IST 718 | WK2 | ASYNC 2.5¶
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# Predictive Model for Los Angeles Dodgers Promotion and Attendance (Python)
# BASED ON EXHIBIT 2.1 FROM MILLER (2015)
# import packages for analysis and modeling
import pandas as pd # data frame operations
import numpy as np # arrays and math functions
from scipy.stats import uniform # for training-and-test split
import statsmodels.api as sm # statistical models (including regression)
import statsmodels.formula.api as smf # R-like model specification
import matplotlib.pyplot as plt # 2D plotting
import seaborn as sns # PROVIDES TRELLIS AND SMALL MULTIPLE PLOTTING
# read in Dodgers bobbleheads data and create data frame
dodgers = pd.read_csv("dodgers.csv")
# examine the structure of the data frame
print("\nContents of dodgers data frame ---------------")
# attendance in thousands for plotting
dodgers['attend_000'] = dodgers['attend']/1000
# print the first five rows of the data frame
print(pd.DataFrame.head(dodgers))
dodgerDF = pd.DataFrame(dodgers)
mondays = dodgers[dodgers['day_of_week'] == 'Monday']
tuesdays = dodgers[dodgers['day_of_week'] == 'Tuesday']
wednesdays = dodgers[dodgers['day_of_week'] == 'Wednesday']
thursdays = dodgers[dodgers['day_of_week'] == 'Thursday']
fridays = dodgers[dodgers['day_of_week'] == 'Friday']
saturdays = dodgers[dodgers['day_of_week'] == 'Saturday']
sundays = dodgers[dodgers['day_of_week'] == 'Sunday']
# convert days' attendance into list of vectors for box plot
data = [mondays['attend_000'], tuesdays['attend_000'],
wednesdays['attend_000'], thursdays['attend_000'],
fridays['attend_000'], saturdays['attend_000'],
sundays['attend_000']]
ordered_day_names = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']
ordered_team_names = (sorted(set(dodgers['opponent']), reverse = True))
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## SUMMARY STATISTICS
np.mean(dodgers['attend'])
# np.min(dodgers['attend'])
# np.max(dodgers['attend'])
sns.boxplot(y="attend", data=dodgers)
plt.show()
# KO -- UPDATED TO CORRECTLY ORDER DAYS OF WEEK
sns.boxplot(x="day_of_week", y="attend", data=dodgers, color = "gray",
order=['Sunday','Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']);
g.add_legend();
plt.show()
# NUMBER OF PROMOTIONS - ONLY WORKS WITH BINARY VARIABLES
#cap = np.count_nonzero(dodgers['cap'])
#print(cap)
#shirt = np.count_nonzero(dodgers['shirt'])
#print(shirt)
#fire = np.count_nonzero(dodgers['fireworks'])
#print(fire)
#bob = np.count_nonzero(dodgers['bobblehead'])
#print(bob)
#print(cap + shirt + fire + bob)
sns.boxplot(x="month", y="attend", data=dodgers, color = "gray")
g.add_legend();
plt.show()
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# exploratory data analysis: box plot for day of the week
# FROM MILLER (2015)
fig, axis = plt.subplots()
axis.set_xlabel('Day of Week')
axis.set_ylabel('Attendance (thousands)')
day_plot = plt.boxplot(data, sym='o', vert=1, whis=1.5)
plt.setp(day_plot['boxes'], color = 'black')
plt.setp(day_plot['whiskers'], color = 'black')
plt.setp(day_plot['fliers'], color = 'black', marker = 'o')
axis.set_xticklabels(ordered_day_names)
plt.show()
plt.savefig('fig_advert_promo_dodgers_eda_day_of_week_Python.pdf',
bbox_inches = 'tight', dpi=None, facecolor='w', edgecolor='b',
orientation='portrait', papertype=None, format=None,
transparent=True, pad_inches=0.25, frameon=None)
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# exploratory data analysis: box plot for day of the week
april = dodgers[dodgers['month'] == 'APR']
may = dodgers[dodgers['month'] == 'MAY']
june = dodgers[dodgers['month'] == 'JUN']
july = dodgers[dodgers['month'] == 'JUL']
august = dodgers[dodgers['month'] == 'AUG']
september = dodgers[dodgers['month'] == 'SEP']
october = dodgers[dodgers['month'] == 'OCT']
data = [april['attend_000'], may['attend_000'],
june['attend_000'], july['attend_000'],
august['attend_000'], september['attend_000'],
october['attend_000']]
ordered_month_names = ['April', 'May', 'June', 'July', 'Aug', 'Sept', 'Oct']
fig, axis = plt.subplots()
axis.set_xlabel('Month')
axis.set_ylabel('Attendance (thousands)')
day_plot = plt.boxplot(data, sym='o', vert=1, whis=1.5)
plt.setp(day_plot['boxes'], color = 'black')
plt.setp(day_plot['whiskers'], color = 'black')
plt.setp(day_plot['fliers'], color = 'black', marker = 'o')
axis.set_xticklabels(ordered_month_names)
plt.show()
## CODE TO SAVE IMAGE FILE
# plt.savefig('fig_advert_promo_dodgers_eda_month_Python.pdf',
# bbox_inches = 'tight', dpi=None, facecolor='w', edgecolor='b',
# orientation='portrait', papertype=None, format=None,
# transparent=True, pad_inches=0.25, frameon=None)
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# trellis/lattice plot attendance by temp, conditioning on skies
