id int64 0 7.95k | code_block stringlengths 1 52.9k | too_long stringclasses 2
values | marks int64 1 5 | graph_vertex_id float64 1 88 | graph_vertex_class stringclasses 12
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
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,157 | df_train[df_train.Customers>7000] | 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 |
2,158 | g = sns.distplot(data['Sales'])
g.set_title("Data Distribution")
' | 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 |
2,159 | zero_sales = data[data['Sales']==0].copy()
data = data[data['Sales']!=0].drop('Open', 1) | No | 4 | 10 | Data_Transform | drop_column | 2,095,107 | 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"] | 6 | A |
2,160 | fig, ax = plt.subplots (1,4, figsize=(20,4))
sns.barplot(['Size'], [len(zero_sales)], ax=ax[0])
sns.countplot('DayOfWeek', data=zero_sales, ax=ax[1])
sns.countplot('Open', data=zero_sales, ax=ax[2])
sns.countplot('Promo', data=zero_sales, ax=ax[3])
plt.tight_layout() | No | 5 | 75 | Visualization | time_series | 2,095,107 | 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"] | 6 | A |
2,161 | plt.figure(figsize=(8,8))
sns.heatmap(data .corr(), cmap='coolwarm') | No | 5 | 80 | Visualization | heatmap | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
2,162 | data.fillna(0, inplace=True)
test.fillna(1, inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "... | A | no applicable ML-specific findings | [] | ["InPlace Checker"] | 6 | A |
2,163 | # Combining train and test data
# Decomposing date features
data['part'] = 'train'
test['part'] = 'test'
all_data = pd.concat([data, test], 0)[data.columns.tolist()+['Id']]
all_data['Date'] = pd.to_datetime(all_data['Date'])
all_data['Month'] = all_data['Date'].dt.month
all_data['Year'] = all_data['Date'].dt.year
all_... | No | 5 | 8 | Data_Transform | feature_engineering | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "InPlace Checker", "fix": "Assign operations to a new DataFrame variable.", "benefits": "Prevents data loss and improves code clarity.", "location": "Line 19"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker"... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "InPlace Checker"] | 6 | A |
2,164 | all_data.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,165 | tmp = all_data[all_data['part']=='train']
sns.regplot('CompetitionOpen', 'Sales', data=tmp, ci=None) | No | 5 | 33 | Visualization | distribution | 2,095,107 | 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"] | 6 | A |
2,166 | b"all_data['PromoOpen'] = 12 * (all_data.Year - all_data.Promo2SinceYear) + \\\n (all_data.WeekOfYear - all_data.Promo2SinceWeek) / 4.0\nall_data['PromoOpen'] = all_data['PromoOpen'].apply(lambda x: x if x > 0 else 0)" | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,167 | tmp = all_data[all_data['part']=='train']
sns.regplot('PromoOpen', 'Sales', data=tmp, ci=None) | No | 5 | 33 | Visualization | distribution | 2,095,107 | 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"] | 6 | A |
2,168 | df_store[pd.isnull(df_store.CompetitionDistance)]
#rows with missing values for Competition Distance, only 3 rows with null which makes sense since 99.73% is filled | No | 2 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,169 | df_store_check_distribution=df_store.drop(df_store[pd.isnull(df_store.CompetitionDistance)].index)
fig, axes = plt.subplots(1, 2, figsize=(17,3.5))
axes[0].boxplot(df_store_check_distribution.CompetitionDistance, showmeans=True,vert=False,)
axes[0].set_xlim(0,max(df_store_check_distribution.CompetitionDistance+1000))
a... | No | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,170 | df_store['CompetitionDistance'].fillna(df_store['CompetitionDistance'].median(), inplace = True) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,171 | df_store.CompetitionOpenSinceMonth.fillna(0, inplace = True)
df_store.CompetitionOpenSinceYear.fillna(0,inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,173 | df_store.Promo2SinceWeek.fillna(0,inplace=True)
df_store.Promo2SinceYear.fillna(0,inplace=True)
df_store.PromoInterval.fillna(0,inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,174 | #Left-join the train to the store dataset since .Why?
