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933 | xgb_model = XGBRegressor(learning_rate=xgb_regressor.best_params_["learning_rate"],
colsample_bytree=xgb_regressor.best_params_["colsample_bytree"],
max_depth=4, alpha=3, subsample=.5,
n_estimators=xgb_regressor.best_params_["n_estimators"], ... | No | 3 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
934 | # XGB with early stopping
xgb_model.fit(X_train, y_train, early_stopping_rounds=4,
eval_set=[(X_test, y_test)], verbose=False)
y_train_pred = xgb_model.predict(X_train)
y_pred = xgb_model.predict(X_test)
print('Train r2 score: ', r2_score(y_train_pred, y_train))
print('Test r2 score: ', r2_score(y_test, y_... | No | 3 | 28 | Model_Train | compute_train_metric | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 10 | 7 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | 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", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 15 | B |
935 | # XGB Feature Importance, relevant features can be selected based on its score
feature_important = xgb_model.get_booster().get_fscore()
keys = list(feature_important.keys())
values = list(feature_important.values())
data = pd.DataFrame(data=values, index=keys, columns=['score']).sort_values(by = 'score', ascending=Tru... | No | 3 | 79 | Visualization | model_coefficients | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 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 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker", "Unit Testing Checker"] | 15 | A |
936 | rf_model_en = RandomForestRegressor(max_depth=200, max_features=0.4, min_samples_leaf=3,
min_samples_split=6, n_estimators=30, n_jobs=-1, oob_score=True)
rf_model_en.fit(X_train, y_train)
y_train_pred = rf_model_en.predict(X_train)
y_pred = rf_model_en.predict(X_test)
print('Train r2 s... | No | 4 | 28 | Model_Train | compute_train_metric | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 13 | 9 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 15 | B |
937 | from numpy import mean
from numpy import std
from sklearn.datasets import make_regression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedKFold
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import StackingRegressor
from matplotlib import pyplot
... | No | 2 | 4 | Model_Train | choose_model_class | 12,343,159 | 1 | A | 4 | 4 | 1 | 0 | 20 | 36 | ok | 9 | 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 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Import Checker", "Missing function docstring", "Unit Testing Checker"] | 15 | A |
938 | # define the base models
base_models = list()
base_models.append(('ridge', ridge_model))
base_models.append(('lasso', lasso_model))
base_models.append(('rf', rf_model_en))
# define meta learner model
learner = LinearRegression()
# define the stacking ensemble
stack1 = StackingRegressor(estimators=base_models, final_est... | No | 5 | 82 | Hyperparam_Tuning | define_search_model | 12,343,159 | 1 | A | 1 | 0 | 0 | 0 | 5 | 7 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 15 | B |
939 | pivot=pd.pivot_table(train_df,columns='Country_Region',index='Date',values='ConfirmedCases',aggfunc=np.sum)
pivot_fatality=pd.pivot_table(train_df,columns='Country_Region',index='Date',values='Fatalities',aggfunc=np.sum)
country_list=[]
value_list=[]
fatality_list=[]
for country in list(pivot.columns):
country_list... | No | 5 | 33 | Visualization | distribution | 8,604,602 | 1 | A | 1 | 1 | 1 | 0 | 5 | 20 | 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 |
940 | top_confirmed=df.sort_values(by='Confirmed',ascending=False)[:10] | No | 5 | 9 | Data_Transform | sort_values | 8,604,602 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
941 | times_series_cntr = train_df.groupby(['Date','Country_Region'])['ConfirmedCases'].sum()\\
.reset_index().set_index('Date')
df_countries_tm = times_series_cntr[times_series_cntr['Country_Region'].isin(list_countries)]
plt.figure(figsize=(16,12))
ax = sns.lineplot(x=df_countries_tm.index, y="Confirm... | No | 5 | 81 | Visualization | relationship | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
942 | from sklearn import linear_model
import numpy
from sklearn.ensemble import RandomForestRegressor
cls = RandomForestRegressor(n_estimators=100)
cls.fit(X_train, Y_train)
pred = cls.predict(X_test)
pred = numpy.exp(pred)
cls.score(X_train, Y_train) | No | 2 | 7 | Model_Train | train_model | 5,466,844 | 1 | A | 2 | 0 | 0 | 0 | 10 | 8 | ok | 12 | 9 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 1"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
943 | output = pd.DataFrame({'Id': test['Id'],
'Prediction': pred})
output.to_csv('submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 5,466,844 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 1"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 3 | A |
944 | # 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 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
945 | #dependencies
import pandas as pd
import keras
import tensorflow as tf
import matplotlib.pyplot as plt
from matplotlib import style
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing, svm
