Mercurial > repos > galaxytrakr > plasmidtrakr

annotate predict_source.py @ 1:fe9ff3859d68 draft

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planemo upload commit 3e1ba2085173abf4b891e9d715399bbbece65bba
author galaxytrakr
date Wed, 29 Apr 2026 15:26:26 +0000
parents f25631df0e9f
children 954eccb7cc48
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f25631df0e9f planemo upload commit 25e4c800a5358b8615dac18ea5e908e31c534020
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1 import argparse
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2 import sys
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3 import os
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4 import pandas as pd
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5 import joblib
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6 import warnings
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7
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8 # Suppress scikit-learn warnings about feature names if they pop up
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9 warnings.filterwarnings("ignore", category=UserWarning)
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10
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11 def main():
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12 parser = argparse.ArgumentParser(description="Predict the source of an isolate using a trained Random Forest model and Mash distances.")
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13 parser.add_argument("-i", "--input", required=True, help="Input Mash screen/dist file for one or more isolates.")
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14
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15 # --- KEY FIX 1: Replaced -m and -f with a single -b (bundle) argument ---
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16 parser.add_argument("-b", "--bundle", required=True, help="Path to the bundled model and features (.joblib file)")
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17
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18 parser.add_argument("-t", "--threshold", type=float, default=0.95, help="Mash identity threshold (default: 0.95)")
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19 parser.add_argument("-o", "--output", default="predictions.tsv", help="Output file for predictions (default: predictions.tsv)")
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20 args = parser.parse_args()
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21
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22 print(f"Loading model bundle: {args.bundle}")
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23
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24 try:
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25 # --- KEY FIX 2: Load the dictionary and extract both pieces ---
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26 bundle = joblib.load(args.bundle)
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27 rf_model = bundle['model']
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28 training_features = bundle['features']
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29 print(f"Successfully loaded model and {len(training_features)} features.")
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30 except Exception as e:
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31 print(f"FATAL: Error loading model bundle: {e}")
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32 sys.exit(1)
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33
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34 print(f"Loading and processing input data: {args.input}")
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35
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36 try:
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37 df = pd.read_csv(args.input, sep='\s+', header=None, engine='python')
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38
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39 # Your format is from 'mash screen', where the columns are:
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40 # Identity, Shared-hashes, Median-multiplicity, P-value, Query-ID
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41 if len(df.columns) >= 5:
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42 print("--> Standard headerless Mash output detected.")
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43 # Keep only the first 5 columns to be safe
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44 df = df.iloc[:, :5]
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45 df.columns = ['Identity', 'Shared_Hashes', 'Median_Multiplicity', 'P_value', 'Plasmid_ID']
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46
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47 # The 'Identity' is already the first column, just convert it to numeric
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48 df['Identity'] = pd.to_numeric(df['Identity'], errors='coerce')
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49
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50 # We need to manually add the 'Run' column. For screen output, the Query-ID (isolate name)
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51 # is not present in the file itself. We must get it from the filename.
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52 run_id = os.path.splitext(os.path.basename(args.input))[0]
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53 df['Run'] = run_id
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54 else:
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55 print(f"FATAL: Input file format not recognized. Expected at least 5 columns for Mash output, but got {len(df.columns)}.")
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56 sys.exit(1)
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57
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58 df.dropna(subset=['Identity'], inplace=True)
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59 df['Run'] = df['Run'].astype(str).str.strip()
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60
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61 except Exception as e:
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62 print(f"FATAL: Error reading input file '{args.input}'. Error: {e}")
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63 sys.exit(1)
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64
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65 print(f"Filtering features (Identity >= {args.threshold})...")
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66 filtered_df = df[df['Identity'] >= args.threshold].copy()
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67
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68 if filtered_df.empty:
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69 print("Warning: No plasmid hits met the identity threshold. Cannot make a prediction.")
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70 sys.exit(0)
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71
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72 new_data_matrix = filtered_df.pivot_table(index='Run', columns='Plasmid_ID', values='Identity', fill_value=0)
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73
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74 print("Aligning input features with the trained model...")
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75 aligned_matrix = pd.DataFrame(0, index=new_data_matrix.index, columns=training_features)
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76 common_plasmids = new_data_matrix.columns.intersection(training_features)
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77 aligned_matrix[common_plasmids] = new_data_matrix[common_plasmids]
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78
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79 print(f"Making predictions for {len(aligned_matrix)} isolate(s)...")
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80 predictions = rf_model.predict(aligned_matrix)
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81 probabilities = rf_model.predict_proba(aligned_matrix)
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82 max_probs = probabilities.max(axis=1)
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83
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84 results_df = pd.DataFrame({
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85 'Run': aligned_matrix.index,
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86 'Predicted_Source': predictions,
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87 'Confidence_Score': max_probs
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88 })
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89
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90 results_df.to_csv(args.output, sep='\t', index=False)
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91 print(f"\n✅ Predictions complete! Saved to {args.output}")
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92 print("--- PREDICTION RESULTS ---")
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93 print(results_df.to_string(index=False))
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94
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95 if __name__ == "__main__":
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96 main()