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「Data Every Day」を毎日やるAdvent Calendar 2020Day 2
@kentakozuka(Kenta Kozuka)

Data Every Day: AV : Healthcare Analytics II

Posted at

tldr

KggleのAV : Healthcare Analytics II
をPredicting Hospital Stays - Data Every Day #024に沿ってやっていきます。

実行環境はGoogle Colaboratorです。

インポート

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

import tensorflow as tf

データのダウンロード

Google Driveをマウントします。

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。

import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path

from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()

Kaggle APIを使ってデータをダウンロードします。

dataset_id = 'nehaprabhavalkar/av-healthcare-analytics-ii'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path

Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.9)





'/content/healthcare/train_data.csv'

api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)

100%|██████████| 4.45M/4.45M [00:00<00:00, 213MB/s]

Downloading train_data.csv.zip to /content









True

os.listdir('/content')

['.config', 'train_data.csv.zip', 'drive', 'sample_data']

import zipfile

zip_path = '/content/train_data.csv.zip'
with zipfile.ZipFile(zip_path) as existing_zip:
    existing_zip.extractall('/content')

os.listdir('/content')

['.config', 'train_data.csv.zip', 'drive', 'train_data.csv', 'sample_data']

データの読み込み

Padasを使ってダウンロードしてきたCSVファイルを読み込みます。

data = pd.read_csv('/content/train_data.csv')

data
case_id Hospital_code Hospital_type_code City_Code_Hospital Hospital_region_code Available Extra Rooms in Hospital Department Ward_Type Ward_Facility_Code Bed Grade patientid City_Code_Patient Type of Admission Severity of Illness Visitors with Patient Age Admission_Deposit Stay
0 1 8 c 3 Z 3 radiotherapy R F 2.0 31397 7.0 Emergency Extreme 2 51-60 4911.0 0-10
1 2 2 c 5 Z 2 radiotherapy S F 2.0 31397 7.0 Trauma Extreme 2 51-60 5954.0 41-50
2 3 10 e 1 X 2 anesthesia S E 2.0 31397 7.0 Trauma Extreme 2 51-60 4745.0 31-40
3 4 26 b 2 Y 2 radiotherapy R D 2.0 31397 7.0 Trauma Extreme 2 51-60 7272.0 41-50
4 5 26 b 2 Y 2 radiotherapy S D 2.0 31397 7.0 Trauma Extreme 2 51-60 5558.0 41-50
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
318433 318434 6 a 6 X 3 radiotherapy Q F 4.0 86499 23.0 Emergency Moderate 3 41-50 4144.0 11-20
318434 318435 24 a 1 X 2 anesthesia Q E 4.0 325 8.0 Urgent Moderate 4 81-90 6699.0 31-40
318435 318436 7 a 4 X 3 gynecology R F 4.0 125235 10.0 Emergency Minor 3 71-80 4235.0 11-20
318436 318437 11 b 2 Y 3 anesthesia Q D 3.0 91081 8.0 Trauma Minor 5 11-20 3761.0 11-20
318437 318438 19 a 7 Y 5 gynecology Q C 2.0 21641 8.0 Emergency Minor 2 11-20 4752.0 0-10

318438 rows × 18 columns

下準備

欠損値の処理

data.isnull().sum()

case_id                                 0
Hospital_code                           0
Hospital_type_code                      0
City_Code_Hospital                      0
Hospital_region_code                    0
Available Extra Rooms in Hospital       0
Department                              0
Ward_Type                               0
Ward_Facility_Code                      0
Bed Grade                             113
patientid                               0
City_Code_Patient                    4532
Type of Admission                       0
Severity of Illness                     0
Visitors with Patient                   0
Age                                     0
Admission_Deposit                       0
Stay                                    0
dtype: int64

欠損値に列全体の平均を挿入します。

def impute_missing_values(data, columns):
    for column in columns:
        data[column] = data[column].fillna(data[column].mean())

impute_columns = ['Bed Grade', 'City_Code_Patient']
impute_missing_values(data, impute_columns)

data.isnull().sum()

case_id                              0
Hospital_code                        0
Hospital_type_code                   0
City_Code_Hospital                   0
Hospital_region_code                 0
Available Extra Rooms in Hospital    0
Department                           0
Ward_Type                            0
Ward_Facility_Code                   0
Bed Grade                            0
patientid                            0
City_Code_Patient                    0
Type of Admission                    0
Severity of Illness                  0
Visitors with Patient                0
Age                                  0
Admission_Deposit                    0
Stay                                 0
dtype: int64