#Because you want to make sure you have all events even if some of them don't have their store information ( which shouldn't happen)
df_train_store = pd.merge(df_train, df_store, how = 'left', on = 'Store')
df_train_store.head()
print ("The Train_Store dataset has ... | No | 4 | 32 | Data_Transform | merge | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,175 | df_train_store['SalesperCustomer']=df_train_store['Sales']/df_train_store['Customers'] | 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 |
2,176 | df_train_store.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,177 | fig, axes = plt.subplots(2, 3,figsize=(17,10) )
palette = itertools.cycle(sns.color_palette(n_colors=4))
plt.subplots_adjust(hspace = 0.28)
#axes[1].df_train_store.groupby(by="StoreType").count().Store.plot(kind='bar')
axes[0,0].bar(df_store.groupby(by="StoreType").count().Store.index,df_store.groupby(by="StoreType").c... | 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 |
2,178 | StoretypeXAssortment = sns.countplot(x="StoreType",hue="Assortment",order=["a","b","c","d"], data=df_store,palette=sns.color_palette("Set2", n_colors=3)).set_title("Number of Different Assortments per Store Type")
df_store.groupby(by=["StoreType","Assortment"]).Assortment.count()
| No | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,179 | df_train_store['Month']=df_train_store.Date.dt.month
df_train_store['Year']=df_train_store.Date.dt.year | No | 3 | 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 |
2,180 |
sns.factorplot(data = df_train_store, x ="Month", y = "Sales",
col = 'Promo', # per store type in cols
hue = 'Promo2',
row = "Year"
,sharex=False)
' | No | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,181 | sns.factorplot(data = df_train_store, x ="Month", y = "SalesperCustomer",
col = 'Promo', # per store type in cols
hue = 'Promo2',
row = "Year"
,sharex=False)' | No | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,182 | sns.factorplot(data = df_train_store, x ="DayOfWeek", y = "Sales",
hue='Promo'
,sharex=False)' | No | 2 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,183 | #33 Stores are opened on Sundays
print ("Number of Stores opened on Sundays:{}" .format(df_train_store[(df_train_store.Open == 1) & (df_train_store.DayOfWeek == 7)]['Store'].unique().shape[0]))' | No | 3 | 54 | EDA | count_unique_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,184 | df_train_store['CompetitionDist_Cat']=pd.cut(df_train_store['CompetitionDistance'], 5) | 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 |
2,185 | df_train_store.groupby(by="CompetitionDist_Cat").Sales.mean(),df_train_store.groupby(by="CompetitionDist_Cat").Customers.mean() | No | 5 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,186 | del df_train_store["CompetitionDist_Cat"] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,187 | df_train_store['Day']=df_train_store.Date.dt.day | 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 |
2,188 | del df_train_store["Date"] | No | 5 | 10 | Data_Transform | drop_column | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,189 | df_train_store['StoreType'].isnull().any(),df_train_store['Assortment'].isnull().any(),df_train_store['StateHoliday'].isnull().any()
#No Null values we can proceed with the transformation | No | 2 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,190 | df_train_store["StoreType"].value_counts(),df_train_store["Assortment"].value_counts(),df_train_store["StateHoliday"].value_counts() | No | 4 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,191 | df_train_store['StateHoliday'] = df_train_store['StateHoliday'].astype('category')
df_train_store['Assortment'] = df_train_store['Assortment'].astype('category')
df_train_store['StoreType'] = df_train_store['StoreType'].astype('category')
df_train_store['PromoInterval']= df_train_store['PromoInterval'].astype('category... | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,192 | df_train_store['StateHoliday_cat'] = df_train_store['StateHoliday'].cat.codes
df_train_store['Assortment_cat'] = df_train_store['Assortment'].cat.codes
df_train_store['StoreType_cat'] = df_train_store['StoreType'].cat.codes
df_train_store['PromoInterval_cat'] = df_train_store['PromoInterval'].cat.codes
| No | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,193 | df_train_store['StateHoliday_cat'] = df_train_store['StateHoliday_cat'].astype('float')
df_train_store['Assortment_cat'] = df_train_store['Assortment_cat'].astype('float')
df_train_store['StoreType_cat'] = df_train_store['StoreType_cat'].astype('float')
df_train_store['PromoInterval_cat'] = df_train_store['PromoInterva... | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,194 | from argparse import Namespace
#There are 1115 stores. Select a small sample to do experimentation on. Select all for full training.
num_sample_stores=1115
#The test set is 47 days. Normally use the last 47 days of the training data for validation. Se to 0 and use all data for traing when submitting to kaggle
valid_... | No | 5 | 59 | Model_Train | init_hyperparams | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,195 | from pathlib import Path
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from fastai import *
from fastai.tabular import *
#display results
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import cufflinks as cf
| No | 5 | 22 | Environment | import_modules | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,196 | plotly.offline.init_notebook_mode(connected=False)
cf.go_offline()
%matplotlib inline
%reload_ext autoreload
%autoreload 2
pd.set_option('display.max_columns', 0)
pd.set_option('display.max_rows', 500) | No | 5 | 23 | Environment | set_options | 2,568,883 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 2 | null |
2,197 | !ls ../input/ | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,198 | path=Path("../input/rossmann-data-engineering/")
traindf=pd.read_feather(path/"train.feather")
testdf=pd.read_feather(path/"test.feather") | No | 5 | 44 | Data_Extraction | load_from_disk | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/rossmann-data-engineering/. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
2,199 | best_type= data.sort_values('revenue', ascending=False)
plt.figure(figsize=(13,12))
sns.barplot(x=best_type['Type'], y=best_type['revenue']) | No | 4 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,200 | data | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,201 | #Select size validation set based on valid_days variable
from datetime import datetime, timedelta
valid_idx=traindata[traindata.Date>=(traindata.Date.max()- timedelta(days=valid_days))].index.tolist() | No | 4 | 8 | Data_Transform | feature_engineering | 2,568,883 | 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"] | 2 | A |
2,202 | #Convert datetime columns to int64 for traning
datecols=traindata.select_dtypes(include="datetime").columns.tolist()
traindata[datecols]=traindata[datecols].astype("int64")
testdf[datecols]=testdf[datecols].astype("int64") | No | 5 | 16 | Data_Transform | data_type_conversions | 2,568,883 | 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"] | 2 | A |
2,203 | procs = [FillMissing, Categorify, Normalize]
dep_var = 'Sales'
#cont_names,cat_names= cont_cat_split(sample_train,dep_var="Sales")
cont_names=[
'CompetitionDistance',
'Week',
'Day',
'Dayofyear',
'Elapsed',
'ratio-sales-customer',
'ratio-saturday-week',
'ratio-sunday-week',
'ratio-promo-nopromo',
'Promo_thiswe... | No | 3 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,204 | max_log_y = np.log(np.max(traindata['Sales']))#*1.2
y_range = torch.tensor([0, max_log_y], device=defaults.device) | No | 4 | 21 | Data_Transform | prepare_x_and_y | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Missing Axis Specification", "fix": "Specify axis in array operations.", "benefits": "Improves performance and avoids unexpected behaviour.", "location": "Line 3"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Chec... | C | Missing Axis Specification=medium | [{"type": "Missing Axis Specification", "severity": "medium"}] | ["Column Selection Checker"] | 2 | C |
2,205 | databunch = (TabularList.from_df(traindata, path="", cat_names=cat_names, cont_names=cont_names, procs=procs,)
.split_by_idx(valid_idx)
.label_from_df(cols=dep_var, label_cls=FloatList, log=True)
.add_test(TabularList.from_df(testdf, path=path, cat_names=cat_names, cont_n... | No | 4 | 13 | Data_Transform | split | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,206 | learn = tabular_learner(databunch, layers=[s.l1,s.l2], ps=[s.ps1,s.ps2], emb_drop=s.emb_drop, y_range=y_range, metrics=exp_rmspe) | No | 5 | 4 | Model_Train | choose_model_class | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,207 | learn.lr_find() | 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 |
2,208 | learn.recorder.plot() | No | 5 | 35 | Visualization | learning_history | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,209 | learn.fit_one_cycle(s.l1epoch, s.lrate, wd=s.wd) | No | 5 | 7 | Model_Train | train_model | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,210 | learn.fit_one_cycle(s.l2epoch, s.lrate/s.lrate_ratio, wd=s.wd) | No | 5 | 7 | Model_Train | train_model | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,211 | learn.fit_one_cycle(s.l3epoch, s.lrate/(s.lrate_ratio*s.lrate_ratio), wd=s.wd) | No | 5 | 7 | Model_Train | train_model | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,212 | valid_preds=learn.get_preds(DatasetType.Valid)
