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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6,503 | # drop address columns
train = train.drop(['Address', 'AddressNumberAndStreet','WnvPresent', 'NumMosquitos'], axis = 1)
test = test.drop(['Id', 'Address', 'AddressNumberAndStreet'], axis = 1) | No | 5 | 10 | Data_Transform | drop_column | 716,075 | 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 |
6,504 | test["created"] = pd.to_datetime(test["created"])<br>test[\'day_of_week\'] = test[\'created\'].dt.weekday | 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 |
6,505 | train['created_day'] = train['created'].dt.day | No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,506 | test['created_day'] = test['created'].dt.day | No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,507 | ### Iterest per Day of Week<br>fig = plt.figure(figsize=(12,6))<br>sns.countplot(x="created_day", hue="interest_level", hue_order=[\'low\', \'medium\', \'high\'], data=train);<br>plt.xlabel(\'created_day\');<br>plt.ylabel(\'Number of occurrences\'); | No | 5 | 75 | Visualization | time_series | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,508 | # Merge with weather data
train = train.merge(weather, on='Date')
test = test.merge(weather, on='Date')
train = train.drop(['Date'], axis = 1)
test = test.drop(['Date'], axis = 1) | Yes | 3 | 32 | Data_Transform | merge | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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": "general_ml", "framework": "General ML", "name... | 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"}] | [] | 3 | B |
6,509 | # Convert categorical data to numbers
lbl = preprocessing.LabelEncoder()
lbl.fit(list(train['Species'].values) + list(test['Species'].values))
train['Species'] = lbl.transform(train['Species'].values)
test['Species'] = lbl.transform(test['Species'].values) | No | 5 | 20 | Data_Transform | categorify | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 11 | 9 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Che... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | D |
6,510 | train["num_features"] = train["features"].apply(len)
test["num_features"] = test["features"].apply(len) | No | 5 | 8 | Data_Transform | feature_engineering | 685,472 | 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 |
6,511 | llimit = np.percentile(train.latitude.values, 1)
ulimit = np.percentile(train.latitude.values, 99)
train['latitude'].ix[train['latitude']<llimit] = llimit
train['latitude'].ix[train['latitude']>ulimit] = ulimit
plt.figure(figsize=(8,6))
sns.distplot(train.latitude.values, bins=50, kde=False)
plt.xlabel('latitude', fon... | Yes | 3 | 33 | Visualization | distribution | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,512 | llimit = np.percentile(train.longitude.values, 1)
ulimit = np.percentile(train.longitude.values, 99)
train['longitude'].ix[train['longitude']<llimit] = llimit
train['longitude'].ix[train['longitude']>ulimit] = ulimit
plt.figure(figsize=(8,6))
sns.distplot(train.longitude.values, bins=50, kde=False)
plt.xlabel('longitu... | Yes | 3 | 33 | Visualization | distribution | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,513 | lbl.fit(list(train['Street'].values) + list(test['Street'].values))
train['Street'] = lbl.transform(train['Street'].values)
test['Street'] = lbl.transform(test['Street'].values) | No | 5 | 20 | Data_Transform | categorify | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 10 | 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": "ScikitLearn", "name": "Cross Valida... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | D |
6,514 | train['price'] | No | 5 | 41 | Debug | show_table | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,515 | lbl.fit(list(train['Trap'].values) + list(test['Trap'].values))
train['Trap'] = lbl.transform(train['Trap'].values)
test['Trap'] = lbl.transform(test['Trap'].values) | No | 5 | 20 | Data_Transform | categorify | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 10 | 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": "ScikitLearn", "name": "Cross Valida... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}] | ["Column Selection Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | D |
6,516 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'price\', shade=True)<br>facet.add_legend()<br>plt.show() | 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 |
6,517 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'price\', shade=True)<br>facet.set(xlim=(0,100000))<br>facet.add_legend()<br>plt.show() | 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 |
