-
Notifications
You must be signed in to change notification settings - Fork 0
/
lwoku.py
307 lines (237 loc) · 10.9 KB
/
lwoku.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
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
# !/usr/bin/env python
"""Utility script with functions to be used in Tactic series:
https://www.kaggle.com/juanmah/tactic-00-baseline
https://www.kaggle.com/juanmah/tactic-01-test-classifiers
"""
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pip._internal as pip
pip.main(['install', '--upgrade', 'numpy==1.17.2'])
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict, cross_val_score
from sklearn.utils.multiclass import unique_labels
import matplotlib.pyplot as plt
__author__ = "Juanma Hernández"
__copyright__ = "Copyright 2019"
__credits__ = ["Juanma Hernández", "Kwabena"]
__license__ = "GPL"
__maintainer__ = "Juanma Hernández"
__email__ = "https://twitter.com/juanmah"
__status__ = "Utility script"
print('> NumPy version: {}'.format(np.__version__))
# Class names
CLASS_NAMES = np.array(
[None, 'Spruce/Fir', 'Lodgepole Pine', 'Ponderosa Pine', 'Cottonwood/Willow', 'Aspen', 'Douglas-fir', 'Krummholz'])
"""" Set model parameters:
- random_state = 42. To get always the same results. And get always the same random split. 42 is the answer to the ultimate question of life, the universe, and everything.
- n_jobs = -1. Use all processors.
- verbose = 0. Per default, not nag this notebook. It could be change for testing.
"""
RANDOM_STATE = 42
N_JOBS = -1
VERBOSE = 0
# noinspection PyPep8Naming
def get_accuracy(estimator, X, y, cv=5, n_jobs=-1):
"""Wrapper to get the accuracy
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like
The data to fit. Can be for example a list, or an array.
y : array-like, optional, default: None
The target variable to try to predict in the case of
supervised learning.
Returns
-------
accuracy: float
The average score of all cross validation scores.
"""
scores = cross_val_score(estimator, X, y, cv=cv, scoring='accuracy', n_jobs=n_jobs)
return np.mean(scores)
# noinspection PyPep8Naming
def get_prediction(estimator, X, y, cv=5, n_jobs=-1):
"""Wrapper to get the prediction
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like
The data to fit. Can be for example a list, or an array.
y : array-like, optional, default: None
The target variable to try to predict in the case of
supervised learning.
Returns
-------
prediction: ndarray
The cross-validated prediction.
"""
y_pred = cross_val_predict(estimator, X, y, cv=cv, n_jobs=n_jobs)
return y_pred
def plot_confusion_matrix(y, y_pred):
# Compute confusion matrix
cm = confusion_matrix(y, y_pred)
# Only use the labels that appear in the data
classes = CLASS_NAMES[unique_labels(y, y_pred)]
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap='Blues')
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=classes, yticklabels=classes,
title='Confusion matrix',
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = 'd'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(j, i, format(cm[i, j], fmt),
ha="center", va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
np.set_printoptions(precision=2)
plt.show()
def plot_features_importance(features, model):
importances = pd.DataFrame({'Features': features,
'Importances': model.feature_importances_})
importances.sort_values(by=['Importances'], axis='index', ascending=False, inplace=True)
plt.figure(figsize=(18, 6))
plt.bar(importances['Features'], importances['Importances'])
plt.xticks(rotation='vertical')
plt.show()
# noinspection PyPep8Naming
def add_features(X):
"""Add features to the independent variables list
The new features are created by calculations on the original features
Most of the new features are get from Kwabena.
Parameters
----------
X : array-like
The data to fit. Can be for example a list, or an array.
Returns
-------
X : array-like
Original data plus the new features.
