How to use the cornac.data.TextModality function in cornac

def test_tfidf_params(self):
        corpus = ['a b c', 'b c d d', 'c b e c f']
        ids = ['u1', 'u2', 'u3']

        modality = TextModality(corpus=corpus, ids=ids, max_vocab=6,
                                tfidf_params={
                                    'binary': False,
                                    'norm': 'l2',
                                    'use_idf': True,
                                    'smooth_idf': True,
                                    'sublinear_tf': False
                                }).build({'u1': 0, 'u2': 1, 'u3': 2})
        npt.assert_array_equal(modality.batch_tfidf([1]),
                               self.modality.batch_tfidf([1]))

        for k, v in {
            'binary': True,
            'norm': 'l1',
            'use_idf': False,
            'smooth_idf': False,
            'sublinear_tf': True

def test_build(self):
        TextModality().build()
        TextModality(corpus=['abc']).build()
        TextModality(corpus=['abc']).build({'b': 0})
        TextModality(corpus=['abc'], ids=['a']).build({'b': 0})

def test_build(self):
        TextModality().build()
        TextModality(corpus=['abc']).build()
        TextModality(corpus=['abc']).build({'b': 0})
        TextModality(corpus=['abc'], ids=['a']).build({'b': 0})

bm.user_text = ImageModality()
        except ValueError:
            assert True

        try:
            bm.item_text = ImageModality()
        except ValueError:
            assert True

        try:
            bm.user_image = TextModality()
        except ValueError:
            assert True

        try:
            bm.item_image = TextModality()
        except ValueError:
            assert True

        try:
            bm.user_graph = TextModality()
        except ValueError:
            assert True

        try:
            bm.item_graph = ImageModality()
        except ValueError:
            assert True

        try:
            bm.sentiment = TextModality()
        except ValueError:

bm.item_text = ImageModality()
        except ValueError:
            assert True

        try:
            bm.user_image = TextModality()
        except ValueError:
            assert True

        try:
            bm.item_image = TextModality()
        except ValueError:
            assert True

        try:
            bm.user_graph = TextModality()
        except ValueError:
            assert True

        try:
            bm.item_graph = ImageModality()
        except ValueError:
            assert True

        try:
            bm.sentiment = TextModality()
        except ValueError:
            assert True

        try:
            bm.sentiment = ImageModality()
        except ValueError:

# limitations under the License.
# ============================================================================
"""Example for Collaborative Deep Learning"""

import cornac
from cornac.data import Reader
from cornac.datasets import citeulike
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer

docs, item_ids = citeulike.load_text()
data = citeulike.load_feedback(reader=Reader(item_set=item_ids))

# build text modality
item_text_modality = TextModality(corpus=docs, ids=item_ids,
                                tokenizer=BaseTokenizer(stop_words='english'),
                                max_vocab=8000, max_doc_freq=0.5)

ratio_split = RatioSplit(data=data, test_size=0.2, exclude_unknowns=True,
                         item_text=item_text_modality, verbose=True, seed=123, rating_threshold=0.5)
cdl = cornac.models.CDL(k=50, autoencoder_structure=[200], max_iter=30,
                        lambda_u=0.1, lambda_v=1, lambda_w=0.1, lambda_n=1000)
rec_300 = cornac.metrics.Recall(k=300)

exp = cornac.Experiment(eval_method=ratio_split,
                        models=[cdl],
                        metrics=[rec_300])
exp.run()

# limitations under the License.
# ============================================================================
"""Example for HFT with Movilen 1m dataset """

import cornac
from cornac.data import Reader
from cornac.datasets import movielens
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer

plots, movie_ids = movielens.load_plot()
ml_1m = movielens.load_feedback(variant='1M', reader=Reader(item_set=movie_ids))

# build text module
item_text_modality = TextModality(corpus=plots, ids=movie_ids,
                                  tokenizer=BaseTokenizer(sep='\t', stop_words='english'),
                                  max_vocab=5000, max_doc_freq=0.5)

ratio_split = RatioSplit(data=ml_1m, test_size=0.2, exclude_unknowns=True,
                         item_text=item_text_modality, verbose=True, seed=123)

hft = cornac.models.HFT(k=10, max_iter=40, grad_iter=5, l2_reg=0.001, lambda_text=0.01, vocab_size=5000, seed=123)

mse = cornac.metrics.MSE()

exp = cornac.Experiment(eval_method=ratio_split,
                        models=[hft],
                        metrics=[mse],
                        user_based=False)
exp.run()

# limitations under the License.
# ============================================================================
"""Example for Collaborative Topic Modeling"""

import cornac
from cornac.data import Reader
from cornac.datasets import citeulike
from cornac.eval_methods import RatioSplit
from cornac.data import TextModality
from cornac.data.text import BaseTokenizer

docs, item_ids = citeulike.load_text()
data = citeulike.load_feedback(reader=Reader(item_set=item_ids))

# build text modality
item_text_modality = TextModality(corpus=docs, ids=item_ids,
                                tokenizer=BaseTokenizer(sep=' ', stop_words='english'),
                                max_vocab=8000, max_doc_freq=0.5)

ratio_split = RatioSplit(data=data, test_size=0.2, exclude_unknowns=True,
                         item_text=item_text_modality, verbose=True, seed=123, rating_threshold=0.5)

ctr = cornac.models.CTR(k=50, max_iter=50, lambda_v=1)

rec_300 = cornac.metrics.Recall(k=300)

exp = cornac.Experiment(eval_method=ratio_split,
                        models=[ctr],
                        metrics=[rec_300])
exp.run()

def item_text(self, input_modality):
        if input_modality is not None and not isinstance(input_modality, TextModality):
            raise ValueError(
                'input_modality has to be instance of TextModality but {}'.format(type(input_modality)))
        self.__item_text = input_modality

def user_text(self, input_modality):
        if input_modality is not None and not isinstance(input_modality, TextModality):
            raise ValueError(
                'input_modality has to be instance of TextModality but {}'.format(type(input_modality)))
        self.__user_text = input_modality