Agent Conversation: openai/gpt-5.4

Model: openai/gpt-5.4

Total steps: 7

Messages: 16

UserStep 0

Task: ml-feature-selection

Feature Selection Method Design

Research Question

Design a novel univariate feature scoring method that identifies the most informative features for classification, generalizing across diverse data modalities (text, vision, tabular).

Background

Feature selection is a fundamental preprocessing step in machine learning. By removing irrelevant or redundant features, it can improve model accuracy, reduce overfitting, and speed up training. Classical univariate methods score each feature independently based on its relationship with the target variable:

Chi-squared test: Measures departure from independence between feature and target using contingency tables. Works best with non-negative, count-like features.
ANOVA F-value (f_classif): Computes the ratio of between-class variance to within-class variance. Effective for normally-distributed features with different means per class.
Mutual Information: Estimates the mutual information between each feature and the target via k-nearest neighbors. Captures non-linear dependencies but is computationally expensive.

Each method has strengths and weaknesses depending on the data distribution. The task is to design a scoring function that performs robustly across different data types and class structures.

Task

Implement the score_features(X, y) function in custom_featsel.py. Given a training feature matrix X and integer class labels y, return a 1-D numpy array of non-negative importance scores (one per feature). The top-k features (by score) will be selected and used to train a LogisticRegression classifier.

Interface

def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:
    """
    Args:
        X: (n_samples, n_features) non-negative float array
        y: (n_samples,) integer class labels

    Returns:
        scores: (n_features,) non-negative float array
    """

Available imports (already at top of file): numpy, scipy (via sklearn), sklearn.feature_selection (mutual_info_classif, chi2, f_classif), sklearn.preprocessing, sklearn.metrics.

Evaluation

Evaluated on three classification benchmarks spanning different data modalities:

20newsgroups: 10,000 TF-IDF text features, 20 classes, top-500 selected
MNIST: 784 pixel intensity features, 10 digit classes, top-200 selected
Madelon: 500 synthetic features (20 informative + 480 noisy), binary classification, top-20 selected

Metric: test classification accuracy using LogisticRegression on the selected features (higher is better).

scikit-learn/custom_featsel.py [EDITABLE — lines 86–112 only]

     1: # Custom feature selection method for MLS-Bench
     2: #
     3: # EDITABLE section: score_features() function.
     4: # FIXED sections: everything else (data loading, classifier, evaluation).
     5: import os
     6: import warnings
     7: import numpy as np
     8: from pathlib import Path
     9: 
    10: from skl

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_featsel.py",
4  "content": "def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:\n    \"\"\"Adaptive pairwise mean+threshold separability score.\"\"\"\n    y = np.asarray(y)\n    cls, inv = np.unique(y, return_inverse=True)\n    n, p = X.shape\n    C, eps = len(cls), 1e-12\n    med = np.median(X, axis=0)\n    cnt = np.bincount(inv, minlength=C).astype(np.float64)\n    pri = cnt / float(n)\n    mu = np.zeros((C, p), dtype=np.float64)\n    var = np.zeros((C, p), dtype=np.float64)\n    q = np.zeros((C, p), dtype=np.float64)\n    for c in range(C):\n        Xc = X[inv == c]\n        mu[c] = Xc.mean(axis=0)\n        var[c] = Xc.var(axis=0)\n        q[c] = (Xc > med).mean(axis=0)\n    w = (pri[:, None] * pri[None, :])[:, :, None]\n    dmu = mu[:, None, :] - mu[None, :, :]\n    cont = 0.5 * np.sum(w * (dmu * dmu) / (var[:, None, :] + var[None, :, :] + eps), axis=(0, 1))\n    dq = q[:, None, :] - q[None, :, :]\n    qv = q[:, None, :] * (1.0 - q[:, None, :]) + q[None, :, :] * (1.0 - q[None, :, :]) + eps\n    binsep = 0.5 * np.sum(w * (dq * dq) / qv, axis=(0, 1))\n    scores = cont + np.sqrt(cont * binsep) + binsep\n    return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)\n",
5  "after_line": 0,
6  "start_line": 86,
7  "end_line": 112
8}

Tool ResultStep 1

OK: Replaced lines 86..112 with 25 line(s) in scikit-learn/custom_featsel.py. Editable range: 86–110.

