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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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4,852 | train = pd.read_csv('../input/bike-sharing-demand/train.csv')
test = pd.read_csv('../input/bike-sharing-demand/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,853 | tt = train.append(test)
tt = tt.reset_index().drop('index', axis=1)
tt.head() | Yes | 3 | 11 | Data_Transform | concatenate | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"}] | [] | 6 | B |
4,854 | sns.distplot(train['count']) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,855 | # Time processing
# Add two columns, date and hour respectively
temp = pd.DatetimeIndex(train['datetime'])
train['year'] = temp.year
train['date'] = temp.date
train['hour'] = temp.hour
# Categorical variables for the day of the week
train['dayofweek'] = pd.DatetimeIndex(train.date).dayofweek | No | 5 | 8 | Data_Transform | feature_engineering | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,856 | # The impact of each time period of the day on count
sns.boxplot(train['hour'], train['count']) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,857 | # The influence of the total days of the week on count
sns.boxplot(train['dayofweek'], train['count']) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,858 | # Changes in count for each day of the week
sns.pointplot(x='hour', y='count', hue='dayofweek', data=train) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,859 | # The impact of different months on count
train['month'] = pd.to_datetime(train['datetime']).dt.month
sns.boxplot(train['month'], train['count']) | Yes | 5 | 81 | Visualization | relationship | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,860 | # The impact of holidays on count
sns.pointplot(x='hour', y='count',hue='workingday', data=train) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,861 | # The impact of weather on count
sns.pointplot(x='hour', y='count', hue='weather', data=train) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,862 | # The influence of season on count
sns.pointplot(x='hour', y='count', hue='season', data=train) | No | 5 | 75 | Visualization | time_series | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,863 | # Pearson coefficient
cor=train[['temp', 'atemp', 'casual', 'registered', 'humidity','windspeed', 'count']].corr()
sns.heatmap(cor, square=True, annot=True) | No | 5 | 80 | Visualization | heatmap | 13,074,892 | 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"}] | [] | 6 | B |
4,864 | temp = pd.DatetimeIndex(tt['datetime'])
tt['year'] = temp.year
tt['hour'] = temp.hour
tt = tt[['hour', 'year', 'workingday', 'holiday', 'season', 'weather', 'atemp', 'count']]
# One-hot coding for discrete variables, such as color red, yellow, and blue coding as [[1,0,0], [0,1,0], [0,0,1]]
tt = pd.get_dummies(tt, colum... | Yes | 3 | 20 | Data_Transform | categorify | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
4,865 | # Extract the training set and test set from the processed data set, [0:10886] and [10886:]
new_train = tt.iloc[:10886, :]
# Pair count+1, then take the logarithm
y = np.log1p(new_train['count'])
new_test = tt.iloc[10886:, :].drop('count',axis=1)
new_train.drop('count', axis=1, inplace=True)
x = new_train
x.head() | Yes | 5 | 10 | Data_Transform | drop_column | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 6 | B |
4,866 | from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=3) | Yes | 5 | 13 | Data_Transform | split | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"}] | [] | 6 | B |
4,867 | from sklearn.linear_model import LinearRegression
lmodel = LinearRegression()
lmodel.fit(x, y)
cross_val_score(lmodel, x, y, cv=5).mean() | Yes | 4 | 7 | Model_Train | train_model | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 5 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | A |
4,868 | lmodel.fit(x_train, y_train)
pre = lmodel.predict(x_test)
mean_squared_error(y_test, pre) | Yes | 3 | 7 | Model_Train | train_model | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 8 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
