### Set up virtual environment and install dependencies

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Creates a virtual environment in the sapientml repository folder, installs Poetry and project dependencies, sets up pre-commit, and installs core (and optionally preprocess/loaddata) in editable mode.

```bash
cd /path/to/AutoML/sapientml
python -m venv venv
. venv/bin/activate
pip install poetry
poetry install
pre-commit install
pip install -e ../core

# optional
pip install -e ../preprocess
pip install -e ../loaddata

```

--------------------------------

### Expected directory structure after installation

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Expected directory structure after successful installation, showing the layout of core and sapientml folders.

```text
AutoML/
├── core/
│   ├── sapientml_core/
│       ├── corpus/
│       │   ├── clean-notebooks/
│       │   ├── annotated-notebooks/
│       │   └── dataset/
│       ├── design/
│       └── training/
│
└── sapientml/
    └──sapientml/

```

--------------------------------

### Workaround for poetry install failure on Ubuntu

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Workaround for Ubuntu when poetry install fails; sets the keyring backend to null.

```bash
PYTHON_KEYRING_BACKEND="keyring.backends.null.Keyring" poetry install

```

--------------------------------

### static_info.json example

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Example output of static_analysis_of_columns.py. It lists dropped columns, renamed columns, and the target column for a pipeline script.

```json
{
    "script0011.py": {
        "drop_api": [
            "Age",
            "Balance",
            "CreditScore",
            "EstimatedSalary",
            "RowNumber",
            "CustomerId",
            "Surname",
            "Tenure",
            "HasCrCard"
        ],
        "rename_api": [],
        "target": "Exited"
    },

}
```

--------------------------------

### Example of label_orders.json

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Shows the format of the label_orders.json file, which contains pairwise label orders. Each entry is a string with two labels separated by '#'.

```json
[
"PREPROCESS:MissingValues:fillna:pandas#PREPROCESS:GenerateColumn:groupby:pandas",
"PREPROCESS:TypeChange:astype:pandas#PREPROCESS:MissingValues:fillna:pandas",
"PREPROCESS:MissingValues:interpolate:sklearn#PREPROCESS:CONVERT_NUM2NUM:where:numpy",
"PREPROCESS:TypeChange:astype:pandas#PREPROCESS:GenerateColumn:date:pandas",
"PREPROCESS:MissingValues:fillna:pandas#PREPROCESS:TypeChange:astype:pandas",
"PREPROCESS:MissingValues:fillna:pandas#PREPROCESS:Category:get_dummies:pandas",
]
```

--------------------------------

### Install sapientml with pip

Source: https://sapientml.readthedocs.io/en/latest/user/installation.html

Use pip to install the sapientml package into your documentation building environment. Follow third-party plugin guidelines if you plan to use plugins.

```bash
pip install sapientml
```

--------------------------------

### dataset-snapshots example

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Example output of dataset_snapshot_extractor. It shows a list with a dictionary mapping line number to a list containing column names, a data label, and the DataFrame type.

```json
[
    {
        "4": [
            [
                "RowNumber",
                "CustomerId",
                "Surname",
                "CreditScore",
                "Geography",
                "Gender",
                "Age",
                "Tenure",
                "Balance",
                "NumOfProducts",
                "HasCrCard",
                "IsActiveMember",
                "EstimatedSalary",
                "Exited"
            ],
            "data",
            "<class 'pandas.core.frame.DataFrame'>"
        ]
    }
]
```

--------------------------------

### Integrate sapientml and core packages

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Installs the core package in editable mode to integrate sapientml and core, then deactivates the virtual environment.

```bash
pip install -e /path/to/AutoML/core
deactivate

```

--------------------------------

### Example of files generated by fit

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

Shows the directory structure of the `outputs` folder after calling `fit`. It includes scripts for hold-out validation, final training, prediction, and a library folder for supporting modules. This is a file tree output, not executable code.

```text
outputs
├── 1_script.py
├── 2_script.py
├── 3_script.py
├── final_predict.py
├── final_script.out.json
├── final_script.py
├── final_train.py
└── lib
    └── sample_dataset.py

