🚀 week-01-environment-setup #1
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WalkthroughThe pull request introduces several changes across multiple files. The CI workflow has been updated to modify how pre-commit checks are executed, adding a dependency installation step. The Changes
Sequence Diagram(s)sequenceDiagram
participant CI
participant PreCommit
participant Git
CI->>PreCommit: Install pre-commit package
PreCommit-->>CI: Installation complete
CI->>PreCommit: Run pre-commit checks on all files
PreCommit-->>CI: Checks complete
CI->>Git: Commit changes
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
.gitignore (2)
98-98: Remove unnecessary empty line.The empty line at line 98 appears to be unnecessary.
-
99-99: Add section comment for better organization.The entry is correct, but consider adding a descriptive comment to maintain the organized structure of the file.
+# Databricks .databricks
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⛔ Files ignored due to path filters (1)
uv.lockis excluded by!**/*.lock
📒 Files selected for processing (4)
- .github/workflows/ci.yml (1 hunks)
- .gitignore (1 hunks)
- databricks.yml (1 hunks)
- pyproject.toml (1 hunks)
✅ Files skipped from review due to trivial changes (1)
- databricks.yml
🧰 Additional context used
🔇 Additional comments (3)
pyproject.toml (3)
6-6: LGTM! Good practice for Python version constraint.The change from strict equality (
==3.11) to a more flexible range (>=3.11, <3.12) is a good practice. This allows for patch version updates while maintaining compatibility, which is important for security fixes.
Line range hint
7-21: Well-structured dependency management.The dependencies are well-organized with appropriate version constraints:
- Upper bounds prevent unexpected breaking changes
- Recent, stable versions of key ML libraries
- Consistent versioning pattern across all dependencies
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23-26: Comprehensive dev dependencies for Databricks development.The development dependencies include all necessary tools for Databricks development and local testing. The version constraints follow the same best practices as the main dependencies.
| uv pip install pre-commit | ||
| uv run pre-commit run --all-files |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider managing pre-commit through project dependencies
The separate installation of pre-commit could lead to version conflicts and adds unnecessary build time overhead. Instead:
- Add pre-commit to your project dependencies (pyproject.toml)
- Remove the separate installation step as
uv syncwill handle it
Apply this diff to streamline the workflow:
- name: Run pre-commit checks
run: |
- uv pip install pre-commit
- uv run pre-commit run --all-files
+ uv run pre-commit run --all-filesAnd add pre-commit to your development dependencies in pyproject.toml:
[project.optional-dependencies]
dev = [
"pre-commit",
# ... other dev dependencies
]
|
Hello Maha! Have you also played with the dataset already? :) |
…us-databricks-course-MahaAmin into w1m2-environment-setup
…databricks/marvelous-databricks-course-MahaAmin into w1m2-environment-setup
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (3)
notebooks/fraud_credit_cards.py (3)
14-14: Move package dependencies torequirements.txtorpyproject.toml.Installing packages directly in notebooks makes dependency management harder and less reproducible. Consider moving these dependencies to your project's dependency management files.
104-119: Refactor classification report visualization.The current implementation has duplicate imports and hardcoded thresholds.
Consider this more maintainable approach:
# At the top of the file with other imports from colorama import Fore, Back, Style # Define constants METRIC_THRESHOLD = 0.8 METRICS = ['precision', 'recall', 'f1-score'] def format_metric(value, metric_name): """Format a metric value with color coding.""" color = Fore.GREEN if value > METRIC_THRESHOLD else Fore.RED return f"{metric_name.title()}: {color}{value:.2f}{Style.RESET_ALL}" # Display report print(f"\n{Back.BLUE}{Fore.WHITE}Classification Report{Style.RESET_ALL}") report = classification_report(y_test, y_pred, output_dict=True) for class_name, metrics in report.items(): if class_name in ['0', '1']: print(f"\nClass {class_name}:") for metric in METRICS: print(f" {format_metric(metrics[metric], metric)}") print(f" Support: {metrics['support']}") else: print(f"\n{class_name}:", metrics)🧰 Tools
🪛 Ruff
104-104: Module level import not at top of file
(E402)
104-104: Redefinition of unused
Forefrom line 30Remove definition:
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104-104: Redefinition of unused
Stylefrom line 30Remove definition:
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108-108: Trailing whitespace
Remove trailing whitespace
(W291)
1-122: Consider production readiness improvements.While the notebook implements the basic fraud detection functionality, consider these improvements for production readiness:
- Move model training code to a proper Python module
- Implement proper logging instead of print statements
- Add docstrings and type hints
- Add unit tests for the preprocessing and evaluation functions
- Consider using MLflow for experiment tracking
Would you like me to help implement any of these improvements?
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seabornimported but unusedRemove unused import:
seaborn(F401)
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matplotlib.pyplotimported but unusedRemove unused import:
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numpyimported but unusedRemove unused import:
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plotly.expressimported but unusedRemove unused import:
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sklearn.preprocessing.MinMaxScalerimported but unusedRemove unused import
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sklearn.preprocessing.QuantileTransformerimported but unusedRemove unused import
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sklearn.preprocessing.OneHotEncoderimported but unusedRemove unused import
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sklearn.ensemble.RandomForestClassifierimported but unusedRemove unused import:
sklearn.ensemble.RandomForestClassifier(F401)
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sklearn.tree.DecisionTreeClassifierimported but unusedRemove unused import:
sklearn.tree.DecisionTreeClassifier(F401)
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sklearn.svm.SVCimported but unusedRemove unused import:
sklearn.svm.SVC(F401)
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sklearn.neighbors.KNeighborsClassifierimported but unusedRemove unused import:
sklearn.neighbors.KNeighborsClassifier(F401)
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sklearn.model_selection.RandomizedSearchCVimported but unusedRemove unused import
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sklearn.model_selection.GridSearchCVimported but unusedRemove unused import
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sklearn.metrics.precision_scoreimported but unusedRemove unused import
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sklearn.metrics.recall_scoreimported but unusedRemove unused import
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sklearn.metrics.f1_scoreimported but unusedRemove unused import
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sklearn.metrics.roc_auc_scoreimported but unusedRemove unused import
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scipy.stats.randintimported but unusedRemove unused import:
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lightgbm.LGBMClassifierimported but unusedRemove unused import:
lightgbm.LGBMClassifier(F401)
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lightgbmimported but unusedRemove unused import:
lightgbm(F401)
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sklearn.preprocessing.LabelEncoderimported but unusedRemove unused import:
sklearn.preprocessing.LabelEncoder(F401)
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104-104: Redefinition of unused
Forefrom line 30Remove definition:
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Stylefrom line 30Remove definition:
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108-108: Trailing whitespace
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(W291)
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📒 Files selected for processing (1)
notebooks/fraud_credit_cards.py(1 hunks)
🧰 Additional context used
🪛 Ruff
notebooks/fraud_credit_cards.py
23-23: seaborn imported but unused
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26-26: numpy imported but unused
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34-34: sklearn.preprocessing.MinMaxScaler imported but unused
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34-34: sklearn.preprocessing.QuantileTransformer imported but unused
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35-35: sklearn.ensemble.RandomForestClassifier imported but unused
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36-36: sklearn.tree.DecisionTreeClassifier imported but unused
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38-38: sklearn.neighbors.KNeighborsClassifier imported but unused
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39-39: sklearn.model_selection.RandomizedSearchCV imported but unused
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40-40: sklearn.metrics.precision_score imported but unused
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41-41: scipy.stats.randint imported but unused
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43-43: lightgbm.LGBMClassifier imported but unused
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45-45: sklearn.preprocessing.LabelEncoder imported but unused
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104-104: Module level import not at top of file
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104-104: Redefinition of unused Fore from line 30
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notebooks/fraud_credit_cards.py
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| # COMMAND ---------- | ||
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| df_tr = pd.read_csv('/Volumes/fraud_credit_cards/data/credit_cards_2023/creditcard_2023.csv') |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling and use configurable data path.