[Current file: scikit-learn/custom_featsel.py | editable: 86–110 | total: 190 lines]
    86  def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:
    87      """Adaptive pairwise mean+threshold separability score."""
    88      y = np.asarray(y)
       ... (19 more lines) ...
   108      binsep = 0.5 * np.sum(w * (dq * dq) / qv, axis=(0, 1))
   109      scores = cont + np.sqrt(cont * binsep) + binsep
   110      return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)

AssistantStep 2test

Tool ResultStep 2

[Test #1] (2 tests remaining; call submit(n=N) to choose which test result to submit as final)

### 20newsgroups (scripts/20newsgroups.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=20newsgroups n_features=10000 k=500
TRAIN_METRICS step=selected dataset=20newsgroups top_k=500
TRAIN_METRICS step=eval dataset=20newsgroups train_acc=0.7576 test_acc=0.4715
Test results (20newsgroups):
  Accuracy: 0.4715 (47.15%)

### mnist (scripts/mnist.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=mnist n_features=784 k=200
TRAIN_METRICS step=selected dataset=mnist top_k=200
TRAIN_METRICS step=eval dataset=mnist train_acc=0.9020 test_acc=0.8904
Test results (mnist):
  Accuracy: 0.8904 (89.04%)

[Leaderboard] Results saved: {'accuracy_20newsgroups': 0.4715, 'elapsed_20newsgroups': 12, 'accuracy_mnist': 0.8904, 'elapsed_mnist': 12, 'accuracy_madelon': 0.6128, 'elapsed_madelon': 12}

AssistantStep 3edit

Tool Input

1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_featsel.py",
4  "content": "def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:\n    \"\"\"Sparse-adaptive rank fusion of chi2, ANOVA, and threshold separability.\"\"\"\n    y = np.asarray(y)\n    _, inv = np.unique(y, return_inverse=True)\n    n, p = X.shape\n    med = np.median(X, axis=0)\n    cnt = np.bincount(inv).astype(np.float64) / float(n)\n    mu = np.zeros((len(cnt), p)); var = np.zeros((len(cnt), p)); q = np.zeros((len(cnt), p))\n    for c in range(len(cnt)):\n        Xc = X[inv == c]\n        mu[c] = Xc.mean(0); var[c] = Xc.var(0); q[c] = (Xc > med).mean(0)\n    w = (cnt[:, None] * cnt[None, :])[:, :, None]\n    dmu = mu[:, None, :] - mu[None, :, :]\n    cont = 0.5 * np.sum(w * (dmu * dmu) / (var[:, None, :] + var[None, :, :] + 1e-12), axis=(0, 1))\n    dq = q[:, None, :] - q[None, :, :]\n    qv = q[:, None, :] * (1.0 - q[:, None, :]) + q[None, :, :] * (1.0 - q[None, :, :]) + 1e-12\n    binsep = 0.5 * np.sum(w * (dq * dq) / qv, axis=(0, 1))\n    raw = cont + np.sqrt(cont * binsep) + binsep\n    c, _ = chi2(X, y)\n    f, _ = f_classif(X, y)\n    def rank_score(s):\n        s = np.nan_to_num(np.log1p(s), nan=0.0, posinf=0.0, neginf=0.0); r = np.empty(p, dtype=np.float64); r[np.argsort(s)] = np.linspace(1.0 / p, 1.0, p); return r\n    z = (X <= 1e-12).mean(0)\n    scores = rank_score(raw) + (1.0 - z) * rank_score(f) + z * rank_score(c)\n    return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)\n",
5  "after_line": 0,
6  "start_line": 86,
7  "end_line": 110
8}

Tool ResultStep 3

OK: Replaced lines 86..110 with 25 line(s) in scikit-learn/custom_featsel.py. Editable range: 86–110.

[Current file: scikit-learn/custom_featsel.py | editable: 86–110 | total: 190 lines]
    86  def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:
    87      """Sparse-adaptive rank fusion of chi2, ANOVA, and threshold separability."""
    88      y = np.asarray(y)
       ... (19 more lines) ...
   108      z = (X <= 1e-12).mean(0)
   109      scores = rank_score(raw) + (1.0 - z) * rank_score(f) + z * rank_score(c)
   110      return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)

AssistantStep 4test

Tool ResultStep 4

[Test #2] (1 test remaining; call submit(n=N) to choose which test result to submit as final)

### 20newsgroups (scripts/20newsgroups.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=20newsgroups n_features=10000 k=500
TRAIN_METRICS step=selected dataset=20newsgroups top_k=500
TRAIN_METRICS step=eval dataset=20newsgroups train_acc=0.7534 test_acc=0.5289
Test results (20newsgroups):
  Accuracy: 0.5289 (52.89%)

### mnist (scripts/mnist.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=mnist n_features=784 k=200
TRAIN_METRICS step=selected dataset=mnist top_k=200
TRAIN_METRICS step=eval dataset=mnist train_acc=0.9023 test_acc=0.8917
Test results (mnist):
  Accuracy: 0.8917 (89.17%)

