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annotate main.py @ 4:e7b4afedc471 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit f514c9038f1aac1ef0ca40a9f4866b1ad0fc7747
author bgruening
date Tue, 11 Feb 2025 10:14:12 +0000
parents 33d53eb476fd
children 49b4ee0d0965
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3dc3c7443c8e planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit bce8b0297bff54e7e29a6106a7f385fd1318c0aa
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1 """
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2 Tabular data prediction using TabPFN
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3 """
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4 import argparse
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5 import time
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6
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7 import matplotlib.pyplot as plt
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8 import numpy as np
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9 import pandas as pd
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10 from sklearn.metrics import (
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11 average_precision_score,
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12 precision_recall_curve,
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13 r2_score,
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14 root_mean_squared_error,
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15 )
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16 from sklearn.preprocessing import label_binarize
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17 from tabpfn import TabPFNClassifier, TabPFNRegressor
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20 def separate_features_labels(data):
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21 df = pd.read_csv(data, sep="\t")
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22 labels = df.iloc[:, -1]
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23 features = df.iloc[:, :-1]
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24 return features, labels
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26
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27 def classification_plot(y_true, y_scores):
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28 plt.figure(figsize=(8, 6))
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29 is_binary = len(np.unique(y_true)) == 2
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30 if is_binary:
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31 # Compute precision-recall curve
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32 precision, recall, _ = precision_recall_curve(y_true, y_scores[:, 1])
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33 average_precision = average_precision_score(y_true, y_scores[:, 1])
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34 plt.plot(
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35 recall,
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36 precision,
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37 label=f"Precision-Recall Curve (AP={average_precision:.2f})",
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38 )
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39 plt.title("Precision-Recall Curve (binary classification)")
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40 else:
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41 y_true_bin = label_binarize(y_true, classes=np.unique(y_true))
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42 n_classes = y_true_bin.shape[1]
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43 class_labels = [f"Class {i}" for i in range(n_classes)]
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44 # Plot PR curve for each class
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45 for i in range(n_classes):
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46 precision, recall, _ = precision_recall_curve(
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47 y_true_bin[:, i], y_scores[:, i]
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48 )
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49 ap_score = average_precision_score(y_true_bin[:, i], y_scores[:, i])
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50 plt.plot(
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51 recall, precision, label=f"{class_labels[i]} (AP = {ap_score:.2f})"
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52 )
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53 # Compute micro-average PR curve
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54 precision, recall, _ = precision_recall_curve(
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55 y_true_bin.ravel(), y_scores.ravel()
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56 )
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57 plt.plot(
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58 recall, precision, linestyle="--", color="black", label="Micro-average"
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59 )
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60 plt.title("Precision-Recall Curve (Multiclass Classification)")
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61 plt.xlabel("Recall")
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62 plt.ylabel("Precision")
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63 plt.legend(loc="lower left")
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64 plt.grid(True)
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65 plt.savefig("output_plot.png")
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66
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67
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68 def regression_plot(xval, yval, title, xlabel, ylabel):
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69 plt.figure(figsize=(8, 6))
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70 plt.xlabel(xlabel)
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71 plt.ylabel(ylabel)
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72 plt.title(title)
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73 plt.legend(loc="lower left")
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74 plt.grid(True)
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75 plt.scatter(xval, yval, alpha=0.8)
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76 xticks = np.arange(len(xval))
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77 plt.plot(xticks, xticks, color="red", linestyle="--", label="y = x")
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78 plt.savefig("output_plot.png")
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79
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80
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81 def train_evaluate(args):
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82 """
2
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83 Train TabPFN and predict
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84 """
2
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85 # prepare train data
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86 tr_features, tr_labels = separate_features_labels(args["train_data"])
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87 # prepare test data
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88 if args["testhaslabels"] == "haslabels":
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89 te_features, te_labels = separate_features_labels(args["test_data"])
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90 else:
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91 te_features = pd.read_csv(args["test_data"], sep="\t")
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92 te_labels = []
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93 s_time = time.time()
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94 if args["selected_task"] == "Classification":
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95 classifier = TabPFNClassifier()
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96 classifier.fit(tr_features, tr_labels)
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97 y_eval = classifier.predict(te_features)
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98 pred_probas_test = classifier.predict_proba(te_features)
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99 if len(te_labels) > 0:
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100 classification_plot(te_labels, pred_probas_test)
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101 else:
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102 regressor = TabPFNRegressor()
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103 regressor.fit(tr_features, tr_labels)
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104 y_eval = regressor.predict(te_features)
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105 if len(te_labels) > 0:
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106 score = root_mean_squared_error(te_labels, y_eval)
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107 r2_metric_score = r2_score(te_labels, y_eval)
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108 regression_plot(
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109 te_labels,
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110 y_eval,
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111 f"Scatter plot: True vs predicted values. RMSE={score:.2f}, R2={r2_metric_score:.2f}",
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112 "True values",
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113 "Predicted values",
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114 )
0
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115 e_time = time.time()
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116 print(
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117 "Time taken by TabPFN for training and prediction: {} seconds".format(
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118 e_time - s_time
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119 )
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120 )
0
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121 te_features["predicted_labels"] = y_eval
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122 te_features.to_csv("output_predicted_data", sep="\t", index=None)
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123
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124
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125 if __name__ == "__main__":
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126 arg_parser = argparse.ArgumentParser()
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127 arg_parser.add_argument("-trdata", "--train_data", required=True, help="Train data")
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128 arg_parser.add_argument("-tedata", "--test_data", required=True, help="Test data")
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129 arg_parser.add_argument(
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130 "-testhaslabels",
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131 "--testhaslabels",
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132 required=True,
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133 help="if test data contain labels",
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134 )
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135 arg_parser.add_argument(
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136 "-selectedtask",
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137 "--selected_task",
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138 required=True,
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139 help="Type of machine learning task",
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140 )
0
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141 # get argument values
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142 args = vars(arg_parser.parse_args())
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143 train_evaluate(args)