Python preprocessing.Binarizer方法代码示例

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def _transform(arr, method):
    if method is not None:
        if method in ["log", "log10"]:
            # arr = np.log(arr, where=(arr > 0))
            # hacky, but np.log(arr, where=arr>0) is really buggy
            arr = arr.copy()
            if method == "log":
                arr[arr > 0] = np.log(arr[arr > 0])
            else:
                arr[arr > 0] = np.log10(arr[arr > 0])
        elif method in ["zero-boost", "simple-all", "simple-nonzero"]:
            arr = pass_to_ranks(arr, method=method)
        elif method == "binarize":
            transformer = Binarizer().fit(arr)
            arr = transformer.transform(arr)
        else:
            msg = "Transform must be one of {log, log10, binarize, zero-boost, simple-all, \
            simple-nonzero, not {}.".format(
                method
            )
            raise ValueError(msg)

    return arr 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def get_model_alias(model_type):
    """
    Get alias model. Raise an exception if not found.

    :param model_type:  A scikit-learn object (e.g., SGDClassifier
                        and Binarizer)
    :return: A string which stands for the type of the input model in
             our conversion framework
    """
    res = _get_sklearn_operator_name(model_type)
    if res is None:
        raise RuntimeError("Unable to find alias for model '{}'. "
                           "The converter is likely missing."
                           "".format(type(model_type)))
    return res


# registered converters 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_model_binarizer(self):
        data = np.array([[1., -1., 2.],
                         [2., 0., 0.],
                         [0., 1., -1.]], dtype=np.float32)
        model = Binarizer(threshold=0.5)
        model_onnx = convert_sklearn(
            model,
            "scikit-learn binarizer",
            [("input", FloatTensorType(data.shape))],
        )
        self.assertTrue(model_onnx is not None)
        dump_data_and_model(
            data,
            model,
            model_onnx,
            basename="SklearnBinarizer-SkipDim1",
        ) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_onnx_helper_load_save(self):
        model = make_pipeline(StandardScaler(), Binarizer(threshold=0.5))
        X = numpy.array([[0.1, 1.1], [0.2, 2.2]])
        model.fit(X)
        model_onnx = convert_sklearn(model, "binarizer",
                                     [("input", FloatTensorType([None, 2]))])
        filename = "temp_onnx_helper_load_save.onnx"
        save_onnx_model(model_onnx, filename)
        model = load_onnx_model(filename)
        new_model = select_model_inputs_outputs(model, "variable")
        assert new_model.graph is not None

        tr1 = self.get_model(model)
        tr2 = self.get_model(new_model)
        X = X.astype(numpy.float32)
        X1 = tr1(X)
        X2 = tr2(X)
        assert X1.shape == (2, 2)
        assert X2.shape == (2, 2) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_onnx_helper_load_save_init(self):
        model = make_pipeline(
            Binarizer(),
            OneHotEncoder(sparse=False, handle_unknown='ignore'),
            StandardScaler())
        X = numpy.array([[0.1, 1.1], [0.2, 2.2], [0.4, 2.2], [0.2, 2.4]])
        model.fit(X)
        model_onnx = convert_sklearn(model, "pipe3",
                                     [("input", FloatTensorType([None, 2]))])
        filename = "temp_onnx_helper_load_save.onnx"
        save_onnx_model(model_onnx, filename)
        model = load_onnx_model(filename)
        new_model = select_model_inputs_outputs(model, "variable")
        assert new_model.graph is not None

        tr1 = self.get_model(model)
        tr2 = self.get_model(new_model)
        X = X.astype(numpy.float32)
        X1 = tr1(X)
        X2 = tr2(X)
        assert X1.shape == (4, 2)
        assert X2.shape == (4, 2) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.preprocessing.Binarizer, pp.Binarizer)
        self.assertIs(df.preprocessing.FunctionTransformer,
                      pp.FunctionTransformer)
        self.assertIs(df.preprocessing.Imputer, pp.Imputer)
        self.assertIs(df.preprocessing.KernelCenterer, pp.KernelCenterer)
        self.assertIs(df.preprocessing.LabelBinarizer, pp.LabelBinarizer)
        self.assertIs(df.preprocessing.LabelEncoder, pp.LabelEncoder)
        self.assertIs(df.preprocessing.MultiLabelBinarizer, pp.MultiLabelBinarizer)
        self.assertIs(df.preprocessing.MaxAbsScaler, pp.MaxAbsScaler)
        self.assertIs(df.preprocessing.MinMaxScaler, pp.MinMaxScaler)
        self.assertIs(df.preprocessing.Normalizer, pp.Normalizer)
        self.assertIs(df.preprocessing.OneHotEncoder, pp.OneHotEncoder)
        self.assertIs(df.preprocessing.PolynomialFeatures, pp.PolynomialFeatures)
        self.assertIs(df.preprocessing.RobustScaler, pp.RobustScaler)
        self.assertIs(df.preprocessing.StandardScaler, pp.StandardScaler) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_transform_1d_frame_int(self):
        arr = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
        idx = pd.Index('a b c d e f g h i'.split(' '))
        df = pdml.ModelFrame(arr, index=idx, columns=['X'])
        self.assertEqual(len(df.columns), 1)

        # reshape arr to 2d
        arr = arr.reshape(-1, 1)

        if pd.compat.PY3:
            models = ['Binarizer', 'Imputer', 'StandardScaler']
            # MinMaxScalar raises TypeError in ufunc
        else:
            models = ['Binarizer', 'Imputer', 'StandardScaler', 'MinMaxScaler']

        for model in models:
            mod1 = getattr(df.preprocessing, model)()
            mod2 = getattr(pp, model)()

            self._assert_transform(df, arr, mod1, mod2)

            mod1 = getattr(df.preprocessing, model)()
            mod2 = getattr(pp, model)()
            self._assert_fit_transform(df, arr, mod1, mod2) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def sklearn_one_hot_vectorize(corpus):
    # The Sklearn one hot vectorize method

