Weave: eval_results (23/04/12 23:04:41)

How do I pass WandbCallback to an XGBoost model for logging metrics and saving the trained model to Weights & Biases Artifacts?

```python
from wandb.xgboost import WandbCallback
import xgboost as XGBClassifier

...
# Start a wandb run
run = wandb.init()

# Pass WandbCallback to the model
bst = XGBClassifier()
bst.fit(X_train, y_train, 
    callbacks=[WandbCallback(log_model=True)])

# Close your wandb run
run.finish()
```

To pass `WandbCallback` to an XGBoost model for logging metrics and saving the trained model to Weights & Biases Artifacts, follow these steps:

1. Install and import the necessary modules:
```python
from wandb.xgboost import WandbCallback
import wandb
import xgboost as xgb
```

2. Initialize a W&B run:
```python
run = wandb.init()
```

3. Train your XGBoost model and pass the `WandbCallback` to the `fit()` method:
```python
# Prepare your data and train-test split, for example X_train, y_train
bst = xgb.XGBClassifier()
bst.fit(X_train, y_train, callbacks=[WandbCallback(log_model=True)])
```

4. When training is complete, finish the W&B run:
```python
run.finish()
```

Here's an example in context:

```python
import wandb
from wandb.xgboost import WandbCallback
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification

# Start a wandb run
run = wandb.init()

# Load or create your dataset
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the XGBoost model with WandbCallback
bst = xgb.XGBClassifier()
bst.fit(X_train, y_train, callbacks=[WandbCallback(log_model=True)])

# Finish the wandb run
run.finish()
```

This will log metrics, the configuration, and save the trained model to Weights & Biases Artifacts.

['https://docs.wandb.ai/guides/integrations/xgboost', 'https://github.com/wandb/examples/blob/master/examples/boosting-algorithms/lightgbm-regression/train.py', 'https://github.com/wandb/wandb/blob/main/wandb/integration/lightgbm/__init__.py']

[0.23293667, 0.28185582, 0.2977323]

How do I log a model as an artifact version in wandb?

```
    art = wandb.Artifact("my-object-detector", type="model")
    art.add_file("saved_model_weights.pt")
    wandb.log_artifact(art)
    ```

To log a model as an artifact version in wandb, follow these steps:

1. Initialize a W&B run to start tracking.
```python
import wandb
wandb.init()
```

2. Serialize your model to disk periodically (and/or at the end of training) using the serialization process provided by your modeling library (e.g., PyTorch or Keras).

3. Create a Model Version as an Artifact and add the serialized model files to it.
```python
art = wandb.Artifact(f'my-model-{wandb.run.id}', type="model")
art.add_file("path/to/model.pth", "model.pth")
```

4. Log the Model Version.
```python
if model_is_best:
    # If the model is the best model so far, add "best" to the aliases
    wandb.log_artifact(art, aliases=["latest", "best"])
else:
    wandb.log_artifact(art)
```

Here's an example in a training loop:

```python
import wandb

# Initialize a W&B run to start tracking
wandb.init()

# Training loop (simplified)
for epoch in range(num_epochs):
    # Train and evaluate the model (omitted)
    
    # Serialize your model (e.g., for PyTorch)
    torch.save(model.state_dict(), "path/to/model.pth")
    
    # Create a Model Version as an Artifact
    art = wandb.Artifact(f'my-model-{wandb.run.id}', type="model")
    art.add_file("path/to/model.pth", "model.pth")
    
    # Log the Model Version
    if model_is_best:
        wandb.log_artifact(art, aliases=["latest", "best"])
    else:
        wandb.log_artifact(art)
```

This will log your trained model as an artifact in your W&B project with proper versioning. Remember to replace the serialization and saving with the appropriate commands for your specific model and training library.

['https://docs.wandb.ai/guides/track/launch', 'https://docs.wandb.ai/guides/models/walkthrough', 'https://docs.wandb.ai/guides/integrations/huggingface', 'https://docs.wandb.ai/guides/integrations/add-wandb-to-any-library']

[0.1773625, 0.21069235, 0.21689638, 0.25404486]

How do I read a CSV file into a Pandas DataFrame and save it to a variable named `new_iris_dataframe` using wandb?

```python
import wandb
import pandas as pd

# Read our CSV into a new DataFrame
new_iris_dataframe = pd.read_csv("iris.csv")
```

With wandb, you can log a CSV file and visualize it in a W&B Dashboard. To read a CSV file into a Pandas DataFrame using wandb, follow these steps:

1. First, import your CSV file and read it into a new DataFrame.

```python
import pandas as pd

# Read the CSV into a new DataFrame
new_iris_dataframe = pd.read_csv("iris.csv")
```

2. Start a new W&B run to track and log data.

```python
import wandb

run = wandb.init(project="My_Project")
```

3. Convert the DataFrame into a W&B Table.

```python
iris_table = wandb.Table(dataframe=new_iris_dataframe)
```

4. Log the table to visualize with a run.

```python
run.log({"iris": iris_table})
```

This will log the `new_iris_dataframe` pandas DataFrame containing the data from the "iris.csv" file.

Here's the complete code snippet:

```python
import pandas as pd
import wandb

# Read the CSV into a new DataFrame
new_iris_dataframe = pd.read_csv("iris.csv")

# Start a W&B run to track and log data
run = wandb.init(project="My_Project")

# Convert the DataFrame into a W&B Table
iris_table = wandb.Table(dataframe=new_iris_dataframe)

# Log the table to visualize with a run
run.log({"iris": iris_table})
```

Note that wandb is not used specifically for reading the CSV file; Pandas is performing that task. Wandb is used for logging and visualizing the data.

['https://docs.wandb.ai/guides/track/log/working-with-csv', 'https://docs.wandb.ai/guides/track/log/working-with-csv']

[0.20116183, 0.26913416]