# and day_night with bobblehead NO/YES shown in distinct colors
import seaborn as sns
sns.set(style="darkgrid")
g = sns.FacetGrid(dodgers, col="skies", hue="fireworks",
hue_order=["YES", "NO"],
hue_kws=dict(marker=["^", "v"]))
g.map(plt.scatter, "temp", "attend", alpha=.7)
g.add_legend();
plt.show()
g = sns.FacetGrid(dodgers, col="day_night", hue="fireworks",
hue_order=["YES", "NO"],)
g.map(plt.scatter, "temp", "attend", alpha=.7)
g.add_legend();
plt.show()
g = sns.FacetGrid(dodgers, col="day_night", hue="bobblehead",
hue_order=["YES", "NO"],)
g.map(plt.scatter, "temp", "attend", alpha=.7)
g.add_legend();
plt.show()
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# DODGER PROMOTIONS BY TYPE
sns.countplot(y="fireworks", data=dodgers, palette="Blues_d",
order=["YES", "NO"])
g.add_legend();
plt.show()
sns.countplot(y="bobblehead", hue="day_night",
data=dodgers, palette="Blues_d",
order=["YES", "NO"])
g.add_legend();
plt.show()
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# Dodgers attendance by Visiting Team
# Figure 2.4 from Miller
# teams = sorted(["opponent"], reverse=True)
sns.swarmplot(x="attend", y="opponent", hue="day_night",
data=dodgers, order =ordered_team_names)
g.add_legend();
plt.show()
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# CREATE A HISTOGRAM OF ATTENDANCE
# CREATE BINS
plt.hist(dodgers['attend'], normed = False, stacked = False, rwidth = .9)
plt.title("Attendance Histogram")
plt.xlabel('Attendance')
plt.ylabel('Frequency')
plt.show()
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# BLOCK FOR ORDERING DATA
# map day_of_week to ordered_day_of_week
day_to_ordered_day = {'Monday' : '1Monday',
'Tuesday' : '2Tuesday',
'Wednesday' : '3Wednesday',
'Thursday' : '4Thursday',
'Friday' : '5Friday',
'Saturday' : '6Saturday',
'Sunday' : '7Sunday'}
dodgers['ordered_day_of_week'] = dodgers['day_of_week'].map(day_to_ordered_day)
# map month to ordered_month
month_to_ordered_month = {'APR' : '1April',
'MAY' : '2May',
'JUN' : '3June',
'JUL' : '4July',
'AUG' : '5Aug',
'SEP' : '6Sept',
'OCT' : '7Oct'}
dodgers['ordered_month'] = dodgers['month'].map(month_to_ordered_month)
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# employ training-and-test regimen for model validation
np.random.seed(1234)
dodgers['runiform'] = uniform.rvs(loc = 0, scale = 1, size = len(dodgers))
dodgers_train = dodgers[dodgers['runiform'] >= 0.33]
dodgers_test = dodgers[dodgers['runiform'] < 0.33]
# check training data frame
print('\ndodgers_train data frame (rows, columns): ',dodgers_train.shape)
print(dodgers_train.head())
# check test data frame
print('\ndodgers_test data frame (rows, columns): ',dodgers_test.shape)
print(dodgers_test.head())
# specify a simple model with bobblehead entered last
my_model = str('attend ~ ordered_month + ordered_day_of_week + bobblehead')
# fit the model to the training set
train_model_fit = smf.ols(my_model, data = dodgers_train).fit()
# summary of model fit to the training set
print(train_model_fit.summary())
# training set predictions from the model fit to the training set
dodgers_train['predict_attend'] = train_model_fit.fittedvalues
# test set predictions from the model fit to the training set
dodgers_test['predict_attend'] = train_model_fit.predict(dodgers_test)
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# compute the proportion of response variance
# accounted for when predicting out-of-sample
print('\nProportion of Test Set Variance Accounted for: ',\
round(np.power(dodgers_test['attend'].corr(dodgers_test['predict_attend']),2),3))
# use the full data set to obtain an estimate of the increase in
# attendance due to bobbleheads, controlling for other factors
my_model_fit = smf.ols(my_model, data = dodgers).fit()
print(my_model_fit.summary())
print('\nEstimated Effect of Bobblehead Promotion on Attendance: ',\
round(my_model_fit.params[13],0))
# Suggestions for the student: Reproduce the figures in this chapter
# using matplotlib, ggplot, and/or rpy2 calls to R graphics.
# Examine regression diagnostics for the fitted model.
# Examine other linear predictors and other explanatory variables.
# See if you can improve upon the model with variable transformations.
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corr = dodgerDF.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
# Set up the matplotlib figure
#f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
plt.show()
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