Warning

This document is for an old release of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.

Source code for galaxy.tools.recommendations

""" Compute tool recommendations """

import json
import logging
import os

import h5py
import numpy as np
import requests
import yaml

from galaxy.tools.parameters import populate_state
from galaxy.tools.parameters.basic import workflow_building_modes
from galaxy.util import DEFAULT_SOCKET_TIMEOUT
from galaxy.workflow.modules import module_factory

log = logging.getLogger(__name__)


[docs]class ToolRecommendations:
[docs] def __init__(self): self.tool_recommendation_model_path = None self.admin_tool_recommendations_path = None self.deprecated_tools = dict() self.admin_recommendations = dict() self.model_data_dictionary = dict() self.reverse_dictionary = dict() self.all_tools = dict() self.tool_weights_sorted = dict() self.session = None self.graph = None self.loaded_model = None self.compatible_tools = None self.standard_connections = None self.max_seq_len = 25
[docs] def get_predictions(self, trans, tool_sequence, remote_model_url): """ Compute tool predictions """ recommended_tools = dict() self.__collect_admin_preferences(trans.app.config.admin_tool_recommendations_path) is_set = self.__set_model(trans, remote_model_url) if is_set is True: # get the recommended tools for a tool sequence recommended_tools = self.__compute_tool_prediction(trans, tool_sequence) else: tool_sequence = "" return tool_sequence, recommended_tools
def __set_model(self, trans, remote_model_url): """ Create model and associated dictionaries for recommendations """ if not self.graph: # import moves from the top of file: in case the tool recommendation feature is disabled, # keras is not downloaded because of conditional requirement and Galaxy does not build try: import tensorflow as tf tf.compat.v1.disable_v2_behavior() except Exception: trans.response.status = 400 return False # set graph and session only once if self.graph is None: self.graph = tf.Graph() self.session = tf.compat.v1.Session(graph=self.graph) model_weights = list() counter_layer_weights = 0 self.tool_recommendation_model_path = self.__download_model(remote_model_url) # read the hdf5 attributes trained_model = h5py.File(self.tool_recommendation_model_path, "r") model_config = json.loads(trained_model["model_config"][()]) # set tensorflow's graph and session to maintain # consistency between model load and predict methods with self.graph.as_default(): with self.session.as_default(): try: # iterate through all the attributes of the model to find weights of neural network layers for item in trained_model.keys(): if "weight_" in item: weight = trained_model[f"weight_{str(counter_layer_weights)}"][()] model_weights.append(weight) counter_layer_weights += 1 self.loaded_model = tf.keras.models.model_from_json(model_config) self.loaded_model.set_weights(model_weights) except Exception as e: log.exception(e) trans.response.status = 400 return False # set the dictionary of tools self.model_data_dictionary = json.loads(trained_model["data_dictionary"][()]) self.reverse_dictionary = {v: k for k, v in self.model_data_dictionary.items()} # set the list of compatible tools self.compatible_tools = json.loads(trained_model["compatible_tools"][()]) tool_weights = json.loads(trained_model["class_weights"][()]) self.standard_connections = json.loads(trained_model["standard_connections"][()]) # sort the tools' usage dictionary tool_pos_sorted = [int(key) for key in tool_weights.keys()] for k in tool_pos_sorted: self.tool_weights_sorted[k] = tool_weights[str(k)] # collect ids and names of all the installed tools for tool_id, tool in trans.app.toolbox.tools(): t_id_renamed = tool_id if t_id_renamed.find("/") > -1: t_id_renamed = t_id_renamed.split("/")[-2] self.all_tools[t_id_renamed] = (tool_id, tool.name) return True def __collect_admin_preferences(self, admin_path): """ Collect preferences for recommendations of tools set by admins as dictionaries of deprecated tools and additional