from keras.models import Sequential
from keras.... | No | 5 | 23 | Environment | set_options | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 31 | 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"] | 14 | A |
946 | df_train = pd.read_csv('../input/covid19-global-forecasting-week-2/train.csv', index_col=0) | No | 5 | 45 | Data_Extraction | load_from_csv | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path"] | 14 | A |
947 | #df_train['Fatalities'].plt.show()
df_train.drop(columns=['Province_State'], inplace=True) | No | 4 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"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 16"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Logging Checker", "fi... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "InPlace Checker", "Logging Checker"] | 14 | B |
948 | df_train.fillna(0, inplace=True)
#df_train.set_index('Date', inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"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 15"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Logging Checker", "fi... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "InPlace Checker", "Logging Checker"] | 14 | B |
949 | testData = pd.read_csv("../input/test.csv")
submission = pd.DataFrame({
"Id": testData["Id"],
"Prediction": pred
})
submission.to_csv('RandomForestSimple.csv',header=True, index=False)' | 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 |
950 | le = preprocessing.LabelEncoder()
df_train['Country_Region'] = le.fit_transform(df_train['Country_Region'])
df_train['Date'] = le.fit_transform(df_train['Date'])
df_train | No | 5 | 20 | Data_Transform | categorify | 8,620,454 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | ok | 13 | 11 | [{"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": "Pipeline Che... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["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"] | 14 | D |
951 | X = df_train.drop(columns=['Fatalities','ConfirmedCases'])
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(X) #t_scaled_data = preprocessing.scale(X)
X= np.array(X)
X = preprocessing.scale(X) | No | 3 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 5 | ok | 11 | 11 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 18"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Loggin... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | D |
952 | scaler.scale_
scale=1/ 1.51515152e-02 | No | 5 | 53 | Other | something_strange | 8,620,454 | 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"] | 14 | A |
953 | # Import necessary libraries
import datetime
import numpy as np
import pandas as pd
import matplotlib as plt
import warnings
warnings.filterwarnings('ignore')
# Import ML libraries
from sklearn import preprocessing
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from s... | No | 5 | 23 | Environment | set_options | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | 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"] | 12 | A |
954 | y = df_train.drop(columns=['Date','Country_Region','Fatalities'])
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(y)
y = np.array(y)
y = preprocessing.scale(y) | No | 2 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 11 | 11 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 18"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Loggin... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | D |
955 | # Load and read files
submission_example = pd.read_csv("../input/covid19-global-forecasting-week-2/submission.csv")
train_df = pd.read_csv('../input/covid19-global-forecasting-week-2/train.csv')
test_df = pd.read_csv('../input/covid19-global-forecasting-week-2/test.csv')
# Rename columns
train_df.rename(columns={'Coun... | No | 3 | 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 |
956 | # Transform the normal date to pandas datetime
train_df['Date'] = pd.to_datetime(train_df['Date'])
test_df['Date'] = pd.to_datetime(test_df['Date'])
display(train_df.head(5))
display(test_df.head(5)) | No | 4 | 16 | Data_Transform | data_type_conversions | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 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": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker"] | 12 | B |
957 | 19698-15523 | No | 5 | 53 | Other | something_strange | 8,620,454 | 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"] | 14 | A |
958 | X.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
959 | X_train,X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) | No | 5 | 13 | Data_Transform | split | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Logging Checker", "fix": "Implement TensorBoard logging.", "benefits": "Enables better experiment tracking and visualization.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Logging Checker"] | 14 | B |
960 | X_train.shape[1] | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
961 | #X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
#X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
#X_train.shape | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | 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"] | 14 | A |
