def dt_predict(input_dict): from discomll.ensemble import forest_distributed_decision_trees predictions_url = forest_distributed_decision_trees.predict(input_dict["dataset"], fitmodel_url=input_dict["fitmodel_url"], save_results=True) return {"string": predictions_url}
def dt_predict(input_dict): from discomll.ensemble import forest_distributed_decision_trees predictions_url = forest_distributed_decision_trees.predict( input_dict["dataset"], fitmodel_url=input_dict["fitmodel_url"], save_results=True) return {"string": predictions_url}
from discomll import dataset from discomll.ensemble import forest_distributed_decision_trees train = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/train/xaaaaa.gz", "http://ropot.ijs.si/data/segmentation/train/xaaabj.gz"], data_type="gzip", generate_urls=True, X_indices=range(2, 21), id_index=0, y_index=1, X_meta=["c" for i in range(2, 21)], delimiter=",") test = dataset.Data(data_tag=["http://ropot.ijs.si/data/segmentation/test/xaaaaa.gz", "http://ropot.ijs.si/data/segmentation/test/xaaabj.gz"], data_type="gzip", generate_urls=True, X_indices=range(2, 21), id_index=0, y_index=1, X_meta=["c" for i in range(2, 21)], delimiter=",") fit_model = forest_distributed_decision_trees.fit(train, trees_per_chunk=1, bootstrap=True, max_tree_nodes=50, min_samples_leaf=10, min_samples_split=5, class_majority=1, separate_max=True, measure="info_gain", accuracy=1, random_state=None, save_results=True) predict_url = forest_distributed_decision_trees.predict(test, fit_model) print predict_url
y_index=1, X_meta=["c" for i in range(2, 21)], delimiter=",") test = dataset.Data(data_tag=[ "http://ropot.ijs.si/data/segmentation/test/xaaaaa.gz", "http://ropot.ijs.si/data/segmentation/test/xaaabj.gz" ], data_type="gzip", generate_urls=True, X_indices=range(2, 21), id_index=0, y_index=1, X_meta=["c" for i in range(2, 21)], delimiter=",") fit_model = forest_distributed_decision_trees.fit(train, trees_per_chunk=1, bootstrap=True, max_tree_nodes=50, min_samples_leaf=10, min_samples_split=5, class_majority=1, separate_max=True, measure="info_gain", accuracy=1, random_state=None, save_results=True) predict_url = forest_distributed_decision_trees.predict(test, fit_model) print predict_url
from disco.core import result_iterator from discomll import dataset from discomll.ensemble import forest_distributed_decision_trees from discomll.utils import model_view train = dataset.Data(data_tag=[["http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"]], X_indices=xrange(0, 4), X_meta="http://ropot.ijs.si/data/datasets_meta/iris_meta.csv", y_index=4, delimiter=",") fit_model = forest_distributed_decision_trees.fit(train, trees_per_chunk=1, bootstrap=False, max_tree_nodes=50, min_samples_leaf=2, min_samples_split=1, class_majority=1, separate_max=True, measure="info_gain", accuracy=1, random_state=None, save_results=True) print model_view.output_model(fit_model) # predict training dataset predictions = forest_distributed_decision_trees.predict(train, fit_model) # output results for k, v in result_iterator(predictions): print k, v[0]