-
Notifications
You must be signed in to change notification settings - Fork 197
/
19_advanced_sklearn.py
192 lines (157 loc) · 5.64 KB
/
19_advanced_sklearn.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
'''
CLASS: Advanced scikit-learn
'''
'''
TASK: Searching for optimal parameters
FUNCTION: GridSearchCV
DOCUMENTATION: http://scikit-learn.org/stable/modules/grid_search.html
DATA: Titanic (n=891, p=5 selected, type=classification)
DATA DICTIONARY: https://www.kaggle.com/c/titanic-gettingStarted/data
'''
# read in and prepare titanic data
import pandas as pd
url = 'https://github.com/justmarkham/DAT7/master/data/titanic.csv'
titanic = pd.read_csv(url)
titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1})
titanic.Age.fillna(titanic.Age.mean(), inplace=True)
embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked').iloc[:, 1:]
titanic = pd.concat([titanic, embarked_dummies], axis=1)
# define X and y
feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S']
X = titanic[feature_cols]
y = titanic.Survived
# use cross-validation to find best max_depth
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import cross_val_score
# try max_depth=2
treeclf = DecisionTreeClassifier(max_depth=2, random_state=1)
cross_val_score(treeclf, X, y, cv=10, scoring='roc_auc').mean()
# try max_depth=3
treeclf = DecisionTreeClassifier(max_depth=3, random_state=1)
cross_val_score(treeclf, X, y, cv=10, scoring='roc_auc').mean()
# use GridSearchCV to automate the search
from sklearn.grid_search import GridSearchCV
treeclf = DecisionTreeClassifier(random_state=1)
max_depth_range = range(1, 21)
param_grid = dict(max_depth=max_depth_range)
grid = GridSearchCV(treeclf, param_grid, cv=10, scoring='roc_auc')
grid.fit(X, y)
# check the results of the grid search
grid.grid_scores_
grid_mean_scores = [result.mean_validation_score for result in grid.grid_scores_]
# plot the results
import matplotlib.pyplot as plt
plt.plot(max_depth_range, grid_mean_scores)
plt.xlabel('Value of max_depth')
plt.ylabel('Cross-Validated AUC')
# what was best?
grid.best_score_
grid.best_params_
grid.best_estimator_
# search multiple parameters simultaneously
max_depth_range = range(1, 21)
leaf_range = range(1, 11)
param_grid = dict(max_depth=max_depth_range, min_samples_leaf=leaf_range)
grid = GridSearchCV(treeclf, param_grid, cv=10, scoring='roc_auc')
grid.fit(X, y)
grid.grid_scores_
grid.best_score_
grid.best_params_
'''
TASK: Standardization of features (aka "center and scale" or "z-score normalization")
FUNCTION: StandardScaler
DOCUMENTATION: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
DATA: Wine (n=178, p=2 selected, type=classification)
DATA DICTIONARY: http://archive.ics.uci.edu/ml/datasets/Wine
'''
# fake data
train = pd.DataFrame({'id':[0,1,2], 'length':[0.9,0.3,0.6], 'mass':[0.1,0.2,0.8], 'rings':[40,50,60]})
test = pd.DataFrame({'length':[0.59], 'mass':[0.79], 'rings':[54.9]})
# define X and y
feature_cols = ['length', 'mass', 'rings']
X = train[feature_cols]
y = train.id
# KNN with k=1
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X, y)
# what "should" it predict? what does it predict?
knn.predict(test)
# standardize the features
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
# compare original to standardized
X.values
X_scaled
# figure out how it standardized
scaler.mean_
scaler.std_
(X.values - scaler.mean_) / scaler.std_
# try this on real data
url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data'
wine = pd.read_csv(url, header=None, usecols=[0, 10, 13])
wine.columns=['label', 'color', 'proline']
wine.head()
wine.describe()
# define X and y
feature_cols = ['color', 'proline']
X = wine[feature_cols]
y = wine.label
# split into train/test
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
# standardize X_train
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
# check that it worked properly
X_train_scaled[:, 0].mean()
X_train_scaled[:, 0].std()
X_train_scaled[:, 1].mean()
X_train_scaled[:, 1].std()
# standardize X_test
X_test_scaled = scaler.transform(X_test)
# is this right?
X_test_scaled[:, 0].mean()
X_test_scaled[:, 0].std()
X_test_scaled[:, 1].mean()
X_test_scaled[:, 1].std()
# KNN accuracy on original data
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
y_pred_class = knn.predict(X_test)
from sklearn import metrics
metrics.accuracy_score(y_test, y_pred_class)
# KNN accuracy on scaled data
knn.fit(X_train_scaled, y_train)
y_pred_class = knn.predict(X_test_scaled)
metrics.accuracy_score(y_test, y_pred_class)
'''
TASK: Chaining steps
FUNCTION: Pipeline
DOCUMENTATION: http://scikit-learn.org/stable/modules/pipeline.html
DATA: Wine (n=178, p=2 selected, type=classification)
DATA DICTIONARY: http://archive.ics.uci.edu/ml/datasets/Wine
'''
# here is proper cross-validation on the original (unscaled) data
feature_cols = ['color', 'proline']
X = wine[feature_cols]
y = wine.label
knn = KNeighborsClassifier(n_neighbors=3)
cross_val_score(knn, X, y, cv=5, scoring='accuracy').mean()
# why is this improper cross-validation on the scaled data?
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
cross_val_score(knn, X_scaled, y, cv=5, scoring='accuracy').mean()
# fix this using Pipeline
from sklearn.pipeline import make_pipeline
pipe = make_pipeline(StandardScaler(), KNeighborsClassifier(n_neighbors=3))
cross_val_score(pipe, X, y, cv=5, scoring='accuracy').mean()
# using GridSearchCV with Pipeline
neighbors_range = range(1, 21)
param_grid = dict(kneighborsclassifier__n_neighbors=neighbors_range)
grid = GridSearchCV(pipe, param_grid, cv=5, scoring='accuracy')
grid.fit(X, y)
grid.best_score_
grid.best_params_