962 | model = XGBRegressor(n_estimators=1000) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
963 | model.fit(X_train, y_train) #,batch_size = 50, epochs= 20) | No | 5 | 7 | Model_Train | train_model | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 8 | 8 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "framework_specific", "framework":... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Memory Release Checker", "Model Evaluation Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | B |
964 | # Use the forest's predict method on the test data
prediction_s = model.predict(X_test)
# Calculate the absolute errors
errors_s = abs(prediction_s - y_test)
# Print out the mean absolute error (mae)
print('Mean Absolute Error:', round(np.mean(errors_s), 2), 'degrees.') | No | 4 | 27 | Model_Train | predict_on_train | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 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 20"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release C... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Memory Release Checker", "Unit Testing Checker"] | 14 | C |
965 | accuracy = model.score(X_test, y_test) #test Accuracy squared error for linreg | No | 5 | 49 | Model_Evaluation | compute_test_metric | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Model Evaluation Checker", "fix": "Evaluate model performance using model.evaluate().", "benefits": "Provides standardized model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Thresh... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Model Evaluation Checker", "Unit Testing Checker"] | 14 | A |
966 | # Shape of training data
print(train_df.shape)
# Number of missing values in each column of training data
missing_train_count_col = (train_df.isnull().sum())
print(missing_train_count_col[missing_train_count_col>0])
# Shape of testing data
print(test_df.shape)
# Number of missing values in each column of training dat... | No | 4 | 39 | EDA | count_missing_values | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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"] | 12 | B |
967 | # define the base models
base_model = list()
base_model.append(('rf1', rf_model))
base_model.append(('rf2', rf_model_en))
base_model.append(('rf3', RandomForestRegressor(max_depth=8, max_features=0.1, min_samples_leaf=3,
min_samples_split=2, n_estimators=250, n_jobs=-1, ... | No | 4 | 7 | Model_Train | train_model | 12,343,159 | 1 | A | 1 | 0 | 0 | 0 | 5 | 8 | ok | 8 | 6 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 15 | B |
968 | df_t = pd.read_csv('../input/covid19-global-forecasting-week-2/test.csv',index_col=0) | No | 5 | 45 | Data_Extraction | load_from_csv | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path"] | 14 | A |
969 | df_t.drop(columns=['Province_State'], inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 15"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold ... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "InPlace Checker"] | 14 | A |
970 | df_t | 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 |
971 | df_t['Country_Region'] = le.fit_transform(df_t['Country_Region'])
df_t['Date'] = le.fit_transform(df_t['Date']) | No | 5 | 20 | Data_Transform | categorify | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 12 | 10 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "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"] | 14 | D |
972 | submission = pd.DataFrame(columns=['Id','Prediction'])
submission['Id'] = test_df['Id']
ridge_pred = ridge_model.predict(test_df.drop('Id', axis=1))
submission['Prediction'] = np.expm1(ridge_pred)
submission.to_csv('submission_ridge.csv',index=False)
lasso_pred = lasso_model.predict(test_df.drop('Id', axis=1))
submis... | Yes | 4 | 25 | Data_Export | save_to_csv | 12,343,159 | 1 | A | 0 | 0 | 0 | 0 | 0 | 29 | ok | 14 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 15 | B |
973 | #df_t = np.array(scaled_data)
#t_scaled_data = scaler.fit_transform(df_t)
#t_scaled_data = preprocessing.scale(df_t) | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | 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"] | 14 | A |
974 | #Libraries to import
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
import pycountry
import plotly_express as px
sns.set_style('darkgrid')
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import OrdinalEn... | No | 4 | 22 | Environment | import_modules | 8,628,909 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
975 | df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-2/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 8,628,909 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
976 | test_predictions = model.predict(subt_data)
test_predictions.shape | No | 4 | 27 | Model_Train | predict_on_train | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Memory Release Checker", "Unit Testing Checker"] | 14 | A |
977 | #test_predictions = test_predictions.reshape(12642,)
#test_predictions = test_predictions.reshape(-1, 3) | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | 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"] | 14 | A |
