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values | graph_vertex_subclass stringclasses 70
values | kernel_id float64 25.8k 14.7M ⌀ | sqale_rating float64 1 4 | maintainability_rating stringclasses 4
values | code_smells int64 0 36 | complexity float64 0 15 ⌀ | cognitive_complexity int64 0 33 | bugs int64 0 7 | sqale_index int64 0 125 | ncloc float64 0 101 ⌀ | mlscent_analysis_status stringclasses 3
values | mlscent_smell_count int64 0 31 | mlscent_unique_smell_count int64 0 12 | mlscent_findings_json stringlengths 2 7.76k | mlscent_ml_rating stringclasses 5
values | mlscent_ml_rating_basis stringclasses 52
values | mlscent_ml_findings_json stringclasses 50
values | mlscent_ignored_findings_json stringclasses 199
values | context_line_count int64 0 33 | combined_rating stringclasses 5
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6,103 | d = devicelabels.dropna(subset=['trainrow'])
Xtr_label = csr_matrix((np.ones(d.shape[0]), (d.trainrow, d.label)),
shape=(gatrain.shape[0],nlabels))
d = devicelabels.dropna(subset=['testrow'])
Xte_label = csr_matrix((np.ones(d.shape[0]), (d.testrow, d.label)),
shape=(gatest.... | No | 4 | 17 | Data_Transform | correct_missing_values | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,104 | Xtrain = hstack((Xtr_brand, Xtr_model, Xtr_app, Xtr_label), format='csr')
Xtest = hstack((Xte_brand, Xte_model, Xte_app, Xte_label), format='csr')
print('All features: train shape {}, test shape {}'.format(Xtrain.shape, Xtest.shape)) | No | 4 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,105 | targetencoder = LabelEncoder().fit(gatrain.gender)
y = targetencoder.transform(gatrain.gender)
nclasses = len(targetencoder.classes_) | No | 5 | 20 | Data_Transform | categorify | 144,515 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,106 | clf = LogisticRegression(C=0.08)#, multi_class='multinomial',solver='lbfgs')
clf.fit(Xtrain[70001:], y[70001:])
pred = pd.DataFrame(clf.predict_proba(Xtrain[70001:]), index=gatrain.iloc[70001:].index, columns=targetencoder.classes_)
pred.head() | Yes | 3 | 7 | Model_Train | train_model | 144,515 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,107 | pred.to_csv('test_gender.csv',index=True) | No | 5 | 25 | Data_Export | save_to_csv | 144,515 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,108 | import sys
import os
import math
import numpy as np
import pandas as pd
import seaborn as sns
from scipy.stats import norm, gumbel_r
from scipy.optimize import linprog
import matplotlib
import matplotlib.pyplot as plt
from sklearn.utils.extmath import cartesian
%matplotlib inline | No | 5 | 23 | Environment | set_options | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
6,109 | submission.to_csv("submission.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,110 | def plot_bag_weight_distributions(bags, size=10000):
plot_distributions(bags, create_bag_weight_sampler, size=size, fit=norm)
def plot_distributions(bags, sampler_builder, size=10000, fit=None):
num_plots = len(bags)
num_cols = int(round(math.sqrt(num_plots)))
num_rows = (num_plots // num_cols)
num... | No | 5 | 33 | Visualization | distribution | null | 1 | A | 1 | 4 | 2 | 0 | 5 | 32 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,111 | example_bags = [
{"horse": 1, "ball": 2},
{"train": 3, "bike": 1},
{"coal": 2, "book": 2},
{"gloves": 12, "book": 12},
]
plot_bag_weight_distributions(example_bags) | No | 5 | 33 | Visualization | distribution | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,112 | def plot_bag_utility_distributions(bags, size=10000, fit=norm):<br> plot_distributions(bags, create_bag_utility_sampler, size=size, fit=fit)<br><br>def create_bag_utility_sampler(bag):<br> bag_weight_sampler, bag_name = create_bag_weight_sampler(bag)<br> def bag_utility_sampler(size=1):<br> samples = ba... | Yes | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,113 | def plot_score_distribution(bags, num_tries=60, size=10000, fit=norm, extremal_fit=gumbel_r):<br> scores = np.zeros(size)<br> for i, bag in enumerate(bags):<br> current_bag_sampler, _ = create_bag_utility_sampler(bag)<br> scores += current_bag_sampler(size)<br> score_mean, score_std = np.mean(sco... | Yes | 4 | 33 | Visualization | distribution | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,114 | def drop_duplicate(candidate_bags, distributions):
