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annotate main.py @ 3:33d53eb476fd draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit c1c3b6d5abd35875890c45baacab073b5e749537
author bgruening
date Mon, 20 Jan 2025 15:45:17 +0000
parents c081e5e1d7ce
children e7b4afedc471
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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 """
3dc3c7443c8e planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/tabpfn commit bce8b0297bff54e7e29a6106a7f385fd1318c0aa
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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 tabpfn import TabPFNClassifier, TabPFNRegressor
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18
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19 def separate_features_labels(data):
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20 df = pd.read_csv(data, sep="\t")
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21 labels = df.iloc[:, -1]
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22 features = df.iloc[:, :-1]
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23 return features, labels
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25
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26 def classification_plot(xval, yval, leg_label, title, xlabel, ylabel):
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27 plt.figure(figsize=(8, 6))
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28 plt.plot(xval, yval, label=leg_label)
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29 plt.xlabel(xlabel)
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30 plt.ylabel(ylabel)
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31 plt.title(title)
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32 plt.legend(loc="lower left")
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33 plt.grid(True)
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34 plt.savefig("output_plot.png")
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36
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37 def regression_plot(xval, yval, title, xlabel, ylabel):
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38 plt.figure(figsize=(8, 6))
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39 plt.xlabel(xlabel)
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40 plt.ylabel(ylabel)
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41 plt.title(title)
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42 plt.legend(loc="lower left")
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43 plt.grid(True)
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44 plt.scatter(xval, yval, alpha=0.8)
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45 xticks = np.arange(len(xval))
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46 plt.plot(xticks, xticks, color="red", linestyle="--", label="y = x")
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47 plt.savefig("output_plot.png")
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48
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49
0
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50 def train_evaluate(args):
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51 """
2
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52 Train TabPFN and predict
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53 """
2
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54 # prepare train data
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55 tr_features, tr_labels = separate_features_labels(args["train_data"])
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56 # prepare test data
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57 if args["testhaslabels"] == "haslabels":
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58 te_features, te_labels = separate_features_labels(args["test_data"])
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59 else:
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60 te_features = pd.read_csv(args["test_data"], sep="\t")
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61 te_labels = []
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62 s_time = time.time()
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63 if args["selected_task"] == "Classification":
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64 classifier = TabPFNClassifier()
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65 classifier.fit(tr_features, tr_labels)
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66 y_eval = classifier.predict(te_features)
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67 pred_probas_test = classifier.predict_proba(te_features)
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68 if len(te_labels) > 0:
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69 precision, recall, thresholds = precision_recall_curve(
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70 te_labels, pred_probas_test[:, 1]
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71 )
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72 average_precision = average_precision_score(
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73 te_labels, pred_probas_test[:, 1]
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74 )
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75 classification_plot(
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76 recall,
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77 precision,
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78 f"Precision-Recall Curve (AP={average_precision:.2f})",
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79 "Precision-Recall Curve",
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80 "Recall",
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81 "Precision",
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82 )
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83 else:
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84 regressor = TabPFNRegressor()
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85 regressor.fit(tr_features, tr_labels)
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86 y_eval = regressor.predict(te_features)
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87 if len(te_labels) > 0:
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88 score = root_mean_squared_error(te_labels, y_eval)
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89 r2_metric_score = r2_score(te_labels, y_eval)
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90 regression_plot(
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91 te_labels,
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92 y_eval,
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93 f"Scatter plot: True vs predicted values. RMSE={score:.2f}, R2={r2_metric_score:.2f}",
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94 "True values",
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95 "Predicted values",
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96 )
0
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97 e_time = time.time()
2
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98 print(
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99 "Time taken by TabPFN for training and prediction: {} seconds".format(
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100 e_time - s_time
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101 )
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102 )
0
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103 te_features["predicted_labels"] = y_eval
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104 te_features.to_csv("output_predicted_data", sep="\t", index=None)
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105
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106
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107 if __name__ == "__main__":
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108 arg_parser = argparse.ArgumentParser()
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109 arg_parser.add_argument("-trdata", "--train_data", required=True, help="Train data")
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110 arg_parser.add_argument("-tedata", "--test_data", required=True, help="Test data")
2
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111 arg_parser.add_argument(
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112 "-testhaslabels",
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113 "--testhaslabels",
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114 required=True,
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115 help="if test data contain labels",
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116 )
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117 arg_parser.add_argument(
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118 "-selectedtask",
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119 "--selected_task",
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120 required=True,
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121 help="Type of machine learning task",
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122 )
0
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123 # get argument values
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124 args = vars(arg_parser.parse_args())
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125 train_evaluate(args)