id int64 0 7.95k | code_block stringlengths 1 52.9k | too_long stringclasses 2
values | marks int64 1 5 | graph_vertex_id float64 1 88 | graph_vertex_class stringclasses 12
values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,990 | train.fillna(method='ffill', inplace=True)
train.tail().T | No | 4 | 17 | Data_Transform | correct_missing_values | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,991 | train.isnull().any().describe() | No | 4 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,992 | Img = []
for i in range(7049):
img = train["Image"][i].split(' ')
img = ['0' if x=='' else x for x in img]
Img.append(img)' | No | 4 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,993 | PATH_WEEK2='/kaggle/input/covid19-global-forecasting-week-2'
df_train = pd.read_csv(f'{PATH_WEEK2}/train.csv')
df_test = pd.read_csv(f'{PATH_WEEK2}/test.csv')
df_train.head()
df_test.head()
df_train.rename(columns={'Country_Region':'Country'}, inplace=True)
df_test.rename(columns={'Country_Region':'Country'}, inplace=T... | No | 3 | 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 |
2,994 | Y_train = train.drop('Image', axis=1)
Y_train = Y_train.values
Y_train = np.array(Y_train, dtype='float')
Y_train.shape, X_train.shape | No | 4 | 21 | Data_Transform | prepare_x_and_y | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,995 | # keras CNN
#
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5,5), padding = 'same', activation = 'relu', input_shape = (96,96,1)))
model.add(MaxPool2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters=32, kernel_size=(3,3), padding = 'same', activation = 'relu'))
model.add(MaxPool... | No | 3 | 4 | Model_Train | choose_model_class | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,996 | optimizer = RMSprop(lr = 0.001, epsilon = 1e-8)
optimizer1 =Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(optimizer1, loss = "mse", metrics = ["accuracy"] ) | No | 3 | 4 | Model_Train | choose_model_class | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,997 |
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
tensorboard = TensorBoard... | No | 4 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,998 | model1 = Sequential([Flatten(input_shape=(96,96,1)),
Dense(128, activation="relu"),
Dropout(0.1),
Dense(64, activation="relu"),
Dense(30)
])
model1.compile(optimizer='adam',
loss=... | No | 3 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,999 | batch_size =100
epochs = 50
history = model.fit(X_train, Y_train, epochs = epochs, batch_size=batch_size, callbacks=callback_list, validation_split=0.1, verbose = 2) | No | 4 | 7 | Model_Train | train_model | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,000 | # Plot the loss and accuracy curves for training and validation
fig, ax = plt.subplots(2,1)
ax[0].plot(history.history['loss'], color='b', label="Training loss")
ax[0].plot(history.history['val_loss'], color='r', label="validation loss",axes =ax[0])
legend = ax[0].legend(loc='best', shadow=True)
ax[1].plot(history.hi... | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,001 | #preparing test data
timag = []
for i in range(0,1783):
timg = test['Image'][i].split(' ')
timg = ['0' if x == '' else x for x in timg]
timag.append(timg) | No | 3 | 14 | Data_Transform | filter | 3,344,044 | 1 | A | 0 | 2 | 3 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,002 | X_test = np.array(timag,dtype = 'float')
X_test = X_test/255
X_test = X_test.reshape(-1,96,96,1)
X_test.shape | No | 4 | 21 | Data_Transform | prepare_x_and_y | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,003 | opt_model = load_model('./optimized_model.h5')
| No | 5 | 30 | Model_Train | load_pretrained | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,004 | pred = model.predict(X_test, batch_size = 100)
pred.shape | No | 4 | 48 | Model_Evaluation | predict_on_test | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,005 | feature = []
for f in list( look_id['FeatureName']):
feature.append(lookid_list.index(f)) | No | 2 | 8 | Data_Transform | feature_engineering | 3,344,044 | 2 | B | 1 | 1 | 1 | 0 | 5 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,006 | rowid = pd.Series(rowid,name = 'RowId')
loc = pd.Series(preded,name = 'Location')
submission = pd.concat([rowid,loc],axis = 1)
submission.to_csv('face_key_detection_submission.csv',index = False) | No | 4 | 25 | Data_Export | save_to_csv | 3,344,044 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,007 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O... | No | 4 | 22 | Environment | import_modules | 4,178,004 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 18"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 4 | A |
3,008 | df = pd.read_csv('../input/training/training.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,009 | print(df.isnull().any().value_counts(), df.shape)
df.dropna(inplace=True)
#df.fillna(method = 'ffill',inplace = True)
#df.reset_index(drop = True, inplace = True)
print(df.isnull().any().value_counts(), df.shape) | No | 3 | 17 | Data_Transform | correct_missing_values | 4,178,004 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["InPlace Checker"] | 4 | B |
3,010 | df = df.sample(frac=1)
img_data = df['Image'].values
df.drop('Image', inplace=True, axis=1) | No | 4 | 10 | Data_Transform | drop_column | 4,178,004 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 4 | A |
3,011 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 11 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 16"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
3,012 | !unzip ../input/facial-keypoints-detection/training.zip -d train | No | 5 | 44 | Data_Extraction | load_from_disk | 10,073,555 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
3,013 | !unzip ../input/facial-keypoints-detection/test.zip -d test | No | 5 | 84 | Other | not_enough_vertices | 10,073,555 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
3,014 | train = pd.read_csv("../working/train/training.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../working/train/training.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
3,015 | test = pd.read_csv("../working/test/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../working/test/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
3,016 | print(test) | 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 |
3,017 | train.head().T | 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 |
3,018 | train.fillna(method='ffill',inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["InPlace Checker"] | 3 | A |
3,019 | print(train) | 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 |
3,020 | len(train["Image"][4].split(' '))' | No | 3 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,021 | images = np.ndarray((7049,9216))
for i in range(7049):
img = np.array(train["Image"][i].split(' '))
img = ['0' if x == '' else x for x in img]
images[i,:] = img' | No | 3 | 14 | Data_Transform | filter | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,022 | Y_test = np.ndarray((1783,9216))
for i in range(1783):
img = np.array(test["Image"][i].split(' '))
img = ['0' if x == '' else x for x in img]
Y_test[i,:] = img' | No | 4 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,023 | images = images.reshape(-1,96,96,1) | No | 5 | 84 | Other | not_enough_vertices | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,024 | Y_test = Y_test.reshape(-1,96,96,1) | No | 5 | 84 | Other | not_enough_vertices | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,025 | images.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 |
3,026 | plt.imshow(images[34].reshape(96,96),cmap='gray') | No | 5 | 84 | Other | not_enough_vertices | 10,073,555 | 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"] | 3 | A |
3,027 | train.drop('Image',axis=1) | No | 5 | 10 | Data_Transform | drop_column | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,028 | Y_train = np.array(train.drop("Image",axis=1),dtype='float')' | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,029 | print(Y_train.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 |
3,030 | model = Sequential()
model.add(Conv2D(32,(3,3),input_shape=(96,96,1),padding = 'SAME',activation='relu'))
model.add(Conv2D(32,(3,3),padding = 'SAME',activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(64,(3,3),padding = 'SAME',activation='relu'))
model.a... | No | 3 | 4 | Model_Train | choose_model_class | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 28 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Batch Normalisation Checker", "Memory Release Checker"] | 3 | A |
3,031 | model.compile(loss='mean_squared_error',optimizer='adam',metrics=['mae']) | No | 3 | 4 | Model_Train | choose_model_class | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
3,032 | model.fit(images,Y_train,epochs=10,batch_size=256,validation_split=0.2) | No | 5 | 7 | Model_Train | train_model | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Early Stopping Checker", "fix": "Implement early stopping using tf.keras.callbacks.EarlyStopping.", "benefits": "Prevents overfitting and reduces unnecessary training time.", "location": ""}, {"detector": "framework_specific", "framework": "TensorF... | A | no applicable ML-specific findings | [] | ["Data Augmentation Checker", "Early Stopping Checker", "Memory Release Checker", "Model Evaluation Checker"] | 3 | A |
3,033 | pred = model.predict(Y_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Memory Release Checker"] | 3 | A |
3,034 | lookid_data = pd.read_csv("/kaggle/input/facial-keypoints-detection/IdLookupTable.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 10,073,555 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/facial-keypoints-detection/IdLookupTable.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
3,035 | lookid_list = list(lookid_data['FeatureName'])
imageID = list(lookid_data['ImageId']-1)
pre_list = list(pred)
rowid = lookid_data['RowId']
rowid=list(rowid)
feature = []
for f in list(lookid_data['FeatureName']):
feature.append(lookid_list.index(f))
preded = []
for x,y in zip(imageID,feature):
preded.append(pre... | Yes | 4 | 25 | Data_Export | save_to_csv | 10,073,555 | 1 | A | 1 | 2 | 2 | 0 | 5 | 15 | 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"] | 3 | A |
3,036 | train_file = 'training.csv'
test_file = 'test.csv'
lookup_file = '../input/facial-keypoints-detection/IdLookupTable.csv'
train = pd.read_csv(train_file)
test = pd.read_csv(test_file)
lookup = pd.read_csv(lookup_file)
| No | 4 | 45 | Data_Extraction | load_from_csv | 11,186,959 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/facial-keypoints-detection/IdLookupTable.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"] | 3 | A |
3,037 | import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator
import matplotlib as mpl
import matplotlib.pyplot as plt
!pip install py7zr
from keras.preprocessing.image import load_img,img_to_array
from py7zr import unpack_7zarchive
... | No | 4 | 87 | Environment | install_modules | 9,829,311 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
3,038 | shutil.unpack_archive('/kaggle/input/cifar-10/train.7z', '/kaggle/working') | No | 4 | 73 | Data_Extraction | load_from_zip | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,039 |
train_dir = os.listdir("./train");
train_dir_len = len(train_dir)
print(".\\\\train:\\t",train_dir_len)
print("files:\\t\\t",train_dir[:3])' | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,040 | train_labels = pd.read_csv('/kaggle/input/cifar-10/trainLabels.csv',dtype=str)
train_images = pd.DataFrame(columns = ['id','label','path'],dtype=str)
test_labels = pd.read_csv('/kaggle/input/cifar-10/sampleSubmission.csv')
train_labels.info() | No | 4 | 45 | Data_Extraction | load_from_csv | 9,829,311 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,041 | path_base = '/kaggle/working/train/'
for index in range(0,train_dir_len):
path = path_base + str(index+1)+'.png'
if os.path.exists(path):
train_images = train_images.append([{ 'id': str(train_labels['id'].iloc[index]),'path': path, 'label':train_labels['label'].iloc[index]}])
train_images.head... | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,042 | train_images.head(2) | 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 |
3,043 | display_groupby = train_images.groupby(['label']).count()
display_groupby.head(10) | No | 4 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,044 | class_names = ['airplane','automobile','bird','cat','deer','dog','frog','horse','ship','truck']
for name in class_names:
index = class_names.index(name)
train_images.loc[train_images.label==name,'label'] = str(index)
display_groupby = train_images.groupby(['label']).count()
display_groupby.head(10) | No | 4 | 60 | Data_Transform | groupby | 9,829,311 | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,045 | path_base = '/kaggle/working/train'
batch_size = 64
train_data_generator = ImageDataGenerator(
rescale=1./255.,
validation_split=0.2,
horizontal_flip=True
)
train_generator = train_data_generator.flow_from_dataframe(dataframe=train_images,
directory="./train/"... | Yes | 4 | 31 | Data_Transform | augment | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,046 | num_classes = 10 | No | 5 | 77 | Other | define_variables | 9,829,311 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,047 | validation_generator = train_data_generator.flow_from_dataframe(dataframe=train_images,
directory="./train/",
x_col="path",
y_col="label",
subset="validation",
batch_size=batch_size,
shuffle=True,
target_size=(32,32),
class_... | No | 5 | 84 | Other | not_enough_vertices | 9,829,311 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,048 | b"train_size = len(train_generator.filenames)\nvalidation_size = len(validation_generator.filenames)\nprint('validation_size:\\t',validation_size)\nprint('train_size:\\t\\t',train_size)" | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,049 | index = 0
fig = plt.figure(figsize = (16,10))
for item in train_images.values[:20]:
index += 1
plt.subplot(5, 5, index)
test_path = item[2]
test_image = load_img(test_path, target_size=(32,32))
plt.imshow(test_image)
plt.colorbar()
plt.grid(False)
plt.axis("off")
plt.title(class_... | No | 5 | 84 | Other | not_enough_vertices | 9,829,311 | 1 | A | 0 | 1 | 1 | 0 | 0 | 13 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,050 | import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import cross_val_score | No | 4 | 22 | Environment | import_modules | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 13 | A |
3,051 | # Import dataset
df = pd.read_csv('../input/loan-default-prediction/train_v2.csv.zip')
df.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/loan-default-prediction/train_v2.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 15"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 13 | A |
3,052 | # Check duplication in dataframe
df[df.duplicated()].shape | No | 5 | 38 | EDA | count_duplicates | 13,885,246 | 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"] | 13 | A |
3,053 | # The number of each data type in the dataframe
df.dtypes.value_counts() | No | 5 | 72 | EDA | count_values | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 13 | A |
3,054 | # Loss Distribution
fig , ax = plt.subplots()
plt.hist(df['loss'], bins = 20, range=(0,100))
ax.set_ylim([0,3000])
plt.show() | No | 5 | 33 | Visualization | distribution | 13,885,246 | 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"] | 13 | A |
3,055 | # Calculate percent of missing in each row
df['num_missing'] = df.isnull().sum(axis = 1)/df.shape[1]
# Drop row that percent of missing more than 20%
missing_row = df[df['num_missing'] > 0.20].index
df.drop(df.index[missing_row], inplace = True)
df.shape | No | 4 | 17 | Data_Transform | correct_missing_values | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 13 | A |
3,056 | # Drop id and num_missing collumn
df.drop(columns = ['id','num_missing'], inplace = True) | No | 5 | 10 | Data_Transform | drop_column | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 15"}] | A | no applicable ML-specific findings | [] | ["InPlace Checker"] | 13 | A |
3,057 | # Calculate percent of missing in each column
col_pct_miss = []
for col in df.columns:
percent_miss = np.mean(df[col].isnull())*100
if percent_miss > 0:
col_pct_miss.append([col, percent_miss])
col_pct_miss_df = pd.DataFrame(col_pct_miss, columns = ['column_name','% of Missing']).sort_values(by = '... | No | 4 | 17 | Data_Transform | correct_missing_values | 13,885,246 | 1 | A | 0 | 2 | 3 | 1 | 0 | 7 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "NumPy", "name": "Inefficient Operat... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Inefficient Operations"] | 13 | C |
3,058 | # Impute missing value in numeric columns with median
numeric_cols = df.select_dtypes(include=['number']).columns.values
for col in numeric_cols:
if col in list(col_pct_miss_df.column_name) :
med = df[col].median()
df[col] = df[col].fillna(med) | No | 5 | 17 | Data_Transform | correct_missing_values | 13,885,246 | 1 | A | 0 | 2 | 3 | 0 | 0 | 5 | 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"] | 13 | A |
3,059 | # Impute missing value in categorical columns with mode
not_numeric_cols = df.select_dtypes(exclude=['number']).columns.values
for col in not_numeric_cols:
if col in list(col_pct_miss_df.column_name):
mode = df[col].mode()[0]
df[col] = df[col].fillna(mode) | No | 5 | 17 | Data_Transform | correct_missing_values | 13,885,246 | 1 | A | 0 | 2 | 3 | 0 | 0 | 5 | 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"] | 13 | A |
3,060 | # Check missing value
df.isnull().sum().value_counts() | No | 5 | 39 | EDA | count_missing_values | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 13 | A |
3,061 | # Drop Highly Corelated Columns
# Create correlation matrix
corr_matrix = df.corr().abs()
# Select upper triangle of correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
# Find index of feature columns with correlation greater than 0.95
to_drop = [column for column i... | No | 5 | 10 | Data_Transform | drop_column | 13,885,246 | 1 | A | 0 | 1 | 0 | 0 | 0 | 4 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 25"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | A | no applicable ML-specific findings | [] | ["Array Creation Efficiency", "Column Selection Checker", "InPlace Checker"] | 13 | A |
3,062 | #Drop Repetitive Columns
num_rows = df.shape[0]
rep_cols = []
for col in df.loc[:, df.columns != 'loss'].columns :
cnts = df[col].value_counts()
top_pct = (cnts/num_rows).iloc[0]
if top_pct > 0.80:
rep_cols.append([col,top_pct])
rep_col_df = pd.DataFrame(rep_cols, columns = ['column_n... | No | 3 | 10 | Data_Transform | drop_column | 13,885,246 | 1 | A | 0 | 2 | 3 | 1 | 0 | 10 | 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"] | 13 | A |
3,063 | cat_cols = df.select_dtypes(exclude=['number']).columns.values
drop_cols = []
keep_cols = []
for col in cat_cols:
if df[col].value_counts().count() > 20000 :
print('column {} has {} categories > drop'.format(col,df[col].value_counts().count()))
drop_cols.append(col)
else :
print('colu... | No | 4 | 10 | Data_Transform | drop_column | 13,885,246 | 1 | A | 0 | 2 | 4 | 0 | 0 | 10 | 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"] | 13 | A |
3,064 | # Binary Encoding
import category_encoders as ce
encoder = ce.BinaryEncoder(cols = keep_cols)
bi_enc_df = encoder.fit_transform(df[keep_cols])
bi_col_name = bi_enc_df.columns
bi_enc_df.head()
#Add Binary Encding to dataframe and drop all categorical columns
df = pd.concat([df,bi_enc_df],axis = 1)
df.head() | No | 3 | 20 | Data_Transform | categorify | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | D |
3,065 | # Add a 'loan_status' collumn which 1 represents default loan and 0 represents not default loan.
df['loan_status'] = np.where(df['loss'] > 0, 1, 0)
df.head() | No | 5 | 8 | Data_Transform | feature_engineering | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 13 | A |
3,066 | # After generate a visualization from loan_status in dataframe.
# We found that the data is imbalance.
ax = sns.countplot(x = 'loan_status', data=df)
plt.show()
df['loan_status'].value_counts() | No | 5 | 33 | Visualization | distribution | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 13 | A |
3,067 | from sklearn.model_selection import train_test_split
X = resample_df.drop(columns = ['loss','loan_status'])
Y = resample_df['loss']
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.2, random_state = 1234, stratify = resample_df['loan_status'])
print('training set = {} records, test set= {} rec... | No | 4 | 21 | Data_Transform | prepare_x_and_y | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Column Selection Checker"] | 13 | B |
3,068 | from sklearn.feature_selection import SelectPercentile , SelectKBest, f_regression , f_classif | No | 5 | 22 | Environment | import_modules | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 13 | A |
3,069 | #Select top 170 important numerical columns with filter method
X_train_num = X_train.drop(columns = bi_col_name)
selector = SelectKBest(score_func = f_regression, k = 170)
selector.fit(X_train_num,Y_train)
select_cols = selector.get_support(indices = True)
select_num_cols = X_train_num.iloc[:,select_cols]
select_num... | No | 5 | 86 | Model_Interpretation | features_selection | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 7 | 7 | [{"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... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | B |
3,070 | #Select top 150 important numerical columns with RFE
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
selector = RFE(LogisticRegression(), n_features_to_select=150, step=1, verbose = 2)
selector = selector.fit(select_num_cols, Y_train)
select_cols = selector.get_support(ind... | No | 5 | 86 | Model_Interpretation | features_selection | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing Checker", "fix": "Apply scaling before scaling-sensitive operations.", "benefits": "Improves model performance and accuracy.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation... | C | Cross Validation Checker=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | C |
3,071 | # Select top 5 important categorical columns with filter method
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
X_train_cat = X_train.select_dtypes(exclude = 'number').copy()
# Create encoder
le = LabelEncoder()
X_train_cat = X_train_cat.apply(lambda col: le.fit_transf... | No | 3 | 86 | Model_Interpretation | features_selection | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 14 | ok | 15 | 10 | [{"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, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Feature selection detected without clear validation strategy. Ensure it's applied with proper validation to avoid selection bias.", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mo... | 13 | D |
3,072 | #Combine categorical and non-categorical dataframe together
def filter_x_df(x):
df = x.copy()
all_filter_col = []
for keep in select_cat_col_name[select_cat_col_name.isin(keep_cols)]:
filter_col = [col for col in df.columns if col.startswith(str(keep))]
for col in filter_col :
... | No | 5 | 11 | Data_Transform | concatenate | 13,885,246 | 1 | A | 0 | 5 | 6 | 0 | 0 | 11 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Missing function docstring"] | 13 | A |
3,073 | filter_X_train.head() | No | 5 | 41 | Debug | show_table | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"}] | [] | 13 | B |
3,074 | from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(filter_X_train)
X_train_scal = scaler.fit_transform(filter_X_train)
X_test_scal = scaler.fit_transform(filter_X_test) | No | 5 | 18 | Data_Transform | normalization | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 13 | 9 | [{"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, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "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"] | 13 | D |
3,075 | from sklearn.neighbors import KNeighborsClassifier
neigh = KNeighborsClassifier()
neigh.fit(X_train_scal, Y_train)
Knn_y_pred_train = neigh.predict(X_train_scal)
Knn_y_pred_test = neigh.predict(X_test_scal)
scores_kn = cross_val_score(estimator = neigh, y = Y_train, X = X_train_scal, cv=5)
print('Cross Validation ... | Yes | 4 | 7 | Model_Train | train_model | 13,885,246 | 1 | A | 1 | 0 | 0 | 0 | 5 | 7 | ok | 9 | 6 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 23"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Ch... | C | Missing Axis Specification=medium, No random seed setting detected=low | [{"type": "Missing Axis Specification", "severity": "medium"}, {"type": "No random seed setting detected", "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"] | 13 | C |
3,076 | from sklearn.linear_model import LogisticRegression
logisticRegr = LogisticRegression()
logisticRegr = logisticRegr.fit(X_train_scal, Y_train)
Lr_y_pred_train = logisticRegr.predict(X_train_scal)
Lr_y_pred_test = logisticRegr.predict(X_test_scal)
scores_lr = cross_val_score(estimator = logisticRegr, y = Y_train, X =... | Yes | 4 | 7 | Model_Train | train_model | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 10 | 7 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 23"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing ... | C | Missing Axis Specification=medium, No random seed setting detected=low, Scaler Missing Checker=medium | [{"type": "Missing Axis Specification", "severity": "medium"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["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"] | 13 | C |
3,077 | from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(max_depth = 70)
rf.fit(filter_X_train, Y_train)
rf_y_pred_train = rf.predict(filter_X_train)
rf_y_pred_test = rf.predict(filter_X_test)
scores_rf = cross_val_score(estimator = rf, y = Y_train, X = filter_X_train, cv=5)
print('Cross Val... | Yes | 4 | 7 | Model_Train | train_model | 13,885,246 | 1 | A | 2 | 0 | 0 | 0 | 10 | 7 | ok | 11 | 7 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 23"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Randomness Cont... | C | Missing Axis Specification=medium, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Missing Axis Specification", "severity": "medium"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control 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"] | 13 | C |
3,078 | from xgboost import XGBClassifier
xgb = XGBClassifier(gamma=0, learning_rate=0.1, max_depth=100, n_estimators=100)
xgb.fit(filter_X_train,Y_train)
xgb_y_pred_train = xgb.predict(filter_X_train)
xgb_y_pred_test = xgb.predict(filter_X_test)
scores_xg = cross_val_score(estimator = rf, y = Y_train, X = filter_X_train, ... | Yes | 4 | 7 | Model_Train | train_model | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 9 | 6 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 23"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Ch... | C | Missing Axis Specification=medium, No random seed setting detected=low | [{"type": "Missing Axis Specification", "severity": "medium"}, {"type": "No random seed setting detected", "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"] | 13 | C |
3,079 | # Split Train Set & Test Set
from sklearn.model_selection import train_test_split
Y = X_sm['loss']
X = X_sm.drop(columns = 'loss')
X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 0.2, random_state = 1234, stratify = Y_sm)
print('training set = {} records, test set= {} records'.format(X_train.sh... | No | 4 | 13 | Data_Transform | split | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Column Selection Checker"] | 13 | B |
3,080 | # Select top 170 important numerical columns with filter method
X_train_num = X_train.drop(columns = bi_col_name)
selector = SelectKBest(score_func = f_regression, k = 170)
selector.fit(X_train_num,Y_train)
select_cols_sm = selector.get_support(indices = True)
select_num_cols_sm = X_train_num.iloc[:,select_cols_sm]
... | No | 5 | 86 | Model_Interpretation | features_selection | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 7 | 7 | [{"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... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | B |
3,081 | # Select top 150 important numerical columns with RFE
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
selector = RFE(LogisticRegression(), n_features_to_select=150, step=1, verbose = 2)
selector = selector.fit(select_num_cols_sm, Y_train)
select_cols_sm = selector.get_supp... | No | 5 | 86 | Model_Interpretation | features_selection | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing Checker", "fix": "Apply scaling before scaling-sensitive operations.", "benefits": "Improves model performance and accuracy.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation... | C | Cross Validation Checker=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | C |
3,082 | # Combine categorical and non-categorical dataframe together
def filter_x_df_sm(x):
df = x.copy()
all_filter_col = []
for keep in select_cat_col_name[select_cat_col_name.isin(keep_cols)]:
filter_col = [col for col in df.columns if col.startswith(str(keep))]
for col in filter_col :
... | No | 5 | 11 | Data_Transform | concatenate | 13,885,246 | 1 | A | 0 | 5 | 6 | 0 | 0 | 10 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Missing function docstring"] | 13 | A |
3,083 | #Standardize
scaler = StandardScaler()
scaler.fit(filter_X_train_sm)
X_train_scal_sm = scaler.fit_transform(filter_X_train_sm)
X_test_scal_sm = scaler.fit_transform(filter_X_test_sm) | No | 5 | 18 | Data_Transform | normalization | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 13 | 9 | [{"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, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "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"] | 13 | D |
3,084 | # Train Logistic Regression model
logisticRegr_sm = LogisticRegression()
logisticRegr_sm = logisticRegr_sm.fit(X_train_scal_sm, Y_train)
Lr_y_pred_train_sm = logisticRegr_sm.predict(X_train_scal_sm)
Lr_y_pred_test_sm = logisticRegr_sm.predict(X_test_scal_sm)
scores_lr_sm = cross_val_score(estimator = logisticRegr_sm... | Yes | 4 | 7 | Model_Train | train_model | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 10 | 7 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 22"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing ... | C | Missing Axis Specification=medium, No random seed setting detected=low, Scaler Missing Checker=medium | [{"type": "Missing Axis Specification", "severity": "medium"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["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"] | 13 | C |
3,085 | cat_cols = df.select_dtypes(exclude=['number']).columns.values
for col in cat_cols:
if df[col].value_counts().count() > 20000 :
print('Column {} has {} categories'.format(col,df[col].value_counts().count())) | No | 5 | 54 | EDA | count_unique_values | 13,885,246 | 1 | A | 0 | 2 | 3 | 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"] | 13 | A |
3,086 | b"featurename = filter_X_train.columns\nimportances = list(rf.feature_importances_)\n\nfeature_importances = [(feature, round(importance, 3)) for feature, importance in zip(featurename, importances)]\nfeature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n\nprint('Top 50 Importance Fea... | No | 5 | 86 | Model_Interpretation | features_selection | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
3,087 | test_df = pd.read_csv('../input/loan-default-prediction/test_v2.csv.zip')
test_df.head() | No | 5 | 45 | Data_Extraction | load_from_csv | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/loan-default-prediction/test_v2.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 14"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 13 | A |
3,088 | # Binary Encoding
encoder = ce.BinaryEncoder(cols = keep_cols)
bi_enc_df = encoder.fit_transform(test_df[keep_cols])
bi_col_name = bi_enc_df.columns
test_df = pd.concat([test_df,bi_enc_df],axis = 1)
test_df.head() | Yes | 4 | 20 | Data_Transform | categorify | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 13 | D |
3,089 | # Create select_test_df by drop some columns in test_df
select_test_df = pd.concat([test_df['id'],test_df[filter_X_train.columns]],axis = 1)
select_test_df.head() | No | 4 | 11 | Data_Transform | concatenate | 13,885,246 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 13 | A |
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