Data Every Day: 動物分類

tldr

KggleのZoo Animal ClassificationをTabular Zoo Animal Classification - Data Every Day #032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

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')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).

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 = 'uciml/zoo-animal-classification'
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Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.10 / client 1.5.9)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/zoo.csv'

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

100%|██████████| 4.27k/4.27k [00:00<00:00, 1.04MB/s]

Downloading zoo.csv to /content









True

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(file_path)

data

	animal_name	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	class_type
0	aardvark	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	1
1	antelope	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	1
2	bass	0	0	1	0	0	1	1	1	1	0	0	1	0	1	0	0	4
3	bear	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	1
4	boar	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	wallaby	1	0	0	1	0	0	0	1	1	1	0	0	2	1	0	1	1
97	wasp	1	0	1	0	1	0	0	0	0	1	1	0	6	0	0	0	6
98	wolf	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	1
99	worm	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	7
100	wren	0	1	1	0	1	0	0	0	1	1	0	0	2	1	0	0	2

101 rows × 18 columns

下準備

不要な列の削除

data = data.drop('animal_name', axis=1)

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 101 entries, 0 to 100
Data columns (total 17 columns):
 #   Column      Non-Null Count  Dtype
---  ------      --------------  -----
 0   hair        101 non-null    int64
 1   feathers    101 non-null    int64
 2   eggs        101 non-null    int64
 3   milk        101 non-null    int64
 4   airborne    101 non-null    int64
 5   aquatic     101 non-null    int64
 6   predator    101 non-null    int64
 7   toothed     101 non-null    int64
 8   backbone    101 non-null    int64
 9   breathes    101 non-null    int64
 10  venomous    101 non-null    int64
 11  fins        101 non-null    int64
 12  legs        101 non-null    int64
 13  tail        101 non-null    int64
 14  domestic    101 non-null    int64
 15  catsize     101 non-null    int64
 16  class_type  101 non-null    int64
dtypes: int64(17)
memory usage: 13.5 KB

データはとてもきれいです。ほぼやることがない。

分割とスケーリング

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

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

X

	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize
0	1.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.50	0.0	0.0	1.0
1	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.50	1.0	0.0	1.0
2	0.0	0.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	1.0	0.00	1.0	0.0	0.0
3	1.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.50	0.0	0.0	1.0
4	1.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.50	1.0	0.0	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	1.0	0.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.25	1.0	0.0	1.0
97	1.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.75	0.0	0.0	0.0
98	1.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	1.0	1.0	0.0	0.0	0.50	1.0	0.0	1.0
99	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.00	0.0	0.0	0.0
100	0.0	1.0	1.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.25	1.0	0.0	0.0

101 rows × 16 columns

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)

トレーニング

ロジスティック回帰

from sklearn.linear_model import LogisticRegression

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

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)

SVM

from sklearn.svm import SVC

svm_model = SVC(C=1.0)
svm_model.fit(X_train, y_train)

SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

ニューラルネット

from sklearn.neural_network import MLPClassifier

nn_model = MLPClassifier(hidden_layer_sizes=(64, 64))
nn_model.fit(X_train, y_train)

/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.
  % self.max_iter, ConvergenceWarning)





MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
              beta_2=0.999, early_stopping=False, epsilon=1e-08,
              hidden_layer_sizes=(64, 64), learning_rate='constant',
              learning_rate_init=0.001, max_fun=15000, max_iter=200,
              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
              power_t=0.5, random_state=None, shuffle=True, solver='adam',
              tol=0.0001, validation_fraction=0.1, verbose=False,
              warm_start=False)

結果

log_acc = log_model.score(X_test, y_test)
svm_acc = svm_model.score(X_test, y_test)
nn_acc = nn_model.score(X_test, y_test)

print('Accuracy Results\n' + '*'*16)
print('Logistic Model:', log_acc)
print('     SVM Model:', svm_acc)
print('      NN Model:', nn_acc)

Accuracy Results
****************
Logistic Model: 0.9354838709677419
     SVM Model: 0.9032258064516129
      NN Model: 1.0

結果としてはニューラルネットが最も高い精度になりました。
ただ、データ自体がとても少ないのでそこは注意が必要だと思いました。