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values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,057 | print(df_test.shape, df_test.columns) | Yes | 3 | 17 | Data_Transform | correct_missing_values | 243,149 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker"] | 6 | A |
2,058 | for c in rlnrs.keys():
X_c = df_test[(df_test.label == c) & (df_test.Open != 0)].drop(['Store', 'Date', 'Sales'], axis=1).values
df_test.loc[(df_test.label == c) & (df_test.Open != 0), 'Sales'] = rlnrs[c].predict(X_c)
df_test.Sales.mean(), df_test.Sales.min(), df_test.Sales.max() | Yes | 2 | 16 | Data_Transform | data_type_conversions | 243,149 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker"] | 6 | A |
2,059 | out = pd.DataFrame({
"Id": df_test.index,
"Sales": df_test.Sales.values
})
out.to_csv('submission.csv', index=False)' | No | 2 | 58 | Debug | show_shape | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,060 | import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
from lightgbm import LGBMRegressor
from sklearn.metrics import make_scorer
from sklearn.preprocessing import LabelEncoder, Imputer, OneHotEncoder, FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.b... | No | 5 | 23 | Environment | set_options | 874,590 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,061 | df_train = pd.read_csv("../input/train.csv", parse_dates=["Date"], date_parser=pd.to_datetime, low_memory=False)
df_test = pd.read_csv("../input/test.csv", parse_dates=["Date"], date_parser=pd.to_datetime, low_memory=False)
df_store = pd.read_csv("../input/store.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,062 | train = df_train.merge(df_store)
test = df_test.merge(df_store) | No | 5 | 32 | Data_Transform | merge | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,063 | print("train")
print("max: ", df_train.Date.min())
print("min:", df_train.Date.max())
print("delta: ", df_train.Date.max() - df_train.Date.min()) | No | 5 | 40 | Debug | show_table_attributes | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,064 | print("test")
print("max: ", df_test.Date.min())
print("min:", df_test.Date.max())
print("delta: ", df_test.Date.max() - df_test.Date.min()) | No | 5 | 40 | Debug | show_table_attributes | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,065 | df_train.groupby("DayOfWeek").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,066 | df_train[(df_train.DayOfWeek == 7) & (df_train.Open == 1)].Store.unique().shape[0] | No | 5 | 54 | EDA | count_unique_values | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,067 | df_train.groupby("StateHoliday").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,068 | df_train.groupby("SchoolHoliday").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,069 | df_train.groupby("Promo").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,070 | train.PromoInterval.unique() | No | 5 | 57 | Debug | show_unique_values | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,071 | def get_Promo2Active(df):
months_map = {v:i+1 for i, v in enumerate(["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sept", "Oct", "Nov", "Dec"])}
def is_Promo2_active(row):
if row.Promo2 == 0:
return 0
current_week, c... | No | 5 | 8 | Data_Transform | feature_engineering | 874,590 | 2 | B | 3 | 6 | 5 | 0 | 25 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,072 | def get_CompetitionActive(df):
def is_competition_active(row):
if np.isnan(row.CompetitionDistance):
return 0
if np.isnan(row.CompetitionOpenSinceMonth) and np.isnan(row.CompetitionOpenSinceYear):
return 1
current_month, current_year = row.Date.month... | No | 5 | 8 | Data_Transform | feature_engineering | 874,590 | 1 | A | 1 | 7 | 7 | 0 | 10 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,073 | train["Promo2Active"] = get_Promo2Active(train)
train["CompetitionActive"] = get_CompetitionActive(train) | No | 5 | 8 | Data_Transform | feature_engineering | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,074 | train[train.Promo2 == 1].groupby("Promo2Active").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 60 | Data_Transform | groupby | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,075 | train.groupby("CompetitionActive").agg({"Sales": "mean"}).plot(kind="bar") | No | 5 | 33 | Visualization | distribution | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,076 | # we already have these, but for the sake of consistence, let's do it again
train = df_train.merge(df_store)
test = df_test.merge(df_store)
train["Promo2Active"] = get_Promo2Active(train)
train["CompetitionActive"] = get_CompetitionActive(train)
test["Promo2Active"] = get_Promo2Active(test)
