How to use the a2ml.api.utils.dataframe.DataFrame.create_dataframe function in a2ml

def test_process_prediction(self):
        model_path = 'tests/fixtures/test_predict_by_model/iris'
        options = fsclient.read_json_file(os.path.join(model_path, "options.json"))
        target_categories = ["setosa", "versicolor", "virginica"]

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        ds.drop([options['targetFeature']])
        results = ["setosa", "versicolor", "virginica", "setosa", "versicolor", "virginica"] 
        results_proba =  None
        proba_classes = None

        ModelHelper.process_prediction(ds, 
            results, results_proba, proba_classes, 
            None, options.get('minority_target_class'), 
            options['targetFeature'], target_categories)

        ds_test = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        self.assertEqual(ds.dtypes, ds_test.dtypes)
        self.assertEqual(ds.df.values.tolist(), ds_test.df.values.tolist())

self.assertEqual( len(res['data']), 6)

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=False, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual( type(res[0]), dict)
        self.assertEqual( res[0][options['targetFeature']], 'setosa')

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        fsclient.remove_file(results_file_path)
        self.assertFalse(fsclient.is_file_exists(results_file_path))
        fsclient.remove_file(predicted_file_path)
        self.assertFalse(fsclient.is_file_exists(predicted_file_path))

        ds.options['data_path'] = None
        ds.loaded_columns = ds.columns
        res = ModelHelper.save_prediction(ds, prediction_id, 
            support_review_model=False, json_result=False, count_in_result=False, prediction_date=prediction_date, 
            model_path=model_path, model_id=options.get('uid'))
        self.assertEqual( res['columns'], ds.columns)
        self.assertEqual( len(res['data']), 6)
        self.assertEqual( type(res['data'][0]), list)

model_path = 'tests/fixtures/test_predict_by_model/iris'
        options = fsclient.read_json_file(os.path.join(model_path, "options.json"))
        target_categories = ["setosa", "versicolor", "virginica"]

        ds = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        ds.drop([options['targetFeature']])
        results = ["setosa", "versicolor", "virginica", "setosa", "versicolor", "virginica"] 
        results_proba =  None
        proba_classes = None

        ModelHelper.process_prediction(ds, 
            results, results_proba, proba_classes, 
            None, options.get('minority_target_class'), 
            options['targetFeature'], target_categories)

        ds_test = DataFrame.create_dataframe(os.path.join(model_path, "iris_test.csv"))
        self.assertEqual(ds.dtypes, ds_test.dtypes)
        self.assertEqual(ds.df.values.tolist(), ds_test.df.values.tolist())

def _predict_on_cloud(self, filename, model_id, threshold, data, columns, output):
        ds = DataFrame.create_dataframe(filename, data, columns)

        pipeline_api = AugerPipelineApi(self.ctx, None, model_id)
        predictions = pipeline_api.predict(ds.get_records(), ds.columns, threshold)

        ds_result = DataFrame.create_dataframe(None, records=predictions['data'], features=predictions['columns'])
        ds_result.options['data_path'] = filename
        return ModelHelper.save_prediction_result(ds_result, 
            prediction_id = None, support_review_model = False, 
            json_result=False, count_in_result=False, prediction_date=None, 
            model_path=None, model_id=model_id, output=output)

def _predict_on_cloud(self, filename, model_id, threshold, data, columns, output):
        ds = DataFrame.create_dataframe(filename, data, columns)

        pipeline_api = AugerPipelineApi(self.ctx, None, model_id)
        predictions = pipeline_api.predict(ds.get_records(), ds.columns, threshold)

        ds_result = DataFrame.create_dataframe(None, records=predictions['data'], features=predictions['columns'])
        ds_result.options['data_path'] = filename
        return ModelHelper.save_prediction_result(ds_result, 
            prediction_id = None, support_review_model = False, 
            json_result=False, count_in_result=False, prediction_date=None, 
            model_path=None, model_id=model_id, output=output)