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MLproject

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+name: MLflow
+
+
+docker_env:
+  image: szymonjadczak/mlflow:latest
+
+entry_points:
+  main:
+    parameters:
+      epochs: {type: float, default: 3}
+    command: "python ./biblioteki_dl.py {epochs}"

mlflow.py

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+import tensorflow as tf
+import os
+import pandas as pd
+import numpy as np
+import csv
+from sklearn.model_selection import train_test_split
+import sys
+import mlflow
+
+# os.system("kaggle datasets download -d tamber/steam-video-games")
+# os.system("unzip -o steam-video-games.zip")
+
+with mlflow.start_run():
+    epoch = int(sys.argv[1])
+
+    steam=pd.read_csv('data.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)
+
+    with open('xtest.csv','w',encoding='UTF-8',newline='') as xtest:
+        writer = csv.writer(xtest)
+        for i in x_test:
+            writer.writerow(i)
+        
+    for i,j in enumerate(y_train):
+        y_train[i] = games[j]
+        
+    for i,j in enumerate(y_test):
+        y_test[i] = games[j]
+
+    np.savetxt("ytest.csv",y_test,delimiter=",",fmt='%d')
+
+
+    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')
+    ])
+    mlflow.log_param("layers dense relu",256)
+    mlflow.log_param("layers droout",0.01)
+    mlflow.log_param("layers dense softmax",1000)
+    mlflow.log_param("iterations",epoch)
+
+
+    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=epoch)
+    eval = model.evaluate(x_test, y_test)
+    
+    mlflow.log_metric("accuraccy", eval[1])
+    
+    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)
+    
+    model.save('./model')