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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,949 | total_epoch = 0
best_loss = 1e6
for i in range(3):
print("Epoch", i + 1)
total_epoch += 1
train_epoch(resolver, optimizer, train_dl)
loss, labels = evaluate(resolver, optimizer, valid_dl)
if loss < best_loss:
best_loss = loss
print(f"Loss improved, saving {total_epoch}")
torch.save(resolver.stat... | No | 5 | 7 | Model_Train | train_model | 3,065,122 | 1 | A | 0 | 2 | 3 | 0 | 0 | 11 | 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": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Magic number"] | 1 | B |
1,950 | for param in resolver.encoder.parameters():
param.requires_grad = True | No | 4 | 59 | Model_Train | init_hyperparams | 3,065,122 | 1 | A | 0 | 1 | 1 | 0 | 0 | 2 | 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"] | 1 | A |
1,951 | lr = 3e-4
optimizer = torch.optim.Adam(resolver.parameters(), lr=lr) | No | 5 | 4 | Model_Train | choose_model_class | 3,065,122 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"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": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Magic number"] | 1 | A |
1,952 | x = np.load('../input/prepare-for-submission/0.npy') | No | 5 | 44 | Data_Extraction | load_from_disk | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/prepare-for-submission/0.npy. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
1,953 | from keras.models import load_model
import tensorflow as tf
def logloss(y, y_):
return tf.losses.log_loss(y,y_)
model = load_model(mf, custom_objects={'logloss':logloss}) | No | 4 | 4 | Model_Train | choose_model_class | 3,663,832 | 1 | A | 0 | 1 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,954 | import gc | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,955 | y1 = model.predict(x) | No | 5 | 27 | Model_Train | predict_on_train | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Memory Release Checker"] | 3 | A |
1,956 | for i in range(6):
print("Epoch", i + 1)
total_epoch += 1
train_epoch(resolver, optimizer, train_dl)
loss, labels = evaluate(resolver, optimizer, valid_dl)
if loss < best_loss:
best_loss = loss
print(f"Loss improved, saving {total_epoch}")
torch.save(resolver.state_dict(), data_path/"model_best.pt... | No | 4 | 2 | Hyperparam_Tuning | find_best_params | 3,065,122 | 1 | A | 0 | 2 | 3 | 0 | 0 | 9 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker"] | 1 | B |
1,957 | resolver.load_state_dict(torch.load(data_path/"model_best.pt")) | No | 5 | 44 | Data_Extraction | load_from_disk | 3,065,122 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"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"] | 1 | A |
1,958 | loss, res = evaluate(resolver, optimizer, test_dl, True)
res_s = np.exp(res) # don't forget that we have log-softmax outputs:
submission = pd.DataFrame(res_s, index=train["ID"], columns=["A", "B", "NEITHER"])
submission.to_csv("submission.csv", index="id")' | Yes | 3 | 27 | Model_Train | predict_on_train | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,959 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_sel... | No | 5 | 23 | Environment | set_options | 244,905 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
1,960 | del x
gc.collect() | No | 1 | 53 | Other | something_strange | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,961 | x = np.load('../input/prepare-for-submission-2/1.npy')
y2 = model.predict(x)
del x
gc.collect() | No | 2 | 27 | Model_Train | predict_on_train | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "Memory Release Checker"] | 3 | A |
1,962 | x = np.load('../input/prepare-for-submission-3/2.npy')
y3 = model.predict(x)
del x
gc.collect() | No | 3 | 27 | Model_Train | predict_on_train | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Memory Release Checker", "fix": "Clear session after model creation/training using tf.keras.backend.clear_session().", "benefits": "Prevents memory leaks and reduces resource usage.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Hardcoded file path", "Memory Release Checker"] | 3 | A |
1,963 | y = np.concatenate([y1, y2, y3]) | No | 5 | 11 | Data_Transform | concatenate | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,964 | b"df = pd.DataFrame()\ndf['ID'] = pd.read_csv('../input/gendered-pronoun-resolution/test_stage_2.tsv', delimiter='\\t')['ID']" | No | 5 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,965 | df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_store = pd.read_csv('../input/store.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,966 | df_s = pd.read_csv('../input/gendered-pronoun-resolution/sample_submission_stage_2.csv') | No | 5 | 45 | Data_Extraction | load_from_csv | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/gendered-pronoun-resolution/sample_submission_stage_2.csv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 3 | A |
