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values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
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values | context_line_count int64 0 33 | combined_rating stringclasses 5
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508 | data = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/train.csv.zip')
data.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 9,655,329 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/restaurant-revenue-prediction/train.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 1 | A |
509 | data.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
510 | y_train = data.pop('revenue') | No | 5 | 10 | Data_Transform | drop_column | 9,655,329 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
511 | data = data.drop(data.columns[[0, 1,2,3,4]], axis=1)
x_train=data[:]
data.head() | No | 4 | 10 | Data_Transform | drop_column | 9,655,329 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
512 | from sklearn.ensemble import GradientBoostingRegressor
model = GradientBoostingRegressor()
model.fit(x_train, y_train) | No | 5 | 7 | Model_Train | train_model | 9,655,329 | 3 | C | 2 | 0 | 0 | 0 | 10 | 3 | 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"] | 1 | C |
513 | from sklearn.ensemble import GradientBoostingRegressor
learning_rates = [1, 0.5, 0.25, 0.1, 0.05, 0.01]
#learing_rates=[]
train_results = []
test_results = []
#beta=range(.05,1.05,.05)
for eta in learning_rates:
#learning_rates.append(eta)
model = GradientBoostingRegressor(learning_rate=eta)
model.fit(x_train,... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 3 | 1 | 1 | 0 | 15 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
514 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(learning_rates, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('Learning Rate')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
515 | from sklearn.ensemble import GradientBoostingRegressor
n_estimators = [ 1, 2, 4, 8, 16, 32, 64, 100, 200, 500, 1000, 2000]
train_results = []
test_results = []
for estimator in n_estimators:
model = GradientBoostingRegressor(n_estimators=estimator)
model.fit(x_train, y_train)
from sklearn.metrics import mean_s... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 2 | 1 | 1 | 0 | 10 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
516 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(n_estimators, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('Number of Estimators')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
517 | from sklearn.ensemble import GradientBoostingRegressor
#min_samples_splits = np.linspace(0.1, 1.0, 10, endpoint=True)
min_samples_splits=[2,3,4,5,6,7,8,9,10]
train_results = []
test_results = []
for min_samples_split in min_samples_splits:
model = GradientBoostingRegressor(min_samples_split=min_samples_split)
mod... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 3 | 1 | 1 | 0 | 15 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
518 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(min_samples_splits, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('Min Samples Split')
plt.show()' | 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 |
519 | from sklearn.ensemble import GradientBoostingRegressor
max_depths = [1,2,3,4,5,6,7,8,9,10]
train_results = []
test_results = []
for max_depth in max_depths:
model = GradientBoostingRegressor(max_depth=max_depth)
model.fit(x_train, y_train)
from sklearn.metrics import mean_squared_error, r2_score
model_score... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 2 | 1 | 1 | 0 | 10 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
520 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(max_depths, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('No. of Depth')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
521 | from sklearn.ensemble import GradientBoostingRegressor
#min_samples_leafs = np.linspace(0.1, 0.5, 5, endpoint=True)
min_samples_leafs = [1,2,3,4,5,6,7,8,9,10]
train_results = []
test_results = []
for min_samples_leaf in min_samples_leafs:
model = GradientBoostingRegressor(min_samples_leaf=min_samples_leaf)
model.... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 3 | 1 | 1 | 0 | 15 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
522 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(min_samples_leafs, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('No. of Samples Leaf')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
523 | from sklearn.ensemble import GradientBoostingRegressor
max_features = list(range(1,data.shape[1]))
train_results = []
test_results = []
for max_feature in max_features:
model = GradientBoostingRegressor(max_features=max_feature)
model.fit(x_train, y_train)
from sklearn.metrics import mean_squared_error, r2_sco... | No | 5 | 2 | Hyperparam_Tuning | find_best_params | 9,655,329 | 1 | A | 2 | 1 | 1 | 0 | 10 | 12 | ok | 10 | 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 | [{"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.", "Only MSE detected for regression. Consider adding R2 score or MAE for a more comprehensive evaluation.", "Unit Testing Checker", "Verbose Mode Checker"] | 1 | B |
524 | from matplotlib.legend_handler import HandlerLine2D
line1, = plt.plot(max_features, train_results, 'b', label="Training MSE")
plt.legend(handler_map={line1: HandlerLine2D(numpoints=2)})
plt.ylabel('Mean Squared Error')
plt.xlabel('No. of Feature')
plt.show()' | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
525 | data1 = pd.read_csv('/kaggle/input/restaurant-revenue-prediction/test.csv.zip')
data1.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 9,655,329 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/restaurant-revenue-prediction/test.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 2"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 1 | A |
526 | data1=data1.drop(data1.columns[[0, 1,2,3,4]], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 9,655,329 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 1 | A |
527 | data1.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 |
528 | sample =pd.read_csv('/kaggle/input/restaurant-revenue-prediction/sampleSubmission.csv') | 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 |
529 | params = {'n_estimators': 225, 'max_depth': 4, 'min_samples_split': 2, 'learning_rate': 0.25, 'loss': 'ls','max_features':10,
'min_samples_leaf':2}
model = ensemble.GradientBoostingRegressor(**params)
model.fit(x_train, y_train)
sample["Prediction"] = model.predict(data1)' | No | 3 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
530 | sample.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 |
531 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 6 | 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 12"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
532 |
import pandas as pd
import numpy as np
| No | 5 | 22 | Environment | import_modules | 10,424,951 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
533 | import pandas as pd
df_train = pd.read_csv('../input/restaurant-revenue-prediction/train.csv.zip')
df_test = pd.read_csv('../input/restaurant-revenue-prediction/test.csv.zip') | No | 4 | 45 | Data_Extraction | load_from_csv | 10,522,332 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 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."] | 4 | A |
534 | train_corr = df_train.corr()
print(train_corr)
train_corr.to_csv("corr.csv", index = False) | No | 4 | 25 | Data_Export | save_to_csv | 10,522,332 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"}] | [] | 4 | B |
535 | import datetime
from sklearn.preprocessing import LabelEncoder
revenue = df_train["revenue"] | No | 4 | 22 | Environment | import_modules | 10,522,332 | 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"] | 4 | A |
536 | del df_train["revenue"] | No | 5 | 10 | Data_Transform | drop_column | 10,522,332 | 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"] | 4 | A |
537 | df_whole = pd.concat([df_train, df_test], axis=0) | 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 |
538 | df_whole["Open Date"] = pd.to_datetime(df_whole["Open Date"])
df_whole["Year"] = df_whole["Open Date"].apply(lambda x:x.year)
df_whole["Month"] = df_whole["Open Date"].apply(lambda x:x.month)
df_whole["Day"] = df_whole["Open Date"].apply(lambda x:x.day) | No | 4 | 8 | Data_Transform | feature_engineering | 10,522,332 | 2 | B | 1 | 0 | 0 | 0 | 10 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | B |
539 | le = LabelEncoder()
df_whole["City"] = le.fit_transform(df_whole["City"]) | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
540 | df_whole["City Group"] = df_whole["City Group"].map({"Other":0, "Big Cities":1}) | No | 5 | 20 | Data_Transform | categorify | 10,522,332 | 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"] | 4 | A |
541 | df_whole["Type"] = df_whole["Type"].map({"FC":0, "IL":1, "DT":2, "MB":3}) | No | 5 | 20 | Data_Transform | categorify | 10,522,332 | 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"] | 4 | A |
542 | df_train = df_whole.iloc[:df_train.shape[0]]
df_test = df_whole.iloc[df_train.shape[0]:] | 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 |
543 | from sklearn.ensemble import RandomForestRegressor
