def violin_plot(data, name):
"""
Plot violin plots for the building types versus the building height.
"""
sns.set_style("ticks")
fig = plt.figure(figsize=(8, 6))
violin = sns.violinplot(x=data['bldg_type'],
y=data['rel_height'],
scale='width',
width=0.75,
color='steelblue')
violin.set_xticklabels(violin.get_xticklabels(),
rotation=45,
horizontalalignment='right')
fig.tight_layout()
sns.despine()
violin.set_xlabel('Building Type')
violin.set_ylabel('Building Height [m]')
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/Violin_BldTypes_" + name + ".pdf",
bbox_inches="tight",
dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def correlation_matrix(data, name):
"""
Compute the correlation matrix for the non-geometric features
and the building height.
"""
sns.set_style("ticks")
corr_matrix = data[[
'rel_height', 'avg_hh_income', 'avg_hh_size', 'pop_density', 'h_mean',
'num_amenities'
]].corr()
features = [
'Building Height', 'Avg. HH. Income', 'Avg. HH. Size',
'Population Density', 'Raster Height', '#Amenities'
]
fig = plt.figure(figsize=(5, 5))
# Create mask to only show one halve of the matrix
mask = np.triu(np.ones_like(corr_matrix, dtype=np.bool))
heatmap = sns.heatmap(corr_matrix,
xticklabels=features,
yticklabels=features,
cmap='RdBu',
annot=True,
linewidth=0.5,
square=True,
mask=mask,
linewidths=.5,
cbar_kws={
"shrink": 0.6,
"label": "Correlation"
},
vmin=-1,
vmax=1)
heatmap.set_xticklabels(heatmap.get_xticklabels(),
rotation=45,
horizontalalignment='right')
heatmap.tick_params(left=False, bottom=False)
fig.tight_layout()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/Correlation_NewFeatures_" + name + ".pdf",
bbox_inches="tight",
dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
plt.clf()
def rf_min_samples_leaf(train_features, train_labels, test_features, test_labels, name):
"""
Plot the minimum samples required in a leaf against the accuracy.
"""
sns.set()
sns.set_style("ticks")
train_results = []
test_results = []
samples_start = np.linspace(2, 24, 12, dtype=int)
samples_end = np.linspace(25, 750, num=30, dtype=int)
min_samples_leaf = np.hstack((samples_start, samples_end))
train_scaled, scaler = ml_funcs.apply_scaling(train_features, 'RF', name, save_scaler=False)
test_scaled = scaler.transform(test_features)
for samples in min_samples_leaf:
print("Samples leaf:", samples)
randomforest = RandomForestRegressor(min_samples_leaf=samples, n_jobs=-1, random_state=0)
randomforest.fit(train_scaled, train_labels)
predict_train = randomforest.predict(train_scaled)
# Accuracy of training data (mean absolute percentage error)
accuracy_train = compute_accuracy(predict_train, train_labels)
train_results.append(accuracy_train)
predict_test = randomforest.predict(test_scaled)
# Accuracy for test data.
accuracy_test = compute_accuracy(predict_test, test_labels)
test_results.append(accuracy_test)
fig = plt.figure(figsize=(10, 6))
sns.lineplot(x=min_samples_leaf, y=train_results, label='Train')
sns.lineplot(x=min_samples_leaf, y=test_results, label='Test')
plt.legend(frameon=False, loc='upper right')
plt.xlabel('Minimum samples in leaf')
plt.ylabel('Accuracy score [%]')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/Min_Samples_Leaf_" + name + ".pdf", bbox_inches="tight", dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def rf_max_depth(train_features, train_labels, test_features, test_labels, name):
"""
Plot the maximum tree depth against the accuracy.
"""
sns.set()
sns.set_style("ticks")
train_results = []
test_results = []
# Maximum depth of the tree.
max_depth = np.linspace(1, 35, 35, dtype=int)
train_scaled, scaler = ml_funcs.apply_scaling(train_features, 'RF', name, save_scaler=False)
test_scaled = scaler.transform(test_features)
for depth in max_depth:
print("Depth:", depth)
randomforest = RandomForestRegressor(max_depth=depth, n_jobs=-1, random_state=0)
randomforest.fit(train_scaled, train_labels)
predict_train = randomforest.predict(train_scaled)
# Accuracy of training data (mean absolute percentage error)
accuracy_train = compute_accuracy(predict_train, train_labels)
train_results.append(accuracy_train)
predict_test = randomforest.predict(test_scaled)
# Accuracy for test data.
accuracy_test = compute_accuracy(predict_test, test_labels)
test_results.append(accuracy_test)
fig = plt.figure(figsize=(10, 6))
sns.lineplot(x=max_depth, y=train_results, label='Train')
sns.lineplot(x=max_depth, y=test_results, label='Test')
plt.legend(frameon=False, loc='lower right')
plt.xlabel('Maximum tree depth')
plt.ylabel('Accuracy score [%]')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/Max_Depth_" + name + ".pdf", bbox_inches="tight", dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def rf_n_estimators(train_features, train_labels, test_features, test_labels, name):
"""
Plot the number of estimators against the accuracy.
