I'm trying to run this SVM using stratified K fold in Python,however I keep on getting the error like below
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.utils import shuffle
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, zero_one_loss, confusion_matrix
import pandas as pd
import numpy as np
z = pd.read_csv('/home/User/datasets/gtzan.csv', header=0)
X = z.iloc[:, :-1]
y = z.iloc[:, -1:]
X = np.array(X)
y = np.array(y)
# Performing standard scaling
scaler = preprocessing.MinMaxScaler()
X_scaled = scaler.fit_transform(X)
# Defining the SVM with 'rbf' kernel
svc = SVC(kernel='rbf', C=100, random_state=50)
#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, shuffle=True)
skf = StratifiedKFold(n_splits=10, shuffle=True)
accuracy_score = []
#skf.get_n_splits(X, y)
for train_index, test_index in skf.split(X, y):
X_train, X_test = X_scaled[train_index], X_scaled[test_index]
y_train, y_test = y[train_index], y[test_index]
# Training the model
svc.fit(X_train, np.ravel(y_train))
# Prediction on test dataste
y_pred = svc.predict(X_test)
# Obtaining the accuracy scores of the model
score = accuracy_score(y_test, y_pred)
accuracy_score.append(score)
# Print the accuarcy of the svm model
print('accuracy score: %0.3f' % np.mean(accuracy_score))
however, it gives me an error like below
Traceback (most recent call last):
File "/home/User/Test_SVM.py", line 55, in <module>
score = accuracy_score(y_test, y_pred)
TypeError: 'list' object is not callable
What makes this score list uncallable and how do I fix this error?
accuracy_scoreis a list in my code and I was also calling the same list as a function, which is overriding the existing functionality of function accuarcy_score. Changed the list name to acc_score which solved the problem.
Related
I need to use Logistic Regression classifier I have dataset the length of each column 2000 this is all my code:
from statistics import mode
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import plot_confusion_matrix
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_val_predict
from sklearn.linear_model import LogisticRegression
# Importing the datasets
###Social_Network_Ads
datasets = pd.read_csv('C:/Users/n3.csv',header=None)
X = datasets.iloc[:, 0:5].values
Y = datasets.iloc[:, 5].values
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.25, random_state = 0)
# instantiate the model (using the default parameters)
model = LogisticRegression()
# fit the model with data
model.fit(X_Train, Y_Train)
predicted = cross_val_predict(mode, X_Train, Y_Train, cv=5)
train_acc = model.score(X_Train, Y_Train)
print("The Accuracy for Training Set is {}".format(train_acc*100))
But in I got on this error:
TypeError: Cannot clone object '<function mode at 0x000000FD6579B9D0>'
(type <class 'function'>): it does not seem to be a scikit-learn
estimator as it does not implement a 'get_params' method.
How solve this?
Change this line
predicted = cross_val_predict(mode, X_Train, Y_Train, cv=5)
to
predicted = cross_val_predict(model, X_Train, Y_Train, cv=5)
You have a simple typo. You want to pass your estimator to the function but instead you passed mode which is imported from statistics. That's why the error tells you that it can not clone an object of type function. You are passing a function but it expects an estimator.
How can I deal with polynomial degree when I want to save a polynomial model, sicne this info is not being saved!
import pandas as pd
import numpy as np
import joblib
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
df = pd.DataFrame({
"a": np.random.uniform(0.0, 1.0, 1000),
"b": np.random.uniform(10.0, 14.0, 1000),
"c": np.random.uniform(100.0, 1000.0, 1000)})
def data():
X_train, X_val, y_train, y_val = train_test_split(df.iloc[:, :2].values,
df.iloc[:, 2].values,
test_size=0.2,
random_state=1340)
return X_train, X_val, y_train, y_val
X_train, X_val, y_train, y_val = data()
poly_reg = PolynomialFeatures(degree = 2)
X_poly = poly_reg.fit_transform(X_train)
poly_reg_model = LinearRegression().fit(X_poly, y_train)
poly_model = joblib.dump(poly_reg_model, 'themodel')
y_pred = poly_reg_model.predict(poly_reg.fit_transform(X_val))
themodel = joblib.load('themodel')
Now, if I try to predict:
themodel.predict(X_val), I am receiving:
ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 6 is different from 2)
I have to do:
pol_feat = PolynomialFeatures(degree=2)
themodel.predict(pol_feat.fit_transform(X_val))
in order to work.
