I have a 2D numpy array which I converted to R matrix and now I need to convert it further to named list:
rpy2.robjects.numpy2ri.activate()
nr,nc = counts.shape
r_mtx = robjects.r.matrix(counts, nrow=nr, ncol=nc)
So, I got the matrix r_mtx, but I am not sure how to make a named list out of it similar to how we do it in R:
named_list <- list(counts=mtx)
I need it to feed into SingleCellExperiment object to do dataset normalization:
https://bioconductor.org/packages/devel/bioc/vignettes/scran/inst/doc/scran.html
I tried using rpy2.rlike.container both TaggedList and OrdDict but can't figure out how to apply them to my case.
Ultimately I solved it (avoiding convertion of numpy array to r matrix), straight making the named list from the numpy array:
named_list = robjects.r.list(counts=counts)
Where counts is a 2D numpy array
Related
I would like to create a meshgrid of variable dimensions by specifying the dimensions with a variable i.e. specifying dim=2, rather than manually changing the expression as in the example below to set a 2D mesh grid.
How would I implement a wrapper function for this?
The problem stems from not being familiar with the syntax that mgrid uses (index_tricks).
import numpy as np
mgrid = np.mgrid[
-5:5:5j,
-5:5:5j,
]
Observed documentation for mgrid, but there seems to be no info on setting the number of dimensions with a variable.
You can create a tuple containing the slice manually, and repeat it some number of times:
import numpy as np
num_dims = 2
mgrid = np.mgrid[(slice(-5, 5, 5j),) * num_dims]
I think some of my question is answered here:1
But the difference that I have is that I'm wondering if it is possible to do the slicing step without having to re-write the datasets to another file first.
Here is the code that reads in a single HDF5 file that is given as an argument to the script:
with h5py.File(args.H5file, 'r') as df:
print('Here are the keys of the input file\n', df.keys())
#interesting point here: you need the [:] behind each of these and we didn't need it when
#creating datasets not using the 'with' formalism above. Adding that even handled the cases
#in the 'hits' and 'truth_hadrons' where there are additional dimensions...go figure.
jetdset = df['jets'][:]
haddset = df['truth_hadrons'][:]
hitdset = df['hits'][:]
Then later I do some slicing operations on these datasets.
Ideally I'd be able to pass a wild-card into args.H5file and then the whole set of files, all with the same data formats, would end up in the three datasets above.
I do not want to store or make persistent these three datasets at the end of the script as the output are plots that use the information in the slices.
Any help would be appreciated!
There are at least 2 ways to access multiple files:
If all files follow a naming pattern, you can use the glob
module. It uses wildcards to find files. (Note: I prefer
glob.iglob; it is an iterator that yields values without creating a list. glob.glob creates a list which you frequently don't need.)
Alternatively, you could input a list of filenames and loop on
the list.
Example of iglob:
import glob
for fname in glob.iglob('img_data_0?.h5'):
with h5py.File(fname, 'r') as h5f:
print('Here are the keys of the input file\n', h5.keys())
Example with a list of names:
filenames = [ 'img_data_01.h5', 'img_data_02.h5', 'img_data_03.h5' ]
for fname in filenames:
with h5py.File(fname, 'r') as h5f:
print('Here are the keys of the input file\n', h5.keys())
Next, your code mentions using [:] when you access a dataset. Whether or not you need to add indices depends on the object you want returned.
If you include [()], it returns the entire dataset as a numpy array. Note [()] is now preferred over [:]. You can use any valid slice notation, e.g., [0,0,:] for a slice of a 3-axis array.
If you don't include [:], it returns a h5py dataset object, which
behaves like a numpy array. (For example, you can get dtype and shape, and slice the data). The advantage? It has a smaller memory footprint. I use h5py dataset objects unless I specifically need an array (for example, passing image data to another package).