978 | #test_predictions = scaler.inverse_transform(test_predictions) | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | 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"] | 14 | A |
979 | display(df_train.head())
display(df_train.describe())
display(df_train.info()) | 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 |
980 | #INVERSE TRANSFORM
#test_predictions = test_predictions.reshape(12642,)
#test_predictions_c = test_predictions* scale | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 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"] | 14 | A |
981 | df_train['Date'] = pd.to_datetime(df_train['Date'], format = '%Y-%m-%d')
df_test['Date'] = pd.to_datetime(df_test['Date'], format = '%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
982 | #test_predictions = test_predictions.reshape(12642,)
test_predictions = test_predictions_c | No | 4 | 12 | Data_Transform | create_dataframe | 8,620,454 | 3 | C | 1 | 0 | 0 | 0 | 5 | 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"] | 14 | C |
983 | print('Minimum date from training set: {}'.format(df_train['Date'].min()))
print('Maximum date from training set: {}'.format(df_train['Date'].max())) | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
984 | b"# Fill null values\ntrain_df['State'].fillna('No State', inplace=True)\ntest_df['State'].fillna('No State', inplace=True)\n\n# Number of missing values in each column of training data\nmissing_train_count_col = (train_df.isnull().sum())\nprint(missing_train_count_col[missing_train_count_col>0])\n\n# Number of missing... | No | 3 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
985 | b"# Apply Label Encoding to train and test data\ntrain_df_encoded = train_df.copy()\ntest_df_encoded = test_df.copy()\n\n# Initialize Label encoder\nle = LabelEncoder()\n\n# Create date time features\ndef create_time_features(df):\n df['date'] = df['Date']\n df['hour'] = df['date'].dt.hour\n df['dayofweek'] = ... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
986 | df_sub = pd.read_csv('../input/covid19-global-forecasting-week-2/submission.csv')
df_sub.drop(columns=['Fatalities','ConfirmedCases'], inplace=True)
save_file_c = pd.DataFrame(test_predictions_c, columns=[['ConfirmedCases']])
result_c = pd.merge(df_sub, save_file_c,left_index=True, right_index=True)
result_c.columns ... | No | 4 | 12 | Data_Transform | create_dataframe | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Merge Parameter Checker", "fix": "Specify how, on, and validate parameters in merge operations.", "benefits": "Ensures accurate data merging and prevents data loss.", "location": "Line 20"}, {"detector": "framework_specific", "framework": "Pandas", "na... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path", "InPlace Checker", "Merge Parameter Checker"] | 14 | A |
987 | print('Minimum date from test set: {}'.format(df_test['Date'].min()))
print('Maximum date from test set: {}'.format(df_test['Date'].max())) | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
988 | result_c = pd.merge(df_t,result_c ,on='ForecastId')
df_t.drop(columns=['Country_Region'], inplace=True)
#result_c.drop(columns=['Country_Region_y','Country_Region_x'], inplace=True | No | 4 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 16"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold ... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "InPlace Checker"] | 14 | A |
989 | # Specify all features for prediction
x_features_drop = ['ConfirmedCases', 'Fatalities', 'Date', 'date']
y_target1 = ['ConfirmedCases']
y_target2 = ['Fatalities']
# Assign features into X, y1, y2 for training and testing
X = train_df_encoded.drop(x_features_drop, axis=1)
y1 = train_df_encoded[y_target1]
y2 = train_df_... | No | 4 | 10 | Data_Transform | drop_column | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | 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"] | 12 | B |
990 | df_map = df_train.copy()
df_map['Date'] = df_map['Date'].astype(str)
df_map = df_map.groupby(['Date','Country_Region'], as_index=False)['ConfirmedCases','Fatalities'].sum() | No | 3 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
991 | def get_iso3_util(country_name):
try:
country = pycountry.countries.get(name=country_name)
return country.alpha_3
except:
if 'Congo' in country_name:
country_name = 'Congo'
elif country_name == 'Diamond Princess' or country_name == 'Laos':
return country_n... | No | 5 | 8 | Data_Transform | feature_engineering | 8,628,909 | 1 | A | 1 | 5 | 9 | 0 | 5 | 22 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
992 | df_map['ln(ConfirmedCases)'] = np.log(df_map.ConfirmedCases + 1)
df_map['ln(Fatalities)'] = np.log(df_map.Fatalities + 1) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
993 | b"# # Split into validaion and training data on 2 features\nrft1_train_X, rft1_val_X, rft1_train_y, rft1_val_y = train_test_split(X, y1, train_size=0.8, test_size=0.2, random_state=1)\nrft2_train_X, rft2_val_X, rft2_train_y, rft2_val_y = train_test_split(X, y2, train_size=0.8, test_size=0.2, random_state=2)\n\n# Define... | No | 5 | 3 | Hyperparam_Tuning | find_best_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
994 | result_c.set_index('ForecastId', inplace=True) | No | 5 | 84 | Other | not_enough_vertices | 8,620,454 | 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"] | 14 | A |