test["CompetitionActive"] ... | No | 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 |
2,077 | train["DayOfYear"] = train.Date.apply(lambda x: x.timetuple().tm_yday)
test["DayOfYear"] = test.Date.apply(lambda x: x.timetuple().tm_yday) | No | 5 | 16 | Data_Transform | data_type_conversions | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,078 | min_date = train.Date.min() # we know that all test data happened later
def date_to_day_number(df):
return (df.Date - min_date).apply(lambda x: x.days) | No | 5 | 8 | Data_Transform | feature_engineering | 874,590 | 1 | A | 0 | 1 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,079 | train["Day"] = date_to_day_number(train)
test["Day"] = date_to_day_number(test) | No | 5 | 8 | Data_Transform | feature_engineering | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,080 | train.sort_values("Day", inplace=True)
test.sort_values("Day", inplace=True) | No | 5 | 9 | Data_Transform | sort_values | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,081 | def rmspe(y_true, y_pred):
w = np.zeros(y_true.shape, dtype=float)
ind = y_true != 0
w[ind] = 1./ (y_true[ind]**2)
return np.sqrt(np.mean(w * (y_true - y_pred)**2))
rmspe_scorer = make_scorer(rmspe, greater_is_better=False) | No | 5 | 84 | Other | not_enough_vertices | 874,590 | 1 | A | 0 | 1 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,082 | train_baseline = train.copy()
train_baseline['Last_Week_Sales'] = train_baseline.groupby("Store")["Sales"].shift()
train_baseline['Last_Week_Diff'] = train_baseline.groupby("Store")["Last_Week_Sales"].diff()
train_baseline.dropna(inplace=True, subset=["Last_Week_Sales", "Last_Week_Diff"])
train_baseline.head()' | No | 2 | 60 | Data_Transform | groupby | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,083 | mean_error = []
for day in range(2, train_baseline.Day.max() + 1):
val = train_baseline[train_baseline.Day == day]
p = val.Last_Week_Sales.values
error = rmspe(val.Sales.values, p)
mean_error.append(error)
print('Mean Error = %.5f' % np.mean(mean_error)) | No | 2 | 60 | Data_Transform | groupby | 874,590 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,084 | class LabelEncoderPipelineFriendly(LabelEncoder):
def fit(self, X, y=None):
"""this would allow us to fit the model based on the X input."""
super(LabelEncoderPipelineFriendly, self).fit(X)
def transform(self, X, y=None):
return super(LabelEncoderPipelineFriendly, self).tra... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | 3 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,085 | def prepare_pipeline(df):
def get_DayOfWeek(df):
return df["DayOfWeek"]
def get_Open(df):
return df[["Open"]]
def get_Promo(df):
return df[["Promo"]]
def get_StateHoliday(df):
return df["StateHoliday"]
def get_SchoolHoliday(df):
return... | No | 5 | 4 | Model_Train | choose_model_class | 874,590 | 2 | B | 12 | 12 | 0 | 0 | 115 | 50 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,086 | pipeline = prepare_pipeline(train) | No | 3 | 4 | Model_Train | choose_model_class | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,087 | x_train, y_train = pipeline.fit_transform(train), train.Sales
x_test = pipeline.transform(test) | No | 4 | 7 | Model_Train | train_model | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,088 | params = {"boosting_type" : ["gbdt"],
"learning_rate": [0.1],
"n_estimators": [200],
"objective": ["regression"],
"reg_alpha": [1.0],# [0.0, 0.5, 1.0], # no time for an actual CV on kaggle
"reg_lambda": [1.0],# [0.0, 0.5, 1.0],
"random_state": [0],
"... | No | 5 | 59 | Model_Train | init_hyperparams | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,089 | gs = GridSearchCV(LGBMRegressor(), params, scoring=rmspe_scorer, cv=2, n_jobs=1)
gs.fit(x_train, y_train) | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,090 | prediction = gs.predict(x_test) | No | 5 | 48 | Model_Evaluation | predict_on_test | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,091 | pd.DataFrame({"Id": test.Id, "Sales": prediction}).to_csv("submission.csv", sep=",", index=False) | No | 5 | 25 | Data_Export | save_to_csv | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,092 | def prepare_pipeline_ts(df, min_shift, max_shift):
def get_shifted_date(df, for_sales=False):
return (df.Date.min() + pd.DateOffset(days_to_shift))
def get_DayOfWeek(df):
return df["DayOfWeek"]
def get_Open(df):
return df[["Open"]]
def get_Promo(df):
r... | No | 5 | 53 | Other | something_strange | 874,590 | 2 | B | 14 | 15 | 2 | 0 | 125 | 61 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,093 | test_size = len(test)
min_shift = (test.Date.max() - test.Date.min()).days
max_shift = 180
