remove endpoints in fastify && update model
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60cd644afa
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1ee1e10e72
@ -243,7 +243,7 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading):
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if not df_copy.empty:
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with open(file_path, 'wb') as file:
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file.write(orjson.dumps(df_copy.to_dict(orient='records')))
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print(df_copy)
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return df_copy
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except Exception as e:
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@ -266,6 +266,7 @@ async def chunked_gather(tickers, con, skip_downloading, chunk_size):
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for chunk in tqdm(chunks(tickers, chunk_size)):
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# Create tasks for each chunk
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print(f"chunk size: {len(chunk)}")
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tasks = [download_data(ticker, con, start_date, end_date, skip_downloading) for ticker in chunk]
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# Await the results for the current chunk
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chunk_results = await asyncio.gather(*tasks)
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@ -357,7 +358,7 @@ async def run():
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AND symbol NOT LIKE '%.%'
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AND symbol NOT LIKE '%-%'
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""")
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warm_start_symbols = ['AAPL'] #[row[0] for row in cursor.fetchall()]
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warm_start_symbols = ['PEP'] #[row[0] for row in cursor.fetchall()]
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print(f'Warm Start Training: Total Tickers {len(warm_start_symbols)}')
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await warm_start_training(warm_start_symbols, con, skip_downloading)
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Binary file not shown.
@ -23,16 +23,11 @@ class ScorePredictor:
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self.pca = PCA(n_components=0.95) # Retain components explaining 95% variance
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.pkl'
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self.model = lgb.LGBMClassifier(
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n_estimators=200, # Number of boosting iterations - good balance between performance and training time
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learning_rate=0.005, # Smaller learning rate for better generalization
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max_depth=5, # Controlled depth to prevent overfitting
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num_leaves=2**5-1, # 2^max_depth, prevents overfitting while maintaining model complexity
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colsample_bytree=0.8, # Use 80% of features per tree to reduce overfitting
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subsample=0.8, # Use 80% of data per tree to reduce overfitting
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min_child_samples=5, # Minimum samples per leaf to ensure reliable splits
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random_state=42, # For reproducibility
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reg_alpha=0.1, # L1 regularization
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reg_lambda=0.1, # L2 regularization
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n_estimators=20_000, # Number of boosting iterations - good balance between performance and training time
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learning_rate=0.001, # Smaller learning rate for better generalization
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max_depth=6, # Controlled depth to prevent overfitting
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num_leaves=2**6-1, # 2^max_depth, prevents overfitting while maintaining model complexity
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colsample_bytree=0.1,
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n_jobs=10, # Use N CPU cores
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verbose=0, # Reduce output noise
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)
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@ -61,10 +61,6 @@ const corsMiddleware = (request, reply, done) => {
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fastify.addHook("onRequest", corsMiddleware);
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//fastify.register(require('./mixpanel/server'), { mixpanel, UAParser });
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fastify.register(require("./get-user-stats/server"), { pb });
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fastify.register(require("./get-community-stats/server"), { pb });
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fastify.register(require("./get-moderators/server"), { pb });
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fastify.register(require("./get-user-data/server"), { pb });
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fastify.register(require("./get-all-comments/server"), { pb });
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fastify.register(require("./get-post/server"), { pb });
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fastify.register(require("./get-one-post/server"), { pb });
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@ -1,27 +0,0 @@
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module.exports = function (fastify, opts, done) {
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const pb = opts.pb;
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fastify.get('/get-community-stats', async (request, reply) => {
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let output;
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let totalUsers = 0;
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let totalPosts = 0;
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let totalComments = 0;
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try {
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totalUsers = (await pb.collection("users").getList(1, 1))?.totalItems;
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totalPosts = (await pb.collection("posts").getList(1, 1))?.totalItems;
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totalComments = (await pb.collection("comments").getList(1, 1))?.totalItems;
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output = { totalUsers, totalPosts, totalComments };
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} catch (e) {
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console.error(e);
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output = { totalUsers, totalPosts, totalComments };
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}
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reply.send({ items: output });
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});
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done();
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};
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@ -1,22 +0,0 @@
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// Declare a route
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module.exports = function (fastify, opts, done) {
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const pb = opts.pb;
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fastify.get('/get-moderators', async (request, reply) => {
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let output;
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try {
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output = await pb.collection("moderators").getFullList({
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expand: 'user'
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})
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}
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catch(e) {
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output = [];
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}
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reply.send({ items: output })
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});
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done();
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};
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@ -1,22 +0,0 @@
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// Declare a route
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module.exports = function (fastify, opts, done) {
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const pb = opts.pb;
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fastify.post('/get-user-data', async (request, reply) => {
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const data = request.body;
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const userId = data?.userId
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let output;
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try {
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output = await pb.collection("users").getOne(userId)
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}
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catch(e) {
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output = {};
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}
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reply.send({ items: output })
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});
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done();
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};
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@ -1,36 +0,0 @@
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// Declare a route
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module.exports = function (fastify, opts, done) {
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const pb = opts.pb;
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fastify.post('/get-user-stats', async (request, reply) => {
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const data = request.body;
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const userId = data?.userId;
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let output;
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try {
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const getNumberOfPosts = await pb.collection("posts").getList(1,1, {
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filter: `user="${userId}"`,
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});
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const numberOfPosts = getNumberOfPosts?.totalItems
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const getNumberOfComments = await pb.collection("comments").getList(1,1, {
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filter: `user="${userId}"`,
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});
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const numberOfComments = getNumberOfComments?.totalItems
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output = {numberOfPosts, numberOfComments}
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console.log(output)
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}
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catch(e) {
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output = {};
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}
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reply.send({ items: output })
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});
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done();
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};
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