wine-sklearn.ipynb 95 KB
# the basic imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# but also reporting on the model
from sklearn.metrics import classification_report, confusion_matrix
# and a couple of utilities
from sklearn.utils import resample# load data, extract just the features, and just the labels
wine_data = pd.read_csv("./WineQT.csv", delimiter=",")
wine_features = wine_data.drop("quality", axis=1).drop("Id", axis=1)
wine_labels = np.ravel(wine_data['quality'])# split the dataset into train and test subsets
# note, while it may be tempting to get creative with variable names, such as
# features_train, features_test, labels_train, labels_test...
# it's WAY TOO MUCH typing, and most examples use x for features (as in, input
# data) and y for labels (as in, result)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(wine_features, wine_labels, test_size=0.2, random_state=50)print("train:", len(x_train), "test:", len(x_test))train: 914 test: 229
print("sample:", resample(x_train, n_samples=5))sample: fixed acidity volatile acidity citric acid residual sugar chlorides \
309 7.0 0.62 0.18 1.5 0.062
258 12.5 0.46 0.63 2.0 0.071
1122 6.4 0.36 0.53 2.2 0.230
378 10.0 0.48 0.24 2.7 0.102
712 8.0 0.58 0.16 2.0 0.120
free sulfur dioxide total sulfur dioxide density pH sulphates \
309 7.0 50.0 0.99510 3.08 0.60
258 6.0 15.0 0.99880 2.99 0.87
1122 19.0 35.0 0.99340 3.37 0.93
378 13.0 32.0 1.00000 3.28 0.56
712 3.0 7.0 0.99454 3.22 0.58
alcohol
309 9.3
258 10.2
1122 12.4
378 10.0
712 11.2
# normalise the data (meaning spread it ALL out on a scale between a..b)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)print("normalised sample:", resample(x_train, n_samples=5))normalised sample: [[ 0.03113943 1.24584416 -0.83529885 -0.48591162 0.09791733 0.05205639 0.53090333 -0.12485948 -0.76774246 1.02627829 -0.80008014] [ 0.31293584 -1.00062031 0.65403462 0.86207888 0.55581673 -0.34777171 -0.684197 -0.64140864 -0.5136124 0.27501217 1.41492055] [-0.53245339 0.42119264 -0.06495395 -0.3273245 -0.06234746 0.15201342 1.40328305 -0.29874731 -0.13241733 -0.60146497 -0.61549675] [-0.41973482 -0.14753254 0.08911503 -0.16873739 0.00633745 -1.0474709 -0.99576119 -0.881783 0.05818021 -0.22583191 0.95346207] [-0.30701626 0.67711897 -1.40021845 2.36865649 0.00633745 -1.0474709 -0.87113551 0.94915362 0.56644031 -0.66407048 -0.80008014]]
# train the SVC model
print("**** TESTING C-Support Vector Classification ****")
from sklearn.svm import SVC
svc_model = SVC()
svc_model.fit(x_train, y_train)
# now test the fitness with the test subset
svc_y_predict = svc_model.predict(x_test)
# visualise it
print("x: predictions, y: labels")
svc_cm = np.array(confusion_matrix(y_test, svc_y_predict, labels=[0,1,2,3,4,5,6,7,8,9,10]))
svc_conf_matrix = pd.DataFrame(svc_cm)
print(svc_conf_matrix)**** TESTING C-Support Vector Classification ****
x: predictions, y: labels
0 1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 2 0 0 0 0 0
4 0 0 0 0 0 6 4 1 0 0 0
5 0 0 0 0 0 78 21 0 0 0 0
6 0 0 0 0 0 29 50 0 0 0 0
7 0 0 0 0 0 1 27 9 0 0 0
8 0 0 0 0 0 0 1 0 0 0 0
9 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0
# visualise the SVC model in a nice picture
sns.heatmap(svc_conf_matrix, annot=True, fmt='g')
plt.show()
# train the NuSVC model
print("**** TESTING Nu-Support Vector Classification ****")
from sklearn.svm import NuSVC
nusvc_model = NuSVC(nu=0.015)
nusvc_model.fit(x_train, y_train)
# now test the fitness with the test subset
nusvc_y_predict = svc_model.predict(x_test)
# visualise it
print("x: predictions, y: labels")
nu_cm = np.array(confusion_matrix(y_test, nusvc_y_predict, labels=[0,1,2,3,4,5,6,7,8,9,10]))
nu_conf_matrix = pd.DataFrame(nu_cm)
print(nu_conf_matrix)**** TESTING Nu-Support Vector Classification ****
x: predictions, y: labels
0 1 2 3 4 5 6 7 8 9 10
0 0 0 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 2 0 0 0 0 0
4 0 0 0 0 0 6 4 1 0 0 0
5 0 0 0 0 0 78 21 0 0 0 0
6 0 0 0 0 0 29 50 0 0 0 0
7 0 0 0 0 0 1 27 9 0 0 0
8 0 0 0 0 0 0 1 0 0 0 0
9 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0
# visualise the NuSVC model in a nice picture
sns.heatmap(nu_conf_matrix, annot=True, fmt='g')
plt.show()type(y_test)numpy.ndarray
# train the SVR model
print("**** TESTING C-Support Vector Regression ****")
from sklearn.svm import SVR
svr_model = SVR(kernel="rbf")
svr_model.fit(x_train, y_train)
# now test the fitness with the test subset
svr_y_predict = svr_model.predict(x_test)**** TESTING C-Support Vector Regression ****
# visualise it
plt.scatter(range(0, len(y_test)), y_test, color = 'blue')
plt.scatter(range(0, len(y_test)), svr_y_predict, color = 'green')<matplotlib.collections.PathCollection at 0x30c74aad0>
What else is there to do?
* improve visualisations
* experiment with model settings
* try different train/test sizes