qbiocode.utils.helper_fn module#

Helper Functions for Data Preprocessing and Model Evaluation#

This module provides utility functions for data preprocessing, feature encoding, and result presentation in machine learning workflows.

Summary#

Functions:

`feature_encoding`	Encode categorical features using various encoding strategies.
`print_results`	Print formatted machine learning model evaluation results.
`scale_train_test`	Scale train and test with a single scaler fit on the training set only.
`scaler_fn`	Apply scaling transformation to input data.

Reference#

scaler_fn(X, scaling='None')[source]#

Apply scaling transformation to input data.

Scales the input data using one of three methods: no scaling, standard scaling (z-score normalization), or min-max scaling to [0, 1] range.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input data to be scaled.
scaling ({'None', 'StandardScaler', 'MinMaxScaler'}, default='None') –
Scaling method to apply:
- ’None’: No scaling, returns original data
- ’StandardScaler’: Standardize features by removing mean and scaling to unit variance
- ’MinMaxScaler’: Scale features to [0, 1] range

Returns:

X_scaled – Scaled data. If scaling=’None’, returns original data unchanged.

Return type:

array-like of shape (n_samples, n_features)

Notes

StandardScaler transforms data to have mean=0 and variance=1:

\[z = \frac{x - \mu}{\sigma}\]

MinMaxScaler transforms data to [0, 1] range:

\[x_{scaled} = \frac{x - x_{min}}{x_{max} - x_{min}}\]

Examples

>>> import numpy as np
>>> from qbiocode.utils import scaler_fn
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> X_scaled = scaler_fn(X, scaling='StandardScaler')
>>> X_minmax = scaler_fn(X, scaling='MinMaxScaler')

See also

sklearn.preprocessing.StandardScaler: Standardize features
sklearn.preprocessing.MinMaxScaler: Scale features to a range

scale_train_test(X_train, X_test, scaling='None')[source]#

Scale train and test with a single scaler fit on the training set only.

Unlike calling scaler_fn() separately on each split (which fits a fresh scaler on the test set using test-set statistics), this fits one scaler on X_train and applies it to both. That is the correct protocol: the test set is transformed with train-derived statistics, exactly as a deployed model would see unseen data.

Parameters:

X_train (array-like) – train / test feature matrices.
X_test (array-like) – train / test feature matrices.
scaling ({'None', 'StandardScaler', 'MinMaxScaler'}) – scaler to fit on X_train.

Returns:

(X_train_scaled, X_test_scaled). When scaling='None' the inputs are returned unchanged.

Return type:

tuple

Raises:

ValueError – if scaling is not one of the three accepted names. A mistyped name used to fall through to the 'None' branch, so scaling='minmaxscaler' returned unscaled data and reported success.

feature_encoding(feature1, sparse_output=False, feature_encoding='None')[source]#

Encode categorical features using various encoding strategies.

Transforms categorical features into numerical representations suitable for machine learning algorithms. Supports one-hot encoding, ordinal encoding, or no encoding.

Parameters:

feature1 (array-like of shape (n_samples,)) – Input categorical feature to be encoded. Should be a 1D array.
sparse_output (bool, default=False) – If True and feature_encoding=’OneHotEncoder’, returns a sparse matrix. If False, returns a dense array. Ignored for other encoding methods.
feature_encoding ({'None', 'OneHotEncoder', 'OrdinalEncoder'}, default='None') –
Encoding method to apply:
- ’None’: No encoding, returns original feature
- ’OneHotEncoder’: Create binary columns for each category
- ’OrdinalEncoder’: Map categories to integer values

Returns:

feature1_encoded – Encoded feature. Shape depends on encoding method:

’None’: shape (n_samples, 1)
’OrdinalEncoder’: shape (n_samples, 1)
’OneHotEncoder’: shape (n_samples, n_categories)

Return type:

array-like

Notes

One-hot encoding creates a binary column for each unique category, useful when categories have no ordinal relationship. Ordinal encoding assigns integer values, suitable when categories have a natural order.

The function automatically reshapes the input to (-1, 1) format required by scikit-learn encoders.

Examples

>>> import numpy as np
>>> from qbiocode.utils import feature_encoding
>>> categories = np.array(['A', 'B', 'C', 'A', 'B'])
>>> # One-hot encoding
>>> encoded_onehot = feature_encoding(categories, feature_encoding='OneHotEncoder')
>>> # Ordinal encoding
>>> encoded_ordinal = feature_encoding(categories, feature_encoding='OrdinalEncoder')

See also

sklearn.preprocessing.OneHotEncoder: Encode categorical features as one-hot
sklearn.preprocessing.OrdinalEncoder: Encode categorical features as integers

print_results(model, accuracy, f1, compile_time, params)[source]#

Print formatted machine learning model evaluation results.

Displays model performance metrics and parameters in a consistent, readable format. Useful for comparing multiple models during experimentation and benchmarking.

Parameters:

model (str) – Name or identifier of the machine learning model.
accuracy (float) – Accuracy score of the model, typically in range [0, 1].
f1 (float) – F1 score of the model, harmonic mean of precision and recall.
compile_time (float) – Time taken to train/compile the model, in seconds.
params (dict) – Dictionary of model hyperparameters and configuration settings.

Returns:

Prints results to stdout.

Return type:

None

Notes

The function formats floating-point numbers to 4 decimal places for consistency. All metrics are printed with descriptive labels.

Examples

>>> from qbiocode.utils import print_results
>>> params = {'n_estimators': 100, 'max_depth': 10}
>>> print_results('RandomForest', 0.9234, 0.9156, 2.345, params)
RandomForest Model Accuracy score: 0.9234
RandomForest Model F1 score: 0.9156
Time taken for RandomForest Model (secs): 2.3450
RandomForest Model Params:  {'n_estimators': 100, 'max_depth': 10}