https://github.com/manojkarthick/pcregression

Python package to build principal components regression model using the scikit-learn library.
https://github.com/manojkarthick/pcregression

machine-learning python scikit-learn

Last synced: 24 days ago
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Python package to build principal components regression model using the scikit-learn library.

• Host: GitHub
• URL: https://github.com/manojkarthick/pcregression
• Owner: manojkarthick
• License: mit
• Created: 2018-03-31T07:39:33.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-04-01T22:57:07.000Z (over 6 years ago)
• Last Synced: 2024-10-13T00:01:29.023Z (24 days ago)
• Topics: machine-learning, python, scikit-learn
• Language: Python
• Size: 13.7 KB
• Stars: 7
• Watchers: 4
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PCRegression

Python package to build principal components regression models using the scikit-learn library. This package follows the same principles as the scikit-learn API and exposes similar `fit` and `predict` methods.

View it on PyPI at: https://pypi.org/project/PCRegression/

### Installation

This tool has been built with python3. Install from PyPI using pip.

```shell

# If Python3 is your default python, use

$ pip3 install PCRegression

# If Python 3.x is not your primary version of python, then use

$ pip install PCRegression

```

**NOTE**:   You can check your python version using `python -V`.

### Examples

General fit and predict API:

```python

X_train, X_test , y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=1)

pcr = PCR(n_components = 7)

pcr.fit(scale(X_train), y_train) # Scale your predictors for best results.

predictions = pcr.predict(scale(X_test))

```

K-Fold Cross validation using PCR:

```python

from sklearn.model_selection import KFold, cross_val_score

pcr = PCR(n_components=7)

kf_10 = KFold( n_splits=10, shuffle=True, random_state=1)

pcr.fit(scale(X_train), y_train)

X_reduced_train = pcr.get_transformed_data()

regr = pcr.get_regression_model()

n = len(X_reduced_train)

mse = list()

score = -1*cross_val_score(regr, np.ones((n,1)), y_train.ravel(), cv=kf_10, scoring='neg_mean_squared_error').mean()    

mse.append(score)

for i in np.arange(1, n):

    score = -1*cross_val_score(regr, X_reduced_train[:,:i], y_train.ravel(), cv=kf_10, scoring='neg_mean_squared_error').mean()

    mse.append(score)

print(mse)

```

### Important Parameters

* `n_components` : Number of components to keep.

* `copy` : Keep a copy of the data passed to the fit method()

* `random_state` : The seed used by the random number generator

* `fit_intercept` : Whether to calculate the intercept for this model

* `normalize` : If True, the regressors X will be normalized before regression.

### Requirements

1. scikit-learn >= 0.13.3

2. numpy >= 1.8.2