# Assignment #8 (demo). Implementation of online regressor#

**mlcourse.ai – Open Machine Learning Course**

Author: Yury Kashnitsky. 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 Notebook + solution.**

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

## Linear regression and Stochastic Gradient Descent#

```
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.metrics import log_loss, mean_squared_error, roc_auc_score
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from matplotlib import pyplot as plt
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 iterationsClass has

`fit`

and`predict`

methodsThe

`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 errorThe

`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.

```
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).

```
# for Jupyter-book, we copy data from GitHub, locally, to save Internet traffic,
# you can specify the data/ folder from the root of your cloned
# https://github.com/Yorko/mlcourse.ai repo, to save Internet traffic
DATA_PATH = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/main/data/"
```

```
data_demo = pd.read_csv(DATA_PATH + "weights_heights.csv")
```

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

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

Perform train/test split and scale data.

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

```
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.

```
# you code here
```

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

```
# you code here
```

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

```
# you code here
```

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

```
# you code here
```

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

and check MSE value.

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

Do the same thing for `LinearRegression`

class from `sklearn.linear_model`

. Evaluate MSE for hold-out set.

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

```
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)
)
```

```
Something's not good.
Linreg's holdout MSE: 9
SGD's holdout MSE: 10
```