df = pd.DataFrame(data=np.hstack((candidate_bags, distributions)), columns=gifts + ["mean", "var"])
df.drop_duplicates(subset=gifts, inplace=True)
return df[gifts].values, df[["mean", "var"]].values
candidate_bags = np.vstack([mixed_item_candiadte_bags, lo... | Yes | 3 | 11 | Data_Transform | concatenate | 166,245 | 1 | A | 0 | 1 | 0 | 0 | 0 | 9 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,115 | pd.read_csv('submission_20.csv').head() | No | 5 | 41 | Debug | show_table | 166,245 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,116 | import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8")) | No | 5 | 88 | Debug | list_files | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 2 | 2 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Dependent Threshold Checker", "fix": "Use threshold-independent metrics alongside threshold-dependent ones.", "benefits": "Provides a comprehensive evaluation of model performance.", "location": ""}, {"detector": "general_ml", "framework": "Genera... | A | no applicable ML-specific findings | [] | ["Dependent Threshold Checker", "Hardcoded file path"] | 11 | A |
6,117 | train = pd.read_json("../input/train.json")
train.info() | No | 5 | 44 | Data_Extraction | load_from_disk | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Hardcoded file path"] | 11 | B |
6,118 | X = train.drop("interest_level", 1)
Y = train["interest_level"].astype("category") | No | 5 | 21 | Data_Transform | prepare_x_and_y | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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, 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", "Dependent Threshold Checker"] | 11 | B |
6,119 | X["street_address"] = X["street_address"].astype(\'category\').cat.codes<br>X["created"] = X["created"].astype(\'category\').cat.codes<br>X["building_id"] = X["building_id"].astype(\'category\').cat.codes<br>X["description"] = X["description"].astype(\'category\').cat.codes<br>X["display_address"] = X["display_address"... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,120 | features = ["bathrooms", "bedrooms", "latitude", "longitude", "price", "street_address", "created",
"description", "display_address"]
X = X[features] | No | 4 | 21 | Data_Transform | prepare_x_and_y | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 11 | A |
6,121 | from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=42) | No | 5 | 13 | Data_Transform | split | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | 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"] | 11 | B |
6,122 | from sklearn.metrics import accuracy_score, log_loss<br>from sklearn.neighbors import KNeighborsClassifier<br>from sklearn.tree import DecisionTreeClassifier<br>from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier<br>from sklearn.naive_bayes import GaussianNB<br>from sklea... | Yes | 3 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 0 | null |
6,123 | test = pd.read_json("../input/test.json")
index = test["listing_id"]
test = test[features] | No | 5 | 44 | Data_Extraction | load_from_disk | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker", "Hardcoded file path"] | 11 | A |
6,124 | olist = list(test.select_dtypes(['object']))
for col in olist:
test[col] = test[col].astype('category').cat.codes | No | 5 | 8 | Data_Transform | feature_engineering | 179,492 | 1 | A | 0 | 1 | 1 | 0 | 0 | 3 | 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": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker"] | 11 | A |
6,125 | favorite_clf = LinearDiscriminantAnalysis()
favorite_clf.fit(X_train, y_train)
test_predictions = favorite_clf.predict_proba(test) | Yes | 3 | 7 | Model_Train | train_model | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 9 | 8 | [{"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, 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"}] | ["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"] | 11 | B |
6,126 | submission = pd.DataFrame({<br> "listing_id": index,<br> "high": test_predictions[:,0],<br> "medium":test_predictions[:,2],<br> "low":test_predictions[:,1]<br> })<br> <br>columnsTitles=["listing_id","high","medium","low"]<br>submission=submission.reindex(columns=columnsTitles)<br>submi... | Yes | 4 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,127 | from keras.models import Sequential
