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values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
305 | # build final model with best params from grid search cv
final_regressor = build_regressor(optimizer=gridsearch.best_params_['optimizer'],
lstm_nodes=gridsearch.best_params_['lstm_nodes']) | No | 5 | 4 | Model_Train | choose_model_class | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
306 | # fit final model
final_regressor.fit(x, y,
epochs=gridsearch.best_params_['nb_epoch'],
batch_size=gridsearch.best_params_['batch_size'],
verbose=0,
shuffle=False) | No | 5 | 7 | Model_Train | train_model | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker"] | 5 | B |
307 | test_data['Date'] = pd.to_datetime(test_data['Date'])
test_data.Date.max()-test_data.Date.min() | No | 3 | 16 | Data_Transform | data_type_conversions | 8,705,213 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 5 | A |
308 | # first batch of predictions
first_predict_date = pd.to_datetime('2020-03-19')
pred_data = {key:region_dfs[key].loc[(region_dfs[key]['Date']>=(first_predict_date-pd.DateOffset(days=n_in)))&
(region_dfs[key]['Date']<first_predict_date)]
for key in region_dfs}
test_reshap... | No | 3 | 27 | Model_Train | predict_on_train | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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 11"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "DataFrame Conversion Checker", "Exception Handling Checker", "Unit Testing Checker"] | 5 | A |
309 | # iterate prediction output back into model input for the next days
pin = [first_input]
pout = [first_pred]
# first prediction is done outside of loop, need to loop for following 41 days
for i in range(42):
p = final_regressor.predict(pin[i])
pout.append(p)
t= np.insert(pin[i],n_in,pout[i],axis=1)[:,1:,:]
... | No | 4 | 27 | Model_Train | predict_on_train | 8,705,213 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 5 | A |
310 | # create the prediction dataframe
pred_df = pd.DataFrame(np.concatenate(pout))
pred_df.columns = ['ConfirmedCases_scaled','Fatalities_scaled']
pred_df['Date'] = np.repeat(test_data['Date'].unique(),294)
pred_df['Province_State'] = list(test_data.drop_duplicates(subset=['Province_State','Country_Region'])['Province_Stat... | No | 4 | 12 | Data_Transform | create_dataframe | 8,705,213 | 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"] | 5 | A |
311 | # inverse scale results
pred_df['ConfirmedCases'] = (
case_scaler.inverse_transform(pred_df['ConfirmedCases_scaled'].values.reshape(1,-1))[0])
pred_df['Fatalities'] = (
fat_scaler.inverse_transform(pred_df['Fatalities_scaled'].values.reshape(1,-1))[0]) | No | 5 | 18 | Data_Transform | normalization | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 10 | 6 | [{"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 8"}, {"detector": "framework_specific", "framework": "Pandas", "name": "DataFram... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "DataFrame Conversion Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 5 | B |
312 | pred_df[pred_df['Date']=='2020-04-30'].sum() | No | 2 | 40 | Debug | show_table_attributes | 8,705,213 | 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"] | 5 | A |
313 | raw_train_data['Date'] = pd.to_datetime(raw_train_data['Date'])
test_check = raw_train_data[raw_train_data['Date']>'2020-03-18']
model_check = pred_df[pred_df['Date']<=test_check['Date'].max()] | No | 4 | 53 | Other | something_strange | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 5 | A |
314 | np.sqrt(mean_squared_log_error(y_true = test_check[['ConfirmedCases','Fatalities']],
y_pred = model_check[['ConfirmedCases','Fatalities']])) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
315 | sub = pred_df[['ConfirmedCases','Fatalities']]
sub['ForecastId'] = test_data['ForecastId'] | No | 5 | 55 | Data_Export | prepare_output | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
316 | sub.sample(20) | No | 5 | 41 | Debug | show_table | 8,705,213 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
317 | sub.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 |
318 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import warnings
warnings.filterwarnings("ignore") | No | 3 | 22 | Environment | import_modules | 8,706,858 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 1 | null |
319 | train = pd.read_csv('../input/covid19-global-forecasting-week-2/train.csv')
train.info() | No | 4 | 45 | Data_Extraction | load_from_csv | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 1 | B |
320 | 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 |
321 | train.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 |
322 | train.describe() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
323 | train.corr() | 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 |
324 | train.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
325 | test = pd.read_csv('../input/covid19-global-forecasting-week-2/test.csv')
