I am trying to make my MultinomialNB work. I use CountVectorizer on my training and test set and of course there are different words in both setzs. So I see, why the error
ValueError: dimension mismatch
occurs, but I dont know how to fix it. I tried CountVectorizer().transform instead of CountVectorizer().fit_transform as was suggested in an other post (SciPy and scikit-learn - ValueError: Dimension mismatch) but that just gives me
NotFittedError: CountVectorizer - Vocabulary wasn't fitted.
how can I use CountVectorizer right?
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report
import sklearn.feature_extraction
df = data
y = df["meal_parent_category"]
X = df['name_cleaned']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
X_train = CountVectorizer().fit_transform(X_train)
X_test = CountVectorizer().fit_transform(X_test)
algo = MultinomialNB()
algo.fit(X_train,y_train)
y = algo.predict(X_test)
print(classification_report(y_test,y_pred))
Ok, so after asking this question I figured it out :)
Here is the solution with vocabulary and such:
df = train
y = df["meal_parent_category_cleaned"]
X = df['name_cleaned']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3)
vectorizer_train = CountVectorizer()
X_train = vectorizer_train.fit_transform(X_train)
vectorizer_test = CountVectorizer(vocabulary=vectorizer_train.vocabulary_)
X_test = vectorizer_test.transform(X_test)
algo = MultinomialNB()
algo.fit(X_train,y_train)
y_pred = algo.predict(X_test)
print(classification_report(y_test,y_pred))
Related
I was trying to create roc curve for multiclass using Naive Bayes But it ending with
ValueError: bad input shape.
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.naive_bayes import BernoulliNB
from scipy import interp
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Binarize the output
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]
# Add noisy features to make the problem harder
random_state = np.random.RandomState(0)
n_samples, n_features = X.shape
X = np.c_[X, random_state.randn(n_samples, 200 * n_features)]
# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5,
random_state=0)
# Learn to predict each class against the other
classifier = BernoulliNB(alpha=1.0, binarize=6, class_prior=None, fit_prior=True)
y_score = classifier.fit(X_train, y_train).predict(X_test)
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (75, 6)
The error because of binarizing the y variable. The estimator can work with string values itself.
Remove the following lines,
y = label_binarize(y, classes=[0, 1, 2])
n_classes = y.shape[1]
You are good to go!
To get the predicted probabilities for roc_curve, use the following:
classifier.fit(X_train, y_train)
y_score = classifier.predict_proba(X_test)
y_score.shape
# (75, 3)
I have plotted few ROC curve to calculate the AUC. I am having ROC curve is actually doesn't plots like a curve. I have attached the images for better understanding. If any one can tell me what is wrong in there. I will be obliged. This is one kind of plot I am getting
This is the another type
However I am not getting a curve like this one.
This is the link to my dataset
https://drive.google.com/open?id=1luj8d863_IOA36cQTo772GEWgUsrXlbJ
I will thankful if anyone can help me understand the problem if any or if my curves are correct then why it is not actually in a curve like structure
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.decomposition import PCA
from keras.layers import Dense, Input
from keras.wrappers.scikit_learn import KerasClassifier
from keras.models import Model,Sequential
from keras.utils import np_utils
from sklearn.model_selection import train_test_split, cross_val_score, KFold
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler
from itertools import cycle
from sklearn.metrics import roc_curve, auc
from imblearn.over_sampling import SMOTE
seed = 7
np.random.seed(seed)
dataset = pd.read_csv('dataset/prostate.csv')
labels = dataset.values[:,-1]
features_set = dataset.iloc[:,0:12600]
oversampler = SMOTE(random_state=0)
oversampler_feature_set, oversampler_labels = oversampler.fit_sample(features_set,labels)
feature_df = pd.DataFrame(oversampler_feature_set)
labels_df = pd.DataFrame(oversampler_labels)
scalar = MinMaxScaler()
scaled_data = scalar.fit_transform(feature_df)
pca = PCA(n_components=30)
pca_data = pd.DataFrame(pca.fit_transform(scaled_data))
recreated_df = pd.concat([pca_data,labels_df], axis=1)
train, test = train_test_split(recreated_df,test_size=0.2)
X_train = train.values[:,0:30]
Y_train = train.values[:,-1]
X_test = test.values[:,0:30]
y_test = test.values[:,-1]
def my_model():
model = Sequential()
model.add(Dense(20, input_dim=30,activation='sigmoid'))
model.add(Dense(10, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=['accuracy'])
return model
estimator = KerasClassifier(build_fn=my_model, epochs=1000, batch_size=10, shuffle=True,verbose=1)
kfold = KFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(estimator,X_train,Y_train, cv=kfold)
results.mean()
estimator.fit(X_train,Y_train)
y_pred = estimator.predict(X_test).ravel()
sensitivity, specificity, thresholds_keras = roc_curve(y_test,y_pred,pos_label=2)
auc_keras = auc(sensitivity,specificity)
plt.figure(1)
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(sensitivity, specificity, label='Keras (area =:.3f})'.format(auc_keras))
plt.xlabel('Specificity')
plt.ylabel('Sensitivity')
plt.title('Prostate')
plt.legend(loc='best')
plt.show()
Getting an error while trying to use naive bayes.