995 | result_c['ConfirmedCases'] = [0 if result_c.loc[i, 'ConfirmedCases'] <= -0
else result_c.loc[i, 'ConfirmedCases'] for i in result_c.index] | No | 5 | 8 | Data_Transform | feature_engineering | 8,620,454 | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 14 | A |
996 | #Fatalities X
X = df_train.drop(columns=['Fatalities', ])
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(X) #t_scaled_data = preprocessing.scale(X)
X= np.array(X)
X = preprocessing.scale(X) | No | 3 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 5 | ok | 11 | 11 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Loggin... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | D |
997 | #Fatalities y
y = df_train.drop(columns=['Date','Country_Region','ConfirmedCases'])
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(y)
y = np.array(y)
y = preprocessing.scale(y) | No | 4 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 11 | 11 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "TensorFlow", "name": "Loggin... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | D |
998 | # Choose best Random Forest Model for y1 and y2
best_rf_model_y1 = model_2
best_rf_model_y2 = model_2
# Assign features to test data
x_test_features_drop = ['Date', 'date']
X_test = test_df_encoded.drop(x_test_features_drop, axis=1)
# Predict the best model for y1 and y2
y1_pred = best_rf_model_y1.predict(X_test)
y2_... | No | 4 | 48 | Model_Evaluation | predict_on_test | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 7 | 6 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 12 | B |
999 | # Save predictions in format used for competition scoring
output = pd.DataFrame({'ForecastId': test_df.ForecastId, 'ConfirmedCases': rnd_y1_pred, 'Fatalities': rnd_y2_pred})
output.to_csv('submission.csv', index=False)
print(output.tail(10))
print('Submission file successfully saved..') | No | 5 | 25 | Data_Export | save_to_csv | 8,625,834 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 12 | A |
1,000 | #X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
#X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
X_train.shape | No | 5 | 58 | Debug | show_shape | 8,620,454 | 3 | C | 1 | 0 | 0 | 0 | 5 | 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"] | 14 | C |
1,001 | px.choropleth(df_map,
locations="iso_alpha",
color="ln(ConfirmedCases)",
hover_name="Country_Region",
hover_data=["ConfirmedCases"] ,
animation_frame="Date",
color_continuous_scale=px.colors.sequential.dense,
title='D... | 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 |
1,002 | model1 = XGBRegressor(n_estimators=1000) | No | 5 | 4 | Model_Train | choose_model_class | 8,620,454 | 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"] | 14 | A |
1,003 | px.choropleth(df_map,
locations="iso_alpha",
color="ln(Fatalities)",
hover_name="Country_Region",
hover_data=["Fatalities"],
animation_frame="Date",
color_continuous_scale=px.colors.sequential.OrRd,
title = 'Daily Death... | 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 |
1,004 | #Compile the model, because this is a binary classification problem, accuracy can be used
#model1.compile(optimizer='Adam', loss= 'mean_squared_error') | No | 1 | 53 | Other | something_strange | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | 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"] | 14 | A |
1,005 | training = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/train.csv")
testing = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-2/test.csv")
data_ = pd.read_csv("/kaggle/input/covid19-demographic-predictors/covid19_by_country.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 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."] | 3 | A |
1,006 | model1.fit(X_train, y_train)#, batch_size = 50, epochs= 20) | No | 5 | 7 | Model_Train | train_model | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Model Evaluation Checker", "fix": "Evaluate model performance using model.evaluate().", "benefits": "Provides standardized model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Model Evaluation Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | B |
1,007 | train_c = result_c
train_c = scaler.fit_transform(train_c)
train_c= np.array(train_c)
train_c = preprocessing.scale(train_c)
#train_c = np.reshape(train_c, (train_c.shape[0], train_c.shape[1], 1)) | No | 4 | 18 | Data_Transform | normalization | 8,620,454 | 1 | A | 1 | 0 | 0 | 0 | 5 | 4 | ok | 7 | 7 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 17"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 14 | D |
1,008 | pred_f = model1.predict(train_c)
#pred_f = pred_f.reshape(-1,3) | No | 5 | 27 | Model_Train | predict_on_train | 8,620,454 | 3 | C | 1 | 0 | 0 | 0 | 5 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Memory Release Checker", "Unit Testing Checker"] | 14 | C |
1,010 | save_file_f = pd.DataFrame(pred_f, columns=['Fatalities'])
save_file_f.index += 1 | No | 5 | 55 | Data_Export | prepare_output | 8,620,454 | 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"] | 14 | A |
1,011 | #Get the top 10 countries
last_date = df_train.Date.max()