test.info() | No | 3 | 45 | Data_Extraction | load_from_csv | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/covid19-global-forecasting-week-2/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 1 | A |
326 | test.describe() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
327 | test.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
328 | #Changing dtype for dates from object to datetime
train['Date'] = pd.to_datetime(train['Date'])
test['Date'] = pd.to_datetime(test['Date']) | No | 5 | 16 | Data_Transform | data_type_conversions | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
329 | train[train['Province_State'].isnull()]['Country_Region'].unique() | No | 3 | 57 | Debug | show_unique_values | 8,706,858 | 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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 1 | B |
330 | train[train['Province_State'].notnull()]['Country_Region'].unique() | No | 4 | 57 | Debug | show_unique_values | 8,706,858 | 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": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 1 | B |
331 | train['Province_State'] = np.where(train['Province_State'].isnull(), train['Country_Region'], train['Province_State'])
test['Province_State'] = np.where(test['Province_State'].isnull(), test['Country_Region'], test['Province_State']) | No | 5 | 17 | Data_Transform | correct_missing_values | 8,706,858 | 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"}] | [] | 1 | B |
332 | train[train['Province_State'] == 'Diamond Princess'] | No | 4 | 41 | Debug | show_table | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
333 | train[train['Province_State'] == 'Diamond Princess']['Country_Region'].unique() | No | 4 | 57 | Debug | show_unique_values | 8,706,858 | 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"}] | [] | 1 | B |
334 | df = train.append(test) | No | 5 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
335 | group = df.groupby(['Province_State', 'Country_Region'])['Date'].count().reset_index()
group | No | 5 | 60 | Data_Transform | groupby | 8,706,858 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
336 | df[df['Province_State'] == 'Georgia']['Country_Region'].unique() | No | 4 | 57 | Debug | show_unique_values | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
337 | #Distinguishing Province/State Georgia according to Country/Region
df['Province_State'] = np.where((df['Country_Region'] == 'Georgia') & (df['Province_State'] == 'Georgia'),
'Country Georgia', df['Province_State']) | No | 5 | 8 | Data_Transform | feature_engineering | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
338 | #Viewing the total number of confirmeed cases and fatalities worldwide
world = train.groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
plt.plot(world['Date'], world['ConfirmedCases'], label = 'Confirmed Cases')
plt.plot(world['Date'], world['Fatalities'], label = 'Fatalities')
plt.legend()
plt.title(... | No | 5 | 75 | Visualization | time_series | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 1 | B |
339 | #Plotting the number of confirmed cases and fatalities for each country
country = train.groupby('Country_Region')['ConfirmedCases', 'Fatalities'].sum().reset_index()
fig = plt.figure(figsize = (15, 25))
ax = fig.add_subplot(111)
ax.barh(country['Country_Region'], country['ConfirmedCases'],label = 'Confirmed Cases')
ax... | No | 4 | 33 | Visualization | distribution | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 1 | B |
340 | #Viewing the top 15 countries with the most confirmed cases
ranked = country.sort_values(by = 'ConfirmedCases', ascending = False)[:15]
ranked | No | 5 | 9 | Data_Transform | sort_values | 8,706,858 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
341 | #Plotting confirmed cases and fatalities for the 15 countries with the most cases
countries = ['China', 'Italy', 'US',
'Spain', 'Germany', 'Iran',
'France', 'Korea, South', 'United Kingdom',
'Switzerland', 'Netherlands', 'Belgium',
'Austria', 'Turkey', 'Canada']
... | No | 4 | 33 | Visualization | distribution | 8,706,858 | 1 | A | 0 | 1 | 1 | 0 | 0 | 12 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 1 | B |
342 | from statsmodels.tsa.seasonal import seasonal_decompose
def trends(country, case):
group = train[train['Country_Region'] == country].groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
decomposition = seasonal_decompose(group[case], freq = 3)
trend = decomposition.trend
seasonal = decom... | No | 3 | 33 | Visualization | distribution | 8,706,858 | 1 | A | 0 | 1 | 0 | 0 | 0 | 24 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Missing function docstring"] | 1 | B |
343 | from statsmodels.tsa.stattools import adfuller
def stationarity_test(country, case):
timeseries = train[train['Country_Region'] == country].groupby('Date')['ConfirmedCases', 'Fatalities'].sum().reset_index()
#Perform Dickey-Fuller test:
print('Results of Dickey-Fuller Test:')
