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7,581 | from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X_train)
X_train_scaled = scaler.transform(X_train) | No | 5 | 18 | Data_Transform | normalization | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 10 | 9 | [{"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... | E | Cross Validation Checker=low, Data Leakage Checker=high, Inconsistent scaling methods=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Inconsistent scaling methods", "severity": "high"}, {"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"] | 7 | E |
7,582 | from sklearn import linear_model
from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error
reg = linear_model.LinearRegression().fit(X_train_scaled, Y)
reg.score(X_train_scaled, Y) | Yes | 4 | 7 | Model_Train | train_model | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,583 | from sklearn import neighbors
knn = neighbors.KNeighborsRegressor(n_neighbors=6)
knn.fit(X_train_scaled, Y)
knn_scores = cross_val_score(knn, X_train_scaled, Y, cv=10)
np.mean(knn_scores) | Yes | 3 | 7 | Model_Train | train_model | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 11"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | A |
7,584 | from sklearn import ensemble
params = {'n_estimators': 500, 'max_depth': 5, 'min_samples_split': 2,
'learning_rate': 0.01, 'loss': 'ls'}
gbr = ensemble.GradientBoostingRegressor(**params)
gbr.fit(X_train_scaled, Y) | Yes | 4 | 7 | Model_Train | train_model | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,585 | Yreg = reg.predict(X_train_scaled)
Yknn = knn.predict(X_train_scaled)
Ygbr = gbr.predict(X_train_scaled) | No | 5 | 27 | Model_Train | predict_on_train | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 6 | 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"] | 7 | B |
7,586 | from sklearn.metrics import mean_squared_log_error
mean_squared_log_error(Y, Yknn) | No | 5 | 28 | Model_Train | compute_train_metric | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,587 | mean_squared_log_error(Y, Ygbr) | No | 5 | 28 | Model_Train | compute_train_metric | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,588 | testdf = pd.read_csv("../input/atividade-3-pmr3508/test.csv",<br> sep=r\'\\s*,\\s*\',<br> engine=\'python\',<br> na_values="?")<br>testdf = testdf.replace(np.nan,\' \', regex=True) | Yes | 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 |
7,589 | testdf["coord"] = testdf["latitude"].map(str) + \', \' + testdf["longitude"].map(str) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,590 | texts = ["Hooli stock price soared after a dip in PiedPiper revenue growth.",<br> "Captain Tsubasa scores a magnificent goal for the Japanese team.",<br> "Merryweather mercenaries are sent on another mission, as government oversight groups call for new sanctions.",<br> "Beyonc releases a new alb... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,591 | test_loc = pd.read_csv("../input/california-prices/test_loc.csv",<br> sep=r\'\\s*"\',<br> engine=\'python\')<br><br>test_loc.columns = [col.replace(\'"\', \'\') for col in test_loc.columns]<br>test_loc = test_loc.replace(\'"\',\'\', regex=True)<br><br>test_loc[\'location\'] = test_loc[\'location\'].st... | Yes | 4 | 78 | Data_Transform | string_transform | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,592 | #import os
print(os.listdir("../input/bbc-test")) | No | 5 | 88 | Debug | list_files | 1,967,574 | 3 | C | 1 | 0 | 0 | 0 | 5 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/bbc-test. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 11"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 9 | C |
7,593 | TEST_PATH = os.path.join("../input/bbc-test", "BBC News Test (1).csv")
#Load the data using pandas : Create a DataFrame
test_df = pd.read_csv(TEST_PATH)
| No | 5 | 45 | Data_Extraction | load_from_csv | 1,967,574 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/bbc-test. 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 |
7,594 | test_cities = pd.DataFrame(test_loc.Id)<br>test_cities = test_cities.set_index(\'Id\')<br>test_cities[\'city\'] = \'\'<br><br>for index, row in test_loc.iterrows():<br> row_id = row[\'Id\']<br> city = row[\'location\'][-2]<br> if city != \'n\':<br> test_cities.at[row_id, \'city\'] = city.lstrip(\' \')<b... | 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 |
7,595 | test_cities = pd.read_csv("../input/california-prices/test_cities.csv",<br> engine=\'python\')<br><br>testdf[\'cities\'] = test_cities[\'city\']<br> | Yes | 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 |
7,596 | import pandas as pd
import numpy | No | 5 | 22 | Environment | import_modules | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,597 | data = pd.read_csv("../input/datasetss/train.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 2"}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path de... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "Import Checker"] | 6 | A |
