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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5,785 | model.fit(X_train1,y_train1) | No | 5 | 7 | Model_Train | train_model | 8,666,356 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
5,786 | (pd.DataFrame([X_train.columns,model.feature_importances_],columns=feature).T).plot.bar() | No | 5 | 79 | Visualization | model_coefficients | 8,666,356 | 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"}] | [] | 3 | B |
5,787 | holidays = X_test1['IsHoliday'].to_numpy()
y_test1 = y_test1.to_numpy() | No | 5 | 16 | Data_Transform | data_type_conversions | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 3 | A |
5,788 | result = model.predict(X_test1) | No | 5 | 48 | Model_Evaluation | predict_on_test | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
5,789 | WMAE = wmae(result, y_test1, holidays)
print(WMAE) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,790 | '''from sklearn.model_selection import GridSearchCV
# Create the parameter grid based on the results of random search
param_grid = {
'bootstrap': [True],
'max_depth': [80, 90, 100, 110],
'max_features': [2, 3],
'min_samples_leaf': [3, 4, 5],
'min_samples_split': [8, 10, 12],
'n_estimators': [10... | Yes | 4 | 5 | Hyperparam_Tuning | define_search_space | 8,666,356 | 1 | A | 1 | 0 | 0 | 0 | 5 | 0 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,792 | grid_search.best_params_ | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,794 | data_dmatrix = xgb.DMatrix(data=X_train,label=y_train) | No | 5 | 84 | Other | not_enough_vertices | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,795 | xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,
# max_depth = 5, alpha = 10, n_estimators = 10) | No | 5 | 4 | Model_Train | choose_model_class | 8,666,356 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
5,796 | xg_reg.fit(X_train1,y_train1)
result = xg_reg.predict(X_test1) | Yes | 4 | 7 | Model_Train | train_model | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 8 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, 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", "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 |
5,801 |
%%time
from sklearn.ensemble import RandomForestRegressor
#model = RandomForestRegressor(bootstrap= True, max_depth= 110, max_features= 3, min_samples_leaf= 3, min_samples_split= 8, n_estimators= 200)
model = RandomForestRegressor(n_estimators= 500)
model.fit(X_train,y_train)
result = model.predict(X_test) | Yes | 4 | 7 | Model_Train | train_model | 8,666,356 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
5,802 | sub = pd.read_csv("/kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv.zip") | No | 5 | 45 | Data_Extraction | load_from_csv | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/walmart-recruiting-store-sales-forecasting/sampleSubmission.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
5,803 | sub["Weekly_Sales"] = result
sub.head() | Yes | 4 | 55 | Data_Export | prepare_output | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 3 | A |
5,804 | sub.to_csv("walmart_predict_sub.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 8,666,356 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
5,805 | import numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import random
import os
import sys
import re
from datetime import datetime
import math
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8")) | No | 4 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 13"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
5,806 | pd.options.display.max_colwidth = 400
from IPython.display import FileLink, FileLinks | Yes | 5 | 23 | Environment | set_options | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 400", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}] | A | no applicable ML-specific findings | [] | ["Magic number"] | 2 | A |
5,807 | train = pd.read_csv('/kaggle/input/web-traffic-time-series-forecasting/train_2.csv.zip')
keys = pd.read_csv('/kaggle/input/web-traffic-time-series-forecasting/key_2.csv.zip')
sub = pd.read_csv('/kaggle/input/web-traffic-time-series-forecasting/sample_submission_2.csv.zip') | No | 5 | 45 | Data_Extraction | load_from_csv | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/web-traffic-time-series-forecasting/train_2.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "genera... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
5,808 | keys.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 |
5,809 | def split_page_col(page):
tokens = page.split('_')
article_name = ''.join(tokens[:-3])
org = tokens[-3]
access = tokens[-2]
crawler = tokens[-1]
return (article_name, org, access, crawler)
def split_page_col_wdate(page):
tokens = page.split('_')
article_name = ''.join(tokens[:-4])
o... | No | 5 | 78 | Data_Transform | string_transform | 7,463,061 | 1 | A | 0 | 2 | 0 | 0 | 0 | 15 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Missing function docstring"] | 2 | A |
5,810 | keys['date'] = keys['Page'].apply(lambda x: x.split('_')[-1])
keys['Page'] = keys['Page'].apply(lambda x: '_'.join(x.split('_')[:-1]))
keys['date'] = pd.to_datetime(keys['date'], format='%Y-%m-%d') | No | 5 | 8 | Data_Transform | feature_engineering | 7,463,061 | 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"] | 2 | A |
5,811 | keys.tail() | No | 5 | 41 | Debug | show_table | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,812 | sub = sub.merge(keys, on='Id', how='left') | No | 5 | 32 | Data_Transform | merge | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,813 | sub['date'] = pd.to_datetime(sub['date'], format='%Y-%m-%d') | No | 5 | 16 | Data_Transform | data_type_conversions | 7,463,061 | 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"] | 2 | A |
5,814 | print(sub['date'].min(), sub['date'].max())
print(sub['date'].max() - sub['date'].min()) | No | 3 | 40 | Debug | show_table_attributes | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
5,815 | print(sub['Page'].nunique())
print(train['Page'].nunique()) | No | 5 | 54 | EDA | count_unique_values | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
5,816 | train.iloc[:, 755:803] | No | 5 | 14 | Data_Transform | filter | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,817 | print(sub.shape)
print(train.shape) | No | 5 | 58 | Debug | show_shape | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,818 | prev_year_data_cols = pd.date_range('2016-09-13', '2016-11-13')
train_flat = pd.melt(train.loc[:, ['Page'] + list(prev_year_data_cols.date.astype(str))], id_vars='Page', var_name='date')
train_flat['date'] = pd.to_datetime(train_flat['date'], format='%Y-%m-%d') | Yes | 4 | 16 | Data_Transform | data_type_conversions | 7,463,061 | 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"] | 2 | A |
5,819 | train_flat.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 |
5,820 | train_flat['prediction_date'] = train_flat['date'] + pd.DateOffset(years=1)
sub = sub[['Page', 'date', 'Id']].merge(train_flat[['Page', 'prediction_date', 'value']], left_on=('Page', 'date'), right_on=('Page', 'prediction_date'))
sub['value'] = sub['value'].fillna(0) | Yes | 3 | 32 | Data_Transform | merge | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,821 | sub[['Id', 'value']].rename(columns={'value': 'visits'}).to_csv('all_submission.csv', index=False)
FileLink('all_submission.csv') | Yes | 5 | 25 | Data_Export | save_to_csv | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,822 | page_median = train.iloc[:, 1:].median(axis=1, skipna=True) | No | 3 | 40 | Debug | show_table_attributes | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,823 | page_median = pd.DataFrame({'Page': train['Page'], 'median': page_median}) | No | 5 | 12 | Data_Transform | create_dataframe | 7,463,061 | 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"] | 2 | A |
5,824 | page_median.head() | No | 5 | 41 | Debug | show_table | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,825 | sub_median = sub.merge(page_median, on='Page')[['Id', 'median']] | No | 5 | 32 | Data_Transform | merge | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,826 | sub_median.isnull().mean() | No | 3 | 40 | Debug | show_table_attributes | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,827 | sub_median | No | 5 | 53 | Other | something_strange | 7,463,061 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,828 | sub_median.rename(columns={'median': 'visits'}).to_csv('submission.csv', index=False)
FileLink('submission.csv') | Yes | 5 | 25 | Data_Export | save_to_csv | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,829 | prev_year_data_cols = pd.date_range('2016-09-13', '2016-11-13')
prev_year_median = train.loc[:, list(prev_year_data_cols.date.astype(str))].median(axis=1, skipna=True)
| Yes | 5 | 77 | Other | define_variables | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,830 | prev_year_median = pd.DataFrame({'Page': train['Page'], 'visits': prev_year_median}) | No | 5 | 12 | Data_Transform | create_dataframe | 7,463,061 | 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"] | 2 | A |
5,831 | sub_prev_year_median = sub.merge(prev_year_median, on='Page')[['Id', 'visits']] | No | 5 | 32 | Data_Transform | merge | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,832 | sub_prev_year_median.isnull().mean() | No | 4 | 57 | Debug | show_unique_values | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,833 | sub_prev_year_median['visits'] = sub_prev_year_median['visits'].fillna(sub_prev_year_median['visits'].median()) | No | 5 | 17 | Data_Transform | correct_missing_values | 7,463,061 | 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"] | 2 | A |
5,835 | median_60 = train.iloc[:, -60:].median(axis=1, skipna=True)
median_60 = pd.DataFrame({'Page': train['Page'], 'visits': median_60})
sub_median_60 = sub.merge(median_60, on='Page')[['Id', 'visits']] | Yes | 4 | 12 | Data_Transform | create_dataframe | 7,463,061 | 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"] | 2 | A |
5,836 | sub_median_60.isnull().mean() | No | 3 | 57 | Debug | show_unique_values | 7,463,061 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
5,837 | sub_median_60['visits'] = sub_median_60['visits'].fillna(0) | No | 5 | 17 | Data_Transform | correct_missing_values | 7,463,061 | 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"] | 2 | A |
5,839 | # Libraries
import numpy as np
import pandas as pd
pd.set_option('max_columns', None)
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn')
%matplotlib inline
import copy
import datetime
import lightgbm as lgb
from scipy import stats
from sklearn.model_selection import train_test_split, Stratif... | Yes | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,840 | class LGBWrapper(object):
"""
A wrapper for lightgbm model so that we will have a single api for various models.
