The Excitement of the Basketball Superliga FBU Ukraine: Tomorrow's Matches
The Basketball Superliga FBU Ukraine is gearing up for another thrilling day of competition, with fans eagerly anticipating the matchups set for tomorrow. This premier league continues to showcase the finest talent in Ukrainian basketball, and each game promises to deliver edge-of-your-seat excitement. As the teams prepare to face off on the court, we delve into the details of tomorrow's schedule, offering expert insights and betting predictions to enhance your viewing experience.
Tomorrow's Match Schedule
The day's fixtures are packed with high-stakes games that will undoubtedly keep fans on their toes. Here’s a breakdown of what to expect:
- Team A vs. Team B: This matchup is one of the most anticipated, with both teams coming off impressive performances last week. Team A has been in stellar form, boasting a strong defensive lineup, while Team B's offensive strategies have been nothing short of spectacular.
- Team C vs. Team D: Known for their strategic gameplay, Team C faces off against the high-scoring Team D. This game is expected to be a classic showdown between defense and offense.
- Team E vs. Team F: With both teams vying for a top spot in the league standings, this game could be a turning point in their season. Fans are eager to see how each team will leverage their strengths.
Expert Betting Predictions
For those looking to add an extra layer of excitement to tomorrow's games, here are some expert betting predictions:
Team A vs. Team B
Analysts predict a close contest, but Team A's recent defensive prowess gives them a slight edge. Bettors might consider placing wagers on Team A to win by a narrow margin.
Team C vs. Team D
Given Team D's high-scoring capabilities, a bet on them to win with a significant point difference could be lucrative. However, don't count out Team C's strategic play; they could pull off an upset.
Team E vs. Team F
With both teams neck and neck in the standings, this game is anyone's guess. A bet on the total points scored might be wise, as both teams are known for their aggressive playstyles.
In-Depth Analysis of Key Players
Tomorrow's games will also be influenced by standout performances from key players across the league. Here’s a closer look at some of the stars to watch:
- Player X (Team A): Known for his exceptional three-point shooting, Player X has been a game-changer for Team A. His ability to perform under pressure makes him a critical player in tomorrow’s matchup.
- Player Y (Team B): With his unmatched defensive skills, Player Y has been pivotal in Team B’s recent successes. His presence on the court could be the deciding factor against Team A.
- Player Z (Team C): As a versatile player who excels in both offense and defense, Player Z is expected to lead Team C’s efforts against Team D.
- Player W (Team D): Known for his scoring ability, Player W is likely to be at the heart of Team D’s strategy against Team C.
- Player V (Team E): With his leadership qualities and playmaking skills, Player V is crucial for Team E as they aim to secure a win against Team F.
- Player U (Team F): As one of the league’s top rebounders, Player U will play a significant role in Team F’s performance against Team E.
Tactical Insights: What to Expect from Each Game
Each matchup in tomorrow’s schedule brings its own unique tactical battles. Here’s what fans can anticipate:
Tactics in Team A vs. Team B
This game is likely to be a battle of wits between two strong defensive units. Look for both teams to focus on controlling the pace and limiting turnovers.
Tactics in Team C vs. Team D
Expect a fast-paced game with an emphasis on three-point shooting from both sides. The team that can capitalize on open shots will likely come out on top.
Tactics in Team E vs. Team F
With both teams fighting for position, expect aggressive defense and quick transitions. Ball control and minimizing fouls will be key strategies.
Past Performances: What History Tells Us
Analyzing past performances can provide valuable insights into how these games might unfold:
- Team A vs. Team B: Historically, this matchup has been closely contested, with both teams splitting wins over recent encounters.
- Team C vs. Team D: In their previous meetings, both teams have shown they can outscore each other significantly, making this game hard to predict.
- Team E vs. Team F: These two have had a fierce rivalry, often resulting in nail-biting finishes that keep fans on the edge of their seats.
The Role of Home Advantage
Home court advantage can play a crucial role in basketball outcomes. Here’s how it might impact tomorrow’s games:
- Team A: Playing at home could boost their confidence and energy levels, potentially giving them an edge over visiting opponents.
- Team C: Known for their strong home support, they might leverage this advantage to overcome any challenges posed by away teams.
- Team E: With their fans’ backing, they could find additional motivation to secure crucial points in their matchup.
Betting Tips and Strategies
For those interested in placing bets, consider these strategies:
- Diversify your bets across different games to spread risk and increase potential rewards.
- Favor bets on total points scored if you anticipate high-scoring games.
- Closely monitor player performances and adjust your bets accordingly based on real-time developments.
- Avoid overconfidence; even expert predictions can go awry due to unexpected factors like injuries or referee decisions.
Potential Impact on League Standings
Tomorrow’s games could significantly impact the league standings:
- A win for any top contender could solidify their position or push them closer to securing a playoff spot.
