Author: Yury Kashnitskiy. Translated by Sergey Oreshkov. This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

**Same assignment as a Kaggle Kernel + solution.**

Here we'll implement a regressor trained with stochastic gradient descent (SGD). Fill in the missing code. If you do evething right, you'll pass a simple embedded test.

In [1]:

```
import numpy as np
import pandas as pd
from tqdm import tqdm
from sklearn.base import BaseEstimator
from sklearn.metrics import mean_squared_error, log_loss, roc_auc_score
from sklearn.model_selection import train_test_split
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
```

Implement class `SGDRegressor`

. Specification:

- class is inherited from
`sklearn.base.BaseEstimator`

- constructor takes parameters
`eta`

– gradient step ($10^{-3}$ by default) and`n_epochs`

– dataset pass count (3 by default) - constructor also creates
`mse_`

and`weights_`

lists in order to track mean squared error and weight vector during gradient descent iterations - Class has
`fit`

and`predict`

methods - The
`fit`

method takes matrix`X`

and vector`y`

(`numpy.array`

objects) as parameters, appends column of ones to`X`

on the left side, initializes weight vector`w`

with**zeros**and then makes`n_epochs`

iterations of weight updates (you may refer to this article for details), and for every iteration logs mean squared error and weight vector`w`

in corresponding lists we created in the constructor. - Additionally the
`fit`

method will create`w_`

variable to store weights which produce minimal mean squared error - The
`fit`

method returns current instance of the`SGDRegressor`

class, i.e.`self`

- The
`predict`

method takes`X`

matrix, adds column of ones to the left side and returns prediction vector, using weight vector`w_`

, created by the`fit`

method.

In [2]:

```
class SGDRegressor(BaseEstimator):
# you code here
def __init__(self):
pass
def fit(self, X, y):
pass
def predict(self, X):
pass
```

Let's test out the algorithm on height/weight data. We will predict heights (in inches) based on weights (in lbs).

In [3]:

```
data_demo = pd.read_csv('../../data/weights_heights.csv')
```

In [4]:

```
plt.scatter(data_demo['Weight'], data_demo['Height']);
plt.xlabel('Weight (lbs)')
plt.ylabel('Height (Inch)')
plt.grid();
```

In [5]:

```
X, y = data_demo['Weight'].values, data_demo['Height'].values
```

Perform train/test split and scale data.

In [6]:

```
X_train, X_valid, y_train, y_valid = train_test_split(X, y,
test_size=0.3,
random_state=17)
```

In [7]:

```
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train.reshape([-1, 1]))
X_valid_scaled = scaler.transform(X_valid.reshape([-1, 1]))
```

Train created `SGDRegressor`

with `(X_train_scaled, y_train)`

data. Leave default parameter values for now.

In [8]:

```
# you code here
```

Draw a chart with training process – dependency of mean squared error from the i-th SGD iteration number.

In [9]:

```
# you code here
```

Print the minimal value of mean squared error and the best weights vector.

In [10]:

```
# you code here
```

Draw chart of model weights ($w_0$ and $w_1$) behavior during training.

In [11]:

```
# you code here
```

Make a prediction for hold-out set `(X_valid_scaled, y_valid)`

and check MSE value.

In [12]:

```
# you code here
sgd_holdout_mse = 10
```

Do the same thing for `LinearRegression`

class from `sklearn.linear_model`

. Evaluate MSE for hold-out set.

In [13]:

```
# you code here
linreg_holdout_mse = 9
```

In [14]:

```
try:
assert (sgd_holdout_mse - linreg_holdout_mse) < 1e-4
print('Correct!')
except AssertionError:
print("Something's not good.\n Linreg's holdout MSE: {}"
"\n SGD's holdout MSE: {}".format(linreg_holdout_mse,
sgd_holdout_mse))
```