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Flying paddle learning competition: League of Legends masters predict 85.365 points in February 2023
Contest Links - Paddle Learn: League of Legends Masters Predictions
Introduction
This question is a typical classification problem, with the League of Legends mobile game as the background, requiring players to predict the player's win or loss in the game based on the real-time game data of the League of Legends players.
the data shows
Each row in the data set is the game data of a player, and the data fields are as follows:
- id: player record id
- win: win or not, label variable
- kills: number of kills
- deaths: number of deaths
- assists: the number of assists
- largestkillingspree: The largest killing spree (game term, meaning big kills. When you kill three opponent heroes in a row without dying in the middle)
- largestmultikill: Maximum mult ikill (game terminology, multiple kills in a short period of time)
- longesttimespentliving: the longest survival time
- doublekills: the number of doublekills
- triplekills: times of doublekills
- quadrakills: number of quadrakills
- pentakills: number of pentakills
- totdmgdealt: total damage
- magicdmgdealt: magic damage
- physicaldmgdealt: physical damage
- truedmgdealt: true damage
- largestcrit: maximum crit damage
- totdmgtochamp: damage to opposing players
- magicdmgtochamp: magic damage to opposing players
- physdmgtochamp: Physical damage to opposing players
- truedmgtochamp: true damage to opposing players
- totheal: treatment amount
- totunitshealed: Total units healed
- dmgtoturrets: damage to turrets
- timecc: Legal control time
- totdmgtaken: damage taken
- magicdmgtaken: magic damage taken
- physdmgtaken: physical damage taken
- truedmgtaken: true damage taken
- wardsplaced: Number of scout wards placed
- wardskilled: number of scout wards destroyed
- firstblood: whether it is firstblood
The label field win in the test set is empty, and players need to predict.
game difficulty
The difficulty of this competition mainly lies in data processing and model selection
- The game data has neither missing values nor particularly obvious outliers. The data distribution is relatively normal. There are not many places that can increase the accuracy rate in feature engineering.
- In fact, even without any data processing, the random forest example, LightGBM and xgboost models can easily get 82-84 points. The neural network model cannot make much progress and is not even as good as the tree model.
Project Highlights
-
Using genetic algorithm to construct more features and using PCA algorithm for feature dimension reduction
-
Using the Dense Connetion idea to design the network structure to prevent the gradient from disappearing
code repository
https://github.com/ZhangzrJerry/paddle-lolmp
- decompress data
!unzip -d data/ data/data137276/test.csv.zip !unzip -d data/ data/data137276/train.csv.zip !pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple/ !mkdir result pretrain
Archive: data/data137276/test.csv.zip
inflating: data/test.csv
Archive: data/data137276/train.csv.zip
inflating: data/train.csv
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple/
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import numpy as np
import pandas as pd
x_train = pd.read_csv('data/train.csv').drop('win',axis=1)
y_train = pd.read_csv('data/train.csv')['win']
x_test = pd.read_csv('data/test.csv')
# Convenient feature engineering after splicing
feature = pd.concat([x_train, x_test])
- data visualization
import matplotlib.pyplot as plt
import seaborn as sns
# view data information
print(feature.describe())
print(feature.info())
# View data distribution characteristics
sns.FacetGrid(pd.melt(feature), col="variable", col_wrap=4, sharex=False, sharey=False).map(sns.distplot, "value")
# View data dependencies
sns.set_context({"figure.figsize":(8,8)})
sns.heatmap(data=feature.corr(),square=True,cmap='RdBu_r')
id kills deaths assists \
count 200000.000000 200000.000000 200000.000000 200000.000000
mean 99999.500000 5.798545 5.810190 8.322870
std 57735.171256 4.605316 3.263815 5.933893
min 0.000000 0.000000 0.000000 0.000000
25% 49999.750000 2.000000 3.000000 4.000000
50% 99999.500000 5.000000 6.000000 7.000000
75% 149999.250000 8.000000 8.000000 12.000000
max 199999.000000 39.000000 23.000000 52.000000
largestkillingspree largestmultikill longesttimespentliving \
count 200000.000000 200000.000000 200000.000000
mean 2.671450 1.332095 630.531655
std 2.537784 0.758037 311.568408
min 0.000000 0.000000 0.000000
25% 0.000000 1.000000 433.000000
50% 2.000000 1.000000 590.000000
75% 4.000000 2.000000 792.000000
max 31.000000 5.000000 3038.000000
doublekills triplekills quadrakills ... totunitshealed \
count 200000.000000 200000.000000 200000.000000 ... 200000.000000
mean 0.540265 0.073220 0.010235 ... 2.253135
std 0.924831 0.295887 0.104548 ... 2.481890
min 0.000000 0.000000 0.000000 ... 0.000000
25% 0.000000 0.000000 0.000000 ... 1.000000
50% 0.000000 0.000000 0.000000 ... 1.000000
75% 1.000000 0.000000 0.000000 ... 3.000000
max 11.000000 7.000000 4.000000 ... 98.000000
dmgtoturrets timecc totdmgtaken magicdmgtaken physdmgtaken \
count 200000.000000 200000.0 200000.000000 200000.000000 200000.000000
mean 2138.209810 0.0 23226.733180 8136.551010 14039.533350
std 2934.306106 0.0 11873.669826 5161.055339 7754.110833
min 0.000000 0.0 0.000000 0.000000 0.000000
25% 0.000000 0.0 15264.000000 4521.000000 8627.000000
50% 986.000000 0.0 21531.000000 7246.000000 12803.000000
75% 3222.250000 0.0 29465.250000 10739.000000 18205.000000
max 55083.000000 0.0 118130.000000 71631.000000 73172.000000
truedmgtaken wardsplaced wardskilled firstblood
count 200000.000000 200000.000000 200000.000000 200000.000000
mean 1049.892170 11.508290 1.782860 0.100380
std 1266.146212 7.539761 2.226049 0.300507
min 0.000000 0.000000 0.000000 0.000000
25% 274.000000 7.000000 0.000000 0.000000
50% 656.000000 10.000000 1.000000 0.000000
75% 1352.000000 14.000000 3.000000 0.000000
max 25140.000000 322.000000 48.000000 1.000000
[8 rows x 31 columns]
<class 'pandas.core.frame.DataFrame'>
Int64Index: 200000 entries, 0 to 19999
Data columns (total 31 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 id 200000 non-null int64
1 kills 200000 non-null int64
2 deaths 200000 non-null int64
3 assists 200000 non-null int64
4 largestkillingspree 200000 non-null int64
5 largestmultikill 200000 non-null int64
6 longesttimespentliving 200000 non-null int64
7 doublekills 200000 non-null int64
8 triplekills 200000 non-null int64
9 quadrakills 200000 non-null int64
10 pentakills 200000 non-null int64
11 totdmgdealt 200000 non-null int64
12 magicdmgdealt 200000 non-null int64
13 physicaldmgdealt 200000 non-null int64
14 truedmgdealt 200000 non-null int64
15 largestcrit 200000 non-null int64
16 totdmgtochamp 200000 non-null int64
17 magicdmgtochamp 200000 non-null int64
18 physdmgtochamp 200000 non-null int64
19 truedmgtochamp 200000 non-null int64
20 totheal 200000 non-null int64
21 totunitshealed 200000 non-null int64
22 dmgtoturrets 200000 non-null int64
23 timecc 200000 non-null int64
24 totdmgtaken 200000 non-null int64
25 magicdmgtaken 200000 non-null int64
26 physdmgtaken 200000 non-null int64
27 truedmgtaken 200000 non-null int64
28 wardsplaced 200000 non-null int64
29 wardskilled 200000 non-null int64
30 firstblood 200000 non-null int64
dtypes: int64(31)
memory usage: 48.8 MB
None
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/seaborn/distributions.py:288: UserWarning: Data must have variance to compute a kernel density estimate.
warnings.warn(msg, UserWarning)
<matplotlib.axes._subplots.AxesSubplot at 0x7f6b5a36cf50>
[External link picture transfer failed, the source site may have an anti-leeching mechanism, it is recommended to save the picture and upload it directly (img-sONywZGi-1677308129213)(main_files/main_5_3.png)]
- data preprocessing
from category_encoders import *
from sklearn.preprocessing import MinMaxScaler
# Sequential encoding of discrete variables
columns= ['kills','deaths','assists', 'largestkillingspree',
'largestmultikill', 'longesttimespentliving', 'doublekills',
'triplekills', 'quadrakills', 'pentakills','firstblood']
m_feature = OrdinalEncoder(cols=columns).fit(feature).transform(feature)
m_feature.columns=[
'id', 'akills', 'adeaths', 'aassists', 'alargestkillingspree',
'alargestmultikill', 'alongesttimespentliving', 'adoublekills',
'atriplekills', 'aquadrakills', 'apentakills', 'totdmgdealt',
'magicdmgdealt', 'physicaldmgdealt', 'truedmgdealt', 'largestcrit',
'totdmgtochamp', 'magicdmgtochamp', 'physdmgtochamp', 'truedmgtochamp',
'totheal', 'totunitshealed', 'dmgtoturrets', 'timecc', 'totdmgtaken',
'magicdmgtaken', 'physdmgtaken', 'truedmgtaken', 'wardsplaced',
