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Zero-shot generation
The coding agent sees only the task. No rubric, no feedback. This is the artefact every repair arm starts from, so the arms differ only in the feedback they get.
Input — prompt to qwen/qwen3-14b
You are solving the Kaggle competition `spooky-author-identification`.
# Overview
## Description
As I scurried across the candlelit chamber, manuscripts in hand, I thought I'd made it. Nothing would be able to hurt me anymore. Little did I know there was one last fright lurking around the corner.
DING! My phone pinged me with a disturbing notification. It was Will, the scariest of Kaggle moderators, sharing news of another data leak.
"ph’nglui mglw’nafh Cthulhu R’lyeh wgah’nagl fhtagn!" I cried as I clumsily dropped my crate of unbound, spooky books. Pages scattered across the chamber floor. How will I ever figure out how to put them back together according to the authors who wrote them? Or are they lost, forevermore? Wait, I thought... I know, machine learning!
In this year's Halloween playground competition, you're challenged to predict the author of excerpts from horror stories by Edgar Allan Poe, Mary Shelley, and HP Lovecraft. We're encouraging you ([with cash prizes!](https://www.kaggle.com/c/spooky-author-identification#Prizes)) to share your insights in the competition's discussion forum and code in Kernels. We've designated prizes to reward authors of kernels and discussion threads that are particularly valuable to the community. Click the ["Prizes" tab](https://www.kaggle.com/c/spooky-author-identification) on this overview page to learn more.
### Getting Started
New to Kernels or working with natural language data? We've put together some starter kernels in [Python](https://www.kaggle.com/rtatman/beginner-s-tutorial-python) and [R](https://www.kaggle.com/rtatman/beginner-s-tutorial-r) to help you hit the ground running.
## Evaluation
Submissions are evaluated using multi-class logarithmic loss. Each id has one true class. For each id, you must submit a predicted probability for each author. The formula is then:
$$\text{log loss} = -\frac{1}{N} \sum_{i=1}^N \sum_{j=1}^M y_{ij} \log(p_{ij})$$
where N is the number of observations in the test set, M is the number of class labels (3 classes), \\(log\\) is the natural logarithm, \\(y_{ij}\\) is 1 if observation \\(i\\) belongs to class \\(j\\) and 0 otherwise, and \\(p_{ij}\\) is the predicted probability that observation \\(i\\) belongs to class \\(j\\).
The submitted probabilities for a given sentences are not required to sum to one because they are rescaled prior to being scored (each row is divided by the row sum). In order to avoid the extremes of the log function, predicted probabilities are replaced with \\(max(min(p,1-10^{-15}),10^{-15})\\).
### Submission File
You must submit a csv file with the id, and a probability for each of the three classes. The order of the rows does not matter. The file must have a header and should look like the following:
```
id,EAP,HPL,MWS
id07943,0.33,0.33,0.33
...
```
## Prizes
### Kernels Prizes
#### Total prize pool of $20,000
**Upvotes**: Six awards of \$2,000 will be given to the authors of the most upvoted kernel in the competition. Self-votes and admin-votes will not be counted. The prizes will be awarded at the close of the competition.
**Themed**: At the close of the competition, Kaggle data scientists will pick one kernel from each of the following categories that they feel is an exceptional example of the analysis type to award the authors each \$2,000:
- an R tutorial,
- a Python tutorial,
- creative feature engineering,
- creative data visualization.
Tip: Add tags like "tutorial", "feature engineering", and "data visualization" to your kernel after you publish it to help others discover it.
### Discussion Prizes
####Total prize pool of $5,000
**Upvotes**: Awards of \$1,000 each will be given to the unique authors of the top five most upvoted discussion topics on the competition.
The contents of the discussion must forward the work done on the competition to be judged at Kaggle's discretion. Only votes from other competitors who have accepted the rules and made a first submission will count towa
… [3550 more chars truncated]Output — generated script
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
Interpreter result
ran clean: False
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 38, in <module>
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'
CWM grading
The world model (claude-opus-5) sees the task, the code and one rubric — never the traceback. It marks every item present or absent from source text alone. Anything it gets right had to come from the rubric.
Input — the rubric shown to the CWM
### P1 — Removed or renamed constructor keyword argument on a library estimator
- **Pattern**: Flags a call to a library model/transformer constructor that passes a keyword argument belonging to the known set of parameters removed or renamed in current library versions, which makes the constructor raise a `TypeError` about an unexpected keyword argument.