オブジェクト型の処理

data.dtypes

case_id                                int64
Hospital_code                          int64
Hospital_type_code                    object
City_Code_Hospital                     int64
Hospital_region_code                  object
Available Extra Rooms in Hospital      int64
Department                            object
Ward_Type                             object
Ward_Facility_Code                    object
Bed Grade                            float64
patientid                              int64
City_Code_Patient                    float64
Type of Admission                     object
Severity of Illness                   object
Visitors with Patient                  int64
Age                                   object
Admission_Deposit                    float64
Stay                                  object
dtype: object

def get_categorical_unique(data):
    # object型の列のみをリストにする
    categorical_columns = [column for column in data.dtypes.index if data.dtypes[column] == 'object']
    # 各列の値を取り出しマップを作る
    categorical_uniques = {column: data[column].unique() for column in categorical_columns}
    return categorical_uniques

get_categorical_unique(data)

{'Age': array(['51-60', '71-80', '31-40', '41-50', '81-90', '61-70', '21-30',
        '11-20', '0-10', '91-100'], dtype=object),
 'Department': array(['radiotherapy', 'anesthesia', 'gynecology', 'TB & Chest disease',
        'surgery'], dtype=object),
 'Hospital_region_code': array(['Z', 'X', 'Y'], dtype=object),
 'Hospital_type_code': array(['c', 'e', 'b', 'a', 'f', 'd', 'g'], dtype=object),
 'Severity of Illness': array(['Extreme', 'Moderate', 'Minor'], dtype=object),
 'Stay': array(['0-10', '41-50', '31-40', '11-20', '51-60', '21-30', '71-80',
        'More than 100 Days', '81-90', '61-70', '91-100'], dtype=object),
 'Type of Admission': array(['Emergency', 'Trauma', 'Urgent'], dtype=object),
 'Ward_Facility_Code': array(['F', 'E', 'D', 'B', 'A', 'C'], dtype=object),
 'Ward_Type': array(['R', 'S', 'Q', 'P', 'T', 'U'], dtype=object)}

各データの特性を見て

  • Labelエンコード
  • Onehotエンコード
  • Ordinalエンコード

のどれでエンコードするか判断する

Onehotエンコード

def onehot_encode(data, columns):
    for column in columns:
        dummies = pd.get_dummies(data[column])
        data = pd.concat([data, dummies], axis=1)
        data = data.drop(column, axis=1)
    return data

onehot_columns =  ['Department', 'Hospital_region_code', 'Hospital_type_code', 'Ward_Facility_Code', 'Ward_Type']

data = onehot_encode(data, onehot_columns)

Ordinalエンコード

categorical_uniques = get_categorical_unique(data)
categorical_uniques

{'Age': array(['51-60', '71-80', '31-40', '41-50', '81-90', '61-70', '21-30',
        '11-20', '0-10', '91-100'], dtype=object),
 'Severity of Illness': array(['Extreme', 'Moderate', 'Minor'], dtype=object),
 'Stay': array(['0-10', '41-50', '31-40', '11-20', '51-60', '21-30', '71-80',
        'More than 100 Days', '81-90', '61-70', '91-100'], dtype=object),
 'Type of Admission': array(['Emergency', 'Trauma', 'Urgent'], dtype=object)}

for column in categorical_uniques:
    categorical_uniques[column] = sorted(categorical_uniques[column])

categorical_uniques

{'Age': ['0-10',
  '11-20',
  '21-30',
  '31-40',
  '41-50',
  '51-60',
  '61-70',
  '71-80',
  '81-90',
  '91-100'],
 'Severity of Illness': ['Extreme', 'Minor', 'Moderate'],
 'Stay': ['0-10',
  '11-20',
  '21-30',
  '31-40',
  '41-50',
  '51-60',
  '61-70',
  '71-80',
  '81-90',
  '91-100',
  'More than 100 Days'],
 'Type of Admission': ['Emergency', 'Trauma', 'Urgent']}

unique_list = categorical_uniques['Type of Admission']
unique_list.insert(0, unique_list.pop(unique_list.index('Urgent')))
unique_list.insert(0, unique_list.pop(unique_list.index('Trauma')))

unique_list = categorical_uniques['Severity of Illness']
unique_list.insert(0, unique_list.pop(unique_list.index('Moderate')))
unique_list.insert(0, unique_list.pop(unique_list.index('Minor')))

categorical_uniques

{'Age': ['0-10',
  '11-20',
  '21-30',
  '31-40',
  '41-50',
  '51-60',
  '61-70',
  '71-80',
  '81-90',
  '91-100'],
 'Severity of Illness': ['Minor', 'Moderate', 'Extreme'],
 'Stay': ['0-10',
  '11-20',
  '21-30',
  '31-40',
  '41-50',
  '51-60',
  '61-70',
  '71-80',
  '81-90',
  '91-100',
  'More than 100 Days'],
 'Type of Admission': ['Trauma', 'Urgent', 'Emergency']}

stay_mappings = {value: index for index, value in enumerate(categorical_uniques['Stay'])}

def ordinal_encode(data, uniques):
    for column in uniques:
        data[column] = data[column].apply(lambda x: uniques[column].index(x))

ordinal_encode(data, categorical_uniques)

data['Stay']

