import argparse

from ml.ds import DataSetBuilderFile
from ml.utils.config import get_settings
from ml.utils.numeric_functions import le
from ml.processing import Transforms
from ml.clf.extended import w_sklearn
from ml.clf.extended import w_tflearn
from ml.clf import ensemble as clf_ensemble


settings = get_settings("ml")
settings.update(get_settings("numerai"))


def predict(classif, path):
    import pandas as pd
    import csv

    df = pd.read_csv(path)
    ids = df["id"].as_matrix()
    data = df.drop(["target", "id", "era", "data_type"], axis=1).as_matrix()
    predictions = []
    for value, label in zip(list(classif.predict(data, raw=True, chunk_size=258)), ids):
        predictions.append([str(label), str(value[1])])
    
    with open("predictions.csv", "w") as csvfile:
        csvwriter = csv.writer(csvfile, delimiter=',', quoting=csv.QUOTE_MINIMAL)
        csvwriter.writerow(["id", "probability"])
        for row in predictions:
            csvwriter.writerow(row)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-name", help="nombre del dataset a utilizar", type=str)
    parser.add_argument("--model-version", type=str)
    parser.add_argument("--path", type=str)
    args = parser.parse_args()

    dataset = DataSetBuilderFile(args.dataset_name)
    predict(classif, args.path, "id")
    print("ok")

@@ -987,16 +987,1084 @@
 w(row)%0A%0A
+def build():%0A    dsb = DataSetBuilder(name=%22numerai%22, compression_level=6, validator=None)%0A    training_data = pd.read_csv('numerai_training_data.csv', header=0)%0A    prediction_data = pd.read_csv('numerai_tournament_data.csv', header=0)%0A%0A    features = %5Bf for f in list(training_data) if %22feature%22 in f%5D%0A    data = training_data%5Bfeatures%5D.as_matrix()%0A    labels = training_data%5B%22target%22%5D.as_matrix()%0A    #test = prediction_data%5Bprediction_data%5B'data_type'%5D == 'test'%5D%0A    #test_data = test%5Bfeatures%5D.as_matrix()%0A    #test_labels = test%5B'target'%5D.as_matrix()%0A    validation = prediction_data%5Bprediction_data%5B'data_type'%5D == 'validation'%5D%0A    validation_data = validation%5Bfeatures%5D.as_matrix()%0A    validation_labels = validation%5B'target'%5D.as_matrix()%0A%0A    data = np.vstack((data, validation_data))%0A    labels = np.append(labels, validation_labels, axis=0)%0A    #dsb.build_dataset(data, labels, test_data=test_data, test_labels=test_labels,%0A    #    validation_data=validation_data, validation_labels=validation_labels)%0A%0A    dsb.build_dataset(data, labels)%0A    dsb.info()%0A
 %0Aif __na

change command numerai