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93 lines
2.9 KiB
Python
93 lines
2.9 KiB
Python
# Demo: (Dropdown, Checkbox, Slider, CheckboxGroup, Number, Radio) -> (Label)
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import pandas as pd
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import numpy as np
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import sklearn
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import gradio as gr
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from sklearn import preprocessing
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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import os
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current_dir = os.path.dirname(os.path.realpath(__file__))
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data = pd.read_csv(os.path.join(current_dir, 'files/titanic.csv'))
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def encode_age(df):
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df.Age = df.Age.fillna(-0.5)
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bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
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categories = pd.cut(df.Age, bins, labels=False)
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df.Age = categories
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return df
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def encode_fare(df):
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df.Fare = df.Fare.fillna(-0.5)
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bins = (-1, 0, 8, 15, 31, 1000)
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categories = pd.cut(df.Fare, bins, labels=False)
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df.Fare = categories
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return df
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def encode_df(df):
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df = encode_age(df)
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df = encode_fare(df)
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sex_mapping = {"male": 0, "female": 1}
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df = df.replace({'Sex': sex_mapping})
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embark_mapping = {"S": 1, "C": 2, "Q": 3}
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df = df.replace({'Embarked': embark_mapping})
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df.Embarked = df.Embarked.fillna(0)
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df["Company"] = 0
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df.loc[(df["SibSp"] > 0), "Company"] = 1
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df.loc[(df["Parch"] > 0), "Company"] = 2
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df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
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df = df[["PassengerId", "Pclass", "Sex", "Age", "Fare", "Embarked", "Company", "Survived"]]
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return df
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train = encode_df(data)
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X_all = train.drop(['Survived', 'PassengerId'], axis=1)
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y_all = train['Survived']
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num_test = 0.20
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X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=23)
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clf = RandomForestClassifier()
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clf.fit(X_train, y_train)
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predictions = clf.predict(X_test)
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def predict_survival(passenger_class, is_male, age, company, fare, embark_point):
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df = pd.DataFrame.from_dict({
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'Pclass': [passenger_class + 1],
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'Sex': [0 if is_male else 1],
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'Age': [age],
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'Company': [(1 if "Sibling" in company else 0) + (2 if "Child" in company else 0)],
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'Fare': [fare],
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'Embarked': [embark_point + 1]
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})
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df = encode_age(df)
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df = encode_fare(df)
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pred = clf.predict_proba(df)[0]
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return {'Perishes': pred[0], 'Survives': pred[1]}
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iface = gr.Interface(
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predict_survival,
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[
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gr.inputs.Dropdown(["first", "second", "third"], type="index"),
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"checkbox",
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gr.inputs.Slider(0, 80),
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gr.inputs.CheckboxGroup(["Sibling", "Child"], label="Travelling with (select all)"),
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gr.inputs.Number().interpret(delta_type="absolute", delta=5),
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gr.inputs.Radio(["S", "C", "Q"], type="index"),
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],
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"label",
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examples=[
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["first", True, 30, [], 50, "S"],
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["second", False, 40, ["Sibling", "Child"], 10, "Q"],
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["third", True, 30, ["Child"], 20, "S"],
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],
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interpretation="default",
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live=True
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)
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if __name__ == "__main__":
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iface.launch()
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