In [1]:
from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.lda import LDA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
%matplotlib inline
In [2]:
smarket_df = pd.read_csv("../data/Smarket.csv")
smarket_df.head()
Out[2]:
In [3]:
# equivalent to the R pairs(df) command.
axes = pd.tools.plotting.scatter_matrix(smarket_df, color="brown")
According to this blog post, Scikit-Learn's LogisticRegression has some significant differences from R's glm - to reproduce R's glm, one should use statsmodels.api.GLM. But my preference is to work with Scikit-Learn because of its greater breadth, so unless there is something that is only available from statsmodels, I am going to use Scikit-Learn. The numbers below are not identical but close.
In [4]:
X = smarket_df[smarket_df.columns[1:-2]]
y = pd.factorize(smarket_df["Direction"])[0]
clf = LogisticRegression()
clf.fit(X, y)
(clf.intercept_, clf.coef_)
Out[4]:
In [5]:
# return probability for each class. R's predict() only returns the probability of the first
# class, so we do the same.
probs = clf.predict_proba(X)
[prob[0] for prob in probs[0:5]]
Out[5]:
In [6]:
ypreds = ["Up" if prob[0] > 0.5 else "Down" for prob in probs]
ypreds[0:5]
Out[6]:
In [7]:
# R uses table() to tabulate the confusion matrix below
yacts = [str(x) for x in smarket_df["Direction"].values]
confusion_matrix(yacts, ypreds)
Out[7]:
In [8]:
accuracy_score(yacts, ypreds)
Out[8]:
In [9]:
# Split dataset into training and test sets
smarket_train_df = smarket_df[smarket_df["Year"] < 2005]
smarket_test_df = smarket_df[smarket_df["Year"] >= 2005]
# train Logistic Regression model with training data
clf2 = LogisticRegression()
Xtrain = smarket_train_df[smarket_df.columns[1:-2]]
ytrain = pd.factorize(smarket_train_df["Direction"])[0]
clf2.fit(Xtrain, ytrain)
# test model with test data
Xtest = smarket_test_df[smarket_df.columns[1:-2]]
ytest = pd.factorize(smarket_test_df["Direction"])[0]
ypred = clf2.predict(Xtest)
# calculate confusion matrix and accuracy
confusion_matrix(ytest, ypred)
Out[9]:
In [10]:
accuracy_score(ytest, ypred)
Out[10]:
In [11]:
clf3 = LogisticRegression()
Xtrain = smarket_train_df[smarket_df.columns[1:3]]
Xtest = smarket_test_df[smarket_df.columns[1:3]]
clf3.fit(Xtrain, ytrain)
ypred = clf3.predict(Xtest)
confusion_matrix(ytest, ypred)
Out[11]:
In [12]:
accuracy_score(ytest, ypred)
Out[12]:
In [13]:
clf4 = LDA()
clf4.fit(Xtrain, ytrain)
ypred = clf4.predict(Xtest)
ypred[0:5]
Out[13]:
In [14]:
confusion_matrix(ytest, ypred)
Out[14]:
In [15]:
accuracy_score(ytest, ypred)
Out[15]:
In [16]:
clf5 = KNeighborsClassifier()
clf5.fit(Xtrain, ytrain)
ypred = clf5.predict(Xtest)
confusion_matrix(ytest, ypred)
Out[16]:
In [17]:
accuracy_score(ytest, ypred)
Out[17]: