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6,970 | result = pd.DataFrame(pred[:,1])
result.index.name = 'id'
result.columns = ['predicted_val']
result.to_csv('output.csv', index=True) | Yes | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,971 | train = train_data[:-500]
test = train_data[-500:] | 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 |
6,972 | import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
| No | 5 | 22 | Environment | import_modules | 1,497,226 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
6,973 | df_train = pd.read_csv('../input/web-club-recruitment-2018/train.csv')
df_test = pd.read_csv('../input/web-club-recruitment-2018/test.csv')
| No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,974 | X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
Y = [i[1] for i in train]
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,1)
test_y = [i[1] for i in test] | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,975 | model.fit({'input': X}, {'targets': Y}, n_epoch=2, validation_set=({'input': test_x}, {'targets': test_y}),
snapshot_step=50000, show_metric=True, run_id=MODEL_NAME) | 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 |
6,976 | X = df_train.loc[:, 'X1':'X23']
y = df_train.loc[:, 'Y']
| No | 5 | 14 | Data_Transform | filter | 1,497,226 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
6,977 | import tensorflow as tf
tf.reset_default_graph() | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,978 | rf.fit(X, y)
| No | 5 | 7 | Model_Train | train_model | 1,497,226 | 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"] | 8 | B |
6,979 | convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)
convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool... | No | 2 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 29 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,980 | model.save(MODEL_NAME) | No | 5 | 50 | Model_Train | save_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,981 | test = df_test.loc[:, 'X1':'X23']
pred = rf.predict_proba(test) | Yes | 4 | 48 | Model_Evaluation | predict_on_test | 1,497,226 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 8 | A |
6,982 | with open('submission_file.csv','w') as f:<br> f.write('id,label\<br>')<br> <br>with open('submission_file.csv','a') as f:<br> for data in tqdm(test_data):<br> img_num = data[1]<br> img_data = data[0]<br> orig = img_data<br> data = img_data.reshape(IMG_SIZE,IMG_SIZE,1)<br> ... | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,984 | import pandas as pd
import matplotlib.pyplot as plt
import sklearn
import os
%matplotlib inline
print(os.listdir("../input/dataset-adult/")) | No | 5 | 88 | Debug | list_files | 1,527,751 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 4 | null |
6,985 | #Adult Data
adult = pd.read_csv("../input/dataset-adult/train_data.csv",sep=",", na_values="?") | No | 5 | 45 | Data_Extraction | load_from_csv | 1,527,751 | 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": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path"] | 4 | A |
6,986 | adult.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 |
6,987 | import pandas as pd
import sklearn | 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 |
6,988 | adult.head(3) | No | 5 | 41 | Debug | show_table | 1,527,751 | 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"] | 4 | A |
6,989 | import os
print(os.listdir('../input')) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,990 | adult.info() | No | 5 | 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 |
6,991 | adult = pd.read_csv("../input/dataadult/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 |
6,992 | adult.describe() | No | 5 | 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 |
6,993 | adult.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 |
6,994 | adult['native.country'].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,995 | adult['race'].value_counts().plot(kind="pie") | No | 5 | 33 | Visualization | distribution | 1,527,751 | 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"] | 4 | A |
6,996 | #distribuio de idade<br>adult["age"].plot(kind=\'hist\',bins=15); | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,997 | #agrupando atributo \'income\' e \'sex\' para cada idade<br>df=adult.groupby(["income","sex"]).mean()<br>df[\'age\'].plot(kind="bar") | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,998 | nadult = adult.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 |
6,999 | #Proporo de sexo por \'income\'<br>df2=adult.groupby(["income","sex"]).size().unstack().plot(kind=\'bar\',stacked=False) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,000 | nadult | 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 |
7,001 | #proporo de sexo por ocupao!<br>df2=adult.groupby(["occupation","sex"])[\'race\'].size().unstack().plot(kind=\'barh\',stacked=True) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,002 | #drop colunas empty e index "Id"
na_adult=adult.set_index("Id").dropna() | No | 5 | 17 | Data_Transform | correct_missing_values | 1,527,751 | 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"] | 4 | A |
7,003 | test_adult= pd.read_csv("../input/dataset-adult/test_data.csv",sep=",",na_values="?") | No | 5 | 45 | Data_Extraction | load_from_csv | 1,527,751 | 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": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path"] | 4 | A |
7,004 | Yadult = nadult.income | No | 4 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,005 | from sklearn.neighbors import KNeighborsClassifier | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,006 | knn = KNeighborsClassifier(n_neighbors=5) | 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 |
7,007 | from sklearn.model_selection import cross_val_score | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,008 | #armazena todos os dados de treino e de teste (numericos e categoricos)
X_adult = na_adult.iloc[:,:-1]
Y_adult = na_adult.income | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,527,751 | 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"] | 4 | A |
7,009 | X_test = test_adult.iloc[:,:] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,527,751 | 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"] | 4 | A |
7,010 | #treino e teste apenas de dados numericos adult
num_cols=["age","education.num","capital.gain","capital.loss","hours.per.week"]
X_num=X_adult[num_cols]
Y_num=Y_adult
X_test= X_test[num_cols] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,527,751 | 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"] | 4 | A |
7,011 | 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 |
7,012 | testadult = pd.read_csv("../input/dataadult/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 |
7,013 | #importacao de bibliotecas de ML
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder | No | 5 | 22 | Environment | import_modules | 1,527,751 | 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"] | 4 | A |
7,014 | YtestPred = knn.predict(Xtestadult) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,015 | arr1= testadult.iloc[:,0].values<br>arr1 = arr1.ravel()<br>dataset = pd.DataFrame({\'Id\':arr1[:],\'income\':YtestPred[:]})<br>dataset.to_csv("Adultscompetition.csv", index = False) | Yes | 3 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,016 | import pandas as pd
import sklearn | 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 |
7,017 | adult = pd.read_csv("../input/adult-db/train_data.csv",header=0, index_col=0, na_values="?") | No | 5 | 45 | Data_Extraction | load_from_csv | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/adult-db/train_data.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 8"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 7 | A |
7,018 | print(adult.shape) | No | 5 | 58 | Debug | show_shape | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,019 | #learning e predict
knn=KNeighborsClassifier(n_neighbors=9) #instacia model
scores = cross_val_score(knn,X_num,Y_num,cv=10) #validacao cruzada
knn.fit(X_num,Y_num)
Y_testpredict=knn.predict(X_test)
scores | Yes | 3 | 4 | Model_Train | choose_model_class | 1,527,751 | 1 | A | 0 | 0 | 0 | 1 | 0 | 5 | ok | 8 | 6 | [{"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": "... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["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"] | 4 | B |
7,020 | #observando tipo de dados..
test_adult.dtypes | No | 5 | 70 | Debug | show_data_types | 1,527,751 | 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"] | 4 | A |
7,021 | #Converte 'object columns para 'str', pois object pode conter dados em outro formato:
convert_cols=['workclass','education','marital.status','occupation','race','relationship',
'sex','native.country']
test_adult[convert_cols] = test_adult[convert_cols].astype(str) | No | 5 | 16 | Data_Transform | data_type_conversions | 1,527,751 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 4 | A |
7,022 | #testes..
test_adult.columns | No | 5 | 71 | Debug | show_columns | 1,527,751 | 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"] | 4 | A |
7,023 | #treino e teste de dados numericos e categoricos:
Xencode_adult= na_adult.iloc[:,:-1].apply(LabelEncoder().fit_transform)
Xencode_test_adult = test_adult.apply(LabelEncoder().fit_transform)
X_adult = Xencode_adult
X_test = Xencode_test_adult | No | 5 | 20 | Data_Transform | categorify | 1,527,751 | 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"] | 4 | A |
7,024 | adult["native.country"].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,025 | import matplotlib.pyplot as plt
%matplotlib inline | 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 |
7,026 | Yfit_adult= LabelEncoder().fit(na_adult["income"])
Y_adult = Yfit_adult.transform(na_adult["income"]) | No | 5 | 20 | Data_Transform | categorify | 1,527,751 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 12 | 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"}] | ["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"] | 4 | D |
7,027 | adult["age"].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 |
7,028 | #learning e predict
knn =KNeighborsClassifier(n_neighbors=10)
scores = cross_val_score(knn,X_adult,Y_adult,cv=10)
knn.fit(X_adult,Y_adult)
scores | Yes | 3 | 4 | Model_Train | choose_model_class | 1,527,751 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 5 | 4 | [{"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": "... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | A |
7,029 | adult["sex"].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,030 | adult["education.num"].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 |
7,031 | Ytest_predict= knn.predict(X_test)
print(Ytest_predict) | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,527,751 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 4 | A |
7,032 | adult["occupation"].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 |
7,033 | pd.unique(adult["relationship"]) | No | 5 | 57 | Debug | show_unique_values | 1,544,504 | 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"] | 7 | A |
7,034 | X_adult.columns | No | 5 | 71 | Debug | show_columns | 1,527,751 | 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"] | 4 | A |
7,035 | #escolha de atributos para melhor predict
atributos=atributos=["age","workclass","education.num","occupation","sex","marital.status","capital.gain","capital.loss"]
X_adult = Xencode_adult[atributos]
X_test = Xencode_test_adult[atributos] | No | 4 | 21 | Data_Transform | prepare_x_and_y | 1,527,751 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 4 | A |
7,036 | #Escolhendo k=27 p kNN
knn =KNeighborsClassifier(n_neighbors=27)
knn.fit(X_adult,Y_adult)
scores | Yes | 3 | 4 | Model_Train | choose_model_class | 1,527,751 | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | ok | 5 | 5 | [{"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"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | B |
7,037 | from sklearn.preprocessing import MinMaxScaler
minmaxscaler = MinMaxScaler()
col_inds = [0,1,4,5,6,7,8,10,11,12] # 0,1 [0,1,3,4,5,6,7,8,9,10,13] 2 [0,1,3,4,5,6,7,8,9,10,11,12,13] 3 [0,1,4,5,6,7,8,10,11,12]
Xadult_unscaled = adult_fill.iloc[:,col_inds].apply(LabelEncoder().fit_transform)
Xadult = minmaxscaler.fit_transf... | Yes | 3 | 18 | Data_Transform | normalization | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 6 | 6 | [{"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, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | D |
7,038 | Ytest_predict= knn.predict(X_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,527,751 | 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"] | 4 | A |
7,039 | #dados de submissao
label_out = Yfit_adult.inverse_transform(Ytest_predict)
df_out = pd.DataFrame({'Id': X_test.index,'income':label_out})
df_out.to_csv('submission_adult.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 1,527,751 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 6 | 6 | [{"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... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | D |
7,040 | pd.read_csv("submission_adult.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 1,527,751 | 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"] | 4 | A |
7,041 | %matplotlib inline
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
import numpy as np | No | 5 | 23 | Environment | set_options | 1,554,855 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
7,042 | testAdult = pd.read_csv("../input/adult-db/test_data.csv",header=0, index_col=0, na_values="?")
testAdult.shape | Yes | 4 | 45 | Data_Extraction | load_from_csv | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/adult-db/test_data.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 8"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 7 | A |
7,043 | adult = pd.read_csv("../input/mydata/train_data.csv",<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> skiprows... | 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 |
7,044 | adult["Country"].value_counts() | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,045 | adult["Age"].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 |
7,046 | 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 |
7,047 | adult["Occupation"].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 |
7,048 | XtestAdult_unscaled = testAdult_fill.iloc[:,col_inds].apply(LabelEncoder().fit_transform)
XtestAdult = minmaxscaler.transform(XtestAdult_unscaled) | Yes | 3 | 20 | Data_Transform | categorify | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"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": "Unit Te... | B | Pipeline Checker=low | [{"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker"] | 7 | B |
7,049 | testAdult = pd.read_csv("../input/mydata/test_data.csv",<br> names=[<br> "ID","Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Martial Status",<br> "Occupation", "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss",<br> "Hours per week", "Country"],<br> skiprows=1... | 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 |
7,050 | knn = KNeighborsClassifier(n_neighbors=34,p=1)
knn.fit(Xadult,Yadult) | No | 5 | 7 | Model_Train | train_model | 1,544,504 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 7 | B |
7,051 | testAdult.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 |
7,052 | YtestAdult = knn.predict(XtestAdult)
YtestAdult | No | 5 | 48 | Model_Evaluation | predict_on_test | 1,544,504 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | 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"] | 7 | A |
7,053 | testAdult['Capital Gain'].plot() | No | 5 | 33 | Visualization | distribution | 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 |
7,054 | prediction = pd.DataFrame(testAdult.index)
prediction["income"] = YtestAdult | Yes | 4 | 12 | Data_Transform | create_dataframe | 1,544,504 | 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"] | 7 | A |
7,055 | prediction.to_csv("adult_prediction_5.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | 1,544,504 | 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"] | 7 | A |
7,056 | import pandas as pd
import numpy as np
import sklearn | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,057 | adult = pd.read_csv("../input/adultdataset/train_data.csv",<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> se... | 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 |
7,058 | nTestAdult = testAdult.dropna()
nTestAdult.shape | Yes | 3 | 17 | Data_Transform | correct_missing_values | 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 |
7,059 | Xadult = nadult[["Age","Education-Num","Capital Gain", "Capital Loss", "Hours per week"]]
Xadult.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 |
7,060 | Yadult = nadult.Target | 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 |
7,061 | testAdult = pd.read_csv("../input/adultdataset/test_data.csv",<br> names=[<br> "id", "Age", "Workclass", "fnlwgt", "Education", "Education-Num", "Martial Status",<br> "Occupation", "Relationship", "Race", "Sex", "Capital Gain", "Capital Loss",<br> "Hours per week", "Country"],<br> sep... | 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 |
7,062 | from sklearn.neighbors import KNeighborsClassifier | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,063 | knn = KNeighborsClassifier(n_neighbors=30) | 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 |
7,064 | YtestPred = knn.predict(XtestAdult) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,065 | result = np.vstack((testAdult["id"], YtestPred)).T
x = ["id","income"]
Resultado = pd.DataFrame(columns = x, data = result)
Resultado.to_csv("Resultados.csv", index = False)
Resultado | Yes | 3 | 25 | Data_Export | save_to_csv | 1,558,400 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 5 | A |
7,066 | import os
os.listdir('../input/adultb') | No | 5 | 88 | Debug | list_files | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/adultb. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 7"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 5 | A |
7,067 | adult=pd.read_csv(\'../input/adultb/train_data.csv\',<br> sep=\',\', 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 |
7,068 | nadult = adult.copy() | No | 4 | 77 | Other | define_variables | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
7,069 | adult.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 |
7,070 | Xadult = adult[['age','education.num',
'capital.gain','capital.loss',
'hours.per.week']] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 1,560,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
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