dftest = adfuller(timeserie... | No | 5 | 47 | Model_Evaluation | statistical_test | 8,706,858 | 1 | A | 0 | 2 | 1 | 0 | 0 | 11 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Missing function docstring"] | 1 | B |
344 | stationarity_test('US', 'ConfirmedCases') | No | 5 | 47 | Model_Evaluation | statistical_test | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
345 | from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error | No | 5 | 22 | Environment | import_modules | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
346 | def comb_p_d_q(pVals,dVals,qVals):
return [(p,d,q) for p in pVals for d in dVals for q in qVals] | No | 5 | 53 | Other | something_strange | 8,706,858 | 2 | B | 3 | 1 | 0 | 0 | 6 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | B |
347 | #List of combinations for pdq
pdq_results = comb_p_d_q([0,1,2],[0,1,2],[0,1,2])
pdq_results | No | 5 | 53 | Other | something_strange | 8,706,858 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
348 | df.drop_duplicates(subset = ['Date', 'Province_State'], keep = 'last', inplace = True) | No | 5 | 19 | Data_Transform | remove_duplicates | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
349 | from datetime import timedelta
TEST_DAYS = 7
TRAIN_LAST = - timedelta(days=TEST_DAYS)
TEST_FIRST = sub_df["Date"].min()
TEST_DAYS = (df["Date"].max() - TEST_FIRST).days + 1
dev_df, test_df = df[df["Date"] < TEST_FIRST].copy(), df[df["Date"] >= TEST_FIRST].copy()
dev_df.shape, test_df.shape | No | 3 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
350 | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
model = Pipeline([('poly', PolynomialFeatures(degree=2, include_bias=False)),
('linear', LinearRegression())])
f... | No | 3 | 22 | Environment | import_modules | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
351 | def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
def evaluate(df):
error = 0
for col in TARGETS:
error += rmse(df[col].values, df["pred_{}".format(col)].values)
return np.round(error/len(TARGETS), 5)
def predict(test_df, first_day, num_days, val=False):
y_pred... | No | 2 | 27 | Model_Train | predict_on_train | null | 1 | A | 1 | 9 | 8 | 0 | 8 | 24 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
352 | for col in TARGETS:
test_df[col] = np.expm1(test_df[col])
test_df["pred_{}".format(col)] = np.expm1(test_df["pred_{}".format(col)]) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
353 | SUB_FIRST = sub_df["Date"].min()
SUB_DAYS = (sub_df["Date"].max() - sub_df["Date"].min()).days + 1
sub_df = dev_df.append(sub_df, sort=False)
for col in TARGETS:
sub_df["prev_{}".format(col)] = sub_df.groupby(loc_group)[col].shift()
sub_df = sub_df[sub_df["Date"] >= SUB_FIRST].copy()
sub_df["ForecastId"] = s... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 2 | 2 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
354 | sub_df.to_csv("submission.csv", index=False, columns=["ForecastId"] + TARGETS) | 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 |
355 | submission = pd.read_csv("../input/covid19-global-forecasting-week-2/submission.csv")
submission | No | 5 | 45 | Data_Extraction | load_from_csv | 8,668,446 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/covid19-global-forecasting-week-2/submission.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 7"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
356 | submission['ConfirmedCases'] = sub_df['ConfirmedCases']
submission['Fatalities'] = sub_df['Fatalities']
submission.to_csv('submission.csv', index=False)
submission | No | 5 | 25 | Data_Export | save_to_csv | 8,668,446 | 1 | A | 0 | 0 | 0 | 1 | 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 |
357 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import matplotlib.pyplot as plt | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
358 | from sklearn.linear_model import (
ElasticNet,
ElasticNetCV,
Lasso,
LassoCV,
LinearRegression,
LogisticRegression,
Ridge,
)
from sklearn.ensemble import (
AdaBoostClassifier,
GradientBoostingClassifier,
GradientBoostingRegressor,
RandomForestClassifier,
RandomForestRegre... | No | 5 | 22 | Environment | import_modules | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 21 | 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"] | 31 | A |
359 | import seaborn as sns
from sklearn.base import BaseEstimator
from xgboost import XGBClassifier, XGBRegressor
import hyperopt as hp
from hyperopt import STATUS_OK, Trials, fmin, hp, tpe | No | 5 | 22 | Environment | import_modules | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | 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"] | 31 | A |
360 | #Import Date
xtrain = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
xtest = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
xsubmission = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/submission.csv")
# view shape of test and train data
print(xtrain.shap... | No | 4 | 45 | Data_Extraction | load_from_csv | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 5 | 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": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 31 | A |
