ml basics

Author: bob7783

numpy_class/classification_example.py

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+# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python
+# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python
+
+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 the function that will get the data
+# yes, sklearn comes with built-in datasets!
+from sklearn.datasets import load_breast_cancer
+
+# load the data
+data = load_breast_cancer()
+
+# check the type of 'data'
+type(data)
+
+# note: it is a Bunch object
+# this basically acts like a dictionary where you can treat the keys like attributes
+data.keys()
+
+# 'data' (the attribute) means the input data
+data.data.shape
+# it has 569 samples, 30 features
+
+# 'targets'
+data.target
+# note how the targets are just 0s and 1s
+# normally, when you have K targets, they are labeled 0..K-1
+
+# their meaning is not lost
+data.target_names
+
+# there are also 569 corresponding targets
+data.target.shape
+
+# you can also determinw the meaning of each feature
+data.feature_names
+
+
+# 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.data, data.target, test_size=0.33)
+
+
+# instantiate a classifer and train it
+from sklearn.ensemble import RandomForestClassifier
+
+
+model = RandomForestClassifier()
+model.fit(X_train, y_train)
+
+
+# evaluate the model's performance
+model.score(X_train, y_train)
+model.score(X_test, y_test)
+
+
+# how you can make predictions
+predictions = model.predict(X_test)
+
+# what did we get?
+predictions
+
+# manually check the accuracy of your predictions
+N = len(y_test)
+np.sum(predictions == y_test) / N # can also just call np.mean()
+
+
+
+# we can even use deep learning to solve the same problem!
+from sklearn.neural_network import MLPClassifier
+
+# 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)
+
+model = MLPClassifier(max_iter=500)
+model.fit(X_train2, y_train)
+
+
+# evaluate the model's performance
+model.score(X_train2, y_train)
+model.score(X_test2, y_test)

numpy_class/regression_example.py

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+# https://deeplearningcourses.com/c/deep-learning-prerequisites-the-numpy-stack-in-python
+# https://www.udemy.com/deep-learning-prerequisites-the-numpy-stack-in-python
+
+# 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