Mercurial > repos > bgruening > sklearn_build_pipeline
annotate simple_model_fit.py @ 24:b1eda492f063 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit f031d8ddfb73cec24572648666ac44ee47f08aad
author | bgruening |
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date | Thu, 11 Aug 2022 09:20:25 +0000 |
parents | 0c74d3164ac8 |
children | 118e230e85ce |
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6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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1 import argparse |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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2 import json |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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3 import pickle |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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4 |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"
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5 import pandas as pd |
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6 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 e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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7 from scipy.io import mmread |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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8 from sklearn.pipeline import Pipeline |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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9 |
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10 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 d6333e7294e67be5968a41f404b66699cad4ae53"
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11 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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12 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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13 # TODO import from galaxy_ml.utils in future versions |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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14 def clean_params(estimator, n_jobs=None): |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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15 """clean unwanted hyperparameter settings |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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16 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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17 If n_jobs is not None, set it into the estimator, if applicable |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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18 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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19 Return |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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20 ------ |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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21 Cleaned estimator object |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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22 """ |
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23 ALLOWED_CALLBACKS = ( |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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24 "EarlyStopping", |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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25 "TerminateOnNaN", |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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26 "ReduceLROnPlateau", |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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27 "CSVLogger", |
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28 "None", |
4de3d598c116
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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29 ) |
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30 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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31 estimator_params = estimator.get_params() |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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32 |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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33 for name, p in estimator_params.items(): |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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34 # all potential unauthorized file write |
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35 if name == "memory" or name.endswith("__memory") or name.endswith("_path"): |
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36 new_p = {name: None} |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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37 estimator.set_params(**new_p) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
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38 elif n_jobs is not None and (name == "n_jobs" or name.endswith("__n_jobs")): |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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39 new_p = {name: n_jobs} |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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40 estimator.set_params(**new_p) |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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41 elif name.endswith("callbacks"): |
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42 for cb in p: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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43 cb_type = cb["callback_selection"]["callback_type"] |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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44 if cb_type not in ALLOWED_CALLBACKS: |
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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
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45 raise ValueError("Prohibited callback type: %s!" % cb_type) |
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46 |
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47 return estimator |
c33145a815ee
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"
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48 |
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49 |
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50 def _get_X_y(params, infile1, infile2): |
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51 """read from inputs and output X and y |
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52 |
6d056a1b2249
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53 Parameters |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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54 ---------- |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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55 params : dict |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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56 Tool inputs parameter |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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57 infile1 : str |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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58 File path to dataset containing features |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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59 infile2 : str |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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60 File path to dataset containing target values |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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61 |
6d056a1b2249
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62 """ |
6d056a1b2249
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63 # store read dataframe object |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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64 loaded_df = {} |
6d056a1b2249
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit eb703290e2589561ea215c84aa9f71bcfe1712c6"
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65 |
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66 input_type = params["input_options"]["selected_input"] |
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67 # tabular input |
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68 if input_type == "tabular": |
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69 header = "infer" if params["input_options"]["header1"] else None |
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70 column_option = params["input_options"]["column_selector_options_1"][ |
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71 "selected_column_selector_option" |
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72 ] |
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73 if column_option in [ |
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74 "by_index_number", |
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75 "all_but_by_index_number", |
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76 "by_header_name", |
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77 "all_but_by_header_name", |
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78 ]: |
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79 c = params["input_options"]["column_selector_options_1"]["col1"] |
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80 else: |
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81 c = None |
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82 |
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83 df_key = infile1 + repr(header) |
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84 df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True) |
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85 loaded_df[df_key] = df |
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86 |
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87 X = read_columns(df, c=c, c_option=column_option).astype(float) |
6d056a1b2249
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88 # sparse input |
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89 elif input_type == "sparse": |
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90 X = mmread(open(infile1, "r")) |
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91 |
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92 # Get target y |
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93 header = "infer" if params["input_options"]["header2"] else None |
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94 column_option = params["input_options"]["column_selector_options_2"][ |
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95 "selected_column_selector_option2" |
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96 ] |
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97 if column_option in [ |
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98 "by_index_number", |
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99 "all_but_by_index_number", |
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100 "by_header_name", |
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101 "all_but_by_header_name", |
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102 ]: |
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103 c = params["input_options"]["column_selector_options_2"]["col2"] |
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104 else: |
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105 c = None |
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106 |
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107 df_key = infile2 + repr(header) |
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108 if df_key in loaded_df: |
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109 infile2 = loaded_df[df_key] |
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110 else: |
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111 infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True) |
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112 loaded_df[df_key] = infile2 |
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113 |
21
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114 y = read_columns( |
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115 infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True |
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116 ) |
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117 if len(y.shape) == 2 and y.shape[1] == 1: |
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118 y = y.ravel() |
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119 |
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120 return X, y |
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121 |
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122 |
19
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123 def main(inputs, infile_estimator, infile1, infile2, out_object, out_weights=None): |
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124 """main |
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125 |
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126 Parameters |
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127 ---------- |
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128 inputs : str |
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129 File path to galaxy tool parameter |
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130 |
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131 infile_estimator : str |
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132 File paths of input estimator |
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133 |
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134 infile1 : str |
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135 File path to dataset containing features |
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136 |
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137 infile2 : str |
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138 File path to dataset containing target labels |
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139 |
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140 out_object : str |
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141 File path for output of fitted model or skeleton |
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142 |
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143 out_weights : str |
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144 File path for output of weights |
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145 |
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146 """ |
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147 with open(inputs, "r") as param_handler: |
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148 params = json.load(param_handler) |
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149 |
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150 # load model |
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151 with open(infile_estimator, "rb") as est_handler: |
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152 estimator = load_model(est_handler) |
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153 estimator = clean_params(estimator, n_jobs=N_JOBS) |
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154 |
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155 X_train, y_train = _get_X_y(params, infile1, infile2) |
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156 |
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157 estimator.fit(X_train, y_train) |
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158 |
13
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159 main_est = estimator |
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160 if isinstance(main_est, Pipeline): |
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161 main_est = main_est.steps[-1][-1] |
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162 if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"): |
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163 if out_weights: |
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164 main_est.save_weights(out_weights) |
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165 del main_est.model_ |
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166 del main_est.fit_params |
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167 del main_est.model_class_ |
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168 if getattr(main_est, "validation_data", None): |
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169 del main_est.validation_data |
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170 if getattr(main_est, "data_generator_", None): |
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171 del main_est.data_generator_ |
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172 |
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173 with open(out_object, "wb") as output_handler: |
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174 pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL) |
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175 |
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176 |
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177 if __name__ == "__main__": |
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178 aparser = argparse.ArgumentParser() |
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179 aparser.add_argument("-i", "--inputs", dest="inputs", required=True) |
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180 aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator") |
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181 aparser.add_argument("-y", "--infile1", dest="infile1") |
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182 aparser.add_argument("-g", "--infile2", dest="infile2") |
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183 aparser.add_argument("-o", "--out_object", dest="out_object") |
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184 aparser.add_argument("-t", "--out_weights", dest="out_weights") |
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185 args = aparser.parse_args() |
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186 |
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187 main( |
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188 args.inputs, |
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189 args.infile_estimator, |
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190 args.infile1, |
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191 args.infile2, |
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192 args.out_object, |
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193 args.out_weights, |
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194 ) |