config

polygon_classification.config.dataset = 'data/polypairs_dataset.csv'

Relative path to the datasets.

Type

str

polygon_classification.config.seed_no = 2020

Seed used by each of the random number generators.

Type

int

polygon_classification.config.test_split_thres = 0.2

Proportion of the dataset to include in the test split. Accepted values should be between 0.0 and 1.0.

Type

float

class polygon_classification.config.MLConf[source]

This class initializes parameters that correspond to the machine learning part of the framework.

These variables define the parameter grid for GridSearchCV:

Variables

  • SVM_hyperparameters (list) – Defines the search space for SVM.

  • MLP_hyperparameters (dict) – Defines the search space for MLP.

  • DecisionTree_hyperparameters (dict) – Defines the search space for Decision Trees.

  • RandomForest_hyperparameters (dict) – Defines the search space for Random Forests and Extra-Trees.

  • XGBoost_hyperparameters (dict) – Defines the search space for XGBoost.

These variables define the parameter grid for RandomizedSearchCV where continuous distributions are used for continuous parameters (whenever this is feasible):

Variables

  • SVM_hyperparameters_dist (dict) – Defines the search space for SVM.

  • MLP_hyperparameters_dist (dict) – Defines the search space for MLP.

  • DecisionTree_hyperparameters_dist (dict) – Defines the search space for Decision Trees.

  • RandomForest_hyperparameters_dist (dict) – Defines the search space for Random Forests and Extra-Trees.

  • XGBoost_hyperparameters_dist (dict) – Defines the search space for XGBoost.

kfold_parameter = 5

The number of outer folds that splits the dataset for the k-fold cross-validation.

Type

int

n_jobs = -1

Number of parallel jobs to be initiated. -1 means to utilize all available processors.

Type

int

extra_features = True

Whether to build additional features or not, i.e., convex hull of polygons and dist of centroids.

Type

bool

hyperparams_search_method = 'randomized'

Search Method to use for finding best hyperparameters. (randomized | grid).

See also

fineTuneClassifiers()

Type

str

max_iter = 3

Number of iterations that RandomizedSearchCV should execute. It applies only when hyperparams_search_method equals to ‘randomized’.

Type

int

score = 'accuracy'

The metric to optimize on hyper-parameter tuning. Possible valid values presented on Scikit predefined values.

Type

str

classifiers = ['RandomForest']

Define the classifiers to apply on code execution. Accepted values are:

  • SVM

  • DecisionTree

  • RandomForest

  • ExtraTrees

  • XGBoost

  • MLP.

Type

list of str

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