Mercurial > repos > bgruening > sklearn_ensemble
annotate model_prediction.py @ 31:af0523c606a7 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5b2ac730ec6d3b762faa9034eddd19ad1b347476"
author | bgruening |
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date | Mon, 16 Dec 2019 05:42:39 -0500 |
parents | 47d4baa183b2 |
children | 19d6c2745d34 |
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dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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1 import argparse |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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2 import json |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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3 import numpy as np |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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4 import pandas as pd |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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5 import warnings |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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6 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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7 from scipy.io import mmread |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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8 from sklearn.pipeline import Pipeline |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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9 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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10 from galaxy_ml.utils import (load_model, read_columns, |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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11 get_module, try_get_attr) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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12 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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13 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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14 N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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15 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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16 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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17 def main(inputs, infile_estimator, outfile_predict, |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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18 infile_weights=None, infile1=None, |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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19 fasta_path=None, ref_seq=None, |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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20 vcf_path=None): |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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21 """ |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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22 Parameter |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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23 --------- |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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24 inputs : str |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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25 File path to galaxy tool parameter |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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26 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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27 infile_estimator : strgit |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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28 File path to trained estimator input |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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29 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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30 outfile_predict : str |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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31 File path to save the prediction results, tabular |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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32 |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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33 infile_weights : str |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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34 File path to weights input |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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35 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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36 infile1 : str |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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37 File path to dataset containing features |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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38 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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39 fasta_path : str |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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40 File path to dataset containing fasta file |
dde0f1654d18
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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41 |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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42 ref_seq : str |
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43 File path to dataset containing the reference genome sequence. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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44 |
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45 vcf_path : str |
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46 File path to dataset containing variants info. |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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47 """ |
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48 warnings.filterwarnings('ignore') |
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49 |
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50 with open(inputs, 'r') as param_handler: |
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51 params = json.load(param_handler) |
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52 |
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53 # load model |
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54 with open(infile_estimator, 'rb') as est_handler: |
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55 estimator = load_model(est_handler) |
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56 |
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57 main_est = estimator |
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58 if isinstance(estimator, Pipeline): |
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59 main_est = estimator.steps[-1][-1] |
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60 if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): |
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61 if not infile_weights or infile_weights == 'None': |
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62 raise ValueError("The selected model skeleton asks for weights, " |
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63 "but dataset for weights wan not selected!") |
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64 main_est.load_weights(infile_weights) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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65 |
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66 # handle data input |
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67 input_type = params['input_options']['selected_input'] |
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68 # tabular input |
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69 if input_type == 'tabular': |
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70 header = 'infer' if params['input_options']['header1'] else None |
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71 column_option = (params['input_options'] |
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72 ['column_selector_options_1'] |
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73 ['selected_column_selector_option']) |
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74 if column_option in ['by_index_number', 'all_but_by_index_number', |
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75 'by_header_name', 'all_but_by_header_name']: |
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76 c = params['input_options']['column_selector_options_1']['col1'] |
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77 else: |
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78 c = None |
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79 |
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80 df = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True) |
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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty
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81 |
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82 X = read_columns(df, c=c, c_option=column_option).astype(float) |
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83 |
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84 if params['method'] == 'predict': |
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85 preds = estimator.predict(X) |
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86 else: |
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87 preds = estimator.predict_proba(X) |
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88 |
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89 # sparse input |
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90 elif input_type == 'sparse': |
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91 X = mmread(open(infile1, 'r')) |
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92 if params['method'] == 'predict': |
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93 preds = estimator.predict(X) |
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94 else: |
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95 preds = estimator.predict_proba(X) |
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96 |
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97 # fasta input |
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98 elif input_type == 'seq_fasta': |
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99 if not hasattr(estimator, 'data_batch_generator'): |
