Mercurial > repos > bgruening > sklearn_ensemble

comparison keras_deep_learning.py @ 31:af0523c606a7 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author bgruening
date Mon, 16 Dec 2019 05:42:39 -0500
parents 47d4baa183b2
children 19d6c2745d34
comparison
equal deleted inserted replaced
30:ab4249158912 31:af0523c606a7
71 "constraint_type": 71 "constraint_type":
72 "MinMaxNorm" 72 "MinMaxNorm"
73 } 73 }
74 """ 74 """
75 constraint_type = config['constraint_type'] 75 constraint_type = config['constraint_type']
76 if constraint_type == 'None': 76 if constraint_type in ('None', ''):
77 return None 77 return None
78 78
79 klass = getattr(keras.constraints, constraint_type) 79 klass = getattr(keras.constraints, constraint_type)
80 options = config.get('constraint_options', {}) 80 options = config.get('constraint_options', {})
81 if 'axis' in options: 81 if 'axis' in options:
90 90
91 def _handle_layer_parameters(params): 91 def _handle_layer_parameters(params):
92 """Access to handle all kinds of parameters 92 """Access to handle all kinds of parameters
93 """ 93 """
94 for key, value in six.iteritems(params): 94 for key, value in six.iteritems(params):
95 if value == 'None': 95 if value in ('None', ''):
96 params[key] = None 96 params[key] = None
97 continue 97 continue
98 98
99 if type(value) in [int, float, bool]\ 99 if type(value) in [int, float, bool]\
100 or (type(value) is str and value.isalpha()): 100 or (type(value) is str and value.isalpha()):
203 Parameters 203 Parameters
204 ----------- 204 -----------
205 config : dictionary, galaxy tool parameters loaded by JSON 205 config : dictionary, galaxy tool parameters loaded by JSON
206 """ 206 """
207 generator_type = config.pop('generator_type') 207 generator_type = config.pop('generator_type')
208 if generator_type == 'none':
209 return None
210
208 klass = try_get_attr('galaxy_ml.preprocessors', generator_type) 211 klass = try_get_attr('galaxy_ml.preprocessors', generator_type)
209 212
210 if generator_type == 'GenomicIntervalBatchGenerator': 213 if generator_type == 'GenomicIntervalBatchGenerator':
211 config['ref_genome_path'] = 'to_be_determined' 214 config['ref_genome_path'] = 'to_be_determined'
212 config['intervals_path'] = 'to_be_determined' 215 config['intervals_path'] = 'to_be_determined'
238 model = get_functional_model(layers_config) 241 model = get_functional_model(layers_config)
239 242
240 json_string = model.to_json() 243 json_string = model.to_json()
241 244
242 with open(outfile, 'w') as f: 245 with open(outfile, 'w') as f:
243 f.write(json_string) 246 json.dump(json.loads(json_string), f, indent=2)
244 247
245 248
246 def build_keras_model(inputs, outfile, model_json, infile_weights=None, 249 def build_keras_model(inputs, outfile, model_json, infile_weights=None,
247 batch_mode=False, outfile_params=None): 250 batch_mode=False, outfile_params=None):
248 """ for `keras_model_builder` tool 251 """ for `keras_model_builder` tool