"""
sns.set()
sns.set_style("ticks")
train_results = []
test_results = []
# The number of trees in the random forest.
n_estimators = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
train_scaled, scaler = ml_funcs.apply_scaling(train_features, 'RF', name, save_scaler=False)
test_scaled = scaler.transform(test_features)
for estimator in n_estimators:
print("Num estimators:", estimator)
randomforest = RandomForestRegressor(n_estimators=estimator, n_jobs=-1, random_state=0)
randomforest.fit(train_scaled, train_labels)
predict_train = randomforest.predict(train_scaled)
# Accuracy of training data (mean absolute percentage error)
accuracy_train = compute_accuracy(predict_train, train_labels)
train_results.append(accuracy_train)
predict_test = randomforest.predict(test_scaled)
# Accuracy for test data.
accuracy_test = compute_accuracy(predict_test, test_labels)
test_results.append(accuracy_test)
fig = plt.figure(figsize=(10, 6))
sns.lineplot(x=n_estimators, y=train_results, label='Train')
sns.lineplot(x=n_estimators, y=test_results, label='Test')
plt.legend(frameon=False, loc='lower right')
plt.xlabel('Number of estimators')
plt.ylabel('Accuracy score [%]')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/N_Estimators_" + name + ".pdf", bbox_inches="tight", dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def svr_C(train_features, train_labels, test_features, test_labels, name):
"""
Plot C against the accuracy.
"""
sns.set()
sns.set_style("ticks")
train_results = []
test_results = []
c_values = np.linspace(1e-4, 1, 10)
train_scaled, scaler = ml_funcs.apply_scaling(train_features, 'SVR', name, save_scaler=False)
test_scaled = scaler.transform(test_features)
for c_val in c_values:
print("C:", c_val)
svr = LinearSVR(C=c_val, max_iter=2000, random_state=0)
svr.fit(train_scaled, train_labels)
predict_train = svr.predict(train_scaled)
# Accuracy of training data (mean absolute percentage error)
accuracy_train = compute_accuracy(predict_train, train_labels)
train_results.append(accuracy_train)
predict_test = svr.predict(test_scaled)
# Accuracy for test data.
accuracy_test = compute_accuracy(predict_test, test_labels)
test_results.append(accuracy_test)
fig = plt.figure(figsize=(10, 6))
sns.lineplot(x=c_values, y=train_results, label='Train')
sns.lineplot(x=c_values, y=test_results, label='Test')
plt.legend(frameon=False, loc='lower right')
plt.xlabel('C')
plt.ylabel('Accuracy score [%]')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/C_" + name + ".pdf", bbox_inches="tight", dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def emperical_cdf(ground_truth, rfr, svr, mlr, city, env):
"""
Plot a cumulative error graph showing how the errors are distributed
over the number of buildings.
"""
sns.set()
sns.set_style("white")
sns.set_style("ticks")
abs_errors_rf = np.sort(abs(ground_truth - rfr))
prop_vals_rf = np.linspace(0, 1, len(abs_errors_rf))
abs_errors_svr = np.sort(abs(ground_truth - svr))
prop_vals_svr = np.linspace(0, 1, len(abs_errors_svr))
abs_errors_mlr = np.sort(abs(ground_truth - mlr))
prop_vals_mlr = np.linspace(0, 1, len(abs_errors_mlr))
fig, ax = plt.subplots()
ax.plot(abs_errors_rf, prop_vals_rf, label='RFR')
ax.plot(abs_errors_svr, prop_vals_svr, label='SVR')
ax.plot(abs_errors_mlr, prop_vals_mlr, label='MLR')
ax.set_xlabel("Error [m]")
ax.set_ylabel("Cumulative Frequency")
if city == 'Seattle':
ax.set_xlim([0, 100])
else:
ax.set_xlim([0, 8])
ax.set_ylim([0, 1])
ax.legend(frameon=False, loc='lower right')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/Cumulative_Errors_" + city + "_" + env + ".pdf",
bbox_inches="tight",
dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
def svr_maxiter_tolerance(train_features, train_labels, test_features, test_labels, name):
"""
Plot a combination of the maximum number of iterations and the tolerance
against the accuracy.
"""
sns.set()
sns.set_style("ticks")
train_results = []
test_results = []
tolerances = [1e-3, 1e-4, 1e-5]
tol_labels = ['1e-3', '1e-4', '1e-5']
max_iter = np.linspace(100, 5000, 50, dtype=int)
train_scaled, scaler = ml_funcs.apply_scaling(train_features, 'SVR', name, save_scaler=False)
test_scaled = scaler.transform(test_features)
for tolerance in tolerances:
temp_train = []
temp_test = []
print("Tolerance:", tolerance)
for iteration in max_iter:
print("Max. iterations:", iteration)
svr = LinearSVR(tol=tolerance, max_iter=iteration, random_state=0)
svr.fit(train_scaled, train_labels)
predict_train = svr.predict(train_scaled)
# Accuracy of training data (mean absolute percentage error)
accuracy_train = compute_accuracy(predict_train, train_labels)
temp_train.append(accuracy_train)
predict_test = svr.predict(test_scaled)
# Accuracy for test data.
accuracy_test = compute_accuracy(predict_test, test_labels)
temp_test.append(accuracy_test)
train_results.append(temp_train)
test_results.append(temp_test)
fig = plt.figure(figsize=(10, 6))
for i in range(len(train_results)):
label_train = 'Train (tol' + tol_labels[i] +')'
sns.lineplot(x=max_iter, y=train_results[i], label=label_train)
label_test = 'Test (tol' + tol_labels[i] +')'
sns.lineplot(x=max_iter, y=test_results[i], label=label_test)
plt.legend(frameon=False, loc='lower left', bbox_to_anchor=(1.0, 0.0))
plt.xlabel('Maximum number of iterations')
plt.ylabel('Accuracy score [%]')
fig.tight_layout()
sns.despine()
if generate_plots.directory_exists("./Figures"):
plt.savefig("./Figures/MaxIter_Tolerance_" + name + ".pdf", bbox_inches="tight", dpi=300,
transparent=True)
else:
print("Directory: ./Figures does not exist!")
