I need to calculate the mean average precision (mAP) of specific keypoints (and not for all keypoints, as it done by default).
Here's my code :
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO('annotations/person_keypoints_val2017.json') # initialize COCO ground truth api
cocoDt = cocoGt.loadRes('detections/results.json') # initialize COCO pred api
cat_ids = cocoGt.getCatIds(catNms=['person'])
imgIds = cocoGt.getImgIds(catIds=cat_ids)
cocoEval = COCOeval(cocoGt, cocoDt, 'keypoints')
cocoEval.params.imgIds = imgIds
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
print(cocoEval.stats[0])
This code prints the mAP for all keypoints ['nose', ...,'right_ankle'] but I need only for few specific keypoints like ['nose', 'left_hip', 'right_hip']
I recently solved this and evaluated only the 13 key points, leaving behind the eyes and the ears as per my application.
Just open the cocoeval.py under pycocotools, then head over to the computeOKS function, where you will encounter two sets of keypoints—ground truth keypoints—and detection keypoints, such as a NumPy array.
Make sure to do proper slicing for that 51 array size Python lists.
For example, if you wish to only check the mAP for nose, the slicing would be as follows:
g= np.array(gt['keypoints'][0:3])
Similarly, do it for a dt array.
Also, set the sigma values of those unwanted key points to 0.
You are all set!
How to use seasonal_decompose. How to deal with various errors while using seasonal_decompose. How can we practically use or implement seasonal_decompose.
Get all imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from statsmodels.tsa.seasonal import seasonal_decompose
Prepare test data
data = {'Unix Timestamp': ['1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12','1.61888E+12'],
'Date': ['4/20/2021 0:02','4/20/2021 0:01','4/20/2021 0:00','4/19/2021 23:59','4/19/2021 23:58','4/19/2021 23:57','4/19/2021 23:56','4/19/2021 23:55','4/19/2021 23:54','4/19/2021 23:53','4/19/2021 23:52','4/19/2021 23:51','4/19/2021 23:50','4/19/2021 23:49','4/19/2021 23:48','4/19/2021 23:47','4/19/2021 23:46','4/20/2021 0:02','4/20/2021 0:01','4/20/2021 0:00','4/19/2021 23:59','4/19/2021 23:58','4/19/2021 23:57','4/19/2021 23:56','4/19/2021 23:55','4/19/2021 23:54','4/19/2021 23:53','4/19/2021 23:52','4/19/2021 23:51','4/19/2021 23:50','4/19/2021 23:49','4/19/2021 23:48','4/19/2021 23:47','4/19/2021 23:46'],
'Symbol': ['BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD','BTCUSD'],
'Open': [55717.47,55768.94,55691.79,55777.86,55803.5,55690.64,55624.69,55651.82,55688.08,55749.28,55704.59,55779.38,55816.61,55843.69,55880.12,55890.88,0,55717.47,55768.94,55691.79,55777.86,55803.5,55690.64,55624.69,55651.82,55688.08,55749.28,55704.59,55779.38,55816.61,55843.69,55880.12,55890.88,0],
'High': [55723,55849.82,55793.15,55777.86,55823.88,55822.91,55713.02,55675.92,55730.21,55749.28,55759.27,55779.38,55835.57,55863.89,55916.47,55918.87,0,55723,55849.82,55793.15,55777.86,55823.88,55822.91,55713.02,55675.92,55730.21,55749.28,55759.27,55779.38,55835.57,55863.89,55916.47,55918.87,0],
'Low': [55541.69,55711.74,55691.79,55677.92,55773.08,55682.56,55624.63,55621.58,55641.46,55688.08,55695.42,55688.66,55769.46,55797.08,55815.99,55826.84,0,55541.69,55711.74,55691.79,55677.92,55773.08,55682.56,55624.63,55621.58,55641.46,55688.08,55695.42,55688.66,55769.46,55797.08,55815.99,55826.84,0]}
df=pd.DataFrame(data)
Perform decomposition
df_seasonal = seasonal_decompose(df)
We get our first error
ValueError: could not convert string to float:
Lets fix the above error, for this run below code
df['Date'] = df['Date'].apply(
lambda x : datetime.datetime.strptime(str(x),'%m/%d/%Y %H:%M')
)
Now if you run seasonal_decompose again, you will get new error
df_seasonal = seasonal_decompose(df)
Now the new error will be
TypeError: float() argument must be a string or a number, not 'Timestamp'
To fix this error we pass one column at a time and the column passed should be a string or a number. Try the decompose using below code
df_seasonal = seasonal_decompose(df['Open'])
Now you get a new error, as shown below
ValueError: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None
There are two solution's to this error
First Solution:- use period parameter for seasonal_decompose
df_seasonal = seasonal_decompose(df['Open'],period = 1) ## here we have data for every minute and hence period is 1 , but this need not be correct.
In above code we have data for every minute and hence period is 1. However, this need not be correct period is actually cycle period of input data. To know more on how to decide on period read this page. To know the complete list of freq abbrevations click here
Second Solution:- create an datetime index for the data along with frequency
df = df.set_index(df.Date).asfreq('2Min') ## M for Months S for Seconds. Here we cannot resample data with frequency 1Min, as data is already in frequency of 1Min, hence we used 2Min here
df_seasonal = seasonal_decompose(df['Open']) ## here we didn't use period and freq argument
In seasonal_decompose we have to set the model ( By default its Addictive). We can either set the model to be Additive or Multiplicative. A rule of thumb for selecting the right model is to see in our plot if the trend and seasonal variation are relatively constant over time, in other words, linear. If yes, then we will select the Additive model. Otherwise, if the trend and seasonal variation increase or decrease over time then we use the Multiplicative model. So that means before we do seasonal_decompose we must plot the preprocessed data over time and see if there are any trends or cycles.
Finally we could run it without error.
Another error that we might see is TypeError: Index(...) must be called with a collection of some kind, 'seasonal' was passed, this again happens due to wrong usage of seasonal_decompose like for example below
df_bt_decomp = seasonal_decompose(df_bt[['Open','High']],period=1) ## this is wrong because we have used two columns together and both are valid metric and not an index.
I am trying to finish this course tooth and nail with the hopes of being able to do this kind of stuff entry level by Spring time. This is my first post here on this incredible resource, and will do my best to conform to posting format. As a potential way to enforce my learning and commit to long term memory, I'm trying the same things on my own dataset of > 500 entries containing data more relevant to me as opposed to dummy data.
I'm learning about the data preprocessing phase where you fill in missing values and separate the columns into their respective X and Y to be fed into the models later on, if I understand correctly.
So in the course example, it's the top left dataset of countries. Then the bottom left is my own database of data I've been keeping for about a year on a multiplayer game I play. It has 100 or so characters you can choose from who are played between 5 different categorical roles.
Course data set (top left) personal dataset (bottom left
personal dataset column transformed results
What's up with the different outputs that are produced, with the only difference being the dataset (.csv file)? The course's dataset looks right; that first column of countries (textual categories) gets turned into binary vectors in the output no? Why is the output on my data set omitting columns, and producing these bizarre looking tuples followed by what looks like a random number? I've tried removing the np.array function, I've tried printing each output at each level, unable to see what's causing the difference. I expected on my dataset it would transform the characters' names into binary vectors (combinations of 1s/0s?) so the computer can understand the difference and map them to the appropriate results. Instead I'm getting that weird looking output I've never seen before.
EDIT: It turns out these bizarre number combinations are what's called a "sparse matrix." Had to do some research starting with the type() which yielded csr_array. If I understood what I Read correctly all the stuff inside takes up one column, so I just tried all rows/columns using [:] and I didn't get an error.
Really appreciate your time and assistance.
EDIT: Thanks to this thread I was able to make my way to the end of this data preprocessing/import/cleaning/ phase exercise, to feature scaling using my own dataset of ~ 550 rows.
import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, StandardScaler
from sklearn.model_selection import train_test_split
# IMPORT RAW DATA // ASSIGN X AND Y RAW
df = pd.read_csv('datasets/winpredictor.csv')
X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values
# TRANSFORM CATEGORICAL DATA
ct = ColumnTransformer(transformers=\
[('encoder', OneHotEncoder(), [0, 1])], remainder='passthrough')
le = LabelEncoder()
X = ct.fit_transform(X)
y = le.fit_transform(y)
# SPLIT THE DATA INTO TRAINING AND TEST SETS
X_train, X_test, y_train, y_test = train_test_split(\
X, y, train_size=.8, test_size=.2, random_state=1)
# FEATURE SCALING
sc = StandardScaler(with_mean=False)
X_train[:, :] = sc.fit_transform(X_train[:, :])
X_test[:, :] = sc.transform(X_test[:, :])
First of all I encourage you to keep working with this course and for sure you will be a perfect Data Science in a few weeks.
Let's talk about your problem. It' seems that you only have a problem of visualization due to the big size of different types of "Hero" (I think you have 37 unique values).
I will explain you the results you have plotted. They programm only indicate you the values of the samples that are different of 0:
(0,10)=1 --> 0 refers to the first sample, and 10 refers to the 10th
value of the sample that is equal to 1.
(0,37)=5 --> 0 refers to the first sample, and 37 refers to the 37th, which is equal to 5.
etc..
So your first sample will be something like:
[0,0,0,0,0,0,0,0,0,0,1,.........., 5, 980,-30, 1000, 6023]
Which is the way to express the first sample of "Jakiro".
["Jakiro",5, 980,-30, 1000, 6023]
To sump up, the first 37 values refers to your OneHotEncoder, and last 5 refers to your initial numerical values.
So it seems to be correct, just a different way to plot the result due to the big size of classes of the categorical variable.
You can try to reduce the number of X rows (to 4 for example), and try the same process. Then you will have a similar output as the course.
I have taken code in relation to the Kalman Filter and am attempting to iterate through each column of data. What I would like to have happen is:
The column data is fed into the filter
The filtered column data (xhat) is placed into another DataFrame (filtered)
The filtered column data (xhat) is used to produce a visual.
I have created a for loop to iterate through the column data, but when I run the cell, I crash the notebook. When it doesn't crash, I get this warning:
C:\Users\perso\Anaconda3\envs\learn-env\lib\site-packages\ipykernel_launcher.py:45: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
Thanks in advance for any help. I hope this question is detailed enough. I bombed on the last one.
'''A Python implementation of the example given in pages 11-15 of "An
Introduction to the Kalman Filter" by Greg Welch and Gary Bishop,
University of North Carolina at Chapel Hill, Department of Computer
Science, TR 95-041,
https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf'''
# by Andrew D. Straw
import numpy as np
import matplotlib.pyplot as plt
# dataframe created to hold filtered data
filtered = pd.DataFrame()
# intial parameters
for column in data:
n_iter = len(data.index) #number of iterations equal to sample numbers
sz = (n_iter,) # size of array
z = data[column] # observations
Q = 1e-5 # process variance
# allocate space for arrays
xhat=np.zeros(sz) # a posteri estimate of x
P=np.zeros(sz) # a posteri error estimate
xhatminus=np.zeros(sz) # a priori estimate of x
Pminus=np.zeros(sz) # a priori error estimate
K=np.zeros(sz) # gain or blending factor
R = 1.0**2 # estimate of measurement variance, change to see effect
# intial guesses
xhat[0] = z[0]
P[0] = 1.0
for k in range(1,n_iter):
# time update
xhatminus[k] = xhat[k-1]
Pminus[k] = P[k-1]+Q
# measurement update
K[k] = Pminus[k]/( Pminus[k]+R )
xhat[k] = xhatminus[k]+K[k]*(z[k]-xhatminus[k])
P[k] = (1-K[k])*Pminus[k]
# add new data to created dataframe
filtered.assign(a = [xhat])
#create visualization of noise reduction
plt.rcParams['figure.figsize'] = (10, 8)
plt.figure()
plt.plot(z,'k+',label='noisy measurements')
plt.plot(xhat,'b-',label='a posteri estimate')
plt.legend()
plt.title('Estimate vs. iteration step', fontweight='bold')
plt.xlabel('column data')
plt.ylabel('Measurement')
This seems like a pretty straightforward error. The warning indicates that you have attempted to plot more figures than the current limit before a warning is created (a parameter you can change but which by default is set to 20). This is because in each iteration of your for loop, you create a new figure. Depending on the size of n_iter, you are opening potentially hundreds or thousands of figures. Each of these figures takes resources to generate and show, so you are creating a very large resource load on your system. Either it is processing very slowly due or is crashing altogether. In any case, the solution is to plot fewer figures.
I don't know exactly what you're plotting in your loop but it seems like each iteration of your loop corresponds to one time step and at each time step you'd like to plot the estimated and actual values. In this case, you need to define a figure and figure options once, outside of the loop, rather than at each iteration. But a better way to do this is probably to generate all of the data you want to plot ahead of time and store it in an easy-to-plot datatype like lists, then plot it once at the end.