from Wine import getWineData
from Util import plot_decision_regions
from sklearn.lda import LDA
from sklearn.linear_model import LogisticRegression
import numpy as np
import matplotlib.pyplot as plt
X_train_std, X_test_std, y_train, y_test = getWineData()
lda = LDA(n_components=2)
X_train_lda = lda.fit_transform(X_train_std, y_train)
lr = LogisticRegression()
lr.fit(X_train_lda, y_train)
plot_decision_regions(X_train_lda, y_train, classifier=lr)
plt.xlabel('LD 1')
plt.ylabel('LD 2')
plt.legend(loc='lower left')
plt.show()
from Wine import getWineData, getWineRawData
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
X_train, X_test, y_train, y_test = getWineData(kind='none')
df_wine = getWineRawData()
feat_labels = df_wine.columns[1:]
forest = RandomForestClassifier(n_estimators=10000, random_state=0, n_jobs=-1)
forest.fit(X_train, y_train)
importances = forest.feature_importances_
indices = np.argsort(importances)[::-1]
plt.title('Feature Importances')
plt.bar(range(X_train.shape[1]),
importances[indices],
color='lightblue',
align='center')
plt.xticks(range(X_train.shape[1]), feat_labels[indices], rotation=90)
plt.xlim([-1, X_train.shape[1]])
plt.tight_layout()
plt.show()