1. Advisor
  2. autokeras
  3. functions
  4. autokeras.utils.to_type_key
View all autokeras analysis

How to use the autokeras.utils.to_type_key function in autokeras

To help you get started, we’ve selected a few autokeras examples, based on popular ways it is used in public projects.

Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.

github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
self.label_encoders[key] = encoder.deserialize(label_encoder)
        for key, label_encoder_state in state['label_encoders_state'].items():
            self.label_encoders[key].set_state(label_encoder_state)
        self.column_names = state['column_names']
        self.column_types = state['column_types']
        self.num_columns = state['num_columns']
        self.shape = state['shape']
        self.num_rows = state['num_rows']
        self.categorical_col = state['categorical_col']
        self.numerical_col = state['numerical_col']
        self.value_counters = utils.to_type_key(state['value_counters'], int)
        self.categorical_categorical = utils.to_type_key(
            state['categorical_categorical'], ast.literal_eval)
        self.numerical_categorical = utils.to_type_key(
            state['numerical_categorical'], ast.literal_eval)
        self.count_frequency = utils.to_type_key(state['count_frequency'], int)
        self.high_level1_col = state['high_level1_col']
        self.high_level2_col = state['high_level2_col']
        self.high_level_cat_cat = utils.to_type_key(
            state['high_level_cat_cat'], ast.literal_eval)
        self.high_level_num_cat = utils.to_type_key(
            state['high_level_num_cat'], ast.literal_eval)
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
self.num_columns = state['num_columns']
        self.shape = state['shape']
        self.num_rows = state['num_rows']
        self.categorical_col = state['categorical_col']
        self.numerical_col = state['numerical_col']
        self.value_counters = utils.to_type_key(state['value_counters'], int)
        self.categorical_categorical = utils.to_type_key(
            state['categorical_categorical'], ast.literal_eval)
        self.numerical_categorical = utils.to_type_key(
            state['numerical_categorical'], ast.literal_eval)
        self.count_frequency = utils.to_type_key(state['count_frequency'], int)
        self.high_level1_col = state['high_level1_col']
        self.high_level2_col = state['high_level2_col']
        self.high_level_cat_cat = utils.to_type_key(
            state['high_level_cat_cat'], ast.literal_eval)
        self.high_level_num_cat = utils.to_type_key(
            state['high_level_num_cat'], ast.literal_eval)
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
def set_state(self, state):
        super().set_state(state)
        for key, label_encoder in state['label_encoders'].items():
            self.label_encoders[key] = encoder.deserialize(label_encoder)
        for key, label_encoder_state in state['label_encoders_state'].items():
            self.label_encoders[key].set_state(label_encoder_state)
        self.column_names = state['column_names']
        self.column_types = state['column_types']
        self.num_columns = state['num_columns']
        self.shape = state['shape']
        self.num_rows = state['num_rows']
        self.categorical_col = state['categorical_col']
        self.numerical_col = state['numerical_col']
        self.value_counters = utils.to_type_key(state['value_counters'], int)
        self.categorical_categorical = utils.to_type_key(
            state['categorical_categorical'], ast.literal_eval)
        self.numerical_categorical = utils.to_type_key(
            state['numerical_categorical'], ast.literal_eval)
        self.count_frequency = utils.to_type_key(state['count_frequency'], int)
        self.high_level1_col = state['high_level1_col']
        self.high_level2_col = state['high_level2_col']
        self.high_level_cat_cat = utils.to_type_key(
            state['high_level_cat_cat'], ast.literal_eval)
        self.high_level_num_cat = utils.to_type_key(
            state['high_level_num_cat'], ast.literal_eval)
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
def get_state(self):
        state = super().get_state()
        label_encoders = {
            key: encoder.serialize(label_encoder)
            for key, label_encoder in self.label_encoders.items()}
        label_encoders_state = {
            key: label_encoder.get_state()
            for key, label_encoder in self.label_encoders.items()}
        state.update({
            'shape': self.shape,
            'num_rows': self.num_rows,
            'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
super().set_state(state)
        for key, label_encoder in state['label_encoders'].items():
            self.label_encoders[key] = encoder.deserialize(label_encoder)
        for key, label_encoder_state in state['label_encoders_state'].items():
            self.label_encoders[key].set_state(label_encoder_state)
        self.column_names = state['column_names']
        self.column_types = state['column_types']
        self.num_columns = state['num_columns']
        self.shape = state['shape']
        self.num_rows = state['num_rows']
        self.categorical_col = state['categorical_col']
        self.numerical_col = state['numerical_col']
        self.value_counters = utils.to_type_key(state['value_counters'], int)
        self.categorical_categorical = utils.to_type_key(
            state['categorical_categorical'], ast.literal_eval)
        self.numerical_categorical = utils.to_type_key(
            state['numerical_categorical'], ast.literal_eval)
        self.count_frequency = utils.to_type_key(state['count_frequency'], int)
        self.high_level1_col = state['high_level1_col']
        self.high_level2_col = state['high_level2_col']
        self.high_level_cat_cat = utils.to_type_key(
            state['high_level_cat_cat'], ast.literal_eval)
        self.high_level_num_cat = utils.to_type_key(
            state['high_level_num_cat'], ast.literal_eval)
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
label_encoders_state = {
            key: label_encoder.get_state()
            for key, label_encoder in self.label_encoders.items()}
        state.update({
            'shape': self.shape,
            'num_rows': self.num_rows,
            'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
def get_state(self):
        state = super().get_state()
        label_encoders = {
            key: encoder.serialize(label_encoder)
            for key, label_encoder in self.label_encoders.items()}
        label_encoders_state = {
            key: label_encoder.get_state()
            for key, label_encoder in self.label_encoders.items()}
        state.update({
            'shape': self.shape,
            'num_rows': self.num_rows,
            'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
state.update({
            'shape': self.shape,
            'num_rows': self.num_rows,
            'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state
github keras-team / autokeras / autokeras / hypermodel / preprocessor.py View on Github external
def get_state(self):
        state = super().get_state()
        label_encoders = {
            key: encoder.serialize(label_encoder)
            for key, label_encoder in self.label_encoders.items()}
        label_encoders_state = {
            key: label_encoder.get_state()
            for key, label_encoder in self.label_encoders.items()}
        state.update({
            'shape': self.shape,
            'num_rows': self.num_rows,
            'categorical_col': self.categorical_col,
            'numerical_col': self.numerical_col,
            'label_encoders': utils.to_type_key(label_encoders, str),
            'label_encoders_state': utils.to_type_key(label_encoders_state, str),
            'value_counters': utils.to_type_key(self.value_counters, str),
            'categorical_categorical': utils.to_type_key(
                self.categorical_categorical, str),
            'numerical_categorical': utils.to_type_key(
                self.numerical_categorical, str),
            'count_frequency': utils.to_type_key(self.count_frequency, str),
            'high_level1_col': self.high_level1_col,
            'high_level2_col': self.high_level2_col,
            'high_level_cat_cat': utils.to_type_key(self.high_level_cat_cat, str),
            'high_level_num_cat': utils.to_type_key(self.high_level_num_cat, str),
            'column_names': self.column_names,
            'column_types': utils.to_type_key(self.column_types, str),
            'num_columns': self.num_columns,
        })
        return state

autokeras

AutoML for deep learning

GitHub
Apache-2.0
Latest version published 6 months ago

Package Health Score

74 / 100
Full package analysis

Popular autokeras functions

Similar packages