[Leaderboard] Results saved: {'accuracy_20newsgroups': 0.5289, 'elapsed_20newsgroups': 14, 'accuracy_mnist': 0.8917, 'elapsed_mnist': 14, 'accuracy_madelon': 0.6103, 'elapsed_madelon': 14}

AssistantStep 5edit

Tool Input

1{
2  "op": "replace",
3  "filename": "scikit-learn/custom_featsel.py",
4  "content": "def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:\n    \"\"\"Sparse-adaptive rank fusion of magnitude, threshold, and ANOVA evidence.\"\"\"\n    n, p = X.shape\n    c, _ = chi2(X, y)\n    f, _ = f_classif(X, y)\n    med = np.median(X, axis=0)\n    b, _ = chi2((X > med).astype(np.float64), y)\n    z = (X <= 1e-12).mean(0)\n    def rank_score(s):\n        s = np.nan_to_num(np.log1p(s), nan=0.0, posinf=0.0, neginf=0.0)\n        r = np.empty(p, dtype=np.float64)\n        r[np.argsort(s)] = np.linspace(1.0 / p, 1.0, p)\n        return r\n    rc = rank_score(c)\n    rf = rank_score(f)\n    rb = rank_score(b)\n    scores = rb + (1.0 - z) * rf + (0.5 + 0.5 * z) * rc\n    return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)\n",
5  "after_line": 0,
6  "start_line": 86,
7  "end_line": 110
8}

Tool ResultStep 5

OK: Replaced lines 86..110 with 18 line(s) in scikit-learn/custom_featsel.py. Editable range: 86–103.

[Current file: scikit-learn/custom_featsel.py | editable: 86–103 | total: 183 lines]
    86  def score_features(X: np.ndarray, y: np.ndarray) -> np.ndarray:
    87      """Sparse-adaptive rank fusion of magnitude, threshold, and ANOVA evidence."""
    88      n, p = X.shape
       ... (12 more lines) ...
   101      rb = rank_score(b)
   102      scores = rb + (1.0 - z) * rf + (0.5 + 0.5 * z) * rc
   103      return np.nan_to_num(scores, nan=0.0, posinf=0.0, neginf=0.0)

AssistantStep 6test

Tool ResultStep 6

[Test #3] (0 tests remaining; call submit(n=N) to choose which test result to submit as final)

[NOTE] This was your last test. You MUST now call submit(n=X) to choose which test result to submit as your final answer.

### 20newsgroups (scripts/20newsgroups.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=20newsgroups n_features=10000 k=500
TRAIN_METRICS step=selected dataset=20newsgroups top_k=500
TRAIN_METRICS step=eval dataset=20newsgroups train_acc=0.7156 test_acc=0.5531
Test results (20newsgroups):
  Accuracy: 0.5531 (55.31%)

### mnist (scripts/mnist.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=mnist n_features=784 k=200
TRAIN_METRICS step=selected dataset=mnist top_k=200
TRAIN_METRICS step=eval dataset=mnist train_acc=0.9019 test_acc=0.8927
Test results (mnist):
  Accuracy: 0.8927 (89.27%)

### madelon (scripts/madelon.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=madelon n_features=500 k=20
TRAIN_METRICS step=selected dataset=madelon top_k=20
T

AssistantStep 7submit

Tool Input

1{
2  "n": 3
3}

Tool ResultStep 7

[submit] Submitting result from test #3 as final.

### 20newsgroups (scripts/20newsgroups.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=20newsgroups n_features=10000 k=500
TRAIN_METRICS step=selected dataset=20newsgroups top_k=500
TRAIN_METRICS step=eval dataset=20newsgroups train_acc=0.7156 test_acc=0.5531
Test results (20newsgroups):
  Accuracy: 0.5531 (55.31%)

### mnist (scripts/mnist.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=mnist n_features=784 k=200
TRAIN_METRICS step=selected dataset=mnist top_k=200
TRAIN_METRICS step=eval dataset=mnist train_acc=0.9019 test_acc=0.8927
Test results (mnist):
  Accuracy: 0.8927 (89.27%)

### madelon (scripts/madelon.sh)
Training progress:
TRAIN_METRICS step=scoring dataset=madelon n_features=500 k=20
TRAIN_METRICS step=selected dataset=madelon top_k=20
TRAIN_METRICS step=eval dataset=madelon train_acc=0.6214 test_acc=0.6231
Test results (madelon):
  Accuracy: 0.6231 (62.31%)

[submit] Finalized test #3 as final.

[Lead