from sklearn.naive_bayes import GaussianNB
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/sjwhitworth/golearn/master/examples/datasets/tennis.csv')
X_train = pd.get_dummies(df[['outlook', 'temp', 'humidity', 'windy']])
y_train = df['play']
gNB = GaussianNB()
gNB.fit(X_train, y_train)
ndf=pd.DataFrame({'outlook':['sunny'], 'temp':['hot'], 'humidity':['normal'], 'windy':[False]})
X_test=pd.get_dummies(ndf[['outlook', 'temp', 'humidity', 'windy']])
gNB.predict(X_test)
ValueError: operands could not be broadcast together with shapes (1,4)
(9,)
Is it a good idea to use get_dummies method in this case?
Obviously not a good practice as pointed by vivek but you here is the code if you want to do anyway:
from sklearn.naive_bayes import GaussianNB
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/sjwhitworth/golearn/master/examples/datasets/tennis.csv')
X_train = pd.get_dummies(df[['outlook', 'temp', 'humidity', 'windy']])
y_train = df['play']
gNB = GaussianNB()
gNB.fit(X_train, y_train)
ndf=pd.DataFrame({'outlook':['sunny'], 'temp':['hot'], 'humidity':['normal'], 'windy':[False]})
X_test=pd.get_dummies(ndf[['outlook', 'temp', 'humidity', 'windy']])
dict1 = {}
X_test.columns
for i in X_train.columns:
if i in X_test.columns:
dict1.update({i:[1]})
else:
dict1.update({i:[0]})
X_test_new = pd.DataFrame(data = dict1)
gNB.predict(X_test_new)
I am attempting to use the anneal.arff dataset with Python scikit-learn's semisupervised algorithm LabelPropagation. The anneal dataset is categorical data, so I preprocessed it so that the output class for each item of instance
looks like [0. 0. 1. 0. 0.]. This is a numeric list that encodes the output class
as 5 possible values with 0's everywhere, and 1. in the position of the corresponding class. This is what I would expect.
For semi-supervised learning, most of the training data must be unlabeled, so
I modified the training set so that the unlabeled data has output [-1, -1, -1, -1, -1]. I previously tried just using -1, but the code emits the same error as shown below.
I train the classifier as follows, Y_train includes labeled and "unlabeled" data:
lp_model = LabelSpreading(gamma=0.25, max_iter=5)
lp_model.fit(X, Y_train)
I receive the error shown below after calling the fit method:
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\semi_supervised\label_propagation.py", line 221, in fit
X, y = check_X_y(X, y)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 526, in check_X_y
y = column_or_1d(y, warn=True)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 562, in column_or_1d
raise ValueError("bad input shape {0}".format(shape))
ValueError: bad input shape (538, 5)
This suggests that something is wrong with the shape of my Y_train list,
but this is the correct shape. What am I doing wrong?
Can LabelPropagation take as training data in this form, or does it only
accept unlabeled data as a scalar -1?
--- edit ---
Here is the code that generates the error. I'm sorry about the confusion over algorithms--I want to use both LabelSpreading and LabelPropagation, and choosing one or the other doesn't fix this error.
from scipy.io import arff
import pandas as pd
import numpy as np
import math
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from copy import deepcopy
from sklearn.semi_supervised import LabelPropagation
from sklearn.semi_supervised import LabelSpreading
f = "../../Documents/UCI/anneal.arff"
dataAsRecArray, meta = arff.loadarff(f)
dataset_raw = pd.DataFrame.from_records(dataAsRecArray)
dataset = pd.get_dummies(dataset_raw)
class_names = [col for col in dataset.columns if 'class_' in col]
print (dataset.shape)
number_of_output_columns = len(class_names)
print (number_of_output_columns)
def run(name, model, dataset, percent):
# Split-out validation dataset
array = dataset.values
X = array[:, 0:-number_of_output_columns]
Y = array[:, -number_of_output_columns:]
validation_size = 0.40
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
num_samples = len(Y_train)
num_labeled_points = math.floor(percent*num_samples)
indices = np.arange(num_samples)
unlabeled_set = indices[num_labeled_points:]
Y_train[unlabeled_set] = [-1, -1, -1, -1, -1]
lp_model = LabelSpreading(gamma=0.25, max_iter=5)
lp_model.fit(X_train, Y_train)
"""
predicted_labels = lp_model.transduction_[unlabeled_set]
print(predicted_labels[:10])
"""
if __name__ == "__main__":
#percentages = [0.1, 0.2, 0.3, 0.4]
percentages = [0.1]
models = []
models.append(('LS', LabelSpreading()))
#models.append(('CART', DecisionTreeClassifier()))
#models.append(('NB', GaussianNB()))
#models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
for percent in percentages:
run(name, model, dataset, percent)
print ("bye")
Your Y_train has shape (538, 5) but should be 1d. LabelPropagation doesn't support multi-label or multi-output multi-class right now.
The error message could be more informative, though :-/
Attempting to create a decision tree with cross validation using sklearn and panads.
My question is in the code below, the cross validation splits the data, which i then use for both training and testing. I will be attempting to find the best depth of the tree by recreating it n times with different max depths set. In using cross validation should i instead be using k folds CV and if so how would I use that within the code I have?
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn import cross_validation
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)
depth = []
for i in range(3,20):
clf = tree.DecisionTreeClassifier(max_depth=i)
clf = clf.fit(x_train,y_train)
depth.append((i,clf.score(x_test,y_test)))
print depth
here is a link to the data that i am using in case that helps anyone. https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope
In your code you are creating a static training-test split. If you want to select the best depth by cross-validation you can use sklearn.cross_validation.cross_val_score inside the for loop.
You can read sklearn's documentation for more information.
Here is an update of your code with CV:
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.cross_validation import cross_val_score
from pprint import pprint
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
# x_train,x_test,y_train,y_test = cross_validation.train_test_split(x,y,test_size=0.4,random_state=0)
depth = []
for i in range(3,20):
clf = tree.DecisionTreeClassifier(max_depth=i)
# Perform 7-fold cross validation
scores = cross_val_score(estimator=clf, X=x, y=y, cv=7, n_jobs=4)
depth.append((i,scores.mean()))
print(depth)
Alternatively, you can use sklearn.grid_search.GridSearchCV and not write the for loop yourself, especially if you want to optimize for more than one hyper-parameter.
import numpy as np
import pandas as pd
from sklearn import tree
from sklearn.model_selection import GridSearchCV
features = ["fLength", "fWidth", "fSize", "fConc", "fConc1", "fAsym", "fM3Long", "fM3Trans", "fAlpha", "fDist", "class"]
df = pd.read_csv('magic04.data',header=None,names=features)
df['class'] = df['class'].map({'g':0,'h':1})
x = df[features[:-1]]
y = df['class']
parameters = {'max_depth':range(3,20)}
clf = GridSearchCV(tree.DecisionTreeClassifier(), parameters, n_jobs=4)
clf.fit(X=x, y=y)
tree_model = clf.best_estimator_
print (clf.best_score_, clf.best_params_)
Edit: changed how GridSearchCV is imported to accommodate learn2day's comment.