4,869 | from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
rfr = RandomForestRegressor(random_state=50, max_features='sqrt', oob_score=True) | Yes | 5 | 4 | Model_Train | choose_model_class | 13,074,892 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 1 | 1 | [{"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 9"}] | A | no applicable ML-specific findings | [] | ["Verbose Mode Checker"] | 6 | B |
4,870 | # Parameter tuning - This step requires a lot of calculation
para = {'n_estimators': np.arange(200, 241, 1)}
rf = GridSearchCV(estimator=rfr, param_grid=para, cv=5)
rf.fit(x, y) | Yes | 5 | 6 | Hyperparam_Tuning | train_on_grid | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 9"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | A |
4,871 | rf.best_params_ | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,872 | rfr = RandomForestRegressor(n_estimators=227, random_state=50, max_features='sqrt',oob_score=True)
cross_val_score(rfr, x, y, cv=5).mean() | No | 4 | 28 | Model_Train | compute_train_metric | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Verbose Mode Checker", "fix": "Enable verbose mode for long training processes.", "benefits": "Provides better insights into model training progress.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | A |
4,873 | rfr.fit(x_train, y_train)
pre = rfr.predict(x_test)
mean_squared_error(y_test, pre) | Yes | 3 | 7 | Model_Train | train_model | 13,074,892 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 8 | 7 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
4,874 | rfr.fit(x,y) | No | 5 | 7 | Model_Train | train_model | 13,074,892 | 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"] | 6 | B |
4,875 | co = rfr.predict(new_test)
m = []
# Decrease the result by one and round up
for i in (np.exp(co) - 1):
n = round(i)
m.append(n)
predict = pd.DataFrame({'datetime': test['datetime'], 'count': m})
predict.to_csv('rfr.csv', index=False)
| Yes | 4 | 25 | Data_Export | save_to_csv | 13,074,892 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | ok | 5 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 6 | B |
4,876 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters() | Yes | 5 | 23 | Environment | set_options | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,877 | # Example
# Converts to log1p(count)
# Print original count back using expm1
print('Test log and exp')
test_count = 100
print('original value', test_count)
x = np.log1p(test_count) # log (x+1)
print('log1p', x)
print('expm1', np.expm1(x)) # exp(x) - 1 | No | 3 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,878 | df = pd.read_csv('../input/bike-sharing-demand/train.csv',parse_dates=['datetime'],index_col=0)
df_test = pd.read_csv('../input/bike-sharing-demand/test.csv',parse_dates=['datetime'],index_col=0)
| No | 5 | 45 | Data_Extraction | load_from_csv | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 3 | [{"detector": "general_ml", "framework": "General ML", "name": "Data loading detected without size checks or batch processing. Consider using generators or batch processing for large datasets.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "... | A | no applicable ML-specific findings | [] | ["Data loading detected without size checks or batch processing. Consider using generators or batch processing for large datasets.", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 4 | A |
4,879 | # We need to convert datetime to numeric for training.
# Let's extract key features into separate numeric columns
def add_features(df):
df['year'] = df.index.year
df['month'] = df.index.month
df['day'] = df.index.day
df['dayofweek'] = df.index.dayofweek
df['hour'] = df.index.hour | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,880 | # Need to predict the missing data
plt.title('Rental Count - Gaps')
df['2011-01':'2011-02']['count'].plot()
plt.show() | No | 5 | 81 | Visualization | relationship | 13,343,252 | 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 |
4,881 | # Rentals change hourly!
