In [0]:
from sklearn import linear_model
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn import metrics
from matplotlib import pyplot as plt
import numpy as np
import os
from imageio import imread
import pandas as pd
from time import time as timer
import tensorflow as tf
%matplotlib inline
from matplotlib import animation
from IPython.display import HTML
In [2]:
if not os.path.exists('data'):
path = os.path.abspath('.')+'/colab_material.tgz'
tf.keras.utils.get_file(path, 'https://github.com/neworldemancer/DSF5/raw/master/colab_material.tgz')
!tar -xvzf colab_material.tgz > /dev/null 2>&1
Datasets¶
In this course we will use several synthetic and real-world datasets to ilustrate the behavior of the models and excercise our skills.
1. Synthetic linear¶
In [0]:
def get_linear(n_d=1, n_points=10, w=None, b=None, sigma=5):
x = np.random.uniform(0, 10, size=(n_points, n_d))
w = w or np.random.uniform(0.1, 10, n_d)
b = b or np.random.uniform(-10, 10)
y = np.dot(x, w) + b + np.random.normal(0, sigma, size=n_points)
print('true w =', w, '; b =', b)
return x, y
In [4]:
x, y = get_linear(n_d=1, sigma=0)
plt.plot(x[:, 0], y, '*')
Out[4]:
In [5]:
n_d = 2
x, y = get_linear(n_d=n_d, n_points=100)
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x[:,0], x[:,1], y, marker='x', color='b',s=40)
Out[5]:
2. House prices¶
Subset of the the hous pricess kaggle dataset: https://www.kaggle.com/c/house-prices-advanced-regression-techniques
In [0]:
def house_prices_dataset(return_df=False, price_max=400000, area_max=40000):
path = 'data/train.csv'
df = pd.read_csv(path, na_values="NaN", keep_default_na=False)
useful_fields = ['LotArea',
'Utilities', 'OverallQual', 'OverallCond',
'YearBuilt', 'YearRemodAdd', 'ExterQual', 'ExterCond',
'HeatingQC', 'CentralAir', 'Electrical',
'1stFlrSF', '2ndFlrSF','GrLivArea',
'FullBath', 'HalfBath',
'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual', 'TotRmsAbvGrd',
'Functional','PoolArea',
'YrSold', 'MoSold'
]
target_field = 'SalePrice'
cleanup_nums = {"Street": {"Grvl": 0, "Pave": 1},
"LotFrontage": {"NA":0},
"Alley": {"NA":0, "Grvl": 1, "Pave": 2},
"LotShape": {"IR3":0, "IR2": 1, "IR1": 2, "Reg":3},
"Utilities": {"ELO":0, "NoSeWa": 1, "NoSewr": 2, "AllPub": 3},
"LandSlope": {"Sev":0, "Mod": 1, "Gtl": 3},
"ExterQual": {"Po":0, "Fa": 1, "TA": 2, "Gd": 3, "Ex":4},
"ExterCond": {"Po":0, "Fa": 1, "TA": 2, "Gd": 3, "Ex":4},
"BsmtQual": {"NA":0, "Po":1, "Fa": 2, "TA": 3, "Gd": 4, "Ex":5},
"BsmtCond": {"NA":0, "Po":1, "Fa": 2, "TA": 3, "Gd": 4, "Ex":5},
"BsmtExposure":{"NA":0, "No":1, "Mn": 2, "Av": 3, "Gd": 4},
"BsmtFinType1":{"NA":0, "Unf":1, "LwQ": 2, "Rec": 3, "BLQ": 4, "ALQ":5, "GLQ":6},
"BsmtFinType2":{"NA":0, "Unf":1, "LwQ": 2, "Rec": 3, "BLQ": 4, "ALQ":5, "GLQ":6},
"HeatingQC": {"Po":0, "Fa": 1, "TA": 2, "Gd": 3, "Ex":4},
"CentralAir": {"N":0, "Y": 1},
"Electrical": {"NA":0, "Mix":1, "FuseP":2, "FuseF": 3, "FuseA": 4, "SBrkr": 5},
"KitchenQual": {"Po":0, "Fa": 1, "TA": 2, "Gd": 3, "Ex":4},
"Functional": {"Sal":0, "Sev":1, "Maj2": 2, "Maj1": 3, "Mod": 4, "Min2":5, "Min1":6, 'Typ':7},
"FireplaceQu": {"NA":0, "Po":1, "Fa": 2, "TA": 3, "Gd": 4, "Ex":5},
"PoolQC": {"NA":0, "Fa": 1, "TA": 2, "Gd": 3, "Ex":4},
"Fence": {"NA":0, "MnWw": 1, "GdWo": 2, "MnPrv": 3, "GdPrv":4},
}
df_X = df[useful_fields].copy()
df_X.replace(cleanup_nums, inplace=True) # convert continous categorial variables to numerical
df_Y = df[target_field].copy()
x = df_X.to_numpy().astype(np.float32)
y = df_Y.to_numpy().astype(np.float32)
if price_max>0:
idxs = y<price_max
x = x[idxs]
y = y[idxs]
if area_max>0:
idxs = x[:,0]<area_max
x = x[idxs]
y = y[idxs]
return (x, y, df) if return_df else (x,y)
In [7]:
x, y, df = house_prices_dataset(return_df=True)
print(x.shape, y.shape)
df.head()
Out[7]:
In [8]:
plt.plot(x[:, 0], y, '.')