6,518 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'price\', shade=True)<br>facet.set(xlim=(100000,200000))<br>facet.add_legend()<br>plt.show() | 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 |
6,519 | # drop columns with -1s
train = train.ix[:,(train != -1).any(axis=0)]
test = test.ix[:,(test != -1).any(axis=0)] | No | 4 | 10 | Data_Transform | drop_column | 716,075 | 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 |
6,520 | facet = sns.FacetGrid(train, hue = "interest_level", aspect=4)<br>facet.map(sns.kdeplot, \'price\', shade=True)<br>facet.set(xlim=(0,10000))<br>facet.add_legend()<br>plt.show() | 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 |
6,521 | # Random Forest Classifier
clf = ensemble.RandomForestClassifier(n_jobs=1, n_estimators=1000, min_samples_split= 2)
clf.fit(train, labels) | No | 5 | 7 | Model_Train | train_model | 716,075 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 6 | 5 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | B |
6,522 | features_drop = ['building_id', 'created', 'description', 'display_address', 'features', 'manager_id', 'photos', 'street_address', 'month_created', 'date_created']
train1 = train.drop(features_drop, axis=1) | No | 5 | 10 | Data_Transform | drop_column | 685,472 | 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 |
6,523 | features_drop = ['building_id', 'created', 'description', 'display_address', 'features', 'manager_id', 'photos', 'street_address']
test1 = test.drop(features_drop, axis=1) | No | 5 | 10 | Data_Transform | drop_column | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,524 | X = train[['bathrooms','bedrooms','latitude','longitude','price','day_of_week','created_day','num_features']]
y = train1['interest_level'] | No | 5 | 21 | Data_Transform | prepare_x_and_y | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,525 | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.33) | Yes | 4 | 22 | Environment | import_modules | 685,472 | 1 | A | 1 | 0 | 0 | 0 | 5 | 4 | 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, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Randomness Control Checker", "severity": "low"}] | [] | 3 | B |
6,526 | X = test[['bathrooms','bedrooms','latitude','longitude','price','day_of_week','created_day','num_features']]
y = clf.predict_proba(X) | Yes | 3 | 21 | Data_Transform | prepare_x_and_y | 685,472 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 3 | A |
6,527 | t = 732
m=8
n=8 | No | 5 | 77 | Other | define_variables | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 3 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 732", "fix": "Not specified", "benefits": "Not specified", "location": "Line 4"}, {"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 8", "fix": "Not specified", "benefits": "Not specified", "locatio... | A | no applicable ML-specific findings | [] | ["Magic number"] | 3 | A |
6,528 | df = pd.read_csv('../input/data.txt',skiprows=2,sep=' ',names=list(map(str,(list(range(n)))))) | No | 5 | 45 | Data_Extraction | load_from_csv | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/data.txt. 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 |
6,529 | sub = pd.DataFrame()
sub["listing_id"] = test["listing_id"]
for label in ["high", "medium", "low"]:
sub[label] = y[:, labels2idx[label]]
sub.to_csv("submission_rf.csv", index=False) | Yes | 3 | 45 | Data_Extraction | load_from_csv | 685,472 | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,530 | from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
from nltk import word_tokenize
import pandas as pd
import numpy as np
import re
from sklearn.model_selection import train_test_split
from collections import defaultdict
from sklearn.linear_model import LogisticRegression
from ... | No | 5 | 22 | Environment | import_modules | 807,552 | 1 | A | 0 | 0 | 0 | 0 | 0 | 12 | 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"] | 7 | A |
6,531 | with open('../input/train.json') as f:
raw_train = json.load(f)
with open('../input/test.json') as f:
raw_test = json.load(f) | No | 5 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,532 | softmax = LogisticRegression(**m_params) | No | 5 | 4 | Model_Train | choose_model_class | 807,552 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Scaler Missing Checker", "fix": "Apply scaling before scaling-sensitive operations.", "benefits": "Improves model performance and accuracy.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Thresh... | C | Scaler Missing Checker=medium | [{"type": "Scaler Missing Checker", "severity": "medium"}] | ["Dependent Threshold Checker"] | 7 | C |
