def final_auto_snapshot():
from kale.utils import pod_utils as _kale_pod_utils
_kale_pod_utils.snapshot_pipeline_step("T",
"final_auto_snapshot",
"/path/to/nb",
before=False)
def loaddata(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "loaddata",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
path = "data/"
PREDICTION_LABEL = 'Survived'
test_df = pd.read_csv(path + "test.csv")
train_df = pd.read_csv(path + "train.csv")
# -----------------------DATA SAVING START---------------------------------
if "test_df" in locals():
_kale_resource_save(test_df,
os.path.join(_kale_data_directory, "test_df"))
else:
print("_kale_resource_save: `test_df` not found.")
if "PREDICTION_LABEL" in locals():
_kale_resource_save(
PREDICTION_LABEL,
os.path.join(_kale_data_directory, "PREDICTION_LABEL"))
else:
print("_kale_resource_save: `PREDICTION_LABEL` not found.")
if "train_df" in locals():
_kale_resource_save(train_df,
os.path.join(_kale_data_directory, "train_df"))
else:
print("_kale_resource_save: `train_df` not found.")
def test():
from kale.utils import pod_utils as _kale_pod_utils
_kale_pod_utils.snapshot_pipeline_step("T",
"test",
"/path/to/nb",
before=True)
_kale_pod_utils.snapshot_pipeline_step("T",
"test",
"/path/to/nb",
before=False)
def test():
import os
import shutil
from kale.utils import pod_utils as _kale_pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/path"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
_kale_pod_utils.snapshot_pipeline_step("T", "test", "/path/to/nb")
def final_auto_snapshot(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "final_auto_snapshot",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
def test():
from kale.utils import mlmd_utils as _kale_mlmd_utils
_kale_mlmd_utils.init_metadata()
from kale.utils import pod_utils as _kale_pod_utils
_kale_mlmd_utils.call("link_input_rok_artifacts")
_kale_pod_utils.snapshot_pipeline_step(
"T",
"test",
"/path/to/nb",
before=True)
_rok_snapshot_task = _kale_pod_utils.snapshot_pipeline_step(
"T",
"test",
"/path/to/nb",
before=False)
_kale_mlmd_utils.call("submit_output_rok_artifact", _rok_snapshot_task)
_kale_mlmd_utils.call("mark_execution_complete")
def results(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "results",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
# -----------------------DATA LOADING START--------------------------------
_kale_directory_file_names = [
os.path.splitext(f)[0] for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
]
if "acc_log" not in _kale_directory_file_names:
raise ValueError("acc_log" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "acc_log"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "acc_log" + ": " +
str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
acc_log = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "acc_random_forest" not in _kale_directory_file_names:
raise ValueError("acc_random_forest" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "acc_random_forest"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " +
"acc_random_forest" + ": " +
str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
acc_random_forest = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "acc_decision_tree" not in _kale_directory_file_names:
raise ValueError("acc_decision_tree" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "acc_decision_tree"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " +
"acc_decision_tree" + ": " +
str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
acc_decision_tree = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "acc_gaussian" not in _kale_directory_file_names:
raise ValueError("acc_gaussian" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "acc_gaussian"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "acc_gaussian" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
acc_gaussian = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "acc_linear_svc" not in _kale_directory_file_names:
raise ValueError("acc_linear_svc" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "acc_linear_svc"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "acc_linear_svc" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
acc_linear_svc = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
# -----------------------DATA LOADING END----------------------------------
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
results = pd.DataFrame({
'Model': [
'Support Vector Machines', 'logistic Regression', 'Random Forest',
'Naive Bayes', 'Decision Tree'
],
'Score': [
acc_linear_svc, acc_log, acc_random_forest, acc_gaussian,
acc_decision_tree
]
})
result_df = results.sort_values(by='Score', ascending=False)
result_df = result_df.set_index('Score')
print(result_df)
def randomforest(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "randomforest",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
# -----------------------DATA LOADING START--------------------------------
_kale_directory_file_names = [
os.path.splitext(f)[0] for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
]
if "train_labels" not in _kale_directory_file_names:
raise ValueError("train_labels" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "train_labels"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "train_labels" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
train_labels = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "train_df" not in _kale_directory_file_names:
raise ValueError("train_df" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "train_df"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "train_df" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
train_df = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
# -----------------------DATA LOADING END----------------------------------
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(train_df, train_labels)
acc_random_forest = round(
random_forest.score(train_df, train_labels) * 100, 2)
# -----------------------DATA SAVING START---------------------------------
if "acc_random_forest" in locals():
_kale_resource_save(
acc_random_forest,
os.path.join(_kale_data_directory, "acc_random_forest"))
else:
print("_kale_resource_save: `acc_random_forest` not found.")
