# Others

## pull

Returns the last printed scoring grid. Use `pull` function after any training function to store the scoring grid in `pandas.DataFrame`.

#### Example

{% code lineNumbers="true" %}

```python
# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# compare models
best_model = compare_models()

# get the scoring grid
results = pull()
```

{% endcode %}

![Output from pull()](/files/Y4hthct49GoJH0aJeaBd)

{% code lineNumbers="true" %}

```python
type(results)
# >>> pandas.core.frame.DataFrame
```

{% endcode %}

## models

Return a table containing all the models available in the imported module of the model library.

#### Example

{% code lineNumbers="true" %}

```python
# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# check model library
models()
```

{% endcode %}

![Output from models()](/files/SCE0ExTKy5OR8nOF3j72)

If you want to see a little more information than this, you can pass `internal=True`.

{% code lineNumbers="true" %}

```python
# loading dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# check model library
models(internal = True)
```

{% endcode %}

![Output from models(internal = True)](/files/2kfN8WNZSdiHwrsSm6Vo)

## get\_config

This function retrieves the global variables created when initializing the [setup](/docs/get-started/functions/initialize.md#setup) function. 

#### **Example**

{% code lineNumbers="true" %}

```python
# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable')

# get X_train
get_config('X_train')
```

{% endcode %}

![Output from get\_config('X\_train')](/files/Ep2T8wLq986AP1hCN9zW)

To check all accessible parameters with `get_config`:

{% code lineNumbers="true" %}

```python
# check all available param
get_config()
```

{% endcode %}

Variables accessible by `get_config` function:

* 'USI' 
* 'X' 
* 'X\_test' 
* 'X\_test\_transformed' 
* 'X\_train' 
* 'X\_train\_transformed' 
* 'X\_transformed' 
* 'data' 
* 'dataset' 
* 'dataset\_transformed' 
* 'exp\_id' 
* 'exp\_name\_log' 
* 'fix\_imbalance' 
* 'fold\_generator' 
* 'fold\_groups\_param' 
* 'fold\_shuffle\_param' 
* 'gpu\_n\_jobs\_param' 
* 'gpu\_param' 
* 'html\_param' 
* 'idx' 
* 'is\_multiclass' 
* 'log\_plots\_param' 
* 'logging\_param' 
* 'memory' 
* 'n\_jobs\_param' 
* 'pipeline' 
* 'seed' 
* 'target\_param' 
* 'test' 
* 'test\_transformed' 
* 'train' 
* 'train\_transformed' 
* 'variable\_and\_property\_keys' 
* 'variables' 
* 'y' 
* 'y\_test' 
* 'y\_test\_transformed' 
* 'y\_train' 
* 'y\_train\_transformed' 
* 'y\_transformed'

## set\_config

This function resets the global variables.

#### **Example**

{% code lineNumbers="true" %}

```python
# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# reset environment seed
set_config('seed', 999) 
```

{% endcode %}

## get\_metrics

Returns the table of all the available metrics in the metric container. All these metrics are used for cross-validation.

{% code lineNumbers="true" %}

```python
# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# get metrics
get_metrics()
```

{% endcode %}

![Output from get\_metrics()](/files/GiRCKGRbJquisbRdURx2)

## add\_metric

Adds a custom metric to the metric container. 

{% code lineNumbers="true" %}

```python
# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', session_id = 123)

# add metric
from sklearn.metrics import log_loss
add_metric('logloss', 'Log Loss', log_loss, greater_is_better = False)
```

{% endcode %}

![Output from add\_metric('logloss', 'Log Loss', log\_loss, greater\_is\_better = False)](/files/VV59V2Lg9RBpvSmvluZS)

Now if you check metric container:

{% code lineNumbers="true" %}

```python
get_metrics()
```

{% endcode %}

![Output from get\_metrics() (after adding log loss metric)](/files/oemzltyc38nbcHqr52Ch)

## remove\_metric

Removes a metric from the metric container.

{% code lineNumbers="true" %}

```python
# remove metric
remove_metric('logloss')
```

{% endcode %}

No Output. Let's check the metric container again.

{% code lineNumbers="true" %}

```python
get_metrics()
```

{% endcode %}

![Output from get\_metrics() (after removing log loss metric)](/files/yMzNWWecnTu20fNbutxS)

## automl

This function returns the best model out of all trained models in the current setup based on the `optimize` parameter. Metrics evaluated can be accessed using the `get_metrics` function.

#### Example

{% code lineNumbers="true" %}

```python
# load dataset 
from pycaret.datasets import get_data 
data = get_data('diabetes') 

# init setup 
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable') 

# compare models
top5 = compare_models(n_select = 5) 

# tune models
tuned_top5 = [tune_model(i) for i in top5]

# ensemble models
bagged_top5 = [ensemble_model(i) for i in tuned_top5]

# blend models
blender = blend_models(estimator_list = top5) 

# stack models
stacker = stack_models(estimator_list = top5) 

# automl 
best = automl(optimize = 'Recall')
print(best)
```

{% endcode %}

![Output from print(best)](/files/TDYvdtJxXW6G59UIFNKg)

## get\_logs

Returns a table of experiment logs. Only works when `log_experiment = True` when initializing the [setup](/docs/get-started/functions/initialize.md#setup) function.

#### Example

{% code overflow="wrap" lineNumbers="true" %}

```python
# load dataset
from pycaret.datasets import get_data
data = get_data('diabetes')

# init setup
from pycaret.classification import *
clf1 = setup(data, target = 'Class variable', log_experiment = True, experiment_name = 'diabetes1')

# compare models
top5 = compare_models()

# check ML logs
get_logs()
```

{% endcode %}

![Output from get\_logs()](/files/7LGFFGwnjaJDYnqsJnYW)

## get\_current\_experiment

Obtain the current experiment object and return a class. This is useful when you are using a functional API and want to move to an OOP API.

{% code lineNumbers="true" %}

```python
# loading dataset
from pycaret.datasets import get_data
data = get_data('insurance')

# init setup using functional API
from pycaret.regression import *
s = setup(data, target = 'charges', session_id = 123)

# compare models
best = compare_models()

# return OOP class for current functional experiment
reg1 = get_current_experiment()
```

{% endcode %}

## set\_current\_experiment

Set the current experiment created using the OOP API to be used with the functional API.

{% code lineNumbers="true" %}

```python
# loading dataset
from pycaret.datasets import get_data
data = get_data('insurance')

# init setup using OOP API
from pycaret.regression import RegressionExperiment
reg1 = RegressionExperiment()
reg1.setup(data, target = 'charges', session_id = 123)

# compare models
best = compare_models()

# set OOP experiment as functional
set_current_experiment(reg1)
```

{% endcode %}


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