Develop Reference

numpy.float64' object is not callable - hyperparameter tuning

I'm trying to do hyperparameter tuning and every time I run this code.
from sklearn.model_selection import GridSearchCV
param_grid = {'C':[0,1,1,100,1000], 'kernel':['rbf','poly','sigmoid','linear'],'degree':[1,2,3,4,5,6]}
grid =GridSearchCV(svc.sc(),param_grid)
grid.fit(X_train,y_train)
I get this error
TypeError Traceback (most recent call last)
<ipython-input-64-74de9eeb3cae> in <module>
3
4 param_grid = {'C':[0,1,1,100,1000], 'kernel':['rbf','poly','sigmoid','linear'],'degree':[1,2,3,4,5,6]}
----> 5 grid =GridSearchCV(svc.sc(),param_grid)
6 grid.fit(X_train,y_train)
TypeError: 'numpy.float64' object is not callable
Any idea what to do? Also svc.sc is the way defined the model.

What is svc.sc()? Either way, you're probably not meant to call it at that point, just pass it as the callback to GridSearchCV, i.e. drop the parentheses:
grid = GridSearchCV(svc.sc, param_grid)

TypeError: __init__() got an unexpected keyword argument 'i'

I am trying to determine the suitable neighbor number for machine learning KNneighbors algorithm.
from sklearn.neighbors import KNeighborsClassifier
training_accuracy = []
test_accuracy = []
n_s = range(1,11)
for i in n_s:
clf = KNeighborsClassifier(i=i)
clf.fit(X_train, y_train)
training_accuracy.append(clf.score(X_train, y_train))
test_accuracy.append(clf.score(X_test, y_test))
The error I am getting:
TypeError Traceback (most recent call last)
<ipython-input-12-80ad682622ef> in <module>
4 n_s = range(1,11)
5 for i in n_s:
----> 6 clf = KNeighborsClassifier(i=i)
7 clf.fit(X_train, y_train)
8 training_accuracy.append(clf.score(X_train, y_train))
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\neighbors\_classification.py in __init__(self, n_neighbors, weights, algorithm, leaf_size, p, metric, metric_params, n_jobs, **kwargs)
152 leaf_size=leaf_size, metric=metric, p=p,
153 metric_params=metric_params,
--> 154 n_jobs=n_jobs, **kwargs)
155 self.weights = _check_weights(weights)
TypeError: __init__() got an unexpected keyword argument 'i'
How can I fix this issue?

Instead of using i parameter, use n_neighbors instead.
for i in n_s:
clf = KNeighborsClassifier(n_neighbors=i)
Check the documentation of KNeighborsClassifier. Here you can see that there is no i parameter and n_neighbors is the first argument.

There is few updation in syntax of algorithm in wrt to time
Now This works for me !!
> for i in range(1,40):
> knn = RandomForestClassifier(i)

Scikit-learn LinearRegression: How to resolve the error " fit() missing 1 required positional argument: 'y' "?

My code:
import pandas as pd
from sklearn.Linear_model import LinearRegression as lr
df = pd.DataFrame({"Match Score":[95,85,80,70,60], "Statistic score":[85,95,70,65,70]})
x =df[["Match Score"]]
y =df["Statistic score"]
lr.fit(x,y)
Error details:
TypeError Traceback (most recent call last)
<ipython-input-19-e644bf405118> in <module>
----> 1 lr.fit(x,y)
TypeError: fit() missing 1 required positional argument: 'y'

You must instantiate the LinearRegression estimator first.
my_lr = lr().fit(x,y)
You also have a typo in your import statements, it's sklearn.linear_model with a small l.

PySpark logistic regression missing method

I am new to Pyspark. I am using logistic regression API. I followed some tutorials and worked this way :
from pyspark.ml.classification import LogisticRegression
train, test = df.randomSplit([0.80, 0.20], seed = some_seed)
LR = LogisticRegression(featuresCol = 'features', labelCol = 'label', maxIter=some_iter)
LR_model = LR.fit(train)
When I call
trainingSummary = LR_model.summary
trainingSummary.roc
I get
--------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-319-bf79768ab64e> in <module>()
1 trainingSummary = LR_model.summary
2
----> 3 trainingSummary.roc
AttributeError: 'LogisticRegressionTrainingSummary' object has no attribute 'roc'
Someone has an idea ?

fit() got an unexpected keyword argument 'criterion'

I am working with sklearn.tree.DecisionTreeClassifier here is the link to it.
I want to use the keyword criterion and set it to "entropy"
I did the following :
model = DecisionTreeClassifier()
model.fit(X_train, y_train, criterion = "entropy")
but it gives this error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-103-509174b2caad> in <module>()
1 model = DecisionTreeClassifier()
----> 2 model.fit(X_train, y_train, criterion = "entropy")
TypeError: fit() got an unexpected keyword argument 'criterion'
It is working fine with the default argument 'gini' but not with this.

You probably want
model = DecisionTreeClassifier(criterion="entropy")
model.fit(X_train, y_train)