361 | # view head of train data
xtrain.head(10) | No | 5 | 41 | Debug | show_table | 8,708,118 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker"] | 31 | B |
362 | # view head of test data
xtest.head(10) | No | 5 | 41 | Debug | show_table | 8,708,118 | 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"] | 31 | A |
363 | # view head of submission data
xsubmission.head(10) | No | 5 | 41 | Debug | show_table | 8,708,118 | 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"] | 31 | A |
364 | # date wise value count
xtrain['Date'].value_counts() | No | 4 | 54 | EDA | count_unique_values | 8,708,118 | 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"] | 31 | B |
365 | # create ConfirmedCasesgroup by Province_State
statevalue = xtrain.groupby('Province_State').max().ConfirmedCases | No | 5 | 60 | Data_Transform | groupby | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, 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"}] | ["Dependent Threshold Checker"] | 31 | B |
366 | # view top state conformed cases in a barplot
top_states = statevalue.sort_values(ascending = False).head(10)
sns.barplot(x=top_states.index, y=top_states.values)
plt.xticks(rotation = 'vertical') | No | 5 | 33 | Visualization | distribution | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
367 | # make data as integer
xtrain.ConfirmedCases = xtrain.ConfirmedCases.astype('int64')
xtrain.Fatalities = xtrain.Fatalities.astype('int64') | No | 5 | 16 | Data_Transform | data_type_conversions | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, 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"}] | ["Dependent Threshold Checker"] | 31 | B |
368 | # Date wise confirm case view in an lineplot
plt.figure(figsize=(15,6))
sns.lineplot(x=xtrain.Date,y=xtrain.ConfirmedCases,markers=True,style=True)
plt.xticks(rotation = 'vertical') | No | 5 | 75 | Visualization | time_series | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
369 | # Date wise Fatalities view in an lineplot
plt.figure(figsize=(15,6))
sns.lineplot(x=xtrain.Date,y=xtrain.Fatalities,markers=True,style=True)
plt.xticks(rotation = 'vertical') | No | 5 | 33 | Visualization | distribution | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
370 | # ConfirmedCases and Fatalities column groupby Country Region
df_xtrain = xtrain.groupby(['Country_Region'])[['ConfirmedCases', 'Fatalities']].max()
print(df_xtrain.sort_values(by=['ConfirmedCases','Fatalities'],ascending=False).head(10)) | No | 4 | 60 | Data_Transform | groupby | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, 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"}] | ["Dependent Threshold Checker"] | 31 | B |
371 | # view countrywise ConfirmedCases and Fatalities in a plot
fig,ax = plt.subplots()
fig.set_figheight(10)
fig.set_figwidth(40)
ax.plot(df_xtrain[:29].index.values,df_xtrain[:29].ConfirmedCases, color="red", marker="o")
ax.set_xlabel("Countries",fontsize=24)
ax.set_ylabel("Confirmed Cases",color="red",fontsize=24)
ax.tic... | 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 |
372 | # ConfirmedCases and Fatalities data Analysis Exclude China and view in two Plot
confirmed_total_date_noChina = xtrain[xtrain['Country_Region']!='China'].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_noChina = xtrain[xtrain['Country_Region']!='China'].groupby(['Date']).agg({'Fatalities':['sum'... | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
373 | #ConfirmedCases and Fatalities data Analysis and Visualization for China
confirmed_total_date_China = xtrain[xtrain['Country_Region']=='China'].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_China = xtrain[xtrain['Country_Region']=='China'].groupby(['Date']).agg({'Fatalities':['sum']})
total_da... | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
374 | #For Itally
confirmed_total_date_Italy = xtrain[xtrain['Country_Region']=='Italy'].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_Italy = xtrain[xtrain['Country_Region']=='Italy'].groupby(['Date']).agg({'Fatalities':['sum']})
total_date_Italy = confirmed_total_date_Italy.join(fatalities_total_d... | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
375 | #For UK
confirmed_total_date_UK = xtrain[xtrain['Country_Region']=='United Kingdom'].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_UK = xtrain[xtrain['Country_Region']=='United Kingdom'].groupby(['Date']).agg({'Fatalities':['sum']})
total_date_UK = confirmed_total_date_UK.join(fatalities_total... | 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 |
376 | #For Australia
confirmed_total_date_Australia = xtrain[xtrain['Country_Region']=='Australia'].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_Australia = xtrain[xtrain['Country_Region']=='Australia'].groupby(['Date']).agg({'Fatalities':['sum']})
total_date_Australia = confirmed_total_date_Austra... | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
377 | pop_italy = 60486683.
