Mercurial > repos > bgruening > sklearn_svm_classifier

annotate train_test_eval.py @ 16:f9639b488779 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 756f8be9c3cd437e131e6410cd625c24fe078e8c"
author bgruening
date Wed, 22 Jan 2020 07:44:40 -0500
parents 2df8f5c30edc
children d67dcd63f6cb
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1 import argparse
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2 import joblib
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3 import json
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4 import numpy as np
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5 import os
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6 import pandas as pd
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7 import pickle
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8 import warnings
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9 from itertools import chain
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10 from scipy.io import mmread
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11 from sklearn.base import clone
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12 from sklearn import (cluster, compose, decomposition, ensemble,
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13 feature_extraction, feature_selection,
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14 gaussian_process, kernel_approximation, metrics,
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15 model_selection, naive_bayes, neighbors,
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16 pipeline, preprocessing, svm, linear_model,
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17 tree, discriminant_analysis)
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18 from sklearn.exceptions import FitFailedWarning
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19 from sklearn.metrics.scorer import _check_multimetric_scoring
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20 from sklearn.model_selection._validation import _score, cross_validate
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21 from sklearn.model_selection import _search, _validation
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22 from sklearn.utils import indexable, safe_indexing
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24 from galaxy_ml.model_validations import train_test_split
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25 from galaxy_ml.utils import (SafeEval, get_scoring, load_model,
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26 read_columns, try_get_attr, get_module)
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29 _fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
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30 setattr(_search, '_fit_and_score', _fit_and_score)
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31 setattr(_validation, '_fit_and_score', _fit_and_score)
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32
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33 N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
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34 CACHE_DIR = os.path.join(os.getcwd(), 'cached')
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35 del os
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36 NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
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37 'nthread', 'callbacks')
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38 ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
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39 'CSVLogger', 'None')
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42 def _eval_swap_params(params_builder):
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43 swap_params = {}
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45 for p in params_builder['param_set']:
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46 swap_value = p['sp_value'].strip()
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47 if swap_value == '':
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48 continue
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49
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50 param_name = p['sp_name']
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51 if param_name.lower().endswith(NON_SEARCHABLE):
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52 warnings.warn("Warning: `%s` is not eligible for search and was "
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53 "omitted!" % param_name)
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54 continue
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55
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56 if not swap_value.startswith(':'):
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57 safe_eval = SafeEval(load_scipy=True, load_numpy=True)
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58 ev = safe_eval(swap_value)
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59 else:
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60 # Have `:` before search list, asks for estimator evaluatio
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61 safe_eval_es = SafeEval(load_estimators=True)
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62 swap_value = swap_value[1:].strip()
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63 # TODO maybe add regular express check
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64 ev = safe_eval_es(swap_value)
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66 swap_params[param_name] = ev
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67
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68 return swap_params
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69
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71 def train_test_split_none(*arrays, **kwargs):
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72 """extend train_test_split to take None arrays
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73 and support split by group names.
