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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7,071 | testAdult = pd.read_csv('../input/adultb/test_data.csv',
sep=',',engine='python',
na_values='?') | No | 5 | 45 | Data_Extraction | load_from_csv | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/adultb/test_data.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 5 | A |
7,072 | testAdult.isnull().sum() | No | 4 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,073 | XtestAdult = testAdult[['age','education.num',
'capital.gain','capital.loss',
'hours.per.week']] | No | 5 | 10 | Data_Transform | drop_column | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
7,074 | result = np.vstack((testAdult["Id"], YtestPred)).T
x = ["id","income"]
submit = pd.DataFrame(columns = x, data = result)
submit.to_csv("Resultados.csv", index = False) | Yes | 3 | 25 | Data_Export | save_to_csv | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 5 | A |
7,075 | import pandas as pd
import sklearn
import numpy as np
import os
from sklearn import preprocessing | No | 5 | 22 | Environment | import_modules | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,076 | adultOriginal = pd.read_csv("../input/adult-db/train_data.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="?") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,077 | adultOriginal.head()
| No | 5 | 41 | Debug | show_table | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,078 | adultOriginal.shape | No | 5 | 58 | Debug | show_shape | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,079 | numAdult_1=adultOriginal.fillna(method='pad')
numAdult_2=numAdult_1.fillna(method='pad') | No | 5 | 17 | Data_Transform | correct_missing_values | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,080 | adult = numAdult_2.apply(preprocessing.LabelEncoder().fit_transform)
adult | No | 5 | 20 | Data_Transform | categorify | 1,563,153 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,081 | adult["sex"].value_counts().plot(kind="bar") | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,082 | adult["race"].value_counts().plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
7,083 | adult["education"].value_counts().plot(kind="bar") | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,084 | test_adult = pd.read_csv(\'../input/adult-db/test_data.csv\',<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="?") | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,085 | numAdultTest_1=test_adult.fillna(method='pad')
numAdultTest_2=numAdultTest_1.fillna(method='pad')
adultTest = numAdultTest_2.apply(preprocessing.LabelEncoder().fit_transform)
adultTest | Yes | 4 | 17 | Data_Transform | correct_missing_values | 1,563,153 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,086 | from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,087 | Xadult = adult.drop(['workclass', 'marital.status', 'sex', 'occupation', 'relationship', 'income', 'capital.gain', 'capital.loss', 'native.country'], axis=1)
Xadult | No | 5 | 10 | Data_Transform | drop_column | 1,563,153 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,088 | XtestAdult = adultTest.drop(['workclass', 'marital.status', 'sex', 'occupation', 'relationship', 'capital.gain', 'capital.loss', 'native.country'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,089 | knn = KNeighborsClassifier(n_neighbors=21) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,090 | data = pd.DataFrame(adultTest.Id) | No | 5 | 12 | Data_Transform | create_dataframe | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,091 | data.to_csv("BaseAdult_KNN.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | 1,563,153 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,092 | import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import numpy as np | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,093 | train = pd.read_csv("../input/dataset/train_data.csv",<br> na_values = \'?\') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,094 | train = train.dropna() | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,095 | Atrain = train[["age","education.num","capital.gain", "capital.loss", "hours.per.week"]]
Btrain = train.income | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
7,096 | knn = KNeighborsClassifier(n_neighbors=15) | No | 5 | 4 | Model_Train | choose_model_class | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
7,097 | knn.fit(Atrain,Btrain) | No | 5 | 7 | Model_Train | train_model | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 5 | B |
7,098 | test = pd.read_csv("../input/dataset/test_data.csv",<br> na_values = \'?\') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,099 | Atest = test[["age","education.num","capital.gain", "capital.loss", "hours.per.week"]] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
7,100 | Bpred=knn.predict(Atest) | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 5 | A |
7,101 | prediction = pd.DataFrame(index = test.index) | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,102 | prediction.to_csv("submition.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 1,564,753 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 5 | A |
7,103 | import os, cv2, re, random
import numpy as np
import pandas as pd
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras import layers, models, optimizers
from keras import backend as K
from sklearn.model_selection import train_test_split
from s... | No | 5 | 23 | Environment | set_options | 1,583,645 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,104 | transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.CIFAR10(root='.', train=True,
download=False, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_... | No | 3 | 42 | Data_Extraction | load_from_url | 1,015,888 | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "PyTorch", "name": "Deterministic Algorithm Usage Checker", "fix": "Enable deterministic algorithms using torch.use_deterministic_algorithms(True).", "benefits": "Ensures consistent results across different hardware and runs.", "location": ""}, {"detector": "framework_sp... | B | Deterministic Algorithm Usage Checker=low, No random seed setting detected=low, Randomness Control Checker (PyTorch-Dataloader)=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker (PyTorch-Dataloader)", "severity": "low"}] | ["No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 2 | B |
