Question

我下面有使用人工神经网络（ANN）预测CSV文件中的类的代码。

如果我想在测试数据上找到预测，我要执行以下操作吗？

    predictions = model.predict(X_test)
    # round predictions
    rounded = [round(x[0]) for x in predictions]
    prediction = pd.DataFrame(rounded,columns=['predictions']).to_csv('prediction.csv')

在这种情况下，我将拥有一个CSV文件，其中包含预测列表（0和1）。我的问题是：

我如何知道预测引用的数据（行）？

我如何找到结果预测的准确性？

import numpy as np 
import pandas as pd 
from keras.layers import Dense, Dropout, BatchNormalization, Activation
import keras.models as md
import keras.layers.core as core
import keras.utils.np_utils as kutils
import keras.layers.convolutional as conv

from keras.layers import MaxPool2D

from subprocess import check_output
dataset = pd.read_csv('mutation-train.csv')

dataset = dataset[['CDS_Mutation',
                   'Primary_Tissue',
                    'Genomic',
                    'Gene_ID',
                    'Official_Symbol',
                    'Histology']]

X = dataset.iloc[:,0:5].values
y = dataset.iloc[:,5].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_0 = LabelEncoder()
X[:, 0] = labelencoder_X_0.fit_transform(X[:, 0])
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2= LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
labelencoder_X_4= LabelEncoder()
X[:, 4] = labelencoder_X_4.fit_transform(X[:, 4])

X = X.astype(float)
labelencoder_y= LabelEncoder()
y = labelencoder_y.fit_transform(y)

onehotencoder0 = OneHotEncoder(categorical_features = [0])
X = onehotencoder0.fit_transform(X).toarray()
X = X[:,0:]
onehotencoder1 = OneHotEncoder(categorical_features = [1])
X = onehotencoder1.fit_transform(X).toarray()
X = X[:,0:]
onehotencoder2 = OneHotEncoder(categorical_features = [2])
X = onehotencoder2.fit_transform(X).toarray()
X = X[:,0:]
onehotencoder4 = OneHotEncoder(categorical_features = [4])
X = onehotencoder4.fit_transform(X).toarray()
X = X[:,0:]

# Splitting the dataset training and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2)

# Feature scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Evaluating the ANN
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

model=Sequential()
model.add(Dense(32, activation = 'relu', input_shape=(X.shape[1],)))
model.add(Dense(16, activation = 'relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ["accuracy"])

# Compile model
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# Fit the model
model.fit(X,y, epochs=3, batch_size=1)

# Evaluate the model
scores = model.evaluate(X,y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

# Calculate predictions
predictions = model.predict(X)
prediction = pd.DataFrame(predictions,columns=['predictions']).to_csv('prediction.csv')

谢谢。

Answer 1

我如何知道预测所参考的数据（行）？

预测的向量和输入的长度和顺序相同。

我如何找到结果预测的准确性？

将输入的预测与基本事实进行比较。将正确的预测除以输入集的大小。

如果您没有输入集的基本事实，那么您将找不到准确性。最好的办法是在模型训练结束时将准确性估计为最终测试的准确性。

Answer 2

您可以轻松地将索引列添加到dataset。然后在train_test_split之后恢复索引的新排列。

我如何知道哪个预测适用于哪些数据？而且，如何评估预测？

2 个答案: