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import numpy as np import pandas as pd iris_dataset = load_iris() print(iris_dataset["data"][:5]) |
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[[ 5.1 3.5 1.4 0.2] [ 4.9 3. 1.4 0.2] [ 4.7 3.2 1.3 0.2] [ 4.6 3.1 1.5 0.2] [ 5. 3.6 1.4 0.2]] |
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import numpy as np import pandas as pd import sklearn from sklearn.datasets import load_iris iris_dataset = load_iris() print(iris_dataset.keys()) #>>> ['DESCR', 'target_names', 'feature_names', 'target', 'data'] #DESCRはデータセットの解説があるという事 #その他はデータの種類を示している print(iris_dataset["DESCR"[:193]]+"\n...") #データセットの詳細を知る為にDESCRを表示 |
結果
150のデータと4つのデータの種類設定コード
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Iris Plants Database ==================== Notes ----- Data Set Characteristics: :Number of Instances: 150 (50 in each of three classes) :Number of Attributes: 4 numeric, predictive attributes and the class :Attribute Information: - sepal length in cm - sepal width in cm - petal length in cm - petal width in cm - class: - Iris-Setosa - Iris-Versicolour - Iris-Virginica :Summary Statistics: ============== ==== ==== ======= ===== ==================== Min Max Mean SD Class Correlation ============== ==== ==== ======= ===== ==================== sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) ============== ==== ==== ======= ===== ==================== :Missing Attribute Values: None :Class Distribution: 33.3% for each of 3 classes. :Creator: R.A. Fisher :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) :Date: July, 1988 This is a copy of UCI ML iris datasets. http://archive.ics.uci.edu/ml/datasets/Iris The famous Iris database, first used by Sir R.A Fisher This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. References ---------- - Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950). - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II conceptual clustering system finds 3 classes in the data. - Many, many more ... ... |
targetnameの種類を知りたい場合（アイリスの名称一覧）
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print(iris_dataset["target_names"]) |
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['setosa' 'versicolor' 'virginica'] |
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import pandas as pd from IPython.display import display data ={"name":["jonh","anna","peter","linda"], "location":["new york","paris","berlin","london"], "age":[24,13,53,33]} data_pandas = pd.DataFrame(data) display(data_pandas) |
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from scipy import sparse import numpy as np %matplotlib inline import matplotlib.pyplot as plt x = np.linspace(-10,10,100) y = np.sin(x) plt.plot(x,y,marker="x") |
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print("NumPy array: \n {}".format(aaa)) |
LSTM時系列解析
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import numpy import matplotlib.pyplot as plt from pandas import read_csv import math from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error #以下にコードを書いてください # データセットの作成 def create_dataset(dataset, look_back): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return numpy.array(dataX), numpy.array(dataY) # 乱数設定 numpy.random.seed(7) # データセットの読み込み dataframe = read_csv('nikkei225.csv', usecols=[1], engine='python', skipfooter=3) dataset = dataframe.values dataset = dataset.astype('float32') # 訓練データ、テストデータに分ける train_size = int(len(dataset) * 0.67) test_size = len(dataset) - train_size train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] # データのスケーリング scaler = MinMaxScaler(feature_range=(0, 1)) scaler_train = scaler.fit(train) train = scaler_train.transform(train) test = scaler_train.transform(test) # データの作成 look_back = 10 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) # データの整形 trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1)) testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1)) # LSTMモデルの作成と学習 model = Sequential() model.add(LSTM(64, return_sequences=True,input_shape=(look_back, 1))) model.add(LSTM(32)) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2) # 予測データの作成 trainPredict = model.predict(trainX) testPredict = model.predict(testX) # スケールしたデータを元に戻す trainPredict = scaler_train.inverse_transform(trainPredict) trainY = scaler_train.inverse_transform([trainY]) testPredict = scaler_train.inverse_transform(testPredict) testY = scaler_train.inverse_transform([testY]) # 予測精度の計算 trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) print('Train Score: %.2f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test Score: %.2f RMSE' % (testScore)) # プロットのためのデータ整形 trainPredictPlot = numpy.empty_like(dataset) trainPredictPlot[:, :] = numpy.nan trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict testPredictPlot = numpy.empty_like(dataset) testPredictPlot[:, :] = numpy.nan testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict # テストデータのプロット plt.plot(dataframe[round(len(dataset)*0.67):]) plt.plot(testPredictPlot) plt.show() |
結果
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Epoch 1/10 44s - loss: 0.0040 Epoch 2/10 44s - loss: 0.0013 Epoch 3/10 43s - loss: 0.0011 Epoch 4/10 44s - loss: 7.8079e-04 Epoch 5/10 44s - loss: 5.9064e-04 Epoch 6/10 44s - loss: 5.5586e-04 Epoch 7/10 43s - loss: 5.2437e-04 Epoch 8/10 43s - loss: 5.4960e-04 Epoch 9/10 43s - loss: 5.3203e-04 Epoch 10/10 44s - loss: 4.9286e-04 Train Score: 270.96 RMSE Test Score: 144.13 RMSE |