- **Detection procedure**:
1. Locate every call whose callee name is a class from a modelling library (imported via `from <lib>... import <Name>` and invoked as `Name(` with keyword arguments).
2. Extract each keyword name used at that call site (text immediately left of `=` inside the parentheses).
3. Fire if any extracted keyword name appears in this removed/renamed list: `multi_class=` on a logistic-regression class; `base_estimator=` on a boosting/bagging wrapper class; `normalize=` on a linear regression/ridge/lasso class; `n_iter=` where the class documents `max_iter`; `sparse=` on a one-hot/encoder class (renamed `sparse_output`); `iid=` on a search/CV class; `presort=` on a tree/boosting class; `early_stopping_rounds=` or `verbose=` passed to a gradient-boosting library's `.fit(` call rather than its constructor.
4. Fire regardless of whether the argument value looks "harmless" (e.g. `'auto'`, `False`, `None`); the exception comes from the keyword's existence, not its value.
5. Do not fire if the keyword is passed to a plain user-defined function or to a dict literal.
- **Predicted errors**:
- Add score for E3 (Type mismatch / TypeError): 3
- **weight**: 3
- **confidence**: high
- **Evidence**: constructor invoked with `multi_class='auto'` inside an estimator list; `TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'`
- **Example**:
- Input:
```
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(C=1.0, max_iter=1000, solver='lbfgs', multi_class='auto')
```
- Output:
```
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'
```
- **Counter-example**:
- Input:
```
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(C=1.0, max_iter=1000, solver='lbfgs')
```
- Why it does not fire: no keyword from the removed/renamed set is present.
### P2 — Row-wise comprehension over a 2-D array that still uses 2-D reductions/slicing
- **Pattern**: Detects a list/generator comprehension that iterates directly over a 2-D array (probability matrix, feature matrix, prediction matrix) while the loop variable is used with `axis=1`, `axis=-1` on a reduction, or with a two-index slice such as `[:, np.newaxis]` / `[:, 0]`, so the 1-D row lacks the assumed second axis.
- **Detection procedure**:
1. Find every comprehension of the form `[<expr> for <v> in <arr>]` where `<arr>` is a name previously assigned from a call whose result is 2-D by construction (`predict_proba(`, `transform(`, `np.array(` of a nested list, `np.vstack(`, `np.zeros((a, b))`, or any variable whose `.shape[1]` or `[:, k]` is used elsewhere in the file).
2. Inside `<expr>`, check whether `<v>` appears with `axis=1`, `axis=-1`, `[:, ` , or `np.newaxis` / `None` in a second slice position.
3. Fire if both step 1 and step 2 hold.
4. Do not fire when the iteration is over a Python list of 2-D arrays (e.g. the name was built by `[m1, m2]` or by appending arrays in a loop).
- **Predicted errors**:
- Add score for E4 (Bounds / IndexError / AxisError): 3
- Add score for E3 (Type mismatch): 1
- **weight**: 3
- **confidence**: low
- **Evidence**: `np.array([probs / probs.sum(axis=1)[:, np.newaxis] for probs in y_proba])` where the iterated object is the 2-D output of `predict_proba(`
- **Example**:
- Input:
```
import numpy as np
proba = model.predict_proba(X_test) # shape (n_rows, n_classes)
proba = np.array([p / p.sum(axis=1)[:, np.newaxis] for p in proba])
```
- Output:
```
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
```
- **Counter-example**:
- Input:
```
proba = model.predict_proba(X_test)
proba = proba / proba.sum(axis=1, keepdims=True)
```
- Why it does not fire: the `axis=1` reduction is applied to the 2-D array itself, not to a row extracted by a comprehension.
### P3 — Dict-literal label mapping with `.map()` and no unmapped-value guard before fitting
- **Pattern**: Identifies a target column converted to numeric codes by `.map(<dict literal or dict variable>)` whose result is passed to a model `fit(` or cast with `.astype(` without any `fillna(`, `dropna(`, `isna()` check, or membership assertion, so any label absent from the dict becomes `NaN` and fitting raises.
- **Detection procedure**:
1. Find a dict literal whose keys are string literals and whose values are integers (or a `dict(zip(...))` built from a hardcoded list of label strings).