"""
X = X.copy()
X['Hydro_Elevation_sum'] = X[['Elevation',
'Vertical_Distance_To_Hydrology']
].sum(axis='columns')
X['Hydro_Elevation_diff'] = X[['Elevation',
'Vertical_Distance_To_Hydrology']
].diff(axis='columns').iloc[:, [1]]
X['Hydro_Euclidean'] = np.sqrt(X['Horizontal_Distance_To_Hydrology'] ** 2 +
X['Vertical_Distance_To_Hydrology'] ** 2)
X['Hydro_Manhattan'] = (X['Horizontal_Distance_To_Hydrology'] +
X['Vertical_Distance_To_Hydrology'].abs())
X['Hydro_Distance_sum'] = X[['Horizontal_Distance_To_Hydrology',
'Vertical_Distance_To_Hydrology']
].sum(axis='columns')
X['Hydro_Distance_diff'] = X[['Horizontal_Distance_To_Hydrology',
'Vertical_Distance_To_Hydrology']
].diff(axis='columns').iloc[:, [1]]
X['Hydro_Fire_sum'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Fire_Points']
].sum(axis='columns')
X['Hydro_Fire_diff'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Fire_Points']
].diff(axis='columns').iloc[:, [1]].abs()
X['Hydro_Fire_mean'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Fire_Points']
].mean(axis='columns')
X['Hydro_Fire_median'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Fire_Points']
].median(axis='columns')
X['Hydro_Road_sum'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways']
].sum(axis='columns')
X['Hydro_Road_diff'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways']
].diff(axis='columns').iloc[:, [1]].abs()
X['Hydro_Road_mean'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways']
].mean(axis='columns')
X['Hydro_Road_median'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways']
].median(axis='columns')
X['Road_Fire_sum'] = X[['Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].sum(axis='columns')
X['Road_Fire_diff'] = X[['Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].diff(axis='columns').iloc[:, [1]].abs()
X['Road_Fire_mean'] = X[['Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].mean(axis='columns')
X['Road_Fire_median'] = X[['Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].median(axis='columns')
X['Hydro_Road_Fire_mean'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].mean(axis='columns')
X['Hydro_Road_Fire_median'] = X[['Horizontal_Distance_To_Hydrology',
'Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points']
].median(axis='columns')
X['Hillshade_sum'] = X[['Hillshade_9am',
'Hillshade_Noon',
'Hillshade_3pm']
].sum(axis='columns')
X['Hillshade_mean'] = X[['Hillshade_9am',
'Hillshade_Noon',
'Hillshade_3pm']
].mean(axis='columns')
X['Hillshade_median'] = X[['Hillshade_9am',
'Hillshade_Noon',
'Hillshade_3pm']
].median(axis='columns')
X['Hillshade_min'] = X[['Hillshade_9am',
'Hillshade_Noon',
'Hillshade_3pm']
].min(axis='columns')
X['Hillshade_max'] = X[['Hillshade_9am',
'Hillshade_Noon',
'Hillshade_3pm']
].max(axis='columns')
# For all 40 Soil_Types, 1=rubbly, 2=stony, 3=very stony, 4=extremely stony, 0=?
stoneyness = [4, 3, 1, 1, 1, 2, 0, 0, 3, 1,
1, 2, 1, 0, 0, 0, 0, 3, 0, 0,
0, 4, 0, 4, 4, 3, 4, 4, 4, 4,
4, 4, 4, 4, 1, 4, 4, 4, 4, 4]
# Compute Soil_Type number from Soil_Type binary columns
X['Stoneyness'] = sum(i * X['Soil_Type{}'.format(i)] for i in range(1, 41))
# Replace Soil_Type number with "stoneyness" value
X['Stoneyness'] = X['Stoneyness'].replace(range(1, 41), stoneyness)
rocks = [1, 0, 1, 1, 1, 1, 0, 0, 0, 1,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 1, 0, 0, 1]
X['Rocks'] = sum(i * X['Soil_Type{}'.format(i)] for i in range(1, 41))
X['Rocks'] = X['Rocks'].replace(range(1, 41), rocks)
# 1=lower montane dry, 2=lower montane, 3=montane dry, 4=montane
# 5=montane dry and montane, 6=montane and subalpine, 7=subalpine, 8=alpine
climatic_zone = [2, 2, 2, 2, 2, 2, 3, 3, 4, 4,
4, 4, 4, 5, 5, 6, 6, 6, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 8, 8, 8, 8, 8, 8]
X['Climatic_zone'] = sum(i * X['Soil_Type{}'.format(i)] for i in range(1, 41))
X['Climatic_zone'] = X['Climatic_zone'].replace(range(1, 41), rocks)
# 1=alluvium, 2=glacial, 3=shale, 4=sandstone
# 5=mixed sedimentary, 6=unspecified, 7=igneous and metamorphic, 8=volcanic
geologic_zone = [7, 7, 7, 7, 7, 7, 5, 5, 2, 7,
7, 7, 7, 1, 1, 1, 1, 7, 1, 1,
1, 2, 2, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7]
X['Geologic_zone'] = sum(i * X['Soil_Type{}'.format(i)] for i in range(1, 41))
X['Geologic_zone'] = X['Geologic_zone'].replace(range(1, 41), rocks)
return X