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4,752 | predict = []
cas = LinearRegression().fit(x2_train, y2_train)
pre_cas = cas.predict(x2_test)
cas_poly = LinearRegression().fit(poly_x2_train, y2_train)
pre_cas_poly = cas_poly.predict(poly_x2_test)
rf_cas = GridSearchCV(rf, parameters, cv=5, verbose=2,scoring = scorer,n_jobs = -1)
rf_cas.fit(x2_train, y2_train)
pre_r... | Yes | 3 | 7 | Model_Train | train_model | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 16 | 5 | [{"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 10"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | 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", "Verbose Mode Checker"] | 8 | B |
4,753 | print("For Random Forest Model: ")
print("\\t Best Parametres for registered are: ",end='')
print(rf_reg.best_params_)
print("\\t Best Parametres for casual are: ",end = '')
print(rf_cas.best_params_)' | No | 2 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,754 | predict1 = []
reg1 = LinearRegression().fit(x1_train, y1_train)
pre_reg1 = reg1.predict(x1_test)
reg1_poly = LinearRegression().fit(poly_x1_train, y1_train)
pre_reg1_poly = reg1_poly.predict(poly_x1_test)
rf1 = RandomForestRegressor(n_estimators = 200,max_depth=80,min_impurity_decrease = 0.001).fit(x1_train, y1_trai... | Yes | 2 | 7 | Model_Train | train_model | 10,931,352 | 1 | A | 0 | 5 | 8 | 0 | 0 | 26 | ok | 21 | 9 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
4,755 | predict2 = []
reg2 = LinearRegression().fit(x2_train, y2_train)
pre_reg2 = reg2.predict(x2_test)
reg2_poly = LinearRegression().fit(poly_x2_train, y2_train)
pre_reg2_poly = reg2_poly.predict(poly_x2_test)
rf2 = RandomForestRegressor(n_estimators = 150,max_depth=60,min_impurity_decrease = 0.0).fit(x2_train, y2_train)... | Yes | 3 | 7 | Model_Train | train_model | 10,931,352 | 1 | A | 0 | 5 | 8 | 0 | 0 | 26 | ok | 21 | 9 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
4,756 | from plotly.subplots import make_subplots
name1 = ['Residual for casual without polynomial features'] *1633
name2 = ['Residual for casual with polynomial features'] *1633
name3 = ['Residual for registered without polynomial features'] *1633
name4 = ['Residual for registered with polynomial features'] *1633
dic = {'... | No | 5 | 56 | Visualization | plot_predictions | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 27 | ok | 5 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexing", "fix": "Use single indexing or .loc[], .iloc[] methods.", "benefits": "Enhances code readability and prevents performance issues.", "location": "Line 24"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Chain Indexi... | A | no applicable ML-specific findings | [] | ["Chain Indexing", "Column Selection Checker"] | 8 | A |
4,757 | rf1 = RandomForestRegressor(n_estimators = 200,max_depth=80,min_impurity_decrease = 0.001).fit(df_train_x,df_cas_train_y)
rf2 = RandomForestRegressor(n_estimators = 150,max_depth=60,min_impurity_decrease = 0.0).fit(df_train_x,df_reg_train_y) | No | 5 | 7 | Model_Train | train_model | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 15 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
4,758 | test_file = pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input/bike-sharing-demand/test.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 8 | A |
4,759 | test=test_file
test.describe() | No | 5 | 40 | Debug | show_table_attributes | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,760 | test['mth'] = pd.DatetimeIndex(test['datetime']).month
test['yr'] = pd.DatetimeIndex(test['datetime']).year
test['dy'] = pd.DatetimeIndex(test['datetime']).day
test['hr'] = pd.DatetimeIndex(test['datetime']).hour
for i in test.groupby("season").count().index:
s = 's' + str(i)
a = []
for j in test.season:
... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,761 | pre_cas = rf1.predict(test)
pre_reg = rf2.predict(test)
final_predictions = pd.DataFrame(pre_cas+pre_reg,columns = ['cout'])
final_predictions.describe | Yes | 4 | 48 | Model_Evaluation | predict_on_test | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 8 | B |
4,762 | final_predictions['datetime']=test_file['datetime']
final_predictions = final_predictions[['datetime','count']] | No | 5 | 55 | Data_Export | prepare_output | 10,931,352 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,763 | #Importing libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import chi2_contingency
