Merge branch 'main' of https://github.com/WecoAI/weco-cli

Author: DhruvSrikanth

examples/spaceship-titanic/README.md

@@ -1,8 +1,6 @@
 # Example: Optimizing a Kaggle Classification Model (Spaceship Titanic)
 
-This example demonstrates using Weco to optimize a Python script designed for the [Spaceship Titanic Kaggle competition](https://www.kaggle.com/competitions/spaceship-titanic/overview). The goal is to improve the model's `accuracy` metric by modifying the feature engineering and modeling steps within the `optimize.py` script.
-
-This example uses the `README.md` file (this file) to provide additional instructions to the LLM.
+This example demonstrates using Weco to optimize a Python script designed for the [Spaceship Titanic Kaggle competition](https://www.kaggle.com/competitions/spaceship-titanic/overview). The goal is to improve the model's `accuracy` metric by directly optimizing the evaluate.py
 
 ## Setup
 
@@ -12,51 +10,58 @@ This example uses the `README.md` file (this file) to provide additional instruc
     ```bash
     pip install -r requirements-test.txt
     ```
-4.  **Prepare Data:** Run the utility script once to download the dataset from Kaggle and place it in the expected `public/` and `private/` subdirectories:
+4.  **Prepare Data:** Run the utility script once to download the dataset from Kaggle and place it in the expected `./data/` subdirectories:
     ```bash
-    python utils.py
+    python get_data.py
     ```
-    After running `utils.py`, your directory structure should look like this:
+    After running `get_data.py`, your directory structure should look like this:
     ```
     .
-    ├── baseline.py
-    ├── evaluate.py
-    ├── optimize.py
-    ├── private
-    │   └── test.csv
-    ├── public
+    ├── competition_description.md
+    ├── data
     │   ├── sample_submission.csv
     │   ├── test.csv
     │   └── train.csv
-    ├── README.md  # This file
+    ├── evaluate.py
+    ├── get_data.py
+    ├── README.md # This file
     ├── requirements-test.txt
-    └── utils.py
+    └── submit.py
     ```
 
 ## Optimization Command
 
 Run the following command to start optimizing the model:
 
 ```bash
-weco run --source optimize.py \
-         --eval-command "python optimize.py && python evaluate.py" \
+weco run --source evaluate.py \
+         --eval-command "python evaluate.py --data-dir ./data" \
          --metric accuracy \
          --maximize true \
          --steps 10 \
          --model gemini-2.5-pro-exp-03-25 \
-         --additional-instructions README.md
+         --additional-instructions "Improve feature engineering, model choice and hyper-parameters."
+         --log-dir .runs/spaceship-titanic
+```
+
+## Submit the solution
+
+Once the optimization finished, you can submit your predictions to kaggle to see the results. Make sure `submission.csv` is present and then simply run the following command.
+
+```bash
+python submit.py
 ```
 
 ### Explanation
 
-*   `--source optimize.py`: The script containing the model training and prediction logic to be optimized. It starts identical to `baseline.py`.
-*   `--eval-command "python optimize.py && python evaluate.py"`: This is a multi-step evaluation.
-    *   `python optimize.py`: Runs the modified script to generate predictions (`submission.csv`).
-    *   `python evaluate.py`: Compares the generated `submission.csv` against the ground truth (using the training data as a proxy evaluation set in this example) and prints the `accuracy` metric.
+*   `--source evaluate.py`: The script provides a baseline as root node and directly optimize the evaluate.py
+*   `--eval-command "python evaluate.py --data-dir ./data/"`: The weco agent will run the `evaluate.py` and update it.
+    *   [optional] `--data-dir`: path to the train and test data.
+    *   [optional] `--seed`: Seed for reproduce the experiment.
 *   `--metric accuracy`: The target metric Weco should optimize.
 *   `--maximize true`: Weco aims to increase the accuracy.
 *   `--steps 10`: The number of optimization iterations.
 *   `--model gemini-2.5-pro-exp-03-25`: The LLM driving the optimization.
-*   `--additional-instructions README.md`: Provides this file as context to the LLM, which might include hints about feature engineering techniques, model types to try, or specific data columns to focus on (you can add such instructions to this file if desired).
+*   `--additional-instructions "Improve feature engineering, model choice and hyper-parameters."`: A simple instruction for model improvement or you can put the path to [`comptition_description.md`](./competition_description.md) within the repo to feed the agent more detailed information.
 