to_drop = len(train[train.Date < train.Date.min() + pd.DateOffset(max_shift)]) | No | 5 | 77 | Other | define_variables | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,094 | import warnings
warnings.filterwarnings("ignore")
#Data Manipulation and Treatment
import numpy as np
import pandas as pd
from datetime import datetime
#Plotting and Visualizations
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from scipy import stats
import itertools
#Scikit-Learn for Modeli... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,095 | def str_to_date(date):
return datetime.strptime(date, '%Y-%m-%d').date() | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,096 | #The training Set
df_train = pd.read_csv("../input/train.csv",sep=',', parse_dates=['Date']
, date_parser=str_to_date,
low_memory = False)
#Additional Information on those stores
df_store = pd.read_csv("../input/store.csv"
, low_memory = False)' | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,097 | df_train.head() | No | 5 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,098 | df_train.tail() | 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 |
2,099 | df_train.dtypes,print ("The Train dataset has {} Rows and {} Variables".format(str(df_train.shape[0]),str(df_train.shape[1]))) | No | 3 | 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 |
2,100 | df_store.tail() | 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 |
2,101 | df_store.dtypes ,print ("The Store dataset has {} Rows (which means unique Shops) and {} Variables".format(str(df_store.shape[0]),str(df_store.shape[1])))
| No | 3 | 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 |
2,102 | df_train.count(0)/df_train.shape[0] * 100 | No | 2 | 54 | EDA | count_unique_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,103 | class bert(nn.Module):
def __init__(self, bert_path):
super().__init__()
BERT = BertModel.from_pretrained(bert_path, config = BertConfig.from_pretrained(bert_path, output_hidden_states = True))
self.BERT = BERT
self.fc = nn.Sequential(nn.BatchNorm1d(self.BERT.config.hidden_size * 3),... | Yes | 3 | 4 | Model_Train | choose_model_class | 11,400,829 | 1 | A | 1 | 4 | 1 | 0 | 5 | 29 | ok | 12 | 11 | [{"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 23"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Selec... | B | Cross-validation not detected=low, Deterministic Algorithm Usage Checker=low, No random seed setting detected=low | [{"type": "Cross-validation not detected", "severity": "low"}, {"type": "Deterministic Algorithm Usage Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Chain Indexing", "Column Selection Checker", "Data Augmentation Checker", "Early stopping not implemented", "Efficient data loading not detected", "Logging Checker", "Missing class docstring", "Missing function docstring"] | 6 | B |
2,104 | epoch_len = 20
model, criteria, optimizer, scheduler = create_model(len(df_train), epoch_len)
set_trainable(model.BERT, False)
aaa = 0
for t in range(epoch_len):
tot_loss = 0
correct_train = 0
val_loss = 0
val_correct = 0
model = model.train()
if GPU:
model = model.cuda()
f... | No | 4 | 7 | Model_Train | train_model | 11,400,829 | 1 | A | 0 | 8 | 17 | 0 | 0 | 51 | ok | 8 | 8 | [{"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": "PyTorch", "name": "Deterministic Al... | B | Cross-validation not detected=low, Deterministic Algorithm Usage Checker=low, No random seed setting detected=low | [{"type": "Cross-validation not detected", "severity": "low"}, {"type": "Deterministic Algorithm Usage Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Data Augmentation Checker", "Early stopping not implemented", "Efficient data loading not detected", "Logging Checker"] | 6 | B |
2,105 | def predict(df, dataloader, model):
tmp_array = np.zeros((len(df), 3))
with torch.no_grad():
model = model.eval()
if GPU:
model = model.cuda()
j = 0
for item in tqdm(dataloader):
token = item[0]
at_mask = item[2]
... | No | 4 | 49 | Model_Evaluation | compute_test_metric | 11,400,829 | 1 | A | 0 | 5 | 6 | 0 | 0 | 20 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "NumPy", "name": "Array Creation Efficiency", "fix": "Specify dtype when creating arrays.", "benefits": "Improves memory efficiency and avoids unnecessary conversions.", "location": "Line 8"}, {"detector": "framework_specific", "framework": "Pandas", "name": "Column Sele... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Array Creation Efficiency", "Column Selection Checker", "Efficient data loading not detected", "Missing function docstring"] | 6 | B |
