By Matthew Millar R&D Scientist at ユニファ
This blog will show a new experimental method for data augmentation geared towards bio-science for deep learning. This is important for several reasons. 1: Collecting data is time-consuming especially in collecting large enough observations for training deep learning models. 2: It can be difficult to collect or sample enough observations due to the lack of access or chances to make collections. 3: Collecting observations can only be done at certain times or during certain periods, or the period of time for sampling has passed so the collection of further/more observations are impossible. 4: There are few species available to collect samples from. These are just 4 simple reasons why data augmentation is needed for biological studies.
Methods for Data Augmentation
The simplest method for data augmentation is to match the generated data both statistically and logically to the observed data. This means that the data that is generated should have a similar look and feel of the real-world data. The two data sets should have similar distributions, mean, modes, etc. to ensure that the data truly simulates the observed sequences. The simulated data should also be logically like the data that is observed. This means that the simulated data should not have outliers model into it as this will confuse any model. The augmented data should flow alongside the observations and almost mirror each observation. But, just copying the real observations is not an appropriate method for data augmentation. The observations should change slightly. For example, common methods for data augmentations in CNN are image rotation, flipping, cropping, changing color, etc. to create “new” unseen images for a CNN to be trained on. This is also true for numerical data, but not as easy as just flipping the numbers from 10 to 01 as they are not the same.
There are very few methods that exist for data augmentation for numerical data. There are even fewer geared specifically towards biodata or biostudies. This blog will show a new method for generating near-infinite observations based simply on the minimum and maximum observations in a data set.
The data set that I am using is a publicly available data set of Body Measurements (BDIMS)(Heinz, Peterson, Johnson, & Kerk, 2003). This data set is the girth and skeletal measurement of 247 men and 260 women.
Now let's get into the coding aspect of it:
CODE
First, let's get all the import statements out of the way.
import numpy as np import pandas as pd %matplotlib inline import matplotlib.pyplot as plt import pymc3 as pm import theano from statsmodels.formula.api import glm as glm_sm import statsmodels.api as sm from pandas.plotting import scatter_matrix from random import randint
Next, we need to do some quick examination of the data we downloaded.
# Read the data in from the csv file data = pd.read_csv("bdims.csv") print(data.columns)
Index(['bia.di', 'bii.di', 'bit.di', 'che.de', 'che.di', 'elb.di', 'wri.di',
'kne.di', 'ank.di', 'sho.gi', 'che.gi', 'wai.gi', 'nav.gi', 'hip.gi',
'thi.gi', 'bic.gi', 'for.gi', 'kne.gi', 'cal.gi', 'ank.gi', 'wri.gi',
'age', 'wgt', 'hgt', 'sex'],
dtype='object')Now we know the colum names. Lets get rid of some of the data we dont want to make it simpler and easier to use.
filter_data = data.filter(['sex','hgt','wgt', 'che.gi','hip.gi', 'kne.gi','thi.gi', 'ank.gi', 'wri.gi', 'wai.gi' ], axis=1) print(filter_data.head())
sex hgt wgt che.gi hip.gi kne.gi thi.gi ank.gi wri.gi wai.gi 0 1 174.0 65.6 89.5 93.5 34.5 51.5 23.5 16.5 71.5 1 1 175.3 71.8 97.0 94.8 36.5 51.5 24.5 17.0 79.0 2 1 193.5 80.7 97.5 95.0 37.0 57.3 21.9 16.9 83.2 3 1 186.5 72.6 97.0 94.0 37.0 53.0 23.0 16.6 77.8 4 1 187.2 78.8 97.5 98.5 37.7 55.4 24.4 18.0 80.0
Much nicer. Now we only want to look at one subject as this is biological data. So we will filter out females from males and just look at males. This process will work on both sexes as the steps will be the same, but doing both at the same time will yield poor results as there are biological differences between males and females in general.
# Split between male and female male_mask = filter_data['sex'] > 0 male = filter_data[male_mask] female = filter_data[~male_mask] # After sperating the two exes lets drop the sex collumn as we dont need it male = male.drop(['sex'], axis=1) male.describe() hgt wgt che.gi hip.gi kne.gi thi.gi ank.gi wri.gi wai.gi count 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 247.000000 mean 177.745344 78.144534 100.989879 97.763158 37.195547 56.497976 23.159109 17.190283 84.533198 std 7.183629 10.512890 7.209018 6.228043 2.272999 4.246667 1.729088 0.907997 8.782241 min 157.200000 53.900000 79.300000 81.500000 31.100000 46.800000 16.400000 14.600000 67.100000 25% 172.900000 70.950000 95.950000 93.250000 35.750000 53.700000 22.000000 16.500000 77.900000 50% 177.800000 77.300000 101.000000 97.400000 37.000000 56.000000 23.000000 17.100000 83.400000 75% 182.650000 85.500000 106.050000 101.550000 38.450000 59.150000 24.300000 17.850000 90.000000 max 198.100000 116.400000 118.700000 118.700000 45.700000 70.000000 29.300000 19.600000 113.200000
Now with the first step of preprocessing, we can get into the process of creating the dataset from only two points! These two points will be the minimum and maximum based on height. Height is chosen because this variable is the dominating variable in biology and bio-mass. Weight is normally heavily dependant on height (pun intended). The dependent variable will be weight. (X = height Y = weight).