plt.plot(df['2011-01-01']['count'])
plt.xticks(fontsize=14, rotation=45)
plt.xlabel('Date')
plt.ylabel('Rental Count')
plt.title('Hourly Rentals for Jan 01, 2011')
plt.show() | No | 5 | 81 | Visualization | relationship | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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 |
4,882 | # Seasonal
plt.plot(df['2011-01']['count'])
plt.xticks(fontsize=14, rotation=45)
plt.xlabel('Date')
plt.ylabel('Rental Count')
plt.title('Jan 2011 Rentals (1 month)')
plt.show() | No | 5 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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 |
4,883 | group_hour = df.groupby(['hour'])
average_by_hour = group_hour['count'].mean() | Yes | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,884 | plt.plot(average_by_hour.index,average_by_hour)
plt.xlabel('Hour')
plt.ylabel('Rental Count')
plt.xticks(np.arange(24))
plt.grid(True)
plt.title('Average Hourly Rental Count') | No | 5 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,885 | # Year to year trend
plt.plot(df['2011']['count'],label='2011')
plt.plot(df['2012']['count'],label='2012')
plt.xticks(fontsize=14, rotation=45)
plt.xlabel('Date')
plt.ylabel('Rental Count')
plt.title('2011 and 2012 Rentals (Year to Year)')
plt.legend()
plt.show() | No | 5 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | 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 |
4,886 | plt.plot(df['2011']['count'].map(np.log1p),label='2011')
plt.plot(df['2012']['count'].map(np.log1p),label='2012')
plt.xticks(fontsize=14, rotation=45)
plt.xlabel('Date')
plt.ylabel('Log(Rental Count)')
plt.title('2011 and 2012 Rentals (Year to Year)')
plt.legend()
plt.show() | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,887 | plt.boxplot([df['count']], labels=['count'])
plt.title('Box Plot - Count')
plt.ylabel('Target')
plt.grid(True) | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,888 | # Let's see how the data distribution changes with log1p
# Evenly distributed
plt.boxplot([df['count'].map(np.log1p)], labels=['log1p(count)'])
plt.title('Box Plot - log1p(Count)')
plt.ylabel('Target')
plt.grid(True) | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,889 | df["count"] = df["count"].map(np.log1p) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,890 | group_year_month = df.groupby(['year','month']) | No | 5 | 60 | Data_Transform | groupby | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,891 | average_year_month = group_year_month['count'].mean() | No | 2 | 8 | Data_Transform | feature_engineering | 13,343,252 | 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 |
4,892 | average_year_month | No | 5 | 41 | Debug | show_table | 13,343,252 | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,893 | for year in average_year_month.index.levels[0]:
plt.plot(average_year_month[year].index,average_year_month[year],label=year)
plt.legend()
plt.xlabel('Month')
plt.ylabel('Count')
plt.grid(True)
plt.title('Average Monthly Rental Count for 2011, 2012')
plt.show() | No | 4 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 0 | 1 | 1 | 0 | 0 | 8 | 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 |
4,894 | group_year_hour = df.groupby(['year','hour'])
average_year_hour = group_year_hour['count'].mean()
for year in average_year_hour.index.levels[0]:
#print (year)
#print(average_year_month[year])
plt.plot(average_year_hour[year].index,average_year_hour[year],label=year)
plt.legend()
plt.xlabel('Hour')
... | Yes | 4 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 1 | 1 | 1 | 0 | 5 | 10 | 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 |
4,895 | group_workingday_hour = df.groupby(['workingday','hour'])
average_workingday_hour = group_workingday_hour['count'].mean() | Yes | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,896 | for workingday in average_workingday_hour.index.levels[0]:
#print (year)
#print(average_year_month[year])
plt.plot(average_workingday_hour[workingday].index,average_workingday_hour[workingday],
label=workingday)
plt.legend()
plt.xlabel('Hour')
plt.ylabel('Count')
plt.xticks(np.arange(2... | No | 4 | 75 | Visualization | time_series | 13,343,252 | 1 | A | 1 | 1 | 1 | 0 | 5 | 10 | 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 |
4,897 | # Let's look at correlation beween features and target
df.corr()['count'] | No | 5 | 40 | Debug | show_table_attributes | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,898 | # Any relation between temperature and rental count?
plt.scatter(x=df.temp,y=df["count"])
plt.grid(True)
plt.xlabel('Temperature')
plt.ylabel('Count')
plt.title('Temperature vs Count')
plt.show()' | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,899 | # Any relation between humidity and rental count?
plt.scatter(x=df.humidity,y=df["count"],label='Humidity')
plt.grid(True)
plt.xlabel('Humidity')
plt.ylabel('Count')
plt.title('Humidity vs Count')
plt.show()' | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,900 | # Save all data
df.to_csv('bike_all.csv',index=True,index_label='datetime',columns=columns) | 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 |
4,901 | # Training = 70% of the data
# Validation = 30% of the data
# Randomize the datset
np.random.seed(5)
l = list(df.index)
np.random.shuffle(l)
df = df.loc[l] | No | 5 | 15 | Data_Transform | randomize_order | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,902 | rows = df.shape[0]
train = int(.7 * rows)
test = rows-train | No | 4 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,903 | rows, train, test | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 3 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,904 | columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,905 | # Write Training Set
df.iloc[:train].to_csv('bike_train.csv'
,index=False,header=False
,columns=columns) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,906 | # Write Validation Set
df.iloc[train:].to_csv('bike_validation.csv'
,index=False,header=False
,columns=columns) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,907 | # Test Data has only input features
df_test.to_csv('bike_test.csv',index=True,index_label='datetime') | 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 |
4,908 | print(','.join(columns)) | No | 3 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,909 | # Write Column List
with open('bike_train_column_list.txt','w') as f:
f.write(','.join(columns)) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,910 | # Install xgboost in notebook instance.
