Python Dataset.from_test示例

示例#1

文件： nn.py 项目： klangner/telstra

def make_submission(network, params, u):
    print('Prepare submission')
    test = Dataset.from_test()
    if params.pca:
        X2 = test.get_pca_features(u)
    else:
        X2 = test.get_features()
    predictions = network.predict(X2)
    save_predictions(predictions, test.df)

示例#2

def make_submission(network, params, u):
    print('Prepare submission')
    test = Dataset.from_test()
    if params.pca:
        X2 = test.get_pca_features(u)
    else:
        X2 = test.get_features()
    predictions = network.predict(X2)
    save_predictions(predictions, test.df)

示例#3

文件： random_forrest.py 项目： fenggami/NetworkPrediction

def prepare_solution():
    train = Dataset.from_train()
    X = train.get_features()
    Y = train.get_labels()
    rf = RandomForestRegressor(n_jobs=-1)
    model = rf.fit(X, Y)
    print('Train dataset score: %f' % loss(Y, model.predict(X)))
    test = Dataset.from_test()
    X2 = test.get_features()
    Y2 = model.predict(X2)
    save_predictions(Y2, test.df)

示例#4

文件： random_forrest.py 项目： klangner/telstra

def prepare_solution():
    train = Dataset.from_train()
    X = train.get_features()
    Y = train.get_labels()
    rf = RandomForestRegressor(n_jobs=-1)
    model = rf.fit(X, Y)
    print('Train score: %f' % loss(Y, model.predict(X)))
    test = Dataset.from_test()
    X2 = test.get_features()
    Y2 = model.predict(X2)
    save_predictions(Y2, test)

示例#5

文件： kmeans.py 项目： klangner/telstra

def submission():
    print('Cross validate K-Means model')
    train = Dataset.from_train()
    test = Dataset.from_test()
    X = train.get_features()
    Y = train.get_labels()
    X2 = test.get_features()
    kmeans = KMeans(n_clusters=8)
    clf = kmeans.fit(X, train.get_multi_labels())
    score = check_score(Y, to_labels(clf.predict(X)))
    print("Train dataset score %f" % (score/len(X)))
    Y2 = to_labels(clf.predict(X2))
    save_predictions(Y2, test.df)

示例#6

文件： kmeans.py 项目： fenggami/NetworkPrediction

def submission():
    print('Cross validate K-Means model')
    train = Dataset.from_train()
    test = Dataset.from_test()
    X = train.get_features()
    Y = train.get_labels()
    X2 = test.get_features()
    kmeans = KMeans(n_clusters=8)
    clf = kmeans.fit(X, train.get_multi_labels())
    score = check_score(Y, to_labels(clf.predict(X)))
    print("Train dataset score %f" % (score / len(X)))
    Y2 = to_labels(clf.predict(X2))
    save_predictions(Y2, test.df)

示例#7

文件： bayes.py 项目： fenggami/NetworkPrediction

def submission():
    print('Cross validate bayes model')
    train = Dataset.from_train()
    test = Dataset.from_test()
    X = train.get_features()
    Y = train.get_labels()
    X2 = test.get_features()
    gnb = bayes.MultinomialNB()
    clf = gnb.fit(X, train.get_multi_labels())
    score = check_score(Y, to_labels(clf.predict(X)))
    print("Train dataset score %f" % (score / len(X)))
    Y2 = to_labels(clf.predict(X2))
    save_predictions(Y2, test.df)

示例#8

文件： bayes.py 项目： klangner/telstra

def submission():
    print('Cross validate bayes model')
    train = Dataset.from_train()
    test = Dataset.from_test()
    X = train.get_features()
    Y = train.get_labels()
    X2 = test.get_features()
    gnb = bayes.MultinomialNB()
    clf = gnb.fit(X, train.get_multi_labels())
    score = check_score(Y, to_labels(clf.predict(X)))
    print("Train dataset score %f" % (score/len(X)))
    Y2 = to_labels(clf.predict(X2))
    save_predictions(Y2, test.df)

示例#9

def train_auto_encoder(restore):
    print('Training auto encoder')
    network = AutoEncoder()
    train_data = Dataset.from_train()
    test_data = Dataset.from_test()
    network.fit_encoder(train_data, test_data, restore=restore)