Source code for mlbox.encoding.categorical_encoder
# coding: utf-8
# Author: Axel ARONIO DE ROMBLAY <axelderomblay@gmail.com>
# License: BSD 3 clause
import numpy as np
import pandas as pd
import warnings
import os
from tensorflow.keras.layers import Dense, Reshape, Dropout, Embedding, concatenate, Input
from tensorflow.keras.models import Model
[docs]class Categorical_encoder():
"""Encodes categorical features.
Several strategies are possible (supervised or not). Works for both
classification and regression tasks.
Parameters
----------
strategy : str, default = "label_encoding"
The strategy to encode categorical features.
Available strategies = {"label_encoding", "dummification",
"random_projection", entity_embedding"}
verbose : bool, default = False
Verbose mode. Useful for entity embedding strategy.
"""
def __init__(self, strategy='label_encoding', verbose=False):
"""Init method for class Categorical_encoder()."""
self.strategy = strategy
self.verbose = verbose
self.__Lcat = []
self.__Lnum = []
self.__Enc = dict()
self.__K = dict()
self.__weights = None
self.__fitOK = False
[docs] def get_params(self, deep=True):
"""Get param that can be defined by the user.
Get strategy parameters and verbose parameters
Parameters
----------
strategy : str, default = "label_encoding"
The strategy to encode categorical features.
Available strategies = {"label_encoding", "dummification",
"random_projection", entity_embedding"}
verbose : bool, default = False
Verbose mode. Useful for entity embedding strategy.
Returns
-------
dict : dictionary
Dictionary that contains strategy and verbose parameters.
"""
dict = {'strategy': self.strategy,
'verbose': self.verbose}
return dict
[docs] def set_params(self, **params):
"""Set param method for Categorical encoder.
Set strategy parameters and verbose parameters
Parameters
----------
strategy : str, default = "label_encoding"
The strategy to encode categorical features.
Available strategies = {"label_encoding", "dummification",
"random_projection", entity_embedding"}
verbose : bool, default = False
Verbose mode. Useful for entity embedding strategy.
"""
self.__fitOK = False
for k, v in params.items():
if k not in self.get_params():
warnings.warn("Invalid parameter(s) for encoder "
"Categorical_encoder. Parameter(s) IGNORED. "
"Check the list of available parameters with "
"`encoder.get_params().keys()`")
else:
setattr(self, k, v)
[docs] def fit(self, df_train, y_train):
"""Fit Categorical Encoder.
Encode categorical variable of a dataframe
following strategy parameters.
Parameters
----------
df_train : pandas.Dataframe of shape = (n_train, n_features).
The training dataset with numerical and categorical features.
NA values are allowed.
y_train : pandas.Series of shape = (n_train, ).
The target for classification or regression tasks.
Returns
-------
object
self
"""
self.__Lcat = df_train.dtypes[df_train.dtypes == 'object'].index
self.__Lnum = df_train.dtypes[df_train.dtypes != 'object'].index
if (len(self.__Lcat) == 0):
self.__fitOK = True
else:
#################################################
# Label Encoding
#################################################
if (self.strategy == 'label_encoding'):
for col in self.__Lcat:
d = dict()
levels = list(df_train[col].unique())
nan = False
if np.NaN in levels:
nan = True
levels.remove(np.NaN)
for enc, level in enumerate([np.NaN]*nan + sorted(levels)):
d[level] = enc # TODO: Optimize loop?
self.__Enc[col] = d
self.__fitOK = True
#################################################
# Dummification
#################################################
elif (self.strategy == 'dummification'):
for col in self.__Lcat:
# TODO: Optimize?
self.__Enc[col] = list(df_train[col].dropna().unique())
self.__fitOK = True
#################################################
# Entity Embedding
#################################################
elif (self.strategy == 'entity_embedding'):
# Parameters
A = 10 # 15 : more complex
B = 5 # 2 or 3 : more complex
# computing interactions
self.__K = {}
for col in self.__Lcat:
exp_ = np.exp(-df_train[col].nunique() * 0.05)
self.__K[col] = np.int(5 * (1 - exp_) + 1)
sum_ = sum([1. * np.log(k) for k in self.__K.values()])
# TODO: Add reference for this formula?
# Number of neurons for layer 1 and 2
n_layer1 = min(1000,
int(A * (len(self.__K) ** 0.5) * sum_ + 1))
n_layer2 = int(n_layer1 / B) + 2
# Dropouts
dropout1 = 0.1
dropout2 = 0.1
# Learning parameters
epochs = 20 # 25 : more iterations
batch_size = 128 # 256 : gradient more stable
# Creating the neural network
embeddings = []
inputs = []
for col in self.__Lcat:
d = dict()
levels = list(df_train[col].unique())
nan = False
if np.NaN in levels:
nan = True
levels.remove(np.NaN)
for enc, level in enumerate([np.NaN]*nan + sorted(levels)):
d[level] = enc # TODO: Optimize loop?