2,106 | a = predict(test_2, test_2_loader, bst_model) | No | 5 | 48 | Model_Evaluation | predict_on_test | 11,400,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "PyTorch", "name": "Deterministic Algorithm Usage Checker", "fix": "Enable deterministic algorithms using torch.use_deterministic_algorithms(True).", "benefits": "Ensures consistent results across different hardware and runs.", "location": ""}, {"detector": "hugging_face... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Efficient data loading not detected"] | 6 | B |
2,107 | bla = test_2[['ID']].merge(pd.DataFrame(torch.nn.functional.softmax(torch.tensor(a), dim = 1).numpy()), left_index=True, right_index=True).set_index('ID')
bla.columns = ['A', 'B', 'NEITHER']
bla.to_csv('sbmsn2.csv') | No | 4 | 25 | Data_Export | save_to_csv | 11,400,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Merge Parameter Checker", "fix": "Specify how, on, and validate parameters in merge operations.", "benefits": "Ensures accurate data merging and prevents data loss.", "location": "Line 7"}, {"detector": "framework_specific", "framework": "PyTorch", "na... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Efficient data loading not detected", "Merge Parameter Checker"] | 6 | B |
2,108 | data.columns | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,109 | data.info() | 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 |
2,110 | data['Type'].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 |
2,111 | import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(14,6))
plt.subplot(1,2,1)
sns.boxplot(data.revenue)
plt.subplot(1,2,2)
sns.distplot(data.revenue, bins=20, kde=False)
plt.show() | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,112 | #City distribution
data["City"].value_counts().plot(kind='bar')' | 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 |
2,113 | data["Type"].value_counts().plot(kind='bar')' | 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 |
2,114 | data["City Group"].value_counts().plot(kind='bar')' | 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 |
2,115 | # Crrelation between revenue and feature (p)s
def numFeaturePlot():
features=(data.loc[:,'P1':'P37']).columns.tolist()
plt.figure(figsize=(35,18))
j=1
while j<len(features):
col=features[j-1]
plt.subplot(6,6,j)
sorted_grp = data.groupby(col)["revenue"].sum().sort_values(ascending... | No | 5 | 81 | Visualization | relationship | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,116 | df_train=df_train.drop(df_train[(df_train.Open == 0) & (df_train.Sales == 0)].index) | 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 |
2,117 | print ("Our new training set has now {} rows ".format(df_train.shape[0])) | No | 3 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,118 | df_train.Sales.describe()
#we see here a minimum of 0 which means some stores even opened got 0 sales on some days. | 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 |
2,119 | df_train=df_train.drop(df_train[(df_train.Open == 1) & (df_train.Sales == 0)].index)
df_train = df_train.reset_index(drop=True) | No | 3 | 14 | Data_Transform | filter | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,120 |
fig, axes = plt.subplots(1, 2, figsize=(17,3.5))
axes[0].boxplot(df_train.Sales, showmeans=True,vert=False)
axes[0].set_xlim(0,max(df_train["Sales"]+1000))
axes[0].set_title('Boxplot For Sales Values')
axes[1].hist(df_train.Sales, cumulative=False, bins=20)
axes[1].set_title("Sales histogram")
axes[1].set_xlim((min(df... | 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 |
2,121 | tst_model = torch.load('/kaggle/input/gendered-model/model1.pth')['model'] | No | 5 | 30 | Model_Train | load_pretrained | 11,400,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "PyTorch", "name": "Deterministic Algorithm Usage Checker", "fix": "Enable deterministic algorithms using torch.use_deterministic_algorithms(True).", "benefits": "Ensures consistent results across different hardware and runs.", "location": ""}, {"detector": "hugging_face... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Efficient data loading not detected", "Hardcoded file path"] | 6 | B |
2,122 | avg = df_train.groupby('Month')["Customers"].mean()
avg_sales = df_train.groupby('Month')['Sales'].mean()
total_sales_customers = df_train.groupby('Store')['Sales', 'Customers'].sum()
total_sales_customers.head()' | No | 4 | 60 | Data_Transform | groupby | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,123 | df_total_sales_customers = pd.DataFrame({'Sales': total_sales_customers['Sales'],
'Customers': total_sales_customers['Customers']},
index = total_sales_customers.index)
df_total_sales_customers = df_total_sales_customers.reset_index()
... | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,124 | avg_sales_customers = df_train.groupby('Store')['Sales', 'Customers'].mean()
avg_sales_customers.head() | No | 4 | 60 | Data_Transform | groupby | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,125 | df_avg_sales_customers = pd.DataFrame({'Sales': avg_sales_customers['Sales'],
'Customers': avg_sales_customers['Customers']},
index = avg_sales_customers.index)
df_avg_sales_customers = df_avg_sales_customers.reset_index()
df_stor... | No | 4 | 12 | Data_Transform | create_dataframe | 244,890 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 4 | A |
2,126 | df_stores_new = df_total_sales_customers.join(df_store.set_index('Store'), on='Store')
df_stores_new.head() | No | 4 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,127 | b"# b\n# , , b , \n# \n\naverage_storetype = df_stores_new.groupby('StoreType')['Sales', 'Customers', 'CompetitionDistance'].mean()\n\nfig, (axis1,axis2,axis3) = plt.subplots(1,3,figsize=(15,4))\nsns.barplot(average_storetype.index, average_storetype['Sales'], ax=axis1)\nsns.barplot(average_sto... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,128 | b"# , , \ncomp_sales_cust = pd.DataFrame()\n\ncomp_sales_cust['Customers'] = average_storetype['Customers']\ncomp_sales_cust['Sales'] = average_storetype['Sales']\ncomp_sales_cust['Comp'] = average_storetype['CompetitionDistance']\ncorr_matrix = comp_sales_cust.corr()\n\nplt.subplots(figsize=(13, 9))\ns... | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,129 | b"# \navg_assort = df_stores_new.groupby('Assortment')['Sales', 'Customers'].mean()\n\nfig, (axis1,axis2) = plt.subplots(1, 2, figsize=(15, 4))\nsns.barplot(avg_assort.index, avg_assort['Sales'], ax=axis1)\nsns.barplot(avg_assort.index, avg_assort['Customers'], ax=axis2)" | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,130 | df_train = pd.read_csv("../input/train.csv")
df_store = pd.read_csv("../input/store.csv")
df_test = pd.read_csv("../input/test.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 244,890 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 2 | [{"detector": "general_ml", "framework": "General ML", "name": "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "fix": "Not specified", "benefits": "Not specified", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness."] | 4 | A |
2,131 | closed_store_ids = df_test["Id"][df_test["Open"] == 0].values
df_train['Year'] = df_train['Date'].apply(lambda x: int(x[:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
df_train["HolidayBin"] = df_train.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})
del df_train['Date']
del df_tr... | No | 3 | 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 |
2,132 | df_test['Year'] = df_test['Date'].apply(lambda x: int(x[:4]))
df_test['Month'] = df_test['Date'].apply(lambda x: int(x[5:7]))
df_test["HolidayBin"] = df_test.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})
del df_test['Date']
del df_test['StateHoliday']
df_test.head()' | No | 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 |
2,133 | df_test = df_test[df_test["Open"] != 0]
df_test[df_test['Store'] == 1].head()' | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,134 | b"# numpy\na = list()\nfor i in df_test['Store']:\n a.append(float(df_store['CompetitionDistance'][df_store['Store'] == i]))\n\ndf_test['CompetitionDistance'] = a\ndf_test.head()" | 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 |
2,135 | a = list()
for i in df_train['Store']:
a.append(float(df_store['CompetitionDistance'][df_store['Store'] == i]))
df_train['CompetitionDistance'] = a
df_train['CompetitionDistance'] = df_train['CompetitionDistance'].fillna(df_train['CompetitionDistance'].mean())
df_train.head() | No | 4 | 8 | Data_Transform | feature_engineering | 244,890 | 1 | A | 1 | 1 | 1 | 0 | 5 | 6 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | [] | 4 | B |
2,136 | b"# , \ndf_train['CompetitionDistance'] = np.log(df_train['CompetitionDistance'])\ndf_test ['CompetitionDistance'] = np.log(df_test ['CompetitionDistance'])" | 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 |
2,137 | gs_ts = GridSearchCV(LGBMRegressor(), params, scoring=rmspe_scorer, cv=2, n_jobs=1)
gs_ts.fit(x_train_ts, y_train_ts) | No | 5 | 6 | Hyperparam_Tuning | train_on_grid | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,138 | pd.DataFrame({"Id": test.Id, "Sales": prediction_ts}).to_csv("submission_ts.csv", sep=",", index=False) | No | 5 | 25 | Data_Export | save_to_csv | 874,590 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,139 | # This method helps in understanding the correlation between the different features and the Revenue.