-Weco will iteratively modify the feature engineering or modeling code within `optimize.py`, run the evaluation pipeline, and use the resulting `accuracy` to guide further improvements. The `baseline.py` file is provided as a reference starting point.
+Weco will iteratively modify the feature engineering or modeling code within `evaluate.py`, run the evaluation pipeline, and use the resulting `accuracy` to guide further improvements.

examples/spaceship-titanic/baseline.py

@@ -0,0 +1,93 @@
+# Overview
+
+## Description
+Welcome to the year 2912, where your data science skills are needed to solve a cosmic mystery. We've received a transmission from four lightyears away and things aren't looking good.
+
+The *Spaceship Titanic* was an interstellar passenger liner launched a month ago. With almost 13,000 passengers on board, the vessel set out on its maiden voyage transporting emigrants from our solar system to three newly habitable exoplanets orbiting nearby stars.
+
+While rounding Alpha Centauri en route to its first destination—the torrid 55 Cancri E—the unwary *Spaceship Titanic* collided with a spacetime anomaly hidden within a dust cloud. Sadly, it met a similar fate as its namesake from 1000 years before. Though the ship stayed intact, almost half of the passengers were transported to an alternate dimension!
+
+![joel-filipe-QwoNAhbmLLo-unsplash.jpg](https://storage.googleapis.com/kaggle-media/competitions/Spaceship%20Titanic/joel-filipe-QwoNAhbmLLo-unsplash.jpg)
+
+To help rescue crews and retrieve the lost passengers, you are challenged to predict which passengers were transported by the anomaly using records recovered from the spaceship’s damaged computer system.
+
+Help save them and change history!
+
+### Acknowledgments
+
+Photos by [Joel Filipe](https://unsplash.com/@joelfilip?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText), [Richard Gatley](https://unsplash.com/@uncle_rickie?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) and [ActionVance](https://unsplash.com/@actionvance?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on Unsplash.
+
+## Evaluation
+
+### Metric
+
+Submissions are evaluated based on their [classification accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy), the percentage of predicted labels that are correct.
+
+### Submission Format
+
+The submission format for the competition is a csv file with the following format:
+
+```
+PassengerId,Transported
+0013_01,False
+0018_01,False
+0019_01,False
+0021_01,False
+etc.
+```
+
+## Frequently Asked Questions
+
+### What is a Getting Started competition?
+
+Getting Started competitions were created by Kaggle data scientists for people who have little to no machine learning background. They are a great place to begin if you are new to data science or just finished a MOOC and want to get involved in Kaggle.
+
+Getting Started competitions are a non-competitive way to get familiar with Kaggle’s platform, learn basic machine learning concepts, and start meeting people in the community. They have no cash prize and are on a rolling timeline.
+
+### How do I create and manage a team?
+
+When you accept the competition rules, a team will be created for you. You can invite others to your team, accept a merger with another team, and update basic information like team name by going to the [Team](https://www.kaggle.com/c/spaceship-titanic/team) page.
+
+We've heard from many Kagglers that teaming up is the best way to learn new skills AND have fun. If you don't have a teammate already, consider asking if anyone wants to team up in the [discussion forum](https://www.kaggle.com/c/spaceship-titanic/discussion).
+
+### What are Notebooks?
+
+Kaggle Notebooks is a cloud computational environment that enables reproducible and collaborative analysis. Notebooks support scripts in Python and R, Jupyter Notebooks, and RMarkdown reports. You can visit the [Notebooks](https://www.kaggle.com/c/spaceship-titanic/notebooks) tab to view all of the publicly shared code for the Spaceship Titanic competition. For more on how to use Notebooks to learn data science, check out our [Courses](https://www.kaggle.com/learn/overview)!
