mlproject

Jenkinsfile_train

@@ -31,7 +31,7 @@ pipeline {
                 steps
                 {
                     catchError {
-                        sh 'python3.8 Zadanie_06_training.py ${BATCH_SIZE} ${EPOCHS}'
+                        sh 'python3.8 Zadanie_06_and_07_training.py ${BATCH_SIZE} ${EPOCHS}'
                     }
                 }
             }

MLproject

@@ -0,0 +1,11 @@
+name: 434788-mlflow
+
+docker_env:
+    image: snowycocoon/ium_434788:4
+
+entry_points:
+    main:
+        parameters:
+            batch_size: {type: int, default: 16}
+            epochs: {type: int, default: 15}
+        command: "python3 Zadanie_08_MLflow.py {batch_size} {epochs}"

Zadanie_08_MLflow.py

@@ -0,0 +1,73 @@
+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 numpy as np
+import sys
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from tensorflow.keras.optimizers import Adam
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+from datetime import datetime
+import os
+import sys
+import mlflow
+
+with mlflow.start_run():
+
+    batch_param = int(sys.argv[1]) if len(sys.argv) > 1 else 16
+    epoch_param = int(sys.argv[2]) if len(sys.argv) > 2 else 5
+
+    mlflow.log_param("batch_size: ", batch_param)
+    mlflow.log_param("epochs: ", epoch_param)
+
+    wine=pd.read_csv('train.csv')
+
+    y = wine['quality']
+    x = wine.drop('quality', axis=1)
+
+    citricacid = x['fixed acidity'] * x['citric acid']
+    citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
+
+    density_acidity = x['fixed acidity'] * x['density']
+    density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
+
+
+    x = wine.join(citric_acidity).join(density_acidity)
+
+    bins = (2, 5, 8)
+    gnames = ['bad', 'nice']
+    y = pd.cut(y, bins = bins, labels = gnames)
+
+    enc = LabelEncoder()
+    yenc = enc.fit_transform(y)
+
+    scale = StandardScaler()
+    scaled_x = scale.fit_transform(x)
+
+    NeuralModel = Sequential([
+                            Dense(128, activation='relu', input_shape=(14,)),
+                            Dense(32, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(64, activation='relu'),
+                            Dense(1, activation='sigmoid')
+    ])
+
+    rms = Adam(lr=0.0003)
+
+    NeuralModel.compile(optimizer=rms, loss='binary_crossentropy', metrics=['accuracy'])
+
+    NeuralModel.fit(scaled_x, yenc, batch_size= batch_param, epochs = epoch_param) #verbose = 1
+
+    NeuralModel.save('wine_model.h5')
+
+    #TO TYLKO NA POTRZEBY ZADANIA
+    y_pred = NeuralModel.predict(scaled_x)
+
+    y_pred = np.around(y_pred, decimals=0)
+
+    results = accuracy_score(yenc,y_pred)
+
+    mlflow.log_metric("Accuracy: ", results)