Closed valer1435 closed 2 weeks ago
@RepoPilotAssistant help
@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.
I want to know which concepts lies under Fedot
@RepoPilotAssistant
@RepoPilotAssistant help
@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:
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.
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.
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.
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.
How I can solve classification problem with Fedot? @RepoPilotAssistant help
@valer1435 To solve a classification problem with Fedot, you can follow these steps:
Import the necessary module:
from fedot.api.main import Fedot
Load your training and test data:
import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
Initialize the Fedot object for classification:
model = Fedot(problem='classification', metric='roc_auc')
Fit the model to your training data:
best_pipeline = model.fit(features=train, target='target')
Make predictions on the test data:
prediction = model.predict(features=test)
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.
What if my data is already loaded in memory as pandas Dataframe? @RepoPilotAssistant help
@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:
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
Initialize the Fedot object for classification:
from fedot.api.main import Fedot
model = Fedot(problem='classification', metric='roc_auc')
Fit the model to your training data:
best_pipeline = model.fit(features=train_df, target='target')
Make predictions on the test data:
prediction = model.predict(features=test_df)
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.
I wonder to understand how fedot works.