2. Find a `.map(` call that receives that dict and whose result is assigned to a target variable (name containing `y`, `label`, `target`) or chained into `.values` / `.astype(`.
3. Check the whole file for any of `fillna(`, `dropna(`, `isna()`, `notna()`, `assert`, or an `in` membership test applied to that mapped result or to the source column.
4. Fire if step 1 and step 2 hold and step 3 finds none.
- **Predicted errors**:
- Add score for E2 (Wrong variable value / silent NaN labels): 1
- Add score for E3 (Type mismatch / ValueError on NaN target): 2
- **weight**: 2
- **confidence**: low
- **Evidence**: `y_train = train['<target>'].map(author_to_label).values.astype(np.float32)` with a hardcoded three-key dict literal and no NaN check; predicted `ValueError: Input y contains NaN.`
- **Example**:
- Input:
```
mapping = {'LABEL_A': 0, 'LABEL_B': 1}
y = df['target'].map(mapping).values.astype('float32')
model.fit(X, y)
```
- Output:
```
ValueError: Input y contains NaN.
```
- **Counter-example**:
- Inpu
… [8160 more chars truncated]Output — per-item verdict
(2/7 fired)
| item | pattern | verdict | judge reasoning |
|---|---|---|---|
| P1 | Removed or renamed constructor keyword argument on a library estimator | PRESENT | LogisticRegression is constructed with the removed `multi_class='auto'` keyword. |
| P2 | Row-wise comprehension over a 2-D array that still uses 2-D reductions/slicing | absent | No comprehension iterating over a 2-D array with axis=1/2-D slicing. |
| P3 | Dict-literal label mapping with `.map()` and no unmapped-value guard before fitting | absent | Labels are encoded with LabelEncoder, no dict-literal `.map()`. |
| P4 | Sparse vectorizer output fed to a dense-only estimator | absent | TF-IDF output feeds LogisticRegression via a pipeline, which accepts sparse input. |
| P5 | Hardcoded absolute path used for read/write with no existence or directory-creation guard | PRESENT | Absolute literal paths used for read_csv and to_csv with no makedirs/exists guard. |
| P6 | Probability columns named by a hardcoded label order instead of the model's class order | absent | Probability columns are indexed via label_encoder.transform, not hardcoded integer positions. |
| P7 | Access to a library attribute/method removed in current versions | absent | No removed numpy aliases, `.ix[`, or DataFrame `.append(` usage. |
Derived feedback handed to the coding agent
A static code review scored your script against a rubric of known error-predictive patterns. These patterns were found PRESENT: - [P1] Flags a call to a library model/transformer constructor that passes a keyword argument belonging to the known set of parameters removed or renamed in current library versions, which makes the constructor raise a `TypeError` about an unexpected keyword argument. Reviewer note: LogisticRegression is constructed with the removed `multi_class='auto'` keyword. - [P5] Detects an absolute path string literal (leading `/` or a drive-letter prefix, containing two or more path separators) passed to a file read or write call, while the file contains no `os.makedirs(`, `Path(...).mkdir(`, or `os.path.exists(` guard for that path's directory. Reviewer note: Absolute literal paths used for read_csv and to_csv with no makedirs/exists guard.
Input — the rubric shown to the CWM
### P1 — Removed or renamed scikit-learn constructor keyword
- **Pattern**: Detects an estimator/transformer constructor call that passes a keyword argument belonging to the set of parameters removed or deprecated in recent scikit-learn releases.
- **Detection procedure**:
1. Locate every call whose callee name is a scikit-learn estimator/transformer class (name ends in `Classifier`, `Regressor`, `Encoder`, `Vectorizer`, `Scaler`, `CV`, or is one of `SVC`, `LogisticRegression`, `PCA`, `KMeans`, etc.) and is imported from a `sklearn.*` module.
2. Read the keyword names passed in that call.
3. Fire if any keyword is one of: `multi_class`, `normalize`, `base_estimator`, `iid`, `n_iter` (on solvers that renamed it to `max_iter`), `presort`, `min_impurity_split`, `sparse` (on `OneHotEncoder`), `n_features` (on hashing/text vectorizers), `loss='ls'`-style renamed values passed as a keyword name that no longer exists.
4. Fire regardless of whether the value passed is the library default (e.g. `'auto'`); the keyword's mere presence is the trigger.
