guys am having import error while trying to import KNeighborsClassifier from sklearn.neighbors import k
its showing the following errors
ImportError: cannot import name 'kNeighborsClassifier' from 'sklearn.neighbors' (/home/themysteriouschemeng/anaconda3/lib/python3.7/site-packages/sklearn/neighbors/init.py)
You have used small k instead of capital K in KNeighborsClassifier.
Your import -from sklearn.neighbors import kNeighborsClassifier
Right import - from sklearn.neighbors import KNeighborsClassifier
Replace small k with capital K in KNeighborsClassifier and this will fix your import issue.
Can you add the code you are using?
Btw the basic code to import is
from sklearn.neighbors import KNeighborsClassifier
model = KNeighborsClassifier(n_neighbors=4)
Related
how to import modules which are in form of
"from sklearn.tree import DecisionTreeRegressor" in Pyscript?
The way you import modules works as follows:
Include the relevant package in the environment
<py-env>
- scikit-learn
</py-env>
Import the module as you would do it in any other python file
<py-script>
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
...
</py-script>
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import check_util.checker as checker
from IPython.display import clear_output
from PIL import Image
import os
import time
import re
from glob import glob
import shutil
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow import keras
print('tensorflow version: {}'.format(tf.__version__))
print('GPU available: {}'.format(tf.test.is_gpu_available()))
When I run this, there is no error. But it shows as indefinitely in progress.
(Current 630..631..632...sec)
I am running a Jupyter notebook but I do not get any output or error telling me if something is wrong. I have tried installing tornado as some other threads have suggested as well as the command pip install notebook --upgrade
While I do not think there is a problem with my code here it is.
Any help is truly appreciated.
import os
import numpy as np
import pandas as pd
import cv2
from glob import glob
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split
if __name__=="_main_":
path="dog-breed-identification/"
train_path = os.path.join(path, "train/*")
train_path = os.path.join(path, "test/*")
train_path = os.path.join(path, "labels.csv")
labels_df = pd.read_csv(labels_path)
#name of column in csv
breed = labels_df["breed"].unique()
print("Number of Breed: ", len(breed))
enter code here
As it turns out, if I delete
if __name__=="_main_":
I get an output
I want to get the distribution of each features in cancer dataset using ggplot but its giving me error.
#pip install plotnine
from plotnine import ggplot
from plotnine import *
from sklearn.datasets import load_breast_cancer
for i in cancer.feature_names:
ggplot(cancer.data)+aes(x=i)+geom_bar(size=10)
This is the error message i got
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
I would recommand to use seaborn for that. Here is an example of plotting the distribution of each in feature in cancer dataset by target:
import seaborn as sns
import pandas as pd
import numpy as np
from sklearn.datasets import load_breast_cancer
# loading data
cancer = load_breast_cancer()
data = pd.DataFrame(np.c_[cancer['data'], cancer['target']],
columns= np.append(cancer['feature_names'], ['target']))
df = data.melt(['target'], var_name='cols', value_name='vals')
g = sns.FacetGrid(df, col='cols', hue="target", palette="Set1", col_wrap=4)
g = (g.map(sns.distplot, "vals", hist=True, ))
from plotnine import ggplot
from plotnine import *
from sklearn.datasets import load_breast_cancer
cancer=load_breast_cancer()
import pandas as pd
import matplotlib.pyplot as plt
data=pd.DataFrame(cancer.data,columns=cancer.feature_names)
for i in data.columns:
print(ggplot(data)+aes(x=i)+geom_density(size=1))
print(ggplot(data)+aes(x=i)+geom_bar(size=10))
I am trying to scale my data using Python 3
But I keep getting this error: I am out of ideas as to what could be the issue? Please can you assist me guys? I would deeply appreciate your help!
import pandas as pd
import numpy as np
from numpy.random import randn
from pandas import Series, DataFrame
from pandas.plotting import scatter_matrix
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rcParams
from pylab import rcParams
import seaborn as sb
import scipy
from scipy import stats
from scipy.stats import pearsonr
from scipy.stats import spearmanr
from scipy.stats import chi2_contingency
import sklearn
from sklearn import preprocessing
from sklearn.preprocessing import scale
mtcars = pd.read_csv('mtcars.csv')
mtcars.columns = ['Car
names','mpg','cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']
mpg = mtcars['mpg']
#Scale your data
mpg_matrix = mpg.reshape(-1,1)
scaled = preprocessing.MinMaxScaler()
scaled_mpg = scaled.fit_transform(mpg_matrix)
plt.plot(scaled_mpg)
plt.show()
mpg_matrix = mpg.numpy.reshape(-1,1)
tr__
File "C:\Anaconda\lib\site-packages\pandas\core\generic.py", line 5067, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'Series' object has no attribute 'numpy'
pandas.core.series.Series doesn't have reshape.
Perhaps:
mpg_matrix = mpg.values.reshape(-1,1)
i.e. get the underlying numpy array and reshape that.