The hardcoded path makes the notebook less portable and lacks error handling for data loading issues.
Consider implementing this pattern:
from pathlib import Path
import os
# Load path from environment variable or config
data_path = os.getenv('CREDIT_CARD_DATA_PATH', '/Volumes/fraud_credit_cards/data/credit_cards_2023/creditcard_2023.csv')
try:
if not Path(data_path).exists():
raise FileNotFoundError(f"Data file not found at {data_path}")
df_tr = pd.read_csv(data_path)
# Validate data
required_columns = ['Class'] # Add all required columns
if not all(col in df_tr.columns for col in required_columns):
raise ValueError("Missing required columns in dataset")
except Exception as e:
print(f"Error loading data: {str(e)}")
raise
notebooks/fraud_credit_cards.py
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| model = Pipeline(steps=[('preprocessor', preprocessor), | ||
| ('classifier', CatBoostClassifier(verbose=False))]) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider adding cross-validation and handling class imbalance.
For fraud detection, it's crucial to handle class imbalance and validate model performance robustly.
Consider implementing these improvements:
from sklearn.model_selection import cross_val_score
from imblearn.pipeline import Pipeline as ImbPipeline
from imblearn.over_sampling import SMOTE
# Define the model with class imbalance handling
model = ImbPipeline(steps=[
('preprocessor', preprocessor),
('smote', SMOTE(random_state=42)),
('classifier', CatBoostClassifier(
verbose=False,
class_weights='balanced'
))
])
# Add cross-validation
cv_scores = cross_val_score(model, X, y, cv=5, scoring='f1')
print(f"Cross-validation F1 scores: {cv_scores}")
print(f"Mean F1: {cv_scores.mean():.3f} (+/- {cv_scores.std() * 2:.3f})")
notebooks/fraud_credit_cards.py
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| import seaborn as sns | ||
| import matplotlib.pyplot as plt | ||
| import pandas as pd | ||
| import numpy as np | ||
| import plotly.express as px | ||
| from IPython.display import display, HTML | ||
| import warnings | ||
| from colorama import Fore, Style | ||
|
|
||
| from sklearn.compose import ColumnTransformer | ||
| from sklearn.pipeline import Pipeline | ||
| from sklearn.preprocessing import StandardScaler, MinMaxScaler, QuantileTransformer, OneHotEncoder | ||
| from sklearn.ensemble import RandomForestClassifier | ||
| from sklearn.tree import DecisionTreeClassifier | ||
| from sklearn.svm import SVC | ||
| from sklearn.neighbors import KNeighborsClassifier | ||
| from sklearn.model_selection import train_test_split, RandomizedSearchCV, GridSearchCV | ||
| from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score | ||
| from scipy.stats import randint | ||
| from catboost import CatBoostClassifier | ||
| from lightgbm import LGBMClassifier | ||
| import lightgbm as lgb | ||
| from sklearn.preprocessing import LabelEncoder | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Clean up unused imports.
Many imported libraries are not used in the code. This adds unnecessary overhead and reduces code readability.
Apply this diff to remove unused imports:
import pandas as pd
from colorama import Fore, Style
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
from catboost import CatBoostClassifier📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| from IPython.display import display, HTML | |
| import warnings | |
| from colorama import Fore, Style | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import StandardScaler, MinMaxScaler, QuantileTransformer, OneHotEncoder | |
| from sklearn.ensemble import RandomForestClassifier | |
| from sklearn.tree import DecisionTreeClassifier | |
| from sklearn.svm import SVC | |
| from sklearn.neighbors import KNeighborsClassifier | |
| from sklearn.model_selection import train_test_split, RandomizedSearchCV, GridSearchCV | |
| from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score | |
| from scipy.stats import randint | |
| from catboost import CatBoostClassifier | |
| from lightgbm import LGBMClassifier | |
| import lightgbm as lgb | |
| from sklearn.preprocessing import LabelEncoder | |
| import pandas as pd | |
| from colorama import Fore, Style | |
| from sklearn.compose import ColumnTransformer | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.metrics import accuracy_score, classification_report | |
| from catboost import CatBoostClassifier |
🧰 Tools
🪛 Ruff
23-23: seaborn imported but unused
Remove unused import: seaborn
(F401)
24-24: matplotlib.pyplot imported but unused
Remove unused import: matplotlib.pyplot
(F401)
26-26: numpy imported but unused
Remove unused import: numpy
(F401)
27-27: plotly.express imported but unused
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28-28: IPython.display.display imported but unused
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28-28: IPython.display.HTML imported but unused
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34-34: sklearn.preprocessing.MinMaxScaler imported but unused
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34-34: sklearn.preprocessing.QuantileTransformer imported but unused
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34-34: sklearn.preprocessing.OneHotEncoder imported but unused
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35-35: sklearn.ensemble.RandomForestClassifier imported but unused
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(F401)
36-36: sklearn.tree.DecisionTreeClassifier imported but unused
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(F401)
37-37: sklearn.svm.SVC imported but unused
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38-38: sklearn.neighbors.KNeighborsClassifier imported but unused
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39-39: sklearn.model_selection.RandomizedSearchCV imported but unused
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39-39: sklearn.model_selection.GridSearchCV imported but unused
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40-40: sklearn.metrics.precision_score imported but unused
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40-40: sklearn.metrics.recall_score imported but unused
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40-40: sklearn.metrics.f1_score imported but unused
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40-40: sklearn.metrics.roc_auc_score imported but unused
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41-41: scipy.stats.randint imported but unused
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43-43: lightgbm.LGBMClassifier imported but unused
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44-44: lightgbm imported but unused
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45-45: sklearn.preprocessing.LabelEncoder imported but unused
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(F401)
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Actionable comments posted: 3
🧹 Outside diff range and nitpick comments (5)
notebooks/modular_fraud_credit_cards.py (5)
128-169: Remove unused parametersX_trainandy_trainfromevaluate_modelfunctionThe parameters
X_trainandy_trainare not used within theevaluate_modelfunction. Removing these unused parameters will simplify the function signature and improve readability.Apply this diff to remove the unused parameters:
def evaluate_model(model, X_train, X_test, y_train, y_test, y_pred): - def evaluate_model(model, X_test, y_test, y_pred): # Generate evaluation metrics # Calculate accuracy accuracy = accuracy_score(y_test, y_pred) # ... return accuracy, precision, recall, mse, f1 -accuracy, precision, recall, mse, f1 = evaluate_model(model, X_train, X_test, y_train, y_test, y_pred) +accuracy, precision, recall, mse, f1 = evaluate_model(model, X_test, y_test, y_pred)
139-144: Mean Squared Error may not be appropriate for classification tasksMean Squared Error (MSE) is typically used for regression problems, not classification. Since this is a classification task, consider removing MSE or replacing it with a more suitable metric like Log Loss.
Apply this diff to remove MSE calculation:
# Calculate Mean Squared Error - mse = mean_squared_error(y_test, y_pred) # Calculate F1 score f1 = f1_score(y_test, y_pred)If you need a probabilistic metric, consider using log loss:
from sklearn.metrics import log_loss # After predicting probabilities with model.predict_proba() y_pred_proba = model.predict_proba(X_test) logloss = log_loss(y_test, y_pred_proba) print("Log Loss:", logloss)
142-142: Correct typo in comment: 'Calcualte' to 'Calculate'There's a typo in the comment. Correcting it improves readability and professionalism.
Apply this diff:
- # Calcualte F1 score + # Calculate F1 score
41-42: Avoid suppressing all warnings indiscriminatelySuppressing all warnings can hide important alerts that might help in debugging or improving the code. It's better to suppress specific warnings if necessary.
Consider specifying the warnings to ignore:
-warnings.filterwarnings("ignore") +warnings.filterwarnings("ignore", category=SomeSpecificWarning)Replace
SomeSpecificWarningwith the actual warning category you intend to suppress.