pop_spain = 46749696.
pop_UK = 67784927.
pop_singapore = 5837230.
total_date_Italy.ConfirmedCases = total_date_Italy.ConfirmedCases/pop_italy*100.
total_date_Italy.Fatalities = total_date_Italy.ConfirmedCases/pop_italy*100.
total_date_Spain.ConfirmedCases = total_date_Spain.ConfirmedCases/pop_spa... | 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 |
378 | # For Itally
confirmed_total_date_Italy = xtrain[(xtrain['Country_Region']=='Italy') & xtrain['ConfirmedCases']!=0].groupby(['Date']).agg({'ConfirmedCases':['sum']})
fatalities_total_date_Italy = xtrain[(xtrain['Country_Region']=='Italy') & xtrain['ConfirmedCases']!=0].groupby(['Date']).agg({'Fatalities':['sum']})
tota... | No | 4 | 12 | Data_Transform | create_dataframe | 8,708,118 | 1 | A | 4 | 0 | 0 | 0 | 20 | 23 | 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"] | 31 | B |
379 | # Plots
plt.figure(figsize=(12,6))
plt.plot(italy_30)
plt.plot(spain_30)
plt.plot(UK_30)
plt.plot(singapore_30)
plt.legend(["Italy", "Spain", "UK", "Singapore"], loc='upper left')
plt.title("COVID-19 infections from the first confirmed case", size=15)
plt.xlabel("Days", size=13)
plt.ylabel("Infected cases", size=13)
pl... | 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 |
380 | # Check if there have any null value
xtrain.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, 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"}] | ["Dependent Threshold Checker"] | 31 | B |
381 | # CHANGE TO PD.DATETIME
xtrain.Date = pd.to_datetime(xtrain.Date, infer_datetime_format=True)
xtest.Date = pd.to_datetime(xtest.Date, infer_datetime_format=True) | No | 5 | 16 | Data_Transform | data_type_conversions | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
382 | # CONCISING THE TRAIN DATASET TO 18TH MARCH 2020.
MIN_TEST_DATE = xtest.Date.min()
xtrain = xtrain.loc[xtrain.Date < MIN_TEST_DATE, :] | No | 4 | 14 | Data_Transform | filter | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
383 | # FILLING MISSING VALUES
xtrain.fillna("", inplace=True)
xtest.fillna("", inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 33"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "InPlace Checker"] | 31 | B |
384 | from statsmodels.tsa.arima_model import ARIMA | 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 |
385 | # DROPPING COUNTRY REGION AND PROVINCE STATE
xtrain.drop(['Country_Region','Province_State'],axis=1,inplace=True)
xtest.drop(['Country_Region','Province_State'],axis=1,inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 33"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "InPlace Checker"] | 31 | B |
386 | # CONVERTING DATE COLUMN TO INTEGER
xtrain.loc[:, 'Date'] = xtrain.Date.dt.strftime("%m%d")
xtest.loc[:, 'Date'] = xtest.Date.dt.strftime("%m%d")' | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
387 | # Region wise Confirmed Cases in LinePlot
sns.lineplot(data=xtrain, x="Date", y="ConfirmedCases", hue="Region")
plt.show() | No | 5 | 75 | Visualization | time_series | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
388 | # Region wise Fatalities in Line Plot.