'wardskilled', 'afirstblood'
]
m_feature=pd.concat([m_feature,feature[columns]],axis=1).drop(['timecc','id'],axis=1)
# Normalize the features
sc = MinMaxScaler().fit(m_feature)
feature = sc.transform(m_feature)
pd.DataFrame(feature).to_csv('pretrain/feature.csv',index=False)
pd.DataFrame(feature[:180000]).to_csv('pretrain/train_feature.csv',index=False)
pd.DataFrame(feature[180000:]).to_csv('pretrain/test_feature.csv',index=False)
- Automatic Mining Features
from gplearn.genetic import SymbolicTransformer
from IPython.display import Image
import pydotplus
import numpy as np
import pandas as pd
function_set = ['add', 'sub', 'mul', 'div', 'log', 'sqrt', 'abs', 'neg', 'max', 'min']
st = SymbolicTransformer(
generations=20,
population_size=1000,
hall_of_fame=100,
n_components=100,
function_set=function_set,
parsimony_coefficient=0.0005,
max_samples=0.9,
verbose=1,
random_state=0,
n_jobs=3
)
st.fit(np.array(pd.read_csv('pretrain/train_feature.csv')),np.array(pd.read_csv('data/train.csv')['win']))
graph = st._best_programs[0].export_graphviz()
graph = pydotplus.graphviz.graph_from_dot_data(graph)
display(Image(graph.create_png()))
pd.DataFrame(
np.concatenate(
[
st.transform(
np.array(
pd.read_csv('pretrain/train_feature.csv')
)
),
np.array(
pd.read_csv('pretrain/train_feature.csv')
)
],
axis=1
)
).to_csv('pretrain/gptrain.csv',index=False)
pd.DataFrame(
np.concatenate(
[
st.transform(
np.array(
pd.read_csv('pretrain/test_feature.csv')
)
),
np.array(
pd.read_csv('pretrain/test_feature.csv')
)
],
axis=1
)
).to_csv('pretrain/gptest.csv',index=False)
| Population Average | Best Individual | ---- ------------------------- ------------------------------------------ ---------- Gen Length Fitness Length Fitness OOB Fitness Time Left 0 12.03 0.0967885 4 0.395269 0.413457 2.82m 1 6.91 0.250165 5 0.512841 0.518938 1.67m 2 4.82 0.322645 7 0.600911 0.603908 1.49m 3 5.58 0.365057 12 0.604385 0.606605 1.34m 4 6.48 0.414963 9 0.615257 0.610385 1.35m 5 8.58 0.468763 9 0.628536 0.620473 1.22m 6 11.07 0.508876 15 0.638086 0.641776 1.18m 7 13.61 0.514324 11 0.640324 0.636264 1.12m 8 14.01 0.540671 39 0.656748 0.657258 1.09m 9 15.80 0.544383 39 0.656818 0.656685 1.02m 10 18.48 0.556065 41 0.656622 0.658435 54.90s 11 19.58 0.569885 50 0.66438 0.666361 49.46s 12 20.93 0.58614 39 0.665209 0.665554 46.77s 13 22.09 0.589301 53 0.666591 0.67299 38.94s 14 23.42 0.597276 59 0.666831 0.671305 33.80s 15 25.60 0.600218 65 0.66804 0.668831 28.90s 16 27.89 0.618124 76 0.667599 0.668118 22.03s 17 31.54 0.6234 62 0.668819 0.660909 15.79s 18 33.64 0.631025 55 0.669813 0.669861 7.87s 19 32.85 0.625123 52 0.672489 0.662052 0.00s
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- feature dimensionality reduction
import pandas as pd
from sklearn.decomposition import PCA
traindata = pd.read_csv('pretrain/gptrain.csv')
testdata = pd.read_csv('pretrain/gptest.csv')
transfer = PCA(n_components=64)
pd.DataFrame(
transfer.fit_transform(traindata)
).to_csv('pretrain/pcatrain.csv',index=False)
pd.DataFrame(
transfer.transform(testdata)
).to_csv('pretrain/pcatest.csv',index=False)
- model building
from paddle import nn
import paddle
import paddle.nn.functional as F
class DenseBlock(nn.Layer):
def __init__(self):
super(DenseBlock, self).__init__()
self.fc1 = nn.Linear(96, 64)
self.fc2 = nn.Linear(64, 32)
pass
def forward(self, input, dense):
x = self.fc1(input)
x = self.fc2(x)
x = F.relu(x)
x = paddle.concat([x, dense],axis=1)
return x
pass
class MyNet(nn.Layer):
def __init__(self):
super(MyNet, self).__init__()
self.fc0 = nn.Linear(64, 96)
self.bl1 = DenseBlock()
self.bl2 = DenseBlock()
self.bl3 = DenseBlock()
self.bl4 = DenseBlock()
self.bl5 = DenseBlock()
self.bl6 = DenseBlock()
self.bl7 = DenseBlock()
self.bl8 = DenseBlock()
self.bl9 = DenseBlock()
self.fc4 = nn.Linear(96, 32)
self.fc5 = nn.Linear(32, 1)
pass
def forward(self, input):
x = self.fc0(input)
x = self.bl1(x, input)
x = self.bl2(x, input)
x = self.bl3(x, input)
x = self.bl4(x, input)
x = self.bl5(x, input)
x = self.bl6(x, input)
x = self.bl7(x, input)
x = self.bl8(x, input)
x = self.bl9(x, input)
x = self.fc4(x)
x = self.fc5(x)
return F.sigmoid(x)
pass
paddle.summary(MyNet(),(180000,64))
W0216 17:52:57.241905 5934 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 8.0, Driver API Version: 11.2, Runtime API Version: 11.2
W0216 17:52:57.244976 5934 gpu_resources.cc:91] device: 0, cuDNN Version: 8.2.
-----------------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===================================================================================
Linear-1 [[180000, 64]] [180000, 96] 6,240
Linear-2 [[180000, 96]] [180000, 64] 6,208
Linear-3 [[180000, 64]] [180000, 32] 2,080
DenseBlock-1 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-4 [[180000, 96]] [180000, 64] 6,208
Linear-5 [[180000, 64]] [180000, 32] 2,080
DenseBlock-2 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-6 [[180000, 96]] [180000, 64] 6,208
Linear-7 [[180000, 64]] [180000, 32] 2,080
DenseBlock-3 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-8 [[180000, 96]] [180000, 64] 6,208
Linear-9 [[180000, 64]] [180000, 32] 2,080
DenseBlock-4 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-10 [[180000, 96]] [180000, 64] 6,208
Linear-11 [[180000, 64]] [180000, 32] 2,080
DenseBlock-5 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-12 [[180000, 96]] [180000, 64] 6,208
Linear-13 [[180000, 64]] [180000, 32] 2,080
DenseBlock-6 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-14 [[180000, 96]] [180000, 64] 6,208
Linear-15 [[180000, 64]] [180000, 32] 2,080
DenseBlock-7 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-16 [[180000, 96]] [180000, 64] 6,208
Linear-17 [[180000, 64]] [180000, 32] 2,080
DenseBlock-8 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-18 [[180000, 96]] [180000, 64] 6,208
Linear-19 [[180000, 64]] [180000, 32] 2,080
DenseBlock-9 [[180000, 96], [180000, 64]] [180000, 96] 0
Linear-20 [[180000, 96]] [180000, 32] 3,104
Linear-21 [[180000, 32]] [180000, 1] 33
===================================================================================
Total params: 83,969
Trainable params: 83,969
Non-trainable params: 0
-----------------------------------------------------------------------------------
Input size (MB): 43.95
Forward/backward pass size (MB): 2550.20
Params size (MB): 0.32
Estimated Total Size (MB): 2594.47
-----------------------------------------------------------------------------------
{'total_params': 83969, 'trainable_params': 83969}
- model training
import paddle.nn.functional as F
import paddle
import pandas as pd
import numpy as np
def train_pm(model, optimizer, feature, label, epoches=1):
# Start training on GPU No. 0
paddle.device.set_device('gpu:0')
print('start training ')
model.train()
for epoch in range(epoches):
feature = paddle.to_tensor(feature)
label = paddle.reshape(paddle.to_tensor(label),(-1,1))
# Run the forward calculation of the model to get the predicted value
logits = model(feature)
loss = F.binary_cross_entropy_with_logits(logits, label)
avg_loss = paddle.mean(loss)
# Backpropagation, update weights, clear gradients
avg_loss.backward()
optimizer.step()
optimizer.clear_grad()
model.eval()
accuracies = []
losses = []
# Run the forward calculation of the model to get the predicted value
logits = model(feature)
# Two classification, the result after sigmoid calculation is divided into two categories with 0.5 as the threshold
# Calculate the predicted probability after sigmoid and perform loss calculation
pred = logits
loss = F.binary_cross_entropy_with_logits(logits, label)
# Compute categories with predicted probabilities less than 0.5
pred2 = pred * (-1.0) + 1.0
# Get the predicted probabilities for the two classes and concatenate them along the first dimension
pred = paddle.concat([pred2, pred], axis=1)
acc = paddle.metric.accuracy(pred, paddle.cast(label, dtype='int64'))
accuracies.append(acc.numpy())
losses.append(loss.numpy())
print("[validation] accuracy/loss: {:.4f}/{:.4f}".format(np.mean(accuracies), np.mean(losses)))
model.train()
# paddle.save(model.state_dict(), 'model/lolmp{}_{}.pdparams'.format(epoch,acc.numpy()))
mynet = MyNet()
train_pm(
mynet,
paddle.optimizer.Adam(
parameters=mynet.parameters(),
learning_rate=0.005
),
np.array(
pd.read_csv('pretrain/pcatrain.csv')
).astype('float32'),
np.array(
pd.read_csv('data/train.csv')['win']
).astype('float32'),
400
)
