=self.missing_rf_cols_,
- index=X.index)
- else:
- discretized_df = pd.DataFrame({}, index=X.index)
- # do discretization based on rf split thresholds
- for col in self.bin_edges_.keys():
- discretized_df[col] = self._discretize_to_bins(
- X[col], self.bin_edges_[col])
- # return onehot encoded data if specified and
- # join discretized columns with rest of X
- X_discretized = self._transform_postprocessing(discretized_df, X)
- return X_discretized</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- </dl>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index 🔍</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="imodels.discretization" href="index.html">imodels.discretization</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-classes">Classes</a></h3>
- <ul>
- <li>
- <h4><code><a title="imodels.discretization.discretizer.AbstractDiscretizer" href="#imodels.discretization.discretizer.AbstractDiscretizer">AbstractDiscretizer</a></code></h4>
- </li>
- <li>
- <h4><code><a title="imodels.discretization.discretizer.BasicDiscretizer" href="#imodels.discretization.discretizer.BasicDiscretizer">BasicDiscretizer</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.discretization.discretizer.BasicDiscretizer.fit" href="#imodels.discretization.discretizer.BasicDiscretizer.fit">fit</a></code></li>
- <li><code><a title="imodels.discretization.discretizer.BasicDiscretizer.transform" href="#imodels.discretization.discretizer.BasicDiscretizer.transform">transform</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.discretization.discretizer.ExtraBasicDiscretizer" href="#imodels.discretization.discretizer.ExtraBasicDiscretizer">ExtraBasicDiscretizer</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.discretization.discretizer.ExtraBasicDiscretizer.fit" href="#imodels.discretization.discretizer.ExtraBasicDiscretizer.fit">fit</a></code></li>
- <li><code><a title="imodels.discretization.discretizer.ExtraBasicDiscretizer.transform" href="#imodels.discretization.discretizer.ExtraBasicDiscretizer.transform">transform</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.discretization.discretizer.RFDiscretizer" href="#imodels.discretization.discretizer.RFDiscretizer">RFDiscretizer</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.discretization.discretizer.RFDiscretizer.fit" href="#imodels.discretization.discretizer.RFDiscretizer.fit">fit</a></code></li>
- <li><code><a title="imodels.discretization.discretizer.RFDiscretizer.reweight_n_bins" href="#imodels.discretization.discretizer.RFDiscretizer.reweight_n_bins">reweight_n_bins</a></code></li>
- <li><code><a title="imodels.discretization.discretizer.RFDiscretizer.transform" href="#imodels.discretization.discretizer.RFDiscretizer.transform">transform</a></code></li>
- </ul>
- </li>
- </ul>
- </li>
- </ul>
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