```

--------------------------------

### feature_analysis_summary.json example

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Example output of determine_used_features. It provides a summary for a pipeline including used columns, unmapped columns, new columns, target, deleted columns, and status.

```json
{
 "script0011.py": {
    "pipeline": "script0011.py",
    "used_cols": [
        "EstimatedSalary",
        "Exited",
        "Age",
        "CreditScore",
        "NumOfProducts",
        "Gender",
        "Geography",
        "Balance",
        "IsActiveMember"
    ],
    "unmapped_cols": [],
    "new_cols": [],
    "target": "Exited",
    "deleted": [
        "Tenure",
        "Surname",
        "HasCrCard",
        "RowNumber",
        "CustomerId"
    ],
    "status": "FINALIZED"
},

}
```

--------------------------------

### Restore SapientML instance from generated model

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

Shows how to save the SapientML model to a pickle file and restore it using `SapientML.from_pretrained`. The example uses the `cls` object from previous sections and saves it as `model.pkl`.

```python
import pickle

cls.fit(train_data)
with open("model.pkl", "wb") as f:
    pickle.dump(sml.model, f)

with open("model.pkl", "rb") as f:
    model = pickle.load(f)
sml = SapientML.from_pretrained(model)

```

--------------------------------

### Fit and predict with SapientML

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

Demonstrates the `fit` and `predict` methods for model training and prediction using generated code. The example uses a public Titanic dataset, splits it into training and test sets, and prints the F1 score of the prediction.

```python
import pandas as pd
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split

train_data = pd.read_csv("https://github.com/sapientml/sapientml/files/12481088/titanic.csv")
train_data, test_data = train_test_split(train_data)
y_true = test_data["survived"].reset_index(drop=True)
test_data.drop(["survived"], axis=1, inplace=True)

cls.fit(train_data, output_dir="./outputs")
y_pred = cls.predict(test_data)

print(f"F1 score: {f1_score(y_true, y_pred)}")

```

--------------------------------

### Example of dependent_labels.json

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Shows the format of the dependent_labels.json file, which maps dependent API pairs to their occurrence count. The number indicates how many pipelines executed that dependent pair.

```json
{
"['PREPROCESS:Category:get_dummies:pandas', 'PREPROCESS:DeleteColumns:drop:pandas']": 79,
"['PREPROCESS:ConvertStr2Date:to_datetime:pandas', 'PREPROCESS:DeleteColumns:drop:pandas']": 27,
"['PREPROCESS:MissingValues:fillna:pandas', 'PREPROCESS:DeleteColumns:drop:pandas']": 16,
"['PREPROCESS:Scaling:log:numpy', 'PREPROCESS:DeleteColumns:drop:pandas']": 12,
}
```

--------------------------------

### Create SapientML instance with target columns

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

Sets up a SapientML instance for a classification task. The `target_columns` parameter is required and specifies which column the model predicts. The `task_type` parameter is optional; if set to `None`, the task type is suggested automatically based on target column values. The example shows a classification task for the 'survived' column.

```python
from sapientml import SapientML

cls = SapientML(
    target_columns=["survived"],
    task_type=None, # suggested automatically from the target columns
)

```

--------------------------------

### Directory structure for SapientML local training

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Expected directory structure after successful setup of the development environment. The corpus is stored under sapientml_core/corpus with clean-notebooks, annotated-notebooks, and dataset subdirectories.

```text
AutoML/
├── core/
│   ├── sapientml_core/
│       ├── corpus/
│       │   ├── clean-notebooks/
│       │   ├── annotated-notebooks/
│       │   └── dataset/
│       ├── design
│       └── training
│
└── sapientml/
    └──sapientml/

```

--------------------------------

### Get GeneratedModel instance after fit

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

After calling `fit`, you can obtain the `GeneratedModel` instance from `cls.model`. The instance behaves like a usual model and provides `fit` and `predict` methods.

```python
cls.fit(train_data)
model = cls.model # obtains GeneratedModel instance

