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Pythonで機械学習入門以前

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2016/6/7 みんなのPython勉強会で発表した資料です。

scikit-learnの初心者向けに、データのまとめ方やドキュメントを読む時の心構えについて書いてあります。

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Pythonで機械学習入門以前

  1. 1. Python 2016/6/7 Python
  2. 2. Python 3 3
  3. 3. Python http://bit.ly/yoseiml
  4. 4. Python • • scikit-learn • Numpy/Scipy •
  5. 5. • • • • • •
  6. 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)
  7. 7. scikit-learn n×m n×1 n
  8. 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.))
  9. 9. scikit-learn • • scikit-learn • • •
  10. 10. • • •
  11. 11.
  12. 12. 0 1 … 0 1 … 1 /1 Python i j (i,j)
  13. 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. 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. 15. • • • np.array
  16. 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()
  17. 17. • lil_matrix • csr_matrix
  18. 18. scikit-learn
  19. 19. • • • • •
  20. 20. scikit-learn …
  21. 21. • • SVM SVC • • SVM • •
  22. 22. scikit-learn
  23. 23. np.meshgrid? np.c_? ravel?? ???
  24. 24. … model = SomeAlogrithm(hyperparameters) model.fit(x,y) prediction = model.predict(z)
  25. 25. • scikit-learn • • scikit-learn numpy matplotlib
  26. 26.
  27. 27.
  28. 28. Python http://bit.ly/yoseiml
  29. 29. scikit-learn • • • • OK

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