So let's find the smallest and largest person in the dataset.
# Find the smallest item based on height # Create a new dataframe of the smallest and larget min_max_male = pd.DataFrame(male[male.hgt == male.hgt.max()]) min_max_male = min_max_male.append(male[male.hgt == male.hgt.min()]) # Sort by height sort_min_mix_male = min_max_male.sort_values('hgt') print(sort_min_mix_male) hgt wgt che.gi hip.gi kne.gi thi.gi ank.gi wri.gi wai.gi 105 157.2 58.4 91.6 91.3 35.5 55.0 20.8 16.4 80.6 126 198.1 85.5 96.9 94.9 39.2 54.4 27.5 17.9 82.5 ax1 = min_max_male.plot.scatter(x='hgt',y='wgt',c='DarkBlue')
# Now use linear regression to fill in some of the missing points import numpy as np from sklearn.linear_model import LinearRegression x = np.array([min_max_male.hgt.min(),min_max_male.hgt.max()]).reshape((-1, 1)) y = np.array([min_max_male.wgt.min(), min_max_male.wgt.max()]) # Define a linear regerssion model model = LinearRegression() model.fit(x, y) r_sq = model.score(x, y) print('coefficient of determination:', r_sq) print('intercept:', model.intercept_) print('slope:', model.coef_)
coefficient of determination: 1.0 intercept: -45.75941320293397 slope: [0.66259169]
Now to make new points.
prediction = [] gen_height = [] for i in range(int(min_max_male.hgt.min()), int(min_max_male.hgt.max())): new_x = np.array(i).reshape((-1, 1)) gen_height.append(i) pred = model.predict(new_x) prediction.append(pred[0]) print(len(prediction)) print(len(gen_height)) print(prediction[0]) print(gen_height[0]) 41 41 58.267481662591706 157 # Lets plot the results import matplotlib.pyplot as plt old_min_hgt = min_max_male.hgt.min() old_max_hgt = min_max_male.hgt.max() old_min_wgt = min_max_male.wgt.min() old_max_wgt = min_max_male.wgt.max() plt.plot(gen_height, prediction, 'ro') plt.plot(old_min_hgt, old_min_wgt, 'bo') plt.plot(old_max_hgt, old_max_wgt, 'bo') plt.show()
Lets create a few more points:
# Now lets fine tune the hieght veriable by a float instead of a int # We can resue the linerar regression model to generate more data # Go from 41 observations to 409000 observatsions # All equally possible to occure in the real world current_hgt = min_max_male.hgt.min() count = 0 large_hgt = [] while current_hgt <= min_max_male.hgt.max(): # increase the height by 0.1 cm current_hgt +=0.0001 large_hgt.append(current_hgt) count +=1 print(len(large_hgt)) 409000 # Now using the newlly generated fine scale height lets get the weight large_pred = [] for h in large_hgt: new_x = np.array(h).reshape((-1, 1)) pred = model.predict(new_x) large_pred.append(pred[0]) print(len(large_pred)) 409000 # Now lest plot everything again plt.plot(large_hgt, large_pred, 'go') plt.plot(gen_height, prediction, 'ro') plt.plot(old_min_hgt, old_min_wgt, 'bo') plt.plot(old_max_hgt, old_max_wgt, 'bo') plt.show()
As you can see perfectly overlaps and each observation makes sense and is logical.
The blue dots are the original, the red is the first step, and the green is fine-tuned steps.
This jumps from 2 observations (min and max) to 41 observations (fully synthetic) to 409000 observations.
But in the real world, biology does not always follow a linear line
Let's introduce some variability into the data generation!
