gabrielziegler3

# META CODE
from sklearn.multiclass import OneVsRestClassifier
from xgboost import XGBClassifier
from sklearn.preprocessing import MultiLabelBinarizer

clf = OneVsRestClassifier(XGBClassifier(n_jobs=-1, max_depth=4))

# You may need to use MultiLabelBinarizer to encode your variables from arrays [[x, y, z]] to a multilabel 
# format before training.
mlb = MultiLabelBinarizer()

gabrielziegler3

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

def plot_confusion_matrix(cm, classes, normalized=True, cmap='bone'):
    plt.figure(figsize=[5, 5])
    norm_cm = cm
    if normalized:
        norm_cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        sns.heatmap(norm_cm, annot=cm, fmt='g', xticklabels=classes, yticklabels=classes, cmap=cmap)

gabrielziegler3

from sklearn.metrics import classification_report

bst = xgb.train(params, dtrain)
pred = bst.predict(dtest)
print(classification_report(y_test, pred))

gabrielziegler3

params = {
    'max_depth': 6,
    'objective': 'multi:softmax',
    'num_class': 3
}

gabrielziegler3

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)

dtrain = xgb.DMatrix(data=X_train, label=y_train)
dtest = xgb.DMatrix(data=X_test)

gabrielziegler3

features = [
    'alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash','magnesium',
    'total_phenols', 'flavanoids', 'nonflavanoid_phenols',
    'proanthocyanins', 'color_intensity', 'hue',
    'od280/od315_of_diluted_wines', 'proline'
]

X = pd.DataFrame(data=X, columns=features)
y = pd.DataFrame(data=y, columns=['classes'])

gabrielziegler3

import pandas as pd
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

X, y = load_wine(return_X_y=True)