6,533 | test_x = [j for i in sorted(val.keys()) for j in val[i]]
true = [i for i in sorted(val.keys()) for j in val[i]] | No | 5 | 53 | Other | something_strange | 807,552 | 2 | B | 1 | 0 | 0 | 0 | 5 | 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": "framework_specific", "framework":... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 7 | B |
6,534 | pred = softmax.predict(tfidf.transform(test_x)) | No | 5 | 48 | Model_Evaluation | predict_on_test | 807,552 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 6 | 5 | [{"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":... | B | No random seed setting detected=low | [{"type": "No random seed setting detected", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 7 | B |
6,535 | accuracy_score(true, pred) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 807,552 | 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", "Unit Testing Checker"] | 7 | A |
6,536 | lab = LabelEncoder()
c_true = lab.fit_transform(true)
c_pred = lab.transform(pred)
print(classification_report(c_true, c_pred, target_names=lab.classes_, digits=5)) | No | 4 | 20 | Data_Transform | categorify | 807,552 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 10 | 9 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, No random seed setting detected=low, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 7 | D |
6,537 | sub_df.to_csv('softmax_reg.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 807,552 | 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"] | 7 | A |
6,538 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
% matplotlib inline
from datetime import datetime
from scipy import stats
pd.options.mode.chained_assignment = None
from scipy.stats import norm, skew
warnings.filterwarnings("ignore", category=DeprecationWarnin... | No | 5 | 23 | Environment | set_options | 817,152 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 8 | null |
6,539 | train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') | No | 5 | 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 |
6,540 | test.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 |
6,541 | train.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 |
6,542 | print('Train Dataset Shape : {0}'.format(train.shape))
print('Test Dataset Shape : {0}'.format(test.shape)) | No | 5 | 58 | Debug | show_shape | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
6,543 | train.dtypes | No | 5 | 70 | Debug | show_data_types | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,544 | sns.boxplot(train['count']) | 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 |
6,545 | new_df = pd.DataFrame(new_df) | No | 5 | 12 | Data_Transform | create_dataframe | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,546 | train = train[np.abs(train["count"]-train["count"].mean())<=(3*train["count"].std())] | Yes | 4 | 14 | Data_Transform | filter | 817,152 | 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"}] | [] | 8 | B |
6,547 | fig,ax = plt.subplots(2,1,figsize = (10,10))<br>sns.distplot(train[\'count\'],ax=ax[0])<br>stats.probplot(train["count"], dist=\'norm\', fit=True, plot=ax[1])<br>print(\'Skewness : {0}\'.format(train[\'count\'].skew()))<br>print(\'Kurt : {0}\'.format(train[\'count\'].kurt())) | 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 |
6,548 | new_df['m'] = new_df['t'].apply(lambda x : int(x)%8) | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,549 | fig,ax = plt.subplots(2,1,figsize = (10,10))<br>#logcount = np.log1p(train[\'count\']).kurt()<br>#rootcount = np.sqrt(train[\'count\']).kurt()<br>#cubiccount = np.power(train[\'count\'],2).kurt()<br>#minVal = min([logcount, rootcount, cubiccount])<br>#if logcount == minVal:<br>best = \'log\'<br>train[\'count_log\'] = n... | 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 |
6,550 | new_df['t'] = new_df['t'].apply(lambda x:x//8) | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,551 | new_df['hour'] = new_df['t'].apply(lambda x : x%24 ) | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,552 | train['date'] = train.datetime.apply(lambda x: x.split()[0])
train['hour'] = train.datetime.apply(lambda x: x.split()[1].split(':')[0])
train['weekday'] = train.date.apply(lambda dateString : datetime.strptime(dateString, '%Y-%m-%d').weekday())
train['month'] = train.date.apply(lambda dateString : datetime.strptime(da... | No | 4 | 8 | Data_Transform | feature_engineering | 817,152 | 1 | A | 0 | 0 | 0 | 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"}] | [] | 8 | B |