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74 """
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75 nones = []
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76 new_arrays = []
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77 for idx, arr in enumerate(arrays):
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78 if arr is None:
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79 nones.append(idx)
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80 else:
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81 new_arrays.append(arr)
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82
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83 if kwargs['shuffle'] == 'None':
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84 kwargs['shuffle'] = None
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85
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86 group_names = kwargs.pop('group_names', None)
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87
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88 if group_names is not None and group_names.strip():
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89 group_names = [name.strip() for name in
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90 group_names.split(',')]
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91 new_arrays = indexable(*new_arrays)
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92 groups = kwargs['labels']
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93 n_samples = new_arrays[0].shape[0]
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94 index_arr = np.arange(n_samples)
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95 test = index_arr[np.isin(groups, group_names)]
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96 train = index_arr[~np.isin(groups, group_names)]
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97 rval = list(chain.from_iterable(
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98 (safe_indexing(a, train),
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99 safe_indexing(a, test)) for a in new_arrays))
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100 else:
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101 rval = train_test_split(*new_arrays, **kwargs)
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102
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103 for pos in nones:
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104 rval[pos * 2: 2] = [None, None]
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105
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106 return rval
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107
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108
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109 def main(inputs, infile_estimator, infile1, infile2,
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110 outfile_result, outfile_object=None,
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111 outfile_weights=None, groups=None,
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112 ref_seq=None, intervals=None, targets=None,
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113 fasta_path=None):
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114 """
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115 Parameter
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116 ---------
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117 inputs : str
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118 File path to galaxy tool parameter
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119
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120 infile_estimator : str
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121 File path to estimator
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122
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123 infile1 : str
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124 File path to dataset containing features
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125
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126 infile2 : str
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127 File path to dataset containing target values
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128
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129 outfile_result : str
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130 File path to save the results, either cv_results or test result
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131
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132 outfile_object : str, optional
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133 File path to save searchCV object
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134
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135 outfile_weights : str, optional
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136 File path to save deep learning model weights
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137
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138 groups : str
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139 File path to dataset containing groups labels
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140
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141 ref_seq : str
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142 File path to dataset containing genome sequence file
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143
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144 intervals : str
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145 File path to dataset containing interval file
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146
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147 targets : str
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148 File path to dataset compressed target bed file
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149
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150 fasta_path : str
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151 File path to dataset containing fasta file
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152 """
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153 warnings.simplefilter('ignore')
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154
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155 with open(inputs, 'r') as param_handler:
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156 params = json.load(param_handler)
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157
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158 # load estimator
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159 with open(infile_estimator, 'rb') as estimator_handler:
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160 estimator = load_model(estimator_handler)
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161
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162 # swap hyperparameter
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163 swapping = params['experiment_schemes']['hyperparams_swapping']
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164 swap_params = _eval_swap_params(swapping)
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165 estimator.set_params(**swap_params)
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166
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167 estimator_params = estimator.get_params()
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168
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169 # store read dataframe object
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170 loaded_df = {}
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171
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172 input_type = params['input_options']['selected_input']
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173 # tabular input
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174 if input_type == 'tabular':
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175 header = 'infer' if params['input_options']['header1'] else None
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176 column_option = (params['input_options']['column_selector_options_1']
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177 ['selected_column_selector_option'])
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178 if column_option in ['by_index_number', 'all_but_by_index_number',
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179 'by_header_name', 'all_but_by_header_name']:
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180 c = params['input_options']['column_selector_options_1']['col1']
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181 else:
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182 c = None
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183
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184 df_key = infile1 + repr(header)
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185 df = pd.read_csv(infile1, sep='\t', header=header,
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186 parse_dates=True)
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187 loaded_df[df_key] = df
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188
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189 X = read_columns(df, c=c, c_option=column_option).astype(float)
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190 # sparse input
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191 elif input_type == 'sparse':
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192 X = mmread(open(infile1, 'r'))
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193
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194 # fasta_file input
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195 elif input_type == 'seq_fasta':
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196 pyfaidx = get_module('pyfaidx')
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197 sequences = pyfaidx.Fasta(fasta_path)
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198 n_seqs = len(sequences.keys())
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199 X = np.arange(n_seqs)[:, np.newaxis]
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200 for param in estimator_params.keys():
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201 if param.endswith('fasta_path'):
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202 estimator.set_params(
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203 **{param: fasta_path})
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204 break
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205 else:
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206 raise ValueError(
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207 "The selected estimator doesn't support "
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208 "fasta file input! Please consider using "
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209 "KerasGBatchClassifier with "
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210 "FastaDNABatchGenerator/FastaProteinBatchGenerator "
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211 "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
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212 "in pipeline!")