+
+### Why did my team disappear from the leaderboard?
+
+To keep with the spirit of getting-started competitions, we have implemented a two month rolling window on submissions. Once a submission is more than two months old, it will be invalidated and no longer count towards the leaderboard.
+
+If your team has no submissions in the previous two months, the team will also drop from the leaderboard. This will keep the leaderboard at a manageable size, freshen it up, and prevent newcomers from getting lost in a sea of abandoned scores.
+
+*"I worked so hard to get that score! Give it back!"* Read more about our decision to implement a rolling leaderboard [here](https://www.kaggle.com/c/titanic/discussion/6240).
+
+### How do I contact Support?
+
+Kaggle does not have a dedicated support team so you’ll typically find that you receive a response more quickly by asking your question in the appropriate forum. (For this competition, you’ll want to use the [Spaceship Titanic discussion forum](https://www.kaggle.com/c/spaceship-titanic/discussion)).
+
+Support is only able to help with issues that are being experienced by all participants. Before contacting support, please check the discussion forum for information on your problem. If you can’t find it, you can post your problem in the forum so a fellow participant or a Kaggle team member can provide help. The forums are full of useful information on the data, metric, and different approaches. We encourage you to use the forums often. If you share your knowledge, you'll find that others will share a lot in turn!
+
+If your problem persists or it seems to be effective all participants then please [contact us](https://www.kaggle.com/contact).
+
+# Dataset Description
+
+In this competition your task is to predict whether a passenger was transported to an alternate dimension during the Spaceship Titanic's collision with the spacetime anomaly. To help you make these predictions, you're given a set of personal records recovered from the ship's damaged computer system.
+
+## File and Data Field Descriptions
+
+- **train.csv** - Personal records for about two-thirds (~8700) of the passengers, to be used as training data.
+    - `PassengerId` - A unique Id for each passenger. Each Id takes the form `gggg_pp` where `gggg` indicates a group the passenger is travelling with and `pp` is their number within the group. People in a group are often family members, but not always.
+    - `HomePlanet` - The planet the passenger departed from, typically their planet of permanent residence.
+    - `CryoSleep` - Indicates whether the passenger elected to be put into suspended animation for the duration of the voyage. Passengers in cryosleep are confined to their cabins.
+    - `Cabin` - The cabin number where the passenger is staying. Takes the form `deck/num/side`, where `side` can be either `P` for *Port* or `S` for *Starboard*.
+    - `Destination` - The planet the passenger will be debarking to.
+    - `Age` - The age of the passenger.
+    - `VIP` - Whether the passenger has paid for special VIP service during the voyage.
+    - `RoomService`, `FoodCourt`, `ShoppingMall`, `Spa`, `VRDeck` - Amount the passenger has billed at each of the *Spaceship Titanic*'s many luxury amenities.
+    - `Name` - The first and last names of the passenger.
+    - `Transported` - Whether the passenger was transported to another dimension. This is the target, the column you are trying to predict.
+- **test.csv** - Personal records for the remaining one-third (~4300) of the passengers, to be used as test data. Your task is to predict the value of `Transported` for the passengers in this set.
+- **sample_submission.csv** - A submission file in the correct format.
+    - `PassengerId` - Id for each passenger in the test set.
+    - `Transported` - The target. For each passenger, predict either `True` or `False`.

