Mercurial > repos > goeckslab > pycaret_predict

annotate pycaret_train.py @ 14:7d78a6afc958 draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam commit 1ffd143e57fa952ee9dd84fc141771520aea0791
author goeckslab
date Wed, 26 Nov 2025 17:49:50 +0000
parents f07850192bc2
children
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1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
goeckslab
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diff changeset
1 import argparse
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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2 import logging
14
7d78a6afc958 planemo upload for repository https://github.com/goeckslab/gleam commit 1ffd143e57fa952ee9dd84fc141771520aea0791
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3 import os
0
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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4
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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5 from pycaret_classification import ClassificationModelTrainer
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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6 from pycaret_regression import RegressionModelTrainer
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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8 logging.basicConfig(level=logging.DEBUG)
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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9 LOG = logging.getLogger(__name__)
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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10
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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11
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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12 def main():
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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13 parser = argparse.ArgumentParser()
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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14 parser.add_argument("--input_file", help="Path to the input file")
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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15 parser.add_argument("--target_col", help="Column number of the target")
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16 parser.add_argument("--output_dir", help="Path to the output directory")
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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17 parser.add_argument(
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18 "--model_type",
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19 choices=["classification", "regression"],
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20 help="Type of the model",
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21 )
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22 parser.add_argument(
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23 "--train_size",
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24 type=float,
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25 default=None,
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26 help="Train size for PyCaret setup",
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27 )
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28 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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29 "--normalize",
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30 action="store_true",
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31 default=None,
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32 help="Normalize data for PyCaret setup",
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33 )
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34 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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35 "--feature_selection",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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36 action="store_true",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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37 default=None,
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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38 help="Perform feature selection for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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39 )
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40 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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41 "--cross_validation",
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42 action="store_true",
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43 default=None,
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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44 help="Enable cross-validation for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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45 )
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46 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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47 "--no_cross_validation",
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48 action="store_true",
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49 default=None,
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50 help="Disable cross-validation for PyCaret setup",
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51 )
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52 parser.add_argument(
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53 "--cross_validation_folds",
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54 type=int,
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55 default=None,
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56 help="Number of cross-validation folds for PyCaret setup",
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57 )
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58 parser.add_argument(
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59 "--remove_outliers",
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60 action="store_true",
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61 default=None,
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62 help="Remove outliers for PyCaret setup",
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63 )
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64 parser.add_argument(
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65 "--remove_multicollinearity",
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66 action="store_true",
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67 default=None,
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68 help="Remove multicollinearity for PyCaret setup",
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69 )
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70 parser.add_argument(
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71 "--polynomial_features",
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72 action="store_true",
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73 default=None,
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74 help="Generate polynomial features for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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75 )
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76 parser.add_argument(
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77 "--feature_interaction",
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78 action="store_true",
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79 default=None,
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80 help="Generate feature interactions for PyCaret setup",
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81 )
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82 parser.add_argument(
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83 "--feature_ratio",
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84 action="store_true",
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85 default=None,
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86 help="Generate feature ratios for PyCaret setup",
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87 )
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88 parser.add_argument(
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89 "--fix_imbalance",
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90 action="store_true",
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91 default=None,
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92 help="Fix class imbalance for PyCaret setup",
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93 )
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94 parser.add_argument(
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95 "--models",
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96 nargs="+",
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97 default=None,
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98 help="Selected models for training",
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99 )
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100 parser.add_argument(
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101 "--tune_model",
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102 action="store_true",
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103 default=False,
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104 help="Tune the best model hyperparameters after training",
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105 )
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106 parser.add_argument(
9
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107 "--test_file",
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108 type=str,
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109 default=None,
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110 help="Path to the test data file",
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111 )
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112 parser.add_argument(
8
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113 "--random_seed",
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114 type=int,
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115 default=42,
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116 help="Random seed for PyCaret setup",
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117 )
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118 parser.add_argument(
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119 "--n-jobs",
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120 dest="n_jobs",
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121 type=int,
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122 default=None,
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123 help="Number of parallel jobs; defaults to GALAXY_SLOTS or 1 if unset/invalid.",
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124 )
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125 parser.add_argument(
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126 "--probability_threshold",
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127 type=float,
8
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128 default=None,
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129 help="Probability threshold for classification decision,",
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130 )
13
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131 parser.add_argument(
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132 "--best_model_metric",
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133 type=str,
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134 default=None,
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135 help="Metric used to select the best model (e.g. AUC, Accuracy, R2, RMSE).",
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136 )
0
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137
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138 args = parser.parse_args()
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139
14
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140 # Derive n_jobs from CLI or GALAXY_SLOTS env var
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141 if args.n_jobs is not None:
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142 n_jobs = args.n_jobs
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143 else:
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144 slots_str = os.environ.get("GALAXY_SLOTS")
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145 try:
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146 n_jobs = int(slots_str) if slots_str is not None else 1
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147 except ValueError:
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148 n_jobs = 1
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149
8
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150 # Normalize cross-validation flags: --no_cross_validation overrides --cross_validation
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151 if args.no_cross_validation:
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152 args.cross_validation = False
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153 # If --cross_validation was passed, args.cross_validation is True
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154 # If neither was passed, args.cross_validation remains None
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155
8
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156 # Build the model_kwargs dict from CLI args
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157 model_kwargs = {
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158 "train_size": args.train_size,
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159 "normalize": args.normalize,
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160 "feature_selection": args.feature_selection,
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diff changeset
161 "cross_validation": args.cross_validation,
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162 "cross_validation_folds": args.cross_validation_folds,
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163 "remove_outliers": args.remove_outliers,
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164 "remove_multicollinearity": args.remove_multicollinearity,
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165 "polynomial_features": args.polynomial_features,
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166 "feature_interaction": args.feature_interaction,
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167 "feature_ratio": args.feature_ratio,
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168 "fix_imbalance": args.fix_imbalance,
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169 "tune_model": args.tune_model,
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170 "n_jobs": n_jobs,
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171 "probability_threshold": args.probability_threshold,
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172 "best_model_metric": args.best_model_metric,
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173 }
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174 LOG.info(f"Model kwargs: {model_kwargs}")
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175
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176 # If the XML passed a comma-separated string in a single list element, split it out
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177 if args.models:
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178 model_kwargs["models"] = args.models[0].split(",")
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179
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180 # Drop None entries so PyCaret uses its default values
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181 model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None}
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182 LOG.info(f"Model kwargs 2: {model_kwargs}")
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183
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184 # Instantiate the appropriate trainer
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185 if args.model_type == "classification":
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186 trainer = ClassificationModelTrainer(
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187 args.input_file,
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188 args.target_col,
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189 args.output_dir,
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190 args.model_type,
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191 args.random_seed,
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192 args.test_file,
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193 **model_kwargs,
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194 )
0
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195 elif args.model_type == "regression":
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196 # regression doesn't support fix_imbalance
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197 model_kwargs.pop("fix_imbalance", None)
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198 trainer = RegressionModelTrainer(
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199 args.input_file,
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200 args.target_col,
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201 args.output_dir,
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202 args.model_type,
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203 args.random_seed,
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204 args.test_file,
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205 **model_kwargs,
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206 )
0
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207 else:
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208 LOG.error("Invalid model type. Please choose 'classification' or 'regression'.")
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209 return
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210
0
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211 trainer.run()
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212
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213
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214 if __name__ == "__main__":
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215 main()