Python preprocessing.MinMaxScaler方法代碼示例

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def make_mnist_data(path, isconv=False):
    X, Y = load_mnist(path, True)
    X = X.astype(np.float64)
    X2, Y2 = load_mnist(path, False)
    X2 = X2.astype(np.float64)
    X3 = np.concatenate((X, X2), axis=0)

    minmaxscale = MinMaxScaler().fit(X3)

    X = minmaxscale.transform(X)
    if isconv:
        X = X.reshape((-1, 1, 28, 28))

    sio.savemat(osp.join(path, 'traindata.mat'), {'X': X, 'Y': Y})

    X2 = minmaxscale.transform(X2)
    if isconv:
        X2 = X2.reshape((-1, 1, 28, 28))

    sio.savemat(osp.join(path, 'testdata.mat'), {'X': X2, 'Y': Y2}) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = BidirectionalLstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = CnnLstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def main():
    data_dir_path = './data'
    model_dir_path = './models'
    ecg_data = pd.read_csv(data_dir_path + '/ecg_discord_test.csv', header=None)
    print(ecg_data.head())
    ecg_np_data = ecg_data.as_matrix()
    scaler = MinMaxScaler()
    ecg_np_data = scaler.fit_transform(ecg_np_data)
    print(ecg_np_data.shape)

    ae = LstmAutoEncoder()

    # fit the data and save model into model_dir_path
    if DO_TRAINING:
        ae.fit(ecg_np_data[:23, :], model_dir_path=model_dir_path, estimated_negative_sample_ratio=0.9)

    # load back the model saved in model_dir_path detect anomaly
    ae.load_model(model_dir_path)
    anomaly_information = ae.anomaly(ecg_np_data[:23, :])
    reconstruction_error = []
    for idx, (is_anomaly, dist) in enumerate(anomaly_information):
        print('# ' + str(idx) + ' is ' + ('abnormal' if is_anomaly else 'normal') + ' (dist: ' + str(dist) + ')')
        reconstruction_error.append(dist)

    visualize_reconstruction_error(reconstruction_error, ae.threshold) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def make_misc_data(path, filename, dim, isconv=False):
    import cPickle
    fo = open(osp.join(path, filename), 'r')
    data = cPickle.load(fo)
    fo.close()
    X = data['data'].astype(np.float64)
    Y = data['labels']

    minmaxscale = MinMaxScaler().fit(X)
    X = minmaxscale.transform(X)

    p = np.random.permutation(X.shape[0])
    X = X[p]
    Y = Y[p]

    N = X.shape[0]

    if isconv:
        X = X.reshape((-1, dim[2], dim[0], dim[1]))
    save_misc_data(path, X, Y, N) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def make_easy_visual_data(path, N=600):
    """Make 3 clusters of 2D data where the cluster centers lie along a line.
    The latent variable would be just their x or y value since that uniquely defines their projection onto the line.
    """

    line = (1.5, 1)
    centers = [(m, m * line[0] + line[1]) for m in (-4, 0, 6)]
    cluster_std = [1, 1, 1.5]
    X, labels = make_blobs(n_samples=N, cluster_std=cluster_std, centers=centers, n_features=len(centers[0]))

    # scale data
    minmaxscale = MinMaxScaler().fit(X)
    X = minmaxscale.transform(X)

    save_misc_data(path, X, labels, N)
    return X, labels 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def applyFeatures(dataset, delta):
    """
    applies rolling mean and delayed returns to each dataframe in the list
    """
    columns = dataset.columns
    close = columns[-3]
    returns = columns[-1]
    for n in delta:
        addFeatures(dataset, close, returns, n)

    dataset = dataset.drop(dataset.index[0:max(delta)]) #drop NaN due to delta spanning

    # normalize columns
    scaler = preprocessing.MinMaxScaler()
    return pd.DataFrame(scaler.fit_transform(dataset),\
            columns=dataset.columns, index=dataset.index) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def get_term_topic(self, X):
        n_features = X.shape[1]
        id2word = self.vocabulary_
        word2topic = {}

        with open('word_topic.txt', 'r') as f:
            for line in f:
                strs = line.decode('utf-8').strip('\n').split('\t')
                word2topic[strs[0]] = strs[2]

        topic = np.zeros((len(id2word),))

        for i, key in enumerate(id2word):
            if key in word2topic:
                topic[id2word[key]] = word2topic[key]
            else:
                print key

        topic = preprocessing.MinMaxScaler().fit_transform(topic)
        # topic = sp.spdiags(topic, diags=0, m=n_features,
        #                    n=n_features, format='csr')
        return topic 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def test_metrics_wrapper():
    # make the features in y be in different scales
    y = np.array([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]]) * [1, 100]

    # With no scaler provided it is relevant which of the two series gets an 80% error
    metric_func_noscaler = model_utils.metric_wrapper(mean_squared_error)

    mse_feature_one_wrong = metric_func_noscaler(y, y * [0.8, 1])
    mse_feature_two_wrong = metric_func_noscaler(y, y * [1, 0.8])

    assert not np.isclose(mse_feature_one_wrong, mse_feature_two_wrong)