examples/spaceship-titanic/competition_description.md

@@ -1,71 +1,43 @@
-import sys
+import argparse
+from pathlib import Path
 import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.dummy import DummyClassifier
 from sklearn.metrics import accuracy_score
-from pathlib import Path
-
-
-class InvalidSubmissionError(Exception):
-    """
-    A custom exception for when the agent submission cannot be graded.
-    """
-
-    pass
-
+import joblib
+import warnings
 
-def prepare_for_accuracy_metric(submission: pd.DataFrame, answers: pd.DataFrame, target_column: str, id_column: str) -> dict:
-    # Answers checks
-    assert target_column in answers.columns, f"Answers must have a `{target_column}` column"
-    assert id_column in answers.columns, f"Answers must have a `{id_column}` column"
+warnings.filterwarnings("ignore", category=UserWarning)  # keep Weco's panel tidy
 
-    # Submission checks
-    if len(submission) != len(answers):
-        raise InvalidSubmissionError("Submission must have the same length as the answers.")
-    if target_column not in submission.columns:
-        raise InvalidSubmissionError(f"Submission must have a `{target_column}` column")
-    if id_column not in submission.columns:
-        raise InvalidSubmissionError(f"Submission must have a `{id_column}` column")
 
-    # sort on id to ensure correct order
-    submission = submission.sort_values(id_column)
-    answers = answers.sort_values(id_column)
+def train(df: pd.DataFrame, test_df: pd.DataFrame, random_state: int = 0) -> float:
+    train_df, val_df = train_test_split(df, test_size=0.10, random_state=random_state, stratify=df["Transported"])
 
-    if (submission[id_column].values != answers[id_column].values).any():
-        raise InvalidSubmissionError(f"Submission and Answers `{id_column}`'s do not match")
+    y_train = train_df.pop("Transported")
+    y_val = val_df.pop("Transported")
 
-    y_pred = submission[target_column].to_numpy()
-    y_true = answers[target_column].to_numpy()
+    model = DummyClassifier(strategy="most_frequent", random_state=random_state)
+    model.fit(train_df, y_train)
+    preds = model.predict(val_df)
+    acc = accuracy_score(y_val, preds)
 
-    return {"y_true": y_true, "y_pred": y_pred}
+    # **Important**: Keep this step!!!
+    # Save the model and generate a submission file on test
+    joblib.dump(model, "model.joblib")
+    test_preds = model.predict(test_df)
+    submission_df = pd.DataFrame({"PassengerId": test_df["PassengerId"], "Transported": test_preds.astype(bool)})
+    submission_df.to_csv("submission.csv", index=False)
 
-
-def grade(submission: pd.DataFrame, answers: pd.DataFrame) -> float:
-    accuracy_inputs = prepare_for_accuracy_metric(
-        submission=submission, answers=answers, target_column="Transported", id_column="PassengerId"
-    )
-    return accuracy_score(**accuracy_inputs)
+    return acc
 
 
 if __name__ == "__main__":
-    # Get the directory where the script is located
-    script_dir = Path(__file__).resolve().parent
-    # The ground truth answers are now in private/test.csv
-    answers_path = script_dir / "private" / "test.csv"
-    # Assume the agent's submission is saved here
-    submission_path = script_dir / "submission.csv"
-
-    # Check if files exist before proceeding
-    if not answers_path.exists():
-        print(f"Error: Answers file not found at {answers_path}")  # Updated path in error message
-        sys.exit(1)
-
-    if not submission_path.exists():
-        print(f"Error: Submission file not found at {submission_path}")
-        sys.exit(1)
-
-    submission = pd.read_csv(submission_path)
-    # Read answers from the updated path
-    answers = pd.read_csv(answers_path)
-
-    # Calculate and print the grade
-    score = grade(submission, answers)
-    print(f"accuracy: {score}")
+    p = argparse.ArgumentParser()
+    p.add_argument("--data-dir", type=Path, default=Path("./data/"))
+    p.add_argument("--seed", type=int, default=0)
+    args = p.parse_args()
+
+    train_df = pd.read_csv(args.data_dir / "train.csv")
+    test_df = pd.read_csv(args.data_dir / "test.csv")
+    acc = train(train_df, test_df, random_state=args.seed)
+    print(f"accuracy: {acc:.6f}")

examples/spaceship-titanic/evaluate.py

@@ -0,0 +1,16 @@
+import kaggle
+import zipfile
+import os
+
+
+def get_data():
+    kaggle.api.competition_download_files("spaceship-titanic")
+    # unzip the data
+    with zipfile.ZipFile("spaceship-titanic.zip", "r") as zip_ref:
+        zip_ref.extractall("data")
+    # remove the zip file
+    os.remove("spaceship-titanic.zip")
+
+
+if __name__ == "__main__":
+    get_data()

examples/spaceship-titanic/get_data.py

@@ -0,0 +1,14 @@
+import argparse
+import kaggle
+from pathlib import Path
+
+
+def submit_submission(submission_path: Path):
+    kaggle.api.competition_submit(submission_path, "My first submission using weco agent", "spaceship-titanic")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--submission-path", "-p", type=Path, default="submission.csv")
+    args = parser.parse_args()
+    submit_submission(args.submission_path)