7,598 | X_test = testdf.copy()
X_test['people_pb'] = X_test.population/X_test.total_bedrooms
X_test['people_ph'] = X_test.population/X_test.households
X_test['income_pr'] = X_test.median_income/X_test.total_rooms
X_test = X_test.filter(['median_age','total_rooms', 'total_bedrooms', 'population', 'households', 'median_income'... | Yes | 3 | 8 | Data_Transform | feature_engineering | 2,077,825 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,599 | data | 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 |
7,600 | X_test_scaled = scaler.transform(X_test) | No | 5 | 18 | Data_Transform | normalization | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,601 | Ypred = knn.predict(X_test_scaled) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,077,825 | 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"] | 7 | A |
7,602 | prediction.to_csv("prediction.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 |
7,603 | data = data.drop('Id', axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,604 | import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import sklearn as skl
from sklearn.neighbors import KNeighborsRegressor as KNR
from sklearn.tree import DecisionTreeRegressor as DTR
from sklearn.linear_model import Lasso, LassoCV
from sklearn.model_selection import cross_val_score as cvs | No | 5 | 22 | Environment | import_modules | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,605 | data.isna().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,606 | arquivo1 = '../input/test.csv'
tester = pd.read_csv(arquivo1, engine = 'python')
tester.shape | Yes | 4 | 45 | Data_Extraction | load_from_csv | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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 8"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 7 | A |
7,607 | X_train = data.drop('median_house_value', axis=1)
y_train = data.median_house_value | No | 5 | 21 | Data_Transform | prepare_x_and_y | 2,078,255 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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 2"}] | A | no applicable ML-specific findings | [] | ["Import Checker"] | 6 | A |
7,608 | from sklearn import linear_model
from sklearn.model_selection import cross_val_score | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,609 | arquivo2 = '../input/train.csv'
trainer = pd.read_csv(arquivo2, engine = 'python')
trainer.shape | Yes | 4 | 45 | Data_Extraction | load_from_csv | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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 8"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 7 | A |
7,610 | trainer.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 |
7,611 | best_knr = KNR(n_neighbors=melhor_knr(train,5,50)[1]) | No | 4 | 4 | Model_Train | choose_model_class | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,612 | best_knr.fit(train,trainer['median_house_value']) | No | 5 | 7 | Model_Train | train_model | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,613 | knr_pred = best_knr.predict(test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,078,637 | 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"] | 7 | A |
7,614 | Submit1 = pd.DataFrame()
Submit1.insert(0, 'Id', tester['Id'])
Submit1.insert(1,'median_house_value', knr_pred) | Yes | 4 | 12 | Data_Transform | create_dataframe | 2,078,637 | 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"] | 7 | A |
7,615 | best_tree = DTR(max_depth=10) | No | 5 | 4 | Model_Train | choose_model_class | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,616 | best_tree.fit(train,target) | No | 5 | 7 | Model_Train | train_model | 2,078,637 | 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"] | 7 | B |
7,617 | tree_pred = best_tree.predict(test)
tree_pred | No | 5 | 48 | Model_Evaluation | predict_on_test | 2,078,637 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 7 | A |
7,618 | lcv = LassoCV().fit(train, target)
lcv.score(train, target) | Yes | 4 | 7 | Model_Train | train_model | 2,078,637 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,619 | lasso = Lasso(max_iter = 100000, selection = 'random')
lasso.fit(train, target)
pred_lasso = lasso.predict(test)
pred_lasso | Yes | 3 | 7 | Model_Train | train_model | 2,078,637 | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | 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"] | 7 | B |
7,620 | # 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 |
7,621 | training_labels[training_labels == 0] = -1
testing_labels[testing_labels == 0] = -1 | No | 5 | 8 | Data_Transform | feature_engineering | 2,083,980 | 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"] | 4 | A |
7,622 | from sklearn.model_selection import cross_val_score, GridSearchCV
clf = GridSearchCV(AveragePerceptron(), param_grid={'learning_rate':[1, 0.1, 0.01], 'margin':[ 0, 0.1], 'decay': [False, True], 'epochs':[50], 'avg_decay':[False, True]}, cv=10, scoring='accuracy', n_jobs=-1)
