Esempio n. 1
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 def test_export(self):
     flow = Flow()
     X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
     y = np.array([1, 1, 2, 2])
     flow.data(X=X, y=y)
     model = Model(SGDClassifier(), "SGDClassfier")
     flow.add_model(model)
     try:
         mkdir("temp")
     except:
         pass
     flow.export("SGDClassfier", export_folder="temp")
     shutil.rmtree("temp")
Esempio n. 2
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from os.path import dirname, join
import sys
from languageflow.flow import Flow
from languageflow.model import Model
from languageflow.transformer.count import CountVectorizer
from sklearn.svm import SVC
from languageflow.validation.validation import TrainTestSplitValidation
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from load_data import load_dataset

if __name__ == '__main__':
    data_file = join(dirname(dirname(dirname(dirname(__file__)))), "data", "fb_bank", "corpus", "train.xlsx")
    X, y = load_dataset(data_file)

    flow = Flow()
    flow.data(X, y)

    transformer = CountVectorizer(ngram_range=(1, 2), max_df=0.5, min_df=8)
    flow.transform(MultiLabelBinarizer())
    flow.transform(transformer)
    flow.add_model(Model(OneVsRestClassifier(SVC(kernel='linear')), "SVC"))
    flow.set_validation(TrainTestSplitValidation(test_size=0.1))

    # flow.train()
    flow.export(model_name="SVC", export_folder="model")
Esempio n. 3
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import sys
from languageflow.flow import Flow
from languageflow.model import Model
from languageflow.transformer.count import CountVectorizer
from sklearn.linear_model import LogisticRegression
from languageflow.validation.validation import TrainTestSplitValidation
from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import MultiLabelBinarizer
from load_data import load_dataset

if __name__ == '__main__':
    data_file = join(dirname(dirname(dirname(dirname(__file__)))), "data",
                     "fb_bank", "corpus", "train.xlsx")
    X, y = load_dataset(data_file)

    flow = Flow()
    flow.data(X, y)

    transformer = CountVectorizer(ngram_range=(1, 3),
                                  max_df=0.8,
                                  min_df=8,
                                  max_features=5000)
    flow.transform(MultiLabelBinarizer())
    flow.transform(transformer)
    flow.add_model(
        Model(OneVsRestClassifier(LogisticRegression()), "LogisticRegression"))
    flow.set_validation(TrainTestSplitValidation(test_size=0.1))

    flow.train()
    flow.export(model_name="LogisticRegression", export_folder="model")