154-154: Consolidate imports ofcoloramamoduleYou have multiple imports from
colorama. It's better to import all needed components at once to keep the imports organized.Apply this diff to consolidate the imports:
from colorama import Fore, Style +from colorama import BackChange to:
-from colorama import Fore, Style +from colorama import Fore, Style, BackThen remove the later import at line 154.
-from colorama import Back
| def load_data(path): | ||
| """ | ||
| Load the data from the given filepath. | ||
| """ | ||
| df = pd.read_csv(filepath) | ||
| return df |
There was a problem hiding this comment.
Fix variable name mismatch in load_data() function
In the load_data function, the parameter is named path, but the variable filepath is used inside the function. This will result in a NameError. Replace filepath with path inside the function to ensure it works correctly.
Apply this diff to fix the issue:
def load_data(path):
"""
Load the data from the given filepath.
"""
- df = pd.read_csv(filepath)
+ df = pd.read_csv(path)
return df📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def load_data(path): | |
| """ | |
| Load the data from the given filepath. | |
| """ | |
| df = pd.read_csv(filepath) | |
| return df | |
| def load_data(path): | |
| """ | |
| Load the data from the given filepath. | |
| """ | |
| df = pd.read_csv(path) | |
| return df |
| def train_and_evaluate_model(X, y, preprocessor, test_size=0.2, random_state=42, n_estimators=100): | ||
| # Define the model | ||
| model = Pipeline(steps=[("preprocessor", preprocessor), ("classifier", CatBoostClassifier(verbose=False))]) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Utilize the n_estimators parameter in CatBoostClassifier
The n_estimators parameter is defined in the function signature but not used in the CatBoostClassifier. To control the number of trees in the model, pass n_estimators to the classifier.
Apply this diff to include n_estimators in the classifier:
def train_and_evaluate_model(X, y, preprocessor, test_size=0.2, random_state=42, n_estimators=100):
# Define the model
model = Pipeline(
steps=[
("preprocessor", preprocessor),
- ("classifier", CatBoostClassifier(verbose=False)),
+ ("classifier", CatBoostClassifier(n_estimators=n_estimators, verbose=False)),
]
)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def train_and_evaluate_model(X, y, preprocessor, test_size=0.2, random_state=42, n_estimators=100): | |
| # Define the model | |
| model = Pipeline(steps=[("preprocessor", preprocessor), ("classifier", CatBoostClassifier(verbose=False))]) | |
| def train_and_evaluate_model(X, y, preprocessor, test_size=0.2, random_state=42, n_estimators=100): | |
| # Define the model | |
| model = Pipeline( | |
| steps=[ | |
| ("preprocessor", preprocessor), | |
| ("classifier", CatBoostClassifier(n_estimators=n_estimators, verbose=False)), | |
| ] | |
| ) |
| filepath = "/Volumes/fraud_credit_cards/data/credit_cards_2023/creditcard_2023.csv" | ||
| df = load_data(filepath) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid hardcoding file paths; make the file path configurable
Hardcoding the file path can cause issues when running the code in different environments or by other users. Consider parameterizing the file path or using a configuration setting to make the code more flexible and reusable.
Apply this diff to parameterize the file path:
-filepath = "/Volumes/fraud_credit_cards/data/credit_cards_2023/creditcard_2023.csv"
+filepath = "/path/to/your/dataset/creditcard_2023.csv" # Replace with your dataset pathAlternatively, you can prompt the user for the file path or pass it as a command-line argument.
Committable suggestion skipped: line range outside the PR's diff.
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Actionable comments posted: 3
🧹 Nitpick comments (4)
main.py (2)
7-10: Consider adding error handling when loading the configuration file.If
project_config.ymlis missing or malformed, an exception will be raised. It might be beneficial to wrap this operation in a try-except block and provide a meaningful error message or fallback behavior.🧰 Tools
🪛 GitHub Actions: CI
[error] Multiple issues found: 2 unsorted imports and 2 unused imports
11-15: Optional: Expand logging usage or environment-based logging configuration.While setting up basic logging is a good start, consider adopting environment-based logging levels or adding more granular log statements for improved debugging.
🧰 Tools
🪛 GitHub Actions: CI
[error] Multiple issues found: 2 unsorted imports and 2 unused imports
src/fraud_credit_cards/data_processor.py (1)
13-13: Remove trailing whitespace from blank lines.Trailing whitespace can introduce formatting inconsistencies. For instance, in line 13 and line 20, remove extra spaces to address the pipeline warnings:
- +- +Also applies to: 20-20
🧰 Tools
🪛 Ruff (0.8.2)
13-13: Blank line contains whitespace
Remove whitespace from blank line
(W293)
🪛 GitHub Actions: CI
[warning] Trailing whitespace detected and fixed
[error] Unsorted imports detected
src/fraud_credit_cards/fraud_model.py (1)
10-10: Remove trailing whitespace to adhere to code style guidelines.Trailing whitespace in blank lines can trigger formatting checks. For instance, line 10 and line 16 contain trailing spaces and should be removed.
Also applies to: 16-16
🧰 Tools
🪛 Ruff (0.8.2)
10-10: Blank line contains whitespace
Remove whitespace from blank line
(W293)
🪛 GitHub Actions: CI
[warning] Trailing whitespace detected and fixed
[error] Multiple issues found: 3 unused imports and 1 unsorted import
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📒 Files selected for processing (4)
config.json(1 hunks)main.py(1 hunks)src/fraud_credit_cards/data_processor.py(1 hunks)src/fraud_credit_cards/fraud_model.py(1 hunks)
✅ Files skipped from review due to trivial changes (1)
- config.json
🧰 Additional context used
🪛 Ruff (0.8.2)
main.py
4-4: fraud_credit_cards.data_processor.DataProcessor imported but unused
Remove unused import: fraud_credit_cards.data_processor.DataProcessor
(F401)
5-5: fraud_credit_cards.fraud_model.FraudModel imported but unused
Remove unused import: fraud_credit_cards.fraud_model.FraudModel
(F401)
src/fraud_credit_cards/data_processor.py
13-13: Blank line contains whitespace
Remove whitespace from blank line
(W293)
20-20: Blank line contains whitespace
Remove whitespace from blank line
(W293)
34-34: Local variable preprocessor is assigned to but never used
Remove assignment to unused variable preprocessor
(F841)
src/fraud_credit_cards/fraud_model.py
3-3: sklearn.metrics.classification_report imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.roc_auc_score imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.r2_score imported but unused
Remove unused import
(F401)
10-10: Blank line contains whitespace
Remove whitespace from blank line
(W293)
16-16: Blank line contains whitespace
Remove whitespace from blank line
(W293)
🪛 GitHub Actions: CI
main.py
[error] Multiple issues found: 2 unsorted imports and 2 unused imports
src/fraud_credit_cards/data_processor.py
[warning] Trailing whitespace detected and fixed
[error] 35-35: Local variable preprocessor is assigned to but never used
[error] Unsorted imports detected
src/fraud_credit_cards/fraud_model.py
[warning] Trailing whitespace detected and fixed
[error] Multiple issues found: 3 unused imports and 1 unsorted import
main.py
Outdated
| import yaml | ||
| import logging | ||
|
|
||
| from fraud_credit_cards.data_processor import DataProcessor | ||
| from fraud_credit_cards.fraud_model import FraudModel |
There was a problem hiding this comment.
Address unused imports and unsorted imports.
