junos
/
stress_at_work_analysis
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Repair preprocessing one hot encoding of test set.

ml_pipeline
Primoz 2023-04-21 13:24:31 +02:00
parent 865225994b
commit 26804cf8ea
1 changed files with 13 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

19
machine_learning/preprocessing.py
View File

@ -33,7 +33,7 @@ class Preprocessing:
Args: Args:
categorical_features (DataFrame): DataFrame including only categorical columns. categorical_features (DataFrame): DataFrame including only categorical columns.
numerical_features (_type_): DataFrame including only numerical columns. numerical_features (_type_): DataFrame including only numerical columns.
mode (int): Mode of the column with which DataFrame is filled. TODO: check mode results mode (int): Mode of the column with which DataFrame is filled.
Returns: Returns:
DataFrame: Hot-One Encoded DataFrame. DataFrame: Hot-One Encoded DataFrame.
@ -46,7 +46,7 @@ class Preprocessing:
if not categorical_features.empty: if not categorical_features.empty:
categorical_features = pd.get_dummies(categorical_features) categorical_features = pd.get_dummies(categorical_features)
return pd.concat([numerical_features, categorical_features], axis=1) return pd.concat([numerical_features, categorical_features], axis=1), categorical_features.columns.tolist()
def one_hot_encode_train_and_test_sets(self, categorical_columns=["gender", "startlanguage", "mostcommonactivity", "homelabel"]): def one_hot_encode_train_and_test_sets(self, categorical_columns=["gender", "startlanguage", "mostcommonactivity", "homelabel"]):
@ -68,20 +68,27 @@ class Preprocessing:
categorical_columns = [col for col in self.train_X.columns if col in categorical_columns] categorical_columns = [col for col in self.train_X.columns if col in categorical_columns]
# For train set # For train set
train_X_categorical_features = self.train_X[categorical_columns].copy() train_X_categorical_features = self.train_X[categorical_columns].copy()
train_X_numerical_features = self.train_X.drop(categorical_columns, axis=1) train_X_numerical_features = self.train_X.drop(categorical_columns, axis=1)
mode_train_X_categorical_features = train_X_categorical_features.mode().iloc[0] mode_train_X_categorical_features = train_X_categorical_features.mode().iloc[0]
self.train_X = self.one_hot_encoder(train_X_categorical_features, train_X_numerical_features, mode_train_X_categorical_features) self.train_X, train_cat_col_names = self.one_hot_encoder(train_X_categorical_features, train_X_numerical_features, mode_train_X_categorical_features)
encoded_categorical_features = [col for col in self.train_X.columns if col.startswith(tuple(categorical_columns))]
# For test set # For test set
test_X_categorical_features = self.test_X[categorical_columns].copy() test_X_categorical_features = self.test_X[categorical_columns].copy()
test_X_numerical_features = self.test_X.drop(categorical_columns, axis=1) test_X_numerical_features = self.test_X.drop(categorical_columns, axis=1)
self.test_X = self.one_hot_encoder(test_X_categorical_features, test_X_numerical_features, mode_train_X_categorical_features) self.test_X, test_cat_col_names = self.one_hot_encoder(test_X_categorical_features, test_X_numerical_features, mode_train_X_categorical_features)
# Create categorical columns that were not found in test set and fill them with 0
missing_cols = [col for col in train_cat_col_names if col not in test_cat_col_names]
self.test_X[missing_cols] = 0
# Sort column names alphabetically
self.train_X = self.train_X.reindex(sorted(self.train_X.columns), axis=1)
self.test_X = self.test_X.reindex(sorted(self.test_X.columns), axis=1)
def imputer(self, interval_feature_list, other_feature_list, groupby_feature="pid"): def imputer(self, interval_feature_list, other_feature_list, groupby_feature="pid"):