Getting Started

1. Installation

You only need two things: Python 3 and your favourite Python IDE to get started. Then simply install via pip.

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pip install photonai

2. Setup New Analysis

Start by importing some utilities and creating a new Hyperpipe instance, naming the analysis and specifying where to save all outputs. 

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from sklearn.model_selection import ShuffleSplit, KFold
from sklearn.datasets import load_breast_cancer
from photonai.base import Hyperpipe, PipelineElement, Switch
from photonai.optimization import IntegerRange, FloatRange

pipe = Hyperpipe('basic_pipe', project_folder='./')

3. Define training, optimization and testing parameters

Select parameters to customize the training, hyperparameter optimization and testing procedure.

Particularly, you can choose the hyperparameter optimization strategy, set parameters, choose performance metrics and choose the performance metric to minimize or maximize, respectively.

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pipe = Hyperpipe('basic_pipe', project_folder='./',

                  # choose hyperparameter optimization strategy
                  optimizer='random_grid_search',

                  # PHOTONAI automatically calculates your preferred metrics
                  metrics=['accuracy', 'balanced_accuracy', 'f1_score'],

                  # this metrics selects the best hyperparameter configuration
                  # in this case mean squared error is minimized
                  best_config_metric='f1_score',

                  # select cross validation strategies
                  outer_cv=ShuffleSplit(n_splits=3, test_size=0.2),
                  inner_cv=KFold(n_splits=10))

4. Build custom pipeline

Select and arrange normalization, dimensionality reduction, feature selection, data augmentation, over- or undersampling algorithms in simple or parallel data streams. You can integrate custom algorithms or choose from our wide range of pre-registered algorithms from established toolboxes.

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pipe += PipelineElement('StandardScaler')

pipe += PipelineElement('PCA',
                        hyperparameters={'n_components': FloatRange(0.5, 0.8, step=0.1)})

pipe += PipelineElement('ImbalancedDataTransformer',
                        hyperparameters={'method_name': ['RandomUnderSampler',
                                                         'RandomOverSampler',
                                                         'SMOTE']})

or_element = Switch('EstimatorSwitch')
or_element += PipelineElement('RandomForestClassifier',
                              hyperparameters={'min_samples_split': IntegerRange(2, 30)})
or_element += PipelineElement('SVC',
                              hyperparameters={'C': FloatRange(0.5, 10),
                                               'kernel': ['linear', 'rbf']})

pipe += or_element

5. Load Data and Train

Load your data and start the (nested-) cross-validated hyperparameter optimization, training and evaluation procedure. You will see an extensive output to monitor the hyperparameter optimization progress, see the results and track the best performances so far.

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X, y = load_breast_cancer(return_X_y=True)
pipe.fit(X, y)