I'm trying to create a heat map in Tableau that will show me the hottest and coldest values per field, at the moment it is currently just assessing all values in the data set and assigning heat based on the lowest and highest values, irrespective of where they appear in the data set. Example below:
Place chickens bears potatoes
a............. 10.......... 100...... 5000
b ............. 70.......... 50...... 7000
c............. 30.......... 5........ 3000
d............. 30.......... 150........100
In the example table above, I'd like each column to have its own individual heat ranking (e.g. Chickens in place B should be hot, place b cold. Bears in place D should be hot, place C cold etc.). Can this be done in Tableau on one screen? I know if I filter on one column it works, but I'd rather have this all in one visualisation if possible.
Thanks!
See the example below.
When you have multiple measures in a viz using the measure names pseudo-field on the color shelf, you can have a separate color legend for each measure name. You can choose to have separate or combined color legends by right-clicking on the measure names pill on the color shelf/marks card.
In the example, I chose to use a different color palette for each measure, but that's not required. You can use the same palette for each legend if you wish, and still get different scales.
Related
I have gone through a couple of YOLO tutorials but I am finding it some what hard to figure if the Anchor boxes for each cell the image is to be divided into is predetermined. In one of the guides I went through, The image was divided into 13x13 cells and it stated each cell predicts 5 anchor boxes(bigger than it, ok here's my first problem because it also says it would first detect what object is present in the small cell before the prediction of the boxes).
How can the small cell predict anchor boxes for an object bigger than it. Also it's said that each cell classifies before predicting its anchor boxes how can the small cell classify the right object in it without querying neighbouring cells if only a small part of the object falls within the cell
E.g. say one of the 13 cells contains only the white pocket part of a man wearing a T-shirt how can that cell classify correctly that a man is present without being linked to its neighbouring cells? with a normal CNN when trying to localize a single object I know the bounding box prediction relates to the whole image so at least I can say the network has an idea of what's going on everywhere on the image before deciding where the box should be.
PS: What I currently think of how the YOLO works is basically each cell is assigned predetermined anchor boxes with a classifier at each end before the boxes with the highest scores for each class is then selected but I am sure it doesn't add up somewhere.
UPDATE: Made a mistake with this question, it should have been about how regular bounding boxes were decided rather than anchor/prior boxes. So I am marking #craq's answer as correct because that's how anchor boxes are decided according to the YOLO v2 paper
I think there are two questions here. Firstly, the one in the title, asking where the anchors come from. Secondly, how anchors are assigned to objects. I'll try to answer both.
Anchors are determined by a k-means procedure, looking at all the bounding boxes in your dataset. If you're looking at vehicles, the ones you see from the side will have an aspect ratio of about 2:1 (width = 2*height). The ones viewed from in front will be roughly square, 1:1. If your dataset includes people, the aspect ratio might be 1:3. Foreground objects will be large, background objects will be small. The k-means routine will figure out a selection of anchors that represent your dataset. k=5 for yolov3, but there are different numbers of anchors for each YOLO version.
It's useful to have anchors that represent your dataset, because YOLO learns how to make small adjustments to the anchor boxes in order to create an accurate bounding box for your object. YOLO can learn small adjustments better/easier than large ones.
The assignment problem is trickier. As I understand it, part of the training process is for YOLO to learn which anchors to use for which object. So the "assignment" isn't deterministic like it might be for the Hungarian algorithm. Because of this, in general, multiple anchors will detect each object, and you need to do non-max-suppression afterwards in order to pick the "best" one (i.e. highest confidence).
There are a couple of points that I needed to understand before I came to grips with anchors:
Anchors can be any size, so they can extend beyond the boundaries of
the 13x13 grid cells. They have to be, in order to detect large
objects.
Anchors only enter in the final layers of YOLO. YOLO's neural network makes 13x13x5=845 predictions (assuming a 13x13 grid and 5 anchors). The predictions are interpreted as offsets to anchors from which to calculate a bounding box. (The predictions also include a confidence/objectness score and a class label.)
YOLO's loss function compares each object in the ground truth with one anchor. It picks the anchor (before any offsets) with highest IoU compared to the ground truth. Then the predictions are added as offsets to the anchor. All other anchors are designated as background.
If anchors which have been assigned to objects have high IoU, their loss is small. Anchors which have not been assigned to objects should predict background by setting confidence close to zero. The final loss function is a combination from all anchors. Since YOLO tries to minimise its overall loss function, the anchor closest to ground truth gets trained to recognise the object, and the other anchors get trained to ignore it.
The following pages helped my understanding of YOLO's anchors:
https://medium.com/#vivek.yadav/part-1-generating-anchor-boxes-for-yolo-like-network-for-vehicle-detection-using-kitti-dataset-b2fe033e5807
https://github.com/pjreddie/darknet/issues/568
I think that your statement about the number of predictions of the network could be misleading. Assuming a 13 x 13 grid and 5 anchor boxes the output of the network has, as I understand it, the following shape: 13 x 13 x 5 x (2+2+nbOfClasses)
13 x 13: the grid
x 5: the anchors
x (2+2+nbOfClasses): (x, y)-coordinates of the center of the bounding box (in the coordinate system of each cell), (h, w)-deviation of the bounding box (deviation to the prior anchor boxes) and a softmax activated class vector indicating a probability for each class.
If you want to have more information about the determination of the anchor priors you can take a look at the original paper in the arxiv: https://arxiv.org/pdf/1612.08242.pdf.
I have two layers: One of the Mississippi River Basin and one of the counties within the 48 states.
USA counties
Misissippi River Basin
I'm having no trouble selecting the counties within the Basin, but I also need to select the portions of the counties only partially within the basin. I know how to select the entirety of the counties that are partially within, but I want to select only the portions that are within the basin shapefile so I can calculate the percentage of those counties that are within the MRB.
Thanks, let me know if you need anything else
If I am reading your question correctly, the end result you want is the proportion of each county that is in the basin?
I would use the Union function to combine the two layers. The output is essentially sub-counties polygons.
You then calculate the area of those shapes that are both county and basin, but selecting by attribute.
Then use Dissolve on the counties using a sum rule on your area field (so that all the pieces are added up if there are unexpected splits).
Then calculate the total area of the counties in a new field and use the two new fields to calculate the proportion.
I hope this helps
I have a small prob in my chart legend. It shows like below at present. I just want to see AP1, AP2, PRIN and just one Total. Is this possible?
You can stop a series from appearing in the legend by going to the series properties and ticking 'Do not show this series in a legend'. How your chart is set up at the moment, you could do this to all but one of your Total series so that only one shows in the legend.
However, it may be more beneficial to make it clear what each separate total is calculating. If each is totalling a different set of data, you should make their styles different and change the Custom Legend Text to specify what is being totalled. If all the totals are supposed to be the same (i.e. Total = AP1 + AP2 + PRIN) you should check your calculations and only include the series on the chart once.
The report user wants to have groups by business unit and within each group they want to chart a set of same 4 metrics one under another: metric1, metric2... metric4. Under each of the merticN chart they want a matrix showing metricN values in rows where the months are the columns (metricN is actually 3 metrics: current month's, YTD and previous YE which is the same for every month).
Question: I know that it is possible to use list, bound to a group, to show charts by groups. Is it possible to insert a matrix for the same group under the chart (not to the right of it)?
I tried inserting nested rectangles, but a matrix inside a nested rectangle can no longer be bound to the same dataset, needless to say to the group (dataset is disabled).
As a proof of concept I can build a matrix with a chart inside the proper group, which does not solve the task of putting charts above the data. The charts, even so small, look fine in design, but in preview mode SSRS plays some dirty tricks. The resulting chart (occupying 100% of the group box and leving no space for group title, of course) is very ugly - horrible combination of scaling (why?!) and being squeezed into a small area. It looks like SSRS is going mad when a chart is in a confined space. The charts are stretched vertically and horizontally, the lines are thick and fuzzy. Only those charts, that end up in a partial group at the end of the page, look OK from graphical POV.
The most scandalous part is that even though data is correctly groupped by the matrix and reflected in the axis auto-scaling ticks and legends, it is not plotted. The charts remain empty. There must be some 'advanced option' hidden somewhere that is necessary for the charts to work within a matrix, or it is simply bugged.
Edit:
The best I've done so far was adding a Total row above a group and inserting a chart into that cell. It is somewhat closer to the requirement with several drawbacks:
The chart is only above the group name, not above whole matrix
Due to p.1 the chart cannot be made large enough
The matrix's column headers are separated from matrix data by the area
height of the chart
However the chart is still not plotting data. Playing with Category/Series Groups 'Synchronize group in' property is not helping.
To have a chart per group and group data in a matrix one on top of another, one should create a list and insert the required row groups with header rows. Then make group columns except the innermost hidden, delete the detail row and insert both chart and matrix into the innermost child group.
Then put the group fields into the group header cells.
This way the group titles appear at the beginning, then the chart, then the matrix and the last two are repeated in every child group.
I have a simple chart, and on the x-axis I have numbers ranging from 10000 to 10031.
The numbers are from a SQL call, and these values have values, which are plotted on the y-axis, just a simple chart.
The chart is only showing six of the numbers between 10000 and 10031, and having the user figure out the other numbers, I suppose to avoid clutter.
I would like all of the x-axis number to show up, and to fit it will need to be rotated 90 degrees.
I cannot find a setting to make all of the numbers show up.
In "Axis Properties", select "Axis Options" and put in "1" for the expression to show all. "2" will show every other, and the default, "0" will mean for Reporting Services to optimize as it sees fit, usually between every fourth or sixth element.