clf.fit(testing_data, testing_labels) | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | 2,083,980 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 4 | A |
7,623 | clf.best_estimator_.score(testing_data, testing_labels) | No | 5 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,624 | with open('submission.csv', 'w') as submission:<br> with open('../input/movie-ratings/movie-ratings/data-splits/data.eval.anon.id', 'r') as example_ids:<br> submission.write('example_id,label\<br>')<br> for example_id, label in zip(example_ids, submission_pred):<br> submission.write('{},{}\<... | No | 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 |
7,625 | %matplotlib inline
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from fastai.structured import *
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.ensemble import RandomForestRegressor
from IPyth... | No | 4 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,626 | df_train = pd.read_csv(f'{PATH}train.csv', parse_dates=['Open Date'])
df_test = pd.read_csv(f'{PATH}test.csv', parse_dates=['Open Date'])
df_joined = pd.concat([df_train.drop('revenue', axis=1), df_test], axis=0) | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,627 | X_train, X_test = prcs(df_joined.copy())
y_train = df_train['revenue'].copy().apply(np.log) | No | 5 | 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 |
7,628 | m = RandomForestRegressor(n_jobs=-1, n_estimators=150, oob_score=True, max_features=0.5)
m.fit(X_train, y_train)
score(m,X_train, y_train) | Yes | 4 | 7 | Model_Train | train_model | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,629 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.exp(predict(m, X_test)))
df_preds.to_csv('submission0.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,630 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.exp(predict(models, X_test)))
df_preds.to_csv('submission1.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,631 | X_train, X_test = prcs(df_joined.copy(), fe=['id'])<br><br># Doble transformacin para que la distribucin sea Normal<br>y_train = df_train['revenue'].copy().apply(np.sqrt).apply(np.log) | 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 |
7,632 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.square(np.exp(predict(models, X_test))))
df_preds.to_csv('submission2.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,633 | X_train, X_test = prcs(df_joined.copy(), fe=['id', 'dummies'])<br><br># Doble transformacin para que la distribucin sea Normal<br>y_train = df_train['revenue'].copy().apply(np.sqrt).apply(np.log) | 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 |
7,634 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.square(np.exp(predict(models, X_test))))
df_preds.to_csv('submission3.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,635 | X_train, X_test = prcs(df_joined.copy(), fe=['id', 'dummies', 'city'])<br><br># Doble transformacin para que la distribucin sea Normal<br>y_train = df_train['revenue'].copy().apply(np.sqrt).apply(np.log) | 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 |
7,636 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.square(np.exp(predict(models, X_test))))
df_preds.to_csv('submission4.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,637 | X_train, X_test = prcs(df_joined.copy(), fe=['id', 'dummies', 'city', 'city_group'])<br><br># Doble transformacin para que la distribucin sea Normal<br>y_train = df_train['revenue'].copy().apply(np.sqrt).apply(np.log) | 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 |
7,638 | df_preds = pd.DataFrame(columns=['Prediction'],index=X_test.index, data=np.square(np.exp(predict(models, X_test))))
df_preds.to_csv('submission5.csv', index=True, index_label='Id')
df_preds.head() | Yes | 4 | 25 | Data_Export | save_to_csv | 2,094,493 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,639 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import warnings
warnings.filterwarnings("ignore") | No | 5 | 23 | Environment | set_options | 2,102,499 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,640 | t = pd.read_csv('../input/training/training.csv')
ts = pd.read_csv('../input/test/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 2,102,499 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,641 | t.shape[0] | No | 5 | 58 | Debug | show_shape | 2,102,499 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,642 | Y = np.array(t.drop('Image', axis=1).fillna(method='ffill'),dtype=float) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 2,102,499 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,643 | #import packages
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Conv2D, MaxPool2D,Flatten, LeakyReLU
