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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1,648 | import tensorflow as tf | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
1,649 | train_data = pd.read_csv("gap-development.tsv", sep = "\\t")
validation_data = pd.read_csv("gap-validation.tsv", sep = "\\t")
test_data = pd.read_csv("gap-test.tsv", sep = "\\t")' | 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 |
1,650 |
merge_data = pd.concat([train_data,validation_data]).reset_index(drop = True)
merge_data = pd.concat([merge_data,train_data]).reset_index(drop = True)
count = 0 | No | 5 | 11 | Data_Transform | concatenate | 3,544,896 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 4 | 4 | [{"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 10"}, {"detector": "framework_specific", "framework": "ScikitLearn"... | 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"}] | ["Merge Parameter Checker"] | 7 | B |
1,651 |
def name_replace(s, r1, r2):
s = str(s).replace(r1,r2)
for r3 in r1.split(' '):
s = str(s).replace(r3,r2)
return s
def shortest_dependency_path(doc, e1=None, e2=None):
edges = []
for token in doc:
for child in token.children:
edges.append(('{0}'.format(token),
... | Yes | 2 | 53 | Other | something_strange | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,652 | feature = get_features(merge_data)
| No | 5 | 8 | Data_Transform | feature_engineering | 3,544,896 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
1,653 | feature | No | 5 | 53 | Other | something_strange | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,654 |
Y = merge_data[["A-coref", "B-coref"]]
Y.columns = ["A","B"]
Y["A"] = Y["A"].astype(int)
Y["B"] = Y["B"].astype(int)
Y["NEITHER"] = 1- (Y["A"] + Y["B"]) | No | 4 | 16 | Data_Transform | data_type_conversions | 3,544,896 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
1,655 | from sklearn import *
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
import xgboost as xgb
from xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler
x1, x2, y1, y2 = model_selection.train_test_split(feature.fillna(-1), Y, test_size=0.2, rando... | No | 4 | 22 | Environment | import_modules | 3,544,896 | 1 | A | 1 | 0 | 0 | 1 | 5 | 10 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "TensorFlow", "name": "Model Evaluation Checker", "fix": "Evaluate model performance using model.evaluate().", "benefits": "Provides standardized model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Model Evaluation Checker"] | 7 | B |
1,656 | scaler = StandardScaler()
x1 = scaler.fit_transform(x1)
x2 = scaler.transform(x2)
model = multiclass.OneVsRestClassifier(ensemble.RandomForestClassifier(max_depth = 7, n_estimators=1000, random_state=33))
# model = multiclass.OneVsRestClassifier(ensemble.ExtraTreesClassifier(n_jobs=-1, n_estimators=100, random_state=33... | No | 3 | 18 | Data_Transform | normalization | 3,544,896 | 2 | B | 2 | 0 | 0 | 0 | 10 | 6 | ok | 16 | 11 | [{"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": "framework_specific", "framework":... | D | Cross Validation Checker=low, Cross-validation not detected=low, Data Leakage Checker=high, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Logging Checker", "Memory Release Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | D |
1,657 | final_test = pd.read_csv("../input/gendered-pronoun-resolution/test_stage_2.tsv", sep = "\\t")
feature = get_features(final_test)
print(feature)
' | No | 3 | 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 |
1,658 |
feature = feature.fillna(-1)
# feature = scaler.transform(feature)
print(feature)
| No | 4 | 17 | Data_Transform | correct_missing_values | 3,544,896 | 2 | B | 1 | 0 | 0 | 0 | 5 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | B |
1,659 |
Y = pd.DataFrame(model.predict_proba(feature).tolist(), columns=["A","B", "NEITHER"])
r = final_test[["ID"]]
submission = pd.concat([r,Y], axis = 1) | No | 3 | 12 | Data_Transform | create_dataframe | 3,544,896 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 7 | 6 | [{"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": "framework_specific", "framework":... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Memory Release Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 7 | B |
