[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:class EntitiyEmbedding:\n    def __init__(self):\n        self.input_model = []\n        self.output_model = []\n        self.features = []\n        self.embeddings = []\n\n    def add(self, feature, input_shape, output_shape):\n        self.features.append(feature)\n        self.embeddings.append(feature)\n        input_model = Input(shape=(1,), name=(feature + \'_input\'))\n        output_model = Embedding(input_shape, output_shape, name=(feature + \'_embedding\'))(input_model)\n        output_model = Reshape(target_shape=(output_shape,))(output_model)\n        self.input_model.append(input_model)\n        self.output_model.append(output_model)\n\n    def dense(self, feature, output_shape):\n        self.features.append(feature)\n        input_model = Input(shape=(1,), name=(feature + \'_input\'))\n        output_model = Dense(output_shape, name=(feature + \'_dense\'))(input_model)\n        self.input_model.append(input_model)\n        self.output_model.append(output_model)\n\n    def concatenate(self):\n        output_model = Concatenate()(self.output_model)\n        output_model = Dense(1000, kernel_initializer="uniform")(output_model)\n        output_model = Activation(\'relu\')(output_model)\n        output_model = Dense(500, kernel_initializer="uniform")(output_model)\n        output_model = Activation(\'relu\')(output_model)\n        output_model = Dense(1)(output_model)\n        output_model = Activation(\'sigmoid\')(output_model)\n        self.model = KerasModel(inputs=self.input_model, outputs=output_model)\n        self.model.compile(loss=\'mean_absolute_error\', optimizer=\'adam\')\n\n    def split_features(self, X):\n        X_list = {}\n        for feature in self.features:\n            X_list[feature + \'_input\'] = X[[feature]]\n        return X_list\n\n    def fit(self, X_train, y_train, X_test, y_test, epochs=12, batch_size=128):\n        self.X_test = X_test\n        self.model.fit(self.split_features(X_train), y_train,\n                       validation_data=(self.split_features(X_test), y_test),\n                       epochs=epochs,\n                       batch_size=batch_size)\n\n    def predict(self, X=None):\n        if X is None:\n            X = self.X_test\n        pred = self.model.predict(self.split_features(X))\n        return pred\n\n    def get_weight(self):\n        weights = {}\n        for feature in self.embeddings:\n            w = self.model.get_layer(feature + \'_embedding\').get_weights()[0]\n            columns = []\n            for i in range(w.shape[1]):\n                columns.append(feature + \'_\' + str(i))\n            w = pd.DataFrame(w, columns=columns)\n            w.index.names = [feature]\n            weights[feature] = w\n        return weights\''}, {'role': 'assistant', 'content': "['modelling']"}]