start training [validation] accuracy/loss: 0.5128/0.6676 [validation] accuracy/loss: 0.8079/0.6187 [validation] accuracy/loss: 0.8114/0.5981 [validation] accuracy/loss: 0.8112/0.5925 [validation] accuracy/loss: 0.8110/0.5919 [validation] accuracy/loss: 0.8105/0.5920 [validation] accuracy/loss: 0.8106/0.5913 [validation] accuracy/loss: 0.7105/0.6138 [validation] accuracy/loss: 0.8112/0.5916 [validation] accuracy/loss: 0.8116/0.5929 [validation] accuracy/loss: 0.8118/0.5949 [validation] accuracy/loss: 0.5492/0.7224 [validation] accuracy/loss: 0.8119/0.5934 [validation] accuracy/loss: 0.8120/0.5932 [validation] accuracy/loss: 0.8122/0.5930 [validation] accuracy/loss: 0.8125/0.5928 [validation] accuracy/loss: 0.8127/0.5923 [validation] accuracy/loss: 0.8131/0.5917 [validation] accuracy/loss: 0.8129/0.5909 [validation] accuracy/loss: 0.8123/0.5899 [validation] accuracy/loss: 0.8070/0.5939 [validation] accuracy/loss: 0.8122/0.5894 [validation] accuracy/loss: 0.8127/0.5894 [validation] accuracy/loss: 0.8132/0.5894 [validation] accuracy/loss: 0.8134/0.5892 [validation] accuracy/loss: 0.8135/0.5890 [validation] accuracy/loss: 0.8138/0.5888 [validation] accuracy/loss: 0.8139/0.5885 [validation] accuracy/loss: 0.8141/0.5881 [validation] accuracy/loss: 0.8141/0.5877 [validation] accuracy/loss: 0.8143/0.5874 [validation] accuracy/loss: 0.8134/0.5871 [validation] accuracy/loss: 0.8123/0.5870 [validation] accuracy/loss: 0.8115/0.5866 [validation] accuracy/loss: 0.8118/0.5860 [validation] accuracy/loss: 0.8130/0.5854 [validation] accuracy/loss: 0.8140/0.5849 [validation] accuracy/loss: 0.8143/0.5844 [validation] accuracy/loss: 0.8133/0.5840 [validation] accuracy/loss: 0.8140/0.5835 [validation] accuracy/loss: 0.8165/0.5831 [validation] accuracy/loss: 0.8169/0.5827 [validation] accuracy/loss: 0.8157/0.5824 [validation] accuracy/loss: 0.8185/0.5822 [validation] accuracy/loss: 0.8189/0.5820 [validation] accuracy/loss: 0.8174/0.5819 [validation] accuracy/loss: 0.8191/0.5817 [validation] accuracy/loss: 0.8198/0.5815 [validation] accuracy/loss: 0.8186/0.5814 [validation] accuracy/loss: 0.8202/0.5812 [validation] accuracy/loss: 0.8200/0.5810 [validation] accuracy/loss: 0.8191/0.5809 [validation] accuracy/loss: 0.8207/0.5807 [validation] accuracy/loss: 0.8198/0.5806 [validation] accuracy/loss: 0.8196/0.5805 [validation] accuracy/loss: 0.8213/0.5804 [validation] accuracy/loss: 0.8198/0.5802 [validation] accuracy/loss: 0.8202/0.5800 [validation] accuracy/loss: 0.8220/0.5799 [validation] accuracy/loss: 0.8204/0.5798 [validation] accuracy/loss: 0.8235/0.5797 [validation] accuracy/loss: 0.8219/0.5794 [validation] accuracy/loss: 0.8241/0.5792 [validation] accuracy/loss: 0.8238/0.5791 [validation] accuracy/loss: 0.8248/0.5789 [validation] accuracy/loss: 0.8250/0.5787 [validation] accuracy/loss: 0.8273/0.5786 [validation] accuracy/loss: 0.8191/0.5793 [validation] accuracy/loss: 0.8311/0.5812 [validation] accuracy/loss: 0.8311/0.5816 [validation] accuracy/loss: 0.8311/0.5794 [validation] accuracy/loss: 0.8211/0.5788 [validation] accuracy/loss: 0.8121/0.5805 [validation] accuracy/loss: 0.8299/0.5782 [validation] accuracy/loss: 0.8339/0.5797 [validation] accuracy/loss: 0.8339/0.5799 [validation] accuracy/loss: 0.8332/0.5787 [validation] accuracy/loss: 0.8291/0.5777 [validation] accuracy/loss: 0.8209/0.5783 [validation] accuracy/loss: 0.8206/0.5784 [validation] accuracy/loss: 0.8290/0.5772 [validation] accuracy/loss: 0.8332/0.5775 [validation] accuracy/loss: 0.8350/0.5779 [validation] accuracy/loss: 0.8349/0.5775 [validation] accuracy/loss: 0.8327/0.5767 [validation] accuracy/loss: 0.8278/0.5768 [validation] accuracy/loss: 0.8258/0.5771 [validation] accuracy/loss: 0.8307/0.5765 [validation] accuracy/loss: 0.8343/0.5764 [validation] accuracy/loss: 0.8363/0.5766 [validation] accuracy/loss: 0.8364/0.5764 [validation] accuracy/loss: 0.8348/0.5759 [validation] accuracy/loss: 0.8310/0.5761 [validation] accuracy/loss: 0.8312/0.5760 [validation] accuracy/loss: 0.8349/0.5757 [validation] accuracy/loss: 0.8370/0.5758 [validation] accuracy/loss: 0.8375/0.5757 [validation] accuracy/loss: 0.8356/0.5755 [validation] accuracy/loss: 0.8333/0.5755 [validation] accuracy/loss: 0.8334/0.5754 [validation] accuracy/loss: 0.8361/0.5752 [validation] accuracy/loss: 0.8378/0.5753 [validation] accuracy/loss: 0.8371/0.5751 [validation] accuracy/loss: 0.8358/0.5750 [validation] accuracy/loss: 0.8352/0.5750 [validation] accuracy/loss: 0.8364/0.5749 [validation] accuracy/loss: 0.8378/0.5749 [validation] accuracy/loss: 0.8372/0.5747 [validation] accuracy/loss: 0.8361/0.5747 [validation] accuracy/loss: 0.8367/0.5746 [validation] accuracy/loss: 0.8385/0.5745 [validation] accuracy/loss: 0.8383/0.5744 [validation] accuracy/loss: 0.8373/0.5744 [validation] accuracy/loss: 0.8381/0.5743 [validation] accuracy/loss: 0.8393/0.5742 [validation] accuracy/loss: 0.8384/0.5741 [validation] accuracy/loss: 0.8378/0.5741 [validation] accuracy/loss: 0.8394/0.5740 [validation] accuracy/loss: 0.8397/0.5739 [validation] accuracy/loss: 0.8386/0.5738 [validation] accuracy/loss: 0.8394/0.5738 [validation] accuracy/loss: 0.8402/0.5737 [validation] accuracy/loss: 0.8392/0.5736 [validation] accuracy/loss: 0.8407/0.5735 [validation] accuracy/loss: 0.8401/0.5734 [validation] accuracy/loss: 0.8407/0.5733 [validation] accuracy/loss: 0.8406/0.5732 [validation] accuracy/loss: 0.8417/0.5732 [validation] accuracy/loss: 0.8388/0.5733 [validation] accuracy/loss: 0.8430/0.5739 [validation] accuracy/loss: 0.8332/0.5744 [validation] accuracy/loss: 0.8351/0.5745 [validation] accuracy/loss: 0.8370/0.5742 [validation] accuracy/loss: 0.8367/0.5739 [validation] accuracy/loss: 0.8384/0.5738 [validation] accuracy/loss: 0.8406/0.5738 [validation] accuracy/loss: 0.8399/0.5734 [validation] accuracy/loss: 0.8383/0.5734 [validation] accuracy/loss: 0.8406/0.5732 [validation] accuracy/loss: 0.8421/0.5731 [validation] accuracy/loss: 0.8410/0.5730 [validation] accuracy/loss: 0.8416/0.5729 [validation] accuracy/loss: 0.8422/0.5728 [validation] accuracy/loss: 0.8413/0.5727 [validation] accuracy/loss: 0.8418/0.5725 [validation] accuracy/loss: 0.8440/0.5725 [validation] accuracy/loss: 0.8431/0.5723 [validation] accuracy/loss: 0.8442/0.5721 [validation] accuracy/loss: 0.8448/0.5720 [validation] accuracy/loss: 0.8431/0.5719 [validation] accuracy/loss: 0.8453/0.5717 [validation] accuracy/loss: 0.8445/0.5716 [validation] accuracy/loss: 0.8457/0.5715 [validation] accuracy/loss: 0.8446/0.5715 [validation] accuracy/loss: 0.8464/0.5714 [validation] accuracy/loss: 0.8419/0.5717 [validation] accuracy/loss: 0.8472/0.5727 [validation] accuracy/loss: 0.8378/0.5728 [validation] accuracy/loss: 0.8369/0.5731 [validation] accuracy/loss: 0.8413/0.5727 [validation] accuracy/loss: 0.8431/0.5720 [validation] accuracy/loss: 0.8409/0.5726 [validation] accuracy/loss: 0.8462/0.5720 [validation] accuracy/loss: 0.8439/0.5717 [validation] accuracy/loss: 0.8411/0.5720 [validation] accuracy/loss: 0.8453/0.5713 [validation] accuracy/loss: 0.8469/0.5718 [validation] accuracy/loss: 0.8435/0.5714 [validation] accuracy/loss: 0.8454/0.5711 [validation] accuracy/loss: 0.8461/0.5711 [validation] accuracy/loss: 0.8448/0.5710 [validation] accuracy/loss: 0.8477/0.5709 [validation] accuracy/loss: 0.8467/0.5708 [validation] accuracy/loss: 0.8470/0.5706 [validation] accuracy/loss: 0.8481/0.5706 [validation] accuracy/loss: 0.8463/0.5704 [validation] accuracy/loss: 0.8480/0.5707 [validation] accuracy/loss: 0.8475/0.5703 [validation] accuracy/loss: 0.8477/0.5705 [validation] accuracy/loss: 0.8490/0.5704 [validation] accuracy/loss: 0.8470/0.5700 [validation] accuracy/loss: 0.8485/0.5706 [validation] accuracy/loss: 0.8462/0.5704 [validation] accuracy/loss: 0.8499/0.5700 [validation] accuracy/loss: 0.8472/0.5703 [validation] accuracy/loss: 0.8495/0.5697 [validation] accuracy/loss: 0.8507/0.5697 [validation] accuracy/loss: 0.8455/0.5702 [validation] accuracy/loss: 0.8503/0.5707 [validation] accuracy/loss: 0.8404/0.5715 [validation] accuracy/loss: 0.8451/0.5710 [validation] accuracy/loss: 0.8486/0.5703 [validation] accuracy/loss: 0.8442/0.5710 [validation] accuracy/loss: 0.8505/0.5698 [validation] accuracy/loss: 0.8469/0.5706 [validation] accuracy/loss: 0.8457/0.5706 [validation] accuracy/loss: 0.8503/0.5694 [validation] accuracy/loss: 0.8501/0.5706 [validation] accuracy/loss: 0.8445/0.5704 [validation] accuracy/loss: 0.8462/0.5708 [validation] accuracy/loss: 0.8478/0.5702 [validation] accuracy/loss: 0.8445/0.5703 [validation] accuracy/loss: 0.8507/0.5702 [validation] accuracy/loss: 0.8500/0.5695 [validation] accuracy/loss: 0.8476/0.5698 [validation] accuracy/loss: 0.8501/0.5694 [validation] accuracy/loss: 0.8504/0.5692 [validation] accuracy/loss: 0.8508/0.5691 [validation] accuracy/loss: 0.8508/0.5691 [validation] accuracy/loss: 0.8518/0.5688 [validation] accuracy/loss: 0.8506/0.5690 [validation] accuracy/loss: 0.8520/0.5690 [validation] accuracy/loss: 0.8479/0.5692 [validation] accuracy/loss: 0.8522/0.5695 [validation] accuracy/loss: 0.8473/0.5694 [validation] accuracy/loss: 0.8529/0.5687 [validation] accuracy/loss: 0.8531/0.5684 [validation] accuracy/loss: 0.8503/0.5686 [validation] accuracy/loss: 0.8525/0.5696 [validation] accuracy/loss: 0.8435/0.5703 [validation] accuracy/loss: 0.8494/0.5695 [validation] accuracy/loss: 0.8518/0.5690 [validation] accuracy/loss: 0.8476/0.5698 [validation] accuracy/loss: 0.8525/0.5687 [validation] accuracy/loss: 0.8507/0.5691 [validation] accuracy/loss: 0.8511/0.5686 [validation] accuracy/loss: 0.8526/0.5692 [validation] accuracy/loss: 0.8469/0.5694 [validation] accuracy/loss: 0.8495/0.5698 [validation] accuracy/loss: 0.8510/0.5686 [validation] accuracy/loss: 0.8505/0.5692 [validation] accuracy/loss: 0.8511/0.5687 [validation] accuracy/loss: 0.8515/0.5691 [validation] accuracy/loss: 0.8517/0.5684 [validation] accuracy/loss: 0.8506/0.5689 [validation] accuracy/loss: 0.8525/0.5686 [validation] accuracy/loss: 0.8504/0.5686 [validation] accuracy/loss: 0.8524/0.5681 [validation] accuracy/loss: 0.8538/0.5682 [validation] accuracy/loss: 0.8504/0.5684 [validation] accuracy/loss: 0.8533/0.5687 [validation] accuracy/loss: 0.8527/0.5678 [validation] accuracy/loss: 0.8536/0.5678 [validation] accuracy/loss: 0.8546/0.5678 [validation] accuracy/loss: 0.8502/0.5684 [validation] accuracy/loss: 0.8545/0.5687 [validation] accuracy/loss: 0.8491/0.5686 [validation] accuracy/loss: 0.8549/0.5677 [validation] accuracy/loss: 0.8542/0.5675 [validation] accuracy/loss: 0.8523/0.5679 [validation] accuracy/loss: 0.8553/0.5677 [validation] accuracy/loss: 0.8522/0.5679 [validation] accuracy/loss: 0.8549/0.5675 [validation] accuracy/loss: 0.8538/0.5677 [validation] accuracy/loss: 0.8539/0.5675 [validation] accuracy/loss: 0.8550/0.5673 [validation] accuracy/loss: 0.8533/0.5677 [validation] accuracy/loss: 0.8536/0.5678 [validation] accuracy/loss: 0.8546/0.5673 [validation] accuracy/loss: 0.8540/0.5680 [validation] accuracy/loss: 0.8503/0.5682 [validation] accuracy/loss: 0.8517/0.5697 [validation] accuracy/loss: 0.8543/0.5676 [validation] accuracy/loss: 0.8467/0.5694 [validation] accuracy/loss: 0.8538/0.5685 [validation] accuracy/loss: 0.8511/0.5689 [validation] accuracy/loss: 0.8483/0.5689 [validation] accuracy/loss: 0.8538/0.5683 [validation] accuracy/loss: 0.8530/0.5675 [validation] accuracy/loss: 0.8540/0.5678 [validation] accuracy/loss: 0.8561/0.5671 [validation] accuracy/loss: 0.8517/0.5678 [validation] accuracy/loss: 0.8551/0.5680 [validation] accuracy/loss: 0.8532/0.5676 [validation] accuracy/loss: 0.8517/0.5679 [validation] accuracy/loss: 0.8550/0.5680 [validation] accuracy/loss: 0.8504/0.5680 [validation] accuracy/loss: 0.8553/0.5677 [validation] accuracy/loss: 0.8554/0.5672 [validation] accuracy/loss: 0.8503/0.5680 [validation] accuracy/loss: 0.8548/0.5685 [validation] accuracy/loss: 0.8532/0.5675 [validation] accuracy/loss: 0.8536/0.5672 [validation] accuracy/loss: 0.8553/0.5681 [validation] accuracy/loss: 0.8511/0.5679 [validation] accuracy/loss: 0.8538/0.5675 [validation] accuracy/loss: 0.8565/0.5673 [validation] accuracy/loss: 0.8500/0.5681 [validation] accuracy/loss: 0.8554/0.5670 [validation] accuracy/loss: 0.8559/0.5675 [validation] accuracy/loss: 0.8516/0.5677 [validation] accuracy/loss: 0.8552/0.5674 [validation] accuracy/loss: 0.8550/0.5675 [validation] accuracy/loss: 0.8515/0.5677 [validation] accuracy/loss: 0.8553/0.5672 [validation] accuracy/loss: 0.8567/0.5666 [validation] accuracy/loss: 0.8540/0.5670 [validation] accuracy/loss: 0.8564/0.5668 [validation] accuracy/loss: 0.8570/0.5663 [validation] accuracy/loss: 0.8572/0.5664 [validation] accuracy/loss: 0.8549/0.5667 [validation] accuracy/loss: 0.8567/0.5663 [validation] accuracy/loss: 0.8568/0.5666 [validation] accuracy/loss: 0.8566/0.5663 [validation] accuracy/loss: 0.8573/0.5662 [validation] accuracy/loss: 0.8569/0.5663 [validation] accuracy/loss: 0.8541/0.5673 [validation] accuracy/loss: 0.8527/0.5676 [validation] accuracy/loss: 0.8570/0.5668 [validation] accuracy/loss: 0.8532/0.5673 [validation] accuracy/loss: 0.8568/0.5662 [validation] accuracy/loss: 0.8566/0.5672 [validation] accuracy/loss: 0.8520/0.5675 [validation] accuracy/loss: 0.8538/0.5679 [validation] accuracy/loss: 0.8557/0.5669 [validation] accuracy/loss: 0.8565/0.5666 [validation] accuracy/loss: 0.8527/0.5682 [validation] accuracy/loss: 0.8558/0.5671 [validation] accuracy/loss: 0.8520/0.5686 [validation] accuracy/loss: 0.8507/0.5681 [validation] accuracy/loss: 0.8507/0.5688 [validation] accuracy/loss: 0.8528/0.5677 [validation] accuracy/loss: 0.8528/0.5677 [validation] accuracy/loss: 