#from keras.layers import LeakyReLU(alpha=0.3) as activation | No | 5 | 22 | Environment | import_modules | 2,102,499 | 2 | B | 1 | 0 | 0 | 0 | 5 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,644 | # Set the CNN model <br># my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out<br><br>model = Sequential()<br><br>model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = \'Same\', <br> activation =\'linear\', input_shape = (96,96,1)))<br>m... | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,645 | model.fit(X, Y, epochs=100, batch_size=128,validation_split = 0.2) | No | 5 | 7 | Model_Train | train_model | 2,102,499 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,646 | Y_ts = model.predict(X_ts) | 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 |
7,647 | look_id = pd.read_csv('../input/IdLookupTable.csv')
look_id.drop('Location',axis=1,inplace=True) | Yes | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,648 | look_id['location_id'] = look_id.FeatureName.map(maps) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,649 | look_id[['RowId','Location']].to_csv('Sub1.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 2,102,499 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,650 | %matplotlib inline
import os
import pandas as pd, numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread
import seaborn as sns # nice visuals
from sklearn.model_selection import train_test_split # splitting data
# quantifying models
from sklearn.metrics import accuracy_score, classification_report, r... | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
7,651 | trn_image, vld_image, trn_label , vld_label = train_test_split(full_train_df['image'],
full_train_df['opacity'],
test_size=0.25,
random_state=2018)
trn_image = np.stack(trn_image... | Yes | 3 | 13 | Data_Transform | split | 2,108,474 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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"}] | [] | 7 | B |
7,652 | out_model = models.Sequential()
out_model.add(layers.Reshape((64, 64, 1), input_shape=trn_image.shape[1:]))
out_model.add(layers.Conv2D(16, (3, 3), padding='valid', activation='relu'))
out_model.add(layers.MaxPool2D((2, 2)))
out_model.add(layers.Conv2D(32, (3, 3), padding='valid', activation='relu'))
out_model.add(laye... | Yes | 4 | 4 | Model_Train | choose_model_class | 2,108,474 | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,653 | from IPython.display import clear_output
fit_results = out_model.fit(trn_image, trn_label,
validation_data=(vld_image, vld_label),
epochs=100)
clear_output() | No | 5 | 7 | Model_Train | train_model | 2,108,474 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,654 | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.plot(fit_results.history['loss'], label='Training')
ax1.plot(fit_results.history['val_loss'], label='Validation')
ax1.legend()
ax1.set_title('Loss History')
ax2.plot(100*np.array(fit_results.history['binary_accuracy']), label='Training')
ax2.plot(100*np.array(f... | No | 5 | 35 | Visualization | learning_history | 2,108,474 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
7,655 | # Import the necessary libraries
import numpy as np
import pandas as pd
import os
import time
import warnings
import gc
from six.moves import urllib
import matplotlib
import matplotlib.pyplot as plt
warnings.filterwarnings('ignore') | No | 5 | 23 | Environment | set_options | 2,122,158 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | 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"] | 33 | A |
7,656 | #Add All the Models Libraries
# Scalers
from sklearn.preprocessing import StandardScaler
from sklearn.utils import shuffle
from sklearn.pipeline import Pipeline
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import LabelEncoder
# Models
from sklearn.linear_model import LogisticRegression #logist... | No | 5 | 22 | Environment | import_modules | 2,122,158 | 1 | A | 0 | 0 | 0 | 0 | 0 | 34 | 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"] | 33 | A |
7,657 | # to make this notebook's output stable across runs
np.random.seed(123)
# To plot pretty figures
%matplotlib inline
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12 | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,658 | train = pd.read_csv("../input/train_users_2.csv")<br>test = pd.read_csv("../input/test_users.csv")<br>id_test = test[\'id\']<br>labels = train[\'country_destination\'].values<br>df_train = train.drop([\'country_destination\'], axis=1)<br>train_flag = df_train.shape[0] | Yes | 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 |
7,659 | #We now concat Training and Test set
df_total = pd.concat((df_train, test),axis=0, ignore_index = True) | No | 5 | 11 | Data_Transform | concatenate | 2,122,158 | 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"] | 33 | A |
7,660 | df_total = df_total.drop(['id','date_first_booking'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 2,122,158 | 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"] | 33 | A |
7,661 | #Date Account created - Capture Date, month and year seperately.