from keras.layers import Dense | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,128 | from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)
# convert integers to dummy variables (i.e. one hot encoded)
dummy_y = np_utils.to_categorical(encoded_Y) | No | 5 | 20 | Data_Transform | categorify | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | 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"] | 11 | D |
6,129 | # Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X.values, dummy_y, nb_epoch=10, batch_size=10) | No | 4 | 7 | Model_Train | train_model | 179,492 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 5 | 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 | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Exception Handling Checker", "Unit Testing Checker", "Verbose Mode Checker"] | 11 | B |
6,130 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from subprocess import check_output
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingC... | No | 5 | 88 | Debug | list_files | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 14"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
6,131 | sub = pd.read_csv('../input/sample_submission.csv')
sub.head() | Yes | 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 |
6,132 | df = pd.read_json('../input/train.json')
df.tail() | Yes | 4 | 44 | Data_Extraction | load_from_disk | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.json. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 7"}] | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 6 | A |
6,133 | df['address'] = df['display_address'].astype('category').cat.codes
df['street_address'] = df['street_address'].astype('category').cat.codes
df['building_id'] = df['building_id'].astype('category').cat.codes
df['manager_id'] = df['manager_id'].astype('category').cat.codes
df['num_features'] = df['features'].apply(len)
d... | No | 5 | 8 | Data_Transform | feature_engineering | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 6 | A |
6,134 | new_feat = ['price','address','manager_id','building_id',
'num_features','created_year','created_month',
'len_description','latitude','longitude','num_pics']
#new_feat = ['price','latitude','longitude','num_pics',
# 'num_features','created_year','created_month','len_description']
X =... | Yes | 3 | 21 | Data_Transform | prepare_x_and_y | 179,634 | 1 | A | 1 | 0 | 0 | 0 | 5 | 7 | 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"] | 6 | B |
6,135 | clf1 = GradientBoostingClassifier(n_estimators=200, max_depth=9)
clf2 = AdaBoostClassifier(n_estimators=200)
clf3 = RandomForestClassifier(n_estimators=300)
estimators = [('gb', clf1), ('ab', clf2), ('rf', clf3)]
vclf = VotingClassifier(estimators=estimators, voting='soft', n_jobs= -1)
vclf.fit(X_train, y_train)
y_va... | Yes | 3 | 4 | Model_Train | choose_model_class | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 8 | ok | 12 | 9 | [{"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, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control 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"] | 6 | B |
6,136 | X_train = df[new_feat].fillna(0)
y_train = df['interest_level']
vclf.fit(X_train, y_train)
df2 = pd.read_json('../input/test.json')
df2['address'] = df2['display_address'].astype('category').cat.codes
df2['street_address'] = df2['street_address'].astype('category').cat.codes
df2['building_id'] = df2['building_id'].as... | Yes | 3 | 8 | Data_Transform | feature_engineering | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 16 | ok | 11 | 10 | [{"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", "Dependent Threshold Checker", "Exception Handling Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker", "Verbose Mode Checker"] | 6 | B |
6,137 | sub = pd.read_csv('submissionVoting.csv')
sub.head() | Yes | 4 | 45 | Data_Extraction | load_from_csv | 179,634 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 6 | A |
6,138 | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss | No | 5 | 22 | Environment | import_modules | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,139 | df = pd.read_json(open("../input/train.json", "r")) | No | 5 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,140 | df.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 |