1,967 | df['A'] = y[:,0] #, 'B', 'NEITHER' | No | 5 | 8 | Data_Transform | feature_engineering | 3,663,832 | 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"] | 3 | A |
1,968 | df['B'] = y[:,1]
df['NEITHER'] = y[:,2] | No | 5 | 8 | Data_Transform | feature_engineering | 3,663,832 | 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"] | 3 | A |
1,969 | y = df_train["Sales"].values | No | 5 | 21 | Data_Transform | prepare_x_and_y | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,970 | df.to_csv('submission.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,971 | df_train['Year'] = df_train['Date'].apply(lambda x: int(x[0:4]))
df_train['Month'] = df_train['Date'].apply(lambda x: int(x[5:7]))
df_train['Day'] = df_train['Date'].apply(lambda x: int(x[8:10]))
df_test['Year'] = df_test['Date'].apply(lambda x: int(x[0:4]))
df_test['Month'] = df_test['Date'].apply(lambda x: int(x[5:7]... | No | 5 | 8 | Data_Transform | feature_engineering | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,972 | b"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n%matplotlib inline\n\nplt.style.use('ggplot')\nplt.rcParams['figure.figsize'] = (12,8)\n\n# \nfont = {'family': 'Verdana',\n 'weight': 'normal'}\nplt.rc('font', **font)" | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,973 | data_train = pd.read_csv("../input/train.csv")
data_test = pd.read_csv("../input/test.csv")
data_store = pd.read_csv("../input/store.csv") | No | 5 | 45 | Data_Extraction | load_from_csv | 244,962 | 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."] | 3 | A |
1,974 | data_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 |
1,975 | df_store.CompetitionDistance.fillna(value=0, inplace=True)
df_test.Open.fillna(value=0, inplace=True)
df_train.StateHoliday[df_train["StateHoliday"] == 0] = "0" | Yes | 5 | 17 | Data_Transform | correct_missing_values | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,976 | print(df_train.shape)
print(df_test.shape)
print(df_store.shape) | No | 5 | 58 | Debug | show_shape | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,977 | fig, (axis1,axis2) = plt.subplots(1,2,figsize=(12,4))
sns.barplot(x='Year', y='Sales', data=df_train, ax=axis1)
sns.barplot(x='Year', y='Customers', data=df_train, ax=axis2) | No | 5 | 75 | Visualization | time_series | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,978 | data_train.shape | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,979 | df_train.query('Open == 1')[['Sales', 'Customers']].hist(bins=100, figsize=(13,7)); | No | 5 | 33 | Visualization | distribution | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,980 | data_test.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 |
1,981 | fig, (axis1,axis2) = plt.subplots(1,2,figsize=(12,4))
sns.barplot(x='Month', y='Sales', data=df_train, ax=axis1)
sns.barplot(x='Month', y='Customers', data=df_train, ax=axis2) | No | 5 | 75 | Visualization | time_series | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,982 | data_store.head(n=3) | No | 5 | 41 | Debug | show_table | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
1,983 | fig, (axis1,axis2) = plt.subplots(1,2,figsize=(12,4))
sns.barplot(x='DayOfWeek', y='Sales', data=df_train, ax=axis1)
sns.barplot(x='DayOfWeek', y='Customers', data=df_train, ax=axis2) | No | 5 | 75 | Visualization | time_series | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,984 | data_train.StateHoliday = data_train.StateHoliday.replace(0,'0')
data_test.StateHoliday = data_test.StateHoliday.replace(0,'0')
data_train.DayOfWeek.value_counts() | No | 4 | 8 | Data_Transform | feature_engineering | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"}] | [] | 3 | B |
1,986 | df_train[['Sales', 'Customers']].corr() | No | 3 | 41 | Debug | show_table | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,987 | df_DayOfWeek = pd.get_dummies(df_train.DayOfWeek, prefix='DayOfWeek')
df_StateHoliday = pd.get_dummies(df_train.StateHoliday, prefix="StateHoliday_")
df_train = pd.concat([df_train, df_DayOfWeek, df_StateHoliday], axis=1)
del df_train["Date"]
del df_train["Day"]
del df_train["Customers"]
del df_train["DayOfWeek"]
del d... | Yes | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,988 | df_StoreType = pd.get_dummies(df_store.StoreType, prefix='StoreType_')
df_Assortment = pd.get_dummies(df_store.Assortment, prefix='Assortment_')
df_store = pd.concat([df_store, df_StoreType, df_Assortment], axis=1)
del df_store["StoreType"]
del df_store["Assortment"]