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100 raise ValueError( |
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101 "To do prediction on sequences in fasta input, " |
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102 "the estimator must be a `KerasGBatchClassifier`" |
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103 "equipped with data_batch_generator!") |
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104 pyfaidx = get_module('pyfaidx') |
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105 sequences = pyfaidx.Fasta(fasta_path) |
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106 n_seqs = len(sequences.keys()) |
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107 X = np.arange(n_seqs)[:, np.newaxis] |
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108 seq_length = estimator.data_batch_generator.seq_length |
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109 batch_size = getattr(estimator, 'batch_size', 32) |
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110 steps = (n_seqs + batch_size - 1) // batch_size |
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111 |
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112 seq_type = params['input_options']['seq_type'] |
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113 klass = try_get_attr( |
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114 'galaxy_ml.preprocessors', seq_type) |
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115 |
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116 pred_data_generator = klass( |
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117 fasta_path, seq_length=seq_length) |
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118 |
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119 if params['method'] == 'predict': |
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120 preds = estimator.predict( |
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121 X, data_generator=pred_data_generator, steps=steps) |
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122 else: |
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123 preds = estimator.predict_proba( |
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124 X, data_generator=pred_data_generator, steps=steps) |
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125 |
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126 # vcf input |
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127 elif input_type == 'variant_effect': |
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128 klass = try_get_attr('galaxy_ml.preprocessors', |
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129 'GenomicVariantBatchGenerator') |
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130 |
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131 options = params['input_options'] |
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132 options.pop('selected_input') |
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133 if options['blacklist_regions'] == 'none': |
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134 options['blacklist_regions'] = None |
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135 |
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136 pred_data_generator = klass( |
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137 ref_genome_path=ref_seq, vcf_path=vcf_path, **options) |
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138 |
31
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139 pred_data_generator.set_processing_attrs() |
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140 |
27
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141 variants = pred_data_generator.variants |
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142 |
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143 # predict 1600 sample at once then write to file |
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144 gen_flow = pred_data_generator.flow(batch_size=1600) |
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145 |
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146 file_writer = open(outfile_predict, 'w') |
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147 header_row = '\t'.join(['chrom', 'pos', 'name', 'ref', |
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148 'alt', 'strand']) |
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149 file_writer.write(header_row) |
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150 header_done = False |
26
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151 |
27
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152 steps_done = 0 |
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153 |
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154 # TODO: multiple threading |
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155 try: |
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156 while steps_done < len(gen_flow): |
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157 index_array = next(gen_flow.index_generator) |
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158 batch_X = gen_flow._get_batches_of_transformed_samples( |
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159 index_array) |
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160 |
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161 if params['method'] == 'predict': |
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162 batch_preds = estimator.predict( |
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163 batch_X, |
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164 # The presence of `pred_data_generator` below is to |
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165 # override model carrying data_generator if there |
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166 # is any. |
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167 data_generator=pred_data_generator) |
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168 else: |
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169 batch_preds = estimator.predict_proba( |
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170 batch_X, |
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171 # The presence of `pred_data_generator` below is to |
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172 # override model carrying data_generator if there |
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173 # is any. |
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174 data_generator=pred_data_generator) |
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175 |
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176 if batch_preds.ndim == 1: |
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177 batch_preds = batch_preds[:, np.newaxis] |
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178 |
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179 batch_meta = variants[index_array] |
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180 batch_out = np.column_stack([batch_meta, batch_preds]) |
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181 |
27
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182 if not header_done: |
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183 heads = np.arange(batch_preds.shape[-1]).astype(str) |
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184 heads_str = '\t'.join(heads) |
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185 file_writer.write("\t%s\n" % heads_str) |
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186 header_done = True |
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187 |
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188 for row in batch_out: |
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189 row_str = '\t'.join(row) |
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190 file_writer.write("%s\n" % row_str) |
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191 |
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192 steps_done += 1 |
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193 |
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194 finally: |
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195 file_writer.close() |
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196 # TODO: make api `pred_data_generator.close()` |
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197 pred_data_generator.close() |
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198 return 0 |
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199 # end input |
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200 |
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201 # output |
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202 if len(preds.shape) == 1: |
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203 rval = pd.DataFrame(preds, columns=['Predicted']) |
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204 else: |
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205 rval = pd.DataFrame(preds) |
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206 |
27
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207 rval.to_csv(outfile_predict, sep='\t', header=True, index=False) |
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208 |
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209 |
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210 if __name__ == '__main__': |
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211 aparser = argparse.ArgumentParser() |
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212 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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213 aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") |
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214 aparser.add_argument("-w", "--infile_weights", dest="infile_weights") |
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215 aparser.add_argument("-X", "--infile1", dest="infile1") |
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216 aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict") |
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217 aparser.add_argument("-f", "--fasta_path", dest="fasta_path") |
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218 aparser.add_argument("-r", "--ref_seq", dest="ref_seq") |
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219 aparser.add_argument("-v", "--vcf_path", dest="vcf_path") |
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220 args = aparser.parse_args() |
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221 |
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222 main(args.inputs, args.infile_estimator, args.outfile_predict, |
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223 infile_weights=args.infile_weights, infile1=args.infile1, |
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224 fasta_path=args.fasta_path, ref_seq=args.ref_seq, |
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225 vcf_path=args.vcf_path) |