    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.preprocessing import Binarizer

    freq    = CountVectorizer()
    vectors = freq.fit_transform(corpus)

    print(len(vectors.toarray()[0]))

    onehot  = Binarizer()
    vectors = onehot.fit_transform(vectors.toarray())

    print(len(vectors[0])) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def _get_sklearn_operator_name(model_type):
    """
    Get operator name of the input argument

    :param model_type:  A scikit-learn object (e.g., SGDClassifier
                        and Binarizer)
    :return: A string which stands for the type of the input model in
             our conversion framework
    """
    if model_type not in sklearn_operator_name_map:
        # "No proper operator name found, it means a local operator.
        return None
    return sklearn_operator_name_map[model_type] 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def test_transform_series_int(self):
        arr = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3])
        s = pdml.ModelSeries(arr, index='a b c d e f g h i'.split(' '))

        # reshape arr to 2d
        arr = arr.reshape(-1, 1)

        if pd.compat.PY3:
            models = ['Binarizer', 'Imputer', 'StandardScaler']
            # MinMaxScalar raises TypeError in ufunc
        else:
            models = ['Binarizer', 'Imputer', 'StandardScaler', 'MinMaxScaler']

        for model in models:
            mod1 = getattr(s.preprocessing, model)()
            mod2 = getattr(pp, model)()
            s.fit(mod1)
            mod2.fit(arr)

            result = s.transform(mod1)
            expected = mod2.transform(arr).flatten()

            self.assertIsInstance(result, pdml.ModelSeries)
            self.assert_numpy_array_almost_equal(result.values, expected)

            mod1 = getattr(s.preprocessing, model)()
            mod2 = getattr(pp, model)()

            result = s.fit_transform(mod1)
            expected = mod2.fit_transform(arr).flatten()

            self.assertIsInstance(result, pdml.ModelSeries)
            self.assert_numpy_array_almost_equal(result.values, expected) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def main():
    x, fc6 = initModel()
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    img_names = load_image_names(args.input_data_dir)

    with open(args.output_image_name_file, 'w') as img_names_file:
        for img_name in img_names:
            img_names_file.write(img_name + '\n')

    t = time.time()
    # 图像太多了，必须分批次
    batch_size = 100
    features = []

    with open(args.output_feature_file, 'w') as output_file:
        for i in range(0, int(math.ceil(len(img_names) / (batch_size * 1.0)))):
            print('batch: %d' % i)
            if (i + 1) * batch_size < len(img_names):
                img_names_batch = img_names[i * batch_size:(i + 1) * batch_size]
            else:
                img_names_batch = img_names[i * batch_size:len(img_names)]
            img_batch = load_images(img_names_batch)
            output = sess.run(fc6, feed_dict={x: img_batch})
            features.append(output)
        features = np.vstack(features)
        # binarizer = preprocessing.Binarizer().fit(features)
        # features = binarizer.transform(features)
        np.save(output_file, features)

    # with open('fc6.npy', 'w') as output_file:
    #     for i in range(0, int(math.ceil(len(imgs) / (batch_size * 1.0)))):
    #         print('batch: %d' % i)
    #         if (i + 1) * batch_size < len(imgs):
    #             img_batch = imgs[i * batch_size:(i + 1) * batch_size]
    #         else:
    #             img_batch = imgs[i * batch_size: len(imgs)]
    #         output = sess.run(fc6, feed_dict={x: img_batch})
    #         features.append(output)
    #     features = np.vstack(features)
    #     np.save(output_file, features)

    print(time.time() - t) 

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import Binarizer [as 别名]
def main():
    t = time.time()
    img = imread(args.img_file_path)
    imgs = [img, watermark(img), rotate(img), crop(img), mirror(img)]
    imgs_norm = image_normalize(imgs)
    dataset_features = np.load('fc6.npy')

    query_start = time.time()
    query_features = extract_feature(imgs_norm)
    binarizer = preprocessing.Binarizer().fit(query_features)
    query_features = binarizer.transform(query_features)
    print(dataset_features)
    # https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html#scipy.spatial.distance.cdist
    cosine = distance.cdist(dataset_features, query_features, 'cosine')
    print(cosine.shape)
    dis = cosine
    inds_all = argsort(dis, axis=0)  # 按列排序 https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
    print('query cost: %f, dataset: %d, query: %d' % (time.time() - query_start, len(dataset_features), len(imgs)))
    img_names = load_image_names()
    fig, axes = plt.subplots(5, 11, figsize=(22, 10), subplot_kw={'xticks': [], 'yticks': []})
    fig.subplots_adjust(hspace=0.15, wspace=0.01, left=.02, right=.98, top=.92, bottom=.08)
    titles = ['original', 'watermark', 'rotate', 'crop', 'mirror']
    for i in range(len(imgs)):
        topK = []
        inds = inds_all[:, i]
        # print(inds)
        for k in range(10):
            topK.append(img_names[inds[k]])
            print(inds[k], dis[inds[k], i], img_names[inds[k]])

        original = axes[i, 0]
        original.set_title(titles[i])
        img = imgs[i]
        original.imshow(img)
        for j in range(10):
            ax = axes[i, j + 1]
            img = imread(topK[j])
            ax.imshow(img)
            title = '%d : %f' % (j + 1, dis[inds[j], i])
            ax.set_title(title)

    savePath = args.img_file_path + '_search_result.jpg'
    plt.savefig(savePath)
    print(time.time() - t)
    # os.system('open -a Preview.app -F ' + savePath)