del df_store["PromoInterval"]' | Yes | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,989 | df = pd.merge(df_train, df_store, how='left', on=['Store']) | No | 5 | 32 | Data_Transform | merge | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,990 | df.fillna(0, 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 |
1,991 | X = df.values[:,1:] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,992 | parametrs = range(40, 241, 40) | No | 5 | 5 | Hyperparam_Tuning | define_search_space | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,993 | b"scores, tst_scr = validation_curve(RandomForestRegressor(n_jobs = 4), X[:20000],\\\n y[:20000], 'n_estimators', parametrs, cv=5, scoring='r2', verbose=2)" | No | 5 | 2 | Hyperparam_Tuning | find_best_params | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,994 | scores_mean = scores.mean(axis=1)
scores_std = scores.std(axis=1)
tst_scr_mean = tst_scr.mean(axis=1)
tst_scr_std = tst_scr.std(axis=1)
plt.plot(parametrs, tst_scr_mean)
plt.fill_between(parametrs, tst_scr_mean + tst_scr_std, tst_scr_mean - tst_scr_std, alpha=0.3)
plt.plot(parametrs, scores_mean)
plt.fill_between(param... | No | 5 | 35 | Visualization | learning_history | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,995 | parametrs = range(3, 24) | No | 5 | 5 | Hyperparam_Tuning | define_search_space | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,996 | b"scores, tst_scr = validation_curve(RandomForestRegressor(n_estimators=120, n_jobs = 4), X[:20000], \\\n y[:20000], 'max_features', parametrs, cv=3, scoring='r2', verbose=2)" | No | 5 | 1 | Hyperparam_Tuning | find_best_score | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,997 | data_train['Year'] = data_train['Date'].apply(lambda x: int(x[:4]))
data_train['Month'] = data_train['Date'].apply(lambda x: int(x[5:7]))
data_train.head() | No | 4 | 8 | Data_Transform | feature_engineering | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"}] | [] | 3 | B |
1,998 | parametrs = range(4, 61, 4) | No | 5 | 5 | Hyperparam_Tuning | define_search_space | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,999 | average_sales_per_month = data_train.groupby('Month')["Sales"].mean()
plt.figure(figsize=(8, 5))
average_sales_per_month.plot(legend=True, marker='o', title="Average sales per month")' | 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 |
2,000 | b"scores, tst_scr = validation_curve(RandomForestRegressor(n_estimators=120, n_jobs = 4, max_features=16), X[:20000], \\\n y[:20000], 'max_depth', parametrs, cv=3, scoring='r2', verbose=2)" | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,001 | average_sales_per_day = data_train.groupby('Date')["Sales"].mean()
fig = plt.subplots(1,1, sharex=True, figsize=(18, 5))
average_sales_per_day.plot(legend=True, title="Average Daily Sales")' | 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 |
2,002 | model = RandomForestRegressor(n_estimators=120, max_depth=20, max_features=16, n_jobs=4, verbose=2) | No | 5 | 4 | Model_Train | choose_model_class | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,003 | model.fit(X, y) | No | 5 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,004 | idx = model.feature_importances_.argsort()[::-1] | No | 5 | 79 | Visualization | model_coefficients | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,005 | ax = sns.barplot(x=model.feature_importances_[idx], y=df.drop('Store', axis=1).columns[idx]) | No | 5 | 79 | Visualization | model_coefficients | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,006 | df_DayOfWeek = pd.get_dummies(df_test.DayOfWeek, prefix='DayOfWeek')
df_StateHoliday = pd.get_dummies(df_test.StateHoliday, prefix="StateHoliday_")
df_StateHoliday = pd.concat([df_StateHoliday, pd.DataFrame(columns=['StateHoliday__b', 'StateHoliday__c'])], axis = 1)' | Yes | 3 | 20 | Data_Transform | categorify | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,007 | columns_corr = ['Sales', 'Customers', 'Promo', 'StateHoliday', 'SchoolHoliday']
data_train[columns_corr].corr(method='pearson') | No | 5 | 40 | Debug | show_table_attributes | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 3 | B |
2,008 | df_StateHoliday.fillna(0, inplace=True) | No | 5 | 17 | Data_Transform | correct_missing_values | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,011 | data_train['StateHoliday'] = data_train['StateHoliday'].replace(0, '0')
data_train["HolidayBin"] = data_train['StateHoliday'].map({"0": 0, "a": 1, "b": 1, "c": 1})
data_train.StateHoliday.unique()' | 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 |
2,013 | del df_test["Date"]
del df_test["Day"]
del df_test["DayOfWeek"]
del df_test["StateHoliday"]
df_test = pd.concat([df_test, df_DayOfWeek, df_StateHoliday], axis=1)