6,141 | df["num_photos"] = df["photos"].apply(len)
df["num_features"] = df["features"].apply(len)
df["num_description_words"] = df["description"].apply(lambda x: len(x.split(" ")))
df["created"] = pd.to_datetime(df["created"])
df["created_year"] = df["created"].dt.year
df["created_month"] = df["created"].dt.month
df["created_d... | No | 5 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,142 | num_feats = ["bathrooms", "bedrooms", "latitude", "longitude", "price",
"num_photos", "num_features", "num_description_words",
"created_year", "created_month", "created_day"]
X = df[num_feats]
y = df["interest_level"]
X.head() | Yes | 4 | 21 | Data_Transform | prepare_x_and_y | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,143 | X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.33) | No | 5 | 13 | Data_Transform | split | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,144 | clf = RandomForestClassifier(n_estimators=1000)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred) | Yes | 3 | 4 | Model_Train | choose_model_class | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,145 | df = pd.read_json(open("../input/test.json", "r"))
df["num_photos"] = df["photos"].apply(len)
df["num_features"] = df["features"].apply(len)
df["num_description_words"] = df["description"].apply(lambda x: len(x.split(" ")))
df["created"] = pd.to_datetime(df["created"])
df["created_year"] = df["created"].dt.year
df["cre... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 10 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,146 | import numpy as np
import pandas as pd
# Look! No scikit learn! | No | 5 | 22 | Environment | import_modules | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,147 | df_train = pd.read_json(open("../input/train.json", "r"))
df_train.set_index("listing_id", inplace=True)
df_test = pd.read_json(open("../input/test.json", "r"))
df_test.set_index("listing_id", inplace=True)
# We will work with a concatenation of the two, then split after the scaling.
df = pd.concat([df_train, df_test]... | Yes | 3 | 44 | Data_Extraction | load_from_disk | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 2 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Hardcoded file path detected: ../input/train.json. Consider using configuration files or environment variables.", "fix": "Not specified", "benefits": "Not specified", "location": "Line 3"}, {"detector": "general_ml", "framework": "General ML", "name": "Har... | A | no applicable ML-specific findings | [] | ["Hardcoded file path"] | 2 | A |
6,148 | df["num_photos"] = df["photos"].apply(len)
df["num_features"] = df["features"].apply(len)
df["num_description_words"] = df["description"].apply(lambda x: len(x.split(" ")))
df["created"] = pd.to_datetime(df["created"])
#df["created_year"] = df["created"].dt.year
df["created_month"] = df["created"].dt.month
df["created_... | No | 5 | 8 | Data_Transform | feature_engineering | 180,496 | 1 | A | 1 | 0 | 0 | 0 | 5 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
6,149 | df["logprice"] = np.log(df.price) | No | 5 | 8 | Data_Transform | feature_engineering | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
6,150 | df.loc[df.bathrooms == 112, "bathrooms"] = 1 | No | 5 | 14 | Data_Transform | filter | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,151 | numeric_feat = ["bathrooms", "bedrooms", "latitude", "longitude", "logprice",
"num_photos", "num_features", "num_description_words",
"created_month", "created_day"]
for col in numeric_feat:
df[col] -= df[col].min()
df[col] /= df[col].max() | No | 5 | 18 | Data_Transform | normalization | 180,496 | 1 | A | 0 | 1 | 1 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 2 | A |
6,152 | X_train = df.loc[df.interest_level.notnull(), numeric_feat]
y_train = pd.get_dummies(df_train[["interest_level"]], prefix="")
y_train = y_train[["_high", "_medium", "_low"]] # Set the order according to submission
X_test = df.loc[df.interest_level.isnull(), numeric_feat] | Yes | 3 | 14 | Data_Transform | filter | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 2 | A |
6,153 | ## A dead simple neural network class in Python+Numpy. Plain SGD, and no regularization.
