# Ensemble classification with Forest and Qiskit devices : Parameters

Dear all,

Can someone please explain to me how the pre-trained set of parameters (params.npy file) were obtained?

If i understand correctly those parameters have to do with the state preparation stage in which we try to encode classical information to quantum states. In that case did you use the similar function:
def get_angles(x): from the “variational classifier demo” to obtain params?

Hey @NikSchet,

The `params` were obtained by training the hybrid model. For example, let’s define:

``````def softmax_ensemble(params, x_point=None):
results = qnodes(params, x=x_point)
softmax = torch.nn.functional.softmax(results, dim=1)
choice = torch.where(softmax == torch.max(softmax))[0][0]
return softmax[choice]
``````

This function evaluates both circuits, converts to softmax vectors, and then returns the “most confident” vector (that is, the one with the largest entry). For example:

``````n_layers = 2
# the first index is for the two models
params = torch.tensor(np.random.random((2, n_layers, n_wires, 3)), requires_grad=True)

print(softmax_ensemble(params, x_train[0]))
``````

The above gives a vector such as `[0.2624, 0.4455, 0.2921]`, denoting a 44.55% probability of class `1`.

This can be compared to the one-hot encoded version of `y_train`:

``````import tensorflow as tf
``````

For example, `y_soft[0] = [1, 0, 0]`.

We then just need to define a cost function to compare the predicted vector with the target, for example:

``````def cost(params, y_point, x_point=None):
``````

This is a simple cost function that gives the absolute error.

Training is then as simple as:

``````opt = torch.optim.Adam([params], lr = 0.1)

for epoch in range(3):
for x_point, y_point in zip(x_train, y_soft):
c = cost(params, y_point=y_point, x_point=x_point)
c.backward()
opt.step()
``````

The training could be improved with random selection of points from the training set, batching, and use of a different cost function.

All the code together (also requires other parts of the tutorial to be run):

``````import tensorflow as tf

n_layers = 2
# the first index is for the two models
params = torch.tensor(np.random.random((2, n_layers, n_wires, 3)), requires_grad=True)

def softmax_ensemble(params, x_point=None):
results = qnodes(params, x=x_point)
softmax = torch.nn.functional.softmax(results, dim=1)
choice = torch.where(softmax == torch.max(softmax))[0][0]
return softmax[choice]

def cost(params, y_point, x_point=None):

opt = torch.optim.Adam([params], lr = 0.1)

for epoch in range(3):
for x_point, y_point in zip(x_train, y_soft):
c = cost(params, y_point=y_point, x_point=x_point)
c.backward()
opt.step()
``````

[quote=“Tom_Bromley, post:2, topic:608”]

Thank you very much for the detailed answer!!

Hello, I have also the same problem. In my case how to get this params.npy file. Because in the (Ensemble classification with Forest and Qiskit devices) they had used dataset having 2 features and 3 classes. In my dataset I have 10 features and 2 classes.
So I want to create this params.npy file fore my own dataset.

I am now facing this error (IndexError: index 4 is out of bounds for axis 2 with size 4)

Hi @Ubaid_Ullah, welcome to the forum!

Could you please share the code you’re using? Since you have a different size for your dataset you might have to change certain parts of the code.

Also, what version of the different libraries are you using?

n_features = 10
n_classes = 2
n_samples = 3042
cols=[22, 38, 41, 24, 35, 1, 40, 5, 4, 16]

sample_train=sample_train[:,[i-1 for i in cols]]
sample_test=sample_test[:,[i-1 for i in cols]]
sample_test.shape

n_wires = 10

dev0 =qml.device(“forest.qvm”, device=“4q-qvm”)
dev1 = qml.device(“qiskit.aer”, wires=10)
devs = [dev0, dev1]

def circuit0(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[1, 0, i], wires=i)

``````qml.CZ(wires=[0, 1])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[2, 3])
qml.CZ(wires=[3, 4])
qml.CZ(wires=[4, 5])
qml.CZ(wires=[5, 6])
qml.CZ(wires=[6, 7])
qml.CZ(wires=[7, 8])
qml.CZ(wires=[8, 9])
qml.CZ(wires=[9, 0])

for i in range(n_wires):
qml.Rot(*params[1, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))
``````