test_df = pd.merge(df_test, df_store, how='left', on=['Store'])
test_df.fillna(0, inplace=True)' | Yes | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,014 | y_test_pred = model.predict(test_df.values[:,2:]) | No | 5 | 48 | Model_Evaluation | predict_on_test | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,015 | average_customers_per_month = data_train.groupby('Month')['Customers'].mean()
average_sales_per_month = data_train.groupby('Month')['Sales'].mean() | No | 4 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"}] | [] | 3 | B |
2,016 | submission = pd.DataFrame({ "Id": test_df.Id, "Sales": y_test_pred.reshape(-1.1)}) | No | 5 | 12 | Data_Transform | create_dataframe | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,017 | plt.figure(figsize=(6, 4))
plt.plot(average_sales_per_month) | No | 5 | 33 | Visualization | distribution | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,018 | plt.figure(figsize=(6, 4))
plt.plot(average_customers_per_month) | No | 5 | 33 | Visualization | distribution | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,019 | submission.to_csv("rossman.csv",index=False) | No | 5 | 25 | Data_Export | save_to_csv | 244,905 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,020 | import pandas as pd
import sklearn
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
import seaborn as sns
%matplotlib inline | No | 5 | 23 | Environment | set_options | 245,675 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
2,021 | total_customers_for_store = data_train.groupby('Store')['Sales', 'Customers'].sum() | No | 5 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"}] | [] | 3 | B |
2,022 | data_total_customers_for_store = pd.DataFrame({'Sales': total_customers_for_store['Sales'],
'Customers': total_customers_for_store['Customers']},
index = total_customers_for_store.index) | No | 5 | 12 | Data_Transform | create_dataframe | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,023 | storedf = pd.read_csv("../input/store.csv")
storedf = storedf [["Store", "Assortment", "CompetitionDistance","Promo2"]]
storedf = storedf.set_index("Store")
storedf.CompetitionDistance = storedf.CompetitionDistance.fillna(storedf.CompetitionDistance.max())
storedf.head() | Yes | 3 | 45 | Data_Extraction | load_from_csv | 245,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,024 | data_total_customers_for_store = data_total_customers_for_store.reset_index() | No | 5 | 61 | Data_Transform | rename_columns | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,025 | average_sales_customers = data_train.groupby('Store')['Sales', 'Customers'].mean() | No | 5 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"}] | [] | 3 | B |
2,026 | def f(traindf):
#traindf = traindf[traindf.Open==1]
traindf = traindf.join(storedf, on="Store")
traindf['isWeekEnd'] = traindf.DayOfWeek>=5
traindf['Month'] = list (map (lambda x: int(x[5:7]), traindf.Date))
traindf['Day'] = list (map (lambda x: int(x[8:]), traindf.Date))
traindf['isWinter'] = ... | Yes | 3 | 14 | Data_Transform | filter | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,027 | data_average_sales_customers = pd.DataFrame({'Sales': average_sales_customers['Sales'],
'Customers': average_sales_customers['Customers']},
index = average_sales_customers.index)
data_average_sales_customers = data_average_sales_custom... | Yes | 4 | 12 | Data_Transform | create_dataframe | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,028 | data_average_sales_customers = pd.DataFrame({'Sales': average_sales_customers['Sales'],
'Customers': average_sales_customers['Customers']},
index = average_sales_customers.index) | No | 5 | 12 | Data_Transform | create_dataframe | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,029 | traindf = pd.read_csv("../input/train.csv", low_memory=False)
traindf = f(traindf)
traindf = traindf[traindf.Open == 1]
ytrain = traindf.Sales.values
del traindf ["Sales"]
del traindf ["Customers"]
del traindf ["Open"]
traindf.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 245,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,030 | data_average_sales_customers = data_average_sales_customers.reset_index() | No | 5 | 84 | Other | not_enough_vertices | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,031 | testdf = pd.read_csv("../input/test.csv")
testdf = f(testdf)
testdf.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 245,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,032 | data_stores_average = data_average_sales_customers.join(data_store.set_index('Store'), on='Store') | No | 5 | 32 | Data_Transform | merge | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,033 | model = RandomForestRegressor(min_samples_leaf=2, max_depth=30, n_estimators=30)
