質問編集履歴
3
微修正
test
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# print(macro_precision)
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# print(macro_recall)
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# print(macro_f_measure)
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# 学習，評価結果を保存
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with open("result.csv", "a") as f:
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2
プログラムの全文を記載（ファイル名のみ修正あり）
test
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test
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@@ -36,6 +36,30 @@
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```Python3
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# -*- coding: utf-8 -*-
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import keras.backend as K
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import os
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from keras.models import Sequential
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from keras.layers.core import Dense, Activation
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from keras.utils import np_utils
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from sklearn import preprocessing
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# precision, recall, f-measureを定義する
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def normalize_y_pred(y_pred):
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def build_multilayer_perceptron():
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"""多層パーセプトロンモデルを構築"""
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model = Sequential()
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model.add(Dense(16, input_shape=(12, )))
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model.add(Activation('relu'))
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model.add(Dense(2))
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model.add(Activation('softmax'))
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return model
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if __name__ == "__main__":
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with open("result.csv", "w") as f:
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print("date,accuracy,precision,recall,f_measure", file=f)
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for i in range(1,
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for i in range(1, 32):
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-
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# ファイルが存在すれば読み込み，以下を実行する
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if os.path.exists(
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if (os.path.exists(
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os.path.join("data", "hoge", "20XX", "01",
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"20XX010" + str(i) + ".txt"))
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"20XX010" + str(i) + ".txt"))
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or os.path.exists(
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os.path.join("data", "hoge", "20XX", "01",
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"20XX01" + str(i) + ".txt"))):
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if i < 10:
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dataset = pd.read_csv(
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dataset = pd.read_csv(
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os.path.join("data", "hoge", "20XX", "01",
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os.path.join("data", "hoge", "20XX", "01",
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-
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"20XX010" + str(i) + ".txt"),
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"20XX010" + str(i) + ".txt"),
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-
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delim_whitespace=True)
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delim_whitespace=True)
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else:
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dataset = pd.read_csv(
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os.path.join("data", "hoge", "20XX", "01",
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"20XX01" + str(i) + ".txt"),
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delim_whitespace=True)
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# 説明変数，ターゲット変数を定義
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X = dataset.iloc[:, [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]
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Y = dataset.iloc[:, 17]
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# データの標準化
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X = preprocessing.scale(X)
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# ラベルをone-hot-encoding形式に変換
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# 0 => [1, 0, 0]
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# 1 => [0, 1, 0]
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# 2 => [0, 0, 1]
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Y = np_utils.to_categorical(Y)
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# 訓練データとテストデータに分割
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train_X, test_X, train_Y, test_Y = train_test_split(
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X, Y, train_size=0.8, test_size=0.2)
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# print(macro_precision)
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# print(macro_recall)
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# print(macro_f_measure)
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# モデル構築
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metrics=[
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'accuracy', macro_precision, macro_recall,
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'accuracy', macro_precision, macro_recall, macro_f_measure
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macro_f_measure
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])
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# モデル訓練
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model.fit(
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model.fit(train_X, train_Y, epochs=1, batch_size=100, verbose=1)
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# モデル評価
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loss, accuracy, macro_precision, macro_recall, macro_f_measure = model.evaluate(
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test_X, test_Y, verbose=0)
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# 学習，評価結果を保存
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338
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339
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+
with open("result.csv", "a") as f:
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print(
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"{0},{1},{2},{3},{4}".format(
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str(i), accuracy, macro_precision, macro_recall,
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macro_f_measure),
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file=f)
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```
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220
352
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|
1
# モデル訓練の箇所を追加
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -208,6 +208,14 @@
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])
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# モデル訓練
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model.fit(
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train_X, train_Y, epochs=1, batch_size=100, verbose=1)
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```
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