SUPPORT_VECTOR_MACHINE

Train a support vector machine (SVM) model for classification tasks. It takes two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data. Params: kernel : 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed' Specifies the kernel type to be used in the algorithm. For detailed information about kernel types: https://scikit-learn.org/stable/modules/svm.html#kernel-functions Returns: out : DataFrame The predictions for the input data.

Python Code

from flojoy import flojoy, DataFrame, Matrix
import pandas as pd
from sklearn import svm, preprocessing
from typing import Literal


@flojoy(deps={"scikit-learn": "1.2.2"})
def SUPPORT_VECTOR_MACHINE(
    train_feature: DataFrame | Matrix,
    train_label: DataFrame | Matrix,
    input_data: DataFrame | Matrix,
    kernel: Literal["linear", "poly", "rbf", "sigmoid", "precomputed"] = "linear",
) -> DataFrame:
    """Train a support vector machine (SVM) model for classification tasks.

    It takes two dataframes of label and feature from labelled training data and a dataframe of unlabelled input data.

    Parameters
    ----------
    kernel : 'linear' | 'poly' | 'rbf' | 'sigmoid' | 'precomputed'
        Specifies the kernel type to be used in the algorithm.
        For detailed information about kernel types:
        https://scikit-learn.org/stable/modules/svm.html#kernel-functions

    Returns
    -------
    DataFrame
        The predictions for the input data.
    """

    le = preprocessing.LabelEncoder()

    if isinstance(train_feature, DataFrame):
        train = train_feature.m.to_numpy()
        col = train_label.m.to_numpy()
        target_name = train_label.m.columns.values[0]

    else:
        train = train_feature.m
        col = train_label.m
        target_name = "target"

    X = train
    Y = le.fit_transform(col)

    clf = svm.SVC(kernel=kernel)
    clf.fit(X, Y)

    if isinstance(input_data, DataFrame):
        input_arr = input_data.m.to_numpy()
    else:
        input_arr = input_data.m

    prediction = clf.predict(input_arr)
    prediction = le.inverse_transform(prediction)
    prediction = pd.DataFrame({target_name: prediction})
    return DataFrame(df=prediction)

Find this Flojoy Block on GitHub

Example

Having problems with this example app? Join our Discord community and we will help you out!

Press enter or space to select a node.You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.

Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.

Download this Flojoy App

In this example, the SUPPORT_VECTOR_MACHINE is passed the iris dataset, split into two parts. The training data contains 120 labels examples, while the input dataset contains 30 samples with the labels removed.

This data is read from disk with two READ_CSV nodes, and the output predictions made by the classifier are visualised in a TABLE.