- Middle-table teams have an opportunity to climb up by capitalizing on slip-ups from direct rivals.
- Losing teams must reassess their strategies if they hope to avoid relegation concerns later in the season.
Fan Engagement and Viewing Experience
bsmilyang/DeepPseudoCell/train.py
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import time
import random
import os
from model import DeepPseudoCell
from data_loader import DeepPseudoCellDataset
def train(net,
train_loader,
valid_loader,
criterion,
optimizer,
num_epochs=100,
device='cuda:0',
save_path='./model',
save_freq=5):
# create save directory
if not os.path.exists(save_path):
os.mkdir(save_path)
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch +1 , num_epochs))
# training phase
net.train()
start = time.time()
total_loss = []
for i_batch,data_batch in enumerate(train_loader):
batch_x = data_batch['x']
batch_y = data_batch['y']
batch_mask = data_batch['mask']
batch_size = batch_x.size(0)
batch_index = batch_x[:,0,:].long()
# forward pass
x_hat = net(batch_x.to(device),batch_index.to(device))
y_hat = torch.gather(x_hat,batch_dim=1,index=batch_index.unsqueeze(-1).repeat(1,x_hat.size(-1)).to(device))
mask = batch_mask.to(device)
output = torch.mul(y_hat.squeeze(),mask)
label = torch.mul(batch_y.to(device),mask)
loss = criterion(output,label)
total_loss.append(loss.item())
# backward pass
net.zero_grad()
loss.backward()
optimizer.step()
print('Epoch {} training loss: {}'.format(epoch+1,np.mean(total_loss)))
print('Epoch {} training time: {}'.format(epoch+1,time.time()-start))
if epoch%save_freq==0:
valid_loss=[]
net.eval()
with torch.no_grad():
for i_batch,data_batch in enumerate(valid_loader):
batch_x = data_batch['x']
batch_y = data_batch['y']
batch_mask = data_batch['mask']
batch_size = batch_x.size(0)
batch_index = batch_x[:,0,:].long()
x_hat = net(batch_x.to(device),batch_index.to(device))
y_hat = torch.gather(x_hat,batch_dim=1,index=batch_index.unsqueeze(-1).repeat(1,x_hat.size(-1)).to(device))
mask = batch_mask.to(device)
output = torch.mul(y_hat.squeeze(),mask)
label = torch.mul(batch_y.to(device),mask)
valid_loss.append(criterion(output,label).item())
print('Epoch {} validation loss: {}'.format(epoch+1,np.mean(valid_loss)))
torch.save(net.state_dict(),os.path.join(save_path,'DeepPseudoCell_epoch_{}.pth'.format(epoch+1)))
bsmilyang/DeepPseudoCell/README.md
# DeepPseudoCell
This repository contains code implementing DeepPseudoCell algorithm described by [Wei et al., Nature Methods (2020)](https://www.nature.com/articles/s41592-020-00948-9).
## Dataset
We use [Lung Cell Painting dataset](https://github.com/chanzuckerberg/cell-painting-lung-cancer) for training DeepPseudoCell.
## Data Preprocessing
We randomly select ~50k cells from each sample as training data.
bash
python preprocessing.py --input_dir ./data/raw_data --output_dir ./data/preprocessed_data --random_seed=42 --sample_ratio=0.5 --num_samples=10 --num_features=64
## Training
bash
python train.py --train_dir ./data/preprocessed_data/train/ --valid_dir ./data/preprocessed_data/valid/ --num_epochs=50 --save_path=./model/DeepPseudoCell/
## Pseudotime inference
bash
python inference.py --input_dir ./data/preprocessed_data/test/ --output_dir ./data/inference_output/
bsmilyang/DeepPseudoCell/preprocessing.py
import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import argparse
parser=argparse.ArgumentParser()
parser.add_argument('--input_dir',type=str,default='./data/raw_data/',help='input directory')
parser.add_argument('--output_dir',type=str,default='./data/preprocessed_data/',help='output directory')
parser.add_argument('--random_seed',type=int,default=42)
parser.add_argument('--sample_ratio',type=float,default=0.5)
parser.add_argument('--num_samples',type=int,default=10)
parser.add_argument('--num_features',type=int,default=64)
args,_=parser.parse_known_args()
def preprocess_data(input_dir,output_dir,sample_ratio,num_samples,num_features):
if not os.path.exists(output_dir):
os.mkdir(output_dir)
file_list=[f for f in os.listdir(input_dir) if f.endswith('.csv')]
for f_name in file_list:
df=pd.read_csv(os.path.join(input_dir,f_name))
if df.shape[0]<sample_ratio*df.shape[0]:
sample_ratio=df.shape[0]/sample_ratio
n_sample=np.int(sample_ratio)
df=df.sample(n=n_sample)
features=list(df.columns.values)[2:]
features=np.array(features)
features=np.random.choice(features,size=num_features)
