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Add imputation and One-Hot Encoding Methods.

ml_pipeline
Primoz 2023-02-22 18:05:01 +01:00
parent 8f6cb3f444
commit 7f6ae9b323
1 changed files with 84 additions and 16 deletions
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96
machine_learning/preprocessing.py
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@ -55,32 +55,100 @@ class Preprocessing:
pass pass
def one_hot_encode(self, categorical_columns=["gender", "startlanguage", "mostcommonactivity", "homelabel"]): def one_hot_encoder(categorical_features, numerical_features, mode):
"""
This code is an implementation of one-hot encoding. It takes in two data sets,
one with categorical features and one with numerical features and a mode parameter.
First it uses the fillna() function to fill in any missing values present in the
categorical data set with the mode value. Then it uses the apply () method to
convert each column of the data set into a category data type which is then
transformed using the pd.get_dummies() function. Finally it concatenates the
numerical data set and the transformed categorical data set using pd.concat() and
returns it.
categorical_columns = [col for col in self.X.columns if col in categorical_columns] Args:
categorical_features (DataFrame): DataFrame including only categorical columns.
numerical_features (_type_): DataFrame including only numerical columns.
mode (int): Mode of the column with which DataFrame is filled. TODO: check mode results
categorical_features = self.X[categorical_columns].copy() Returns:
mode_categorical_features = categorical_features.mode().iloc[0] DataFrame: Hot-One Encoded DataFrame.
"""
# fillna with mode # Fill train set with mode
categorical_features = categorical_features.fillna(mode_categorical_features) categorical_features = categorical_features.fillna(mode)
# one-hot encoding # one-hot encoding
categorical_features = categorical_features.apply(lambda col: col.astype("category")) categorical_features = categorical_features.apply(lambda col: col.astype("category"))
if not categorical_features.empty: if not categorical_features.empty:
categorical_features = pd.get_dummies(categorical_features) categorical_features = pd.get_dummies(categorical_features)
numerical_features = self.X.drop(categorical_columns, axis=1) return pd.concat([numerical_features, categorical_features], axis=1)
train_x = pd.concat([numerical_features, categorical_features], axis=1)
# TODO: has to return a train set (or 54 participans in logo) and a test set (1 participant in logo) def one_hot_encode_train_and_test_sets(self, categorical_columns=["gender", "startlanguage", "mostcommonactivity", "homelabel"]):
"""
This code is used to transform categorical data into numerical representations.
It first identifies the categorical columns, then copies them and saves them as
a new dataset. The missing data is filled with the mode (most frequent value in
the respective column). This new dataset is then subjected to one-hot encoding,
which is a process of transforming categorical data into machine interpretable
numerical form by converting categories into multiple binary outcome variables.
These encoded values are then concatenated to the numerical features prior to
being returned as the final dataset.
Args:
categorical_columns (list, optional): List of categorical columns in the dataset.
Defaults to ["gender", "startlanguage", "mostcommonactivity", "homelabel"].
TODO: TESTING
"""
categorical_columns = [col for col in self.train_X.columns if col in categorical_columns]
# For train set
train_X_categorical_features = self.train_X[categorical_columns].copy()
train_X_numerical_features = self.train_X.drop(categorical_columns, axis=1)
mode_train_X_categorical_features = train_X_categorical_features.mode()
self.train_X = one_hot_encoder(train_X_categorical_features, train_X_numerical_features, mode_train_X_categorical_features)
# For test set
test_X_categorical_features = self.test_X[categorical_columns].copy()
test_X_numerical_features = self.test_X.drop(categorical_columns, axis=1)
self.test_X = one_hot_encoder(test_X_categorical_features, test_X_numerical_features, mode_train_X_categorical_features)
def imputer(method="mean"): def imputer(self, interval_feature_list, other_feature_list, groupby_feature="pid"):
# TODO: This has to be done in context of CV method - so that test data has only information to mean of train data (it is imputed with train data mean or median etc.)
# TODO: has to return train set (or 54 participans in logo) and test test (1 participant in logo) # TODO: TESTING
pass
if groupby:
# Interval numerical features # TODO: How can we get and assign appropriate groupby means and assign them to correct columns?
# VVVVV ...... IN PROGRES ...... VVVVV
means = self.train_X[interval_feature_list].groupby(groupby_feature).mean()
self.train_X[self.train_X.loc[:, ~self.train_X.columns.isin([groupby_feature] + other_feature_list)]] = \
self.train_X[interval_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.mean()))
self.test_X[self.test_X.loc[:, ~self.test_X.columns.isin([groupby_feature] + other_feature_list)]] = \
self.test_X[interval_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.mean()))
# Other features
self.train_X[self.train_X.loc[:, ~self.train_X.columns.isin([groupby_feature] + interval_feature_list)]] = \
self.train_X[other_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.median()))
else:
# Interval numerical features
means = self.train_X[interval_feature_list].mean()
self.train_X[interval_feature_list].fillna(means, inplace=True)
self.test_X[interval_feature_list].fillna(means, inplace=True)
# Other features
medians = self.train_X[other_feature_list].median()
self.train_X[other_feature_list].fillna(medians, inplace=True)
self.test_X[other_feature_list].fillna(medians, inplace=True)