from scipy.stats import spearmanr
%matplotlib inline
import itertools
import os
import calendar
from datetime import datetime
from scipy import stats
from scipy.spe... | Yes | 5 | 88 | Debug | list_files | 11,713,576 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 8 | null |
4,764 | def change_to_categorical(data, max_cat=10):
df = data.copy()
for col in df.columns:
if df[col].dtype == object or str(df[col].dtype).startswith(('int','float')):
count = len(df[col].unique())
if count <= max_cat:
df[col] = df[col].astype('category')
return df | No | 4 | 16 | Data_Transform | data_type_conversions | 11,713,576 | 1 | A | 0 | 5 | 7 | 0 | 0 | 8 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: change_to_categorical. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,765 | def get_count_plot(x, df, ax, y=None, value_counts = None, print_percent = False):
if value_counts is None:
counts = df[x].value_counts().sort_index()
else:
counts = df[value_counts]
#counts.plot.bar()
#sns bars are just more colorful :P
if y is None:
sns.countplot(x, data=df, ax=ax)
else:
s... | No | 4 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 4 | 5 | 0 | 0 | 11 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: get_count_plot. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,766 | def get_count_plot_for_categorical(df, n_cols = 2, y='cnt', list_cat=None, value_counts=None, print_percent=False):
if list_cat is None:
num_col, cat_col = get_numerical_and_categorical_col(df)
else:
cat_col = list_cat
f, axs, n_rows = get_fig_and_axis_for_subplots(len(cat_col), n_cols)
for i, col in en... | No | 5 | 33 | Visualization | distribution | 11,713,576 | 2 | B | 3 | 3 | 3 | 0 | 15 | 9 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: get_count_plot_for_categorical. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | B |
4,767 | def get_target_dist_with_categorical(df, n_cols = 2, y='cnt', list_cat=None, plot_type = 'box'):
if list_cat is None:
num_col, cat_col = get_numerical_and_categorical_col(df)
else:
cat_col = list_cat
f, axs, n_rows = get_fig_and_axis_for_subplots(len(cat_col), n_cols)
for i, col in enumerate(cat_col):
... | 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 |
4,768 | def get_plot_for_numerical(df, n_cols = 2, plot_type='probability',list_col=None, hist=True, kde=True):
if list_col is None:
num_col, cat_col = get_numerical_and_categorical_col(df)
else:
num_col = list_col
f, axs, n_rows = get_fig_and_axis_for_subplots(len(num_col), n_cols)
for i, col in enumerate(... | 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 |
4,769 | # visualize correlation matrix
def visualize_corr_matrix(data):
numerical_col, cat_col = get_numerical_and_categorical_col(data)
df = data[numerical_col]
corr = df.corr()# plot the heatmap
#generating masks for upper triangle so that values are not repeated
mask_ut=np.triu(np.ones(corr.shape)).astyp... | No | 5 | 80 | Visualization | heatmap | 11,713,576 | 1 | A | 1 | 1 | 0 | 0 | 5 | 6 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: visualize_corr_matrix. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,770 | def remove_outliers_for_variable_by_quantiles(data, col, q1=.25, q2=.75):
df = data.copy()
median = df[col].median()
q25, q75 = df[col].quantile([q1,q2])
iqr = q75-q25
upper_wh = q75 +1.5*iqr
lower_wh = q25 - 1.5*iqr
whiskers = int(np.floor(lower_wh)), int(np.ceil(upper_wh))
df.drop(df[~df[col].between(... | No | 5 | 8 | Data_Transform | feature_engineering | 11,713,576 | 1 | A | 1 | 1 | 0 | 0 | 5 | 10 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: remove_outliers_for_variable_by_quantiles. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,771 | def remove_outliers_for_variable_by_std(data, col):
df = data.copy()
df = df[np.abs(df[col]-df[col].mean())<=(3*df[col].std())]
return df | No | 5 | 14 | Data_Transform | filter | 11,713,576 | 1 | A | 0 | 1 | 0 | 0 | 0 | 4 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: remove_outliers_for_variable_by_std. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,772 | #loop for chi square values
def calculate_chi_square_values(df, alpha=.05):
chi2_dict = {}
numerical_col, cat_col = get_numerical_and_categorical_col(df)