6,553 | new_df['day'] = new_df['t'].apply(lambda x : x//24 ) | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,554 | 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 |
6,555 | categorical = [\'date\',\'weekday\',\'month\',\'hour\',\'season\',\'holiday\',\'workingday\',\'weather\']<br>numeric = ["temp","atemp","casual","registered","humidity","windspeed","count","count_log"] | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,556 | new_df['mm']=new_df['m']
new_df['nn']= new_df['n'] | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,557 | new_df['id'] = new_df['t'].map(str)+':'+new_df['mm'].map(str)+':'+new_df['nn'].map(str) | No | 5 | 8 | Data_Transform | feature_engineering | 736,747 | 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 |
6,558 | for idx in categorical:
train[idx].astype('category') | Yes | 4 | 16 | Data_Transform | data_type_conversions | 817,152 | 1 | A | 0 | 1 | 1 | 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"}] | [] | 8 | B |
6,559 | fig,axes = plt.subplots(ncols=2 ,nrows=2)
fig.set_size_inches(15,10)
sns.boxplot(data=train,x='season',y='count',ax=axes[0][0])
sns.boxplot(data=train,x='holiday',y='count',ax=axes[0][1])
sns.boxplot(data=train,x='workingday',y='count',ax=axes[1][0])
sns.boxplot(data=train,x='weather',y='count',ax=axes[1][1])
fig1,axe... | No | 5 | 33 | Visualization | distribution | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
6,560 | plt.subplots(figsize=(15,8))
sns.heatmap(train[numeric].corr(),annot=True) | No | 5 | 80 | Visualization | heatmap | 817,152 | 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"}] | [] | 8 | B |
6,561 | corr = train[numeric].drop(\'count\', axis=1).corr()<br>corr =corr.drop(\'count_log\', axis=1).corr() # We already examined SalePrice correlations<br>plt.figure(figsize=(12, 10))<br>sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.4)], <br> cmap=\'viridis\', vmax=1.0, vmin=-1.0, linewidths=0.1,<br> an... | No | 5 | 80 | Visualization | heatmap | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,562 | new_df = df_r.set_index('id').join(new_df.set_index('id')) | No | 4 | 32 | Data_Transform | merge | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,563 | ### count,month
plt.figure(figsize=(15,8))
monthagg = pd.DataFrame(train.groupby('month')['count'].mean()).reset_index()
sns.barplot(data=monthagg, x='month',y='count').set(title = 'Month Vs Count') | No | 4 | 33 | Visualization | distribution | 817,152 | 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"}] | [] | 8 | B |
6,564 | from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(new_df.drop(['val','t','mm','nn'],axis=1)) | No | 5 | 18 | Data_Transform | normalization | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 6 | 6 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Pipeline Checker", "fix": "Use Pipelines for all scikit-learn estimators.", "benefits": "Prevents data leakage and ensures correct model evaluation.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross V... | D | Cross Validation Checker=low, Data Leakage Checker=high, Pipeline Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Data Leakage Checker", "severity": "high"}, {"type": "Pipeline Checker", "severity": "low"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 3 | D |
6,565 | ### count,season,hour
plt.figure(figsize=(15,8))
houragg = pd.DataFrame(train.groupby(['hour','season'])['count'].mean()).reset_index()
sns.pointplot(data=houragg,x=houragg['hour'],y=houragg['count'],hue=houragg['season']).set(title='Hour,Season Vs Count') | Yes | 4 | 33 | Visualization | distribution | 817,152 | 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"}] | [] | 8 | B |
6,566 | scaler.transform(new_df.drop(['val','t','mm','nn'],axis=1)) | No | 5 | 18 | Data_Transform | normalization | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
6,567 | ### count,hour,weekday
plt.figure(figsize=(15,8))
hourweekagg = pd.DataFrame(train.groupby(['hour','weekday'])['count'].mean()).reset_index()
sns.pointplot(data=hourweekagg,x=hourweekagg['hour'],y=hourweekagg['count'],hue=hourweekagg['weekday']).set(title='Hour,Week Vs Count') | Yes | 4 | 33 | Visualization | distribution | 817,152 | 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"}] | [] | 8 | B |
6,568 | from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.base import B... | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,569 | new_df[['m1','m2','m3','m4','n','m','hour','day']]=scaler.transform(new_df.drop(['val','t','mm','nn'],axis=1)) | No | 5 | 18 | Data_Transform | normalization | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 3 | A |