def featureCatPlot(col):
plt.figure(figsize=(15,6))
i=1
if not data[col].dtype.name=='int64' and not data[col].dtype.name=='float64':
plt.subplot(1,2,i)
sns.boxplot(x=col,y='revenue',dat... | Yes | 5 | 33 | Visualization | distribution | null | 1 | A | 3 | 3 | 2 | 0 | 14 | 19 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,140 | featureCatPlot('City Group')
| 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 |
2,141 | # Splitting 01/31/2018 as 01, 31, 2018
train_date=data['Open Date'].str.split('/', n = 2, expand = True)
data['month']=train_date[0]
data['days']=train_date[1]
data['year']=train_date[2]
test_date=test_data['Open Date'].str.split('/', n = 2, expand = True)
test_data['month']=test_date[0]
test_data['days']=test_date[1]... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,142 | featureCatPlot('month') | 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 |
2,143 | data.sort_values('revenue', ascending=False)[:20] | No | 5 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,144 | top_6= data.sort_values('revenue', ascending=False)[:20]
plt.figure(figsize=(13,12))
plt.title("The top 6 resturants")
sns.barplot(x=top_6['City'], y=top_6['revenue'])
' | No | 4 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,145 | best_month= data.sort_values('revenue', ascending=False)[:20]
| No | 5 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,146 | plt.figure(figsize=(13,12))
sns.barplot(x=best_month['month'], y=best_month['revenue'])
plt.xticks(rotation=60) | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,147 | # 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 in
import warnings
warnings.filterwarnings("ignore")
import numpy as np # linear algebra
i... | No | 4 | 23 | Environment | set_options | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 25"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
2,148 | sales = pd.read_csv("../input/train.csv")
sales.head() | No | 5 | 45 | Data_Extraction | load_from_csv | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 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 7"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
2,149 | sales.info() | 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 |
2,150 | stores = pd.read_csv("../input/store.csv")
stores.head() | No | 4 | 45 | Data_Extraction | load_from_csv | 2,095,107 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/store.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 7"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
2,151 | stores.info() | 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 |
2,152 | df_train.Customers.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 |
2,153 | #Number of stores in sales not in stores data
print(len(set(sales['Store']) - set(stores['Store']))) | No | 5 | 72 | EDA | count_values | 2,095,107 | 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"] | 6 | A |
2,154 | #Number of test stores not in train stores
print(len(set(test['Store']) - set(data['Store']))) | No | 5 | 72 | EDA | count_values | 2,095,107 | 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"] | 6 | A |
2,155 |
fig, axes = plt.subplots(1, 2, figsize=(17,3.5))
axes[0].boxplot(df_train.Customers, showmeans=True,vert=False)
axes[0].set_xlim(0,max(df_train["Customers"]+100))
axes[0].set_title('Boxplot For Customer Values')
axes[1].hist(df_train.Customers, cumulative=False, bins=20)
axes[1].set_title("Customers histogram")
axes[1... | 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 |
2,156 | #Number of train stores not in test stores
print(len(set(data['Store']) - set(test['Store']))) | No | 5 | 72 | EDA | count_values | 2,095,107 | 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"] | 6 | A |
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