Test4

[valer1435]

How to use fedot in classification task? @RepoPilotAssistant help

[RepoPilotAssistant]

It seems like the chat history provided does not contain any specific question or context to derive an answer from. Please provide more details or the specific question you would like answered regarding the Fedot library.

[valer1435]

How to use fedot in classification task? @RepoPilotAssistant

[RepoPilotAssistant]

To use FEDOT for a classification task, you can initialize the Fedot class with the problem parameter set to 'classification'. Here is a concise example:

from fedot.api.main import Fedot

# Initialize FEDOT for classification task
model = Fedot(problem='classification', timeout=5.0)

# Fit the model
model.fit(features=train_features, target=train_target)

# Predict
predictions = model.predict(features=test_features)

In this example:

• problem='classification' specifies that the task is classification.
• timeout=5.0 sets the time for model design in minutes.
• fit method is used to train the model on your training data (train_features and train_target).
• predict method is used to make predictions on your test data (test_features).

[valer1435]

@RepoPilotAssistant What should be as a features and target?

[RepoPilotAssistant]

For a classification task using FEDOT, features should be your input data containing the attributes or predictors, and target should be the corresponding labels or classes you are trying to predict. Here is a more detailed example:

from fedot.api.main import Fedot
from fedot.core.data.data import InputData
from fedot.core.repository.tasks import Task, TaskTypesEnum

# Assuming you have your data in pandas DataFrame format
import pandas as pd

# Load your data
data = pd.read_csv('path_to_your_data.csv')

# Split data into features and target
features_df = data.drop(columns=['target_column'])  # Replace 'target_column' with your actual target column name
target_df = data['target_column']  # Replace 'target_column' with your actual target column name

# Convert to FEDOT's InputData format
data = InputData.from_dataframe(features_df, target_df, task=Task(TaskTypesEnum.classification))

# Split data into train and test sets
train_data, test_data = train_test_data_setup(data)

# Initialize FEDOT for classification task
model = Fedot(problem='classification', timeout=5.0)

# Fit the model
model.fit(features=train_data)

# Predict
predictions = model.predict(features=test_data)

In this example:

• features_df is your DataFrame containing all columns except the target column.
• target_df is your DataFrame containing only the target column.
• InputData.from_dataframe is used to convert your pandas DataFrame into FEDOT's InputData format.
• train_test_data_setup is a function to split your data into train and test sets.
• model.fit is used to train the model on the training data.
• model.predict is used to make predictions on the test data.

[RepoPilotAssistant]

To use FEDOT for a classification task, you need to follow these steps:

1. Import the necessary object:

   from fedot import Fedot

2. Load and split your data into train and test sets. FEDOT uses its own data object notation (InputData). You can create this from a file or DataFrame:

   from fedot.core.data.data import InputData
   data = InputData.from_dataframe(features_df, target_df, task=Task(TaskTypesEnum.classification))
   train, test = train_test_data_setup(data)

3. Initialize the FEDOT object, specifying the problem type as classification, and optionally define metrics and timeout:

   model = Fedot(problem='classification', metric='roc_auc', timeout=5)

4. Train the model using the fit() method:

   best_pipeline = model.fit(features=train)

5. After training, you can view the structure of the resulting pipeline in text or plot format:

   best_pipeline.print_structure()
   best_pipeline.show()

6. Obtain predictions for the test data using the predict_proba() method if you want to calculate ROC-AUC:

   prediction = model.predict_proba(features=test)

7. Evaluate the quality of predictions using the get_metrics() method:

   print(model.get_metrics(rounding_order=4))

8. Optionally, visualize the prediction and save the model:

   model.plot_prediction()
   best_pipeline.save(path='path_to_save_and_load', create_subdir=False, is_datetime_in_path=False)

9. To load a fitted pipeline, use the load() method:

   from fedot.core.pipelines.pipeline import Pipeline
   loaded_pipeline = Pipeline().load('path_to_save_and_load')