0         0
1         4
2         3
3         4
4         4
         ..
318433    1
318434    3
318435    1
318436    1
318437    0
Name: Stay, Length: 318438, dtype: int64

すべてが数値になっていることを確認

(data.dtypes == 'object').sum()

0

スケーリング

data = data.set_index('case_id')

y = data['Stay']
X = data.drop(['Stay'], axis=1)

y

case_id
1         0
2         4
3         3
4         4
5         4
         ..
318434    1
318435    3
318436    1
318437    1
318438    0
Name: Stay, Length: 318438, dtype: int64

scaler = sp.StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X), index=X.index, columns=X.columns)

X
Hospital_code City_Code_Hospital Available Extra Rooms in Hospital Bed Grade patientid City_Code_Patient Type of Admission Severity of Illness Visitors with Patient Age Admission_Deposit TB & Chest disease anesthesia gynecology radiotherapy surgery X Y Z a b c d e f g A B C D E F P Q R S T U
case_id
1 -1.195176 -0.571055 -0.169177 -0.716855 -0.904442 -0.053458 1.212557 1.646648 -0.727923 0.461600 0.027835 -0.176175 -0.320416 -1.902171 3.188572 -0.061529 -0.848727 -0.790317 2.020115 -0.905268 -0.525686 2.435861 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 -0.458683 1.350633 -0.126891 -0.707202 1.220175 -0.568572 -0.068263 -0.005316
2 -1.890124 0.073580 -1.025217 -0.716855 -0.904442 -0.053458 -0.974973 1.646648 -0.727923 0.461600 0.987556 -0.176175 -0.320416 -1.902171 3.188572 -0.061529 -0.848727 -0.790317 2.020115 -0.905268 -0.525686 2.435861 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 -0.458683 1.350633 -0.126891 -0.707202 -0.819554 1.758792 -0.068263 -0.005316
3 -0.963527 -1.215691 -1.025217 -0.716855 -0.904442 -0.053458 -0.974973 1.646648 -0.727923 0.461600 -0.124910 -0.176175 3.120939 -1.902171 -0.313620 -0.061529 1.178235 -0.790317 -0.495021 -0.905268 -0.525686 -0.410533 -0.26155 3.443224 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 2.180153 -0.740394 -0.126891 -0.707202 -0.819554 1.758792 -0.068263 -0.005316
4 0.889668 -0.893373 -1.025217 -0.716855 -0.904442 -0.053458 -0.974973 1.646648 -0.727923 0.461600 2.200319 -0.176175 -0.320416 -1.902171 3.188572 -0.061529 -0.848727 1.265315 -0.495021 -0.905268 1.902277 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 2.268564 -0.458683 -0.740394 -0.126891 -0.707202 1.220175 -0.568572 -0.068263 -0.005316
5 0.889668 -0.893373 -1.025217 -0.716855 -0.904442 -0.053458 -0.974973 1.646648 -0.727923 0.461600 0.623175 -0.176175 -0.320416 -1.902171 3.188572 -0.061529 -0.848727 1.265315 -0.495021 -0.905268 1.902277 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 2.268564 -0.458683 -0.740394 -0.126891 -0.707202 -0.819554 1.758792 -0.068263 -0.005316
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
318434 -1.426825 0.395897 -0.169177 1.574123 0.546379 3.342582 1.212557 0.138090 -0.161049 -0.067622 -0.677923 -0.176175 -0.320416 -1.902171 3.188572 -0.061529 1.178235 -0.790317 -0.495021 1.104645 -0.525686 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 -0.458683 1.350633 -0.126891 1.414024 -0.819554 -0.568572 -0.068263 -0.005316
318435 0.658018 -1.215691 -1.025217 1.574123 -1.722559 0.158795 0.118792 0.138090 0.405826 2.049268 1.673071 -0.176175 3.120939 -1.902171 -0.313620 -0.061529 1.178235 -0.790317 -0.495021 1.104645 -0.525686 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 2.180153 -0.740394 -0.126891 1.414024 -0.819554 -0.568572 -0.068263 -0.005316
318436 -1.311001 -0.248738 -0.169177 1.574123 1.566288 0.583300 1.212557 -1.370469 -0.161049 1.520045 -0.594189 -0.176175 -0.320416 0.525715 -0.313620 -0.061529 1.178235 -0.790317 -0.495021 1.104645 -0.525686 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 -0.440807 -0.458683 1.350633 -0.126891 -0.707202 1.220175 -0.568572 -0.068263 -0.005316
318437 -0.847702 -0.893373 -0.169177 0.428634 0.667022 0.158795 -0.974973 -1.370469 0.972701 -1.655290 -1.030342 -0.176175 3.120939 -1.902171 -0.313620 -0.061529 -0.848727 1.265315 -0.495021 -0.905268 1.902277 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 -0.354009 2.268564 -0.458683 -0.740394 -0.126891 1.414024 -0.819554 -0.568572 -0.068263 -0.005316
318438 0.078895 0.718215 1.542903 -0.716855 -1.161314 0.158795 1.212557 -1.370469 -0.727923 -1.655290 -0.118469 -0.176175 -0.320416 0.525715 -0.313620 -0.061529 -0.848727 1.265315 -0.495021 1.104645 -0.525686 -0.410533 -0.26155 -0.290425 -0.186494 -0.116679 -0.309922 -0.352282 2.824791 -0.440807 -0.458683 -0.740394 -0.126891 1.414024 -0.819554 -0.568572 -0.068263 -0.005316