1,660 | print(submission)
submission.to_csv('submission.csv', index=False) | No | 4 | 25 | Data_Export | save_to_csv | 3,544,896 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 7 | A |
1,661 | !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 |
1,662 | import time
import os
import random
import numpy as np
import pandas as pd
import torch
from torch.optim import Adam
from torch.utils.data import Dataset
from torch.nn import Module, Linear, Dropout
import torch.nn.functional as F
from pytorch_pretrained_bert.modeling import BertModel, BertLayer
from pytorch_pretrain... | No | 5 | 22 | Environment | import_modules | 3,577,796 | 1 | A | 0 | 0 | 0 | 0 | 0 | 18 | ok | 1 | 1 | [{"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": ""}] | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | [] | 10 | B |
1,663 | # Model
bert_model = "bert-large-cased"
n_bertlayers = 22
dropout = 0.1
# Preprocessing
do_lower_case = False
# Training
train_batch_size = 4
gradient_accumulation_steps = 5
lr = 1e-5
num_train_epochs = 2
warmup_proportion = 0.1
optim = "bertadam"
weight_decay = False
# Others
n_models = 10
eval_batch_size = 32
de... | No | 4 | 59 | Model_Train | init_hyperparams | 3,577,796 | 1 | A | 0 | 0 | 0 | 0 | 0 | 15 | ok | 9 | 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": "general_ml",... | B | Deterministic Algorithm Usage Checker=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}] | ["Magic number"] | 10 | B |
1,664 | mf = '../input/fork-of-fork-of-densenet201/model.hdf5'
import os
os.system('ls '+mf) | No | 5 | 88 | Debug | list_files | 3,663,832 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/fork-of-fork-of-densenet201/model.hdf5. 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 |
1,665 | plt.figure(figsize=(16,8))
for i,country in enumerate(list_countries):
Fatal_diff=Fatal_pivot[(Fatal_pivot[country]>0)][country].diff().fillna(0)
Fatal_diff=Fatal_diff[Fatal_diff>0]
Fatal_diff.plot(color=colors[i],label=country,lw=5)
plt.title('Number of daily new Fatalities',fontsize=15)
plt.legend... | No | 5 | 33 | Visualization | distribution | 8,604,602 | 1 | A | 0 | 1 | 1 | 0 | 0 | 8 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 5 | A |
1,666 | # all prediction is correct
y_pred = [
[0., 1.],
[1., 0.],
[1., 0.],
[0., 1.]
]
log_loss(y_true, y_pred) | No | 5 | 84 | Other | not_enough_vertices | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,667 | # all prediction is wrong
y_pred = [
[1., 0.],
[0., 1.],
[0., 1.],
[1., 0.]
]
log_loss(y_true, y_pred) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,668 | samples = df.sample(n=10, random_state=100)
for _, s in samples.iterrows():
display_entry(s) | No | 4 | 41 | Debug | show_table | 3,127,294 | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,669 | import spacy
nlp = spacy.load('en_core_web_lg')
displacy.render(nlp(samples.iloc[-2]['Text']), style='dep', jupyter=True, options={'distance': 150}) | No | 4 | 22 | Environment | import_modules | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,670 | # following: https://www.kaggle.com/keyit92/end2end-coref-resolution-by-attention-rnn/data
train_df = read_df("../input/googlegapcoreference/gap-test.tsv")
test_df = read_df("../input/googlegapcoreference/gap-development.tsv")
dev_df = read_df("../input/googlegapcoreference/gap-validation.tsv") | No | 5 | 45 | Data_Extraction | load_from_csv | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/googlegapcoreference/gap-test.tsv. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 8"}, {"detector": "general_ml", "framework": "Ge... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 5 | A |
1,671 | print(f"Train: {train_df.shape}\
Test: {test_df.shape}\
Development: {dev_df.shape}")' | No | 5 | 58 | Debug | show_shape | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,672 | sample = train_df.sample(random_state=555)
display_entry(sample.iloc[0]) | No | 5 | 41 | Debug | show_table | 3,127,294 | 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"}] | [] | 5 | B |
1,673 | # just testing if there is any entry with more than one answer
train_df[train_df[['A-coref', 'B-coref']].sum(axis=1) > 1] | No | 4 | 41 | Debug | show_table | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"}] | [] | 5 | B |