- **Predicted errors**:
- Add score for E3 (Type mismatch / TypeError): 3
- **weight**: 3
- **confidence**: high
- **Evidence**: `LogisticRegression(..., multi_class='auto')` and `TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'`
- **Example**:
- Input:
```
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(C=1.0, max_iter=1000, multi_class='auto')
```
- Output:
```
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'
```
- **Counter-example**:
- Input:
```
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(C=1.0, max_iter=1000, solver='lbfgs')
```
- Why it does not fire: every keyword passed is still a live constructor parameter.
### P2 — Iterating a 2-D array then reducing the element with `axis=1`
- **Pattern**: Flags a comprehension or `for` loop that iterates directly over a 2-D array/matrix and then calls a reduction (`.sum(`, `.mean(`, `.max(`, `.argmax(`) with `axis=1` on the loop variable.
- **Detection procedure**:
1. Find a name bound to the result of a call known to return a 2-D array (`predict_proba(`, `transform(`, `np.array([[`, `.values` of a multi-column frame, `np.vstack(`, `np.zeros((a, b))`).
2. Find a `for <var> in <that name>` header, including inside a list comprehension.
3. Inside that loop/comprehension body, check whether `<var>` is used with a reduction call carrying `axis=1`, or is indexed with two subscripts such as `<var>[:, k]`.
4. Fire if steps 1–3 all hold; the loop variable is a 1-D row, so `axis=1` is out of bounds.
- **Predicted errors**:
- Add score for E4 (Bounds / IndexError): 3
- Add score for E3 (Type mismatch / TypeError): 1
- **weight**: 3
- **confidence**: low
- **Evidence**: `np.array([probs / probs.sum(axis=1)[:, np.newaxis] for probs in y_proba])` where the iterated object is the 2-D output of `predict_proba(`
- **Example**:
- Input:
```
import numpy as np
p = np.random.rand(4, 3)
p = np.array([row / row.sum(axis=1)[:, np.newaxis] for row in p])
```
- Output:
```
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
```
- **Counter-example**:
- Input:
```
import numpy as np
p = np.random.rand(4, 3)
p = p / p.sum(axis=1)[:, np.newaxis]
```
- Why it does not fire: the reduction is applied to the 2-D array itself, not to a row produced by iteration.
### P3 — Hardcoded label-to-index dict applied with `.map()` before fitting
- **Pattern**: Identifies a target column transformed by `.map(` with a dict literal of fixed string keys, whose result is passed to a `fit(` call or converted with `.astype(` a numeric dtype, with no membership check on the column's unique values.
- **Detection procedure**:
1. Find a dict literal whose keys are string literals and whose values are integers, assigned to a variable or written inline.
2. Find a call `<series>.map(<that dict>)` on a DataFrame column.
3. Check that the mapped result is then either `.astype(` a numeric dtype, `.values`-extracted, or passed as the second argument of a `fit(` / `fit_transform(` call.
4. Check that nowhere in the file is there a guard such as `assert`, `isin(`, `dropna(` on the mapped result, or a `fillna(` applied to it.
5. Fire if 1–4 hold: any category absent from the dict becomes `NaN` and reaches the estimator.
- **Predicted errors**:
- Add score for E4 (Missing key / unmapped category): 2
- Add score for E2 (Wrong variable value): 1
- **weight**: 2
- **confidence**: low
- **Evidence**: `y_train = train['label_col'].map(label_to_index).values.astype(np.float32)` with `label_to_index = {'LABEL_A': 0, 'LABEL_B': 1}` and no coverage check
- **Example**:
- Input:
```
import pandas as pd
from sklearn.linear_model import LogisticRegression
df = pd.DataFrame({'col_a': ['LABEL_A', 'LABEL_C'], 'f': [0.1, 0.2]})
m = {'LABEL_A': 0, 'LABEL_B': 1}
y = df['col_a'].map(m).values.astype('float32')
LogisticRegression().fit(df[['f']], y)
```
- Output:
```
ValueError: Input y contains NaN.
```
- **Counter-example**:
- Input:
```
from sklearn.preprocessing import LabelEncoder
y = LabelEncoder().fit_transform(df['col_a'])
```
- Why it does not fire: the encoding is derived from the data itself, so no category can be unmapped.
### P4 — Fixed column indices into `predict_proba` output sized by a hardcoded label list
- **Pattern**: Matches code that indexes a probability matrix with literal column numbers (`[:, 0]`, `[:, 1]`, `[:, 2]`, ...) whose highest index is fixed in source rather than derived from the fitted estimator's class array.
- **Detection procedure**:
1. Find a name assigned from a call to `predict_proba(` or `decision_function(`.
2. Find two or more subscripts of the form `<that
… [2925 more chars truncated]Output — per-item verdict
(2/5 fired)
| item | pattern | verdict | judge reasoning |
|---|---|---|---|
| P1 | Removed or renamed scikit-learn constructor keyword | PRESENT | LogisticRegression is constructed with the removed multi_class='auto' keyword. |
| P2 | Iterating a 2-D array then reducing the element with `axis=1` | absent | No loop or comprehension iterates over a 2-D array with axis=1 reductions. |
| P3 | Hardcoded label-to-index dict applied with `.map()` before fitting | absent | Labels are encoded with LabelEncoder, not a hardcoded dict .map(). |
| P4 | Fixed column indices into `predict_proba` output sized by a hardcoded label list | absent | Probability columns are indexed via label_encoder.transform, not integer literals. |
| P5 | Writing to a deep absolute path literal with no directory creation | PRESENT | to_csv writes to a deep absolute path literal with no os.makedirs anywhere. |
Derived feedback handed to the coding agent
A static code review scored your script against a rubric of known error-predictive patterns. These patterns were found PRESENT: - [P1] Detects an estimator/transformer constructor call that passes a keyword argument belonging to the set of parameters removed or deprecated in recent scikit-learn releases. Reviewer note: LogisticRegression is constructed with the removed multi_class='auto' keyword. - [P5] Detects a write call (`to_csv(`, `to_parquet(`, `open(` in a write mode, `savefig(`, `np.save(`, `torch.save(`) whose destination is an absolute path string literal containing two or more path separators, while the file contains no directory-creation call. Reviewer note: to_csv writes to a deep absolute path literal with no os.makedirs anywhere.
Repair
Same broken script into all four arms; only the feedback differs. original error gone is the outcome that matters — if control keeps the bug and a rubric arm drops it, the rubric carried the repair signal.
| arm | feedback | ran clean | original error gone | ending error |
|---|---|---|---|---|
| control | no information — isolates re-roll luck | False | False | TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class' |
| rubric_1 | fired items from the general rubric | False | True | ValueError: The 'liblinear' solver does not support multiclass classification (n_classes > |
| rubric_2 | fired items from the specific rubric | False | True | ValueError: The 'liblinear' solver does not support multiclass classification (n_classes > |
| oracle | the raw traceback — ceiling for what feedback can buy | False | False | TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class' |
Input — full repair prompt
Your previous script failed. Here it is:
```python
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
```
Find and fix the bug.
Return the full corrected script in a single ```python block.Output — repaired script
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Map author names to their corresponding indices for consistent submission format
author_to_index = {author: idx for idx, author in enumerate(label_encoder.classes_)}
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe using author_to_index mapping
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, author_to_index['EAP']],
'HPL': test_preds[:, author_to_index['HPL']],
'MWS': test_preds[:, author_to_index['MWS']]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
Interpreter result
ran clean: False · original error gone: False
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 41, in <module>
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'
Input — full repair prompt
Your previous script failed. Here it is:
```python
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
```
A static code review scored your script against a rubric of known error-predictive patterns. These patterns were found PRESENT:
- [P1] Flags a call to a library model/transformer constructor that passes a keyword argument belonging to the known set of parameters removed or renamed in current library versions, which makes the constructor raise a `TypeError` about an unexpected keyword argument.
Reviewer note: LogisticRegression is constructed with the removed `multi_class='auto'` keyword.
- [P5] Detects an absolute path string literal (leading `/` or a drive-letter prefix, containing two or more path separators) passed to a file read or write call, while the file contains no `os.makedirs(`, `Path(...).mkdir(`, or `os.path.exists(` guard for that path's directory.
Reviewer note: Absolute literal paths used for read_csv and to_csv with no makedirs/exists guard.
