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6,403 | import time
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
| No | 5 | 23 | Environment | set_options | 244,995 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 9 | null |
6,404 | trainData = pd.read_csv('../input/train.csv', low_memory=False)
trainData
| No | 5 | 45 | Data_Extraction | load_from_csv | 244,995 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | A |
6,405 | supply = pd.read_csv('../input/store.csv')
supply.set_index('Store', inplace=True)
supply
| No | 5 | 45 | Data_Extraction | load_from_csv | 244,995 | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/store.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | A |
6,406 | trainData
| No | 5 | 41 | Debug | show_table | 244,995 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
6,407 | #: 2 . customers, sales<br>from sklearn.tree import DecisionTreeRegressor<br>from sklearn.ensemble import RandomForestRegressor<br>from sklearn.linear_model import LinearRegression<br>from sklearn.model_selection import GridSearchCV<br>from sklearn.model_selection import RandomizedSearchCV<br>from sklearn.mo... | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,408 | X_train, customers_train, sales_train = trainData.drop(['Sales', 'Customers'], axis=1).values, trainData['Customers'].values, trainData['Sales'].values
| No | 5 | 21 | Data_Transform | prepare_x_and_y | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 9 | B |
6,409 | X1_train = np.concatenate((X_train, np.reshape(customers_train, (customers_train.shape[0], 1))), axis=1)
| No | 5 | 11 | Data_Transform | concatenate | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
6,410 | forest1 = RandomForestRegressor()
forest2 = RandomForestRegressor()
forest1.fit(X_train, customers_train)
forest2.fit(X1_train, sales_train)
| Yes | 3 | 7 | Model_Train | train_model | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 13 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, 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", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 9 | B |
6,411 | # <br>testData = pd.read_csv('../input/test.csv')<br> | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,412 | testData['UnixTime'] = testData['Date'].map(toUnixTime)
testData.drop('Date', axis=1, inplace=True)
| Yes | 3 | 8 | Data_Transform | feature_engineering | 244,995 | 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 11"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 9 | A |
6,413 | testData.StateHoliday.replace({'a': 1, 'b': 2, 'c': 3, '0': 0}, inplace=True)
unixTime = testData['UnixTime'].values
testData['UnixTime'] = (unixTime - np.mean(unixTime))/np.std(unixTime)
| No | 4 | 18 | Data_Transform | normalization | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 12"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "f... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker", "InPlace Checker"] | 9 | C |
6,414 | testData
| No | 5 | 41 | Debug | show_table | 244,995 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
6,415 | nanToMedian(testData)
X_test = testData.drop('Id', axis=1).values
| Yes | 3 | 10 | Data_Transform | drop_column | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 9 | A |
6,416 | customers_test = forest1.predict(X_test)
X1_test = np.concatenate((X_test, np.reshape(customers_test, (customers_test.shape[0], 1))), axis=1)
sales_predicted = forest2.predict(X1_test)
| Yes | 3 | 32 | Data_Transform | merge | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 4 | [{"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... | B | No random seed setting detected=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"] | 9 | B |
6,417 | Result = pd.DataFrame(testData.Id)
Result['Sales'] = sales_predicted
Result.set_index('Id')
Result.to_csv('./result.csv', index=False)
| No | 5 | 25 | Data_Export | save_to_csv | 244,995 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path"] | 9 | A |
6,418 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
6,419 | df_store = pd.read_csv('../input/store.csv', index_col=None) | No | 5 | 45 | Data_Extraction | load_from_csv | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/store.csv. 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 |
6,420 | df_store.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 |
6,421 | df_train = pd.read_csv('../input/train.csv', index_col=None, low_memory=False) | No | 5 | 45 | Data_Extraction | load_from_csv | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.csv. 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 |
6,422 | y_hat_R = R_model.predict(X_test)
print(eval_rmspe(y_hat_R, y_mytest)) | Yes | 4 | 48 | Model_Evaluation | predict_on_test | 242,073 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 6 | A |
6,423 | print(df_train.shape)
df_train.head() | Yes | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,424 | #, <br>from sklearn.ensemble import RandomForestRegressor | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,425 | df_train.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 |
6,426 | df_train.replace({'StateHoliday': {0: '0'}}, inplace=True)
df_train.StateHoliday.unique() | Yes | 3 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,427 | df_train.DayOfWeek = df_train.DayOfWeek.astype(str) # for dictvectorizer | No | 5 | 16 | Data_Transform | data_type_conversions | 245,807 | 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 |
6,428 | # <br>X_train_full, y_train_full = df_data.drop('Sales', axis=1).values, df_data['Sales'].values | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,429 | X_test_full = df_test.drop('Id', axis=1).values | Yes | 4 | 10 | Data_Transform | drop_column | 242,073 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
6,430 | # , <br>forest = RandomForestRegressor(n_jobs=1, n_estimators=150, max_features=7, max_depth=100)<br>forest.fit(X_train_full, y_train_full) | Yes | 4 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,431 | y_hat_full = forest.predict(X_test_full) | No | 5 | 48 | Model_Evaluation | predict_on_test | 242,073 | 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"] | 6 | A |
6,432 | submission = submission.append(pd.Series(y_hat_full, index=store_ids))<br>submission = pd.DataFrame({ "Id": submission.index, "Sales": submission.values})<br>submission.to_csv(\'submission.csv\', index=False) | 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 |
6,433 | df = df_train[df_train.Open != 0].merge(df_store, on='Store').fillna(1)
df.drop(['Store', 'Date', 'Customers'], axis=1, inplace=True) | Yes | 3 | 17 | Data_Transform | correct_missing_values | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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 |
6,434 | df.shape, df.columns | Yes | 3 | 58 | Debug | show_shape | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,435 | y_train = df.Sales.values | No | 5 | 21 | Data_Transform | prepare_x_and_y | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,436 | print(X_train.shape, y_train.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 |
6,437 | from sklearn.ensemble import RandomForestRegressor
rgr = RandomForestRegressor(n_estimators=25, verbose=True, n_jobs=8)
rgr.fit(X_train, y_train)
print(rgr.score(X_train, y_train)) | Yes | 3 | 4 | Model_Train | choose_model_class | 245,807 | 2 | B | 2 | 0 | 0 | 0 | 10 | 4 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
6,438 | df_test = pd.read_csv('../input/test.csv', index_col=None) | No | 5 | 45 | Data_Extraction | load_from_csv | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/test.csv. 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 |
6,439 | print(df_test.shape)
df_test.head() | Yes | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,440 | features_to_use = ["bathrooms", "bedrooms", "latitude", "longitude", "price"] | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,441 | df_test.replace({'StateHoliday': {0: '0'}}, inplace=True)
df_test.StateHoliday.unique() | Yes | 3 | 57 | Debug | show_unique_values | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,442 | df_pred = df_test[df_test.Open != 0].merge(df_store, on='Store').fillna(1)
df_pred.drop(['Id', 'Store', 'Date'], axis=1, inplace=True) | Yes | 3 | 17 | Data_Transform | correct_missing_values | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,443 | df_pred.shape | No | 5 | 58 | Debug | show_shape | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,444 | X_test.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 |
6,445 | rgr.predict(X_test)[:10] | No | 5 | 48 | Model_Evaluation | predict_on_test | 245,807 | 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 |
6,446 | df_test.loc[df_test.Open != 0,'Sales'] = rgr.predict(X_test)
df_test.loc[df_test.Open == 0, 'Sales'] = 0 | No | 5 | 48 | Model_Evaluation | predict_on_test | 245,807 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
6,447 | out = pd.DataFrame({<br> "Id": df_test.Id,<br> "Sales": df_test.Sales.values<br>})<br>out.to_csv(\'submission.csv\', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,448 | from mlbox.preprocessing import *
