biblioteki_dl

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szymonj98 2022-04-24 12:25:00 +02:00
parent 8863f36ca8
commit ea5a76edbc
2 changed files with 123 additions and 1 deletions

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@ -7,5 +7,6 @@ RUN mkdir /.kaggle && chmod o+w /.kaggle
RUN pip3 install pandas
RUN pip3 install numpy
RUN pip3 install sklearn
RUN pip3 install tensorflow
COPY ./steam-200k.csv ./
COPY ./kagle.py ./
COPY ./biblioteki_dl.py ./

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biblioteki_dl.py Normal file
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@ -0,0 +1,121 @@
import tensorflow as tf
import os
import pandas as pd
import numpy as np
import csv
from sklearn.model_selection import train_test_split
os.system("kaggle datasets download -d tamber/steam-video-games")
os.system("unzip -o steam-video-games.zip")
steam=pd.read_csv('steam-200k.csv',usecols=[0,1,2,3],names=['userId','game','behavior','hoursPlayed'])
steam.isnull().values.any()
steam['userId'] = steam.userId.astype(str)
purchaseCount = steam[steam["behavior"] != "play"]["game"].value_counts()
playCount = steam[steam["behavior"] != "purchase"]["game"].value_counts()
playerPurchaseCount = steam[steam["behavior"] != "play"]["userId"].value_counts()
playerPlayCount = steam[steam["behavior"] != "purchase"]["userId"].value_counts()
steam = steam[steam['behavior'] != 'purchase']
steam = steam.groupby("game").filter(lambda x: len(x)>10)
size=int(len(steam)/10)
meanGame = steam[steam["behavior"] != "purchase"].groupby("game").mean()
meanGame = meanGame.to_dict()
meanGame = meanGame['hoursPlayed']
purchaseCount = purchaseCount.to_dict()
playCount = playCount.to_dict()
playerPurchaseCount = playerPurchaseCount.to_dict()
playerPlayCount = playerPlayCount.to_dict()
steam['meanTime'] = 0;
steam['purchaseCount'] = 0;
steam['playCount'] = 0;
steam['playerPurchaseCount'] =0;
steam['playerPlayCount'] =0;
steam['playPercent'] =0;
for i in steam.index:
steam.at[i,'meanTime'] = meanGame[steam.at[i,'game']]
steam.at[i,'purchaseCount'] = purchaseCount[steam.at[i,'game']]
steam.at[i,'playCount'] = playCount[steam.at[i,'game']]
steam.at[i,'playerPurchaseCount'] = playerPurchaseCount[steam.at[i,'userId']]
steam.at[i,'playerPlayCount'] = playerPlayCount[steam.at[i,'userId']]
steam.at[i,'playPercent'] = playerPlayCount[steam.at[i,'userId']]/playerPurchaseCount[steam.at[i,'userId']]
steam_train, steam_test = train_test_split(steam, test_size=size, random_state=1, stratify=steam["game"])
steam_train, steam_dev = train_test_split(steam_train, test_size=size, random_state=1, stratify=steam_train["game"])
print(steam)
games = {}
for i in steam['game']:
games[i] = 0
j=0
for key,game in games.items():
games[key]=j
j=j+1
for i in steam['game']:
i = games[i]
invGames = {v: k for k, v in games.items()}
x_train = steam_train[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
y_train = steam_train['game']
x_test = steam_test[['hoursPlayed','purchaseCount','playCount','playerPlayCount','playerPurchaseCount']]
y_test = steam_test['game']
x_train = np.array(x_train)
y_train = np.array(y_train)
x_test = np.array(x_test)
y_test = np.array(y_test)
for i,j in enumerate(y_train):
y_train[i] = games[j]
for i,j in enumerate(y_test):
y_test[i] = games[j]
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(5,1)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dropout(0.01),
tf.keras.layers.Dense(1000, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
y_train = np.array(y_train).astype(np.float32)
y_test = np.array(y_test).astype(np.float32)
model.fit(x_train, y_train, epochs=100)
model.evaluate(x_test, y_test)
prediction = model.predict(x_test)
classes_x=np.argmax(prediction,axis=1)
rows = []
for j,i in enumerate(classes_x):
row = [invGames[i],invGames[y_test[j]]]
rows.append(row)
with open('results.csv','w',encoding='UTF-8',newline='') as f:
writer = csv.writer(f)
writer.writerow(["predicted", "expected"])
for row in rows:
writer.writerow(row)