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Note: This is a generated markdown export from the Jupyter notebook file classification_sdg.ipynb. You can also view the notebook with the nbviewer from Jupyter.

Classification with stochastic gradient descent (SGD)

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

from sklearn import linear_model, datasets, metrics, model_selection, preprocessing, pipeline

Load the data set

wine = datasets.load_wine()
print(wine.DESCR)
.. _wine_dataset:

Wine recognition dataset
------------------------

**Data Set Characteristics:**

    :Number of Instances: 178 (50 in each of three classes)
    :Number of Attributes: 13 numeric, predictive attributes and the class
    :Attribute Information:
 		- Alcohol
 		- Malic acid
 		- Ash
		- Alcalinity of ash  
 		- Magnesium
		- Total phenols
 		- Flavanoids
 		- Nonflavanoid phenols
 		- Proanthocyanins
		- Color intensity
 		- Hue
 		- OD280/OD315 of diluted wines
 		- Proline

    - class:
            - class_0
            - class_1
            - class_2
		
    :Summary Statistics:
    
    ============================= ==== ===== ======= =====
                                   Min   Max   Mean     SD
    ============================= ==== ===== ======= =====
    Alcohol:                      11.0  14.8    13.0   0.8
    Malic Acid:                   0.74  5.80    2.34  1.12
    Ash:                          1.36  3.23    2.36  0.27
    Alcalinity of Ash:            10.6  30.0    19.5   3.3
    Magnesium:                    70.0 162.0    99.7  14.3
    Total Phenols:                0.98  3.88    2.29  0.63
    Flavanoids:                   0.34  5.08    2.03  1.00
    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12
    Proanthocyanins:              0.41  3.58    1.59  0.57
    Colour Intensity:              1.3  13.0     5.1   2.3
    Hue:                          0.48  1.71    0.96  0.23
    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71
    Proline:                       278  1680     746   315
    ============================= ==== ===== ======= =====

    :Missing Attribute Values: None
    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML Wine recognition datasets.
https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

The data is the results of a chemical analysis of wines grown in the same
region in Italy by three different cultivators. There are thirteen different
measurements taken for different constituents found in the three types of
wine.

Original Owners: 

Forina, M. et al, PARVUS - 
An Extendible Package for Data Exploration, Classification and Correlation. 
Institute of Pharmaceutical and Food Analysis and Technologies,
Via Brigata Salerno, 16147 Genoa, Italy.

Citation:

Lichman, M. (2013). UCI Machine Learning Repository
[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
School of Information and Computer Science. 

.. topic:: References

  (1) S. Aeberhard, D. Coomans and O. de Vel, 
  Comparison of Classifiers in High Dimensional Settings, 
  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  
  Mathematics and Statistics, James Cook University of North Queensland. 
  (Also submitted to Technometrics). 

  The data was used with many others for comparing various 
  classifiers. The classes are separable, though only RDA 
  has achieved 100% correct classification. 
  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) 
  (All results using the leave-one-out technique) 

  (2) S. Aeberhard, D. Coomans and O. de Vel, 
  "THE CLASSIFICATION PERFORMANCE OF RDA" 
  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of 
  Mathematics and Statistics, James Cook University of North Queensland. 
  (Also submitted to Journal of Chemometrics).

X = pd.DataFrame(wine.data, columns=wine.feature_names)
y = wine.target

Stratify the data by the target label

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.5, stratify=y)

df_train = pd.DataFrame(y_train, columns=['target'])
df_train['type'] = 'train'

df_test = pd.DataFrame(y_test, columns=['target'])
df_test['type'] = 'test'

df_set = df_train.append(df_test)

_ = sns.countplot(x='target', hue='type', data=df_set)     

print('train samples:', len(X_train))
print('test samples', len(X_test))
train samples: 89
test samples 89

png

model = pipeline.make_pipeline(preprocessing.StandardScaler(), linear_model.SGDClassifier(max_iter=1000, tol=1e-3))
model.fit(X_train, y_train)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('sgdclassifier', SGDClassifier())])

predicted = model.predict(X_test)

confusion_matrix = pd.DataFrame(metrics.confusion_matrix(y_test, predicted))
confusion_matrix
0 1 2
0 29 1 0
1 0 32 3
2 0 0 24 
_ = sns.heatmap(confusion_matrix, annot=True, cmap="Blues")

png

print("accuracy: {:.3f}".format(metrics.accuracy_score(y_test, predicted)))
print("precision: {:.3f}".format(metrics.precision_score(y_test, predicted, average='weighted')))
print("recall: {:.3f}".format(metrics.recall_score(y_test, predicted, average='weighted')))
print("f1 score: {:.3f}".format(metrics.f1_score(y_test, predicted, average='weighted')))
accuracy: 0.955
precision: 0.958
recall: 0.955
f1 score: 0.955