1,674 | # adding a column with the answer
def get_answer(row):
if row['A-coref']:
return 'A'
if row['B-coref']:
return 'B'
return 'NEITHER'
train_df['answer'] = train_df.apply(get_answer, axis=1) | No | 5 | 8 | Data_Transform | feature_engineering | 3,127,294 | 1 | A | 0 | 3 | 2 | 0 | 0 | 7 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Missing function docstring"] | 5 | B |
1,675 | train_df['Text-length'].hist() | No | 5 | 33 | Visualization | distribution | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker"] | 5 | B |
1,676 | train_df.groupby(pd.qcut(train_df['Text-length'], q=[0, .25, .5, .75, 1.]))['answer'].value_counts().unstack() | No | 5 | 60 | Data_Transform | groupby | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker"] | 5 | B |
1,677 | train_df_A = train_df[train_df['answer'] == 'A']
train_df_B = train_df[train_df['answer'] == 'B']
train_df_NEITHER = train_df[train_df['answer'] == 'NEITHER']
X_A_A = train_df_A.rename(columns={
'A': 'RE',
'A-offset': 'RE-offset',
'A-offset-end': 'RE-offset-end'
})[['Text', 'Pronoun', 'RE', 'RE-offset', 'R... | Yes | 3 | 61 | Data_Transform | rename_columns | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 46 | ok | 4 | 4 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low, Cross-validation not detected=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker"] | 5 | B |
1,678 | X_df.shape | No | 5 | 58 | Debug | show_shape | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,679 | X_df.sample(random_state=1) | No | 5 | 41 | Debug | show_table | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,680 | from textacy import similarity
def add_features(df, re_col, inplace=False):
if inplace:
df_ = df
else:
df_ = df.copy()
df_['URL_last_part'] = df_['URL'].str.rsplit('/', n=1, expand=True)[1].apply(preprocess_so)
df_['URL_distance_jaro_winkler'] = df_.apply(lambda row: simi... | No | 4 | 8 | Data_Transform | feature_engineering | 3,127,294 | 1 | A | 0 | 2 | 2 | 0 | 0 | 13 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path d... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Hardcoded file path", "Missing function docstring"] | 5 | A |
1,681 | X_df.hist(column='URL_distance_jaro_winkler', by='y', figsize=(20, 5), bins=20, sharey=True) | No | 5 | 33 | Visualization | distribution | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,682 | X_df.hist(column='URL_distance_levenshtein', by='y', figsize=(20, 5), bins=20, sharey=True) | No | 5 | 33 | Visualization | distribution | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,683 | X_df.hist(column='URL_distance_token_sort_ratio', by='y', figsize=(20, 5), bins=20, sharey=True) | No | 5 | 33 | Visualization | distribution | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
1,684 | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
X = X_df[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length', 'referred-expression']]
y = X_df['y']
X_train, X_test, y_train, y_test = train_test_split(X, y, test... | Yes | 4 | 21 | Data_Transform | prepare_x_and_y | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | ok | 7 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Cross-validation not detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}] | ["Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 5 | B |
1,685 | import numpy as np
def transform_to_submit(y_pred_, referred_expression):
y_pred_comp = 1 - y_pred_
all_zero = np.zeros_like(y_pred_).reshape((-1, 1))
y_pred = np.hstack((
np.where(referred_expression == 'A', y_pred_, y_pred_comp).reshape((-1, 1)),
np.where(referred_ex... | No | 4 | 55 | Data_Export | prepare_output | 3,127,294 | 1 | A | 3 | 1 | 0 | 0 | 10 | 16 | ok | 7 | 4 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Array Creation Efficiency", "Exception Handling Checker", "Missing function docstring", "Unit Testing Checker"] | 5 | A |
1,686 | y_true | 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 |
1,687 | X_features = X.drop(columns='referred-expression')
X_referred_expression = X['referred-expression']
lr.fit(X_features, y) | Yes | 3 | 7 | Model_Train | train_model | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 5 | 5 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Valida... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 5 | B |
1,688 | df_A = add_features(df, 'A', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]
df_B = add_features(df, 'B', inplace=False)[['URL_distance_token_sort_ratio', 'URL_distance_levenshtein', 'URL_distance_jaro_winkler', 'Text-length']]
y_pred_A = lr.pre... | Yes | 3 | 27 | Model_Train | predict_on_train | 3,127,294 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | ok | 10 | 7 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Array Creation Efficiency", "Column Selection Checker", "Exception Handling Checker", "Magic number", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 5 | B |
1,689 | import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 2 | 3 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: /kaggle/input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 5"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 0 | A |
1,690 | os.environ["SEED"] = "420"
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
from transformers import BertModel, BertTokenizer, AdamW, get_linear_schedule_with_warmup, BertConfig
import re
from tqdm import tqdm | No | 5 | 23 | Environment | set_options | 11,400,829 | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | 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 |
1,691 | df_train = pd.read_csv("/kaggle/input/gapvalidation/gap-test.tsv", delimiter="\\t")
df_val = pd.read_csv("/kaggle/input/gapvalidation/gap-validation.tsv", delimiter="\\t")
df_test = pd.read_csv("/kaggle/input/gapvalidation/gap-development.tsv", delimiter="\\t")
test_2 = pd.read_csv("/kaggle/input/gendered-pronoun-resol... | No | 4 | 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 |
1,692 | tokenizer.add_tokens(['[A]', '[B]', '[P]'])
def insert_tag(row):
to_be_inserted = sorted([
(row["A-offset"], " [A] "),
(row["B-offset"], " [B] "),
(row["Pronoun-offset"], " [P] ")
], key=lambda x: x[0], reverse=True)
text = row["Text"]
for offset, tag in to_be_inserted:
t... | Yes | 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 |
1,693 | class modified_dataset(torch.utils.data.Dataset):
def __init__(self, df, tokenizer):
p_text = []
offsets = []
at_mask = []
self.y_lst = df[['A-coref', 'B-coref']].apply(lambda row: target(row), axis = 1)
for row in tqdm(range(len(df))):
tokens, offset = tokenize(i... | Yes | 3 | 44 | Data_Extraction | load_from_disk | 11,400,829 | 1 | A | 2 | 10 | 2 | 6 | 2 | 70 | 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": "framework_sp... | B | Deterministic Algorithm Usage Checker=low, Randomness Control Checker (PyTorch-Dataloader)=low | [{"type": "Deterministic Algorithm Usage Checker", "severity": "low"}, {"type": "Randomness Control Checker (PyTorch-Dataloader)", "severity": "low"}] | [] | 6 | B |
1,694 | subs = df_weo['Subject Descriptor'].unique()[:-1]
df_weo_agg = df_weo[['Country']][df_weo['Country'].duplicated()==False].reset_index(drop=True)
for sub in subs[:]:
df_tmp = df_weo[['Country', '2019']][df_weo['Subject Descriptor']==sub].reset_index(drop=True)
df_tmp = df_tmp[df_tmp['Country'].duplicated()==Fal... | No | 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 |
1,695 | # merge
df_traintest5 = pd.merge(df_traintest4, df_weo_agg, on='Country_Region', how='left')
print(df_traintest5.shape)
df_traintest5.head() | No | 3 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,696 | # add Life expectancy
# Life expectancy at birth obtained from http://hdr.undp.org/en/data
df_life = pd.read_csv("../input/smokingstats/Life expectancy at birth.csv")
tmp = df_life.iloc[:,1].values.tolist()
df_life = df_life[['Country', '2018']]
def func(x):
x_new = 0
try:
x_new = float(x.replace(",", "... | Yes | 2 | 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 |
1,697 | df_life = df_life[['Country', '2018']]
df_life.columns = ['Country_Region', 'LifeExpectancy'] | 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 |
1,698 | # merge
df_traintest6 = pd.merge(df_traintest5, df_life, on='Country_Region', how='left')
print(len(df_traintest6))
df_traintest6.head() | No | 4 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,699 | # add additional info from countryinfo dataset
df_country = pd.read_csv("../input/countryinfo/covid19countryinfo.csv")
df_country.head() | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,700 | df_country['Country_Region'] = df_country['country']
df_country = df_country[df_country['country'].duplicated()==False] | No | 3 | 19 | Data_Transform | remove_duplicates | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,701 | print(df_country[df_country['country'].duplicated()].shape) | No | 5 | 38 | EDA | count_duplicates | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,702 | df_country[df_country['country'].duplicated()] | No | 2 | 38 | EDA | count_duplicates | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,703 | df_traintest7 = pd.merge(df_traintest6,
df_country.drop(['tests', 'testpop', 'country'], axis=1),
on=['Country_Region',], how='left')
print(df_traintest7.shape)
df_traintest7.head() | No | 3 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,704 | def encode_label(df, col, freq_limit=0):
df[col][pd.isna(df[col])] = 'nan'
tmp = df[col].value_counts()
cols = tmp.index.values
freq = tmp.values
num_cols = (freq>=freq_limit).sum()
print("col: {}, num_cat: {}, num_reduced: {}".format(col, len(cols), num_cols))
col_new = '{}_le'.format(col)... | Yes | 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 |
1,705 | df_traintest8['cases/day'] = df_traintest8['cases/day'].astype(np.float)
df_traintest8['fatal/day'] = df_traintest8['fatal/day'].astype(np.float) | No | 5 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,706 | # covert object type to float
def func(x):
x_new = 0
try:
x_new = float(x.replace(",", ""))
except:
# print(x)
x_new = np.nan
return x_new
cols = [
'Gross_domestic_product__constant_prices',
'Gross_domestic_product__current_prices',
'Gross_domestic_product__deflator... | No | 3 | 16 | Data_Transform | data_type_conversions | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,707 | df_traintest8[df_traintest8['place_id']=='China/Hubei'].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 |
1,708 | for col in df_traintest8.columns:
print("'{}',".format(col))' | No | 5 | 71 | Debug | show_columns | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,709 | day_before_valid = 71 # 3-11 day before of validation
day_before_public = 78 # 3-18 last day of train
day_before_launch = 85 # 4-1 last day before launch | No | 2 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,710 | def calc_score(y_true, y_pred):
y_true[y_true<0] = 0
score = metrics.mean_squared_error(np.log(y_true.clip(0, 1e10)+1), np.log(y_pred[:]+1))**0.5
return score | No | 5 | 84 | Other | not_enough_vertices | null | 1 | A | 0 | 1 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,711 | # train model to predict fatalities/day
# params
SEED = 42
params = {'num_leaves': 8,
'min_data_in_leaf': 5, # 42,
'objective': 'regression',
'max_depth': 8,
'learning_rate': 0.02,
'boosting': 'gbdt',
'bagging_freq': 5, # 5
'bagging_fraction': 0.8,... | No | 5 | 59 | Model_Train | init_hyperparams | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 20 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,712 | # train model to predict fatalities/day
# features are selected manually based on valid score
col_target = 'fatal/day'
col_var = [
'Lat', 'Long',
'days_since_1cases',
# 'days_since_10cases',
# 'days_since_100cases',
# 'days_since_1fatal',
# 'days_since_10fatal', 'days_since_100fatal',
# '... | No | 4 | 13 | Data_Transform | split | 8,687,249 | 1 | A | 0 | 0 | 0 | 0 | 0 | 63 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "Pandas", "name": "DataFrame Conversion Checker", "fix": "Use .to_numpy() instead of .values.", "benefits": "Ensures future compatibility and avoids unexpected behaviour.", "location": "Line 75"}, {"detector": "framework_specific", "framework": "Pandas", "name": "DataFra... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "DataFrame Conversion Checker", "Hardcoded file path"] | 3 | B |
1,713 | y_true = df_valid['fatal/day'].values
y_pred = np.exp(model.predict(X_valid))-1
score = calc_score(y_true, y_pred)
print("{:.6f}".format(score))' | No | 4 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,714 | # display feature importance
tmp = pd.DataFrame()
tmp["feature"] = col_var
tmp["importance"] = model.feature_importance()
tmp = tmp.sort_values('importance', ascending=False)
tmp.head(30)' | No | 4 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,715 | # train with all data before public
df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_test = df_traintest8[pd.isna(df_traintest8['ForecastId'])==Fals... | No | 4 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,716 | # train model to predict fatalities/day
col_target2 = 'cases/day'
col_var2 = [
'Lat', 'Long',
'days_since_1cases',
# 'days_since_10cases',
# 'days_since_100cases',
# 'days_since_1fatal',
# 'days_since_10fatal', 'days_since_100fatal',
# 'cases/day_(1-1)',
# 'cases/day_(1-7)',
# '... | No | 4 | 13 | Data_Transform | split | 8,687,249 | 1 | A | 0 | 0 | 0 | 0 | 0 | 61 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "Pandas", "name": "DataFrame Conversion Checker", "fix": "Use .to_numpy() instead of .values.", "benefits": "Ensures future compatibility and avoids unexpected behaviour.", "location": "Line 72"}, {"detector": "framework_specific", "framework": "Pandas", "name": "DataFra... | B | Cross Validation Checker=low, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "DataFrame Conversion Checker", "Hardcoded file path"] | 3 | B |
1,717 | # display feature importance
tmp = pd.DataFrame()
tmp["feature"] = col_var2
tmp["importance"] = model2.feature_importance()
tmp = tmp.sort_values('importance', ascending=False)
tmp.head(30)' | No | 3 | 79 | Visualization | model_coefficients | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,718 | y_true = df_valid['cases/day'].values
y_pred = np.exp(model2.predict(X_valid))-1
score = calc_score(y_true, y_pred)
print("{:.6f}".format(score))
' | No | 4 | 28 | Model_Train | compute_train_metric | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,719 | df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
X_train = df_train[col_var2]
X_valid = df_valid[col_var2]
y_train = np.log(df_train[col_target2].value... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,720 | # train model to predict fatalities/day
df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (day_before_public<df_traintest8['day'])]
df_test = df_traintest8[pd.isna(df_traintest8['ForecastId'])==F... | No | 2 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 14 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,721 | # train with all data
df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId']))]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId']))]
X_train = df_train[col_var]
X_valid = df_valid[col_var]
y_train = np.log(df_train[col_target].values.clip(0, 1e10)+1)
y_valid = np.log(df_valid[col_target].values.clip(... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,722 | # train model to predict cases/day
df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (day_before_public<df_traintest8['day'])]
X_train = df_train[col_var2]
X_valid = df_valid[col_var2]
y_train = ... | No | 2 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,723 | # train with all data
df_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
df_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_public)]
X_train = df_train[col_var2]
X_valid = df_valid[col_var2]
y_train = np.log(df_tr... | No | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,724 | # remove overlap for public LB prediction
df_tmp = df_traintest8[
((df_traintest8['day']<=day_before_public) & (pd.isna(df_traintest8['ForecastId'])))
| ((day_before_public<df_traintest8['day']) & (pd.isna(df_traintest8['ForecastId'])==False))].reset_index(drop=True)
df_tmp = df_tmp.drop([
'cases/day_(1-1)... | No | 5 | 14 | Data_Transform | filter | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 16 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,725 | # remove overlap for private LB prediction
df_tmp = df_traintest8[
((df_traintest8['day']<=day_before_private) & (pd.isna(df_traintest8['ForecastId'])))
| ((day_before_private<df_traintest8['day']) & (pd.isna(df_traintest8['ForecastId'])==False))].reset_index(drop=True)
df_tmp = df_tmp.drop([
'cases/day_(1... | No | 4 | 14 | Data_Transform | filter | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 16 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,726 | # predict test data in public
