def get_outliers_per_gene(data, gene, method="mad", cutoff=2): result = None if method == "mad": result = out.mad_outliers(data, gene, cutoff) elif method == "kmeans": result = out.cluster_outliers(data, gene, cutoff) return result
def stream_detect_outliers_in_data(data, method, min_dist, max_samples, mining_id): # if exists -> get from db processing_result = None store_path = APP_CONFIG["application_files_location"] + APP_CONFIG["application_store_name"] data = dr.get_data_frame_from_hdf(data, store_path) # For demo purposes (cleaning) data = data[-(data.sum(1) <= 5)] if method == "kmeans_outliers": return om.cluster_outliers(data, data.index, max_samples=max_samples, min_dist=min_dist, mining_id=mining_id) if method == "mad": return om.mad_outliers(data, data.index, max_samples)