Pythonで機械学習入門以前
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This document discusses Python and machine learning libraries like scikit-learn. It provides code examples for loading data, fitting models, and making predictions using scikit-learn algorithms. It also covers working with NumPy arrays and loading data from files like CSVs.
![from sklearn import datasets
from sklearn.svm import SVC
iris=datasets.load_iris()
data_train=iris.data[:-10,:]
target_train=iris.target[:-10]
data_eval=iris.data[-10:,:]
target_eval=iris.target[-10:]
svc=SVC()
svc.fit(data_train,target_train)
predicted=svc.predict(data_eval)
print("Accuracy: {}".format((target_eval==predicted).sum()/10.))](https://image.slidesharecdn.com/start-python-160608032428/85/Python-8-320.jpg)
![0 1 2
3 4 5
6 7 8
9 10 11
a
1 [3,4,5]
0 [0,3,6,9]
(2,1) a[2,1]
1 a[1,:]
0 a[:,0]
(2,1) 7
>>> import numpy as np
>>> a=np.arange(12).reshape(4,3)
>>> a
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[ 9, 10, 11]])
>>> a[1,:]
array([3, 4, 5])
>>> a[2,1]
7
>>> a[:,0]
array([0, 3, 6, 9])
>>>](https://image.slidesharecdn.com/start-python-160608032428/85/Python-13-320.jpg)
![csv 9 10
import numpy as np
import csv
data = []
target = []
filename = "input_data.csv"
with open(filename) as f:
for row in csv.reader(f):
data.append([float(x) for x in row[:9]])
target.append(float(row[9]))
data = np.array(data)
target = np.array(target)](https://image.slidesharecdn.com/start-python-160608032428/85/Python-14-320.jpg)
![MovieLens
from scipy import sparse
items = []
users = []
ratings = []
for line in open("ml-100k/u.data"):
a = line.split("t")
users.append(int(a[0]))
items.append(int(a[1]))
ratings.append(int(a[2]))
n_users = max(users)
n_items = max(items)
mat = sparse.lil_matrix((n_users, n_items))
for u, i, r in zip(users, items, ratings):
mat[u - 1, i - 1] = r
mat = mat.tocsr()](https://image.slidesharecdn.com/start-python-160608032428/85/Python-16-320.jpg)
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Pythonで機械学習入門以前
- 2. Python 3 3
- 5. • • • • • •
- 6. scikit-learn model = SomeAlogrithm(hyperparameters) model.fit(x,y) prediction = model.predict(z) model = SomeAlogrithm(hyperparameters) model.fit(x) prediction_x = model.labels_ prediction_z = model.predict(z) model = SomeAlogrithm(hyperparameters) model.fit(x) transformed = model.transform(z)
- 8. from sklearn import datasets from sklearn.svm import SVC iris=datasets.load_iris() data_train=iris.data[:-10,:] target_train=iris.target[:-10] data_eval=iris.data[-10:,:] target_eval=iris.target[-10:] svc=SVC() svc.fit(data_train,target_train) predicted=svc.predict(data_eval) print("Accuracy: {}".format((target_eval==predicted).sum()/10.))
- 10. • • •
- 11. …
- 12. 0 1 … 0 1 … 1 /1 Python i j (i,j)
- 13. 0 1 2 3 4 5 6 7 8 9 10 11 a 1 [3,4,5] 0 [0,3,6,9] (2,1) a[2,1] 1 a[1,:] 0 a[:,0] (2,1) 7 >>> import numpy as np >>> a=np.arange(12).reshape(4,3) >>> a array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) >>> a[1,:] array([3, 4, 5]) >>> a[2,1] 7 >>> a[:,0] array([0, 3, 6, 9]) >>>
- 14. csv 9 10 import numpy as np import csv data = [] target = [] filename = "input_data.csv" with open(filename) as f: for row in csv.reader(f): data.append([float(x) for x in row[:9]]) target.append(float(row[9])) data = np.array(data) target = np.array(target)
- 15. • • • np.array
- 16. MovieLens from scipy import sparse items = [] users = [] ratings = [] for line in open("ml-100k/u.data"): a = line.split("t") users.append(int(a[0])) items.append(int(a[1])) ratings.append(int(a[2])) n_users = max(users) n_items = max(items) mat = sparse.lil_matrix((n_users, n_items)) for u, i, r in zip(users, items, ratings): mat[u - 1, i - 1] = r mat = mat.tocsr()
- 18. scikit-learn
- 19. • • • • •
- 20. scikit-learn …
- 21. • • SVM SVC • • SVM • •
- 22. scikit-learn
- 24. … model = SomeAlogrithm(hyperparameters) model.fit(x,y) prediction = model.predict(z)
- 25. • scikit-learn • • scikit-learn numpy matplotlib
- 26. …
- 27. …