# predict the cases and fatatilites one day at a time and use the predicts as next day's feature recursively.
df_preds = []
for i, place in enumerate(places[:]):
df_interest = copy.deepcopy(df_traintest9[df_traintest9['place_id']==place].reset_index(drop=True))
df_interest['cases/d... | Yes | 2 | 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 |
1,727 | # predict test data in public
df_preds_pri = []
for i, place in enumerate(places[:]):
df_interest = copy.deepcopy(df_traintest10[df_traintest10['place_id']==place].reset_index(drop=True))
df_interest['cases/day'][(pd.isna(df_interest['ForecastId']))==False] = -1
df_interest['fatal/day'][(pd.isna(df_interest... | Yes | 2 | 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 |
1,728 | places_sort = df_traintest10[['place_id', 'ConfirmedCases']][df_traintest10['day']==day_before_private]
places_sort = places_sort.sort_values('ConfirmedCases', ascending=False).reset_index(drop=True)['place_id'].values
print(len(places_sort))
places_sort[:5] | No | 3 | 9 | Data_Transform | sort_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,729 | print("Fatalities / Public")
plt.figure(figsize=(30,30))
for i in range(30):
plt.subplot(5,6,i+1)
idx = i * 10
df_interest = df_preds[df_preds['place_id']==places_sort[idx]].reset_index(drop=True)
tmp = df_interest['fatal/day'].values
tmp = np.cumsum(tmp)
sns.lineplot(x=df_interest['day'], y=tmp... | 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 |
1,730 | print("Confirmed Cases / Public")
plt.figure(figsize=(30,30))
for i in range(30):
plt.subplot(5,6,i+1)
idx = i * 10
df_interest = df_preds[df_preds['place_id']==places_sort[idx]].reset_index(drop=True)
tmp = df_interest['cases/day'].values
tmp = np.cumsum(tmp)
sns.lineplot(x=df_interest['day'], ... | 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 |
1,731 | print("Fatalities / Private")
plt.figure(figsize=(30,30))
for i in range(30):
plt.subplot(5,6,i+1)
idx = i * 10
df_interest = df_preds_pri[df_preds_pri['place_id']==places_sort[idx]].reset_index(drop=True)
tmp = df_interest['fatal/day'].values
tmp = np.cumsum(tmp)
sns.lineplot(x=df_interest['day... | 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 |
1,732 | print("ConfirmedCases / Private")
plt.figure(figsize=(30,30))
for i in range(30):
plt.subplot(5,6,i+1)
idx = i * 10
df_interest = df_preds_pri[df_preds_pri['place_id']==places_sort[idx]].reset_index(drop=True)
tmp = df_interest['cases/day'].values
tmp = np.cumsum(tmp)
sns.lineplot(x=df_interest[... | 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 |
1,733 | # merge 2 preds
df_preds[df_preds['day']>day_before_private] = df_preds_pri[df_preds['day']>day_before_private] | 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 |
1,734 | df_preds.to_csv("df_preds.csv", index=None) | 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 |
1,735 | # load sample submission
df_sub = pd.read_csv("../input/covid19-global-forecasting-week-2/submission.csv")
print(len(df_sub))
df_sub.head() | No | 3 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,736 | # merge prediction with sub
df_sub = pd.merge(df_sub, df_traintest3[['ForecastId', 'place_id', 'day']])
df_sub = pd.merge(df_sub, df_preds[['place_id', 'day', 'cases_pred', 'fatal_pred']], on=['place_id', 'day',], how='left')
df_sub.head(10) | No | 4 | 32 | Data_Transform | merge | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,737 | # save
df_sub['ConfirmedCases'] = df_sub['cases_pred']
df_sub['Fatalities'] = df_sub['fatal_pred']
df_sub = df_sub[['ForecastId', 'ConfirmedCases', 'Fatalities']]
df_sub.to_csv("submission.csv", index=None)
df_sub.head(10)' | No | 3 | 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 |
1,738 | %matplotlib inline | Yes | 3 | 22 | Environment | import_modules | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,739 | # Imports
# pandas
import pandas as pd
from pandas import Series,DataFrame
# numpy, matplotlib, seaborn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline
# machine learning
from sklearn.linear_model import LinearRegression
from sklearn.linear_model... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
1,740 | # get rossmann, store, & test csv files as a DataFrame
rossmann_df = pd.read_csv("../input/train.csv")
store_df = pd.read_csv("../input/store.csv")
test_df = pd.read_csv("../input/test.csv")
# preview the data
rossmann_df.head() | No | 4 | 45 | Data_Extraction | load_from_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,741 | rossmann_df.info()
print("----------------------------")
store_df.info()
print("----------------------------")
test_df.info() | No | 5 | 40 | Debug | show_table_attributes | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,742 | # Open
fig, (axis1) = plt.subplots(1,1,figsize=(15,4))
sns.countplot(x='Open',hue='DayOfWeek', data=rossmann_df,palette="husl", ax=axis1)
# fill NaN values in test_df with Open=1 if DayOfWeek != 7
test_df["Open"][test_df["Open"] != test_df["Open"]] = (test_df["DayOfWeek"] != 7).astype(int)
# Drop Open column
# rossma... | Yes | 2 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 1 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,743 | # Date
# Create Year and Month columns
rossmann_df['Year'] = rossmann_df['Date'].apply(lambda x: int(str(x)[:4]))
rossmann_df['Month'] = rossmann_df['Date'].apply(lambda x: int(str(x)[5:7]))
test_df['Year'] = test_df['Date'].apply(lambda x: int(str(x)[:4]))
test_df['Month'] = test_df['Date'].apply(lambda x: int(str... | Yes | 2 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 13 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
1,744 | # .... contiune with Date
# Plot average sales & customers for every year
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='Year', y='Sales', data=rossmann_df, ax=axis1)
sns.barplot(x='Year', y='Customers', data=rossmann_df, ax=axis2)
# Drop Date column
# rossmann_df.drop(['Date'], axis=1,inplace=... | 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 |
1,745 | # Customers
fig, (axis1,axis2) = plt.subplots(2,1,figsize=(15,8))
# Plot max, min values, & 2nd, 3rd quartile
sns.boxplot([rossmann_df["Customers"]], whis=np.inf, ax=axis1)
# group by date and get average customers, and precent change
average_customers = rossmann_df.groupby('Date')["Customers"].mean()
# pct_cha... | 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 |
1,746 | # DayOfWeek
# In both cases where the store is closed and opened
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='DayOfWeek', y='Sales', data=rossmann_df, order=[1,2,3,4,5,6,7], ax=axis1)
sns.barplot(x='DayOfWeek', y='Customers', data=rossmann_df, order=[1,2,3,4,5,6,7], ax=axis2) | 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 |
1,747 | # Promo
# Plot average sales & customers with/without promo
fig, (axis1,axis2) = plt.subplots(1,2,figsize=(15,4))
sns.barplot(x='Promo', y='Sales', data=rossmann_df, ax=axis1)
sns.barplot(x='Promo', y='Customers', data=rossmann_df, ax=axis2) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
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