在深度学习的模型构建过程中数据集的重要性不言而喻，其建立过程包括以下几点：
-
数据读入和借助可视化工具辅助数据分析
-
根据基本的分析结果对特征数据进行选择，舍弃不重要的特征
-
对于类别数据进行 独热编码或映射
-
数据特征的标准化和数据集的划分
在前面几个项目的学习中对于不同的数据来源，其实际的处理过程各有差异，在此对几个经典的例子放在一起进行一个对比和总结。
本笔记所示代码源自 Udacity Deep Learning Nano Degree，版权归属于 Udacity，Jupyter notebook 完整代码请见 我的 GihHub 。
Data processing example from student admission project
In [1]:
# Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 3 rows of our data
data[:3]
Out[1]:
admit gre gpa rank
0 0 380 3.61 3
1 1 660 3.67 3
2 1 800 4.00 1
In [2]:
# Importing matplotlib
import matplotlib.pyplot as plt
# Function to help us plot
def plot_points(data):
X = np.array(data[['gre','gpa']])
y = np.array(data['admit'])
admitted = X[np.argwhere(y==1)]
rejected = X[np.argwhere(y==0)]
plt.scatter([s[0][0] for s in rejected], [s[0][1] for s in rejected], s = 25, color = 'red', edgecolor = 'k')
plt.scatter([s[0][0] for s in admitted], [s[0][1] for s in admitted], s = 25, color = 'cyan', edgecolor = 'k')
plt.xlabel('Test (GRE)')
plt.ylabel('Grades (GPA)')
# Plotting the points
plot_points(data)
plt.show()
Student admission
In [3]:
# Make dummy variables for rank
one_hot_data = pd.concat([data, pd.get_dummies(data['rank'], prefix='rank')], axis=1)
# Drop the previous rank column
one_hot_data = one_hot_data.drop('rank', axis=1)
# Print the first 3 rows of our data
one_hot_data[:3]
Out[3]:
admit gre gpa rank_1 rank_2 rank_3 rank_4
0 0 380 3.61 0 0 1 0
1 1 660 3.67 0 0 1 0
2 1 800 4.00 1 0 0 0
In [4]:
# Scaling the data
processed_data = one_hot_data[:]
# Scaling the columns
processed_data['gre'] = processed_data['gre'] / 800
processed_data['gpa'] = processed_data['gpa'] / 4.0
processed_data[:3]
Out[4]:
admit gre gpa rank_1 rank_2 rank_3 rank_4
0 0 0.475 0.9025 0 0 1 0
1 1 0.825 0.9175 0 0 1 0
2 1 1.000 1.0000 1 0 0 0
In [5]:
# choose the data randomly
sample = np.random.choice(processed_data.index, size=int(len(processed_data)*0.9), replace=False)
train_data, test_data = processed_data.iloc[sample], processed_data.drop(sample)
print("Number of training samples is", len(train_data))
print("Number of testing samples is", len(test_data))
print(train_data[:3])
print(test_data[:3])
Out [5]:
Number of training samples is 360
Number of testing samples is 40
admit gre gpa rank_1 rank_2 rank_3 rank_4
302 1 0.5 0.7875 0 1 0 0
121 1 0.6 0.6675 0 1 0 0
249 0 0.8 0.9325 0 0 1 0
admit gre gpa rank_1 rank_2 rank_3 rank_4
3 1 0.800 0.7975 0 0 0 1
12 1 0.950 1.0000 1 0 0 0
13 0 0.875 0.7700 0 1 0 0
In [6]:
import keras
# Separate data and one-hot encode the output
# Note: We're also turning the data into numpy arrays, in order to train the model in Keras
# use keras.utils.to_categorical to one-hot encoding targets
features = np.array(train_data.drop('admit', axis=1))
targets = np.array(keras.utils.to_categorical(train_data['admit'], 2))
features_test = np.array(test_data.drop('admit', axis=1))
targets_test = np.array(keras.utils.to_categorical(test_data['admit'], 2))
print(features[:3])
print(targets[:3])
[[ 0.5 0.7875 0. 1. 0. 0. ]
[ 0.6 0.6675 0. 1. 0. 0. ]
[ 0.8 0.9325 0. 0. 1. 0. ]]
[[ 0. 1.]
[ 0. 1.]
[ 1. 0.]]
Data processing example from bike rental project
In [7]:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [8]:
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
In [9]:
# check to see what style is available in your current working environment
plt.style.available
Out[9]:
['seaborn-deep',
'seaborn-talk',
'seaborn-paper',
'bmh',
'grayscale',
'seaborn-bright',
'seaborn-colorblind',
'ggplot',
'seaborn-notebook',
'seaborn-muted',
'dark_background',
'seaborn-dark-palette',
'seaborn-white',
'seaborn-darkgrid',
'classic',
'seaborn-poster',
'seaborn-pastel',
'fivethirtyeight',
'seaborn-ticks',
'_classic_test',
'seaborn-whitegrid',
'seaborn-dark',
'seaborn']
In [10]:
# choose the style you like
plt.style.use('ggplot')
fig, ax = plt.subplots(nrows=1, ncols=1) # add this line to take control of the figure configuration later
rides[:24 * 10].plot(x='dteday', y='cnt', ax=ax, figsize=(10, 5)) #set ax=ax to take control of the figure
ax.legend().set_visible(False)
ax.set(title='Rental counts in the first 10 days', ylabel='Rental Counts', xlabel='Date');
# this very semicolon stop plt printing out working messages
Bike rental
In [11]:
# this demonstrates how you can one-hot encoding more than one column using pandas
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
In [12]:
# scaling the data with standard values
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data[each] = (data[each] - mean) / std
# this line should be write this way for simplicity's sake
In [13]:
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
In [14]:
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
网友评论