#
df_train_columns = [col for col in df_train.columns if col not in ["Id", "Open Date"]]
#RandomForest
rf = RandomForestRegressor(
n_estimators=200,
max_depth=5,
max_features=0.5,
random_state=449,
n_jobs=-1
)
rf.fit(df_train[df_train_columns],... | No | 2 | 7 | Model_Train | train_model | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
544 | prediction = rf.predict(df_test[df_train_columns]) | 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 |
545 | submission = pd.DataFrame({"Id":df_test.Id, "Prediction":prediction})
submission.to_csv("TFI_submission.csv", index=False) | No | 4 | 25 | Data_Export | save_to_csv | 10,522,332 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
546 | df = pd.read_csv('../input/restaurant-revenue-prediction/train.csv.zip',
index_col='Id')
df | No | 5 | 45 | Data_Extraction | load_from_csv | 10,702,707 | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/restaurant-revenue-prediction/train.csv.zip. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 5 | A |
547 | y = df.revenue
X = df.drop(columns=['revenue'], axis=1) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 10,702,707 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
548 | y
| 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 |
549 | X | 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 |
550 | for col in X.columns:
print(col, df[col].dtype) | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
551 | y.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 |
552 | X_num = X.select_dtypes(exclude=['object'])
X_num | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
553 | df.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 |
554 | X_num.shape
| No | 5 | 58 | Debug | show_shape | 10,702,707 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
555 | for col in X_num.columns:
if X_num[col].isna().sum() > 0:
print(col, X_num[col].isna().sum() / len(X_num) ) | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
556 | from sklearn.impute import SimpleImputer
imputer = SimpleImputer()
X_num_imputed = pd.DataFrame(imputer.fit_transform(X_num)) | No | 4 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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... | D | Cross Validation Checker=low, Data Leakage Checker=high | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | D |
557 | parameters = {
'n_estimators': list(range(100, 1001, 100)),
'max_leaf_nodes': list(range(2, 70, 5)),
'max_depth': list(range(6, 70, 5))
}
parameters | No | 5 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
558 | my_randome_state=1486
| No | 5 | 77 | Other | define_variables | 10,702,707 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 1486", "fix": "Not specified", "benefits": "Not specified", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Magic number"] | 5 | A |
559 | from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestRegressor
gsearch = GridSearchCV(estimator=RandomForestRegressor(random_state=my_randome_state),
param_grid = parameters,
scoring='neg_mean_absolute_error',
n_j... | No | 4 | 5 | Hyperparam_Tuning | define_search_space | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 3 | 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 3"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | A |
560 | gsearch.fit(X_num_imputed, y)
| No | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
561 | best_n_estimators = gsearch.best_params_.get('n_estimators')
best_n_estimators | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
562 | best_max_leaf_nodes = gsearch.best_params_.get('max_leaf_nodes')
best_max_leaf_nodes | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
563 | best_max_depth = gsearch.best_params_.get('max_depth')
best_max_depth | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
564 | final_model = RandomForestRegressor(n_estimators=best_n_estimators, random_state=my_randome_state,
max_leaf_nodes=best_max_leaf_nodes,
max_depth=best_max_depth) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
565 | final_model.fit(X_num_imputed, y)
| No | 5 | 7 | Model_Train | train_model | 10,702,707 | 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"] | 5 | B |
566 | X_test = pd.read_csv('../input/restaurant-revenue-prediction/test.csv.zip',
index_col='Id')
X_test | No | 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 |
567 | X_test_num = X_test.select_dtypes(exclude=['object'])
| No | 5 | 14 | Data_Transform | filter | 10,702,707 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
568 | for col in X_test_num.columns:
if X_test_num[col].isna().sum() > 0:
print(col, X_test_num[col].isna().sum() / len(X_test_num) ) | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
