Zajęcia 7 z Sacred (Zadanie 2 nie jest skończone - awaria Jenkinsa)

Dockerfile

@@ -13,6 +13,7 @@ RUN pip3 install --user pandas
 RUN pip3 install --user numpy
 RUN pip3 install --user matplotlib
 RUN pip3 install --user tensorflow
+RUN pip3 install --user sacred
 
 
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)

Zajęcia4_oraz_5/Dockerfile

@@ -13,6 +13,7 @@ RUN pip3 install --user pandas
 RUN pip3 install --user numpy
 RUN pip3 install --user matplotlib
 RUN pip3 install --user tensorflow
+RUN pip3 install --user sacred
 
 
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)

Zajęcia7/Zadanie_1_Sacred.py

@@ -0,0 +1,76 @@
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+
+ex = Experiment("file_observer", interactive=True)
+
+ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+
+    url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+    wget.download(url, out='Zajęcia7/winequality-red.csv', bar=None)
+
+    wine=pd.read_csv('Zajęcia7/winequality-red.csv')
+    wine
+
+    y = wine.quality
+    y.head()
+
+    x = wine.drop(['quality'], axis= 1)
+    x.head()
+
+    x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=test_size_param, train_size=train_size_param, random_state=21)
+
+    def regression_model():
+        model = Sequential()
+        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+        model.add(Dense(64,activation = "relu"))
+        model.add(Dense(1,activation = "relu"))
+        
+        model.compile(optimizer = "adam", loss = "mean_squared_error")
+        return model
+
+    model = regression_model()
+    model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+    model.save('Zajęcia7/saved_model')
+
+    y_pred = model.predict(x_test)
+
+    y_pred[:5]
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    y_pred[:5]
+
+    print(accuracy_score(y_test, y_pred))
+
+    _run.info["Final Results: "] = classification_report(y_test,y_pred)
+
+    return(classification_report(y_test,y_pred))
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model()) ## Nie musimy przekazywać wartości
+
+
+r = ex.run()
+ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")

Zajęcia7/Zadanie_2_Sacred.py

@@ -0,0 +1,84 @@
+'''
+Zadanie na dzień 09.05.2021 nie jest możliwe do skończenia bez dostępu do Jenkinsa!
+'''
+
+
+
+
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+from sacred.observers import MongoObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+
+ex = Experiment("sacred_scopes", interactive=True)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
+                                  db_name='sacred'))  # Tutaj podajemy dane uwierzytelniające i nazwę bazy skonfigurowane w pliku .env podczas uruchamiania bazy.
+# W przypadku instancji na Jenkinsie url będzie wyglądał następująco: mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+
+    url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+    wget.download(url, out='Zajęcia7/winequality-red.csv', bar=None)
+
+    wine=pd.read_csv('Zajęcia7/winequality-red.csv')
+    wine
+
+    y = wine.quality
+    y.head()
+
+    x = wine.drop(['quality'], axis= 1)
+    x.head()
+
+    x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=test_size_param, train_size=train_size_param, random_state=21)
+
+    def regression_model():
+        model = Sequential()
+        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+        model.add(Dense(64,activation = "relu"))
+        model.add(Dense(1,activation = "relu"))
+        
+        model.compile(optimizer = "adam", loss = "mean_squared_error")
+        return model
+
+    model = regression_model()
+    model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+    model.save('Zajęcia7/saved_model')
+
+    y_pred = model.predict(x_test)
+
+    y_pred[:5]
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    y_pred[:5]
+
+    print(accuracy_score(y_test, y_pred))
+
+    _run.info["Final Results: "] = classification_report(y_test,y_pred)
+
+    return(classification_report(y_test,y_pred))
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model()) ## Nie musimy przekazywać wartości
+
+
+r = ex.run()
+ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")

Zajęcia7/my_runs/1/config.json

@@ -0,0 +1,5 @@
+{
+  "seed": 93742377,
+  "test_size_param": 0.2,
+  "train_size_param": 0.8
+}

Zajęcia7/my_runs/1/info.json

@@ -0,0 +1,4 @@
+{
+  "Final Results: ": "              precision    recall  f1-score   support\n\n           3       0.00      0.00      0.00         1\n           4       0.00      0.00      0.00        16\n           5       0.69      0.65      0.67       127\n           6       0.58      0.69      0.63       131\n           7       0.56      0.52      0.54        42\n           8       0.00      0.00      0.00         3\n\n    accuracy                           0.61       320\n   macro avg       0.30      0.31      0.31       320\nweighted avg       0.58      0.61      0.60       320\n",
+  "prepare_model_ts": "2021-05-09 23:25:48.528529"
+}