The pipeline flagged two imports (DataProcessor and FraudModel) as unused and also reported unsorted imports. To resolve these errors, consider removing the unused imports and sorting the remaining imports. Below is a suggested fix:
-import yaml
-import logging
-from fraud_credit_cards.data_processor import DataProcessor
-from fraud_credit_cards.fraud_model import FraudModel
+import logging
+import yaml📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import yaml | |
| import logging | |
| from fraud_credit_cards.data_processor import DataProcessor | |
| from fraud_credit_cards.fraud_model import FraudModel | |
| import logging | |
| import yaml |
🧰 Tools
🪛 Ruff (0.8.2)
4-4: fraud_credit_cards.data_processor.DataProcessor imported but unused
Remove unused import: fraud_credit_cards.data_processor.DataProcessor
(F401)
5-5: fraud_credit_cards.fraud_model.FraudModel imported but unused
Remove unused import: fraud_credit_cards.fraud_model.FraudModel
(F401)
🪛 GitHub Actions: CI
[error] Multiple issues found: 2 unsorted imports and 2 unused imports
| preprocessor = ColumnTransformer( | ||
| transformers=[ | ||
| ('num', numeric_transformer, numeric_features)]) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Use the preprocessor assignment or remove it.
preprocessor is assigned but never used. Since you have self.preprocessor in the class, consider storing it there and removing the redundant line self.preprocessor = None:
- self.preprocessor = None
...
- preprocessor = ColumnTransformer(
+ self.preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features)])Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Ruff (0.8.2)
34-34: Local variable preprocessor is assigned to but never used
Remove assignment to unused variable preprocessor
(F841)
🪛 GitHub Actions: CI
[warning] Trailing whitespace detected and fixed
[error] 35-35: Local variable preprocessor is assigned to but never used
[error] Unsorted imports detected
| from sklearn.pipeline import Pipeline | ||
| from catboost import CatBoostClassifier | ||
| from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score, mean_squared_error, r2_score |
There was a problem hiding this comment.
Remove unused imports and sort the remaining ones.
classification_report, roc_auc_score, and r2_score are imported but never used. Below is a suggested fix that removes the unused imports and places them in alphabetical order:
-from sklearn.pipeline import Pipeline
-from catboost import CatBoostClassifier
-from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score, mean_squared_error, r2_score
+from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
from sklearn.pipeline import Pipeline📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| from sklearn.pipeline import Pipeline | |
| from catboost import CatBoostClassifier | |
| from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score, mean_squared_error, r2_score | |
| from catboost import CatBoostClassifier | |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error | |
| from sklearn.pipeline import Pipeline |
🧰 Tools
🪛 Ruff (0.8.2)
3-3: sklearn.metrics.classification_report imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.roc_auc_score imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.r2_score imported but unused
Remove unused import
(F401)
🪛 GitHub Actions: CI
[warning] Trailing whitespace detected and fixed
[error] Multiple issues found: 3 unused imports and 1 unsorted import
There was a problem hiding this comment.
Actionable comments posted: 3
♻️ Duplicate comments (1)
src/fraud_credit_cards/data_processor.py (1)
22-37:⚠️ Potential issueFix unused preprocessor and improve preprocessing logic
The preprocessor is created but never used or stored. This is a critical issue that needs to be fixed:
def preprocess_data(self): # Spliting the data into features and target target = self.config['target'] self.X = self.df.drop(target, axis=1) self.y = self.df[target] # Define numeric features (remove categorical columns) numeric_features = self.X.select_dtypes(include=['int64', 'float64']).columns.tolist() # Define preprocessing steps numeric_transformer = Pipeline(steps=[ ('scaler', StandardScaler())]) - preprocessor = ColumnTransformer( + self.preprocessor = ColumnTransformer( transformers=[ ('num', numeric_transformer, numeric_features)]) + + # Fit and transform the data + self.X = self.preprocessor.fit_transform(self.X)🧰 Tools
🪛 Ruff (0.8.2)
35-35: Local variable
preprocessoris assigned to but never usedRemove assignment to unused variable
preprocessor(F841)
🧹 Nitpick comments (12)
dbconnect_example.py (1)
1-5: Add basic error handling & trailing newline.This code correctly demonstrates how to connect to Databricks and read a table. However, consider adding basic error handling for table existence or connectivity issues. Also, please address the missing trailing newline at the end of the file, as recommended by static analysis.
Here's a sample fix for the trailing newline:
df.show(5) +🧰 Tools
🪛 Ruff (0.8.2)
5-5: No newline at end of file
Add trailing newline
(W292)
pyproject.toml (1)
44-44: Ruff formatting style.Setting
"indent-style = 'space'"is fine. Verify this aligns with your team’s standard, as mixing styles can lead to confusion.src/fraud_credit_cards/fraud_model.py (3)
10-10: Eliminate trailing whitespace.Remove stray whitespace on lines 10 and 16 to satisfy linting rules and maintain consistency.
10- + 16- +Also applies to: 16-16
🧰 Tools
🪛 Ruff (0.8.2)
10-10: Blank line contains whitespace
Remove whitespace from blank line
(W293)
34-34: Fix typographical error."Calcualte" is misspelled. Please correct to "Calculate" for clarity.
- # Calcualte F1 score + # Calculate F1 score
17-37: Optional: Expand evaluation metrics.Although the current metric set is sufficient, consider including ROC-AUC or confusion matrix data for a more holistic view of model performance.
README.md (1)
28-29: Add language specifiers to code blocksAdd language specifiers to the code blocks for better syntax highlighting:
-``` +```bash uv venv -p 3.11.11 .venv source .venv/bin/activateApply similar changes to other code blocks.
Also applies to: 37-38, 43-44, 47-48
src/fraud_credit_cards/data_processor.py (1)
40-42: Improve documentation for split_data methodAdd docstring to explain the method's parameters and return values:
def split_data(self, test_size=0.2, random_state=42): + """ + Split the dataset into training and test sets. + + Args: + test_size (float): Proportion of the dataset to include in the test split + random_state (int): Random state for reproducibility + + Returns: + tuple: (X_train, X_test, y_train, y_test) + """ # Split the data into training and test sets return train_test_split(self.X, self.y, test_size=test_size, random_state=random_state)main.py (1)
47-48: Improve logging levelsThe debug message contains important shape information that should be at INFO level:
-logger.info(f"Data split into training and test sets.") -logger.debug(f"Training set shape: {X_train.shape}, Test set shape: {X_test.shape}") +logger.info("Data split into training and test sets.") +logger.info(f"Training set shape: {X_train.shape}, Test set shape: {X_test.shape}")🧰 Tools
🪛 Ruff (0.8.2)
47-47: f-string without any placeholders
Remove extraneous
fprefix(F541)
project_config.yml (1)
1-1: Add documentation and improve YAML structure.While the configuration is valid, consider adding documentation and following YAML best practices:
- Add a comment explaining the purpose of the
targetconfiguration- Consider using a more descriptive structure
+# Configuration for the fraud detection model +# target: Specifies the target column for classification + model: target: ClassAlso, please add a newline at the end of the file to fix the yamllint error.
