RepoChat Arena

import pandas as pd
import numpy as np
from sktime.forecasting.model_selection import (
ForecastingOptunaSearchCV,
ExpandingWindowSplitter,
temporal_train_test_split
)
from sktime.forecasting.base import ForecastingHorizon
from sktime.performance_metrics.forecasting import MeanAbsolutePercentageError
from sktime.forecasting.compose import TransformedTargetForecaster
from sktime.forecasting.statsforecast import (
StatsForecastMSTL,
StatsForecastAutoETS,
StatsForecastAutoARIMA,
StatsForecastAutoTheta
)
from sktime.transformations.series.detrend import Detrender
from sktime.transformations.series.deseasonalize import Deseasonalizer
import optuna
import warnings

warnings.filterwarnings('ignore')

# Load your time series data

# Ensure 'pivot_table' is defined and contains the 'PAN4_PIBPMG4' series

y = pivot_table['PAN4_PIBPMG4']

# Split the data into train and test sets

y_train, y_test = temporal_train_test_split(y, test_size=8)

# Define the forecasting horizon

fh = ForecastingHorizon(np.arange(1, 9), is_relative=True)

# Set up cross-validation with an expanding window splitter

cv = ExpandingWindowSplitter(fh=fh, initial_window=len(y_train) - 8)

# Define the parameter space for tuning

param_distributions = {
'forecaster**season_length': optuna.distributions.CategoricalDistribution([(4,), (8,)]),
'forecaster**trend_forecaster': optuna.distributions.CategoricalDistribution([
StatsForecastAutoETS(model="ZZZ"),
StatsForecastAutoARIMA(seasonal=True),
StatsForecastAutoTheta()
]),
'forecaster\_\_stl_kwargs': {
'robust': optuna.distributions.CategoricalDistribution([True, False]),
'period': optuna.distributions.IntUniformDistribution(4, 8)
}
}

# Initialize the MSTL forecaster

mstl_forecaster = StatsForecastMSTL()

# Create a pipeline with optional transformations

forecaster = TransformedTargetForecaster(steps=[
("detrender", Detrender()),
("deseasonalizer", Deseasonalizer()),
("mstl_forecaster", mstl_forecaster)
])

# Set up the OptunaSearchCV

optuna_search = ForecastingOptunaSearchCV(
forecaster=forecaster,
cv=cv,
param_distributions=param_distributions,
scoring=MeanAbsolutePercentageError(symmetric=True),
n_trials=100,
random_state=42
)

# Fit the model

optuna_search.fit(y_train)

# Predict

y_pred = optuna_search.predict(fh)

# Evaluate

mape = MeanAbsolutePercentageError(symmetric=True)
final_mape = mape(y_test, y_pred)
print(f"Final sMAPE: {final_mape:.2f}")

# Plot results

import matplotlib.pyplot as plt

plt.figure(figsize=(15, 7))
plt.plot(y_train.index, y_train.values, label='Training Data', color='blue')
plt.plot(y_test.index, y_test.values, label='Test Data', color='green')
plt.plot(y_pred.index, y_pred.values, label='Predictions', color='red', linestyle='--')
plt.title('MSTL Forecast Results with Optuna Optimization')
plt.legend()
plt.grid(True)
plt.show()

# Save the best model

from joblib import dump
dump(optuna*search.best_forecaster*, 'best_mstl_model_optuna.joblib')
print("\nBest model saved as 'best_mstl_model_optuna.joblib'")

# Print additional optimization results

print("\nOptimization Results:")
print("="\*50)
print(f"Number of completed trials: {len(optuna*search.cv_results*)}")
print(f"Best trial number: {optuna*search.best_index*}")
print(f"Best sMAPE achieved during optimization: {optuna*search.best_score*:.2f}")

# Print best parameters

print("\nBest Parameters Found:")
print("="\*50)
for param, value in optuna*search.best_params*.items():
print(f"{param}: {value}")

The following files are found in the repository:
{file structure}
Please provide a list of files that you would like to search for answering the user query.
Enclose the file paths in a list in a markdown code block as shown below:
```
1. [[ filepath_1 ]]\n
2. [[ filepath_2 ]]\n
3. [[ filepath_3 ]]\n
...
```
Think step-by-step and strategically reason about the files you choose to maximize the chances of finding the answer to the query. Only pick the files that are most likely to contain the information you are looking for in decreasing order of relevance. Once you have selected the files, please submit your response in the appropriate format mentioned above (markdown numbered list in a markdown code block). The filepath within [[ and ]] should contain the complete path of the file in the repository.
{query}

Here is a list of files in the repository that may help you answer the query:
{pairs of (file_name, file_content)}
___

[INSTRUCTION]

You are an expert software engineer. Answer the following user query using provided context retrieved from the {repository_name} repository.