from mlbox.optimisation import *
from mlbox.prediction import * | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,449 | paths = ["../input/train.json", "../input/test.json"]
target_name = "interest_level" | No | 5 | 77 | Other | define_variables | 415,588 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,450 | rd = Reader()
df = rd.train_test_split(paths, target_name) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,451 | df["train"].head() | No | 5 | 41 | Debug | show_table | 415,588 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,452 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import os<br><br>import h2o<br>from h2o.estimators.random_forest import H2... | Yes | 4 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,453 | import numpy as np
import pandas as pd | No | 5 | 22 | Environment | import_modules | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,454 | brats = pd.read_csv("../input/child_wishlist_v2.csv", header=None, index_col=0).as_matrix()
gifts = pd.read_csv("../input/gift_goodkids_v2.csv", header=None, index_col=0).as_matrix() | No | 5 | 45 | Data_Extraction | load_from_csv | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/child_wishlist_v2.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "general_ml", "framework": "General ML", "... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
6,455 | TRIPS_COUNT = 5_001
TWINS_COUNT = 40_000
TWINS_START = TRIPS_COUNT
TWINS_END = TWINS_START + TWINS_COUNT
GIFTS_LIMIT = 1_000
BRAT_PREF_COUNT = brats.shape[1]
GIFT_PREF_COUNT = gifts.shape[1] | No | 5 | 77 | Other | define_variables | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 5001", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 40000", "fix": "Not specified", "benefits": "Not specified", "lo... | A | no applicable ML-specific findings | [] | ["Magic number"] | 2 | A |
6,456 | submit = pd.read_csv("save/"+target_name+"_predictions.csv")[["high", "medium", "low"]]
submit["listing_id"] = df["test"].listing_id.astype(int).values
submit.to_csv("mlbox.csv", index=False) | Yes | 3 | 45 | Data_Extraction | load_from_csv | 415,588 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,457 | print("Quantity of Gifts:")
pd.Series(gift_cnt).value_counts() | No | 5 | 72 | EDA | count_values | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,458 | import os, operator, math
import pandas as pd
import numpy as np
import datetime as dt
from tqdm import tqdm
import matplotlib.pyplot as plt
from scipy.optimize import linear_sum_assignment
from collections import defaultdict, Counter | Yes | 3 | 22 | Environment | import_modules | 515,984 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,459 | scores = pd.DataFrame(rows)
scores.head() | Yes | 4 | 12 | Data_Transform | create_dataframe | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,460 | submission = pd.DataFrame({
"ChildId": range(len(brats)),
"GiftId": assigned
}) | No | 5 | 12 | Data_Transform | create_dataframe | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,461 | child_data = pd.read_csv('../input/santa-gift-matching/child_wishlist_v2.csv',
header=None).drop(0, 1).values
gift_data = pd.read_csv('../input/santa-gift-matching/gift_goodkids_v2.csv',
header=None).drop(0, 1).values
n_children = 1000000
n_gift_type = 1000
n_gift_qu... | Yes | 3 | 77 | Other | define_variables | 515,984 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 7 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 1000000", "fix": "Not specified", "benefits": "Not specified", "location": "Line 8"}, {"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 1000", "fix": "Not specified", "benefits": "Not specified", "... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "Magic number"] | 2 | A |
6,462 | submission.to_csv("greedy_v2.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | 504,569 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,463 | import os, cv2, re, random
import numpy as np
import pandas as pd
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras import layers, models, optimizers
from keras import backend as K
from sklearn.model_selection import train_test_split | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,464 | initial_sub = '../input/max-flow-with-min-cost-v2-0-9267/subm_0.926447635166.csv'
subm = pd.read_csv(initial_sub)
subm['gift_rank'] = subm.groupby('GiftId').rank() - 1
subm['gift_id'] = subm['GiftId'] * 1000 + subm['gift_rank']
subm['gift_id'] = subm['gift_id'].astype(np.int64)
current_gift_ids = subm['gift_id'].values | Yes | 3 | 8 | Data_Transform | feature_engineering | 515,984 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path"] | 2 | A |