🧰 Tools
🪛 yamllint (1.35.1)
[error] 1-1: no new line character at the end of file
(new-line-at-end-of-file)
notes/commands.md (3)
1-4: Fix markdown heading hierarchy.The document structure should follow proper heading hierarchy:
-# Commands +# Databricks CLI Commands -### Install Databricks CLI +## Install Databricks CLI🧰 Tools
🪛 Markdownlint (0.37.0)
3-3: Expected: h2; Actual: h3
Heading levels should only increment by one level at a time(MD001, heading-increment)
5-9: Add language specifier to code blocks.Specify the shell language for the code blocks to enable syntax highlighting:
-``` +```shell brew tap databricks/tap brew install databricks🧰 Tools
🪛 Markdownlint (0.37.0)
5-5: null
Fenced code blocks should have a language specified(MD040, fenced-code-language)
13-15: Add workspace URL placeholder explanation.The command contains a placeholder that needs clarification:
+# Replace <workspace-url> with your Databricks workspace URL +# Example: https://your-workspace.cloud.databricks.com🧰 Tools
🪛 Markdownlint (0.37.0)
13-13: null
Fenced code blocks should have a language specified(MD040, fenced-code-language)
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⛔ Files ignored due to path filters (1)
uv.lockis excluded by!**/*.lock
📒 Files selected for processing (9)
.gitignore(3 hunks)README.md(1 hunks)dbconnect_example.py(1 hunks)main.py(1 hunks)notes/commands.md(1 hunks)project_config.yml(1 hunks)pyproject.toml(2 hunks)src/fraud_credit_cards/data_processor.py(1 hunks)src/fraud_credit_cards/fraud_model.py(1 hunks)
🧰 Additional context used
🪛 Ruff (0.8.2)
dbconnect_example.py
5-5: No newline at end of file
Add trailing newline
(W292)
src/fraud_credit_cards/fraud_model.py
3-3: sklearn.metrics.classification_report imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.roc_auc_score imported but unused
Remove unused import
(F401)
3-3: sklearn.metrics.r2_score imported but unused
Remove unused import
(F401)
10-10: Blank line contains whitespace
Remove whitespace from blank line
(W293)
16-16: Blank line contains whitespace
Remove whitespace from blank line
(W293)
src/fraud_credit_cards/data_processor.py
14-14: Blank line contains whitespace
Remove whitespace from blank line
(W293)
21-21: Blank line contains whitespace
Remove whitespace from blank line
(W293)
35-35: Local variable preprocessor is assigned to but never used
Remove assignment to unused variable preprocessor
(F841)
38-38: Blank line contains whitespace
Remove whitespace from blank line
(W293)
main.py
7-7: sklearn.metrics.accuracy_score imported but unused
Remove unused import: sklearn.metrics.accuracy_score
(F401)
47-47: f-string without any placeholders
Remove extraneous f prefix
(F541)
🪛 Markdownlint (0.37.0)
README.md
25-25: Punctuation: ':'
Trailing punctuation in heading
(MD026, no-trailing-punctuation)
23-23: null
Bare URL used
(MD034, no-bare-urls)
27-27: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
36-36: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
42-42: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
46-46: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
notes/commands.md
3-3: Expected: h2; Actual: h3
Heading levels should only increment by one level at a time
(MD001, heading-increment)
5-5: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
13-13: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
19-19: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
25-25: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
🪛 yamllint (1.35.1)
project_config.yml
[error] 1-1: no new line character at the end of file
(new-line-at-end-of-file)
🔇 Additional comments (11)
pyproject.toml (3)
2-4: Project name and description updates look good.The rename to "fraud_credit_cards" and updated description are clear. Ensure the new naming convention is reflected in docs and other code references.
6-6: Validate Python version range.Pinning Python to ">=3.11, <3.12" is quite narrow. Confirm that this range is intentional. If broader version support is desired (3.10, 3.9, etc.), adjust accordingly.
7-22: Dependency list changes.• The new dependencies (colorama, catboost, gecs, databricks) align with the updated project scope.
• Make sure that removing or replacing lightgbm no longer breaks any existing code.
• Confirm no version conflicts among the newly introduced packages..gitignore (3)
13-13: Ignore catboost info folder.Adding
catboost_info/helps avoid cluttering the repo with CatBoost artifacts. This is an appropriate addition.
33-33: Ignore local venv.Adding
venv/ensures that local Python virtual environments don’t pollute source control. This is a standard best practice.
99-100: Ignore Databricks configurations.Ignoring
.databricksavoids committing local Databricks settings. This is reasonable, but ensure it doesn’t hide necessary shared configuration files.src/fraud_credit_cards/fraud_model.py (1)
3-3: Remove unused imports.
classification_report,roc_auc_score, andr2_scoreare imported but never used. Please remove them to keep imports lean.-from sklearn.metrics import accuracy_score, classification_report, precision_score, recall_score, f1_score, roc_auc_score, mean_squared_error, r2_score +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error🧰 Tools
🪛 Ruff (0.8.2)
3-3:
sklearn.metrics.classification_reportimported but unusedRemove unused import
(F401)
3-3:
sklearn.metrics.roc_auc_scoreimported but unusedRemove unused import
(F401)
3-3:
sklearn.metrics.r2_scoreimported but unusedRemove unused import
(F401)
README.md (1)
6-6: Verify dataset accessibilityThe Kaggle dataset link is mentioned twice. Please ensure:
- The dataset is accessible to all course participants
- Consider adding instructions for downloading and setting up the dataset
Also applies to: 14-14
main.py (1)
1-8: Clean up importsRemove unused imports and sort them:
-import yaml -import logging +import logging +import yaml from colorama import Back, Fore, Style from fraud_credit_cards.data_processor import DataProcessor from fraud_credit_cards.fraud_model import FraudModel -from sklearn.metrics import accuracy_score, classification_report +from sklearn.metrics import classification_report🧰 Tools
🪛 Ruff (0.8.2)
7-7:
sklearn.metrics.accuracy_scoreimported but unusedRemove unused import:
sklearn.metrics.accuracy_score(F401)
project_config.yml (1)
1-1: Verify the target column name.Please ensure that "Class" matches exactly with the column name in your dataset. This is crucial as it's used by the DataProcessor class mentioned in the AI summary.
✅ Verification successful
Target column name "Class" is correctly specified
The verification confirms that "Class" is the correct target column name:
- Found in configuration files with
"target": "Class"- Used in the notebook's
preprocess_datafunction with default parametertarget_column="Class"- Referenced in the main script for classification reporting
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Verify if "Class" exists as a column name in the dataset # Look for CSV files that might contain the schema or data fd -e csv -x head -n 1 {} \; 2>/dev/null | grep -i "class"Length of output: 49
Script:
#!/bin/bash # Let's check for any Python files that might use DataProcessor or handle data loading rg -l "DataProcessor|read_csv|target.*class" --type py # Also check for any configuration or schema files fd -e json -e yaml -e yml | xargs cat 2>/dev/null | grep -i "class"Length of output: 269
Script:
#!/bin/bash # Let's check the data_processor.py to see how the target column is used rg -A 5 "target.*class" src/fraud_credit_cards/data_processor.py # Also check main.py for target column usage cat main.py | grep -i "class"Length of output: 351
Script:
#!/bin/bash # Let's check the actual DataProcessor implementation and data loading ast-grep --pattern 'class DataProcessor { $$$ }' # Also check the notebooks for any target column usage rg "target.*=.*['\"]Class['\"]" notebooks/Length of output: 189
🧰 Tools
🪛 yamllint (1.35.1)
[error] 1-1: no new line character at the end of file
(new-line-at-end-of-file)
notes/commands.md (1)
26-27: 🛠️ Refactor suggestionVerify data paths and add prerequisites.
The data copy command assumes specific paths that need verification:
Consider adding:
- Prerequisites about volume creation
- Instructions for creating the target directory if it doesn't exist
- Example with placeholder paths
+# Prerequisites: +# 1. Ensure the local data directory exists +# 2. Create the target volume using Databricks workspace +# 3. Create the target directory structure + +# Example with placeholder paths: databricks fs cp data/data.csv dbfs:/Volumes/mlops_dev/house_prices/data/data.csv + +# Create directory if needed: +databricks fs mkdirs dbfs:/Volumes/mlops_dev/house_prices/data
| def load_data(self, file_path): | ||
| """ | ||
| Load the data from the given filepath. | ||
| """ | ||
| df = pd.read_csv(file_path) | ||
| return df |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for file operations
The load_data method should handle potential file operation errors:
def load_data(self, file_path):
"""
Load the data from the given filepath.
