How fedot works?

[valer1435]

I wonder to understand how fedot works.

[valer1435]

@RepoPilotAssistant help

[RepoPilotAssistant]

@valer1435 I don't know the answer to your question based on the provided information. Please refer to the documentation or source files for more details. If you have a specific example or step-by-step guide you need, please provide more context so I can assist you better.

[valer1435]

I want to know which concepts lies under Fedot

[valer1435]

@RepoPilotAssistant

[valer1435]

@RepoPilotAssistant help

[RepoPilotAssistant]

@valer1435 Based on the provided information, Fedot encompasses several key concepts including its AutoML API, data handling, pipeline composition, and model tuning. Here’s a brief overview:

1. AutoML API: The main class Fedot allows you to define the type of modeling problem (classification, regression, or time series forecasting), set parameters like timeout, task-specific parameters, and more. It provides methods for fitting models, tuning hyperparameters, and making predictions.

2. Data Handling: Fedot uses its own data object notation (InputData) which can be created from various formats like CSV files, pandas DataFrames, or numpy arrays. It supports different task types such as classification, regression, and time series forecasting.

3. Pipeline Composition: The fit method composes and fits a new pipeline or fits a predefined one. Pipelines can be visualized and saved for later use.

4. Model Tuning: The tune method allows for hyperparameters tuning of the current pipeline, with options to set metrics, iterations, timeout, and more.

For more detailed examples and step-by-step guides, you can refer to the specific sections in the documentation such as the "Classification example" and "Working with data" sections. If you need a specific example or further assistance, please provide more context.

[valer1435]

How I can solve classification problem with Fedot? @RepoPilotAssistant help

[RepoPilotAssistant]

@valer1435 To solve a classification problem with Fedot, you can follow these steps:

1. Import the necessary module:

   from fedot.api.main import Fedot

2. Load your training and test data:

   import pandas as pd
   train = pd.read_csv('train.csv')
   test = pd.read_csv('test.csv')

3. Initialize the Fedot object for classification:

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

4. Fit the model to your training data:

   best_pipeline = model.fit(features=train, target='target')

5. Make predictions on the test data:

   prediction = model.predict(features=test)

6. Evaluate the model's performance:

   metrics = model.get_metrics()
   print(metrics)

Here's a complete example:

from fedot.api.main import Fedot
import pandas as pd

# Load data
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

# Initialize Fedot for classification
model = Fedot(problem='classification', metric='roc_auc')

# Fit the model
best_pipeline = model.fit(features=train, target='target')

# Make predictions
prediction = model.predict(features=test)

# Evaluate the model
metrics = model.get_metrics()
print(metrics)

This example demonstrates how to use Fedot to automate the machine learning pipeline design for a classification problem. For more detailed options and parameters, you can refer to the Fedot documentation.

[valer1435]

What if my data is already loaded in memory as pandas Dataframe? @RepoPilotAssistant help

[RepoPilotAssistant]

@valer1435 If your data is already loaded in memory as a pandas DataFrame, you can directly use it with the Fedot API without needing to read from a CSV file. Here's how you can proceed:

1. Ensure your data is in a pandas DataFrame:

   import pandas as pd
   train_df = pd.DataFrame(...)  # Your training data
   test_df = pd.DataFrame(...)    # Your test data

2. Initialize the Fedot object for classification:

   from fedot.api.main import Fedot
   model = Fedot(problem='classification', metric='roc_auc')

3. Fit the model to your training data:

   best_pipeline = model.fit(features=train_df, target='target')

4. Make predictions on the test data:

   prediction = model.predict(features=test_df)

5. Evaluate the model's performance:

   metrics = model.get_metrics()
   print(metrics)

Here's a complete example:

from fedot.api.main import Fedot
import pandas as pd

# Assume train_df and test_df are already loaded pandas DataFrames
train_df = pd.DataFrame(...)  # Your training data
test_df = pd.DataFrame(...)   # Your test data

# Initialize Fedot for classification
model = Fedot(problem='classification', metric='roc_auc')

# Fit the model
best_pipeline = model.fit(features=train_df, target='target')

# Make predictions
prediction = model.predict(features=test_df)

# Evaluate the model
metrics = model.get_metrics()
print(metrics)

This example shows how to use Fedot with data already in memory as pandas DataFrames. For more detailed options and parameters, you can refer to the Fedot documentation.