Examples of each method:
jets_dset = h5f['jets'] # w/out [()] returns a h5py dataset object
jets_arr = h5f['jets'][()] # with [()] returns a numpy array object
Finally, if you want to create a single array that merges values from 3 datasets, you have to create an array big enough to hold the data, then load with slice notation. Alternatively, you can use np.concatenate() (However, be careful, as concatenating a lot of data can be slow.)
A simple example is shown below. It assumes you know the shape of the dataset, and they are the same for all 3 files. (a0, a1 are the axes lengths for 1 dataset) If you don't know them, you can get them from the .shape attribute
Example for method 1 (pre-allocating array jets3x_arr):
a0, a1 = 100, 100
jets3x_arr = np.empty(shape=(a0, a1, 3)) # add dtype= if not float
for cnt, fname in enumerate(glob.iglob('img_data_0?.h5')):
with h5py.File(fname, 'r') as h5f:
jets3x_arr[:,:,cnt] = h5f['jets']
Example for method 2 (using np.concatenate()):
a0, a1 = 100, 100
for cnt, fname in enumerate(glob.iglob('img_data_0?.h5')):
with h5py.File(fname, 'r') as h5f:
if cnt == 0:
jets3x_arr= h5f['jets'][()].reshape(a0,a1,1)
else:
jets3x_arr= np.concatenate(\
(jets3x_arr, h5f['jets'][()].reshape(a0,a1,1)), axis=2)
Below is a screenshot of the branches of data in my .hdf5 file. I am trying to extract the existing column names (ie. experiment_id, session_id....) from this particular BlinkStartEvent segment.
I have the following codes that was able to access to this section of the data and extract the numerical data as well. But for some reason, I cannot extract the corresponding column names, which I wish to append onto a separate list so I can create a dictionary out of this entire dataset. I thought .keys() was supposed to do it, but it didn't.
import h5py
def traverse_datasets(hdf_file):
def h5py_dataset_iterator(g, prefix=''):
for key in g.keys():
#print(key)
item = g[key]
path = f'{prefix}/{key}'
if isinstance(item, h5py.Dataset): # test for dataset
yield (path, item)
elif isinstance(item, h5py.Group): # test for group (go down)
yield from h5py_dataset_iterator(item, path)
for path, _ in h5py_dataset_iterator(hdf_file):
yield path
with h5py.File(filenameHDF[0], 'r') as f:
for dset in traverse_datasets(f):
if str(dset[-15:]) == 'BlinkStartEvent':
print('-----Path:', dset) # path that leads to the data
print('-----Shape:', f[dset].shape) #the length dimension of the data
print('-----Data type:', f[dset].dtype) #prints out the unicode for all columns
data2 = f[dset][()] # The entire dataset
# print('Check column names', f[dset].keys()) # I tried this but I got a AttributeError: 'Dataset' object has no attribute 'keys' error
I got the following as the output:
-----Path: /data_collection/events/eyetracker/BlinkStartEvent
-----Shape: (220,)
-----Data type: [('experiment_id', '<u4'), ('session_id', '<u4'), ('device_id', '<u2'), ('event_id', '<u4'), ('type', 'u1'), ('device_time', '<f4'), ('logged_time', '<f4'), ('time', '<f4'), ('confidence_interval', '<f4'), ('delay', '<f4'), ('filter_id', '<i2'), ('eye', 'u1'), ('status', 'u1')]
Traceback (most recent call last):
File "C:\Users\angjw\Dropbox\NUS PVT\Analysis\PVT analysis_hdf5access.py", line 64, in <module>
print('Check column names', f[dset].keys())
AttributeError: 'Dataset' object has no attribute 'keys'
What am I getting wrong here?
Also, is there a more efficient way to access the data such that I can do something (hypothetical) like:
data2[0]['experiment_id'] = 1
data2[1]['time'] = 78.35161
data2[2]['logged_time'] = 80.59253
rather than having to go through the process of setting up a dictionary for every single row of data?