"""
- df = pd.read_csv(file_path)
- return df
+ try:
+ df = pd.read_csv(file_path)
+ return df
+ except FileNotFoundError:
+ raise FileNotFoundError(f"Data file not found at: {file_path}")
+ except pd.errors.EmptyDataError:
+ raise ValueError("The data file is empty")
+ except Exception as e:
+ raise Exception(f"Error loading data: {str(e)}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def load_data(self, file_path): | |
| """ | |
| Load the data from the given filepath. | |
| """ | |
| df = pd.read_csv(file_path) | |
| return df | |
| def load_data(self, file_path): | |
| """ | |
| Load the data from the given filepath. | |
| """ | |
| try: | |
| df = pd.read_csv(file_path) | |
| return df | |
| except FileNotFoundError: | |
| raise FileNotFoundError(f"Data file not found at: {file_path}") | |
| except pd.errors.EmptyDataError: | |
| raise ValueError("The data file is empty") | |
| except Exception as e: | |
| raise Exception(f"Error loading data: {str(e)}") |
| print(yaml.dump(config, default_flow_style=False)) | ||
|
|
||
| # initialize DataProcessor | ||
| data_processor = DataProcessor("data/creditcard_2023.csv", config) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Avoid hardcoding file paths
The data file path should be configurable:
-data_processor = DataProcessor("data/creditcard_2023.csv", config)
+data_processor = DataProcessor(config.get('data_path', 'data/creditcard_2023.csv'), config)Consider adding the data path to your configuration file.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| data_processor = DataProcessor("data/creditcard_2023.csv", config) | |
| data_processor = DataProcessor(config.get('data_path', 'data/creditcard_2023.csv'), config) |
main.py
Outdated
| # load configurations | ||
| with open('project_config.yml', 'r') as file: | ||
| config = yaml.safe_load(file) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for configuration loading
Add proper error handling for the configuration file:
# load configurations
-with open('project_config.yml', 'r') as file:
- config = yaml.safe_load(file)
+try:
+ with open('project_config.yml', 'r') as file:
+ config = yaml.safe_load(file)
+except FileNotFoundError:
+ logger.error("Configuration file 'project_config.yml' not found")
+ raise
+except yaml.YAMLError as e:
+ logger.error(f"Error parsing configuration file: {e}")
+ raise📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| # load configurations | |
| with open('project_config.yml', 'r') as file: | |
| config = yaml.safe_load(file) | |
| # load configurations | |
| try: | |
| with open('project_config.yml', 'r') as file: | |
| config = yaml.safe_load(file) | |
| except FileNotFoundError: | |
| logger.error("Configuration file 'project_config.yml' not found") | |
| raise | |
| except yaml.YAMLError as e: | |
| logger.error(f"Error parsing configuration file: {e}") | |
| raise |
There was a problem hiding this comment.
Actionable comments posted: 6
♻️ Duplicate comments (2)
notebooks/modular_fraud_credit_cards.py (2)
53-58:⚠️ Potential issueFix variable name mismatch in
load_data()functionThe parameter is named
pathbut the function usesfilepathinternally, which will cause aNameError.def load_data(path): """ Load the data from the given filepath. """ - df = pd.read_csv(filepath) + df = pd.read_csv(path) return df
102-115: 🛠️ Refactor suggestionUtilize the
n_estimatorsparameter inCatBoostClassifierThe
n_estimatorsparameter is defined but not used in the classifier initialization.def train_and_evaluate_model(X, y, preprocessor, test_size=0.2, random_state=42, n_estimators=100): # Define the model model = Pipeline( steps=[ ("preprocessor", preprocessor), - ("classifier", CatBoostClassifier(verbose=False)), + ("classifier", CatBoostClassifier(n_estimators=n_estimators, verbose=False)), ] )
🧹 Nitpick comments (13)
pyproject.toml (1)
7-23: Review dependency version constraints and organization.
- Consider grouping related dependencies:
dependencies = [ + # Core ML libraries "scikit-learn>=1.5.1, <2", - "cloudpickle>=3.0.0, <4", + "catboost==1.2.0", + "numpy>=1.26.4, <2", + "scipy>=1.14.1, <2", + + # Data processing "pandas>=2.2.2, <3", "pyarrow>=15.0.2, <16", - "cffi>=1.17.1, <2", - "scipy>=1.14.1, <2", + "cloudpickle>=3.0.0, <4", + + # Visualization and utilities "matplotlib>=3.9.2, <4", - "databricks-feature-engineering>=0.6, <1", "colorama==0.4.6", - "catboost==1.2.0", + + # Databricks integration + "databricks-feature-engineering>=0.6, <1", + "databricks>=0.2", "gecs==0.1.1", - "databricks>=0.2", + + # Development tools + "cffi>=1.17.1, <2", "pre-commit>=4.0.1", ]
- Version constraints:
- Consider using upper bounds for
databricks>=0.2to prevent potential breaking changes- Verify if exact versions (
==) forcatboostandcoloramaare necessarysrc/fraud_credit_cards/fraud_model.py (2)
12-15: Add comprehensive docstrings and type hints.The class and its constructor should be properly documented:
class FraudModel: + """A machine learning model for fraud detection using CatBoost classifier. + + This class implements a pipeline that combines preprocessing steps with a CatBoost + classifier for detecting fraudulent transactions. + """ + def __init__(self, preprocessor): + """Initialize the fraud detection model. + + Args: + preprocessor: A sklearn-compatible preprocessor implementing fit_transform + """ self.model = Pipeline(steps=[("preprocessor", preprocessor), ("classifier", CatBoostClassifier(verbose=False))])
39-39: Fix typo in comment.- # Calcualte F1 score + # Calculate F1 scoresrc/fraud_credit_cards/data_processor.py (2)
14-14: Remove redundant preprocessor initializationSince
preprocessoris properly initialized inpreprocess_data(), the initialNoneassignment is unnecessary.- self.preprocessor = None
29-35: Consider handling categorical featuresThe preprocessing pipeline only handles numeric features. Consider adding support for categorical features using encoders like
OneHotEncoderorLabelEncoder.README.md (2)
27-29: Fix markdown code block formattingAdd language specifiers to code blocks for better syntax highlighting:
-``` +```bash uv venv -p 3.11.11 .venv source .venv/bin/activateApply this to all code blocks in the file.
Also applies to: 36-38, 42-44, 46-48, 54-56
🧰 Tools
🪛 Markdownlint (0.37.0)
27-27: null
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23-23: Use proper markdown link formatReplace bare URL with proper markdown link format:
-Check out the documentation on how to install it: https://docs.astral.sh/uv/getting-started/installation/ +Check out the [documentation on how to install it](https://docs.astral.sh/uv/getting-started/installation/)🧰 Tools
🪛 Markdownlint (0.37.0)
23-23: null
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main.py (2)
11-27: Improve print_evaluation function
- Add docstring to document the function's purpose and parameters
- Make color thresholds configurable
def print_evaluation(y_test, y_pred, accuracy): + """ + Print classification evaluation metrics with color-coded output. + + Args: + y_test: True labels + y_pred: Predicted labels + accuracy: Model accuracy score + """ + THRESHOLD = 0.8 # Move to config for key, value in report.items(): if key in ["0", "1"]: - color = Fore.GREEN if value["precision"] > 0.8 else Fore.RED + color = Fore.GREEN if value["precision"] > THRESHOLD else Fore.RED
51-51: Adjust log level for debug messageThe debug message won't be visible with the current INFO log level. Either:
- Change the message to INFO level if the information is important, or
- Add a way to configure debug logging through the config file
-logger.debug(f"Training set shape: {X_train.shape}, Test set shape: {X_test.shape}") +logger.info(f"Training set shape: {X_train.shape}, Test set shape: {X_test.shape}")notebooks/modular_fraud_credit_cards.py (4)
14-14: Review package versioning strategy and remove unused dependencyConsider the following suggestions:
- Use version ranges instead of fixed versions to allow for compatible updates and security patches
- Remove the unused
gecspackage as it's not utilized in the code-# MAGIC %pip install colorama==0.4.6 catboost==1.2.0 gecs==0.1.1 +# MAGIC %pip install "colorama>=0.4.6,<0.5.0" "catboost>=1.2.0,<2.0.0"
42-43: Avoid global warning suppressionGlobal warning suppression can hide important issues and make debugging more difficult. Consider:
- Only suppress specific warnings where necessary
- Document why each warning is being suppressed
-warnings.filterwarnings("ignore", category=FutureWarning) -warnings.filterwarnings("ignore") +# Suppress specific warnings with documentation +warnings.filterwarnings("ignore", category=FutureWarning, message="specific message")
74-89: Enhance preprocessing function with validation and proper loggingConsider adding input validation and using proper logging instead of print statements.