def circuit1(params, x=None):
for i in range(n_wires):
qml.RX(x[i % n_features], wires=i)
qml.Rot(*params[0, 0, i], wires=i)

``````qml.CZ(wires=[0, 1])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[2, 3])
qml.CZ(wires=[3, 4])
qml.CZ(wires=[4, 5])
qml.CZ(wires=[5, 6])
qml.CZ(wires=[6, 7])
qml.CZ(wires=[7, 8])
qml.CZ(wires=[8, 9])
qml.CZ(wires=[9, 0])

for i in range(n_wires):
qml.Rot(*params[0, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(qml.PauliZ(2))
``````

qnodes = qml.QNodeCollection(
[qml.QNode(circuit0, dev0, interface=“torch”),
qml.QNode(circuit1, dev1, interface=“torch”)]
)

print(“Predicting on training dataset”)
p_train, p_train_0, p_train_1, choices_train = predict(params, x=sample_train)
print(“Predicting on test dataset”)
p_test, p_test_0, p_test_1, choices_test = predict(params, x=sample_test)

I am using this code. and using the following library version.

ackage Version

absl-py 1.0.0
appdirs 1.4.4
argon2-cffi 20.1.0
astunparse 1.6.3
async-generator 1.10
attrs 20.3.0
autoray 0.2.5
backcall 0.2.0
bleach 4.0.0
cachetools 4.2.4
certifi 2021.10.8
cffi 1.14.6
charset-normalizer 2.0.9
cloudpickle 2.0.0
cryptography 36.0.0
cvxpy 1.1.11
cycler 0.11.0
debugpy 1.5.1
decorator 5.1.0
defusedxml 0.7.1
dill 0.3.4
dlx 1.0.4
docker 5.0.3
docplex 2.22.213
ecos 2.0.8
entrypoints 0.3
fastdtw 0.3.4
flatbuffers 2.0
fonttools 4.28.3
fsspec 2021.11.1
future 0.18.2
gast 0.4.0
grpcio 1.42.0
h11 0.9.0
h5py 3.2.1
httpcore 0.11.1
httpx 0.15.5
idna 3.3
immutables 0.6
inflection 0.5.1
ipykernel 6.4.1
ipython 7.29.0
ipython-genutils 0.2.0
ipywidgets 7.6.5
iso8601 0.1.16
jax 0.2.26
jaxlib 0.1.75
jedi 0.18.0
Jinja2 3.0.2
joblib 1.1.0
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 7.0.6
jupyter-console 6.4.0
jupyter-core 4.9.1
jupyterlab-pygments 0.1.2
jupyterlab-widgets 1.0.0
keras 2.7.0
Keras-Preprocessing 1.1.2
kiwisolver 1.3.2
lark 0.11.3
libclang 12.0.0
llvmlite 0.37.0
locket 0.2.1
lxml 4.6.4
Markdown 3.3.6
MarkupSafe 2.0.1
matplotlib 3.5.0
matplotlib-inline 0.1.2
mistune 0.8.4
more-itertools 8.12.0
mpmath 1.2.1
msgpack 0.6.2
nbclient 0.5.3
nbconvert 6.1.0
nbformat 5.1.3
nest-asyncio 1.5.1
networkx 2.6.3
ninja 1.10.2.3
notebook 6.4.6
ntlm-auth 1.5.0
numba 0.54.1
numpy 1.20.3
oauthlib 3.1.1
opt-einsum 3.3.0
osqp 0.6.2.post0
packaging 21.3
pandas 1.3.4
pandocfilters 1.4.3
parso 0.8.2
partd 1.2.0
pbr 5.8.0
PennyLane 0.20.0
PennyLane-Forest 0.20.0
PennyLane-Lightning 0.20.1
PennyLane-qiskit 0.20.0
pexpect 4.8.0
pickleshare 0.7.5
Pillow 8.4.0
pip 21.2.4
ply 3.11
prometheus-client 0.12.0
prompt-toolkit 3.0.20
protobuf 3.19.1
psutil 5.8.0
ptyprocess 0.7.0
py 1.11.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.21
pydantic 1.8.2
Pygments 2.10.0
PyJWT 1.7.1
pylatexenc 2.10
pyparsing 3.0.4
pyquil 2.28.2
pyrsistent 0.18.0
python-constraint 1.4.0
python-dateutil 2.8.2
python-rapidjson 1.5
pytz 2021.3
PyYAML 6.0
pyzmq 22.3.0
qcs-api-client 0.8.0
qdldl 0.1.5.post0
qiskit 0.34.0
qiskit-aer 0.10.1
qiskit-aer-gpu 0.9.1
qiskit-aqua 0.9.5
qiskit-ibmq-provider 0.18.3
qiskit-ignis 0.7.0
qiskit-machine-learning 0.2.1
qiskit-terra 0.19.1
qtconsole 5.1.1
QtPy 1.10.0
Quandl 3.7.0
requests 2.26.0
requests-ntlm 1.1.0
requests-oauthlib 1.3.0
retry 0.9.2
retrying 1.3.3
retworkx 0.10.2
rfc3339 6.2
rfc3986 1.5.0
rpcq 3.9.2
rsa 4.8
ruamel.yaml 0.17.17
ruamel.yaml.clib 0.2.6
scikit-learn 1.0.1
scipy 1.7.3
scs 2.1.4
semantic-version 2.6.0
Send2Trash 1.8.0
setuptools 58.0.4
setuptools-scm 6.3.2
sip 4.19.13
six 1.16.0
sniffio 1.2.0
sparse 0.13.0
stevedore 3.5.0
symengine 0.8.1
sympy 1.9
tensorboard 2.7.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0
tensorflow 2.7.0
tensorflow-estimator 2.7.0
tensorflow-io-gcs-filesystem 0.23.0
termcolor 1.1.0
testpath 0.5.0
toml 0.10.2
tomli 1.2.2
toolz 0.11.2
torch 1.10.0
torchvision 0.11.1
traitlets 5.1.1
tweedledum 1.1.1
typing_extensions 4.0.1
urllib3 1.26.7
wcwidth 0.2.5
webencodings 0.5.1
websocket-client 1.2.3
Werkzeug 2.0.2
wheel 0.37.0
widgetsnbextension 3.5.1
wrapt 1.13.3
yfinance 0.1.67
zipp 3.6.0