for i in cat_col:
for j in cat_col:
if i!=j and (j+' '+i) not in chi2_dict.keys():
chi2, p, dof, ex = chi2_contingenc... | No | 5 | 47 | Model_Evaluation | statistical_test | 11,713,576 | 2 | B | 5 | 5 | 7 | 0 | 21 | 9 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: calculate_chi_square_values. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 10"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | B |
4,773 | def rmsle(y, y_,convertExp=True):
if convertExp:
y = inv_boxcox(y, fitted_lambda),
y_ = inv_boxcox(y_, fitted_lambda)
log1 = np.nan_to_num(np.array([np.log(v + 1) for v in y]))
log2 = np.nan_to_num(np.array([np.log(v + 1) for v in y_]))
calc = (log1 - log2) ** 2
return np.sqrt(n... | No | 5 | 84 | Other | not_enough_vertices | 11,713,576 | 1 | A | 1 | 2 | 1 | 0 | 2 | 8 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Missing docstring for function: rmsle. Consider adding documentation for parameters and return values.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 9"}] | A | no applicable ML-specific findings | [] | ["Missing function docstring"] | 8 | A |
4,774 | def plot_prediction(test, test_pred, train, train_pred, convert_to_original_form = False):
if convert_to_original_form:
test = inv_boxcox(test, fitted_lambda),
test_pred = inv_boxcox(test_pred, fitted_lambda)
train = inv_boxcox(train, fitted_lambda),
train_pred = inv_boxcox(train_pre... | No | 5 | 56 | Visualization | plot_predictions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,775 | df_hour = pd.read_csv("/kaggle/input/bike-sharing-demand/train.csv")
df_test = pd.read_csv("/kaggle/input/bike-sharing-demand/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 8 | A |
4,776 | df_hour.head() | No | 5 | 41 | Debug | show_table | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,777 | df_hour["dteday"] = df_hour.datetime.apply(lambda x : x.split()[0])
df_hour["yr"] = df_hour.datetime.apply(lambda x : x.split()[0][:4])
df_hour['yr'] = df_hour.yr.map({'2011': 0, '2012':1})
df_hour["hr"] = df_hour.datetime.apply(lambda x : x.split()[1].split(":")[0])
df_hour["weekday"] = df_hour.dteday.apply(lambda dat... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,778 | #performing same on test
#performing same on test
df_test["dteday"] = df_test.datetime.apply(lambda x : x.split()[0])
df_test["yr"] = df_test.datetime.apply(lambda x : x.split()[0][:4])
df_test['yr'] = df_test.yr.map({'2011': 0, '2012':1})
df_test["hr"] = df_test.datetime.apply(lambda x : x.split()[1].split(":")[0])
df... | Yes | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,779 | df_hour['cnt'] = df_hour['count']
del df_hour['count']
df_hour.shape | Yes | 4 | 61 | Data_Transform | rename_columns | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,780 | df_hour.info() | No | 5 | 40 | Debug | show_table_attributes | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,781 | df_hour.columns | No | 5 | 71 | Debug | show_columns | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,782 | #Creating a copy of df to preserve original df
df = df_hour.copy() | No | 5 | 12 | Data_Transform | create_dataframe | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,783 | #Analysing which of these are categorical variables
for col in df.columns:
print('Count of unique values for ', col, ': ', len(df[col].unique())) | No | 5 | 54 | EDA | count_unique_values | 11,713,576 | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,784 | '''We know maximum categories for any categorical col is 24 (for month)
Hence we can use this to def function to convert variable to categorical type'''
df = change_to_categorical(df, max_cat=24) | No | 5 | 16 | Data_Transform | data_type_conversions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,785 | dataTypeDf = (df.dtypes.astype(str).value_counts()).reset_index().rename(columns={"index":"variableType",0:"count"})
fig,ax = plt.subplots()
fig.set_size_inches(12,5)
get_count_plot('variableType',dataTypeDf, ax, 'count', value_counts='count', print_percent=True)' | No | 3 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,786 | df[get_numerical_and_categorical_col(df)[0]].describe() | No | 5 | 40 | Debug | show_table_attributes | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,787 | df[get_numerical_and_categorical_col(df)[1]].describe(include='all') | No | 5 | 40 | Debug | show_table_attributes | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,788 | get_plot_for_numerical(df,3) | No | 3 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,789 | plt.figure(figsize=(7,5))