"""
def __init__(self):
self.model = lgb.LGBMClassifier()
def fit(self, X_train, y_train, X_valid=None, y_valid=None, X_holdout=None, y_holdout=None, params=None):
eval... | Yes | 5 | 53 | Other | something_strange | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,841 | class ClassifierModel(object):
"""
A wrapper class for classification models.
It can be used for training and prediction.
Can plot feature importance and training progress (if relevant for model).
"""
def __init__(self, columns: list = None, model_wrapper=None):
"""
:param ori... | Yes | 5 | 53 | Other | something_strange | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,842 | data_dict = {}
for i in glob.glob('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MDataFiles_Stage1/*'):
name = i.split('/')[-1].split('.')[0]
if name != 'MTeamSpellings':
data_dict[name] = pd.read_csv(i)
else:
data_dict[name] = pd.read_csv(i, encoding='cp1252'... | No | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 2 | 4 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,843 | data_dict.keys() | No | 2 | 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 |
5,844 | data_dict['MNCAATourneySeeds'].head() | No | 5 | 41 | Debug | show_table | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,845 | data_dict['MNCAATourneyCompactResults'].head() | No | 5 | 41 | Debug | show_table | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,846 | data_dict['MNCAATourneyCompactResults'].groupby(['Season'])['WScore'].mean().plot(kind='line');
plt.title('Mean scores of winning teams by season in tourneys'); | No | 5 | 75 | Visualization | time_series | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,847 | data_dict['MRegularSeasonCompactResults'] | No | 5 | 41 | Debug | show_table | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,848 | data_dict['MRegularSeasonCompactResults'].groupby(['Season'])['WScore'].mean().plot();
plt.title('Mean scores of winning teams by season in regular plays'); | No | 5 | 75 | Visualization | time_series | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,849 | # process seed
data_dict['MNCAATourneySeeds'] = data_dict['MNCAATourneySeeds'].loc[data_dict['MNCAATourneySeeds']['Season'] <= 2014]
data_dict['MNCAATourneySeeds']['Seed'] = data_dict['MNCAATourneySeeds']['Seed'].apply(lambda x: int(x[1:3]))
# take only useful columns
data_dict['MNCAATourneySeeds'] = data_dict['MNCAATo... | Yes | 3 | 8 | Data_Transform | feature_engineering | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,850 | team_win_score = data_dict['MRegularSeasonCompactResults'].groupby(['Season', 'WTeamID']).agg({'WScore':['sum', 'count']}).reset_index()
team_win_score.columns = ['Season', 'WTeamID', 'WScore_sum', 'WScore_count']
team_loss_score = data_dict['MRegularSeasonCompactResults'].groupby(['Season', 'LTeamID']).agg({'LScore':[... | Yes | 3 | 32 | Data_Transform | merge | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,851 | df_win = df.copy()
df_los = df.copy()
df_win = df_win[['WSeed', 'LSeed', 'x_score', 'y_score', 'x_count', 'y_count']]
df_los = df_los[['LSeed', 'WSeed', 'y_score', 'x_score', 'y_count', 'x_count']]
df_win.columns = ['Seed_1', 'Seed_2', 'Score_1', 'Score_2', 'Count_1', 'Count_2']
df_los.columns = ['Seed_1', 'Seed_2', 'S... | Yes | 4 | 12 | Data_Transform | create_dataframe | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,852 | df_win['Seed_diff'] = df_win['Seed_1'] - df_win['Seed_2']
df_win['Score_diff'] = df_win['Score_1'] - df_win['Score_2']
df_los['Seed_diff'] = df_los['Seed_1'] - df_los['Seed_2']
df_los['Score_diff'] = df_los['Score_1'] - df_los['Score_2']
df_win['Count_diff'] = df_win['Count_1'] - df_win['Count_2']
df_win['Mean_score1'... | No | 3 | 8 | Data_Transform | feature_engineering | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,853 | df_win['result'] = 1
df_los['result'] = 0
data = pd.concat((df_win, df_los)).reset_index(drop=True) | No | 4 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,854 | for col in ['Score_1', 'Score_2', 'Count_1', 'Count_2', 'Score_diff', 'Count_diff']:
print(col)
data[col] = data[col].fillna(0).astype(int) | No | 5 | 17 | Data_Transform | correct_missing_values | 7,982,339 | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,855 | X = data.drop(['result'], axis=1)
y = data['result'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,856 | # some of params are from this kernel: https://www.kaggle.com/ratan123/march-madness-2020-ncaam-simple-lightgbm-on-kfold
param = {'n_estimators':10000,
'num_leaves': 400,
'min_child_weight': 0.034,
'feature_fraction': 0.379,
'bagging_fraction': 0.418,
'min_data_in_leaf'... | Yes | 3 | 59 | Model_Train | init_hyperparams | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,857 | test = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MSampleSubmissionStage1_2020.csv')
