mlcv-tutorial

Assisting library for the ML4CV tutorial based on scikit-learn.

It is recommended to use Python 3.6 in a virtual environment and install the latest stable versions of the dependencies. If not present, mlcv-tutorial will attempt to install them automatically.

Installation

Dependencies

mlcv-tutorial requires:

• numpy (>= 1.13.3)
• scipy (>= 0.19.1)
• scikit-learn (>=0.19.0)
• requests (>=2.14.2)
• matplotlib (>=2.0.2)

User installation

1. Create a virtual environment. If you use pip:

   python3 -m venv /path/to/new/virtual/environment_name
   

   or if you use conda:

   conda create -n environment_name python=3.6 anaconda
   

2. Enter the virtual environment:

   source activate environment_name
   

3. Install or upgrade the package:

   pip install --upgrade git+https://github.com/johny-c/mlcv-tutorial.git
   

4. To exit the virtual environment:

   source deactivate
   

Usage

Enter the virtual environment you created. Upgrade regularly to get the latest version. Open a python script, import the package and use it in your own work!

from mlcv.templates.base import Solution

class MyEstimator(Solution):

    def __init__(param1=3, param2='gaussian'):
        # Store the passed parameters in your estimator instance
        self.param1 = param1
        self.param2 = param2

    def fit(X, y):
        # Train your estimator on the training inputs X and training targets y
        return self

    def predict(X):
        # Predict targets for the given testing inputs X.
        return y_pred

    def score(y_pred, y_true):
        # Evaluate your model
        return accuracy

Have a look at the examples directory for a complete use case.