```

--------------------------------

### Create sample main.py for SapientML training

Source: https://sapientml.readthedocs.io/en/latest/dev/training.html

Driver code to run each training step. Explicitly calls the train method from SapientMLGenerator with a tag and num_parallelization argument. The tag determines the cache folder name (e.g., ./.cache/2.5.1-test) and num_parallelization defaults to 200.

```python
from sapientml_core.generator import SapientMLGenerator

print("Training started")
print("=================")
cls = SapientMLGenerator()
cls.train(<tag>, <num_parallelization>)
print("=================")
print("Training ended")

```

--------------------------------

### Clone sapientml and core repositories

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Clones the sapientml and core repositories, and optionally preprocess and loaddata, into a new AutoML directory.

```bash
mkdir AutoML
cd AutoML
git clone https://github.com/sapientml/sapientml.git
git clone https://github.com/sapientml/core.git

# optional
git clone https://github.com/sapientml/preprocess.git
git clone https://github.com/sapientml/loaddata.git

```

--------------------------------

### Download and set up sapientml corpus

Source: https://sapientml.readthedocs.io/en/latest/dev/setup.html

Downloads and extracts the sapientml corpus (version 0.1.4) inside sapientml_core, runs the pull script, cleans up the zip, and deactivates the environment.

```bash
. venv/bin/activate
cd /path/to/AutoML/core/sapientml_core
pip install dvc
wget https://github.com/user-attachments/files/16861353/sapientml-corpus-0.1.4.zip
unzip sapientml-corpus-0.1.4.zip -d corpus
cd corpus
bash ./scripts/pull.sh
cd ..
rm -f sapientml-corpus-0.1.4.zip
deactivate

```

--------------------------------

### sapientml_preprocess.PreprocessConfig

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Configuration arguments for sapientml_preprocess.Preprocess class.

```APIDOC
## sapientml_preprocess.PreprocessConfig

### Description
Configuration arguments for sapientml_preprocess.Preprocess class.

### Constructor
```python
sapientml_preprocess.PreprocessConfig(*, initial_timeout: int = 600, timeout_for_test: int = 0, cancel: CancellationToken | None = None, project_name: str | None = None, debug: bool = False, use_pos_list: list[str] | None = ['名詞', '動詞', '助動詞', '形容詞', '副詞'], use_word_stemming: bool = True)
```

### Parameters
- **initial_timeout** (int) - Optional - Timelimit to execute each generated script. Ignored when hyperparameter_tuning=True and hyperparameter_tuning_timeout is set.
- **timeout_for_test** (int) - Optional - Timelimit to execute test script (final_script) and Visualization.
- **cancel** (CancellationToken) - Optional - Object to interrupt evaluations.
- **project_name** (str) - Optional - Project name.
- **debug** (bool) - Optional - Debug mode or not.
- **use_pos_list** (Optional[list[str]]) - Optional - List of parts of speech to use. Default ['名詞', '動詞', '助動詞', '形容詞', '副詞'].
- **use_word_stemming** (bool) - Optional - Whether to use word stemming. Default True.

### Notes
Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be validated to form a valid model.
```

--------------------------------

### Files contained in GeneratedModel

Source: https://sapientml.readthedocs.io/en/latest/user/usage.html

Shows the directory structure of a generated model containing source code and `.pkl` files for preprocessors and the trained model. This is a file tree output, not executable code.

```text
model
├── final_predict.py
├── final_train.py
├── lib
│   └── sample_dataset.py
├── model.pkl
├── ordinalEncoder.pkl
├── simpleimputer-numeric.pkl
└── simpleimputer-string.pkl