visualize_corr_matrix(df) | No | 3 | 80 | Visualization | heatmap | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,790 | '''Let's start creating a list which contains all the variables to be deleted.
We can delete them once we're done with our exploratory analysis'''
cols_to_remove = ['registered','casual','windspeed']
#besides atemp should be deleted immediately for obvious reasons!
del df['atemp']
del df_test['atemp'] | No | 5 | 10 | Data_Transform | drop_column | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,791 | get_plot_for_numerical(df, 3, plot_type='box') | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,792 | get_target_dist_with_categorical(df,n_cols=3) | No | 5 | 53 | Other | something_strange | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,793 | get_target_dist_with_categorical(df, n_cols=2, plot_type='violin') | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,794 | #Let's perform categorical test chi2 to decide which categorical columns to delete
chi2_dict = calculate_chi_square_values(df)
chi2_dict | No | 5 | 47 | Model_Evaluation | statistical_test | 11,713,576 | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,795 | sns.pointplot(x='hr',y='cnt',data=df, hue='season', markers = 'x') | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,796 | sns.pointplot(x='hr',y='cnt',data=df, hue='weekday', markers = 'x') | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,797 | #to visualize similar plot for type of user, we would need to use melt
#what melt would do, take each hour and generate rows for value variables. Next we'll use this to find mean for each hour and for each type of users
hr_users_type = pd.melt(df[["hr","casual","registered"]], id_vars=['hr'], value_vars=['casual', 'reg... | Yes | 2 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,798 | hr_users_type_mean = pd.DataFrame(hr_users_type.groupby(["hr","variable"],sort=True)["value"].mean()).reset_index()
hr_users_type_mean.head() | Yes | 4 | 12 | Data_Transform | create_dataframe | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,799 | sns.pointplot(x=hr_users_type_mean["hr"], y=hr_users_type_mean["value"],hue=hr_users_type_mean["variable"],hue_order=["casual","registered"], data=hr_users_type_mean, join=True) | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,800 | from sklearn.ensemble import RandomForestRegressor
np.random.seed(42)
# drop target columns
df_original = df.copy()
drop_cols=['cnt', 'dteday','registered','casual']
X = df.drop(drop_cols, axis = 1) # X = independent columns (potential predictors)
y = df['cnt'] # y = target column (what we want to predict)
# instantiat... | Yes | 3 | 7 | Model_Train | train_model | 11,713,576 | 1 | A | 2 | 0 | 0 | 0 | 10 | 15 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
4,801 | df_cleaned = df.copy()
df_cleaned.drop(cols_to_remove, axis=1, inplace=True)
df_test.drop(cols_to_remove, axis=1, inplace=True, errors='ignore')
df_cleaned.head() | Yes | 5 | 10 | Data_Transform | drop_column | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,802 | df_cleaned.describe() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,803 | df_cleaned.drop(['cnt','cnt_log','box_cox_reverse'], axis=1, inplace=True)
df_cleaned.rename(columns={'cnt_box_cox':'count_transformed'}, inplace=True) | Yes | 4 | 10 | Data_Transform | drop_column | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,804 | sc = StandardScaler() | No | 2 | 18 | Data_Transform | normalization | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,805 | target = 'count_transformed'
X = df_cleaned.drop(target, axis=1)
y = df_cleaned[target]
seed=23
X_train, X_test, y_train, y_test = split(X, y, test_size=.3, random_state=seed) | Yes | 5 | 13 | Data_Transform | split | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 23", "fix": "Not specified", "benefits": "Not specified", "location": "Line 12"}] | A | no applicable ML-specific findings | [] | ["Magic number"] | 8 | A |
4,806 | X_train = sc.fit_transform(X_train)