6,570 | target = train['count']
target_log=train['count_log']
train = train.drop('count_log',axis=1)
train = train.drop('count',axis=1)
train = train.drop('atemp',axis=1)
train = train.drop('date',axis=1)
train = train.drop('casual',axis=1)
train = train.drop('registered',axis=1)
m_dum = pd.get_dummies(train['month'],prefix='m... | Yes | 3 | 10 | Data_Transform | drop_column | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 26 | 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"}] | [] | 8 | B |
6,571 | train = new_df[new_df['val'] !=-1] | No | 5 | 14 | Data_Transform | filter | 736,747 | 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 |
6,572 | X_train = train.drop(['val','t','mm','nn'], axis=1)
y_train = train['val'].values | No | 5 | 21 | Data_Transform | prepare_x_and_y | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 3 | B |
6,573 | test = new_df[new_df['val'] == -1] | No | 5 | 14 | Data_Transform | filter | 736,747 | 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 |
6,574 | X_test = test.drop(['val','t','mm','nn'], axis=1) | No | 5 | 10 | Data_Transform | drop_column | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,575 | gbr = GradientBoostingRegressor(n_estimators=2000, learning_rate=0.01, max_depth=4).fit(train.values, target_log) | No | 5 | 7 | Model_Train | train_model | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 5 | 4 | [{"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"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
6,576 | import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import adam | No | 5 | 22 | Environment | import_modules | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,577 | model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(8,)))
model.add(Dense(256, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(1))
model.summary() | No | 5 | 84 | Other | not_enough_vertices | 736,747 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,578 | model_gbr = GradientBoostingRegressor(n_estimators=1500,max_depth=5,learning_rate=0.01).fit(train.values,target_log) | No | 5 | 7 | Model_Train | train_model | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 5 | 4 | [{"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"}] | ["Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 8 | B |
6,579 | prediction = model_gbr.predict(test.values)
prediction = np.expm1(prediction) | Yes | 4 | 48 | Model_Evaluation | predict_on_test | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Unit Testing Checker", "fix": "Write unit tests for data processing and model components.", "benefits": "Ensures code reliability and prevents bugs.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Excepti... | A | no applicable ML-specific findings | [] | ["Exception Handling Checker", "Unit Testing Checker"] | 8 | A |
6,580 | output = pd.DataFrame()
output['datetime'] = testid
output['count'] = prediction
output.to_csv('output.csv',index=False) | Yes | 3 | 25 | Data_Export | save_to_csv | 817,152 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 8 | A |
6,581 | import glob
import os, sys
import random
from tqdm import tqdm
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense
from keras import applications
import seaborn as sns
import pandas as ... | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 14 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,582 | train_data_dir = '../input/dogs-vs-cats-redux-kernels-edition/train'
test_data_dir = '../input/dogs-vs-cats-redux-kernels-edition/test'
# Make sure you include https://www.kaggle.com/keras/vgg16/data as your data source
vgg_model_path = '../input/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
epochs = 20
ba... | No | 5 | 77 | Other | define_variables | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,583 | # Wrap training data into pandas' DataFrame.
lst = list(gen_image_label(train_data_dir))
random.shuffle(lst)
if training_n_bound is not None:
lst = lst[:training_n_bound]
train_df = pd.DataFrame(lst, columns=['label', 'id', 'filename'])
train_df = train_df.sort_values(by=['label', 'id'])
train_df.head(3) | Yes | 3 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,584 | train_df['label_code'] = train_df.label.map({'cat':0, 'dog':1})
train_df.head(3) | Yes | 3 | 41 | Debug | show_table | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,585 | # Wrap testing data into pandas' DataFrame.
