super wyniki ewaluacji

Zadanie_06_evaluate.py

@@ -1,25 +1,48 @@
 import pandas as pd
 import numpy as np
-from os import path
 from tensorflow import keras
-import sys
 import matplotlib.pyplot as plt
-from sklearn.metrics import accuracy_score, classification_report
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import StandardScaler, LabelEncoder
 
 model = keras.models.load_model('wine_model.h5')
 print('evaluating')
 
-test_df =pd.read_csv('test.csv')
+data =pd.read_csv('test.csv')
-y_test = test_df.quality
-x_test = test_df.drop(['quality'], axis= 1)
 
-x_test=((x_test-x_test.min())/(x_test.max()-x_test.min())) #Normalizacja
+#########################################
 
-y_pred = model.predict(x_test)
+y = data['quality']
+x = data.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 = data.join(citric_acidity).join(density_acidity)
+
+print(y)
+
+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)
+
+##################################
+
+y_pred = model.predict(scaled_x)
 
 y_pred = np.around(y_pred, decimals=0)
 
-results = accuracy_score(y_test,y_pred)
+results = accuracy_score(yenc,y_pred)
 with open('results.txt', 'a+', encoding="UTF-8") as f:
     f.write(str(results) +"\n")
 

Zadanie_06_training.py

@@ -4,33 +4,47 @@ 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
 
 
 wine=pd.read_csv('train.csv')
-wine
 
-y = wine.quality
+y = wine['quality']
-y.head()
+x = wine.drop('quality', axis=1)
 
-x = wine.drop(['quality'], axis= 1)
+citricacid = x['fixed acidity'] * x['citric acid']
-x.head()
+citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
 
-x=((x-x.min())/(x.max()-x.min())) #Normalizacja
+density_acidity = x['fixed acidity'] * x['density']
+density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
 
-x_train, x_test, y_train, y_test = train_test_split(x,y , test_size=0.2,train_size=0.8, random_state=21)
 
-def regression_model():
+x = wine.join(citric_acidity).join(density_acidity)
-    model = Sequential()
-    model.add(Dense(4,activation = "relu", input_shape = (x_train.shape[1],)))
-    model.add(Dense(8,activation = "relu"))
-    model.add(Dense(8,activation = "relu"))
-    model.add(Dense(1,activation = "relu"))
 
-    model.compile(optimizer = "adam", loss = "mean_squared_error")
+bins = (2, 5, 8)
-    return model
+gnames = ['bad', 'nice']
+y = pd.cut(y, bins = bins, labels = gnames)
 
-model = regression_model()
+enc = LabelEncoder()
-model.fit(x_train, y_train, batch_size=int(sys.argv[1]), epochs = int(sys.argv[1])) #verbose = 1
+yenc = enc.fit_transform(y)
 
-model.save('wine_model.h5')
+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, epochs=15) #verbose = 1
+
+NeuralModel.save('wine_model.h5')