df_countries = df_train[df_train['Date']==last_date]
df_countries = df_countries.groupby('Country_Region', as_index=False)['ConfirmedCases','Fatalities'].sum()
df_countries = df_countries.nlargest(10,'ConfirmedCases')
#Get the trend for top 10 countries
df_trend... | No | 4 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,012 | #change the name of the 'country' feature to match 'Country_Region' on the train set
data_['Country_Region']= data_.Country
data_.drop('Country',axis=1, inplace =True) | No | 4 | 10 | Data_Transform | drop_column | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 3 | A |
1,013 | px.line(df_trend, x='Date', y='Cases', color='Country', title='COVID19 Cases growth for top 10 worst affected countries') | No | 5 | 75 | Visualization | time_series | 8,628,909 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,014 | result = pd.merge(result_c, save_file_f, left_index=True, right_index=True) | No | 5 | 32 | Data_Transform | merge | 8,620,454 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Merge Parameter Checker", "fix": "Specify how, on, and validate parameters in merge operations.", "benefits": "Ensures accurate data merging and prevents data loss.", "location": "Line 15"}, {"detector": "framework_specific", "framework": "ScikitLearn"... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Merge Parameter Checker"] | 14 | A |
1,015 | training.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,017 | px.line(df_trend, x='Date', y='Deaths', color='Country', title='COVID19 Deaths growth for top 10 worst affected countries') | No | 5 | 75 | Visualization | time_series | 8,628,909 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,018 | print(data_.shape)
print(training.shape) | No | 5 | 58 | Debug | show_shape | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,019 | result | 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 |
1,020 | px.line(df_trend, x='Date', y='ln(Cases)', color='Country', title='COVID19 Cases growth for top 10 worst affected countries(Logarithmic Scale)') | No | 5 | 75 | Visualization | time_series | 8,628,909 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,022 | #missing values
training.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 3 | B |
1,023 | px.line(df_trend, x='Date', y='ln(Deaths)', color='Country', title='COVID19 Deaths growth for top 10 worst affected countries(Logarithmic Scale)') | No | 5 | 75 | Visualization | time_series | 8,628,909 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,024 | submission = result | No | 5 | 77 | Other | define_variables | 8,620,454 | 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"] | 14 | A |
1,025 | #missing values
data_.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,026 | data_['Quarantine_date'] = pd.to_datetime(data_.Quarantine)
data_['Restrictions_date'] = pd.to_datetime(data_.Restrictions)
data_['Schools_date'] = pd.to_datetime(data_.Schools)
data_.drop(['Schools', 'Restrictions', 'Quarantine'], axis =1, inplace = True) | No | 4 | 16 | Data_Transform | data_type_conversions | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 3 | A |
1,027 | submission['Fatalities'] = [0 if submission.loc[i, 'Fatalities'] < 0
else submission.loc[i, 'Fatalities'] for i in submission.index] | No | 5 | 8 | Data_Transform | feature_engineering | 8,620,454 | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 14 | A |
1,028 | training.Date = pd.to_datetime(training.Date) | No | 5 | 16 | Data_Transform | data_type_conversions | 8,658,083 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,030 | submission | No | 5 | 41 | Debug | show_table | 8,620,454 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 14 | null |
1,031 | training = training.fillna({'Province_State': 'Unknown'})
testing = testing.fillna({'Province_State': 'Unknown'}) | No | 5 | 17 | Data_Transform | correct_missing_values | 8,658,083 | 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"}] | [] | 3 | B |
1,032 | df_us = df_train[df_train['Country_Region']=='US']
df_us['Date'] = df_us['Date'].astype(str)
df_us['state_code'] = df_us.apply(lambda x: us_state_abbrev.get(x.Province_State,float('nan')), axis=1)
df_us['ln(ConfirmedCases)'] = np.log(df_us.ConfirmedCases + 1)
df_us['ln(Fatalities)'] = np.log(df_us.Fatalities + 1) | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,033 | submission.drop(columns=['Country_Region','Date'], inplace=True) | No | 5 | 10 | Data_Transform | drop_column | 8,620,454 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 14 | null |
1,034 | data_.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,035 | px.choropleth(df_us,
locationmode="USA-states",
scope="usa",
locations="state_code",
color="ln(ConfirmedCases)",
hover_name="Province_State",
hover_data=["ConfirmedCases"],
animation_frame="Date",
color_conti... | 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 |
1,036 | px.choropleth(df_us,
locationmode="USA-states",
scope="usa",
locations="state_code",
color="ln(Fatalities)",
hover_name="Province_State",
hover_data=["Fatalities"],
animation_frame="Date",
color_continuous_sc... | 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 |
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