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213
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214 elif input_type == 'refseq_and_interval':
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215 path_params = {
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216 'data_batch_generator__ref_genome_path': ref_seq,
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217 'data_batch_generator__intervals_path': intervals,
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218 'data_batch_generator__target_path': targets
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219 }
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220 estimator.set_params(**path_params)
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221 n_intervals = sum(1 for line in open(intervals))
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222 X = np.arange(n_intervals)[:, np.newaxis]
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223
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224 # Get target y
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225 header = 'infer' if params['input_options']['header2'] else None
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226 column_option = (params['input_options']['column_selector_options_2']
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227 ['selected_column_selector_option2'])
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228 if column_option in ['by_index_number', 'all_but_by_index_number',
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229 'by_header_name', 'all_but_by_header_name']:
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230 c = params['input_options']['column_selector_options_2']['col2']
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231 else:
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232 c = None
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233
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234 df_key = infile2 + repr(header)
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235 if df_key in loaded_df:
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236 infile2 = loaded_df[df_key]
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237 else:
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238 infile2 = pd.read_csv(infile2, sep='\t',
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239 header=header, parse_dates=True)
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240 loaded_df[df_key] = infile2
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241
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242 y = read_columns(
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243 infile2,
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244 c=c,
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245 c_option=column_option,
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246 sep='\t',
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247 header=header,
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248 parse_dates=True)
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249 if len(y.shape) == 2 and y.shape[1] == 1:
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250 y = y.ravel()
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251 if input_type == 'refseq_and_interval':
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252 estimator.set_params(
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253 data_batch_generator__features=y.ravel().tolist())
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254 y = None
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255 # end y
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256
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257 # load groups
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258 if groups:
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259 groups_selector = (params['experiment_schemes']['test_split']
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260 ['split_algos']).pop('groups_selector')
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261
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262 header = 'infer' if groups_selector['header_g'] else None
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263 column_option = \
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264 (groups_selector['column_selector_options_g']
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265 ['selected_column_selector_option_g'])
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266 if column_option in ['by_index_number', 'all_but_by_index_number',
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267 'by_header_name', 'all_but_by_header_name']:
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268 c = groups_selector['column_selector_options_g']['col_g']
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269 else:
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270 c = None
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271
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272 df_key = groups + repr(header)
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273 if df_key in loaded_df:
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274 groups = loaded_df[df_key]
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275
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276 groups = read_columns(
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277 groups,
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278 c=c,
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279 c_option=column_option,
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280 sep='\t',
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281 header=header,
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282 parse_dates=True)
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283 groups = groups.ravel()
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284
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285 # del loaded_df
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286 del loaded_df
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287
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288 # handle memory
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289 memory = joblib.Memory(location=CACHE_DIR, verbose=0)
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290 # cache iraps_core fits could increase search speed significantly
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291 if estimator.__class__.__name__ == 'IRAPSClassifier':
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292 estimator.set_params(memory=memory)
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293 else:
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294 # For iraps buried in pipeline
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295 new_params = {}
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296 for p, v in estimator_params.items():
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297 if p.endswith('memory'):
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298 # for case of `__irapsclassifier__memory`
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299 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
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300 # cache iraps_core fits could increase search
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301 # speed significantly
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302 new_params[p] = memory
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303 # security reason, we don't want memory being
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304 # modified unexpectedly
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305 elif v:
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306 new_params[p] = None
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307 # handle n_jobs
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308 elif p.endswith('n_jobs'):
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309 # For now, 1 CPU is suggested for iprasclassifier
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310 if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
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311 new_params[p] = 1
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312 else:
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313 new_params[p] = N_JOBS
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314 # for security reason, types of callback are limited
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315 elif p.endswith('callbacks'):
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316 for cb in v:
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317 cb_type = cb['callback_selection']['callback_type']
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318 if cb_type not in ALLOWED_CALLBACKS:
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319 raise ValueError(
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320 "Prohibited callback type: %s!" % cb_type)
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321
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322 estimator.set_params(**new_params)
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323
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324 # handle scorer, convert to scorer dict
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325 scoring = params['experiment_schemes']['metrics']['scoring']
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326 scorer = get_scoring(scoring)
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327 scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)
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328
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329 # handle test (first) split
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330 test_split_options = (params['experiment_schemes']
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331 ['test_split']['split_algos'])
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332
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333 if test_split_options['shuffle'] == 'group':
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334 test_split_options['labels'] = groups
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335 if test_split_options['shuffle'] == 'stratified':
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336 if y is not None:
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337 test_split_options['labels'] = y
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338 else:
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339 raise ValueError("Stratified shuffle split is not "
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340 "applicable on empty target values!")