So, how can i store this info in order to be able to use the model for prediction?
You have to pickle trained PolynomialFeatures also:
# train and pickle
poly_reg = PolynomialFeatures(degree = 2)
X_poly = poly_reg.fit_transform(X_train)
poly_reg_model = LinearRegression().fit(X_poly, y_train)
joblib.dump(poly_reg_model, 'themodel')
joblib.dump(poly_reg, 'poilynomia_features_model')
# load and predict
poilynomia_features_model = joblib.load('poilynomia_features_model')
themodel = joblib.load('themodel')
X_val_prep = poilynomia_features_model.transform(X_val)
predictions = themodel.predict(X_val_prep)
But better will wrap all the steps in the single pipeline:
pipeline = Pipeline(steps=[('poilynomia', PolynomialFeatures()),
('lr', LinearRegression())])
pipeline.fit(X_train, y_train)
pipeline.predict(X_val)
I'm trying to change the cut-off point into a logistic regression with the Scikit learn library but I don't see the way even having read the documentation for it. In SPSS it gives you the option to change that parameter but here I don't get it. I put algorithm code. Any help? Thank you
X = np.array(dataS)
y = np.array(target)
X.shape
from sklearn import linear_model
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import seaborn as sb
import warnings
warnings.filterwarnings("ignore")
model = linear_model.LogisticRegression()
model.fit(X,y)
predictions = model.predict(X)
model.score(X,y)
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, y,
test_size=validation_size, random_state=seed)
name='Logistic Regression'
kfold = model_selection.KFold(n_splits=161, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
predictions = model.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
import math
import numpy as np
import pandas as pd
#from pandas import DataFrame
from sklearn import preprocessing,cross_validation
from sklearn.linear_model import LogisticRegression
#from sklearn.cross_validation import train_test_split
from numpy import loadtxt, where
from pylab import scatter, show, legend, xlabel, ylabel
# scale larger positive and values to between -1,1 depending on the largest
# value in the data
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))
df = pd.read_excel("Cryotherapy.xlsx", header=0)
# clean up data
df.columns = ["sex","age","Time","Number_of_Warts", "Type",
"Area","Result_of_Treatment"]
x = df["Result_of_Treatment"]
X = df[["Type","Area",]]
X = np.array(X)
X = min_max_scaler.fit_transform(X)
Y = df["Result_of_Treatment"]
Y = np.array(Y)
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y,
test_size=0.4)
# train scikit learn model
clf = LogisticRegression()
clf.fit(X_train, Y_train)
accuracy = clf.score(X_test,Y_test)
print(accuracy)
Try passing a random_state into the train_test_split function. If you do not do this then the data is gonna be shuffled randomly each time -> producing different train and test sets.
Example:
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4, random_state=1)
I am new to python and also learning machine learning. I got a data-set for titanic and trying to predict who survived and who did not. But my code seems to have an issue with the y_pred, as none of them is close to 1 or above one. Find attached also the y_test and y_pred images.
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('train.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 3].values
# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 2:3])
X[:, 2:3] = imputer.transform(X[:, 2:3])
#Encoding Categorical variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
# Dummy variable trap
X = X[:, 1:]
# Splitting the Dataset into Training Set and Test Set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
# Split the dataset into training and test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_tratin, y_test = train_test_split(X, y, test_size = 0.2,)
# Fitting the Multiple Linear Regression to the training set
""" regressor is an object of LinearRegression() class in line 36 """
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
Thanks for the help everyone, I have been able to sort it out.
The problem was y in the importing dataset was seen as a vector and not a matrix
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('train.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 3:].values
# Taking care of missing data
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 2:3])
X[:, 2:3] = imputer.transform(X[:, 2:3])
#Encoding Categorical variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
# Dummy variable trap
X = X[:, 1:]
# Splitting the Dataset into Training Set and Test Set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
# Fitting the Multiple Linear Regression to the training set
""" regressor is an object of LinearRegression() class in line 36 """
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Predicting the test set result
y_pred = regressor.predict(X_test)