# Define a new line using all the data from the real data set # Define a linear regerssion model X = np.array(male.hgt).reshape(-1, 1) Y = np.array(male.wgt).reshape(-1, 1) model2 = LinearRegression() model2.fit(X,Y) r_sq2 = model2.score(X,Y) print('coefficient of determination:', r_sq2) print('intercept:', model2.intercept_) print('slope:', model2.coef_)
coefficient of determination: 0.28594874074704446 intercept: [-60.95336414] slope: [[0.78256845]]
# Linear regresion using real data y_pred = model2.predict(X) # Now plot all the data plt.plot(X, y_pred, color='blue', linewidth=3) plt.plot(male.hgt, male.wgt, 'yo') plt.plot(large_hgt, large_pred, 'go') plt.plot(gen_height, prediction, 'ro') plt.plot(old_min_hgt, old_min_wgt, 'bo') plt.plot(old_max_hgt, old_max_wgt, 'bo') plt.show()
# The slope of the line is b, and a is the intercept found from Sklenar linear model # Simple Linear regressoin model Y = a + bX that will be the model for out MCMC alpha = -45.75941320293397 # Intercept beta = [0.66259169] # Slope X = np.array(large_hgt) Y = np.array(large_pred) print(len(X)) print(len(Y)) 409000 409000 # Weight Histogram hist = male.hist(column='wgt')
#Normal distribution. mu is the mean, and sigma is the standard deviation. # Seeing that the weight is normally distributed (basically) we can use that knowledge to generate new data via a normally # Distrubuted method #for random.normalvariate(mu, sigma) std = np.std(X, axis=0) real_std = np.std(male.wgt, axis=0) print(std) print(real_std) 11.806813005284504 10.491587167890629 temp_min_max = [] temp_min_max.append(male.wgt.max()) temp_min_max.append(male.wgt.min()) mean = np.mean(temp_min_max) real_mean = np.mean(male.wgt) print(mean) print(real_mean) 85.15 78.14453441295547
Looking at the mean and standard deviation they are close enough for this example. Lets make a Million data points for our new dataset! That should be enough for any deep learning dataset.
new_X = [] new_Y = [] for i in range(0,1000000): index = randint(0, len(X) -1) new_X.append(X[index]) new_Y.append(np.random.normal(mean,std)) plt.plot(new_X, new_Y, 'go',marker='^') plt.plot(male.hgt, male.wgt, 'yo') plt.plot(large_hgt, large_pred, 'go') plt.plot(gen_height, prediction, 'ro') plt.plot(old_min_hgt, old_min_wgt, 'bo') plt.plot(old_max_hgt, old_max_wgt, 'bo') plt.show()
So how can we fix this?
Let's perform some rejections by using a concept of banding. So if the observation falls outside the bands it won't get plotted. The bands themselves set up an upper and lower limit so that all predictions will have to fall within these limits. To form these limit expert knowledge of the observed phenomenon is needed especially for only two observations, luckily for us, we have more than two observations so we can define out limits based on the full real dataset.
# Use upper and lower limits to reject samples def make_sample(lower, upper, mean, std): sample = np.random.normal(mean,std) if lower < sample < upper: return sample else: make_sample(lower, upper, mean, std) # Define bands for each interval # The more bands the finer the level of rejection # Each item in the array is defined as # [band lower, band upper, lower limit, upper limit] band1 = [0, 155, 50, 70] band2 = [156,160, 55, 70] band3 = [161, 165, 56, 75] band4 = [166, 170, 57, 80] band5 = [171, 175, 60, 88] band6 = [176, 180, 60, 94] band7 = [181, 185, 60, 100] band8 = [186, 190, 63, 105] band9 = [191, 195, 64, 110] band10 = [196, 299, 65, 110] # Put all the bands into a single array for easy use bands = [] bands.append(band1) bands.append(band2) bands.append(band3) bands.append(band4) bands.append(band5) bands.append(band6) bands.append(band7) bands.append(band8) bands.append(band9) bands.append(band10) new_X = [] new_Y = [] for i in range(0, 1000000): index = randint(0, len(X) -1) for band in bands: if band[0] <= X[index] <= band[1]: new_X.append(X[index]) new_Y.append(make_sample(band[2], band[3], mean, std)) plt.plot(new_X, new_Y, 'go',marker='^') plt.plot(male.hgt, male.wgt, 'yo') plt.plot(large_hgt, large_pred, 'go') plt.plot(gen_height, prediction, 'ro') plt.plot(old_min_hgt, old_min_wgt, 'bo') plt.plot(old_max_hgt, old_max_wgt, 'bo') plt.show()
Which gives us this!
Conclusion
From this blog, we saw how to use only two observations, the minimum and maximum, and how to create a fully synthetic dataset that can be used for deep learning.
The main idea when building a fully synthetic dataset is to ensure it is statistically and logically similar to that of the observed/real dataset. This gives the benefit of creating a large training dataset and then using the real data as a testing set. This can give very good results when creating a deep learning model as you won't have to train the model on the very limited (and precious) real data that can be very difficult to capture or collect.
This approach can be improved significantly, especially in the banding section. By adding a larger number of bands, smoothing out the lower and upper limits, and even using more complex algorithms like a random walk can improve the final results. But, this method still needs to be vetted before use in different models and/or real-world applications. The next step would be to model more independent variables, other phenomenons, and improve the generation steps.
References:
Heinz G, Peterson LJ, Johnson RW, Kerk CJ. 2003. Exploring Relationships in Body Dimensions. Journal of Statistics Education 11(2).