+import logging + def preprocess_data(df, target_column="Class"): + if not isinstance(df, pd.DataFrame): + raise TypeError("Input must be a pandas DataFrame") + if target_column not in df.columns: + raise ValueError(f"Target column '{target_column}' not found in DataFrame") + # Spliting the data into features and target X = df.drop(target_column, axis=1) y = df[target_column] # Define numeric features (remove categorical columns) numeric_features = X.select_dtypes(include=["int64", "float64"]).columns.tolist() # Define preprocessing steps numeric_transformer = Pipeline(steps=[("scaler", StandardScaler())]) preprocessor = ColumnTransformer(transformers=[("num", numeric_transformer, numeric_features)]) - print("Features Shape: ", X.shape) - print("Target Shape: ", y.shape) + logging.info(f"Features Shape: {X.shape}") + logging.info(f"Target Shape: {y.shape}") return X, y, preprocessor
1-175: Add essential machine learning practicesConsider adding the following essential components:
- Error handling for model training and prediction
- Cross-validation strategy instead of a single train-test split
- Model persistence mechanism to save and load the trained model
- Model validation metrics beyond basic classification metrics (e.g., ROC-AUC, confusion matrix)
Would you like me to provide example implementations for any of these components?
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⛔ Files ignored due to path filters (1)
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📒 Files selected for processing (11)
.gitignore(3 hunks).pre-commit-config.yaml(0 hunks)README.md(1 hunks)dbconnect_example.py(1 hunks)main.py(1 hunks)notebooks/modular_fraud_credit_cards.py(1 hunks)notes/commands.md(1 hunks)project_config.yml(1 hunks)pyproject.toml(2 hunks)src/fraud_credit_cards/data_processor.py(1 hunks)src/fraud_credit_cards/fraud_model.py(1 hunks)
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- .pre-commit-config.yaml
🚧 Files skipped from review as they are similar to previous changes (3)
- project_config.yml
- .gitignore
- dbconnect_example.py
🧰 Additional context used
🪛 Markdownlint (0.37.0)
README.md
25-25: Punctuation: ':'
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(MD026, no-trailing-punctuation)
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notes/commands.md
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🔇 Additional comments (2)
src/fraud_credit_cards/data_processor.py (1)
16-21: Add error handling for file operationsThe
load_datamethod should handle potential file operation errors.main.py (1)
34-35: Add error handling for configuration loadingAdd proper error handling for the configuration file.
| # Commands | ||
|
|
||
| ### Install Databricks CLI | ||
|
|
||
| ``` | ||
| brew tap databricks/tap | ||
|
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| brew install databricks | ||
| ``` | ||
|
|
||
| ### Initiate Authenticaton | ||
|
|
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| ``` | ||
| databricks auth login --configure-cluster --host <workspace-url> | ||
| ``` | ||
|
|
||
| ### Show Databricks Profiles | ||
|
|
||
| ``` | ||
| databricks auth profiles | ||
| ``` | ||
|
|
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| ### Copy Data to Databricks Volumes | ||
|
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| ``` | ||
| databricks fs cp data/data.csv dbfs:/Volumes/mlops_dev/house_prices/data/data.csv | ||
| ``` |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fix markdown structure and enhance command documentation.
- Fix heading hierarchy:
-# Commands
+# Databricks CLI Commands
+
+## Installation and Setup
-### Install Databricks CLI
+### Install CLI
-### Initiate Authenticaton
+### Configure Authentication
-### Show Databricks Profiles
+### Manage Profiles
-### Copy Data to Databricks Volumes
+### Data Operations- Add language specifiers to code blocks:
-```
+```bash- Add clarifying notes:
- For authentication: Specify where to find the workspace URL (e.g., Databricks workspace settings)
- For data copy: Document the expected directory structure and volume setup prerequisites
🧰 Tools
🪛 Markdownlint (0.37.0)
3-3: Expected: h2; Actual: h3
Heading levels should only increment by one level at a time
(MD001, heading-increment)
5-5: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
13-13: null
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(MD040, fenced-code-language)
19-19: null
Fenced code blocks should have a language specified
(MD040, fenced-code-language)
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| def train(self, X_train, y_train): | ||
| self.model.fit(X_train, y_train) | ||
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| def predict(self, X): | ||
| return self.model.predict(X) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to training and prediction methods.
def train(self, X_train, y_train):
+ """Train the fraud detection model.
+
+ Args:
+ X_train: Training features
+ y_train: Training labels
+
+ Raises:
+ ValueError: If input data is empty or contains invalid values
+ """
+ if X_train is None or y_train is None:
+ raise ValueError("Training data cannot be None")
+ if len(X_train) == 0 or len(y_train) == 0:
+ raise ValueError("Training data cannot be empty")
self.model.fit(X_train, y_train)
def predict(self, X):
+ """Generate predictions for input data.
+
+ Args:
+ X: Input features
+
+ Returns:
+ array-like: Predicted labels
+
+ Raises:
+ ValueError: If model hasn't been trained or input is invalid
+ """
+ if not hasattr(self, 'model') or not self.model:
+ raise ValueError("Model must be trained before prediction")
+ if X is None or len(X) == 0:
+ raise ValueError("Input data cannot be None or empty")
return self.model.predict(X)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def train(self, X_train, y_train): | |
| self.model.fit(X_train, y_train) | |
| def predict(self, X): | |
| return self.model.predict(X) | |
| def train(self, X_train, y_train): | |
| """Train the fraud detection model. | |
| Args: | |
| X_train: Training features | |
| y_train: Training labels | |
| Raises: | |
| ValueError: If input data is empty or contains invalid values | |
| """ | |
| if X_train is None or y_train is None: | |
| raise ValueError("Training data cannot be None") | |
| if len(X_train) == 0 or len(y_train) == 0: | |
| raise ValueError("Training data cannot be empty") | |
| self.model.fit(X_train, y_train) | |
| def predict(self, X): | |
| """Generate predictions for input data. | |
| Args: | |
| X: Input features | |
| Returns: | |
| array-like: Predicted labels | |
| Raises: | |
| ValueError: If model hasn't been trained or input is invalid | |
| """ | |
| if not hasattr(self, 'model') or not self.model: | |
| raise ValueError("Model must be trained before prediction") | |
| if X is None or len(X) == 0: | |
| raise ValueError("Input data cannot be None or empty") | |
| return self.model.predict(X) |
| def evaluate(self, X_test, y_test): | ||
| # Predict on the test set | ||
| y_pred = self.predict(X_test) | ||
|
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||
| # Generate evaluation metrics | ||
| # Calculate accuracy | ||
| accuracy = accuracy_score(y_test, y_pred) | ||
|
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| # Calculate precision | ||
| precision = precision_score(y_test, y_pred) | ||
|
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| # Calculate recall | ||
| recall = recall_score(y_test, y_pred) | ||
|
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| # Calculate Mean-Squared Error | ||
| mse = mean_squared_error(y_test, y_pred) | ||
|
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| # Calcualte F1 score | ||
| f1 = f1_score(y_test, y_pred) | ||
|
|
||
| return y_pred, accuracy, precision, recall, mse, f1 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve evaluation metrics and error handling.
- MSE might not be the most appropriate metric for binary classification. Consider using:
- ROC AUC score
- Confusion matrix
- Classification report
- Add error handling for edge cases:
def evaluate(self, X_test, y_test):
+ """Evaluate model performance on test data.
+
+ Args:
+ X_test: Test features
+ y_test: True labels
+
+ Returns:
+ tuple: (predictions, accuracy, precision, recall, f1_score)
+
+ Raises:
+ ValueError: If model hasn't been trained
+ """
+ if not hasattr(self, 'model') or not self.model:
+ raise ValueError("Model must be trained before evaluation")
+
# Predict on the test set
y_pred = self.predict(X_test)
# Generate evaluation metrics
- # Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
-
- # Calculate precision
precision = precision_score(y_test, y_pred)
-
- # Calculate recall
recall = recall_score(y_test, y_pred)
-
- # Calculate Mean-Squared Error
- mse = mean_squared_error(y_test, y_pred)
-
- # Calcualte F1 score
f1 = f1_score(y_test, y_pred)
- return y_pred, accuracy, precision, recall, mse, f1
+ return y_pred, accuracy, precision, recall, f1📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def evaluate(self, X_test, y_test): | |
| # Predict on the test set | |
| y_pred = self.predict(X_test) | |
| # Generate evaluation metrics | |
| # Calculate accuracy | |
| accuracy = accuracy_score(y_test, y_pred) | |
| # Calculate precision | |
| precision = precision_score(y_test, y_pred) | |
| # Calculate recall | |
| recall = recall_score(y_test, y_pred) | |
| # Calculate Mean-Squared Error | |
| mse = mean_squared_error(y_test, y_pred) | |
| # Calcualte F1 score | |
| f1 = f1_score(y_test, y_pred) | |
| return y_pred, accuracy, precision, recall, mse, f1 | |
| def evaluate(self, X_test, y_test): | |
| """Evaluate model performance on test data. | |
| Args: | |
| X_test: Test features | |
| y_test: True labels | |
| Returns: | |
| tuple: (predictions, accuracy, precision, recall, f1_score) | |
| Raises: | |
| ValueError: If model hasn't been trained | |
| """ | |
| if not hasattr(self, 'model') or not self.model: | |
| raise ValueError("Model must be trained before evaluation") | |
| # Predict on the test set | |
| y_pred = self.predict(X_test) | |
| # Generate evaluation metrics | |
| accuracy = accuracy_score(y_test, y_pred) | |
| precision = precision_score(y_test, y_pred) | |
| recall = recall_score(y_test, y_pred) | |
| f1 = f1_score(y_test, y_pred) | |
| return y_pred, accuracy, precision, recall, f1 |
| def split_data(self, test_size=0.2, random_state=42): | ||
| # Split the data into training and test sets | ||
| return train_test_split(self.X, self.y, test_size=test_size, random_state=random_state) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add validation in split_data method
Ensure that preprocess_data() was called before splitting the data:
def split_data(self, test_size=0.2, random_state=42):
+ if self.X is None or self.y is None:
+ raise ValueError("Call preprocess_data() before splitting the data")
return train_test_split(self.X, self.y, test_size=test_size, random_state=random_state)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def split_data(self, test_size=0.2, random_state=42): | |
| # Split the data into training and test sets | |
| return train_test_split(self.X, self.y, test_size=test_size, random_state=random_state) | |
| def split_data(self, test_size=0.2, random_state=42): | |
| if self.X is None or self.y is None: | |
| raise ValueError("Call preprocess_data() before splitting the data") | |
| # Split the data into training and test sets | |
| return train_test_split(self.X, self.y, test_size=test_size, random_state=random_state) |
| target = self.config["target"] | ||
| self.X = self.df.drop(target, axis=1) | ||
| self.y = self.df[target] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add validation for target column
Add validation to ensure the target column exists in the DataFrame:
target = self.config["target"]
+ if target not in self.df.columns:
+ raise ValueError(f"Target column '{target}' not found in DataFrame")
self.X = self.df.drop(target, axis=1)
self.y = self.df[target]📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| target = self.config["target"] | |
| self.X = self.df.drop(target, axis=1) | |
| self.y = self.df[target] | |
| target = self.config["target"] | |
| if target not in self.df.columns: | |
| raise ValueError(f"Target column '{target}' not found in DataFrame") | |
| self.X = self.df.drop(target, axis=1) | |
| self.y = self.df[target] |
| def evaluate_model(model, X_train, X_test, y_train, y_test, y_pred): | ||
| # Generate evaluation metrics | ||
| # Calculate accuracy | ||
| accuracy = accuracy_score(y_test, y_pred) | ||
|
|
||
| # Calculate precision | ||
| precision = precision_score(y_test, y_pred) | ||
|
|
||
| # Calculate recall | ||
| recall = recall_score(y_test, y_pred) | ||
|
|
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| # Calculate Mean-Squared Error | ||
| mse = mean_squared_error(y_test, y_pred) | ||
|
|
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| # Calcualte F1 score | ||
| f1 = f1_score(y_test, y_pred) | ||
|
|
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| print("Accuracy:", accuracy) | ||
| print("Precision:", precision) | ||
| print("Recall:", recall) | ||
| print("MSE:", mse) | ||
| print("F1 Score:", f1) | ||
|
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| # Display classification report with colors and heading | ||
| from colorama import Back | ||
|
|
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| print("\n" + Back.BLUE + Fore.WHITE + "Classification Report" + Style.RESET_ALL) | ||
| report = classification_report(y_test, y_pred, output_dict=True) | ||
| for key, value in report.items(): | ||
| if key in ["0", "1"]: | ||
| color = Fore.GREEN if value["precision"] > 0.8 else Fore.RED | ||
| print(f"Class {key}:") | ||
| print(f" Precision: {color}{value['precision']:.2f}{Style.RESET_ALL}") | ||
| color = Fore.GREEN if value["recall"] > 0.8 else Fore.RED | ||
| print(f" Recall: {color}{value['recall']:.2f}{Style.RESET_ALL}") | ||
| color = Fore.GREEN if value["f1-score"] > 0.8 else Fore.RED | ||
| print(f" F1-score: {color}{value['f1-score']:.2f}{Style.RESET_ALL}") | ||
| print(f" Support: {value['support']}") | ||
| else: | ||
| print(key + ":", value) | ||
| return accuracy, precision, recall, mse, f1 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Refactor evaluation function for better maintainability
The function has several issues that could be improved:
- Move the colorama import to the top with other imports
- Extract the report formatting into a separate function
- Make the performance thresholds configurable
- Consider splitting into smaller, focused functions
def evaluate_model(model, X_train, X_test, y_train, y_test, y_pred):
+ threshold = 0.8 # Make this a parameter
# Generate evaluation metrics
metrics = {
"accuracy": accuracy_score(y_test, y_pred),
"precision": precision_score(y_test, y_pred),
"recall": recall_score(y_test, y_pred),
"mse": mean_squared_error(y_test, y_pred),
"f1": f1_score(y_test, y_pred)
}
for metric_name, value in metrics.items():
print(f"{metric_name}: {value}")
- # Display classification report with colors and heading
- from colorama import Back
+ _display_colored_report(y_test, y_pred, threshold)
+ return tuple(metrics.values())
+
+def _display_colored_report(y_test, y_pred, threshold=0.8):
print("\n" + Back.BLUE + Fore.WHITE + "Classification Report" + Style.RESET_ALL)
report = classification_report(y_test, y_pred, output_dict=True)
for key, value in report.items():
if key in ["0", "1"]:
- color = Fore.GREEN if value["precision"] > 0.8 else Fore.RED
+ color = Fore.GREEN if value["precision"] > threshold else Fore.REDCommittable suggestion skipped: line range outside the PR's diff.
Summary by CodeRabbit
New Features
pyproject.toml.DataProcessorclass for data handling.FraudModelclass for machine learning model encapsulation.Chores
.gitignorefile to exclude.databricksandcatboost_infofiles from version control.pyproject.toml.README.mdby removing unnecessary whitespace..github/CODEOWNERSfile.colorama,catboost,gecs, anddatabricks) topyproject.toml.notes/commands.md.project_config.ymlto specify target class settings.