def sigmoid(X):
return 1.0 / ( 1.0 + np.exp(-X) )
def softmax(X):
_sum = np.exp(X).sum()
return np.exp(X) / _sum
class neuralnet(object):
def __init__(self, num_input, num_hidden, num_output):
self._W1 = (... | No | 5 | 4 | Model_Train | choose_model_class | null | 1 | A | 1 | 5 | 0 | 0 | 0 | 25 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,154 | # Some hyper-parameters to tune.
num_hidden = 17 # I think I get about 1 epoch/sec with this size on the docker instance
n_epochs = 100
eta = 0.01 | No | 5 | 59 | Model_Train | init_hyperparams | 180,496 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 1 | 1 | [{"detector": "general_ml", "framework": "General ML", "name": "Magic number detected: 0.01", "fix": "Not specified", "benefits": "Not specified", "location": "Line 6"}] | A | no applicable ML-specific findings | [] | ["Magic number"] | 2 | A |
6,155 | import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import xgboost as xgb
from sklearn import model_selection, preprocessing, ensemble
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
| 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 |
6,156 | data_path = "../input/"
train_file = data_path + "train.json"
test_file = data_path + "test.json"
train_df = pd.read_json(train_file)
test_df = pd.read_json(test_file)
print(train_df.shape)
print(test_df.shape)
| Yes | 3 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,157 | # count of photos #
train_df["num_photos"] = train_df["photos"].apply(len)
test_df["num_photos"] = test_df["photos"].apply(len)
# count of "features" #
train_df["num_features"] = train_df["features"].apply(len)
test_df["num_features"] = test_df["features"].apply(len)
# count of words present in description column #
t... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,158 | categorical = ["display_address", "manager_id", "building_id", "street_address"]<br>for f in categorical:<br> if train_df[f].dtype==\'object\':<br> #print(f)<br> lbl = preprocessing.LabelEncoder()<br> lbl.fit(list(train_df[f].values) + list(test_df[f].values))<br> trai... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,159 | train_df[\'features\'] = train_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))<br>test_df[\'features\'] = test_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))<br>print(train_df["features"].head())<br>tfidf = CountVectorizer(stop_words=\'english\', max_features... | 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 |
6,160 | train_X = sparse.hstack([train_df[features_to_use], tr_sparse]).tocsr()
test_X = sparse.hstack([test_df[features_to_use], te_sparse]).tocsr()
target_num_map = {'high':0, 'medium':1, 'low':2}
train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))
print(train_X.shape, test_X.shape)
| Yes | 3 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,161 | import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import xgboost as xgb
from sklearn import model_selection, preprocessing, ensemble
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer | 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 |
6,162 | data_path = "../input/"
train_file = data_path + "train.json"
test_file = data_path + "test.json"
train_df = pd.read_json(train_file)
test_df = pd.read_json(test_file)
print(train_df.shape)
print(test_df.shape) | Yes | 4 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 7 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,163 | # count of photos #
train_df["num_photos"] = train_df["photos"].apply(len)
test_df["num_photos"] = test_df["photos"].apply(len)
# count of "features" #
train_df["num_features"] = train_df["features"].apply(len)
test_df["num_features"] = test_df["features"].apply(len)
# count of words present in description column #
t... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 17 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,164 | categorical = ["display_address", "manager_id", "building_id", "street_address"]<br>for f in categorical:<br> if train_df[f].dtype==\'object\':<br> #print(f)<br> lbl = preprocessing.LabelEncoder()<br> lbl.fit(list(train_df[f].values) + list(test_df[f].values))<br> trai... | No | 5 | 20 | Data_Transform | categorify | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,165 | train_df[\'features\'] = train_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))<br>test_df[\'features\'] = test_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))<br>print(train_df["features"].head())<br>tfidf = CountVectorizer(stop_words=\'english\', max_features... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,166 | train_X = sparse.hstack([train_df[features_to_use], tr_sparse]).tocsr()
