From db6511c2daa0f6240ee4fed43701d46ea10cd78e Mon Sep 17 00:00:00 2001
From: Alex Rubinsteyn <alex.rubinsteyn@gmail.com>
Date: Wed, 27 Jan 2016 17:34:21 -0500
Subject: [PATCH] added option to save incomplete matrix
:use 20 rounds of MICE for matrix completion
---
experiments/matrix-completion-accuracy.py | 31 ++++++++++++++++++-----
1 file changed, 24 insertions(+), 7 deletions(-)
diff --git a/experiments/matrix-completion-accuracy.py b/experiments/matrix-completion-accuracy.py
index 2553c9e8..d09cc714 100644
--- a/experiments/matrix-completion-accuracy.py
+++ b/experiments/matrix-completion-accuracy.py
@@ -35,6 +35,7 @@ import sklearn.metrics
from sklearn.cross_validation import StratifiedKFold
from scipy import stats
import numpy as np
+import pandas as pd
from dataset_paths import PETERS2009_CSV_PATH
@@ -50,6 +51,11 @@ parser.add_argument(
default=50000.0,
type=float)
+parser.add_argument(
+ "--save-incomplete-affinity-matrix",
+ default=None,
+ help="Path to CSV which will contains the incomplete affinity matrix")
+
parser.add_argument(
"--only-human",
default=False,
@@ -127,12 +133,12 @@ imputation_methods = {
"svdImpute-5": IterativeSVD(5, verbose=VERBOSE),
"svdImpute-10": IterativeSVD(10, verbose=VERBOSE),
"svdImpute-20": IterativeSVD(20, verbose=VERBOSE),
- "colSims": SimilarityWeightedAveraging(
+ "similarityWeightedAveraging": SimilarityWeightedAveraging(
orientation="columns",
verbose=VERBOSE),
"meanFill": SimpleFill("mean"),
"zeroFill": SimpleFill("zero"),
- "MICE": MICE(verbose=VERBOSE),
+ "MICE": MICE(n_burn_in=5, n_imputations=20, verbose=VERBOSE),
"knnImpute-3": KNN(3, orientation="columns", verbose=VERBOSE, print_interval=1),
"knnImpute-7": KNN(7, orientation="columns", verbose=VERBOSE, print_interval=1),
"knnImpute-15": KNN(15, orientation="columns", verbose=VERBOSE, print_interval=1),
@@ -221,6 +227,17 @@ if __name__ == "__main__":
X, peptide_order, allele_order = \
dense_matrix_from_nested_dictionary(peptide_to_allele_to_affinity)
+ if args.save_incomplete_affinity_matrix:
+ print("Saving incomplete data to %s" % args.save_incomplete_affinity_matrix)
+ column_names = [None] * len(allele_order)
+ for (name, position) in allele_order.items():
+ column_names[position] = name
+ row_names = [None] * len(peptide_order)
+ for (name, position) in peptide_order.items():
+ row_names[position] = name
+ df = pd.DataFrame(X, columns=column_names, index=row_names)
+ df.to_csv(args.save_incomplete_affinity_matrix, index_label="peptide")
+
scores = ScoreSet()
missing_mask = np.isnan(X)
@@ -236,11 +253,6 @@ if __name__ == "__main__":
assert len(observed_indices) == n_observed
kfold = StratifiedKFold(observed_y, n_folds=5, shuffle=True)
- biscaler = BiScaler(
- scale_rows=args.normalize_rows,
- center_rows=args.normalize_rows,
- scale_columns=args.normalize_columns,
- center_columns=args.normalize_rows)
for fold_idx, (_, indirect_test_indices) in enumerate(kfold):
@@ -268,6 +280,11 @@ if __name__ == "__main__":
empty_col_mask.sum()))
X_fold_reduced = X_fold[ok_mesh]
+ biscaler = BiScaler(
+ scale_rows=args.normalize_rows,
+ center_rows=args.normalize_rows,
+ scale_columns=args.normalize_columns,
+ center_columns=args.normalize_columns)
X_fold_reduced_scaled = biscaler.fit_transform(X=X_fold_reduced)
for (method_name, solver) in sorted(imputation_methods.items()):
print("CV fold %d/%d, running %s" % (
--
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