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341
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342 X_train, X_test, y_train, y_test, groups_train, groups_test = \
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343 train_test_split_none(X, y, groups, **test_split_options)
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344
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345 exp_scheme = params['experiment_schemes']['selected_exp_scheme']
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346
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347 # handle validation (second) split
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348 if exp_scheme == 'train_val_test':
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349 val_split_options = (params['experiment_schemes']
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350 ['val_split']['split_algos'])
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351
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352 if val_split_options['shuffle'] == 'group':
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353 val_split_options['labels'] = groups_train
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354 if val_split_options['shuffle'] == 'stratified':
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355 if y_train is not None:
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356 val_split_options['labels'] = y_train
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357 else:
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358 raise ValueError("Stratified shuffle split is not "
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359 "applicable on empty target values!")
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360
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361 X_train, X_val, y_train, y_val, groups_train, groups_val = \
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362 train_test_split_none(X_train, y_train, groups_train,
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363 **val_split_options)
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364
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365 # train and eval
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366 if hasattr(estimator, 'validation_data'):
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367 if exp_scheme == 'train_val_test':
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368 estimator.fit(X_train, y_train,
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369 validation_data=(X_val, y_val))
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370 else:
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371 estimator.fit(X_train, y_train,
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372 validation_data=(X_test, y_test))
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373 else:
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374 estimator.fit(X_train, y_train)
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375
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376 if hasattr(estimator, 'evaluate'):
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377 scores = estimator.evaluate(X_test, y_test=y_test,
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378 scorer=scorer,
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379 is_multimetric=True)
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380 else:
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381 scores = _score(estimator, X_test, y_test, scorer,
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382 is_multimetric=True)
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383 # handle output
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384 for name, score in scores.items():
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385 scores[name] = [score]
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386 df = pd.DataFrame(scores)
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387 df = df[sorted(df.columns)]
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388 df.to_csv(path_or_buf=outfile_result, sep='\t',
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389 header=True, index=False)
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390
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391 memory.clear(warn=False)
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392
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393 if outfile_object:
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394 main_est = estimator
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395 if isinstance(estimator, pipeline.Pipeline):
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396 main_est = estimator.steps[-1][-1]
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397
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398 if hasattr(main_est, 'model_') \
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399 and hasattr(main_est, 'save_weights'):
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400 if outfile_weights:
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401 main_est.save_weights(outfile_weights)
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402 del main_est.model_
153f237ddb36 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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403 del main_est.fit_params
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404 del main_est.model_class_
153f237ddb36 planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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405 del main_est.validation_data
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406 if getattr(main_est, 'data_generator_', None):
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407 del main_est.data_generator_
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408
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409 with open(outfile_object, 'wb') as output_handler:
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410 pickle.dump(estimator, output_handler,
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411 pickle.HIGHEST_PROTOCOL)
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412
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413
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414 if __name__ == '__main__':
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415 aparser = argparse.ArgumentParser()
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416 aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
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417 aparser.add_argument("-e", "--estimator", dest="infile_estimator")
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418 aparser.add_argument("-X", "--infile1", dest="infile1")
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419 aparser.add_argument("-y", "--infile2", dest="infile2")
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420 aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
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421 aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
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422 aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
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423 aparser.add_argument("-g", "--groups", dest="groups")
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424 aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
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425 aparser.add_argument("-b", "--intervals", dest="intervals")
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426 aparser.add_argument("-t", "--targets", dest="targets")
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427 aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
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428 args = aparser.parse_args()
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429
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430 main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
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431 args.outfile_result, outfile_object=args.outfile_object,
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432 outfile_weights=args.outfile_weights, groups=args.groups,
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433 ref_seq=args.ref_seq, intervals=args.intervals,
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434 targets=args.targets, fasta_path=args.fasta_path)