sns.lineplot(data=xtrain, x="Date", y="Fatalities", hue="Region")
plt.show() | No | 5 | 33 | Visualization | distribution | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
389 | # CREATING X AND Y for Train Dataset
X1 = xtrain.drop(["ConfirmedCases", "Fatalities"], axis=1)
X2 = xtrain.drop(["ConfirmedCases", "Fatalities"], axis=1)
y1 = xtrain["ConfirmedCases"]
y2 = xtrain["Fatalities"] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 31 | B |
390 | # Create TEST 1 AND TEST 2 for Test dataset
test_1 = xtest.copy()
test_2 = xtest.copy() | No | 5 | 12 | Data_Transform | create_dataframe | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
391 | for f2 in ["Region"]:
me2 = MeanEncoding(f2, C=0.01 * len(X2[f2].unique()))
me2.fit(X2, y2)
X2 = me2.transform(X2)
test_2 = me2.transform(test_2) | No | 4 | 7 | Model_Train | train_model | 8,708,118 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | 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, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"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", "Verbose Mode Checker"] | 31 | D |
392 | for f1 in ["Region"]:
me1 = MeanEncoding(f1, C=0.01 * len(X1[f1].unique()))
me1.fit(X1, y1)
X1 = me1.transform(X1)
test_1 = me1.transform(test_1) | No | 5 | 20 | Data_Transform | categorify | 8,708,118 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | 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, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"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", "Verbose Mode Checker"] | 31 | D |
393 | # View Test_1
test_1 | No | 5 | 53 | Other | something_strange | 8,708,118 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 31 | A |
394 | # View Test_2
test_2 | No | 5 | 41 | Debug | show_table | 8,708,118 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 31 | A |
395 | # Load some Basic Library
import matplotlib.pyplot as plt
from sklearn import model_selection
import numpy as np | No | 5 | 22 | Environment | import_modules | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 31 | A |
396 | # FUNCTION FOR COMPARING DIFFERENT REGRESSORS
def algorithim_boxplot_comparison(
X, y, algo_list=[], random_state=3, scoring="r2", n_splits=10
):
results = []
names = []
for algo_name, algo_model in algo_list:
kfold = model_selection.KFold(
shuffle=True, n_splits=n_splits, rando... | No | 5 | 84 | Other | not_enough_vertices | 8,708,118 | 1 | A | 0 | 2 | 1 | 0 | 0 | 30 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Inefficient Operations", "fix": "Optimize numerical operations.", "benefits": "Improves performance and reduces execution time.", "location": "Line 39"}, {"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Inefficient Operations", "Missing function docstring", "Unit Testing Checker"] | 31 | C |
397 | # REGRESSORS
lr = LinearRegression(n_jobs=-1)
rfr = RandomForestRegressor(random_state=96, n_jobs=-1)
gbr = GradientBoostingRegressor(random_state=96)
xgbr = XGBRegressor() | No | 5 | 82 | Hyperparam_Tuning | define_search_model | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 3 | 2 | [{"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 34"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Verbose Mode Checker"] | 31 | A |
398 | df_out = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
soln = pd.DataFrame({'ForecastId': test_df.ForecastId, 'ConfirmedCases': Y_pred_CC, 'Fatalities': Y_pred_Fat})
df_out = pd.concat([df_out, soln], axis=0)
df_out.ForecastId = df_out.ForecastId.astype('int') | No | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
399 | # APPENDING THE REGRESSORS IN A LIST
models = []
models.append(('lr',lr))
models.append(('rfr',rfr))
models.append(('gbr',gbr))
models.append(('xgbr',xgbr)) | No | 5 | 82 | Hyperparam_Tuning | define_search_model | 8,708,118 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | 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"] | 31 | A |
400 | df_out.to_csv('submission.csv', index=False)
print("Your submission was successfully saved!")' | 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 |
401 | \ | 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 |
402 | def model_eval(case):
state = ['Italy']
for s in state:
train_ts = train[train['Province_State'] == s][:50]
test_ts = train[train['Province_State'] == s][50:]
a = 9999
for pdq in pdq_results:
try:
model = ARIMA(train_ts[case], order = pdq, dat... | No | 3 | 48 | Model_Evaluation | predict_on_test | 8,706,858 | 1 | A | 2 | 4 | 9 | 0 | 10 | 20 | ok | 11 | 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... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "Magic number", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
403 | # 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 | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
404 | model_eval('ConfirmedCases') | No | 5 | 53 | Other | something_strange | 8,706,858 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
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