self.__Enc[col] = d
var = Input(shape=(1,))
inputs.append(var)
emb = Embedding(input_dim=len(self.__Enc[col]),
output_dim=self.__K[col],
input_length=1)(var)
emb = Reshape(target_shape=(self.__K[col],))(emb)
embeddings.append(emb)
if (len(self.__Lcat) > 1):
emb_layer = concatenate(embeddings)
else:
emb_layer = embeddings[0]
lay1 = Dense(n_layer1,
kernel_initializer='uniform',
activation='relu')(emb_layer)
lay1 = Dropout(dropout1)(lay1)
lay2 = Dense(n_layer2,
kernel_initializer='uniform',
activation='relu')(lay1)
lay2 = Dropout(dropout2)(lay2)
# Learning the weights
if ((y_train.dtype == object) | (y_train.dtype == 'int')):
# Classification
if (y_train.nunique() == 2):
outputs = Dense(1,
kernel_initializer='normal',
activation='sigmoid')(lay2)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='binary_crossentropy',
optimizer='adam')
model.fit(
[df_train[col].apply(lambda x: self.__Enc[col][x]).values
for col in self.__Lcat],
pd.get_dummies(y_train,
drop_first=True).astype(int).values,
epochs=epochs,
batch_size=batch_size,
verbose=int(self.verbose)
)
else:
outputs = Dense(y_train.nunique(),
kernel_initializer='normal',
activation='softmax')(lay2)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='binary_crossentropy',
optimizer='adam')
model.fit(
[df_train[col].apply(lambda x: self.__Enc[col][x]).values
for col in self.__Lcat],
pd.get_dummies(y_train,
drop_first=False).astype(int).values,
epochs=epochs,
batch_size=batch_size,
verbose=int(self.verbose)
)
else:
# Regression
outputs = Dense(1, kernel_initializer='normal')(lay2)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(
[df_train[col].apply(lambda x: self.__Enc[col][x]).values
for col in self.__Lcat],
y_train.values,
epochs=epochs,
batch_size=batch_size,
verbose=int(self.verbose)
)
self.__weights = model.get_weights()
self.__fitOK = True
#################################################
# Random Projection
#################################################
elif(self.strategy == 'random_projection'):
for col in self.__Lcat:
exp_ = np.exp(-df_train[col].nunique() * 0.05)
# TODO: Add reference to formula used here below?
self.__K[col] = np.int(5 * (1 - exp_)) + 1
d = dict()
levels = list(df_train[col].unique())
nan = False
if np.NaN in levels:
nan = True
levels.remove(np.NaN)
for k in range(self.__K[col]):
if (k == 0):
levels = sorted(levels)
else:
np.random.seed(k)
np.random.shuffle(levels)
for enc, level in enumerate([np.NaN] * nan + levels):
if(k == 0):
d[level] = [enc]
else:
d[level] = d[level] + [enc]
self.__Enc[col] = d
self.__fitOK = True
else:
raise ValueError("Categorical encoding strategy is not valid")
return self
[docs] def fit_transform(self, df_train, y_train):
"""Fits Categorical Encoder and transforms the dataset.
Fit categorical encoder following strategy parameter and transform the
dataset df_train.
Parameters
----------
df_train : pandas.Dataframe of shape = (n_train, n_features)
The training dataset with numerical and categorical features.
NA values are allowed.
y_train : pandas.Series of shape = (n_train, ).
The target for classification or regression tasks.
Returns
-------
pandas.Dataframe of shape = (n_train, n_features)
Training dataset with numerical and encoded categorical features.
"""
self.fit(df_train, y_train)
return self.transform(df_train)
[docs] def transform(self, df):
"""Transform categorical variable of df dataset.
Transform df DataFrame encoding categorical features with the strategy
parameter if self.__fitOK is set to True.
Parameters
----------
df : pandas.Dataframe of shape = (n_train, n_features)
The training dataset with numerical and categorical features.
NA values are allowed.
Returns
-------
pandas.Dataframe of shape = (n_train, n_features)
The dataset with numerical and encoded categorical features.