    # With a scaler provided it is not relevant which of the two series gets an 80%
    # error
    scaler = MinMaxScaler().fit(y)
    metric_func_scaler = model_utils.metric_wrapper(mean_squared_error, scaler=scaler)

    mse_feature_one_wrong = metric_func_scaler(y, y * [0.8, 1])
    mse_feature_two_wrong = metric_func_scaler(y, y * [1, 0.8])

    assert np.isclose(mse_feature_one_wrong, mse_feature_two_wrong) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def build_ensemble(**kwargs):
    """Generate ensemble."""

    ens = SuperLearner(**kwargs)
    prep = {'Standard Scaling': [StandardScaler()],
            'Min Max Scaling': [MinMaxScaler()],
            'No Preprocessing': []}

    est = {'Standard Scaling':
               [ElasticNet(), Lasso(), KNeighborsRegressor()],
           'Min Max Scaling':
               [SVR()],
           'No Preprocessing':
               [RandomForestRegressor(random_state=SEED),
                GradientBoostingRegressor()]}

    ens.add(est, prep)

    ens.add(GradientBoostingRegressor(), meta=True)

    return ens 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def test_build_meowa_factory():

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    from sklearn.preprocessing import MinMaxScaler
    X = MinMaxScaler().fit_transform(X)

    l = nfpc.FuzzyPatternClassifier(membership_factory=t_factory,
                                    aggregation_factory=nfpc.MEOWAFactory())

    from sklearn.model_selection import cross_val_score

    scores = cross_val_score(l, X, y, cv=10)
    mean = np.mean(scores)

    assert 0.80 < mean 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def test_build_ps_owa_factory():

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    from sklearn.preprocessing import MinMaxScaler
    X = MinMaxScaler().fit_transform(X)

    l = nfpc.FuzzyPatternClassifier(
        membership_factory=t_factory,
        aggregation_factory=nfpc.GAOWAFactory(optimizer=nfpc.ps_owa_optimizer())
    )

    from sklearn.model_selection import cross_val_score

    scores = cross_val_score(l, X, y, cv=10)
    mean = np.mean(scores)

    print("mean", mean)

    assert 0.92 < mean 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def test_classifier_iris():

    iris = load_iris()

    X = iris.data
    y = iris.target

    from sklearn.preprocessing import MinMaxScaler
    X = MinMaxScaler().fit_transform(X)

    l = fpcga.FuzzyPatternClassifierGA(iterations=100, random_state=1)

    from sklearn.model_selection import cross_val_score

    scores = cross_val_score(l, X, y, cv=10)

    assert len(scores) == 10
    assert np.mean(scores) > 0.6
    mean = np.mean(scores)

    print("mean", mean)

    assert 0.92 == pytest.approx(mean, 0.01) 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def scale_target_for_each_time_group(self, X, tgc_wo_time):
        # Go through groups and standard scale them
        if len(tgc_wo_time) > 0:
            X_groups = X.groupby(tgc_wo_time)
        else:
            X_groups = [([None], X)]

        self.scalers = {}
        scaled_ys = []
        for key, X_grp in X_groups:
            # Create dict key to store the min max scaler
            grp_hash = self.get_hash(key)
            # Scale target for current group
            self.scalers[grp_hash] = MinMaxScaler()
            y_skl = self.scalers[grp_hash].fit_transform(X_grp[['y']].values)
            # Put back in a DataFrame to keep track of original index
            y_skl_df = pd.DataFrame(y_skl, columns=['y'])
            y_skl_df.index = X_grp.index
            scaled_ys.append(y_skl_df)
        # Set target back in original frame but keep original
        X['y_orig'] = X['y']
        X['y'] = pd.concat(tuple(scaled_ys), axis=0)
        return X 

# 需要導入模塊: from sklearn import preprocessing [as 別名]
# 或者: from sklearn.preprocessing import MinMaxScaler [as 別名]
def _pp_min_max_scale(df):
    """
    特征值歸一化處理
    """
    print("  start minmax scaling...")
    # drop掉id和price_date字段
    # df = df.drop(['id', 'price_date'], axis=1)
    # 保存index信息及column信息
    index = df.index
    columns = df.columns
    # 對特征進行歸一化
    feature_scaled = preprocessing.MinMaxScaler().fit_transform(df.iloc[:, :-1])

    target = np.array(df.iloc[:, -1])
    target.shape = (len(target), 1)

    # 合並歸一化後的X和未做歸一化的y（歸一化後Pandas 的 DataFrame類型會轉換成numpy的ndarray類型）
    df_scaled = pd.DataFrame(np.hstack((feature_scaled, target)))
    # 重新設置索引及column信息
    df_scaled.index = index
    df_scaled.columns = columns

    print("  minmax scaling finished.")
    return df_scaled