Mercurial > repos > galaxytrakr > plasmidtrakr

comparison predict_source.py @ 0:f25631df0e9f draft

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planemo upload commit 25e4c800a5358b8615dac18ea5e908e31c534020
author galaxytrakr
date Wed, 29 Apr 2026 15:04:37 +0000
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children 954eccb7cc48
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-1:000000000000 0:f25631df0e9f
1 import argparse
2 import sys
3 import os
4 import pandas as pd
5 import joblib
6 import warnings
7
8 # Suppress scikit-learn warnings about feature names if they pop up
9 warnings.filterwarnings("ignore", category=UserWarning)
10
11 def main():
12 parser = argparse.ArgumentParser(description="Predict the source of an isolate using a trained Random Forest model and Mash distances.")
13 parser.add_argument("-i", "--input", required=True, help="Input Mash screen/dist file for one or more isolates.")
14
15 # --- KEY FIX 1: Replaced -m and -f with a single -b (bundle) argument ---
16 parser.add_argument("-b", "--bundle", required=True, help="Path to the bundled model and features (.joblib file)")
17
18 parser.add_argument("-t", "--threshold", type=float, default=0.95, help="Mash identity threshold (default: 0.95)")
19 parser.add_argument("-o", "--output", default="predictions.tsv", help="Output file for predictions (default: predictions.tsv)")
20 args = parser.parse_args()
21
22 print(f"Loading model bundle: {args.bundle}")
23
24 try:
25 # --- KEY FIX 2: Load the dictionary and extract both pieces ---
26 bundle = joblib.load(args.bundle)
27 rf_model = bundle['model']
28 training_features = bundle['features']
29 print(f"Successfully loaded model and {len(training_features)} features.")
30 except Exception as e:
31 print(f"FATAL: Error loading model bundle: {e}")
32 sys.exit(1)
33
34 print(f"Loading and processing input data: {args.input}")
35
36 try:
37 df = pd.read_csv(args.input, sep='\s+', header=None, engine='python')
38
39 # Your format is from 'mash screen', where the columns are:
40 # Identity, Shared-hashes, Median-multiplicity, P-value, Query-ID
41 if len(df.columns) >= 5:
42 print("--> Standard headerless Mash output detected.")
43 # Keep only the first 5 columns to be safe
44 df = df.iloc[:, :5]
45 df.columns = ['Identity', 'Shared_Hashes', 'Median_Multiplicity', 'P_value', 'Plasmid_ID']
46
47 # The 'Identity' is already the first column, just convert it to numeric
48 df['Identity'] = pd.to_numeric(df['Identity'], errors='coerce')
49
50 # We need to manually add the 'Run' column. For screen output, the Query-ID (isolate name)
51 # is not present in the file itself. We must get it from the filename.
52 run_id = os.path.splitext(os.path.basename(args.input))[0]
53 df['Run'] = run_id
54 else:
55 print(f"FATAL: Input file format not recognized. Expected at least 5 columns for Mash output, but got {len(df.columns)}.")
56 sys.exit(1)
57
58 df.dropna(subset=['Identity'], inplace=True)
59 df['Run'] = df['Run'].astype(str).str.strip()
60
61 except Exception as e:
62 print(f"FATAL: Error reading input file '{args.input}'. Error: {e}")
63 sys.exit(1)
64
65 print(f"Filtering features (Identity >= {args.threshold})...")
66 filtered_df = df[df['Identity'] >= args.threshold].copy()
67
68 if filtered_df.empty:
69 print("Warning: No plasmid hits met the identity threshold. Cannot make a prediction.")
70 sys.exit(0)
71
72 new_data_matrix = filtered_df.pivot_table(index='Run', columns='Plasmid_ID', values='Identity', fill_value=0)
73
74 print("Aligning input features with the trained model...")
75 aligned_matrix = pd.DataFrame(0, index=new_data_matrix.index, columns=training_features)
76 common_plasmids = new_data_matrix.columns.intersection(training_features)
77 aligned_matrix[common_plasmids] = new_data_matrix[common_plasmids]
78
79 print(f"Making predictions for {len(aligned_matrix)} isolate(s)...")
80 predictions = rf_model.predict(aligned_matrix)
81 probabilities = rf_model.predict_proba(aligned_matrix)
82 max_probs = probabilities.max(axis=1)
83
84 results_df = pd.DataFrame({
85 'Run': aligned_matrix.index,
86 'Predicted_Source': predictions,
87 'Confidence_Score': max_probs
88 })
89
90 results_df.to_csv(args.output, sep='\t', index=False)
91 print(f"\n✅ Predictions complete! Saved to {args.output}")
92 print("--- PREDICTION RESULTS ---")
93 print(results_df.to_string(index=False))
94
95 if __name__ == "__main__":
96 main()