"""
if self.__fitOK:
if len(self.__Lcat) == 0:
return df[self.__Lnum]
else:
#################################################
# Label Encoding
#################################################
if (self.strategy == 'label_encoding'):
for col in self.__Lcat:
# Handling unknown levels
unknown_levels = list(set(df[col].values)
- set(self.__Enc[col].keys()))
if (len(unknown_levels) != 0):
new_enc = len(self.__Enc[col])
for unknown_level in unknown_levels:
d = self.__Enc[col]
# TODO: make sure no collisions introduced
d[unknown_level] = new_enc
self.__Enc[col] = d
if (len(self.__Lnum) == 0):
return pd.concat(
[pd.DataFrame(
df[col].apply(lambda x: self.__Enc[col][x]).values,
columns=[col], index=df.index
) for col in self.__Lcat],
axis=1)[df.columns]
else:
return pd.concat(
[df[self.__Lnum]]
+ [pd.DataFrame(
df[col].apply(lambda x: self.__Enc[col][x]).values,
columns=[col],
index=df.index
) for col in self.__Lcat],
axis=1)[df.columns]
#################################################
# Dummification
#################################################
elif (self.strategy == 'dummification'):
sub_var = []
missing_var = []
for col in self.__Lcat:
# Handling unknown and missing levels
unique_levels = set(df[col].values)
sub_levels = unique_levels & set(self.__Enc[col])
missing_levels = [col + "_" + str(s)
for s in list(set(self.__Enc[col]) - sub_levels)]
sub_levels = [col + "_" + str(s)
for s in list(sub_levels)]
sub_var = sub_var + sub_levels
missing_var = missing_var + missing_levels
if (len(missing_var) != 0):
return pd.SparseDataFrame(
pd.concat(
[pd.get_dummies(df,
sparse=True)[list(self.__Lnum)
+ sub_var]]
+ [pd.DataFrame(np.zeros((df.shape[0],
len(missing_var))),
columns=missing_var,
index=df.index)],
axis=1
)[list(self.__Lnum)+sorted(missing_var+sub_var)])
else:
return pd.get_dummies(df, sparse=True)[list(self.__Lnum) + sorted(sub_var)]
#################################################
# Entity Embedding
#################################################
elif (self.strategy == 'entity_embedding'):
def get_embeddings(x, col, i):
if int(self.__Enc[col][x]) < \
np.shape(self.__weights[i])[0]:
return self.__weights[i][int(self.__Enc[col][x]), :]
return np.mean(self.__weights[i], axis=0)
for col in self.__Lcat:
# Handling unknown levels
unknown_levels = list(set(df[col].values)
- set(self.__Enc[col].keys())
)
if (len(unknown_levels) != 0):
new_enc = len(self.__Enc[col])
for unknown_level in unknown_levels:
d = self.__Enc[col]
d[unknown_level] = new_enc
self.__Enc[col] = d
if (len(self.__Lnum) == 0):
return pd.concat(
[pd.DataFrame(
df[col].apply(lambda x: get_embeddings(x, col, i)).tolist(),
columns=[col + "_emb" + str(k + 1)
for k in range(self.__K[col])],
index=df.index
)
for i, col in enumerate(self.__Lcat)], axis=1)
else:
return pd.concat(
[df[self.__Lnum]]
+ [pd.DataFrame(
df[col].apply(lambda x: get_embeddings(x, col, i)).tolist(),
columns=[col + "_emb" + str(k + 1)
for k in range(self.__K[col])],
index=df.index
)
for i, col in enumerate(self.__Lcat)], axis=1)
#################################################
# Random Projection
#################################################
else:
for col in self.__Lcat:
unknown_levels = list(set(df[col].values)
- set(self.__Enc[col].keys())
)
if (len(unknown_levels) != 0):
new_enc = len(self.__Enc[col])
for unknown_level in unknown_levels:
d = self.__Enc[col]
d[unknown_level] = [new_enc
for _ in range(self.__K[col])]
self.__Enc[col] = d
if (len(self.__Lnum) == 0):
return pd.concat(
[pd.DataFrame(
df[col].apply(lambda x: self.__Enc[col][x]).tolist(),
columns=[col + "_proj" + str(k + 1)
for k in range(self.__K[col])],
index=df.index
) for col in self.__Lcat], axis=1)
else:
return pd.concat(
[df[self.__Lnum]]
+ [pd.DataFrame(
df[col].apply(lambda x: self.__Enc[col][x]).tolist(),
columns=[col + "_proj" + str(k + 1)
for k in range(self.__K[col])],
index=df.index) for col in self.__Lcat], axis=1)
else:
raise ValueError("Call fit or fit_transform function before")