#### Command to install xgboost
!pip install xgboost==0.90 | No | 5 | 87 | Environment | install_modules | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,911 | import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error, mean_absolute_error
# XGBoost
import xgboost as xgb | No | 5 | 22 | Environment | import_modules | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,912 | column_list_file = 'bike_train_column_list.txt'
train_file = 'bike_train.csv'
validation_file = 'bike_validation.csv'
test_file = 'bike_test.csv' | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,913 | columns = ''
with open(column_list_file,'r') as f:
columns = f.read().split(',') | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,914 | # Specify the column names as the file does not have column header
df_train = pd.read_csv(train_file,names=columns)
df_validation = pd.read_csv(validation_file,names=columns) | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,915 | X_train = df_train.iloc[:,1:] # Features: 1st column onwards
y_train = df_train.iloc[:,0].ravel() # Target: 0th column
X_validation = df_validation.iloc[:,1:]
y_validation = df_validation.iloc[:,0].ravel() | No | 5 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,916 | # XGBoost Training Parameter Reference:
# https://github.com/dmlc/xgboost/blob/master/doc/parameter.md
#regressor = xgb.XGBRegressor(max_depth=5,eta=0.1,subsample=0.7,num_round=150)
regressor = xgb.XGBRegressor(max_depth=5,n_estimators=150) | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,917 | regressor | No | 2 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,918 | regressor.fit(X_train,y_train, eval_set = [(X_train, y_train), (X_validation, y_validation)]) | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,919 | eval_result = regressor.evals_result() | No | 5 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,920 | training_rounds = range(len(eval_result['validation_0']['rmse'])) | No | 3 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,921 | plt.scatter(x=training_rounds,y=eval_result['validation_0']['rmse'],label='Training Error')
plt.scatter(x=training_rounds,y=eval_result['validation_1']['rmse'],label='Validation Error')
plt.grid(True)
plt.xlabel('Iteration')
plt.ylabel('RMSE')
plt.title('Training Vs Validation Error')
plt.legend()
plt.show() | No | 4 | 35 | Visualization | learning_history | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,922 | xgb.plot_importance(regressor)
plt.show() | No | 5 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,923 | # Verify Quality using Validation dataset
# Compare actual vs predicted performance with dataset not seen by the model before
df = pd.read_csv(validation_file,names=columns) | No | 5 | 45 | Data_Extraction | load_from_csv | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,924 | X_test = df.iloc[:,1:]
print(X_test[:5]) | Yes | 4 | 13 | Data_Transform | split | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 4 | A |
4,925 | result = regressor.predict(X_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,926 | result[:5] | No | 4 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,927 | df['count_predicted'] = result | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,928 | # Negative Values are predicted
df['count_predicted'].describe() | No | 5 | 40 | Debug | show_table_attributes | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
4,929 | df[df['count_predicted'] < 0] | No | 5 | 14 | Data_Transform | filter | 13,343,252 | 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 |
4,930 | df['count_predicted'].hist()
plt.title('Predicted Count Histogram')
plt.show() | No | 5 | 33 | Visualization | distribution | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
4,931 | def adjust_count(x):
if x < 0:
return 0
else:
return x | No | 3 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 2 | 2 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,932 | df['count_predicted'] = df['count_predicted'].map(adjust_count) | No | 3 | 8 | Data_Transform | feature_engineering | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker"] | 4 | A |
4,933 | df['count'] = df['count'].map(np.expm1)