318438 rows × 38 columns

トレーニング

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8)

log_model = LogisticRegression()
log_model.fit(X_train, y_train)

/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)





LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)

nn_model = tf.keras.Sequential([
    tf.keras.layers.Dense(16, activation='relu', input_shape=(38, )),
    tf.keras.layers.Dense(16, activation='relu'),
    tf.keras.layers.Dense(11, activation='softmax'),
])

nn_model.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_3 (Dense)              (None, 16)                624       
_________________________________________________________________
dense_4 (Dense)              (None, 16)                272       
_________________________________________________________________
dense_5 (Dense)              (None, 11)                187       
=================================================================
Total params: 1,083
Trainable params: 1,083
Non-trainable params: 0
_________________________________________________________________

nn_model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'],
)

batch_size = 32
epochs = 10

history = nn_model.fit(
    X_train,
    y_train,
    validation_split=0.2,
    batch_size=batch_size,
    epochs=epochs,
)

Epoch 1/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.6387 - accuracy: 0.3739 - val_loss: 1.5888 - val_accuracy: 0.3951
Epoch 2/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5738 - accuracy: 0.3990 - val_loss: 1.5667 - val_accuracy: 0.4029
Epoch 3/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5593 - accuracy: 0.4034 - val_loss: 1.5563 - val_accuracy: 0.4081
Epoch 4/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5528 - accuracy: 0.4057 - val_loss: 1.5530 - val_accuracy: 0.4080
Epoch 5/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5488 - accuracy: 0.4075 - val_loss: 1.5494 - val_accuracy: 0.4100
Epoch 6/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5462 - accuracy: 0.4083 - val_loss: 1.5463 - val_accuracy: 0.4104
Epoch 7/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5445 - accuracy: 0.4084 - val_loss: 1.5462 - val_accuracy: 0.4093
Epoch 8/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5427 - accuracy: 0.4096 - val_loss: 1.5454 - val_accuracy: 0.4086
Epoch 9/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5416 - accuracy: 0.4102 - val_loss: 1.5452 - val_accuracy: 0.4117
Epoch 10/10
6369/6369 [==============================] - 8s 1ms/step - loss: 1.5404 - accuracy: 0.4109 - val_loss: 1.5431 - val_accuracy: 0.4120

print(f'Logistic Regression Acc: {log_model.score(X_test, y_test)}')
print(f'     Neural Network Acc: {nn_model.evaluate(X_test, y_test, verbose=0)[1]}')

Logistic Regression Acc: 0.38908428589373195
     Neural Network Acc: 0.41164740920066833

plt.figure(figsize=(14, 10))

plt.plot(range(epochs), history.history['loss'], label='Training Loss')
plt.plot(range(epochs), history.history['val_loss'], label='Validation Loss')

plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend(loc='upper right')

plt.show()

AV _ Healthcare Analytics II_60_0.png

np.argmin(history.history['val_loss']) + 1

10

今回はsklearnのロジスティック回帰とtensorflowの両方でモデルを構築しました。
比較するとtensorflowのほうが若干良い精度がでていました。
NNいいですね。

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