6,465 | import os, cv2, itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline | No | 5 | 23 | Environment | set_options | 595,311 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 2 | null |
6,466 | train_images_dogs_cats.sort(key=natural_keys)
train_images_dogs_cats = train_images_dogs_cats[0:1300] + train_images_dogs_cats[12500:13800]
test_images_dogs_cats.sort(key=natural_keys) | Yes | 4 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,467 | def prepare_data(list_of_images):<br> """<br> Returns two arrays: <br> x is an array of resized images<br> y is an array of labels<br> """<br> x = [] # images as arrays<br> y = [] # labels<br> <br> for image in list_of_images:<br> x.append(cv2.resize(cv2.imread(image), (img_wid... | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,468 | from sklearn.linear_model import LogisticRegressionCV
from sklearn.metrics import confusion_matrix
clf = LogisticRegressionCV()
X_train_lr, y_train_lr = X_train.T, y_train.T.ravel()
clf.fit(X_train_lr, y_train_lr) | Yes | 3 | 22 | Environment | import_modules | 595,311 | 1 | A | 1 | 0 | 0 | 0 | 5 | 5 | ok | 7 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing Checker", "fix": "Apply scaling before scaling-sensitive operations.", "benefits": "Improves model performance and accuracy.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation... | C | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Scaler Missing Checker=medium | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Scaler Missing Checker", "severity": "medium"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 2 | C |
6,469 | X, Y = prepare_data(train_images_dogs_cats)
print(K.image_data_format()) | Yes | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,470 | # First split the data in two sets, 80% for training, 20% for Val/Test)
X_train, X_val, Y_train, Y_val = train_test_split(X,Y, test_size=0.2, random_state=1) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,471 | submission = pd.DataFrame(np.hstack([test_idx, clf.predict_proba(X_test_lr)]), columns=['id', 'cat', 'dog']) | No | 5 | 12 | Data_Transform | create_dataframe | 595,311 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"] | 2 | A |
6,472 | nb_train_samples = len(X_train)
nb_validation_samples = len(X_val)
batch_size = 16 | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,473 | model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), input_shape=(img_width, img_height, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(32, (3, 3)))
model.add(layers.Activation('relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
mod... | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 20 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,474 | submission = submission.drop([\'cat\'], axis=1)<br>submission = submission.rename(index=str, columns={"dog": "label"})<br>submission[\'id\'] = submission[\'id\'].astype(int)<br>submission.sort_values(\'id\', inplace=True) | Yes | 3 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,475 | submission.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 |
6,476 | submission.to_csv('STahamtan_Dog_vs_Cat_Submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 595,311 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,477 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processi... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,478 | train = pd.read_json("../input/train.json")
test = pd.read_json("../input/test.json") | No | 5 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,479 | test.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 |
6,480 | history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=30,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size
)
| No | 4 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,481 | model.save_weights('model_wieghts.h5')
model.save('model_keras.h5') | Yes | 4 | 50 | Model_Train | save_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,482 | train['interest_level'].value_counts() | No | 5 | 72 | EDA | count_values | 685,472 | 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 |
6,483 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'bathrooms\', shade=True)<br>facet.add_legend()<br>plt.show() | 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 |
6,484 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'bathrooms\', shade=True)<br>facet.set(xlim=(0,2))<br>facet.add_legend()<br>plt.show() | 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 |