You're close. The dataset's .dtype gives you the dataset as a NumPy dtype. Adding .descr returns it as a list of (field name, field type) tuples. See code below to print the field names inside your loop:
for (f_name,f_type) in f[dset].dtype.descr:
print(f_name)
There are better ways to work with HDF5 data than creating a dictionary for every single row of data (unless you absolutely want a dictionary for some reason). h5py is designed to work with dataset objects similar to NumPy arrays. (However, not all NumPy operations work on h5py dataset objects). The following code accesses the data and returns 2 similar (but slightly different) data objects.
# this returns a h5py dataset object that behaves like a NumPy array:
dset_obj = f[dset]
# this returns a NumPy array:
dset_arr = f[dset][()]
You can slice data from either object using standard NumPy slicing notation (using field names and row values). Continuing from above...
# returns row 0 from field 'experiment_id'
val0 = dset_obj[0]['experiment_id']
# returns row 1 from field 'time'
val1 = dset_obj[1]['time']
# returns row 2 from field 'logged_time'
val2 = dset_obj[2]['logged_time']
(You will get the same values if you replace dset_obj with dset_arr above.)
You can also slice entire fields/columns like this:
# returns field 'experiment_id' as a NumPy array
expr_arr = dset_obj['experiment_id']
# returns field 'time' as a NumPy array
time_arr = dset_obj['time']
# returns field 'logged_time' as a NumPy array
logtime_arr = dset_obj['logged_time']
That should answer your initial questions. If not, please add comments (or modify the post), and I will update my answer.
My previous answer used the h5py package (same package as your code). There is another Python package that I like to use with HDF5 data: PyTables (aka tables). Both are very similar, and each has unique strengths.
h5py attempts to map the HDF5 feature set to NumPy as closely as possible. Also, it uses Python dictionary syntax to iterate over object names and values. So, it is easy to learn if you are familiar with NumPy. Otherwise, you have to learn some NumPy basics (like interrogating dtypes). Homogeneous data is returned as a np.array and heterogeneous data (like yours) is returned as a np.recarray.
PyTables builds an additional abstraction layer on top of HDF5 and NumPy. Two unique capabilities I like are: 1) recursive iteration over nodes (groups or datasets), so a custom dataset generator isn't required, and 2) heterogeneous data is accessed with a "Table" object that has more methods than basic NumPy recarray methods. (Plus it can do complex queries on tables, has advanced indexing capabilities, and is fast!)
To compare them, I rewrote your h5py code with PyTables so you can "see" the difference. I incorporated all the operations in your question, and included the equivalent calls from my h5py answer. Differences to note:
The f.walk_nodes() method is a built-in method that replaces your
your generator. However, it returns an object (a Table object in this
case), not the Table (dataset) name. So, the code is slightly
different to work with the object instead of the name.
Use Table.read() to load the data into a NumPy (record) array. Different examples show how to load the entire Table into an array, or load a single column referencing the field name.
Code below:
import tables as tb
with tb.File(filenameHDF[0], 'r') as f:
for tb_obj in f.walk_nodes('/','Table'):
if str(tb_obj.name[-15:]) == 'BlinkStartEvent':
print('-----Name:', tb_obj.name) # Table name without the path
print('-----Path:', tb_obj._v_pathname) # path that leads to the data
print('-----Shape:', tb_obj.shape) # the length dimension of the data
print('-----Data type:', tb_obj.dtype) # prints out the np.dtype for all column names/variable types
print('-----Field/Column names:', tb_obj.colnames) #prints out the names of all columns as a list
data2 = tb_obj.read() # The entire Table (dataset) into array data2
# returns field 'experiment_id' as a NumPy (record) array
expr_arr = tb_obj.read(field='experiment_id')
# returns field 'time' as a NumPy (record) array
time_arr = tb_obj.read(field='time')
# returns field 'logged_time' as a NumPy (record) array
logtime_arr = tb_obj.read(field='logged_time')
I started playing with Decision Trees lately and I wanted to train my own simple model with some manufactured data. I wanted to use this model to predict some further mock data, just to get a feel of how it works, but then I got stuck. Once your model is trained, how do you pass data to predict()?
http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
Docs state:
clf.predict(X)
Parameters:
X : array-like or sparse matrix of shape = [n_samples, n_features]
But when trying to pass np.array, np.ndarray, list, tuple or DataFrame it just throws an error. Can you help me understand why please?