test_X = sparse.hstack([test_df[features_to_use], te_sparse]).tocsr()
target_num_map = {'high':0, 'medium':1, 'low':2}
train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))
print(train_X.shape, test_X.shape) | Yes | 3 | 11 | Data_Transform | concatenate | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,167 | out_df.to_csv("xgb_starter2.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,168 | df = pd.read_json(open("../input/train.json", \'r\'))<br>df.head() | Yes | 4 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,169 | df['num_photos'] = df['photos'].apply(len)
df['num_features'] = df['features'].apply(len)
df['num_description_words'] = df['description'].apply(lambda x: len(x.split(' ')))
df['created'] = pd.to_datetime(df['created'])
df['created_year'] = df['created'].dt.year
df['created_month'] = df['created'].dt.month
df['created_d... | No | 5 | 8 | Data_Transform | feature_engineering | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 9 | 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 |
6,170 | # price: removing values in 99 percentile
df = remove_outlier(df, 'price', [99])
# Latitude & Longitude:
# removing outliers: values in the 1/99 percentiles
df = remove_outlier(df, 'latitude', [1, 99])
df = remove_outlier(df, 'longitude', [1, 99]) | No | 4 | 8 | Data_Transform | feature_engineering | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
6,171 | num_feats = ['bathrooms', 'bedrooms', 'latitude', 'longitude', 'price', 'num_photos',
'num_features', 'num_description_words', 'created_year', 'created_month',
'created_day', 'created_hour', 'created_minute']
X = df[num_feats]
y = df['interest_level']
X.head() | Yes | 4 | 21 | Data_Transform | prepare_x_and_y | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 6 | ok | 1 | 1 | [{"detector": "framework_specific", "framework": "Pandas", "name": "Column Selection Checker", "fix": "Select necessary columns after importing DataFrame.", "benefits": "Clarifies data usage and improves performance.", "location": ""}] | A | no applicable ML-specific findings | [] | ["Column Selection Checker"] | 5 | A |
6,172 | X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.33,
random_state=0) | No | 5 | 13 | Data_Transform | split | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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 |
6,173 | clf = RandomForestClassifier(n_estimators=1000)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_test)
log_loss(y_test, y_val_pred) | Yes | 3 | 4 | Model_Train | choose_model_class | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 11 | 9 | [{"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, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control 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"] | 5 | B |
6,174 | # fitting the model on the entire data without split
clf.fit(X, y) | No | 5 | 7 | Model_Train | train_model | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 4 | 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"] | 5 | B |
6,175 | df = pd.read_json(open("../input/test.json", \'r\'))<br>df[\'num_photos\'] = df[\'photos\'].apply(len)<br>df[\'num_features\'] = df[\'features\'].apply(len)<br>df[\'num_description_words\'] = df[\'description\'].apply(lambda x: len(x.split(\' \')))<br>df[\'created\'] = pd.to_datetime(df[\'created\'])<br>df[\'created_ye... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | null | 0 | 0 | 0 | null | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,176 | y = clf.predict_proba(X) | No | 5 | 27 | Model_Train | predict_on_train | 183,411 | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | 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"] | 5 | A |
6,177 | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import log_loss | No | 5 | 22 | Environment | import_modules | 187,669 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 5 | A |
6,178 | clf = ExtraTreesClassifier(n_estimators=100)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