6,485 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'bathrooms\', shade=True)<br>facet.set(xlim=(2,6))<br>facet.add_legend()<br>plt.show() | 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 |
6,486 | for dataset in train_test:
dataset.loc[ dataset['bathrooms'] <= 2, 'bathrooms'] = 2,
dataset.loc[(dataset['bathrooms'] > 2) & (dataset['bathrooms'] <= 4), 'bathrooms'] = 1,
dataset.loc[ dataset['bathrooms'] > 4, 'bathrooms'] = 0
| No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,487 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'bedrooms\', shade=True)<br>facet.add_legend()<br>plt.show() | 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 |
6,488 | for dataset in train_test:
dataset.loc[ dataset['bedrooms'] <= 2, 'bedrooms'] = 0,
dataset.loc[(dataset['bedrooms'] > 2) & (dataset['bedrooms'] <= 4), 'bedrooms'] = 1,
dataset.loc[ dataset['bedrooms'] > 4, 'bedrooms'] = 2 | No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,489 | counter = range(1, len(test_images_dogs_cats) + 1)<br>solution = pd.DataFrame({"id": counter, "label":list(prediction_probabilities)})<br>cols = [\'label\']<br><br>for col in cols:<br> solution[col] = solution[col].map(lambda x: str(x).lstrip(\'[\').rstrip(\']\')).astype(float)<br><br>solution.to_csv("dogsVScats.csv... | 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 |
6,490 | import pandas as pd
import numpy as np
from sklearn import ensemble, preprocessing | No | 5 | 22 | Environment | import_modules | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,491 | # Load dataset
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sampleSubmission.csv')
weather = pd.read_csv('../input/weather.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 5 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 3 | A |
6,492 | sum(train['building_id']=='0') | No | 5 | 72 | EDA | count_values | 685,472 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,493 | # Not using codesum for this benchmark
weather = weather.drop('CodeSum', axis=1) | No | 5 | 10 | Data_Transform | drop_column | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,494 | train["created"] = pd.to_datetime(train["created"])
train["month_created"] = train["created"].dt.month | Yes | 3 | 16 | Data_Transform | data_type_conversions | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,495 | train["month_created"] | No | 5 | 41 | Debug | show_table | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,496 | train['month_created'].value_counts() | No | 5 | 72 | EDA | count_values | 685,472 | 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 |
6,497 | # Split station 1 and 2 and join horizontally
weather_stn1 = weather[weather['Station']==1]
weather_stn2 = weather[weather['Station']==2]
weather_stn1 = weather_stn1.drop('Station', axis=1)
weather_stn2 = weather_stn2.drop('Station', axis=1)
weather = weather_stn1.merge(weather_stn2, on='Date') | Yes | 3 | 14 | Data_Transform | filter | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 3 | A |
6,498 | def bar_chart(feature):<br> low = train[train['interest_level']=='low'][feature].value_counts() # survived <br> medium = train[train['interest_level']=='medium'][feature].value_counts()<br> high = train[train['interest_level']=='high'][feature].value_counts()<br> df = pd.DataFrame([low, medium, high])<b... | Yes | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,499 | train["created"] = pd.to_datetime(train["created"])<br>train["date_created"] = train["created"].dt.date<br>cnt_srs = train[\'date_created\'].value_counts()<br><br>plt.figure(figsize=(12,4))<br>ax = plt.subplot(111)<br>ax.bar(cnt_srs.index, cnt_srs.values, alpha=0.8)<br>ax.xaxis_date()<br>plt.xticks(rotation=\'vertical\... | Yes | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,500 | train['month'] = train.Date.apply(create_month)
train['day'] = train.Date.apply(create_day)
test['month'] = test.Date.apply(create_month)
test['day'] = test.Date.apply(create_day) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,501 | # Add integer latitude/longitude columns
train['Lat_int'] = train.Latitude.apply(int)
train['Long_int'] = train.Longitude.apply(int)
test['Lat_int'] = test.Latitude.apply(int)
test['Long_int'] = test.Longitude.apply(int) | No | 5 | 16 | Data_Transform | data_type_conversions | 716,075 | 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, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker"] | 3 | B |
6,502 | train['day_of_week'] = train['created'].dt.weekday | No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
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