7,105 | def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5... | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 1 | 1 | 0 | 0 | 5 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,106 | class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 1... | No | 5 | 4 | Model_Train | choose_model_class | 1,015,888 | 1 | A | 0 | 3 | 1 | 0 | 0 | 20 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "PyTorch", "name": "Deterministic Algorithm Usage Checker", "fix": "Enable deterministic algorithms using torch.use_deterministic_algorithms(True).", "benefits": "Ensures consistent results across different hardware and runs.", "location": ""}, {"detector": "framework_sp... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Batch Normalisation Checker", "Missing class docstring", "Missing function docstring"] | 2 | B |
7,107 | import torch.optim as optim
criterion = nn.CrossEntropyLoss()
if use_gpu:
criterion = criterion.cuda()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) | No | 3 | 28 | Model_Train | compute_train_metric | null | 1 | A | 1 | 1 | 1 | 0 | 5 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,108 | # loop over the dataset multiple times
for epoch in tqdm_notebook(range(10)):
running_loss = 0.0
for i, data in tqdm_notebook(enumerate(trainloader, 0)):
# get the inputs
inputs, labels = data
if torch.cuda.is_available():
# in versions of Torch < 0.4.0 we have to wrap th... | No | 5 | 7 | Model_Train | train_model | 1,015,888 | 1 | A | 1 | 4 | 9 | 0 | 5 | 17 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "PyTorch", "name": "Deterministic Algorithm Usage Checker", "fix": "Enable deterministic algorithms using torch.use_deterministic_algorithms(True).", "benefits": "Ensures consistent results across different hardware and runs.", "location": ""}, {"detector": "framework_sp... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Logging Checker"] | 2 | B |
7,109 | dataiter = iter(testloader)
images, labels = dataiter.next()
# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(32))) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,110 | # in PyTorch 0.4.0 you won't need the Variable wrapper
outputs = net(Variable(images).cuda()) if use_gpu else net(Variable(images)) | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,111 | _, predicted = torch.max(outputs.data, 1)
print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
for j in range(32))) | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,112 | all_pred = np.empty((0, 10), float) | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,113 | for data in tqdm_notebook(testloader):
images, _ = data
if use_gpu:
images = images.cuda()
outputs = net(Variable(images))
curr_pred = F.softmax(outputs).data.cpu().numpy()
all_pred = np.vstack([all_pred, curr_pred]) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,114 | all_pred.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,115 | pd.DataFrame(all_pred, columns=classes).to_csv('baseline.csv', index_label='id') | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,116 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processi... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,117 | train_path = '../input/train'
path_name = train_path + '/**/*.jpg' | No | 4 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,118 | train_image_paths = glob(path_name, recursive=True) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,119 | train_image_paths[:10] | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,120 | train_categories = list(map(os.path.basename,train_image_paths)) | No | 3 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,121 | train_categories[:3] | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,122 | labels =[]
for category in train_categories:
labels.append(category[:3]) | No | 3 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,123 | labels[:10] | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,124 | len(labels) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,125 | len(train_image_paths) | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,126 | num_classes = len(np.unique(labels))
num_classes | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,127 | #Encode labels with value between 0 and n_classes-1.
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
loadedLabels = np.asarray(labels)
encoder.fit(loadedLabels)
encoded_loadedLabels = encoder.transform(loadedLabels) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 9 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
7,128 | # Encode labels to hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])
from keras.utils.np_utils import to_categorical
labels_Hot = to_categorical(encoded_loadedLabels, num_classes = num_classes) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,129 | labels_Hot[:3] | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,130 | df = pd.DataFrame() | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,131 | df | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,132 | df['path']=train_image_paths | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,133 | df['path'].head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,134 | df['labels'] = list(labels_Hot) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,135 | from keras.preprocessing.image import ImageDataGenerator
IMG_SIZE = (128, 128)
core_idg = ImageDataGenerator(samplewise_center=True,
samplewise_std_normalization=True,
horizontal_flip = True,
vertical_flip = False,
... | No | 4 | 31 | Data_Transform | augment | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,136 | def flow_from_dataframe(img_data_gen, in_df, path_col, y_col, **dflow_args):
base_dir = os.path.dirname(in_df[path_col].values[0])
print('## Ignore next message from keras, values are replaced anyways')
df_gen = img_data_gen.flow_from_directory(base_dir,
class_mode = 's... | No | 3 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 14 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,137 | from sklearn.model_selection import train_test_split
train_df, valid_df = train_test_split(df,
test_size = 0.25,
random_state = 2018) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 0 | B |
7,138 | len(train_df) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,139 | len(valid_df) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,140 | train_gen = flow_from_dataframe(core_idg, train_df,
path_col = 'path',
y_col = 'labels',
target_size = IMG_SIZE,
batch_size = 32)
valid_gen = flow_from_dataframe(core_idg, valid_df,
