#!/usr/bin/env python # coding: utf-8 #
# # # ## [mlcourse.ai](https://mlcourse.ai) – Open Machine Learning Course # # Author: [Yury Kashnitskiy](https://yorko.github.io). Translated by [Sergey Oreshkov](https://www.linkedin.com/in/sergeoreshkov/). This material is subject to the terms and conditions of the [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license. Free use is permitted for any non-commercial purpose. # #
Assignment #8 (demo) # # ##
Implementation of online regressor # # **Same assignment as a [Kaggle Kernel](https://www.kaggle.com/kashnitsky/a8-demo-implementing-online-regressor) + [solution](https://www.kaggle.com/kashnitsky/a8-demo-implementing-online-regressor-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. # ##
Linear regression and Stochastic Gradient Descent # 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 get_ipython().run_line_magic('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](https://medium.com/open-machine-learning-course/open-machine-learning-course-topic-8-vowpal-wabbit-fast-learning-with-gigabytes-of-data-60f750086237) 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))