```

--------------------------------

### SapientML.fit()

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Generates ML scripts for input data.

```APIDOC
## SapientML.fit

### Description
Generate ML scripts for input data.

### Method
fit

### Signature
fit(training_data: DataFrame | str, validation_data: DataFrame | str | None = None, test_data: DataFrame | str | None = None, save_datasets_format: Literal['csv', 'pickle'] = 'pickle', csv_encoding: Literal['UTF-8', 'SJIS'] = 'UTF-8', csv_delimiter: str = ',', ignore_columns: list[str] | None = None, output_dir: str = './outputs', codegen_only: bool = False)

### Parameters
- **training_data** (DataFrame | str) - Required - Training data.
- **validation_data** (DataFrame | str | None) - Optional - Validation data.
- **test_data** (DataFrame | str | None) - Optional - Test data.
- **save_datasets_format** ('csv', 'pickle') - Optional - Format to save datasets, default 'pickle'.
- **csv_encoding** ('UTF-8', 'SJIS') - Optional - Encoding for CSV files, default 'UTF-8'.
- **csv_delimiter** (str) - Optional - Delimiter for CSV files, default ','.
- **ignore_columns** (list[str] | None) - Optional - Columns to ignore.
- **output_dir** (str) - Optional - Output directory, default './outputs'.
- **codegen_only** (bool) - Optional - If True, only generate code, default False.
```

--------------------------------

### sapientml.Config

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Configuration arguments for sapientml.generator.CodeBlockGenerator and/or sapientml.generator.PipelineGenerator.

```APIDOC
## sapientml.Config

### Description
Configuration arguments for sapientml.generator.CodeBlockGenerator and/or sapientml.generator.PipelineGenerator.

### Constructor
```python
sapientml.Config(*, initial_timeout: int = 600, timeout_for_test: int = 0, cancel: CancellationToken | None = None, project_name: str | None = None, debug: bool = False)
```

### Parameters
- **initial_timeout** (int) - Optional - Timelimit to execute each generated script. Ignored when hyperparameter_tuning=True and hyperparameter_tuning_timeout is set.
- **timeout_for_test** (int) - Optional - Timelimit to execute test script (final_script) and Visualization.
- **cancel** (CancellationToken) - Optional - Object to interrupt evaluations.
- **project_name** (str) - Optional - Project name.
- **debug** (bool) - Optional - Debug mode or not.

### Notes
Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be validated to form a valid model.
```

--------------------------------

### sapientml_core.SapientMLConfig

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Configuration arguments for SapientMLGenerator.

```APIDOC
## sapientml_core.SapientMLConfig

### Description
Configuration arguments for SapientMLGenerator.

### Constructor
```python
sapientml_core.SapientMLConfig(*, initial_timeout: int = 600, timeout_for_test: int = 0, cancel: CancellationToken | None = None, project_name: str | None = None, debug: bool = False, n_models: int = 3, seed_for_model: int = 42, id_columns_for_prediction: list[str] | None = None, use_word_list: list[str] | dict[str, list[str]] | None = None, hyperparameter_tuning: bool = False, hyperparameter_tuning_n_trials: int = 10, hyperparameter_tuning_timeout: int = 0, hyperparameter_tuning_random_state: int = 1023, predict_option: Literal['default', 'probability'] = 'default', permutation_importance: bool = True, add_explanation: bool = False)
```

### Parameters
- **initial_timeout** (int) - Optional - Timelimit to execute each generated script. Ignored when hyperparameter_tuning=True and hyperparameter_tuning_timeout is set.
- **timeout_for_test** (int) - Optional - Timelimit to execute test script (final_script) and Visualization.
- **cancel** (CancellationToken) - Optional - Object to interrupt evaluations.
- **project_name** (str) - Optional - Project name.
- **debug** (bool) - Optional - Debug mode or not.
- **n_models** (int) - Optional - Number of output models to be tried. Default 3.
- **seed_for_model** (int) - Optional - Random seed for models such as RandomForestClassifier. Default 42.
- **id_columns_for_prediction** (Optional[list[str]]) - Optional - Name of the dataframe columns that outputs the prediction result. Default None.
- **use_word_list** (Optional[Union[list[str], dict[str, list[str]]]]) - Optional - List of words to be used as features when generating explanatory variables from text. If dict type is specified, key must be a column name and value must be a list of words. Default None.
- **hyperparameter_tuning** (bool) - Optional - On/Off of hyperparameter tuning. Default False.
- **hyperparameter_tuning_n_trials** (int) - Optional - The number of trials of hyperparameter tuning. Default 10.
- **hyperparameter_tuning_timeout** (int) - Optional - Time limit for hyperparameter tuning in each generated script. Ignored when hyperparameter_tuning is False. Default 0.
- **hyperparameter_tuning_random_state** (int) - Optional - Random seed for hyperparameter tuning. Default 1023.
- **predict_option** (Literal['default', 'probability']) - Optional - Specify predict method (default: predict(), probability: predict_proba()). Default 'default'.
- **permutation_importance** (bool) - Optional - On/Off of outputting permutation importance calculation code. Default True.
- **add_explanation** (bool) - Optional - If True, outputs ipynb files including EDA and explanation. Default False.

### Methods
#### postinit()
Set initial_timeout and hyperparameter_tuning_timeout. If initial_timeout is set as None and hyperparameter_tuning is false, set initial_timeout as INITIAL_TIMEOUT. For hyperparameter_tuning_timeout, if both initial_timeout and hyperparameter_tuning_timeout are set as None, set hyperparameter_tuning_timeout as INITIAL_TIMEOUT. If initial_timeout is set and hyperparameter_tuning is True, and hyperparameter_tuning_timeout is None, set the hyperparameter_tuning_timeout to unlimited (hyperparameter_tuning_timeout = self.initial_timeout). Since initial_timeout always precedes hyperparameter_tuning_timeout, it can be expressed that there is no time limit for hyperparameters during actual execution.

### Notes
Create a new model by parsing and validating input data from keyword arguments. Raises ValidationError if the input data cannot be validated to form a valid model.
```

--------------------------------

### SapientML.from_pretrained

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Factory method to create a SapientML instance from a pretrained model.

```APIDOC
## from_pretrained(model)

### Description
The factory method of SapientML from a pretrained model built by source code previously generated by SapientML.

### Parameters
- **model** (str, bytes-like object, or GeneratedModel) - Required - A pretrained model built by source code previously generated by SapientML.

### Returns
- **sml** (SapientML) - a new SapientML instance loaded from the pretrained model.
```

--------------------------------

### SapientML

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Constructor of the SapientML class.

```APIDOC
## SapientML

### Description
Constructor of the SapientML class. Initializes the main class for generating machine learning scripts.

### Method
Constructor

### Signature
SapientML(target_columns: list[str], task_type: Literal['classification', 'regression'] | None = None, adaptation_metric: str | None = None, split_method: Literal['random', 'time', 'group'] = 'random', split_seed: int = 17, split_train_size: float = 0.75, split_column_name: str | None = None, time_split_num: int = 5, time_split_index: int = 4, split_stratification: bool | None = None, model_type: str = 'sapientml', **kwargs)

### Parameters
- **target_columns** (list[str]) - Required - Names of target columns.
- **task_type** ('classification', 'regression', or None) - Optional - Specify task type classification, regression.
- **adaptation_metric** (str) - Optional - Metric for evaluation. Classification: 'f1', 'auc', 'ROC_AUC', 'accuracy', 'Gini', 'LogLoss', 'MCC', 'QWK'. Regression: 'r2', 'RMSLE', 'RMSE', 'MAE'.
- **split_method** ('random', 'time', or 'group') - Optional - Method of train-test split. 'random' uses random split. 'time' requires 'split_column_name'. 'group' requires 'split_column_name'.
- **split_seed** (int) - Optional - Random seed for train-test split. Ignored when split_method='time'.
- **split_train_size** (float) - Optional - The ratio of training size to input data. Ignored when split_method='time'.
- **split_column_name** (str) - Optional - Name of the column used to split. Ignored when split_method='random'.
- **time_split_num** (int) - Optional - Passed to TimeSeriesSplit's n_splits. Valid only when split_method='time'.
- **time_split_index** (int) - Optional - The index of the split from TimeSeriesSplit. Valid only when split_method='time'.
- **split_stratification** (bool) - Optional - To perform stratification in train-test split. Valid only when task_type='classification'.
- **model_type** (str) - Optional - Model type, default 'sapientml'.
- **kwargs** - Optional - Additional keyword arguments for configurations of PipelineGenerators and CodeBlockGenerators from plugins.

### Description
You can pass all the keyword arguments for configurations of PipelineGenerators and CodeBlockGenerators from plugins.
```

