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- import numpy as np
- from scipy.io.arff import loadarff
- from sklearn import metrics
- import matplotlib.pyplot as plt
- from sklearn.datasets import fetch_openml
- from sklearn.model_selection import train_test_split
- import os
- path_to_current_file = os.path.dirname(os.path.abspath(__file__))
- def viz_classification_preds(probs, y_test):
- '''look at prediction breakdown
- '''
- plt.subplot(121)
- plt.hist(probs[:, 1][y_test == 0], label='Class 0')
- plt.hist(probs[:, 1][y_test == 1], label='Class 1', alpha=0.8)
- plt.ylabel('Count')
- plt.xlabel('Predicted probability of class 1')
- plt.legend()
- plt.subplot(122)
- preds = np.argmax(probs, axis=1)
- plt.title('ROC curve')
- fpr, tpr, thresholds = metrics.roc_curve(y_test, preds)
- plt.xlabel('False positive rate')
- plt.ylabel('True positive rate')
- plt.plot(fpr, tpr)
- plt.tight_layout()
- plt.show()
- def get_ames_data():
- try:
- housing = fetch_openml(name="house_prices", as_frame=True, parser='auto')
- except:
- housing = fetch_openml(name="house_prices", as_frame=True)
- housing_target = housing['target'].values
- housing_data_numeric = housing['data'].select_dtypes('number').drop(columns=['Id']).dropna(axis=1)
- feature_names = housing_data_numeric.columns.values
- X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(
- housing_data_numeric.values, housing_target, test_size=0.75)
- return X_train_reg, X_test_reg, y_train_reg, y_test_reg, feature_names
- def get_diabetes_data():
- '''load (classification) data on diabetes
- '''
- data = loadarff(os.path.join(path_to_current_file, "../tests/test_data/diabetes.arff"))
- data_np = np.array(list(map(lambda x: np.array(list(x)), data[0])))
- X = data_np[:, :-1].astype('float32')
- y_text = data_np[:, -1].astype('str')
- y = (y_text == 'tested_positive').astype(int) # labels 0-1
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.75) # split
- feature_names = ["#Pregnant", "Glucose concentration test", "Blood pressure(mmHg)",
- "Triceps skin fold thickness(mm)",
- "2-Hour serum insulin (mu U/ml)", "Body mass index", "Diabetes pedigree function", "Age (years)"]
- return X_train, X_test, y_train, y_test, feature_names
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