dvc repro

.gitignore

@@ -160,3 +160,4 @@ IUM08/*
 mlruns
 my_model
 dvcstore
+/prediction_results.csv

data/.gitignore

@@ -0,0 +1 @@
+/prepared

dvc.lock

@@ -0,0 +1,46 @@
+schema: '2.0'
+stages:
+  prepare:
+    cmd: python3 script_prepare.py data/Car_Prices_Poland_Kaggle.csv
+    deps:
+    - path: data/Car_Prices_Poland_Kaggle.csv
+      md5: 9170e9b525149cb1f571f318cd604913
+      size: 9894367
+    - path: script_prepare.py
+      md5: f1dfe33a503f5acc687c53dee448f71b
+      size: 1899
+    outs:
+    - path: data/Car_Prices_Poland_Kaggle_dev.csv
+      md5: cf9355749edc79f588e264de5b2bf1f0
+      size: 1648309
+    - path: data/Car_Prices_Poland_Kaggle_test.csv
+      md5: cf9355749edc79f588e264de5b2bf1f0
+      size: 1648309
+    - path: data/Car_Prices_Poland_Kaggle_train.csv
+      md5: 8818f758e2de344a4b9ad712379b81e1
+      size: 6597472
+  train:
+    cmd: python3 lab05_deepLearning.py 50
+    deps:
+    - path: data/Car_Prices_Poland_Kaggle_dev.csv
+      md5: cf9355749edc79f588e264de5b2bf1f0
+      size: 1648309
+    - path: data/Car_Prices_Poland_Kaggle_test.csv
+      md5: cf9355749edc79f588e264de5b2bf1f0
+      size: 1648309
+    - path: data/Car_Prices_Poland_Kaggle_train.csv
+      md5: 8818f758e2de344a4b9ad712379b81e1
+      size: 6597472
+    outs:
+    - path: CarPrices_pytorch_model.pkl
+      md5: cff6a79945bbf839058a4fd1b2dcc98f
+      size: 30039
+    - path: prediction_results.csv
+      md5: 62b9e54cdfebc7f1dfb060e18e9b8738
+      size: 585197
+  evaluate:
+    cmd: python3 lab10_evaluate.py
+    deps:
+    - path: CarPrices_pytorch_model.pkl
+      md5: cff6a79945bbf839058a4fd1b2dcc98f
+      size: 30039

dvc.yaml

@@ -0,0 +1,23 @@
+stages:
+  prepare:
+    cmd:  python3 script_prepare.py data/Car_Prices_Poland_Kaggle.csv
+    deps:
+    - data/Car_Prices_Poland_Kaggle.csv
+    - script_prepare.py
+    outs:
+    - data/Car_Prices_Poland_Kaggle_dev.csv
+    - data/Car_Prices_Poland_Kaggle_train.csv
+    - data/Car_Prices_Poland_Kaggle_test.csv
+  train:
+    cmd:  python3 lab05_deepLearning.py 50
+    deps:
+    - data/Car_Prices_Poland_Kaggle_dev.csv
+    - data/Car_Prices_Poland_Kaggle_train.csv
+    - data/Car_Prices_Poland_Kaggle_test.csv
+    outs:
+    - CarPrices_pytorch_model.pkl
+    - prediction_results.csv
+  evaluate:
+    cmd:  python3 lab10_evaluate.py
+    deps:
+    - CarPrices_pytorch_model.pkl

lab06_evaluation.py

@@ -90,9 +90,9 @@ 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)
+# print_metrics(labels_test, pred)
 
-draw_plot()
+# draw_plot()
 