X_test = sc.fit_transform(X_test) | No | 4 | 18 | Data_Transform | normalization | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,807 | from sklearn.linear_model import LinearRegression,Ridge,Lasso
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.metrics import mean_squared_error as mse
from sklearn import metrics
# Initialize logistic regression model
lr = LinearRegression()
# Train the model
lr.fit(X_train,y = y_tra... | Yes | 3 | 7 | Model_Train | train_model | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 9 | 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"] | 8 | B |
4,808 | coeff_df = pd.DataFrame(lr.coef_, X.columns, columns=['Coefficient'])
coeff_df | No | 4 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,809 | plot_prediction(y_test, y_pred, y_train, y_pred_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,810 | ridge = Ridge()
ridge_param = {'max_iter':[3000], 'alpha':[.1,.03,.3,1,3,10, 30, 100,300]}
rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)
grid_ridge = GridSearchCV(ridge,
ridge_param,
scoring = rmsle_scorer,
cv = 10)
gr... | Yes | 3 | 6 | Hyperparam_Tuning | train_on_grid | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | ok | 9 | 5 | [{"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 12"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | 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", "Verbose Mode Checker"] | 8 | B |
4,811 | fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(grid_ridge.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sns.pointplot(data=df,x="alpha",y="rmsle",ax=ax) | No | 5 | 81 | Visualization | relationship | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 8 | A |
4,812 | plot_prediction(y_test, y_pred_ridge, y_train, y_pred_ridge_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,813 | lasso = Lasso()
alpha = 1/np.array([.1,.03,.3,1,3,10, 30, 100,300,1000])
lasso_param = {'max_iter':[3000], 'alpha':alpha}
rmsle_scorer = metrics.make_scorer(rmsle, greater_is_better=False)
random_lasso = RandomizedSearchCV(lasso,
lasso_param,
scoring = rmsle_scorer,... | No | 3 | 6 | Hyperparam_Tuning | train_on_grid | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 14 | ok | 8 | 5 | [{"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 13"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": ... | 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", "Verbose Mode Checker"] | 8 | B |
4,814 | fig,ax= plt.subplots()
fig.set_size_inches(12,5)
df = pd.DataFrame(random_lasso.cv_results_)
df["alpha"] = df["params"].apply(lambda x:x["alpha"])
df["rmsle"] = df["mean_test_score"].apply(lambda x:-x)
sns.pointplot(data=df,x="alpha",y="rmsle",ax=ax) | No | 5 | 84 | Other | not_enough_vertices | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"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": ""}] | A | no applicable ML-specific findings | [] | ["Unit Testing Checker"] | 8 | A |
4,815 | plot_prediction(y_test, y_pred_lasso, y_train, y_pred_lasso_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,816 | from sklearn.tree import DecisionTreeRegressor as dt
dt_m = dt(random_state=0)
dt_m.fit(X_train,y_train)
y_pred_dt=dt_m.predict(X_test)
y_pred_dt_train=dt_m.predict(X_train)
print('RMSLE for test: ',rmsle(y_test, y_pred_dt, True))
print('RMSLE for train: ',rmsle(y_train, y_pred_dt_train, True)) | Yes | 4 | 7 | Model_Train | train_model | 11,713,576 | 1 | A | 1 | 0 | 0 | 0 | 5 | 7 | ok | 8 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not 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"] | 8 | B |
4,817 | plot_prediction(y_test, y_pred_dt, y_train, y_pred_dt_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,818 | from sklearn.ensemble import RandomForestRegressor as rfr
rf = rfr(n_estimators=100)
rf.fit(X_train, y_train)
y_pred_rf = rf.predict(X_test)
y_pred_rf_train = rf.predict(X_train)
print('RMSLE for test: ',rmsle(y_test, y_pred_rf, True))
print('RMSLE for train: ',rmsle(y_train, y_pred_rf_train, True)) | Yes | 3 | 7 | Model_Train | train_model | 11,713,576 | 1 | A | 2 | 0 | 0 | 0 | 10 | 7 | ok | 9 | 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"] | 8 | B |
4,819 | plot_prediction(y_test, y_pred_rf, y_train, y_pred_rf_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,820 | from sklearn.ensemble import GradientBoostingRegressor
gbm = GradientBoostingRegressor(n_estimators=3000,alpha=.03)
gbm.fit(X_train,y_train)
y_pred_gbm = gbm.predict(X_test)
y_pred_gbm_train = gbm.predict(X_train)
print('RMSLE for test: ',rmsle(y_test, y_pred_gbm, True))
print('RMSLE for train: ',rmsle(y_train, y_pred_... | Yes | 3 | 7 | Model_Train | train_model | 11,713,576 | 1 | A | 2 | 0 | 0 | 0 | 10 | 7 | ok | 10 | 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"] | 8 | B |
4,821 | plot_prediction(y_test, y_pred_gbm, y_train, y_pred_gbm_train, True) | No | 4 | 56 | Visualization | plot_predictions | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
4,822 | df_test = df_test.sort_values(by='datetime')
datetime_series = df_test.datetime
df_test_for_model = df_test.copy()
df_test_for_model.drop(['datetime'], inplace=True, axis=1)
X_test_ndarry = df_test_for_model.to_numpy()
final_X_test = sc.fit_transform(X_test_ndarry)
final_y_pred = inv_boxcox(gbm.predict(final_X_test), f... | Yes | 3 | 21 | Data_Transform | prepare_x_and_y | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 9 | 8 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}] | ["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"] | 8 | D |
4,823 | final_y_pred.shape, datetime_series.shape | No | 5 | 58 | Debug | show_shape | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,824 | submission = pd.DataFrame({'datetime':datetime_series, 'count':np.round(final_y_pred)}) | No | 5 | 55 | Data_Export | prepare_output | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,825 | submission_rf = pd.DataFrame({'datetime':datetime_series, 'count':final_y_pred_rf}) | No | 5 | 12 | Data_Transform | create_dataframe | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,826 | sns.distplot(final_y_pred, hist=True, kde=True,
color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 4}) | No | 5 | 33 | Visualization | distribution | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,827 | #Just to avoid any missing values
submission[submission['count'].isna()] | No | 4 | 39 | EDA | count_missing_values | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,828 | #let's see values around it to fill these
#intuition knn
submission.iloc[721],submission.iloc[720],submission.iloc[719] | No | 4 | 41 | Debug | show_table | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,829 | #By this and also by our initial analysis, afternoon is not a preferable time to ride bike
submission.fillna(0, inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,830 | submission.iloc[725],submission.iloc[726],submission.iloc[727] | No | 4 | 41 | Debug | show_table | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,831 | submission.to_csv('bike_predictions_rounded.csv', index=False)
submission.to_csv('bike_predictions_random_forest.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 11,713,576 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
4,832 | import pandas as pd
import calendar
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.special import boxcox, inv_boxcox
from datetime import datetime
from numpy import arange
from pandas import read_csv
from sklearn.linear_model import Ridge,Lasso
from sklearn.model_selection import Re... | Yes | 4 | 45 | Data_Extraction | load_from_csv | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 16 | ok | 6 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Hardcoded file path", "Import Checker"] | 10 | B |
4,833 | #Fig Will show count distribution
sns.distplot(train_df['count'])
plt.show()
| No | 5 | 33 | Visualization | distribution | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Import Checker"] | 10 | A |
4,834 | #Fig Will show count distribution post log transformation
train_df['count']=train_df['count'].apply(lambda x:np.log(x))
sns.distplot(train_df['count'])
plt.show()
print (train_df['count'])
train_df.shape | Yes | 4 | 33 | Visualization | distribution | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 4 | 3 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Import Checker"] | 10 | B |
4,835 | #Visualize Count wrt categorical variables
cat_names=['season', 'holiday', 'workingday', 'weather']
i=0
for name in cat_names:
i=i+1
plt.subplot(2,2,i)
sns.countplot(name,data=train_df)
plt.show() | No | 5 | 33 | Visualization | distribution | 12,980,177 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
4,836 | #Visualize data wrt continous variables.