recommendations """ if not self.admin_tool_recommendations_path and admin_path is not None: self.admin_tool_recommendations_path = os.path.join(os.getcwd(), admin_path) if os.path.exists(self.admin_tool_recommendations_path): with open(self.admin_tool_recommendations_path) as admin_recommendations: admin_recommendation_preferences = yaml.safe_load(admin_recommendations) if admin_recommendation_preferences: for tool_id in admin_recommendation_preferences: tool_info = admin_recommendation_preferences[tool_id] if "is_deprecated" in tool_info[0]: self.deprecated_tools[tool_id] = tool_info[0]["text_message"] else: if tool_id not in self.admin_recommendations: self.admin_recommendations[tool_id] = tool_info def __download_model(self, model_url, download_local="database/"): """ Download the model from remote server """ local_dir = os.path.join(os.getcwd(), download_local, "tool_recommendation_model.hdf5") # read model from remote model_binary = requests.get(model_url, timeout=DEFAULT_SOCKET_TIMEOUT) # save model to a local directory with open(local_dir, "wb") as model_file: model_file.write(model_binary.content) return local_dir def __get_tool_extensions(self, trans, tool_id): """ Get the input and output extensions of a tool """ payload = {"type": "tool", "tool_id": tool_id, "_": "true"} inputs = payload.get("inputs", {}) trans.workflow_building_mode = workflow_building_modes.ENABLED module = module_factory.from_dict(trans, payload) if "tool_state" not in payload: module_state = {} populate_state(trans, module.get_inputs(), inputs, module_state, check=False) module.recover_state(module_state) inputs = module.get_all_inputs(connectable_only=True) outputs = module.get_all_outputs() input_extensions = list() output_extensions = list() for i_ext in inputs: input_extensions.extend(i_ext["extensions"]) for o_ext in outputs: output_extensions.extend(o_ext["extensions"]) return input_extensions, output_extensions def __filter_tool_predictions(self, trans, prediction_data, tool_ids, tool_scores, last_tool_name): """ Filter tool predictions based on datatype compatibility and tool connections. Add admin preferences to recommendations. """ last_compatible_tools = list() if last_tool_name in self.compatible_tools: last_compatible_tools = self.compatible_tools[last_tool_name].split(",") prediction_data["is_deprecated"] = False # get the list of datatype extensions of the last tool of the tool sequence _, last_output_extensions = self.__get_tool_extensions(trans, self.all_tools[last_tool_name][0]) prediction_data["o_extensions"] = list(set(last_output_extensions)) t_ids_scores = zip(tool_ids, tool_scores) # form the payload of the predicted tools to be shown for child, score in t_ids_scores: c_dict = dict() for t_id in self.all_tools: # select the name and tool id if it is installed in Galaxy if ( t_id == child and score > 0.0 and child in last_compatible_tools and child not in self.deprecated_tools ): full_tool_id = self.all_tools[t_id][0] pred_input_extensions, _ = self.__get_tool_extensions(trans, full_tool_id) c_dict["name"] = self.all_tools[t_id][1] c_dict["tool_id"] = full_tool_id c_dict["i_extensions"] = list(set(pred_input_extensions)) prediction_data["children"].append(c_dict) break # incorporate preferences set by admins if self.admin_tool_recommendations_path: # filter out deprecated tools t_ids_scores = [ (tid, score) for tid, score in zip(tool_ids, tool_scores) if tid not in self.deprecated_tools ] # set the property if the last tool of the sequence is deprecated if last_tool_name in self.deprecated_tools: prediction_data["is_deprecated"] = True prediction_data["message"] = self.deprecated_tools[last_tool_name] # add the recommendations given by admins for tool_id in self.admin_recommendations: if last_tool_name == tool_id: admin_recommendations = self.admin_recommendations[tool_id] if trans.app.config.overwrite_model_recommendations is True: prediction_data["children"] = admin_recommendations else: prediction_data["children"].extend(admin_recommendations) break # get the root name for displaying after tool run for t_id in self.all_tools: if t_id == last_tool_name: prediction_data["name"] = self.all_tools[t_id][1] break return prediction_data def __get_predicted_tools(self, base_tools, predictions, topk): """ Get predicted tools. If predicted tools are less in number, combine them with published tools """ intersection = list(set(predictions).intersection(set(base_tools))) return intersection[:topk] def __sort_by_usage(self, t_list, class_weights, d_dict): """ Sort predictions by usage/class weights """ tool_dict = dict() for tool in t_list: t_id = d_dict[tool] tool_dict[tool] = class_weights[t_id] tool_dict = dict(sorted(tool_dict.items(), key=lambda kv: kv[1], reverse=True)) return list(tool_dict.keys()), list(tool_dict.values()) def __separate_predictions(self, base_tools, predictions, last_tool_name, weight_values, topk): """ Get predictions from published and normal workflows """ last_base_tools = list() predictions = predictions * weight_values prediction_pos = np.argsort(predictions, axis=-1) topk_prediction_pos = prediction_pos[-topk:] # get tool ids pred_tool_ids = [self.reverse_dictionary[int(tool_pos)] for tool_pos in topk_prediction_pos] if last_tool_name in base_tools: last_base_tools = base_tools[last_tool_name] if type(last_base_tools).__name__ == "str": # get published or compatible tools for the last tool in a sequence of tools last_base_tools = last_base_tools.split(",") # get predicted tools p_tools = self.__get_predicted_tools(last_base_tools, pred_tool_ids, topk) sorted_c_t, sorted_c_v = self.__sort_by_usage(p_tools, self.tool_weights_sorted, self.model_data_dictionary) return sorted_c_t, sorted_c_v def __compute_tool_prediction(self, trans, tool_sequence): """ Compute the predicted tools for a tool sequences Return a payload with the tool sequences and recommended tools Return an empty payload with just the tool sequence if anything goes wrong within the try block """ topk = trans.app.config.topk_recommendations prediction_data = dict() tool_sequence = tool_sequence.split(",")[::-1] prediction_data["name"] = ",".join(tool_sequence) prediction_data["children"] = list() last_tool_name = tool_sequence[-1] # do prediction only if the last is present in the collections of tools if last_tool_name in self.model_data_dictionary: sample = np.zeros(self.max_seq_len) # get tool names without slashes and create a sequence vector for idx, tool_name in enumerate(tool_sequence): if tool_name.find("/") > -1: tool_name = tool_name.split("/")[-2] try: sample[idx] = int(self.model_data_dictionary[tool_name]) except Exception: log.exception(f"Failed to find tool {tool_name} in model") return prediction_data sample = np.reshape(sample, (1, self.max_seq_len)) # boost the predicted scores using tools' usage weight_values = list(self.tool_weights_sorted.values()) # predict next tools for a test path try: # use the same graph and session to predict with self.graph.as_default(): with self.session.as_default(): prediction = self.loaded_model.predict(sample) except Exception as e: log.exception(e) return prediction_data # get dimensions nw_dimension = prediction.shape[1] prediction = np.reshape(prediction, (nw_dimension,)) half_len = int(nw_dimension / 2) # get recommended tools from published workflows pub_t, pub_v = self.__separate_predictions( self.standard_connections, prediction[:half_len], last_tool_name, weight_values, topk ) # get recommended tools from normal workflows c_t, c_v = self.__separate_predictions( self.compatible_tools, prediction[half_len:], last_tool_name, weight_values, topk ) # combine predictions coming from different workflows # promote recommended tools coming from published workflows # to the top and then show other recommendations pub_t.extend(c_t) pub_v.extend(c_v) # remove duplicates if any pub_t = list(dict.fromkeys(pub_t)) pub_v = list(dict.fromkeys(pub_v)) prediction_data = self.__filter_tool_predictions(trans, prediction_data, pub_t, pub_v, last_tool_name) return prediction_data