 
 

lab08_deepLearining_mlflow.py

@@ -1,13 +1,10 @@
 #!/usr/bin/python
 
 from urllib.parse import urlparse
-import mlflow
 import numpy as np
 import torch
 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, f1_score
 import torch.nn.functional as F
 import pandas as pd

lab10_evaluate.py

@@ -0,0 +1,96 @@
+#!/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
+import os
+import matplotlib.pyplot as plt
+
+
+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"""
+    dataset = dataset.dropna()
+    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.StandardScaler()
+    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}")
+
+    if len(sys.argv) != 2:
+        return
+
+
+    build_number = sys.argv[1]
+    print(f"Build number: {build_number}")
+    field_names = ['BUILD_NUMBER', 'F1', 'ACCURACY']
+    dict = {'BUILD_NUMBER': build_number, 'F1': f1, 'ACCURACY': accuracy }
+    filename = "./metrics.csv"
+    file_exists = os.path.isfile(filename)
+
+    with open(filename, 'a') as metrics_file:
+        dictwriter_object = DictWriter(metrics_file, fieldnames=field_names)
+        if not file_exists:
+            dictwriter_object.writeheader()
+        dictwriter_object.writerow(dict)
+        metrics_file.close()
+
+
+"""
+Load model and data
+"""
+model = torch.load("CarPrices_pytorch_model.pkl")
+cars_dev = pd.read_csv('data/Car_Prices_Poland_Kaggle_dev.csv', usecols=[1, 4, 5, 6, 10], sep=',', names=[str(i) for i in range(5)])
+
+
+"""
+Prepare data
+"""
+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()
+
+"""
+Make predictions
+"""
+pred = model(x_test)
+pred = pred.detach().numpy()
+print_metrics(labels_test, pred)
+
+
+

script_prepare.py

@@ -0,0 +1,60 @@
+import subprocess
+import sys
+import pandas as pd
+import os
+import numpy as np
+
+
+try:
+    dataset_path = sys.argv[1]
+except Exception as e:
+    print("Exception while retrieving dataset path")
+    print(e)
+
+
+def divide_dataset(dataset, path):
+    """Split dataset to dev, train, test datasets. """
+
+    print('Shuffle dataset...')
+    shuf_path = 'data/Car_Prices_Poland_Kaggle_shuf.csv'
+    os.system(f'tail -n +2 {path} | shuf > {shuf_path}')
+
+    len1 = len(dataset) // 6
+    len2 = (len1 * 2) + 1
+
+    print('Dividing dataset...')
+    os.system(f'head -n {len1} {shuf_path} > data/Car_Prices_Poland_Kaggle_dev.csv')
+    os.system(f'head -n {len1} {shuf_path} | tail -n {len1} > data/Car_Prices_Poland_Kaggle_test.csv')
+    os.system(f'tail -n +{len2} {shuf_path} > data/Car_Prices_Poland_Kaggle_train.csv')
+
+    os.system(f'rm {shuf_path}')
+    print("Len match: " + str(sum([len1 * 2, len2]) == len(dataset)))
+    os.system('cat Car_Prices_Poland_Kaggle_train.csv | wc -l')
+    os.system('cat Car_Prices_Poland_Kaggle_dev.csv | wc -l')
+    os.system('cat Car_Prices_Poland_Kaggle_test.csv | wc -l')
+
+    print('Dataset devided')
+
+
+def normalize_dataset(dataset):
+    """Drop unnecessary columns and set numeric values to [0,1] range"""
+
+    print(f'--------------- Initial dataset length ---------------')
+    print(len(dataset))
+
+    # drop columns
+    dataset.drop(columns=["Unnamed: 0", "generation_name"], inplace=True)
+    dataset = dataset.dropna()
+
+    # normalize numbers to [0, 1]
+    for column in dataset.columns:
+        if isinstance(dataset.iloc[1][column], np.int64) or isinstance(dataset.iloc[1][column], np.float64):
+            dataset[column] = (dataset[column] - dataset[column].min()) / (dataset[column].max() - dataset[column].min())
+    return dataset
+
+
+cars = pd.read_csv(dataset_path)
+df = pd.DataFrame(cars)
+df = normalize_dataset(df)
+divide_dataset(df, dataset_path)
+