[QUERT CONTEXT]

{issue/PR content}

[USER QUERY]

{user query}

INFO:__main__:Generated assets successfully.
INFO:backend.main:Generating video to output.mp4 from tmpmpc_r1vm
Free port: 55427
Exposed directory /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm
Starting up http-server, serving /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm

http-server version: 14.1.1

http-server settings:
CORS: true
Cache: 3600 seconds
Connection Timeout: 120 seconds
Directory Listings: visible
AutoIndex: visible
Serve GZIP Files: false
Serve Brotli Files: false
Default File Extension: none

Available on:
http://localhost:55427
Hit CTRL-C to stop the server

INFO:backend.utils.generate_video:Exposed directory /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm
INFO:backend.utils.generate_video:Generating video to /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm/output.mp4
(node:93666) ExperimentalWarning: CommonJS module /opt/homebrew/lib/node_modules/npm/node_modules/debug/src/node.js is loading ES Module /opt/homebrew/lib/node_modules/npm/node_modules/supports-color/index.js using require().
Support for loading ES Module in require() is an experimental feature and might change at any time
(Use `node --trace-warnings ...` to show where the warning was created)
Bundled code ━━━━━━━━━━━━━━━━━━ 550ms
[Fri Dec 06 2024 00:02:39 GMT+0100 (Central European Standard Time)] "GET /audio.wav" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/131.0.6778.87 Safari/537.36"
(node:93654) [DEP0066] DeprecationWarning: OutgoingMessage.prototype.\_headers is deprecated
(Use `node --trace-deprecation ...` to show where the warning was created)
Composition Arxflix
Codec h264
Output /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm/output.mp4
Concurrency 1x
Rendering frames ━━━━━━━━━━━━━━━━━━ 0/2353
Encoding video ━━━━━━━━━━━━━━━━━━ 0/2353[Fri Dec 06 2024 00:02:39 GMT+0100 (Central European Standard Time)] "GET /subtitles.srt" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/131.0.6778.87 Safari/537.36"
[Fri Dec 06 2024 00:02:39 GMT+0100 (Central European Standard Time)] "GET /rich.json" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) HeadlessChrome/131.0.6778.87 Safari/537.36"
node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:187 Error: No "src" prop was passed to .
at ImgRefForwarding (http://localhost:3000/bundle.js:26884:15)
at Nh (http://localhost:3000/bundle.js:25838:137)
at Yi (http://localhost:3000/bundle.js:25870:57)
at Vk (http://localhost:3000/bundle.js:25971:275)
at Uk (http://localhost:3000/bundle.js:25957:389)
at Tk (http://localhost:3000/bundle.js:25957:320)
at Ik (http://localhost:3000/bundle.js:25957:180)
at Nk (http://localhost:3000/bundle.js:25948:88)
at Gk (http://localhost:3000/bundle.js:25945:429)
at J (http://localhost:3000/bundle.js:34117:203)

An error occurred while rendering frame 0:
Error No "src" prop was passed to .

at node_modules/.pnpm/remotion@4.0.171_react-dom@18.3.1_react@18.3.1__react@18.3.1/node_modules/remotion/dist/cjs/Img.js:19
17 │ const { delayPlayback } = (0, use_buffer_state_js_1.useBufferState)();
18 │ const sequenceContext = (0, react_1.useContext)(SequenceContext_js_1.SequenceContext);
19 │ if (!src) {
20 │ throw new Error('No "src" prop was passed to .');
21 │ }
22 │ (0, react_1.useImperativeHandle)(ref, () => {

at Nh (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:159)
at Yi (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:191)
at Vk (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:292)
at Uk (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:278)
at Tk (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:278)
at Ik (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:278)
at Nk (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:269)
at Gk (node_modules/.pnpm/react-dom@18.3.1_react@18.3.1/node_modules/react-dom/cjs/react-dom.production.min.js:266)
at J (node_modules/.pnpm/scheduler@0.23.2/node_modules/scheduler/cjs/scheduler.production.min.js:12)
at R (node_modules/.pnpm/scheduler@0.23.2/node_modules/scheduler/cjs/scheduler.production.min.js:13)
INFO:backend.utils.generate_video:Generated video to /var/folders/3j/jv5_hbgn59g9yxccxtfcvz4r0000gp/T/tmpmpc_r1vm/output.mp4
http-server stopped.
INFO:**main**:Generated video successfully.
^CKeyboard interruption in main thread... closing server.

require("dotenv").config();

const { Telegraf, session } = require("telegraf");
const { registerCommands } = require("./commands");
const { registerHears } = require("./hears");
const { registerScenes } = require("./scenes");
const sequelize = require("./database");
const User = require("./models/user");
const { t, i18next } = require("./i18n");
const Plans = require("./models/plans");

// sync database
(async () => {
try {
await sequelize.sync({ force: true });
console.log("database synchronized successfully.");

const bot = new Telegraf(process.env.BOT_TOKEN);

const defaultPlan = await Plans.findOne({ where: { isDefault: true } });
if (!defaultPlan) {
await Plans.create({
name: "atom",
isDefault: true,
price: 0,
});
console.log("default plan created.");
}

// telegraf.js sessions
bot.use(
session({
defaultSession: () => ({ counter: 0, userName: "" }), // Initialize session variables
})
);
// user check/creation middleware
bot.use(async (ctx, next) => {
const telegramId = ctx.from.id;
try {
let user = await User.findOne({ where: { telegramId } });
if (!user) {
user = await User.create({
name: ctx.from.first_name || "unknown",
telegramId: telegramId,
});
console.log(
`new user created: ${user.name} (id: ${user.telegramId})`
);
}

ctx.session.isAuthorized = true;
ctx.session.user = user;
ctx.session.language = user.language || "ru";

i18next.changeLanguage(ctx.session.language);
return next();
} catch (error) {
console.error("error during user check/creation:", error);
return ctx.reply(t("error_user_creation"));
}
});

// register commands, hears, and scenes
registerCommands(bot);
registerHears(bot);
registerScenes(bot);

// start the bot
bot.launch();
console.log("bot is running.");

// enable graceful stop
process.once("SIGINT", () => bot.stop("SIGINT"));
process.once("SIGTERM", () => bot.stop("SIGTERM"));
} catch (error) {
console.error("error initializing bot:", error);
}
})();