traindata["SalesPreds"]=pd.Series(index=traindata.iloc[valid_idx].index,data=np.exp(valid_preds[0].numpy().T[0])) | No | 4 | 48 | Model_Evaluation | predict_on_test | 2,568,883 | 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"] | 2 | A |
2,213 | #Define error function
def rmspe_metric(act,pred):
return np.sqrt(np.mean(((act-pred)/act)**2)) | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,214 | rmspe_metric(traindata.Sales,traindata.SalesPreds) | No | 5 | 28 | Model_Train | compute_train_metric | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,215 | #Sort stores by how much error
store_rmspe=traindata.groupby(["Store"]).apply(lambda x:rmspe_metric(x.Sales,x.SalesPreds)).sort_values(ascending=False) | No | 5 | 28 | Model_Train | compute_train_metric | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,216 | store_rmspe.iplot(kind="histogram") | 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 |
2,217 | store_rmspe[:10] | No | 5 | 41 | Debug | show_table | 2,568,883 | 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"] | 2 | A |
2,218 | t=traindata.set_index("Date") | No | 5 | 61 | Data_Transform | rename_columns | 2,568,883 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,219 | #Stores with most error
for store in store_rmspe.index[:4].tolist():
t[t.Store==store][["Sales","SalesPreds"]].iplot(kind="bar",barmode="overlay",title="Store {}".format(store)) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,220 | #Stores with least error
for store in store_rmspe.index[-4:].tolist():
t[t.Store==store][["Sales","SalesPreds"]].iplot(kind="bar",barmode="overlay",title="Store {}".format(store)) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,221 | test_preds=learn.get_preds(DatasetType.Test)
testdf["Sales"]=np.exp(test_preds[0].data).numpy().T[0]
testdf[["Id","Sales"]]=testdf[["Id","Sales"]].astype("int")
testdf[["Id","Sales"]].to_csv("rossmann_submission.csv",index=False) | No | 3 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,223 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from xgboost import XGBRegressor | No | 5 | 22 | Environment | import_modules | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,224 | train_data = pd.read_csv("../input/train.csv",low_memory= False)
test_data = pd.read_csv("../input/test.csv",low_memory= False)
store_data = pd.read_csv("../input/store.csv",low_memory= False)
test_copy = test_data | No | 5 | 45 | Data_Extraction | load_from_csv | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "general_ml", "framework": "General ML", "name": "Hard... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
2,225 | print("Shape of Train data :", train_data.shape)
print("Shape of Test data :", test_data.shape)
print("Shape of Store data :", store_data.shape) | No | 5 | 58 | Debug | show_shape | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,226 | train_data.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,227 | store_data.head(100) | No | 5 | 41 | Debug | show_table | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,228 | train_data.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,229 | test_data.isnull().sum() | No | 5 | 39 | EDA | count_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,230 | store_data.isnull().sum().sort_values(ascending = False) | No | 5 | 39 | EDA | count_missing_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,231 | store_data['Promo2SinceWeek'].unique() | No | 5 | 57 | Debug | show_unique_values | 3,237,641 | 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"] | 2 | A |
2,232 | train_data['Store'].unique() | No | 5 | 57 | Debug | show_unique_values | 3,237,641 | 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"] | 2 | A |
2,233 | train_data['DayOfWeek'].unique() | No | 5 | 57 | Debug | show_unique_values | 3,237,641 | 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"] | 2 | A |
2,234 | train_data['Open'].unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,235 | train_data['StateHoliday'].unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,236 | train_data['Promo'].unique() | No | 5 | 57 | Debug | show_unique_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,237 | store_data['CompetitionOpenSinceMonth'].unique() | No | 5 | 57 | Debug | show_unique_values | 3,237,641 | 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"] | 2 | A |
2,238 | print(sum(train_data["Open"] == 0))
print(sum(train_data["Open"] == 1)) | No | 5 | 72 | EDA | count_values | 3,237,641 | 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"] | 2 | A |
2,239 | print(sum(test_data["Open"] == 0))
print(sum(test_data["Open"] == 1)) | No | 5 | 72 | EDA | count_values | 3,237,641 | 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"] | 2 | A |
2,240 | print(sum(train_data["StateHoliday"] == 'a'))
print(sum(train_data["StateHoliday"] == 'b'))
