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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7,813 | df['Intersection']=df['Address'].apply(lambda x : 1 if '/' in x else 0)
df['Block']=df['Address'].apply(lambda x : 1 if 'Block' in x else 0)
df_test['Intersection']=df_test['Address'].apply(lambda x : 1 if '/' in x else 0)
df_test['Block']=df_test['Address'].apply(lambda x : 1 if 'Block' in x else 0) | No | 5 | 8 | Data_Transform | feature_engineering | 2,236,557 | 1 | A | 0 | 4 | 4 | 0 | 0 | 4 | ok | 2 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
7,814 | Id=df['Id']
df=df.drop(['Descript','Resolution','Id'],axis=1)
#----------test data---------
Id_test=df_test['Id']
df_test=df_test.drop(['Descript','Resolution','Id'],axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,815 | lasso = linear_model.Lasso(alpha=0.1)
score1 = cross_val_score(lasso, X_train, y_train, cv=10)
score1.mean() | No | 5 | 28 | Model_Train | compute_train_metric | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,816 | ridge = linear_model.Ridge(alpha=0.1)
score2 = cross_val_score(ridge, X_train, y_train, cv=10)
score2.mean() | No | 5 | 28 | Model_Train | compute_train_metric | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,817 | from sklearn.neighbors import KNeighborsRegressor | 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 |
7,818 | score3 = []
neighbor = []
for k in range(10, 100, 10):
knn = KNeighborsRegressor(n_neighbors=k, weights='distance')
score3.append(cross_val_score(knn, X_train, y_train, cv=10).mean())
neighbor.append(k) | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 2,078,255 | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,819 | score3 | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,820 | plt.plot(neighbor, score3, 'ro') | No | 5 | 33 | Visualization | distribution | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,821 | score3 = []
neighbor = []
for k in range(1, 32, 2):
knn = KNeighborsRegressor(n_neighbors=k, weights='distance')
score3.append(cross_val_score(knn, X_train, y_train, cv=10).mean())
neighbor.append(k) | No | 5 | 84 | Other | not_enough_vertices | 2,078,255 | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,822 | neighbor | No | 5 | 41 | Debug | show_table | 2,078,255 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,823 | knn = KNeighborsRegressor(n_neighbors=21)
score3 = cross_val_score(knn, X_train, y_train, cv=10)
score3.mean() | No | 5 | 28 | Model_Train | compute_train_metric | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,824 | score4 = []
trees = []
for n in range(10,101,10):
regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=n)
score4.append(cross_val_score(regr, X_train, y_train, cv=10).mean())
trees.append(n) | No | 2 | 27 | Model_Train | predict_on_train | 2,078,255 | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | A |
7,825 | plt.plot(trees, score4, 'ro') | No | 5 | 56 | Visualization | plot_predictions | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,826 | regr = RandomForestRegressor(max_depth=2, random_state=0, n_estimators=80)
score4.append(cross_val_score(regr, X_train, y_train, cv=10).mean()) | No | 5 | 28 | Model_Train | compute_train_metric | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | A |
7,827 | score4[-1] | No | 5 | 41 | Debug | show_table | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection C... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Import Checker"] | 6 | A |
7,828 | df["raw_radial"]=np.sqrt(np.power(df[\'X\'],2) + np.power(df[\'Y\'],2))<br><br>df_test["raw_radial"]=np.sqrt(np.power(df_test[\'X\'],2) + np.power(df_test[\'Y\'],2)) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,829 | test = pd.read_csv('../input/datasetss/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path de... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "Import Checker"] | 6 | A |
7,830 | le_res=le.fit_transform(df['Category'])
cat=pd.DataFrame(le_res)
cat.columns=['Category']
df=df.drop(labels=['Category'],axis=1)
df=pd.concat([cat,df],axis=1)
df.columns | Yes | 3 | 20 | Data_Transform | categorify | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 2 | D |
7,831 | X_test = test.drop('Id',axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,832 | lasso.fit(X_train, y_train) | 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 |
7,833 | prediction1 = lasso.predict(X_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker"] | 6 | A |
7,834 | df_test.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,835 | df=df[['Address', 'Minute', 'Hour', 'Day', 'Month', 'Year',
'District', 'DayOfWeek', 'X', 'Y', 'rot45_X', 'rot45_Y', 'rot30_X',
'rot30_Y', 'radial60', 'Intersection', 'Block', 'raw_radial',
'closest_centers_f', 'label']]
df_test=df_test[['Address', 'Minute', 'Hour', 'Day', 'Month', 'Year', 'Distr... | No | 5 | 10 | Data_Transform | drop_column | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,836 | prediction1 = abs(prediction)
prediction1[0] | Yes | 4 | 55 | Data_Export | prepare_output | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection C... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Import Checker"] | 6 | A |
7,837 | prediction = pd.DataFrame({'Id':test.Id,'median_house_value': prediction1[0]}) | No | 5 | 55 | Data_Export | prepare_output | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection C... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Import Checker"] | 6 | A |
7,838 | prediction | 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 |
7,839 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processi... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,840 | train_df = pd.read_csv('../input/train.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 |
7,841 | train_df.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 |
7,842 | train_df['president'].value_counts().plot(kind = 'bar')
plt.show() | No | 5 | 33 | Visualization | distribution | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 5 | 5 | [{"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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 8 | B |
7,850 | #Independent Column
X=df
X.shape
| No | 5 | 58 | Debug | show_shape | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,851 | #Dependent
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,shuffle=False) | No | 5 | 13 | Data_Transform | split | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | [] | 2 | B |
7,853 | train_data = []
for i, row in train_df.iterrows():
for text in row['text'].split('.'):
train_data.append([row['president'], text])
train_data = pd.DataFrame(train_data, columns=['president', 'text']) | No | 5 | 12 | Data_Transform | create_dataframe | 2,279,813 | 1 | A | 0 | 2 | 3 | 0 | 0 | 5 | ok | 5 | 5 | [{"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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 8 | B |
7,854 | train_data['president'].value_counts().plot(kind = 'bar')
plt.show() | No | 5 | 33 | Visualization | distribution | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 5 | 5 | [{"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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 8 | B |
7,855 | train_data.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 |
7,856 | def remove_punctuation_numbers(text):
punc_numbers = string.punctuation + '0123456789'
return ''.join([l for l in text if l not in punc_numbers]) | No | 5 | 78 | Data_Transform | string_transform | 2,279,813 | 1 | A | 0 | 2 | 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"] | 8 | A |
7,857 | def tokeniser(text):
return TreebankWordTokenizer().tokenize(text) | No | 5 | 78 | Data_Transform | string_transform | 2,279,813 | 1 | A | 0 | 1 | 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"] | 8 | A |
7,858 | def lemmetizer(tokens):
wordnet_lemmatizer = WordNetLemmatizer()
return [wordnet_lemmatizer.lemmatize(word) for word in tokens] | No | 5 | 84 | Other | not_enough_vertices | 2,279,813 | 1 | A | 0 | 1 | 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"] | 8 | A |
7,859 | def remove_stop_words(tokens):
return [t for t in tokens if t not in set(stopwords.words('english'))] | No | 5 | 84 | Other | not_enough_vertices | 2,279,813 | 1 | A | 0 | 2 | 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"] | 8 | A |
7,860 | def data_cleaner(text):
text = text.lower()
text = remove_punctuation_numbers(text)
lst = tokeniser(text)
lst = remove_stop_words(lst)
return ' '.join(lemmetizer(lst)) | No | 5 | 78 | Data_Transform | string_transform | 2,279,813 | 1 | A | 0 | 1 | 0 | 0 | 0 | 6 | 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", "Missing function docstring"] | 8 | A |
7,861 | train_data['clean_text'] = train_data['text'].apply(data_cleaner) | No | 5 | 8 | Data_Transform | feature_engineering | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 8 | B |
7,862 | for pres in train_data['president'].unique():
words =[]
for sentence in train_data[train_data['president'] == pres].clean_text:
words.extend(tokeniser(sentence))
wordcloud = WordCloud().generate_from_frequencies(frequencies=Counter(words))
plt.figure(figsize=(12,8))
plt.imshow(wordcloud... | No | 5 | 53 | Other | something_strange | 2,279,813 | 1 | A | 0 | 2 | 3 | 0 | 0 | 10 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 8 | B |
7,863 | train_data.president = train_data.president.map({'deKlerk':0,'Mandela':1,
'Mbeki':2, 'Motlanthe':3,
'Zuma': 4, 'Ramaphosa':5}) | No | 5 | 20 | Data_Transform | categorify | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 2 | 2 | [{"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"] | 8 | B |
7,864 | X = train_data.clean_text
y = train_data.president
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.33,
random_state=0,
stratify=y) | Yes | 4 | 13 | Data_Transform | split | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 2 | 2 | [{"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"] | 8 | B |
7,865 | vect = CountVectorizer(ngram_range=(1,2)) | No | 5 | 84 | Other | not_enough_vertices | 2,279,813 | 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"] | 8 | A |
7,866 | X_train_ = vect.fit_transform(X_train) | No | 5 | 8 | Data_Transform | feature_engineering | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"] | 8 | D |
7,867 | log = LogisticRegressionCV(dual=False, penalty='l2', multi_class='multinomial') | No | 5 | 4 | Model_Train | choose_model_class | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"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": "Dependent Thresh... | C | Scaler Missing Checker=medium | [{"type": "Scaler Missing Checker", "severity": "medium"}] | ["Dependent Threshold Checker"] | 8 | C |
7,868 | log.fit(X_train_, y_train) | No | 5 | 7 | Model_Train | train_model | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"] | 8 | B |
7,869 | print(accuracy_score(y_train, log.predict(X_train_))) | No | 5 | 28 | Model_Train | compute_train_metric | 2,279,813 | 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"] | 8 | A |
7,870 | print(accuracy_score(y_test, log.predict(vect.transform(X_test)))) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 6 | 5 | [{"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":... | 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"] | 8 | B |
7,871 | test_data = pd.read_csv('../input/test.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 |
7,872 | test_data.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 |
7,873 | test_data.text = test_data.text.apply(data_cleaner) | No | 5 | 8 | Data_Transform | feature_engineering | 2,279,813 | 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"] | 8 | A |
7,874 | test_data['president'] = log.predict(vect.transform(test_data.text)) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 7 | 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": "Dependent Th... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "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"] | 8 | B |
7,875 | test_data.drop('text', axis=1, inplace=True,) | No | 5 | 10 | Data_Transform | drop_column | 2,279,813 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 9"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold C... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "InPlace Checker"] | 8 | A |
7,876 | test_data.to_csv('Thapelo_log.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 2,279,813 | 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"] | 8 | A |
7,877 | import pandas as pd
import numpy as np
import string
import re
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='whitegrid', palette='muted',
rc={'figure.figsize': (15,10)}) | Yes | 4 | 22 | Environment | import_modules | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,878 | print(train.shape, test.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,879 | pres = {'deKlerk': 0,
'Mandela': 1,
'Mbeki': 2,
'Motlanthe': 3,
'Zuma': 4,
'Ramaphosa': 5}
train.replace({'president': pres}, inplace=True) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,880 | # speech number: intro lines
starts = {
0: 1,
1: 1,
2: 1,
3: 12,
4: 12,
5: 5,
6: 1,
7: 1,
8: 8,
9: 9,
10: 12,
11: 14,
12: 14,
13: 15,
14: 15,
15: 15,
16: 15,
17: 15,
18: 15,
19: 15,
20: 20,
21: 1,
22: 15,
23: 20,
24: 20,... | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 33 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,881 | def divide_on(df, char):
# iterate over text column of DataFrame, splitting at each occurrence of char
sentences = []
# let's split the data into senteces
for i, row in df.iterrows():
# skip the intro lines of the speech
for sentence in row['text'].split(char)[starts[i]:]:... | No | 5 | 78 | Data_Transform | string_transform | null | 1 | A | 0 | 3 | 3 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,882 | train = divide_on(train, '.') | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,883 | train.head(5) | 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 |
7,884 | train['president'].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 |
7,885 | # proportion of total
train['president'].value_counts()/train.shape[0] | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,886 | train['sentence'] = None
test['president'] = None
df = pd.concat([train, test], axis=0, sort=False) | No | 5 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,887 | # reorder columns
df = df[['sentence', 'text', 'president']] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,888 | df.tail() | 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 |
7,889 | def fixup(text):<br> <br> # remove punctuation<br> text = \'\'.join([char for char in text if char == \'-\' or char not in string.punctuation])<br> # remove special characters<br> text = text.replace(r\'^[*-]\', \'\')<br> # remove numbers<br> text = \'\'.join([char for char in text if not char.isdi... | No | 5 | 78 | Data_Transform | string_transform | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,890 | # get length of sentence as variable
df['length'] = df['text'].apply(len) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,891 | # what are our longest sentences?
df.sort_values(by='length', ascending=False).head(10) | 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 |
7,892 | df.loc[3930][1] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,893 | # what are our shortest sentences?
df.sort_values(by='length').head(5) | 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 |
7,894 | # let's check the shortest sentences in our test set
df[pd.isnull(df['president'])].sort_values(by='length').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 |
7,895 | # sentences with just a few characters are of no use to us
df = df[df['length']>10] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,896 | # what are our shortest sentences now?
df.sort_values(by='length').head(5) | 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 |
7,897 | df['president'].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 |
7,898 | from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from sklearn.preprocessin... | No | 5 | 22 | Environment | import_modules | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,899 | tfidf = TfidfVectorizer(strip_accents='unicode', ngram_range=(1,3), stop_words='english', min_df=6)
X = tfidf.fit_transform(df['text']).todense()
X.shape | Yes | 4 | 8 | Data_Transform | feature_engineering | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 11 | 9 | [{"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, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=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"}] | ["Column Selection 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"] | 7 | D |
7,900 | tfidf.get_feature_names() | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,901 | X = pd.DataFrame(data=X, columns=tfidf.get_feature_names()) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,902 | df = df.drop(columns=['text', 'length'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,903 | X = pd.DataFrame(np.hstack((df, X))) | No | 5 | 12 | Data_Transform | create_dataframe | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,904 | HYPER_PARAMS = {
'learning_rate': 0.02,
'n_estimators':800,
'max_depth': 6,
'subsample': 0.8,
'colsample_bytree': 0.8,
'max_delta_step': 1,
'objective': 'multi:softmax',
'nthread': 4,
'seed': 1747
}
model = xgb.XGBClassifier(**HYPER_PARAMS)
model.fit(X,y)
| No | 5 | 7 | Model_Train | train_model | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | 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"] | 2 | B |
7,905 | X.columns = ['sentence_id', 'president_id'] + tfidf.get_feature_names() | No | 5 | 61 | Data_Transform | rename_columns | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,906 | y_pred=model.predict_proba(df_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,236,557 | 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"] | 2 | A |
7,907 | train = X[pd.isnull(X['sentence_id'])]
test = X[pd.notnull(X['sentence_id'])] | No | 5 | 14 | Data_Transform | filter | 2,297,927 | 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 |
7,908 | X_train = train.drop(['sentence_id', 'president_id'], axis=1)
X_test = test.drop(['sentence_id', 'president_id'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,297,927 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"}] | [] | 7 | B |
7,909 | y_pred= pd.DataFrame(y_pred, index=Id_test,columns = le.classes_) | No | 5 | 12 | Data_Transform | create_dataframe | 2,236,557 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
7,910 | y_pred.to_csv("submit.csv", float_format = \'%.5F\') | 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 |
7,918 | def one_hot_encode(label):
# initialize zero array
vec = [0, 0, 0, 0, 0, 0]
# set index of array corresponding to label = 1
vec[label] = 1
return vec
# save encoded labels as target for model
y_train = np.vstack(row for row in train['president_id'].apply(one_hot_encode).values) | No | 5 | 20 | Data_Transform | categorify | 2,297,927 | 1 | A | 1 | 1 | 0 | 0 | 5 | 5 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "DataFrame Conversion Checker", "fix": "Use .to_numpy() instead of .values.", "benefits": "Ensures future compatibility and avoids unexpected behaviour.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "DataFrame Conversion Checker", "Missing function docstring"] | 7 | B |
7,919 | y_train[600] | 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 |
7,920 | print('Train size:', X_train.shape)
print('Test size:', X_test.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,921 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processi... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,922 | # importing the modulus
from keras.preprocessing.image import ImageDataGenerator,img_to_array,load_img
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense , Activation
from keras import applications
import re | No | 5 | 22 | Environment | import_modules | 2,309,282 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
7,923 | def create_model(lyrs=[X_train.shape[1], 1028, 512, 256], act='relu', opt='Adam', dr=0.25):
model = Sequential()
# create first hidden layer
model.add(Dense(lyrs[0], input_dim=X_train.shape[1], activation=act))
# create additional hidden layers
for i in range(1,len(lyrs)):
mod... | No | 5 | 4 | Model_Train | choose_model_class | 2,297,927 | 1 | A | 0 | 2 | 1 | 0 | 0 | 9 | 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"] | 7 | A |
7,924 | model = create_model()
print(model.summary()) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,925 | # train model on full train set, with 80/20 CV split<br>training = model.fit(X_train, y_train, epochs=5, batch_size=64, validation_split=0.2)<br>val_acc = np.mean(training.history[\'val_acc\'])<br>print("\<br>%s: %.2f%%" % (\'val_acc\', val_acc*100)) | Yes | 4 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,926 | # summarize history for accuracy
plt.plot(training.history['acc'])
plt.plot(training.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show() | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,927 | predictions = model.predict(X_test) | 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 |
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