Mercurial > repos > goeckslab > pycaret_predict

annotate pycaret_train.py @ 9:c6c1f8777aae draft default tip

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planemo upload for repository https://github.com/goeckslab/gleam commit 4a11e8a4c4e9daa884bddedfa47090476c517667
author goeckslab
date Thu, 31 Jul 2025 15:41:24 +0000
parents 1aed7d47c5ec
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
goeckslab
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2 import logging
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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3
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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4 from pycaret_classification import ClassificationModelTrainer
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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5 from pycaret_regression import RegressionModelTrainer
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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6
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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7 logging.basicConfig(level=logging.DEBUG)
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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8 LOG = logging.getLogger(__name__)
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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9
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 def main():
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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12 parser = argparse.ArgumentParser()
1f20fe57fdee planemo upload for repository https://github.com/goeckslab/Galaxy-Pycaret commit d79b0f722b7d09505a526d1a4332f87e548a3df1
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13 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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14 parser.add_argument("--target_col", help="Column number of the target")
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1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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15 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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16 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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17 "--model_type",
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18 choices=["classification", "regression"],
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19 help="Type of the model",
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20 )
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21 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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22 "--train_size",
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23 type=float,
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24 default=None,
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25 help="Train size for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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26 )
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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27 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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28 "--normalize",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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29 action="store_true",
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30 default=None,
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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31 help="Normalize data for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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32 )
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33 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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34 "--feature_selection",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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35 action="store_true",
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36 default=None,
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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37 help="Perform feature selection for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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38 )
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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39 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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40 "--cross_validation",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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41 action="store_true",
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42 default=None,
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43 help="Enable cross-validation for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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44 )
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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45 parser.add_argument(
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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46 "--no_cross_validation",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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47 action="store_true",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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48 default=None,
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49 help="Disable cross-validation for PyCaret setup",
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50 )
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51 parser.add_argument(
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52 "--cross_validation_folds",
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53 type=int,
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54 default=None,
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55 help="Number of cross-validation folds for PyCaret setup",
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56 )
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57 parser.add_argument(
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58 "--remove_outliers",
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59 action="store_true",
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60 default=None,
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61 help="Remove outliers for PyCaret setup",
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62 )
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63 parser.add_argument(
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64 "--remove_multicollinearity",
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65 action="store_true",
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66 default=None,
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67 help="Remove multicollinearity for PyCaret setup",
1aed7d47c5ec planemo upload for repository https://github.com/goeckslab/gleam commit 8112548ac44b7a4769093d76c722c8fcdeaaef54
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68 )
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69 parser.add_argument(
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70 "--polynomial_features",
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71 action="store_true",
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72 default=None,
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73 help="Generate polynomial features for PyCaret setup",
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74 )
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75 parser.add_argument(
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76 "--feature_interaction",
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77 action="store_true",
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78 default=None,
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79 help="Generate feature interactions for PyCaret setup",
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80 )
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81 parser.add_argument(
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82 "--feature_ratio",
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83 action="store_true",
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84 default=None,
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85 help="Generate feature ratios for PyCaret setup",
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86 )
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87 parser.add_argument(
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88 "--fix_imbalance",
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89 action="store_true",
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90 default=None,
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91 help="Fix class imbalance for PyCaret setup",
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92 )
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93 parser.add_argument(
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94 "--models",
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95 nargs="+",
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96 default=None,
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97 help="Selected models for training",
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98 )
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99 parser.add_argument(
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100 "--tune_model",
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101 action="store_true",
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102 default=False,
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103 help="Tune the best model hyperparameters after training",
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104 )
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105 parser.add_argument(
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106 "--test_file",
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107 type=str,
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108 default=None,
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109 help="Path to the test data file",
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110 )
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111 parser.add_argument(
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112 "--random_seed",
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113 type=int,
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114 default=42,
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115 help="Random seed for PyCaret setup",
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116 )
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117 parser.add_argument(
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118 "--probability_threshold",
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119 type=float,
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120 default=None,
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121 help="Probability threshold for classification decision,",
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122 )
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123
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124 args = parser.parse_args()
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125
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126 # Normalize cross-validation flags: --no_cross_validation overrides --cross_validation
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127 if args.no_cross_validation:
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128 args.cross_validation = False
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129 # If --cross_validation was passed, args.cross_validation is True
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130 # If neither was passed, args.cross_validation remains None
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131
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132 # Build the model_kwargs dict from CLI args
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133 model_kwargs = {
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134 "train_size": args.train_size,
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135 "normalize": args.normalize,
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136 "feature_selection": args.feature_selection,
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137 "cross_validation": args.cross_validation,
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138 "cross_validation_folds": args.cross_validation_folds,
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139 "remove_outliers": args.remove_outliers,
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140 "remove_multicollinearity": args.remove_multicollinearity,
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141 "polynomial_features": args.polynomial_features,
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142 "feature_interaction": args.feature_interaction,
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143 "feature_ratio": args.feature_ratio,
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144 "fix_imbalance": args.fix_imbalance,
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145 "tune_model": args.tune_model,
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146 "probability_threshold": args.probability_threshold,
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147 }
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148 LOG.info(f"Model kwargs: {model_kwargs}")
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149
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150 # If the XML passed a comma-separated string in a single list element, split it out
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151 if args.models:
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152 model_kwargs["models"] = args.models[0].split(",")
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153
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154 # Drop None entries so PyCaret uses its default values
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155 model_kwargs = {k: v for k, v in model_kwargs.items() if v is not None}
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156 LOG.info(f"Model kwargs 2: {model_kwargs}")
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157
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158 # Instantiate the appropriate trainer
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159 if args.model_type == "classification":
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160 trainer = ClassificationModelTrainer(
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161 args.input_file,
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162 args.target_col,
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163 args.output_dir,
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164 args.model_type,
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165 args.random_seed,
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166 args.test_file,
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167 **model_kwargs,
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168 )
0
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169 elif args.model_type == "regression":
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170 # regression doesn't support fix_imbalance
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171 model_kwargs.pop("fix_imbalance", None)
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172 trainer = RegressionModelTrainer(
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173 args.input_file,
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174 args.target_col,
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175 args.output_dir,
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176 args.model_type,
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177 args.random_seed,
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178 args.test_file,
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179 **model_kwargs,
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180 )
0
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181 else:
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182 LOG.error("Invalid model type. Please choose 'classification' or 'regression'.")
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183 return
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184
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185 trainer.run()
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186
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187
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188 if __name__ == "__main__":
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189 main()