cont_names=['temp','atemp','humidity','windspeed']
i=0
for name in cont_names:
i=i+1
plt.subplot(2,2,i)
sns.boxplot(name,data=train_df)
plt.show()
#Windspeed seems to be skewed | No | 5 | 33 | Visualization | distribution | 12,980,177 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
4,837 | #Splitting out Datetime attribute in dataframe and dropping unwanted variables as per before analysis
new_df=train_df.copy(deep=True)
new_df['day']=new_df['datetime'].apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d %H:%M:%S").weekday()])
new_df['datetime'] = pd.to_datetime(new_df['dat... | Yes | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,838 | #adding dummy varibles to categorical variables dropping the souce columns
weather_df=pd.get_dummies(final_df['weather'],prefix='w',drop_first=True)
yr_df=pd.get_dummies(final_df['year'],prefix='y',drop_first=True)
month_df=pd.get_dummies(final_df['month'],prefix='m',drop_first=True)
hour_df=pd.get_dummies(final_df['ho... | Yes | 4 | 20 | Data_Transform | categorify | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | ok | 3 | 2 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Import Checker"] | 10 | A |
4,839 | print(final_df.columns.to_series().groupby(final_df.dtypes).groups)
final_df.head(5) | Yes | 4 | 41 | Debug | show_table | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
4,840 | #Initializing training set
X=final_df.iloc[:,final_df.columns!='count'].values
Y=final_df.iloc[:,5].values | No | 5 | 21 | Data_Transform | prepare_x_and_y | 12,980,177 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Import Checker"] | 10 | A |
4,841 | #Ridge Regression Implementation 10 Folds
# define model
model = Ridge()
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# define grid
grid = dict()
grid['alpha'] = arange(0, 1, 0.01)
# define search
search = GridSearchCV(model, grid, scoring='neg_mean_squared_error', cv=cv... | Yes | 5 | 6 | Hyperparam_Tuning | train_on_grid | 12,980,177 | 1 | A | 1 | 0 | 0 | 0 | 5 | 8 | ok | 8 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | A |
4,842 | #Lasso Regression Implementation 10 Folds
# define model
model = Lasso()
# define model evaluation method
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# define grid
grid = dict()
grid['alpha'] = arange(0, 1, 0.01)
# define search
search = GridSearchCV(model, grid, scoring='neg_mean_squared_error', cv=cv... | Yes | 5 | 6 | Hyperparam_Tuning | train_on_grid | 12,980,177 | 1 | A | 1 | 0 | 0 | 0 | 5 | 8 | ok | 8 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | A |
4,843 | #Decision TreeImplementation 10 Folds
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
dtm = DecisionTreeRegressor(random_state=42)
param_grid = {"criterion": ["mse", "mae"],
}
search = GridSearchCV(dtm,param_grid, scoring='neg_mean_squared_error', cv=cv)
results = search.fit(X, Y)
# summariz... | Yes | 4 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,844 | #Decision Tree with Pruning with 10 Folds
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
dtm = DecisionTreeRegressor(random_state=42)
param_grid = {"criterion": ["mse", "mae"],
"max_depth": [2, 6, 8],
}
search = GridSearchCV(dtm,param_grid, scoring='neg_mean_squared_error', cv=... | Yes | 5 | 6 | Hyperparam_Tuning | train_on_grid | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,845 | grid = dict()