log_loss(y_val, y_val_pred) | Yes | 3 | 4 | Model_Train | choose_model_class | 187,669 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | ok | 9 | 8 | [{"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, 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"}] | ["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"] | 5 | B |
6,179 | import numpy as np # linear algebra<br>import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)<br>import matplotlib.pyplot as plt<br>import seaborn as sns<br>from subprocess import check_output<br><br># Load and describe data<br># print(check_output(["ls", "../input"]).decode("utf8"))<br>df = pd.read_jso... | No | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 1 | ok | 0 | 0 | [] | A | no applicable ML-specific findings | [] | [] | 0 | A |
6,180 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt #plotting library
%matplotlib inline
import seaborn as sns #plotting library
sns.set(color_codes=True)
sns.set_style("white")
| No | 5 | 23 | Environment | set_options | 201,234 | 1 | A | 0 | null | 0 | 0 | 0 | null | invalid_python | 0 | 0 | [] | null | invalid_python | [] | [] | 4 | null |
6,181 | import sklearn.ensemble
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
| No | 5 | 22 | Environment | import_modules | 201,234 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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"] | 4 | A |
6,182 | # This Python 3 environment comes with many helpful analytics libraries installed<br>train_data = pd.read_json("../input/train.json")<br>test_data = pd.read_json("../input/test.json")<br>display_count = 2<br>target = \'interest_level\'<br> | Yes | 4 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,183 | train_data.iloc[0]
| No | 5 | 14 | Data_Transform | filter | 201,234 | 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"] | 4 | A |
6,184 | train_data['rooms'] = train_data['bedrooms'] + train_data['bathrooms']
train_data['living_rooms'] = train_data['bedrooms'] - train_data['bathrooms']
train_data['even_rooms'] = train_data['rooms'].apply(lambda x : (x%2) == 0)
| No | 5 | 8 | Data_Transform | feature_engineering | 201,234 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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"] | 4 | B |
6,185 | def price_per_room(row):
rooms = row['rooms']
if rooms == 0:
return -1
price_per_room = row['price'] / rooms
return price_per_room
train_data['price_per_room'] = train_data.apply(lambda row: price_per_room(row), axis=1)
| No | 5 | 8 | Data_Transform | feature_engineering | 201,234 | 1 | A | 0 | 2 | 1 | 0 | 0 | 7 | ok | 3 | 3 | [{"detector": "framework_specific", "framework": "ScikitLearn", "name": "Cross Validation Checker", "fix": "Implement cross-validation techniques for robust model evaluation.", "benefits": "Enhances model performance and reduces overfitting.", "location": ""}, {"detector": "framework_specific", "framework": "ScikitLear... | B | Cross Validation Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}] | ["Dependent Threshold Checker", "Missing function docstring"] | 4 | B |
6,186 | train_data['created'] = pd.to_datetime(train_data['created'])
train_data['year'] = train_data['created'].dt.year
train_data['month'] = train_data['created'].dt.month
train_data['day'] = train_data['created'].dt.day
train_data['hour'] = train_data['created'].dt.hour
train_data['month'] = train_data['month'].apply(lambd... | No | 5 | 8 | Data_Transform | feature_engineering | 201,234 | 1 | A | 0 | 2 | 2 | 0 | 0 | 7 | ok | 4 | 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, 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"}] | ["Dependent Threshold Checker"] | 4 | B |
6,187 | train_data['MMDD'] = train_data.apply(lambda x: str(x.month) + str(x.day), axis=1)
| No | 5 | 8 | Data_Transform | feature_engineering | 201,234 | 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"] | 4 | B |
6,188 | month_counts = train_data.groupby(['day', 'hour']).agg({target: 'count'})
month_counts = month_counts.sort_values(target, ascending=False)
| Yes | 3 | 60 | Data_Transform | groupby | 201,234 | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | ok | 4 | 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, 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"}] | ["Dependent Threshold Checker"] | 4 | B |
6,189 | train_data['months_with_less_listings'] = train_data.apply(lambda x: 0 if x['month'] == 31 else 1, axis=1)
