added evaluation

lab05_deepLearning.py

@@ -6,7 +6,7 @@ from torch import nn
 from torch.autograd import Variable
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+from sklearn.metrics import accuracy_score, f1_score
 import torch.nn.functional as F
 import pandas as pd
 from sklearn import preprocessing
@@ -33,28 +33,31 @@ def load_dataset_raw():
     return cars
 
 
-def remove_rows(dataset):
-    # dataset.drop(dataset[dataset['mark'] == 'alfa-romeo'].index, inplace=True)
-    # dataset.drop(dataset[dataset['mark'] == 'chevrolet'].index, inplace=True)
-    # dataset.drop(dataset[dataset['mark'] == 'mitsubishi'].index, inplace=True)
-    # dataset.drop(dataset[dataset['mark'] == 'mini'].index, inplace=True)
-    # audi bmw ford opel volkswagen
+def load_dataset_files():
+    """ Load shuffled, splitted dev and train files from .csv files. """
 
-    new_data = dataset.loc[(dataset['mark'] == 'audi') | (dataset['mark'] == 'bmw') | (dataset['mark'] == 'ford') | (dataset['mark'] == 'opel') | (dataset['mark'] == 'volkswagen')]
-    return new_data
-    # dataset = dataset.drop(dataset)
-    # return dataset
+    cars_dev = pd.read_csv('./Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
+    cars_train = pd.read_csv('./Car_Prices_Poland_Kaggle_train.csv', usecols=[1, 4, 5, 6, 10], sep=',', names= [str(i) for i in range(5)])
+
+    return cars_dev, cars_train
 
 
-def prepare_dataset_raw(dataset):
+def remove_rows(data_dev, data_train):
+    dev_removed_rows = data_dev.loc[(data_dev['0'] == 'audi') | (data_dev['0'] == 'bmw') | (data_dev['0'] == 'ford') | (data_dev['0'] == 'opel') | (data_dev['0'] == 'volkswagen')]
+    train_removed_rows = data_train.loc[(data_train['0'] == 'audi') | (data_train['0'] == 'bmw') | (data_train['0'] == 'ford') | (data_train['0'] == 'opel') | (data_train['0'] == 'volkswagen')]
+
+    return dev_removed_rows, train_removed_rows
+
+
+def prepare_labels_features(dataset):
     """ Label make column"""
     le = preprocessing.LabelEncoder()
-    mark_column = np.array(dataset[:]['mark'])
+    mark_column = np.array(dataset[:]['0'])
     le.fit(mark_column)
 
     print(list(le.classes_))
     lab = le.transform(mark_column)
-    feat = dataset.drop(['mark'], axis=1).to_numpy()
+    feat = dataset.drop(['0'], axis=1).to_numpy()
 
     mm_scaler = preprocessing.MinMaxScaler()
     feat = mm_scaler.fit_transform(feat)
@@ -62,6 +65,9 @@ def prepare_dataset_raw(dataset):
     return lab, feat
 
 
+
+
+
 # def draw_plot(lbl):
 # need to import matplotlib to work
 #     plt.hist(lbl, bins=[i for i in range(len(set(lbl)))], edgecolor="black")
@@ -70,17 +76,15 @@ def prepare_dataset_raw(dataset):
 
 # Prepare dataset
 print("Loading dataset...")
-dataset = load_dataset_raw()
+dev, train = load_dataset_files()
 print("Dataset loaded")
 
 print("Preparing dataset...")
-dataset = remove_rows(dataset)
-labels, features = prepare_dataset_raw(dataset)
+dev, train = remove_rows(dev, train)
+labels_train, features_train = prepare_labels_features(train)
+labels_test, features_test = prepare_labels_features(dev)
 print("Dataset prepared")
 
-
-features_train, features_test, labels_train, labels_test = train_test_split(features, labels, random_state=42,
-                                                                            shuffle=True)
 # Training
 model = Model(features_train.shape[1])
 optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
@@ -92,7 +96,7 @@ try:
 except Exception as e:
     print(e)
     print("Setting default epochs value to 1000.")
-    epochs = 1000
+    epochs = 100
 
 print(f"Number of epochs: {epochs}")
 
@@ -113,7 +117,8 @@ print("Model training finished")
 x_test = Variable(torch.from_numpy(features_test)).float()
 pred = model(x_test)
 pred = pred.detach().numpy()
-print("The accuracy is", accuracy_score(labels_test, np.argmax(pred, axis=1)))
+print(f"The accuracy metric is: {accuracy_score(labels_test, np.argmax(pred, axis=1))}")
+
 
 # Checking for first value
 # print(np.argmax(model(x_test[0]).detach().numpy(), axis=0))

lab06_evaluation.py

@@ -0,0 +1,75 @@
+#!/usr/bin/python
+
+import torch
+from torch import nn
+import pandas as pd
+from sklearn import preprocessing
+import numpy as np
+from torch.autograd import Variable
+from sklearn.metrics import accuracy_score, f1_score
+from csv import DictWriter
+import torch.nn.functional as F
+import sys
+
+class Model(nn.Module):
+    def __init__(self, input_dim):
+        super(Model, self).__init__()
+        self.layer1 = nn.Linear(input_dim, 100)
+        self.layer2 = nn.Linear(100, 60)
+        self.layer3 = nn.Linear(60, 5)
+
+    def forward(self, x):
+        x = F.relu(self.layer1(x))
+        x = F.relu(self.layer2(x))
+        x = F.softmax(self.layer3(x))  # To check with the loss function
+        return x
+
+def prepare_labels_features(dataset):
+    """ Label make column"""
+    le = preprocessing.LabelEncoder()
+    mark_column = np.array(dataset[:]['0'])
+    le.fit(mark_column)
+
+    print(list(le.classes_))
+    lab = le.transform(mark_column)
+    feat = dataset.drop(['0'], axis=1).to_numpy()
+
+    mm_scaler = preprocessing.MinMaxScaler()
+    feat = mm_scaler.fit_transform(feat)
+
+    return lab, feat
+
+
+def print_metrics(test_labels, predictions):
+    # take column with max predicted score
+    f1 = f1_score(labels_test, np.argmax(predictions, axis=1), average='weighted')
+    accuracy = accuracy_score(test_labels, np.argmax(predictions, axis=1))
+    print(f"The F1_score metric is: {f1}")
+    print(f"The accuracy metric is: {accuracy}")
+
+    try:
+        # build_number = sys.argv[1]
+        build_number = 1
+        field_names = ['BUILD_NUMBER', 'F1', 'ACCURACY']
+        dict = {'BUILD_NUMBER': build_number, 'F1': f1, 'ACCURACY': accuracy }
+
+        with open('metrics.csv', 'a') as metrics_file:
+            dictwriter_object = DictWriter(metrics_file, fieldnames=field_names)
+            dictwriter_object.writerow(dict)
+            metrics_file.close()
+    except Exception as e:
+        print(e)
+
+
+model = torch.load("CarPrices_pytorch_model.pkl")
+cars_dev = pd.read_csv('./Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names=[str(i) for i in range(5)])
+cars_dev = cars_dev.loc[(cars_dev['0'] == 'audi') | (cars_dev['0'] == 'bmw') | (cars_dev['0'] == 'ford') | (cars_dev['0'] == 'opel') | (cars_dev['0'] == 'volkswagen')]
+labels_test, features_test = prepare_labels_features(cars_dev)
+
+x_test = Variable(torch.from_numpy(features_test)).float()
+pred = model(x_test)
+pred = pred.detach().numpy()
+print_metrics(labels_test, pred)
+
+
+