features=np.append(['ImageId','Metadata_Cell_ID'],features)
df=df[features]
df.to_csv(os.path.join(output_dir,f_name),index=False)
def split_data(input_dir,output_dir,num_samples):
if not os.path.exists(os.path.join(output_dir,'train')):
os.mkdir(os.path.join(output_dir,'train'))
if not os.path.exists(os.path.join(output_dir,'valid')):
os.mkdir(os.path.join(output_dir,'valid'))
if not os.path.exists(os.path.join(output_dir,'test')):
os.mkdir(os.path.join(output_dir,'test'))
file_list=[f for f in os.listdir(input_dir) if f.endswith('.csv')]
num_train=int(num_samples*0.8)
num_valid=int(num_samples*0.1)
file_list_train=random.sample(file_list,num_train)
file_list_valid=random.sample([f for f in file_list if f not in file_list_train],num_valid)
file_list_test=[f for f in file_list if f not in file_list_train and f not in file_list_valid]
for f_name_train,f_name_valid,f_name_test in zip(file_list_train,file_list_valid,file_list_test):
preprocess_data(input_dir=os.path.join(output_dir,'train'),output_dir=os.path.join(output_dir,'train'),sample_ratio=args.sample_ratio,num_samples=args.num_samples,num_features=args.num_features)
preprocess_data(input_dir=os.path.join(output_dir,'valid'),output_dir=os.path.join(output_dir,'valid'),sample_ratio=args.sample_ratio,num_samples=args.num_samples,num_features=args.num_features)
preprocess_data(input_dir=os.path.join(output_dir,'test'),output_dir=os.path.join(output_dir,'test'),sample_ratio=args.sample_ratio,num_samples=args.num_samples,num_features=args.num_features)
if __name__=='__main__':
split_data(args.input_dir,args.output_dir,args.num_samples)# coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import json
import logging
import os
from six.moves import urllib
from sklearn.preprocessing import MinMaxScaler
logger=logging.getLogger(__name__)
def _parse_args():
parser=argparse.ArgumentParser()
parser.add_argument('--input',dest='input',default='./data/raw_data/',help='Input directory')
parser.add_argument('--output',dest='output',default='./data/preprocessed_data/',help='Output directory')
args,_=parser.parse_known_args()
return args
def get_feature_mapping():
mapping_url='https://raw.githubusercontent.com/czbiohub/cell-painting-moa-pipeline/master/configs/mapping.json'
mapping_filename=os.path.basename(mapping_url)
mapping_file_path=os.path.join('./resources',mapping_filename)
if not os.path.exists(mapping_file_path):
def _progress(count,size,total):
sys.stdout.write('r>> Downloading %s %.1f%%' % (mapping_filename,float(count * size)/float(total) *100))
sys.stdout.flush()
def _download(url,filename):
filepath,_=urllib.request.urlretrieve(url,filename=_progress)
print()
return filepath
mapping_file_path=_download(mapping_url,mapping_file_path)
with open(mapping_file_path) as mapping_file:
feature_mapping=json.load(mapping_file)
return feature_mapping
def preprocess(args):
input_directory=args.input
output_directory=args.output
feature_mapping=get_feature_mapping()
features=list(feature_mapping.keys())
logger.info('Features: {}'.format(features))
logger.info('Input directory: {}'.format(input_directory))
logger.info('Output directory: {}'.format(output_directory))
logger.info('Reading files...')
files=[os.path.join(root,f) for root,d,f_list in os.walk(input_directory) for f in f_list if '.csv'==os.path.splitext(f)[1]]
logger.info('Found {} files'.format(len(files)))
logger.info('Reading files...')
data={}
for file_path in files:
logger.info(file_path)
df=pd.read_csv(file_path)
image_id=os.path.basename(file_path).split('.')[0]
if image_id not in data:
data[image_id]=df
else:
logger.warning('{} already exists'.format(image_id))
df_new=df[df.columns[~df.columns.isin(data[image_id].columns)]]
logger.warning('{} new features found'.format(df_new.shape[1]))
data[image_id]=pd.concat([data[image_id],df_new],axis=1)
logger.info('Processing files...')
for image_id,image_df in data.items():
logger.info(image_id)
image_df=image_df[['Metadata_Plate','Metadata_Well']+features]
plate_well=image_df.iloc[0][['Metadata_Plate','Metadata_Well']]
if plate_well not in plate_well_to_image_id:
plate_well_to_image_id[plate_well]=[]
plate_well_to_image_id[plate_well].append(image_id)
well_to_image_ids=image_df.Metadata_Well.unique()
well_to_image_ids=[image_id+'_'+well for well,image_id,image_df,image_df_new,image_idx,image_df_concat,image_df