569 | X_test_num_imputed = pd.DataFrame(imputer.transform(X_test_num))
X_test_num_imputed.columns = X_test_num.columns
X_test_num_imputed | No | 5 | 61 | Data_Transform | rename_columns | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
570 | preds_test = final_model.predict(X_test_num_imputed)
| No | 5 | 48 | Model_Evaluation | predict_on_test | 10,702,707 | 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"] | 5 | A |
571 | len(preds_test)
| No | 5 | 40 | Debug | show_table_attributes | 10,702,707 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
572 | # Save test predictions to file
output = pd.DataFrame({'Id': X_test.index,
'Prediction': preds_test})
output | No | 5 | 55 | Data_Export | prepare_output | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
573 | output.to_csv('submission.csv', index=False)
print('done!') | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
574 | data_train = pd.read_csv('../input/restaurant-revenue-prediction/train.csv.zip',index_col='Id', parse_dates=["Open Date"])
data_test = pd.read_csv('../input/restaurant-revenue-prediction/test.csv.zip',index_col='Id', parse_dates=["Open Date"])
data_train' | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
575 | data_train.describe() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
576 | data_train.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
577 | for i in data_train.columns:
print(i ,': ',len(data_train[i].unique())) | No | 5 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
578 | columnsForDrop = ['Open Date']
data_train.drop(columns=columnsForDrop, inplace=True)
################################
data_test.drop(columns=columnsForDrop, inplace=True)
data_train | No | 4 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
579 | s = (data_train.dtypes == 'object')
object_cols = list(s[s].index)
print("Categorical variables:")
print(object_cols)' | No | 4 | 37 | EDA | count_data_types | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
580 | from sklearn.preprocessing import LabelEncoder
# Make copy to avoid changing original data
label_X_train = data_train.copy()
label_X_test = data_test.copy()
# Apply label encoder to each column with categorical data
label_encoder = LabelEncoder()
for col in object_cols:
label_encoder.fit(pd.concat([data_train[co... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 8 | ok | 10 | 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... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "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"] | 0 | D |
581 | data_train = label_X_train
data_test = label_X_test
data_train | No | 4 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
582 | y = data_train.revenue
############################################
X = data_train.drop(columns=['revenue']) | 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 |
583 | from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, classification_report, f1_score
from sklearn.neighbors import KNeighborsClassifier | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 0 | A |
584 | from xgboost import XGBRegressor
from sklearn.metrics import mean_absolute_error ,explained_variance_score, mean_squared_error
#########################################################################3
from sklearn.ensemble import RandomForestRegressor
parameters = {'max_depth': list(range(6, 30, 10)),
... | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 1 | 0 | 0 | 0 | 5 | 15 | ok | 6 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Randomness Control Checker", "fix": "Set random_state in estimators for reproducibility.", "benefits": "Ensures reproducible results and consistent model behaviour.", "location": "Line 15"}, {"detector": "framework_specific", "framework": "ScikitL... | B | Randomness Control Checker=low | [{"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 0 | B |
585 | print(gsearch.best_params_.get('n_estimators'))
print(gsearch.best_params_.get('max_leaf_nodes'))
print(gsearch.best_params_.get('max_depth')) | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
586 | print(data_train.shape)
print(data_test.shape)
print(X.shape) | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
587 | final_model = RandomForestRegressor(
max_depth = gsearch.best_params_.get('max_depth'),
max_leaf_nodes = gsearch.best_params_.get('max_leaf_nodes'),
n_estimators = gsearch.best_params_.get('n_estimators'),random_state=1, n_jobs=4)
final_model.fit(X, y) | No | 4 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
588 | preds = final_model.predict(data_test)
print(preds.shape)
print(data_test.shape) | No | 4 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