date_ac = np.vstack(df_total.date_account_created.astype(str).apply(lambda x:list(map(int,x.split('-')))).values)
df_total['Day'] = date_ac[:,0]
df_total['Month']= date_ac[:,1]
df_total['year'] = date_ac[:,2]
df_total = df_total.drop(['date_account_crea... | Yes | 3 | 11 | Data_Transform | concatenate | 2,122,158 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "DataFrame Conversion Checker", "fix": "Use .to_numpy() instead of .values.", "benefits": "Ensures future compatibility and avoids unexpected behaviour.", "location": "Line 36"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "DataFrame Conversion Checker", "Dependent Threshold Checker"] | 33 | A |
7,662 | #Time Stamp first active
time_stp = np.vstack(df_total.timestamp_first_active.astype(str)
.apply(lambda x: list(map(int,[x[:4],x[4:6],x[6:8],x[8:]]))).values)
df_total['tfa_day'] = time_stp[:,0]
df_total['tfa_Month'] = time_stp[:,1]
df_total['tfa_year'] = time_stp[:,2]
df_total = df_total.drop([... | Yes | 3 | 11 | Data_Transform | concatenate | 2,122,158 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "DataFrame Conversion Checker", "fix": "Use .to_numpy() instead of .values.", "benefits": "Ensures future compatibility and avoids unexpected behaviour.", "location": "Line 36"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "DataFrame Conversion Checker", "Dependent Threshold Checker"] | 33 | A |
7,663 | forest_class = RandomForestClassifier(random_state = 42)
n_estimators = [100, 500]
min_samples_split = [10, 20]
param_grid_forest = {'n_estimators' : n_estimators, 'min_samples_split' : min_samples_split}
rand_search_forest = GridSearchCV(forest_class, param_grid_forest, cv = 4, refit = True,
... | Yes | 3 | 6 | Hyperparam_Tuning | train_on_grid | 2,122,158 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 6 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 34"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 33 | A |
7,664 | df = pd.read_csv('../input/train.csv', parse_dates=[0]) | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,665 | test = pd.read_csv('../input/test.csv', parse_dates=[0]) | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,666 | df.rename(columns={'count': 'rentals'}, inplace=True) | No | 5 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,667 | df['rentals'] = np.log(df['rentals']) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,668 | import math | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,669 | pd.concat([df, test]) | No | 5 | 11 | Data_Transform | concatenate | 2,146,505 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
7,670 | df = pd.concat([df, test]) | No | 5 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,671 | df['year'] = df.datetime.dt.year
df['hour'] = df.datetime.dt.hour
df['dayofweek'] = df.datetime.dt.dayofweek | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,672 | train, valid = train_test_split(df, random_state=42) | 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 |
7,673 | removed_cols = ['rentals', 'casual', 'registered', 'datetime'] | 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 |
7,674 | feats = [c for c in df.columns if c not in removed_cols] | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,675 | from sklearn.tree import DecisionTreeRegressor | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,676 | dt = DecisionTreeRegressor(random_state=42, max_depth=2) | 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 |
7,677 | dt.fit(train[feats], train['rentals']) | No | 5 | 7 | Model_Train | train_model | 1,788,208 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | B |
7,678 | from fastai.structured import draw_tree | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
7,679 | from sklearn.metrics import mean_squared_error | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
7,680 | mean_squared_error(train['rentals'], train['preds']) | No | 5 | 28 | Model_Train | compute_train_metric | 2,146,505 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
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