Compare values in numpy array and Pandas dataframe

Question

I have an array of type numpy.ndarray and pandas DataFrame and need a way to compare each value to each other.

Below is one of the ways I've tried to do it. I've also used pd.get(labels) to pull the values out and was returned None. y_test is a pandas DataFrame and preds is a numpy array of predictions.

Tried converting both of them to lists as well as numpy arrays for comparison:

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy',
              optimizer='adam',metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5, batch_size=2000)

preds = model.predict(x_test)
preds[preds>=0.5] = 1
preds[preds<0.5] = 0
print(type(preds))
print(y_test.get('labels'))
total = 0
for i in range(len(preds)):
    if int(preds[i]) == y_test[i]:
        total = total + 1

This is what I am getting - TypeError: 'NoneType' object is not callable - KeyError: 0

Francesco Zambolin · Accepted Answer · 2019-06-27 16:45:17Z

1

So if y_test is a dataframe, then you can just ask for the values like this to get a numpy array:

y_test_array = y_test["labels"].values

Then print this out to know how many items are equal:

sum(y_test_array == preds)# number of items with same value
sum(y_test_array == preds)/len(preds)# %

I hope is what you asked.

answered Jun 27, 2019 at 16:45

Francesco Zambolin

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Sunderam Dubey · Accepted Answer · 2022-11-28 13:10:18Z

0

I guess you wanted to find the performance of your model so just use .evaluate() method, I am assuming that you are using a keras model here.

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy',
              optimizer='adam',metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5, batch_size=2000)

score= model.evaluate(x_test,y_test)

edited Nov 28, 2022 at 13:10

Sunderam Dubey

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answered Jun 27, 2019 at 16:49

Parthasarathy Subburaj

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