grid['n_estimators'] = [1000]
grid['max_depth'] = [125,150,175]
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
model = RandomForestRegressor()
search = GridSearchCV(model,param_grid=grid, scoring='neg_mean_squared_error', cv=cv)
results = search.fit(X, Y)
# summarize
print('MSE (NEGATIVE): ... | Yes | 4 | 6 | Hyperparam_Tuning | train_on_grid | 12,980,177 | 1 | A | 1 | 0 | 0 | 0 | 5 | 9 | ok | 10 | 6 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Randomness Control Checker=low | [{"type": "Randomness Control Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
4,846 | def grid_search():
from sklearn.ensemble import GradientBoostingRegressor
print ('lets go')
model = GradientBoostingRegressor()
# define the grid of values to search
grid = dict()
grid['n_estimators'] = [4000]
grid['learning_rate'] = [ 0.001, 0.01, 0.1]
grid['max_depth'] = [4]
cv =... | Yes | 5 | 6 | Hyperparam_Tuning | train_on_grid | 12,980,177 | 1 | A | 3 | 1 | 0 | 0 | 15 | 16 | ok | 9 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | A |
4,847 | from sklearn.ensemble import GradientBoostingRegressor
rgr=GradientBoostingRegressor(learning_rate=0.1,n_estimators=4000, max_depth=4)
rgr.fit(X,Y) | Yes | 5 | 7 | Model_Train | train_model | 12,980,177 | 2 | B | 1 | 0 | 0 | 0 | 5 | 3 | ok | 7 | 5 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Exception Handling Checker", "Import Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 10 | B |
4,848 | test_df=pd.read_csv('/kaggle/input/bike-sharing-demand/test.csv')
test_df['day']=test_df['datetime'].apply(lambda dateString : calendar.day_name[datetime.strptime(dateString,"%Y-%m-%d %H:%M:%S").weekday()])
test_df['datetime']=pd.to_datetime(test_df['datetime'], format='%Y-%m-%d %H:%M:%S')
test_df['month']=test_df['dat... | Yes | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,849 | temp=pd.read_csv('../input/bike-sharing-demand/sampleSubmission.csv')
X_test=test_df.iloc[:,:].values
y_output=rgr.predict(X_test)
y_output
op=pd.DataFrame({'count':np.exp(y_output)})
op['datetime']=temp['datetime']
op.to_csv('finalSubmission.csv', index=False) | Yes | 3 | 25 | Data_Export | save_to_csv | 12,980,177 | 1 | A | 0 | 0 | 0 | 1 | 0 | 7 | ok | 7 | 6 | [{"detector": "framework_specific", "framework": "General", "name": "Import Checker", "fix": "Use standard naming conventions for imported modules.", "benefits": "Improves code readability and maintainability.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "General", "name": "Import Checker", ... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Exception Handling Checker", "Hardcoded file path", "Import Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 10 | A |
4,850 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
# import numpy as np # linear algebra
# import pandas as pd # data processing, CSV file I/O... | No | 5 | 88 | Debug | list_files | null | 1 | A | 1 | 2 | 3 | 0 | 5 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
4,851 | import pandas as pd
import numpy as np
import seaborn as sns
from scipy import stats
from datetime import datetime
import matplotlib.pyplot as plt
%matplotlib inline | No | 5 | 23 | Environment | set_options | 13,074,892 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 6 | null |
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