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import os
import pandas as pd
from sklearn.ensemble import IsolationForest
import numpy as np
from sklearn.model_selection import train_test_split
import gradio as gr
#Data preparation
n_samples, n_outliers = 120, 40
rng = np.random.RandomState(0)
covariance = np.array([[0.5, -0.1], [0.7, 0.4]])
cluster_1 = 0.4 * rng.randn(n_samples, 2) @ covariance + np.array([2, 2]) # general deformed cluster
cluster_2 = 0.3 * rng.randn(n_samples, 2) + np.array([-2, -2]) # spherical cluster
outliers = rng.uniform(low=-4, high=4, size=(n_outliers, 2))
X = np.concatenate([cluster_1, cluster_2, outliers]) #120+120+40 = 280 with 2D
y = np.concatenate(
[np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)]
)
#Visualize the data as a scatter plot
# def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
# if passenger_class is None or embark_point is None:
# return None
# df = pd.DataFrame.from_dict(
# {
# "Pclass": [passenger_class + 1],
# "Sex": [0 if is_male else 1],
# "Age": [age],
# "Fare": [fare],
# "Embarked": [embark_point + 1],
# "Company": [
# (1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)
# ]
# }
# )
# df = encode_age(df)
# df = encode_fare(df)
# pred = clf.predict_proba(df)[0]
# return {"Perishes": float(pred[0]), "Survives": float(pred[1])}
# demo = gr.Interface(
# predict_survival,
# [
# gr.Dropdown(["first", "second", "third"], type="index"),
# "checkbox",
# gr.Slider(0, 80, value=25),
# gr.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
# gr.Number(value=20),
# gr.Radio(["S", "C", "Q"], type="index"),
# ],
# "label",
# examples=[
# ["first", True, 30, [], 50, "S"],
# ["second", False, 40, ["Sibling", "Child"], 10, "Q"],
# ["third", True, 30, ["Child"], 20, "S"],
# ],
# interpretation="default",
# live=True,
# )
# if __name__ == "__main__":
# demo.launch() |