--------------------------------

### GeneratedModel

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Constructor for GeneratedModel. Instantiating this class by yourself is not intended.

```APIDOC
## GeneratedModel(input_dir: PathLike, save_datasets_format: Literal['csv', 'pickle'], timeout: int, csv_encoding: Literal['UTF-8', 'SJIS'], csv_delimiter: str, params: dict)

### Description
The constructor of GeneratedModel. Instantiating this class by yourself is not intended.

### Parameters
- **input_dir** (PathLike) - Required - Directory path containing training/prediction scripts and trained models.
- **save_datasets_format** ('csv' or 'pickle') - Required - Data format when the input dataframes are written to files. Ignored when all inputs are specified as file path.
- **timeout** (int) - Required - Timeout for the execution of training and prediction.
- **csv_encoding** ('UTF-8' or 'SJIS') - Required - Encoding method when csv files are involved. Ignored when only pickle files are involved.
- **csv_delimiter** (str) - Required - Delimiter to read csv files.
- **params** (dict) - Required - Additional parameters.
```

--------------------------------

### GeneratedModel.fit

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Generate ML scripts for input data.

```APIDOC
## fit(X: DataFrame, y: DataFrame | Series | None = None)

### Description
Generate ML scripts for input data.

### Parameters
- **X** (pandas.DataFrame) - Required - Training dataframe. Contains target values if y is None.
- **y** (pandas.DataFrame or pandas.Series) - Optional - The target values.

### Returns
- **self** (GeneratedModel) - GeneratedModel object itself.
```

--------------------------------

### GeneratedModel.save

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Save generated code to output_dir folder.

```APIDOC
## save(output_dir: PathLike)

### Description
Save generated code to output_dir folder.

### Parameters
- **output_dir** (Path-like object) - Required - Training dataframe.

### Returns
- **self** (GeneratedModel) - GeneratedModel object itself.
```

--------------------------------

### SapientML

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Main class for SapientML. It is used to generate ML scripts and train models.

```APIDOC
## SapientML

### Description
Main class for SapientML. It is used to generate ML scripts and train models.

### Constructor Parameters
- **training_data** (pandas.DataFrame or str) - Required - Training dataframe. When str, this is regarded as a file path.
- **validation_data** (pandas.DataFrame, str, or None) - Optional - Validation dataframe. When str, this is regarded as file paths. When None, validation data is extracted from training data by split.
- **test_data** (pandas.DataFrame, str, or None) - Optional - Test dataframes. When str, they are regarded as file paths. When None, test data is extracted from training data by split.
- **save_datasets_format** ('csv' or 'pickle') - Optional - Data format when the input dataframes are written to files. Ignored when all inputs are specified as file path.
- **csv_encoding** ('UTF-8' or 'SJIS') - Optional - Encoding method when csv files are involved. Ignored when only pickle files are involved.
- **csv_delimiter** (str) - Optional - Delimiter to read csv files.
- **ignore_columns** (list[str]) - Optional - Column names which must not be used and must be dropped.
- **output_dir** (str) - Optional - Output directory.
- **codegen_only** (bool) - Optional - Do not conduct fit() of GeneratedModel if True.

### Returns
- **self** (SapientML) - SapientML object itself.
```

--------------------------------

### SapientML.predict

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Predicts the output of the test_data.

```APIDOC
## predict(test_data: DataFrame)

### Description
Predicts the output of the test_data.

### Parameters
- **test_data** (pd.DataFrame) - Required - Dataframe used for predicting the result.

### Returns
- **result** (pd.DataFrame) - It returns the prediction_result.csv result in dataframe format.
```

--------------------------------

### GeneratedModel.predict

Source: https://sapientml.readthedocs.io/en/latest/reference/api.html

Predicts the output of the test_data and store in the prediction_result.csv.

```APIDOC
## predict(X: DataFrame)

### Description
Predicts the output of the test_data and store in the prediction_result.csv.

### Parameters
- **X** (pd.DataFrame) - Required - Dataframe used for predicting the result.

### Returns
- **result_df** (pd.DataFrame) - It returns the prediction_result.csv result in dataframe format.
```

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