Example #1
0
def wrf_fit(input_dict):
    from discomll.ensemble import distributed_weighted_forest_rand

    random_state = None if input_dict["seed"] == "None" else int(input_dict["seed"])

    fitmodel_url = distributed_weighted_forest_rand.fit(input_dict["dataset"],
                                                        trees_per_chunk=input_dict["trees_per_subset"],
                                                        max_tree_nodes=input_dict["tree_nodes"],
                                                        num_medoids=input_dict["num_medoids"],
                                                        min_samples_leaf=input_dict["min_samples_leaf"],
                                                        min_samples_split=input_dict["min_samples_split"],
                                                        class_majority=input_dict["majority"],
                                                        measure=input_dict["measure"],
                                                        accuracy=input_dict["accuracy"],
                                                        separate_max=input_dict["separate_max"] == "true",
                                                        random_state=random_state,
                                                        save_results=True)
    return {"fitmodel_url": fitmodel_url}
Example #2
0
def wrf_fit(input_dict):
    from discomll.ensemble import distributed_weighted_forest_rand

    random_state = None if input_dict["seed"] == "None" else int(
        input_dict["seed"])

    fitmodel_url = distributed_weighted_forest_rand.fit(
        input_dict["dataset"],
        trees_per_chunk=input_dict["trees_per_subset"],
        max_tree_nodes=input_dict["tree_nodes"],
        num_medoids=input_dict["num_medoids"],
        min_samples_leaf=input_dict["min_samples_leaf"],
        min_samples_split=input_dict["min_samples_split"],
        class_majority=input_dict["majority"],
        measure=input_dict["measure"],
        accuracy=input_dict["accuracy"],
        separate_max=input_dict["separate_max"] == "true",
        random_state=random_state,
        save_results=True)
    return {"fitmodel_url": fitmodel_url}
from disco.core import result_iterator

from discomll import dataset
from discomll.ensemble import distributed_weighted_forest_rand
from discomll.utils import accuracy

train = dataset.Data(data_tag=[
    ["http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"]],
    id_index=0,
    X_indices=xrange(1, 10),
    X_meta="http://ropot.ijs.si/data/datasets_meta/breastcancer_meta.csv",
    y_index=10,
    delimiter=",")

fit_model = distributed_weighted_forest_rand.fit(train, trees_per_chunk=3, max_tree_nodes=50, min_samples_leaf=5,
                                                 min_samples_split=10, class_majority=1,
                                                 measure="info_gain", num_medoids=10, accuracy=1, separate_max=True,
                                                 random_state=None, save_results=True)

# predict training dataset
predictions = distributed_weighted_forest_rand.predict(train, fit_model)

# output results
for k, v in result_iterator(predictions):
    print k, v[0]

# measure accuracy
ca = accuracy.measure(train, predictions)
print ca
Example #4
0
test = dataset.Data(data_tag=[
    "http://ropot.ijs.si/data/lymphography/test/xaaaaa.gz",
    "http://ropot.ijs.si/data/lymphography/test/xaaabj.gz"
],
                    data_type="gzip",
                    generate_urls=True,
                    X_indices=range(2, 20),
                    id_index=0,
                    y_index=1,
                    X_meta=[
                        "d", "d", "d", "d", "d", "d", "d", "d", "c", "c", "d",
                        "d", "d", "d", "d", "d", "d", "c"
                    ],
                    delimiter=",")

fit_model = distributed_weighted_forest_rand.fit(train,
                                                 trees_per_chunk=3,
                                                 max_tree_nodes=50,
                                                 min_samples_leaf=10,
                                                 min_samples_split=5,
                                                 class_majority=1,
                                                 measure="info_gain",
                                                 num_medoids=10,
                                                 accuracy=1,
                                                 separate_max=True,
                                                 random_state=None,
                                                 save_results=True)
predict_url = distributed_weighted_forest_rand.predict(test, fit_model)
print predict_url