I’m trying to replicate your problem and this will make it much easier.
You can upload .txt and .py files here.

@CatalinaAlbornoz I covert my code and dataset into txt file and tried to send it to you but it seems (New user cannot upload attachment) how to send you my code and dataset?

Hi @Ubaid_Ullah!

Thank you for sharing your data. I finally made it work. Here’s what I found:

In the “Make predictions” section you had changed
`params = torch.tensor(np.random.random((2, n_layers, n_wires, 3)), requires_grad=True)`

to

`params = torch.tensor(np.random.random((2, n_layers, n_wires, 10)), requires_grad=True)`

However if you look carefully at the circuits you notice that when using the rotations you are saying that the parameters have size 3, not 10. This will then show an error.

`qml.Rot(*params[1, 0, i], wires=i)`

The next thing to take into account is that in the cost function you are subtracting two values.

` return torch.sum(torch.abs(softmax_ensemble(params, x_point=x_point) - y_point))`

Hence they must have the same shape. `y_point` comes from `y_soft` and if you look carefully `y_soft` depends on the number of classes.

`y_soft = torch.tensor(tf.one_hot(label_train, n_classes).numpy(), requires_grad=True)`

However, the number of classes is not affecting the softmax part of the cost equation. The ideal solution would be to change the softmax logic. However you can also simply hardwire “3” instead of `n_classes`.

These 2 changes should get you running.

Let me know if this works for you!