df['count_predicted'] = df['count_predicted'].map(np.expm1) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,934 | # Actual Vs Predicted
plt.plot(df['count'], label='Actual')
plt.plot(df['count_predicted'],label='Predicted')
plt.xlabel('Sample')
plt.ylabel('Count')
plt.xlim([100,150])
plt.title('Validation Dataset - Predicted Vs. Actual')
plt.legend()
plt.show() | No | 4 | 56 | Visualization | plot_predictions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,935 | # Over prediction and Under Prediction needs to be balanced
# Training Data Residuals
residuals = (df['count'] - df['count_predicted'])
plt.hist(residuals)
plt.grid(True)
plt.xlabel('Actual - Predicted')
plt.ylabel('Count')
plt.title('Residuals Distribution')
plt.axvline(color='r')
plt.show() | Yes | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,936 | print("RMSE: {0:0.2f}".format(mean_squared_error(df['count'],df['count_predicted'])**.5))' | No | 5 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,937 | # RMSlE - Root Mean Squared Log Error
# RMSLE Metric is used by Kaggle for this competition
# RMSE Cost Function - Magnitude of difference matters
# RMSLE cost function - "Only Percentage difference matters"
# Reference:Katerina Malahova, Khor SoonHin
# https://www.slideshare.net/KhorSoonHin/rmsle-cost-function
def... | No | 5 | 84 | Other | not_enough_vertices | 13,343,252 | 1 | A | 0 | 3 | 2 | 0 | 0 | 6 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 16"}, {"detector": "framework_specific", "framework": "NumPy", "name": "Array Creat... | A | no applicable ML-specific findings | [] | ["Array Creation Efficiency", "Missing function docstring"] | 4 | A |
4,938 | print('RMSLE')
print(compute_rmsle(100,50),
compute_rmsle(1000,500),
compute_rmsle(10000,5000)) | No | 2 | 49 | Model_Evaluation | compute_test_metric | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,939 | print('RMSLE')
print(compute_rmsle(100,25),
compute_rmsle(1000,250),
compute_rmsle(10000,2500)) | No | 2 | 49 | Model_Evaluation | compute_test_metric | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,940 | print('RMSE')
print(mean_squared_error([100],[50])**.5,
mean_squared_error([1000],[500])**.5,
mean_squared_error([10000],[5000])**.5) | No | 2 | 49 | Model_Evaluation | compute_test_metric | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,941 | print('RMSE')
print(mean_squared_error([100],[25])**.5,
mean_squared_error([1000],[250])**.5,
mean_squared_error([10000],[2500])**.5) | No | 2 | 49 | Model_Evaluation | compute_test_metric | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,942 | print("RMSLE: {0}".format(compute_rmsle(df['count'],df['count_predicted'])))' | No | 5 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,943 | # Prepare Data for Submission to Kaggle
df_test = pd.read_csv(test_file,parse_dates=['datetime']) | 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 |
4,944 | X_test = df_test.iloc[:,1:] # Exclude datetime for prediction | No | 4 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,945 | np.expm1(result) | No | 4 | 8 | Data_Transform | feature_engineering | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,946 | # Convert result to actual count
df_test["count"] = np.expm1(result) | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,947 | df_test[df_test["count"] < 0] | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,948 | df_test[['datetime','count']].to_csv('predicted_count_log.csv',index=False) | No | 5 | 25 | Data_Export | save_to_csv | 13,343,252 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
4,949 | submission.iloc[1258:1269, 1]= submission.iloc[1258:1269, 1]*0.5
submission.iloc[4492:4515, 1]= submission.iloc[4492:4515, 1]*0.5
#
submission.iloc[6308:6330, 1]= submission.iloc[6308:6330, 1]*0.5
submission.iloc[3041:3063, 1]= submission.iloc[3041:3063, 1]*0.5
#
submission.iloc[6332:6354, 1]= submission.iloc[6332:635... | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,950 | submission.drop("holiday",1,inplace=True)
submission.to_csv("allrf2.csv", index=False) | 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 |
4,951 | import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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