lst = list(gen_image_label(test_data_dir))
test_df = pd.DataFrame(lst, columns=['label', 'id', 'filename'])
test_df = test_df.sort_values(by=['label', 'id'])
test_df['label_code'] = test_df.label.map({'cat':0, 'dog':1})
test_df.head(3) | Yes | 3 | 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 |
6,586 | sns.countplot(train_df.label)
plt.title('Number of training images per category') | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,587 | model.fit(train_embeddings[train_indices,:],
train_labels,
epochs=epochs,
batch_size=batch_size,
validation_data=(train_embeddings[validate_indices,:],
validation_labels))
model.save_weights(embedding_fc_model) | Yes | 3 | 7 | Model_Train | train_model | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,588 | from sklearn.metrics import f1_score, accuracy_score
pred_validation = model.predict(train_embeddings[validate_indices,:])
f1 = f1_score(validation_labels, pred_validation > 0.5)
acc = accuracy_score(validation_labels, pred_validation > 0.5)
(f1, acc) | Yes | 4 | 49 | Model_Evaluation | compute_test_metric | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | 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"] | 0 | A |
6,589 | pred_test = model.predict(test_embeddings)
pred_test.shape | Yes | 3 | 48 | Model_Evaluation | predict_on_test | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,590 | results = pd.DataFrame({'id': pd.Series(test_df.id.values[:pred_test.shape[0]]),
'label': pd.Series(pred_test.T[0])})
results.to_csv('submission.csv', index=False)
results.head(10) | Yes | 4 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,591 | df = pd.DataFrame(np.concatenate((X,
train_labels[:train_embeddings[train_indices,:].shape[0]].reshape(train_embeddings[train_indices,:].shape[0],1)),
axis=1),
columns=['X', 'Y', 'Z', 'label']) | No | 5 | 12 | Data_Transform | create_dataframe | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,592 | g = sns.FacetGrid(df, hue="label", size=7)
g.map(plt.scatter, "X", "Y", alpha=.5)
g.add_legend();
g = sns.FacetGrid(df, hue="label", size=7)
g.map(plt.scatter, "Y", "Z", alpha=.5)
g.add_legend();
g = sns.FacetGrid(df, hue="label", size=7)
g.map(plt.scatter, "X", "Z", alpha=.5)
g.add_legend(); | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,593 | from nltk import word_tokenize
from nltk.corpus import stopwords
import pandas as pd
import numpy as np
import re
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.base import BaseEsti... | No | 5 | 22 | Environment | import_modules | 951,820 | 1 | A | 0 | 0 | 0 | 0 | 0 | 15 | 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"] | 9 | A |
6,594 | train, val = train_test_split(raw_train, test_size=0.2, random_state=2018) | No | 5 | 13 | Data_Transform | split | 951,820 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"] | 9 | B |
6,595 | pred = []
test_pred = []
for i in range(3):
p.get_params()['lr'].class_weight = {0: 1, 1:w[i] }
p.fit(train_x, train_y[i])
pred.append(p.decision_function(val_x))
test_pred.append(p.decision_function(test_x)) | Yes | 3 | 7 | Model_Train | train_model | 951,820 | 1 | A | 0 | 1 | 1 | 0 | 0 | 7 | ok | 6 | 6 | [{"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", "Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 9 | B |
6,596 | accuracy_score(val_y, np.argmax(np.array(pred), axis=0)) | No | 5 | 49 | Model_Evaluation | compute_test_metric | 951,820 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 3 | 3 | [{"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 10"}, {"detector": "framework_specific", "framework": "ScikitLearn", "name": "Depen... | A | no applicable ML-specific findings | [] | ["Array Creation Efficiency", "Dependent Threshold Checker", "Unit Testing Checker"] | 9 | A |
6,597 | sub_df = pd.DataFrame()
sub_df['id'] = [i['id'] for i in raw_test]
sub_df['sentiment'] = np.argmax(np.array(test_pred), axis=0)
sub_df['sentiment']= sub_df['sentiment'].apply(lambda x: lab.classes_[x]) | Yes | 4 | 8 | Data_Transform | feature_engineering | 951,820 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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": "NumPy", "name": "Array Creation Eff... | A | no applicable ML-specific findings | [] | ["Array Creation Efficiency", "Column Selection Checker", "Dependent Threshold Checker"] | 9 | A |
6,598 | sub_df.to_csv('nb.csv', index=False) | No | 5 | 25 | Data_Export | save_to_csv | 951,820 | 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"] | 9 | A |
6,599 | # This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processi... | Yes | 4 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,600 | # initializations related to threading stuff
batch_size = 500
num_train_images = 25000
num_test_images = 12500
num_train_threads = int(num_train_images/batch_size) # 50
num_test_threads = int(num_test_images/batch_size) # 25
lock = Lock() | No | 4 | 77 | Other | define_variables | 1,002,729 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,601 | print(train_x.shape)
print(len(train_y)) | No | 5 | 58 | Debug | show_shape | 1,002,729 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
6,602 | test_x =get_testing_data()
print(test_x.shape) | Yes | 4 | 58 | Debug | show_shape | 1,002,729 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 3 | A |
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