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829 | regressor_models={
'Linear Regression':LinearRegression(),
'Decision Tree Regressor':DecisionTreeRegressor(),
'Random Forest Regressor':RandomForestRegressor(),
'SVR':SVR(),
} | No | 3 | 23 | Environment | set_options | 13,503,938 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 3 | [{"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": "Randomness Contr... | C | Randomness Control Checker=low, Scaler Missing Checker=medium | [{"type": "Randomness Control Checker", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Verbose Mode Checker"] | 9 | C |
830 | def get_rmse_score(model,x_train,x_test,y_train,y_test):
model.fit(x_train,y_train)
y_predicted=model.predict(x_test)
r2_score=model.score(x_test,y_test)
rmse=sqrt(mean_squared_error(y_test,y_predicted))
return rmse,r2_score | No | 4 | 28 | Model_Train | compute_train_metric | 13,503,938 | 1 | A | 0 | 1 | 0 | 0 | 0 | 6 | ok | 9 | 8 | [{"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, 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"}] | ["Exception Handling Checker", "Missing function docstring", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 9 | B |
831 | x_train,x_test,y_train,y_test=train_test_split(X,Y,random_state=42) | No | 5 | 13 | Data_Transform | split | 13,503,938 | 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"}] | [] | 9 | B |
832 | svr_model=SVR()
svr_model.fit(x_train,y_train)
Y_Test_predictions=svr_model.predict(X_Test) | No | 3 | 27 | Model_Train | predict_on_train | 13,503,938 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 9 | 8 | [{"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, Cross-validation not detected=low, No random seed setting detected=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"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"] | 9 | C |
833 | predictions=[]
for index in range(len(Y_Test_predictions)):
predictions.append([test_indexes[index],Y_Test_predictions[index]]) | No | 3 | 48 | Model_Evaluation | predict_on_test | 13,503,938 | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | 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": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 9 | A |
834 | predictions_df=pd.DataFrame(predictions,columns=['Id','Prediction'])
predictions_df.to_csv('SVM_Predictions.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 13,503,938 | 1 | A | 0 | 0 | 0 | 0 | 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"] | 9 | A |
835 | # 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 | 4 | 22 | Environment | import_modules | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 6 | 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 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
836 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore") | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
837 | train_path = '../input/restaurant-revenue-prediction/train.csv.zip'
df_train = pd.read_csv(train_path)
df_train.head() | No | 3 | 41 | Debug | show_table | 14,177,670 | 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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path"] | 11 | B |
838 | test_path = '../input/restaurant-revenue-prediction/test.csv.zip'
df_test = pd.read_csv(test_path, index_col='Id')
df_test.head() | No | 3 | 41 | Debug | show_table | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/test.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 11 | A |
839 | from datetime import date, datetime
def calculate_age(born):
born = datetime.strptime(born, "%m/%d/%Y").date()
today = date.today()
return today.year - born.year - ((today.month, today.day) < (born.month, born.day))
df_train['Age'] = df_train['Open Date'].apply(calculate_age)
df_test['Age'] = ... | No | 3 | 41 | Debug | show_table | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
840 | # Find the sets of categorical variables and numberical variales for feature analyses
numerical_features = df_train.select_dtypes([np.number]).columns.tolist()
categorical_features = df_train.select_dtypes(exclude = [np.number,np.datetime64]).columns.tolist()
print(categorical_features)
print(numerical_features) | No | 3 | 40 | Debug | show_table_attributes | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 3 | 3 | [{"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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 11 | B |
841 | print(df_train['revenue'].describe())
sns.distplot(a=df_train['revenue'], kde=True).set(xlabel='revenue', ylabel='P(revenue)') | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
842 | df_train[df_train['revenue'] > 10000000 ] | 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 |
843 | # Drop outliers
df_train = df_train[df_train['revenue'] < 10000000 ]
df_train.reset_index(drop=True).head() | No | 3 | 10 | Data_Transform | drop_column | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 4 | [{"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"] | 11 | B |
844 | fig, ax = plt.subplots(3, 1, figsize=(40, 30))
for variable, subplot in zip(categorical_features, ax.flatten()):
df_2 = df_train[[variable,'revenue']].groupby(variable).revenue.sum().reset_index()
df_2.columns = [variable,'total_revenue']
sns.barplot(x=variable, y='total_revenue', data=df_2 , ax=subplot)
... | No | 5 | 33 | Visualization | distribution | 14,177,670 | 1 | A | 0 | 3 | 5 | 0 | 0 | 13 | ok | 3 | 3 | [{"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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 11 | B |
845 | fig, ax = plt.subplots(13, 3, figsize=(30, 35))
for variable, subplot in zip(numerical_features, ax.flatten()):
sns.regplot(x=df_train[variable], y=df_train['revenue'], ax=subplot)
subplot.set_xlabel(variable,fontsize=20)
subplot.set_ylabel('Revenue',fontsize=20)
fig.tight_layout() | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
846 | plt.figure(figsize=(45,25))
mask = np.triu(np.ones_like(df_train.corr(), dtype=np.bool))
sns.heatmap(df_train.corr(),annot=True, mask=mask)
sns.set(font_scale=1.4) | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
847 | fig, ax = plt.subplots(3, 1, figsize=(40, 30))
for variable, subplot in zip(categorical_features, ax.flatten()):
sns.swarmplot(x=variable, y='revenue', data=df_train, ax=subplot,size=10)
subplot.set_xlabel(variable,fontsize=20)
subplot.set_ylabel('Revenue',fontsize=20)
for label in subplot.get_xticklabe... | No | 5 | 33 | Visualization | distribution | 14,177,670 | 1 | A | 0 | 3 | 5 | 0 | 0 | 11 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
848 | #Lets take a look at city group field
print("--- Train set ---")
print(df_train['City Group'].value_counts())
print("---- Test set ----")
print(df_test['City Group'].value_counts())' | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
849 | #Lets take a look at type field
print("--- Train set ---")
print(df_train['Type'].value_counts())
print("---- Test set ----")
print(df_test['Type'].value_counts())
' | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
850 | y = df_train['revenue']
df_train=df_train.drop('revenue', axis=1) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 11 | B |
851 | print("Shapes: Train set ", df_train.shape ,", Test ",df_test.shape)
df_full = pd.concat([df_train,df_test])
print("Full dataset shapes: ", df_full.shape) | No | 4 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
852 | print('There are {} cities which restaurant location have been collected.'.format(len(df_full['City'].unique()))) | No | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
853 | df_full = df_full.drop('City', axis=1)
df_full.shape | No | 4 | 10 | Data_Transform | drop_column | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
854 | p_name = ['P'+str(i) for i in range(1,38)] | No | 3 | 58 | Debug | show_shape | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
855 | from sklearn.decomposition import PCA
pca = PCA().fit(df_full[p_name])
plt.figure(figsize=(7,5))
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of Components')
plt.ylabel('Explained variance')
plt.yticks(np.arange(0.1,1.1,0.05))
plt.xticks(np.arange(0,41,2))
plt.grid(True) | No | 3 | 23 | Environment | set_options | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 9 | ok | 9 | 8 | [{"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": "Pipeline Checker... | D | Cross Validation Checker=low, Data Leakage Checker=high, Pipeline Checker=low, Randomness Control Checker=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Pipeline Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
856 | pca_list = ['pca'+str(i) for i in range(1,30,1)]
df_full[pca_list] = PCA(n_components=29).fit_transform(df_full[p_name])
df_full.drop(p_name,axis=1,inplace=True) | No | 3 | 33 | Visualization | distribution | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 11 | 10 | [{"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 14"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | D | Cross Validation Checker=low, Data Leakage Checker=high, Pipeline Checker=low, Randomness Control Checker=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Pipeline Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Column Selection Checker", "Exception Handling Checker", "InPlace Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 11 | D |
857 | df_full.info() | No | 4 | 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 |
858 | df=pd.get_dummies(df_full, dtype=float) | No | 3 | 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 |
859 | # Get number of train sets
numTrain=df_train.shape[0]
train = df[:numTrain]
test = df[numTrain:] | No | 3 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
860 | sns.distplot(a=y, kde=True).set(xlabel='revenue', ylabel='P(revenue)') | No | 5 | 33 | Visualization | distribution | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
861 | print("Kurtosis: {}".format(y.kurt()))
print("Skewness: {}".format(y.skew())) | No | 5 | 40 | Debug | show_table_attributes | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
862 | from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso, Ridge, ElasticNet
from sklearn.ensemble import AdaBoostRegressor
from xgboost import XGBRegressor
| No | 4 | 21 | Data_Transform | prepare_x_and_y | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
863 | best_estimators=[] | No | 5 | 77 | Other | define_variables | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
864 | ## Parameters
params = {
"alpha" : [.01, .1, .5, .7, .9, .95, .99, 1, 5, 10, 20],
"fit_intercept" : [True, False],
"normalize" : [True,False],
"solver" : ['svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'],
"tol" : [0.0001, 0.001, 0.01, 0.1],
"random_state" : [42]
}
## Ridge
ridge = Ridge()... | No | 4 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
865 | params = {
'alpha' : [.01, .1, .5, .7, .9, .95, .99, 1, 5, 10, 20],
'fit_intercept' : [True, False],
'normalize' : [True,False],
'tol' : [0.0001, 0.001, 0.01, 0.1],
"random_state" : [42]
}
## Lasso
lasso = Lasso()
lasso_grid = GridSearchCV(lasso, params, scoring='r2', cv=5, n_jobs=-1)
lasso_grid.fi... | No | 3 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
866 | # Parameters
params = {
"alpha" : [.01, .1, .5, .7, .9, .95, .99, 1, 5, 10, 20],
"fit_intercept" : [True, False],
"normalize" : [True,False],
"tol" : [0.0001, 0.001, 0.01, 0.1],
"random_state" : [42]
}
## Elastic Net
EL = ElasticNet()
EL_grid = GridSearchCV(EL, params, scoring='r2', cv=5, n_jobs=-1... | No | 3 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
867 | # parameters
params = {
"learning_rate": [.1, .5, .7, .9, .95, .99, 1],
"colsample_bytree": [.3, .4, .5, .6],
"max_depth": [2, 4],
"alpha": [1, 3, 5],
"subsample": [.5],
"n_estimators": [30, 70, 100, 200],
"random_state" : [42]
}
## XGBoost Regressor
XGBR = XGBRegressor()
XGBR_grid = GridS... | No | 3 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
868 | ## parameters
params = {
"n_estimators": [10, 30, 50, 100],
"learning_rate": [.01, 0.1, 0.5, 0.9, 0.95, 1],
"random_state" : [42]
}
## XGBoost Regressor
AdaBoostR = AdaBoostRegressor()
AdaBoostR_grid = GridSearchCV(AdaBoostR, params, scoring='r2', cv=5, n_jobs=-1)
AdaBoostR_grid.fit(X_train, y_train)
##... | No | 3 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
869 | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
## pipeline
pipelines = []
for name,model in best_estimators:
pipeline = Pipeline([("Scaler",StandardScaler()),
(name,model)
])
pipelines.append(["Scaled_"+name,pipeline]) | No | 3 | 5 | Hyperparam_Tuning | define_search_space | 14,177,670 | 1 | A | 1 | 1 | 1 | 0 | 5 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
870 | from sklearn.model_selection import KFold, cross_val_score
## Create a dataframe to store all the models' cross validation score
evaluate = pd.DataFrame(columns=["model","cv","std"])
## Encoded dataset
for name,model in pipelines:
kfold = KFold(n_splits=10,random_state=42)
cv = cross_val_score(model, X_train... | No | 2 | 22 | Environment | import_modules | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
871 | from sklearn.ensemble import VotingRegressor | No | 3 | 56 | Visualization | plot_predictions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
872 | ## Creating a list for all combinations models
votings = []
## All models
votings.append(("Scaled_all_models",Pipeline([("Scaler",StandardScaler()),
("Votings",VotingRegressor([("XGBoostR",XGBR_grid.best_estimator_),
... | No | 5 | 82 | Hyperparam_Tuning | define_search_model | 14,177,670 | 1 | A | 0 | 0 | 0 | 0 | 0 | 20 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 11 | A |
873 | !pip install python-googlegeocoder | No | 4 | 22 | Environment | import_modules | 8,604,602 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 5 | null |
874 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from googlegeocoder import GoogleGeocoder
from bokeh.plotting import figure, save
import plotly.graph_objects as go
import sklearn
import warnings
warnings.filterwarnings("ignore")
plt.style.use('ggplot')
pd.plotting.register_m... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
875 | train_df=pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/train.csv')
test_df=pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/test.csv')
all_data=pd.concat([train_df,test_df],axis=0)
all_data.reset_index(drop=True) | No | 3 | 45 | Data_Extraction | load_from_csv | 8,604,602 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 3 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 5 | A |
876 | train_df.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 |
877 | train_df['Country_Region'].nunique() | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
878 | print("fill blanks and add region for counting")
train_df.drop('Province_State',axis=1,inplace=True)' | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
879 | # Resetting Date column into Datetime object and making it an index of dataframe
train_df['Date']=pd.to_datetime(train_df['Date'])
train_df.set_index('Date',inplace=True) | No | 4 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
880 | from sklearn.neighbors import KNeighborsRegressor
params_knn = {
'n_neighbors' : [3, 5, 7, 9, 11],
}
knn_model = KNeighborsRegressor()
knn_regressor = GridSearchCV(knn_model, params_knn, scoring='neg_root_mean_squared_error', cv=10, n_jobs=-1)
knn_regressor.fit(X_train, y_train)
print(f'Optimal neighbors: {knn_re... | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
881 | knn_model = KNeighborsRegressor(n_neighbors=knn_regressor.best_params_["n_neighbors"])
knn_model.fit(X_train, y_train)
y_train_pred = knn_model.predict(X_train)
y_pred = knn_model.predict(X_test)
print('Train r2 score: ', r2_score(y_train_pred, y_train))
print('Test r2 score: ', r2_score(y_test, y_pred))
train_rmse = n... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
882 | from sklearn.ensemble import RandomForestRegressor
params_rf = {
'max_depth': [10, 30, 35, 50, 65, 75, 100],
'max_features': [.3, .4, .5, .6],
'min_samples_leaf': [3, 4, 5],
'min_samples_split': [8, 10, 12],
'n_estimators': [30, 50, 100, 200]
}
rf = RandomForestRegressor()
rf_regressor = GridSearc... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
883 | rf_model = RandomForestRegressor(max_depth=rf_regressor.best_params_["max_depth"],
max_features=rf_regressor.best_params_["max_features"],
min_samples_leaf=rf_regressor.best_params_["min_samples_leaf"],
min_samples_spl... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
884 | #
# City, City Group, Type
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df_all["City"] = le.fit_transform(df_all["City"])
df_all["City Group"] = le.fit_transform(df_all["City Group"])
df_all["Type"] = le.fit_transform(df_all["Type"])
# df_all' | No | 4 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 12 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
885 | #
df_train_fin = df_all.iloc[:df_train.shape[0]] # df_train
df_test_fin = df_all.iloc[df_train.shape[0]:] # df_test' | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
886 | from sklearn.ensemble import RandomForestRegressor
# IDOpenDate
out_columns = ["Id", "Open Date"]
columns = []
for i in df_train_fin.columns:
if i not in out_columns:
columns.append(i)
x_train = df_train_fin[columns]
#
rfr = RandomForestRegressor(
n_estimators=200,
max_depth=5,
max_feat... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
887 | pred = rfr.predict(df_test_fin[columns]) | No | 5 | 48 | Model_Evaluation | predict_on_test | 11,410,370 | 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": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
888 | df_submission | 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 |
889 | df_submission['Prediction'] = pred
df_submission.to_csv('/kaggle/working/RandamForest_submission01.csv', index=False) | No | 4 | 25 | Data_Export | save_to_csv | 11,410,370 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path"] | 4 | A |
890 | # 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 | 5 | 88 | Debug | list_files | null | 1 | A | 1 | 2 | 3 | 0 | 5 | 8 | 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 |
891 | train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/test.csv')
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/submission.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
894 | from plotly.offline import iplot
from plotly import tools
import plotly.graph_objects as go
import plotly.express as px
import plotly.offline as py
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
import plotly.io as pio
pio.templates.default = "plotly_dark"
py.init_notebook_mode(connected=T... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
895 | latest_grouped = train.groupby('Country_Region')['ConfirmedCases', 'Fatalities'].sum().reset_index() | No | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
896 | fig = px.bar(latest_grouped.sort_values('ConfirmedCases', ascending=False)[:20][::-1],
x='ConfirmedCases', y='Country_Region',
title='Confirmed Cases Worldwide', text='ConfirmedCases', height=1000, orientation='h')
fig.show() | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
897 | europe = list(['Austria','Belgium','Bulgaria','Croatia','Cyprus','Czechia','Denmark','Estonia','Finland','France','Germany','Greece','Hungary','Ireland',
'Italy', 'Latvia','Luxembourg','Lithuania','Malta','Norway','Netherlands','Poland','Portugal','Romania','Slovakia','Slovenia',
'Spain', ... | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
898 | temp = train[train['Country_Region'].isin(europe)]
temp = temp.groupby(['Date', 'Country_Region'])['ConfirmedCases'].sum().reset_index()
temp['Date'] = pd.to_datetime(temp['Date']).dt.strftime('%m/%d/%Y')
temp['size'] = temp['ConfirmedCases'].pow(0.3) * 3.5
fig = px.scatter_geo(temp, locations="Country_Region", locati... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
899 | fig = px.bar(europe_grouped_latest.sort_values('ConfirmedCases', ascending=False)[:10][::-1],
x='ConfirmedCases', y='Country_Region', color_discrete_sequence=['#84DCC6'],
title='Confirmed Cases in Europe', text='ConfirmedCases', orientation='h')
fig.show() | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
900 | usa = train[train['Country_Region'] == "US"]
usa_latest = usa[usa['Date'] == max(usa['Date'])]
usa_latest = usa_latest.groupby('Province_State')['ConfirmedCases', 'Fatalities'].max().reset_index()
fig = px.bar(usa_latest.sort_values('ConfirmedCases', ascending=False)[:10][::-1],
x='ConfirmedCases', y='Pro... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
901 | ch = train[train['Country_Region'] == "China"]
ch = ch[ch['Date'] == max(ch['Date'])]
ch = ch.groupby('Province_State')['ConfirmedCases', 'Fatalities'].max().reset_index()
fig = px.bar(ch.sort_values('ConfirmedCases', ascending=False)[:10][::-1],
x='ConfirmedCases', y='Province_State', color_discrete_sequ... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
902 | province_encoded = {state:index for index, state in enumerate(train['Province_State'].unique())} | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
903 | train['province_encoded'] = train['Province_State'].apply(lambda x: province_encoded[x])
train.head() | No | 4 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
904 | country_encoded = dict(enumerate(train['Country_Region'].unique()))
country_encoded = dict(map(reversed, country_encoded.items())) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
905 | train['country_encoded'] = train['Country_Region'].apply(lambda x: country_encoded[x])
train.head() | No | 4 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
906 | from datetime import datetime
import time | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
909 | train['Mon'] = train['Date'].apply(lambda x: int(x.split('-')[1]))
train['Day'] = train['Date'].apply(lambda x: int(x.split('-')[2])) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
910 | train['serial'] = train['Mon'] * 30 + train['Day']
train.head() | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
911 | train['serial'] = train['serial'] - train['serial'].min() | 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 |
912 | gdp2020 = pd.read_csv('/kaggle/input/gdp2020/GDP2020.csv')
population2020 = pd.read_csv('/kaggle/input/population2020/population2020.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 |
913 | gdp2020 = gdp2020.rename(columns={"rank":"rank_gdp"})
gdp2020_numeric_list = [list(gdp2020)[0]] + list(gdp2020)[2:-1]
gdp2020.head() | No | 4 | 61 | Data_Transform | rename_columns | null | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
914 | set(train['Country_Region']) - set(population2020['name']) | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
915 | set(train['Country_Region']) - set(gdp2020['country']) | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
916 | population2020 = population2020.rename(columns={"rank":"rank_pop"})
population2020_numeric_list = [list(population2020)[0]] + list(gdp2020)[2:]
population2020.head() | No | 4 | 61 | Data_Transform | rename_columns | null | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
917 | train = pd.merge(train, population2020, how='left', left_on = 'Country_Region', right_on = 'name')
train = pd.merge(train, gdp2020, how='left', left_on = 'Country_Region', right_on = 'country') | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
918 | train = train.fillna(-1) | 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 |
919 | # numeric_features_X = ['Lat','Long', 'province_encoded' ,'country_encoded','Mon','Day']
numeric_features_X = ['province_encoded' ,'country_encoded','Mon','Day'] + population2020_numeric_list + gdp2020_numeric_list
numeric_features_Y = ['ConfirmedCases', 'Fatalities']
train_numeric_X = train[numeric_features_X]
train_n... | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
920 | test['province_encoded'] = test['Province_State'].apply(lambda x: province_encoded[x] if x in province_encoded else max(province_encoded.values())+1) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
921 | test['country_encoded'] = test['Country_Region'].apply(lambda x: country_encoded[x] if x in country_encoded else max(country_encoded.values())+1) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
922 | test['Mon'] = test['Date'].apply(lambda x: int(x.split('-')[1]))
test['Day'] = test['Date'].apply(lambda x: int(x.split('-')[2])) | No | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
923 | test['serial'] = test['Mon'] * 30 + test['Day']
test['serial'] = test['serial'] - test['serial'].min() | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
924 | test = pd.merge(test, population2020, how='left', left_on = 'Country_Region', right_on = 'name')
test = pd.merge(test, gdp2020, how='left', left_on = 'Country_Region', right_on = 'country') | No | 5 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
925 | test_numeric_X = test[numeric_features_X]
test_numeric_X.isnull().sum() | No | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
926 | test_numeric_X = test_numeric_X.fillna(-1) | 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 |
927 | from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression | 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 |
928 | # Random Forest Model Feature Importance
rf_feature_importance = pd.Series(index = X_train.columns, data = np.abs(rf_model.feature_importances_))
n_features = (rf_feature_importance>0).sum()
print(f'{n_features} features with reduction of {(1-n_features/len(rf_feature_importance))*100:2.2f}%')
rf_feature_importance.sor... | No | 2 | 79 | Visualization | model_coefficients | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 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 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 15 | A |
929 | import lightgbm as lgbm
params_lgbm = {
'learning_rate': [.01, .1, .5, .7, .9, .95, .99, 1],
'boosting': ['gbdt'],
'metric': ['l1'],
'feature_fraction': [.3, .4, .5, 1],
'num_leaves': [20],
'min_data': [10],
'max_depth': [10],
'n_estimators': [10, 30, 50, 100]
}
lgb = lgbm.LGBMRegresso... | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
930 | lgb_model = lgbm.LGBMRegressor(learning_rate=lgb_regressor.best_params_["learning_rate"], boosting='gbdt',
metric='l1', feature_fraction=lgb_regressor.best_params_["feature_fraction"],
num_leaves=20, min_data=10, max_depth=10,
... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
931 | # LightGBM Feature Importance
lgb_feature_importance = pd.Series(index = X_train.columns, data = np.abs(lgb_model.feature_importances_))
n_features = (lgb_feature_importance>0).sum()
print(f'{n_features} features with reduction of {(1-n_features/len(lgb_feature_importance))*100:2.2f}%')
lgb_feature_importance.sort_valu... | No | 5 | 79 | Visualization | model_coefficients | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 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 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 15 | A |
932 | params_xgb = {
'learning_rate': [.1, .5, .7, .9, .95, .99, 1],
'colsample_bytree': [.3, .4, .5, .6],
'max_depth': [4],
'alpha': [3],
'subsample': [.5],
'n_estimators': [30, 70, 100, 200]
}
xgb_model = XGBRegressor()
xgb_regressor = GridSearchCV(xgb_model, params_xgb, scoring='neg_root_mean_squa... | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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