print(sum(train_data["StateHoliday"] == 'c'))
print(sum(train_data["StateHoliday"] == 0))' | No | 5 | 72 | EDA | count_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,241 | plt.plot(train_data['DayOfWeek'],train_data['Customers']) | No | 5 | 81 | Visualization | relationship | 3,237,641 | 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"] | 2 | A |
2,242 | train_data[['Sales','Customers','Promo','SchoolHoliday']].corr(method='pearson') | No | 5 | 40 | Debug | show_table_attributes | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,243 | train_data['Mon'] = train_data["Date"].apply(lambda x : int(x[5:7]))
train_data['Yr'] = train_data["Date"].apply(lambda x : int(x[:4]))
train_data["HolidayBin"] = train_data.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})' | No | 4 | 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 |
2,244 | test_data['Mon'] = test_data["Date"].apply(lambda x : int(x[5:7]))
test_data['Yr'] = test_data["Date"].apply(lambda x : int(x[:4]))
test_data["HolidayBin"] = test_data.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})' | No | 5 | 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 |
2,245 | train_data = train_data.merge(store_data)
test_data =test_data.merge(store_data) | No | 5 | 32 | Data_Transform | merge | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,246 | train_data.isnull().sum().sort_values(ascending= False) | No | 5 | 39 | EDA | count_missing_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,247 | test_data.isnull().sum().sort_values(ascending= False) | No | 5 | 39 | EDA | count_missing_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,248 | test_data[test_data['Open'].isnull()] | No | 5 | 14 | Data_Transform | filter | 3,237,641 | 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"] | 2 | A |
2,249 | for i in train_data['Promo2SinceWeek'].unique() :
print(i ,':', sum(train_data['Promo2SinceWeek'] == i ))
| No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 1 | 1 | 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"] | 2 | A |
2,250 | for i in train_data['CompetitionOpenSinceMonth'].unique() :
print(i ,':', sum(train_data['CompetitionOpenSinceMonth'] == i )) | No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 1 | 1 | 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"] | 2 | A |
2,251 | for i in train_data['Promo2SinceYear'].unique() :
print(i ,':', sum(train_data['Promo2SinceYear'] == i )) | No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 1 | 1 | 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"] | 2 | A |
2,252 | for i in train_data['CompetitionOpenSinceYear'].unique() :
print(i ,':', sum(train_data['CompetitionOpenSinceYear'] == i )) | No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 1 | 1 | 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"] | 2 | A |
2,253 | train_data = train_data.drop(['Customers', 'Store','Date','StateHoliday'],axis= 1 )
test_data = test_data.drop(['Date','StateHoliday','Store','Id'],axis= 1 ) | No | 5 | 10 | Data_Transform | drop_column | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
2,254 | sum(train_data['Open'] == 0) | No | 5 | 72 | EDA | count_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 1 | 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"] | 2 | A |
2,255 | train_data = train_data.drop(train_data[train_data['Open'] == 0].index.tolist()) | No | 5 | 10 | Data_Transform | drop_column | 3,237,641 | 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"] | 2 | A |
2,256 | train_data[train_data['HolidayBin'].isnull()] | No | 5 | 14 | Data_Transform | filter | 3,237,641 | 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"] | 2 | A |
2,257 | train_data['CompetitionOpenSinceMonth'] = train_data['CompetitionOpenSinceMonth'].fillna(9.0)
train_data['HolidayBin'] = train_data['HolidayBin'].fillna(0)
train_data['Promo2SinceWeek'] = train_data['Promo2SinceWeek'].fillna(40.0)
train_data['Promo2SinceYear'] = train_data['Promo2SinceYear'].fillna(2012.0)
train_data['... | No | 5 | 17 | Data_Transform | correct_missing_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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"] | 2 | A |
2,258 | test_data['Open'] = test_data['Open'].fillna(1)
test_data['CompetitionOpenSinceMonth'] = test_data['CompetitionOpenSinceMonth'].fillna(9.0)
test_data['CompetitionDistance'] = test_data['CompetitionDistance'].fillna(train_data['CompetitionDistance'].mean())
test_data['CompetitionOpenSinceYear'] = test_data['CompetitionO... | No | 4 | 17 | Data_Transform | correct_missing_values | 3,237,641 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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"] | 2 | A |
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