def datapreprocessing(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "datapreprocessing",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
# -----------------------DATA LOADING START--------------------------------
_kale_directory_file_names = [
os.path.splitext(f)[0] for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
]
if "test_df" not in _kale_directory_file_names:
raise ValueError("test_df" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "test_df"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "test_df" + ": " +
str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
test_df = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "train_df" not in _kale_directory_file_names:
raise ValueError("train_df" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "train_df"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "train_df" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
train_df = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
# -----------------------DATA LOADING END----------------------------------
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
data = [train_df, test_df]
for dataset in data:
dataset['relatives'] = dataset['SibSp'] + dataset['Parch']
dataset.loc[dataset['relatives'] > 0, 'not_alone'] = 0
dataset.loc[dataset['relatives'] == 0, 'not_alone'] = 1
dataset['not_alone'] = dataset['not_alone'].astype(int)
train_df['not_alone'].value_counts()
# This does not contribute to a person survival probability
train_df = train_df.drop(['PassengerId'], axis=1)
import re
deck = {"A": 1, "B": 2, "C": 3, "D": 4, "E": 5, "F": 6, "G": 7, "U": 8}
data = [train_df, test_df]
for dataset in data:
dataset['Cabin'] = dataset['Cabin'].fillna("U0")
dataset['Deck'] = dataset['Cabin'].map(
lambda x: re.compile("([a-zA-Z]+)").search(x).group())
dataset['Deck'] = dataset['Deck'].map(deck)
dataset['Deck'] = dataset['Deck'].fillna(0)
dataset['Deck'] = dataset['Deck'].astype(int)
# we can now drop the cabin feature
train_df = train_df.drop(['Cabin'], axis=1)
test_df = test_df.drop(['Cabin'], axis=1)
data = [train_df, test_df]
for dataset in data:
mean = train_df["Age"].mean()
std = test_df["Age"].std()
is_null = dataset["Age"].isnull().sum()
# compute random numbers between the mean, std and is_null
rand_age = np.random.randint(mean - std, mean + std, size=is_null)
# fill NaN values in Age column with random values generated
age_slice = dataset["Age"].copy()
age_slice[np.isnan(age_slice)] = rand_age
dataset["Age"] = age_slice
dataset["Age"] = train_df["Age"].astype(int)
train_df["Age"].isnull().sum()
train_df['Embarked'].describe()
# fill with most common value
common_value = 'S'
data = [train_df, test_df]
for dataset in data:
dataset['Embarked'] = dataset['Embarked'].fillna(common_value)
train_df.info()
# -----------------------DATA SAVING START---------------------------------
if "test_df" in locals():
_kale_resource_save(test_df,
os.path.join(_kale_data_directory, "test_df"))
else:
print("_kale_resource_save: `test_df` not found.")
if "train_df" in locals():
_kale_resource_save(train_df,
os.path.join(_kale_data_directory, "train_df"))
else:
print("_kale_resource_save: `train_df` not found.")
def featureengineering(rok_workspace_aidays01_2rlcyd0k8_url: str):
import os
import shutil
from kale.utils import pod_utils
from kale.marshal import resource_save as _kale_resource_save
from kale.marshal import resource_load as _kale_resource_load
_kale_data_directory = "/home/jovyan/examples/titanic-ml-dataset/.titanic_dataset_ml.ipynb.kale.marshal.dir"
if not os.path.isdir(_kale_data_directory):
os.makedirs(_kale_data_directory, exist_ok=True)
pod_utils.snapshot_pipeline_step(
"titanic-ml-fylgn", "featureengineering",
"/home/jovyan/examples/titanic-ml-dataset/titanic_dataset_ml.ipynb")
# -----------------------DATA LOADING START--------------------------------
_kale_directory_file_names = [
os.path.splitext(f)[0] for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
]
if "PREDICTION_LABEL" not in _kale_directory_file_names:
raise ValueError("PREDICTION_LABEL" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "PREDICTION_LABEL"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " +
"PREDICTION_LABEL" + ": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
PREDICTION_LABEL = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "test_df" not in _kale_directory_file_names:
raise ValueError("test_df" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "test_df"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "test_df" + ": " +
str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
test_df = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
if "train_df" not in _kale_directory_file_names:
raise ValueError("train_df" + " does not exists in directory")
_kale_load_file_name = [
f for f in os.listdir(_kale_data_directory)
if os.path.isfile(os.path.join(_kale_data_directory, f))
and os.path.splitext(f)[0] == "train_df"
]
if len(_kale_load_file_name) > 1:
raise ValueError("Found multiple files with name " + "train_df" +
": " + str(_kale_load_file_name))
_kale_load_file_name = _kale_load_file_name[0]
train_df = _kale_resource_load(
os.path.join(_kale_data_directory, _kale_load_file_name))
# -----------------------DATA LOADING END----------------------------------
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib import style
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
data = [train_df, test_df]
for dataset in data:
dataset['Fare'] = dataset['Fare'].fillna(0)
dataset['Fare'] = dataset['Fare'].astype(int)
data = [train_df, test_df]
titles = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in data:
# extract titles
dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\.',
expand=False)
# replace titles with a more common title or as Rare
dataset['Title'] = dataset['Title'].replace([
'Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev',
'Sir', 'Jonkheer', 'Dona'
], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')
# convert titles into numbers
dataset['Title'] = dataset['Title'].map(titles)
# filling NaN with 0, to get safe
dataset['Title'] = dataset['Title'].fillna(0)
train_df = train_df.drop(['Name'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
genders = {"male": 0, "female": 1}
data = [train_df, test_df]
for dataset in data:
dataset['Sex'] = dataset['Sex'].map(genders)
train_df = train_df.drop(['Ticket'], axis=1)
test_df = test_df.drop(['Ticket'], axis=1)
ports = {"S": 0, "C": 1, "Q": 2}
data = [train_df, test_df]
for dataset in data:
dataset['Embarked'] = dataset['Embarked'].map(ports)
data = [train_df, test_df]
for dataset in data:
dataset['Age'] = dataset['Age'].astype(int)
dataset.loc[dataset['Age'] <= 11, 'Age'] = 0
dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1
dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2
dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3
dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4
dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5
dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6
dataset.loc[dataset['Age'] > 66, 'Age'] = 6
# let's see how it's distributed train_df['Age'].value_counts()
data = [train_df, test_df]
for dataset in data:
dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454),
'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31),
'Fare'] = 2
dataset.loc[(dataset['Fare'] > 31) & (dataset['Fare'] <= 99),
'Fare'] = 3
dataset.loc[(dataset['Fare'] > 99) & (dataset['Fare'] <= 250),
'Fare'] = 4
dataset.loc[dataset['Fare'] > 250, 'Fare'] = 5
dataset['Fare'] = dataset['Fare'].astype(int)
data = [train_df, test_df]
for dataset in data:
dataset['Age_Class'] = dataset['Age'] * dataset['Pclass']
for dataset in data:
dataset['Fare_Per_Person'] = dataset['Fare'] / (dataset['relatives'] +
1)
dataset['Fare_Per_Person'] = dataset['Fare_Per_Person'].astype(int)
# Let's take a last look at the training set, before we start training the models.
train_df.head(10)
train_labels = train_df[PREDICTION_LABEL]
train_df = train_df.drop(PREDICTION_LABEL, axis=1)
# -----------------------DATA SAVING START---------------------------------
if "train_labels" in locals():
_kale_resource_save(train_labels,
os.path.join(_kale_data_directory, "train_labels"))
else:
print("_kale_resource_save: `train_labels` not found.")
if "train_df" in locals():
_kale_resource_save(train_df,
os.path.join(_kale_data_directory, "train_df"))
else:
print("_kale_resource_save: `train_df` not found.")