Mercurial > repos > bgruening > create_tool_recommendation_model

annotate optimise_hyperparameters.py @ 2:76251d1ccdcc draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/tool_recommendation_model commit 6fa2a0294d615c9f267b766337dca0b2d3637219"
author bgruening
date Fri, 11 Oct 2019 18:24:54 -0400
parents 9bf25dbe00ad
children 5b3c08710e47
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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 """
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2 Find the optimal combination of hyperparameters
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3 """
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5 import numpy as np
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6 from hyperopt import fmin, tpe, hp, STATUS_OK, Trials
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8 from keras.models import Sequential
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9 from keras.layers import Dense, GRU, Dropout
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10 from keras.layers.embeddings import Embedding
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11 from keras.layers.core import SpatialDropout1D
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12 from keras.optimizers import RMSprop
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13 from keras.callbacks import EarlyStopping
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15 import utils
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18 class HyperparameterOptimisation:
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20 @classmethod
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21 def __init__(self):
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22 """ Init method. """
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24 @classmethod
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25 def train_model(self, config, reverse_dictionary, train_data, train_labels, class_weights):
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26 """
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27 Train a model and report accuracy
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28 """
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29 l_recurrent_activations = config["activation_recurrent"].split(",")
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30 l_output_activations = config["activation_output"].split(",")
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32 # convert items to integer
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33 l_batch_size = list(map(int, config["batch_size"].split(",")))
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34 l_embedding_size = list(map(int, config["embedding_size"].split(",")))
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35 l_units = list(map(int, config["units"].split(",")))
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37 # convert items to float
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38 l_learning_rate = list(map(float, config["learning_rate"].split(",")))
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39 l_dropout = list(map(float, config["dropout"].split(",")))
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40 l_spatial_dropout = list(map(float, config["spatial_dropout"].split(",")))
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41 l_recurrent_dropout = list(map(float, config["recurrent_dropout"].split(",")))
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43 optimize_n_epochs = int(config["optimize_n_epochs"])
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44 validation_split = float(config["validation_share"])
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46 # get dimensions
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47 dimensions = len(reverse_dictionary) + 1
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48 best_model_params = dict()
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49 early_stopping = EarlyStopping(monitor='val_loss', mode='min', verbose=1, min_delta=1e-4)
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51 # specify the search space for finding the best combination of parameters using Bayesian optimisation
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52 params = {
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53 "embedding_size": hp.quniform("embedding_size", l_embedding_size[0], l_embedding_size[1], 1),
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54 "units": hp.quniform("units", l_units[0], l_units[1], 1),
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55 "batch_size": hp.quniform("batch_size", l_batch_size[0], l_batch_size[1], 1),
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56 "activation_recurrent": hp.choice("activation_recurrent", l_recurrent_activations),
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57 "activation_output": hp.choice("activation_output", l_output_activations),
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58 "learning_rate": hp.loguniform("learning_rate", np.log(l_learning_rate[0]), np.log(l_learning_rate[1])),
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59 "dropout": hp.uniform("dropout", l_dropout[0], l_dropout[1]),
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60 "spatial_dropout": hp.uniform("spatial_dropout", l_spatial_dropout[0], l_spatial_dropout[1]),
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61 "recurrent_dropout": hp.uniform("recurrent_dropout", l_recurrent_dropout[0], l_recurrent_dropout[1])
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62 }
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63
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64 def create_model(params):
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65 model = Sequential()
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66 model.add(Embedding(dimensions, int(params["embedding_size"]), mask_zero=True))
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67 model.add(SpatialDropout1D(params["spatial_dropout"]))
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68 model.add(GRU(int(params["units"]), dropout=params["dropout"], recurrent_dropout=params["recurrent_dropout"], return_sequences=True, activation=params["activation_recurrent"]))
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69 model.add(Dropout(params["dropout"]))
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70 model.add(GRU(int(params["units"]), dropout=params["dropout"], recurrent_dropout=params["recurrent_dropout"], return_sequences=False, activation=params["activation_recurrent"]))
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71 model.add(Dropout(params["dropout"]))
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72 model.add(Dense(dimensions, activation=params["activation_output"]))
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73 optimizer_rms = RMSprop(lr=params["learning_rate"])
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74 model.compile(loss=utils.weighted_loss(class_weights), optimizer=optimizer_rms)
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75 model_fit = model.fit(
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76 train_data,
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77 train_labels,
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78 batch_size=int(params["batch_size"]),
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79 epochs=optimize_n_epochs,
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80 shuffle="batch",
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81 verbose=2,
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82 validation_split=validation_split,
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83 callbacks=[early_stopping]
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84 )
2
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85 return {'loss': model_fit.history["val_loss"][-1], 'status': STATUS_OK, 'model': model}
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86 # minimize the objective function using the set of parameters above
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87 trials = Trials()
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88 learned_params = fmin(create_model, params, trials=trials, algo=tpe.suggest, max_evals=int(config["max_evals"]))
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89 best_model = trials.results[np.argmin([r['loss'] for r in trials.results])]['model']
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90
0
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91 # set the best params with respective values
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92 for item in learned_params:
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93 item_val = learned_params[item]
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94 if item == 'activation_output':
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95 best_model_params[item] = l_output_activations[item_val]
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96 elif item == 'activation_recurrent':
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97 best_model_params[item] = l_recurrent_activations[item_val]
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98 else:
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99 best_model_params[item] = item_val
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100 return best_model_params, best_model