test = test.drop(['Pred'], axis=1)
test['Season'] = test['ID'].apply(lambda x: int(x.split('_')[0]))
test['Team1'] = test['ID'].apply(lambda x: int(x.split('_')[1]))
test['Team2'] = test['ID']... | Yes | 2 | 45 | Data_Extraction | load_from_csv | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,858 | test['x_score'] = test['WScore_sum_x'] + test['LScore_sum_y']
test['y_score'] = test['WScore_sum_y'] + test['LScore_sum_x']
test['x_count'] = test['WScore_count_x'] + test['LScore_count_y']
test['y_count'] = test['WScore_count_y'] + test['WScore_count_x'] | No | 5 | 8 | Data_Transform | feature_engineering | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,859 | test = test[['Seed_x', 'Seed_y', 'x_score', 'y_score', 'x_count', 'y_count']]
test.columns = ['Seed_1', 'Seed_2', 'Score_1', 'Score_2', 'Count_1', 'Count_2'] | No | 4 | 10 | Data_Transform | drop_column | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,860 | test['Seed_diff'] = test['Seed_1'] - test['Seed_2']
test['Score_diff'] = test['Score_1'] - test['Score_2']
test['Seed_diff'] = test['Seed_1'] - test['Seed_2']
test['Score_diff'] = test['Score_1'] - test['Score_2']
test['Count_diff'] = test['Count_1'] - test['Count_2']
test['Mean_score1'] = test['Score_1'] / test['Coun... | No | 5 | 8 | Data_Transform | feature_engineering | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,861 | test_preds = lgb_model.predict(test) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,862 | plt.hist(test_preds); | No | 5 | 56 | Visualization | plot_predictions | 7,982,339 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,863 | submission_df = pd.read_csv('../input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MSampleSubmissionStage1_2020.csv')
submission_df['Pred'] = test_preds
submission_df | Yes | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,864 | submission_df.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 |
5,865 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from itertools import cycle, islice
import seaborn as sb
import matplotlib.dates as dates
import datetime as dt
import plotly.offline as py
py.init_notebook_mode(connected=True)
fr... | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 17 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 17"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
5,866 | train_data = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-4/train.csv")#index_col=0
display(train_data.head())
test_data = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-4/test.csv")#index_col=0
display(test_data.head()) | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 2 | B | 2 | 0 | 0 | 0 | 10 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,867 | sum_df = pd.pivot_table(train_data, values=['ConfirmedCases','Fatalities'], index=['Date'],aggfunc=np.sum)
display(sum_df.max()) | Yes | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,868 | def getColumnInfo(df):
n_province = df['Province_State'].nunique()
n_country = df['Country_Region'].nunique()
n_days = df['Date'].nunique()
start_date = df['Date'].unique()[0]
end_date = df['Date'].unique()[-1]
return n_province, n_country, n_days, start_date, end_date
n_train = tra... | No | 3 | 54 | EDA | count_unique_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,869 | prob_confirm_check_train = train_data.ConfirmedCases.value_counts(normalize=True)
prob_fatal_check_train = train_data.Fatalities.value_counts(normalize=True)
n_confirm_train = train_data.ConfirmedCases.value_counts()[1:].sum()
n_fatal_train = train_data.Fatalities.value_counts()[1:].sum()
print('Percentage of confirm... | No | 3 | 72 | EDA | count_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,870 | from itertools import cycle, islice
discrete_col = list(islice(cycle(['orange', 'r', 'g', 'k', 'b', 'c', 'm']), None, len(train_data_by_country_confirm.head(30))))
plt.rcParams.update({'font.size': 22})
train_data_by_country_confirm.head(20).plot(figsize=(20,15), kind='barh', color=discrete_col)
plt.legend(["Confirmed ... | 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 |
5,871 | def reformat_time(reformat, ax):
ax.xaxis.set_major_locator(dates.WeekdayLocator())
ax.xaxis.set_major_formatter(dates.DateFormatter('%b %d'))
if reformat: #reformat again if you wish
date_list = train_data_by_date.reset_index()["Date"].tolist()
x_ticks = [dt.datetime.strftime(t,'%Y-%m-%... | No | 4 | 75 | Visualization | time_series | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,872 | fig = plt.figure()
fig,(ax4,ax5) = plt.subplots(1,2,figsize=(20, 8))
#train_data_by_date.loc[(train_data_by_date.ConfirmedCases > 200)]#useless, its already summed.
train_data_by_date.MortalityRate.plot(ax=ax4, x_compat=True, legend='Mortality Rate',color='r')#tell pandas not to use its own datetime format
reformat_tim... | No | 4 | 75 | Visualization | time_series | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,873 | train_data_by_max_date = train_data_by_country.query('(Date == @max_train_date) & (ConfirmedCases > 100)')
train_data_by_max_date.loc[:, 'MortalityRate'] = train_data_by_max_date.loc[:,'Fatalities']/train_data_by_max_date.loc[:,'ConfirmedCases']
train_data_by_mortality = train_data_by_max_date.sort_values('MortalityRat... | 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 |
5,874 | #import plotly.io as pio # to set shahin plot layout
world_df = train_data_by_country.query('Date == @max_train_date')
world_df.loc[:,'Date'] = world_df.loc[:,'Date'].apply(str)
world_df.loc[:,'Confirmed_log'] = round(np.log10(world_df.loc[:,'ConfirmedCases'] + 1), 3)
world_df.loc[:,'Fatalities... | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,875 | fig2 = px.scatter_geo(world_df,
locations="Country_Region",
locationmode="country names",
color="ConfirmedCases", size='ConfirmedCases',
hover_name="Country_Region",
hover_data=['ConfirmedCases', 'Fatalities', ... | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,877 | from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
from tqdm import tqdm
plt.rcParams.update({'font.size': 12})
fig,(ax0,ax1) = plt.subplots(1,2,figsize=(20, 8))
countries_europe = ['Italy', 'France', 'Spain', 'Germany', 'United Kingd... | Yes | 5 | 56 | Visualization | plot_predictions | null | 1 | A | 5 | 1 | 1 | 0 | 25 | 40 | ok | 12 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | B |
5,878 | from scipy.optimize.minpack import curve_fit
from sklearn.metrics import r2_score
from scipy.special import expit
def Gompertz(a, c, t, t0):
Q = a * np.exp(-np.exp(-c*(t-t0)))
return Q
def Boltzman(a, c, t, t0):
Q = a / (1 + np.exp(-c*(t-t0)))
return Q
emerging_countries = ['Albania', 'Andorra', 'A... | Yes | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,879 | nCountries= train_data['Country_Region'].unique()
isState = bool
x_train = range(n_train_days)
x_test = range(n_train_days+n_test_days-overlap_days)
for country in tqdm(nCountries):
fig,(ax0,ax1) = plt.subplots(1,2,figsize=(20,8))
fig,(ax2,ax3) = plt.subplots(1,2,figsize=(20,8))
#print('\
\
\
\
country... | Yes | 3 | 56 | Visualization | plot_predictions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,880 | submit_data = pd.read_csv("/kaggle/input/covid19-global-forecasting-week-4/submission.csv")#, index_col=0)
test_data['Fatalities'] = test_data['Fatalities'].fillna(0.0).astype(int)
test_data['ConfirmedCases'] = test_data['ConfirmedCases'].fillna(0.0).astype(int)
submit_data['Fatalities'] = test_data['Fatalities'].ast... | Yes | 4 | 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 |
5,881 | display(submit_data.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 |
5,882 | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
df = pd.read_csv('../input/44352/training_solutions_rev1.csv')
df_train, df_test = train_test_split(df, test_size=.2)
df_train.shape, df_test.shape | No | 3 | 45 | Data_Extraction | load_from_csv | 4,373,850 | 1 | A | 1 | 0 | 0 | 0 | 5 | 6 | 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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Hardcoded file path"] | 3 | B |
5,883 | from skimage.transform import resize
from tqdm import tqdm
import matplotlib.pyplot as plt
%matplotlib inline
ORIG_SHAPE = (424,424)
CROP_SIZE = (256,256)
IMG_SHAPE = (64,64)
def get_image(path, x1,y1, shape, crop_size):
x = plt.imread(path)
x = x[x1:x1+crop_size[0], y1:y1+crop_size[1]]
x = res... | Yes | 3 | 44 | Data_Extraction | load_from_disk | 4,373,850 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 3 | null |
