zadania z dvc

.dvc/config

@@ -0,0 +1,4 @@
+[core]
+    remote = ium_ssh_remote
+['remote "ium_ssh_remote"']
+    url = ssh://ium-sftp@tzietkiewicz.vm.wmi.amu.edu.pl/ium-sftp

.gitignore

@@ -0,0 +1,4 @@
+/vgsales.csv
+/10_x.csv
+/10_y.csv
+/vgsales_model_dvc.h5

DVC_prepare.py

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+import pandas as pd
+
+vgsales=pd.read_csv('vgsales.csv')
+
+vgsales['Nintendo'] = vgsales['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
+
+Y = vgsales['Nintendo']
+X = vgsales.drop(['Rank','Name','Platform','Year','Genre','Publisher','Nintendo'],axis = 1)
+
+
+X.to_csv(r'10_x.csv', index=False)
+Y.to_csv(r'10_y.csv', index=False)

DVC_train.py

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+import sys
+from tensorflow.keras.backend import batch_dot, mean
+import pandas as pd
+import numpy as np
+from six import int2byte
+from sklearn import preprocessing
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import train_test_split
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.layers import Input, Dense, Activation,Dropout
+from tensorflow.keras.models import Model
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.models import Sequential
+
+
+X=pd.read_csv('10_x.csv')
+Y=pd.read_csv('10_y.csv')
+
+X_train, X_test, y_train, y_test = train_test_split(X,Y , test_size=0.2,train_size=0.8, random_state=21)
+
+model = Sequential()
+model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
+model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
+
+early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
+
+
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+
+model.fit(X_train, y_train, epochs=15, batch_size=16, validation_data=(X_test, y_test))
+
+
+prediction = model.predict(X_test)
+
+
+rmse = mean_squared_error(y_test, prediction)
+
+
+model.save('vgsales_model_dvc.h5')

dvc.lock

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+schema: '2.0'
+stages:
+  prepare:
+    cmd: python3 DVC_prepare.py
+    deps:
+    - path: DVC_prepare.py
+      md5: 4a18290ac1a5c889a63ff2dee652dcef
+      size: 331
+    - path: vgsales.csv
+      md5: 67fa6f5efdc100db4586aa80556e8620
+      size: 1355781
+    outs:
+    - path: 10_x.csv
+      md5: ac06118118e3e8dd519820c78d1ec868
+      size: 384424
+    - path: 10_y.csv
+      md5: e1543e161f0da6d5887c8c7baf0210c7
+      size: 33205
+  train:
+    cmd: python3 DVC_train.py
+    deps:
+    - path: 10_x.csv
+      md5: ac06118118e3e8dd519820c78d1ec868
+      size: 384424
+    - path: 10_y.csv
+      md5: e1543e161f0da6d5887c8c7baf0210c7
+      size: 33205
+    - path: DVC_train.py
+      md5: 5650806d29bdf745ee046411d3b75f1e
+      size: 1305
+    outs:
+    - path: vgsales_model_dvc.h5
+      md5: a2c4dae4a46d3554654cee5b4e4c42c7
+      size: 25512

dvc.yaml

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+stages:
+  prepare:
+    cmd: python3 DVC_prepare.py
+    deps:
+    - DVC_prepare.py
+    - vgsales.csv
+    outs:
+    - 10_x.csv
+    - 10_y.csv
+  train:
+    cmd: python3 DVC_train.py
+    deps:
+    - 10_x.csv
+    - 10_y.csv
+    - DVC_train.py
+    outs:
+    - vgsales_model_dvc.h5

vgsales.csv.dvc

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+outs:
+- md5: 67fa6f5efdc100db4586aa80556e8620
+  size: 1355781
+  path: vgsales.csv