igel:几行命令实现机器学习自学习

• 安装
• 实战
• 初始化
• 训练
• 验证
• 预测
• 实例

自动学习igel

安装

• pip install igel

igel支持的模型

实战

初始化

$ igel init -type "classification" -model "NeuralNetwork" -target "recurrence"

• igel.yaml

dataset:
  preprocess:
    missing_values: mean
    scale:
      method: standard
      target: inputs
  split:
    shuffle: true
    test_size: 0.2
  type: csv
model:
  algorithm: NeuralNetwork
  type: classification
target:
- recurrence

训练

$ igel fit -dp "../data/cryablation.csv" -yml "./igel.yml" -cmd "fit"

验证

$ igel evaluate -dp path_to_the_evaluation_dataset

预测

$ igel predict -dp path_to_the_new_dataset

实例

• igel.yaml

# dataset operations
dataset:
    split:  # split options
        test_size: 0.2  # 0.2 means 20% for the test data, so 80% are automatically for training
        shuffle: True   # whether to shuffle the data before/while splitting
        stratify: default  # If not None, data is split in a stratified fashion, using this as the class labels.
    preprocess: # preprocessing options
        missing_values: mean    # other possible values: [drop, median, most_frequent, constant] check the docs for more
        encoding:
            type: labelEncoding  # other possible values: [labelEncoding]
            column: Species
        scale:  # scaling options
            method: standard    # standardization will scale values to have a 0 mean and 1 standard deviation  | you can also try minmax
            target: inputs  # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled

# model definition
model:
    type: classification
    algorithm: LogisticRegression

# target you want to predict
target:
    - Species

• fit.py

from igel import Igel
mock_fit_params = {'data_path': '../iris/train-Iris.csv',
                   'yaml_path': './iris.yaml',
                   'cmd': 'fit'}
Igel(**mock_fit_params)

• evaluate.py

from igel import Igel

mock_eval_params = {'data_path': '../iris/eval-Iris.csv',
                    'cmd': 'evaluate'}
Igel(**mock_eval_params)

• predict.py

from igel import Igel

mock_pred_params = {'data_path': '../iris/test-Iris.csv',
                    'cmd': 'predict'}
Igel(**mock_pred_params)

参考：https://github.com/nidhaloff/igel