Mercurial > repos > bgruening > create_tool_recommendation_model

annotate predict_tool_usage.py @ 5:4f7e6612906b draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
author bgruening
date Fri, 06 May 2022 09:05:18 +0000
parents 5b3c08710e47
children e94dc7945639
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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1 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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2 Predict tool usage to weigh the predicted tools
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3 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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5 import collections
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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6 import csv
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7 import os
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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8 import warnings
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9
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10 import numpy as np
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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11 import utils
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12 from sklearn.model_selection import GridSearchCV
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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13 from sklearn.pipeline import Pipeline
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14 from sklearn.svm import SVR
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16 warnings.filterwarnings("ignore")
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18 main_path = os.getcwd()
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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21 class ToolPopularity:
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22 def __init__(self):
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23 """ Init method. """
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25 def extract_tool_usage(self, tool_usage_file, cutoff_date, dictionary):
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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26 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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27 Extract the tool usage over time for each tool
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28 """
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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29 tool_usage_dict = dict()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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30 all_dates = list()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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31 all_tool_list = list(dictionary.keys())
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32 with open(tool_usage_file, "rt") as usage_file:
4f7e6612906b "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 5eebc0cb44e71f581d548b7e842002705dd155eb"
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33 tool_usage = csv.reader(usage_file, delimiter="\t")
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34 for index, row in enumerate(tool_usage):
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35 row = [item.strip() for item in row]
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36 if (str(row[1]).strip() > cutoff_date) is True:
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37 tool_id = utils.format_tool_id(row[0])
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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38 if tool_id in all_tool_list:
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39 all_dates.append(row[1])
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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40 if tool_id not in tool_usage_dict:
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41 tool_usage_dict[tool_id] = dict()
9bf25dbe00ad "planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 7fac577189d01cedd01118a77fc2baaefe7d5cad"
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42 tool_usage_dict[tool_id][row[1]] = int(row[2])
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43 else:
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44 curr_date = row[1]
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45 # merge the usage of different version of tools into one
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46 if curr_date in tool_usage_dict[tool_id]:
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47 tool_usage_dict[tool_id][curr_date] += int(row[2])
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48 else:
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49 tool_usage_dict[tool_id][curr_date] = int(row[2])
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50 # get unique dates
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51 unique_dates = list(set(all_dates))
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52 for tool in tool_usage_dict:
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53 usage = tool_usage_dict[tool]
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54 # extract those dates for which tool's usage is not present in raw data
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55 dates_not_present = list(set(unique_dates) ^ set(usage.keys()))
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56 # impute the missing values by 0
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57 for dt in dates_not_present:
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58 tool_usage_dict[tool][dt] = 0
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59 # sort the usage list by date
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60 tool_usage_dict[tool] = collections.OrderedDict(sorted(usage.items()))
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61 return tool_usage_dict
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62
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63 def learn_tool_popularity(self, x_reshaped, y_reshaped):
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64 """
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65 Fit a curve for the tool usage over time to predict future tool usage
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66 """
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67 epsilon = 0.0
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68 cv = 5
5
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69 s_typ = "neg_mean_absolute_error"
0
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70 n_jobs = 4
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71 s_error = 1
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72 tr_score = False
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73 try:
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74 pipe = Pipeline(steps=[("regressor", SVR(gamma="scale"))])
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75 param_grid = {
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76 "regressor__kernel": ["rbf", "poly", "linear"],
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77 "regressor__degree": [2, 3],
0
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78 }
5
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79 search = GridSearchCV(
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80 pipe,
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81 param_grid,
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82 cv=cv,
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83 scoring=s_typ,
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84 n_jobs=n_jobs,
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85 error_score=s_error,
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86 return_train_score=tr_score,
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87 )
0
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88 search.fit(x_reshaped, y_reshaped.ravel())
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89 model = search.best_estimator_
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90 # set the next time point to get prediction for
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91 prediction_point = np.reshape([x_reshaped[-1][0] + 1], (1, 1))
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92 prediction = model.predict(prediction_point)
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93 if prediction < epsilon:
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94 prediction = [epsilon]
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95 return prediction[0]
5
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96 except Exception as e:
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97 print(e)
0
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98 return epsilon
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99
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100 def get_pupularity_prediction(self, tools_usage):
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101 """
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102 Get the popularity prediction for each tool
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103 """
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104 usage_prediction = dict()
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105 for tool_name, usage in tools_usage.items():
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106 y_val = list()
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107 x_val = list()
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108 for x, y in usage.items():
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109 x_val.append(x)
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110 y_val.append(y)
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111 x_pos = np.arange(len(x_val))
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112 x_reshaped = x_pos.reshape(len(x_pos), 1)
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113 y_reshaped = np.reshape(y_val, (len(x_pos), 1))
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114 prediction = np.round(self.learn_tool_popularity(x_reshaped, y_reshaped), 8)
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115 usage_prediction[tool_name] = prediction
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116 return usage_prediction