numpy_class.7z

# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python # https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python # YouTube direct link: http://bit.ly/2LENC50 # Get the data from: # https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise from __future__ import print_function, division from future.utils import iteritems from builtins import range, input # Note: you may need to update your version of future # sudo pip install -U future # just in case we need it import numpy as np import pandas as pd # load the data # important note: this is where we will usually put data files df = pd.read_csv('../large_files/airfoil_self_noise.dat', sep='\t', header=None) # check the data df.head() df.info() # get the inputs data = df[[0,1,2,3,4]].values # get the outputs target = df[5].values # tiny update: pandas is moving from .as_matrix() to the equivalent .values # normally we would put all of our imports at the top # but this lets us tell a story from sklearn.model_selection import train_test_split # split the data into train and test sets # this lets us simulate how our model will perform in the future X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.33) # instantiate a classifer and train it from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X_train, y_train) # evaluate the model's performance print(model.score(X_train, y_train)) print(model.score(X_test, y_test)) # how you can make predictions predictions = model.predict(X_test) # what did we get? predictions # we can even use random forest to solve the same problem! from sklearn.ensemble import RandomForestRegressor model2 = RandomForestRegressor() model2.fit(X_train, y_train) # evaluate the model's performance print(model2.score(X_train, y_train)) print(model2.score(X_test, y_test)) # we can even use deep learning to solve the same problem! from sklearn.neural_network import MLPRegressor # you'll learn why scaling is needed in a later course from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train2 = scaler.fit_transform(X_train) X_test2 = scaler.transform(X_test) scaler2 = StandardScaler() y_train2 = scaler2.fit_transform(np.expand_dims(y_train, -1)).ravel() y_test2 = scaler2.fit_transform(np.expand_dims(y_test, -1)).ravel() model = MLPRegressor(max_iter=500) model.fit(X_train2, y_train2) # evaluate the model's performance print(model.score(X_train2, y_train2)) print(model.score(X_test2, y_test2)) # not as good as a random forest! # but not as bad as linear regression