plt.xlabel('area, sq.ft')
plt.ylabel('price, $');
3. Blobs¶
In [9]:
x, y = make_blobs(n_samples=1000, centers=[[0,0], [5,5], [10, 0]])
colors = "bry"
for i, color in enumerate(colors):
idx = y == i
plt.scatter(x[idx, 0], x[idx, 1], c=color, edgecolor='gray', s=25)
4. Fashion MNIST¶
Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes. (from https://github.com/zalandoresearch/fashion-mnist)
In [10]:
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
Let's chech few samples:
In [11]:
n = 3
fig, ax = plt.subplots(n, n, figsize=(2*n, 2*n))
ax = [ax_xy for ax_y in ax for ax_xy in ax_y]
for axi, im_idx in zip(ax, np.random.choice(len(train_images), n**2)):
im = train_images[im_idx]
im_class = train_labels[im_idx]
axi.imshow(im, cmap='gray')
axi.text(1, 4, f'{im_class}', color='r', size=16)
plt.tight_layout(0,0,0)
Each training and test example is assigned to one of the following labels:
| Label | Description |
|---|---|
| 0 | T-shirt/top |
| 1 | Trouser |
| 2 | Pullover |
| 3 | Dress |
| 4 | Coat |
| 5 | Sandal |
| 6 | Shirt |
| 7 | Sneaker |
| 8 | Bag |
| 9 | Ankle boot |
EXERCISE 1.¶
In [12]:
# Solution:
x, y = house_prices_dataset()
# 1. make train/test split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
# 2. fit the model
reg = linear_model.LinearRegression()
reg.fit(x_train, y_train)
# 3. evaluate MSE, MAD, and R2 on train and test datasets
#prediction:
y_p_train = reg.predict(x_train)
y_p_test = reg.predict(x_test)
# mse
print('train mse =', np.std(y_train - y_p_train))
print('test mse =', np.std(y_test - y_p_test))
# mse
print('train mae =', np.mean(np.abs(y_train - y_p_train)))
print('test mae =', np.mean(np.abs(y_test - y_p_test)))
# R2
print('train R2 =', reg.score(x_train, y_train))
print('test R2 =', reg.score(x_test, y_test))
# 4. plot y vs predicted y for test and train parts
plt.plot(y_train, y_p_train, 'b.', label='train')
plt.plot(y_test, y_p_test, 'r.', label='test')
plt.plot([0], [0], 'w.') # dummy to have origin
plt.xlabel('true')
plt.ylabel('predicted')
plt.gca().set_aspect('equal')
plt.legend()
Out[12]:
EXERCISE 2.¶
In [0]:
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
We will reshape 2-d images to 1-d arrays for use in scikit-learn:
In [0]:
n_train = len(train_labels)
x_train = train_images.reshape((n_train, -1))
y_train = train_labels
n_test = len(test_labels)
x_test = test_images.reshape((n_test, -1))
y_test = test_labels
Now use a multinomial logistic regression classifier, and measure the accuracy:
In [15]:
#solution
# 1. Create classifier
multi_class = 'multinomial'
clf = linear_model.LogisticRegression(solver='sag', max_iter=20,
multi_class=multi_class)
# 2. fit the model
t1 = timer()
clf.fit(x_train, y_train)
t2 = timer()
print ('training time: %.1fs'%(t2-t1))
# 3. evaluate accuracy on train and test datasets
print("training score : %.3f" % (clf.score(x_train, y_train)))
print("test score : %.3f" % (clf.score(x_test, y_test)))