Matthew Morgan Project Three Notebook¶
For Project three I used a data set located at https://catalog.data.gov/dataset/3-year-recidivism-for-offenders-released-from-prison that indicates many variable for people such as sex and race and whether they were re-admitted to prison within 3 years of having been released.
I took a sample of a little over 100 people from the data set and created a simplified data set. I coded their sex as either 1 (male), or 0 (female); their race as either 1 (white non-hispanic), 2 (white hispanic), 3 (black non-hispanic), 4 (black hispanic), or 5 (american indian non-hispanic); and there recidivism as either 1 (re-admittded) or 0 (not readmitted).
I then used this data set for the fllowing program to see if there is any relationship between sex and race on recidivism. The model didn't perform well. The accuracy in training was 0. There probably isn't enough data to properly form a good model.
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import os
try:
inputFunc = raw_input
except NameError:
inputFunc = input
import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar
from pandas.tseries.offsets import CustomBusinessDay
import numpy as np
import seaborn as sns
from statsmodels.formula.api import ols
from sklearn import linear_model
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from patsy import dmatrices
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import random
def evaluate(pred, labels_test):
acc = accuracy_score(pred, labels_test)
print ("Accuracey: %s"%acc)
tn, fp, fn, tp = confusion_matrix(labels_test, pred).ravel()
recall = tp / (tp + fp)
percision = tp / (tp + fn)
f1 = (2 / ((1/recall)+(1/percision)))
print ("")
print ("True Negatives: %s"%tn)
print ("False Positives: %s"%fp)
print ("False Negatives: %s"%fn)
print ("True Positives: %s"%tp)
print ("Recall: %s"%recall)
print ("Precision: %s"%percision)
print ("F1 Score: %s"%f1)
def plot_bound(Z_val,data,col1,col2,binary):
x_min = float(data.iloc[:,[col1]].min())-float(data.iloc[:,[col1]].min())*0.10
x_max = float(data.iloc[:,[col1]].max()+float(data.iloc[:,[col1]].min())*0.10)
y_min = 0.0;
y_max = float(training.iloc[:,[col2]].max())+float(training.iloc[:,[col2]].max())*0.10
h_x = (x_max-x_min)/100
h_y = (y_max-y_min)/100
xx, yy = np.meshgrid(np.arange(x_min, x_max, h_x), np.arange(y_min, y_max, h_y))
if binary == 1:
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = np.where(Z=="Y",1,0)
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]
Z = Z.reshape(xx.shape)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.pcolormesh(xx, yy, Z)
plt.show()
Data¶
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raw_data_df = pd.read_csv('Jail2.csv', parse_dates=[0])
raw_data_df.head()
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raw_data_df["recidivism_Y_N"] = "N"
raw_data_df.head()
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raw_data_df.loc[raw_data_df['Recidivism'] == 1, 'recidivism_Y_N'] = "Y"
raw_data_df.head()
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Rec_lin_df = raw_data_df[[
'Sex',
'Race',
'Recidivism'
]].copy()
Rec_lin_df.head()
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Rec_class_df = raw_data_df[[
'recidivism_Y_N',
'Sex',
'Race'
]].copy()
Rec_class_df.head()
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Taining and Validation¶
Rec_lin_df¶
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data = Rec_lin_df
data = data[data["Recidivism"]<=1]
holdout = data.sample(frac=0.05)
training = data.loc[~data.index.isin(holdout.index)]
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sns.lmplot(x="Sex", y="Recidivism", data=training, x_estimator=np.mean, order=1)
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sns.lmplot(x="Race", y="Recidivism", data=training, x_estimator=np.mean, order=1)
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model = ols("Recidivism ~ Race + Sex", training).fit()
model.summary()
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# Rerun with SciKitLearn because it's easy to check accuracy
features_train = training.drop("Recidivism", axis=1).as_matrix(columns=None)
labels_train = training["Recidivism"].as_matrix(columns=None)
features_test = holdout.drop("Recidivism", axis=1).as_matrix(columns=None)
labels_test = holdout["Recidivism"].as_matrix(columns=None)
lm = linear_model.LinearRegression()
clf = lm.fit(features_train, labels_train)
pred = clf.predict(features_test)
accuracy = metrics.r2_score(labels_test, pred)
print("R squared:",lm.score(features_train,labels_train))
print("Accuracy:",accuracy)
Rec_class_df¶
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data = Rec_class_df
holdout = data.sample(frac=0.05)
training = data.loc[~data.index.isin(holdout.index)]
features_train = training.drop("recidivism_Y_N", axis=1).as_matrix(columns=None)
labels_train = training["recidivism_Y_N"].as_matrix(columns=None)
features_test = holdout.drop("recidivism_Y_N", axis=1).as_matrix(columns=None)
labels_test = holdout["recidivism_Y_N"].as_matrix(columns=None)
print("Percentage of Ys: %s\n"%(len(data[data["recidivism_Y_N"]=="Y"])/len(data)))
feature_1_no = [features_test[ii][0] for ii in range(0, len(features_test)) if labels_test[ii]=="N"]
feature_2_no = [features_test[ii][1] for ii in range(0, len(features_test)) if labels_test[ii]=="N"]
feature_1_yes = [features_test[ii][0] for ii in range(0, len(features_test)) if labels_test[ii]=="Y"]
feature_2_yes = [features_test[ii][1] for ii in range(0, len(features_test)) if labels_test[ii]=="Y"]
plt.scatter(feature_1_yes, feature_2_yes, color = "g", label="Recidivism")
plt.scatter(feature_1_no, feature_2_no, color = "r", label="No Recidivism")
plt.legend()
plt.xlabel("Race")
plt.ylabel("Sex")
plt.show()
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# Logistic Regression
model = LogisticRegression(fit_intercept = False, C = 1e9)
clf = model.fit(features_train, labels_train)
pred = clf.predict(features_test)
print("Logistic Regression")
evaluate(pred, labels_test)
plot_bound("Y",holdout,1,2,0)
# Test some spot
x_test = 70
y_test = 160000
print("")
print(clf.predict([[x_test,y_test]])[0])
print(clf.predict_proba([[x_test,y_test]])[0][1])
print("")
from sklearn import tree
clf = tree.DecisionTreeClassifier(min_samples_split=40)
clf = clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
print("\nDecision Tree")
evaluate(pred, labels_test)
plot_bound("Y",holdout,1,2,0)
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
print("Random Forest")
evaluate(pred, labels_test)
plot_bound("Y",holdout,1,2,0)
from sklearn.svm import SVC
clf = SVC(kernel="rbf",probability=True)
clf = clf.fit(features_train, labels_train)
pred = clf.predict(features_test)
print("SVM")
evaluate(pred, labels_test)
#plot_bound("Y",holdout,1,2,0) # plot doesn't work with SVM
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