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2,576 | test_merged['PromoInterval'].value_counts() | No | 5 | 72 | EDA | count_values | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 7 | A |
2,577 | test_merged['StateHoliday']=test_merged['StateHoliday'].map({'0':0,'a':1})
test_merged['StateHoliday']=test_merged['StateHoliday'].astype(int)
test_merged['StoreType']=test_merged['StoreType'].map({'a':0,'b':1,'c':2,'d':3})
test_merged['StoreType']=test_merged['StoreType'].astype(int)
test_merged['Assortment']=test_m... | No | 5 | 20 | Data_Transform | categorify | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | 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 |
2,578 | test_merged1=test_merged.drop('Id',1) | No | 5 | 10 | Data_Transform | drop_column | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,579 | test_merged1.head() | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,580 | X_train.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 |
2,581 | test_merged1.shape | No | 5 | 58 | Debug | show_shape | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,582 | y_pred=dt.predict(test_merged1[X_train.columns]) | No | 5 | 48 | Model_Evaluation | predict_on_test | 6,511,492 | 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"] | 7 | A |
2,583 | y_pred | 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 |
2,584 | y_pred_exp=np.exp(y_pred)-1 | No | 5 | 55 | Data_Export | prepare_output | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,585 | submission_pred=pd.DataFrame(test_merged['Id'],columns=['Id']) | No | 5 | 12 | Data_Transform | create_dataframe | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 7 | A |
2,586 | submission_pred['Sales']=y_pred_exp | No | 3 | 12 | Data_Transform | create_dataframe | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 7 | A |
2,587 | submission_pred['Id']=np.arange(1,len(submission_pred)+1) | No | 3 | 12 | Data_Transform | create_dataframe | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 7 | A |
2,588 | submission_pred | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,589 | # Credit: kaggle.com
def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def rmspe(y, yhat):
w = ToWeight(y)
rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
rmse_val=np.sqrt(mean_squared_error(y_val_exp,y_pred_val_exp))
rmspe_val=rms... | No | 5 | 49 | Model_Evaluation | compute_test_metric | 6,511,492 | 1 | A | 1 | 2 | 0 | 0 | 10 | 12 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 17"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Che... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Missing function docstring"] | 7 | C |
2,590 | from sklearn.model_selection import GridSearchCV
params={'max_depth':list(range(5,20))}
base_model=DecisionTreeRegressor()
cv_model=GridSearchCV(base_model,param_grid=params,return_train_score=True).fit(X_train,y_train) | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 5 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 11"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | A |
2,591 | df_cv_results=pd.DataFrame(cv_model.cv_results_).sort_values(by='mean_test_score',ascending=False) | No | 5 | 12 | Data_Transform | create_dataframe | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,592 | df_cv_results.set_index('param_max_depth')['mean_test_score'].plot.line()
df_cv_results.set_index('param_max_depth')['mean_train_score'].plot.line()
plt.show() | No | 4 | 3 | Hyperparam_Tuning | find_best_model_class | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Unit Testing Checker"] | 7 | B |
2,593 | df_cv_results=pd.DataFrame(cv_model.cv_results_).sort_values(by='mean_test_score',ascending=False)[['param_max_depth','mean_test_score','mean_train_score']]
df_cv_results | No | 5 | 55 | Data_Export | prepare_output | 6,511,492 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,594 | def get_rmspe_score(model,input_values,y_actual):
y_predicted=model.predict(input_values)
y_actual=np.exp(y_actual)-1
y_predicted=np.exp(y_predicted)-1
score=rmspe(y_actual,y_predicted)
return score
params={'max_depth':list(range(5,8))}
base_model=DecisionTreeRegressor()
cv_model=GridSearchCV(base_... | No | 4 | 3 | Hyperparam_Tuning | find_best_model_class | 6,511,492 | 1 | A | 0 | 1 | 0 | 0 | 0 | 10 | ok | 9 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 17"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | B | No random seed setting detected=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"] | 7 | B |
2,595 | from sklearn.ensemble import AdaBoostRegressor
model_ada=AdaBoostRegressor(n_estimators=5).fit(X_train,y_train) | No | 5 | 7 | Model_Train | train_model | 6,511,492 | 3 | C | 2 | 0 | 0 | 0 | 10 | 2 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | C |
2,596 | model_ada.estimators_[0] | No | 4 | 7 | Model_Train | train_model | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,597 | features=X_train.columns
draw_tree(model_ada.estimators_[0],features) | No | 5 | 79 | Visualization | model_coefficients | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,598 | import xgboost as xgb | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,599 | dtrain=xgb.DMatrix(X_train,y_train)
dvalidate=xgb.DMatrix(X_val,y_val)
param={'max_depth':5,'eta':1,'ojective':'reg:linear'}
model_xg=xgb.train(param,dtrain,200)
pred_y=model_xg.predict(dvalidate)
val_y_inv=np.exp(y_val)-1
pred_y_inv=np.exp(pred_y)-1
rmspe_val=rmspe(val_y_inv,pred_y_inv)
print(rmspe_val) | No | 3 | 7 | Model_Train | train_model | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | 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, 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", "Unit Testing Checker"] | 7 | B |
2,600 | test_merged=test_merged.drop(['Id','Customers'],1) | No | 3 | 6 | Hyperparam_Tuning | train_on_grid | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,601 | y_pred_xg=model_xg.predict(xgb.DMatrix(test_merged[X_train.columns])) | No | 5 | 48 | Model_Evaluation | predict_on_test | 6,511,492 | 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"] | 7 | A |
2,602 | y_pred_xg_exp=np.exp(y_pred_xg)-1 | No | 5 | 55 | Data_Export | prepare_output | 6,511,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,603 | y_pred_xg_exp | No | 5 | 41 | Debug | show_table | 6,511,492 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
2,604 | submission_predicted1 = pd.DataFrame({'Id': test['Id'], 'Sales': y_pred_xg_exp})
testop0=(test[test['Open']==0]['Open']).index
Sales1=[]
for i in range(41088):
if i in testop0:
Sales1.append(0)
else:
Sales1.append(submission_predicted1['Sales'][i])
submission_predicted1['Sales']=Sales1
print(sub... | No | 4 | 25 | Data_Export | save_to_csv | 6,511,492 | 1 | A | 0 | 2 | 4 | 0 | 0 | 11 | ok | 5 | 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": "Unit Testing... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 7 | B |
2,605 | store=pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
data=pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test=pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
submission=pd.read_csv('/kaggle/input/rossmann-store-sales/sample_submission.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 5 | 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."] | 6 | A |
2,606 | print(data.shape)
print(store.shape)
print(test.shape)
print(submission.shape) | No | 5 | 58 | Debug | show_shape | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,607 | data.dtypes # date and stateholidays as categorical columns | No | 5 | 70 | Debug | show_data_types | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,608 | #lets dive into the statistical description of data
data.describe(include='object') # we have 942 similar dates and 5 similar etries in state holidays | No | 5 | 40 | Debug | show_table_attributes | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,609 | #we have sales and customer column in which we have to focus more on the customer segment as this will also be one of the major factor affecting the sales
data.describe()[['Sales','Customers']].loc['max'] | No | 5 | 40 | Debug | show_table_attributes | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,610 | data.Store.nunique()
data.Store.value_counts().head(20).plot.bar()
data.Store.value_counts().tail(20).value_counts()
data.Store.value_counts() | No | 4 | 72 | EDA | count_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,611 | # lets check for the store to be open
data.Open.value_counts() | No | 5 | 72 | EDA | count_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,612 | #record of dayof week for the store
data.DayOfWeek.value_counts()
#record for the store
data.groupby(['DayOfWeek'])[['Sales']].mean() # the first day of the week ,the sales is higher | No | 3 | 72 | EDA | count_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,613 | #checking the null values
data.isnull().sum()
store.isnull().sum() # the store data has more null values so for furthur imputations we have to fill it
test.isnull().sum() # there is some missing values in test data too | No | 5 | 39 | EDA | count_missing_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,614 | # convert datetime column to date
data['Date']=pd.to_datetime(data['Date'],format='%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,615 | # lets take one store and make visualisations to se the pattern
store_id=data.Store.unique()[0]
print(store_id)
store_rows=data[data['Store']==store_id]
store_rows.resample('1D',on='Date')['Sales'].sum().plot.line(figsize=(10,8))
#plotting the 1day sales for the selected store_id over past 2 years before 2015 | No | 5 | 81 | Visualization | relationship | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,616 | #carry on our analysis over the picked store
store_rows[store_rows['Sales']==0] # showing the analysis of no sales for the store 1 | No | 5 | 14 | Data_Transform | filter | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,617 | test['Date']=pd.to_datetime(test['Date'],format='%Y-%m-%d')
store_test_rows=test[test['Store']==store_id]
store_test_rows['Date'].min(),store_test_rows['Date'].max() | No | 3 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,618 | store_test_rows['Open'].value_counts() # the shop will be closed(7days )means the sales will be zero for the given days
#lets proceed ahead to the exploration for predictig the sales for the dates on test data | No | 5 | 72 | EDA | count_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,619 | store_rows['Sales'].plot.hist()
#it is slightly skewed in target column means there are certain rows where slaes data is missing | No | 5 | 33 | Visualization | distribution | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,620 | store.head()
store[store['Store']==store_id].T # we dont know what value imputaion we can do to fill the missinng values so we are takinng a single store and anlaysing the data | No | 4 | 41 | Debug | show_table | 6,518,412 | 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"] | 6 | A |
2,621 | #missing value treatment
store.isna().sum()
# its obvious technically to say that the week that have zero promos so we can fill it with zero
store['Promo2SinceWeek']=store['Promo2SinceWeek'].fillna(0) | No | 4 | 17 | Data_Transform | correct_missing_values | 6,518,412 | 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"] | 6 | A |
2,622 | store['Promo2SinceYear']=store['Promo2SinceYear'].fillna(store['Promo2SinceYear'].mode().iloc[0])
#technically its wrong to say promos missed for 2 years but for current scenrio we can fill it with mode
store['PromoInterval']=store['PromoInterval'].fillna(store['PromoInterval'].mode().iloc[0])
#its obvious that there... | No | 5 | 17 | Data_Transform | correct_missing_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,623 | store['Promo2SinceYear'].mode()
data_merged=data.merge(store,on='Store',how='left')
print(data.shape)
print(data_merged.shape)
#just have a look over the missing value after merging the data over store data/sometimes there is missing value after the merge
print(data_merged.isna().sum().sum())
| No | 3 | 32 | Data_Transform | merge | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,624 | #lets do the decision tree regresssion
#Encoding
#3 cat_cols,1date_col,rest are numerical
data_merged.dtypes
data_merged['day']=data_merged['Date'].dt.day
data_merged['month']=data_merged['Date'].dt.month
data_merged['year']=data_merged['Date'].dt.year
# will give the day of week extracion
# data_merged['dayofwee... | No | 4 | 17 | Data_Transform | correct_missing_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,625 | data_merged.dtypes
#stateholiday,assortment,promointerval,storetype-cat_cols
data_merged['StateHoliday'].unique()
data_merged['StateHoliday']=data_merged['StateHoliday'].map({'0':0,0:0,'a':1,'b':2,'c':3})
data_merged['StateHoliday']=data_merged['StateHoliday'].astype(int) | No | 4 | 20 | Data_Transform | categorify | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,626 | #check the assortment columns for missing value imputation
data_merged['Assortment'].unique() | No | 5 | 57 | Debug | show_unique_values | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,627 | #data_merged.dtypes
#stateholiday,assortment,promointerval,storetype-cat_cols
data_merged['Assortment'].unique()
data_merged['Assortment']=data_merged['Assortment'].map({'a':1,'b':2,'c':3})
data_merged['Assortment']=data_merged['Assortment'].astype(int) | No | 3 | 20 | Data_Transform | categorify | 6,518,412 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | B |
2,628 | map_promo={'Jan,Apr,Jul,Oct':1,'Feb,May,Aug,Nov':2,'Mar,Jun,Sept,Dec':3}
data_merged['PromoInterval']=data_merged['PromoInterval'].map(map_promo) | 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 |
2,629 | #trainn and test validate
features=data_merged.columns.drop(['Sales','Date'])
from sklearn.model_selection import train_test_split
train_x,validate_x,train_y,validate_y=train_test_split(data_merged[features],np.log(data_merged['Sales']+1),test_size=0.2,random_state=1)
train_x.shape,validate_x.shape,train_y.shape,vali... | No | 4 | 21 | Data_Transform | prepare_x_and_y | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 6 | B |
2,630 | #apply decision tree regressor
from sklearn.tree import DecisionTreeRegressor
model_dt=DecisionTreeRegressor(max_depth=11,random_state=1).fit(train_x,train_y)
validate_y_pred=model_dt.predict(validate_x) | No | 4 | 7 | Model_Train | train_model | 6,518,412 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 8 | 7 | [{"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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
2,631 | validate_y_pred=model_dt.predict(validate_x)
from sklearn.metrics import mean_squared_error
def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def rmspe(y, yhat):
w = ToWeight(y)
rmspe= np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
vali... | No | 4 | 28 | Model_Train | compute_train_metric | 6,518,412 | 1 | A | 1 | 2 | 0 | 0 | 10 | 16 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Che... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Exception Handling Checker", "Missing function docstring", "Unit Testing Checker"] | 6 | C |
2,634 | pd.Series(model_dt.feature_importances_,index=features) | No | 5 | 79 | Visualization | model_coefficients | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,635 | #checking the most important feature for test data from the merged data
import matplotlib.pyplot as plt
plt.figure(figsize=(10,5))
yvalues=model_dt.feature_importances_
xvalues=features
plt.bar(xvalues,yvalues)
plt.xticks(rotation=90,color='red')
plt.show() | No | 5 | 79 | Visualization | model_coefficients | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,636 | #to check the importance of each column over the merged data on sales (target variable)
data_merged.corr().loc['Sales'].sort_values(ascending=False) | No | 4 | 80 | Visualization | heatmap | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,637 | #we rae finding the average number of customers as it will help in addition of customers column in test data will help in prediction of customers
store_avg_cust=data.groupby(['Store'])[['Customers']].mean().reset_index().astype(int) | No | 3 | 60 | Data_Transform | groupby | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,638 | test1=test.merge(store_avg_cust,on='Store',how='left')
test1.shape,test.shape | No | 4 | 32 | Data_Transform | merge | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,639 | test_merged['Open']=test_merged['Open'].fillna(1)
test_merged['Date']=pd.to_datetime(test_merged['Date'],format='%Y-%m-%d')
test_merged['day']=test_merged['Date'].dt.day
test_merged['month']=test_merged['Date'].dt.month
test_merged['year']=test_merged['Date'].dt.year
test_merged['StateHoliday']=test_merged['StateHoli... | Yes | 4 | 16 | Data_Transform | data_type_conversions | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,640 | test_merged['Assortment']=test_merged['Assortment'].map({'a':1,'b':2,'c':3})
test_merged['Assortment']=test_merged['Assortment'].astype(int)
test_merged['StoreType']=test_merged['StoreType'].map({'a':1,'b':2,'c':3,'d':4})
test_merged['StoreType']=test_merged['StoreType'].astype(int)
map_promo={'Jan,Apr,Jul,Oct':1,'Feb,... | No | 4 | 20 | Data_Transform | categorify | 6,518,412 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
2,641 | test_pred=model_dt.predict(test_merged[features])
test_pred_inv=np.exp(test_pred)-1
submission_predicted=pd.DataFrame({'Id':test['Id'],'Sales':test_pred_inv})
submission_predicted | No | 4 | 48 | Model_Evaluation | predict_on_test | 6,518,412 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | 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"] | 6 | A |
2,642 | submission_predicted.to_csv('submission.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,643 | def ToWeight(y):
w = np.zeros(y.shape, dtype=float)
ind = y != 0
w[ind] = 1./(y[ind]**2)
return w
def rmspe(y, yhat):
w = ToWeight(y)
rmspe = np.sqrt(np.mean( w * (y - yhat)**2 ))
return rmspe
validate_y_inv=np.exp(validate_y)-1
validate_y_pred_inv=np.exp(validate_y_pred)-1
rmse_val=np.sqr... | No | 4 | 28 | Model_Train | compute_train_metric | 6,518,412 | 1 | A | 1 | 2 | 0 | 0 | 10 | 14 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 15"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Che... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "Missing function docstring"] | 6 | C |
2,644 | import numpy as np
from sklearn.model_selection import GridSearchCV
def get_rmspe_score(model,input_values,y_actual):
y_predicted=model.predict(input_values)
Y_actual=np.exp(y_actual)-1
y_predicted=np.exp(y_predicted)-1
score=rmspe(y_actual,y_predicted)
return score
parameter={'max_depth':list(... | Yes | 3 | 6 | Hyperparam_Tuning | train_on_grid | 6,518,412 | 1 | A | 2 | 1 | 0 | 0 | 7 | 12 | ok | 9 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 18"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | B | No random seed setting detected=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"] | 6 | B |
2,645 | from fastai.tabular import *
import os, tarfile
import random
import matplotlib.pyplot as plt
import pandas as pd
import re
from datetime import *
%matplotlib inline
%reload_ext autoreload
%autoreload 2
np.random.seed(23)
np.set_printoptions(threshold=50, edgeitems=20) | No | 5 | 23 | Environment | set_options | 6,606,006 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,646 | !ls -al /kaggle/input/rossmann-time-series-data-engineering | No | 5 | 88 | Debug | list_files | 6,606,006 | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,647 | OUTPUT = '/kaggle/working/'
PATH='/kaggle/input/rossmann-time-series-data-engineering/'
df = pd.read_feather(f'{PATH}df')
train_df = pd.read_feather(f'{PATH}joined2')
test_df = pd.read_feather(f'{PATH}joined2_test')
train_df.shape, test_df.shape | No | 4 | 44 | Data_Extraction | load_from_disk | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,648 | cat_vars = ['Store', 'DayOfWeek', 'Promo',
'StateHoliday', 'SchoolHoliday', 'Year', 'Month', 'Week', 'Day',
'Is_year_end', 'Is_year_start', 'StoreType', 'Assortment',
'Promo2', 'PromoInterval', 'State',
'Events', 'CompetitionMonthsOpen',
'Promo2Weeks',
'SchoolHoliday_bw',... | No | 5 | 10 | Data_Transform | drop_column | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,649 | test_df['Date'].min(), test_df['Date'].max(), len(test_df) | No | 5 | 40 | Debug | show_table_attributes | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,650 | cut = train_df['Date'][(train_df['Date'] == train_df['Date'][len(test_df)])].index.max()
valid_idx = range(cut) ; valid_idx | No | 5 | 14 | Data_Transform | filter | 6,606,006 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,651 | train_df['Date'][0], train_df['Date'][cut] | No | 5 | 41 | Debug | show_table | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,652 | procs=[FillMissing, Categorify, Normalize]
datalist = (TabularList.from_df(df, path=OUTPUT, cat_names=cat_vars, cont_names=cont_vars, procs=procs,)
.split_by_idx(valid_idx=valid_idx)
.label_from_df(cols=dep_var, label_cls=FloatList, log=True)
.add_test(TabularList.from_d... | No | 3 | 13 | Data_Transform | split | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,653 | defaults.device | No | 5 | 84 | Other | not_enough_vertices | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,654 | max_log_y = np.log(np.max(train_df['Sales'])*1.2) # whether it is better to have +20% max sales need to be verified
y_range = torch.tensor([0, max_log_y], device=defaults.device)
learn = tabular_learner(data, layers=[1000,500], ps=[0.001,0.01], emb_drop=0.04,
y_range=y_range, metrics=exp_rmspe... | No | 2 | 4 | Model_Train | choose_model_class | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,655 | learn.to_fp16 | No | 2 | 4 | Model_Train | choose_model_class | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,656 | learn.data.batch_size
| No | 5 | 23 | Environment | set_options | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,657 | import fastai
fastai.__version__ | 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 |
2,658 | learn.lr_find(end_lr=100, wd=0.3)
learn.recorder.plot() | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,659 | learn.fit_one_cycle(5, 3e-3, wd=0.3) | No | 5 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,660 | learn.save('bs512_5ep_2e-3_wd0.3') | No | 5 | 50 | Model_Train | save_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,661 | learn.fit_one_cycle(5, 1e-3, wd=0.3) | No | 5 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,662 | learn.recorder.plot_losses() | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,663 | learn.save('bs512_2_5ep_1e-3_wd0.3') | No | 5 | 50 | Model_Train | save_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,664 | learn.fit_one_cycle(5, 3e-4, wd=0.3)
learn.recorder.plot_losses() | No | 4 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,665 | learn.save('bs512_3_5ep_3e-4_wd0.3') | No | 5 | 50 | Model_Train | save_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,666 | data = datalist.databunch(bs=128)
learn.data = data
learn.data.batch_size | No | 4 | 23 | Environment | set_options | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,667 | learn.fit_one_cycle(5, 1e-3, wd=0.2)
learn.recorder.plot_losses() | No | 3 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,668 | learn.save('bs128_5ep_1e-3_wd0.2') | No | 5 | 50 | Model_Train | save_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,669 | learn.fit_one_cycle(5, 1e-3, wd=0.1)
learn.recorder.plot_losses() | No | 4 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,670 | learn.fit_one_cycle(20, 1e-3, wd=0.1)
learn.recorder.plot_losses() | No | 4 | 7 | Model_Train | train_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,672 |
learn.save('last') | No | 5 | 50 | Model_Train | save_model | 6,606,006 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,673 | test_preds=learn.get_preds(DatasetType.Test)
test_df["Sales"]=np.exp(test_preds[0].data).numpy().T[0]
test_df[["Id","Sales"]]=test_df[["Id","Sales"]].astype("int")
test_df[["Id","Sales"]].to_csv("rossmann_submission.csv",index=False) | No | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,677 | import pandas as pd
from pandas import DataFrame as df
import numpy as np | No | 5 | 22 | Environment | import_modules | 7,004,483 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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 2"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 5 | A |
2,678 | path= "../input/rossmann-store-sales/" | No | 5 | 77 | Other | define_variables | 7,004,483 | 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"] | 5 | A |
2,679 | store_data=pd.read_csv(path+"store.csv")
train_data=pd.read_csv(path+"train.csv")
test_data=pd.read_csv(path+"test.csv")
store_data=store_data.fillna(-1)
print(store_data.keys(),"\
",train_data.keys())
' | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,680 | months_dict={
"-1":0, "Jan":1, "Feb":2, "Mar":3, "Apr":4, "May":5, "Jun":6,
"Jul":7, "Aug":8, "Sept":9, "Oct":10, "Nov":11, "Dec":12
}
characters_dict={
"a":0, "b":1, "c":2, "d":3, "e":4, "f":5, "g":6, "h":7, "i":8, "j":9,"k":10,"l":11,"m":12,"n":13,
"o":14, "p":15, "q"... | Yes | 2 | 14 | Data_Transform | filter | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,681 |
preprocessed_store_data = pre_process_store_data(store_data,months_dict,characters_dict)
preprocessed_train_data, preprocessed_train_labels = pre_process_train_data(train_data)
preprocessed_dataset = connect_store_data_with_train_data(preprocessed_train_data,preprocessed_store_data)
| No | 2 | 14 | Data_Transform | filter | 7,004,483 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 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": "Cross Validat... | 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"}] | ["Import Checker"] | 5 | B |
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