589 | testData = pd.read_csv("../input/restaurant-revenue-prediction/test.csv.zip")
submission = pd.DataFrame({
"Id": testData["Id"],
"Prediction": preds
})
submission.to_csv('RandomForestSimple.csv',header=True, index=False)
print('Done')' | 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 |
590 | df_train=pd.read_csv("/kaggle/input/restaurant-revenue-prediction/train.csv.zip",index_col='Id')
X_test = pd.read_csv('../input/restaurant-revenue-prediction/test.csv.zip',index_col='Id')
df_train.shape,X_test.shape
' | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
591 | train_with_missing = [col for col in df_train.columns if df_train[col].isnull().any()]
test_with_missing = [col for col in X_test.columns if X_test[col].isnull().any()]
train_with_missing,test_with_missing | No | 3 | 17 | Data_Transform | correct_missing_values | 11,097,956 | 1 | A | 0 | 2 | 0 | 2 | 0 | 3 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker"] | 4 | B |
592 | y_train = df_train.revenue
X_train = df_train.drop(columns=['revenue'], axis=1) | No | 5 | 21 | Data_Transform | prepare_x_and_y | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 4 | B |
593 | X_train.shape,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 |
594 | b"import matplotlib.pyplot as plt\n\nd_names = ('train.csv.zip', 'test.csv.zip')\ny_pos = range(len(d_names))\n \nplt.bar(\n y_pos, \n (X_train.shape[0], X_test.shape[0]), \n align='center', \n alpha=0.8\n)\nplt.xticks(y_pos, d_names)\nplt.ylabel('Number of rows') \nplt.title(' Wow!')\nplt.show()" | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
595 | bad_label_cols = list(set(X_train.columns)-set(X_test.columns))
bad_label_cols | No | 4 | 37 | EDA | count_data_types | 11,097,956 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
596 | X_train['City'].value_counts() | No | 5 | 72 | EDA | count_values | 11,097,956 | 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"] | 4 | B |
597 | X_train.Type.value_counts() | No | 5 | 72 | EDA | count_values | 11,097,956 | 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"}] | [] | 4 | B |
598 | X_train['year'] = pd.DatetimeIndex(X_train['Open Date']).year
X_train.drop(columns=['Open Date','City'],inplace=True) | No | 4 | 10 | Data_Transform | drop_column | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "InPlace Checker"] | 4 | B |
599 | X_test['year'] = pd.DatetimeIndex(X_test['Open Date']).year
X_test.drop(columns=['Open Date','City'],inplace=True) | No | 4 | 10 | Data_Transform | drop_column | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 6"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker",... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 4 | A |
600 | from sklearn.preprocessing import OneHotEncoder
# Apply one-hot encoder to each column with categorical data
OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(X_train[object_cols]))
OH_cols_test = pd.DataFrame(OH_encoder.transform(X_test[object_cols]... | Yes | 3 | 20 | Data_Transform | categorify | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 11 | 10 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Che... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Column Selection Checker", "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"] | 4 | D |
601 | OH_X_train.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
602 | OH_X_test.head() | No | 5 | 41 | Debug | show_table | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
603 | train_stats=OH_X_train.describe().transpose()
train_stats | No | 5 | 40 | Debug | show_table_attributes | 11,097,956 | 1 | A | 0 | 0 | 0 | 1 | 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"}] | [] | 4 | B |
604 | def norm(x):
return (x - train_stats['mean']) / train_stats['std']
normed_train_data = norm(OH_X_train)
normed_test_data = norm(OH_X_test) | No | 5 | 18 | Data_Transform | normalization | 11,097,956 | 1 | A | 0 | 1 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
605 | normed_test_data.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 |
606 | parameters = {
'n_estimators': list(range(10, 300, 20)),
'learning_rate': [l / 100 for l in range(5, 100, 20)],
'max_depth': list(range(1, 20,3)),
'gamma':[2,3],
'eta':[0.8,0.9],
'reg_alpha':[0.5,0.6,0.7,0.8],
'reg_lambda':[0.5,0.6,0.7,0.8]
}
parameters | No | 5 | 5 | Hyperparam_Tuning | define_search_space | 11,097,956 | 1 | A | 0 | 0 | 0 | 1 | 0 | 10 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
607 | my_randome_state=70 | No | 5 | 77 | Other | define_variables | 11,097,956 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 70", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Magic number"] | 4 | A |
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