Return the full corrected script in a single ```python block.Output — repaired script
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Ensure the submission directory exists
os.makedirs(os.path.dirname(submission_path), exist_ok=True)
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
Interpreter result
ran clean: False · original error gone: True
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 45, in <module>
pipeline.fit(X_train, y_train)
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/base.py", line 1403, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/pipeline.py", line 649, in fit
self._final_estimator.fit(Xt, y, **last_step_params["fit"])
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/base.py", line 1403, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py", line 1488, in fit
raise ValueError(
ValueError: The 'liblinear' solver does not support multiclass classification (n_classes >= 3). Either use another solver or wrap the estimator in a OneVsRestClassifier to keep applying a one-versus-rest scheme.
Input — full repair prompt
Your previous script failed. Here it is:
```python
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
```
A static code review scored your script against a rubric of known error-predictive patterns. These patterns were found PRESENT:
- [P1] Detects an estimator/transformer constructor call that passes a keyword argument belonging to the set of parameters removed or deprecated in recent scikit-learn releases.
Reviewer note: LogisticRegression is constructed with the removed multi_class='auto' keyword.
- [P5] Detects a write call (`to_csv(`, `to_parquet(`, `open(` in a write mode, `savefig(`, `np.save(`, `torch.save(`) whose destination is an absolute path string literal containing two or more path separators, while the file contains no directory-creation call.
Reviewer note: to_csv writes to a deep absolute path literal with no os.makedirs anywhere.
Return the full corrected script in a single ```python block.Output — repaired script
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Ensure directory exists before saving CSV
os.makedirs(os.path.dirname(submission_path), exist_ok=True)
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
Interpreter result
ran clean: False · original error gone: True
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 42, in <module>
pipeline.fit(X_train, y_train)
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/base.py", line 1403, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/pipeline.py", line 649, in fit
self._final_estimator.fit(Xt, y, **last_step_params["fit"])
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/base.py", line 1403, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace-vast/edwardosunny/envs/ml/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py", line 1488, in fit
raise ValueError(
ValueError: The 'liblinear' solver does not support multiclass classification (n_classes >= 3). Either use another solver or wrap the estimator in a OneVsRestClassifier to keep applying a one-versus-rest scheme.
Input — full repair prompt
Your previous script failed. Here it is:
```python
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
```
It failed with:
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 38, in <module>
('clf', LogisticRegression(solver='liblinear', multi_class='auto', C=1.0, random_state=42))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'
Return the full corrected script in a single ```python block.Output — repaired script
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import os
# Set paths
train_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/train.csv'
test_path = '/workspace-vast/edwardosunny/mle-bench/data/spooky-author-identification/prepared/public/test.csv'
submission_path = '/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/submission.csv'
# Load data
train_df = pd.read_csv(train_path)
test_df = pd.read_csv(test_path)
# Label encoding for authors
label_encoder = LabelEncoder()
train_df['author_encoded'] = label_encoder.fit_transform(train_df['author'])
# Split data into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(
train_df['text'],
train_df['author_encoded'],
test_size=0.2,
random_state=42
)
# Create a TF-IDF vectorizer
vectorizer = TfidfVectorizer(stop_words='english', max_features=10000)
# Create a pipeline with TF-IDF and logistic regression
pipeline = Pipeline([
('tfidf', vectorizer),
('clf', LogisticRegression(solver='lbfgs', multi_class='multinomial', C=1.0, random_state=42))
])
# Train the model
pipeline.fit(X_train, y_train)
# Predict on validation set (for checking log loss)
y_pred_val = pipeline.predict_proba(X_val)
val_log_loss = log_loss(y_val, y_pred_val)
print(f"Validation Log Loss: {val_log_loss}")
# Predict on test set
test_preds = pipeline.predict_proba(test_df['text'])
# Create submission dataframe
submission = pd.DataFrame({
'id': test_df['id'],
'EAP': test_preds[:, label_encoder.transform(['EAP'])[0]],
'HPL': test_preds[:, label_encoder.transform(['HPL'])[0]],
'MWS': test_preds[:, label_encoder.transform(['MWS'])[0]]
})
# Save submission to CSV
submission.to_csv(submission_path, index=False)
print(f"Submission saved to {submission_path}")
Interpreter result
ran clean: False · original error gone: False
--- stderr ---
Traceback (most recent call last):
File "/workspace-vast/edwardosunny/mle-bench/runs/spooky-author-identification/solution.py", line 38, in <module>
('clf', LogisticRegression(solver='lbfgs', multi_class='multinomial', C=1.0, random_state=42))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: LogisticRegression.__init__() got an unexpected keyword argument 'multi_class'