predict.py

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error
import gradio as gr

# Charger et préparer les données
df = pd.read_csv("datatset/sphist.csv")
df['Date'] = pd.to_datetime(df["Date"])
df = df.sort_values(by='Date', ascending=True)

year_i = -1
day_i = -1
mean_d = np.nan
std_d = np.nan
std_d_v = np.nan
df['std 5'] = np.nan
df['mean 5'] = np.nan
mean_y = np.nan
std_y = np.nan
ratio = np.nan
df['mean 365'] = np.nan
df['std 365'] = np.nan
j = 0
for i, elt in df.iterrows():
    if df.iloc[j]['Date'] - df.iloc[0]['Date'] > timedelta(days=365):
        if year_i == -1:
            year_i = 0
        mean_y = np.mean(df.iloc[year_i:j]['Open'])
        std_y = np.std(df.iloc[year_i:j]['Open'])
        year_i += 1
    df.iloc[j, df.columns.get_loc("std 365")] = std_y
    df.iloc[j, df.columns.get_loc("mean 365")] = mean_y
    if df.iloc[j]['Date'] - df.iloc[0]['Date'] > timedelta(days=5):
        if day_i == -1:
            day_i = 0
        mean_d = np.mean(df.iloc[day_i:j]["Open"])
        std_d = np.std(df.iloc[day_i:j]['Open'])
        std_d_v = np.std(df.iloc[day_i:j]['Volume'])
        day_i += 1
    df.iloc[j, df.columns.get_loc("mean 5")] = mean_d
    df.iloc[j, df.columns.get_loc("std 5")] = std_d
    j += 1

# Ajouter des indicateurs utiles pour notre modèle de ML
df['5 Days Open'] = df['Open'].rolling(center=False, window=5).mean()
df['Year'] = df['Date'].apply(lambda x: x.year)
df['5 Days High'] = df['High'].rolling(center=False, window=5).mean()
df['5 Days Low'] = df['Low'].rolling(center=False, window=5).mean()
df['5 Days Volume'] = df['Volume'].rolling(center=False, window=5).mean()

# Déplacer la colonne d'un jour
df['5 Days Open'] = df['5 Days Open'].shift(1)
df = df.dropna(axis=0)
df = df.drop(df[df["Date"] < datetime(year=1951, month=1, day=3)].index, axis=0)
test = df[df['Date'] >= datetime(year=2013, month=1, day=1)]
train = df[df['Date'] < datetime(year=2013, month=1, day=1)]

# Entraîner le modèle
lr = LinearRegression().fit(train.drop(columns=["Open", 'High', 'Low', 'Volume', 'Adj Close', 'Close', 'Date']), train["Close"])
pred = lr.predict(test.drop(columns=["Open", 'High', 'Low', 'Volume', 'Adj Close', 'Close', 'Date']))

# Calculer les erreurs
err = mean_absolute_error(test["Close"], pred)
errP = mean_absolute_percentage_error(test["Close"], pred)

# Créer le DataFrame pour le tableau
result_df = pd.DataFrame({'Predictions': pred, 'Actual Close': test['Close']})

# Fonction pour afficher l'erreur et le tableau
def display_results():
    return str(err), str(errP), result_df

# Créer l'interface Gradio
with gr.Blocks() as demo:
    gr.Markdown("# Linear Regression Model Results")
    gr.Markdown("""This model was trained on S&P 500 stock price before 2013. The predictions below are taken betweek 2013 and 2015.
                
    0.4% of average error was reached using LinearRegression.""")
    with gr.Row():
        with gr.Column():
            error = gr.Textbox(label="Mean Absolute Error")
            errorP = gr.Textbox(label="Mean Absolute Percentage Error")
            table = gr.Dataframe(label="Predictions vs Actual Close Prices")

    with gr.Row():
        with gr.Column():
            btn = gr.Button("Show Results")

    btn.click(display_results, outputs=[error, errorP, table])

# Lancer l'interface Gradio
demo.launch()