Zajęcia7/my_runs/1/metrics.json

@@ -0,0 +1 @@
+{}

Zajęcia7/my_runs/1/run.json

@@ -0,0 +1,82 @@
+{
+  "artifacts": [
+    "saved_model.pb"
+  ],
+  "command": "my_main",
+  "experiment": {
+    "base_dir": "c:\\Users\\domstr2\\Desktop\\Git Repositories\\ium_434788\\Zaj\u0119cia7",
+    "dependencies": [
+      "numpy==1.19.2",
+      "pandas==1.1.3",
+      "sacred==0.8.2",
+      "scikit-learn==0.23.2",
+      "tensorflow==2.4.1",
+      "wget==3.2"
+    ],
+    "mainfile": "Zadanie_1_Sacred.py",
+    "name": "file_observer",
+    "repositories": [
+      {
+        "commit": "fcc6e77ef0297c583ccde2a4eb5924839a8f2f09",
+        "dirty": true,
+        "url": "https://git.wmi.amu.edu.pl/s434788/ium_434788.git"
+      }
+    ],
+    "sources": [
+      [
+        "Zadanie_1_Sacred.py",
+        "_sources\\Zadanie_1_Sacred_30ef87dbd210931ef4b8384e66e7736f.py"
+      ]
+    ]
+  },
+  "heartbeat": "2021-05-09T21:26:06.005402",
+  "host": {
+    "ENV": {},
+    "cpu": "Unknown",
+    "gpus": {
+      "driver_version": "465.89",
+      "gpus": [
+        {
+          "model": "NVIDIA GeForce GTX 970",
+          "persistence_mode": false,
+          "total_memory": 4096
+        }
+      ]
+    },
+    "hostname": "DESKTOP-1NBQAAH",
+    "os": [
+      "Windows",
+      "Windows-10-10.0.19041-SP0"
+    ],
+    "python_version": "3.8.5"
+  },
+  "meta": {
+    "command": "my_main",
+    "options": {
+      "--beat-interval": null,
+      "--capture": null,
+      "--comment": null,
+      "--debug": false,
+      "--enforce_clean": false,
+      "--file_storage": null,
+      "--force": false,
+      "--help": false,
+      "--loglevel": null,
+      "--mongo_db": null,
+      "--name": null,
+      "--pdb": false,
+      "--print-config": false,
+      "--priority": null,
+      "--queue": false,
+      "--s3": null,
+      "--sql": null,
+      "--tiny_db": null,
+      "--unobserved": false
+    }
+  },
+  "resources": [],
+  "result": null,
+  "start_time": "2021-05-09T21:25:48.516529",
+  "status": "COMPLETED",
+  "stop_time": "2021-05-09T21:26:06.004401"
+}

Zajęcia7/my_runs/_sources/Zadanie_1_Sacred_30ef87dbd210931ef4b8384e66e7736f.py

@@ -0,0 +1,76 @@
+from tensorflow.keras.models import Sequential, load_model
+from tensorflow.keras.layers import Dense
+from sklearn.metrics import accuracy_score, classification_report
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import wget
+import numpy as np
+from sacred.observers import FileStorageObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+
+ex = Experiment("file_observer", interactive=True)
+
+ex.observers.append(FileStorageObserver('Zajęcia7/my_runs'))
+
+@ex.config
+def my_config():
+    train_size_param = 0.8
+    test_size_param = 0.2
+
+@ex.capture
+def prepare_model(train_size_param, test_size_param, _run):
+    _run.info["prepare_model_ts"] = str(datetime.now())
+
+    url = 'https://git.wmi.amu.edu.pl/s434788/ium_434788/raw/branch/master/winequality-red.csv'
+    wget.download(url, out='Zajęcia7/winequality-red.csv', bar=None)
+
+    wine=pd.read_csv('Zajęcia7/winequality-red.csv')
+    wine
+
+    y = wine.quality
+    y.head()
+
+    x = wine.drop(['quality'], axis= 1)
+    x.head()
+
+    x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+
+    x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=test_size_param, train_size=train_size_param, random_state=21)
+
+    def regression_model():
+        model = Sequential()
+        model.add(Dense(32,activation = "relu", input_shape = (x_train.shape[1],)))
+        model.add(Dense(64,activation = "relu"))
+        model.add(Dense(1,activation = "relu"))
+        
+        model.compile(optimizer = "adam", loss = "mean_squared_error")
+        return model
+
+    model = regression_model()
+    model.fit(x_train, y_train, epochs = 600, verbose = 1)
+
+    model.save('Zajęcia7/saved_model')
+
+    y_pred = model.predict(x_test)
+
+    y_pred[:5]
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    y_pred[:5]
+
+    print(accuracy_score(y_test, y_pred))
+
+    _run.info["Final Results: "] = classification_report(y_test,y_pred)
+
+    return(classification_report(y_test,y_pred))
+
+@ex.main
+def my_main(train_size_param, test_size_param):
+    print(prepare_model()) ## Nie musimy przekazywać wartości
+
+
+r = ex.run()
+ex.add_artifact("Zajęcia7/saved_model/saved_model.pb")