Some notebooks do not run successfully

[teoguso]

/notebooks/hatespeech/rulesbased_hatespeech.ipynb and /notebooks/hatespeech/transferlearning_hatespeech.ipynb are currently failing. See the errors below.

rulesbased_hatespeech.ipynb

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-8bfbf01c1139> in <module>
     10 
     11 data = pd.read_csv("../../data/external/hatespeech/hs_data.csv")
---> 12 data['normalized'] = data['text'].apply(lambda comment: normalize(comment))
     13 
     14 # Generate vectors

~/anaconda3/envs/find-out/lib/python3.7/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)
   4040             else:
   4041                 values = self.astype(object).values
-> 4042                 mapped = lib.map_infer(values, f, convert=convert_dtype)
   4043 
   4044         if len(mapped) and isinstance(mapped[0], Series):

pandas/_libs/lib.pyx in pandas._libs.lib.map_infer()

<ipython-input-1-8bfbf01c1139> in <lambda>(comment)
     10 
     11 data = pd.read_csv("../../data/external/hatespeech/hs_data.csv")
---> 12 data['normalized'] = data['text'].apply(lambda comment: normalize(comment))
     13 
     14 # Generate vectors

~/Code/find-out/src/utils/preprocess_text_pipelines.py in normalize(dataframe)
     62     pipeline.register_processor(remove_stopwords)
     63 
---> 64     dataframe["cleaned"] = dataframe["text"].apply(pipeline.process_text)
     65     dataframe["normalized"] = normalizer(dataframe["cleaned"])
     66     return dataframe

TypeError: string indices must be integers

transferlearning_hatespeech.ipynb

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-bd19b623da49> in <module>
     82 
     83 ### EDA of PREDICTIONS
---> 84 df_original = returns_predictions(path_to_original_model)
     85 df_all = returns_predictions(path_to_fine_tuned_model)
     86 df_last = returns_predictions(path_to_fine_tuned_model_last)

<ipython-input-1-bd19b623da49> in returns_predictions(path_to_model, differences)
     41     """
     42     data = pd.read_csv(path_to_target_data)
---> 43     best_model = create_model(datasets[6], vocab_size)
     44     best_model.load_weights(path_to_model)
     45     predictions = get_predictions(best_model, datasets[4])

~/Code/find-out/src/models/hatespeech/model_translearn_hatespeech.py in create_model(word_embedding_matrix, vocab_size)
     15             weights=[word_embedding_matrix],
     16             input_length=140,
---> 17             trainable=False,
     18         )
     19     )

~/anaconda3/envs/find-out/lib/python3.7/site-packages/keras/engine/sequential.py in add(self, layer)
    179                 self.inputs = network.get_source_inputs(self.outputs[0])
    180         elif self.outputs:
--> 181             output_tensor = layer(self.outputs[0])
    182             if isinstance(output_tensor, list):
    183                 raise TypeError('All layers in a Sequential model '

~/anaconda3/envs/find-out/lib/python3.7/site-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
    472             if all([s is not None
    473                     for s in to_list(input_shape)]):
--> 474                 output_shape = self.compute_output_shape(input_shape)
    475             else:
    476                 if isinstance(input_shape, list):

~/anaconda3/envs/find-out/lib/python3.7/site-packages/keras/layers/embeddings.py in compute_output_shape(self, input_shape)
    125                 raise ValueError(
    126                     '"input_length" is %s, but received input has shape %s' %
--> 127                     (str(self.input_length), str(input_shape)))
    128             else:
    129                 for i, (s1, s2) in enumerate(zip(in_lens, input_shape[1:])):

ValueError: "input_length" is 140, but received input has shape (None, None, 40)