other-setup-parameters.md

description
All other setup related parameters

Other setup parameters

Required Parameters

There are only two non-optional parameters in the setup function.

PARAMETERS

• data: pandas.DataFrame
  ****Shape (n_samples, n_features), where n_samples is the number of samples and n_features is the number of features.
• target: str
  ****Name of the target column to be passed in as a string.

Experiment Logging

PyCaret can automatically log entire experiments including setup parameters, model hyperparameters, performance metrics, and pipeline artifacts. The default settings use MLflow as the logging backend. wandb is also available as an option for logging backend. A parameter in the setup can be enabled to automatically track all the metrics, hyperparameters, and other important information about your machine learning model.

PARAMETERS

• log_experiment: bool, default = bool or string 'mlflow' or 'wandb'
  A (list of) PyCaret BaseLogger or str (one of mlflow, wandb) corresponding to a logger to determine which experiment loggers to use. Setting to True will use the MLFlow backend by default.
• experiment_name: str, default = None
  Name of the experiment for logging. When set to None, a default name is used.
• experiment_custom_tags: dict, default = None
  ****Dictionary of tag_name: String -> value: (String, but will be string-ified if not) passed to the mlflow.set_tags to add new custom tags for the experiment.
• log_plots: bool, default = False
  When set to True, applicable analysis plots are logged as an image file.
• log_profile: bool, default = False
  When set to True, the data profile is logged as an HTML file.
• log_data: bool, default = False
  When set to True, train and test dataset are logged as a CSV file.

Example

# 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')

# model training
best_model = compare_models() 

To initialize MLflow server you must run the following command from within the notebook or from the command line. Once the server is initialized, you can track your experiment on https://localhost:5000.

# init server
!mlflow ui

Configure MLflow tracking server

When no backend is configured Data is stored locally at the provided file (or ./mlruns if empty). To configure the backend use mlflow.set_tracking_uri before executing the setup function.

• An empty string, or a local file path, prefixed with file:/. Data is stored locally at the provided file (or ./mlruns if empty).
• An HTTP URI like https://my-tracking-server:5000.
• A Databricks workspace, provided as the string “databricks” or, to use a Databricks CLI profile, “databricks://<profileName>”.

# set tracking uri 
import mlflow 
mlflow.set_tracking_uri('file:/c:/users/mlflow-server')

# 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')

PyCaret on Databricks

When using PyCaret on Databricks experiment_name parameter in the setup must include complete path to storage. See example below on how to log experiments when using Databricks:

# 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 = '/Users/[email protected]/experiment-name-here')

Model Selection

Following parameters in the setup can be used for setting parameters for model selection process. These are not related to data preprocessing but can influence your model selection process.

PARAMETERS

• train_size: float, default = 0.7
  ****The proportion of the dataset to be used for training and validation.

• test_data: pandas.DataFrame, default = None
  ****If not None, the test_data is used as a hold-out set and the train_size is ignored. test_data must be labeled and the shape of the data and test_data must match.

• data_split_shuffle: bool, default = True
  When set to False, prevents shuffling of rows during train_test_split.

• data_split_stratify: bool or list, default = False
  Controls stratification during the train_test_split. When set to True, it will stratify by target column. To stratify on any other columns, pass a list of column names. Ignored when data_split_shuffle is False.

• fold_strategy: str or scikit-learn CV generator object, default = ‘stratifiedkfold’
  Choice of cross-validation strategy. Possible values are:

  • ‘kfold’
  • ‘stratifiedkfold’
  • ‘groupkfold’
  • ‘timeseries’
  • a custom CV generator object compatible with scikit-learn.

• fold: int, default = 10
  ****The number of folds to be used in cross-validation. Must be at least 2. This is a global setting that can be over-written at the function level by using the fold parameter. Ignored when fold_strategy is a custom object.

• fold_shuffle: bool, default = False
  ****Controls the shuffle parameter of CV. Only applicable when fold_strategy is kfold or stratifiedkfold. Ignored when fold_strategy is a custom object.

• fold_groups: str or array-like, with shape (n_samples,), default = None

  Optional group labels when ‘GroupKFold’ is used for the cross-validation. It takes an array with shape (n_samples, ) where n_samples is the number of rows in the training dataset. When the string is passed, it is interpreted as the column name in the dataset containing group labels.

Other Miscellaneous

Following parameters in the setup can be used for controlling other experiment settings such as using GPU for training or setting verbosity of the experiment. They do not affect the data in any way.

PARAMETERS

• n_jobs: int, default = -1
  ****The number of jobs to run in parallel (for functions that support parallel processing) -1 means using all processors. To run all functions on single processor set n_jobs to None.
• use_gpu: bool or str, default = False
  ****When set to True, it will use GPU for training with algorithms that support it and fall back to CPU if they are unavailable. When set to force it will only use GPU-enabled algorithms and raise exceptions when they are unavailable. When False all algorithms are trained using CPU only.
• html: bool, default = True
  When set to False, prevents the runtime display of the monitor. This must be set to False when the environment does not support IPython. For example, command line terminal, Databricks, PyCharm, Spyder, and other similar IDEs.
• session_id: int, default = None
  ****Controls the randomness of the experiment. It is equivalent to random_state in scikit-learn. When None, a pseudo-random number is generated. This can be used for later reproducibility of the entire experiment.
• silent: bool, default = False
  Controls the confirmation input of data types when setup is executed. When executing in completely automated mode or on a remote kernel, this must be True.
• verbose: bool, default = True
  When set to False, Information grid is not printed.
• profile: bool, default = False
  When set to True, an interactive EDA report is displayed.
• profile_kwargs: dict, default = {} (empty dict)
  Dictionary of arguments passed to the ProfileReport method used to create the EDA report. Ignored if profile is False.
• custom_pipeline: (str, transformer) or list of (str, transformer), default = None
  ****When passed, will append the custom transformers in the preprocessing pipeline and are applied on each CV fold separately and on the final fit. All the custom transformations are applied after train_test_split and before PyCaret's internal transformations.
• preprocess: bool, default = True
  When set to False, no transformations are applied except for train_test_split and custom transformations passed in custom_pipeline parameter. Data must be ready for modeling (no missing values, no dates, categorical data encoding) when preprocess is set to False.