... | No | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 16 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,141 | t_x, t_y = next(train_gen) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,142 | t_x.shape[1:] | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,143 | from keras.applications import VGG16
from keras.applications.vgg16 import preprocess_input
import keras
from keras import backend as K
from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_cat... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,144 | pretrained_model_1 = VGG16(include_top=False, input_shape=t_x.shape[1:])
base_model = pretrained_model_1 # Topless
optimizer1 = keras.optimizers.Adam()
# Add top layer
x = base_model.output
x = Conv2D(100, kernel_size = (3,3), padding = 'valid')(x)
x = Flatten()(x)
x = Dropout(0.75)(x)
predictions = Dense(num_classes, ... | Yes | 4 | 30 | Model_Train | load_pretrained | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 15 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,145 | model.fit_generator(train_gen,steps_per_epoch=100,validation_data = (test_X, test_Y),
epochs = 10) | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,146 | test_image_paths = glob('../input/test/*.jpg', recursive=True) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,147 | img_width = 150
img_height = 150
TRAIN_DIR = '../input/train/'
TEST_DIR = '../input/test/'
train_images_dogs_cats = [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR)] # use this for full dataset
test_images_dogs_cats = [TEST_DIR+i for i in os.listdir(TEST_DIR)] | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,148 | train_images_dogs_cats.sort(key=natural_keys)
train_images_dogs_cats_trim = train_images_dogs_cats[0:1300]
train_images_dogs_cats_trim += train_images_dogs_cats[12500:13800]
test_images_dogs_cats.sort(key=natural_keys) | Yes | 4 | 9 | Data_Transform | sort_values | 1,583,645 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,149 | X, Y = prepare_data(train_images_dogs_cats_trim)
# First split the data in two sets, 80% for training, 20% for Val/Test)
X_train, X_val, Y_train, Y_val = train_test_split(X,Y, test_size=0.2, random_state=1) | Yes | 3 | 21 | Data_Transform | prepare_x_and_y | 1,583,645 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,150 | model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), input_shape=(img_width, img_height, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(32, (3, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
mod... | No | 5 | 4 | Model_Train | choose_model_class | 1,583,645 | 1 | A | 0 | 0 | 0 | 0 | 0 | 16 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,151 | X_test, Y_test = prepare_data(test_images_dogs_cats) | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,152 | Y_pred = model.predict(np.array(X_val))
#####predict cat | predict dog
for i in range(0,5):
if Y_pred[i, 0] >= 0.5:
print('I am {:.2%} sure this is a Dog'.format(Y_pred[i][0]))
else:
print('I am {:.2%} sure this is a Cat'.format(1-Y_pred[i][0]))
plt.imshow(X_val[i])
plt.s... | Yes | 2 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 2 | 4 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,153 | XtestAdult = nTestAdult[["Age","Education-Num","Capital Gain", "Capital Loss", "Hours per week"]]
XtestAdult.head() | No | 4 | 41 | Debug | show_table | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,154 | # Validating the score on validation data
score = model.evaluate_generator(validation_generator)
print('Test score:', score[0])
print('Test accuracy:', score[1]) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 1,583,645 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,155 | knn = KNeighborsClassifier(n_neighbors=3) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,157 | from sklearn.metrics import accuracy_score | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 0 | A |
7,158 | UCITest = pd.read_csv("../input/mydata/adult.test",<br> names=[<br> "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Martial Status",<br> "Occupation", "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss",<br> "Hours per week", "Country", "Target"],<br> sep=r\'\\s... | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,159 | UCITest.head() | No | 5 | 41 | Debug | show_table | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,160 | nUCITest = UCITest.dropna() | No | 5 | 17 | Data_Transform | correct_missing_values | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,161 | nUCITest.head() | No | 5 | 41 | Debug | show_table | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,162 | XUCITest = nUCITest[["Age","Education-Num","Capital Gain", "Capital Loss", "Hours per week"]] | No | 5 | 77 | Other | define_variables | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,163 | XUCITest.head() | No | 5 | 41 | Debug | show_table | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,164 | YUCIPred = knn.predict(XUCITest)
YUCIPred | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,554,855 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
7,165 | accuracy_score(YUCIPred, YUCITest) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Unit Testing Checker"] | 3 | A |
7,166 | knn.fit(Xadult, Yadult) | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,167 | YUCIPred = knn.predict(XUCITest) | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
7,168 | accuracy_score(YUCITest, YUCIPred) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Unit Testing Checker"] | 3 | A |
7,169 | accuracies = {}
for i in range(1, 100):
knn = KNeighborsClassifier(n_neighbors=i)
knn.fit(Xadult, Yadult)
scores = cross_val_score(knn, Xadult, Yadult, cv=10)
Ypred = knn.predict(XUCITest)
accuracy = accuracy_score(YUCITest,Ypred)
accuracies[i] = accuracy
print('k={}, accuracy={}, CVmean={}... | Yes | 4 | 49 | Model_Evaluation | compute_test_metric | 1,554,855 | 1 | A | 0 | 1 | 1 | 0 | 0 | 9 | ok | 9 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | C | No random seed setting detected=low, Only accuracy metric=medium | [{"type": "No random seed setting detected", "severity": "low"}, {"type": "Only accuracy metric", "severity": "medium"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | C |
7,170 | ks = list(accuracies.keys())
acc = list(accuracies.values())
plt.plot(ks, acc)
plt.show() | No | 5 | 33 | Visualization | distribution | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
7,171 | knn = KNeighborsClassifier(n_neighbors=28) | No | 5 | 4 | Model_Train | choose_model_class | 1,554,855 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 3 | A |
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