Code below:
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
import graphviz
import pandas as pd
import numpy as np
import random
from sklearn import tree
pd.options.display.max_seq_items=5000
pd.options.display.max_rows=20
pd.options.display.max_columns=150
lenght = 50000
miles_commuting = [random.choice([2,3,4,5,7,10,20,25,30]) for x in range(lenght)]
salary = [random.choice([1300,1600,1800,1900,2300,2500,2700,3300,4000]) for x in range(lenght)]
full_time = [random.choice([1,0,1,1,0,1]) for x in range(lenght)]
DataFrame = pd.DataFrame({'CommuteInMiles':miles_commuting,'Salary':salary,'FullTimeEmployee':full_time})
DataFrame['Moving'] = np.where((DataFrame.CommuteInMiles > 20) & (DataFrame.Salary > 2000) & (DataFrame.FullTimeEmployee == 1),1,0)
DataFrame['TargetLabel'] = np.where((DataFrame.Moving == 1),'Considering move','Not moving')
target = DataFrame.loc[:,'Moving']
data = DataFrame.loc[:,['CommuteInMiles','Salary','FullTimeEmployee']]
target_names = DataFrame.TargetLabel
features = data.columns.values
clf = tree.DecisionTreeClassifier()
clf = clf.fit(data, target)
clf.predict(?????) #### <===== What should go here?
clf.predict([30,4000,1])
ValueError: Expected 2D array, got 1D array instead:
array=[3.e+01 4.e+03 1.e+00].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
clf.predict(np.array(30,4000,1))
ValueError: only 2 non-keyword arguments accepted
Where is your "mock data" that you want to predict?
Your data should be of the same shape that you used when calling fit(). From the code above, I see that your X has three columns ['CommuteInMiles','Salary','FullTimeEmployee']. You need to have those many columns in your prediction data, number of rows can be arbitrary.
Now when you do
clf.predict([30,4000,1])
The model is not able to understand that these are columns of a same row or data of different rows.
So you need to convert that into 2-d array, where inner array represents the single row.
Do this:
clf.predict([[30,4000,1]]) #<== Observe the two square brackets
You can have multiple rows to be predicted, each in inner list. Something like this:
X_test = [[30,4000,1],
[35,15000,0],
[40,2000,1],]
clf.predict(X_test)
Now as for your last error clf.predict(np.array(30,4000,1)), this has nothing to do with predict(). You are using the np.array() wrong.
According to the documentation, the signature of np.array is:
(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
Leaving the first (object) all others are keyword arguments, so they need to be used as such. But when you do this: np.array(30,4000,1), each value is considered as input to separate param here: object=30, dtype=4000, copy=1. This is not allowed and hence error. If you want to make a numpy array from list, you need to pass a list.
Like this: np.array([30,4000,1])
Now this will be considered correctly as input to object param.
Click here to see an image that contains a screenshot sample of the data.
I have a CSV file with a column for temperature range with values like "20-25" stored as string. I need to convert this to 22.5 as a float.
Need this to be done for the entire column of such values, not a single value.I want to know how this can be done in Python as i am very new to it.
Notice in the sample data image that there are NaN values as well in the records
Like said in the reactions split the array using "-" as argument.
Second, create a float array of it. Finally, take the average using numpy.
import numpy as np
temp_input = ["20-25", "36-40", "10-11", "23-24"]
# split and convert to float
# [t.split("-") for t in temp_input] is an inline iterator
tmp = np.array([t.split("-") for t in temp_input], dtype=np.float32)
# average the tmp array
temp_output = np.average(tmp, axis=1)
And here's a oneliner:
temp_output = [np.average(np.array(t.split('-'), dtype=np.float32)) for t in temp_input]