5,884 | from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense, BatchNormalization, GlobalMaxPooling2D
from keras import backend as K
def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)))
m... | Yes | 5 | 4 | Model_Train | choose_model_class | 4,373,850 | 1 | A | 0 | 1 | 0 | 0 | 0 | 33 | 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"] | 3 | A |
5,885 | batch_size = 128
model.fit(X_train, y_train, epochs=30, validation_data=(X_test, y_test)) | No | 5 | 7 | Model_Train | train_model | 4,373,850 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
5,886 | import os
from tqdm import tqdm
def test_image_generator(ids, shape=IMG_SHAPE):
x1 = (ORIG_SHAPE[0]-CROP_SIZE[0])//2
y1 = (ORIG_SHAPE[1]-CROP_SIZE[1])//2
x_batch = []
for i in ids:
x = get_image('../input/44352/images_test_rev1/'+i, x1, y1, shape=IMG_SHAPE, crop_size=CROP_SIZE)
x_batch.... | Yes | 2 | 48 | Model_Evaluation | predict_on_test | 4,373,850 | 1 | A | 0 | 4 | 5 | 0 | 0 | 28 | ok | 12 | 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 14"}, {"detector": "framework_specific", "framework": "NumPy", "name": "Array Creat... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Array Creation Efficiency", "Column Selection Checker", "Exception Handling Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 3 | B |
5,887 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
from catboost.datasets import amazon
train, test = amazon()
print(train.shape, test.shape)
target = "ACTION"
col4train = [x for x in train.columns if x not in [target, "ROLE_TITLE"]]
y = train[target].v... | No | 3 | 58 | Debug | show_shape | 3,402,586 | 1 | A | 0 | 1 | 0 | 0 | 0 | 9 | 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"] | 8 | A |
5,888 | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_validate
# returns model instance
def get_model():
params = {
"n_estimators":300,
"n_jobs": 3,
"random_state":5436,
}
return ExtraTreesCl... | Yes | 3 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
5,889 | MJTCP = 32292 #Michael Jordan total career points
#for each column in dataset creates N column with random integers
def assign_rnd_integer(dataset, number_of_times = 5, seed = MJTCP):
new_dataset = pd.DataFrame()
np.random.seed(seed)
for c in dataset.columns:
for i in range(number_of_times):
... | Yes | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
5,890 | new_train, new_test = transform_dataset(
train[col4train], test[col4train],
assign_rnd_integer, {"number_of_times":5}
)
print(new_train.shape, new_test.shape)
new_train.head(5) | Yes | 3 | 41 | Debug | show_table | 3,402,586 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 8 | B |
5,891 | validate_model(
model = get_model(),
data = [new_train.values, y]
) | No | 3 | 28 | Model_Train | compute_train_metric | 3,402,586 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
5,892 | new_train, new_test = transform_dataset(
train[col4train], test[col4train],
assign_rnd_integer, {"number_of_times":1}
)
print(new_train.shape, new_test.shape)
validate_model(
model = get_model(),
data = [new_train.values, y]
) | Yes | 3 | 28 | Model_Train | compute_train_metric | 3,402,586 | 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": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
5,893 | new_train, new_test = transform_dataset(
train[col4train], test[col4train],
assign_rnd_integer, {"number_of_times":10}
)
print(new_train.shape, new_test.shape)
validate_model(
model = get_model(),
data = [new_train.values, y]
) | Yes | 4 | 28 | Model_Train | compute_train_metric | 3,402,586 | 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": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
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