0.8557/0.5672 [validation] accuracy/loss: 0.8543/0.5674 [validation] accuracy/loss: 0.8536/0.5672 [validation] accuracy/loss: 0.8563/0.5669 [validation] accuracy/loss: 0.8567/0.5664 [validation] accuracy/loss: 0.8558/0.5667 [validation] accuracy/loss: 0.8570/0.5661 [validation] accuracy/loss: 0.8568/0.5663 [validation] accuracy/loss: 0.8563/0.5664 [validation] accuracy/loss: 0.8569/0.5662 [validation] accuracy/loss: 0.8568/0.5663 [validation] accuracy/loss: 0.8582/0.5658 [validation] accuracy/loss: 0.8569/0.5659 [validation] accuracy/loss: 0.8589/0.5659 [validation] accuracy/loss: 0.8555/0.5662 [validation] accuracy/loss: 0.8587/0.5663 [validation] accuracy/loss: 0.8537/0.5667 [validation] accuracy/loss: 0.8588/0.5659 [validation] accuracy/loss: 0.8568/0.5659 [validation] accuracy/loss: 0.8586/0.5655 [validation] accuracy/loss: 0.8594/0.5657 [validation] accuracy/loss: 0.8516/0.5673 [validation] accuracy/loss: 0.8557/0.5674 [validation] accuracy/loss: 0.8584/0.5661 [validation] accuracy/loss: 0.8503/0.5677 [validation] accuracy/loss: 0.8581/0.5661 [validation] accuracy/loss: 0.8589/0.5658 [validation] accuracy/loss: 0.8538/0.5666 [validation] accuracy/loss: 0.8590/0.5658 [validation] accuracy/loss: 0.8578/0.5659 [validation] accuracy/loss: 0.8573/0.5656 [validation] accuracy/loss: 0.8582/0.5666 [validation] accuracy/loss: 0.8529/0.5669 [validation] accuracy/loss: 0.8561/0.5666 [validation] accuracy/loss: 0.8583/0.5659 [validation] accuracy/loss: 0.8563/0.5660 [validation] accuracy/loss: 0.8580/0.5664 [validation] accuracy/loss: 0.8584/0.5655 [validation] accuracy/loss: 0.8578/0.5658 [validation] accuracy/loss: 0.8573/0.5664 [validation] accuracy/loss: 0.8574/0.5657 [validation] accuracy/loss: 0.8583/0.5660 [validation] accuracy/loss: 0.8571/0.5657 [validation] accuracy/loss: 0.8586/0.5659 [validation] accuracy/loss: 0.8563/0.5663 [validation] accuracy/loss: 0.8588/0.5654 [validation] accuracy/loss: 0.8584/0.5657 [validation] accuracy/loss: 0.8569/0.5656 [validation] accuracy/loss: 0.8586/0.5666 [validation] accuracy/loss: 0.8537/0.5667 [validation] accuracy/loss: 0.8567/0.5662 [validation] accuracy/loss: 0.8584/0.5656 [validation] accuracy/loss: 0.8570/0.5658 [validation] accuracy/loss: 0.8590/0.5655 [validation] accuracy/loss: 0.8581/0.5656 [validation] accuracy/loss: 0.8586/0.5655 [validation] accuracy/loss: 0.8604/0.5651 [validation] accuracy/loss: 0.8559/0.5658 [validation] accuracy/loss: 0.8593/0.5657 [validation] accuracy/loss: 0.8582/0.5654 [validation] accuracy/loss: 0.8598/0.5649 [validation] accuracy/loss: 0.8598/0.5655 [validation] accuracy/loss: 0.8570/0.5655 [validation] accuracy/loss: 0.8599/0.5655 [validation] accuracy/loss: 0.8565/0.5655 [validation] accuracy/loss: 0.8604/0.5651 [validation] accuracy/loss: 0.8595/0.5653 [validation] accuracy/loss: 0.8584/0.5653 [validation] accuracy/loss: 0.8602/0.5651 [validation] accuracy/loss: 0.8564/0.5659 [validation] accuracy/loss: 0.8594/0.5649 [validation] accuracy/loss: 0.8596/0.5654 [validation] accuracy/loss: 0.8540/0.5665 [validation] accuracy/loss: 0.8599/0.5656 [validation] accuracy/loss: 0.8580/0.5659 [validation] accuracy/loss: 0.8548/0.5660 [validation] accuracy/loss: 0.8591/0.5663
- forecast result
pd.DataFrame(
np.where(
mynet(
paddle.to_tensor(
np.array(
pd.read_csv('pretrain/pcatest.csv')
).astype('float32')
)
).numpy() > 0.5, 1, 0
),
columns=['win']
).to_csv('submission.csv',index=False)
!zip result/submission.zip submission.csv !rm submission.csv
adding: submission.csv (deflated 90%)
[validation] accuracy/loss: 0.8599/0.5656
[validation] accuracy/loss: 0.8580/0.5659
[validation] accuracy/loss: 0.8548/0.5660
[validation] accuracy/loss: 0.8591/0.5663
- forecast result
pd.DataFrame(
np.where(
mynet(
paddle.to_tensor(
np.array(
pd.read_csv('pretrain/pcatest.csv')
).astype('float32')
)
).numpy() > 0.5, 1, 0
),
columns=['win']
).to_csv('submission.csv',index=False)
!zip result/submission.zip submission.csv !rm submission.csv
adding: submission.csv (deflated 90%)
project summary
In fact, the project still has a lot of room for improvement
-
The model can use Network-in-Network to further optimize the network structure
-
Although 180,000 samples can be trained at one time, it is still possible to improve the number of training batches
-
The project does not have a clear training set/test set division, you can try to use cross-validation to reduce overfitting