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Perceptron in Python

I'm using sklearn library. I have a question about the attribute: n_iter_. When executing the code I get TypeError: __init__() got an unexpected keyword argument 'n_iter_'. Also try using n_iter but I get the same error, or maybe I am misspelling the attribute. It is not all the code, if you need more information, let me know
from sklearn.linear_model import Perceptron
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
ppn= Perceptron(n_iter_=40, eta0= 0.1, random_state=1)
ppn.fit(X_train_std, y_train)

Perceptron Model in sklearn.linear_model doesn't have n_iter_ as a parameter. It has following parameters with similar names.
max_iter: int, default=1000
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not the partial_fit method.
and
n_iter_no_change : int, default=5
Number of iterations with no improvement to wait before early stopping.
New in version 0.20.
By looking at your code it looks like you intended to use max_iter.
So do
ppn=Perceptron(max_iter=40, eta0= 0.1, random_state=1)
ppn.fit(X_train_std, y_train)
Note:
You should first upgrade your sklearn using
pip install sklearn -upgrade

The attribute given in the documentation is n_iter and not n_iter_
So this should work:
from sklearn.linear_model import Perceptron
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
ppn=Perceptron(n_iter=40, eta0= 0.1, random_state=1)
ppn.fit(X_train_std, y_train)

First check which Scikit-learn version you have installed. You can do that by executing
python -c "import sklearn;print(sklearn.__version__)"
on your terminal/environment to which you have the python that executes your code.
Perceptron initial parameters have changed from n_iter to max_iter in version 0.20. The best way to keep up, head to the documentation or source code of the correct version and read the params: e.g.
documentation: perceptron docs v.0.23
source code: perceptions.0.23 code

Create file OLS in Python Statsmodels

I dont have much knowledge in Python but I have to crack this for an assessment completion,
Question:
Run the following code to load the required libraries and create the data set to fit the model.
from sklearn.datasets import load_boston
import pandas as pd
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
print(dataset.head())
I have to perform the following steps to complete this scenario.
For the boston dataset loaded in the above code snippet, perform linear regression.
Use the target variable as the dependent variable.
Use the RM variable as the independent variable.
Fit a single linear regression model using statsmodels package in python.
Import statsmodels packages appropriately in your code.
Upon fitting the model, Identify the coefficients.
Finally print the model summary in your code.
You can write your code using vim app.py .
Press i for insert mode.
Press esc and then :wq to save and quit the editor.
Please help me to understand how to get this completed. Your valuable comments are much appreciated
Thanks in Advance

from sklearn.datasets import load_boston
import pandas as pd
boston = load_boston()
dataset = pd.DataFrame(boston.data, columns=boston.feature_names)
dataset['target'] = boston.target
print(dataset.head())
import statsmodels.api as sm
import statsmodels.formula.api as smf
X = dataset["RM"]
y = dataset['target']
X = sm.add_constant(X)
model = smf.OLS(y,X).fit()
predictions = model.predict(X)
print(model.summary())

AttributeError: 'GridSearchCV' object has no attribute 'cv_results_'

I try to apply this code :
pipe = make_pipeline(TfidfVectorizer(min_df=5), LogisticRegression())
param_grid = {'logisticregression__C': [ 0.001, 0.01, 0.1, 1, 10, 100],
"tfidfvectorizer__ngram_range": [(1, 1),(1, 2),(1, 3)]}
grid = GridSearchCV(pipe, param_grid, cv=5)
grid.fit(text_train, Y_train)
scores = grid.cv_results_['mean_test_score'].reshape(-1, 3).T
# visualize heat map
heatmap = mglearn.tools.heatmap(
scores, xlabel="C", ylabel="ngram_range", cmap="viridis", fmt="%.3f",
xticklabels=param_grid['logisticregression__C'],
yticklabels=param_grid['tfidfvectorizer__ngram_range'])
plt.colorbar(heatmap)
But I have this error :
AttributeError: 'GridSearchCV' object has no attribute 'cv_results_'

Update your scikit-learn, cv_results_ has been introduced in 0.18.1, earlier it was called grid_scores_ and had slightly different structure http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV

from sklearn.model_selection import GridSearchCV
use this clf.cv_results_

Solved !
Uninstall and install conda scikit learn in 0.18.1 How to upgrade scikit-learn package in anaconda.
When I import GridSearch :
from sklearn.model_selection import GridSearchCV

First, you should update your sklearn, using:
pip install -U scikit-learn
After that, check if you are include the wrong module:
from sklearn.grid_search import GridSearchCV
Change to new path:
from sklearn.model_selection import GridSearchCV
(this is the right way)

How to solve error in python program named: AttributeError: 'module' object has no attribute 'TensorFlowLinearClassifier'

This is the code which uses tensorflow library.
import tensorflow.contrib.learn as skflow
from sklearn import datasets, metrics
iris = datasets.load_iris()
print iris
classifier = skflow.TensorFlowLinearClassifier(n_classes=3)
classifier.fit(iris.data, iris.target)
score=metrics.accuracy_score(iris.target,classifier.predict(iris.data))
print ("Accracy: %f" % score)
I have created a python virtual environment and installed tensorflow in it. I tried to use conda as well this results in similar error

They have changed the name to LinearClassifier, therefore this will work
classifier = skflow.LinearClassifier(n_classes=3)

try using from tensorflow.contrib.learn import TensorFlowLinearClassifier

scikit-learn GridSearchCV doesn't work as samples increase

The following script runs fine on my machine with n_samples=1000, but dies (no error, just stops working) with n_samples=10000. This only happens using the Anaconda python distribution (numpy 1.8.1) but is fine with Enthought's (numpy 1.9.2). Any ideas what would be causing this?
from sklearn.linear_model import LogisticRegression
from sklearn.grid_search import GridSearchCV
from sklearn.metrics.scorer import log_loss_scorer
from sklearn.cross_validation import KFold
from sklearn import datasets
import numpy as np
X, y = datasets.make_classification(n_samples=10000, n_features=50,
n_informative=35, n_redundant=10,
random_state=1984)
lr = LogisticRegression(random_state=1984)
param_grid = {'C': np.logspace(-1, 2, 4, base=2)}
kf = KFold(n=y.size, n_folds=5, shuffle=True, random_state=1984)
gs = GridSearchCV(estimator=lr, param_grid=param_grid, scoring=log_loss_scorer, cv=kf, verbose=100,
n_jobs=-1)
gs.fit(X, y)
Note: I'm using sklearn 0.16.1 in both distributions and am using OS X.
I've noticed that upgrading to numpy version 1.9.2 with Enthought distribution (by updating manually) breaks the grid search. I haven't had any luck downgrading Anaconda numpy version to 1.8.1 though.

Are you on windows? If so, you need to protect the code with
if __name__ == "__main__":
do_stuff()
Otherwise multiprocessing will not work.

Per Andreas's comment, the problem seems to be with multi threading in the linear algebra library. I solved it with the following command in the terminal:
export VECLIB_MAXIMUM_THREADS=1
My (weak) understanding is that this limits the linear algebra's library use of multiple threads and lets multiprocessing handle multithreading as it wants.