| No | 5 | 8 | Data_Transform | feature_engineering | 201,234 | 1 | A | 0 | 1 | 1 | 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"] | 4 | B |
6,190 | build_group = train_data.groupby([target, 'building_id'])
| No | 5 | 60 | Data_Transform | groupby | 201,234 | 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"] | 4 | B |
6,191 | buildings_with_all_listings = unstacked_df.ix[unstacked_df['frequency'] > 1]
| No | 5 | 14 | Data_Transform | filter | 201,234 | 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"] | 4 | A |
6,192 | print(df.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 |
6,193 | clf = RandomForestClassifier(n_estimators=1000)
clf.fit(X_train, y_train)
y_val_pred = clf.predict_proba(X_val)
print(y_val_pred.shape)
log_loss(y_val, y_val_pred) | Yes | 3 | 4 | Model_Train | choose_model_class | 201,726 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 11 | 9 | [{"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, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control 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"] | 5 | B |
6,194 | df = pd.read_json(open("../input/test.json", "r"))
print(df.shape)
df["num_photos"] = df["photos"].apply(len)
df["num_features"] = df["features"].apply(len)
df["num_description_words"] = df["description"].apply(lambda x: len(x.split(" ")))
df["created"] = pd.to_datetime(df["created"])
df["created_year"] = df["created"]... | Yes | 4 | 8 | Data_Transform | feature_engineering | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 11 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,195 | sub = pd.DataFrame()
sub["listing_id"] = df["listing_id"]
for label in ["high", "medium", "low"]:
sub[label] = y[:, labels2idx[label]]
sub.to_csv("submission_rf.csv", index=False) | No | 5 | 25 | Data_Export | save_to_csv | null | 1 | A | 0 | 1 | 1 | 0 | 0 | 5 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,196 | # 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... | No | 5 | 88 | Debug | list_files | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 0 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,197 | df = pd.read_json(open("../input/train.json", "r"))
df.head(5)
| Yes | 3 | 44 | Data_Extraction | load_from_disk | null | 1 | A | 0 | 0 | 0 | 0 | 0 | 2 | no_ml_import | 0 | 0 | [] | null | no_ml_import | [] | [] | 0 | null |
6,198 | df.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 |
6,199 | numeric_features = ["bathrooms", "bedrooms", "latitude", "longitude", "price"]
X = df[numeric_features]
y = df["interest_level"]
X.head()
| No | 3 | 21 | Data_Transform | prepare_x_and_y | 202,889 | 1 | A | 0 | 0 | 0 | 0 | 0 | 4 | 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 |
6,200 | X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.33)
random_forest_classifier = RandomForestClassifier(n_estimators=1500)
random_forest_classifier.fit(X_train, y_train)
y_val_pred = random_forest_classifier.predict_proba(X_val)
log_loss(y_val, y_val_pred)
| Yes | 3 | 13 | Data_Transform | split | 202,889 | 1 | A | 0 | 0 | 0 | 0 | 0 | 5 | ok | 12 | 9 | [{"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, No random seed setting detected=low, Randomness Control Checker=low | [{"type": "Cross Validation Checker", "severity": "low"}, {"type": "Cross-validation not detected", "severity": "low"}, {"type": "No random seed setting detected", "severity": "low"}, {"type": "Randomness Control 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"] | 5 | B |
6,201 | df_test = pd.read_json(open("../input/test.json", "r"))
X_test = df_test[numeric_features]
y_test = random_forest_classifier.predict_proba(X_test)
| Yes | 3 | 44 | Data_Extraction | load_from_disk | 202,889 | 1 | A | 0 | 0 | 0 | 0 | 0 | 3 | 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": "Dependent Th... | A | no applicable ML-specific findings | [] | ["Column Selection Checker", "Dependent Threshold Checker", "Exception Handling Checker", "Hardcoded file path", "No error handling detected in data processing. Consider adding try-except blocks or validation checks for robustness.", "Unit Testing Checker"] | 5 | A |
6,202 | plt.figure(figsize=(8, 4))<br>price_group.head(5)["total_price"].plot(kind=\'barh\',color="orange")<br>plt.show()<br>plt.close()<br> | 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 |
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