%time model.fit(traindf.values, ytrain) | No | 5 | 7 | Model_Train | train_model | 245,675 | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,034 | data_stores_average.head(n=3) | No | 5 | 41 | Debug | show_table | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,035 | data_stores_new = data_total_customers_for_store.join(data_store.set_index('Store'), on='Store') | No | 5 | 32 | Data_Transform | merge | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,036 | average_store_type = data_stores_new.groupby('StoreType')['Sales', 'Customers', 'CompetitionDistance'].mean() | No | 5 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,038 | y = model.predict(testdf.values[:, 2:])
df = pd.DataFrame([])
df['Sales'] = y
df['Sales'][testdf.Open == 0] = 0
df = df.set_index(testdf.Id)
pd.DataFrame.to_csv(df, 'ans.csv')
df.head() | Yes | 4 | 27 | Model_Train | predict_on_train | 245,675 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
2,039 | Data_cmp = pd.DataFrame()
Data_cmp['Customers'] = average_store_type['Sales']
Data_cmp['Sales'] = average_store_type['Customers']
Data_cmp['Comp'] = average_store_type['CompetitionDistance'] | No | 4 | 12 | Data_Transform | create_dataframe | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,040 | columns_corr = ['Sales', 'Customers', 'Comp']
Data_cmp[columns_corr].corr(method='pearson') | No | 5 | 40 | Debug | show_table_attributes | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,041 | average_assort = data_stores_new.groupby('Assortment')['Sales', 'Customers'].mean() | No | 5 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,043 | closed_store_data = data_test["Id"][data_test["Open"] == 0].values
data_train.StateHoliday = data_train.StateHoliday.replace(0,'0')
data_test.StateHoliday = data_test.StateHoliday.replace(0,'0')' | 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 |
2,044 | data_train['Year'] = data_train['Date'].apply(lambda x: int(x[:4]))
data_train['Month'] = data_train['Date'].apply(lambda x: int(x[5:7]))
data_train["HolidayBin"] = data_train.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})' | 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 |
2,045 | del data_train['Date']
del data_train['StateHoliday'] | No | 5 | 10 | Data_Transform | drop_column | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,046 | data_test['Year'] = data_test['Date'].apply(lambda x: int(x[:4]))
data_test['Month'] = data_test['Date'].apply(lambda x: int(x[5:7]))
data_test["HolidayBin"] = data_test.StateHoliday.map({"0": 0, "a": 1, "b": 1, "c": 1})' | 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 |
2,047 | del data_test['Date']
del data_test['StateHoliday'] | No | 5 | 10 | Data_Transform | drop_column | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,048 | data_test = data_test[data_test["Open"] != 0] | No | 5 | 14 | Data_Transform | filter | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,049 | data_test[data_test['Store'] == 1].head() | No | 5 | 41 | Debug | show_table | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,050 | arr_tmp = []
for i in data_test['Store']:
arr_tmp.append(float(data_store['CompetitionDistance'][data_store['Store'] == i]))
data_test['CompetitionDistance'] = arr_tmp | No | 3 | 8 | Data_Transform | feature_engineering | 244,962 | 1 | A | 0 | 1 | 1 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,051 | arr_tmp = []
for i in data_train['Store']:
arr_tmp.append(float(data_store['CompetitionDistance'][data_store['Store'] == i]))
data_train['CompetitionDistance'] = arr_tmp
data_train['CompetitionDistance'] = data_train['CompetitionDistance'].fillna(data_train['CompetitionDistance'].mean()) | No | 3 | 8 | Data_Transform | feature_engineering | 244,962 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | 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"}] | [] | 3 | B |
2,052 | train_stores = dict(list(data_train.groupby('Store')))
test_stores = dict(list(data_test.groupby('Store'))) | No | 4 | 60 | Data_Transform | groupby | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}] | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | [] | 3 | B |
2,053 | from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV | No | 5 | 22 | Environment | import_modules | 244,962 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
2,055 | #
result = pd.Series()
for i in test_stores:
store = train_stores[i]
X_train = store.drop(["Sales", "Store", "Customers"],axis=1)
Y_train = store["Sales"]
X_test = test_stores[i].copy()
store_ind = X_test["Id"]
X_test.drop(["Id","Store"], axis=1,inplace=True)
X_train = X_train.fil... | Yes | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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